From 20fc5964b61fb6dc85f668101b220a4150583b54 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Fri, 13 Mar 2026 08:35:43 +0100
Subject: [PATCH 01/46] Read and display parquet files schema for relational
 data source

---
 gradle.properties                             |   2 +
 plugins/parquet-adapter/build.gradle          |  79 ++++
 plugins/parquet-adapter/docs/README.md        | 228 ++++++++++
 .../docs/images/parquet_schema.png            | Bin 0 -> 117091 bytes
 plugins/parquet-adapter/gradle.properties     |  27 ++
 .../db/adapter/parquet/ParquetPlugin.java     |  54 +++
 .../db/adapter/parquet/ParquetSource.java     | 405 ++++++++++++++++++
 .../parquet/io/ParquetFileDiscovery.java      |  59 +++
 .../parquet/model/ParquetTypeConverter.java   |  70 +++
 .../planning/ParquetProjectScanRule.java      |  51 +++
 .../adapter/parquet/planning/ParquetScan.java |  87 ++++
 .../schema/ParquetFilterableTable.java        |  74 ++++
 .../parquet/schema/ParquetNamespace.java      |  82 ++++
 .../parquet/schema/ParquetScannableTable.java |  54 +++
 .../adapter/parquet/schema/ParquetTable.java  |  65 +++
 .../schema/ParquetTranslatableTable.java      |  64 +++
 .../util/HadoopConfigurationFactory.java      |  40 ++
 .../resources/orders_db/customers.parquet     | Bin 0 -> 241898 bytes
 .../resources/orders_db/order_items.parquet   | Bin 0 -> 2249170 bytes
 .../main/resources/orders_db/orders.parquet   | Bin 0 -> 1125450 bytes
 .../main/resources/orders_db/products.parquet | Bin 0 -> 106585 bytes
 .../src/test/java/ParquetTest.java            |  20 +
 .../resources/orders_db/customers.parquet     | Bin 0 -> 241898 bytes
 .../resources/orders_db/order_items.parquet   | Bin 0 -> 2249170 bytes
 .../test/resources/orders_db/orders.parquet   | Bin 0 -> 1125450 bytes
 .../test/resources/orders_db/products.parquet | Bin 0 -> 106585 bytes
 settings.gradle                               |   1 +
 27 files changed, 1462 insertions(+)
 create mode 100644 plugins/parquet-adapter/build.gradle
 create mode 100644 plugins/parquet-adapter/docs/README.md
 create mode 100644 plugins/parquet-adapter/docs/images/parquet_schema.png
 create mode 100644 plugins/parquet-adapter/gradle.properties
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/io/ParquetFileDiscovery.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetTypeConverter.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetProjectScanRule.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetScan.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetFilterableTable.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetScannableTable.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTranslatableTable.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/util/HadoopConfigurationFactory.java
 create mode 100644 plugins/parquet-adapter/src/main/resources/orders_db/customers.parquet
 create mode 100644 plugins/parquet-adapter/src/main/resources/orders_db/order_items.parquet
 create mode 100644 plugins/parquet-adapter/src/main/resources/orders_db/orders.parquet
 create mode 100644 plugins/parquet-adapter/src/main/resources/orders_db/products.parquet
 create mode 100644 plugins/parquet-adapter/src/test/java/ParquetTest.java
 create mode 100644 plugins/parquet-adapter/src/test/resources/orders_db/customers.parquet
 create mode 100644 plugins/parquet-adapter/src/test/resources/orders_db/order_items.parquet
 create mode 100644 plugins/parquet-adapter/src/test/resources/orders_db/orders.parquet
 create mode 100644 plugins/parquet-adapter/src/test/resources/orders_db/products.parquet

diff --git a/gradle.properties b/gradle.properties
index c2dea07078..e612e1544f 100644
--- a/gradle.properties
+++ b/gradle.properties
@@ -91,3 +91,5 @@ typesafe_config_version = 1.2.1
 unirest_version = 3.14.5
 web3j_version = 5.0.0
 zip4j_version = 2.11.5
+parquet_version = 1.14.4
+hadoop_version = 3.4.1
diff --git a/plugins/parquet-adapter/build.gradle b/plugins/parquet-adapter/build.gradle
new file mode 100644
index 0000000000..814f11eef4
--- /dev/null
+++ b/plugins/parquet-adapter/build.gradle
@@ -0,0 +1,79 @@
+group "org.polypheny"
+
+
+dependencies {
+    compileOnly project(":core")
+    compileOnly project(":dbms")
+    compileOnly project(":plugins:sql-language")
+
+    implementation(group: "org.apache.parquet", name: "parquet-hadoop", version: parquet_version) {
+//        transitive = false
+    }
+    implementation group: "org.apache.parquet", name: "parquet-column", version: parquet_version
+    implementation group: "org.apache.parquet", name: "parquet-common", version: parquet_version
+    implementation group: "org.apache.hadoop", name: "hadoop-common", version: hadoop_version
+    implementation group: "org.apache.hadoop", name: "hadoop-mapreduce-client-core", version: hadoop_version
+
+    // --- Test Compile ---
+    testImplementation project(path: ":dbms", configuration: "test")
+    testImplementation project(path: ":core", configuration: "tests")
+    testImplementation project(path: ":core")
+    testImplementation project(path: ":plugins:sql-language")
+}
+
+configurations.configureEach {
+    exclude group: "javax.annotation", module: "javax.annotation-api"
+}
+
+sourceSets {
+    main {
+        java {
+            srcDirs = ["src/main/java"]
+        }
+        resources {
+            srcDirs = ["src/main/resources"]
+        }
+        output.resourcesDir = file(project.buildDir.absolutePath + "/classes")
+    }
+    test {
+        java {
+            srcDirs = ["src/test/java"]
+            destinationDirectory.set(file(project.buildDir.absolutePath + "/test-classes"))
+        }
+        resources {
+            srcDirs = ["src/test/resources"]
+        }
+        output.resourcesDir = file(project.buildDir.absolutePath + "/test-classes")
+    }
+}
+
+compileJava {
+    dependsOn(":config:processResources")
+    dependsOn(":core:processResources")
+    dependsOn(":information:processResources")
+    dependsOn(":plugins:sql-language:processResources")
+}
+
+delombok {
+    dependsOn(":plugins:sql-language:processResources")
+}
+
+/**
+ * JARs
+ */
+jar {
+    manifest {
+        attributes "Manifest-Version": "1.0"
+        attributes "Copyright": "The Polypheny Project (polypheny.org)"
+        attributes "Version": "$project.version"
+    }
+}
+java {
+    withJavadocJar()
+    withSourcesJar()
+}
+
+licensee {
+    allow('MIT')
+    allow('Apache-2.0')
+}
diff --git a/plugins/parquet-adapter/docs/README.md b/plugins/parquet-adapter/docs/README.md
new file mode 100644
index 0000000000..d1da32266f
--- /dev/null
+++ b/plugins/parquet-adapter/docs/README.md
@@ -0,0 +1,228 @@
+# Parquet Module Implementation
+
+## Overview
+
+The Parquet adapter exposes Parquet files as Polypheny source tables, derives schema from Parquet metadata, restores tables during adapter activation, and provides the infrastructure needed for query planning over data stored in Parquet files.
+
+## Build and Integration Changes
+
+1\. To integrate the Parquet adapter as a plugin, the Gradle configuration must include the Parquet module and its dependencies.
+
+Affected files:
+- `settings.gradle`
+- `gradle.properties`
+- `plugins/parquet-adapter/build.gradle`
+
+<br>
+
+2\. To make Parquet the default data source, `PolyphenyDB.java` sets `defaultSourceName` to `parquet`.<br>
+During `restore()`, `Catalog.defaultSource` is populated from the configured adapter state.
+
+
+
+
+## Main Execution Flow
+
+1\. `ParquetPlugin` registers the adapter template on initialization and removes it on shutdown.
+
+2\. `ParquetSource` is instantiated and configured with the Parquet source location.
+
+3\. `ParquetFileDiscovery` identifies the available `.parquet` files in the configured source.
+
+4\. `ParquetSource` reads Parquet metadata and extracts exported columns.
+
+5\. `ParquetNamespace` creates table wrappers for each discovered file.
+
+6\. Restored tables are registered in the adapter catalog.
+
+7\. Query execution uses the planning layer through `ParquetScan` and related rules.
+
+
+## Package Structure and Responsibilities
+
+### `root`
+
+Contains main classes
+
+#### 1. `ParquetPlugin`
+
+Registers the adapter template (`ParquetSource`) on initialization and removes the template on stop.
+
+#### 2. `ParquetSource`
+
+Core adapter entry point. Responsible for extracting column information from Parquet metadata, creating the information page, restoring tables, and coordinating namespace/table creation.
+
+Main functions:
+
+- `constructor()`
+
+- `setParquetDir()`
+
+- `createInformationPage()`
+
+- `getExportedColumns()`
+
+- `computePhysicalTableName()`
+
+- `getValidTableName()`
+
+- `getExportedColumnsFromFile()`
+
+- `getExportedColumnFromField()`
+
+- `getValidColumnNameFromField()`
+
+- `getInformationTable()`
+
+- `enableInformationPage()`
+
+- `restoreTable()` called from `AdapterRestore.activate()`
+
+- `createParquetTable()`
+
+
+Restore flow:
+
+- `restoreTable()` calls `parquetNamespace.createParquetTable()` to create physical tables by flavor.
+
+- Restored tables are then added to `adapterCatalog`.
+
+
+Create/refresh flow:
+
+- `createParquetTable()` calls `parquetNamespace.createParquetTable()` and replaces the affected tables in `adapterCatalog`.
+
+### `io`
+Contains classes related to Parquet file access and discovery, including locating input files and handling Parquet-specific input operations.
+
+#### 3. `ParquetFileDiscovery`
+
+Helper functionality for identifying valid Parquet files.
+
+Main functions:
+
+- `listParquetFiles(dir)`
+
+- `isParquetFile(filename)`
+
+
+### `schema`
+
+Contains namespace and table wrapper classes that expose Parquet-backed tables to Polypheny.
+
+
+#### 4. `ParquetNamespace`
+
+Creates table wrappers for Parquet files and chooses table creation by flavor.
+
+Main functions:
+
+- `createParquetTable()`
+
+- `createTable()` to create the concrete table according to flavor
+
+
+#### 5. `ParquetTable`
+
+Base table abstraction containing metadata and other shared information.
+
+Main elements:
+
+- `Flavor` enum
+
+
+#### 6. `ParquetFilterableTable` extends `ParquetTable`
+
+Currently a placeholder or dummy implementation for filterable behavior.
+
+#### 7. `ParquetScannableTable` extends `ParquetTable`
+
+Currently a placeholder or dummy implementation for scannable behavior.
+
+#### 8. `ParquetTranslatableTable` extends `ParquetTable`
+
+Currently a placeholder or dummy implementation for translatable behavior.
+
+
+
+### `model`
+
+Contains Parquet-to-Polypheny type conversion logic.
+
+#### 9. `ParquetTypeConverter`
+
+Converts Parquet types to Polypheny types.
+
+Main function:
+
+- `fromParquetTypeToPolyType()`
+
+
+### `planning`
+
+Contains relational scan and planner rule classes for Parquet query support.
+
+#### 10. `ParquetScan`
+
+Represents the Parquet scan operator in the planning/execution layer.
+
+#### 11. `ParquetProjectScanRule`
+
+Planner rule associated with Parquet scans.
+
+
+### `util`
+
+Contains reusable infrastructure helpers such as Hadoop configuration setup for Parquet reading.
+
+#### 12. `HadoopConfigurationFactory`
+
+Creates Hadoop `Configuration` instances for Parquet readers and ensures the proper classloader and local filesystem registration are available in the plugin environment.
+
+
+
+## Schema Extraction and Naming Rules
+
+- Table names are derived from Parquet file names and normalized through `getValidTableName()`.
+
+- Column metadata is extracted from Parquet footer/schema metadata.
+
+- Column names are normalized through `getValidColumnNameFromField()`.
+
+- Type mapping is delegated to `ParquetTypeConverter`.
+
+- The document should explicitly mention how unsupported or ambiguous Parquet types are handled.
+
+
+## Flavor Selection
+
+Appropriate table wrapper is created for a discovered Parquet file, for example `filterable`, `scannable`, or `translatable`.
+Currently only one flavor is active in practice.
+
+
+## Information Page
+The information page displays the following column metadata:
+
+- Position
+- Column name
+- Type
+- Nullable
+- Filename
+- Primary key flag
+
+
+The following figure shows an example of the displayed schema information.
+![Schema display](images/parquet_schema.png)
+
+
+
+## Current Gaps and Future Work
+
+- Replace placeholder filterable, scannable, and translatable table variants with real implementations.
+- Add explicit support for Hadoop-backed remote filesystems.
+- Add write support
+- Handle nested parquet schema
+- implement partition discovery (?)
+- Document error handling for invalid files, missing metadata, and unsupported types.
+- Add tests and document the tested scenarios for discovery, schema extraction, restore, and planning.
+
diff --git a/plugins/parquet-adapter/docs/images/parquet_schema.png b/plugins/parquet-adapter/docs/images/parquet_schema.png
new file mode 100644
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HcmV?d00001

diff --git a/plugins/parquet-adapter/gradle.properties b/plugins/parquet-adapter/gradle.properties
new file mode 100644
index 0000000000..4d61545e5e
--- /dev/null
+++ b/plugins/parquet-adapter/gradle.properties
@@ -0,0 +1,27 @@
+#
+# Copyright 2019-2026 The Polypheny Project
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+pluginVersion = 0.0.1
+
+pluginId = parquet-adapter
+pluginClass = org.polypheny.db.adapter.parquet.ParquetPlugin
+pluginProvider = The Polypheny Project
+pluginDependencies = sql-language
+pluginUrlPath =
+pluginCategories = source
+pluginPolyDependencies =
+pluginIsSystemComponent = false
+pluginIsUiVisible = true
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
new file mode 100644
index 0000000000..ec0107d0c3
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
@@ -0,0 +1,54 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import org.polypheny.db.adapter.AdapterManager;
+import org.polypheny.db.plugins.PluginContext;
+import org.polypheny.db.plugins.PolyPlugin;
+
+/**
+ * Plugin entry point for the Parquet adapter.
+ */
+public class ParquetPlugin extends PolyPlugin {
+
+    private long id;
+
+    /**
+     * Constructor
+     * Create Plugin Instance
+     */
+    public ParquetPlugin( PluginContext context ) {
+        super( context );
+    }
+
+    /**
+     * Registers the adapter template once the catalog is ready.
+     */
+    @Override
+    public void afterCatalogInit() {
+        this.id = AdapterManager.addAdapterTemplate( ParquetSource.class, "Parquet", ParquetSource::new );
+    }
+
+    /**
+     * Removes the adapter template on shutdown.
+     */
+    @Override
+    public void stop() {
+        AdapterManager.removeAdapterTemplate( id );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
new file mode 100644
index 0000000000..97e7bb3b7a
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
@@ -0,0 +1,405 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import lombok.Getter;
+import lombok.experimental.Delegate;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.parquet.hadoop.ParquetFileReader;
+import org.apache.parquet.hadoop.util.HadoopInputFile;
+import org.apache.hadoop.fs.Path;
+import org.apache.parquet.schema.Type;
+
+import org.polypheny.db.adapter.ConnectionMethod;
+import org.polypheny.db.adapter.DataSource;
+import org.polypheny.db.adapter.DeployMode;
+import org.polypheny.db.adapter.RelationalDataSource;
+import org.polypheny.db.adapter.RelationalScanDelegate;
+import org.polypheny.db.adapter.annotations.AdapterProperties;
+import org.polypheny.db.adapter.annotations.AdapterSettingDirectory;
+import org.polypheny.db.adapter.annotations.AdapterSettingInteger;
+import org.polypheny.db.adapter.annotations.AdapterSettingList;
+import org.polypheny.db.adapter.annotations.AdapterSettingString;
+import org.polypheny.db.adapter.parquet.io.ParquetFileDiscovery;
+import org.polypheny.db.adapter.parquet.model.ParquetTypeConverter;
+import org.polypheny.db.adapter.parquet.schema.ParquetNamespace;
+import org.polypheny.db.adapter.parquet.schema.ParquetTable;
+
+
+import org.polypheny.db.adapter.parquet.util.HadoopConfigurationFactory;
+import org.polypheny.db.catalog.catalogs.RelAdapterCatalog;
+import org.polypheny.db.catalog.entity.allocation.AllocationTable;
+import org.polypheny.db.catalog.entity.allocation.AllocationTableWrapper;
+import org.polypheny.db.catalog.entity.logical.LogicalTableWrapper;
+import org.polypheny.db.catalog.entity.physical.PhysicalEntity;
+import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.catalog.logistic.DataModel;
+import org.polypheny.db.information.InformationGroup;
+import org.polypheny.db.information.InformationTable;
+import org.polypheny.db.prepare.Context;
+import org.polypheny.db.transaction.PolyXid;
+import java.io.File;
+import java.net.MalformedURLException;
+import java.net.URL;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import java.util.stream.Collectors;
+import org.pf4j.Extension;
+import org.polypheny.db.type.PolyType;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+@Extension
+@AdapterProperties(
+        name = "Parquet",
+        description = "An adapter for querying Parquet files. The location of the directory containing the Parquet files can be specified. Currently, this adapter only supports read operations.",
+        usedModes = DeployMode.EMBEDDED,
+        defaultMode = DeployMode.EMBEDDED)
+@AdapterSettingList(name = "method", options = { "upload", "link" }, defaultValue = "upload", description = "If the supplied file(s) should be uploaded or a link to the local filesystem is used (sufficient permissions are required).", position = 1)
+@AdapterSettingDirectory(subOf = "method_upload", name = "directory", defaultValue = "classpath://orders_db", description = "You can upload one or multiple .parquet files.", position = 2)
+@AdapterSettingString(subOf = "method_link", defaultValue = "classpath://orders_db", name = "directoryName", description = "You can select a path to a folder or specific .parquet files.", position = 2)
+@AdapterSettingInteger(
+        name = "maxStringLength",
+        defaultValue = 255,
+        position = 3,
+        description = "Which length (number of characters including whitespace) should be used for the varchar columns. Make sure this is equal or larger than the longest string in any of the columns.")
+public class ParquetSource extends DataSource<RelAdapterCatalog> implements RelationalDataSource {
+
+    @Delegate(excludes = Excludes.class)
+    private final RelationalScanDelegate delegate;
+
+    private final ConnectionMethod connectionMethod;
+    private final int maxStringLength;
+    private final ParquetTypeConverter parquetTypeConverter;
+    private URL parquetDir;
+
+    @Getter
+    private ParquetNamespace currentNamespace;
+    private Map<String, List<ExportedColumn>> exportedColumns;
+
+    private static final Logger log = LoggerFactory.getLogger( ParquetSource.class );
+
+
+    /**
+     * Constructor
+     * Creates the Parquet source from adapter settings.
+     */
+    public ParquetSource(final long storeId, final String uniqueName, final Map<String, String> settings, final DeployMode mode ) {
+        super( storeId, uniqueName, settings, mode, true, new RelAdapterCatalog( storeId ), Set.of( DataModel.RELATIONAL ) );
+
+        this.parquetTypeConverter = new ParquetTypeConverter();
+        this.connectionMethod = settings.containsKey( "method" )
+                ? ConnectionMethod.from( settings.get( "method" ).toUpperCase() )
+                : ConnectionMethod.UPLOAD;
+
+        this.maxStringLength = Integer.parseInt( settings.get( "maxStringLength" ) );
+        if ( maxStringLength < 1 ) {
+            throw new GenericRuntimeException( "Invalid value for maxStringLength: " + maxStringLength );
+        }
+
+        setParquetDir( settings );
+        createInformationPage();
+        enableInformationPage();
+
+        this.delegate = new RelationalScanDelegate( this, adapterCatalog );
+    }
+
+    /**
+     * Get directory information from settings and populate parquetDir variable
+     */
+    private void setParquetDir( Map<String, String> settings ) {
+        String dir = settings.getOrDefault( "directory", "." );
+        if ( connectionMethod == ConnectionMethod.LINK ) {
+            dir = settings.getOrDefault( "directoryName", "." );
+        }
+
+        if ( dir.startsWith( "classpath://" ) ) {
+            parquetDir = this.getClass().getClassLoader().getResource( dir.replace( "classpath://", "" ) + "/" );
+        } else {
+            try {
+                parquetDir = new File( dir ).toURI().toURL();
+            } catch ( MalformedURLException e ) {
+                throw new GenericRuntimeException( e );
+            }
+        }
+    }
+
+    /**
+     * Removes invalid characters and creates a valid table name
+     */
+    private static String getValidTableName(String name){
+        return name.trim().replaceAll( "[^a-z0-9_]+", "" );
+    }
+
+
+    /**
+     * Build valid physical table name from file name
+     */
+    private static String computePhysicalTableName( String fileName ) {
+        String physicalTableName = fileName.toLowerCase();
+        if ( physicalTableName.endsWith( ".parquet" ) ) {
+            physicalTableName = physicalTableName.substring( 0, physicalTableName.length() - ".parquet".length() );
+        }
+        return getValidTableName(physicalTableName);
+    }
+
+
+    /**
+     * Build valid column name from field data
+     */
+    private String getValidColumnNameFromField(Type field) {
+        return field.getName().toLowerCase().trim().replaceAll( "[^a-z0-9_]+", "_" );
+    }
+
+    /**
+     * Get parquet file fields information from metadata
+     */
+    private List<ExportedColumn> getExportedColumnsFromFile( String fileName, String physicalTableName ) {
+        try {
+            Path path = new Path(new URL( parquetDir, fileName ).toURI());
+            Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
+            try ( ParquetFileReader reader = ParquetFileReader.open( HadoopInputFile.fromPath(path, conf))) {
+                List<Type> schemaFields = reader.getFooter().getFileMetaData().getSchema().getFields();
+                List<ExportedColumn> columns = new ArrayList<>();
+                int position = 0;
+                for ( Type field : schemaFields ) {
+                    ExportedColumn column = getExportedColumnFromField(field, fileName, physicalTableName, position);
+                    columns.add(column);
+                    position++;
+                }
+                return columns;
+            }
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( e );
+        }
+    }
+
+
+    /**
+     * Extract metadata information from single parquet field and create column object
+     */
+    private ExportedColumn getExportedColumnFromField(Type field, String fileName, String physicalTableName, int position) {
+        String columnName = getValidColumnNameFromField(field);
+        PolyType polyType = parquetTypeConverter.fromParquetTypeToPolyType( field );
+        Integer length = polyType == PolyType.VARCHAR ? maxStringLength : null;
+        return new ExportedColumn(
+                    columnName,
+                    polyType,
+                    null,
+                    length,
+                    null,
+                    null,
+                    null,
+                    false,
+                    fileName,
+                    physicalTableName,
+                    field.getName(),
+                    position,
+                    position == 0 );
+    }
+
+    /**
+     * Create information page containing exported columns details
+     * for all tables loaded from parquet files
+     */
+    private void createInformationPage() {
+        for ( Map.Entry<String, List<ExportedColumn>> entry : getExportedColumns().entrySet() ) {
+            if ( entry.getValue().isEmpty() ) {
+                continue;
+            }
+
+            InformationGroup group = new InformationGroup( informationPage, entry.getValue().get( 0 ).physicalSchemaName() );
+            informationGroups.add( group );
+            InformationTable table = getInformationTable(group, entry);
+            informationElements.add( table );
+        }
+    }
+
+    /**
+     * Extract columns from all parquet files
+     * @return Map<String, List<ExportedColumn>> - column list per table name
+     */
+    @Override
+    public Map<String, List<ExportedColumn>> getExportedColumns() {
+        if ( connectionMethod == ConnectionMethod.UPLOAD && exportedColumns != null ) {
+            return exportedColumns;
+        }
+
+        Map<String, List<ExportedColumn>> columns = new HashMap<>();
+        Set<String> fileNames = ParquetFileDiscovery.listParquetFiles( parquetDir );
+
+        for ( String fileName : fileNames ) {
+            String physicalTableName = computePhysicalTableName( fileName );
+            columns.put( physicalTableName, getExportedColumnsFromFile( fileName, physicalTableName ) );
+        }
+
+        this.exportedColumns = columns;
+        return columns;
+
+    }
+
+
+    /**
+     * Builds InformationTable from exported columns
+     */
+    private InformationTable getInformationTable(InformationGroup group, Map.Entry<String, List<ExportedColumn>> entry) {
+        List<String> columns = Arrays.asList("Position", "Column Name", "Type", "Nullable", "Filename", "Primary");
+        InformationTable table = new InformationTable(group, columns);
+        for ( ExportedColumn exportedColumn : entry.getValue() ) {
+            table.addRow(
+                    exportedColumn.physicalPosition(),
+                    exportedColumn.name(),
+                    exportedColumn.getDisplayType(),
+                    exportedColumn.nullable() ? "✔" : "",
+                    exportedColumn.physicalSchemaName(),
+                    exportedColumn.primary() ? "✔" : "" );
+        }
+        return table;
+    }
+
+    /**
+     * Updates the active namespace wrapper for this source.
+     */
+    @Override
+    public void updateNamespace( String name, long id ) {
+        currentNamespace = new ParquetNamespace( id, adapterId, parquetDir, ParquetTable.Flavor.FILTERABLE );
+    }
+
+    /**
+     * Returns this source as relational adapter interface.
+     */
+    @Override
+    public RelationalDataSource asRelationalDataSource() {
+        return this;
+    }
+
+
+    /**
+     * Parquet source is read-only, so truncate is not supported.
+     */
+    @Override
+    public void truncate( Context context, long allocId ) {
+        throw new GenericRuntimeException( "Parquet adapter does not support truncate" );
+    }
+
+    /**
+     * Prepare does nothing, because data source is read-only.
+     */
+    @Override
+    public boolean prepare( PolyXid xid ) {
+        log.debug( "Parquet Store does not support prepare()." );
+        return true;
+    }
+
+    /**
+     * Do nothing - read only
+     */
+    @Override
+    public void commit( PolyXid xid ) {
+        log.debug( "Parquet Store does not support commit()." );
+        // do nothing
+    }
+
+    /**
+     * Do nothing - read only
+     */
+    @Override
+    public void rollback( PolyXid xid ) {
+        log.debug( "Parquet Store does not support rollback()." );
+        // do nothing
+    }
+
+    /**
+     * Cleans up information page state.
+     */
+    @Override
+    public void shutdown() {
+        removeInformationPage();
+    }
+
+    /**
+     * Reloads settings that affect source location.
+     */
+    @Override
+    protected void reloadSettings( List<String> updatedSettings ) {
+        if ( updatedSettings.contains( "directory" ) || updatedSettings.contains( "directoryName" ) ) {
+            setParquetDir( settings );
+        }
+    }
+
+    //region Not Delegated Methods
+
+    /**
+     * Creates and registers a physical Parquet table entry.
+     */
+    @Override
+    public List<PhysicalEntity> createTable( Context context, LogicalTableWrapper logical, AllocationTableWrapper allocation ) {
+        PhysicalTable table = adapterCatalog.createTable(
+                logical.table.getNamespaceName(),
+                logical.table.name,
+                logical.columns.stream().collect( Collectors.toMap( c -> c.id, c -> c.name ) ),
+                logical.table,
+                logical.columns.stream().collect( Collectors.toMap( t -> t.id, t -> t ) ),
+                logical.pkIds, allocation );
+
+        ParquetTable physical = currentNamespace.createParquetTable( table.id, table, this );
+        adapterCatalog.replacePhysical( physical );
+        return List.of( physical );
+    }
+
+
+    /**
+     * Restores table metadata after restart
+     */
+    @Override
+    public void restoreTable( AllocationTable alloc, List<PhysicalEntity> entities, Context context ) {
+        PhysicalEntity table = entities.get( 0 );
+        updateNamespace( table.namespaceName, table.namespaceId );
+        ParquetTable physical = currentNamespace.createParquetTable( table.id, table.unwrapOrThrow( PhysicalTable.class ), this );
+        adapterCatalog.addPhysical( alloc,physical );
+    }
+
+
+    /**
+     * Updates logical column naming in adapter catalog
+     */
+    @Override
+    public void renameLogicalColumn( long id, String newColumnName ) {
+        adapterCatalog.renameLogicalColumn( id, newColumnName );
+    }
+    //endregion
+    /**
+     * Methods excluded from Lombok delegate forwarding.
+     * These methods should not be delegated.
+     */
+    @SuppressWarnings("unused")
+    private interface Excludes {
+
+        void renameLogicalColumn( long id, String newColumnName );
+
+        void createTable( Context context, LogicalTableWrapper logical, AllocationTableWrapper allocation );
+
+        void restoreTable( AllocationTable alloc, List<PhysicalEntity> entities, Context context );
+
+    }
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/io/ParquetFileDiscovery.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/io/ParquetFileDiscovery.java
new file mode 100644
index 0000000000..59a27f3a48
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/io/ParquetFileDiscovery.java
@@ -0,0 +1,59 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.io;
+
+import java.io.File;
+import java.net.URL;
+import java.util.Arrays;
+import java.util.Set;
+import java.util.stream.Collectors;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.util.Sources;
+
+/**
+ * Utility methods for discovering Parquet files.
+ */
+public class ParquetFileDiscovery {
+
+    private ParquetFileDiscovery() {
+    }
+
+
+    /**
+     * Lists Parquet files below the configured source location.
+     */
+    public static Set<String> listParquetFiles( URL baseDir ) {
+        if ( Sources.of( baseDir ).file().isFile() ) {
+            return Set.of( Sources.of( baseDir ).file().getName() );
+        }
+
+        File[] files = Sources.of( baseDir ).file().listFiles( ( d, name ) -> isParquetFile( name ) );
+        if ( files == null ) {
+            throw new GenericRuntimeException( "No .parquet files were found." );
+        }
+        return Arrays.stream( files ).map( File::getName ).collect( Collectors.toSet() );
+    }
+
+
+    /**
+     * Checks whether the file name looks like a Parquet file.
+     */
+    public static boolean isParquetFile( String fileName ) {
+        return fileName != null && fileName.toLowerCase().endsWith( ".parquet" );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetTypeConverter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetTypeConverter.java
new file mode 100644
index 0000000000..fc859eee29
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetTypeConverter.java
@@ -0,0 +1,70 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.model;
+
+import org.apache.parquet.schema.LogicalTypeAnnotation;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.type.PolyType;
+
+public class ParquetTypeConverter {
+
+    /**
+     * Convert original parquet type into {@link PolyType}
+     * @param field parquet type
+     * @return {@link PolyType}
+     */
+    public PolyType fromParquetTypeToPolyType( Type field ) {
+
+        // treat nested types as string
+        if ( !field.isPrimitive() ) {
+            return PolyType.VARCHAR;
+        }
+
+        // not nested types
+        PrimitiveType primitive = field.asPrimitiveType();
+        LogicalTypeAnnotation logical = primitive.getLogicalTypeAnnotation();
+
+        // date types
+        if ( logical instanceof LogicalTypeAnnotation.DateLogicalTypeAnnotation ) {
+            return PolyType.DATE;
+        }
+        if ( logical instanceof LogicalTypeAnnotation.TimeLogicalTypeAnnotation ) {
+            return PolyType.TIME;
+        }
+        if ( logical instanceof LogicalTypeAnnotation.TimestampLogicalTypeAnnotation ) {
+            return PolyType.TIMESTAMP;
+        }
+
+        // string like types
+        if ( logical instanceof LogicalTypeAnnotation.StringLogicalTypeAnnotation
+                || logical instanceof LogicalTypeAnnotation.EnumLogicalTypeAnnotation
+                || logical instanceof LogicalTypeAnnotation.JsonLogicalTypeAnnotation ) {
+            return PolyType.VARCHAR;
+        }
+
+        // primitive types
+        return switch ( primitive.getPrimitiveTypeName() ) {
+            case BOOLEAN -> PolyType.BOOLEAN;
+            case INT32 -> PolyType.INTEGER;
+            case INT64 -> PolyType.BIGINT;
+            case FLOAT -> PolyType.REAL;
+            case DOUBLE -> PolyType.DOUBLE;
+            case FIXED_LEN_BYTE_ARRAY, BINARY, INT96 -> PolyType.VARBINARY;
+        };
+    }
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetProjectScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetProjectScanRule.java
new file mode 100644
index 0000000000..063eb34064
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetProjectScanRule.java
@@ -0,0 +1,51 @@
+package org.polypheny.db.adapter.parquet.planning;
+
+import java.util.List;
+import org.polypheny.db.algebra.core.AlgFactories;
+import org.polypheny.db.algebra.logical.relational.LogicalRelProject;
+import org.polypheny.db.plan.AlgOptRule;
+import org.polypheny.db.plan.AlgOptRuleCall;
+import org.polypheny.db.rex.RexIndexRef;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.tools.AlgBuilderFactory;
+
+
+public class ParquetProjectScanRule extends AlgOptRule {
+
+    public static final ParquetProjectScanRule INSTANCE = new ParquetProjectScanRule( AlgFactories.LOGICAL_BUILDER );
+
+
+    public ParquetProjectScanRule( AlgBuilderFactory algBuilderFactory ) {
+        super(
+                operand( LogicalRelProject.class, operand( ParquetScan.class, none() ) ),
+                algBuilderFactory,
+                "ParquetProjectScanRule" );
+    }
+
+
+    @Override
+    public void onMatch( AlgOptRuleCall call ) {
+        final LogicalRelProject project = call.alg( 0 );
+        final ParquetScan scan = call.alg( 1 );
+        int[] fields = getProjectFields( project.getProjects() );
+        if ( fields == null ) {
+            return;
+        }
+        call.transformTo( new ParquetScan( scan.getCluster(), scan.getEntity(), scan.parquetTable, fields ) );
+    }
+
+
+    private int[] getProjectFields( List<RexNode> exps ) {
+        final int[] fields = new int[exps.size()];
+        for ( int i = 0; i < exps.size(); i++ ) {
+            final RexNode exp = exps.get( i );
+            if ( exp instanceof RexIndexRef ) {
+                fields[i] = ((RexIndexRef) exp).getIndex();
+            } else {
+                return null;
+            }
+        }
+        return fields;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetScan.java
new file mode 100644
index 0000000000..de6dab3759
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetScan.java
@@ -0,0 +1,87 @@
+package org.polypheny.db.adapter.parquet.planning;
+
+import java.util.List;
+import org.apache.calcite.linq4j.tree.Blocks;
+import org.apache.calcite.linq4j.tree.Expressions;
+import org.apache.calcite.linq4j.tree.Primitive;
+import org.polypheny.db.adapter.parquet.schema.ParquetTable;
+import org.polypheny.db.adapter.parquet.schema.ParquetTranslatableTable;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.AlgWriter;
+import org.polypheny.db.algebra.core.relational.RelScan;
+import org.polypheny.db.algebra.enumerable.EnumerableAlg;
+import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.enumerable.PhysType;
+import org.polypheny.db.algebra.enumerable.PhysTypeImpl;
+import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.algebra.type.AlgDataTypeFactory;
+import org.polypheny.db.algebra.type.AlgDataTypeField;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgOptCost;
+import org.polypheny.db.plan.AlgPlanner;
+import org.polypheny.db.plan.AlgTraitSet;
+
+
+public class ParquetScan extends RelScan<ParquetTable> implements EnumerableAlg {
+
+    final ParquetTranslatableTable parquetTable;
+    final int[] fields;
+
+
+    public ParquetScan( AlgCluster cluster, ParquetTable table, ParquetTranslatableTable parquetTable, int[] fields ) {
+        super( cluster, cluster.traitSetOf( EnumerableConvention.INSTANCE ), table );
+        this.parquetTable = parquetTable;
+        this.fields = fields;
+    }
+
+    @Override
+    public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
+        return new ParquetScan( getCluster(), entity, parquetTable, fields );
+    }
+
+
+    @Override
+    public AlgWriter explainTerms( AlgWriter pw ) {
+        return super.explainTerms( pw ).item( "fields", Primitive.asList( fields ) );
+    }
+
+
+    @Override
+    public AlgDataType deriveRowType() {
+        final List<AlgDataTypeField> fieldList = entity.getTupleType().getFields();
+        final AlgDataTypeFactory.Builder builder = getCluster().getTypeFactory().builder();
+        for ( int field : fields ) {
+            builder.add( fieldList.get( field ) );
+        }
+        return builder.build();
+    }
+
+
+    @Override
+    public void register( AlgPlanner planner ) {
+        planner.addRule( ParquetProjectScanRule.INSTANCE );
+    }
+
+
+    @Override
+    public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
+        return super.computeSelfCost( planner, mq ).multiplyBy( ((double) fields.length + 2D) / ((double) entity.getTupleType().getFieldCount() + 2D) );
+    }
+
+
+    @Override
+    public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
+        PhysType physType = PhysTypeImpl.of( implementor.getTypeFactory(), getTupleType(), pref.preferArray() );
+        return implementor.result(
+                physType,
+                Blocks.toBlock(
+                        Expressions.call(
+                                entity.asExpression( ParquetTranslatableTable.class ),
+                                "project",
+                                implementor.getRootExpression(),
+                                Expressions.constant( fields ) ) ) );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetFilterableTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetFilterableTable.java
new file mode 100644
index 0000000000..bc35e7cc43
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetFilterableTable.java
@@ -0,0 +1,74 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.schema;
+
+import org.apache.calcite.linq4j.AbstractEnumerable;
+import org.apache.calcite.linq4j.Enumerable;
+import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.ParquetSource;
+import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.schema.types.FilterableEntity;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+
+/**
+ * Filterable table
+ */
+public class ParquetFilterableTable extends ParquetTable implements FilterableEntity {
+
+    public ParquetFilterableTable( long id, Source source, PhysicalTable table, List<PolyType> fieldTypes, int[] fields, ParquetSource csvSource ) {
+        super( id, source, table, fieldTypes, fields, csvSource );
+    }
+
+    /**
+     * Pushes supported filters.
+     */
+    @Override
+    public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> filters ) {
+        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
+        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+        return new AbstractEnumerable<>() {
+            @Override
+            public Enumerator<PolyValue[]> enumerator() {
+                return new Enumerator<>() {
+                    @Override
+                    public PolyValue[] current() {
+                        return new PolyValue[0];
+                    }
+                    @Override
+                    public boolean moveNext() {
+                        return false;
+                    }
+                    @Override
+                    public void reset() {
+
+                    }
+                    @Override
+                    public void close() {
+
+                    }
+                };
+            }
+        };
+    }
+}
+
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java
new file mode 100644
index 0000000000..cd435fbf38
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java
@@ -0,0 +1,82 @@
+package org.polypheny.db.adapter.parquet.schema;
+
+import java.net.MalformedURLException;
+import java.net.URL;
+import java.util.ArrayList;
+import java.util.List;
+import org.jetbrains.annotations.Nullable;
+import org.polypheny.db.adapter.parquet.ParquetSource;
+import org.polypheny.db.catalog.entity.physical.PhysicalColumn;
+import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.plan.Convention;
+import org.polypheny.db.schema.Namespace;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.util.Source;
+import org.polypheny.db.util.Sources;
+
+/**
+ * Create wrappers for parquet files
+ */
+public class ParquetNamespace extends Namespace {
+
+    private final URL directoryUrl;
+    private final ParquetTable.Flavor flavor;
+
+
+    /**
+     * Constructor:
+     * Creates a namespace bound to a directory URL and table flavor.
+     */
+    public ParquetNamespace( long id, long adapterId, URL directoryUrl, ParquetTable.Flavor flavor ) {
+        super( id, adapterId );
+        this.directoryUrl = directoryUrl;
+        this.flavor = flavor;
+    }
+
+
+    /**
+     * Creates a Parquet table wrapper for a physical table entry.
+     */
+    public ParquetTable createParquetTable( long id, PhysicalTable table, ParquetSource sourceAdapter ) {
+        List<PolyType> fieldTypes = new ArrayList<>();
+        List<Integer> fieldIds = new ArrayList<>( table.columns.size() );
+
+        List<PhysicalColumn> columns = table.getColumns();
+
+        for ( int i = 0; i < columns.size(); i++ ) {
+            PhysicalColumn column = columns.get( i );
+            fieldTypes.add( column.type );
+            fieldIds.add( i );
+        }
+
+        Source parquetSource;
+        try {
+            parquetSource = Sources.of( new URL( directoryUrl, table.name + ".parquet" ) );
+        } catch ( MalformedURLException e ) {
+            throw new GenericRuntimeException( e );
+        }
+
+        int[] fields = fieldIds.stream().mapToInt( i -> i ).toArray();
+        return createTable( id, parquetSource, table, fieldTypes, fields, sourceAdapter );
+    }
+
+
+    /**
+     * Instantiates the table class based on configured flavor.
+     */
+    private ParquetTable createTable( long id, Source source, PhysicalTable table, List<PolyType> fieldTypes, int[] fields, ParquetSource parquetSource ) {
+        return switch ( flavor ) {
+            case TRANSLATABLE -> new ParquetTranslatableTable( id, source, table, fieldTypes, fields, parquetSource );
+            case SCANNABLE -> new ParquetScannableTable( id, source, table, fieldTypes, fields, parquetSource );
+            case FILTERABLE -> new ParquetFilterableTable( id, source, table, fieldTypes, fields, parquetSource );
+        };
+    }
+
+
+    @Override
+    protected @Nullable Convention getConvention() {
+        return null;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetScannableTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetScannableTable.java
new file mode 100644
index 0000000000..7c0d6fef7b
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetScannableTable.java
@@ -0,0 +1,54 @@
+package org.polypheny.db.adapter.parquet.schema;
+
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.AbstractEnumerable;
+import org.apache.calcite.linq4j.Enumerable;
+import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.ParquetSource;
+import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.schema.types.ScannableEntity;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+/**
+ * Scannable table
+ */
+public class ParquetScannableTable extends ParquetTable implements ScannableEntity {
+
+    public ParquetScannableTable( long id, Source source, PhysicalTable table, java.util.List<PolyType> fieldTypes, int[] fields, ParquetSource parquetSource ) {
+        super( id, source, table, fieldTypes, fields, parquetSource );
+    }
+
+
+    @Override
+    public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
+        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
+        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+        return new AbstractEnumerable<>() {
+            @Override
+            public Enumerator<PolyValue[]> enumerator() {
+                return new Enumerator<>() {
+                    @Override
+                    public PolyValue[] current() {
+                        return new PolyValue[0];
+                    }
+                    @Override
+                    public boolean moveNext() {
+                        return false;
+                    }
+                    @Override
+                    public void reset() {
+
+                    }
+                    @Override
+                    public void close() {
+
+                    }
+                };
+            }
+        };
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java
new file mode 100644
index 0000000000..c95e2d04c1
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java
@@ -0,0 +1,65 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.schema;
+
+import org.polypheny.db.adapter.parquet.ParquetSource;
+import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.util.Source;
+import java.util.List;
+
+/**
+ * Base class for Parquet physical tables.
+ */
+public class ParquetTable extends PhysicalTable {
+    protected final Source source;
+    protected List<PolyType> fieldTypes;
+    protected final int[] fields;
+    protected final ParquetSource parquetSource;
+
+    /**
+     * Creates a Parquet table wrapper from a physical table definition.
+     */
+    ParquetTable( long id, Source source, PhysicalTable table, List<PolyType> fieldTypes, int[] fields, ParquetSource parquetSource ) {
+        super(
+                id,
+                table.allocationId,
+                table.logicalId,
+                table.name,
+                table.columns,
+                table.namespaceId,
+                table.namespaceName,
+                table.uniqueFieldIds,
+                table.adapterId );
+        this.source = source;
+        this.fieldTypes = fieldTypes;
+        this.fields = fields;
+        this.parquetSource = parquetSource;
+    }
+
+
+    /**
+     * Various degrees of table "intelligence".
+     * Table execution flavors used by the adapter.
+     */
+    public enum Flavor {
+        SCANNABLE, FILTERABLE, TRANSLATABLE
+    }
+
+}
+
+
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTranslatableTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTranslatableTable.java
new file mode 100644
index 0000000000..eae8f5cfc7
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTranslatableTable.java
@@ -0,0 +1,64 @@
+package org.polypheny.db.adapter.parquet.schema;
+
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.AbstractEnumerable;
+import org.apache.calcite.linq4j.Enumerable;
+import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.ParquetSource;
+import org.polypheny.db.adapter.parquet.planning.ParquetScan;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.schema.types.TranslatableEntity;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+/**
+ * Translatable table
+ */
+public class ParquetTranslatableTable extends ParquetTable implements TranslatableEntity {
+
+    protected ParquetTranslatableTable( long id, Source source, PhysicalTable table, List<PolyType> fieldTypes, int[] fields, ParquetSource parquetSource ) {
+        super( id, source, table, fieldTypes, fields, parquetSource );
+    }
+
+
+    public Enumerable<PolyValue[]> project( final DataContext dataContext, final int[] fields ) {
+        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
+        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+        return new AbstractEnumerable<>() {
+            @Override
+            public Enumerator<PolyValue[]> enumerator() {
+                return new Enumerator<>() {
+                    @Override
+                    public PolyValue[] current() {
+                        return new PolyValue[0];
+                    }
+                    @Override
+                    public boolean moveNext() {
+                        return false;
+                    }
+                    @Override
+                    public void reset() {
+
+                    }
+                    @Override
+                    public void close() {
+
+                    }
+                };
+            }
+        };
+    }
+
+
+    @Override
+    public AlgNode toAlg( AlgCluster cluster, AlgTraitSet traitSet ) {
+        return new ParquetScan( cluster, this, this, fields );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/util/HadoopConfigurationFactory.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/util/HadoopConfigurationFactory.java
new file mode 100644
index 0000000000..12e6a8c2b9
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/util/HadoopConfigurationFactory.java
@@ -0,0 +1,40 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.util;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.LocalFileSystem;
+import org.apache.hadoop.fs.local.LocalFs;
+
+/**
+ * Creates Hadoop configurations with the correct classloader and
+ * built-in local filesystem registrations for plugin environments.
+ */
+public final class HadoopConfigurationFactory {
+
+    private HadoopConfigurationFactory() {
+    }
+
+    public static Configuration create( ClassLoader classLoader ) {
+        Configuration conf = new Configuration( false );
+        conf.setClassLoader( classLoader );
+        conf.set( "fs.file.impl", LocalFileSystem.class.getName() );
+        conf.set( "fs.AbstractFileSystem.file.impl", LocalFs.class.getName() );
+        return conf;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/resources/orders_db/customers.parquet b/plugins/parquet-adapter/src/main/resources/orders_db/customers.parquet
new file mode 100644
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diff --git a/plugins/parquet-adapter/src/main/resources/orders_db/order_items.parquet b/plugins/parquet-adapter/src/main/resources/orders_db/order_items.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..6cfdffe94a7e1a6516a6debf57149e0b0c216d31
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diff --git a/plugins/parquet-adapter/src/main/resources/orders_db/orders.parquet b/plugins/parquet-adapter/src/main/resources/orders_db/orders.parquet
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diff --git a/plugins/parquet-adapter/src/main/resources/orders_db/products.parquet b/plugins/parquet-adapter/src/main/resources/orders_db/products.parquet
new file mode 100644
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diff --git a/plugins/parquet-adapter/src/test/java/ParquetTest.java b/plugins/parquet-adapter/src/test/java/ParquetTest.java
new file mode 100644
index 0000000000..8e1a9cf1b5
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/ParquetTest.java
@@ -0,0 +1,20 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+public class ParquetTest {
+
+}
diff --git a/plugins/parquet-adapter/src/test/resources/orders_db/customers.parquet b/plugins/parquet-adapter/src/test/resources/orders_db/customers.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..9dad32f216db14353ef565fef5b2fd3cccbf580a
GIT binary patch
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diff --git a/plugins/parquet-adapter/src/test/resources/orders_db/order_items.parquet b/plugins/parquet-adapter/src/test/resources/orders_db/order_items.parquet
new file mode 100644
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diff --git a/plugins/parquet-adapter/src/test/resources/orders_db/orders.parquet b/plugins/parquet-adapter/src/test/resources/orders_db/orders.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..aa50dd8cd7d122b372cfbf0dd45a0d385cae9731
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diff --git a/plugins/parquet-adapter/src/test/resources/orders_db/products.parquet b/plugins/parquet-adapter/src/test/resources/orders_db/products.parquet
new file mode 100644
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US9qTN>wi?N%v4m?Se)Yj0}MA7z5oCK

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HcmV?d00001

diff --git a/settings.gradle b/settings.gradle
index 914f8b5c94..fc25e34b8b 100644
--- a/settings.gradle
+++ b/settings.gradle
@@ -57,6 +57,7 @@ include 'plugins:mongodb-adapter'
 include 'plugins:file-adapter'
 include 'plugins:google-sheet-adapter'
 include 'plugins:excel-adapter'
+include 'plugins:parquet-adapter'
 
 // other plugins
 include 'plugins:notebooks'

From 282165d65e2f80d44db362e78e67cbefb064d96e Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Fri, 13 Mar 2026 15:24:09 +0100
Subject: [PATCH 02/46] Update licenses

---
 plugins/parquet-adapter/build.gradle | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/plugins/parquet-adapter/build.gradle b/plugins/parquet-adapter/build.gradle
index 814f11eef4..a5ec9bb02e 100644
--- a/plugins/parquet-adapter/build.gradle
+++ b/plugins/parquet-adapter/build.gradle
@@ -76,4 +76,7 @@ java {
 licensee {
     allow('MIT')
     allow('Apache-2.0')
+    allow('BSD-2-Clause')
+    allow('BSD-3-Clause')
+    allow('CDDL-1.1')
 }

From ad6747e2c57a661a3491b487269cb2a85716296e Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Tue, 17 Mar 2026 20:43:36 +0100
Subject: [PATCH 03/46] Support data read, filter, projection

---
 .../java/org/polypheny/db/PolyphenyDb.java    |   3 +-
 plugins/parquet-adapter/build.gradle          |  21 +-
 plugins/parquet-adapter/docs/README.md        | 292 +++++------
 .../docs/images/customers_data.png            | Bin 0 -> 70418 bytes
 .../docs/images/parquet_schema.png            | Bin 117091 -> 105528 bytes
 .../docs/images/query_filter.png              | Bin 0 -> 60380 bytes
 .../docs/images/query_projection.png          | Bin 0 -> 24124 bytes
 .../db/adapter/parquet/ParquetSource.java     |  27 +-
 .../parquet/execution/ParquetEnumerator.java  | 342 +++++++++++++
 .../parquet/execution/PredicateEvaluator.java | 119 +++++
 .../parquet/execution/ValueComparator.java}   |  12 +-
 .../parquet/execution/ValueExtractor.java     |  28 +
 .../adapter/parquet/model/ParquetFilter.java  |  13 +
 .../parquet/model/ParquetTypeConverter.java   |  70 ---
 .../schema/ParquetFilterableTable.java        |  77 ++-
 .../parquet/schema/ParquetTypeConverter.java  | 478 ++++++++++++++++++
 .../db/adapter/parquet/ParquetPluginTest.java | 177 +++++++
 17 files changed, 1389 insertions(+), 270 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/images/customers_data.png
 create mode 100644 plugins/parquet-adapter/docs/images/query_filter.png
 create mode 100644 plugins/parquet-adapter/docs/images/query_projection.png
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetEnumerator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/PredicateEvaluator.java
 rename plugins/parquet-adapter/src/{test/java/ParquetTest.java => main/java/org/polypheny/db/adapter/parquet/execution/ValueComparator.java} (61%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetFilter.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetTypeConverter.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTypeConverter.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java

diff --git a/dbms/src/main/java/org/polypheny/db/PolyphenyDb.java b/dbms/src/main/java/org/polypheny/db/PolyphenyDb.java
index 2182f39e5b..1d3c774b90 100644
--- a/dbms/src/main/java/org/polypheny/db/PolyphenyDb.java
+++ b/dbms/src/main/java/org/polypheny/db/PolyphenyDb.java
@@ -129,7 +129,8 @@ public class PolyphenyDb {
     public String defaultStoreName = "hsqldb";
 
     @Option(name = { "-defaultSource" }, description = "Type of default source")
-    public static String defaultSourceName = "csv";
+    //public static String defaultSourceName = "csv";
+    public static String defaultSourceName = "parquet";
 
     @Option(name = { "-c", "--config" }, description = "Path to the configuration file")
     protected String applicationConfPath;
diff --git a/plugins/parquet-adapter/build.gradle b/plugins/parquet-adapter/build.gradle
index a5ec9bb02e..582f57b310 100644
--- a/plugins/parquet-adapter/build.gradle
+++ b/plugins/parquet-adapter/build.gradle
@@ -11,8 +11,25 @@ dependencies {
     }
     implementation group: "org.apache.parquet", name: "parquet-column", version: parquet_version
     implementation group: "org.apache.parquet", name: "parquet-common", version: parquet_version
-    implementation group: "org.apache.hadoop", name: "hadoop-common", version: hadoop_version
-    implementation group: "org.apache.hadoop", name: "hadoop-mapreduce-client-core", version: hadoop_version
+    implementation(group: "org.apache.hadoop", name: "hadoop-common", version: hadoop_version) {
+        exclude group: "com.google.re2j", module: "re2j"
+        exclude group: "com.jcraft", module: "jsch"
+        exclude group: "jakarta.activation", module: "jakarta.activation-api"
+        exclude group: "javax.activation", module: "activation"
+        exclude group: "javax.servlet", module: "javax.servlet-api"
+        exclude group: "javax.servlet.jsp", module: "jsp-api"
+        exclude group: "javax.ws.rs", module: "jsr311-api"
+        exclude group: "javax.xml.bind", module: "jaxb-api"
+        exclude group: "javax.xml.stream", module: "stax-api"
+        exclude group: "org.bouncycastle", module: "bcprov-jdk18on"
+    }
+    implementation (group: "org.apache.hadoop", name: "hadoop-mapreduce-client-core", version: hadoop_version) {
+        exclude group: "jakarta.activation", module: "jakarta.activation-api"
+        exclude group: "javax.servlet", module: "javax.servlet-api"
+        exclude group: "javax.ws.rs", module: "jsr311-api"
+        exclude group: "aopalliance", module: "aopalliance"
+        exclude group: "jakarta.xml.bind", module: "jakarta.xml.bind-api"
+    }
 
     // --- Test Compile ---
     testImplementation project(path: ":dbms", configuration: "test")
diff --git a/plugins/parquet-adapter/docs/README.md b/plugins/parquet-adapter/docs/README.md
index d1da32266f..c456ca6cd9 100644
--- a/plugins/parquet-adapter/docs/README.md
+++ b/plugins/parquet-adapter/docs/README.md
@@ -2,7 +2,17 @@
 
 ## Overview
 
-The Parquet adapter exposes Parquet files as Polypheny source tables, derives schema from Parquet metadata, restores tables during adapter activation, and provides the infrastructure needed for query planning over data stored in Parquet files.
+The Parquet adapter exposes `.parquet` files as Polypheny source tables.
+At a high level, the adapter is responsible for:
+- discovering Parquet files from the configured source location
+- extracting relational schema metadata from Parquet file footers
+- restoring and registering tables whose data is read from Parquet files
+- executing read queries over Parquet data
+- applying projections and supported predicate filtering during query execution
+
+The adapter is read-only and currently targets relational source integration for embedded deployments.
+
+The following sections describe the architecture, execution flow, package responsibilities, supported query behavior, and current limitations in more detail.
 
 ## Build and Integration Changes
 
@@ -13,191 +23,133 @@ Affected files:
 - `gradle.properties`
 - `plugins/parquet-adapter/build.gradle`
 
-<br>
 
 2\. To make Parquet the default data source, `PolyphenyDB.java` sets `defaultSourceName` to `parquet`.<br>
 During `restore()`, `Catalog.defaultSource` is populated from the configured adapter state.
 
 
-
-
-## Main Execution Flow
-
-1\. `ParquetPlugin` registers the adapter template on initialization and removes it on shutdown.
-
-2\. `ParquetSource` is instantiated and configured with the Parquet source location.
-
-3\. `ParquetFileDiscovery` identifies the available `.parquet` files in the configured source.
-
-4\. `ParquetSource` reads Parquet metadata and extracts exported columns.
-
-5\. `ParquetNamespace` creates table wrappers for each discovered file.
-
-6\. Restored tables are registered in the adapter catalog.
-
-7\. Query execution uses the planning layer through `ParquetScan` and related rules.
+## Execution Flow
+
+1. **Plugin registration**
+    - **Responsible class:** `ParquetPlugin`
+    - Registers the Parquet adapter template during startup.
+    - Removes the template during shutdown.
+2. **Schema initialization**
+    - **Responsible classes:** `ParquetSource`, `ParquetFileDiscovery`, `ParquetTypeConverter`
+    - Resolves the configured Parquet directory or file location.
+    - Discovers available `.parquet` files.
+    - Builds table schemas from Parquet file metadata.
+    - Maps Parquet field types to Polypheny types.
+    - Normalizes physical table names and column names.
+    - Initializes the information page from the extracted column definitions.
+3. **Namespace and table creation**
+    - **Responsible classes:** `ParquetSource`, `ParquetNamespace`, `ParquetTable`
+    - Creates table wrappers for discovered Parquet files.
+    - Chooses the active table flavor.
+    - Registers physical tables in the adapter catalog.
+4. **Table restore during startup**
+    - **Responsible classes:** `ParquetSource`, `ParquetNamespace`
+    - During application startup, the adapter calls `restoreTable(...)` for persisted Parquet tables.
+    - The adapter recreates table wrappers and re-registers them in the adapter catalog.
+5. **Query entry into the adapter**
+    - **Responsible class:** `ParquetFilterableTable`
+    - Receives scan requests from the query engine.
+    - Inspects projected columns and query conditions.
+    - Translates supported query conditions into adapter-level filters.
+6. **Projection and filter pushdown**
+    - **Responsible classes:** `ParquetFilterableTable`, `ParquetEnumerator`, `ParquetFilter`
+    - Pushes supported filter conditions into adapter-specific filter objects.
+    - Pushes column projection into the Parquet reader by limiting the read schema to required columns.
+    - Ensures that columns needed for filtering are included even if they are not part of the final output.
+7. **Row-group pruning**
+    - **Responsible classes:** `ParquetEnumerator`, `PredicateEvaluator`
+    - Uses pushed filter conditions together with Parquet statistics to skip row groups that cannot match.
+
+## Supported Filter Conditions
+
+The active `FILTERABLE` execution path currently pushes only simple comparison conditions into the adapter.
+
+Supported operators:
+- `=`
+- `!=`
+- `>`
+- `>=`
+- `<`
+- `<=`
+
+Supported condition shape:
+- `column OP literal`
+
+Notes:
+- The left-hand side must be a column reference.
+- The right-hand side must be a literal value.
+- Simple casts on the column side are accepted.
+- Unsupported conditions remain outside the adapter-specific filter flow.
 
 
 ## Package Structure and Responsibilities
 
 ### `root`
-
-Contains main classes
-
-#### 1. `ParquetPlugin`
-
-Registers the adapter template (`ParquetSource`) on initialization and removes the template on stop.
-
-#### 2. `ParquetSource`
-
-Core adapter entry point. Responsible for extracting column information from Parquet metadata, creating the information page, restoring tables, and coordinating namespace/table creation.
-
-Main functions:
-
-- `constructor()`
-
-- `setParquetDir()`
-
-- `createInformationPage()`
-
-- `getExportedColumns()`
-
-- `computePhysicalTableName()`
-
-- `getValidTableName()`
-
-- `getExportedColumnsFromFile()`
-
-- `getExportedColumnFromField()`
-
-- `getValidColumnNameFromField()`
-
-- `getInformationTable()`
-
-- `enableInformationPage()`
-
-- `restoreTable()` called from `AdapterRestore.activate()`
-
-- `createParquetTable()`
-
-
-Restore flow:
-
-- `restoreTable()` calls `parquetNamespace.createParquetTable()` to create physical tables by flavor.
-
-- Restored tables are then added to `adapterCatalog`.
-
-
-Create/refresh flow:
-
-- `createParquetTable()` calls `parquetNamespace.createParquetTable()` and replaces the affected tables in `adapterCatalog`.
+Core adapter entry points and lifecycle integration.
+- `ParquetPlugin`: registers and unregisters the adapter template.
+- `ParquetSource`: main adapter implementation; manages source settings, schema export, information-page setup, namespace updates, table creation, and table restore.
 
 ### `io`
-Contains classes related to Parquet file access and discovery, including locating input files and handling Parquet-specific input operations.
-
-#### 3. `ParquetFileDiscovery`
-
-Helper functionality for identifying valid Parquet files.
-
-Main functions:
-
-- `listParquetFiles(dir)`
-
-- `isParquetFile(filename)`
-
+Parquet file access helpers.
+- `ParquetFileDiscovery`: discovers valid `.parquet` files in the configured source.
 
 ### `schema`
-
-Contains namespace and table wrapper classes that expose Parquet-backed tables to Polypheny.
-
-
-#### 4. `ParquetNamespace`
-
-Creates table wrappers for Parquet files and chooses table creation by flavor.
-
-Main functions:
-
-- `createParquetTable()`
-
-- `createTable()` to create the concrete table according to flavor
-
-
-#### 5. `ParquetTable`
-
-Base table abstraction containing metadata and other shared information.
-
-Main elements:
-
-- `Flavor` enum
-
-
-#### 6. `ParquetFilterableTable` extends `ParquetTable`
-
-Currently a placeholder or dummy implementation for filterable behavior.
-
-#### 7. `ParquetScannableTable` extends `ParquetTable`
-
-Currently a placeholder or dummy implementation for scannable behavior.
-
-#### 8. `ParquetTranslatableTable` extends `ParquetTable`
-
-Currently a placeholder or dummy implementation for translatable behavior.
-
-
+Table wrappers and schema-related conversion logic.
+- `ParquetNamespace`: creates Parquet table wrappers for the active table flavor.
+- `ParquetTable`: base class for Parquet-backed physical tables.
+- `ParquetFilterableTable`: active table implementation for filter-aware scans.
+- `ParquetScannableTable`: scaffold for scan-based execution.
+- `ParquetTranslatableTable`: scaffold for planner-driven execution.
+- `ParquetTypeConverter`: converts Parquet schema types, runtime values, and filter literals into Polypheny-compatible representations.
 
 ### `model`
+Internal data representation.
+- `ParquetFilter`: immutable representation of a pushed filter condition.
 
-Contains Parquet-to-Polypheny type conversion logic.
-
-#### 9. `ParquetTypeConverter`
-
-Converts Parquet types to Polypheny types.
-
-Main function:
-
-- `fromParquetTypeToPolyType()`
-
+### `execution`
+Runtime data access and filter evaluation.
+Classes responsible for reading Parquet data and returning rows.
+- `ParquetEnumerator`: drives Parquet reading, projection handling, row-group iteration, and row production.
+- `PredicateEvaluator`: uses Parquet statistics to decide whether row groups can be skipped.
+- `ValueExtractor`: extracts raw values from Parquet records.
+- `ValueComparator`: compares runtime values during filter evaluation.
 
 ### `planning`
-
-Contains relational scan and planner rule classes for Parquet query support.
-
-#### 10. `ParquetScan`
-
-Represents the Parquet scan operator in the planning/execution layer.
-
-#### 11. `ParquetProjectScanRule`
-
-Planner rule associated with Parquet scans.
-
+Classes that integrate the Parquet adapter with Polypheny’s query planner.
+Handling query-plan representation and optimization.
+- `ParquetScan`: Parquet scan operator for the planning layer.
+- `ParquetProjectScanRule`: planner rule for projection handling over Parquet scans.
 
 ### `util`
+Shared infrastructure helpers.
+- `HadoopConfigurationFactory`: builds Hadoop configuration objects for Parquet access inside the plugin environment.
 
-Contains reusable infrastructure helpers such as Hadoop configuration setup for Parquet reading.
-
-#### 12. `HadoopConfigurationFactory`
-
-Creates Hadoop `Configuration` instances for Parquet readers and ensures the proper classloader and local filesystem registration are available in the plugin environment.
-
-
+## Schema Mapping Rules
 
-## Schema Extraction and Naming Rules
+- Table names are derived from Parquet file names and normalized before registration.
+- Column names are derived from Parquet field names and normalized before registration.
+- Column types are mapped from Parquet schema types to Polypheny types.
+- String-like Parquet fields are mapped to `TEXT`.
+- Unlike SQL types with fixed length declarations, Parquet does not impose a fixed maximum string length.
 
-- Table names are derived from Parquet file names and normalized through `getValidTableName()`.
-
-- Column metadata is extracted from Parquet footer/schema metadata.
+## Flavor Selection
 
-- Column names are normalized through `getValidColumnNameFromField()`.
+The adapter defines three table flavors through `ParquetTable.Flavor`:
 
-- Type mapping is delegated to `ParquetTypeConverter`.
+- `FILTERABLE`
+- `SCANNABLE`
+- `TRANSLATABLE`
 
-- The document should explicitly mention how unsupported or ambiguous Parquet types are handled.
+The active flavor is selected by `ParquetSource` when the namespace is created.
 
+In the current implementation, `ParquetSource.updateNamespace()` selects `FILTERABLE`, so discovered and restored Parquet tables are exposed through `ParquetFilterableTable`.
 
-## Flavor Selection
-
-Appropriate table wrapper is created for a discovered Parquet file, for example `filterable`, `scannable`, or `translatable`.
-Currently only one flavor is active in practice.
+`SCANNABLE` and `TRANSLATABLE` remain available as structural alternatives, but they are not currently used as the default execution path.
 
 
 ## Information Page
@@ -214,15 +166,27 @@ The information page displays the following column metadata:
 The following figure shows an example of the displayed schema information.
 ![Schema display](images/parquet_schema.png)
 
+## Data Presentation
+![Schema display](images/customers_data.png)
+
+## Query Projection
+![Schema display](images/query_projection.png)
 
+## Query Filter
+![Schema display](images/query_filter.png)
 
 ## Current Gaps and Future Work
+### Relational Parquet data source implementation
+- Complete the `SCANNABLE` and `TRANSLATABLE` execution paths so they provide real runtime behavior comparable to the active `FILTERABLE` path.
+- Extend filter support beyond simple comparisons of the form `column OP literal`.
+  Planned examples include `BETWEEN`, `IN`, `IS NULL`, `IS NOT NULL`, and more complex boolean combinations such as `AND` and `OR`.
+- Improve handling of nested and complex Parquet schemas instead of treating them only as textual representations.
+- Add partition-aware file discovery and query pruning for partitioned Parquet datasets.
+- Add support for remote and Hadoop-backed filesystems beyond the current local-file configuration.
+- Document and strengthen error handling for invalid files, unsupported schema constructs, and conversion failures.
+- Automated test coverage for schema extraction, table restore, projection pushdown, filter pushdown, row-group pruning, and runtime type conversion.
+- Add write support if the adapter is later extended beyond its current read-only design.
+
+### Document Parquet data source implementation
 
-- Replace placeholder filterable, scannable, and translatable table variants with real implementations.
-- Add explicit support for Hadoop-backed remote filesystems.
-- Add write support
-- Handle nested parquet schema
-- implement partition discovery (?)
-- Document error handling for invalid files, missing metadata, and unsupported types.
-- Add tests and document the tested scenarios for discovery, schema extraction, restore, and planning.
 
diff --git a/plugins/parquet-adapter/docs/images/customers_data.png b/plugins/parquet-adapter/docs/images/customers_data.png
new file mode 100644
index 0000000000000000000000000000000000000000..0708ec39f355b2621b24840520979823255aaa52
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diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
index 97e7bb3b7a..063f9af5e6 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
@@ -32,11 +32,10 @@
 import org.polypheny.db.adapter.RelationalScanDelegate;
 import org.polypheny.db.adapter.annotations.AdapterProperties;
 import org.polypheny.db.adapter.annotations.AdapterSettingDirectory;
-import org.polypheny.db.adapter.annotations.AdapterSettingInteger;
 import org.polypheny.db.adapter.annotations.AdapterSettingList;
 import org.polypheny.db.adapter.annotations.AdapterSettingString;
 import org.polypheny.db.adapter.parquet.io.ParquetFileDiscovery;
-import org.polypheny.db.adapter.parquet.model.ParquetTypeConverter;
+import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
 import org.polypheny.db.adapter.parquet.schema.ParquetNamespace;
 import org.polypheny.db.adapter.parquet.schema.ParquetTable;
 
@@ -78,18 +77,18 @@
 @AdapterSettingList(name = "method", options = { "upload", "link" }, defaultValue = "upload", description = "If the supplied file(s) should be uploaded or a link to the local filesystem is used (sufficient permissions are required).", position = 1)
 @AdapterSettingDirectory(subOf = "method_upload", name = "directory", defaultValue = "classpath://orders_db", description = "You can upload one or multiple .parquet files.", position = 2)
 @AdapterSettingString(subOf = "method_link", defaultValue = "classpath://orders_db", name = "directoryName", description = "You can select a path to a folder or specific .parquet files.", position = 2)
-@AdapterSettingInteger(
-        name = "maxStringLength",
-        defaultValue = 255,
-        position = 3,
-        description = "Which length (number of characters including whitespace) should be used for the varchar columns. Make sure this is equal or larger than the longest string in any of the columns.")
+//@AdapterSettingInteger(
+//        name = "maxStringLength",
+//        defaultValue = 255,
+//        position = 3,
+//        description = "Which length (number of characters including whitespace) should be used for the varchar columns. Make sure this is equal or larger than the longest string in any of the columns.")
 public class ParquetSource extends DataSource<RelAdapterCatalog> implements RelationalDataSource {
 
     @Delegate(excludes = Excludes.class)
     private final RelationalScanDelegate delegate;
 
     private final ConnectionMethod connectionMethod;
-    private final int maxStringLength;
+    //private final int maxStringLength;
     private final ParquetTypeConverter parquetTypeConverter;
     private URL parquetDir;
 
@@ -112,10 +111,10 @@ public ParquetSource(final long storeId, final String uniqueName, final Map<Stri
                 ? ConnectionMethod.from( settings.get( "method" ).toUpperCase() )
                 : ConnectionMethod.UPLOAD;
 
-        this.maxStringLength = Integer.parseInt( settings.get( "maxStringLength" ) );
-        if ( maxStringLength < 1 ) {
-            throw new GenericRuntimeException( "Invalid value for maxStringLength: " + maxStringLength );
-        }
+//        this.maxStringLength = Integer.parseInt( settings.get( "maxStringLength" ) );
+//        if ( maxStringLength < 1 ) {
+//            throw new GenericRuntimeException( "Invalid value for maxStringLength: " + maxStringLength );
+//        }
 
         setParquetDir( settings );
         createInformationPage();
@@ -201,12 +200,12 @@ private List<ExportedColumn> getExportedColumnsFromFile( String fileName, String
     private ExportedColumn getExportedColumnFromField(Type field, String fileName, String physicalTableName, int position) {
         String columnName = getValidColumnNameFromField(field);
         PolyType polyType = parquetTypeConverter.fromParquetTypeToPolyType( field );
-        Integer length = polyType == PolyType.VARCHAR ? maxStringLength : null;
+        //Integer length = polyType == PolyType.VARCHAR ? maxStringLength : null;
         return new ExportedColumn(
                     columnName,
                     polyType,
                     null,
-                    length,
+                    null,
                     null,
                     null,
                     null,
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetEnumerator.java
new file mode 100644
index 0000000000..a3630ce533
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetEnumerator.java
@@ -0,0 +1,342 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.execution;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.LinkedHashSet;
+import java.util.List;
+import java.util.Set;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.Enumerator;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.Path;
+import org.apache.parquet.column.page.PageReadStore;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.convert.GroupRecordConverter;
+import org.apache.parquet.hadoop.ParquetFileReader;
+import org.apache.parquet.hadoop.metadata.BlockMetaData;
+import org.apache.parquet.hadoop.util.HadoopInputFile;
+import org.apache.parquet.io.ColumnIOFactory;
+import org.apache.parquet.io.MessageColumnIO;
+import org.apache.parquet.io.RecordReader;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.model.ParquetFilter;
+import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
+import org.polypheny.db.adapter.parquet.util.HadoopConfigurationFactory;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+public class ParquetEnumerator implements Enumerator<PolyValue[]> {
+
+    private final AtomicBoolean cancelFlag;
+    private final List<ParquetFilter> filters;
+    private final ParquetFileReader fileReader;
+    private final MessageType schema;
+    private final MessageType projectionSchema;
+    private final int[] projectionIndexes;
+    private final List<BlockMetaData> blocks;
+    private final ValueExtractor valueExtractor;
+    private final PredicateEvaluator predicateEvaluator;
+    private final ParquetTypeConverter typeConverter;
+    private final int columnCount;
+
+    private RecordReader<Group> recordReader;
+    private long rowCountInGroup;
+    private long rowIndexInGroup;
+    private int blockIndex;
+    private PolyValue[] current;
+
+
+    /**
+     * Constructor
+     * @param source file
+     * @param cancelFlag used in moveNext()
+     * @param fields columns
+     * @param filters list of parquet filters
+     * @throws GenericRuntimeException on failure
+     */
+
+    public ParquetEnumerator(
+            Source source,
+            AtomicBoolean cancelFlag,
+            int[] fields,
+            List<ParquetFilter> filters ) throws GenericRuntimeException {
+
+        this.cancelFlag = cancelFlag;
+        this.filters = filters == null ? List.of() : filters;
+        this.valueExtractor = new ValueExtractor();
+        this.predicateEvaluator = new PredicateEvaluator();
+        this.typeConverter = new ParquetTypeConverter();
+
+        try {
+            // create file reader
+            Path path = new Path( source.url().toURI() );
+            Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
+            this.fileReader = ParquetFileReader.open( HadoopInputFile.fromPath( path, conf ) );
+
+            this.schema = fileReader.getFooter().getFileMetaData().getSchema();
+            this.columnCount = fields.length == 0 ? schema.getFieldCount() : Math.min( fields.length, schema.getFieldCount() );
+            // combine projection columns (fields) with filter columns
+            this.projectionIndexes = computeProjectionIndexes( fields, this.filters, this.columnCount );
+            this.projectionSchema = buildProjectionSchema( schema, projectionIndexes );
+            this.fileReader.setRequestedSchema( projectionSchema );
+            this.blocks = fileReader.getFooter().getBlocks();
+
+            this.blockIndex = 0;
+            this.recordReader = null;
+            this.rowCountInGroup = 0;
+            this.rowIndexInGroup = 0;
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Unable to open parquet file: " + source.path(), e );
+        }
+    }
+
+    //region Enumerator operations
+
+
+    /**
+     * return current row
+     *
+     * @return current row
+     */
+    @Override
+    public PolyValue[] current() {
+        return current;
+    }
+
+
+    /**
+     * Move to the next matching row
+     *
+     * @return True if there is a new row or False otherwise.
+     */
+    @Override
+    public boolean moveNext() {
+        try {
+            for ( ; ; ) {
+                if ( cancelFlag.get() ) {
+                    current = null;
+                    return false;
+                }
+
+                if ( !ensureRecordReader() ) {
+                    current = null;
+                    return false;
+                }
+
+                Group group = recordReader.read();
+                rowIndexInGroup++;
+                if ( group == null ) {
+                    recordReader = null;
+                    continue;
+                }
+
+                PolyValue[] row = extractRow( group );
+                if ( !matchesFilters( row ) ) {
+                    continue;
+                }
+
+                current = row;
+                return true;
+            }
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Error while reading parquet data", e );
+        }
+    }
+
+
+    /**
+     * Reset is not supported
+     */
+    @Override
+    public void reset() {
+        throw new UnsupportedOperationException();
+    }
+
+
+    /**
+     * Close reader
+     */
+    @Override
+    public void close() {
+        try {
+            fileReader.close();
+        } catch ( IOException e ) {
+            throw new GenericRuntimeException( "Error closing parquet reader", e );
+        }
+    }
+    //endregion
+
+
+    /**
+     * Ensures a record reader is available for the current row group.
+     *
+     * @return true if reader was created, false - if no compatible row groups found
+     * @throws IOException when read fails
+     */
+    private boolean ensureRecordReader() throws IOException {
+        if ( recordReader != null && rowIndexInGroup < rowCountInGroup ) {
+            return true;
+        }
+
+        while ( blockIndex < blocks.size() ) {
+            BlockMetaData block = blocks.get( blockIndex );
+            // check if block contains filter values according to metadata in block
+            // if not - row group can be skipped
+            if ( !predicateEvaluator.mightContain( block, schema, filters ) ) {
+                fileReader.skipNextRowGroup();
+                blockIndex++;
+                continue;
+            }
+
+            // create reader for compatible row group
+
+            PageReadStore pages = fileReader.readNextRowGroup();
+            blockIndex++;
+
+            if ( pages == null ) {
+                return false;
+            }
+
+            MessageColumnIO columnIO = new ColumnIOFactory().getColumnIO( this.projectionSchema );
+            this.recordReader = columnIO.getRecordReader( pages, new GroupRecordConverter( this.projectionSchema ) );
+            this.rowCountInGroup = pages.getRowCount();
+            this.rowIndexInGroup = 0;
+            return true;
+        }
+
+        return false;
+    }
+
+
+    /**
+     * Extracts one row using the requested read schema.
+     */
+    private PolyValue[] extractRow( Group group ) {
+        PolyValue[] row = new PolyValue[columnCount];
+        for ( int readIndex = 0; readIndex < this.projectionIndexes.length; readIndex++ ) {
+            var type = projectionSchema.getType( readIndex );
+            var value = valueExtractor.extractValue( group, readIndex, type );
+            row[readIndex] = typeConverter.fromObjToPolyValue( type, value );
+        }
+        return row;
+    }
+
+
+    /**
+     * Evaluates pushed filters against one extracted row.
+     */
+    private boolean matchesFilters( PolyValue[] row ) {
+        if ( filters.isEmpty() ) {
+            return true;
+        }
+
+        // each filter - for specific column
+        for ( ParquetFilter filter : filters ) {
+            // validate column index
+            int idx = filter.columnIndex();
+            if ( idx < 0 || idx >= row.length || idx >= projectionIndexes.length ) {
+                return false; //invalid filter column
+            }
+
+            // apply filter only for primitive types
+            int schemaFieldIndex = projectionIndexes[idx];
+            Type fieldType = schema.getType( schemaFieldIndex );
+            if ( !fieldType.isPrimitive() ) {
+                return false;
+            }
+
+            Object actual = row[idx];
+            if ( actual == null ) {
+                return false;
+            }
+
+            PrimitiveType primitiveType = fieldType.asPrimitiveType();
+            Object expected = this.typeConverter.fromLiteralToPrimitive( primitiveType, filter.literalValue() );
+            if ( expected == null ) {
+                // incorrect filter value
+                return false;
+            }
+
+            Integer cmp = ValueComparator.compareValues( primitiveType, actual, expected );
+
+            if ( cmp == null || !matchesOperator( filter, cmp ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Applies comparison result to a filter operator.
+     */
+    private boolean matchesOperator( ParquetFilter filter, int cmp ) {
+        return switch ( filter.operator() ) {
+            case EQUALS -> cmp == 0;
+            case NOT_EQUALS -> cmp != 0;
+            case GREATER_THAN -> cmp > 0;
+            case GREATER_THAN_OR_EQUAL -> cmp >= 0;
+            case LESS_THAN -> cmp < 0;
+            case LESS_THAN_OR_EQUAL -> cmp <= 0;
+            default -> false;
+        };
+    }
+
+
+    /**
+     * Computes the set of columns that must be read:
+     * combines projection columns (fields) with filter columns
+     */
+    private static int[] computeProjectionIndexes( int[] projectedFields, List<ParquetFilter> filters, int columnCount ) {
+        Set<Integer> fields = new LinkedHashSet<>();
+        for ( int field : projectedFields ) {
+            if ( field >= 0 && field < columnCount ) {
+                fields.add( field );
+            }
+        }
+        for ( ParquetFilter filter : filters ) {
+            int index = filter.columnIndex();
+            if ( index >= 0 && index < columnCount ) {
+                fields.add( index );
+            }
+        }
+        return fields.stream().mapToInt( Integer::intValue ).toArray();
+    }
+
+
+    /**
+     * Builds the projected read schema.
+     */
+    private static MessageType buildProjectionSchema( MessageType fullSchema, int[] projectedFieldIndexes ) {
+        if ( projectedFieldIndexes.length == 0 ) {
+            return fullSchema;
+        }
+
+        List<Type> fields = new ArrayList<>( projectedFieldIndexes.length );
+        for ( int index : projectedFieldIndexes ) {
+            fields.add( fullSchema.getType( index ) );
+        }
+        return new MessageType( fullSchema.getName(), fields );
+    }
+
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/PredicateEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/PredicateEvaluator.java
new file mode 100644
index 0000000000..34dc413f38
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/PredicateEvaluator.java
@@ -0,0 +1,119 @@
+package org.polypheny.db.adapter.parquet.execution;
+
+import java.util.List;
+import org.apache.parquet.column.statistics.Statistics;
+import org.apache.parquet.hadoop.metadata.BlockMetaData;
+import org.apache.parquet.hadoop.metadata.ColumnChunkMetaData;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.model.ParquetFilter;
+import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
+import org.polypheny.db.algebra.constant.Kind;
+
+/**
+ * Checks whether a row group can be skipped for a given filter (predicate)
+ */
+public class PredicateEvaluator {
+
+    private final ParquetTypeConverter typeConverter = new ParquetTypeConverter();
+
+
+    /**
+     * Returns whether the row group may contain matches.
+     */
+    public boolean mightContain( BlockMetaData block, MessageType schema, List<ParquetFilter> filters ) {
+        if ( filters == null || filters.isEmpty() ) {
+            return true;
+        }
+
+        for ( ParquetFilter filter : filters ) {
+            int i = filter.columnIndex();
+            if ( i < 0 || i >= schema.getFieldCount() ) {
+                continue;
+            }
+
+            Type type = schema.getType( i );
+            if ( !type.isPrimitive() ) {
+                continue;
+            }
+
+            PrimitiveType primitive = type.asPrimitiveType();
+            ColumnChunkMetaData column = findColumn( block.getColumns(), schema.getFieldName( i ) );
+            if ( column == null ) {
+                continue;
+            }
+
+            Statistics<?> stats = column.getStatistics();
+            if ( stats == null || stats.isEmpty() ) {
+                continue;
+            }
+
+            long rowCount = block.getRowCount();
+            long nullCount = stats.isNumNullsSet() ? stats.getNumNulls() : -1L;
+            if ( nullCount == rowCount && rowCount > 0 ) {
+                return false;
+            }
+
+            Object expected = typeConverter.fromLiteralToPrimitive( primitive, filter.literalValue() );
+            if ( expected == null ) {
+                continue;
+            }
+
+            if ( !stats.hasNonNullValue() ) {
+                return false;
+            }
+
+            Object min = stats.genericGetMin();
+            Object max = stats.genericGetMax();
+            if ( min == null || max == null ) {
+                continue;
+            }
+
+            Integer cmpMin = ValueComparator.compareValues( primitive, expected, min );
+            Integer cmpMax = ValueComparator.compareValues( primitive, expected, max );
+            if ( cmpMin == null || cmpMax == null ) {
+                continue;
+            }
+
+            if ( canSkipBlock( filter.operator(), primitive, cmpMin, cmpMax, min, max, expected, rowCount, nullCount ) ) {
+                return false;
+            }
+        }
+
+        return true;
+    }
+
+
+    /**
+     * Applies operator-specific pruning rules.
+     */
+    private boolean canSkipBlock( Kind operator, PrimitiveType primitive, int cmpMin, int cmpMax, Object min, Object max, Object expected, long rowCount, long nullCount ) {
+        return switch ( operator ) {
+            case EQUALS -> cmpMin < 0 || cmpMax > 0;
+            case GREATER_THAN -> cmpMax >= 0;
+            case GREATER_THAN_OR_EQUAL -> cmpMax > 0;
+            case LESS_THAN -> cmpMin <= 0;
+            case LESS_THAN_OR_EQUAL -> cmpMin < 0;
+            case NOT_EQUALS -> {
+                Integer cmpMinMax = ValueComparator.compareValues( primitive, min, max );
+                Integer cmpMinExpected = ValueComparator.compareValues( primitive, min, expected );
+                yield cmpMinMax != null && cmpMinExpected != null && cmpMinMax == 0 && cmpMinExpected == 0 && nullCount == 0 && rowCount > 0;
+            }
+            default -> false;
+        };
+    }
+
+
+    /**
+     * Finds column metadata by top-level Parquet field name.
+     */
+    private ColumnChunkMetaData findColumn( List<ColumnChunkMetaData> columns, String fieldName ) {
+        for ( ColumnChunkMetaData column : columns ) {
+            if ( column.getPath() != null && fieldName.equals( column.getPath().toDotString() ) ) {
+                return column;
+            }
+        }
+        return null;
+    }
+}
diff --git a/plugins/parquet-adapter/src/test/java/ParquetTest.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueComparator.java
similarity index 61%
rename from plugins/parquet-adapter/src/test/java/ParquetTest.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueComparator.java
index 8e1a9cf1b5..ab663dbb3b 100644
--- a/plugins/parquet-adapter/src/test/java/ParquetTest.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueComparator.java
@@ -14,7 +14,17 @@
  * limitations under the License.
  */
 
+package org.polypheny.db.adapter.parquet.execution;
 
-public class ParquetTest {
+import org.apache.parquet.schema.PrimitiveType;
 
+public class ValueComparator {
+
+    public static Integer compareValues( PrimitiveType primitive, Object left, Object right ) {
+        try {
+            return primitive.comparator().compare( left, right );
+        } catch ( Exception e ) {
+            return null;
+        }
+    }
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java
new file mode 100644
index 0000000000..4f3c70ff73
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java
@@ -0,0 +1,28 @@
+package org.polypheny.db.adapter.parquet.execution;
+
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Type;
+
+/**
+ * Central conversion layer between Parquet and Polypheny value representations.
+ */
+public class ValueExtractor {
+
+    public Object extractValue( Group group, int index, Type field ) {
+        if ( !field.isPrimitive() ) {
+            return group.getValueToString( index, 0 );
+        }
+
+        PrimitiveType primitive = field.asPrimitiveType();
+
+        return switch ( primitive.getPrimitiveTypeName() ) {
+            case BOOLEAN -> group.getBoolean( index, 0 );
+            case INT32 -> group.getInteger( index, 0 );
+            case INT64 -> group.getLong( index, 0 );
+            case FLOAT -> group.getFloat( index, 0 );
+            case DOUBLE -> group.getDouble( index, 0 );
+            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> group.getBinary( index, 0 );
+        };
+    }
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetFilter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetFilter.java
new file mode 100644
index 0000000000..5a44cd29cb
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetFilter.java
@@ -0,0 +1,13 @@
+package org.polypheny.db.adapter.parquet.model;
+
+import org.polypheny.db.algebra.constant.Kind;
+
+/**
+ * Immutable Filter description
+ * @param columnIndex - index of filter column
+ * @param operator - filter operation
+ * @param literalValue - filter value
+ */
+public record ParquetFilter(int columnIndex, Kind operator, String literalValue) {
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetTypeConverter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetTypeConverter.java
deleted file mode 100644
index fc859eee29..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetTypeConverter.java
+++ /dev/null
@@ -1,70 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.model;
-
-import org.apache.parquet.schema.LogicalTypeAnnotation;
-import org.apache.parquet.schema.PrimitiveType;
-import org.apache.parquet.schema.Type;
-import org.polypheny.db.type.PolyType;
-
-public class ParquetTypeConverter {
-
-    /**
-     * Convert original parquet type into {@link PolyType}
-     * @param field parquet type
-     * @return {@link PolyType}
-     */
-    public PolyType fromParquetTypeToPolyType( Type field ) {
-
-        // treat nested types as string
-        if ( !field.isPrimitive() ) {
-            return PolyType.VARCHAR;
-        }
-
-        // not nested types
-        PrimitiveType primitive = field.asPrimitiveType();
-        LogicalTypeAnnotation logical = primitive.getLogicalTypeAnnotation();
-
-        // date types
-        if ( logical instanceof LogicalTypeAnnotation.DateLogicalTypeAnnotation ) {
-            return PolyType.DATE;
-        }
-        if ( logical instanceof LogicalTypeAnnotation.TimeLogicalTypeAnnotation ) {
-            return PolyType.TIME;
-        }
-        if ( logical instanceof LogicalTypeAnnotation.TimestampLogicalTypeAnnotation ) {
-            return PolyType.TIMESTAMP;
-        }
-
-        // string like types
-        if ( logical instanceof LogicalTypeAnnotation.StringLogicalTypeAnnotation
-                || logical instanceof LogicalTypeAnnotation.EnumLogicalTypeAnnotation
-                || logical instanceof LogicalTypeAnnotation.JsonLogicalTypeAnnotation ) {
-            return PolyType.VARCHAR;
-        }
-
-        // primitive types
-        return switch ( primitive.getPrimitiveTypeName() ) {
-            case BOOLEAN -> PolyType.BOOLEAN;
-            case INT32 -> PolyType.INTEGER;
-            case INT64 -> PolyType.BIGINT;
-            case FLOAT -> PolyType.REAL;
-            case DOUBLE -> PolyType.DOUBLE;
-            case FIXED_LEN_BYTE_ARRAY, BINARY, INT96 -> PolyType.VARBINARY;
-        };
-    }
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetFilterableTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetFilterableTable.java
index bc35e7cc43..61338ca990 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetFilterableTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetFilterableTable.java
@@ -21,12 +21,19 @@
 import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.DataContext;
 import org.polypheny.db.adapter.parquet.ParquetSource;
+import org.polypheny.db.adapter.parquet.execution.ParquetEnumerator;
+import org.polypheny.db.adapter.parquet.model.ParquetFilter;
+import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.rex.RexCall;
+import org.polypheny.db.rex.RexIndexRef;
+import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNode;
 import org.polypheny.db.schema.types.FilterableEntity;
 import org.polypheny.db.type.PolyType;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.Source;
+import java.util.ArrayList;
 import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
 
@@ -39,36 +46,70 @@ public ParquetFilterableTable( long id, Source source, PhysicalTable table, List
         super( id, source, table, fieldTypes, fields, csvSource );
     }
 
+
     /**
      * Pushes supported filters.
      */
     @Override
     public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> filters ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
+        final List<ParquetFilter> pushedFilters = new ArrayList<>();
+        filters.removeIf( filter -> addFilter( filter, pushedFilters ) );
+
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                return new Enumerator<>() {
-                    @Override
-                    public PolyValue[] current() {
-                        return new PolyValue[0];
-                    }
-                    @Override
-                    public boolean moveNext() {
-                        return false;
-                    }
-                    @Override
-                    public void reset() {
-
-                    }
-                    @Override
-                    public void close() {
-
-                    }
-                };
+                return new ParquetEnumerator( source, cancelFlag, fields, pushedFilters );
             }
         };
     }
+
+
+    /**
+     * Translates a Rex filter into adapter filter form when possible.
+     */
+    private boolean addFilter( RexNode filter, List<ParquetFilter> pushedFilters ) {
+        if ( !isSupportedOperator( filter.getKind() ) ) {
+            return false;
+        }
+
+        RexCall call = (RexCall) filter;
+        RexNode left = call.getOperands().get( 0 );
+        if ( left.isA( Kind.CAST ) ) {
+            left = ((RexCall) left).operands.get( 0 );
+        }
+        RexNode right = call.getOperands().get( 1 );
+
+        if ( !(left instanceof RexIndexRef) || !(right instanceof RexLiteral literal) ) {
+            return false;
+        }
+
+        if ( literal.getValue() == null ) {
+            return false;
+        }
+
+        int index = ((RexIndexRef) left).getIndex();
+        if ( index < 0 || index >= columns.size() ) {
+            return false;
+        }
+
+        pushedFilters.add( new ParquetFilter( index, filter.getKind(), literal.getValue().toString() ) );
+        return true;
+    }
+
+
+    /**
+     * Checks whether the operator can be handled by the reader.
+     */
+    private boolean isSupportedOperator( Kind kind ) {
+        return kind == Kind.EQUALS
+                || kind == Kind.NOT_EQUALS
+                || kind == Kind.GREATER_THAN
+                || kind == Kind.GREATER_THAN_OR_EQUAL
+                || kind == Kind.LESS_THAN
+                || kind == Kind.LESS_THAN_OR_EQUAL;
+    }
+
 }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTypeConverter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTypeConverter.java
new file mode 100644
index 0000000000..1d8f1d4587
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTypeConverter.java
@@ -0,0 +1,478 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.schema;
+
+import org.apache.parquet.io.api.Binary;
+import org.apache.parquet.schema.LogicalTypeAnnotation;
+import org.apache.parquet.schema.LogicalTypeAnnotation.DateLogicalTypeAnnotation;
+import org.apache.parquet.schema.LogicalTypeAnnotation.EnumLogicalTypeAnnotation;
+import org.apache.parquet.schema.LogicalTypeAnnotation.JsonLogicalTypeAnnotation;
+import org.apache.parquet.schema.LogicalTypeAnnotation.StringLogicalTypeAnnotation;
+import org.apache.parquet.schema.LogicalTypeAnnotation.TimeLogicalTypeAnnotation;
+import org.apache.parquet.schema.LogicalTypeAnnotation.TimestampLogicalTypeAnnotation;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeNameConverter;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyBinary;
+import org.polypheny.db.type.entity.PolyBoolean;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.numerical.PolyDouble;
+import org.polypheny.db.type.entity.numerical.PolyFloat;
+import org.polypheny.db.type.entity.numerical.PolyInteger;
+import org.polypheny.db.type.entity.numerical.PolyLong;
+import org.polypheny.db.type.entity.temporal.PolyDate;
+import org.polypheny.db.type.entity.temporal.PolyTime;
+import org.polypheny.db.type.entity.temporal.PolyTimestamp;
+import org.polypheny.db.util.temporal.DateTimeUtils;
+import java.nio.charset.StandardCharsets;
+import java.time.LocalDate;
+import java.time.LocalDateTime;
+import java.time.LocalTime;
+import java.time.ZoneOffset;
+import java.time.format.DateTimeFormatter;
+import java.time.format.DateTimeParseException;
+import java.util.Date;
+
+import static io.activej.common.StringFormatUtils.parseLocalDateTime;
+
+public class ParquetTypeConverter {
+
+    /**
+     * Convert original parquet type into {@link PolyType}
+     * @param field parquet type
+     * @return {@link PolyType}
+     */
+    public PolyType fromParquetTypeToPolyType( Type field ) {
+
+        // treat nested types as string
+        if ( !field.isPrimitive() ) {
+            return PolyType.TEXT;
+        }
+
+        // not nested types
+        PrimitiveType primitive = field.asPrimitiveType();
+        LogicalTypeAnnotation logical = primitive.getLogicalTypeAnnotation();
+
+        // date types
+        if ( logical instanceof DateLogicalTypeAnnotation ) {
+            return PolyType.DATE;
+        }
+        if ( logical instanceof TimeLogicalTypeAnnotation ) {
+            return PolyType.TIME;
+        }
+        if ( logical instanceof TimestampLogicalTypeAnnotation ) {
+            return PolyType.TIMESTAMP;
+        }
+
+        // string like types
+        if ( logical instanceof StringLogicalTypeAnnotation
+                || logical instanceof EnumLogicalTypeAnnotation
+                || logical instanceof JsonLogicalTypeAnnotation ) {
+            return PolyType.TEXT;
+        }
+
+        // primitive types - existing in parquet
+        return switch ( primitive.getPrimitiveTypeName() ) {
+            case BOOLEAN -> PolyType.BOOLEAN;
+            case INT32 -> PolyType.INTEGER;
+            case INT64 -> PolyType.BIGINT;
+            case FLOAT -> PolyType.FLOAT;
+            case DOUBLE -> PolyType.DOUBLE;
+            case FIXED_LEN_BYTE_ARRAY, BINARY, INT96 -> PolyType.VARBINARY;
+        };
+    }
+
+
+    /**
+     * Convert value from object to appropriate PolyValue
+     * @param fieldType - type of value/column
+     * @param value - object to convert
+     * @return converted PolyValue
+     */
+    public PolyValue fromObjToPolyValue( Type fieldType, Object value ) {
+        if ( value == null ) {
+            return PolyNull.NULL;
+        }
+
+        var polyType = fromParquetTypeToPolyType( fieldType );
+
+        return switch ( polyType ) {
+            case BOOLEAN -> fromObjToPolyBoolean( value );
+            case VARBINARY -> fromObjToPolyBinary( value );
+            case INTEGER -> fromObjToPolyInteger( value );
+            case BIGINT -> fromObjToPolyLong( value );
+            case FLOAT -> fromObjToPolyFloat( value );
+            case DOUBLE -> fromObjToPolyDouble( value );
+            case DATE -> fromObjToPolyDate( value );
+            case TIME -> fromObjToPolyTime( value );
+            case TIMESTAMP -> fromObjToPolyTimestamp( value );
+            case VARCHAR, TEXT -> fromObjToPolyString( value );
+            default -> throw new IllegalStateException("Unexpected value: " + fieldType);
+        };
+    }
+
+
+    /**
+     * Convert literal to primitive type
+     * @param primitive - type to convert
+     * @param literal - string value
+     * @return converted object of primitive type
+     */
+    public Object fromLiteralToPrimitive( PrimitiveType primitive, String literal ) {
+        try {
+            Object date = fromLiteralToDate( primitive, literal );
+            if (date != null)
+                return date;
+            // convert to primitive type
+            var converter = new ParquetPrimitiveTypeNameConverter(literal);
+            return primitive.getPrimitiveTypeName().convert( converter );
+        } catch ( Exception e ) {
+            return null;
+        }
+    }
+
+    private Object fromLiteralToDate(PrimitiveType primitive, String literal) {
+        LogicalTypeAnnotation logical = primitive.getLogicalTypeAnnotation();
+
+        if ( logical instanceof DateLogicalTypeAnnotation ) {
+            LocalDate date = parseLocalDate( literal );
+            return date == null ? Integer.parseInt( literal ) : (int) date.toEpochDay();
+        }
+
+        if ( logical instanceof TimeLogicalTypeAnnotation timeLogical ) {
+            long millis = normalizeTimeLiteralToMillis( literal );
+            return switch ( timeLogical.getUnit() ) {
+                case MILLIS -> primitive.getPrimitiveTypeName() == PrimitiveTypeName.INT32 ? (int) millis : millis;
+                case MICROS -> millis * 1_000L;
+                case NANOS -> millis * 1_000_000L;
+            };
+        }
+
+        if ( logical instanceof TimestampLogicalTypeAnnotation timestampLogical ) {
+            long millis = normalizeTimestampLiteralToMillis( literal );
+            return switch ( timestampLogical.getUnit() ) {
+                case MILLIS -> millis;
+                case MICROS -> millis * 1_000L;
+                case NANOS -> millis * 1_000_000L;
+            };
+        }
+        return null;
+    }
+
+    /**
+     * Parses an ISO local date string.
+     */
+    private LocalDate parseLocalDate( String value ) {
+        try {
+            return LocalDate.parse( value, DateTimeFormatter.ISO_LOCAL_DATE );
+        } catch ( DateTimeParseException e ) {
+            return null;
+        }
+    }
+
+    /**
+     * Parses an ISO local time string.
+     */
+    private LocalTime parseLocalTime( String value ) {
+        try {
+            return LocalTime.parse( value, DateTimeFormatter.ISO_LOCAL_TIME );
+        } catch ( DateTimeParseException e ) {
+            return null;
+        }
+    }
+
+    /**
+     * Parses time literal and normalizes it to milliseconds.
+     */
+    private long normalizeTimeLiteralToMillis( String literal ) {
+        LocalTime localTime = parseLocalTime( literal );
+        if ( localTime != null ) {
+            return localTime.toNanoOfDay() / 1_000_000L;
+        }
+        return normalizeTimeToMillis( Long.parseLong( literal ) );
+    }
+
+    /**
+     * Parses timestamp literal and normalizes it to milliseconds.
+     */
+    private long normalizeTimestampLiteralToMillis( String literal ) {
+        LocalDateTime localDateTime = parseLocalDateTime( literal );
+        if ( localDateTime != null ) {
+            return localDateTime.toInstant( ZoneOffset.UTC ).toEpochMilli();
+        }
+        return normalizeTimestampToMillis( Long.parseLong( literal ) );
+    }
+
+    /**
+     * Normalizes time units to milliseconds.
+     */
+    private long normalizeTimeToMillis( long value ) {
+        long abs = Math.abs( value );
+        if ( abs > 86_400_000_000L ) {
+            return value / 1_000_000L;
+        }
+        if ( abs > 86_400_000L ) {
+            return value / 1_000L;
+        }
+        return value;
+    }
+
+    /**
+     * Normalizes timestamp units to milliseconds.
+     */
+    private long normalizeTimestampToMillis( long value ) {
+        long abs = Math.abs( value );
+        if ( abs > 100_000_000_000_000_000L ) {
+            return value / 1_000_000L;
+        }
+        if ( abs > 100_000_000_000_000L ) {
+            return value / 1_000L;
+        }
+        return value;
+    }
+
+    private record ParquetPrimitiveTypeNameConverter( String literal ) implements PrimitiveTypeNameConverter<Object, Exception> {
+
+        /**
+         * Parses a float literal.
+         */
+        @Override
+        public Object convertFLOAT( PrimitiveTypeName primitiveTypeName ) {
+            return Float.parseFloat( literal );
+        }
+
+
+        /**
+         * Parses a double literal.
+         */
+        @Override
+        public Object convertDOUBLE( PrimitiveTypeName primitiveTypeName ) {
+            return Double.parseDouble( literal );
+        }
+
+
+        /**
+         * Parses a 32-bit integer literal.
+         */
+        @Override
+        public Object convertINT32( PrimitiveTypeName primitiveTypeName ) {
+            return Integer.parseInt( literal );
+        }
+
+
+        /**
+         * Parses a 64-bit integer literal.
+         */
+        @Override
+        public Object convertINT64( PrimitiveTypeName primitiveTypeName ) {
+            return Long.parseLong( literal );
+        }
+
+
+        /**
+         * Encodes the literal as binary for INT96.
+         */
+        @Override
+        public Object convertINT96( PrimitiveTypeName primitiveTypeName ) {
+            return Binary.fromConstantByteArray( literal.getBytes( StandardCharsets.UTF_8 ) );
+        }
+
+
+        /**
+         * Encodes the literal as binary for fixed byte arrays.
+         */
+        @Override
+        public Object convertFIXED_LEN_BYTE_ARRAY( PrimitiveTypeName primitiveTypeName ) {
+            return Binary.fromConstantByteArray( literal.getBytes( StandardCharsets.UTF_8 ) );
+        }
+
+
+        /**
+         * Parses a boolean literal.
+         */
+        @Override
+        public Object convertBOOLEAN( PrimitiveTypeName primitiveTypeName ) {
+            return Boolean.parseBoolean( literal );
+        }
+
+
+        /**
+         * Encodes the literal as binary.
+         */
+        @Override
+        public Object convertBINARY( PrimitiveTypeName primitiveTypeName ) {
+            return Binary.fromConstantByteArray( literal.getBytes( StandardCharsets.UTF_8 ) );
+        }
+
+    }
+
+    /**
+     * Converts to boolean value.
+     */
+    private PolyValue fromObjToPolyBoolean( Object value ) {
+        if ( value instanceof Boolean b ) {
+            return PolyBoolean.of( b );
+        }
+        return PolyBoolean.of( Boolean.parseBoolean( String.valueOf( value ) ) );
+    }
+
+
+    /**
+     * Converts to binary value.
+     */
+    private PolyValue fromObjToPolyBinary( Object value ) {
+        if ( value instanceof byte[] bytes ) {
+            return PolyBinary.of( bytes );
+        }
+        if ( value instanceof Binary binary ) {
+            return PolyBinary.of( binary.getBytes() );
+        }
+        return PolyBinary.of( String.valueOf( value ).getBytes( StandardCharsets.UTF_8 ) );
+    }
+
+    /**
+     * Converts to string value.
+     */
+    private PolyValue fromObjToPolyString( Object value ) {
+        if ( value instanceof Binary binary ) {
+            return PolyString.of( binary.toStringUsingUTF8() );
+        }
+        if ( value instanceof byte[] bytes ) {
+            return PolyString.of( new String( bytes, StandardCharsets.UTF_8 ) );
+        }
+        return PolyString.of( String.valueOf( value ) );
+    }
+
+
+    /**
+     * Converts to integer value.
+     */
+    private PolyValue fromObjToPolyInteger( Object value ) {
+        if ( value instanceof Number n ) {
+            return PolyInteger.of( n );
+        }
+        return PolyInteger.of( Integer.parseInt( String.valueOf( value ) ) );
+    }
+
+
+    /**
+     * Converts to long value.
+     */
+    private PolyValue fromObjToPolyLong( Object value ) {
+        if ( value instanceof Number n ) {
+            return PolyLong.of( n );
+        }
+        return PolyLong.of( Long.parseLong( String.valueOf( value ) ) );
+    }
+
+
+    /**
+     * Converts to float value.
+     */
+    private PolyValue fromObjToPolyFloat( Object value ) {
+        if ( value instanceof Number n ) {
+            return PolyFloat.of( n );
+        }
+        return PolyFloat.of( Float.parseFloat( String.valueOf( value ) ) );
+    }
+
+
+    /**
+     * Converts to double value.
+     */
+    private PolyValue fromObjToPolyDouble( Object value ) {
+        if ( value instanceof Number n ) {
+            return PolyDouble.of( n );
+        }
+        return PolyDouble.of( Double.parseDouble( String.valueOf( value ) ) );
+    }
+
+    /**
+     * Converts to date value.
+     */
+    private PolyValue fromObjToPolyDate( Object value ) {
+        if ( value instanceof Number n ) {
+            long numeric = n.longValue();
+            if ( Math.abs( numeric ) < 10_000_000L ) {
+                return PolyDate.of( numeric * DateTimeUtils.MILLIS_PER_DAY );
+            }
+            return PolyDate.of( numeric );
+        }
+        if ( value instanceof LocalDate localDate ) {
+            return PolyDate.of( localDate.toEpochDay() * DateTimeUtils.MILLIS_PER_DAY );
+        }
+        if ( value instanceof Date date ) {
+            return PolyDate.of( date );
+        }
+
+        LocalDate localDate = parseLocalDate( String.valueOf( value ) );
+        if ( localDate == null ) {
+            throw new IllegalArgumentException( "Cannot parse DATE object: " + value );
+        }
+        return PolyDate.of( localDate.toEpochDay() * DateTimeUtils.MILLIS_PER_DAY );
+    }
+
+
+    /**
+     * Converts to time value.
+     */
+    private PolyValue fromObjToPolyTime( Object value ) {
+        long millis;
+        if ( value instanceof Number n ) {
+            millis = normalizeTimeToMillis( n.longValue() );
+        } else if ( value instanceof LocalTime localTime ) {
+            millis = localTime.toNanoOfDay() / 1_000_000L;
+        } else {
+            LocalTime localTime = parseLocalTime( String.valueOf( value ) );
+            if ( localTime == null ) {
+                throw new IllegalArgumentException( "Cannot parse TIME object: " + value );
+            }
+            millis = localTime.toNanoOfDay() / 1_000_000L;
+        }
+        return PolyTime.of( (int) (millis % DateTimeUtils.MILLIS_PER_DAY) );
+    }
+
+
+    /**
+     * Converts to timestamp value.
+     */
+    private PolyValue fromObjToPolyTimestamp( Object value ) {
+        long millis;
+        if ( value instanceof Number n ) {
+            millis = normalizeTimestampToMillis( n.longValue() );
+        } else if ( value instanceof LocalDateTime localDateTime ) {
+            millis = localDateTime.toInstant( ZoneOffset.UTC ).toEpochMilli();
+        } else if ( value instanceof Date date ) {
+            millis = date.getTime();
+        } else {
+            LocalDateTime localDateTime = parseLocalDateTime( String.valueOf( value ) );
+            if ( localDateTime == null ) {
+                millis = Long.parseLong( String.valueOf( value ) );
+            } else {
+                millis = localDateTime.toInstant( ZoneOffset.UTC ).toEpochMilli();
+            }
+        }
+        return PolyTimestamp.of( millis );
+    }
+
+
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
new file mode 100644
index 0000000000..669b46aa10
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
@@ -0,0 +1,177 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import com.google.common.collect.Ordering;
+import org.polypheny.db.util.Sources;
+import org.polypheny.db.util.Util;
+import java.sql.Connection;
+import java.sql.DriverManager;
+import java.sql.ResultSet;
+import java.sql.SQLException;
+import java.sql.Statement;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.List;
+import java.util.Properties;
+import java.util.function.Consumer;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+class ParquetPluginTest {
+    private void checkSql( String sql, String model, Consumer<ResultSet> fn ) throws SQLException {
+        Connection connection = null;
+        Statement statement = null;
+        try {
+            Properties info = new Properties();
+            info.put( "model", jsonPath( model ) );
+            connection = DriverManager.getConnection( "jdbc:polyphenydbembedded:", info );
+            statement = connection.createStatement();
+            final ResultSet resultSet = statement.executeQuery( sql );
+            fn.accept( resultSet );
+        } finally {
+            close( connection, statement );
+        }
+    }
+    private String jsonPath( String model ) {
+        return resourcePath( model + ".json" );
+    }
+
+    private String resourcePath( String path ) {
+        return Sources.of( ParquetPluginTest.class.getResource( "/" + path ) ).file().getAbsolutePath();
+    }
+
+    private void close( Connection connection, Statement statement ) {
+        if ( statement != null ) {
+            try {
+                statement.close();
+            } catch ( SQLException e ) {
+                // ignore
+            }
+        }
+        if ( connection != null ) {
+            try {
+                connection.close();
+            } catch ( SQLException e ) {
+                // ignore
+            }
+        }
+    }
+
+    /**
+     * Returns a function that checks the contents of a result set against an expected string.
+     */
+    private static Consumer<ResultSet> expect( final String... expected ) {
+        return resultSet -> {
+            try {
+                final List<String> lines = new ArrayList<>();
+                ParquetPluginTest.collect( lines, resultSet );
+                assertEquals( Arrays.asList( expected ), lines );
+            } catch ( SQLException e ) {
+                throw new RuntimeException( e );
+            }
+        };
+    }
+
+    private static void collect( List<String> result, ResultSet resultSet ) throws SQLException {
+        final StringBuilder buf = new StringBuilder();
+        while ( resultSet.next() ) {
+            buf.setLength( 0 );
+            int n = resultSet.getMetaData().getColumnCount();
+            String sep = "";
+            for ( int i = 1; i <= n; i++ ) {
+                buf.append( sep )
+                        .append( resultSet.getMetaData().getColumnLabel( i ) )
+                        .append( "=" )
+                        .append( resultSet.getString( i ) );
+                sep = "; ";
+            }
+            result.add( Util.toLinux( buf.toString() ) );
+        }
+    }
+
+    /**
+     * Returns a function that checks the contents of a result set against an expected string.
+     */
+    private static Consumer<ResultSet> expectUnordered( String... expected ) {
+        final List<String> expectedLines = Ordering.natural().immutableSortedCopy( Arrays.asList( expected ) );
+        return resultSet -> {
+            try {
+                final List<String> lines = new ArrayList<>();
+                ParquetPluginTest.collect( lines, resultSet );
+                Collections.sort( lines );
+                assertEquals( expectedLines, lines );
+            } catch ( SQLException e ) {
+                throw new RuntimeException( e );
+            }
+        };
+    }
+
+    /**
+     * Fluent API to perform test actions.
+     */
+    private class Fluent {
+
+        private final String model;
+        private final String sql;
+        private final Consumer<ResultSet> expect;
+
+
+        Fluent( String model, String sql, Consumer<ResultSet> expect ) {
+            this.model = model;
+            this.sql = sql;
+            this.expect = expect;
+        }
+
+        /**
+         * Runs the test.
+         */
+        Fluent ok() {
+            try {
+                checkSql( sql, model, expect );
+                return this;
+            } catch ( SQLException e ) {
+                throw new RuntimeException( e );
+            }
+        }
+
+        /**
+         * Assigns a function to call to test whether output is correct.
+         */
+        Fluent checking( Consumer<ResultSet> expect ) {
+            return new Fluent( model, sql, expect );
+        }
+
+        /**
+         * Sets the rows that are expected to be returned from the SQL query.
+         */
+        Fluent returns( String... expectedLines ) {
+            return checking( expect( expectedLines ) );
+        }
+
+        /**
+         * Sets the rows that are expected to be returned from the SQL query,
+         * in no particular order.
+         */
+        Fluent returnsUnordered( String... expectedLines ) {
+            return checking( expectUnordered( expectedLines ) );
+        }
+
+    }
+
+}

From f62d11a928e73451a1e3d08070207f5a6cae1cf9 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Wed, 18 Mar 2026 15:46:58 +0100
Subject: [PATCH 04/46] create Unit Tests, store nested data in JSON format

---
 plugins/parquet-adapter/build.gradle          |  14 +
 plugins/parquet-adapter/docs/README.md        |  22 +
 .../docs/images/query_filter.png              | Bin 60380 -> 57092 bytes
 .../db/adapter/parquet/ParquetSource.java     |  10 -
 .../parquet/execution/ValueExtractor.java     |  75 ++-
 .../db/adapter/parquet/ParquetPluginTest.java | 429 +++++++++++++-----
 6 files changed, 426 insertions(+), 124 deletions(-)

diff --git a/plugins/parquet-adapter/build.gradle b/plugins/parquet-adapter/build.gradle
index 582f57b310..fe2642a05f 100644
--- a/plugins/parquet-adapter/build.gradle
+++ b/plugins/parquet-adapter/build.gradle
@@ -32,10 +32,24 @@ dependencies {
     }
 
     // --- Test Compile ---
+    testImplementation project(":dbms")
     testImplementation project(path: ":dbms", configuration: "test")
     testImplementation project(path: ":core", configuration: "tests")
     testImplementation project(path: ":core")
     testImplementation project(path: ":plugins:sql-language")
+
+    testImplementation(group: "org.polypheny", name: "polypheny-jdbc-driver", version: polypheny_jdbc_driver_version) {
+        exclude(group: "com.fasterxml.jackson.core")
+        exclude(group: "com.fasterxml.jackson.databind")
+    }
+}
+
+test {
+    useJUnitPlatform()
+    testLogging {
+        events "failed", "standard_out", "standard_error"
+        showStandardStreams = true
+    }
 }
 
 configurations.configureEach {
diff --git a/plugins/parquet-adapter/docs/README.md b/plugins/parquet-adapter/docs/README.md
index c456ca6cd9..7488dfa0b0 100644
--- a/plugins/parquet-adapter/docs/README.md
+++ b/plugins/parquet-adapter/docs/README.md
@@ -190,3 +190,25 @@ The following figure shows an example of the displayed schema information.
 ### Document Parquet data source implementation
 
 
+# Unit Tests
+To run tests for parquet adapter:
+- `.\gradlew.bat :plugins:parquet-adapter:test`
+
+`plugins/parquet-adapter/build.gradle` should contain: 
+- DBMS as a test dependency
+- Polypheny JDBC driver dependency
+
+## `ParquetPluginTest.java` 
+contains the following tests:
+1. `importsAllTablesAndReadsRows()`
+2. `parquetSourceIsReadOnly()`
+3. `readsExpectedRowsFromCustomers()`
+4. `filtersRowsWithWhereClause()`
+5. `projectsOnlyRequestedColumns()`
+6. `supportsGreaterThanFilter()`
+7. `supportsAllComparisonFilterOperations()`
+8. `rejectsUpdateOnParquetSource()`
+9. `projectsAndFiltersOtherTables()`
+10. `returnsNestedShippingAddressAsJSON()`
+
+
diff --git a/plugins/parquet-adapter/docs/images/query_filter.png b/plugins/parquet-adapter/docs/images/query_filter.png
index 7803b8330450dc3efc349d1dd15b1e12c07a20b2..2ba81b1e64ba9eb1bfbfae345686bbacc185d3a4 100644
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diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
index 063f9af5e6..e787bb7721 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
@@ -77,11 +77,6 @@
 @AdapterSettingList(name = "method", options = { "upload", "link" }, defaultValue = "upload", description = "If the supplied file(s) should be uploaded or a link to the local filesystem is used (sufficient permissions are required).", position = 1)
 @AdapterSettingDirectory(subOf = "method_upload", name = "directory", defaultValue = "classpath://orders_db", description = "You can upload one or multiple .parquet files.", position = 2)
 @AdapterSettingString(subOf = "method_link", defaultValue = "classpath://orders_db", name = "directoryName", description = "You can select a path to a folder or specific .parquet files.", position = 2)
-//@AdapterSettingInteger(
-//        name = "maxStringLength",
-//        defaultValue = 255,
-//        position = 3,
-//        description = "Which length (number of characters including whitespace) should be used for the varchar columns. Make sure this is equal or larger than the longest string in any of the columns.")
 public class ParquetSource extends DataSource<RelAdapterCatalog> implements RelationalDataSource {
 
     @Delegate(excludes = Excludes.class)
@@ -111,11 +106,6 @@ public ParquetSource(final long storeId, final String uniqueName, final Map<Stri
                 ? ConnectionMethod.from( settings.get( "method" ).toUpperCase() )
                 : ConnectionMethod.UPLOAD;
 
-//        this.maxStringLength = Integer.parseInt( settings.get( "maxStringLength" ) );
-//        if ( maxStringLength < 1 ) {
-//            throw new GenericRuntimeException( "Invalid value for maxStringLength: " + maxStringLength );
-//        }
-
         setParquetDir( settings );
         createInformationPage();
         enableInformationPage();
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java
index 4f3c70ff73..2004cb9e06 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java
@@ -4,14 +4,21 @@
 import org.apache.parquet.schema.PrimitiveType;
 import org.apache.parquet.schema.Type;
 
+
 /**
  * Central conversion layer between Parquet and Polypheny value representations.
  */
 public class ValueExtractor {
 
+    /*
+    The function reads one Parquet field from the current row.
+    If the field is primitive, function returns the primitive Java value.
+    If the field is nested, it returns JSON text built from the nested group.
+     */
     public Object extractValue( Group group, int index, Type field ) {
         if ( !field.isPrimitive() ) {
-            return group.getValueToString( index, 0 );
+            //return group.getValueToString( index, 0 );
+            return toJson( group.getGroup( index, 0 ) );
         }
 
         PrimitiveType primitive = field.asPrimitiveType();
@@ -25,4 +32,70 @@ public Object extractValue( Group group, int index, Type field ) {
             case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> group.getBinary( index, 0 );
         };
     }
+
+    /*
+    The function converts a nested Parquet group into a JSON object string:
+    Iterates over all fields inside that group.
+    Skips missing fields.
+    For primitive nested fields, delegates to toJsonPrimitive(...).
+    For nested sub-groups, calls itself recursively.
+     */
+    private String toJson( Group group ) {
+        StringBuilder json = new StringBuilder();
+        json.append( "{" );
+
+        String sep = "";
+        for ( int i = 0; i < group.getType().getFieldCount(); i++ ) {
+            Type field = group.getType().getType( i );
+            String name = field.getName();
+
+            if ( group.getFieldRepetitionCount( i ) == 0 ) {
+                continue;
+            }
+
+            json.append( sep ).append( "\"" ).append( name ).append( "\":" );
+
+            if ( field.isPrimitive() ) {
+                json.append( toJsonPrimitive( group, i, field ) );
+            } else {
+                json.append( toJson( group.getGroup( i, 0 ) ) );
+            }
+
+            sep = ",";
+        }
+
+        json.append( "}" );
+        return json.toString();
+    }
+
+    /*
+    Converts one primitive Parquet field into a JSON-compatible string fragment.
+     */
+    private String toJsonPrimitive( Group group, int index, Type field ) {
+        PrimitiveType primitive = field.asPrimitiveType();
+
+        return switch ( primitive.getPrimitiveTypeName() ) {
+            case BOOLEAN -> String.valueOf( group.getBoolean( index, 0 ) );
+            case INT32 -> String.valueOf( group.getInteger( index, 0 ) );
+            case INT64 -> String.valueOf( group.getLong( index, 0 ) );
+            case FLOAT -> String.valueOf( group.getFloat( index, 0 ) );
+            case DOUBLE -> String.valueOf( group.getDouble( index, 0 ) );
+            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 ->
+                    "\"" + escapeJson( group.getBinary( index, 0 ).toStringUsingUTF8() ) + "\"";
+        };
+    }
+
+    /*
+    Escapes characters that would break JSON syntax.
+     */
+    private String escapeJson( String value ) {
+        return value
+                .replace( "\\", "\\\\" )
+                .replace( "\"", "\\\"" )
+                .replace( "\n", "\\n" )
+                .replace( "\r", "\\r" )
+                .replace( "\t", "\\t" );
+    }
+
+
 }
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
index 669b46aa10..c4a5e5dd97 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
@@ -16,162 +16,365 @@
 
 package org.polypheny.db.adapter.parquet;
 
-import com.google.common.collect.Ordering;
+import org.junit.jupiter.api.AfterAll;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.polypheny.db.TestHelper;
+import org.polypheny.db.TestHelper.JdbcConnection;
 import org.polypheny.db.util.Sources;
-import org.polypheny.db.util.Util;
 import java.sql.Connection;
-import java.sql.DriverManager;
 import java.sql.ResultSet;
 import java.sql.SQLException;
 import java.sql.Statement;
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.Collections;
-import java.util.List;
-import java.util.Properties;
-import java.util.function.Consumer;
+import com.google.common.collect.ImmutableList;
+import java.sql.Timestamp;
 
 import static org.junit.jupiter.api.Assertions.*;
 
 class ParquetPluginTest {
-    private void checkSql( String sql, String model, Consumer<ResultSet> fn ) throws SQLException {
-        Connection connection = null;
-        Statement statement = null;
-        try {
-            Properties info = new Properties();
-            info.put( "model", jsonPath( model ) );
-            connection = DriverManager.getConnection( "jdbc:polyphenydbembedded:", info );
-            statement = connection.createStatement();
-            final ResultSet resultSet = statement.executeQuery( sql );
-            fn.accept( resultSet );
-        } finally {
-            close( connection, statement );
+
+    private static final String SOURCE_NAME = "parquet_test";
+    private static TestHelper helper;
+    private static boolean sourceCreated = false;
+
+    //region initialization and finalization
+    @BeforeAll
+    static void start() throws SQLException {
+        // starts the Polypheny test instance
+        helper = TestHelper.getInstance();
+
+        // build absolute path to file directory
+        String parquetDir = Sources.of( ParquetPluginTest.class.getResource( "/orders_db/" ) )
+                .file()
+                .getAbsolutePath()
+                .replace( "\\", "\\\\" );
+
+        // open a JDBC connection to the running Polypheny test server
+        try ( JdbcConnection jdbcConnection = new JdbcConnection( false ) ) {
+            Connection connection = jdbcConnection.getConnection();
+            // creates the Parquet source adapter and imports the parquet files as source tables
+             try ( Statement statement = connection.createStatement() ) {
+                String sql = "ALTER ADAPTERS ADD \"" + SOURCE_NAME + "\" USING 'Parquet' AS 'SOURCE' "
+                        + "WITH '{method:\"link\",directory:\"classpath://orders_db\",directoryName:\"" + parquetDir + "\"}'";
+
+                System.out.println( sql );
+                statement.executeUpdate( sql );
+                connection.commit();
+                sourceCreated = true;
+            }
         }
     }
-    private String jsonPath( String model ) {
-        return resourcePath( model + ".json" );
+
+
+    @AfterAll
+    static void end() throws SQLException {
+        if ( sourceCreated ) {
+            // open a JDBC connection
+            try ( JdbcConnection jdbcConnection = new JdbcConnection( false ) ) {
+                Connection connection = jdbcConnection.getConnection();
+                try ( Statement statement = connection.createStatement() ) {
+                    // remove the source adapter and the imported source tables from the catalog
+                    statement.executeUpdate( "ALTER ADAPTERS DROP \"" + SOURCE_NAME + "\"" );
+                    connection.commit();
+                }
+            }
+            // verify no transactions left open
+            helper.checkAllTrxClosed();
+        }
     }
+    //endregion
 
-    private String resourcePath( String path ) {
-        return Sources.of( ParquetPluginTest.class.getResource( "/" + path ) ).file().getAbsolutePath();
+    // validate that tables loaded and contain data
+    @Test
+    void importsAllTablesAndReadsRows() throws SQLException {
+        assertTableHasRows( "customers" );
+        assertTableHasRows( "orders" );
+        assertTableHasRows( "order_items" );
+        assertTableHasRows( "products" );
     }
 
-    private void close( Connection connection, Statement statement ) {
-        if ( statement != null ) {
-            try {
-                statement.close();
-            } catch ( SQLException e ) {
-                // ignore
+    // validate that tables are read-only, this means delete attempt is invalid
+    @Test
+    void parquetSourceIsReadOnly() throws SQLException {
+        try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
+            Connection connection = jdbcConnection.getConnection();
+            try ( Statement statement = connection.createStatement() ) {
+                assertThrows( SQLException.class, () -> statement.executeUpdate( "DELETE FROM customers" ) );
             }
         }
-        if ( connection != null ) {
-            try {
-                connection.close();
-            } catch ( SQLException e ) {
-                // ignore
+    }
+
+    private void assertTableHasRows( String tableName ) throws SQLException {
+        try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
+            Connection connection = jdbcConnection.getConnection();
+            try ( Statement statement = connection.createStatement();
+                    ResultSet resultSet = statement.executeQuery( "SELECT COUNT(*) FROM " + tableName ) ) {
+
+                assertTrue( resultSet.next(), "COUNT query returned no row for table " + tableName );
+                assertTrue( resultSet.getLong( 1 ) > 0, "Expected table " + tableName + " to contain rows" );
+            }
+        }
+    }
+
+    // read 3 rows and compare result with expected
+    @Test
+    void readsExpectedRowsFromCustomers() throws SQLException {
+        try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
+            Connection connection = jdbcConnection.getConnection();
+            try ( Statement statement = connection.createStatement();
+                    ResultSet resultSet = statement.executeQuery(
+                            "SELECT customer_id, name, email, country, signup_date "
+                                    + "FROM customers "
+                                    + "WHERE customer_id <= 3 "
+                                    + "ORDER BY customer_id" ) ) {
+
+                TestHelper.checkResultSet(
+                        resultSet,
+                        ImmutableList.of(
+                                new Object[]{ 1L, "Carla Schmid", "carla.schmid2317@mail.com", "Israel", Timestamp.valueOf( "2021-05-17 00:00:00" ) },
+                                new Object[]{ 2L, "Urs Meier", "urs.meier3475@demo.net", "UK", Timestamp.valueOf( "2024-10-25 00:00:00" ) },
+                                new Object[]{ 3L, "Rita Garcia", "rita.garcia8608@mail.com", "France", Timestamp.valueOf( "2023-02-04 00:00:00" ) }
+                        )
+                );
             }
         }
     }
+    // check filter pushdown - where close
+    @Test
+    void filtersRowsWithWhereClause() throws SQLException {
+        try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
+            Connection connection = jdbcConnection.getConnection();
+            try ( Statement statement = connection.createStatement();
+                    ResultSet resultSet = statement.executeQuery(
+                            "SELECT customer_id, name, email, country, signup_date "
+                                    + "FROM customers "
+                                    + "WHERE country = 'France' "
+                                    + "ORDER BY customer_id "
+                                    + "LIMIT 3" ) ) {
 
-    /**
-     * Returns a function that checks the contents of a result set against an expected string.
-     */
-    private static Consumer<ResultSet> expect( final String... expected ) {
-        return resultSet -> {
-            try {
-                final List<String> lines = new ArrayList<>();
-                ParquetPluginTest.collect( lines, resultSet );
-                assertEquals( Arrays.asList( expected ), lines );
-            } catch ( SQLException e ) {
-                throw new RuntimeException( e );
+                TestHelper.checkResultSet(
+                        resultSet,
+                        ImmutableList.of(
+                                new Object[]{ 3L, "Rita Garcia", "rita.garcia8608@mail.com", "France", Timestamp.valueOf( "2023-02-04 00:00:00" ) },
+                                new Object[]{ 8L, "Sara Dubois", "sara.dubois8067@mail.com", "France", Timestamp.valueOf( "2022-10-11 00:00:00" ) },
+                                new Object[]{ 17L, "Noah Horvat", "noah.horvat6459@example.org", "France", Timestamp.valueOf( "2021-02-20 00:00:00" ) }
+                        )
+                );
             }
-        };
+        }
     }
+    // text projection - get only requested columns
+    @Test
+    void projectsOnlyRequestedColumns() throws SQLException {
+        try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
+            Connection connection = jdbcConnection.getConnection();
+            try ( Statement statement = connection.createStatement();
+                    ResultSet resultSet = statement.executeQuery(
+                            "SELECT customer_id, name "
+                                    + "FROM customers "
+                                    + "WHERE customer_id <= 3 "
+                                    + "ORDER BY customer_id" ) ) {
 
-    private static void collect( List<String> result, ResultSet resultSet ) throws SQLException {
-        final StringBuilder buf = new StringBuilder();
-        while ( resultSet.next() ) {
-            buf.setLength( 0 );
-            int n = resultSet.getMetaData().getColumnCount();
-            String sep = "";
-            for ( int i = 1; i <= n; i++ ) {
-                buf.append( sep )
-                        .append( resultSet.getMetaData().getColumnLabel( i ) )
-                        .append( "=" )
-                        .append( resultSet.getString( i ) );
-                sep = "; ";
+                TestHelper.checkResultSet(
+                        resultSet,
+                        ImmutableList.of(
+                                new Object[]{ 1L, "Carla Schmid" },
+                                new Object[]{ 2L, "Urs Meier" },
+                                new Object[]{ 3L, "Rita Garcia" }
+                        )
+                );
             }
-            result.add( Util.toLinux( buf.toString() ) );
         }
     }
+    // test comparison filter
+    @Test
+    void supportsGreaterThanFilter() throws SQLException {
+        try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
+            Connection connection = jdbcConnection.getConnection();
+            try ( Statement statement = connection.createStatement();
+                    ResultSet resultSet = statement.executeQuery(
+                            "SELECT customer_id, name "
+                                    + "FROM customers "
+                                    + "WHERE customer_id > 3 "
+                                    + "ORDER BY customer_id "
+                                    + "LIMIT 3" ) ) {
 
-    /**
-     * Returns a function that checks the contents of a result set against an expected string.
-     */
-    private static Consumer<ResultSet> expectUnordered( String... expected ) {
-        final List<String> expectedLines = Ordering.natural().immutableSortedCopy( Arrays.asList( expected ) );
-        return resultSet -> {
-            try {
-                final List<String> lines = new ArrayList<>();
-                ParquetPluginTest.collect( lines, resultSet );
-                Collections.sort( lines );
-                assertEquals( expectedLines, lines );
-            } catch ( SQLException e ) {
-                throw new RuntimeException( e );
+                TestHelper.checkResultSet(
+                        resultSet,
+                        ImmutableList.of(
+                                new Object[]{ 4L, "Lars Cohen" },
+                                new Object[]{ 5L, "Lars Keller" },
+                                new Object[]{ 6L, "Will Novak" }
+                        )
+                );
             }
-        };
+        }
     }
 
-    /**
-     * Fluent API to perform test actions.
-     */
-    private class Fluent {
+    // test filter operations
+    @Test
+    void supportsAllComparisonFilterOperations() throws SQLException {
+        assertCustomerFilterResult(
+                "customer_id = 3",
+                ImmutableList.of(
+                        new Object[]{ 3L, "Rita Garcia" }
+                )
+        );
 
-        private final String model;
-        private final String sql;
-        private final Consumer<ResultSet> expect;
+        assertCustomerFilterResult(
+                "customer_id != 3",
+                ImmutableList.of(
+                        new Object[]{ 1L, "Carla Schmid" },
+                        new Object[]{ 2L, "Urs Meier" },
+                        new Object[]{ 4L, "Lars Cohen" }
+                )
+        );
 
+        assertCustomerFilterResult(
+                "customer_id > 3",
+                ImmutableList.of(
+                        new Object[]{ 4L, "Lars Cohen" },
+                        new Object[]{ 5L, "Lars Keller" },
+                        new Object[]{ 6L, "Will Novak" }
+                )
+        );
 
-        Fluent( String model, String sql, Consumer<ResultSet> expect ) {
-            this.model = model;
-            this.sql = sql;
-            this.expect = expect;
-        }
+        assertCustomerFilterResult(
+                "customer_id >= 3",
+                ImmutableList.of(
+                        new Object[]{ 3L, "Rita Garcia" },
+                        new Object[]{ 4L, "Lars Cohen" },
+                        new Object[]{ 5L, "Lars Keller" }
+                )
+        );
 
-        /**
-         * Runs the test.
-         */
-        Fluent ok() {
-            try {
-                checkSql( sql, model, expect );
-                return this;
-            } catch ( SQLException e ) {
-                throw new RuntimeException( e );
+        assertCustomerFilterResult(
+                "customer_id < 3",
+                ImmutableList.of(
+                        new Object[]{ 1L, "Carla Schmid" },
+                        new Object[]{ 2L, "Urs Meier" }
+                )
+        );
+
+        assertCustomerFilterResult(
+                "customer_id <= 3",
+                ImmutableList.of(
+                        new Object[]{ 1L, "Carla Schmid" },
+                        new Object[]{ 2L, "Urs Meier" },
+                        new Object[]{ 3L, "Rita Garcia" }
+                )
+        );
+    }
+
+    private void assertCustomerFilterResult( String filter, ImmutableList<Object[]> expected ) throws SQLException {
+        try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
+            Connection connection = jdbcConnection.getConnection();
+            try ( Statement statement = connection.createStatement();
+                    ResultSet resultSet = statement.executeQuery(
+                            "SELECT customer_id, name "
+                                    + "FROM customers "
+                                    + "WHERE " + filter + " "
+                                    + "ORDER BY customer_id "
+                                    + "LIMIT 3" ) ) {
+
+                TestHelper.checkResultSet( resultSet, expected );
             }
         }
+    }
 
-        /**
-         * Assigns a function to call to test whether output is correct.
-         */
-        Fluent checking( Consumer<ResultSet> expect ) {
-            return new Fluent( model, sql, expect );
+    // test that update not available
+    @Test
+    void rejectsUpdateOnParquetSource() throws SQLException {
+        try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
+            Connection connection = jdbcConnection.getConnection();
+            try ( Statement statement = connection.createStatement() ) {
+                assertThrows(
+                        SQLException.class,
+                        () -> statement.executeUpdate(
+                                "UPDATE customers "
+                                        + "SET country = 'Switzerland' "
+                                        + "WHERE customer_id = 1" )
+                );
+            }
         }
+    }
 
-        /**
-         * Sets the rows that are expected to be returned from the SQL query.
-         */
-        Fluent returns( String... expectedLines ) {
-            return checking( expect( expectedLines ) );
+    private void assertQueryResult( String sql, ImmutableList<Object[]> expected ) throws SQLException {
+        try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
+            Connection connection = jdbcConnection.getConnection();
+            try ( Statement statement = connection.createStatement();
+                    ResultSet resultSet = statement.executeQuery( sql ) ) {
+                TestHelper.checkResultSet( resultSet, expected );
+            }
         }
+    }
 
-        /**
-         * Sets the rows that are expected to be returned from the SQL query,
-         * in no particular order.
-         */
-        Fluent returnsUnordered( String... expectedLines ) {
-            return checking( expectUnordered( expectedLines ) );
-        }
+    // test data in orders, order_items and products
+    // using filter and projection
+    @Test
+    void projectsAndFiltersOtherTables() throws SQLException {
+        assertQueryResult(
+                "SELECT order_id, status, total_price "
+                        + "FROM orders "
+                        + "WHERE order_id <= 3 "
+                        + "ORDER BY order_id",
+                ImmutableList.of(
+                        new Object[]{ 1L, "paid", 12916.51 },
+                        new Object[]{ 2L, "delivered", 2551.43 },
+                        new Object[]{ 3L, "delivered", 14631.22 }
+                )
+        );
 
+        assertQueryResult(
+                "SELECT order_item_id, product_id, quantity "
+                        + "FROM order_items "
+                        + "WHERE order_id = 1 "
+                        + "ORDER BY order_item_id "
+                        + "LIMIT 3",
+                ImmutableList.of(
+                        new Object[]{ 1L, 1491L, 3L },
+                        new Object[]{ 2L, 1525L, 2L },
+                        new Object[]{ 3L, 1784L, 5L }
+                )
+        );
+
+        assertQueryResult(
+                "SELECT product_id, name, category "
+                        + "FROM products "
+                        + "WHERE product_id <= 3 "
+                        + "ORDER BY product_id",
+                ImmutableList.of(
+                        new Object[]{ 1L, "Mouse 198", "Grocery" },
+                        new Object[]{ 2L, "Yoga Mat 396", "Home" },
+                        new Object[]{ 3L, "Notebook 301", "Books" }
+                )
+        );
+    }
+
+    // check that nested data returned as string
+    @Test
+    void returnsNestedShippingAddressAsJSON() throws SQLException {
+        try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
+            Connection connection = jdbcConnection.getConnection();
+            try ( Statement statement = connection.createStatement();
+                    ResultSet resultSet = statement.executeQuery(
+                            "SELECT shipping_address "
+                                    + "FROM orders "
+                                    + "WHERE order_id = 1" ) ) {
+
+                assertTrue( resultSet.next() );
+
+                Object value = resultSet.getObject( 1 );
+                String shippingAddress = assertInstanceOf( String.class, value );
+
+                assertEquals(
+                        "{\"city\":\"Lucerne\",\"street\":\"Gartenweg 103\",\"zip\":7101}",
+                        shippingAddress
+                );
+            }
+        }
     }
 
+
+
 }

From 2befdebd602978d0e7bf547cb9c7ddf85c2b162d Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Wed, 25 Mar 2026 10:39:05 +0100
Subject: [PATCH 05/46] Implement Scannable flavor

---
 plugins/parquet-adapter/docs/README.md        |  18 +-
 plugins/parquet-adapter/docs/flavors.md       | 154 ++++++++++++++++++
 .../db/adapter/parquet/ParquetSource.java     |   4 +-
 .../parquet/schema/ParquetScannableTable.java |  27 +--
 4 files changed, 178 insertions(+), 25 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/flavors.md

diff --git a/plugins/parquet-adapter/docs/README.md b/plugins/parquet-adapter/docs/README.md
index 7488dfa0b0..25fa6d0c51 100644
--- a/plugins/parquet-adapter/docs/README.md
+++ b/plugins/parquet-adapter/docs/README.md
@@ -102,8 +102,8 @@ Parquet file access helpers.
 Table wrappers and schema-related conversion logic.
 - `ParquetNamespace`: creates Parquet table wrappers for the active table flavor.
 - `ParquetTable`: base class for Parquet-backed physical tables.
-- `ParquetFilterableTable`: active table implementation for filter-aware scans.
-- `ParquetScannableTable`: scaffold for scan-based execution.
+- `ParquetFilterableTable`: active table implementation for filter-aware scans; translates supported query conditions into adapter-specific filters and delegates execution to `ParquetEnumerator`.
+- `ParquetScannableTable`: scan-only table implementation; returns rows through `ParquetEnumerator` without adapter-side filter pushdown and uses an empty filter list for plain Parquet scanning.
 - `ParquetTranslatableTable`: scaffold for planner-driven execution.
 - `ParquetTypeConverter`: converts Parquet schema types, runtime values, and filter literals into Polypheny-compatible representations.
 
@@ -145,11 +145,21 @@ The adapter defines three table flavors through `ParquetTable.Flavor`:
 - `SCANNABLE`
 - `TRANSLATABLE`
 
+These flavors represent different levels of integration with the query engine.
+
 The active flavor is selected by `ParquetSource` when the namespace is created.
 
-In the current implementation, `ParquetSource.updateNamespace()` selects `FILTERABLE`, so discovered and restored Parquet tables are exposed through `ParquetFilterableTable`.
+- `SCANNABLE` is the simplest execution path. 
+It performs plain row scanning through `ParquetScannableTable` and delegates reading to `ParquetEnumerator` without adapter-side filter pushdown. 
+
+- `FILTERABLE` extends the scan-based model by accepting supported filter conditions from the query engine. 
+In this path, `ParquetFilterableTable` translates supported expressions into `ParquetFilter` objects, 
+and `ParquetEnumerator` applies projection pushdown, filter evaluation, and row-group pruning. 
+***This is the default and currently active flavor.***
 
-`SCANNABLE` and `TRANSLATABLE` remain available as structural alternatives, but they are not currently used as the default execution path.
+- `TRANSLATABLE` is the planner-oriented flavor. 
+Instead of only returning rows directly, it allows the adapter to expose adapter-specific relational nodes and planner rules. 
+This path is intended for deeper integration with query planning through `ParquetScan` and `ParquetProjectScanRule`.
 
 
 ## Information Page
diff --git a/plugins/parquet-adapter/docs/flavors.md b/plugins/parquet-adapter/docs/flavors.md
new file mode 100644
index 0000000000..f768bd0108
--- /dev/null
+++ b/plugins/parquet-adapter/docs/flavors.md
@@ -0,0 +1,154 @@
+# Parquet Table Flavors
+
+## Overview
+
+The Parquet adapter defines three table flavors through `ParquetTable.Flavor`:
+
+- `SCANNABLE`
+- `FILTERABLE`
+- `TRANSLATABLE`
+
+These flavors represent different integration styles between the adapter and the Polypheny query engine. 
+They represent different execution and planning strategies for reading the same Parquet data.
+The active flavor is selected when the Parquet namespace creates table wrappers.
+
+## Where Flavor Selection Happens
+
+Flavor-based table creation is implemented in `ParquetNamespace`.
+
+- `ParquetNamespace.createParquetTable(...)` prepares the metadata and source reference for a physical Parquet table.
+- `ParquetNamespace.createTable(...)` instantiates the concrete table implementation according to the configured flavor.
+
+The current Parquet source selects the `FILTERABLE` flavor in `ParquetSource.updateNamespace(...)`.
+
+
+## Purpose of Each Flavor
+
+### SCANNABLE
+
+`SCANNABLE` is the simplest integration model.
+
+In this flavor, the adapter behaves mainly as a row producer. 
+The query engine asks the table to provide rows, and the adapter returns them as an enumerable result stream.
+
+Characteristics:
+
+- simple implementation model
+- filtering is handled outside the adapter
+- less efficient (it does not provide optimization)
+
+
+### FILTERABLE
+
+`FILTERABLE` extends the scan-based model by allowing the adapter to receive filter conditions from the query engine.
+
+In this flavor, the adapter inspects query filters and pushes supported conditions into its own execution logic. 
+This allows the Parquet reader to reduce the amount of data it reads and processes.
+
+Characteristics:
+
+- supports filter pushdown
+- supports projection-aware reading
+- allows row-group pruning using Parquet metadata
+- significantly more efficient than plain scan-only execution
+
+This is the active flavor in the current Parquet adapter implementation.
+
+### TRANSLATABLE
+
+`TRANSLATABLE` is the most advanced integration style.
+
+In this flavor, the adapter participates directly in query planning. 
+Instead of only returning rows at execution time, it provides adapter-specific relational nodes and planner rules.
+
+Characteristics:
+
+- deeper integration with the query planner
+- enables more advanced optimization
+- more flexible than scan-only or filterable execution
+
+
+## Flavor Workflows
+
+The following sections describe how the same logical SQL query flows through each flavor.
+
+Example query:
+
+```sql
+SELECT customer_id, name
+FROM customers
+WHERE customer_id > 3
+```
+
+### 1. SCANNABLE Workflow
+The adapter mainly acts as a generic row reader.
+
+Main class:
+
+- `ParquetScannableTable`
+
+Flow:
+
+- The query engine decides a table scan is needed.
+- The scannable table is asked to provide rows.
+- The adapter reads rows from the Parquet file.
+- Filtering may be applied later by the query engine rather than inside the adapter.
+- Projection may also be handled outside the adapter depending on the execution path.
+
+Practical consequence:
+
+- more data may be read than actually needed
+- pushdown opportunities are limited
+- execution is simpler but less efficient
+- appropriate when correctness and simplicity are more important than optimization.
+
+### 2. FILTERABLE Workflow
+
+The adapter receives a list of query filters and decides which ones it can handle internally.
+
+Main classes:
+
+- `ParquetFilterableTable`
+- `ParquetEnumerator`
+- `PredicateEvaluator`
+
+Flow:
+
+- The query engine calls ParquetFilterableTable.scan(...).
+- The table inspects incoming filter expressions.
+- Supported filters are converted into adapter-specific ParquetFilter objects.
+- These filters are passed into ParquetEnumerator.
+- The enumerator computes the required projection columns.
+- Row-group metadata is checked through PredicateEvaluator.
+- Row groups that cannot match the pushed filters are skipped.
+- Matching rows are read, filtered, and returned.
+
+Practical consequence:
+
+- fewer columns may be read
+- fewer row groups may be read
+- less work is done outside the adapter
+
+
+### 3. TRANSLATABLE Workflow
+
+The adapter participates in planning.
+
+Main classes:
+
+- `ParquetTranslatableTable`
+- `ParquetScan`
+- `ParquetProjectScanRule`
+
+Flow:
+
+- The planner encounters a Parquet-backed table.
+- The translatable table produces an adapter-specific relational node.
+- The query planner can rewrite the query plan using rules that know about your Parquet adapter.
+- Projection and other operations can be integrated into adapter-specific planning structures.
+
+Practical consequence:
+
+- optimization can happen earlier and more explicitly
+- this approach supports more sophisticated future optimizations
+
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
index e787bb7721..2bb8605e0e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
@@ -35,6 +35,7 @@
 import org.polypheny.db.adapter.annotations.AdapterSettingList;
 import org.polypheny.db.adapter.annotations.AdapterSettingString;
 import org.polypheny.db.adapter.parquet.io.ParquetFileDiscovery;
+import org.polypheny.db.adapter.parquet.schema.ParquetTable.Flavor;
 import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
 import org.polypheny.db.adapter.parquet.schema.ParquetNamespace;
 import org.polypheny.db.adapter.parquet.schema.ParquetTable;
@@ -271,7 +272,8 @@ private InformationTable getInformationTable(InformationGroup group, Map.Entry<S
      */
     @Override
     public void updateNamespace( String name, long id ) {
-        currentNamespace = new ParquetNamespace( id, adapterId, parquetDir, ParquetTable.Flavor.FILTERABLE );
+        ParquetTable.Flavor flavor = Flavor.FILTERABLE;
+        currentNamespace = new ParquetNamespace( id, adapterId, parquetDir, flavor );
     }
 
     /**
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetScannableTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetScannableTable.java
index 7c0d6fef7b..f23e3e4d65 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetScannableTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetScannableTable.java
@@ -1,11 +1,14 @@
 package org.polypheny.db.adapter.parquet.schema;
 
+import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.calcite.linq4j.AbstractEnumerable;
 import org.apache.calcite.linq4j.Enumerable;
 import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.DataContext;
 import org.polypheny.db.adapter.parquet.ParquetSource;
+import org.polypheny.db.adapter.parquet.execution.ParquetEnumerator;
+import org.polypheny.db.adapter.parquet.model.ParquetFilter;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
 import org.polypheny.db.schema.types.ScannableEntity;
 import org.polypheny.db.type.PolyType;
@@ -21,34 +24,18 @@ public ParquetScannableTable( long id, Source source, PhysicalTable table, java.
         super( id, source, table, fieldTypes, fields, parquetSource );
     }
 
-
     @Override
     public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+        // create parquet enumerator
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                return new Enumerator<>() {
-                    @Override
-                    public PolyValue[] current() {
-                        return new PolyValue[0];
-                    }
-                    @Override
-                    public boolean moveNext() {
-                        return false;
-                    }
-                    @Override
-                    public void reset() {
-
-                    }
-                    @Override
-                    public void close() {
-
-                    }
-                };
+                // empty filter list
+                List<ParquetFilter> filterList = java.util.List.of();
+                return new ParquetEnumerator( source, cancelFlag, fields, filterList );
             }
         };
     }
-
 }

From 00fd30001aa80a588a55a64340e854de4fb2b459 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Wed, 25 Mar 2026 17:00:33 +0100
Subject: [PATCH 06/46] Implement filter push down, fix flavor issue

---
 plugins/parquet-adapter/docs/README.md        |  93 ++++---
 plugins/parquet-adapter/docs/flavors.md       |  25 +-
 .../db/adapter/parquet/ParquetSource.java     |  79 +++---
 .../parquet/execution/ParquetEnumerator.java  | 226 +++++++-----------
 .../execution/ParquetPredicateBuilder.java    | 197 +++++++++++++++
 .../parquet/execution/PredicateEvaluator.java | 119 ---------
 .../parquet/execution/ValueComparator.java    |  30 ---
 .../{ParquetFilter.java => FilterInfo.java}   |   5 +-
 .../planning/ParquetProjectScanRule.java      |   2 +-
 .../adapter/parquet/planning/ParquetScan.java |  12 +-
 .../schema/ParquetFilterableTable.java        | 115 ---------
 .../parquet/schema/ParquetNamespace.java      |  10 +-
 .../parquet/schema/ParquetScannableTable.java |  41 ----
 .../adapter/parquet/schema/ParquetTable.java  | 160 ++++++++++++-
 .../schema/ParquetTranslatableTable.java      |  64 -----
 .../parquet/schema/ParquetTypeConverter.java  | 173 +++++++-------
 .../db/adapter/parquet/ParquetPluginTest.java |  14 ++
 .../parquet/ParquetPredicateBuilderTest.java  |  46 ++++
 18 files changed, 698 insertions(+), 713 deletions(-)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetPredicateBuilder.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/PredicateEvaluator.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueComparator.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/{ParquetFilter.java => FilterInfo.java} (57%)
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetFilterableTable.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetScannableTable.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTranslatableTable.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPredicateBuilderTest.java

diff --git a/plugins/parquet-adapter/docs/README.md b/plugins/parquet-adapter/docs/README.md
index 25fa6d0c51..06b8ff97d3 100644
--- a/plugins/parquet-adapter/docs/README.md
+++ b/plugins/parquet-adapter/docs/README.md
@@ -8,7 +8,7 @@ At a high level, the adapter is responsible for:
 - extracting relational schema metadata from Parquet file footers
 - restoring and registering tables whose data is read from Parquet files
 - executing read queries over Parquet data
-- applying projections and supported predicate filtering during query execution
+- applying projection pushdown and supported native predicate pushdown during query execution
 
 The adapter is read-only and currently targets relational source integration for embedded deployments.
 
@@ -45,29 +45,21 @@ During `restore()`, `Catalog.defaultSource` is populated from the configured ada
 3. **Namespace and table creation**
     - **Responsible classes:** `ParquetSource`, `ParquetNamespace`, `ParquetTable`
     - Creates table wrappers for discovered Parquet files.
-    - Chooses the active table flavor.
+    - Table Wrapper implements Filterable, Scannable and Translatable functionality
     - Registers physical tables in the adapter catalog.
 4. **Table restore during startup**
     - **Responsible classes:** `ParquetSource`, `ParquetNamespace`
     - During application startup, the adapter calls `restoreTable(...)` for persisted Parquet tables.
     - The adapter recreates table wrappers and re-registers them in the adapter catalog.
 5. **Query entry into the adapter**
-    - **Responsible class:** `ParquetFilterableTable`
-    - Receives scan requests from the query engine.
-    - Inspects projected columns and query conditions.
-    - Translates supported query conditions into adapter-level filters.
-6. **Projection and filter pushdown**
-    - **Responsible classes:** `ParquetFilterableTable`, `ParquetEnumerator`, `ParquetFilter`
-    - Pushes supported filter conditions into adapter-specific filter objects.
-    - Pushes column projection into the Parquet reader by limiting the read schema to required columns.
-    - Ensures that columns needed for filtering are included even if they are not part of the final output.
-7. **Row-group pruning**
-    - **Responsible classes:** `ParquetEnumerator`, `PredicateEvaluator`
-    - Uses pushed filter conditions together with Parquet statistics to skip row groups that cannot match.
+    - **Responsible class:** `ParquetFilterableTable`, `ParquetNamespace`, `ParquetTable`
+    - The query engine resolves the registered Parquet-backed physical table.
+    - The adapter uses the table metadata and source information to access the underlying .parquet file.
+    - Projection and supported filter information are passed into the Parquet execution layer.
 
 ## Supported Filter Conditions
 
-The active `FILTERABLE` execution path currently pushes only simple comparison conditions into the adapter.
+The active `FILTERABLE` execution path currently pushes only simple comparison conditions that can be translated into native Parquet predicates.
 
 Supported operators:
 - `=`
@@ -84,8 +76,18 @@ Notes:
 - The left-hand side must be a column reference.
 - The right-hand side must be a literal value.
 - Simple casts on the column side are accepted.
+- Only pushdown-safe conditions are removed from the outer filter list.
 - Unsupported conditions remain outside the adapter-specific filter flow.
 
+Supported pushdown types:
+- `BOOLEAN`: `=` and `!=`
+- `INTEGER`, `BIGINT`, `FLOAT`, `DOUBLE`, `DATE`, `TIME`, `TIMESTAMP`: all six comparison operators
+- `VARCHAR`, `CHAR`, `TEXT`: `=` and `!=`
+
+Notes:
+- Pushdown uses Parquet native filter APIs through `FilterCompat.Filter`.
+- Legacy Parquet `INT96` timestamp columns are currently not pushed down.
+
 
 ## Package Structure and Responsibilities
 
@@ -100,24 +102,20 @@ Parquet file access helpers.
 
 ### `schema`
 Table wrappers and schema-related conversion logic.
-- `ParquetNamespace`: creates Parquet table wrappers for the active table flavor.
+- `ParquetNamespace`: creates Parquet table wrappers.
 - `ParquetTable`: base class for Parquet-backed physical tables.
-- `ParquetFilterableTable`: active table implementation for filter-aware scans; translates supported query conditions into adapter-specific filters and delegates execution to `ParquetEnumerator`.
-- `ParquetScannableTable`: scan-only table implementation; returns rows through `ParquetEnumerator` without adapter-side filter pushdown and uses an empty filter list for plain Parquet scanning.
-- `ParquetTranslatableTable`: scaffold for planner-driven execution.
 - `ParquetTypeConverter`: converts Parquet schema types, runtime values, and filter literals into Polypheny-compatible representations.
 
 ### `model`
 Internal data representation.
-- `ParquetFilter`: immutable representation of a pushed filter condition.
+- `FilterInfo`: immutable representation of a pushed filter condition.
 
 ### `execution`
 Runtime data access and filter evaluation.
 Classes responsible for reading Parquet data and returning rows.
-- `ParquetEnumerator`: drives Parquet reading, projection handling, row-group iteration, and row production.
-- `PredicateEvaluator`: uses Parquet statistics to decide whether row groups can be skipped.
+- `ParquetEnumerator`: drives Parquet reading, projection handling, native filter configuration, row-group iteration, and row production.
+- `ParquetPredicateBuilder`: translates adapter filters into native Parquet predicates.
 - `ValueExtractor`: extracts raw values from Parquet records.
-- `ValueComparator`: compares runtime values during filter evaluation.
 
 ### `planning`
 Classes that integrate the Parquet adapter with Polypheny’s query planner.
@@ -139,23 +137,15 @@ Shared infrastructure helpers.
 
 ## Flavor Selection
 
-The adapter defines three table flavors through `ParquetTable.Flavor`:
-
-- `FILTERABLE`
-- `SCANNABLE`
-- `TRANSLATABLE`
-
-These flavors represent different levels of integration with the query engine.
-
-The active flavor is selected by `ParquetSource` when the namespace is created.
+The adapter supports different levels of integration with the query engine:
 
 - `SCANNABLE` is the simplest execution path. 
 It performs plain row scanning through `ParquetScannableTable` and delegates reading to `ParquetEnumerator` without adapter-side filter pushdown. 
 
 - `FILTERABLE` extends the scan-based model by accepting supported filter conditions from the query engine. 
-In this path, `ParquetFilterableTable` translates supported expressions into `ParquetFilter` objects, 
-and `ParquetEnumerator` applies projection pushdown, filter evaluation, and row-group pruning. 
-***This is the default and currently active flavor.***
+In this path, `ParquetFilterableTable` translates supported expressions into `FilterInfo` objects,
+`ParquetPredicateBuilder` converts them into native Parquet predicates,
+and `ParquetEnumerator` applies projection pushdown and native predicate pushdown through `ParquetFileReader`. 
 
 - `TRANSLATABLE` is the planner-oriented flavor. 
 Instead of only returning rows directly, it allows the adapter to expose adapter-specific relational nodes and planner rules. 
@@ -185,19 +175,6 @@ The following figure shows an example of the displayed schema information.
 ## Query Filter
 ![Schema display](images/query_filter.png)
 
-## Current Gaps and Future Work
-### Relational Parquet data source implementation
-- Complete the `SCANNABLE` and `TRANSLATABLE` execution paths so they provide real runtime behavior comparable to the active `FILTERABLE` path.
-- Extend filter support beyond simple comparisons of the form `column OP literal`.
-  Planned examples include `BETWEEN`, `IN`, `IS NULL`, `IS NOT NULL`, and more complex boolean combinations such as `AND` and `OR`.
-- Improve handling of nested and complex Parquet schemas instead of treating them only as textual representations.
-- Add partition-aware file discovery and query pruning for partitioned Parquet datasets.
-- Add support for remote and Hadoop-backed filesystems beyond the current local-file configuration.
-- Document and strengthen error handling for invalid files, unsupported schema constructs, and conversion failures.
-- Automated test coverage for schema extraction, table restore, projection pushdown, filter pushdown, row-group pruning, and runtime type conversion.
-- Add write support if the adapter is later extended beyond its current read-only design.
-
-### Document Parquet data source implementation
 
 
 # Unit Tests
@@ -221,4 +198,24 @@ contains the following tests:
 9. `projectsAndFiltersOtherTables()`
 10. `returnsNestedShippingAddressAsJSON()`
 
+## `ParquetPredicateBuilderTest.java`
+contains focused unit tests for predicate translation behavior.
+
+Current test coverage includes:
+1. `rejectsInt96TimestampPredicatePushdown()`
 
+
+## Current Gaps and Future Work
+### Relational Parquet data source implementation
+- Add write support - extended adapter beyond its current read-only design.
+- Add partition-aware file discovery and query pruning for partitioned Parquet datasets.
+- Add support for remote files.
+- Extend filter support beyond simple comparisons of the form `column OP literal`.
+  Planned examples include `BETWEEN`, `IN`, `IS NULL`, `IS NOT NULL`, and more complex boolean combinations such as `AND` and `OR`.
+- Add optional native predicate support for additional Parquet physical encodings where safe and well-tested.
+  One current example is legacy `INT96` timestamps, which are intentionally excluded from pushdown.
+- Improve handling of nested and complex Parquet schemas instead of treating them only as textual representations.
+- Document and strengthen error handling for invalid files, unsupported schema constructs, and conversion failures.
+
+
+### Document Parquet data source implementation
diff --git a/plugins/parquet-adapter/docs/flavors.md b/plugins/parquet-adapter/docs/flavors.md
index f768bd0108..455c464d5f 100644
--- a/plugins/parquet-adapter/docs/flavors.md
+++ b/plugins/parquet-adapter/docs/flavors.md
@@ -43,13 +43,13 @@ Characteristics:
 `FILTERABLE` extends the scan-based model by allowing the adapter to receive filter conditions from the query engine.
 
 In this flavor, the adapter inspects query filters and pushes supported conditions into its own execution logic. 
-This allows the Parquet reader to reduce the amount of data it reads and processes.
+This allows the Parquet reader to reduce the amount of data it reads and processes using native predicate pushdown.
 
 Characteristics:
 
 - supports filter pushdown
 - supports projection-aware reading
-- allows row-group pruning using Parquet metadata
+- uses Parquet native filter stages such as statistics, dictionary, column index, bloom filter, and record filtering when available
 - significantly more efficient than plain scan-only execution
 
 This is the active flavor in the current Parquet adapter implementation.
@@ -110,25 +110,31 @@ Main classes:
 
 - `ParquetFilterableTable`
 - `ParquetEnumerator`
-- `PredicateEvaluator`
+- `ParquetPredicateBuilder`
 
 Flow:
 
 - The query engine calls ParquetFilterableTable.scan(...).
 - The table inspects incoming filter expressions.
-- Supported filters are converted into adapter-specific ParquetFilter objects.
+- Only pushdown-safe filters are converted into adapter-specific `FilterInfo` objects.
 - These filters are passed into ParquetEnumerator.
-- The enumerator computes the required projection columns.
-- Row-group metadata is checked through PredicateEvaluator.
-- Row groups that cannot match the pushed filters are skipped.
-- Matching rows are read, filtered, and returned.
+- The enumerator builds the requested projection schema from the projected columns.
+- `ParquetPredicateBuilder` translates adapter filters into native Parquet predicates.
+- `ParquetFileReader` is opened with `ParquetReadOptions` that enable native predicate pushdown stages.
+- Matching rows are read directly from the projected Parquet schema and returned.
 
 Practical consequence:
 
 - fewer columns may be read
-- fewer row groups may be read
+- fewer row groups and pages may be read when the Parquet file contains usable metadata
 - less work is done outside the adapter
 
+Current limitations:
+
+- Only simple `column OP literal` predicates are pushed down.
+- `BOOLEAN` and string-like fields currently support only `=` and `!=`.
+- Legacy `INT96` timestamp columns are intentionally not pushed down.
+
 
 ### 3. TRANSLATABLE Workflow
 
@@ -151,4 +157,3 @@ Practical consequence:
 
 - optimization can happen earlier and more explicitly
 - this approach supports more sophisticated future optimizations
-
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
index 2bb8605e0e..e246ba08a8 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
@@ -35,7 +35,6 @@
 import org.polypheny.db.adapter.annotations.AdapterSettingList;
 import org.polypheny.db.adapter.annotations.AdapterSettingString;
 import org.polypheny.db.adapter.parquet.io.ParquetFileDiscovery;
-import org.polypheny.db.adapter.parquet.schema.ParquetTable.Flavor;
 import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
 import org.polypheny.db.adapter.parquet.schema.ParquetNamespace;
 import org.polypheny.db.adapter.parquet.schema.ParquetTable;
@@ -99,7 +98,7 @@ public class ParquetSource extends DataSource<RelAdapterCatalog> implements Rela
      * Constructor
      * Creates the Parquet source from adapter settings.
      */
-    public ParquetSource(final long storeId, final String uniqueName, final Map<String, String> settings, final DeployMode mode ) {
+    public ParquetSource( final long storeId, final String uniqueName, final Map<String, String> settings, final DeployMode mode ) {
         super( storeId, uniqueName, settings, mode, true, new RelAdapterCatalog( storeId ), Set.of( DataModel.RELATIONAL ) );
 
         this.parquetTypeConverter = new ParquetTypeConverter();
@@ -114,6 +113,7 @@ public ParquetSource(final long storeId, final String uniqueName, final Map<Stri
         this.delegate = new RelationalScanDelegate( this, adapterCatalog );
     }
 
+
     /**
      * Get directory information from settings and populate parquetDir variable
      */
@@ -134,10 +134,11 @@ private void setParquetDir( Map<String, String> settings ) {
         }
     }
 
+
     /**
      * Removes invalid characters and creates a valid table name
      */
-    private static String getValidTableName(String name){
+    private static String getValidTableName( String name ) {
         return name.trim().replaceAll( "[^a-z0-9_]+", "" );
     }
 
@@ -150,31 +151,32 @@ private static String computePhysicalTableName( String fileName ) {
         if ( physicalTableName.endsWith( ".parquet" ) ) {
             physicalTableName = physicalTableName.substring( 0, physicalTableName.length() - ".parquet".length() );
         }
-        return getValidTableName(physicalTableName);
+        return getValidTableName( physicalTableName );
     }
 
 
     /**
      * Build valid column name from field data
      */
-    private String getValidColumnNameFromField(Type field) {
+    private String getValidColumnNameFromField( Type field ) {
         return field.getName().toLowerCase().trim().replaceAll( "[^a-z0-9_]+", "_" );
     }
 
+
     /**
      * Get parquet file fields information from metadata
      */
     private List<ExportedColumn> getExportedColumnsFromFile( String fileName, String physicalTableName ) {
         try {
-            Path path = new Path(new URL( parquetDir, fileName ).toURI());
+            Path path = new Path( new URL( parquetDir, fileName ).toURI() );
             Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
-            try ( ParquetFileReader reader = ParquetFileReader.open( HadoopInputFile.fromPath(path, conf))) {
+            try ( ParquetFileReader reader = ParquetFileReader.open( HadoopInputFile.fromPath( path, conf ) ) ) {
                 List<Type> schemaFields = reader.getFooter().getFileMetaData().getSchema().getFields();
                 List<ExportedColumn> columns = new ArrayList<>();
                 int position = 0;
                 for ( Type field : schemaFields ) {
-                    ExportedColumn column = getExportedColumnFromField(field, fileName, physicalTableName, position);
-                    columns.add(column);
+                    ExportedColumn column = getExportedColumnFromField( field, fileName, physicalTableName, position );
+                    columns.add( column );
                     position++;
                 }
                 return columns;
@@ -188,26 +190,27 @@ private List<ExportedColumn> getExportedColumnsFromFile( String fileName, String
     /**
      * Extract metadata information from single parquet field and create column object
      */
-    private ExportedColumn getExportedColumnFromField(Type field, String fileName, String physicalTableName, int position) {
-        String columnName = getValidColumnNameFromField(field);
+    private ExportedColumn getExportedColumnFromField( Type field, String fileName, String physicalTableName, int position ) {
+        String columnName = getValidColumnNameFromField( field );
         PolyType polyType = parquetTypeConverter.fromParquetTypeToPolyType( field );
         //Integer length = polyType == PolyType.VARCHAR ? maxStringLength : null;
         return new ExportedColumn(
-                    columnName,
-                    polyType,
-                    null,
-                    null,
-                    null,
-                    null,
-                    null,
-                    false,
-                    fileName,
-                    physicalTableName,
-                    field.getName(),
-                    position,
-                    position == 0 );
+                columnName,
+                polyType,
+                null,
+                null,
+                null,
+                null,
+                null,
+                false,
+                fileName,
+                physicalTableName,
+                field.getName(),
+                position,
+                position == 0 );
     }
 
+
     /**
      * Create information page containing exported columns details
      * for all tables loaded from parquet files
@@ -220,14 +223,16 @@ private void createInformationPage() {
 
             InformationGroup group = new InformationGroup( informationPage, entry.getValue().get( 0 ).physicalSchemaName() );
             informationGroups.add( group );
-            InformationTable table = getInformationTable(group, entry);
+            InformationTable table = getInformationTable( group, entry );
             informationElements.add( table );
         }
     }
 
+
     /**
      * Extract columns from all parquet files
-     * @return Map<String, List<ExportedColumn>> - column list per table name
+     *
+     * @return Map<String, List < ExportedColumn>> - column list per table name
      */
     @Override
     public Map<String, List<ExportedColumn>> getExportedColumns() {
@@ -252,9 +257,9 @@ public Map<String, List<ExportedColumn>> getExportedColumns() {
     /**
      * Builds InformationTable from exported columns
      */
-    private InformationTable getInformationTable(InformationGroup group, Map.Entry<String, List<ExportedColumn>> entry) {
-        List<String> columns = Arrays.asList("Position", "Column Name", "Type", "Nullable", "Filename", "Primary");
-        InformationTable table = new InformationTable(group, columns);
+    private InformationTable getInformationTable( InformationGroup group, Map.Entry<String, List<ExportedColumn>> entry ) {
+        List<String> columns = Arrays.asList( "Position", "Column Name", "Type", "Nullable", "Filename", "Primary" );
+        InformationTable table = new InformationTable( group, columns );
         for ( ExportedColumn exportedColumn : entry.getValue() ) {
             table.addRow(
                     exportedColumn.physicalPosition(),
@@ -267,15 +272,16 @@ private InformationTable getInformationTable(InformationGroup group, Map.Entry<S
         return table;
     }
 
+
     /**
      * Updates the active namespace wrapper for this source.
      */
     @Override
     public void updateNamespace( String name, long id ) {
-        ParquetTable.Flavor flavor = Flavor.FILTERABLE;
-        currentNamespace = new ParquetNamespace( id, adapterId, parquetDir, flavor );
+        currentNamespace = new ParquetNamespace( id, adapterId, parquetDir );
     }
 
+
     /**
      * Returns this source as relational adapter interface.
      */
@@ -293,6 +299,7 @@ public void truncate( Context context, long allocId ) {
         throw new GenericRuntimeException( "Parquet adapter does not support truncate" );
     }
 
+
     /**
      * Prepare does nothing, because data source is read-only.
      */
@@ -302,6 +309,7 @@ public boolean prepare( PolyXid xid ) {
         return true;
     }
 
+
     /**
      * Do nothing - read only
      */
@@ -311,6 +319,7 @@ public void commit( PolyXid xid ) {
         // do nothing
     }
 
+
     /**
      * Do nothing - read only
      */
@@ -320,6 +329,7 @@ public void rollback( PolyXid xid ) {
         // do nothing
     }
 
+
     /**
      * Cleans up information page state.
      */
@@ -328,6 +338,7 @@ public void shutdown() {
         removeInformationPage();
     }
 
+
     /**
      * Reloads settings that affect source location.
      */
@@ -340,6 +351,7 @@ protected void reloadSettings( List<String> updatedSettings ) {
 
     //region Not Delegated Methods
 
+
     /**
      * Creates and registers a physical Parquet table entry.
      */
@@ -367,7 +379,7 @@ public void restoreTable( AllocationTable alloc, List<PhysicalEntity> entities,
         PhysicalEntity table = entities.get( 0 );
         updateNamespace( table.namespaceName, table.namespaceId );
         ParquetTable physical = currentNamespace.createParquetTable( table.id, table.unwrapOrThrow( PhysicalTable.class ), this );
-        adapterCatalog.addPhysical( alloc,physical );
+        adapterCatalog.addPhysical( alloc, physical );
     }
 
 
@@ -379,6 +391,8 @@ public void renameLogicalColumn( long id, String newColumnName ) {
         adapterCatalog.renameLogicalColumn( id, newColumnName );
     }
     //endregion
+
+
     /**
      * Methods excluded from Lombok delegate forwarding.
      * These methods should not be delegated.
@@ -393,4 +407,5 @@ private interface Excludes {
         void restoreTable( AllocationTable alloc, List<PhysicalEntity> entities, Context context );
 
     }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetEnumerator.java
index a3630ce533..29df37fd2d 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetEnumerator.java
@@ -18,26 +18,25 @@
 
 import java.io.IOException;
 import java.util.ArrayList;
-import java.util.LinkedHashSet;
 import java.util.List;
-import java.util.Set;
 import java.util.concurrent.atomic.AtomicBoolean;
+import java.util.stream.IntStream;
 import org.apache.calcite.linq4j.Enumerator;
+import org.apache.parquet.ParquetReadOptions;
+import org.apache.parquet.filter2.compat.FilterCompat;
 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.Path;
 import org.apache.parquet.column.page.PageReadStore;
 import org.apache.parquet.example.data.Group;
 import org.apache.parquet.example.data.simple.convert.GroupRecordConverter;
 import org.apache.parquet.hadoop.ParquetFileReader;
-import org.apache.parquet.hadoop.metadata.BlockMetaData;
 import org.apache.parquet.hadoop.util.HadoopInputFile;
 import org.apache.parquet.io.ColumnIOFactory;
 import org.apache.parquet.io.MessageColumnIO;
 import org.apache.parquet.io.RecordReader;
 import org.apache.parquet.schema.MessageType;
-import org.apache.parquet.schema.PrimitiveType;
 import org.apache.parquet.schema.Type;
-import org.polypheny.db.adapter.parquet.model.ParquetFilter;
+import org.polypheny.db.adapter.parquet.model.FilterInfo;
 import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
 import org.polypheny.db.adapter.parquet.util.HadoopConfigurationFactory;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
@@ -47,63 +46,91 @@
 public class ParquetEnumerator implements Enumerator<PolyValue[]> {
 
     private final AtomicBoolean cancelFlag;
-    private final List<ParquetFilter> filters;
     private final ParquetFileReader fileReader;
-    private final MessageType schema;
     private final MessageType projectionSchema;
-    private final int[] projectionIndexes;
-    private final List<BlockMetaData> blocks;
     private final ValueExtractor valueExtractor;
-    private final PredicateEvaluator predicateEvaluator;
     private final ParquetTypeConverter typeConverter;
-    private final int columnCount;
+    private final boolean useNativeFilter;
 
     private RecordReader<Group> recordReader;
     private long rowCountInGroup;
     private long rowIndexInGroup;
-    private int blockIndex;
     private PolyValue[] current;
 
 
     /**
-     * Constructor
+     * Constructor:
+     * creates file reader with push-down filter configuration
+     *
      * @param source file
      * @param cancelFlag used in moveNext()
      * @param fields columns
-     * @param filters list of parquet filters
      * @throws GenericRuntimeException on failure
      */
+    public ParquetEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields ) {
+        this( source, cancelFlag, fields, List.of() );
+    }
+
 
-    public ParquetEnumerator(
-            Source source,
-            AtomicBoolean cancelFlag,
-            int[] fields,
-            List<ParquetFilter> filters ) throws GenericRuntimeException {
+    /**
+     * Constructor:
+     * creates file reader with push-down filter configuration
+     *
+     * @param source file
+     * @param cancelFlag used in moveNext()
+     * @param fields columns
+     * @param filters list of parquet filters
+     * @throws GenericRuntimeException on failure
+     */
+    public ParquetEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields, List<FilterInfo> filters ) {
 
         this.cancelFlag = cancelFlag;
-        this.filters = filters == null ? List.of() : filters;
         this.valueExtractor = new ValueExtractor();
-        this.predicateEvaluator = new PredicateEvaluator();
         this.typeConverter = new ParquetTypeConverter();
 
         try {
-            // create file reader
+            // build HadoopInputFile object from provided path
             Path path = new Path( source.url().toURI() );
             Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
-            this.fileReader = ParquetFileReader.open( HadoopInputFile.fromPath( path, conf ) );
+            var inputFile = HadoopInputFile.fromPath( path, conf );
+
+            // create parquet file schema
+            MessageType schema;
+            try ( ParquetFileReader schemaReader = ParquetFileReader.open( inputFile ) ) {
+                schema = schemaReader.getFooter().getFileMetaData().getSchema();
+            }
+
+            // create projection from provided projected fields and the whole schema:
+            // if no projection provided return all existing in schema columns
+            int[] projectedFields = buildProjectedFields( fields, schema.getFieldCount() );
+            // build new parquet schema based on provided projection
+            this.projectionSchema = buildProjectionSchema( schema, projectedFields );
+
+            // translate filters provided by Polypheny into a Parquet-native filter object
+            var recordFilter = new ParquetPredicateBuilder().translate( schema, filters == null ? List.of() : filters );
+            // check if there is an actual predicate to apply or is there no filter at all
+            this.useNativeFilter = FilterCompat.isFilteringRequired( recordFilter );
+
+            // set configuration for pushing down
+            ParquetReadOptions.Builder readOptionsBuilder = ParquetReadOptions.builder()
+                    .useStatsFilter()
+                    .useDictionaryFilter()
+                    .useColumnIndexFilter()
+                    .useBloomFilter()
+                    .useRecordFilter();
+
+            if ( useNativeFilter ) {
+                readOptionsBuilder.withRecordFilter( recordFilter );
+            }
 
-            this.schema = fileReader.getFooter().getFileMetaData().getSchema();
-            this.columnCount = fields.length == 0 ? schema.getFieldCount() : Math.min( fields.length, schema.getFieldCount() );
-            // combine projection columns (fields) with filter columns
-            this.projectionIndexes = computeProjectionIndexes( fields, this.filters, this.columnCount );
-            this.projectionSchema = buildProjectionSchema( schema, projectionIndexes );
+            // enable Parquet to apply the filter internally
+            this.fileReader = ParquetFileReader.open( inputFile, readOptionsBuilder.build() );
             this.fileReader.setRequestedSchema( projectionSchema );
-            this.blocks = fileReader.getFooter().getBlocks();
 
-            this.blockIndex = 0;
             this.recordReader = null;
             this.rowCountInGroup = 0;
             this.rowIndexInGroup = 0;
+
         } catch ( Exception e ) {
             throw new GenericRuntimeException( "Unable to open parquet file: " + source.path(), e );
         }
@@ -136,25 +163,20 @@ public boolean moveNext() {
                     current = null;
                     return false;
                 }
-
+                // ensure that reader deals with a correct row group
                 if ( !ensureRecordReader() ) {
                     current = null;
                     return false;
                 }
-
+                // read one row from current row group
                 Group group = recordReader.read();
                 rowIndexInGroup++;
                 if ( group == null ) {
                     recordReader = null;
                     continue;
                 }
-
-                PolyValue[] row = extractRow( group );
-                if ( !matchesFilters( row ) ) {
-                    continue;
-                }
-
-                current = row;
+                // convert to PolyValue object array
+                current = extractRow( group );
                 return true;
             }
         } catch ( Exception e ) {
@@ -197,33 +219,19 @@ private boolean ensureRecordReader() throws IOException {
             return true;
         }
 
-        while ( blockIndex < blocks.size() ) {
-            BlockMetaData block = blocks.get( blockIndex );
-            // check if block contains filter values according to metadata in block
-            // if not - row group can be skipped
-            if ( !predicateEvaluator.mightContain( block, schema, filters ) ) {
-                fileReader.skipNextRowGroup();
-                blockIndex++;
-                continue;
-            }
+        PageReadStore pages = useNativeFilter
+                ? fileReader.readNextFilteredRowGroup()
+                : fileReader.readNextRowGroup();
 
-            // create reader for compatible row group
-
-            PageReadStore pages = fileReader.readNextRowGroup();
-            blockIndex++;
-
-            if ( pages == null ) {
-                return false;
-            }
-
-            MessageColumnIO columnIO = new ColumnIOFactory().getColumnIO( this.projectionSchema );
-            this.recordReader = columnIO.getRecordReader( pages, new GroupRecordConverter( this.projectionSchema ) );
-            this.rowCountInGroup = pages.getRowCount();
-            this.rowIndexInGroup = 0;
-            return true;
+        if ( pages == null ) {
+            return false;
         }
 
-        return false;
+        MessageColumnIO columnIO = new ColumnIOFactory().getColumnIO( this.projectionSchema );
+        this.recordReader = columnIO.getRecordReader( pages, new GroupRecordConverter( this.projectionSchema ) );
+        this.rowCountInGroup = pages.getRowCount();
+        this.rowIndexInGroup = 0;
+        return true;
     }
 
 
@@ -231,8 +239,8 @@ private boolean ensureRecordReader() throws IOException {
      * Extracts one row using the requested read schema.
      */
     private PolyValue[] extractRow( Group group ) {
-        PolyValue[] row = new PolyValue[columnCount];
-        for ( int readIndex = 0; readIndex < this.projectionIndexes.length; readIndex++ ) {
+        PolyValue[] row = new PolyValue[projectionSchema.getFieldCount()];
+        for ( int readIndex = 0; readIndex < projectionSchema.getFieldCount(); readIndex++ ) {
             var type = projectionSchema.getType( readIndex );
             var value = valueExtractor.extractValue( group, readIndex, type );
             row[readIndex] = typeConverter.fromObjToPolyValue( type, value );
@@ -242,89 +250,22 @@ private PolyValue[] extractRow( Group group ) {
 
 
     /**
-     * Evaluates pushed filters against one extracted row.
+     * If no projection provided return all existing in schema columns
      */
-    private boolean matchesFilters( PolyValue[] row ) {
-        if ( filters.isEmpty() ) {
-            return true;
-        }
-
-        // each filter - for specific column
-        for ( ParquetFilter filter : filters ) {
-            // validate column index
-            int idx = filter.columnIndex();
-            if ( idx < 0 || idx >= row.length || idx >= projectionIndexes.length ) {
-                return false; //invalid filter column
-            }
-
-            // apply filter only for primitive types
-            int schemaFieldIndex = projectionIndexes[idx];
-            Type fieldType = schema.getType( schemaFieldIndex );
-            if ( !fieldType.isPrimitive() ) {
-                return false;
-            }
-
-            Object actual = row[idx];
-            if ( actual == null ) {
-                return false;
-            }
-
-            PrimitiveType primitiveType = fieldType.asPrimitiveType();
-            Object expected = this.typeConverter.fromLiteralToPrimitive( primitiveType, filter.literalValue() );
-            if ( expected == null ) {
-                // incorrect filter value
-                return false;
-            }
-
-            Integer cmp = ValueComparator.compareValues( primitiveType, actual, expected );
-
-            if ( cmp == null || !matchesOperator( filter, cmp ) ) {
-                return false;
-            }
-        }
-        return true;
-    }
-
-
-    /**
-     * Applies comparison result to a filter operator.
-     */
-    private boolean matchesOperator( ParquetFilter filter, int cmp ) {
-        return switch ( filter.operator() ) {
-            case EQUALS -> cmp == 0;
-            case NOT_EQUALS -> cmp != 0;
-            case GREATER_THAN -> cmp > 0;
-            case GREATER_THAN_OR_EQUAL -> cmp >= 0;
-            case LESS_THAN -> cmp < 0;
-            case LESS_THAN_OR_EQUAL -> cmp <= 0;
-            default -> false;
-        };
-    }
-
-
-    /**
-     * Computes the set of columns that must be read:
-     * combines projection columns (fields) with filter columns
-     */
-    private static int[] computeProjectionIndexes( int[] projectedFields, List<ParquetFilter> filters, int columnCount ) {
-        Set<Integer> fields = new LinkedHashSet<>();
-        for ( int field : projectedFields ) {
-            if ( field >= 0 && field < columnCount ) {
-                fields.add( field );
-            }
+    private static int[] buildProjectedFields( int[] projectedFields, int schemaFieldCount ) {
+        if ( projectedFields == null || projectedFields.length == 0 ) {
+            return IntStream.range( 0, schemaFieldCount ).toArray();
         }
-        for ( ParquetFilter filter : filters ) {
-            int index = filter.columnIndex();
-            if ( index >= 0 && index < columnCount ) {
-                fields.add( index );
-            }
-        }
-        return fields.stream().mapToInt( Integer::intValue ).toArray();
+        return projectedFields;
     }
 
 
     /**
-     * Builds the projected read schema.
+     * Builds the projected parquet schema.
+     *
+     * @param fullSchema - full parquet file schema
+     * @param projectedFieldIndexes - required projection
+     * @return org.apache.parquet.schema.MessageType - new schema
      */
     private static MessageType buildProjectionSchema( MessageType fullSchema, int[] projectedFieldIndexes ) {
         if ( projectedFieldIndexes.length == 0 ) {
@@ -338,5 +279,4 @@ private static MessageType buildProjectionSchema( MessageType fullSchema, int[]
         return new MessageType( fullSchema.getName(), fields );
     }
 
-
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetPredicateBuilder.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetPredicateBuilder.java
new file mode 100644
index 0000000000..1eee574e48
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetPredicateBuilder.java
@@ -0,0 +1,197 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.execution;
+
+import java.util.List;
+import org.apache.parquet.filter2.compat.FilterCompat;
+import org.apache.parquet.filter2.predicate.FilterApi;
+import org.apache.parquet.filter2.predicate.FilterPredicate;
+import org.apache.parquet.io.api.Binary;
+import org.apache.parquet.schema.LogicalTypeAnnotation;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.model.FilterInfo;
+import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
+import org.polypheny.db.algebra.constant.Kind;
+
+/**
+ * Translates adapter filters into parquet-native predicates.
+ */
+public class ParquetPredicateBuilder {
+
+    private final ParquetTypeConverter typeConverter = new ParquetTypeConverter();
+
+
+    /**
+     * Build parquet filter for pushdown from adapter filter
+     * @param schema - parquet file native schema
+     * @param filters - adapter level filters
+     * @return FilterCompat.Filter - filter in parquet format
+     */
+    public FilterCompat.Filter translate( MessageType schema, List<FilterInfo> filters ) {
+        FilterPredicate predicate = null;
+
+        for ( FilterInfo filter : filters ) {
+            FilterPredicate next = buildPredicate( schema, filter );
+            if ( next == null ) {
+                throw new IllegalArgumentException( "Unsupported parquet predicate: " + filter );
+            }
+            predicate = predicate == null ? next : FilterApi.and( predicate, next );
+        }
+
+        // return filter for given predicate
+        return predicate == null ? FilterCompat.NOOP : FilterCompat.get( predicate );
+    }
+
+
+    /**
+     * Build parquet filter predicate from provided filter info
+     * @param schema - parquet schema
+     * @param filter - adapter level filter info
+     * @return FilterPredicate object
+     */
+    private FilterPredicate buildPredicate( MessageType schema, FilterInfo filter ) {
+        int index = filter.columnIndex();
+        if ( index < 0 || index >= schema.getFieldCount() ) {
+            return null;
+        }
+
+        Type type = schema.getType( index );
+        if ( !type.isPrimitive() ) {
+            return null;
+        }
+
+        PrimitiveType primitive = type.asPrimitiveType();
+        Object expected = typeConverter.fromPolyValueToParquetObj( primitive, filter.polyValue() );
+        if ( expected == null ) {
+            return null;
+        }
+
+        String columnName = schema.getFieldName( index );
+        return switch ( primitive.getPrimitiveTypeName() ) {
+            case BOOLEAN -> buildBooleanPredicate( filter.operator(), columnName, expected );
+            case INT32 -> buildIntPredicate( filter.operator(), columnName, expected );
+            case INT64 -> buildLongPredicate( filter.operator(), columnName, expected );
+            case FLOAT -> buildFloatPredicate( filter.operator(), columnName, expected );
+            case DOUBLE -> buildDoublePredicate( filter.operator(), columnName, expected );
+            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> buildBinaryPredicate( filter.operator(), columnName, expected, primitive.getLogicalTypeAnnotation() );
+        };
+    }
+
+
+    private FilterPredicate buildBooleanPredicate( Kind operator, String columnName, Object expected ) {
+        if ( !(expected instanceof Boolean value) ) {
+            return null;
+        }
+        return switch ( operator ) {
+            case EQUALS -> FilterApi.eq( FilterApi.booleanColumn( columnName ), value );
+            case NOT_EQUALS -> FilterApi.notEq( FilterApi.booleanColumn( columnName ), value );
+            default -> null;
+        };
+    }
+
+
+    private FilterPredicate buildIntPredicate( Kind operator, String columnName, Object expected ) {
+        if ( !(expected instanceof Integer value) ) {
+            return null;
+        }
+        return switch ( operator ) {
+            case EQUALS -> FilterApi.eq( FilterApi.intColumn( columnName ), value );
+            case NOT_EQUALS -> FilterApi.notEq( FilterApi.intColumn( columnName ), value );
+            case GREATER_THAN -> FilterApi.gt( FilterApi.intColumn( columnName ), value );
+            case GREATER_THAN_OR_EQUAL -> FilterApi.gtEq( FilterApi.intColumn( columnName ), value );
+            case LESS_THAN -> FilterApi.lt( FilterApi.intColumn( columnName ), value );
+            case LESS_THAN_OR_EQUAL -> FilterApi.ltEq( FilterApi.intColumn( columnName ), value );
+            default -> null;
+        };
+    }
+
+
+    private FilterPredicate buildLongPredicate( Kind operator, String columnName, Object expected ) {
+        if ( !(expected instanceof Long value) ) {
+            return null;
+        }
+        return switch ( operator ) {
+            case EQUALS -> FilterApi.eq( FilterApi.longColumn( columnName ), value );
+            case NOT_EQUALS -> FilterApi.notEq( FilterApi.longColumn( columnName ), value );
+            case GREATER_THAN -> FilterApi.gt( FilterApi.longColumn( columnName ), value );
+            case GREATER_THAN_OR_EQUAL -> FilterApi.gtEq( FilterApi.longColumn( columnName ), value );
+            case LESS_THAN -> FilterApi.lt( FilterApi.longColumn( columnName ), value );
+            case LESS_THAN_OR_EQUAL -> FilterApi.ltEq( FilterApi.longColumn( columnName ), value );
+            default -> null;
+        };
+    }
+
+
+    private FilterPredicate buildFloatPredicate( Kind operator, String columnName, Object expected ) {
+        if ( !(expected instanceof Float value) ) {
+            return null;
+        }
+        return switch ( operator ) {
+            case EQUALS -> FilterApi.eq( FilterApi.floatColumn( columnName ), value );
+            case NOT_EQUALS -> FilterApi.notEq( FilterApi.floatColumn( columnName ), value );
+            case GREATER_THAN -> FilterApi.gt( FilterApi.floatColumn( columnName ), value );
+            case GREATER_THAN_OR_EQUAL -> FilterApi.gtEq( FilterApi.floatColumn( columnName ), value );
+            case LESS_THAN -> FilterApi.lt( FilterApi.floatColumn( columnName ), value );
+            case LESS_THAN_OR_EQUAL -> FilterApi.ltEq( FilterApi.floatColumn( columnName ), value );
+            default -> null;
+        };
+    }
+
+
+    private FilterPredicate buildDoublePredicate( Kind operator, String columnName, Object expected ) {
+        if ( !(expected instanceof Double value) ) {
+            return null;
+        }
+        return switch ( operator ) {
+            case EQUALS -> FilterApi.eq( FilterApi.doubleColumn( columnName ), value );
+            case NOT_EQUALS -> FilterApi.notEq( FilterApi.doubleColumn( columnName ), value );
+            case GREATER_THAN -> FilterApi.gt( FilterApi.doubleColumn( columnName ), value );
+            case GREATER_THAN_OR_EQUAL -> FilterApi.gtEq( FilterApi.doubleColumn( columnName ), value );
+            case LESS_THAN -> FilterApi.lt( FilterApi.doubleColumn( columnName ), value );
+            case LESS_THAN_OR_EQUAL -> FilterApi.ltEq( FilterApi.doubleColumn( columnName ), value );
+            default -> null;
+        };
+    }
+
+
+    private FilterPredicate buildBinaryPredicate( Kind operator, String columnName, Object expected, LogicalTypeAnnotation logicalType ) {
+        Binary value = expected instanceof Binary binary
+                ? binary
+                : Binary.fromString( expected.toString() );
+
+        return switch ( operator ) {
+            case EQUALS -> FilterApi.eq( FilterApi.binaryColumn( columnName ), value );
+            case NOT_EQUALS -> FilterApi.notEq( FilterApi.binaryColumn( columnName ), value );
+            case GREATER_THAN, GREATER_THAN_OR_EQUAL, LESS_THAN, LESS_THAN_OR_EQUAL -> logicalType == null ? null : buildOrderedBinaryPredicate( operator, columnName, value );
+            default -> null;
+        };
+    }
+
+
+    private FilterPredicate buildOrderedBinaryPredicate( Kind operator, String columnName, Binary value ) {
+        return switch ( operator ) {
+            case GREATER_THAN -> FilterApi.gt( FilterApi.binaryColumn( columnName ), value );
+            case GREATER_THAN_OR_EQUAL -> FilterApi.gtEq( FilterApi.binaryColumn( columnName ), value );
+            case LESS_THAN -> FilterApi.lt( FilterApi.binaryColumn( columnName ), value );
+            case LESS_THAN_OR_EQUAL -> FilterApi.ltEq( FilterApi.binaryColumn( columnName ), value );
+            default -> null;
+        };
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/PredicateEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/PredicateEvaluator.java
deleted file mode 100644
index 34dc413f38..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/PredicateEvaluator.java
+++ /dev/null
@@ -1,119 +0,0 @@
-package org.polypheny.db.adapter.parquet.execution;
-
-import java.util.List;
-import org.apache.parquet.column.statistics.Statistics;
-import org.apache.parquet.hadoop.metadata.BlockMetaData;
-import org.apache.parquet.hadoop.metadata.ColumnChunkMetaData;
-import org.apache.parquet.schema.MessageType;
-import org.apache.parquet.schema.PrimitiveType;
-import org.apache.parquet.schema.Type;
-import org.polypheny.db.adapter.parquet.model.ParquetFilter;
-import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
-import org.polypheny.db.algebra.constant.Kind;
-
-/**
- * Checks whether a row group can be skipped for a given filter (predicate)
- */
-public class PredicateEvaluator {
-
-    private final ParquetTypeConverter typeConverter = new ParquetTypeConverter();
-
-
-    /**
-     * Returns whether the row group may contain matches.
-     */
-    public boolean mightContain( BlockMetaData block, MessageType schema, List<ParquetFilter> filters ) {
-        if ( filters == null || filters.isEmpty() ) {
-            return true;
-        }
-
-        for ( ParquetFilter filter : filters ) {
-            int i = filter.columnIndex();
-            if ( i < 0 || i >= schema.getFieldCount() ) {
-                continue;
-            }
-
-            Type type = schema.getType( i );
-            if ( !type.isPrimitive() ) {
-                continue;
-            }
-
-            PrimitiveType primitive = type.asPrimitiveType();
-            ColumnChunkMetaData column = findColumn( block.getColumns(), schema.getFieldName( i ) );
-            if ( column == null ) {
-                continue;
-            }
-
-            Statistics<?> stats = column.getStatistics();
-            if ( stats == null || stats.isEmpty() ) {
-                continue;
-            }
-
-            long rowCount = block.getRowCount();
-            long nullCount = stats.isNumNullsSet() ? stats.getNumNulls() : -1L;
-            if ( nullCount == rowCount && rowCount > 0 ) {
-                return false;
-            }
-
-            Object expected = typeConverter.fromLiteralToPrimitive( primitive, filter.literalValue() );
-            if ( expected == null ) {
-                continue;
-            }
-
-            if ( !stats.hasNonNullValue() ) {
-                return false;
-            }
-
-            Object min = stats.genericGetMin();
-            Object max = stats.genericGetMax();
-            if ( min == null || max == null ) {
-                continue;
-            }
-
-            Integer cmpMin = ValueComparator.compareValues( primitive, expected, min );
-            Integer cmpMax = ValueComparator.compareValues( primitive, expected, max );
-            if ( cmpMin == null || cmpMax == null ) {
-                continue;
-            }
-
-            if ( canSkipBlock( filter.operator(), primitive, cmpMin, cmpMax, min, max, expected, rowCount, nullCount ) ) {
-                return false;
-            }
-        }
-
-        return true;
-    }
-
-
-    /**
-     * Applies operator-specific pruning rules.
-     */
-    private boolean canSkipBlock( Kind operator, PrimitiveType primitive, int cmpMin, int cmpMax, Object min, Object max, Object expected, long rowCount, long nullCount ) {
-        return switch ( operator ) {
-            case EQUALS -> cmpMin < 0 || cmpMax > 0;
-            case GREATER_THAN -> cmpMax >= 0;
-            case GREATER_THAN_OR_EQUAL -> cmpMax > 0;
-            case LESS_THAN -> cmpMin <= 0;
-            case LESS_THAN_OR_EQUAL -> cmpMin < 0;
-            case NOT_EQUALS -> {
-                Integer cmpMinMax = ValueComparator.compareValues( primitive, min, max );
-                Integer cmpMinExpected = ValueComparator.compareValues( primitive, min, expected );
-                yield cmpMinMax != null && cmpMinExpected != null && cmpMinMax == 0 && cmpMinExpected == 0 && nullCount == 0 && rowCount > 0;
-            }
-            default -> false;
-        };
-    }
-
-
-    /**
-     * Finds column metadata by top-level Parquet field name.
-     */
-    private ColumnChunkMetaData findColumn( List<ColumnChunkMetaData> columns, String fieldName ) {
-        for ( ColumnChunkMetaData column : columns ) {
-            if ( column.getPath() != null && fieldName.equals( column.getPath().toDotString() ) ) {
-                return column;
-            }
-        }
-        return null;
-    }
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueComparator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueComparator.java
deleted file mode 100644
index ab663dbb3b..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueComparator.java
+++ /dev/null
@@ -1,30 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.execution;
-
-import org.apache.parquet.schema.PrimitiveType;
-
-public class ValueComparator {
-
-    public static Integer compareValues( PrimitiveType primitive, Object left, Object right ) {
-        try {
-            return primitive.comparator().compare( left, right );
-        } catch ( Exception e ) {
-            return null;
-        }
-    }
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetFilter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/FilterInfo.java
similarity index 57%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetFilter.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/FilterInfo.java
index 5a44cd29cb..2a2895944e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/ParquetFilter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/FilterInfo.java
@@ -1,13 +1,14 @@
 package org.polypheny.db.adapter.parquet.model;
 
 import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.entity.PolyValue;
 
 /**
  * Immutable Filter description
  * @param columnIndex - index of filter column
  * @param operator - filter operation
- * @param literalValue - filter value
+ * @param polyValue - filter value
  */
-public record ParquetFilter(int columnIndex, Kind operator, String literalValue) {
+public record FilterInfo(int columnIndex, Kind operator, PolyValue polyValue ) {
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetProjectScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetProjectScanRule.java
index 063eb34064..052648715c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetProjectScanRule.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetProjectScanRule.java
@@ -31,7 +31,7 @@ public void onMatch( AlgOptRuleCall call ) {
         if ( fields == null ) {
             return;
         }
-        call.transformTo( new ParquetScan( scan.getCluster(), scan.getEntity(), scan.parquetTable, fields ) );
+        call.transformTo( new ParquetScan( scan.getCluster(), scan.getEntity(), fields ) );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetScan.java
index de6dab3759..2ad0da3da4 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetScan.java
@@ -5,7 +5,6 @@
 import org.apache.calcite.linq4j.tree.Expressions;
 import org.apache.calcite.linq4j.tree.Primitive;
 import org.polypheny.db.adapter.parquet.schema.ParquetTable;
-import org.polypheny.db.adapter.parquet.schema.ParquetTranslatableTable;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.AlgWriter;
 import org.polypheny.db.algebra.core.relational.RelScan;
@@ -26,19 +25,18 @@
 
 public class ParquetScan extends RelScan<ParquetTable> implements EnumerableAlg {
 
-    final ParquetTranslatableTable parquetTable;
-    final int[] fields;
+    private final int[] fields;
 
 
-    public ParquetScan( AlgCluster cluster, ParquetTable table, ParquetTranslatableTable parquetTable, int[] fields ) {
+    public ParquetScan( AlgCluster cluster, ParquetTable table, int[] fields ) {
         super( cluster, cluster.traitSetOf( EnumerableConvention.INSTANCE ), table );
-        this.parquetTable = parquetTable;
         this.fields = fields;
     }
 
+
     @Override
     public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
-        return new ParquetScan( getCluster(), entity, parquetTable, fields );
+        return new ParquetScan( getCluster(), entity, fields );
     }
 
 
@@ -78,7 +76,7 @@ public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
                 physType,
                 Blocks.toBlock(
                         Expressions.call(
-                                entity.asExpression( ParquetTranslatableTable.class ),
+                                entity.asExpression( ParquetTable.class ),
                                 "project",
                                 implementor.getRootExpression(),
                                 Expressions.constant( fields ) ) ) );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetFilterableTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetFilterableTable.java
deleted file mode 100644
index 61338ca990..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetFilterableTable.java
+++ /dev/null
@@ -1,115 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.schema;
-
-import org.apache.calcite.linq4j.AbstractEnumerable;
-import org.apache.calcite.linq4j.Enumerable;
-import org.apache.calcite.linq4j.Enumerator;
-import org.polypheny.db.adapter.DataContext;
-import org.polypheny.db.adapter.parquet.ParquetSource;
-import org.polypheny.db.adapter.parquet.execution.ParquetEnumerator;
-import org.polypheny.db.adapter.parquet.model.ParquetFilter;
-import org.polypheny.db.algebra.constant.Kind;
-import org.polypheny.db.catalog.entity.physical.PhysicalTable;
-import org.polypheny.db.rex.RexCall;
-import org.polypheny.db.rex.RexIndexRef;
-import org.polypheny.db.rex.RexLiteral;
-import org.polypheny.db.rex.RexNode;
-import org.polypheny.db.schema.types.FilterableEntity;
-import org.polypheny.db.type.PolyType;
-import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.util.Source;
-import java.util.ArrayList;
-import java.util.List;
-import java.util.concurrent.atomic.AtomicBoolean;
-
-/**
- * Filterable table
- */
-public class ParquetFilterableTable extends ParquetTable implements FilterableEntity {
-
-    public ParquetFilterableTable( long id, Source source, PhysicalTable table, List<PolyType> fieldTypes, int[] fields, ParquetSource csvSource ) {
-        super( id, source, table, fieldTypes, fields, csvSource );
-    }
-
-
-    /**
-     * Pushes supported filters.
-     */
-    @Override
-    public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> filters ) {
-        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
-        final List<ParquetFilter> pushedFilters = new ArrayList<>();
-        filters.removeIf( filter -> addFilter( filter, pushedFilters ) );
-
-        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
-        return new AbstractEnumerable<>() {
-            @Override
-            public Enumerator<PolyValue[]> enumerator() {
-                return new ParquetEnumerator( source, cancelFlag, fields, pushedFilters );
-            }
-        };
-    }
-
-
-    /**
-     * Translates a Rex filter into adapter filter form when possible.
-     */
-    private boolean addFilter( RexNode filter, List<ParquetFilter> pushedFilters ) {
-        if ( !isSupportedOperator( filter.getKind() ) ) {
-            return false;
-        }
-
-        RexCall call = (RexCall) filter;
-        RexNode left = call.getOperands().get( 0 );
-        if ( left.isA( Kind.CAST ) ) {
-            left = ((RexCall) left).operands.get( 0 );
-        }
-        RexNode right = call.getOperands().get( 1 );
-
-        if ( !(left instanceof RexIndexRef) || !(right instanceof RexLiteral literal) ) {
-            return false;
-        }
-
-        if ( literal.getValue() == null ) {
-            return false;
-        }
-
-        int index = ((RexIndexRef) left).getIndex();
-        if ( index < 0 || index >= columns.size() ) {
-            return false;
-        }
-
-        pushedFilters.add( new ParquetFilter( index, filter.getKind(), literal.getValue().toString() ) );
-        return true;
-    }
-
-
-    /**
-     * Checks whether the operator can be handled by the reader.
-     */
-    private boolean isSupportedOperator( Kind kind ) {
-        return kind == Kind.EQUALS
-                || kind == Kind.NOT_EQUALS
-                || kind == Kind.GREATER_THAN
-                || kind == Kind.GREATER_THAN_OR_EQUAL
-                || kind == Kind.LESS_THAN
-                || kind == Kind.LESS_THAN_OR_EQUAL;
-    }
-
-}
-
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java
index cd435fbf38..3f5ecfef8b 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java
@@ -21,17 +21,15 @@
 public class ParquetNamespace extends Namespace {
 
     private final URL directoryUrl;
-    private final ParquetTable.Flavor flavor;
 
 
     /**
      * Constructor:
      * Creates a namespace bound to a directory URL and table flavor.
      */
-    public ParquetNamespace( long id, long adapterId, URL directoryUrl, ParquetTable.Flavor flavor ) {
+    public ParquetNamespace( long id, long adapterId, URL directoryUrl ) {
         super( id, adapterId );
         this.directoryUrl = directoryUrl;
-        this.flavor = flavor;
     }
 
 
@@ -66,11 +64,7 @@ public ParquetTable createParquetTable( long id, PhysicalTable table, ParquetSou
      * Instantiates the table class based on configured flavor.
      */
     private ParquetTable createTable( long id, Source source, PhysicalTable table, List<PolyType> fieldTypes, int[] fields, ParquetSource parquetSource ) {
-        return switch ( flavor ) {
-            case TRANSLATABLE -> new ParquetTranslatableTable( id, source, table, fieldTypes, fields, parquetSource );
-            case SCANNABLE -> new ParquetScannableTable( id, source, table, fieldTypes, fields, parquetSource );
-            case FILTERABLE -> new ParquetFilterableTable( id, source, table, fieldTypes, fields, parquetSource );
-        };
+        return new ParquetTable( id, source, table, fieldTypes, fields, parquetSource );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetScannableTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetScannableTable.java
deleted file mode 100644
index f23e3e4d65..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetScannableTable.java
+++ /dev/null
@@ -1,41 +0,0 @@
-package org.polypheny.db.adapter.parquet.schema;
-
-import java.util.List;
-import java.util.concurrent.atomic.AtomicBoolean;
-import org.apache.calcite.linq4j.AbstractEnumerable;
-import org.apache.calcite.linq4j.Enumerable;
-import org.apache.calcite.linq4j.Enumerator;
-import org.polypheny.db.adapter.DataContext;
-import org.polypheny.db.adapter.parquet.ParquetSource;
-import org.polypheny.db.adapter.parquet.execution.ParquetEnumerator;
-import org.polypheny.db.adapter.parquet.model.ParquetFilter;
-import org.polypheny.db.catalog.entity.physical.PhysicalTable;
-import org.polypheny.db.schema.types.ScannableEntity;
-import org.polypheny.db.type.PolyType;
-import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.util.Source;
-
-/**
- * Scannable table
- */
-public class ParquetScannableTable extends ParquetTable implements ScannableEntity {
-
-    public ParquetScannableTable( long id, Source source, PhysicalTable table, java.util.List<PolyType> fieldTypes, int[] fields, ParquetSource parquetSource ) {
-        super( id, source, table, fieldTypes, fields, parquetSource );
-    }
-
-    @Override
-    public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
-        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
-        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
-        // create parquet enumerator
-        return new AbstractEnumerable<>() {
-            @Override
-            public Enumerator<PolyValue[]> enumerator() {
-                // empty filter list
-                List<ParquetFilter> filterList = java.util.List.of();
-                return new ParquetEnumerator( source, cancelFlag, fields, filterList );
-            }
-        };
-    }
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java
index c95e2d04c1..537a030628 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java
@@ -16,21 +16,44 @@
 
 package org.polypheny.db.adapter.parquet.schema;
 
+import org.apache.calcite.linq4j.AbstractEnumerable;
+import org.apache.calcite.linq4j.Enumerable;
+import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.DataContext;
 import org.polypheny.db.adapter.parquet.ParquetSource;
+import org.polypheny.db.adapter.parquet.execution.ParquetEnumerator;
+import org.polypheny.db.adapter.parquet.model.FilterInfo;
+import org.polypheny.db.adapter.parquet.planning.ParquetScan;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.rex.RexCall;
+import org.polypheny.db.rex.RexIndexRef;
+import org.polypheny.db.rex.RexLiteral;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.schema.types.FilterableEntity;
+import org.polypheny.db.schema.types.ScannableEntity;
+import org.polypheny.db.schema.types.TranslatableEntity;
 import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.Source;
+import java.util.ArrayList;
 import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
 
 /**
  * Base class for Parquet physical tables.
  */
-public class ParquetTable extends PhysicalTable {
+public class ParquetTable extends PhysicalTable implements FilterableEntity, ScannableEntity, TranslatableEntity {
+
     protected final Source source;
     protected List<PolyType> fieldTypes;
     protected final int[] fields;
     protected final ParquetSource parquetSource;
 
+
     /**
      * Creates a Parquet table wrapper from a physical table definition.
      */
@@ -53,13 +76,140 @@ public class ParquetTable extends PhysicalTable {
 
 
     /**
-     * Various degrees of table "intelligence".
-     * Table execution flavors used by the adapter.
+     * Returns enumerable for FilterableEntity.
+     *
+     * @param dataContext data context
+     * @param filters filters to push down.
+     * @return enumerable.
+     */
+    @Override
+    public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> filters ) {
+        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
+        final List<FilterInfo> pushedFilters = new ArrayList<>();
+        filters.removeIf( filter -> addFilter( filter, pushedFilters ) );
+
+        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+        return new AbstractEnumerable<>() {
+            @Override
+            public Enumerator<PolyValue[]> enumerator() {
+                return new ParquetEnumerator( source, cancelFlag, fields, pushedFilters );
+            }
+        };
+    }
+
+
+    /**
+     * Returns enumerable for ScannableEntity.
+     *
+     * @param dataContext data context
+     * @return enumerable.
+     */
+    @Override
+    public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
+        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
+        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+        // create parquet enumerator
+        return new AbstractEnumerable<>() {
+            @Override
+            public Enumerator<PolyValue[]> enumerator() {
+                return new ParquetEnumerator( source, cancelFlag, fields );
+            }
+        };
+    }
+
+
+    /**
+     * Returns {@link AlgNode} as part of TranslatableEntity.
+     *
+     * @param cluster cluster
+     * @param traitSet trial set.
+     * @return {@link AlgNode}
+     */
+    @Override
+    public AlgNode toAlg( AlgCluster cluster, AlgTraitSet traitSet ) {
+        return new ParquetScan( cluster, this, fields );
+    }
+
+
+    /**
+     * This method is called from the {@link ParquetScan} via reflection.
+     *
+     * @param dataContext data context
+     * @param fields a list of fields to return.
+     * @return enumerable.
+     */
+    public Enumerable<PolyValue[]> project( final DataContext dataContext, final int[] fields ) {
+        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
+        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+        return new AbstractEnumerable<>() {
+            @Override
+            public Enumerator<PolyValue[]> enumerator() {
+                return new ParquetEnumerator( source, cancelFlag, fields );
+            }
+        };
+    }
+
+
+    /**
+     * Translates a Rex filter into adapter filter form when possible.
      */
-    public enum Flavor {
-        SCANNABLE, FILTERABLE, TRANSLATABLE
+    private boolean addFilter( RexNode filter, List<FilterInfo> pushedFilters ) {
+        if ( !isSupportedOperator( filter.getKind() ) ) {
+            return false;
+        }
+
+        RexCall call = (RexCall) filter;
+        RexNode left = call.getOperands().get( 0 );
+        if ( left.isA( Kind.CAST ) ) {
+            left = ((RexCall) left).operands.get( 0 );
+        }
+        RexNode right = call.getOperands().get( 1 );
+
+        if ( !(left instanceof RexIndexRef) || !(right instanceof RexLiteral literal) ) {
+            return false;
+        }
+
+        if ( literal.getValue() == null ) {
+            return false;
+        }
+
+        int index = ((RexIndexRef) left).getIndex();
+        if ( index < 0 || index >= columns.size() ) {
+            return false;
+        }
+
+        if ( !isPushdownSupported( index, filter.getKind(), literal ) ) {
+            return false;
+        }
+
+        pushedFilters.add( new FilterInfo( index, filter.getKind(), literal.getValue() ) );
+        return true;
     }
 
+
+    /**
+     * Checks whether the operator can be handled by the reader.
+     */
+    private boolean isSupportedOperator( Kind kind ) {
+        return kind == Kind.EQUALS
+                || kind == Kind.NOT_EQUALS
+                || kind == Kind.GREATER_THAN
+                || kind == Kind.GREATER_THAN_OR_EQUAL
+                || kind == Kind.LESS_THAN
+                || kind == Kind.LESS_THAN_OR_EQUAL;
+    }
+
+
+    private boolean isPushdownSupported( int index, Kind kind, RexLiteral literal ) {
+        PolyType type = fieldTypes.get( index );
+        return switch ( type ) {
+            case BOOLEAN, VARCHAR, CHAR, TEXT -> kind == Kind.EQUALS || kind == Kind.NOT_EQUALS;
+            case INTEGER, BIGINT, FLOAT, DOUBLE, DATE, TIME, TIMESTAMP -> true;
+            default -> false;
+        } && literal.getValue() != null;
+    }
+
+
 }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTranslatableTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTranslatableTable.java
deleted file mode 100644
index eae8f5cfc7..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTranslatableTable.java
+++ /dev/null
@@ -1,64 +0,0 @@
-package org.polypheny.db.adapter.parquet.schema;
-
-import java.util.List;
-import java.util.concurrent.atomic.AtomicBoolean;
-import org.apache.calcite.linq4j.AbstractEnumerable;
-import org.apache.calcite.linq4j.Enumerable;
-import org.apache.calcite.linq4j.Enumerator;
-import org.polypheny.db.adapter.DataContext;
-import org.polypheny.db.adapter.parquet.ParquetSource;
-import org.polypheny.db.adapter.parquet.planning.ParquetScan;
-import org.polypheny.db.algebra.AlgNode;
-import org.polypheny.db.catalog.entity.physical.PhysicalTable;
-import org.polypheny.db.plan.AlgCluster;
-import org.polypheny.db.plan.AlgTraitSet;
-import org.polypheny.db.schema.types.TranslatableEntity;
-import org.polypheny.db.type.PolyType;
-import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.util.Source;
-
-/**
- * Translatable table
- */
-public class ParquetTranslatableTable extends ParquetTable implements TranslatableEntity {
-
-    protected ParquetTranslatableTable( long id, Source source, PhysicalTable table, List<PolyType> fieldTypes, int[] fields, ParquetSource parquetSource ) {
-        super( id, source, table, fieldTypes, fields, parquetSource );
-    }
-
-
-    public Enumerable<PolyValue[]> project( final DataContext dataContext, final int[] fields ) {
-        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
-        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
-        return new AbstractEnumerable<>() {
-            @Override
-            public Enumerator<PolyValue[]> enumerator() {
-                return new Enumerator<>() {
-                    @Override
-                    public PolyValue[] current() {
-                        return new PolyValue[0];
-                    }
-                    @Override
-                    public boolean moveNext() {
-                        return false;
-                    }
-                    @Override
-                    public void reset() {
-
-                    }
-                    @Override
-                    public void close() {
-
-                    }
-                };
-            }
-        };
-    }
-
-
-    @Override
-    public AlgNode toAlg( AlgCluster cluster, AlgTraitSet traitSet ) {
-        return new ParquetScan( cluster, this, this, fields );
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTypeConverter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTypeConverter.java
index 1d8f1d4587..4b647e90a8 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTypeConverter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTypeConverter.java
@@ -26,7 +26,6 @@
 import org.apache.parquet.schema.LogicalTypeAnnotation.TimestampLogicalTypeAnnotation;
 import org.apache.parquet.schema.PrimitiveType;
 import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
-import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeNameConverter;
 import org.apache.parquet.schema.Type;
 import org.polypheny.db.type.PolyType;
 import org.polypheny.db.type.entity.PolyBinary;
@@ -50,6 +49,7 @@
 import java.time.format.DateTimeFormatter;
 import java.time.format.DateTimeParseException;
 import java.util.Date;
+import java.util.Objects;
 
 import static io.activej.common.StringFormatUtils.parseLocalDateTime;
 
@@ -57,6 +57,7 @@ public class ParquetTypeConverter {
 
     /**
      * Convert original parquet type into {@link PolyType}
+     *
      * @param field parquet type
      * @return {@link PolyType}
      */
@@ -103,6 +104,7 @@ public PolyType fromParquetTypeToPolyType( Type field ) {
 
     /**
      * Convert value from object to appropriate PolyValue
+     *
      * @param fieldType - type of value/column
      * @param value - object to convert
      * @return converted PolyValue
@@ -125,31 +127,43 @@ public PolyValue fromObjToPolyValue( Type fieldType, Object value ) {
             case TIME -> fromObjToPolyTime( value );
             case TIMESTAMP -> fromObjToPolyTimestamp( value );
             case VARCHAR, TEXT -> fromObjToPolyString( value );
-            default -> throw new IllegalStateException("Unexpected value: " + fieldType);
+            default -> throw new IllegalStateException( "Unexpected value: " + fieldType );
         };
     }
 
 
     /**
-     * Convert literal to primitive type
-     * @param primitive - type to convert
-     * @param literal - string value
-     * @return converted object of primitive type
+     * Convert a typed literal to a parquet primitive value.
      */
-    public Object fromLiteralToPrimitive( PrimitiveType primitive, String literal ) {
+    public Object fromPolyValueToParquetObj( PrimitiveType primitive, PolyValue literal ) {
+        if ( literal == null || literal.isNull() ) {
+            return null;
+        }
+
         try {
-            Object date = fromLiteralToDate( primitive, literal );
-            if (date != null)
+            Object date = fromPolyValueToDate( primitive, literal );
+            if ( date != null ) {
                 return date;
-            // convert to primitive type
-            var converter = new ParquetPrimitiveTypeNameConverter(literal);
-            return primitive.getPrimitiveTypeName().convert( converter );
+            }
+
+            return switch ( primitive.getPrimitiveTypeName() ) {
+                case BOOLEAN -> literal.isBoolean() ? literal.asBoolean().value : Boolean.parseBoolean( literal.toString() );
+                case INT32 -> literal.asNumber().intValue();
+                case INT64 -> literal.asNumber().longValue();
+                case FLOAT -> literal.asNumber().floatValue();
+                case DOUBLE -> literal.asNumber().doubleValue();
+                case INT96 -> null;
+                case FIXED_LEN_BYTE_ARRAY, BINARY -> literal.isBinary()
+                        ? Binary.fromConstantByteArray( literal.asBinary().value )
+                        : Binary.fromString( literal.isString() ? literal.asString().value : literal.toString() );
+            };
         } catch ( Exception e ) {
             return null;
         }
     }
 
-    private Object fromLiteralToDate(PrimitiveType primitive, String literal) {
+
+    private Object fromStringToDate( PrimitiveType primitive, String literal ) {
         LogicalTypeAnnotation logical = primitive.getLogicalTypeAnnotation();
 
         if ( logical instanceof DateLogicalTypeAnnotation ) {
@@ -177,6 +191,57 @@ private Object fromLiteralToDate(PrimitiveType primitive, String literal) {
         return null;
     }
 
+
+    private Object fromPolyValueToDate( PrimitiveType primitive, PolyValue literal ) {
+        LogicalTypeAnnotation logical = primitive.getLogicalTypeAnnotation();
+
+        if ( logical instanceof DateLogicalTypeAnnotation ) {
+            if ( literal.isDate() ) {
+                return Math.toIntExact( literal.asDate().getDaysSinceEpoch() );
+            }
+            if ( literal.isNumber() ) {
+                return literal.asNumber().intValue();
+            }
+            return fromStringToDate( primitive, literal.toString() );
+        }
+
+        if ( logical instanceof TimeLogicalTypeAnnotation timeLogical ) {
+            long millis;
+            if ( literal.isTime() ) {
+                millis = Objects.requireNonNull( literal.asTime().ofDay ).longValue();
+            } else if ( literal.isNumber() ) {
+                millis = normalizeTimeToMillis( literal.asNumber().longValue() );
+            } else {
+                millis = normalizeTimeLiteralToMillis( literal.toString() );
+            }
+            return switch ( timeLogical.getUnit() ) {
+                case MILLIS -> primitive.getPrimitiveTypeName() == PrimitiveTypeName.INT32 ? (int) millis : millis;
+                case MICROS -> millis * 1_000L;
+                case NANOS -> millis * 1_000_000L;
+            };
+        }
+
+        if ( logical instanceof TimestampLogicalTypeAnnotation timestampLogical ) {
+            long millis;
+            if ( literal.isTimestamp() ) {
+                //noinspection DataFlowIssue
+                millis = literal.asTimestamp().millisSinceEpoch;
+            } else if ( literal.isNumber() ) {
+                millis = normalizeTimestampToMillis( literal.asNumber().longValue() );
+            } else {
+                millis = normalizeTimestampLiteralToMillis( literal.toString() );
+            }
+            return switch ( timestampLogical.getUnit() ) {
+                case MILLIS -> millis;
+                case MICROS -> millis * 1_000L;
+                case NANOS -> millis * 1_000_000L;
+            };
+        }
+
+        return null;
+    }
+
+
     /**
      * Parses an ISO local date string.
      */
@@ -188,6 +253,7 @@ private LocalDate parseLocalDate( String value ) {
         }
     }
 
+
     /**
      * Parses an ISO local time string.
      */
@@ -199,6 +265,7 @@ private LocalTime parseLocalTime( String value ) {
         }
     }
 
+
     /**
      * Parses time literal and normalizes it to milliseconds.
      */
@@ -210,6 +277,7 @@ private long normalizeTimeLiteralToMillis( String literal ) {
         return normalizeTimeToMillis( Long.parseLong( literal ) );
     }
 
+
     /**
      * Parses timestamp literal and normalizes it to milliseconds.
      */
@@ -221,6 +289,7 @@ private long normalizeTimestampLiteralToMillis( String literal ) {
         return normalizeTimestampToMillis( Long.parseLong( literal ) );
     }
 
+
     /**
      * Normalizes time units to milliseconds.
      */
@@ -235,6 +304,7 @@ private long normalizeTimeToMillis( long value ) {
         return value;
     }
 
+
     /**
      * Normalizes timestamp units to milliseconds.
      */
@@ -249,80 +319,6 @@ private long normalizeTimestampToMillis( long value ) {
         return value;
     }
 
-    private record ParquetPrimitiveTypeNameConverter( String literal ) implements PrimitiveTypeNameConverter<Object, Exception> {
-
-        /**
-         * Parses a float literal.
-         */
-        @Override
-        public Object convertFLOAT( PrimitiveTypeName primitiveTypeName ) {
-            return Float.parseFloat( literal );
-        }
-
-
-        /**
-         * Parses a double literal.
-         */
-        @Override
-        public Object convertDOUBLE( PrimitiveTypeName primitiveTypeName ) {
-            return Double.parseDouble( literal );
-        }
-
-
-        /**
-         * Parses a 32-bit integer literal.
-         */
-        @Override
-        public Object convertINT32( PrimitiveTypeName primitiveTypeName ) {
-            return Integer.parseInt( literal );
-        }
-
-
-        /**
-         * Parses a 64-bit integer literal.
-         */
-        @Override
-        public Object convertINT64( PrimitiveTypeName primitiveTypeName ) {
-            return Long.parseLong( literal );
-        }
-
-
-        /**
-         * Encodes the literal as binary for INT96.
-         */
-        @Override
-        public Object convertINT96( PrimitiveTypeName primitiveTypeName ) {
-            return Binary.fromConstantByteArray( literal.getBytes( StandardCharsets.UTF_8 ) );
-        }
-
-
-        /**
-         * Encodes the literal as binary for fixed byte arrays.
-         */
-        @Override
-        public Object convertFIXED_LEN_BYTE_ARRAY( PrimitiveTypeName primitiveTypeName ) {
-            return Binary.fromConstantByteArray( literal.getBytes( StandardCharsets.UTF_8 ) );
-        }
-
-
-        /**
-         * Parses a boolean literal.
-         */
-        @Override
-        public Object convertBOOLEAN( PrimitiveTypeName primitiveTypeName ) {
-            return Boolean.parseBoolean( literal );
-        }
-
-
-        /**
-         * Encodes the literal as binary.
-         */
-        @Override
-        public Object convertBINARY( PrimitiveTypeName primitiveTypeName ) {
-            return Binary.fromConstantByteArray( literal.getBytes( StandardCharsets.UTF_8 ) );
-        }
-
-    }
 
     /**
      * Converts to boolean value.
@@ -348,6 +344,7 @@ private PolyValue fromObjToPolyBinary( Object value ) {
         return PolyBinary.of( String.valueOf( value ).getBytes( StandardCharsets.UTF_8 ) );
     }
 
+
     /**
      * Converts to string value.
      */
@@ -405,6 +402,7 @@ private PolyValue fromObjToPolyDouble( Object value ) {
         return PolyDouble.of( Double.parseDouble( String.valueOf( value ) ) );
     }
 
+
     /**
      * Converts to date value.
      */
@@ -474,5 +472,4 @@ private PolyValue fromObjToPolyTimestamp( Object value ) {
     }
 
 
-
 }
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
index c4a5e5dd97..7905252155 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
@@ -211,6 +211,20 @@ void supportsGreaterThanFilter() throws SQLException {
         }
     }
 
+    @Test
+    void supportsDateFilterLiterals() throws SQLException {
+        assertQueryResult(
+                "SELECT customer_id, name "
+                        + "FROM customers "
+                        + "WHERE signup_date = TIMESTAMP '2023-02-04 00:00:00' "
+                        + "AND customer_id = 3 "
+                        + "ORDER BY customer_id",
+                ImmutableList.of(
+                        new Object[]{ 3L, "Rita Garcia" }
+                )
+        );
+    }
+
     // test filter operations
     @Test
     void supportsAllComparisonFilterOperations() throws SQLException {
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPredicateBuilderTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPredicateBuilderTest.java
new file mode 100644
index 0000000000..2fdd96d907
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPredicateBuilderTest.java
@@ -0,0 +1,46 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import java.util.List;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+import org.apache.parquet.schema.Types;
+import org.junit.jupiter.api.Test;
+import org.polypheny.db.adapter.parquet.execution.ParquetPredicateBuilder;
+import org.polypheny.db.adapter.parquet.model.FilterInfo;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.entity.temporal.PolyTimestamp;
+
+import static org.junit.jupiter.api.Assertions.assertThrows;
+
+class ParquetPredicateBuilderTest {
+
+    @Test
+    void rejectsInt96TimestampPredicatePushdown() {
+        MessageType schema = Types.buildMessage()
+                .optional( PrimitiveTypeName.INT96 )
+                .named( "ts" )
+                .named( "test_schema" );
+
+        FilterInfo filter = new FilterInfo( 0, Kind.EQUALS, PolyTimestamp.of( 1_700_000_000_000L ) );
+
+        assertThrows(
+                IllegalArgumentException.class,
+                () -> new ParquetPredicateBuilder().translate( schema, List.of( filter ) ) );
+    }
+}

From 3dbd127dc77540929e967bc3b643ed6915aae1a8 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Tue, 31 Mar 2026 14:57:54 +0200
Subject: [PATCH 07/46] add document support, improve relational filter
 pushdown

---
 .../org/polypheny/db/ddl/DdlManagerImpl.java  |  20 ++-
 plugins/parquet-adapter/docs/README.md        |  27 ++-
 .../db/adapter/parquet/ParquetSource.java     |  88 ++++++++-
 .../execution/AbstractParquetEnumerator.java  |  79 ++++++++
 .../execution/ParquetDocumentEnumerator.java  |  46 +++++
 ...numerator.java => ParquetGroupReader.java} | 169 ++++--------------
 .../execution/ParquetPredicateBuilder.java    |  93 ++--------
 .../execution/ParquetTableEnumerator.java     |  47 +++++
 .../ParquetTableFilterTranslator.java         | 153 ++++++++++++++++
 .../parquet/execution/ValueExtractor.java     |  99 ++++++++--
 .../{FilterInfo.java => AdapterFilter.java}   |   2 +-
 .../parquet/planning/ParquetDocumentScan.java |  91 ++++++++++
 .../planning/ParquetDocumentScanRule.java     |  45 +++++
 ...ParquetScan.java => ParquetTableScan.java} |   8 +-
 ...canRule.java => ParquetTableScanRule.java} |  14 +-
 .../parquet/schema/ParquetDocument.java       |  88 +++++++++
 .../parquet/schema/ParquetNamespace.java      |  36 ++--
 .../adapter/parquet/schema/ParquetTable.java  | 109 +++--------
 ... => ParquetTableFilterTranslatorTest.java} |  10 +-
 19 files changed, 853 insertions(+), 371 deletions(-)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/AbstractParquetEnumerator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetDocumentEnumerator.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/{ParquetEnumerator.java => ParquetGroupReader.java} (56%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetTableEnumerator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetTableFilterTranslator.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/{FilterInfo.java => AdapterFilter.java} (78%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScan.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScanRule.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/{ParquetScan.java => ParquetTableScan.java} (90%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/{ParquetProjectScanRule.java => ParquetTableScanRule.java} (71%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetDocument.java
 rename plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/{ParquetPredicateBuilderTest.java => ParquetTableFilterTranslatorTest.java} (77%)

diff --git a/dbms/src/main/java/org/polypheny/db/ddl/DdlManagerImpl.java b/dbms/src/main/java/org/polypheny/db/ddl/DdlManagerImpl.java
index 545315f7b2..92976c6e5d 100644
--- a/dbms/src/main/java/org/polypheny/db/ddl/DdlManagerImpl.java
+++ b/dbms/src/main/java/org/polypheny/db/ddl/DdlManagerImpl.java
@@ -215,12 +215,12 @@ public void createStore( String uniqueName, String adapterName, AdapterType adap
     public void createSource( Transaction transaction, String uniqueName, String adapterName, long namespace, AdapterType adapterType, Map<String, String> config, DeployMode mode ) {
         uniqueName = uniqueName.toLowerCase();
         DataSource<?> adapter = (DataSource<?>) AdapterManager.getInstance().addAdapter( catalog, adapterName, uniqueName, adapterType, mode, config );
-        namespace = adapter.getCurrentNamespace() == null ? namespace : adapter.getCurrentNamespace().getId(); // TODO: clean implementation. Sources should either create their own namespace or there should be default namespaces for different models.
+        long sourceNamespace = adapter.getCurrentNamespace() == null ? namespace : adapter.getCurrentNamespace().getId(); // TODO: clean implementation. Sources should either create their own namespace or there should be default namespaces for different models.
         if ( adapter.supportsRelational() ) {
-            createRelationalSource( transaction, adapter, namespace );
+            createRelationalSource( transaction, adapter, resolveSourceNamespace( uniqueName, sourceNamespace, DataModel.RELATIONAL ) );
         }
         if ( adapter.supportsDocument() ) {
-            createDocumentSource( adapter, namespace );
+            createDocumentSource( adapter, resolveSourceNamespace( uniqueName, sourceNamespace, DataModel.DOCUMENT ) );
         }
         if ( adapter.supportsGraph() ) {
             // TODO: implement graph source creation
@@ -230,6 +230,20 @@ public void createSource( Transaction transaction, String uniqueName, String ada
     }
 
 
+    private long resolveSourceNamespace( String uniqueName, long namespaceId, DataModel model ) {
+        LogicalNamespace namespace = catalog.getSnapshot().getNamespace( namespaceId ).orElseThrow();
+        if ( namespace.dataModel == model ) {
+            return namespaceId;
+        }
+
+        String sourceNamespaceName = uniqueName + "_" + model.name().toLowerCase();
+        return catalog.getSnapshot().getNamespace( sourceNamespaceName )
+                .filter( ns -> ns.dataModel == model )
+                .map( ns -> ns.id )
+                .orElseGet( () -> createNamespace( sourceNamespaceName, model, true, false, false, null ) );
+    }
+
+
     private void createDocumentSource( DataSource<?> adapter, long namespace ) {
         List<ExportedDocument> exportedCollections;
         try {
diff --git a/plugins/parquet-adapter/docs/README.md b/plugins/parquet-adapter/docs/README.md
index 06b8ff97d3..9ace268ed9 100644
--- a/plugins/parquet-adapter/docs/README.md
+++ b/plugins/parquet-adapter/docs/README.md
@@ -103,9 +103,10 @@ Parquet file access helpers.
 ### `schema`
 Table wrappers and schema-related conversion logic.
 - `ParquetNamespace`: creates Parquet table wrappers.
-- `ParquetTable`: base class for Parquet-backed physical tables.
+- `ParquetTable`: extends PhysicalTable and implements all 3 flavors.
+  - implements `scan()` functions
 - `ParquetTypeConverter`: converts Parquet schema types, runtime values, and filter literals into Polypheny-compatible representations.
-
+- `ParquetDocument`
 ### `model`
 Internal data representation.
 - `FilterInfo`: immutable representation of a pushed filter condition.
@@ -113,15 +114,27 @@ Internal data representation.
 ### `execution`
 Runtime data access and filter evaluation.
 Classes responsible for reading Parquet data and returning rows.
-- `ParquetEnumerator`: drives Parquet reading, projection handling, native filter configuration, row-group iteration, and row production.
-- `ParquetPredicateBuilder`: translates adapter filters into native Parquet predicates.
-- `ValueExtractor`: extracts raw values from Parquet records.
+- `AbstractParquetEnumerator`: basic enumerator functionality general for relational and document data source. Contains abstract `extractRow()` function that should be implemented in derived classes
+- `ParquetTableEnumerator`: implements extractRow() function for RELATIONAL data source.
+- `ParquetDocumentEnumerator`: implements extractRow() function for DOCUMENT data source.
+
+- `ParquetGroupReader`: contains read row group logic
+  - creates native parquet file reader
+  - reads data by row groups
+  - pushes filters down
+  
+- `ParquetTableFilterTranslator`: Translates adapter filters into parquet-native predicates.
+- `ParquetTableFilterBuilder`: Translates a Rex filter into adapter filter when possible.
+
+- `ValueExtractor`: conversion layer between Parquet and Polypheny value representations.
 
 ### `planning`
 Classes that integrate the Parquet adapter with Polypheny’s query planner.
 Handling query-plan representation and optimization.
-- `ParquetScan`: Parquet scan operator for the planning layer.
-- `ParquetProjectScanRule`: planner rule for projection handling over Parquet scans.
+- `ParquetTableScan`: Parquet scan operator for the planning layer.
+- `ParquetTableScanRule`: planner rule for projection handling over Parquet scans.
+- `ParquetDocumentScan`: 
+- `ParquetDocumentScanRule`:
 
 ### `util`
 Shared infrastructure helpers.
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
index e246ba08a8..6f26919459 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
@@ -28,27 +28,35 @@
 import org.polypheny.db.adapter.ConnectionMethod;
 import org.polypheny.db.adapter.DataSource;
 import org.polypheny.db.adapter.DeployMode;
+import org.polypheny.db.adapter.DocumentDataSource;
+import org.polypheny.db.adapter.DocumentScanDelegate;
 import org.polypheny.db.adapter.RelationalDataSource;
-import org.polypheny.db.adapter.RelationalScanDelegate;
+import org.polypheny.db.adapter.Scannable;
 import org.polypheny.db.adapter.annotations.AdapterProperties;
 import org.polypheny.db.adapter.annotations.AdapterSettingDirectory;
 import org.polypheny.db.adapter.annotations.AdapterSettingList;
 import org.polypheny.db.adapter.annotations.AdapterSettingString;
 import org.polypheny.db.adapter.parquet.io.ParquetFileDiscovery;
+import org.polypheny.db.adapter.parquet.schema.ParquetDocument;
 import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
 import org.polypheny.db.adapter.parquet.schema.ParquetNamespace;
 import org.polypheny.db.adapter.parquet.schema.ParquetTable;
 
 
 import org.polypheny.db.adapter.parquet.util.HadoopConfigurationFactory;
-import org.polypheny.db.catalog.catalogs.RelAdapterCatalog;
+import org.polypheny.db.catalog.catalogs.AdapterCatalog;
+import org.polypheny.db.catalog.catalogs.DocAdapterCatalog;
+import org.polypheny.db.catalog.entity.allocation.AllocationCollection;
 import org.polypheny.db.catalog.entity.allocation.AllocationTable;
 import org.polypheny.db.catalog.entity.allocation.AllocationTableWrapper;
+import org.polypheny.db.catalog.entity.logical.LogicalCollection;
 import org.polypheny.db.catalog.entity.logical.LogicalTableWrapper;
+import org.polypheny.db.catalog.entity.physical.PhysicalCollection;
 import org.polypheny.db.catalog.entity.physical.PhysicalEntity;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.catalog.logistic.DataModel;
+import org.polypheny.db.catalog.logistic.EntityType;
 import org.polypheny.db.information.InformationGroup;
 import org.polypheny.db.information.InformationTable;
 import org.polypheny.db.prepare.Context;
@@ -77,10 +85,10 @@
 @AdapterSettingList(name = "method", options = { "upload", "link" }, defaultValue = "upload", description = "If the supplied file(s) should be uploaded or a link to the local filesystem is used (sufficient permissions are required).", position = 1)
 @AdapterSettingDirectory(subOf = "method_upload", name = "directory", defaultValue = "classpath://orders_db", description = "You can upload one or multiple .parquet files.", position = 2)
 @AdapterSettingString(subOf = "method_link", defaultValue = "classpath://orders_db", name = "directoryName", description = "You can select a path to a folder or specific .parquet files.", position = 2)
-public class ParquetSource extends DataSource<RelAdapterCatalog> implements RelationalDataSource {
+public class ParquetSource extends DataSource<DocAdapterCatalog> implements RelationalDataSource, DocumentDataSource, Scannable {
 
     @Delegate(excludes = Excludes.class)
-    private final RelationalScanDelegate delegate;
+    private final DocumentScanDelegate delegate;
 
     private final ConnectionMethod connectionMethod;
     //private final int maxStringLength;
@@ -99,7 +107,7 @@ public class ParquetSource extends DataSource<RelAdapterCatalog> implements Rela
      * Creates the Parquet source from adapter settings.
      */
     public ParquetSource( final long storeId, final String uniqueName, final Map<String, String> settings, final DeployMode mode ) {
-        super( storeId, uniqueName, settings, mode, true, new RelAdapterCatalog( storeId ), Set.of( DataModel.RELATIONAL ) );
+        super( storeId, uniqueName, settings, mode, true, new DocAdapterCatalog( storeId ), Set.of( DataModel.RELATIONAL, DataModel.DOCUMENT ) );
 
         this.parquetTypeConverter = new ParquetTypeConverter();
         this.connectionMethod = settings.containsKey( "method" )
@@ -110,7 +118,7 @@ public ParquetSource( final long storeId, final String uniqueName, final Map<Str
         createInformationPage();
         enableInformationPage();
 
-        this.delegate = new RelationalScanDelegate( this, adapterCatalog );
+        this.delegate = new DocumentScanDelegate( this, adapterCatalog );
     }
 
 
@@ -279,6 +287,7 @@ private InformationTable getInformationTable( InformationGroup group, Map.Entry<
     @Override
     public void updateNamespace( String name, long id ) {
         currentNamespace = new ParquetNamespace( id, adapterId, parquetDir );
+        putNamespace( currentNamespace );
     }
 
 
@@ -291,6 +300,12 @@ public RelationalDataSource asRelationalDataSource() {
     }
 
 
+    @Override
+    public DocumentDataSource asDocumentDataSource() {
+        return this;
+    }
+
+
     /**
      * Parquet source is read-only, so truncate is not supported.
      */
@@ -382,6 +397,53 @@ public void restoreTable( AllocationTable alloc, List<PhysicalEntity> entities,
         adapterCatalog.addPhysical( alloc, physical );
     }
 
+    @Override
+    public void dropTable( Context context, long allocId ) {
+        adapterCatalog.removeAllocAndPhysical( allocId );
+    }
+
+
+    /**
+     * for each parquet file create document
+     * @return list of documents
+     */
+    @Override
+    public List<ExportedDocument> getExportedCollections() {
+        return getExportedColumns().keySet().stream()
+                .map( name -> new ExportedDocument( name, false, EntityType.SOURCE ) )
+                .toList();
+    }
+
+
+    @Override
+    public List<PhysicalEntity> createCollection( Context context, LogicalCollection logical, AllocationCollection allocation ) {
+        // create polypheny level document objects
+        PhysicalCollection collection = adapterCatalog.createCollection(
+                logical.getNamespaceName(),
+                logical.getName(),
+                logical,
+                allocation );
+
+        ParquetDocument physical = currentNamespace.createParquetCollection( collection, this );
+        adapterCatalog.replacePhysical( physical );
+        return List.of( physical );
+    }
+
+
+    @Override
+    public void restoreCollection( AllocationCollection alloc, List<PhysicalEntity> entities, Context context ) {
+        PhysicalEntity collection = entities.get( 0 );
+        updateNamespace( collection.namespaceName, collection.namespaceId );
+        ParquetDocument physical = currentNamespace.createParquetCollection( collection.unwrapOrThrow( PhysicalCollection.class ), this );
+        adapterCatalog.addPhysical( alloc, physical );
+    }
+
+
+    @Override
+    public void dropCollection( Context context, AllocationCollection allocation ) {
+        adapterCatalog.removeAllocAndPhysical( allocation.id );
+    }
+
 
     /**
      * Updates logical column naming in adapter catalog
@@ -390,6 +452,12 @@ public void restoreTable( AllocationTable alloc, List<PhysicalEntity> entities,
     public void renameLogicalColumn( long id, String newColumnName ) {
         adapterCatalog.renameLogicalColumn( id, newColumnName );
     }
+
+
+    @Override
+    public AdapterCatalog getCatalog() {
+        return adapterCatalog;
+    }
     //endregion
 
 
@@ -406,6 +474,14 @@ private interface Excludes {
 
         void restoreTable( AllocationTable alloc, List<PhysicalEntity> entities, Context context );
 
+        void dropTable( Context context, long allocId );
+
+        void createCollection( Context context, LogicalCollection logical, AllocationCollection allocation );
+
+        void restoreCollection( AllocationCollection alloc, List<PhysicalEntity> entities, Context context );
+
+        void dropCollection( Context context, AllocationCollection allocation );
+
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/AbstractParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/AbstractParquetEnumerator.java
new file mode 100644
index 0000000000..b57b3c3b0f
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/AbstractParquetEnumerator.java
@@ -0,0 +1,79 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.execution;
+
+import org.apache.calcite.linq4j.Enumerator;
+import org.apache.parquet.example.data.Group;
+import org.polypheny.db.adapter.parquet.model.AdapterFilter;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+
+public abstract class AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
+
+    protected final ParquetGroupReader reader;
+    protected final ValueExtractor valueExtractor;
+    private PolyValue[] current;
+
+
+    public AbstractParquetEnumerator(Source source, AtomicBoolean cancelFlag, int[] fields, List<AdapterFilter> filters ) {
+        this.reader = new ParquetGroupReader( source, cancelFlag, fields, filters );
+        this.valueExtractor = new ValueExtractor();
+    }
+
+
+    @Override
+    public PolyValue[] current() {
+        return current;
+    }
+
+
+    @Override
+    public boolean moveNext() {
+        try {
+            Group group = reader.next();
+            if ( group == null ) {
+                current = null;
+                return false;
+            }
+            current = extractRow( group );
+            return true;
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Error while reading parquet data", e );
+        }
+    }
+
+
+    @Override
+    public void reset() {
+        throw new UnsupportedOperationException();
+    }
+
+
+    @Override
+    public void close() {
+        reader.close();
+    }
+
+    /*
+    extractValue logic depends on source type (relational/document)
+     */
+    protected abstract PolyValue[] extractRow( Group group );
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetDocumentEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetDocumentEnumerator.java
new file mode 100644
index 0000000000..cf2b030faf
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetDocumentEnumerator.java
@@ -0,0 +1,46 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.execution;
+
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.Enumerator;
+import org.apache.parquet.example.data.Group;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.document.PolyDocument;
+import org.polypheny.db.util.Source;
+
+public class ParquetDocumentEnumerator extends AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
+
+    private final String documentPrefix;
+
+    public ParquetDocumentEnumerator( Source source, AtomicBoolean cancelFlag ) {
+        super(source, cancelFlag, null, List.of());
+        this.documentPrefix = source.path();
+    }
+
+    @Override
+    protected PolyValue[] extractRow( Group group ) {
+        PolyDocument document = valueExtractor.extractDocument(
+                group,
+                reader.getProjectionSchema(),
+                PolyString.of( documentPrefix + "#" + reader.getCurrentRowNumber() ) );
+        return new PolyValue[]{ document };
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetGroupReader.java
similarity index 56%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetEnumerator.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetGroupReader.java
index 29df37fd2d..40293e5ebf 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetGroupReader.java
@@ -21,14 +21,14 @@
 import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
 import java.util.stream.IntStream;
-import org.apache.calcite.linq4j.Enumerator;
-import org.apache.parquet.ParquetReadOptions;
-import org.apache.parquet.filter2.compat.FilterCompat;
+import lombok.Getter;
 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.Path;
+import org.apache.parquet.ParquetReadOptions;
 import org.apache.parquet.column.page.PageReadStore;
 import org.apache.parquet.example.data.Group;
 import org.apache.parquet.example.data.simple.convert.GroupRecordConverter;
+import org.apache.parquet.filter2.compat.FilterCompat;
 import org.apache.parquet.hadoop.ParquetFileReader;
 import org.apache.parquet.hadoop.util.HadoopInputFile;
 import org.apache.parquet.io.ColumnIOFactory;
@@ -36,82 +36,47 @@
 import org.apache.parquet.io.RecordReader;
 import org.apache.parquet.schema.MessageType;
 import org.apache.parquet.schema.Type;
-import org.polypheny.db.adapter.parquet.model.FilterInfo;
-import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
+import org.polypheny.db.adapter.parquet.model.AdapterFilter;
 import org.polypheny.db.adapter.parquet.util.HadoopConfigurationFactory;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
-import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.Source;
 
-public class ParquetEnumerator implements Enumerator<PolyValue[]> {
+public class ParquetGroupReader implements AutoCloseable {
 
     private final AtomicBoolean cancelFlag;
     private final ParquetFileReader fileReader;
-    private final MessageType projectionSchema;
-    private final ValueExtractor valueExtractor;
-    private final ParquetTypeConverter typeConverter;
     private final boolean useNativeFilter;
 
+    @Getter
+    private final MessageType projectionSchema;
+
     private RecordReader<Group> recordReader;
     private long rowCountInGroup;
     private long rowIndexInGroup;
-    private PolyValue[] current;
-
-
-    /**
-     * Constructor:
-     * creates file reader with push-down filter configuration
-     *
-     * @param source file
-     * @param cancelFlag used in moveNext()
-     * @param fields columns
-     * @throws GenericRuntimeException on failure
-     */
-    public ParquetEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields ) {
-        this( source, cancelFlag, fields, List.of() );
-    }
 
+    @Getter
+    private long currentRowNumber = -1;
 
-    /**
-     * Constructor:
-     * creates file reader with push-down filter configuration
-     *
-     * @param source file
-     * @param cancelFlag used in moveNext()
-     * @param fields columns
-     * @param filters list of parquet filters
-     * @throws GenericRuntimeException on failure
-     */
-    public ParquetEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields, List<FilterInfo> filters ) {
 
+    public ParquetGroupReader( Source source, AtomicBoolean cancelFlag, int[] fields, List<AdapterFilter> filters ) {
         this.cancelFlag = cancelFlag;
-        this.valueExtractor = new ValueExtractor();
-        this.typeConverter = new ParquetTypeConverter();
 
         try {
-            // build HadoopInputFile object from provided path
             Path path = new Path( source.url().toURI() );
             Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
             var inputFile = HadoopInputFile.fromPath( path, conf );
 
-            // create parquet file schema
             MessageType schema;
             try ( ParquetFileReader schemaReader = ParquetFileReader.open( inputFile ) ) {
                 schema = schemaReader.getFooter().getFileMetaData().getSchema();
             }
 
-            // create projection from provided projected fields and the whole schema:
-            // if no projection provided return all existing in schema columns
             int[] projectedFields = buildProjectedFields( fields, schema.getFieldCount() );
-            // build new parquet schema based on provided projection
             this.projectionSchema = buildProjectionSchema( schema, projectedFields );
 
-            // translate filters provided by Polypheny into a Parquet-native filter object
-            var recordFilter = new ParquetPredicateBuilder().translate( schema, filters == null ? List.of() : filters );
-            // check if there is an actual predicate to apply or is there no filter at all
+            var recordFilter = new ParquetTableFilterTranslator().translate( schema, filters == null ? List.of() : filters );
             this.useNativeFilter = FilterCompat.isFilteringRequired( recordFilter );
 
-            // set configuration for pushing down
             ParquetReadOptions.Builder readOptionsBuilder = ParquetReadOptions.builder()
                     .useStatsFilter()
                     .useDictionaryFilter()
@@ -123,61 +88,34 @@ public ParquetEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields,
                 readOptionsBuilder.withRecordFilter( recordFilter );
             }
 
-            // enable Parquet to apply the filter internally
             this.fileReader = ParquetFileReader.open( inputFile, readOptionsBuilder.build() );
             this.fileReader.setRequestedSchema( projectionSchema );
-
-            this.recordReader = null;
-            this.rowCountInGroup = 0;
-            this.rowIndexInGroup = 0;
-
         } catch ( Exception e ) {
             throw new GenericRuntimeException( "Unable to open parquet file: " + source.path(), e );
         }
     }
 
-    //region Enumerator operations
-
-
-    /**
-     * return current row
-     *
-     * @return current row
-     */
-    @Override
-    public PolyValue[] current() {
-        return current;
-    }
-
 
-    /**
-     * Move to the next matching row
-     *
-     * @return True if there is a new row or False otherwise.
-     */
-    @Override
-    public boolean moveNext() {
+    public Group next() {
         try {
             for ( ; ; ) {
                 if ( cancelFlag.get() ) {
-                    current = null;
-                    return false;
+                    return null;
                 }
-                // ensure that reader deals with a correct row group
+
                 if ( !ensureRecordReader() ) {
-                    current = null;
-                    return false;
+                    return null;
                 }
-                // read one row from current row group
+
                 Group group = recordReader.read();
                 rowIndexInGroup++;
                 if ( group == null ) {
                     recordReader = null;
                     continue;
                 }
-                // convert to PolyValue object array
-                current = extractRow( group );
-                return true;
+
+                currentRowNumber++;
+                return group;
             }
         } catch ( Exception e ) {
             throw new GenericRuntimeException( "Error while reading parquet data", e );
@@ -185,35 +123,6 @@ public boolean moveNext() {
     }
 
 
-    /**
-     * Reset is not supported
-     */
-    @Override
-    public void reset() {
-        throw new UnsupportedOperationException();
-    }
-
-
-    /**
-     * Close reader
-     */
-    @Override
-    public void close() {
-        try {
-            fileReader.close();
-        } catch ( IOException e ) {
-            throw new GenericRuntimeException( "Error closing parquet reader", e );
-        }
-    }
-    //endregion
-
-
-    /**
-     * Ensures a record reader is available for the current row group.
-     *
-     * @return true if reader was created, false - if no compatible row groups found
-     * @throws IOException when read fails
-     */
     private boolean ensureRecordReader() throws IOException {
         if ( recordReader != null && rowIndexInGroup < rowCountInGroup ) {
             return true;
@@ -227,31 +136,14 @@ private boolean ensureRecordReader() throws IOException {
             return false;
         }
 
-        MessageColumnIO columnIO = new ColumnIOFactory().getColumnIO( this.projectionSchema );
-        this.recordReader = columnIO.getRecordReader( pages, new GroupRecordConverter( this.projectionSchema ) );
+        MessageColumnIO columnIO = new ColumnIOFactory().getColumnIO( projectionSchema );
+        this.recordReader = columnIO.getRecordReader( pages, new GroupRecordConverter( projectionSchema ) );
         this.rowCountInGroup = pages.getRowCount();
         this.rowIndexInGroup = 0;
         return true;
     }
 
 
-    /**
-     * Extracts one row using the requested read schema.
-     */
-    private PolyValue[] extractRow( Group group ) {
-        PolyValue[] row = new PolyValue[projectionSchema.getFieldCount()];
-        for ( int readIndex = 0; readIndex < projectionSchema.getFieldCount(); readIndex++ ) {
-            var type = projectionSchema.getType( readIndex );
-            var value = valueExtractor.extractValue( group, readIndex, type );
-            row[readIndex] = typeConverter.fromObjToPolyValue( type, value );
-        }
-        return row;
-    }
-
-
-    /**
-     * If no projection provided return all existing in schema columns
-     */
     private static int[] buildProjectedFields( int[] projectedFields, int schemaFieldCount ) {
         if ( projectedFields == null || projectedFields.length == 0 ) {
             return IntStream.range( 0, schemaFieldCount ).toArray();
@@ -260,13 +152,6 @@ private static int[] buildProjectedFields( int[] projectedFields, int schemaFiel
     }
 
 
-    /**
-     * Builds the projected parquet schema.
-     *
-     * @param fullSchema - full parquet file schema
-     * @param projectedFieldIndexes - required projection
-     * @return org.apache.parquet.schema.MessageType - new schema
-     */
     private static MessageType buildProjectionSchema( MessageType fullSchema, int[] projectedFieldIndexes ) {
         if ( projectedFieldIndexes.length == 0 ) {
             return fullSchema;
@@ -279,4 +164,14 @@ private static MessageType buildProjectionSchema( MessageType fullSchema, int[]
         return new MessageType( fullSchema.getName(), fields );
     }
 
+
+    @Override
+    public void close() {
+        try {
+            fileReader.close();
+        } catch ( IOException e ) {
+            throw new GenericRuntimeException( "Error closing parquet reader", e );
+        }
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetPredicateBuilder.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetPredicateBuilder.java
index 1eee574e48..c28df5acec 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetPredicateBuilder.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetPredicateBuilder.java
@@ -16,85 +16,14 @@
 
 package org.polypheny.db.adapter.parquet.execution;
 
-import java.util.List;
-import org.apache.parquet.filter2.compat.FilterCompat;
 import org.apache.parquet.filter2.predicate.FilterApi;
 import org.apache.parquet.filter2.predicate.FilterPredicate;
 import org.apache.parquet.io.api.Binary;
 import org.apache.parquet.schema.LogicalTypeAnnotation;
-import org.apache.parquet.schema.MessageType;
-import org.apache.parquet.schema.PrimitiveType;
-import org.apache.parquet.schema.Type;
-import org.polypheny.db.adapter.parquet.model.FilterInfo;
-import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
 import org.polypheny.db.algebra.constant.Kind;
 
-/**
- * Translates adapter filters into parquet-native predicates.
- */
 public class ParquetPredicateBuilder {
-
-    private final ParquetTypeConverter typeConverter = new ParquetTypeConverter();
-
-
-    /**
-     * Build parquet filter for pushdown from adapter filter
-     * @param schema - parquet file native schema
-     * @param filters - adapter level filters
-     * @return FilterCompat.Filter - filter in parquet format
-     */
-    public FilterCompat.Filter translate( MessageType schema, List<FilterInfo> filters ) {
-        FilterPredicate predicate = null;
-
-        for ( FilterInfo filter : filters ) {
-            FilterPredicate next = buildPredicate( schema, filter );
-            if ( next == null ) {
-                throw new IllegalArgumentException( "Unsupported parquet predicate: " + filter );
-            }
-            predicate = predicate == null ? next : FilterApi.and( predicate, next );
-        }
-
-        // return filter for given predicate
-        return predicate == null ? FilterCompat.NOOP : FilterCompat.get( predicate );
-    }
-
-
-    /**
-     * Build parquet filter predicate from provided filter info
-     * @param schema - parquet schema
-     * @param filter - adapter level filter info
-     * @return FilterPredicate object
-     */
-    private FilterPredicate buildPredicate( MessageType schema, FilterInfo filter ) {
-        int index = filter.columnIndex();
-        if ( index < 0 || index >= schema.getFieldCount() ) {
-            return null;
-        }
-
-        Type type = schema.getType( index );
-        if ( !type.isPrimitive() ) {
-            return null;
-        }
-
-        PrimitiveType primitive = type.asPrimitiveType();
-        Object expected = typeConverter.fromPolyValueToParquetObj( primitive, filter.polyValue() );
-        if ( expected == null ) {
-            return null;
-        }
-
-        String columnName = schema.getFieldName( index );
-        return switch ( primitive.getPrimitiveTypeName() ) {
-            case BOOLEAN -> buildBooleanPredicate( filter.operator(), columnName, expected );
-            case INT32 -> buildIntPredicate( filter.operator(), columnName, expected );
-            case INT64 -> buildLongPredicate( filter.operator(), columnName, expected );
-            case FLOAT -> buildFloatPredicate( filter.operator(), columnName, expected );
-            case DOUBLE -> buildDoublePredicate( filter.operator(), columnName, expected );
-            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> buildBinaryPredicate( filter.operator(), columnName, expected, primitive.getLogicalTypeAnnotation() );
-        };
-    }
-
-
-    private FilterPredicate buildBooleanPredicate( Kind operator, String columnName, Object expected ) {
+    public static FilterPredicate buildBoolean( Kind operator, String columnName, Object expected ) {
         if ( !(expected instanceof Boolean value) ) {
             return null;
         }
@@ -106,10 +35,11 @@ private FilterPredicate buildBooleanPredicate( Kind operator, String columnName,
     }
 
 
-    private FilterPredicate buildIntPredicate( Kind operator, String columnName, Object expected ) {
+    public static FilterPredicate buildInt( Kind operator, String columnName, Object expected ) {
         if ( !(expected instanceof Integer value) ) {
             return null;
         }
+
         return switch ( operator ) {
             case EQUALS -> FilterApi.eq( FilterApi.intColumn( columnName ), value );
             case NOT_EQUALS -> FilterApi.notEq( FilterApi.intColumn( columnName ), value );
@@ -122,7 +52,7 @@ private FilterPredicate buildIntPredicate( Kind operator, String columnName, Obj
     }
 
 
-    private FilterPredicate buildLongPredicate( Kind operator, String columnName, Object expected ) {
+    public static FilterPredicate buildLong( Kind operator, String columnName, Object expected ) {
         if ( !(expected instanceof Long value) ) {
             return null;
         }
@@ -138,7 +68,7 @@ private FilterPredicate buildLongPredicate( Kind operator, String columnName, Ob
     }
 
 
-    private FilterPredicate buildFloatPredicate( Kind operator, String columnName, Object expected ) {
+    public static FilterPredicate buildFloat( Kind operator, String columnName, Object expected ) {
         if ( !(expected instanceof Float value) ) {
             return null;
         }
@@ -154,7 +84,7 @@ private FilterPredicate buildFloatPredicate( Kind operator, String columnName, O
     }
 
 
-    private FilterPredicate buildDoublePredicate( Kind operator, String columnName, Object expected ) {
+    public static FilterPredicate buildDouble( Kind operator, String columnName, Object expected ) {
         if ( !(expected instanceof Double value) ) {
             return null;
         }
@@ -170,21 +100,19 @@ private FilterPredicate buildDoublePredicate( Kind operator, String columnName,
     }
 
 
-    private FilterPredicate buildBinaryPredicate( Kind operator, String columnName, Object expected, LogicalTypeAnnotation logicalType ) {
-        Binary value = expected instanceof Binary binary
-                ? binary
-                : Binary.fromString( expected.toString() );
+    public static FilterPredicate buildBinary( Kind operator, String columnName, Object expected, LogicalTypeAnnotation logicalType ) {
+        Binary value = expected instanceof Binary binary ? binary : Binary.fromString( expected.toString() );
 
         return switch ( operator ) {
             case EQUALS -> FilterApi.eq( FilterApi.binaryColumn( columnName ), value );
             case NOT_EQUALS -> FilterApi.notEq( FilterApi.binaryColumn( columnName ), value );
-            case GREATER_THAN, GREATER_THAN_OR_EQUAL, LESS_THAN, LESS_THAN_OR_EQUAL -> logicalType == null ? null : buildOrderedBinaryPredicate( operator, columnName, value );
+            case GREATER_THAN, GREATER_THAN_OR_EQUAL, LESS_THAN, LESS_THAN_OR_EQUAL -> logicalType == null ? null : buildOrderedBinary( operator, columnName, value );
             default -> null;
         };
     }
 
 
-    private FilterPredicate buildOrderedBinaryPredicate( Kind operator, String columnName, Binary value ) {
+    public static FilterPredicate buildOrderedBinary( Kind operator, String columnName, Binary value ) {
         return switch ( operator ) {
             case GREATER_THAN -> FilterApi.gt( FilterApi.binaryColumn( columnName ), value );
             case GREATER_THAN_OR_EQUAL -> FilterApi.gtEq( FilterApi.binaryColumn( columnName ), value );
@@ -193,5 +121,4 @@ private FilterPredicate buildOrderedBinaryPredicate( Kind operator, String colum
             default -> null;
         };
     }
-
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetTableEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetTableEnumerator.java
new file mode 100644
index 0000000000..fb74e89fa9
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetTableEnumerator.java
@@ -0,0 +1,47 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.execution;
+
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.Enumerator;
+import org.apache.parquet.example.data.Group;
+import org.polypheny.db.adapter.parquet.model.AdapterFilter;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+public class ParquetTableEnumerator extends AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
+
+    public ParquetTableEnumerator(Source source, AtomicBoolean cancelFlag, int[] fields ) {
+        super( source, cancelFlag, fields, List.of() );
+    }
+
+    public ParquetTableEnumerator(Source source, AtomicBoolean cancelFlag, int[] fields, List<AdapterFilter> filters ) {
+        super( source, cancelFlag, fields, filters );
+    }
+
+    protected PolyValue[] extractRow( Group group ) {
+        var projectionSchema = reader.getProjectionSchema();
+        PolyValue[] row = new PolyValue[projectionSchema.getFieldCount()];
+        for ( int readIndex = 0; readIndex < projectionSchema.getFieldCount(); readIndex++ ) {
+            var type = projectionSchema.getType( readIndex );
+            row[readIndex] = valueExtractor.extractValue( group, readIndex, type );
+        }
+        return row;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetTableFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetTableFilterTranslator.java
new file mode 100644
index 0000000000..9ae03676ca
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetTableFilterTranslator.java
@@ -0,0 +1,153 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.execution;
+
+import java.util.List;
+import org.apache.parquet.filter2.compat.FilterCompat;
+import org.apache.parquet.filter2.predicate.FilterApi;
+import org.apache.parquet.filter2.predicate.FilterPredicate;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.model.AdapterFilter;
+import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.rex.RexCall;
+import org.polypheny.db.rex.RexIndexRef;
+import org.polypheny.db.rex.RexLiteral;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.type.PolyType;
+
+/**
+ * Translates adapter filters into parquet-native predicates.
+ */
+public class ParquetTableFilterTranslator {
+
+    private final ParquetTypeConverter typeConverter = new ParquetTypeConverter();
+
+
+    /**
+     * Translates a Rex filter into adapter filter form when possible.
+     */
+    public AdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFilter ) {
+        if ( !isSupportedOperator( polyFilter.getKind() ) ) {
+            return null;
+        }
+
+        RexCall call = (RexCall) polyFilter;
+        RexNode left = call.getOperands().get( 0 );
+        if ( left.isA( Kind.CAST ) ) {
+            left = ((RexCall) left).operands.get( 0 );
+        }
+        RexNode right = call.getOperands().get( 1 );
+
+        if ( !(left instanceof RexIndexRef) || !(right instanceof RexLiteral literal) ) {
+            return null;
+        }
+
+        if ( literal.getValue() == null ) {
+            return null;
+        }
+
+        int index = ((RexIndexRef) left).getIndex();
+        if ( index < 0 || index >= fieldTypes.size() ) {
+            return null;
+        }
+
+        if ( !isPushdownSupported( fieldTypes, index, polyFilter.getKind(), literal ) ) {
+            return null;
+        }
+
+        return new AdapterFilter( index, polyFilter.getKind(), literal.getValue() );
+    }
+
+
+    /**
+     * Translate adapter filters to Parquet filter for pushdown
+     *
+     * @param schema - parquet file native schema
+     * @param adpaterFilters - adapter level filters
+     * @return FilterCompat.Filter - filter in parquet format
+     */
+    public FilterCompat.Filter translate( MessageType schema, List<AdapterFilter> adpaterFilters ) {
+        FilterPredicate predicate = null;
+
+        for ( AdapterFilter filter : adpaterFilters ) {
+            FilterPredicate next = buildParquetFilterPredicate( schema, filter );
+            if ( next == null ) {
+                throw new IllegalArgumentException( "Unsupported parquet predicate: " + filter );
+            }
+            predicate = predicate == null ? next : FilterApi.and( predicate, next );
+        }
+
+        // return filter for given predicate
+        return predicate == null ? FilterCompat.NOOP : FilterCompat.get( predicate );
+    }
+
+
+    /**
+     * Build parquet filter predicate from provided filter info
+     *
+     * @param schema - parquet schema
+     * @param adapterFilter - adapter level filter info
+     * @return FilterPredicate object
+     */
+    private FilterPredicate buildParquetFilterPredicate( MessageType schema, AdapterFilter adapterFilter ) {
+        int index = adapterFilter.columnIndex();
+        if ( index < 0 || index >= schema.getFieldCount() ) {
+            return null;
+        }
+
+        Type type = schema.getType( index );
+        if ( !type.isPrimitive() ) {
+            return null;
+        }
+
+        PrimitiveType primitive = type.asPrimitiveType();
+        Object expected = typeConverter.fromPolyValueToParquetObj( primitive, adapterFilter.polyValue() );
+        if ( expected == null ) {
+            return null;
+        }
+
+        String columnName = schema.getFieldName( index );
+        return switch ( primitive.getPrimitiveTypeName() ) {
+            case BOOLEAN -> ParquetPredicateBuilder.buildBoolean( adapterFilter.operator(), columnName, expected );
+            case INT32 -> ParquetPredicateBuilder.buildInt( adapterFilter.operator(), columnName, expected );
+            case INT64 -> ParquetPredicateBuilder.buildLong( adapterFilter.operator(), columnName, expected );
+            case FLOAT -> ParquetPredicateBuilder.buildFloat( adapterFilter.operator(), columnName, expected );
+            case DOUBLE -> ParquetPredicateBuilder.buildDouble( adapterFilter.operator(), columnName, expected );
+            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> ParquetPredicateBuilder.buildBinary( adapterFilter.operator(), columnName, expected, primitive.getLogicalTypeAnnotation() );
+        };
+    }
+
+    /**
+     * Checks whether the operator can be handled by the reader.
+     */
+    private boolean isSupportedOperator( Kind kind ) {
+        return kind == Kind.EQUALS || kind == Kind.NOT_EQUALS || kind == Kind.GREATER_THAN || kind == Kind.GREATER_THAN_OR_EQUAL || kind == Kind.LESS_THAN || kind == Kind.LESS_THAN_OR_EQUAL;
+    }
+
+
+    private boolean isPushdownSupported( List<PolyType> fieldTypes, int index, Kind kind, RexLiteral literal ) {
+        PolyType type = fieldTypes.get( index );
+        return switch ( type ) {
+            case BOOLEAN, VARCHAR, CHAR, TEXT -> kind == Kind.EQUALS || kind == Kind.NOT_EQUALS;
+            case INTEGER, BIGINT, FLOAT, DOUBLE, DATE, TIME, TIMESTAMP -> true;
+            default -> false;
+        } && literal.getValue() != null;
+    }
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java
index 2004cb9e06..586d0f1382 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java
@@ -1,8 +1,20 @@
 package org.polypheny.db.adapter.parquet.execution;
 
+import java.util.ArrayList;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
 import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.GroupType;
+import org.apache.parquet.schema.MessageType;
 import org.apache.parquet.schema.PrimitiveType;
 import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
+import org.polypheny.db.type.entity.PolyList;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.document.PolyDocument;
 
 
 /**
@@ -10,26 +22,91 @@
  */
 public class ValueExtractor {
 
+    private final ParquetTypeConverter typeConverter = new ParquetTypeConverter();
+
     /*
     The function reads one Parquet field from the current row.
     If the field is primitive, function returns the primitive Java value.
     If the field is nested, it returns JSON text built from the nested group.
      */
-    public Object extractValue( Group group, int index, Type field ) {
-        if ( !field.isPrimitive() ) {
-            //return group.getValueToString( index, 0 );
-            return toJson( group.getGroup( index, 0 ) );
+    public PolyValue extractValue( Group group, int index, Type type ) {
+        if ( !type.isPrimitive() ) {
+            return PolyString.of( toJson( group.getGroup( index, 0 ) ));
         }
+        PrimitiveType primitive = type.asPrimitiveType();
+        Object value = extractPrimitiveValue( group, index, primitive, 0 );
+        return typeConverter.fromObjToPolyValue( type, value );
+    }
+
+
+    public PolyDocument extractDocument( Group group, MessageType schema, PolyString generatedId ) {
+        Map<PolyString, PolyValue> values = new LinkedHashMap<>();
+
+        for ( int i = 0; i < schema.getFieldCount(); i++ ) {
+            Type field = schema.getType( i );
+            if ( group.getFieldRepetitionCount( i ) == 0 ) {
+                continue;
+            }
+            values.put( PolyString.of( field.getName() ), extractDocumentValue( group, i, field ) );
+        }
+
+        PolyString idKey = PolyString.of( "_id" );
+        if ( !values.containsKey( idKey ) || values.get( idKey ).isNull() ) {
+            values.put( idKey, generatedId );
+        }
+
+        return PolyDocument.ofDocument( values );
+    }
+
+
+    public PolyValue extractDocumentValue( Group group, int index, Type field ) {
+        int repetitionCount = group.getFieldRepetitionCount( index );
+        if ( repetitionCount == 0 ) {
+            return PolyNull.NULL;
+        }
+
+        if ( repetitionCount > 1 ) {
+            List<PolyValue> values = new ArrayList<>( repetitionCount );
+            for ( int occurrence = 0; occurrence < repetitionCount; occurrence++ ) {
+                values.add( extractDocumentValue( group, index, field, occurrence ) );
+            }
+            return PolyList.of( values );
+        }
+
+        return extractDocumentValue( group, index, field, 0 );
+    }
+
+
+    private PolyValue extractDocumentValue( Group group, int index, Type field, int occurrence ) {
+        if ( field.isPrimitive() ) {
+            return typeConverter.fromObjToPolyValue( field, extractPrimitiveValue( group, index, field.asPrimitiveType(), occurrence ) );
+        }
+
+        return extractNestedDocument( group.getGroup( index, occurrence ), field.asGroupType() );
+    }
+
+
+    private PolyDocument extractNestedDocument( Group group, GroupType type ) {
+        Map<PolyString, PolyValue> values = new LinkedHashMap<>();
+        for ( int i = 0; i < type.getFieldCount(); i++ ) {
+            Type nestedField = type.getType( i );
+            if ( group.getFieldRepetitionCount( i ) == 0 ) {
+                continue;
+            }
+            values.put( PolyString.of( nestedField.getName() ), extractDocumentValue( group, i, nestedField ) );
+        }
+        return PolyDocument.ofDocument( values );
+    }
 
-        PrimitiveType primitive = field.asPrimitiveType();
 
+    private Object extractPrimitiveValue( Group group, int index, PrimitiveType primitive, int occurrence ) {
         return switch ( primitive.getPrimitiveTypeName() ) {
-            case BOOLEAN -> group.getBoolean( index, 0 );
-            case INT32 -> group.getInteger( index, 0 );
-            case INT64 -> group.getLong( index, 0 );
-            case FLOAT -> group.getFloat( index, 0 );
-            case DOUBLE -> group.getDouble( index, 0 );
-            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> group.getBinary( index, 0 );
+            case BOOLEAN -> group.getBoolean( index, occurrence );
+            case INT32 -> group.getInteger( index, occurrence );
+            case INT64 -> group.getLong( index, occurrence );
+            case FLOAT -> group.getFloat( index, occurrence );
+            case DOUBLE -> group.getDouble( index, occurrence );
+            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> group.getBinary( index, occurrence );
         };
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/FilterInfo.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/AdapterFilter.java
similarity index 78%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/FilterInfo.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/AdapterFilter.java
index 2a2895944e..48f7ff0799 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/FilterInfo.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/AdapterFilter.java
@@ -9,6 +9,6 @@
  * @param operator - filter operation
  * @param polyValue - filter value
  */
-public record FilterInfo(int columnIndex, Kind operator, PolyValue polyValue ) {
+public record AdapterFilter(int columnIndex, Kind operator, PolyValue polyValue ) {
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScan.java
new file mode 100644
index 0000000000..5edbd34780
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScan.java
@@ -0,0 +1,91 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.planning;
+
+import java.util.List;
+import org.apache.calcite.linq4j.tree.Blocks;
+import org.apache.calcite.linq4j.tree.Expressions;
+import org.polypheny.db.adapter.parquet.schema.ParquetDocument;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.AlgWriter;
+import org.polypheny.db.algebra.core.document.DocumentScan;
+import org.polypheny.db.algebra.enumerable.EnumerableAlg;
+import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.enumerable.PhysType;
+import org.polypheny.db.algebra.enumerable.PhysTypeImpl;
+import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.algebra.type.AlgDataTypeField;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgOptCost;
+import org.polypheny.db.plan.AlgPlanner;
+import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.schema.types.ScannableEntity;
+
+public class ParquetDocumentScan extends DocumentScan<ParquetDocument> implements EnumerableAlg {
+
+
+    public ParquetDocumentScan( AlgCluster cluster, ParquetDocument collection ) {
+        super( cluster, cluster.traitSetOf( EnumerableConvention.INSTANCE ), collection );
+    }
+
+
+    @Override
+    public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
+        return new ParquetDocumentScan( getCluster(), entity );
+    }
+
+
+    @Override
+    public AlgWriter explainTerms( AlgWriter pw ) {
+        return super.explainTerms( pw );
+    }
+
+
+    @Override
+    public AlgDataType deriveRowType() {
+        final List<AlgDataTypeField> fieldList = entity.getTupleType().getFields();
+        return getCluster().getTypeFactory().builder().add( fieldList.get( 0 ) ).build();
+    }
+
+
+    @Override
+    public void register( AlgPlanner planner ) {
+        planner.addRule( ParquetDocumentScanRule.INSTANCE );
+    }
+
+
+    @Override
+    public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
+        return super.computeSelfCost( planner, mq );
+    }
+
+
+    @Override
+    public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
+        PhysType physType = PhysTypeImpl.of( implementor.getTypeFactory(), getTupleType(), pref.preferArray() );
+        return implementor.result(
+                physType,
+                Blocks.toBlock(
+                        Expressions.call(
+                                entity.asExpression( ScannableEntity.class ),
+                                "scan",
+                                implementor.getRootExpression() ) ) );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScanRule.java
new file mode 100644
index 0000000000..f4fd8692b6
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScanRule.java
@@ -0,0 +1,45 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.planning;
+
+import org.polypheny.db.adapter.parquet.schema.ParquetDocument;
+import org.polypheny.db.algebra.core.AlgFactories;
+import org.polypheny.db.algebra.logical.document.LogicalDocumentScan;
+import org.polypheny.db.plan.AlgOptRule;
+import org.polypheny.db.plan.AlgOptRuleCall;
+import org.polypheny.db.tools.AlgBuilderFactory;
+
+public class ParquetDocumentScanRule extends AlgOptRule {
+
+    public static final ParquetDocumentScanRule INSTANCE = new ParquetDocumentScanRule( AlgFactories.LOGICAL_BUILDER );
+
+
+    private ParquetDocumentScanRule( AlgBuilderFactory algBuilderFactory ) {
+        super(
+                operand( LogicalDocumentScan.class, none() ),
+                algBuilderFactory,
+                ParquetDocumentScanRule.class.getSimpleName() );
+    }
+
+
+    @Override
+    public void onMatch( AlgOptRuleCall call ) {
+        final LogicalDocumentScan scan = call.alg( 0 );
+        call.transformTo( new ParquetDocumentScan( scan.getCluster(), scan.getEntity().unwrapOrThrow( ParquetDocument.class ) ) );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetTableScan.java
similarity index 90%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetScan.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetTableScan.java
index 2ad0da3da4..cb257ba052 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetTableScan.java
@@ -23,12 +23,12 @@
 import org.polypheny.db.plan.AlgTraitSet;
 
 
-public class ParquetScan extends RelScan<ParquetTable> implements EnumerableAlg {
+public class ParquetTableScan extends RelScan<ParquetTable> implements EnumerableAlg {
 
     private final int[] fields;
 
 
-    public ParquetScan( AlgCluster cluster, ParquetTable table, int[] fields ) {
+    public ParquetTableScan(AlgCluster cluster, ParquetTable table, int[] fields ) {
         super( cluster, cluster.traitSetOf( EnumerableConvention.INSTANCE ), table );
         this.fields = fields;
     }
@@ -36,7 +36,7 @@ public ParquetScan( AlgCluster cluster, ParquetTable table, int[] fields ) {
 
     @Override
     public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
-        return new ParquetScan( getCluster(), entity, fields );
+        return new ParquetTableScan( getCluster(), entity, fields );
     }
 
 
@@ -59,7 +59,7 @@ public AlgDataType deriveRowType() {
 
     @Override
     public void register( AlgPlanner planner ) {
-        planner.addRule( ParquetProjectScanRule.INSTANCE );
+        planner.addRule( ParquetTableScanRule.INSTANCE );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetProjectScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetTableScanRule.java
similarity index 71%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetProjectScanRule.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetTableScanRule.java
index 052648715c..5efbda659f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetProjectScanRule.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetTableScanRule.java
@@ -10,28 +10,28 @@
 import org.polypheny.db.tools.AlgBuilderFactory;
 
 
-public class ParquetProjectScanRule extends AlgOptRule {
+public class ParquetTableScanRule extends AlgOptRule {
 
-    public static final ParquetProjectScanRule INSTANCE = new ParquetProjectScanRule( AlgFactories.LOGICAL_BUILDER );
+    public static final ParquetTableScanRule INSTANCE = new ParquetTableScanRule( AlgFactories.LOGICAL_BUILDER );
 
 
-    public ParquetProjectScanRule( AlgBuilderFactory algBuilderFactory ) {
+    public ParquetTableScanRule(AlgBuilderFactory algBuilderFactory ) {
         super(
-                operand( LogicalRelProject.class, operand( ParquetScan.class, none() ) ),
+                operand( LogicalRelProject.class, operand( ParquetTableScan.class, none() ) ),
                 algBuilderFactory,
-                "ParquetProjectScanRule" );
+                ParquetTableScanRule.class.getSimpleName() );
     }
 
 
     @Override
     public void onMatch( AlgOptRuleCall call ) {
         final LogicalRelProject project = call.alg( 0 );
-        final ParquetScan scan = call.alg( 1 );
+        final ParquetTableScan scan = call.alg( 1 );
         int[] fields = getProjectFields( project.getProjects() );
         if ( fields == null ) {
             return;
         }
-        call.transformTo( new ParquetScan( scan.getCluster(), scan.getEntity(), fields ) );
+        call.transformTo( new ParquetTableScan( scan.getCluster(), scan.getEntity(), fields ) );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetDocument.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetDocument.java
new file mode 100644
index 0000000000..39380c1779
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetDocument.java
@@ -0,0 +1,88 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.schema;
+
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.AbstractEnumerable;
+import org.apache.calcite.linq4j.Enumerable;
+import org.apache.calcite.linq4j.Enumerator;
+import org.apache.calcite.linq4j.tree.Expression;
+import org.apache.calcite.linq4j.tree.Expressions;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.ParquetSource;
+import org.polypheny.db.adapter.parquet.execution.ParquetDocumentEnumerator;
+import org.polypheny.db.adapter.parquet.planning.ParquetDocumentScan;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.catalog.entity.physical.PhysicalCollection;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.schema.types.ScannableEntity;
+import org.polypheny.db.schema.types.TranslatableEntity;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+public class ParquetDocument extends PhysicalCollection implements ScannableEntity, TranslatableEntity {
+
+    private final Source source;
+    private final ParquetSource parquetSource;
+
+
+    public ParquetDocument( PhysicalCollection collection, Source source, ParquetSource parquetSource ) {
+        super(
+                collection.id,
+                collection.allocationId,
+                collection.logicalId,
+                collection.namespaceId,
+                collection.name,
+                collection.namespaceName,
+                collection.adapterId );
+        this.source = source;
+        this.parquetSource = parquetSource;
+    }
+
+
+    @Override
+    public Expression asExpression() {
+        Expression argExp = Expressions.constant( this.id );
+        return Expressions.convert_(
+                Expressions.call(
+                        Expressions.call( this.parquetSource.asExpression(), "getAdapterCatalog" ),
+                        "getPhysical",
+                        argExp ),
+                ScannableEntity.class );
+    }
+
+
+    @Override
+    public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
+        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
+        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+        return new AbstractEnumerable<>() {
+            @Override
+            public Enumerator<PolyValue[]> enumerator() {
+                return new ParquetDocumentEnumerator( source, cancelFlag );
+            }
+        };
+    }
+
+
+    @Override
+    public AlgNode toAlg( AlgCluster cluster, AlgTraitSet traitSet ) {
+        return new ParquetDocumentScan( cluster, this );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java
index 3f5ecfef8b..f415ae3559 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java
@@ -2,16 +2,13 @@
 
 import java.net.MalformedURLException;
 import java.net.URL;
-import java.util.ArrayList;
-import java.util.List;
 import org.jetbrains.annotations.Nullable;
 import org.polypheny.db.adapter.parquet.ParquetSource;
-import org.polypheny.db.catalog.entity.physical.PhysicalColumn;
+import org.polypheny.db.catalog.entity.physical.PhysicalCollection;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.plan.Convention;
 import org.polypheny.db.schema.Namespace;
-import org.polypheny.db.type.PolyType;
 import org.polypheny.db.util.Source;
 import org.polypheny.db.util.Sources;
 
@@ -37,34 +34,23 @@ public ParquetNamespace( long id, long adapterId, URL directoryUrl ) {
      * Creates a Parquet table wrapper for a physical table entry.
      */
     public ParquetTable createParquetTable( long id, PhysicalTable table, ParquetSource sourceAdapter ) {
-        List<PolyType> fieldTypes = new ArrayList<>();
-        List<Integer> fieldIds = new ArrayList<>( table.columns.size() );
-
-        List<PhysicalColumn> columns = table.getColumns();
-
-        for ( int i = 0; i < columns.size(); i++ ) {
-            PhysicalColumn column = columns.get( i );
-            fieldTypes.add( column.type );
-            fieldIds.add( i );
-        }
-
-        Source parquetSource;
         try {
-            parquetSource = Sources.of( new URL( directoryUrl, table.name + ".parquet" ) );
+            Source parquetSource = Sources.of( new URL( directoryUrl, table.name + ".parquet" ) );
+            return new ParquetTable( id, parquetSource, table, sourceAdapter );
+
         } catch ( MalformedURLException e ) {
             throw new GenericRuntimeException( e );
         }
-
-        int[] fields = fieldIds.stream().mapToInt( i -> i ).toArray();
-        return createTable( id, parquetSource, table, fieldTypes, fields, sourceAdapter );
     }
 
 
-    /**
-     * Instantiates the table class based on configured flavor.
-     */
-    private ParquetTable createTable( long id, Source source, PhysicalTable table, List<PolyType> fieldTypes, int[] fields, ParquetSource parquetSource ) {
-        return new ParquetTable( id, source, table, fieldTypes, fields, parquetSource );
+    public ParquetDocument createParquetCollection( PhysicalCollection collection, ParquetSource sourceAdapter ) {
+        try {
+            Source parquetSource = Sources.of( new URL( directoryUrl, collection.name + ".parquet" ) );
+            return new ParquetDocument( collection, parquetSource, sourceAdapter );
+        } catch ( MalformedURLException e ) {
+            throw new GenericRuntimeException( e );
+        }
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java
index 537a030628..e9d2a3afb6 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java
@@ -21,17 +21,14 @@
 import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.DataContext;
 import org.polypheny.db.adapter.parquet.ParquetSource;
-import org.polypheny.db.adapter.parquet.execution.ParquetEnumerator;
-import org.polypheny.db.adapter.parquet.model.FilterInfo;
-import org.polypheny.db.adapter.parquet.planning.ParquetScan;
+import org.polypheny.db.adapter.parquet.execution.ParquetTableEnumerator;
+import org.polypheny.db.adapter.parquet.execution.ParquetTableFilterTranslator;
+import org.polypheny.db.adapter.parquet.model.AdapterFilter;
+import org.polypheny.db.adapter.parquet.planning.ParquetTableScan;
 import org.polypheny.db.algebra.AlgNode;
-import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
 import org.polypheny.db.plan.AlgCluster;
 import org.polypheny.db.plan.AlgTraitSet;
-import org.polypheny.db.rex.RexCall;
-import org.polypheny.db.rex.RexIndexRef;
-import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNode;
 import org.polypheny.db.schema.types.FilterableEntity;
 import org.polypheny.db.schema.types.ScannableEntity;
@@ -42,6 +39,7 @@
 import java.util.ArrayList;
 import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
+import java.util.stream.IntStream;
 
 /**
  * Base class for Parquet physical tables.
@@ -49,15 +47,16 @@
 public class ParquetTable extends PhysicalTable implements FilterableEntity, ScannableEntity, TranslatableEntity {
 
     protected final Source source;
-    protected List<PolyType> fieldTypes;
-    protected final int[] fields;
+    protected final int[] fieldIndexes;
+    private final List<PolyType> fieldTypes;
     protected final ParquetSource parquetSource;
+    protected final ParquetTableFilterTranslator filterTranslator;
 
 
     /**
      * Creates a Parquet table wrapper from a physical table definition.
      */
-    ParquetTable( long id, Source source, PhysicalTable table, List<PolyType> fieldTypes, int[] fields, ParquetSource parquetSource ) {
+    ParquetTable( long id, Source source, PhysicalTable table, ParquetSource parquetSource ) {
         super(
                 id,
                 table.allocationId,
@@ -69,9 +68,10 @@ public class ParquetTable extends PhysicalTable implements FilterableEntity, Sca
                 table.uniqueFieldIds,
                 table.adapterId );
         this.source = source;
-        this.fieldTypes = fieldTypes;
-        this.fields = fields;
+        this.fieldIndexes = IntStream.range( 0, table.columns.size() ).toArray();
+        this.fieldTypes = columns.stream().map( c -> c.type ).toList();
         this.parquetSource = parquetSource;
+        this.filterTranslator = new ParquetTableFilterTranslator();
     }
 
 
@@ -79,20 +79,26 @@ public class ParquetTable extends PhysicalTable implements FilterableEntity, Sca
      * Returns enumerable for FilterableEntity.
      *
      * @param dataContext data context
-     * @param filters filters to push down.
+     * @param polyFilters polyFilters to push down.
      * @return enumerable.
      */
     @Override
-    public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> filters ) {
+    public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> polyFilters ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
-        final List<FilterInfo> pushedFilters = new ArrayList<>();
-        filters.removeIf( filter -> addFilter( filter, pushedFilters ) );
+        final List<AdapterFilter> adapterFilters = new ArrayList<>();
+        polyFilters.removeIf( polyFilter -> {
+            var adapterFilter = filterTranslator.translate( fieldTypes, polyFilter );
+            if ( adapterFilter != null ) {
+                return adapterFilters.add( adapterFilter );
+            }
+            return false;
+        } );
 
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                return new ParquetEnumerator( source, cancelFlag, fields, pushedFilters );
+                return new ParquetTableEnumerator( source, cancelFlag, fieldIndexes, adapterFilters );
             }
         };
     }
@@ -112,7 +118,7 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                return new ParquetEnumerator( source, cancelFlag, fields );
+                return new ParquetTableEnumerator( source, cancelFlag, fieldIndexes );
             }
         };
     }
@@ -127,12 +133,12 @@ public Enumerator<PolyValue[]> enumerator() {
      */
     @Override
     public AlgNode toAlg( AlgCluster cluster, AlgTraitSet traitSet ) {
-        return new ParquetScan( cluster, this, fields );
+        return new ParquetTableScan( cluster, this, fieldIndexes );
     }
 
 
     /**
-     * This method is called from the {@link ParquetScan} via reflection.
+     * This method is called from the {@link ParquetTableScan} via reflection.
      *
      * @param dataContext data context
      * @param fields a list of fields to return.
@@ -144,72 +150,11 @@ public Enumerable<PolyValue[]> project( final DataContext dataContext, final int
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                return new ParquetEnumerator( source, cancelFlag, fields );
+                return new ParquetTableEnumerator( source, cancelFlag, fields );
             }
         };
     }
 
-
-    /**
-     * Translates a Rex filter into adapter filter form when possible.
-     */
-    private boolean addFilter( RexNode filter, List<FilterInfo> pushedFilters ) {
-        if ( !isSupportedOperator( filter.getKind() ) ) {
-            return false;
-        }
-
-        RexCall call = (RexCall) filter;
-        RexNode left = call.getOperands().get( 0 );
-        if ( left.isA( Kind.CAST ) ) {
-            left = ((RexCall) left).operands.get( 0 );
-        }
-        RexNode right = call.getOperands().get( 1 );
-
-        if ( !(left instanceof RexIndexRef) || !(right instanceof RexLiteral literal) ) {
-            return false;
-        }
-
-        if ( literal.getValue() == null ) {
-            return false;
-        }
-
-        int index = ((RexIndexRef) left).getIndex();
-        if ( index < 0 || index >= columns.size() ) {
-            return false;
-        }
-
-        if ( !isPushdownSupported( index, filter.getKind(), literal ) ) {
-            return false;
-        }
-
-        pushedFilters.add( new FilterInfo( index, filter.getKind(), literal.getValue() ) );
-        return true;
-    }
-
-
-    /**
-     * Checks whether the operator can be handled by the reader.
-     */
-    private boolean isSupportedOperator( Kind kind ) {
-        return kind == Kind.EQUALS
-                || kind == Kind.NOT_EQUALS
-                || kind == Kind.GREATER_THAN
-                || kind == Kind.GREATER_THAN_OR_EQUAL
-                || kind == Kind.LESS_THAN
-                || kind == Kind.LESS_THAN_OR_EQUAL;
-    }
-
-
-    private boolean isPushdownSupported( int index, Kind kind, RexLiteral literal ) {
-        PolyType type = fieldTypes.get( index );
-        return switch ( type ) {
-            case BOOLEAN, VARCHAR, CHAR, TEXT -> kind == Kind.EQUALS || kind == Kind.NOT_EQUALS;
-            case INTEGER, BIGINT, FLOAT, DOUBLE, DATE, TIME, TIMESTAMP -> true;
-            default -> false;
-        } && literal.getValue() != null;
-    }
-
-
 }
 
 
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPredicateBuilderTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetTableFilterTranslatorTest.java
similarity index 77%
rename from plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPredicateBuilderTest.java
rename to plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetTableFilterTranslatorTest.java
index 2fdd96d907..2103a7fc5f 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPredicateBuilderTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetTableFilterTranslatorTest.java
@@ -21,14 +21,14 @@
 import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
 import org.apache.parquet.schema.Types;
 import org.junit.jupiter.api.Test;
-import org.polypheny.db.adapter.parquet.execution.ParquetPredicateBuilder;
-import org.polypheny.db.adapter.parquet.model.FilterInfo;
+import org.polypheny.db.adapter.parquet.execution.ParquetTableFilterTranslator;
+import org.polypheny.db.adapter.parquet.model.AdapterFilter;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.type.entity.temporal.PolyTimestamp;
 
 import static org.junit.jupiter.api.Assertions.assertThrows;
 
-class ParquetPredicateBuilderTest {
+class ParquetTableFilterTranslatorTest {
 
     @Test
     void rejectsInt96TimestampPredicatePushdown() {
@@ -37,10 +37,10 @@ void rejectsInt96TimestampPredicatePushdown() {
                 .named( "ts" )
                 .named( "test_schema" );
 
-        FilterInfo filter = new FilterInfo( 0, Kind.EQUALS, PolyTimestamp.of( 1_700_000_000_000L ) );
+        AdapterFilter filter = new AdapterFilter( 0, Kind.EQUALS, PolyTimestamp.of( 1_700_000_000_000L ) );
 
         assertThrows(
                 IllegalArgumentException.class,
-                () -> new ParquetPredicateBuilder().translate( schema, List.of( filter ) ) );
+                () -> new ParquetTableFilterTranslator().translate( schema, List.of( filter ) ) );
     }
 }

From 9ac86ec9794b9cbf96053830f4c1302911af8d57 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Tue, 31 Mar 2026 18:45:47 +0200
Subject: [PATCH 08/46] add comments

---
 .../main/java/org/polypheny/db/ddl/DdlManagerImpl.java | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/dbms/src/main/java/org/polypheny/db/ddl/DdlManagerImpl.java b/dbms/src/main/java/org/polypheny/db/ddl/DdlManagerImpl.java
index 92976c6e5d..77cf03b3ca 100644
--- a/dbms/src/main/java/org/polypheny/db/ddl/DdlManagerImpl.java
+++ b/dbms/src/main/java/org/polypheny/db/ddl/DdlManagerImpl.java
@@ -216,6 +216,7 @@ public void createSource( Transaction transaction, String uniqueName, String ada
         uniqueName = uniqueName.toLowerCase();
         DataSource<?> adapter = (DataSource<?>) AdapterManager.getInstance().addAdapter( catalog, adapterName, uniqueName, adapterType, mode, config );
         long sourceNamespace = adapter.getCurrentNamespace() == null ? namespace : adapter.getCurrentNamespace().getId(); // TODO: clean implementation. Sources should either create their own namespace or there should be default namespaces for different models.
+        // Support more than one data model per adapter
         if ( adapter.supportsRelational() ) {
             createRelationalSource( transaction, adapter, resolveSourceNamespace( uniqueName, sourceNamespace, DataModel.RELATIONAL ) );
         }
@@ -230,6 +231,15 @@ public void createSource( Transaction transaction, String uniqueName, String ada
     }
 
 
+    /**
+     * Support more than one data model per adapter
+     * if there is existing namespace for the provided adapter model (RELATIONAL\DOCUMENT) - use it
+     * otherwise create appropriate namespace
+     * @param uniqueName - adapter name
+     * @param namespaceId - namesapce id of adapter to check
+     * @param model - RELATIONAL\DOCUMENT
+     * @return namespace id
+     */
     private long resolveSourceNamespace( String uniqueName, long namespaceId, DataModel model ) {
         LogicalNamespace namespace = catalog.getSnapshot().getNamespace( namespaceId ).orElseThrow();
         if ( namespace.dataModel == model ) {

From 276b9440856a4c30ed98612d6be1be02fb14a98f Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Mon, 6 Apr 2026 15:00:31 +0200
Subject: [PATCH 09/46] split relational and document code into two data
 sources and implement filters on top of document

---
 .../java/org/polypheny/db/PolyphenyDb.java    |   3 +-
 .../db/adapter/parquet/ParquetPlugin.java     |  13 +-
 .../db/adapter/parquet/ParquetSource.java     | 487 ------------------
 .../document/ParquetDocumentSource.java       | 169 ++++++
 .../execution/ParquetDocEnumerator.java}      |  16 +-
 .../execution/ParquetDocFilterTranslator.java |  59 +++
 .../execution/ParquetDocValueExtractor.java   |  42 ++
 .../document/planning/ParquetDocFilter.java   |  63 +++
 .../planning/ParquetDocFilterRule.java        |  87 ++++
 .../planning/ParquetDocScan.java}             |  60 ++-
 .../schema/ParquetDocument.java               |  76 ++-
 .../execution/AbstractParquetEnumerator.java  |  79 ---
 .../parquet/execution/ValueExtractor.java     | 178 -------
 .../planning/ParquetDocumentScanRule.java     |  45 --
 .../relational/ParquetRelationalSource.java   | 141 +++++
 .../execution/ParquetRelEnumerator.java}      |  16 +-
 .../ParquetRelFilterTranslator.java}          |  51 +-
 .../execution/ParquetRelValueExtractor.java   |  22 +
 .../planning/ParquetRelScan.java}             |  14 +-
 .../planning/ParquetRelScanRule.java}         |  16 +-
 .../schema/ParquetRelTable.java}              |  57 +-
 .../parquet/shared/AbstractParquetSource.java | 279 ++++++++++
 .../execution/AbstractParquetEnumerator.java  | 146 ++++++
 .../AbstractParquetValueExtractor.java        |  76 +++
 .../ParquetFilterTranslationSupport.java      |  92 ++++
 .../execution/ParquetGroupReader.java         |   9 +-
 .../execution/ParquetPredicateBuilder.java    |   2 +-
 .../execution/ParquetValueExtractor.java      |  12 +
 .../{ => shared}/io/ParquetFileDiscovery.java |   2 +-
 .../{ => shared}/model/AdapterFilter.java     |   8 +-
 .../{ => shared}/schema/ParquetNamespace.java |  12 +-
 .../schema/ParquetTypeConverter.java          |   2 +-
 .../util/HadoopConfigurationFactory.java      |   2 +-
 ...va => ParquetRelFilterTranslatorTest.java} |   8 +-
 34 files changed, 1432 insertions(+), 912 deletions(-)
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{execution/ParquetDocumentEnumerator.java => document/execution/ParquetDocEnumerator.java} (64%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilter.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{planning/ParquetDocumentScan.java => document/planning/ParquetDocScan.java} (50%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{ => document}/schema/ParquetDocument.java (50%)
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/AbstractParquetEnumerator.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScanRule.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{execution/ParquetTableEnumerator.java => relational/execution/ParquetRelEnumerator.java} (65%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{execution/ParquetTableFilterTranslator.java => relational/execution/ParquetRelFilterTranslator.java} (76%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{planning/ParquetTableScan.java => relational/planning/ParquetRelScan.java} (83%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{planning/ParquetTableScanRule.java => relational/planning/ParquetRelScanRule.java} (68%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{schema/ParquetTable.java => relational/schema/ParquetRelTable.java} (67%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{ => shared}/execution/ParquetGroupReader.java (93%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{ => shared}/execution/ParquetPredicateBuilder.java (99%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{ => shared}/io/ParquetFileDiscovery.java (97%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{ => shared}/model/AdapterFilter.java (56%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{ => shared}/schema/ParquetNamespace.java (75%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{ => shared}/schema/ParquetTypeConverter.java (99%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{ => shared}/util/HadoopConfigurationFactory.java (96%)
 rename plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/{ParquetTableFilterTranslatorTest.java => ParquetRelFilterTranslatorTest.java} (83%)

diff --git a/dbms/src/main/java/org/polypheny/db/PolyphenyDb.java b/dbms/src/main/java/org/polypheny/db/PolyphenyDb.java
index 1d3c774b90..2182f39e5b 100644
--- a/dbms/src/main/java/org/polypheny/db/PolyphenyDb.java
+++ b/dbms/src/main/java/org/polypheny/db/PolyphenyDb.java
@@ -129,8 +129,7 @@ public class PolyphenyDb {
     public String defaultStoreName = "hsqldb";
 
     @Option(name = { "-defaultSource" }, description = "Type of default source")
-    //public static String defaultSourceName = "csv";
-    public static String defaultSourceName = "parquet";
+    public static String defaultSourceName = "csv";
 
     @Option(name = { "-c", "--config" }, description = "Path to the configuration file")
     protected String applicationConfPath;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
index ec0107d0c3..ae34ed09df 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
@@ -17,20 +17,25 @@
 package org.polypheny.db.adapter.parquet;
 
 import org.polypheny.db.adapter.AdapterManager;
+import org.polypheny.db.adapter.parquet.document.ParquetDocumentSource;
+import org.polypheny.db.adapter.parquet.relational.ParquetRelationalSource;
 import org.polypheny.db.plugins.PluginContext;
 import org.polypheny.db.plugins.PolyPlugin;
 
 /**
  * Plugin entry point for the Parquet adapter.
  */
+@SuppressWarnings("unused")
 public class ParquetPlugin extends PolyPlugin {
 
-    private long id;
+    private long relationalId;
+    private long documentId;
 
     /**
      * Constructor
      * Create Plugin Instance
      */
+    @SuppressWarnings("unused")
     public ParquetPlugin( PluginContext context ) {
         super( context );
     }
@@ -40,7 +45,8 @@ public ParquetPlugin( PluginContext context ) {
      */
     @Override
     public void afterCatalogInit() {
-        this.id = AdapterManager.addAdapterTemplate( ParquetSource.class, "Parquet", ParquetSource::new );
+        this.relationalId = AdapterManager.addAdapterTemplate( ParquetRelationalSource.class, ParquetRelationalSource.NAME, ParquetRelationalSource::new );
+        this.documentId = AdapterManager.addAdapterTemplate( ParquetDocumentSource.class, ParquetDocumentSource.NAME, ParquetDocumentSource::new );
     }
 
     /**
@@ -48,7 +54,8 @@ public void afterCatalogInit() {
      */
     @Override
     public void stop() {
-        AdapterManager.removeAdapterTemplate( id );
+        AdapterManager.removeAdapterTemplate( relationalId );
+        AdapterManager.removeAdapterTemplate( documentId );
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
deleted file mode 100644
index 6f26919459..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetSource.java
+++ /dev/null
@@ -1,487 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet;
-
-import lombok.Getter;
-import lombok.experimental.Delegate;
-
-import org.apache.hadoop.conf.Configuration;
-import org.apache.parquet.hadoop.ParquetFileReader;
-import org.apache.parquet.hadoop.util.HadoopInputFile;
-import org.apache.hadoop.fs.Path;
-import org.apache.parquet.schema.Type;
-
-import org.polypheny.db.adapter.ConnectionMethod;
-import org.polypheny.db.adapter.DataSource;
-import org.polypheny.db.adapter.DeployMode;
-import org.polypheny.db.adapter.DocumentDataSource;
-import org.polypheny.db.adapter.DocumentScanDelegate;
-import org.polypheny.db.adapter.RelationalDataSource;
-import org.polypheny.db.adapter.Scannable;
-import org.polypheny.db.adapter.annotations.AdapterProperties;
-import org.polypheny.db.adapter.annotations.AdapterSettingDirectory;
-import org.polypheny.db.adapter.annotations.AdapterSettingList;
-import org.polypheny.db.adapter.annotations.AdapterSettingString;
-import org.polypheny.db.adapter.parquet.io.ParquetFileDiscovery;
-import org.polypheny.db.adapter.parquet.schema.ParquetDocument;
-import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
-import org.polypheny.db.adapter.parquet.schema.ParquetNamespace;
-import org.polypheny.db.adapter.parquet.schema.ParquetTable;
-
-
-import org.polypheny.db.adapter.parquet.util.HadoopConfigurationFactory;
-import org.polypheny.db.catalog.catalogs.AdapterCatalog;
-import org.polypheny.db.catalog.catalogs.DocAdapterCatalog;
-import org.polypheny.db.catalog.entity.allocation.AllocationCollection;
-import org.polypheny.db.catalog.entity.allocation.AllocationTable;
-import org.polypheny.db.catalog.entity.allocation.AllocationTableWrapper;
-import org.polypheny.db.catalog.entity.logical.LogicalCollection;
-import org.polypheny.db.catalog.entity.logical.LogicalTableWrapper;
-import org.polypheny.db.catalog.entity.physical.PhysicalCollection;
-import org.polypheny.db.catalog.entity.physical.PhysicalEntity;
-import org.polypheny.db.catalog.entity.physical.PhysicalTable;
-import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
-import org.polypheny.db.catalog.logistic.DataModel;
-import org.polypheny.db.catalog.logistic.EntityType;
-import org.polypheny.db.information.InformationGroup;
-import org.polypheny.db.information.InformationTable;
-import org.polypheny.db.prepare.Context;
-import org.polypheny.db.transaction.PolyXid;
-import java.io.File;
-import java.net.MalformedURLException;
-import java.net.URL;
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-import java.util.Set;
-import java.util.stream.Collectors;
-import org.pf4j.Extension;
-import org.polypheny.db.type.PolyType;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-
-@Extension
-@AdapterProperties(
-        name = "Parquet",
-        description = "An adapter for querying Parquet files. The location of the directory containing the Parquet files can be specified. Currently, this adapter only supports read operations.",
-        usedModes = DeployMode.EMBEDDED,
-        defaultMode = DeployMode.EMBEDDED)
-@AdapterSettingList(name = "method", options = { "upload", "link" }, defaultValue = "upload", description = "If the supplied file(s) should be uploaded or a link to the local filesystem is used (sufficient permissions are required).", position = 1)
-@AdapterSettingDirectory(subOf = "method_upload", name = "directory", defaultValue = "classpath://orders_db", description = "You can upload one or multiple .parquet files.", position = 2)
-@AdapterSettingString(subOf = "method_link", defaultValue = "classpath://orders_db", name = "directoryName", description = "You can select a path to a folder or specific .parquet files.", position = 2)
-public class ParquetSource extends DataSource<DocAdapterCatalog> implements RelationalDataSource, DocumentDataSource, Scannable {
-
-    @Delegate(excludes = Excludes.class)
-    private final DocumentScanDelegate delegate;
-
-    private final ConnectionMethod connectionMethod;
-    //private final int maxStringLength;
-    private final ParquetTypeConverter parquetTypeConverter;
-    private URL parquetDir;
-
-    @Getter
-    private ParquetNamespace currentNamespace;
-    private Map<String, List<ExportedColumn>> exportedColumns;
-
-    private static final Logger log = LoggerFactory.getLogger( ParquetSource.class );
-
-
-    /**
-     * Constructor
-     * Creates the Parquet source from adapter settings.
-     */
-    public ParquetSource( final long storeId, final String uniqueName, final Map<String, String> settings, final DeployMode mode ) {
-        super( storeId, uniqueName, settings, mode, true, new DocAdapterCatalog( storeId ), Set.of( DataModel.RELATIONAL, DataModel.DOCUMENT ) );
-
-        this.parquetTypeConverter = new ParquetTypeConverter();
-        this.connectionMethod = settings.containsKey( "method" )
-                ? ConnectionMethod.from( settings.get( "method" ).toUpperCase() )
-                : ConnectionMethod.UPLOAD;
-
-        setParquetDir( settings );
-        createInformationPage();
-        enableInformationPage();
-
-        this.delegate = new DocumentScanDelegate( this, adapterCatalog );
-    }
-
-
-    /**
-     * Get directory information from settings and populate parquetDir variable
-     */
-    private void setParquetDir( Map<String, String> settings ) {
-        String dir = settings.getOrDefault( "directory", "." );
-        if ( connectionMethod == ConnectionMethod.LINK ) {
-            dir = settings.getOrDefault( "directoryName", "." );
-        }
-
-        if ( dir.startsWith( "classpath://" ) ) {
-            parquetDir = this.getClass().getClassLoader().getResource( dir.replace( "classpath://", "" ) + "/" );
-        } else {
-            try {
-                parquetDir = new File( dir ).toURI().toURL();
-            } catch ( MalformedURLException e ) {
-                throw new GenericRuntimeException( e );
-            }
-        }
-    }
-
-
-    /**
-     * Removes invalid characters and creates a valid table name
-     */
-    private static String getValidTableName( String name ) {
-        return name.trim().replaceAll( "[^a-z0-9_]+", "" );
-    }
-
-
-    /**
-     * Build valid physical table name from file name
-     */
-    private static String computePhysicalTableName( String fileName ) {
-        String physicalTableName = fileName.toLowerCase();
-        if ( physicalTableName.endsWith( ".parquet" ) ) {
-            physicalTableName = physicalTableName.substring( 0, physicalTableName.length() - ".parquet".length() );
-        }
-        return getValidTableName( physicalTableName );
-    }
-
-
-    /**
-     * Build valid column name from field data
-     */
-    private String getValidColumnNameFromField( Type field ) {
-        return field.getName().toLowerCase().trim().replaceAll( "[^a-z0-9_]+", "_" );
-    }
-
-
-    /**
-     * Get parquet file fields information from metadata
-     */
-    private List<ExportedColumn> getExportedColumnsFromFile( String fileName, String physicalTableName ) {
-        try {
-            Path path = new Path( new URL( parquetDir, fileName ).toURI() );
-            Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
-            try ( ParquetFileReader reader = ParquetFileReader.open( HadoopInputFile.fromPath( path, conf ) ) ) {
-                List<Type> schemaFields = reader.getFooter().getFileMetaData().getSchema().getFields();
-                List<ExportedColumn> columns = new ArrayList<>();
-                int position = 0;
-                for ( Type field : schemaFields ) {
-                    ExportedColumn column = getExportedColumnFromField( field, fileName, physicalTableName, position );
-                    columns.add( column );
-                    position++;
-                }
-                return columns;
-            }
-        } catch ( Exception e ) {
-            throw new GenericRuntimeException( e );
-        }
-    }
-
-
-    /**
-     * Extract metadata information from single parquet field and create column object
-     */
-    private ExportedColumn getExportedColumnFromField( Type field, String fileName, String physicalTableName, int position ) {
-        String columnName = getValidColumnNameFromField( field );
-        PolyType polyType = parquetTypeConverter.fromParquetTypeToPolyType( field );
-        //Integer length = polyType == PolyType.VARCHAR ? maxStringLength : null;
-        return new ExportedColumn(
-                columnName,
-                polyType,
-                null,
-                null,
-                null,
-                null,
-                null,
-                false,
-                fileName,
-                physicalTableName,
-                field.getName(),
-                position,
-                position == 0 );
-    }
-
-
-    /**
-     * Create information page containing exported columns details
-     * for all tables loaded from parquet files
-     */
-    private void createInformationPage() {
-        for ( Map.Entry<String, List<ExportedColumn>> entry : getExportedColumns().entrySet() ) {
-            if ( entry.getValue().isEmpty() ) {
-                continue;
-            }
-
-            InformationGroup group = new InformationGroup( informationPage, entry.getValue().get( 0 ).physicalSchemaName() );
-            informationGroups.add( group );
-            InformationTable table = getInformationTable( group, entry );
-            informationElements.add( table );
-        }
-    }
-
-
-    /**
-     * Extract columns from all parquet files
-     *
-     * @return Map<String, List < ExportedColumn>> - column list per table name
-     */
-    @Override
-    public Map<String, List<ExportedColumn>> getExportedColumns() {
-        if ( connectionMethod == ConnectionMethod.UPLOAD && exportedColumns != null ) {
-            return exportedColumns;
-        }
-
-        Map<String, List<ExportedColumn>> columns = new HashMap<>();
-        Set<String> fileNames = ParquetFileDiscovery.listParquetFiles( parquetDir );
-
-        for ( String fileName : fileNames ) {
-            String physicalTableName = computePhysicalTableName( fileName );
-            columns.put( physicalTableName, getExportedColumnsFromFile( fileName, physicalTableName ) );
-        }
-
-        this.exportedColumns = columns;
-        return columns;
-
-    }
-
-
-    /**
-     * Builds InformationTable from exported columns
-     */
-    private InformationTable getInformationTable( InformationGroup group, Map.Entry<String, List<ExportedColumn>> entry ) {
-        List<String> columns = Arrays.asList( "Position", "Column Name", "Type", "Nullable", "Filename", "Primary" );
-        InformationTable table = new InformationTable( group, columns );
-        for ( ExportedColumn exportedColumn : entry.getValue() ) {
-            table.addRow(
-                    exportedColumn.physicalPosition(),
-                    exportedColumn.name(),
-                    exportedColumn.getDisplayType(),
-                    exportedColumn.nullable() ? "✔" : "",
-                    exportedColumn.physicalSchemaName(),
-                    exportedColumn.primary() ? "✔" : "" );
-        }
-        return table;
-    }
-
-
-    /**
-     * Updates the active namespace wrapper for this source.
-     */
-    @Override
-    public void updateNamespace( String name, long id ) {
-        currentNamespace = new ParquetNamespace( id, adapterId, parquetDir );
-        putNamespace( currentNamespace );
-    }
-
-
-    /**
-     * Returns this source as relational adapter interface.
-     */
-    @Override
-    public RelationalDataSource asRelationalDataSource() {
-        return this;
-    }
-
-
-    @Override
-    public DocumentDataSource asDocumentDataSource() {
-        return this;
-    }
-
-
-    /**
-     * Parquet source is read-only, so truncate is not supported.
-     */
-    @Override
-    public void truncate( Context context, long allocId ) {
-        throw new GenericRuntimeException( "Parquet adapter does not support truncate" );
-    }
-
-
-    /**
-     * Prepare does nothing, because data source is read-only.
-     */
-    @Override
-    public boolean prepare( PolyXid xid ) {
-        log.debug( "Parquet Store does not support prepare()." );
-        return true;
-    }
-
-
-    /**
-     * Do nothing - read only
-     */
-    @Override
-    public void commit( PolyXid xid ) {
-        log.debug( "Parquet Store does not support commit()." );
-        // do nothing
-    }
-
-
-    /**
-     * Do nothing - read only
-     */
-    @Override
-    public void rollback( PolyXid xid ) {
-        log.debug( "Parquet Store does not support rollback()." );
-        // do nothing
-    }
-
-
-    /**
-     * Cleans up information page state.
-     */
-    @Override
-    public void shutdown() {
-        removeInformationPage();
-    }
-
-
-    /**
-     * Reloads settings that affect source location.
-     */
-    @Override
-    protected void reloadSettings( List<String> updatedSettings ) {
-        if ( updatedSettings.contains( "directory" ) || updatedSettings.contains( "directoryName" ) ) {
-            setParquetDir( settings );
-        }
-    }
-
-    //region Not Delegated Methods
-
-
-    /**
-     * Creates and registers a physical Parquet table entry.
-     */
-    @Override
-    public List<PhysicalEntity> createTable( Context context, LogicalTableWrapper logical, AllocationTableWrapper allocation ) {
-        PhysicalTable table = adapterCatalog.createTable(
-                logical.table.getNamespaceName(),
-                logical.table.name,
-                logical.columns.stream().collect( Collectors.toMap( c -> c.id, c -> c.name ) ),
-                logical.table,
-                logical.columns.stream().collect( Collectors.toMap( t -> t.id, t -> t ) ),
-                logical.pkIds, allocation );
-
-        ParquetTable physical = currentNamespace.createParquetTable( table.id, table, this );
-        adapterCatalog.replacePhysical( physical );
-        return List.of( physical );
-    }
-
-
-    /**
-     * Restores table metadata after restart
-     */
-    @Override
-    public void restoreTable( AllocationTable alloc, List<PhysicalEntity> entities, Context context ) {
-        PhysicalEntity table = entities.get( 0 );
-        updateNamespace( table.namespaceName, table.namespaceId );
-        ParquetTable physical = currentNamespace.createParquetTable( table.id, table.unwrapOrThrow( PhysicalTable.class ), this );
-        adapterCatalog.addPhysical( alloc, physical );
-    }
-
-    @Override
-    public void dropTable( Context context, long allocId ) {
-        adapterCatalog.removeAllocAndPhysical( allocId );
-    }
-
-
-    /**
-     * for each parquet file create document
-     * @return list of documents
-     */
-    @Override
-    public List<ExportedDocument> getExportedCollections() {
-        return getExportedColumns().keySet().stream()
-                .map( name -> new ExportedDocument( name, false, EntityType.SOURCE ) )
-                .toList();
-    }
-
-
-    @Override
-    public List<PhysicalEntity> createCollection( Context context, LogicalCollection logical, AllocationCollection allocation ) {
-        // create polypheny level document objects
-        PhysicalCollection collection = adapterCatalog.createCollection(
-                logical.getNamespaceName(),
-                logical.getName(),
-                logical,
-                allocation );
-
-        ParquetDocument physical = currentNamespace.createParquetCollection( collection, this );
-        adapterCatalog.replacePhysical( physical );
-        return List.of( physical );
-    }
-
-
-    @Override
-    public void restoreCollection( AllocationCollection alloc, List<PhysicalEntity> entities, Context context ) {
-        PhysicalEntity collection = entities.get( 0 );
-        updateNamespace( collection.namespaceName, collection.namespaceId );
-        ParquetDocument physical = currentNamespace.createParquetCollection( collection.unwrapOrThrow( PhysicalCollection.class ), this );
-        adapterCatalog.addPhysical( alloc, physical );
-    }
-
-
-    @Override
-    public void dropCollection( Context context, AllocationCollection allocation ) {
-        adapterCatalog.removeAllocAndPhysical( allocation.id );
-    }
-
-
-    /**
-     * Updates logical column naming in adapter catalog
-     */
-    @Override
-    public void renameLogicalColumn( long id, String newColumnName ) {
-        adapterCatalog.renameLogicalColumn( id, newColumnName );
-    }
-
-
-    @Override
-    public AdapterCatalog getCatalog() {
-        return adapterCatalog;
-    }
-    //endregion
-
-
-    /**
-     * Methods excluded from Lombok delegate forwarding.
-     * These methods should not be delegated.
-     */
-    @SuppressWarnings("unused")
-    private interface Excludes {
-
-        void renameLogicalColumn( long id, String newColumnName );
-
-        void createTable( Context context, LogicalTableWrapper logical, AllocationTableWrapper allocation );
-
-        void restoreTable( AllocationTable alloc, List<PhysicalEntity> entities, Context context );
-
-        void dropTable( Context context, long allocId );
-
-        void createCollection( Context context, LogicalCollection logical, AllocationCollection allocation );
-
-        void restoreCollection( AllocationCollection alloc, List<PhysicalEntity> entities, Context context );
-
-        void dropCollection( Context context, AllocationCollection allocation );
-
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java
new file mode 100644
index 0000000000..7054e709c4
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java
@@ -0,0 +1,169 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.document;
+
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import lombok.experimental.Delegate;
+import org.pf4j.Extension;
+import org.polypheny.db.adapter.DeployMode;
+import org.polypheny.db.adapter.DocumentDataSource;
+import org.polypheny.db.adapter.DocumentScanDelegate;
+import org.polypheny.db.adapter.annotations.AdapterProperties;
+import org.polypheny.db.adapter.annotations.AdapterSettingDirectory;
+import org.polypheny.db.adapter.annotations.AdapterSettingList;
+import org.polypheny.db.adapter.annotations.AdapterSettingString;
+import org.polypheny.db.catalog.entity.allocation.AllocationCollection;
+import org.polypheny.db.catalog.entity.allocation.AllocationGraph;
+import org.polypheny.db.catalog.entity.allocation.AllocationTable;
+import org.polypheny.db.catalog.entity.allocation.AllocationTableWrapper;
+import org.polypheny.db.catalog.entity.logical.LogicalCollection;
+import org.polypheny.db.catalog.entity.logical.LogicalGraph;
+import org.polypheny.db.catalog.entity.logical.LogicalTableWrapper;
+import org.polypheny.db.catalog.entity.physical.PhysicalCollection;
+import org.polypheny.db.catalog.entity.physical.PhysicalEntity;
+import org.polypheny.db.catalog.logistic.DataModel;
+import org.polypheny.db.catalog.logistic.EntityType;
+import org.polypheny.db.prepare.Context;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+
+@Extension
+@AdapterProperties(
+        name = ParquetDocumentSource.NAME,
+        description = "A document adapter for querying Parquet files as read-only document collections.",
+        usedModes = DeployMode.EMBEDDED,
+        defaultMode = DeployMode.EMBEDDED)
+@AdapterSettingList(name = "method", options = { "upload", "link", "url" }, defaultValue = "upload", description = "If the supplied file(s) should be uploaded or a link to the local filesystem is used (sufficient permissions are required).", position = 1)
+@AdapterSettingDirectory(subOf = "method_upload", name = "directory", defaultValue = "classpath://orders_db", description = "You can upload one or multiple .parquet files.", position = 2)
+@AdapterSettingString(subOf = "method_link", defaultValue = "classpath://orders_db", name = "directoryName", description = "You can select a path to a folder or specific .parquet files.", position = 2)
+@AdapterSettingString(subOf = "method_url", defaultValue = "", name = "url", description = "URL to the Parquet file(s) to be integrated as this source.", position = 2)
+public class ParquetDocumentSource extends AbstractParquetSource implements DocumentDataSource {
+
+    @Delegate(excludes = Excludes.class)
+    private final DocumentScanDelegate delegate;
+
+    public static final String NAME = "Parquet Document";
+
+
+    public ParquetDocumentSource( long storeId, String uniqueName, Map<String, String> settings, DeployMode mode ) {
+        super( storeId, uniqueName, settings, mode, Set.of( DataModel.DOCUMENT ) );
+        this.delegate = new DocumentScanDelegate( this, adapterCatalog );
+    }
+
+
+    @Override
+    public DocumentDataSource asDocumentDataSource() {
+        return this;
+    }
+
+
+    @Override
+    public List<ExportedDocument> getExportedCollections() {
+        return getExportedColumns().keySet().stream()
+                .map( name -> new ExportedDocument( name, false, EntityType.SOURCE ) )
+                .toList();
+    }
+
+
+    @Override
+    public List<PhysicalEntity> createCollection( Context context, LogicalCollection logical, AllocationCollection allocation ) {
+        PhysicalCollection collection = adapterCatalog.createCollection(
+                logical.getNamespaceName(),
+                logical.getName(),
+                logical,
+                allocation );
+
+        var physical = currentNamespace.createParquetCollection( collection, this );
+        adapterCatalog.replacePhysical( physical );
+        return List.of( physical );
+    }
+
+
+    @Override
+    public void restoreCollection( AllocationCollection alloc, List<PhysicalEntity> entities, Context context ) {
+        PhysicalEntity collection = entities.get( 0 );
+        updateNamespace( collection.namespaceName, collection.namespaceId );
+        var physical = currentNamespace.createParquetCollection( collection.unwrapOrThrow( PhysicalCollection.class ), this );
+        adapterCatalog.addPhysical( alloc, physical );
+    }
+
+
+    @Override
+    public void dropCollection( Context context, AllocationCollection allocation ) {
+        adapterCatalog.removeAllocAndPhysical( allocation.id );
+    }
+
+
+    @Override
+    public List<PhysicalEntity> createTable( Context context, LogicalTableWrapper logical, AllocationTableWrapper allocation ) {
+        log.debug( "NOT SUPPORTED: document Parquet source does not support method createTable()." );
+        return null;
+    }
+
+
+    @Override
+    public void restoreTable( AllocationTable alloc, List<PhysicalEntity> entities, Context context ) {
+        log.debug( "NOT SUPPORTED: document Parquet source does not support method restoreTable()." );
+    }
+
+
+    @Override
+    public void dropTable( Context context, long allocId ) {
+        log.debug( "NOT SUPPORTED: document Parquet source does not support method dropTable()." );
+    }
+
+
+    @Override
+    public List<PhysicalEntity> createGraph( Context context, LogicalGraph logical, AllocationGraph allocation ) {
+        log.debug( "NOT SUPPORTED: Parquet document source does not support method createGraph()." );
+        return null;
+    }
+
+
+    @Override
+    public void restoreGraph( AllocationGraph alloc, List<PhysicalEntity> entities, Context context ) {
+        log.debug( "NOT SUPPORTED: Parquet document source does not support method restoreGraph()." );
+    }
+
+
+    @Override
+    public void dropGraph( Context context, AllocationGraph allocation ) {
+        log.debug( "NOT SUPPORTED: Parquet document source does not support method dropGraph()." );
+    }
+
+
+    @Override
+    public void renameLogicalColumn( long id, String newColumnName ) {
+        adapterCatalog.renameLogicalColumn( id, newColumnName );
+    }
+
+
+    @SuppressWarnings("unused")
+    private interface Excludes {
+
+        void renameLogicalColumn( long id, String newColumnName );
+
+        void createCollection( Context context, LogicalCollection logical, AllocationCollection allocation );
+
+        void restoreCollection( AllocationCollection alloc, List<PhysicalEntity> entities, Context context );
+
+        void dropCollection( Context context, AllocationCollection allocation );
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetDocumentEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
similarity index 64%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetDocumentEnumerator.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
index cf2b030faf..94caff2a6e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetDocumentEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
@@ -14,29 +14,35 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.execution;
+package org.polypheny.db.adapter.parquet.document.execution;
 
 import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.parquet.example.data.Group;
+import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
+import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
 import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.type.entity.document.PolyDocument;
 import org.polypheny.db.util.Source;
 
-public class ParquetDocumentEnumerator extends AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
+public class ParquetDocEnumerator extends AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
 
     private final String documentPrefix;
+    private final ParquetDocValueExtractor documentValueExtractor;
 
-    public ParquetDocumentEnumerator( Source source, AtomicBoolean cancelFlag ) {
-        super(source, cancelFlag, null, List.of());
+
+    public ParquetDocEnumerator( Source source, AtomicBoolean cancelFlag, List<AdapterFilter> filters ) {
+        super( source, cancelFlag, null, filters, new ParquetDocValueExtractor() );
+        this.documentValueExtractor = (ParquetDocValueExtractor) valueExtractor;
         this.documentPrefix = source.path();
     }
 
+
     @Override
     protected PolyValue[] extractRow( Group group ) {
-        PolyDocument document = valueExtractor.extractDocument(
+        PolyDocument document = documentValueExtractor.extractDocument(
                 group,
                 reader.getProjectionSchema(),
                 PolyString.of( documentPrefix + "#" + reader.getCurrentRowNumber() ) );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
new file mode 100644
index 0000000000..0f0ebd102f
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
@@ -0,0 +1,59 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.document.execution;
+
+
+import java.util.List;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.parquet.shared.execution.ParquetFilterTranslationSupport;
+import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
+import org.polypheny.db.rex.RexNameRef;
+import org.polypheny.db.rex.RexNode;
+
+public class ParquetDocFilterTranslator {
+
+    public AdapterFilter translate( List<ExportedColumn> columns, RexNode filter ) {
+        ParquetFilterTranslationSupport.ParsedFilter parsed = ParquetFilterTranslationSupport.parse( filter );
+        if ( parsed == null ) {
+            return null;
+        }
+
+        RexNode left = parsed.left();
+        RexNode right = parsed.right();
+
+        if ( !(left instanceof RexNameRef nameRef) ) {
+            return null;
+        }
+
+        if ( nameRef.names.size() != 1 ) {
+            return null;
+        }
+
+        String fieldName = nameRef.names.get( 0 );
+        if ( !ParquetFilterTranslationSupport.isValueOperand( right ) ) {
+            return null;
+        }
+
+        for ( ExportedColumn column : columns ) {
+            if ( column.name().equalsIgnoreCase( fieldName ) ) {
+                return ParquetFilterTranslationSupport.toAdapterFilter( column.physicalPosition(), filter.getKind(), right );
+            }
+        }
+        return null;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
new file mode 100644
index 0000000000..4b2dc09bb8
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
@@ -0,0 +1,42 @@
+package org.polypheny.db.adapter.parquet.document.execution;
+
+import java.util.LinkedHashMap;
+import java.util.Map;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetValueExtractor;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.document.PolyDocument;
+
+
+public class ParquetDocValueExtractor extends AbstractParquetValueExtractor {
+
+    @Override
+    public PolyValue extractValue( Group group, int index, Type type ) {
+        return extractStructuredValue( group, index, type );
+    }
+
+
+    public PolyDocument extractDocument( Group group, MessageType schema, PolyString generatedId ) {
+        Map<PolyString, PolyValue> values = new LinkedHashMap<>();
+
+        for ( int i = 0; i < schema.getFieldCount(); i++ ) {
+            Type field = schema.getType( i );
+            if ( group.getFieldRepetitionCount( i ) == 0 ) {
+                continue;
+            }
+            values.put( PolyString.of( AbstractParquetSource.normalizeFieldName( field.getName() ) ), extractValue( group, i, field ) );
+        }
+
+        PolyString idKey = PolyString.of( "_id" );
+        if ( !values.containsKey( idKey ) || values.get( idKey ).isNull() ) {
+            values.put( idKey, generatedId );
+        }
+
+        return PolyDocument.ofDocument( values );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilter.java
new file mode 100644
index 0000000000..5ad3d9321f
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilter.java
@@ -0,0 +1,63 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.document.planning;
+
+import lombok.Getter;
+import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.core.Filter;
+import org.polypheny.db.algebra.enumerable.EnumerableAlg;
+import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgOptCost;
+import org.polypheny.db.plan.AlgPlanner;
+import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.rex.RexNode;
+
+@Getter
+public class ParquetDocFilter extends Filter implements EnumerableAlg {
+
+    private final ParquetDocument entity;
+
+
+    public ParquetDocFilter( AlgCluster cluster, AlgTraitSet traitSet, AlgNode input, RexNode condition, ParquetDocument entity ) {
+        super( cluster, traitSet.replace( EnumerableConvention.INSTANCE ), input, condition );
+        this.entity = entity;
+    }
+
+
+    @Override
+    public ParquetDocFilter copy( AlgTraitSet traitSet, AlgNode input, RexNode condition ) {
+        return new ParquetDocFilter( getCluster(), traitSet, input, condition, entity );
+    }
+
+
+    @Override
+    public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
+        return super.computeSelfCost( planner, mq ).multiplyBy( ((double) entity.getParquetSource().getExportedColumns().size() + 2D) / ((double) entity.getTupleType().getFieldCount() + 2D) );
+    }
+
+
+    @Override
+    public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
+        var scan = (ParquetDocScan) input;
+        return scan.implement( implementor, pref );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java
new file mode 100644
index 0000000000..d0005e5fe7
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java
@@ -0,0 +1,87 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.document.planning;
+
+import java.util.ArrayList;
+import java.util.List;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.parquet.document.execution.ParquetDocFilterTranslator;
+import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
+import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.logical.relational.LogicalRelFilter;
+import org.polypheny.db.plan.AlgOptRule;
+import org.polypheny.db.plan.AlgOptRuleCall;
+import org.polypheny.db.rex.RexCall;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.tools.AlgBuilderFactory;
+
+public class ParquetDocFilterRule extends AlgOptRule {
+
+    private final ParquetDocFilterTranslator translator = new ParquetDocFilterTranslator();
+    private final ParquetDocument document;
+
+
+    public ParquetDocFilterRule( AlgBuilderFactory algBuilderFactory, ParquetDocument document ) {
+        super( operand( LogicalRelFilter.class, any() ), algBuilderFactory, ParquetDocFilterRule.class.getSimpleName() );
+        this.document = document;
+    }
+
+
+    @Override
+    public void onMatch( AlgOptRuleCall call ) {
+        LogicalRelFilter filter = call.alg( 0 );
+
+        List<ExportedColumn> columns = document.getParquetSource().getExportedColumns().get( document.name );
+        List<RexNode> predicates = splitConjunctions( filter.getCondition() );
+        if ( columns == null || predicates.isEmpty() ) {
+            return;
+        }
+
+        List<AdapterFilter> adapterFilters = new ArrayList<>();
+        for ( RexNode predicate : predicates ) {
+            AdapterFilter adapterFilter = translator.translate( columns, predicate );
+            if ( adapterFilter == null ) {
+                return;
+            }
+            adapterFilters.add( adapterFilter );
+        }
+
+        call.transformTo(
+                new ParquetDocFilter(
+                        filter.getCluster(),
+                        filter.getTraitSet().replace( EnumerableConvention.INSTANCE ),
+                        new ParquetDocScan( filter.getCluster(), document, adapterFilters ),
+                        filter.getCondition(),
+                        document ) );
+    }
+
+
+    private List<RexNode> splitConjunctions( RexNode node ) {
+        if ( node.getKind() != Kind.AND ) {
+            return List.of( node );
+        }
+        List<RexNode> predicates = new ArrayList<>();
+        RexCall call = (RexCall) node;
+        for ( RexNode operand : call.getOperands() ) {
+            predicates.addAll( splitConjunctions( operand ) );
+        }
+        return predicates;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
similarity index 50%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScan.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
index 5edbd34780..7267d9fe52 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
@@ -14,78 +14,108 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.planning;
+package org.polypheny.db.adapter.parquet.document.planning;
 
 import java.util.List;
+import lombok.Getter;
 import org.apache.calcite.linq4j.tree.Blocks;
+import org.apache.calcite.linq4j.tree.Expression;
 import org.apache.calcite.linq4j.tree.Expressions;
-import org.polypheny.db.adapter.parquet.schema.ParquetDocument;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
+import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.AlgWriter;
+import org.polypheny.db.algebra.core.AlgFactories;
 import org.polypheny.db.algebra.core.document.DocumentScan;
 import org.polypheny.db.algebra.enumerable.EnumerableAlg;
 import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
 import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.enumerable.EnumUtils;
 import org.polypheny.db.algebra.enumerable.PhysType;
 import org.polypheny.db.algebra.enumerable.PhysTypeImpl;
 import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
 import org.polypheny.db.algebra.type.AlgDataType;
 import org.polypheny.db.algebra.type.AlgDataTypeField;
+import org.polypheny.db.algebra.type.DocumentType;
 import org.polypheny.db.plan.AlgCluster;
 import org.polypheny.db.plan.AlgOptCost;
 import org.polypheny.db.plan.AlgPlanner;
 import org.polypheny.db.plan.AlgTraitSet;
-import org.polypheny.db.schema.types.ScannableEntity;
+import org.polypheny.db.type.entity.PolyValue;
 
-public class ParquetDocumentScan extends DocumentScan<ParquetDocument> implements EnumerableAlg {
+@Getter
+public class ParquetDocScan extends DocumentScan<ParquetDocument> implements EnumerableAlg {
 
+    private final List<AdapterFilter> filters;
 
-    public ParquetDocumentScan( AlgCluster cluster, ParquetDocument collection ) {
-        super( cluster, cluster.traitSetOf( EnumerableConvention.INSTANCE ), collection );
+
+    public ParquetDocScan( AlgCluster cluster, ParquetDocument entity, List<AdapterFilter> filters ) {
+        super( cluster, cluster.traitSetOf( EnumerableConvention.INSTANCE ), entity );
+        this.filters = List.copyOf( filters );
     }
 
 
     @Override
     public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
-        return new ParquetDocumentScan( getCluster(), entity );
+        return new ParquetDocScan( getCluster(), entity, filters );
     }
 
 
     @Override
     public AlgWriter explainTerms( AlgWriter pw ) {
-        return super.explainTerms( pw );
+        return super.explainTerms( pw ).item( "filters", filters );
     }
 
 
     @Override
     public AlgDataType deriveRowType() {
-        final List<AlgDataTypeField> fieldList = entity.getTupleType().getFields();
+        final List<AlgDataTypeField> fieldList = DocumentType.ofId().getFields();
         return getCluster().getTypeFactory().builder().add( fieldList.get( 0 ) ).build();
     }
 
 
     @Override
-    public void register( AlgPlanner planner ) {
-        planner.addRule( ParquetDocumentScanRule.INSTANCE );
+    public String algCompareString() {
+        return super.algCompareString() + "$filters=" + filters;
     }
 
 
     @Override
     public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
-        return super.computeSelfCost( planner, mq );
+        return super.computeSelfCost( planner, mq ).multiplyBy( ((double) entity.getParquetSource().getExportedColumns().size() + 2D) / ((double) entity.getTupleType().getFieldCount() + 2D) );
     }
 
 
     @Override
     public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
         PhysType physType = PhysTypeImpl.of( implementor.getTypeFactory(), getTupleType(), pref.preferArray() );
+        Expression runtimeFilters = EnumUtils.expressionList( filters.stream().map( ParquetDocScan::toFilterExpression ).toList() );
+
         return implementor.result(
                 physType,
                 Blocks.toBlock(
                         Expressions.call(
-                                entity.asExpression( ScannableEntity.class ),
-                                "scan",
-                                implementor.getRootExpression() ) ) );
+                                entity.asExpression(),
+                                "scanFiltered",
+                                implementor.getRootExpression(),
+                                runtimeFilters ) ) );
+    }
+
+
+    private static Expression toFilterExpression( AdapterFilter filter ) {
+        return Expressions.new_(
+                AdapterFilter.class,
+                Expressions.constant( filter.columnIndex() ),
+                Expressions.constant( filter.operator(), Kind.class ),
+                filter.polyValue() == null ? Expressions.constant( null, PolyValue.class ) : filter.polyValue().asExpression(),
+                Expressions.constant( filter.dynamicParamIndex(), Long.class ) );
+    }
+
+
+    @Override
+    public void register( AlgPlanner planner ) {
+        planner.addRuleDuringRuntime( new ParquetDocFilterRule( AlgFactories.LOGICAL_BUILDER, entity ) );
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetDocument.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
similarity index 50%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetDocument.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
index 39380c1779..f32143203f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetDocument.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
@@ -14,19 +14,29 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.schema;
+package org.polypheny.db.adapter.parquet.document.schema;
 
+import java.util.ArrayList;
+import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
+import lombok.Getter;
 import org.apache.calcite.linq4j.AbstractEnumerable;
 import org.apache.calcite.linq4j.Enumerable;
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.calcite.linq4j.tree.Expression;
 import org.apache.calcite.linq4j.tree.Expressions;
 import org.polypheny.db.adapter.DataContext;
-import org.polypheny.db.adapter.parquet.ParquetSource;
-import org.polypheny.db.adapter.parquet.execution.ParquetDocumentEnumerator;
-import org.polypheny.db.adapter.parquet.planning.ParquetDocumentScan;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.parquet.document.execution.ParquetDocEnumerator;
+import org.polypheny.db.adapter.parquet.document.planning.ParquetDocScan;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
 import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.algebra.type.AlgDataTypeFactory;
+import org.polypheny.db.algebra.type.AlgDataTypeField;
+import org.polypheny.db.algebra.type.AlgDataTypeFieldImpl;
+import org.polypheny.db.algebra.type.DocumentType;
 import org.polypheny.db.catalog.entity.physical.PhysicalCollection;
 import org.polypheny.db.plan.AlgCluster;
 import org.polypheny.db.plan.AlgTraitSet;
@@ -38,10 +48,11 @@
 public class ParquetDocument extends PhysicalCollection implements ScannableEntity, TranslatableEntity {
 
     private final Source source;
-    private final ParquetSource parquetSource;
+    @Getter
+    private final AbstractParquetSource parquetSource;
 
 
-    public ParquetDocument( PhysicalCollection collection, Source source, ParquetSource parquetSource ) {
+    public ParquetDocument( PhysicalCollection collection, Source source, AbstractParquetSource parquetSource ) {
         super(
                 collection.id,
                 collection.allocationId,
@@ -63,18 +74,24 @@ public Expression asExpression() {
                         Expressions.call( this.parquetSource.asExpression(), "getAdapterCatalog" ),
                         "getPhysical",
                         argExp ),
-                ScannableEntity.class );
+                ParquetDocument.class );
     }
 
 
     @Override
     public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
+        return scanFiltered( dataContext, List.of() );
+    }
+
+
+    public Enumerable<PolyValue[]> scanFiltered( DataContext dataContext, List<AdapterFilter> filters ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+        final List<AdapterFilter> resolvedFilters = filters.stream().map( filter -> resolveFilter( dataContext, filter ) ).toList();
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                return new ParquetDocumentEnumerator( source, cancelFlag );
+                return new ParquetDocEnumerator( source, cancelFlag, resolvedFilters );
             }
         };
     }
@@ -82,7 +99,48 @@ public Enumerator<PolyValue[]> enumerator() {
 
     @Override
     public AlgNode toAlg( AlgCluster cluster, AlgTraitSet traitSet ) {
-        return new ParquetDocumentScan( cluster, this );
+        return new ParquetDocScan( cluster, this, List.of() );
+    }
+
+
+    @Override
+    public AlgDataType getTupleType() {
+        return getTupleType( AlgDataTypeFactory.DEFAULT );
+    }
+
+
+    @Override
+    public AlgDataType getTupleType( AlgDataTypeFactory typeFactory ) {
+        return buildDocumentType( typeFactory );
+    }
+
+
+    private AlgDataType buildDocumentType( AlgDataTypeFactory typeFactory ) {
+        List<ExportedColumn> columns = parquetSource.getExportedColumns().get( name );
+        if ( columns == null || columns.isEmpty() ) {
+            return DocumentType.ofId();
+        }
+
+        List<AlgDataTypeField> fields = new ArrayList<>();
+        for ( ExportedColumn column : columns ) {
+            AlgDataType type = typeFactory.createPolyType( column.type() );
+            if ( column.nullable() ) {
+                type = typeFactory.createTypeWithNullability( type, true );
+            }
+            fields.add( new AlgDataTypeFieldImpl( -1L, column.name(), column.physicalPosition(), type ) );
+        }
+        return new DocumentType( fields );
+    }
+
+
+    private AdapterFilter resolveFilter( DataContext dataContext, AdapterFilter filter ) {
+        if ( filter.dynamicParamIndex() == null ) {
+            return filter;
+        }
+        return new AdapterFilter(
+                filter.columnIndex(),
+                filter.operator(),
+                dataContext.getParameterValue( filter.dynamicParamIndex() ) );
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/AbstractParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/AbstractParquetEnumerator.java
deleted file mode 100644
index b57b3c3b0f..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/AbstractParquetEnumerator.java
+++ /dev/null
@@ -1,79 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.execution;
-
-import org.apache.calcite.linq4j.Enumerator;
-import org.apache.parquet.example.data.Group;
-import org.polypheny.db.adapter.parquet.model.AdapterFilter;
-import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
-import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.util.Source;
-import java.util.List;
-import java.util.concurrent.atomic.AtomicBoolean;
-
-public abstract class AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
-
-    protected final ParquetGroupReader reader;
-    protected final ValueExtractor valueExtractor;
-    private PolyValue[] current;
-
-
-    public AbstractParquetEnumerator(Source source, AtomicBoolean cancelFlag, int[] fields, List<AdapterFilter> filters ) {
-        this.reader = new ParquetGroupReader( source, cancelFlag, fields, filters );
-        this.valueExtractor = new ValueExtractor();
-    }
-
-
-    @Override
-    public PolyValue[] current() {
-        return current;
-    }
-
-
-    @Override
-    public boolean moveNext() {
-        try {
-            Group group = reader.next();
-            if ( group == null ) {
-                current = null;
-                return false;
-            }
-            current = extractRow( group );
-            return true;
-        } catch ( Exception e ) {
-            throw new GenericRuntimeException( "Error while reading parquet data", e );
-        }
-    }
-
-
-    @Override
-    public void reset() {
-        throw new UnsupportedOperationException();
-    }
-
-
-    @Override
-    public void close() {
-        reader.close();
-    }
-
-    /*
-    extractValue logic depends on source type (relational/document)
-     */
-    protected abstract PolyValue[] extractRow( Group group );
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java
deleted file mode 100644
index 586d0f1382..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ValueExtractor.java
+++ /dev/null
@@ -1,178 +0,0 @@
-package org.polypheny.db.adapter.parquet.execution;
-
-import java.util.ArrayList;
-import java.util.LinkedHashMap;
-import java.util.List;
-import java.util.Map;
-import org.apache.parquet.example.data.Group;
-import org.apache.parquet.schema.GroupType;
-import org.apache.parquet.schema.MessageType;
-import org.apache.parquet.schema.PrimitiveType;
-import org.apache.parquet.schema.Type;
-import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
-import org.polypheny.db.type.entity.PolyList;
-import org.polypheny.db.type.entity.PolyNull;
-import org.polypheny.db.type.entity.PolyString;
-import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.type.entity.document.PolyDocument;
-
-
-/**
- * Central conversion layer between Parquet and Polypheny value representations.
- */
-public class ValueExtractor {
-
-    private final ParquetTypeConverter typeConverter = new ParquetTypeConverter();
-
-    /*
-    The function reads one Parquet field from the current row.
-    If the field is primitive, function returns the primitive Java value.
-    If the field is nested, it returns JSON text built from the nested group.
-     */
-    public PolyValue extractValue( Group group, int index, Type type ) {
-        if ( !type.isPrimitive() ) {
-            return PolyString.of( toJson( group.getGroup( index, 0 ) ));
-        }
-        PrimitiveType primitive = type.asPrimitiveType();
-        Object value = extractPrimitiveValue( group, index, primitive, 0 );
-        return typeConverter.fromObjToPolyValue( type, value );
-    }
-
-
-    public PolyDocument extractDocument( Group group, MessageType schema, PolyString generatedId ) {
-        Map<PolyString, PolyValue> values = new LinkedHashMap<>();
-
-        for ( int i = 0; i < schema.getFieldCount(); i++ ) {
-            Type field = schema.getType( i );
-            if ( group.getFieldRepetitionCount( i ) == 0 ) {
-                continue;
-            }
-            values.put( PolyString.of( field.getName() ), extractDocumentValue( group, i, field ) );
-        }
-
-        PolyString idKey = PolyString.of( "_id" );
-        if ( !values.containsKey( idKey ) || values.get( idKey ).isNull() ) {
-            values.put( idKey, generatedId );
-        }
-
-        return PolyDocument.ofDocument( values );
-    }
-
-
-    public PolyValue extractDocumentValue( Group group, int index, Type field ) {
-        int repetitionCount = group.getFieldRepetitionCount( index );
-        if ( repetitionCount == 0 ) {
-            return PolyNull.NULL;
-        }
-
-        if ( repetitionCount > 1 ) {
-            List<PolyValue> values = new ArrayList<>( repetitionCount );
-            for ( int occurrence = 0; occurrence < repetitionCount; occurrence++ ) {
-                values.add( extractDocumentValue( group, index, field, occurrence ) );
-            }
-            return PolyList.of( values );
-        }
-
-        return extractDocumentValue( group, index, field, 0 );
-    }
-
-
-    private PolyValue extractDocumentValue( Group group, int index, Type field, int occurrence ) {
-        if ( field.isPrimitive() ) {
-            return typeConverter.fromObjToPolyValue( field, extractPrimitiveValue( group, index, field.asPrimitiveType(), occurrence ) );
-        }
-
-        return extractNestedDocument( group.getGroup( index, occurrence ), field.asGroupType() );
-    }
-
-
-    private PolyDocument extractNestedDocument( Group group, GroupType type ) {
-        Map<PolyString, PolyValue> values = new LinkedHashMap<>();
-        for ( int i = 0; i < type.getFieldCount(); i++ ) {
-            Type nestedField = type.getType( i );
-            if ( group.getFieldRepetitionCount( i ) == 0 ) {
-                continue;
-            }
-            values.put( PolyString.of( nestedField.getName() ), extractDocumentValue( group, i, nestedField ) );
-        }
-        return PolyDocument.ofDocument( values );
-    }
-
-
-    private Object extractPrimitiveValue( Group group, int index, PrimitiveType primitive, int occurrence ) {
-        return switch ( primitive.getPrimitiveTypeName() ) {
-            case BOOLEAN -> group.getBoolean( index, occurrence );
-            case INT32 -> group.getInteger( index, occurrence );
-            case INT64 -> group.getLong( index, occurrence );
-            case FLOAT -> group.getFloat( index, occurrence );
-            case DOUBLE -> group.getDouble( index, occurrence );
-            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> group.getBinary( index, occurrence );
-        };
-    }
-
-    /*
-    The function converts a nested Parquet group into a JSON object string:
-    Iterates over all fields inside that group.
-    Skips missing fields.
-    For primitive nested fields, delegates to toJsonPrimitive(...).
-    For nested sub-groups, calls itself recursively.
-     */
-    private String toJson( Group group ) {
-        StringBuilder json = new StringBuilder();
-        json.append( "{" );
-
-        String sep = "";
-        for ( int i = 0; i < group.getType().getFieldCount(); i++ ) {
-            Type field = group.getType().getType( i );
-            String name = field.getName();
-
-            if ( group.getFieldRepetitionCount( i ) == 0 ) {
-                continue;
-            }
-
-            json.append( sep ).append( "\"" ).append( name ).append( "\":" );
-
-            if ( field.isPrimitive() ) {
-                json.append( toJsonPrimitive( group, i, field ) );
-            } else {
-                json.append( toJson( group.getGroup( i, 0 ) ) );
-            }
-
-            sep = ",";
-        }
-
-        json.append( "}" );
-        return json.toString();
-    }
-
-    /*
-    Converts one primitive Parquet field into a JSON-compatible string fragment.
-     */
-    private String toJsonPrimitive( Group group, int index, Type field ) {
-        PrimitiveType primitive = field.asPrimitiveType();
-
-        return switch ( primitive.getPrimitiveTypeName() ) {
-            case BOOLEAN -> String.valueOf( group.getBoolean( index, 0 ) );
-            case INT32 -> String.valueOf( group.getInteger( index, 0 ) );
-            case INT64 -> String.valueOf( group.getLong( index, 0 ) );
-            case FLOAT -> String.valueOf( group.getFloat( index, 0 ) );
-            case DOUBLE -> String.valueOf( group.getDouble( index, 0 ) );
-            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 ->
-                    "\"" + escapeJson( group.getBinary( index, 0 ).toStringUsingUTF8() ) + "\"";
-        };
-    }
-
-    /*
-    Escapes characters that would break JSON syntax.
-     */
-    private String escapeJson( String value ) {
-        return value
-                .replace( "\\", "\\\\" )
-                .replace( "\"", "\\\"" )
-                .replace( "\n", "\\n" )
-                .replace( "\r", "\\r" )
-                .replace( "\t", "\\t" );
-    }
-
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScanRule.java
deleted file mode 100644
index f4fd8692b6..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetDocumentScanRule.java
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.planning;
-
-import org.polypheny.db.adapter.parquet.schema.ParquetDocument;
-import org.polypheny.db.algebra.core.AlgFactories;
-import org.polypheny.db.algebra.logical.document.LogicalDocumentScan;
-import org.polypheny.db.plan.AlgOptRule;
-import org.polypheny.db.plan.AlgOptRuleCall;
-import org.polypheny.db.tools.AlgBuilderFactory;
-
-public class ParquetDocumentScanRule extends AlgOptRule {
-
-    public static final ParquetDocumentScanRule INSTANCE = new ParquetDocumentScanRule( AlgFactories.LOGICAL_BUILDER );
-
-
-    private ParquetDocumentScanRule( AlgBuilderFactory algBuilderFactory ) {
-        super(
-                operand( LogicalDocumentScan.class, none() ),
-                algBuilderFactory,
-                ParquetDocumentScanRule.class.getSimpleName() );
-    }
-
-
-    @Override
-    public void onMatch( AlgOptRuleCall call ) {
-        final LogicalDocumentScan scan = call.alg( 0 );
-        call.transformTo( new ParquetDocumentScan( scan.getCluster(), scan.getEntity().unwrapOrThrow( ParquetDocument.class ) ) );
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
new file mode 100644
index 0000000000..474efabeff
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
@@ -0,0 +1,141 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational;
+
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import java.util.stream.Collectors;
+import org.pf4j.Extension;
+import org.polypheny.db.adapter.DeployMode;
+import org.polypheny.db.adapter.RelationalDataSource;
+import org.polypheny.db.adapter.annotations.AdapterProperties;
+import org.polypheny.db.adapter.annotations.AdapterSettingDirectory;
+import org.polypheny.db.adapter.annotations.AdapterSettingList;
+import org.polypheny.db.adapter.annotations.AdapterSettingString;
+import org.polypheny.db.catalog.entity.allocation.AllocationCollection;
+import org.polypheny.db.catalog.entity.allocation.AllocationGraph;
+import org.polypheny.db.catalog.entity.allocation.AllocationTable;
+import org.polypheny.db.catalog.entity.allocation.AllocationTableWrapper;
+import org.polypheny.db.catalog.entity.logical.LogicalCollection;
+import org.polypheny.db.catalog.entity.logical.LogicalGraph;
+import org.polypheny.db.catalog.entity.logical.LogicalTableWrapper;
+import org.polypheny.db.catalog.entity.physical.PhysicalEntity;
+import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.catalog.logistic.DataModel;
+import org.polypheny.db.prepare.Context;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+
+@Extension
+@AdapterProperties(
+        name = ParquetRelationalSource.NAME,
+        description = "A relational adapter for querying Parquet files. The location of the directory containing the Parquet files can be specified. Currently, this adapter only supports read operations.",
+        usedModes = DeployMode.EMBEDDED,
+        defaultMode = DeployMode.EMBEDDED)
+@AdapterSettingList(name = "method", options = { "upload", "link", "url" }, defaultValue = "upload", description = "If the supplied file(s) should be uploaded or a link to the local filesystem is used (sufficient permissions are required).", position = 1)
+@AdapterSettingDirectory(subOf = "method_upload", name = "directory", defaultValue = "classpath://orders_db", description = "You can upload one or multiple .parquet files.", position = 2)
+@AdapterSettingString(subOf = "method_link", defaultValue = "classpath://orders_db", name = "directoryName", description = "You can select a path to a folder or specific .parquet files.", position = 2)
+@AdapterSettingString(subOf = "method_url", defaultValue = "", name = "url", description = "URL to the Parquet file(s) to be integrated as this source.", position = 2)
+public class ParquetRelationalSource extends AbstractParquetSource implements RelationalDataSource {
+
+    public static final String NAME = "Parquet Relational";
+
+    public ParquetRelationalSource(long storeId, String uniqueName, Map<String, String> settings, DeployMode mode ) {
+        super( storeId, uniqueName, settings, mode, Set.of( DataModel.RELATIONAL ) );
+    }
+
+    @Override
+    public RelationalDataSource asRelationalDataSource() {
+        return this;
+    }
+
+
+    @Override
+    public List<PhysicalEntity> createTable( Context context, LogicalTableWrapper logical, AllocationTableWrapper allocation ) {
+        PhysicalTable table = adapterCatalog.createTable(
+                logical.table.getNamespaceName(),
+                logical.table.name,
+                logical.columns.stream().collect( Collectors.toMap( c -> c.id, c -> c.name ) ),
+                logical.table,
+                logical.columns.stream().collect( Collectors.toMap( t -> t.id, t -> t ) ),
+                logical.pkIds, allocation );
+
+        var physical = currentNamespace.createParquetTable( table.id, table, this );
+        adapterCatalog.replacePhysical( physical );
+        return List.of( physical );
+    }
+
+
+    @Override
+    public void restoreTable( AllocationTable alloc, List<PhysicalEntity> entities, Context context ) {
+        PhysicalEntity table = entities.get( 0 );
+        updateNamespace( table.namespaceName, table.namespaceId );
+        var physical = currentNamespace.createParquetTable( table.id, table.unwrapOrThrow( PhysicalTable.class ), this );
+        adapterCatalog.addPhysical( alloc, physical );
+    }
+
+
+    @Override
+    public void dropTable( Context context, long allocId ) {
+        adapterCatalog.removeAllocAndPhysical( allocId );
+    }
+
+
+    @Override
+    public List<PhysicalEntity> createCollection( Context context, LogicalCollection logical, org.polypheny.db.catalog.entity.allocation.AllocationCollection allocation ) {
+        log.debug( "NOT SUPPORTED: relational Parquet source does not support method createCollection()." );
+        return null;
+    }
+
+
+    @Override
+    public void restoreCollection( AllocationCollection alloc, List<PhysicalEntity> entities, Context context ) {
+        log.debug( "NOT SUPPORTED: relational Parquet source does not support method restoreCollection()." );
+    }
+
+
+    @Override
+    public void dropCollection( Context context, AllocationCollection allocation ) {
+        log.debug( "NOT SUPPORTED: relational Parquet source does not support method dropCollection()." );
+    }
+
+
+    @Override
+    public List<PhysicalEntity> createGraph( Context context, LogicalGraph logical, AllocationGraph allocation ) {
+        log.debug( "NOT SUPPORTED: Parquet source does not support method createGraph()." );
+        return null;
+    }
+
+
+    @Override
+    public void restoreGraph( AllocationGraph alloc, List<PhysicalEntity> entities, Context context ) {
+        log.debug( "NOT SUPPORTED: Parquet source does not support method restoreGraph()." );
+    }
+
+
+    @Override
+    public void dropGraph( Context context, AllocationGraph allocation ) {
+        log.debug( "NOT SUPPORTED: Parquet source does not support method dropGraph()." );
+    }
+
+
+    @Override
+    public void renameLogicalColumn( long id, String newColumnName ) {
+        adapterCatalog.renameLogicalColumn( id, newColumnName );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetTableEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
similarity index 65%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetTableEnumerator.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
index fb74e89fa9..5382ded0d8 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetTableEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
@@ -14,24 +14,25 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.execution;
+package org.polypheny.db.adapter.parquet.relational.execution;
 
 import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.parquet.example.data.Group;
-import org.polypheny.db.adapter.parquet.model.AdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
+import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.Source;
 
-public class ParquetTableEnumerator extends AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
+public class ParquetRelEnumerator extends AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
 
-    public ParquetTableEnumerator(Source source, AtomicBoolean cancelFlag, int[] fields ) {
-        super( source, cancelFlag, fields, List.of() );
+    public ParquetRelEnumerator(Source source, AtomicBoolean cancelFlag, int[] fields ) {
+        super( source, cancelFlag, fields, List.of(), new ParquetRelValueExtractor() );
     }
 
-    public ParquetTableEnumerator(Source source, AtomicBoolean cancelFlag, int[] fields, List<AdapterFilter> filters ) {
-        super( source, cancelFlag, fields, filters );
+    public ParquetRelEnumerator(Source source, AtomicBoolean cancelFlag, int[] fields, List<AdapterFilter> filters ) {
+        super( source, cancelFlag, fields, filters, new ParquetRelValueExtractor() );
     }
 
     protected PolyValue[] extractRow( Group group ) {
@@ -43,5 +44,4 @@ protected PolyValue[] extractRow( Group group ) {
         }
         return row;
     }
-
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetTableFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
similarity index 76%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetTableFilterTranslator.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
index 9ae03676ca..6ae6c08841 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetTableFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.execution;
+package org.polypheny.db.adapter.parquet.relational.execution;
 
 import java.util.List;
 import org.apache.parquet.filter2.compat.FilterCompat;
@@ -23,19 +23,19 @@
 import org.apache.parquet.schema.MessageType;
 import org.apache.parquet.schema.PrimitiveType;
 import org.apache.parquet.schema.Type;
-import org.polypheny.db.adapter.parquet.model.AdapterFilter;
-import org.polypheny.db.adapter.parquet.schema.ParquetTypeConverter;
+import org.polypheny.db.adapter.parquet.shared.execution.ParquetFilterTranslationSupport;
+import org.polypheny.db.adapter.parquet.shared.execution.ParquetPredicateBuilder;
+import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
 import org.polypheny.db.algebra.constant.Kind;
-import org.polypheny.db.rex.RexCall;
 import org.polypheny.db.rex.RexIndexRef;
-import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNode;
 import org.polypheny.db.type.PolyType;
 
 /**
  * Translates adapter filters into parquet-native predicates.
  */
-public class ParquetTableFilterTranslator {
+public class ParquetRelFilterTranslator {
 
     private final ParquetTypeConverter typeConverter = new ParquetTypeConverter();
 
@@ -44,35 +44,28 @@ public class ParquetTableFilterTranslator {
      * Translates a Rex filter into adapter filter form when possible.
      */
     public AdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFilter ) {
-        if ( !isSupportedOperator( polyFilter.getKind() ) ) {
+        ParquetFilterTranslationSupport.ParsedFilter parsed = ParquetFilterTranslationSupport.parse( polyFilter );
+        if ( parsed == null ) {
             return null;
         }
 
-        RexCall call = (RexCall) polyFilter;
-        RexNode left = call.getOperands().get( 0 );
-        if ( left.isA( Kind.CAST ) ) {
-            left = ((RexCall) left).operands.get( 0 );
-        }
-        RexNode right = call.getOperands().get( 1 );
-
-        if ( !(left instanceof RexIndexRef) || !(right instanceof RexLiteral literal) ) {
-            return null;
-        }
+        RexNode left = parsed.left();
+        RexNode right = parsed.right();
 
-        if ( literal.getValue() == null ) {
+        if ( !(left instanceof RexIndexRef indexRef) || !ParquetFilterTranslationSupport.isValueOperand( right ) ) {
             return null;
         }
 
-        int index = ((RexIndexRef) left).getIndex();
+        int index = indexRef.getIndex();
         if ( index < 0 || index >= fieldTypes.size() ) {
             return null;
         }
 
-        if ( !isPushdownSupported( fieldTypes, index, polyFilter.getKind(), literal ) ) {
+        if ( !isPushdownSupported( fieldTypes, index, polyFilter.getKind(), right ) ) {
             return null;
         }
 
-        return new AdapterFilter( index, polyFilter.getKind(), literal.getValue() );
+        return ParquetFilterTranslationSupport.toAdapterFilter( index, polyFilter.getKind(), right );
     }
 
 
@@ -80,13 +73,13 @@ public AdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFilter )
      * Translate adapter filters to Parquet filter for pushdown
      *
      * @param schema - parquet file native schema
-     * @param adpaterFilters - adapter level filters
+     * @param adapterFilters - adapter level filters
      * @return FilterCompat.Filter - filter in parquet format
      */
-    public FilterCompat.Filter translate( MessageType schema, List<AdapterFilter> adpaterFilters ) {
+    public FilterCompat.Filter translate( MessageType schema, List<AdapterFilter> adapterFilters ) {
         FilterPredicate predicate = null;
 
-        for ( AdapterFilter filter : adpaterFilters ) {
+        for ( AdapterFilter filter : adapterFilters ) {
             FilterPredicate next = buildParquetFilterPredicate( schema, filter );
             if ( next == null ) {
                 throw new IllegalArgumentException( "Unsupported parquet predicate: " + filter );
@@ -137,17 +130,13 @@ private FilterPredicate buildParquetFilterPredicate( MessageType schema, Adapter
     /**
      * Checks whether the operator can be handled by the reader.
      */
-    private boolean isSupportedOperator( Kind kind ) {
-        return kind == Kind.EQUALS || kind == Kind.NOT_EQUALS || kind == Kind.GREATER_THAN || kind == Kind.GREATER_THAN_OR_EQUAL || kind == Kind.LESS_THAN || kind == Kind.LESS_THAN_OR_EQUAL;
-    }
-
-
-    private boolean isPushdownSupported( List<PolyType> fieldTypes, int index, Kind kind, RexLiteral literal ) {
+    private boolean isPushdownSupported( List<PolyType> fieldTypes, int index, Kind kind, RexNode valueNode ) {
         PolyType type = fieldTypes.get( index );
         return switch ( type ) {
             case BOOLEAN, VARCHAR, CHAR, TEXT -> kind == Kind.EQUALS || kind == Kind.NOT_EQUALS;
             case INTEGER, BIGINT, FLOAT, DOUBLE, DATE, TIME, TIMESTAMP -> true;
             default -> false;
-        } && literal.getValue() != null;
+        } && (valueNode instanceof org.polypheny.db.rex.RexDynamicParam
+                || (valueNode instanceof org.polypheny.db.rex.RexLiteral literal && literal.getValue() != null));
     }
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java
new file mode 100644
index 0000000000..99c03810d1
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java
@@ -0,0 +1,22 @@
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetValueExtractor;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+public class ParquetRelValueExtractor extends AbstractParquetValueExtractor {
+
+    @Override
+    public PolyValue extractValue( Group group, int index, Type type ) {
+        if ( !type.isPrimitive() ) {
+            return extractStructuredValue( group, index, type );
+        }
+        PrimitiveType primitive = type.asPrimitiveType();
+        Object value = extractPrimitiveValue( group, index, primitive, 0 );
+        return typeConverter.fromObjToPolyValue( type, value );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetTableScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
similarity index 83%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetTableScan.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
index cb257ba052..90e7137835 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetTableScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
@@ -1,10 +1,10 @@
-package org.polypheny.db.adapter.parquet.planning;
+package org.polypheny.db.adapter.parquet.relational.planning;
 
 import java.util.List;
 import org.apache.calcite.linq4j.tree.Blocks;
 import org.apache.calcite.linq4j.tree.Expressions;
 import org.apache.calcite.linq4j.tree.Primitive;
-import org.polypheny.db.adapter.parquet.schema.ParquetTable;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.AlgWriter;
 import org.polypheny.db.algebra.core.relational.RelScan;
@@ -23,12 +23,12 @@
 import org.polypheny.db.plan.AlgTraitSet;
 
 
-public class ParquetTableScan extends RelScan<ParquetTable> implements EnumerableAlg {
+public class ParquetRelScan extends RelScan<ParquetRelTable> implements EnumerableAlg {
 
     private final int[] fields;
 
 
-    public ParquetTableScan(AlgCluster cluster, ParquetTable table, int[] fields ) {
+    public ParquetRelScan(AlgCluster cluster, ParquetRelTable table, int[] fields ) {
         super( cluster, cluster.traitSetOf( EnumerableConvention.INSTANCE ), table );
         this.fields = fields;
     }
@@ -36,7 +36,7 @@ public ParquetTableScan(AlgCluster cluster, ParquetTable table, int[] fields ) {
 
     @Override
     public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
-        return new ParquetTableScan( getCluster(), entity, fields );
+        return new ParquetRelScan( getCluster(), entity, fields );
     }
 
 
@@ -59,7 +59,7 @@ public AlgDataType deriveRowType() {
 
     @Override
     public void register( AlgPlanner planner ) {
-        planner.addRule( ParquetTableScanRule.INSTANCE );
+        planner.addRule( ParquetRelScanRule.INSTANCE );
     }
 
 
@@ -76,7 +76,7 @@ public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
                 physType,
                 Blocks.toBlock(
                         Expressions.call(
-                                entity.asExpression( ParquetTable.class ),
+                                entity.asExpression( ParquetRelTable.class ),
                                 "project",
                                 implementor.getRootExpression(),
                                 Expressions.constant( fields ) ) ) );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetTableScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java
similarity index 68%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetTableScanRule.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java
index 5efbda659f..9536dcd885 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/planning/ParquetTableScanRule.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java
@@ -1,4 +1,4 @@
-package org.polypheny.db.adapter.parquet.planning;
+package org.polypheny.db.adapter.parquet.relational.planning;
 
 import java.util.List;
 import org.polypheny.db.algebra.core.AlgFactories;
@@ -10,28 +10,28 @@
 import org.polypheny.db.tools.AlgBuilderFactory;
 
 
-public class ParquetTableScanRule extends AlgOptRule {
+public class ParquetRelScanRule extends AlgOptRule {
 
-    public static final ParquetTableScanRule INSTANCE = new ParquetTableScanRule( AlgFactories.LOGICAL_BUILDER );
+    public static final ParquetRelScanRule INSTANCE = new ParquetRelScanRule( AlgFactories.LOGICAL_BUILDER );
 
 
-    public ParquetTableScanRule(AlgBuilderFactory algBuilderFactory ) {
+    public ParquetRelScanRule(AlgBuilderFactory algBuilderFactory ) {
         super(
-                operand( LogicalRelProject.class, operand( ParquetTableScan.class, none() ) ),
+                operand( LogicalRelProject.class, operand( ParquetRelScan.class, none() ) ),
                 algBuilderFactory,
-                ParquetTableScanRule.class.getSimpleName() );
+                ParquetRelScanRule.class.getSimpleName() );
     }
 
 
     @Override
     public void onMatch( AlgOptRuleCall call ) {
         final LogicalRelProject project = call.alg( 0 );
-        final ParquetTableScan scan = call.alg( 1 );
+        final ParquetRelScan scan = call.alg( 1 );
         int[] fields = getProjectFields( project.getProjects() );
         if ( fields == null ) {
             return;
         }
-        call.transformTo( new ParquetTableScan( scan.getCluster(), scan.getEntity(), fields ) );
+        call.transformTo( new ParquetRelScan( scan.getCluster(), scan.getEntity(), fields ) );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
similarity index 67%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index e9d2a3afb6..50a35b16f1 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -14,17 +14,17 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.schema;
+package org.polypheny.db.adapter.parquet.relational.schema;
 
 import org.apache.calcite.linq4j.AbstractEnumerable;
 import org.apache.calcite.linq4j.Enumerable;
 import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.DataContext;
-import org.polypheny.db.adapter.parquet.ParquetSource;
-import org.polypheny.db.adapter.parquet.execution.ParquetTableEnumerator;
-import org.polypheny.db.adapter.parquet.execution.ParquetTableFilterTranslator;
-import org.polypheny.db.adapter.parquet.model.AdapterFilter;
-import org.polypheny.db.adapter.parquet.planning.ParquetTableScan;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelEnumerator;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
 import org.polypheny.db.plan.AlgCluster;
@@ -44,19 +44,19 @@
 /**
  * Base class for Parquet physical tables.
  */
-public class ParquetTable extends PhysicalTable implements FilterableEntity, ScannableEntity, TranslatableEntity {
+public class ParquetRelTable extends PhysicalTable implements FilterableEntity, ScannableEntity, TranslatableEntity {
 
     protected final Source source;
     protected final int[] fieldIndexes;
     private final List<PolyType> fieldTypes;
-    protected final ParquetSource parquetSource;
-    protected final ParquetTableFilterTranslator filterTranslator;
+    protected final AbstractParquetSource parquetSource;
+    protected final ParquetRelFilterTranslator filterTranslator;
 
 
     /**
      * Creates a Parquet table wrapper from a physical table definition.
      */
-    ParquetTable( long id, Source source, PhysicalTable table, ParquetSource parquetSource ) {
+    public ParquetRelTable(long id, Source source, PhysicalTable table, AbstractParquetSource parquetSource ) {
         super(
                 id,
                 table.allocationId,
@@ -71,7 +71,7 @@ public class ParquetTable extends PhysicalTable implements FilterableEntity, Sca
         this.fieldIndexes = IntStream.range( 0, table.columns.size() ).toArray();
         this.fieldTypes = columns.stream().map( c -> c.type ).toList();
         this.parquetSource = parquetSource;
-        this.filterTranslator = new ParquetTableFilterTranslator();
+        this.filterTranslator = new ParquetRelFilterTranslator();
     }
 
 
@@ -94,11 +94,14 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> poly
             return false;
         } );
 
+        // check for dynamic filters
+        final List<AdapterFilter> resolvedFilters = resolveDynamicFilters( dataContext, adapterFilters );
+
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                return new ParquetTableEnumerator( source, cancelFlag, fieldIndexes, adapterFilters );
+                return new ParquetRelEnumerator( source, cancelFlag, fieldIndexes, resolvedFilters );
             }
         };
     }
@@ -118,7 +121,7 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                return new ParquetTableEnumerator( source, cancelFlag, fieldIndexes );
+                return new ParquetRelEnumerator( source, cancelFlag, fieldIndexes );
             }
         };
     }
@@ -133,12 +136,12 @@ public Enumerator<PolyValue[]> enumerator() {
      */
     @Override
     public AlgNode toAlg( AlgCluster cluster, AlgTraitSet traitSet ) {
-        return new ParquetTableScan( cluster, this, fieldIndexes );
+        return new ParquetRelScan( cluster, this, fieldIndexes );
     }
 
 
     /**
-     * This method is called from the {@link ParquetTableScan} via reflection.
+     * This method is called from the {@link ParquetRelScan} via reflection.
      *
      * @param dataContext data context
      * @param fields a list of fields to return.
@@ -150,11 +153,33 @@ public Enumerable<PolyValue[]> project( final DataContext dataContext, final int
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                return new ParquetTableEnumerator( source, cancelFlag, fields );
+                return new ParquetRelEnumerator( source, cancelFlag, fields );
             }
         };
     }
 
+
+    /**
+     * Support parametrized queries
+     * @param dataContext context
+     * @param filters filters
+     * @return list of adapter filters
+     */
+    private List<AdapterFilter> resolveDynamicFilters( DataContext dataContext, List<AdapterFilter> filters ) {
+        List<AdapterFilter> resolved = new ArrayList<>( filters.size() );
+        for ( AdapterFilter filter : filters ) {
+            if ( filter.dynamicParamIndex() == null ) {
+                // regular filter
+                resolved.add( filter );
+                continue;
+            }
+            // get filter value by index
+            PolyValue value = dataContext.getParameterValue( filter.dynamicParamIndex() );
+            resolved.add( new AdapterFilter( filter.columnIndex(), filter.operator(), value ) );
+        }
+        return resolved;
+    }
+
 }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
new file mode 100644
index 0000000000..b805217467
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
@@ -0,0 +1,279 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared;
+
+import java.io.File;
+import java.net.MalformedURLException;
+import java.net.URL;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.Path;
+import org.apache.parquet.hadoop.ParquetFileReader;
+import org.apache.parquet.hadoop.util.HadoopInputFile;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.ConnectionMethod;
+import org.polypheny.db.adapter.DataSource;
+import org.polypheny.db.adapter.DeployMode;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.Scannable;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetFileDiscovery;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetNamespace;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
+import org.polypheny.db.catalog.catalogs.AdapterCatalog;
+import org.polypheny.db.catalog.catalogs.DocAdapterCatalog;
+import org.polypheny.db.catalog.logistic.DataModel;
+import org.polypheny.db.information.InformationGroup;
+import org.polypheny.db.information.InformationTable;
+import org.polypheny.db.prepare.Context;
+import org.polypheny.db.transaction.PolyXid;
+import org.polypheny.db.type.PolyType;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+public abstract class AbstractParquetSource extends DataSource<DocAdapterCatalog> implements Scannable {
+
+    protected final ConnectionMethod connectionMethod;
+    protected final ParquetTypeConverter parquetTypeConverter;
+    protected URL parquetDir;
+
+    protected ParquetNamespace currentNamespace;
+    private Map<String, List<ExportedColumn>> exportedColumns;
+
+    protected static final Logger log = LoggerFactory.getLogger( AbstractParquetSource.class );
+
+
+    protected AbstractParquetSource( long storeId, String uniqueName, Map<String, String> settings, DeployMode mode, Set<DataModel> supportedModels ) {
+        super( storeId, uniqueName, settings, mode, true, new DocAdapterCatalog( storeId ), supportedModels );
+        this.parquetTypeConverter = new ParquetTypeConverter();
+        this.connectionMethod = settings.containsKey( "method" )
+                ? ConnectionMethod.from( settings.get( "method" ).toUpperCase() )
+                : ConnectionMethod.UPLOAD;
+        setParquetDir( settings );
+        createInformationPage();
+        enableInformationPage();
+    }
+
+
+    protected void setParquetDir( Map<String, String> settings ) {
+        switch ( connectionMethod ) {
+            case LINK -> {
+                String dir = settings.get( "directoryName" );
+                if ( dir.startsWith( "classpath://" ) ) {
+                    parquetDir = this.getClass().getClassLoader().getResource( dir.replace( "classpath://", "" ) + "/" );
+                } else {
+                    try {
+                        parquetDir = new File( dir ).toURI().toURL();
+                    } catch ( MalformedURLException e ) {
+                        throw new org.polypheny.db.catalog.exceptions.GenericRuntimeException( e );
+                    }
+                }
+            }
+            case UPLOAD -> {
+                String dir = settings.get( "directory" );
+                if ( dir.startsWith( "classpath://" ) ) {
+                    parquetDir = this.getClass().getClassLoader().getResource( dir.replace( "classpath://", "" ) + "/" );
+                } else {
+                    try {
+                        parquetDir = new File( dir ).toURI().toURL();
+                    } catch ( MalformedURLException e ) {
+                        throw new org.polypheny.db.catalog.exceptions.GenericRuntimeException( e );
+                    }
+                }
+            }
+            case URL -> {
+                String dir = settings.get( "url" );
+                try {
+                    parquetDir = new URL( dir );
+                } catch ( MalformedURLException e ) {
+                    throw new org.polypheny.db.catalog.exceptions.GenericRuntimeException( e );
+                }
+            }
+        }
+    }
+
+
+    protected static String getValidTableName( String name ) {
+        return name.trim().replaceAll( "[^a-z0-9_]+", "" );
+    }
+
+
+    protected static String computePhysicalTableName( String fileName ) {
+        String physicalTableName = fileName.toLowerCase();
+        if ( physicalTableName.endsWith( ".parquet" ) ) {
+            physicalTableName = physicalTableName.substring( 0, physicalTableName.length() - ".parquet".length() );
+        }
+        return getValidTableName( physicalTableName );
+    }
+
+
+    public static String normalizeFieldName( String name ) {
+        return name.toLowerCase().trim().replaceAll( "[^a-z0-9_]+", "_" );
+    }
+
+
+    protected String getValidColumnNameFromField( Type field ) {
+        return normalizeFieldName( field.getName() );
+    }
+
+
+    private List<ExportedColumn> getExportedColumnsFromFile( String fileName, String physicalTableName ) {
+        try {
+            Path path = new Path( new URL( parquetDir, fileName ).toURI() );
+            Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
+            try ( ParquetFileReader reader = ParquetFileReader.open( HadoopInputFile.fromPath( path, conf ) ) ) {
+                List<Type> schemaFields = reader.getFooter().getFileMetaData().getSchema().getFields();
+                List<ExportedColumn> columns = new ArrayList<>();
+                int position = 0;
+                for ( Type field : schemaFields ) {
+                    columns.add( getExportedColumnFromField( field, fileName, physicalTableName, position++ ) );
+                }
+                return columns;
+            }
+        } catch ( Exception e ) {
+            throw new org.polypheny.db.catalog.exceptions.GenericRuntimeException( e );
+        }
+    }
+
+
+    private ExportedColumn getExportedColumnFromField( Type field, String fileName, String physicalTableName, int position ) {
+        String columnName = getValidColumnNameFromField( field );
+        PolyType polyType = parquetTypeConverter.fromParquetTypeToPolyType( field );
+        return new ExportedColumn(
+                columnName,
+                polyType,
+                null,
+                null,
+                null,
+                null,
+                null,
+                false,
+                fileName,
+                physicalTableName,
+                field.getName(),
+                position,
+                position == 0 );
+    }
+
+
+    public Map<String, List<ExportedColumn>> getExportedColumns() {
+        if ( connectionMethod == ConnectionMethod.UPLOAD && exportedColumns != null ) {
+            return exportedColumns;
+        }
+
+        Map<String, List<ExportedColumn>> columns = new HashMap<>();
+        Set<String> fileNames = ParquetFileDiscovery.listParquetFiles( parquetDir );
+        for ( String fileName : fileNames ) {
+            String physicalTableName = computePhysicalTableName( fileName );
+            columns.put( physicalTableName, getExportedColumnsFromFile( fileName, physicalTableName ) );
+        }
+        this.exportedColumns = columns;
+        return columns;
+    }
+
+
+    private void createInformationPage() {
+        for ( Map.Entry<String, List<ExportedColumn>> entry : getExportedColumns().entrySet() ) {
+            if ( entry.getValue().isEmpty() ) {
+                continue;
+            }
+            InformationGroup group = new InformationGroup( informationPage, entry.getValue().get( 0 ).physicalSchemaName() );
+            informationGroups.add( group );
+            informationElements.add( getInformationTable( group, entry ) );
+        }
+    }
+
+
+    private InformationTable getInformationTable( InformationGroup group, Map.Entry<String, List<ExportedColumn>> entry ) {
+        List<String> columns = Arrays.asList( "Position", "Column Name", "Type", "Nullable", "Filename", "Primary" );
+        InformationTable table = new InformationTable( group, columns );
+        for ( ExportedColumn exportedColumn : entry.getValue() ) {
+            table.addRow(
+                    exportedColumn.physicalPosition(),
+                    exportedColumn.name(),
+                    exportedColumn.getDisplayType(),
+                    exportedColumn.nullable() ? "✔" : "",
+                    exportedColumn.physicalSchemaName(),
+                    exportedColumn.primary() ? "✔" : "" );
+        }
+        return table;
+    }
+
+
+    @Override
+    public void updateNamespace( String name, long id ) {
+        currentNamespace = new ParquetNamespace( id, adapterId, parquetDir );
+        putNamespace( currentNamespace );
+    }
+
+
+    @Override
+    public ParquetNamespace getCurrentNamespace() {
+        return currentNamespace;
+    }
+
+
+    @Override
+    public AdapterCatalog getCatalog() {
+        return adapterCatalog;
+    }
+
+
+    @Override
+    public void truncate( Context context, long allocId ) {
+        throw new org.polypheny.db.catalog.exceptions.GenericRuntimeException( "Parquet adapter does not support truncate" );
+    }
+
+
+    @Override
+    public boolean prepare( PolyXid xid ) {
+        log.debug( "Parquet Store does not support prepare()." );
+        return true;
+    }
+
+
+    @Override
+    public void commit( PolyXid xid ) {
+        log.debug( "Parquet Store does not support commit()." );
+    }
+
+
+    @Override
+    public void rollback( PolyXid xid ) {
+        log.debug( "Parquet Store does not support rollback()." );
+    }
+
+
+    @Override
+    public void shutdown() {
+        removeInformationPage();
+    }
+
+
+    @Override
+    protected void reloadSettings( List<String> updatedSettings ) {
+        if ( updatedSettings.contains( "directory" ) || updatedSettings.contains( "directoryName" ) ) {
+            setParquetDir( settings );
+        }
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
new file mode 100644
index 0000000000..777113f5b4
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
@@ -0,0 +1,146 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.execution;
+
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.Enumerator;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+public abstract class AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
+
+    protected final ParquetGroupReader reader;
+    protected final ParquetValueExtractor valueExtractor;
+    protected final List<AdapterFilter> filters;
+    private PolyValue[] current;
+
+
+    public AbstractParquetEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields, List<AdapterFilter> filters, ParquetValueExtractor valueExtractor ) {
+        this.reader = new ParquetGroupReader( source, cancelFlag, fields, filters );
+        this.valueExtractor = valueExtractor;
+        this.filters = filters == null ? List.of() : List.copyOf( filters );
+    }
+
+
+    @Override
+    public PolyValue[] current() {
+        return current;
+    }
+
+
+    @Override
+    public boolean moveNext() {
+        try {
+            for ( ; ; ) {
+                // group (single row) can be still filtered, while parquet filter works on the row group level
+                Group group = reader.next();
+                if ( group == null ) {
+                    current = null;
+                    return false;
+                }
+                if ( !accept( group ) ) {
+                    continue;
+                }
+                current = extractRow( group );
+                return true;
+            }
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Error while reading parquet data", e );
+        }
+    }
+
+
+    @Override
+    public void reset() {
+        throw new UnsupportedOperationException();
+    }
+
+
+    @Override
+    public void close() {
+        reader.close();
+    }
+
+
+    /*
+    extractValue logic depends on source type (relational/document)
+     */
+    protected abstract PolyValue[] extractRow( Group group );
+
+
+    protected boolean accept( Group group ) {
+        for ( AdapterFilter filter : filters ) {
+            if ( !matches( group, filter ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    private boolean matches( Group group, AdapterFilter filter ) {
+        // if filter is invalid row should be displayed
+        if ( filter.polyValue() == null || filter.columnIndex() < 0 || filter.columnIndex() >= reader.getProjectionSchema().getFieldCount() ) {
+            return true;
+        }
+
+        // this row does not have a value for that Parquet field
+        if ( group.getFieldRepetitionCount( filter.columnIndex() ) == 0 ) {
+            return false;
+        }
+
+        // call overloaded functionality
+        PolyValue actual = extractFilterValue( group, filter );
+        PolyValue expected = filter.polyValue();
+
+        if ( actual == null || actual.isNull() || expected.isNull() ) {
+            return false;
+        }
+
+        // apply filter on the row level
+        return switch ( filter.operator() ) {
+            case EQUALS -> actual.equals( expected );
+            case NOT_EQUALS -> !actual.equals( expected );
+            case GREATER_THAN -> compare( actual, expected ) > 0;
+            case GREATER_THAN_OR_EQUAL -> compare( actual, expected ) >= 0;
+            case LESS_THAN -> compare( actual, expected ) < 0;
+            case LESS_THAN_OR_EQUAL -> compare( actual, expected ) <= 0;
+            default -> true;
+        };
+    }
+
+
+    protected PolyValue extractFilterValue( Group group, AdapterFilter filter ) {
+        Type field = reader.getProjectionSchema().getType( filter.columnIndex() );
+        return valueExtractor.extractValue( group, filter.columnIndex(), field );
+    }
+
+
+    protected int compare( PolyValue actual, PolyValue expected ) {
+        try {
+            return actual.compareTo( expected );
+        } catch ( Exception e ) {
+            return actual.toString().compareTo( expected.toString() );
+        }
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java
new file mode 100644
index 0000000000..7afe393e27
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java
@@ -0,0 +1,76 @@
+package org.polypheny.db.adapter.parquet.shared.execution;
+
+import java.util.ArrayList;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.GroupType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.type.entity.PolyList;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.document.PolyDocument;
+
+
+public abstract class AbstractParquetValueExtractor implements ParquetValueExtractor {
+
+    protected final ParquetTypeConverter typeConverter = new ParquetTypeConverter();
+
+
+    protected PolyValue extractStructuredValue( Group group, int index, Type field ) {
+        int repetitionCount = group.getFieldRepetitionCount( index );
+        if ( repetitionCount == 0 ) {
+            return PolyNull.NULL;
+        }
+
+        if ( repetitionCount > 1 ) {
+            List<PolyValue> values = new ArrayList<>( repetitionCount );
+            for ( int occurrence = 0; occurrence < repetitionCount; occurrence++ ) {
+                values.add( extractStructuredValue( group, index, field, occurrence ) );
+            }
+            return PolyList.of( values );
+        }
+
+        return extractStructuredValue( group, index, field, 0 );
+    }
+
+
+    protected PolyValue extractStructuredValue( Group group, int index, Type field, int occurrence ) {
+        if ( field.isPrimitive() ) {
+            return typeConverter.fromObjToPolyValue( field, extractPrimitiveValue( group, index, field.asPrimitiveType(), occurrence ) );
+        }
+
+        return extractNestedDocument( group.getGroup( index, occurrence ), field.asGroupType() );
+    }
+
+
+    protected PolyDocument extractNestedDocument( Group group, GroupType type ) {
+        Map<PolyString, PolyValue> values = new LinkedHashMap<>();
+        for ( int i = 0; i < type.getFieldCount(); i++ ) {
+            Type nestedField = type.getType( i );
+            if ( group.getFieldRepetitionCount( i ) == 0 ) {
+                continue;
+            }
+            values.put( PolyString.of( AbstractParquetSource.normalizeFieldName( nestedField.getName() ) ), extractStructuredValue( group, i, nestedField ) );
+        }
+        return PolyDocument.ofDocument( values );
+    }
+
+
+    protected Object extractPrimitiveValue( Group group, int index, PrimitiveType primitive, int occurrence ) {
+        return switch ( primitive.getPrimitiveTypeName() ) {
+            case BOOLEAN -> group.getBoolean( index, occurrence );
+            case INT32 -> group.getInteger( index, occurrence );
+            case INT64 -> group.getLong( index, occurrence );
+            case FLOAT -> group.getFloat( index, occurrence );
+            case DOUBLE -> group.getDouble( index, occurrence );
+            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> group.getBinary( index, occurrence );
+        };
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java
new file mode 100644
index 0000000000..faffdb5944
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java
@@ -0,0 +1,92 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.execution;
+
+import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.rex.RexCall;
+import org.polypheny.db.rex.RexDynamicParam;
+import org.polypheny.db.rex.RexLiteral;
+import org.polypheny.db.rex.RexNode;
+
+public final class ParquetFilterTranslationSupport {
+
+    private ParquetFilterTranslationSupport() {
+    }
+
+
+    public static ParsedFilter parse( RexNode filter ) {
+        if ( !(filter instanceof RexCall call) || !isSupportedOperator( filter.getKind() ) || call.getOperands().size() != 2 ) {
+            return null;
+        }
+
+        RexNode left = unwrapCast( call.getOperands().get( 0 ) );
+        RexNode right = unwrapCast( call.getOperands().get( 1 ) );
+
+        if ( isValueOperand( left ) && !isValueOperand( right ) ) {
+            RexNode tmp = left;
+            left = right;
+            right = tmp;
+        }
+
+        return new ParsedFilter( left, right );
+    }
+
+
+    public static AdapterFilter toAdapterFilter( int columnIndex, Kind operator, RexNode valueNode ) {
+        if ( valueNode instanceof RexLiteral literal ) {
+            if ( literal.getValue() == null ) {
+                return null;
+            }
+            return new AdapterFilter( columnIndex, operator, literal.getValue() );
+        }
+
+        if ( valueNode instanceof RexDynamicParam dynamicParam ) {
+            return new AdapterFilter( columnIndex, operator, null, dynamicParam.getIndex() );
+        }
+
+        return null;
+    }
+
+
+    public static boolean isValueOperand( RexNode node ) {
+        return node instanceof RexLiteral || node instanceof RexDynamicParam;
+    }
+
+
+    public static boolean isSupportedOperator( Kind kind ) {
+        return kind == Kind.EQUALS
+                || kind == Kind.NOT_EQUALS
+                || kind == Kind.GREATER_THAN
+                || kind == Kind.GREATER_THAN_OR_EQUAL
+                || kind == Kind.LESS_THAN
+                || kind == Kind.LESS_THAN_OR_EQUAL;
+    }
+
+
+    public static RexNode unwrapCast( RexNode node ) {
+        while ( node.isA( Kind.CAST ) ) {
+            node = ((RexCall) node).getOperands().get( 0 );
+        }
+        return node;
+    }
+
+
+    public record ParsedFilter(RexNode left, RexNode right) {
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetGroupReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetGroupReader.java
similarity index 93%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetGroupReader.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetGroupReader.java
index 40293e5ebf..a6ae03fdf8 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetGroupReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetGroupReader.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.execution;
+package org.polypheny.db.adapter.parquet.shared.execution;
 
 import java.io.IOException;
 import java.util.ArrayList;
@@ -36,8 +36,9 @@
 import org.apache.parquet.io.RecordReader;
 import org.apache.parquet.schema.MessageType;
 import org.apache.parquet.schema.Type;
-import org.polypheny.db.adapter.parquet.model.AdapterFilter;
-import org.polypheny.db.adapter.parquet.util.HadoopConfigurationFactory;
+import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.util.Source;
 
@@ -74,7 +75,7 @@ public ParquetGroupReader( Source source, AtomicBoolean cancelFlag, int[] fields
             int[] projectedFields = buildProjectedFields( fields, schema.getFieldCount() );
             this.projectionSchema = buildProjectionSchema( schema, projectedFields );
 
-            var recordFilter = new ParquetTableFilterTranslator().translate( schema, filters == null ? List.of() : filters );
+            var recordFilter = new ParquetRelFilterTranslator().translate( schema, filters == null ? List.of() : filters );
             this.useNativeFilter = FilterCompat.isFilteringRequired( recordFilter );
 
             ParquetReadOptions.Builder readOptionsBuilder = ParquetReadOptions.builder()
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetPredicateBuilder.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetPredicateBuilder.java
similarity index 99%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetPredicateBuilder.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetPredicateBuilder.java
index c28df5acec..99903ea423 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/execution/ParquetPredicateBuilder.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetPredicateBuilder.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.execution;
+package org.polypheny.db.adapter.parquet.shared.execution;
 
 import org.apache.parquet.filter2.predicate.FilterApi;
 import org.apache.parquet.filter2.predicate.FilterPredicate;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java
new file mode 100644
index 0000000000..6d7b1ec4e0
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java
@@ -0,0 +1,12 @@
+package org.polypheny.db.adapter.parquet.shared.execution;
+
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+public interface ParquetValueExtractor {
+
+    PolyValue extractValue( Group group, int index, Type type );
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/io/ParquetFileDiscovery.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
similarity index 97%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/io/ParquetFileDiscovery.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
index 59a27f3a48..4ba5c75988 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/io/ParquetFileDiscovery.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.io;
+package org.polypheny.db.adapter.parquet.shared.io;
 
 import java.io.File;
 import java.net.URL;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/AdapterFilter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/model/AdapterFilter.java
similarity index 56%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/AdapterFilter.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/model/AdapterFilter.java
index 48f7ff0799..833ff4f6aa 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/model/AdapterFilter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/model/AdapterFilter.java
@@ -1,4 +1,4 @@
-package org.polypheny.db.adapter.parquet.model;
+package org.polypheny.db.adapter.parquet.shared.model;
 
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.type.entity.PolyValue;
@@ -9,6 +9,10 @@
  * @param operator - filter operation
  * @param polyValue - filter value
  */
-public record AdapterFilter(int columnIndex, Kind operator, PolyValue polyValue ) {
+public record AdapterFilter(int columnIndex, Kind operator, PolyValue polyValue, Long dynamicParamIndex ) {
+
+    public AdapterFilter( int columnIndex, Kind operator, PolyValue polyValue ) {
+        this( columnIndex, operator, polyValue, null );
+    }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
similarity index 75%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
index f415ae3559..1b7236699e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetNamespace.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
@@ -1,9 +1,11 @@
-package org.polypheny.db.adapter.parquet.schema;
+package org.polypheny.db.adapter.parquet.shared.schema;
 
 import java.net.MalformedURLException;
 import java.net.URL;
 import org.jetbrains.annotations.Nullable;
-import org.polypheny.db.adapter.parquet.ParquetSource;
+import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.catalog.entity.physical.PhysicalCollection;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
@@ -33,10 +35,10 @@ public ParquetNamespace( long id, long adapterId, URL directoryUrl ) {
     /**
      * Creates a Parquet table wrapper for a physical table entry.
      */
-    public ParquetTable createParquetTable( long id, PhysicalTable table, ParquetSource sourceAdapter ) {
+    public ParquetRelTable createParquetTable( long id, PhysicalTable table, AbstractParquetSource sourceAdapter ) {
         try {
             Source parquetSource = Sources.of( new URL( directoryUrl, table.name + ".parquet" ) );
-            return new ParquetTable( id, parquetSource, table, sourceAdapter );
+            return new ParquetRelTable( id, parquetSource, table, sourceAdapter );
 
         } catch ( MalformedURLException e ) {
             throw new GenericRuntimeException( e );
@@ -44,7 +46,7 @@ public ParquetTable createParquetTable( long id, PhysicalTable table, ParquetSou
     }
 
 
-    public ParquetDocument createParquetCollection( PhysicalCollection collection, ParquetSource sourceAdapter ) {
+    public ParquetDocument createParquetCollection( PhysicalCollection collection, AbstractParquetSource sourceAdapter ) {
         try {
             Source parquetSource = Sources.of( new URL( directoryUrl, collection.name + ".parquet" ) );
             return new ParquetDocument( collection, parquetSource, sourceAdapter );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTypeConverter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
similarity index 99%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTypeConverter.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
index 4b647e90a8..cd078c12b6 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/schema/ParquetTypeConverter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.schema;
+package org.polypheny.db.adapter.parquet.shared.schema;
 
 import org.apache.parquet.io.api.Binary;
 import org.apache.parquet.schema.LogicalTypeAnnotation;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/util/HadoopConfigurationFactory.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/util/HadoopConfigurationFactory.java
similarity index 96%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/util/HadoopConfigurationFactory.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/util/HadoopConfigurationFactory.java
index 12e6a8c2b9..36350b4081 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/util/HadoopConfigurationFactory.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/util/HadoopConfigurationFactory.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.util;
+package org.polypheny.db.adapter.parquet.shared.util;
 
 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.LocalFileSystem;
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetTableFilterTranslatorTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
similarity index 83%
rename from plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetTableFilterTranslatorTest.java
rename to plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
index 2103a7fc5f..5d418a7b7b 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetTableFilterTranslatorTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
@@ -21,14 +21,14 @@
 import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
 import org.apache.parquet.schema.Types;
 import org.junit.jupiter.api.Test;
-import org.polypheny.db.adapter.parquet.execution.ParquetTableFilterTranslator;
-import org.polypheny.db.adapter.parquet.model.AdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.type.entity.temporal.PolyTimestamp;
 
 import static org.junit.jupiter.api.Assertions.assertThrows;
 
-class ParquetTableFilterTranslatorTest {
+class ParquetRelFilterTranslatorTest {
 
     @Test
     void rejectsInt96TimestampPredicatePushdown() {
@@ -41,6 +41,6 @@ void rejectsInt96TimestampPredicatePushdown() {
 
         assertThrows(
                 IllegalArgumentException.class,
-                () -> new ParquetTableFilterTranslator().translate( schema, List.of( filter ) ) );
+                () -> new ParquetRelFilterTranslator().translate( schema, List.of( filter ) ) );
     }
 }

From a34c931d3ba60b334c66c0fdab0f664a6f822c18 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Tue, 7 Apr 2026 15:30:53 +0200
Subject: [PATCH 10/46] add documentation and comments

---
 plugins/parquet-adapter/docs/README.md        | 186 ++++++++++--------
 .../document/ParquetDocumentSource.java       |   5 +
 .../execution/ParquetDocEnumerator.java       |   9 +
 .../execution/ParquetDocFilterTranslator.java |   9 +
 .../execution/ParquetDocValueExtractor.java   |  15 +-
 .../document/planning/ParquetDocFilter.java   |   4 +
 .../planning/ParquetDocFilterRule.java        |  17 ++
 .../document/planning/ParquetDocScan.java     |   7 +-
 .../document/schema/ParquetDocument.java      |  23 +++
 .../relational/ParquetRelationalSource.java   |   5 +
 .../execution/ParquetRelEnumerator.java       |   4 +
 .../execution/ParquetRelValueExtractor.java   |   5 +-
 .../relational/planning/ParquetRelScan.java   |   4 +-
 .../planning/ParquetRelScanRule.java          |   5 +-
 .../relational/schema/ParquetRelTable.java    |   4 +-
 .../parquet/shared/AbstractParquetSource.java |   5 +
 .../execution/AbstractParquetEnumerator.java  |  18 +-
 .../AbstractParquetValueExtractor.java        |   5 +-
 .../ParquetFilterTranslationSupport.java      |   4 +
 .../shared/execution/ParquetGroupReader.java  |   8 +
 .../execution/ParquetPredicateBuilder.java    |   4 +
 .../execution/ParquetValueExtractor.java      |   5 +-
 .../shared/schema/ParquetNamespace.java       |   1 +
 .../shared/schema/ParquetTypeConverter.java   |   3 +
 24 files changed, 267 insertions(+), 88 deletions(-)

diff --git a/plugins/parquet-adapter/docs/README.md b/plugins/parquet-adapter/docs/README.md
index 9ace268ed9..c0b7b66888 100644
--- a/plugins/parquet-adapter/docs/README.md
+++ b/plugins/parquet-adapter/docs/README.md
@@ -35,31 +35,30 @@ During `restore()`, `Catalog.defaultSource` is populated from the configured ada
     - Registers the Parquet adapter template during startup.
     - Removes the template during shutdown.
 2. **Schema initialization**
-    - **Responsible classes:** `ParquetSource`, `ParquetFileDiscovery`, `ParquetTypeConverter`
+    - **Responsible classes:** `AbstractParquetSource`, `ParquetFileDiscovery`, `ParquetTypeConverter`
     - Resolves the configured Parquet directory or file location.
     - Discovers available `.parquet` files.
-    - Builds table schemas from Parquet file metadata.
+    - Builds relational table or document collection metadata from Parquet file metadata.
     - Maps Parquet field types to Polypheny types.
     - Normalizes physical table names and column names.
     - Initializes the information page from the extracted column definitions.
-3. **Namespace and table creation**
-    - **Responsible classes:** `ParquetSource`, `ParquetNamespace`, `ParquetTable`
-    - Creates table wrappers for discovered Parquet files.
-    - Table Wrapper implements Filterable, Scannable and Translatable functionality
-    - Registers physical tables in the adapter catalog.
-4. **Table restore during startup**
-    - **Responsible classes:** `ParquetSource`, `ParquetNamespace`
-    - During application startup, the adapter calls `restoreTable(...)` for persisted Parquet tables.
-    - The adapter recreates table wrappers and re-registers them in the adapter catalog.
+3. **Namespace and entity creation**
+    - **Responsible classes:** `ParquetNamespace`, `ParquetRelTable`, `ParquetDocument`
+    - Creates table or collection wrappers for discovered Parquet files.
+    - Registers physical entities in the adapter catalog.
+4. **Restore during startup**
+    - **Responsible classes:** `ParquetRelationalSource`, `ParquetDocumentSource`, `ParquetNamespace`
+    - Recreates relational tables and document collections from persisted adapter state.
+    - Re-registers the wrappers in the adapter catalog.
 5. **Query entry into the adapter**
-    - **Responsible class:** `ParquetFilterableTable`, `ParquetNamespace`, `ParquetTable`
-    - The query engine resolves the registered Parquet-backed physical table.
-    - The adapter uses the table metadata and source information to access the underlying .parquet file.
+    - **Responsible classes:** `ParquetRelScan`, `ParquetDocScan`, `AbstractParquetEnumerator`, `ParquetGroupReader`
+    - The query engine resolves the registered Parquet-backed physical entity.
     - Projection and supported filter information are passed into the Parquet execution layer.
+    - Rows are read from the Parquet file and converted into Polypheny relational tuples or documents.
 
 ## Supported Filter Conditions
 
-The active `FILTERABLE` execution path currently pushes only simple comparison conditions that can be translated into native Parquet predicates.
+The active predicate pushdown path currently supports only simple comparison conditions that can be translated into native Parquet predicates.
 
 Supported operators:
 - `=`
@@ -71,13 +70,14 @@ Supported operators:
 
 Supported condition shape:
 - `column OP literal`
+- `column OP dynamic-parameter`
 
 Notes:
 - The left-hand side must be a column reference.
-- The right-hand side must be a literal value.
-- Simple casts on the column side are accepted.
-- Only pushdown-safe conditions are removed from the outer filter list.
-- Unsupported conditions remain outside the adapter-specific filter flow.
+- The right-hand side must be a literal value or dynamic parameter.
+- Simple casts on either side are unwrapped before translation.
+- Only pushdown-safe conditions are translated into adapter-specific filters.
+- Unsupported conditions remain outside the Parquet-specific filter flow.
 
 Supported pushdown types:
 - `BOOLEAN`: `=` and `!=`
@@ -91,54 +91,86 @@ Notes:
 
 ## Package Structure and Responsibilities
 
-### `root`
-Core adapter entry points and lifecycle integration.
-- `ParquetPlugin`: registers and unregisters the adapter template.
-- `ParquetSource`: main adapter implementation; manages source settings, schema export, information-page setup, namespace updates, table creation, and table restore.
-
-### `io`
-Parquet file access helpers.
-- `ParquetFileDiscovery`: discovers valid `.parquet` files in the configured source.
-
-### `schema`
-Table wrappers and schema-related conversion logic.
-- `ParquetNamespace`: creates Parquet table wrappers.
-- `ParquetTable`: extends PhysicalTable and implements all 3 flavors.
-  - implements `scan()` functions
-- `ParquetTypeConverter`: converts Parquet schema types, runtime values, and filter literals into Polypheny-compatible representations.
-- `ParquetDocument`
-### `model`
-Internal data representation.
-- `FilterInfo`: immutable representation of a pushed filter condition.
-
-### `execution`
-Runtime data access and filter evaluation.
-Classes responsible for reading Parquet data and returning rows.
-- `AbstractParquetEnumerator`: basic enumerator functionality general for relational and document data source. Contains abstract `extractRow()` function that should be implemented in derived classes
-- `ParquetTableEnumerator`: implements extractRow() function for RELATIONAL data source.
-- `ParquetDocumentEnumerator`: implements extractRow() function for DOCUMENT data source.
-
-- `ParquetGroupReader`: contains read row group logic
-  - creates native parquet file reader
-  - reads data by row groups
-  - pushes filters down
-  
-- `ParquetTableFilterTranslator`: Translates adapter filters into parquet-native predicates.
-- `ParquetTableFilterBuilder`: Translates a Rex filter into adapter filter when possible.
-
-- `ValueExtractor`: conversion layer between Parquet and Polypheny value representations.
-
-### `planning`
-Classes that integrate the Parquet adapter with Polypheny’s query planner.
-Handling query-plan representation and optimization.
-- `ParquetTableScan`: Parquet scan operator for the planning layer.
-- `ParquetTableScanRule`: planner rule for projection handling over Parquet scans.
-- `ParquetDocumentScan`: 
-- `ParquetDocumentScanRule`:
-
-### `util`
-Shared infrastructure helpers.
-- `HadoopConfigurationFactory`: builds Hadoop configuration objects for Parquet access inside the plugin environment.
+### `org.polypheny.db.adapter.parquet`
+
+Contains parquet functionality for Relational and Document data source.
+
+- `ParquetPlugin` - plugin entry point. Registers the relational and document adapter templates after catalog initialization and removes them again during shutdown.
+
+### `org.polypheny.db.adapter.parquet.document`
+
+Document-model integration for exposing Parquet files as read-only Polypheny document collections.
+
+- `ParquetDocumentSource` - document adapter implementation. Owns adapter metadata, performs discovery of Parquet-backed document collections, document restore, and delegation into the document scan APIs.
+
+#### `document.execution`
+
+- `ParquetDocEnumerator` - document runtime enumerator. Reads Parquet rows and create from each row a single `PolyDocument`.
+- `ParquetDocFilterTranslator` - translates supported document filter expressions into adapter-level `AdapterFilter` instances.
+- `ParquetDocValueExtractor` - converts Parquet groups and nested values into Polypheny document values and synthesizes `_id` values when they are missing.
+
+#### `document.planning`
+
+- `ParquetDocFilter` - enumerable filter node used when a document filter can stay inside the Parquet-specific execution path.
+- `ParquetDocFilterRule` - planner rule that recognizes supported document filters and rewrites them into `ParquetDocFilter` plus `ParquetDocScan`.
+- `ParquetDocScan` - document scan algebra node that builds the enumerable execution call for reading Parquet-backed documents.
+
+#### `document.schema`
+
+- `ParquetDocument` - physical collection wrapper for the document model. Represents one Parquet-backed collection inside Polypheny and connects planning and execution to the source adapter.
+
+### `org.polypheny.db.adapter.parquet.relational`
+
+Relational-model integration for exposing Parquet files as source tables.
+
+- `ParquetRelationalSource` - relational adapter implementation. Manages exported table discovery, schema registration, information-page content, and restore of relational Parquet tables.
+
+#### `relational.execution`
+
+- `ParquetRelEnumerator` - relational runtime enumerator. Reads projected Parquet rows and returns them as relational tuples.
+- `ParquetRelFilterTranslator` - converts adapter-level filters into Parquet native predicates for relational scans.
+- `ParquetRelValueExtractor` - converts Parquet field values into Polypheny relational values with relational-specific scalar handling.
+
+#### `relational.planning`
+
+- `ParquetRelScan` - relational scan algebra node for Parquet-backed physical tables.
+- `ParquetRelScanRule` - planner rule that rewrites compatible scans and projections to the Parquet-specific relational scan node.
+
+#### `relational.schema`
+
+- `ParquetRelTable` - physical table wrapper for the relational model. Exposes the Parquet-backed table to Polypheny and ties the planner, scanner, and adapter metadata together.
+
+### `org.polypheny.db.adapter.parquet.shared`
+
+Shared infrastructure used by both the relational and document adapters.
+
+- `AbstractParquetSource` - common base class for both source types. Handles settings, file discovery, exported schema derivation, information-page setup, name normalization, and shared restore behavior.
+
+#### `shared.execution`
+
+- `AbstractParquetEnumerator` - common enumerator base. Manages row iteration, projection handling, cancellation support, and reader lifecycle for both models.
+- `AbstractParquetValueExtractor` - shared conversion base for mapping Parquet primitive and structured values into Polypheny values.
+- `ParquetFilterTranslationSupport` - reusable helper for parsing supported Rex predicates and turning them into adapter-level filters.
+- `ParquetGroupReader` - low-level Parquet row-group reader. Opens the file, applies projection and native predicates, and streams groups to the enumerators.
+- `ParquetPredicateBuilder` - creates native Parquet filter predicates for the supported comparison operators and value types.
+- `ParquetValueExtractor` - small interface implemented by value extractors that can convert a Parquet field into a `PolyValue`.
+
+#### `shared.io`
+
+- `ParquetFileDiscovery` - locates valid `.parquet` files below the configured source location and filters out unsupported files.
+
+#### `shared.model`
+
+- `AdapterFilter` - immutable filter description shared across planning and execution. Carries the target column index, comparison operator, literal value, and optional dynamic parameter index.
+
+#### `shared.schema`
+
+- `ParquetNamespace` - namespace helper that creates the correct Parquet-backed physical wrapper for either a relational table or a document collection.
+- `ParquetTypeConverter` - converts Parquet schema types and runtime values into the Polypheny type system.
+
+#### `shared.util`
+
+- `HadoopConfigurationFactory` - builds Hadoop `Configuration` instances that work correctly inside the plugin classloader environment.
 
 ## Schema Mapping Rules
 
@@ -152,17 +184,17 @@ Shared infrastructure helpers.
 
 The adapter supports different levels of integration with the query engine:
 
-- `SCANNABLE` is the simplest execution path. 
-It performs plain row scanning through `ParquetScannableTable` and delegates reading to `ParquetEnumerator` without adapter-side filter pushdown. 
+- `SCANNABLE` is the simplest execution path.
+It performs plain row scanning through `ParquetScannableTable` and delegates reading to `ParquetEnumerator` without adapter-side filter pushdown.
 
-- `FILTERABLE` extends the scan-based model by accepting supported filter conditions from the query engine. 
-In this path, `ParquetFilterableTable` translates supported expressions into `FilterInfo` objects,
+- `FILTERABLE` extends the scan-based model by accepting supported filter conditions from the query engine.
+In this path, translated filters are converted into adapter-level filter objects,
 `ParquetPredicateBuilder` converts them into native Parquet predicates,
-and `ParquetEnumerator` applies projection pushdown and native predicate pushdown through `ParquetFileReader`. 
+and the Parquet enumerators apply projection pushdown and native predicate pushdown through `ParquetGroupReader`.
 
-- `TRANSLATABLE` is the planner-oriented flavor. 
-Instead of only returning rows directly, it allows the adapter to expose adapter-specific relational nodes and planner rules. 
-This path is intended for deeper integration with query planning through `ParquetScan` and `ParquetProjectScanRule`.
+- `TRANSLATABLE` is the planner-oriented flavor.
+Instead of only returning rows directly, it allows the adapter to expose adapter-specific relational nodes and planner rules.
+This path is intended for deeper integration with query planning through `ParquetRelScan`, `ParquetRelScanRule`, `ParquetDocScan`, and `ParquetDocFilterRule`.
 
 
 ## Information Page
@@ -194,11 +226,11 @@ The following figure shows an example of the displayed schema information.
 To run tests for parquet adapter:
 - `.\gradlew.bat :plugins:parquet-adapter:test`
 
-`plugins/parquet-adapter/build.gradle` should contain: 
+`plugins/parquet-adapter/build.gradle` should contain:
 - DBMS as a test dependency
 - Polypheny JDBC driver dependency
 
-## `ParquetPluginTest.java` 
+## `ParquetPluginTest.java`
 contains the following tests:
 1. `importsAllTablesAndReadsRows()`
 2. `parquetSourceIsReadOnly()`
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java
index 7054e709c4..4212b124ee 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java
@@ -42,6 +42,11 @@
 import org.polypheny.db.prepare.Context;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 
+/**
+ * Document adapter implementation.
+ * Owns adapter metadata, performs discovery of Parquet-backed document collections,
+ * document restore, and delegation into the document scan APIs.
+ */
 @Extension
 @AdapterProperties(
         name = ParquetDocumentSource.NAME,
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
index 94caff2a6e..2609775e22 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
@@ -27,6 +27,10 @@
 import org.polypheny.db.type.entity.document.PolyDocument;
 import org.polypheny.db.util.Source;
 
+/**
+ * Document enumerator.
+ * Reads Parquet rows and create from each row a single PolyDocument.
+ */
 public class ParquetDocEnumerator extends AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
 
     private final String documentPrefix;
@@ -40,6 +44,11 @@ public ParquetDocEnumerator( Source source, AtomicBoolean cancelFlag, List<Adapt
     }
 
 
+    /**
+     * Creates document from parquet file row
+     * @param group - parquet file row
+     * @return document converted to PolyValue list
+     */
     @Override
     protected PolyValue[] extractRow( Group group ) {
         PolyDocument document = documentValueExtractor.extractDocument(
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
index 0f0ebd102f..7ff67e8a8c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
@@ -24,8 +24,17 @@
 import org.polypheny.db.rex.RexNameRef;
 import org.polypheny.db.rex.RexNode;
 
+/**
+ * Translates supported document filter expressions into adapter-level `AdapterFilter` instances.
+ */
 public class ParquetDocFilterTranslator {
 
+    /**
+     * Translate Polypheny filter to AdapterFilter
+     * @param columns - list of valid columns
+     * @param filter - RexNode
+     * @return AdapterFilter
+     */
     public AdapterFilter translate( List<ExportedColumn> columns, RexNode filter ) {
         ParquetFilterTranslationSupport.ParsedFilter parsed = ParquetFilterTranslationSupport.parse( filter );
         if ( parsed == null ) {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
index 4b2dc09bb8..0751c62708 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
@@ -11,7 +11,10 @@
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.type.entity.document.PolyDocument;
 
-
+/**
+ * Converts Parquet groups and nested values into Polypheny document values and
+ * synthesizes `_id` values when they are missing.
+ */
 public class ParquetDocValueExtractor extends AbstractParquetValueExtractor {
 
     @Override
@@ -20,17 +23,27 @@ public PolyValue extractValue( Group group, int index, Type type ) {
     }
 
 
+    /**
+     * Convert one Parquet row (Group) into one Polypheny document (PolyDocument)
+     * @param group - parquet row
+     * @param schema - file schema (fields)
+     * @param generatedId - id for the row
+     * @return - document
+     */
     public PolyDocument extractDocument( Group group, MessageType schema, PolyString generatedId ) {
         Map<PolyString, PolyValue> values = new LinkedHashMap<>();
 
         for ( int i = 0; i < schema.getFieldCount(); i++ ) {
             Type field = schema.getType( i );
+            // If repetition count = 0, the field is absent in this row, so skip it
             if ( group.getFieldRepetitionCount( i ) == 0 ) {
                 continue;
             }
+            // store result in the map under the normalized name
             values.put( PolyString.of( AbstractParquetSource.normalizeFieldName( field.getName() ) ), extractValue( group, i, field ) );
         }
 
+        // if _id is missing or null, insert the provided generatedId
         PolyString idKey = PolyString.of( "_id" );
         if ( !values.containsKey( idKey ) || values.get( idKey ).isNull() ) {
             values.put( idKey, generatedId );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilter.java
index 5ad3d9321f..119493ae91 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilter.java
@@ -30,6 +30,10 @@
 import org.polypheny.db.plan.AlgTraitSet;
 import org.polypheny.db.rex.RexNode;
 
+/**
+ * Enumerable filter node used when a document filter can stay
+ * inside the Parquet-specific execution path.
+ */
 @Getter
 public class ParquetDocFilter extends Filter implements EnumerableAlg {
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java
index d0005e5fe7..54e2ef9b6e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java
@@ -31,6 +31,10 @@
 import org.polypheny.db.rex.RexNode;
 import org.polypheny.db.tools.AlgBuilderFactory;
 
+/**
+ * Planner rule that recognizes supported document filters
+ * and rewrites them into `ParquetDocFilter` plus `ParquetDocScan`
+ */
 public class ParquetDocFilterRule extends AlgOptRule {
 
     private final ParquetDocFilterTranslator translator = new ParquetDocFilterTranslator();
@@ -43,11 +47,22 @@ public ParquetDocFilterRule( AlgBuilderFactory algBuilderFactory, ParquetDocumen
     }
 
 
+    /**
+     * On match read the matched node as LogicalRelFilter filter:
+     * - split received filter information into predicates
+     * - translate them into AdapterFilters
+     * - produce a ParquetDocScan with those filters
+     * - wrap it in ParquetDocFilter
+     * @param call Rule call - passed in by query optimizer when this rule matches, re-written
+     */
     @Override
     public void onMatch( AlgOptRuleCall call ) {
         LogicalRelFilter filter = call.alg( 0 );
 
+        // column list is needed because filter translation
+        // is done against actual exported field names and physical positions
         List<ExportedColumn> columns = document.getParquetSource().getExportedColumns().get( document.name );
+        // separate predicates - the rule validates each predicate individually
         List<RexNode> predicates = splitConjunctions( filter.getCondition() );
         if ( columns == null || predicates.isEmpty() ) {
             return;
@@ -62,6 +77,8 @@ public void onMatch( AlgOptRuleCall call ) {
             adapterFilters.add( adapterFilter );
         }
 
+        // replaces the generic logical filter with a new ParquetDocScan
+        // carrying the translated adapterFilter wrapped in a ParquetDocFilter
         call.transformTo(
                 new ParquetDocFilter(
                         filter.getCluster(),
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
index 7267d9fe52..7ea54a3d68 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
@@ -44,6 +44,11 @@
 import org.polypheny.db.plan.AlgTraitSet;
 import org.polypheny.db.type.entity.PolyValue;
 
+/**
+ * The planner node that sets up document reading.
+ * The real read starts when the generated executable plan calls
+ * the entity’s scanFiltered() method
+ */
 @Getter
 public class ParquetDocScan extends DocumentScan<ParquetDocument> implements EnumerableAlg {
 
@@ -91,7 +96,7 @@ public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
     public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
         PhysType physType = PhysTypeImpl.of( implementor.getTypeFactory(), getTupleType(), pref.preferArray() );
         Expression runtimeFilters = EnumUtils.expressionList( filters.stream().map( ParquetDocScan::toFilterExpression ).toList() );
-
+        // create runtime code that will call scanFiltered() on the ParquetDocument entity
         return implementor.result(
                 physType,
                 Blocks.toBlock(
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
index f32143203f..c0ac48a8fb 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
@@ -45,6 +45,10 @@
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.Source;
 
+/**
+ * Physical collection wrapper for the document model.
+ * Represents one Parquet-backed collection inside Polypheny
+ */
 public class ParquetDocument extends PhysicalCollection implements ScannableEntity, TranslatableEntity {
 
     private final Source source;
@@ -66,6 +70,13 @@ public ParquetDocument( PhysicalCollection collection, Source source, AbstractPa
     }
 
 
+    /**
+     * Build xpression tree:
+     * - get the adapter catalog from parquetSource
+     * - ask it for the physical entity with this document’s id
+     * - cast the result to ParquetDocument
+     * @return Expression
+     */
     @Override
     public Expression asExpression() {
         Expression argExp = Expressions.constant( this.id );
@@ -84,6 +95,12 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
     }
 
 
+    /**
+     * creates enumerable with resolve filters
+     * @param dataContext context
+     * @param filters - adapter filters
+     * @return ParquetDocEnumerator
+     */
     public Enumerable<PolyValue[]> scanFiltered( DataContext dataContext, List<AdapterFilter> filters ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
@@ -133,6 +150,12 @@ private AlgDataType buildDocumentType( AlgDataTypeFactory typeFactory ) {
     }
 
 
+    /**
+     * add dynamic parameters to adapter filter if needed
+     * @param dataContext context
+     * @param filter adapter filter
+     * @return AdapterFilter
+     */
     private AdapterFilter resolveFilter( DataContext dataContext, AdapterFilter filter ) {
         if ( filter.dynamicParamIndex() == null ) {
             return filter;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
index 474efabeff..b14c5b870d 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
@@ -40,6 +40,11 @@
 import org.polypheny.db.prepare.Context;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 
+/**
+ * Relational adapter implementation.
+ * Manages exported table discovery, schema registration, information-page content,
+ * and restore of relational Parquet tables.
+ */
 @Extension
 @AdapterProperties(
         name = ParquetRelationalSource.NAME,
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
index 5382ded0d8..ff87127e94 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
@@ -25,6 +25,10 @@
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.Source;
 
+/**
+ * Relational runtime enumerator.
+ * Reads projected Parquet rows and returns them as relational tuples
+ */
 public class ParquetRelEnumerator extends AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
 
     public ParquetRelEnumerator(Source source, AtomicBoolean cancelFlag, int[] fields ) {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java
index 99c03810d1..043e376910 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java
@@ -6,7 +6,10 @@
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetValueExtractor;
 import org.polypheny.db.type.entity.PolyValue;
 
-
+/**
+ * Converts Parquet field values into Polypheny relational values
+ * with relational-specific scalar handling.
+ */
 public class ParquetRelValueExtractor extends AbstractParquetValueExtractor {
 
     @Override
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
index 90e7137835..3a363cab3c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
@@ -22,7 +22,9 @@
 import org.polypheny.db.plan.AlgPlanner;
 import org.polypheny.db.plan.AlgTraitSet;
 
-
+/**
+ * Relational scan algebra node for Parquet-backed physical tables
+ */
 public class ParquetRelScan extends RelScan<ParquetRelTable> implements EnumerableAlg {
 
     private final int[] fields;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java
index 9536dcd885..c9c8163547 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java
@@ -9,7 +9,10 @@
 import org.polypheny.db.rex.RexNode;
 import org.polypheny.db.tools.AlgBuilderFactory;
 
-
+/**
+ * Planner rule that rewrites compatible scans and projections to the Parquet-specific
+ * relational scan node.
+ */
 public class ParquetRelScanRule extends AlgOptRule {
 
     public static final ParquetRelScanRule INSTANCE = new ParquetRelScanRule( AlgFactories.LOGICAL_BUILDER );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index 50a35b16f1..7cb0497502 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -42,7 +42,9 @@
 import java.util.stream.IntStream;
 
 /**
- * Base class for Parquet physical tables.
+ * Physical table wrapper for the relational model.
+ * Exposes the Parquet-backed table to Polypheny and ties the planner,
+ * scanner, and adapter metadata together.
  */
 public class ParquetRelTable extends PhysicalTable implements FilterableEntity, ScannableEntity, TranslatableEntity {
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
index b805217467..6e1d451d91 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
@@ -50,6 +50,11 @@
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
+/**
+ * Base class for both source types.
+ * Handles settings, file discovery, exported schema derivation, information-page setup,
+ * name normalization, and shared restore behavior.
+ */
 public abstract class AbstractParquetSource extends DataSource<DocAdapterCatalog> implements Scannable {
 
     protected final ConnectionMethod connectionMethod;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
index 777113f5b4..2a260e752c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
@@ -26,6 +26,11 @@
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.Source;
 
+/**
+ * Enumerator base class.
+ * Manages row iteration, projection handling, cancellation support,
+ * and reader lifecycle for both models.
+ */
 public abstract class AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
 
     protected final ParquetGroupReader reader;
@@ -51,7 +56,8 @@ public PolyValue[] current() {
     public boolean moveNext() {
         try {
             for ( ; ; ) {
-                // group (single row) can be still filtered, while parquet filter works on the row group level
+                // group (single row) can be still filtered, while parquet filter
+                // works on the row group level
                 Group group = reader.next();
                 if ( group == null ) {
                     current = null;
@@ -81,12 +87,18 @@ public void close() {
     }
 
 
-    /*
-    extractValue logic depends on source type (relational/document)
+    /**
+     * extractValue logic depends on source type (relational/document)
      */
     protected abstract PolyValue[] extractRow( Group group );
 
 
+    /**
+     * apply filter on adapter level for each row
+     * by calling matches()
+     * @param group - parquet row
+     * @return boolean
+     */
     protected boolean accept( Group group ) {
         for ( AdapterFilter filter : filters ) {
             if ( !matches( group, filter ) ) {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java
index 7afe393e27..4bc93f11e4 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java
@@ -16,7 +16,10 @@
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.type.entity.document.PolyDocument;
 
-
+/**
+ * Conversion base class for mapping
+ * Parquet primitive and structured values into Polypheny values
+ */
 public abstract class AbstractParquetValueExtractor implements ParquetValueExtractor {
 
     protected final ParquetTypeConverter typeConverter = new ParquetTypeConverter();
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java
index faffdb5944..e5a9679450 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java
@@ -23,6 +23,10 @@
 import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNode;
 
+/**
+ * Helper for parsing supported Rex predicates and
+ * turning them into adapter-level filters
+ */
 public final class ParquetFilterTranslationSupport {
 
     private ParquetFilterTranslationSupport() {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetGroupReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetGroupReader.java
index a6ae03fdf8..cab9c4e940 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetGroupReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetGroupReader.java
@@ -42,6 +42,11 @@
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.util.Source;
 
+/**
+ * low-level Parquet row-group reader.
+ * Opens the file, applies projection and native predicates,
+ * and streams groups to the enumerators
+ */
 public class ParquetGroupReader implements AutoCloseable {
 
     private final AtomicBoolean cancelFlag;
@@ -78,6 +83,7 @@ public ParquetGroupReader( Source source, AtomicBoolean cancelFlag, int[] fields
             var recordFilter = new ParquetRelFilterTranslator().translate( schema, filters == null ? List.of() : filters );
             this.useNativeFilter = FilterCompat.isFilteringRequired( recordFilter );
 
+            // create configuration for the native parquet filter
             ParquetReadOptions.Builder readOptionsBuilder = ParquetReadOptions.builder()
                     .useStatsFilter()
                     .useDictionaryFilter()
@@ -86,6 +92,7 @@ public ParquetGroupReader( Source source, AtomicBoolean cancelFlag, int[] fields
                     .useRecordFilter();
 
             if ( useNativeFilter ) {
+                // push down filter
                 readOptionsBuilder.withRecordFilter( recordFilter );
             }
 
@@ -129,6 +136,7 @@ private boolean ensureRecordReader() throws IOException {
             return true;
         }
 
+        // push down filters
         PageReadStore pages = useNativeFilter
                 ? fileReader.readNextFilteredRowGroup()
                 : fileReader.readNextRowGroup();
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetPredicateBuilder.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetPredicateBuilder.java
index 99903ea423..9273dc6740 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetPredicateBuilder.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetPredicateBuilder.java
@@ -22,6 +22,10 @@
 import org.apache.parquet.schema.LogicalTypeAnnotation;
 import org.polypheny.db.algebra.constant.Kind;
 
+/**
+ * Creates native Parquet filter predicates
+ * for the supported comparison operators and value types
+ */
 public class ParquetPredicateBuilder {
     public static FilterPredicate buildBoolean( Kind operator, String columnName, Object expected ) {
         if ( !(expected instanceof Boolean value) ) {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java
index 6d7b1ec4e0..8fc515f76f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java
@@ -4,7 +4,10 @@
 import org.apache.parquet.schema.Type;
 import org.polypheny.db.type.entity.PolyValue;
 
-
+/**
+ * Interface implemented by value extractors
+ * that can convert a Parquet field into a `PolyValue`
+ */
 public interface ParquetValueExtractor {
 
     PolyValue extractValue( Group group, int index, Type type );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
index 1b7236699e..130d87cc24 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
@@ -16,6 +16,7 @@
 
 /**
  * Create wrappers for parquet files
+ * either a relational table or a document collection.
  */
 public class ParquetNamespace extends Namespace {
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
index cd078c12b6..86263719a5 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
@@ -53,6 +53,9 @@
 
 import static io.activej.common.StringFormatUtils.parseLocalDateTime;
 
+/**
+ * converts Parquet schema types and runtime values into the Polypheny type system
+ */
 public class ParquetTypeConverter {
 
     /**

From c37f72750d2f711ca318730982e9587965a2f91a Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Wed, 8 Apr 2026 17:13:34 +0200
Subject: [PATCH 11/46] Workflow - Extract Parquet

---
 plugins/parquet-adapter/build.gradle          |   3 +-
 plugins/parquet-adapter/docs/README.md        |  66 +++--
 .../execution/ParquetDocEnumerator.java       |   4 +-
 .../execution/ParquetDocFilterTranslator.java |  12 +-
 .../execution/ParquetDocValueExtractor.java   |   7 +-
 .../planning/ParquetDocFilterRule.java        |  16 +-
 .../document/planning/ParquetDocScan.java     |  10 +-
 .../document/schema/ParquetDocument.java      |  18 +-
 .../execution/ParquetRelEnumerator.java       |   4 +-
 .../execution/ParquetRelFilterTranslator.java |  78 +-----
 .../execution/ParquetRelValueExtractor.java   |   6 +-
 .../relational/schema/ParquetRelTable.java    |  22 +-
 .../parquet/shared/AbstractParquetSource.java |   3 +-
 .../execution/AbstractParquetEnumerator.java  |  25 +-
 .../ParquetFilterTranslationSupport.java      |   8 +-
 .../shared/filter/ParquetAdapterFilter.java   |  34 +++
 .../ParquetNativeFilterBuilder.java}          |  87 ++++++-
 .../ParquetSourceReader.java}                 |  94 ++++---
 .../parquet/shared/model/AdapterFilter.java   |  18 --
 .../schema/ParquetFieldNameNormalizer.java    |  34 +++
 .../shared/schema/ParquetTypeConverter.java   |   2 +-
 .../ParquetRelFilterTranslatorTest.java       |   8 +-
 plugins/workflow-engine/build.gradle          |  28 +++
 .../impl/extract/ExtractParquetActivity.java  | 233 ++++++++++++++++++
 .../io/parquet/ParquetWorkflowSupport.java    | 214 ++++++++++++++++
 .../dag/activities/ActivityRegistryTest.java  |   6 +
 26 files changed, 803 insertions(+), 237 deletions(-)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/{execution/ParquetPredicateBuilder.java => filter/ParquetNativeFilterBuilder.java} (61%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/{execution/ParquetGroupReader.java => io/ParquetSourceReader.java} (65%)
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/model/AdapterFilter.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetFieldNameNormalizer.java
 create mode 100644 plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/ExtractParquetActivity.java
 create mode 100644 plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/io/parquet/ParquetWorkflowSupport.java

diff --git a/plugins/parquet-adapter/build.gradle b/plugins/parquet-adapter/build.gradle
index fe2642a05f..350ddb5c2e 100644
--- a/plugins/parquet-adapter/build.gradle
+++ b/plugins/parquet-adapter/build.gradle
@@ -5,7 +5,6 @@ dependencies {
     compileOnly project(":core")
     compileOnly project(":dbms")
     compileOnly project(":plugins:sql-language")
-
     implementation(group: "org.apache.parquet", name: "parquet-hadoop", version: parquet_version) {
 //        transitive = false
     }
@@ -23,7 +22,7 @@ dependencies {
         exclude group: "javax.xml.stream", module: "stax-api"
         exclude group: "org.bouncycastle", module: "bcprov-jdk18on"
     }
-    implementation (group: "org.apache.hadoop", name: "hadoop-mapreduce-client-core", version: hadoop_version) {
+    implementation(group: "org.apache.hadoop", name: "hadoop-mapreduce-client-core", version: hadoop_version) {
         exclude group: "jakarta.activation", module: "jakarta.activation-api"
         exclude group: "javax.servlet", module: "javax.servlet-api"
         exclude group: "javax.ws.rs", module: "jsr311-api"
diff --git a/plugins/parquet-adapter/docs/README.md b/plugins/parquet-adapter/docs/README.md
index c0b7b66888..5fa028cb26 100644
--- a/plugins/parquet-adapter/docs/README.md
+++ b/plugins/parquet-adapter/docs/README.md
@@ -1,4 +1,4 @@
-# Parquet Module Implementation
+# Parquet Adapter Implementation
 
 ## Overview
 
@@ -10,7 +10,7 @@ At a high level, the adapter is responsible for:
 - executing read queries over Parquet data
 - applying projection pushdown and supported native predicate pushdown during query execution
 
-The adapter is read-only and currently targets relational source integration for embedded deployments.
+The adapter is read-only and supports relational and document source integration.
 
 The following sections describe the architecture, execution flow, package responsibilities, supported query behavior, and current limitations in more detail.
 
@@ -24,7 +24,7 @@ Affected files:
 - `plugins/parquet-adapter/build.gradle`
 
 
-2\. To make Parquet the default data source, `PolyphenyDB.java` sets `defaultSourceName` to `parquet`.<br>
+2\. Optional: To make Parquet the default data source, `PolyphenyDB.java` sets `defaultSourceName` to `parquet`.<br>
 During `restore()`, `Catalog.defaultSource` is populated from the configured adapter state.
 
 
@@ -89,7 +89,7 @@ Notes:
 - Legacy Parquet `INT96` timestamp columns are currently not pushed down.
 
 
-## Package Structure and Responsibilities
+## Parquet Adapter - Package Structure
 
 ### `org.polypheny.db.adapter.parquet`
 
@@ -151,27 +151,29 @@ Shared infrastructure used by both the relational and document adapters.
 - `AbstractParquetEnumerator` - common enumerator base. Manages row iteration, projection handling, cancellation support, and reader lifecycle for both models.
 - `AbstractParquetValueExtractor` - shared conversion base for mapping Parquet primitive and structured values into Polypheny values.
 - `ParquetFilterTranslationSupport` - reusable helper for parsing supported Rex predicates and turning them into adapter-level filters.
-- `ParquetGroupReader` - low-level Parquet row-group reader. Opens the file, applies projection and native predicates, and streams groups to the enumerators.
-- `ParquetPredicateBuilder` - creates native Parquet filter predicates for the supported comparison operators and value types.
 - `ParquetValueExtractor` - small interface implemented by value extractors that can convert a Parquet field into a `PolyValue`.
 
 #### `shared.io`
 
+- `ParquetSourceReader` - low-level Parquet row-group reader. Opens the file, applies projection and native predicates, and streams groups to the enumerators.
 - `ParquetFileDiscovery` - locates valid `.parquet` files below the configured source location and filters out unsupported files.
 
-#### `shared.model`
+#### `shared.filter`
 
-- `AdapterFilter` - immutable filter description shared across planning and execution. Carries the target column index, comparison operator, literal value, and optional dynamic parameter index.
+- `ParquetAdapterFilter` - immutable filter description shared across planning and execution. Carries the target column index, comparison operator, literal value, and optional dynamic parameter index.
+- `ParquetNativeFilterBuilder`- creates native Parquet filter predicates for the supported comparison operators and value types.
 
 #### `shared.schema`
 
 - `ParquetNamespace` - namespace helper that creates the correct Parquet-backed physical wrapper for either a relational table or a document collection.
 - `ParquetTypeConverter` - converts Parquet schema types and runtime values into the Polypheny type system.
+- `ParquetFieldNameNormalizer` - creates normalized filed names
 
 #### `shared.util`
 
 - `HadoopConfigurationFactory` - builds Hadoop `Configuration` instances that work correctly inside the plugin classloader environment.
 
+
 ## Schema Mapping Rules
 
 - Table names are derived from Parquet file names and normalized before registration.
@@ -180,22 +182,6 @@ Shared infrastructure used by both the relational and document adapters.
 - String-like Parquet fields are mapped to `TEXT`.
 - Unlike SQL types with fixed length declarations, Parquet does not impose a fixed maximum string length.
 
-## Flavor Selection
-
-The adapter supports different levels of integration with the query engine:
-
-- `SCANNABLE` is the simplest execution path.
-It performs plain row scanning through `ParquetScannableTable` and delegates reading to `ParquetEnumerator` without adapter-side filter pushdown.
-
-- `FILTERABLE` extends the scan-based model by accepting supported filter conditions from the query engine.
-In this path, translated filters are converted into adapter-level filter objects,
-`ParquetPredicateBuilder` converts them into native Parquet predicates,
-and the Parquet enumerators apply projection pushdown and native predicate pushdown through `ParquetGroupReader`.
-
-- `TRANSLATABLE` is the planner-oriented flavor.
-Instead of only returning rows directly, it allows the adapter to expose adapter-specific relational nodes and planner rules.
-This path is intended for deeper integration with query planning through `ParquetRelScan`, `ParquetRelScanRule`, `ParquetDocScan`, and `ParquetDocFilterRule`.
-
 
 ## Information Page
 The information page displays the following column metadata:
@@ -220,7 +206,37 @@ The following figure shows an example of the displayed schema information.
 ## Query Filter
 ![Schema display](images/query_filter.png)
 
+# Workflow
+
+## 1. Extract Parquet
+
+### Code Changes
+
+- ### `org.polypheny.db.workflow.dag.activities.impl.extract.ExtractParquetActivity`
+
+Workflow node definition for reading Parquet files into a workflow output pipe:
+1. It defines how the activity appears in the workflow UI using settings. This provides all needed information for editor to show options.
+2. It decides what type of data the node will output: Relational or Document
+3. It executes the extraction at runtime as follows: the activity resolves the input files, iterates over them, and delegates the actual reading/conversion work to `ParquetWorkflowSupport.java`, which turns Parquet rows into either documents or relational rows and pushes them into the workflow output pipe.
+
+- ### `org.polypheny.db.workflow.io.parquet.ParquetWorkflowSupport`
+
+Helper class contains Parquet extraction logic for the workflow engine.
+It maps Parquet schemas to workflow output types, estimates row counts, generates dynamic activity names, and converts Parquet rows into either Polypheny documents or relational tuples.
+
+### Tests:
+
+- Read parquet file as Relational Data Source and store to CSV file
+- Read parquet file as Document Data Source and store to JSON file
+- Add file name as column
+- Add key id column
+
+
+![Schema display](images/workflow1.png)
+
+![Schema display](images/workflow2.png)
 
+## 2. Load to Parquet File
 
 # Unit Tests
 To run tests for parquet adapter:
@@ -243,7 +259,7 @@ contains the following tests:
 9. `projectsAndFiltersOtherTables()`
 10. `returnsNestedShippingAddressAsJSON()`
 
-## `ParquetPredicateBuilderTest.java`
+## `ParquetRelFilterTranslatorTest.java`
 contains focused unit tests for predicate translation behavior.
 
 Current test coverage includes:
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
index 2609775e22..b1375cc41c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
@@ -20,8 +20,8 @@
 import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.parquet.example.data.Group;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
-import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
 import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.type.entity.document.PolyDocument;
@@ -37,7 +37,7 @@ public class ParquetDocEnumerator extends AbstractParquetEnumerator implements E
     private final ParquetDocValueExtractor documentValueExtractor;
 
 
-    public ParquetDocEnumerator( Source source, AtomicBoolean cancelFlag, List<AdapterFilter> filters ) {
+    public ParquetDocEnumerator( Source source, AtomicBoolean cancelFlag, List<ParquetAdapterFilter> filters ) {
         super( source, cancelFlag, null, filters, new ParquetDocValueExtractor() );
         this.documentValueExtractor = (ParquetDocValueExtractor) valueExtractor;
         this.documentPrefix = source.path();
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
index 7ff67e8a8c..22ad9c0e81 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
@@ -19,23 +19,23 @@
 
 import java.util.List;
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.execution.ParquetFilterTranslationSupport;
-import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
 import org.polypheny.db.rex.RexNameRef;
 import org.polypheny.db.rex.RexNode;
 
 /**
- * Translates supported document filter expressions into adapter-level `AdapterFilter` instances.
+ * Translates supported document filter expressions into Parquet filter instances.
  */
 public class ParquetDocFilterTranslator {
 
     /**
-     * Translate Polypheny filter to AdapterFilter
+     * Translate Polypheny filter to ParquetFilter
      * @param columns - list of valid columns
      * @param filter - RexNode
-     * @return AdapterFilter
+     * @return ParquetFilter
      */
-    public AdapterFilter translate( List<ExportedColumn> columns, RexNode filter ) {
+    public ParquetAdapterFilter translate( List<ExportedColumn> columns, RexNode filter ) {
         ParquetFilterTranslationSupport.ParsedFilter parsed = ParquetFilterTranslationSupport.parse( filter );
         if ( parsed == null ) {
             return null;
@@ -59,7 +59,7 @@ public AdapterFilter translate( List<ExportedColumn> columns, RexNode filter ) {
 
         for ( ExportedColumn column : columns ) {
             if ( column.name().equalsIgnoreCase( fieldName ) ) {
-                return ParquetFilterTranslationSupport.toAdapterFilter( column.physicalPosition(), filter.getKind(), right );
+                return ParquetFilterTranslationSupport.toParquetAdapterFilter( column.physicalPosition(), filter.getKind(), right );
             }
         }
         return null;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
index 0751c62708..c08dd2c585 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
@@ -17,6 +17,8 @@
  */
 public class ParquetDocValueExtractor extends AbstractParquetValueExtractor {
 
+    private static final PolyString ID_KEY = PolyString.of( "_id" );
+
     @Override
     public PolyValue extractValue( Group group, int index, Type type ) {
         return extractStructuredValue( group, index, type );
@@ -44,9 +46,8 @@ public PolyDocument extractDocument( Group group, MessageType schema, PolyString
         }
 
         // if _id is missing or null, insert the provided generatedId
-        PolyString idKey = PolyString.of( "_id" );
-        if ( !values.containsKey( idKey ) || values.get( idKey ).isNull() ) {
-            values.put( idKey, generatedId );
+        if ( !values.containsKey( ID_KEY ) || values.get( ID_KEY ).isNull() ) {
+            values.put( ID_KEY, generatedId );
         }
 
         return PolyDocument.ofDocument( values );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java
index 54e2ef9b6e..830f4c4d7c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java
@@ -21,7 +21,7 @@
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
 import org.polypheny.db.adapter.parquet.document.execution.ParquetDocFilterTranslator;
 import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
-import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.algebra.enumerable.EnumerableConvention;
 import org.polypheny.db.algebra.logical.relational.LogicalRelFilter;
@@ -50,7 +50,7 @@ public ParquetDocFilterRule( AlgBuilderFactory algBuilderFactory, ParquetDocumen
     /**
      * On match read the matched node as LogicalRelFilter filter:
      * - split received filter information into predicates
-     * - translate them into AdapterFilters
+     * - translate them into ParquetFilters
      * - produce a ParquetDocScan with those filters
      * - wrap it in ParquetDocFilter
      * @param call Rule call - passed in by query optimizer when this rule matches, re-written
@@ -68,22 +68,22 @@ public void onMatch( AlgOptRuleCall call ) {
             return;
         }
 
-        List<AdapterFilter> adapterFilters = new ArrayList<>();
+        List<ParquetAdapterFilter> parquetAdapterFilters = new ArrayList<>();
         for ( RexNode predicate : predicates ) {
-            AdapterFilter adapterFilter = translator.translate( columns, predicate );
-            if ( adapterFilter == null ) {
+            ParquetAdapterFilter parquetAdapterFilter = translator.translate( columns, predicate );
+            if ( parquetAdapterFilter == null ) {
                 return;
             }
-            adapterFilters.add( adapterFilter );
+            parquetAdapterFilters.add( parquetAdapterFilter );
         }
 
         // replaces the generic logical filter with a new ParquetDocScan
-        // carrying the translated adapterFilter wrapped in a ParquetDocFilter
+        // carrying the translated parquet filter wrapped in a ParquetDocFilter
         call.transformTo(
                 new ParquetDocFilter(
                         filter.getCluster(),
                         filter.getTraitSet().replace( EnumerableConvention.INSTANCE ),
-                        new ParquetDocScan( filter.getCluster(), document, adapterFilters ),
+                        new ParquetDocScan( filter.getCluster(), document, parquetAdapterFilters ),
                         filter.getCondition(),
                         document ) );
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
index 7ea54a3d68..728038f278 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
@@ -23,7 +23,7 @@
 import org.apache.calcite.linq4j.tree.Expressions;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
-import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.AlgWriter;
 import org.polypheny.db.algebra.core.AlgFactories;
@@ -52,10 +52,10 @@
 @Getter
 public class ParquetDocScan extends DocumentScan<ParquetDocument> implements EnumerableAlg {
 
-    private final List<AdapterFilter> filters;
+    private final List<ParquetAdapterFilter> filters;
 
 
-    public ParquetDocScan( AlgCluster cluster, ParquetDocument entity, List<AdapterFilter> filters ) {
+    public ParquetDocScan( AlgCluster cluster, ParquetDocument entity, List<ParquetAdapterFilter> filters ) {
         super( cluster, cluster.traitSetOf( EnumerableConvention.INSTANCE ), entity );
         this.filters = List.copyOf( filters );
     }
@@ -108,9 +108,9 @@ public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
     }
 
 
-    private static Expression toFilterExpression( AdapterFilter filter ) {
+    private static Expression toFilterExpression( ParquetAdapterFilter filter ) {
         return Expressions.new_(
-                AdapterFilter.class,
+                ParquetAdapterFilter.class,
                 Expressions.constant( filter.columnIndex() ),
                 Expressions.constant( filter.operator(), Kind.class ),
                 filter.polyValue() == null ? Expressions.constant( null, PolyValue.class ) : filter.polyValue().asExpression(),
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
index c0ac48a8fb..e29fcaaff2 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
@@ -29,8 +29,8 @@
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
 import org.polypheny.db.adapter.parquet.document.execution.ParquetDocEnumerator;
 import org.polypheny.db.adapter.parquet.document.planning.ParquetDocScan;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
-import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.type.AlgDataType;
 import org.polypheny.db.algebra.type.AlgDataTypeFactory;
@@ -98,13 +98,13 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
     /**
      * creates enumerable with resolve filters
      * @param dataContext context
-     * @param filters - adapter filters
+     * @param filters - parquet filters
      * @return ParquetDocEnumerator
      */
-    public Enumerable<PolyValue[]> scanFiltered( DataContext dataContext, List<AdapterFilter> filters ) {
+    public Enumerable<PolyValue[]> scanFiltered( DataContext dataContext, List<ParquetAdapterFilter> filters ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
-        final List<AdapterFilter> resolvedFilters = filters.stream().map( filter -> resolveFilter( dataContext, filter ) ).toList();
+        final List<ParquetAdapterFilter> resolvedFilters = filters.stream().map( filter -> resolveFilter( dataContext, filter ) ).toList();
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
@@ -151,16 +151,16 @@ private AlgDataType buildDocumentType( AlgDataTypeFactory typeFactory ) {
 
 
     /**
-     * add dynamic parameters to adapter filter if needed
+     * add dynamic parameters to parquet filter if needed
      * @param dataContext context
-     * @param filter adapter filter
-     * @return AdapterFilter
+     * @param filter parquet filter
+     * @return ParquetFilter
      */
-    private AdapterFilter resolveFilter( DataContext dataContext, AdapterFilter filter ) {
+    private ParquetAdapterFilter resolveFilter( DataContext dataContext, ParquetAdapterFilter filter ) {
         if ( filter.dynamicParamIndex() == null ) {
             return filter;
         }
-        return new AdapterFilter(
+        return new ParquetAdapterFilter(
                 filter.columnIndex(),
                 filter.operator(),
                 dataContext.getParameterValue( filter.dynamicParamIndex() ) );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
index ff87127e94..c9b8ee8123 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
@@ -20,8 +20,8 @@
 import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.parquet.example.data.Group;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
-import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.Source;
 
@@ -35,7 +35,7 @@ public ParquetRelEnumerator(Source source, AtomicBoolean cancelFlag, int[] field
         super( source, cancelFlag, fields, List.of(), new ParquetRelValueExtractor() );
     }
 
-    public ParquetRelEnumerator(Source source, AtomicBoolean cancelFlag, int[] fields, List<AdapterFilter> filters ) {
+    public ParquetRelEnumerator(Source source, AtomicBoolean cancelFlag, int[] fields, List<ParquetAdapterFilter> filters ) {
         super( source, cancelFlag, fields, filters, new ParquetRelValueExtractor() );
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
index 6ae6c08841..b919d2502c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
@@ -17,16 +17,8 @@
 package org.polypheny.db.adapter.parquet.relational.execution;
 
 import java.util.List;
-import org.apache.parquet.filter2.compat.FilterCompat;
-import org.apache.parquet.filter2.predicate.FilterApi;
-import org.apache.parquet.filter2.predicate.FilterPredicate;
-import org.apache.parquet.schema.MessageType;
-import org.apache.parquet.schema.PrimitiveType;
-import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.execution.ParquetFilterTranslationSupport;
-import org.polypheny.db.adapter.parquet.shared.execution.ParquetPredicateBuilder;
-import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
-import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.rex.RexIndexRef;
 import org.polypheny.db.rex.RexNode;
@@ -37,13 +29,10 @@
  */
 public class ParquetRelFilterTranslator {
 
-    private final ParquetTypeConverter typeConverter = new ParquetTypeConverter();
-
-
     /**
-     * Translates a Rex filter into adapter filter form when possible.
+     * Translates a Rex filter into Parquet filter form when possible.
      */
-    public AdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFilter ) {
+    public ParquetAdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFilter ) {
         ParquetFilterTranslationSupport.ParsedFilter parsed = ParquetFilterTranslationSupport.parse( polyFilter );
         if ( parsed == null ) {
             return null;
@@ -65,66 +54,7 @@ public AdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFilter )
             return null;
         }
 
-        return ParquetFilterTranslationSupport.toAdapterFilter( index, polyFilter.getKind(), right );
-    }
-
-
-    /**
-     * Translate adapter filters to Parquet filter for pushdown
-     *
-     * @param schema - parquet file native schema
-     * @param adapterFilters - adapter level filters
-     * @return FilterCompat.Filter - filter in parquet format
-     */
-    public FilterCompat.Filter translate( MessageType schema, List<AdapterFilter> adapterFilters ) {
-        FilterPredicate predicate = null;
-
-        for ( AdapterFilter filter : adapterFilters ) {
-            FilterPredicate next = buildParquetFilterPredicate( schema, filter );
-            if ( next == null ) {
-                throw new IllegalArgumentException( "Unsupported parquet predicate: " + filter );
-            }
-            predicate = predicate == null ? next : FilterApi.and( predicate, next );
-        }
-
-        // return filter for given predicate
-        return predicate == null ? FilterCompat.NOOP : FilterCompat.get( predicate );
-    }
-
-
-    /**
-     * Build parquet filter predicate from provided filter info
-     *
-     * @param schema - parquet schema
-     * @param adapterFilter - adapter level filter info
-     * @return FilterPredicate object
-     */
-    private FilterPredicate buildParquetFilterPredicate( MessageType schema, AdapterFilter adapterFilter ) {
-        int index = adapterFilter.columnIndex();
-        if ( index < 0 || index >= schema.getFieldCount() ) {
-            return null;
-        }
-
-        Type type = schema.getType( index );
-        if ( !type.isPrimitive() ) {
-            return null;
-        }
-
-        PrimitiveType primitive = type.asPrimitiveType();
-        Object expected = typeConverter.fromPolyValueToParquetObj( primitive, adapterFilter.polyValue() );
-        if ( expected == null ) {
-            return null;
-        }
-
-        String columnName = schema.getFieldName( index );
-        return switch ( primitive.getPrimitiveTypeName() ) {
-            case BOOLEAN -> ParquetPredicateBuilder.buildBoolean( adapterFilter.operator(), columnName, expected );
-            case INT32 -> ParquetPredicateBuilder.buildInt( adapterFilter.operator(), columnName, expected );
-            case INT64 -> ParquetPredicateBuilder.buildLong( adapterFilter.operator(), columnName, expected );
-            case FLOAT -> ParquetPredicateBuilder.buildFloat( adapterFilter.operator(), columnName, expected );
-            case DOUBLE -> ParquetPredicateBuilder.buildDouble( adapterFilter.operator(), columnName, expected );
-            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> ParquetPredicateBuilder.buildBinary( adapterFilter.operator(), columnName, expected, primitive.getLogicalTypeAnnotation() );
-        };
+        return ParquetFilterTranslationSupport.toParquetAdapterFilter( index, polyFilter.getKind(), right );
     }
 
     /**
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java
index 043e376910..c17085299a 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java
@@ -4,6 +4,7 @@
 import org.apache.parquet.schema.PrimitiveType;
 import org.apache.parquet.schema.Type;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetValueExtractor;
+import org.polypheny.db.type.entity.PolyNull;
 import org.polypheny.db.type.entity.PolyValue;
 
 /**
@@ -14,7 +15,10 @@ public class ParquetRelValueExtractor extends AbstractParquetValueExtractor {
 
     @Override
     public PolyValue extractValue( Group group, int index, Type type ) {
-        if ( !type.isPrimitive() ) {
+        if ( group.getFieldRepetitionCount( index ) == 0 ) {
+            return PolyNull.NULL;
+        }
+        if ( !type.isPrimitive() || group.getFieldRepetitionCount( index ) > 1 ) {
             return extractStructuredValue( group, index, type );
         }
         PrimitiveType primitive = type.asPrimitiveType();
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index 7cb0497502..aaa14ecb6e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -20,8 +20,8 @@
 import org.apache.calcite.linq4j.Enumerable;
 import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
-import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
@@ -87,17 +87,17 @@ public ParquetRelTable(long id, Source source, PhysicalTable table, AbstractParq
     @Override
     public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> polyFilters ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
-        final List<AdapterFilter> adapterFilters = new ArrayList<>();
+        final List<ParquetAdapterFilter> parquetAdapterFilters = new ArrayList<>();
         polyFilters.removeIf( polyFilter -> {
-            var adapterFilter = filterTranslator.translate( fieldTypes, polyFilter );
-            if ( adapterFilter != null ) {
-                return adapterFilters.add( adapterFilter );
+            var parquetFilter = filterTranslator.translate( fieldTypes, polyFilter );
+            if ( parquetFilter != null ) {
+                return parquetAdapterFilters.add( parquetFilter );
             }
             return false;
         } );
 
         // check for dynamic filters
-        final List<AdapterFilter> resolvedFilters = resolveDynamicFilters( dataContext, adapterFilters );
+        final List<ParquetAdapterFilter> resolvedFilters = resolveDynamicFilters( dataContext, parquetAdapterFilters );
 
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
         return new AbstractEnumerable<>() {
@@ -165,11 +165,11 @@ public Enumerator<PolyValue[]> enumerator() {
      * Support parametrized queries
      * @param dataContext context
      * @param filters filters
-     * @return list of adapter filters
+     * @return list of parquet filters
      */
-    private List<AdapterFilter> resolveDynamicFilters( DataContext dataContext, List<AdapterFilter> filters ) {
-        List<AdapterFilter> resolved = new ArrayList<>( filters.size() );
-        for ( AdapterFilter filter : filters ) {
+    private List<ParquetAdapterFilter> resolveDynamicFilters( DataContext dataContext, List<ParquetAdapterFilter> filters ) {
+        List<ParquetAdapterFilter> resolved = new ArrayList<>( filters.size() );
+        for ( ParquetAdapterFilter filter : filters ) {
             if ( filter.dynamicParamIndex() == null ) {
                 // regular filter
                 resolved.add( filter );
@@ -177,7 +177,7 @@ private List<AdapterFilter> resolveDynamicFilters( DataContext dataContext, List
             }
             // get filter value by index
             PolyValue value = dataContext.getParameterValue( filter.dynamicParamIndex() );
-            resolved.add( new AdapterFilter( filter.columnIndex(), filter.operator(), value ) );
+            resolved.add( new ParquetAdapterFilter( filter.columnIndex(), filter.operator(), value ) );
         }
         return resolved;
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
index 6e1d451d91..63b7488db5 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
@@ -37,6 +37,7 @@
 import org.polypheny.db.adapter.Scannable;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetFileDiscovery;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetNamespace;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetFieldNameNormalizer;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
 import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
 import org.polypheny.db.catalog.catalogs.AdapterCatalog;
@@ -132,7 +133,7 @@ protected static String computePhysicalTableName( String fileName ) {
 
 
     public static String normalizeFieldName( String name ) {
-        return name.toLowerCase().trim().replaceAll( "[^a-z0-9_]+", "_" );
+        return ParquetFieldNameNormalizer.normalizeFieldName( name );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
index 2a260e752c..bc656158a9 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
@@ -21,7 +21,8 @@
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.parquet.example.data.Group;
 import org.apache.parquet.schema.Type;
-import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.Source;
@@ -33,16 +34,16 @@
  */
 public abstract class AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
 
-    protected final ParquetGroupReader reader;
+    protected final ParquetSourceReader reader;
     protected final ParquetValueExtractor valueExtractor;
-    protected final List<AdapterFilter> filters;
+    protected final List<ParquetAdapterFilter> filters;
     private PolyValue[] current;
 
 
-    public AbstractParquetEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields, List<AdapterFilter> filters, ParquetValueExtractor valueExtractor ) {
-        this.reader = new ParquetGroupReader( source, cancelFlag, fields, filters );
-        this.valueExtractor = valueExtractor;
+    public AbstractParquetEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields, List<ParquetAdapterFilter> filters, ParquetValueExtractor valueExtractor ) {
         this.filters = filters == null ? List.of() : List.copyOf( filters );
+        this.reader = new ParquetSourceReader( source, cancelFlag, fields, this.filters );
+        this.valueExtractor = valueExtractor;
     }
 
 
@@ -83,7 +84,11 @@ public void reset() {
 
     @Override
     public void close() {
-        reader.close();
+        try {
+            reader.close();
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Error closing parquet reader", e );
+        }
     }
 
 
@@ -100,7 +105,7 @@ public void close() {
      * @return boolean
      */
     protected boolean accept( Group group ) {
-        for ( AdapterFilter filter : filters ) {
+        for ( ParquetAdapterFilter filter : filters ) {
             if ( !matches( group, filter ) ) {
                 return false;
             }
@@ -109,7 +114,7 @@ protected boolean accept( Group group ) {
     }
 
 
-    private boolean matches( Group group, AdapterFilter filter ) {
+    private boolean matches( Group group, ParquetAdapterFilter filter ) {
         // if filter is invalid row should be displayed
         if ( filter.polyValue() == null || filter.columnIndex() < 0 || filter.columnIndex() >= reader.getProjectionSchema().getFieldCount() ) {
             return true;
@@ -141,7 +146,7 @@ private boolean matches( Group group, AdapterFilter filter ) {
     }
 
 
-    protected PolyValue extractFilterValue( Group group, AdapterFilter filter ) {
+    protected PolyValue extractFilterValue( Group group, ParquetAdapterFilter filter ) {
         Type field = reader.getProjectionSchema().getType( filter.columnIndex() );
         return valueExtractor.extractValue( group, filter.columnIndex(), field );
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java
index e5a9679450..91a5170815 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java
@@ -16,7 +16,7 @@
 
 package org.polypheny.db.adapter.parquet.shared.execution;
 
-import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.rex.RexCall;
 import org.polypheny.db.rex.RexDynamicParam;
@@ -51,16 +51,16 @@ public static ParsedFilter parse( RexNode filter ) {
     }
 
 
-    public static AdapterFilter toAdapterFilter( int columnIndex, Kind operator, RexNode valueNode ) {
+    public static ParquetAdapterFilter toParquetAdapterFilter( int columnIndex, Kind operator, RexNode valueNode ) {
         if ( valueNode instanceof RexLiteral literal ) {
             if ( literal.getValue() == null ) {
                 return null;
             }
-            return new AdapterFilter( columnIndex, operator, literal.getValue() );
+            return new ParquetAdapterFilter( columnIndex, operator, literal.getValue() );
         }
 
         if ( valueNode instanceof RexDynamicParam dynamicParam ) {
-            return new AdapterFilter( columnIndex, operator, null, dynamicParam.getIndex() );
+            return new ParquetAdapterFilter( columnIndex, operator, null, dynamicParam.getIndex() );
         }
 
         return null;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
new file mode 100644
index 0000000000..9590a6f9ca
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
@@ -0,0 +1,34 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.filter;
+
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.entity.PolyValue;
+
+/**
+ * Immutable filter description
+ * @param columnIndex - index of filter column
+ * @param operator - filter operation
+ * @param polyValue - filter value
+ */
+public record ParquetAdapterFilter(int columnIndex, Kind operator, PolyValue polyValue, Long dynamicParamIndex ) {
+
+    public ParquetAdapterFilter(int columnIndex, Kind operator, PolyValue polyValue ) {
+        this( columnIndex, operator, polyValue, null );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetPredicateBuilder.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
similarity index 61%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetPredicateBuilder.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
index 9273dc6740..64e8cb43c0 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetPredicateBuilder.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
@@ -14,20 +14,84 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.shared.execution;
+package org.polypheny.db.adapter.parquet.shared.filter;
 
+import java.util.List;
+import org.apache.parquet.filter2.compat.FilterCompat;
 import org.apache.parquet.filter2.predicate.FilterApi;
 import org.apache.parquet.filter2.predicate.FilterPredicate;
 import org.apache.parquet.io.api.Binary;
 import org.apache.parquet.schema.LogicalTypeAnnotation;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
 import org.polypheny.db.algebra.constant.Kind;
 
 /**
- * Creates native Parquet filter predicates
- * for the supported comparison operators and value types
+ * Builds native Parquet predicates from shared filter descriptions.
  */
-public class ParquetPredicateBuilder {
-    public static FilterPredicate buildBoolean( Kind operator, String columnName, Object expected ) {
+public final class ParquetNativeFilterBuilder {
+
+    private static final ParquetTypeConverter TYPE_CONVERTER = new ParquetTypeConverter();
+
+
+    private ParquetNativeFilterBuilder() {
+    }
+
+
+    /**
+     * Build parquet native filter predicates from adapter level filter
+     * @param schema contains fields
+     * @param filters adapter level filters
+     * @return FilterCompat.Filter - parquet native
+     */
+    public static FilterCompat.Filter build( MessageType schema, List<ParquetAdapterFilter> filters ) {
+        if ( filters == null || filters.isEmpty() ) {
+            return FilterCompat.NOOP;
+        }
+
+        FilterPredicate predicate = null;
+        for ( ParquetAdapterFilter filter : filters ) {
+            FilterPredicate next = buildPredicate( schema, filter );
+            if ( next == null ) {
+                throw new IllegalArgumentException( "Unsupported parquet predicate: " + filter );
+            }
+            predicate = predicate == null ? next : FilterApi.and( predicate, next );
+        }
+        return FilterCompat.get( predicate );
+    }
+
+
+    private static FilterPredicate buildPredicate( MessageType schema, ParquetAdapterFilter filter ) {
+        int index = filter.columnIndex();
+        if ( index < 0 || index >= schema.getFieldCount() ) {
+            return null;
+        }
+
+        Type type = schema.getType( index );
+        if ( !type.isPrimitive() ) {
+            return null;
+        }
+
+        PrimitiveType primitive = type.asPrimitiveType();
+        Object expected = TYPE_CONVERTER.fromPolyValueToParquetObj( primitive, filter.polyValue() );
+        if ( expected == null ) {
+            return null;
+        }
+
+        String columnName = schema.getFieldName( index );
+        return switch ( primitive.getPrimitiveTypeName() ) {
+            case BOOLEAN -> buildBoolean( filter.operator(), columnName, expected );
+            case INT32 -> buildInt( filter.operator(), columnName, expected );
+            case INT64 -> buildLong( filter.operator(), columnName, expected );
+            case FLOAT -> buildFloat( filter.operator(), columnName, expected );
+            case DOUBLE -> buildDouble( filter.operator(), columnName, expected );
+            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> buildBinary( filter.operator(), columnName, expected, primitive.getLogicalTypeAnnotation() );
+        };
+    }
+
+    private static FilterPredicate buildBoolean( Kind operator, String columnName, Object expected ) {
         if ( !(expected instanceof Boolean value) ) {
             return null;
         }
@@ -39,7 +103,7 @@ public static FilterPredicate buildBoolean( Kind operator, String columnName, Ob
     }
 
 
-    public static FilterPredicate buildInt( Kind operator, String columnName, Object expected ) {
+    private static FilterPredicate buildInt( Kind operator, String columnName, Object expected ) {
         if ( !(expected instanceof Integer value) ) {
             return null;
         }
@@ -56,7 +120,7 @@ public static FilterPredicate buildInt( Kind operator, String columnName, Object
     }
 
 
-    public static FilterPredicate buildLong( Kind operator, String columnName, Object expected ) {
+    private static FilterPredicate buildLong( Kind operator, String columnName, Object expected ) {
         if ( !(expected instanceof Long value) ) {
             return null;
         }
@@ -72,7 +136,7 @@ public static FilterPredicate buildLong( Kind operator, String columnName, Objec
     }
 
 
-    public static FilterPredicate buildFloat( Kind operator, String columnName, Object expected ) {
+    private static FilterPredicate buildFloat( Kind operator, String columnName, Object expected ) {
         if ( !(expected instanceof Float value) ) {
             return null;
         }
@@ -88,7 +152,7 @@ public static FilterPredicate buildFloat( Kind operator, String columnName, Obje
     }
 
 
-    public static FilterPredicate buildDouble( Kind operator, String columnName, Object expected ) {
+    private static FilterPredicate buildDouble( Kind operator, String columnName, Object expected ) {
         if ( !(expected instanceof Double value) ) {
             return null;
         }
@@ -104,7 +168,7 @@ public static FilterPredicate buildDouble( Kind operator, String columnName, Obj
     }
 
 
-    public static FilterPredicate buildBinary( Kind operator, String columnName, Object expected, LogicalTypeAnnotation logicalType ) {
+    private static FilterPredicate buildBinary( Kind operator, String columnName, Object expected, LogicalTypeAnnotation logicalType ) {
         Binary value = expected instanceof Binary binary ? binary : Binary.fromString( expected.toString() );
 
         return switch ( operator ) {
@@ -116,7 +180,7 @@ public static FilterPredicate buildBinary( Kind operator, String columnName, Obj
     }
 
 
-    public static FilterPredicate buildOrderedBinary( Kind operator, String columnName, Binary value ) {
+    private static FilterPredicate buildOrderedBinary( Kind operator, String columnName, Binary value ) {
         return switch ( operator ) {
             case GREATER_THAN -> FilterApi.gt( FilterApi.binaryColumn( columnName ), value );
             case GREATER_THAN_OR_EQUAL -> FilterApi.gtEq( FilterApi.binaryColumn( columnName ), value );
@@ -125,4 +189,5 @@ public static FilterPredicate buildOrderedBinary( Kind operator, String columnNa
             default -> null;
         };
     }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetGroupReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
similarity index 65%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetGroupReader.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
index cab9c4e940..74b8e4defe 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetGroupReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
@@ -14,13 +14,12 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.shared.execution;
+package org.polypheny.db.adapter.parquet.shared.io;
 
 import java.io.IOException;
 import java.util.ArrayList;
 import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
-import java.util.stream.IntStream;
 import lombok.Getter;
 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.Path;
@@ -29,43 +28,46 @@
 import org.apache.parquet.example.data.Group;
 import org.apache.parquet.example.data.simple.convert.GroupRecordConverter;
 import org.apache.parquet.filter2.compat.FilterCompat;
-import org.apache.parquet.hadoop.ParquetFileReader;
+import org.apache.parquet.hadoop.metadata.BlockMetaData;
 import org.apache.parquet.hadoop.util.HadoopInputFile;
 import org.apache.parquet.io.ColumnIOFactory;
 import org.apache.parquet.io.MessageColumnIO;
 import org.apache.parquet.io.RecordReader;
 import org.apache.parquet.schema.MessageType;
 import org.apache.parquet.schema.Type;
-import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetNativeFilterBuilder;
 import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.util.Source;
 
 /**
- * low-level Parquet row-group reader.
- * Opens the file, applies projection and native predicates,
- * and streams groups to the enumerators
+ * Shared low-level Parquet file reader used by plugin modules that need direct
+ * row access, projection support, and optional native Parquet filtering without
+ * depending on adapter-specific planning code.
  */
-public class ParquetGroupReader implements AutoCloseable {
+public class ParquetSourceReader implements AutoCloseable {
 
     private final AtomicBoolean cancelFlag;
-    private final ParquetFileReader fileReader;
+    private final org.apache.parquet.hadoop.ParquetFileReader fileReader;
     private final boolean useNativeFilter;
-
     @Getter
     private final MessageType projectionSchema;
 
     private RecordReader<Group> recordReader;
     private long rowCountInGroup;
     private long rowIndexInGroup;
-
     @Getter
     private long currentRowNumber = -1;
 
 
-    public ParquetGroupReader( Source source, AtomicBoolean cancelFlag, int[] fields, List<AdapterFilter> filters ) {
-        this.cancelFlag = cancelFlag;
+    public ParquetSourceReader(Source source ) {
+        this( source, null, null, null );
+    }
+
+
+    public ParquetSourceReader(Source source, AtomicBoolean cancelFlag, int[] fields, List<ParquetAdapterFilter> filters ) {
+        this.cancelFlag = cancelFlag == null ? new AtomicBoolean( false ) : cancelFlag;
 
         try {
             Path path = new Path( source.url().toURI() );
@@ -73,30 +75,25 @@ public ParquetGroupReader( Source source, AtomicBoolean cancelFlag, int[] fields
             var inputFile = HadoopInputFile.fromPath( path, conf );
 
             MessageType schema;
-            try ( ParquetFileReader schemaReader = ParquetFileReader.open( inputFile ) ) {
+            try ( org.apache.parquet.hadoop.ParquetFileReader schemaReader = org.apache.parquet.hadoop.ParquetFileReader.open( inputFile ) ) {
                 schema = schemaReader.getFooter().getFileMetaData().getSchema();
             }
 
             int[] projectedFields = buildProjectedFields( fields, schema.getFieldCount() );
             this.projectionSchema = buildProjectionSchema( schema, projectedFields );
-
-            var recordFilter = new ParquetRelFilterTranslator().translate( schema, filters == null ? List.of() : filters );
+            var recordFilter = ParquetNativeFilterBuilder.build( schema, filters == null ? List.of() : filters );
             this.useNativeFilter = FilterCompat.isFilteringRequired( recordFilter );
 
-            // create configuration for the native parquet filter
-            ParquetReadOptions.Builder readOptionsBuilder = ParquetReadOptions.builder()
+            ParquetReadOptions readOptions = ParquetReadOptions.builder()
                     .useStatsFilter()
                     .useDictionaryFilter()
                     .useColumnIndexFilter()
                     .useBloomFilter()
-                    .useRecordFilter();
-
-            if ( useNativeFilter ) {
-                // push down filter
-                readOptionsBuilder.withRecordFilter( recordFilter );
-            }
+                    .useRecordFilter()
+                    .withRecordFilter( recordFilter )
+                    .build();
 
-            this.fileReader = ParquetFileReader.open( inputFile, readOptionsBuilder.build() );
+            this.fileReader = org.apache.parquet.hadoop.ParquetFileReader.open( inputFile, readOptions );
             this.fileReader.setRequestedSchema( projectionSchema );
         } catch ( Exception e ) {
             throw new GenericRuntimeException( "Unable to open parquet file: " + source.path(), e );
@@ -110,7 +107,6 @@ public Group next() {
                 if ( cancelFlag.get() ) {
                     return null;
                 }
-
                 if ( !ensureRecordReader() ) {
                     return null;
                 }
@@ -131,16 +127,40 @@ public Group next() {
     }
 
 
+    public long getEstimatedRowCount() {
+        // use metadata to estimate row count
+        return fileReader.getFooter().getBlocks().stream().mapToLong( BlockMetaData::getRowCount ).sum();
+    }
+
+
+    public static MessageType readSchema( Source source ) {
+        try {
+            Path path = new Path( source.url().toURI() );
+            Configuration conf = HadoopConfigurationFactory.create( ParquetSourceReader.class.getClassLoader() );
+            var inputFile = HadoopInputFile.fromPath( path, conf );
+            try ( org.apache.parquet.hadoop.ParquetFileReader schemaReader = org.apache.parquet.hadoop.ParquetFileReader.open( inputFile ) ) {
+                return schemaReader.getFooter().getFileMetaData().getSchema();
+            }
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Unable to inspect parquet schema for " + source.path(), e );
+        }
+    }
+
+
+    @Override
+    public void close() throws IOException {
+        fileReader.close();
+    }
+
+
     private boolean ensureRecordReader() throws IOException {
         if ( recordReader != null && rowIndexInGroup < rowCountInGroup ) {
             return true;
         }
 
-        // push down filters
         PageReadStore pages = useNativeFilter
                 ? fileReader.readNextFilteredRowGroup()
                 : fileReader.readNextRowGroup();
-
         if ( pages == null ) {
             return false;
         }
@@ -155,7 +175,11 @@ private boolean ensureRecordReader() throws IOException {
 
     private static int[] buildProjectedFields( int[] projectedFields, int schemaFieldCount ) {
         if ( projectedFields == null || projectedFields.length == 0 ) {
-            return IntStream.range( 0, schemaFieldCount ).toArray();
+            int[] allFields = new int[schemaFieldCount];
+            for ( int i = 0; i < schemaFieldCount; i++ ) {
+                allFields[i] = i;
+            }
+            return allFields;
         }
         return projectedFields;
     }
@@ -173,14 +197,4 @@ private static MessageType buildProjectionSchema( MessageType fullSchema, int[]
         return new MessageType( fullSchema.getName(), fields );
     }
 
-
-    @Override
-    public void close() {
-        try {
-            fileReader.close();
-        } catch ( IOException e ) {
-            throw new GenericRuntimeException( "Error closing parquet reader", e );
-        }
-    }
-
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/model/AdapterFilter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/model/AdapterFilter.java
deleted file mode 100644
index 833ff4f6aa..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/model/AdapterFilter.java
+++ /dev/null
@@ -1,18 +0,0 @@
-package org.polypheny.db.adapter.parquet.shared.model;
-
-import org.polypheny.db.algebra.constant.Kind;
-import org.polypheny.db.type.entity.PolyValue;
-
-/**
- * Immutable Filter description
- * @param columnIndex - index of filter column
- * @param operator - filter operation
- * @param polyValue - filter value
- */
-public record AdapterFilter(int columnIndex, Kind operator, PolyValue polyValue, Long dynamicParamIndex ) {
-
-    public AdapterFilter( int columnIndex, Kind operator, PolyValue polyValue ) {
-        this( columnIndex, operator, polyValue, null );
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetFieldNameNormalizer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetFieldNameNormalizer.java
new file mode 100644
index 0000000000..151da66f77
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetFieldNameNormalizer.java
@@ -0,0 +1,34 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.schema;
+
+import java.util.Locale;
+
+/**
+ * Shared Parquet field-name normalization used across Parquet consumers.
+ */
+public final class ParquetFieldNameNormalizer {
+
+    private ParquetFieldNameNormalizer() {
+    }
+
+
+    public static String normalizeFieldName( String name ) {
+        return name.toLowerCase( Locale.ROOT ).trim().replaceAll( "[^a-z0-9_]+", "_" );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
index 86263719a5..d9ba46f01b 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
@@ -68,7 +68,7 @@ public PolyType fromParquetTypeToPolyType( Type field ) {
 
         // treat nested types as string
         if ( !field.isPrimitive() ) {
-            return PolyType.TEXT;
+            return PolyType.DOCUMENT;
         }
 
         // not nested types
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
index 5d418a7b7b..f7752ff2bb 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
@@ -21,8 +21,8 @@
 import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
 import org.apache.parquet.schema.Types;
 import org.junit.jupiter.api.Test;
-import org.polypheny.db.adapter.parquet.shared.model.AdapterFilter;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetNativeFilterBuilder;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.type.entity.temporal.PolyTimestamp;
 
@@ -37,10 +37,10 @@ void rejectsInt96TimestampPredicatePushdown() {
                 .named( "ts" )
                 .named( "test_schema" );
 
-        AdapterFilter filter = new AdapterFilter( 0, Kind.EQUALS, PolyTimestamp.of( 1_700_000_000_000L ) );
+        ParquetAdapterFilter filter = new ParquetAdapterFilter( 0, Kind.EQUALS, PolyTimestamp.of( 1_700_000_000_000L ) );
 
         assertThrows(
                 IllegalArgumentException.class,
-                () -> new ParquetRelFilterTranslator().translate( schema, List.of( filter ) ) );
+                () -> ParquetNativeFilterBuilder.build( schema, List.of( filter ) ) );
     }
 }
diff --git a/plugins/workflow-engine/build.gradle b/plugins/workflow-engine/build.gradle
index 280bb26423..9703a16c8c 100644
--- a/plugins/workflow-engine/build.gradle
+++ b/plugins/workflow-engine/build.gradle
@@ -8,6 +8,7 @@ configurations {
 
 dependencies {
     implementation group: 'com.cronutils', name: 'cron-utils', version: cron_utils_version  // Apache 2.0
+    implementation project(":plugins:parquet-adapter")
     compileOnly project(":core")
     compileOnly project(":dbms")
     compileOnly project(":webui")
@@ -19,6 +20,30 @@ dependencies {
     compileOnly group: "org.apache.commons", name: "commons-collections4", version: commons_collections_version
     compileOnly group: "com.opencsv", name: "opencsv", version: opencsv_version
     compileOnly group: "org.codehaus.janino", name: "commons-compiler", version: commons_compiler_version  // BSD
+    compileOnly(group: "org.apache.parquet", name: "parquet-hadoop", version: parquet_version) {
+//        transitive = false
+    }
+    compileOnly group: "org.apache.parquet", name: "parquet-column", version: parquet_version
+    compileOnly group: "org.apache.parquet", name: "parquet-common", version: parquet_version
+    compileOnly(group: "org.apache.hadoop", name: "hadoop-common", version: hadoop_version) {
+        exclude group: "com.google.re2j", module: "re2j"
+        exclude group: "com.jcraft", module: "jsch"
+        exclude group: "jakarta.activation", module: "jakarta.activation-api"
+        exclude group: "javax.activation", module: "activation"
+        exclude group: "javax.servlet", module: "javax.servlet-api"
+        exclude group: "javax.servlet.jsp", module: "jsp-api"
+        exclude group: "javax.ws.rs", module: "jsr311-api"
+        exclude group: "javax.xml.bind", module: "jaxb-api"
+        exclude group: "javax.xml.stream", module: "stax-api"
+        exclude group: "org.bouncycastle", module: "bcprov-jdk18on"
+    }
+    compileOnly(group: "org.apache.hadoop", name: "hadoop-mapreduce-client-core", version: hadoop_version) {
+        exclude group: "jakarta.activation", module: "jakarta.activation-api"
+        exclude group: "javax.servlet", module: "javax.servlet-api"
+        exclude group: "javax.ws.rs", module: "jsr311-api"
+        exclude group: "aopalliance", module: "aopalliance"
+        exclude group: "jakarta.xml.bind", module: "jakarta.xml.bind-api"
+    }
 
 
     // --- Test Compile ---
@@ -27,6 +52,7 @@ dependencies {
     testImplementation project(path: ":core", configuration: "tests")
     testImplementation project(path: ":core")
     testImplementation project(path: ":webui")
+    testImplementation project(":plugins:parquet-adapter")
     testImplementation project(path: ":plugins:sql-language")
     testImplementation project(":plugins:jdbc-adapter-framework")
 
@@ -68,6 +94,7 @@ compileJava {
     dependsOn(":core:processResources")
     dependsOn(":information:processResources")
     dependsOn(":webui:processResources")
+    dependsOn(":plugins:parquet-adapter:classes")
     dependsOn(":plugins:sql-language:processResources")
     dependsOn(":plugins:mql-language:processResources")
     dependsOn(":plugins:cypher-language:processResources")
@@ -75,6 +102,7 @@ compileJava {
 }
 
 delombok {
+    dependsOn(":plugins:parquet-adapter:classes")
     dependsOn(":plugins:sql-language:processResources")
     dependsOn(":webui:processResources")
     dependsOn(":plugins:sql-language:processResources")
diff --git a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/ExtractParquetActivity.java b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/ExtractParquetActivity.java
new file mode 100644
index 0000000000..c6a604a9f2
--- /dev/null
+++ b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/ExtractParquetActivity.java
@@ -0,0 +1,233 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.workflow.dag.activities.impl.extract;
+
+import static org.polypheny.db.workflow.dag.settings.GroupDef.ADVANCED_GROUP;
+
+import java.util.List;
+import java.util.Set;
+import java.util.function.Supplier;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.transaction.Transaction;
+import org.polypheny.db.util.Source;
+import org.polypheny.db.workflow.dag.activities.Activity;
+import org.polypheny.db.workflow.dag.activities.Activity.ActivityCategory;
+import org.polypheny.db.workflow.dag.activities.Activity.PortType;
+import org.polypheny.db.workflow.dag.activities.ActivityException;
+import org.polypheny.db.workflow.dag.activities.ActivityException.InvalidSettingException;
+import org.polypheny.db.workflow.dag.activities.Pipeable;
+import org.polypheny.db.workflow.dag.activities.TypePreview;
+import org.polypheny.db.workflow.dag.activities.TypePreview.DocType;
+import org.polypheny.db.workflow.dag.activities.TypePreview.RelType;
+import org.polypheny.db.workflow.dag.activities.TypePreview.UnknownType;
+import org.polypheny.db.workflow.dag.annotations.ActivityDefinition;
+import org.polypheny.db.workflow.dag.annotations.ActivityDefinition.OutPort;
+import org.polypheny.db.workflow.dag.annotations.BoolSetting;
+import org.polypheny.db.workflow.dag.annotations.EnumSetting;
+import org.polypheny.db.workflow.dag.annotations.FileSetting;
+import org.polypheny.db.workflow.dag.annotations.IntSetting;
+import org.polypheny.db.workflow.dag.settings.FileValue;
+import org.polypheny.db.workflow.dag.settings.FileValue.SourceType;
+import org.polypheny.db.workflow.dag.settings.SettingDef.Settings;
+import org.polypheny.db.workflow.dag.settings.SettingDef.SettingsPreview;
+import org.polypheny.db.workflow.engine.execution.context.ExecutionContext;
+import org.polypheny.db.workflow.engine.execution.context.PipeExecutionContext;
+import org.polypheny.db.workflow.engine.execution.pipe.InputPipe;
+import org.polypheny.db.workflow.engine.execution.pipe.OutputPipe;
+import org.polypheny.db.workflow.engine.storage.reader.CheckpointReader;
+import org.polypheny.db.workflow.io.parquet.ParquetWorkflowSupport;
+
+/**
+ * Workflow node definition for reading Parquet files into a workflow.
+ * Fulfills the following functions:
+ * 1. defines how the activity appears in the workflow UI.
+ *    It declares the name, description, category, settings, and output port.
+ *    So, the workflow editor can show an Extract Parquet node with options.
+ * 2. decides what type of data the node will output (document or relational)
+ * 3. executes the extraction at runtime.
+ */
+@ActivityDefinition(type = "extractParquet", displayName = "Extract Parquet", categories = { ActivityCategory.EXTRACT, ActivityCategory.DOCUMENT, ActivityCategory.RELATIONAL, ActivityCategory.ESSENTIALS, ActivityCategory.EXTERNAL },
+        inPorts = {},
+        outPorts = { @OutPort(type = PortType.ANY, description = "The extracted Parquet data.") },
+        shortDescription = "Extracts one or multiple Parquet files into either a document collection or a relational table.")
+@FileSetting(key = "file", displayName = "File Location", pos = 0,
+        multi = true, modes = { SourceType.ABS_FILE, SourceType.URL },
+        shortDescription = "Select the Parquet file or folder to extract. In case of multiple files, the union of their rows is computed.")
+@EnumSetting(key = "outputModel", displayName = "Output Type", pos = 1,
+        options = { ParquetWorkflowSupport.OUTPUT_DOCUMENT, ParquetWorkflowSupport.OUTPUT_RELATIONAL },
+        displayOptions = { "Document", "Relational" },
+        defaultValue = ParquetWorkflowSupport.OUTPUT_DOCUMENT,
+        shortDescription = "Choose whether the Parquet rows should be exposed as documents or as a relational table.")
+@BoolSetting(key = "nameField", displayName = "Add File Name Field", pos = 2,
+        shortDescription = "Adds the source file name as a field or column in the output.")
+@IntSetting(key = "maxCount", displayName = "Maximum Row Count", defaultValue = -1, min = -1, pos = 3, group = ADVANCED_GROUP,
+        shortDescription = "The maximum number of rows to extract per file or -1 to extract all rows.")
+@SuppressWarnings("unused")
+public class ExtractParquetActivity implements Activity, Pipeable {
+
+    private static final Set<String> EXTENSIONS = ParquetWorkflowSupport.EXTENSIONS;
+    private List<Source> sources;
+
+
+    /**
+     * Check what node will output:
+     * - is the output document or relational
+     * - if relational, what columns and types should we expect
+     * - if we can’t know yet, should we show an unknown type
+     * @param inTypes a list of {@link TypePreview}s representing the input tuple types.
+     * @param settings the SettingsPreview representing the available settings, i.e. all settings that do not contain variables.
+     * @return List<TypePreview>
+     * @throws ActivityException - exception
+     */
+    @Override
+    public List<TypePreview> previewOutTypes( List<TypePreview> inTypes, SettingsPreview settings ) throws ActivityException {
+        if ( !settings.keysPresent( "outputModel" ) ) {
+            // outputModel is not set yet -> UnknownType
+            return UnknownType.of().asOutTypes();
+        }
+
+        String outputModel = settings.getString( "outputModel" );
+        // Document -> no schema
+        if ( ParquetWorkflowSupport.OUTPUT_DOCUMENT.equals( outputModel ) ) {
+            if ( settings.keysPresent( "nameField" ) && settings.getBool( "nameField" ) ) {
+                return DocType.of( Set.of( "fileName" ) ).asOutTypes();
+            }
+            return DocType.of().asOutTypes();
+        }
+
+        // Relational -> need schema
+        if ( settings.keysPresent( "file" ) ) {
+            // inspect selected file
+            try {
+                List<Source> previewSources = settings.getOrThrow( "file", FileValue.class ).getSources( EXTENSIONS );
+                if ( previewSources.isEmpty() ) {
+                    // no files found
+                    throw new InvalidSettingException( "No parquet files found", "file" );
+                }
+                boolean addNameField = settings.keysPresent( "nameField" ) && settings.getBool( "nameField" );
+                // read the Parquet schema and build a relational preview type
+                return RelType.of( ParquetWorkflowSupport.getOutputType( previewSources.get( 0 ), outputModel, addNameField ) ).asOutTypes();
+            } catch ( InvalidSettingException e ) {
+                throw e;
+            } catch ( Exception e ) {
+                throw new InvalidSettingException( "Invalid location: " + e.getMessage(), "file" );
+            }
+        }
+        return UnknownType.ofRel().asOutTypes();
+    }
+
+
+    @Override
+    public void execute( List<CheckpointReader> inputs, Settings settings, ExecutionContext ctx ) throws Exception {
+        Pipeable.super.execute( inputs, settings, ctx );
+    }
+
+
+    /**
+     * Decide the final, concrete output schema for the activity before execution starts
+     * @param inTypes the types of the input pipes - ignored, because activity has no input ports
+     * @param settings the resolved settings
+     * @return AlgDataType
+     * @throws Exception - exception
+     */
+    @Override
+    public AlgDataType lockOutputType( List<AlgDataType> inTypes, Settings settings ) throws Exception {
+        sources = settings.get( "file", FileValue.class ).getSources( EXTENSIONS );
+        if ( sources.isEmpty() ) {
+            throw new InvalidSettingException( "No parquet files found", "file" );
+        }
+        return ParquetWorkflowSupport.getOutputType( sources.get( 0 ), settings.getString( "outputModel" ), settings.getBool( "nameField" ) );
+    }
+
+
+    /**
+     * Produce the rows/documents and sends them to the activity’s output port
+     * @param inputs the InputPipes to iterate over. For inactive edges, the pipe is null (important for non-default DataStateMergers).
+     * @param output the output pipe for sending output tuples to that respect the locked output type, or null if this activity has no output
+     * @param settings the resolved settings
+     * @param ctx ExecutionContext to be used for access to the transaction (interrupt checking is done automatically by the pipes)
+     * @throws Exception exception
+     */
+    @Override
+    public void pipe( List<InputPipe> inputs, OutputPipe output, Settings settings, PipeExecutionContext ctx ) throws Exception {
+        String outputModel = settings.getString( "outputModel" );
+        boolean addNameField = settings.getBool( "nameField" );
+        int maxCount = settings.getInt( "maxCount" );
+
+        for ( Source source : sources ) {
+            ctx.logInfo( "Extracting " + source.path() + " as " + outputModel );
+            // write for the next stage in pipe
+            if ( ParquetWorkflowSupport.OUTPUT_RELATIONAL.equals( outputModel ) ) {
+                ParquetWorkflowSupport.writeRows( output, source, addNameField, maxCount );
+            } else {
+                ParquetWorkflowSupport.writeDocuments( output, source, addNameField, maxCount );
+            }
+        }
+    }
+
+
+    @Override
+    public void reset() {
+        sources = null;
+    }
+
+
+    /**
+     * Compute a more specific display name for the node based on the current settings
+     * @param inTypes a list of {@link TypePreview}s representing the input tuple types.
+     * @param settings the SettingsPreview representing the available settings, i.e. all settings that do not contain variables.
+     * @return String name
+     */
+    @Override
+    public String getDynamicName( List<TypePreview> inTypes, SettingsPreview settings ) {
+        if ( settings.keysPresent( "file" ) && settings.keysPresent( "outputModel" ) ) {
+            try {
+                List<Source> previewSources = settings.getOrThrow( "file", FileValue.class ).getSources( EXTENSIONS );
+                if ( !previewSources.isEmpty() ) {
+                    // if file data found generate more specific activity name
+                    return ParquetWorkflowSupport.dynamicName( settings.getString( "outputModel" ), previewSources );
+                }
+            } catch ( Exception ignored ) {
+            }
+        }
+        return null;
+    }
+
+
+    /**
+     * Gives the workflow engine a rough estimate of how many output rows this activity will produce
+     * @param inTypes the types of the inputs. For inactive edges, the entry is null (important for non-default DataStateMergers).
+     * @param settings the resolved settings
+     * @param inCounts the list of input tuple counts. -1 if the estimation is not possible and null for inactive edges.
+     * @param transactionSupplier to be used for access to a transaction
+     * @return long
+     */
+    @Override
+    public long estimateTupleCount( List<AlgDataType> inTypes, Settings settings, List<Long> inCounts, Supplier<Transaction> transactionSupplier ) {
+        try {
+            // check if sources is available
+            if ( sources == null ) {
+                // resolves the files from settings
+                sources = settings.get( "file", FileValue.class ).getSources( EXTENSIONS );
+            }
+            return ParquetWorkflowSupport.estimateTupleCount( sources );
+        } catch ( Exception e ) {
+            return -1;
+        }
+    }
+
+}
diff --git a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/io/parquet/ParquetWorkflowSupport.java b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/io/parquet/ParquetWorkflowSupport.java
new file mode 100644
index 0000000000..4016623d20
--- /dev/null
+++ b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/io/parquet/ParquetWorkflowSupport.java
@@ -0,0 +1,214 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.workflow.io.parquet;
+
+import java.net.URISyntaxException;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Set;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.document.execution.ParquetDocValueExtractor;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelValueExtractor;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.algebra.type.AlgDataTypeFactory;
+import org.polypheny.db.algebra.type.AlgDataTypeFactory.Builder;
+import org.polypheny.db.algebra.type.DocumentType;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetFieldNameNormalizer;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.document.PolyDocument;
+import org.polypheny.db.type.entity.numerical.PolyLong;
+import org.polypheny.db.util.Source;
+import org.polypheny.db.workflow.dag.activities.Activity;
+import org.polypheny.db.workflow.dag.activities.ActivityUtils;
+import org.polypheny.db.workflow.engine.execution.pipe.OutputPipe;
+
+/**
+ * Parquet-reading/conversion helper layer
+ */
+public final class ParquetWorkflowSupport {
+
+    public static final Set<String> EXTENSIONS = Set.of( "parquet" );
+    public static final String OUTPUT_DOCUMENT = "document";
+    public static final String OUTPUT_RELATIONAL = "relational";
+
+    private static final AlgDataTypeFactory FACTORY = AlgDataTypeFactory.DEFAULT;
+    private static final PolyString FILE_NAME_KEY = PolyString.of( "fileName" );
+
+    private static final ParquetTypeConverter parquetTypeConverter = new ParquetTypeConverter();
+    private static final ParquetDocValueExtractor parquetDocValueExtractor = new ParquetDocValueExtractor();
+    private static final ParquetRelValueExtractor parquetRelValueExtractor = new ParquetRelValueExtractor();
+
+    private static final int MAX_NAME_LENGTH = 40;
+    private static final int TRUNCATED_LENGTH = 37;
+
+
+    /**
+     * Create dynamic name for activity node
+     * @param outputModel - relational or document
+     * @param sources - parquet files
+     * @return String
+     * @throws URISyntaxException exception
+     */
+    public static String dynamicName( String outputModel, List<Source> sources ) throws URISyntaxException {
+        String prefix = OUTPUT_RELATIONAL.equals( outputModel ) ? "Extract Parquet Table" : "Extract Parquet Document";
+        if ( sources.size() > 1 ) {
+            return prefix + "s";
+        }
+        String name = ActivityUtils.resourceNameFromSource( sources.get( 0 ) );
+        if ( name.length() > MAX_NAME_LENGTH ) {
+            name = name.substring( 0, TRUNCATED_LENGTH ) + "...";
+        }
+        return prefix + ": " + name;
+    }
+
+
+    /**
+     * Convert a Parquet file schema into the workflow engine’s output type
+     *
+     * @param source - parquet file
+     * @param outputModel - workflow output type
+     * @param addNameField - if add filename as column
+     * @return AlgDataType
+     */
+    public static AlgDataType getOutputType( Source source, String outputModel, boolean addNameField ) {
+
+        if ( OUTPUT_DOCUMENT.equals( outputModel ) ) {
+            return DocumentType.ofId();
+        }
+
+        MessageType schema = ParquetSourceReader.readSchema( source );
+
+        Builder builder = FACTORY.builder();
+        // PK added by the workflow layer, it does not come from file
+        builder.add( Activity.PK_COL, null, PolyType.BIGINT );
+        for ( Type field : schema.getFields() ) {
+            var type = parquetTypeConverter.fromParquetTypeToPolyType( field );
+            // PolyDocument type provided as json string
+            if (type == PolyType.DOCUMENT) {
+                type = PolyType.TEXT;
+            }
+            String fieldName = ParquetFieldNameNormalizer.normalizeFieldName( field.getName() );
+            builder.add( fieldName, null, type);
+        }
+        if ( addNameField ) {
+            // add source file name to each row
+            builder.add( FILE_NAME_KEY.value, null, PolyType.VARCHAR, 255 );
+        }
+        return builder.uniquify().build();
+    }
+
+
+    /**
+     * Call reader functionality to estimate row count
+     * @param sources - parquet files
+     * @return long - estimation
+     */
+    public static long estimateTupleCount( List<Source> sources ) {
+        long estimate = 0;
+        for ( Source source : sources ) {
+            try ( ParquetSourceReader reader = new ParquetSourceReader( source ) ) {
+                estimate += Math.max( reader.getEstimatedRowCount(), 0 );
+            } catch ( Exception e ) {
+                return -1;
+            }
+        }
+        return estimate == 0 ? -1 : estimate;
+    }
+
+
+    /**
+     * Read parquet file and write documents to the output pipe
+     * Document created from each row of parquet file
+     * @param output - OutputPipe
+     * @param source - parquet file
+     * @param addNameField - if add filename as column
+     * @param maxCount - max number of rows
+     * @throws Exception - exception
+     */
+    public static void writeDocuments( OutputPipe output, Source source, boolean addNameField, int maxCount ) throws Exception {
+        String fileName = ActivityUtils.resourceNameFromSource( source );
+        PolyString polyName = PolyString.of( fileName );
+        try ( ParquetSourceReader reader = new ParquetSourceReader( source ) ) {
+            long written = 0;
+            for ( Group row = reader.next(); row != null; row = reader.next() ) {
+                if ( maxCount >= 0 && written >= maxCount ) {
+                    return;
+                }
+                PolyDocument doc = parquetDocValueExtractor.extractDocument(
+                        row,
+                        reader.getProjectionSchema(),
+                        PolyString.of( fileName + "#" + reader.getCurrentRowNumber() )
+                );
+                if ( addNameField ) {
+                    doc.put( FILE_NAME_KEY, polyName );
+                }
+                if ( !output.put( doc ) ) {
+                    return;
+                }
+                written++;
+            }
+        }
+    }
+
+
+    /**
+     * Read parquet file and write table to the output pipe
+     *
+     * @param output - output pipe
+     * @param source - parquet file
+     * @param addNameCol - if add filename as additional column
+     * @param maxCount - rows to read/write
+     * @throws Exception - exception
+     */
+    public static void writeRows( OutputPipe output, Source source, boolean addNameCol, int maxCount ) throws Exception {
+        String fileName = ActivityUtils.resourceNameFromSource( source );
+        PolyString polyName = PolyString.of( fileName );
+        try ( ParquetSourceReader reader = new ParquetSourceReader( source ) ) {
+            MessageType schema = reader.getProjectionSchema();
+            long written = 0;
+            for ( Group row = reader.next(); row != null; row = reader.next() ) {
+                if ( maxCount >= 0 && written >= maxCount ) {
+                    return;
+                }
+                List<PolyValue> values = new ArrayList<>( schema.getFieldCount() + (addNameCol ? 2 : 1) );
+                values.add( PolyLong.of( reader.getCurrentRowNumber() ) );
+                for ( int i = 0; i < schema.getFieldCount(); i++ ) {
+                    Type field = schema.getType( i );
+                    var polyValue = parquetRelValueExtractor.extractValue( row, i, field );
+                    // PolyDocument type provided as json string
+                    if (polyValue instanceof PolyDocument document) {
+                        polyValue = document.toPolyJson();
+                    }
+                    values.add( polyValue );
+                }
+                if ( addNameCol ) {
+                    values.add( polyName );
+                }
+                if ( !output.put( values ) ) {
+                    return;
+                }
+                written++;
+            }
+        }
+    }
+}
diff --git a/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/dag/activities/ActivityRegistryTest.java b/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/dag/activities/ActivityRegistryTest.java
index 89989af261..9434c92c88 100644
--- a/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/dag/activities/ActivityRegistryTest.java
+++ b/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/dag/activities/ActivityRegistryTest.java
@@ -71,6 +71,12 @@ public void serializationTest() {
         assertTrue( serialized.length() > 100 ); // 100 is somewhat arbitrary, just ensure it has some content
     }
 
+    // validate Parquet Activity registration
+    @Test
+    public void extractParquetActivityIsRegistered() {
+        assertTrue( ActivityRegistry.getRegistry().containsKey( "extractParquet" ) );
+    }
+
 
     @Test
     public void checkActivityPortsTest() {

From c307926ffc861bd1c50c290ebc941d99cb4ec576 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Sat, 11 Apr 2026 12:11:41 +0200
Subject: [PATCH 12/46] Workflow - Load Parquet

---
 plugins/parquet-adapter/docs/README.md        |  92 +++++-
 .../images/extract_workflow_act_settings.png  | Bin 0 -> 20863 bytes
 .../images/load_workflow_act_settings.png     | Bin 0 -> 27339 bytes
 .../parquet-adapter/docs/images/workflow1.png | Bin 0 -> 41963 bytes
 .../parquet-adapter/docs/images/workflow2.png | Bin 0 -> 40109 bytes
 .../shared/execution/BufferedIterator.java    |  63 ++++
 .../shared/io/ParquetSourceReader.java        |   7 +-
 .../shared/io/ParquetSourceWriter.java        | 260 +++++++++++++++
 .../schema/ParquetFieldNameNormalizer.java    |  32 ++
 .../schema/ParquetMessageTypeBuilder.java     |  99 ++++++
 .../shared/schema/ParquetTypeConverter.java   |  14 +
 .../shared/schema/inference/FieldSchema.java  |  48 +++
 .../shared/schema/inference/SchemaState.java  | 168 ++++++++++
 .../shared/schema/inference/ValueKind.java    |  31 ++
 .../shared/schema/inference/ValueSchema.java  | 309 ++++++++++++++++++
 ...ivity.java => ParquetExtractActivity.java} |  29 +-
 .../impl/load/ParquetLoadActivity.java        | 183 +++++++++++
 .../ParquetWorkflowExtractSupport.java}       |   6 +-
 .../parquet/ParquetWorkflowLoadSupport.java   | 225 +++++++++++++
 19 files changed, 1545 insertions(+), 21 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/images/extract_workflow_act_settings.png
 create mode 100644 plugins/parquet-adapter/docs/images/load_workflow_act_settings.png
 create mode 100644 plugins/parquet-adapter/docs/images/workflow1.png
 create mode 100644 plugins/parquet-adapter/docs/images/workflow2.png
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/BufferedIterator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceWriter.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetMessageTypeBuilder.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/FieldSchema.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/SchemaState.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueKind.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueSchema.java
 rename plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/{ExtractParquetActivity.java => ParquetExtractActivity.java} (88%)
 create mode 100644 plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/load/ParquetLoadActivity.java
 rename plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/{io/parquet/ParquetWorkflowSupport.java => parquet/ParquetWorkflowExtractSupport.java} (97%)
 create mode 100644 plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowLoadSupport.java

diff --git a/plugins/parquet-adapter/docs/README.md b/plugins/parquet-adapter/docs/README.md
index 5fa028cb26..91dca1f947 100644
--- a/plugins/parquet-adapter/docs/README.md
+++ b/plugins/parquet-adapter/docs/README.md
@@ -1,5 +1,7 @@
 # Parquet Adapter Implementation
 
+---
+
 ## Overview
 
 The Parquet adapter exposes `.parquet` files as Polypheny source tables.
@@ -206,30 +208,97 @@ The following figure shows an example of the displayed schema information.
 ## Query Filter
 ![Schema display](images/query_filter.png)
 
+
+
+
 # Workflow
 
+---
+
 ## 1. Extract Parquet
 
-### Code Changes
+- ### ParquetExtractActivity
 
-- ### `org.polypheny.db.workflow.dag.activities.impl.extract.ExtractParquetActivity`
+`org.polypheny.db.workflow.dag.activities.impl.extract.ParquetExtractActivity`
 
 Workflow node definition for reading Parquet files into a workflow output pipe:
 1. It defines how the activity appears in the workflow UI using settings. This provides all needed information for editor to show options.
 2. It decides what type of data the node will output: Relational or Document
 3. It executes the extraction at runtime as follows: the activity resolves the input files, iterates over them, and delegates the actual reading/conversion work to `ParquetWorkflowSupport.java`, which turns Parquet rows into either documents or relational rows and pushes them into the workflow output pipe.
 
-- ### `org.polypheny.db.workflow.io.parquet.ParquetWorkflowSupport`
+#### ***Settings***
+
+#### `file`
+- Display name: `File Location`
+- Type: file setting
+- Required: yes
+- Multiple values: yes
+- Supported source types:
+    - absolute file path
+    - URL
+- Purpose:
+  Select one or more Parquet files, or a folder containing Parquet files, to extract from.
+- Behavior:
+  If multiple files are selected, the activity outputs the union of their rows.
+
+#### `outputModel`
+- Display name: `Output Type`
+- Type: enum
+- Required: yes
+- Default: `document`
+- Available values:
+    - `document`
+    - `relational`
+- Purpose:
+  Defines whether the extracted Parquet rows are exposed as workflow documents or as a relational table.
+
+#### `nameField`
+- Display name: `Add File Name Field`
+- Type: boolean
+- Required: no
+- Default: `false`
+- Purpose:
+  Adds the source file name to the output.
+- Behavior:
+    - in document mode: adds a `fileName` field
+    - in relational mode: adds a `fileName` column
+
+#### `maxCount`
+- Display name: `Maximum Row Count`
+- Type: integer
+- Required: no
+- Default: `-1`
+- Minimum: `-1`
+- Group: advanced
+- Purpose:
+  Limits how many rows are extracted per file.
+- Behavior:
+    - `-1`: extract all rows
+    - `>= 0`: extract at most that many rows from each selected file
+
+#### ***Notes***
+
+- The activity has no input ports and one output port of type `ANY`.
+- The concrete output type is determined from `outputModel`.
+- In relational mode, the schema is derived from the selected Parquet file.
+- In document mode, the output is a document stream with generated document IDs where needed.
+
+- ### ParquetWorkflowExtractSupport
+
+`org.polypheny.db.workflow.parquet.ParquetWorkflowExtractSupport`
 
 Helper class contains Parquet extraction logic for the workflow engine.
 It maps Parquet schemas to workflow output types, estimates row counts, generates dynamic activity names, and converts Parquet rows into either Polypheny documents or relational tuples.
 
+
+
 ### Tests:
 
 - Read parquet file as Relational Data Source and store to CSV file
 - Read parquet file as Document Data Source and store to JSON file
 - Add file name as column
 - Add key id column
+- Set Maximum number of rows = 5 in Advanced settings
 
 
 ![Schema display](images/workflow1.png)
@@ -246,6 +315,23 @@ To run tests for parquet adapter:
 - DBMS as a test dependency
 - Polypheny JDBC driver dependency
 
+
+TODO: describe all properties of activities
+CONFLICT_MODE property
+How the modes behave:
+
+stringify
+
+if values are incompatible, fall back to ValueSchema.stringType()
+this means the field will be written as a Parquet string column/field
+later values are converted to string when written
+fail
+
+throw a GenericRuntimeException
+the export stops because the sampled values do not agree on a schema
+
+
+
 ## `ParquetPluginTest.java`
 contains the following tests:
 1. `importsAllTablesAndReadsRows()`
diff --git a/plugins/parquet-adapter/docs/images/extract_workflow_act_settings.png b/plugins/parquet-adapter/docs/images/extract_workflow_act_settings.png
new file mode 100644
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diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/BufferedIterator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/BufferedIterator.java
new file mode 100644
index 0000000000..9b9b9f3e85
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/BufferedIterator.java
@@ -0,0 +1,63 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.execution;
+
+import java.util.Iterator;
+import java.util.List;
+import java.util.function.Function;
+
+/**
+ * Represents a single iterator that replays the buffered rows first
+ * and then continues with the remaining original input rows.
+ */
+public class BufferedIterator<T, R> implements Iterator<R> {
+
+    private final List<R> buffered;
+    private final Iterator<T> remainder;
+    private final Function<T, R> converter;
+    private int index = 0;
+
+
+    /**
+     * Creates an instance of the iterator with the provided values.
+     *
+     * @param buffered - first sample rows used to infer schema (buffered)
+     * @param remainder - remaining rows
+     * @param converter -
+     */
+    public BufferedIterator( List<R> buffered, Iterator<T> remainder, Function<T, R> converter ) {
+        this.buffered = buffered;
+        this.remainder = remainder;
+        this.converter = converter;
+    }
+
+
+    @Override
+    public boolean hasNext() {
+        return index < buffered.size() || remainder.hasNext();
+    }
+
+
+    @Override
+    public R next() {
+        if ( index < buffered.size() ) {
+            return buffered.get( index++ );
+        }
+        return converter.apply( remainder.next() );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
index 74b8e4defe..46e6fbd0b0 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
@@ -17,6 +17,7 @@
 package org.polypheny.db.adapter.parquet.shared.io;
 
 import java.io.IOException;
+import java.net.URI;
 import java.util.ArrayList;
 import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
@@ -70,7 +71,8 @@ public ParquetSourceReader(Source source, AtomicBoolean cancelFlag, int[] fields
         this.cancelFlag = cancelFlag == null ? new AtomicBoolean( false ) : cancelFlag;
 
         try {
-            Path path = new Path( source.url().toURI() );
+            URI uri = source.isFile() ? source.file().toURI() : source.url().toURI();
+            Path path = new Path( uri );
             Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
             var inputFile = HadoopInputFile.fromPath( path, conf );
 
@@ -135,7 +137,8 @@ public long getEstimatedRowCount() {
 
     public static MessageType readSchema( Source source ) {
         try {
-            Path path = new Path( source.url().toURI() );
+            URI uri = source.isFile() ? source.file().toURI() : source.url().toURI();
+            Path path = new Path( uri );
             Configuration conf = HadoopConfigurationFactory.create( ParquetSourceReader.class.getClassLoader() );
             var inputFile = HadoopInputFile.fromPath( path, conf );
             try ( org.apache.parquet.hadoop.ParquetFileReader schemaReader = org.apache.parquet.hadoop.ParquetFileReader.open( inputFile ) ) {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceWriter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceWriter.java
new file mode 100644
index 0000000000..db37658cd5
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceWriter.java
@@ -0,0 +1,260 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io;
+
+import java.io.File;
+import java.io.IOException;
+import java.util.Iterator;
+import java.util.List;
+import org.apache.hadoop.fs.Path;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.ExampleParquetWriter;
+import org.apache.parquet.hadoop.metadata.CompressionCodecName;
+import org.apache.parquet.io.api.Binary;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.FieldSchema;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.SchemaState;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.ValueKind;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.ValueSchema;
+import org.polypheny.db.algebra.type.DocumentType;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.document.PolyDocument;
+
+public class ParquetSourceWriter implements AutoCloseable {
+
+    public static final String COMPRESSION_SNAPPY = "snappy";
+    public static final String COMPRESSION_GZIP = "gzip";
+    public static final String COMPRESSION_UNCOMPRESSED = "uncompressed";
+
+    private static final ParquetTypeConverter parquetTypeConverter = new ParquetTypeConverter();
+
+    private final ParquetWriter<Group> writer;
+    private final SimpleGroupFactory groupFactory;
+    private final SchemaState schemaState;
+    private final boolean keepId;
+
+
+    /**
+     * Create Parquet writer
+     *
+     * @param file - file to write
+     * @param schema - parquet type schema
+     * @param compression - compression type setting
+     */
+    public ParquetSourceWriter( File file, MessageType schema, String compression, SchemaState schemaState, boolean keepId ) {
+        try {
+            this.schemaState = schemaState;
+            this.keepId = keepId;
+            this.groupFactory = new SimpleGroupFactory( schema );
+            this.writer = ExampleParquetWriter.builder( new Path( file.toURI() ) )
+                    .withType( schema )
+                    .withCompressionCodec( getCompressionCodec( compression ) )
+                    .build();
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Unable to open parquet file for writing: " + file.getAbsolutePath(), e );
+        }
+    }
+
+
+    @FunctionalInterface
+    public interface ProgressListener {
+
+        void rowWritten( long written );
+
+    }
+
+
+    /**
+     * Write rows to parquet file
+     */
+    public <T> void writeRows( Iterator<T> rows, ProgressListener progressListener ) {
+        long written = 0;
+
+        while ( rows.hasNext() ) {
+            try {
+                var row = rows.next();
+
+                // creates a new empty Parquet row object according to the given Parquet schema
+                Group group = groupFactory.newGroup();
+                if ( row instanceof PolyDocument document ) {
+                    populateGroupWithDocument( group, schemaState.getFields(), document, keepId );
+                } else {
+                    //noinspection unchecked
+                    populateGroupWithRelationalRow( group, schemaState.getFields(), (List<PolyValue>) row );
+                }
+
+                writer.write( group );
+                written++;
+
+                if ( progressListener != null ) {
+                    progressListener.rowWritten( written );
+                }
+            } catch ( Exception e ) {
+                throw new GenericRuntimeException( "Error while writing parquet data", e );
+            }
+        }
+    }
+
+
+    /**
+     * Get parquet compression format according to chosen settings
+     *
+     * @param compression - settings
+     * @return CompressionCodecName
+     */
+    private static CompressionCodecName getCompressionCodec( String compression ) {
+        return switch ( compression ) {
+            case COMPRESSION_SNAPPY -> CompressionCodecName.SNAPPY;
+            case COMPRESSION_GZIP -> CompressionCodecName.GZIP;
+            case COMPRESSION_UNCOMPRESSED -> CompressionCodecName.UNCOMPRESSED;
+            default -> throw new IllegalArgumentException( "Unknown compression: " + compression );
+        };
+    }
+
+
+    /**
+     * Close writer
+     *
+     * @throws IOException - exception
+     */
+    @Override
+    public void close() throws IOException {
+        writer.close();
+    }
+
+
+    /**
+     * write row
+     *
+     * @param group - empty parquet row with schema columns
+     * @param fields - columns
+     * @param row - input data
+     */
+    private static void populateGroupWithRelationalRow( Group group, List<FieldSchema> fields, List<PolyValue> row ) {
+        for ( FieldSchema field : fields ) {
+            populateGroupWithValue( group, field, row.get( field.getSourceIndex() ) );
+        }
+    }
+
+
+    private static void populateGroupWithSingleValue( Group group, int fieldIndex, ValueSchema schema, PolyValue value ) {
+        if ( schema.kind() == ValueKind.GROUP ) {
+            if ( !value.isDocument() ) {
+                throw new GenericRuntimeException( "Expected a document value for nested Parquet group field." );
+            }
+            Group child = group.addGroup( fieldIndex );
+            populateGroupWithDocument( child, schema.nested(), value.asDocument(), true );
+            return;
+        }
+        addPrimitiveValue( group, fieldIndex, schema, value );
+    }
+
+
+    private static void populateGroupWithRepeatedValue( Group group, int fieldIndex, ValueSchema schema, PolyValue value ) {
+        if ( value == null || value.isNull() ) {
+            return;
+        }
+        if ( !value.isList() ) {
+            if ( schema.kind() == ValueKind.GROUP ) {
+                throw new GenericRuntimeException( "Expected a list for repeated group field." );
+            }
+            addPrimitiveValue( group, fieldIndex, schema, parquetTypeConverter.valueToPolyString( value ) );
+            return;
+        }
+
+        for ( PolyValue item : value.asList() ) {
+            if ( item == null || item.isNull() ) {
+                continue;
+            }
+            if ( schema.kind() == ValueKind.GROUP ) {
+                if ( !item.isDocument() ) {
+                    throw new GenericRuntimeException( "Expected a document inside repeated group field." );
+                }
+                Group child = group.addGroup( fieldIndex );
+                populateGroupWithDocument( child, schema.nested(), item.asDocument(), true );
+            } else {
+                addPrimitiveValue( group, fieldIndex, schema, item );
+            }
+        }
+    }
+
+
+    private static void populateGroupWithDocument( Group group, List<FieldSchema> fields, PolyDocument document, boolean keepId ) {
+        for ( FieldSchema field : fields ) {
+            if ( !keepId && DocumentType.DOCUMENT_ID.equals( field.getSourceName() ) ) {
+                continue;
+            }
+            populateGroupWithValue( group, field, document.get( PolyString.of( field.getSourceName() ) ) );
+        }
+    }
+
+
+    private static void populateGroupWithValue( Group group, FieldSchema field, PolyValue value ) {
+        if ( value == null || value.isNull() ) {
+            return;
+        }
+
+        int fieldIndex = group.getType().getFieldIndex( field.getParquetName() );
+        if ( field.getValueSchema().repeated() ) {
+            if ( !value.isList() ) {
+                value = parquetTypeConverter.valueToPolyString( value );
+            }
+            populateGroupWithRepeatedValue( group, fieldIndex, field.getValueSchema().elementSchema(), value );
+            return;
+        }
+        populateGroupWithSingleValue( group, fieldIndex, field.getValueSchema(), value );
+    }
+
+
+    /**
+     * Write one scalar (primitive) value into a Parquet Group field
+     *
+     * @param group - row to write
+     * @param fieldIndex - column to write
+     * @param schema - ValueSchema
+     * @param value - PolyValue to write
+     */
+    private static void addPrimitiveValue( Group group, int fieldIndex, ValueSchema schema, PolyValue value ) {
+        if ( value == null || value.isNull() ) {
+            return;
+        }
+
+        PrimitiveType type = (PrimitiveType) group.getType().getType( fieldIndex );
+        PolyValue valueToWrite = schema.kind() == ValueKind.STRING ? parquetTypeConverter.valueToPolyString( value ) : value;
+        Object parquetValue = parquetTypeConverter.fromPolyValueToParquetObj( type, valueToWrite );
+        if ( parquetValue == null ) {
+            throw new GenericRuntimeException( "Value '" + value + "' is incompatible with the inferred Parquet schema." );
+        }
+
+        switch ( schema.primitiveTypeName() ) {
+            case BOOLEAN -> group.add( fieldIndex, (boolean) parquetValue );
+            case INT32 -> group.add( fieldIndex, (int) parquetValue );
+            case INT64 -> group.add( fieldIndex, (long) parquetValue );
+            case FLOAT -> group.add( fieldIndex, (float) parquetValue );
+            case DOUBLE -> group.add( fieldIndex, (double) parquetValue );
+            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> group.add( fieldIndex, (Binary) parquetValue );
+            default -> throw new GenericRuntimeException( "Unsupported Parquet primitive type: " + schema.primitiveTypeName() );
+        }
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetFieldNameNormalizer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetFieldNameNormalizer.java
index 151da66f77..0f281d3a75 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetFieldNameNormalizer.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetFieldNameNormalizer.java
@@ -16,7 +16,12 @@
 
 package org.polypheny.db.adapter.parquet.shared.schema;
 
+import org.polypheny.db.adapter.parquet.shared.schema.inference.FieldSchema;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.ValueKind;
+import java.util.LinkedHashSet;
+import java.util.List;
 import java.util.Locale;
+import java.util.Set;
 
 /**
  * Shared Parquet field-name normalization used across Parquet consumers.
@@ -31,4 +36,31 @@ public static String normalizeFieldName( String name ) {
         return name.toLowerCase( Locale.ROOT ).trim().replaceAll( "[^a-z0-9_]+", "_" );
     }
 
+
+    /**
+     * Normalize and uniquify Parquet names:
+     * convert source names into valid Parquet-safe names and resolves duplicates
+     *
+     * @param fields - schema fields
+     */
+    public static void uniquifyParquetNames( List<FieldSchema> fields ) {
+        Set<String> seen = new LinkedHashSet<>();
+        for ( FieldSchema field : fields ) {
+            String baseName = normalizeFieldName( field.getSourceName() );
+            if ( baseName.isBlank() ) {
+                baseName = "field";
+            }
+            String candidate = baseName;
+            int suffix = 2;
+            while ( seen.contains( candidate ) ) {
+                candidate = baseName + "_" + suffix++;
+            }
+            field.setParquetName( candidate );
+            seen.add( candidate );
+            if ( field.getValueSchema().kind() == ValueKind.GROUP ) {
+                uniquifyParquetNames( field.getValueSchema().nested() );
+            }
+        }
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetMessageTypeBuilder.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetMessageTypeBuilder.java
new file mode 100644
index 0000000000..f16ef940dc
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetMessageTypeBuilder.java
@@ -0,0 +1,99 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.schema;
+
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Type;
+import org.apache.parquet.schema.Types;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.FieldSchema;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.SchemaState;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.ValueKind;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.ValueSchema;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * Builds a Parquet MessageType from inferred workflow field schemas,
+ * including primitive, repeated, and nested group fields.
+ */
+public class ParquetMessageTypeBuilder {
+
+    private final SchemaState schemaState;
+    private final String schemaName;
+
+
+    public ParquetMessageTypeBuilder( SchemaState schemaState, String schemaName ) {
+        this.schemaState = schemaState;
+        this.schemaName = schemaName;
+    }
+
+
+    public MessageType build() {
+        if ( schemaState.getFields().isEmpty() ) {
+            throw new GenericRuntimeException( "Cannot write Parquet file without any fields." );
+        }
+
+        Types.MessageTypeBuilder builder = Types.buildMessage();
+        for ( FieldSchema field : schemaState.getFields() ) {
+            builder.addField( toParquetType( field.getParquetName(), field.getValueSchema() ) );
+        }
+        return builder.named( this.schemaName );
+    }
+
+    private static Type toParquetType( String name, ValueSchema schema ) {
+        if ( schema.repeated() ) {
+            return toRepeatedParquetType( name, schema.elementSchema() );
+        }
+        return toSingleParquetType( name, schema );
+    }
+
+
+    private static Type toRepeatedParquetType( String name, ValueSchema schema ) {
+        if ( schema.kind() == ValueKind.GROUP ) {
+            List<Type> children = new ArrayList<>( schema.nested().size() );
+            for ( FieldSchema child : schema.nested() ) {
+                children.add( toParquetType( child.getParquetName(), child.getValueSchema() ) );
+            }
+            return new org.apache.parquet.schema.GroupType( Type.Repetition.REPEATED, name, children );
+        }
+        return buildPrimitiveType( name, schema, Type.Repetition.REPEATED );
+    }
+
+
+    private static Type toSingleParquetType( String name, ValueSchema schema ) {
+        if ( schema.kind() == ValueKind.GROUP ) {
+            List<Type> children = new ArrayList<>( schema.nested().size() );
+            for ( FieldSchema child : schema.nested() ) {
+                children.add( toParquetType( child.getParquetName(), child.getValueSchema() ) );
+            }
+            return new org.apache.parquet.schema.GroupType( Type.Repetition.OPTIONAL, name, children );
+        }
+        return buildPrimitiveType( name, schema, Type.Repetition.OPTIONAL );
+    }
+
+
+    private static Type buildPrimitiveType( String name, ValueSchema schema, Type.Repetition repetition ) {
+        Types.PrimitiveBuilder<PrimitiveType> builder = Types.primitive( schema.primitiveTypeName(), repetition );
+        if ( schema.logicalType() != null ) {
+            builder.as( schema.logicalType() );
+        }
+        return builder.named( name );
+    }
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
index d9ba46f01b..a62e65b1e2 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
@@ -166,6 +166,20 @@ public Object fromPolyValueToParquetObj( PrimitiveType primitive, PolyValue lite
     }
 
 
+    /**
+     * Convert PolyValue to PolyString
+     * @param value - to convert
+     * @return PolyString
+     */
+    public PolyString valueToPolyString( PolyValue value ) {
+        return switch ( value.getType() ) {
+            case NULL -> PolyString.of( null );
+            case ANY, DOCUMENT, GRAPH, NODE, EDGE, PATH, ARRAY, MAP -> value.toPolyJson();
+            default -> PolyString.convert( value );
+        };
+    }
+
+
     private Object fromStringToDate( PrimitiveType primitive, String literal ) {
         LogicalTypeAnnotation logical = primitive.getLogicalTypeAnnotation();
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/FieldSchema.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/FieldSchema.java
new file mode 100644
index 0000000000..73488f20bd
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/FieldSchema.java
@@ -0,0 +1,48 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.schema.inference;
+
+import lombok.Getter;
+import lombok.Setter;
+
+/**
+ *  Information about a single column/field
+ */
+@Getter
+public class FieldSchema {
+
+    private final String sourceName; // original name from the workflow input
+    private final int sourceIndex; // for relational input
+    @Setter
+    private String parquetName; // normalized field name that will actually be used in the Parquet schema
+    @Setter
+    private ValueSchema valueSchema; // kind of value the field contains
+
+
+    public FieldSchema( String sourceName, String parquetName, int sourceIndex, boolean repeated, ValueSchema valueSchema ) {
+        this.sourceName = sourceName;
+        this.parquetName = parquetName;
+        this.sourceIndex = sourceIndex;
+        this.valueSchema = repeated ? ValueSchema.repeated( valueSchema ) : valueSchema;
+    }
+
+
+    public FieldSchema copy() {
+        return new FieldSchema( sourceName, parquetName, sourceIndex, false, valueSchema.copy() );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/SchemaState.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/SchemaState.java
new file mode 100644
index 0000000000..0dae326dcb
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/SchemaState.java
@@ -0,0 +1,168 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.schema.inference;
+
+import lombok.Getter;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.algebra.type.AlgDataTypeField;
+import org.polypheny.db.algebra.type.DocumentType;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.document.PolyDocument;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+
+import static org.polypheny.db.adapter.parquet.shared.schema.ParquetFieldNameNormalizer.uniquifyParquetNames;
+
+@Getter
+public class SchemaState {
+
+    public static final String CONFLICT_STRINGIFY = "stringify";
+    public static final String CONFLICT_FAIL = "fail";
+
+    private final List<FieldSchema> fields = new ArrayList<>();
+    // defines what to do when sampled values for the same field are incompatible during schema inference
+    // defined in activity settings
+    private final String conflictMode;
+
+
+    public SchemaState( String conflictMode ) {
+        this.conflictMode = conflictMode;
+    }
+
+
+    /**
+     * creates the initial schema model for relational export before any sample rows are inspected
+     *
+     * @param inputType - relational input type
+     * @param keepPk - boolean
+     */
+    public void init( AlgDataType inputType, boolean keepPk ) {
+        List<AlgDataTypeField> fields = inputType.getFields();
+        int start = keepPk ? 0 : 1;
+        for ( int i = start; i < fields.size(); i++ ) {
+            AlgDataTypeField field = fields.get( i );
+            String sourceName = field.getName();
+            // create an entry for each exported column
+            ValueSchema valueSchema = fromPolyType( field.getType().getPolyType() );
+            if ( valueSchema == null ) {
+                valueSchema = ValueSchema.stringType();
+            }
+            this.fields.add( new FieldSchema( sourceName, sourceName, i, false, valueSchema ) );
+        }
+        // normalize and uniquify names of parquet file columns
+        uniquifyParquetNames( this.fields );
+    }
+
+
+    public void addField( FieldSchema field ) {
+        this.fields.add( field );
+    }
+
+
+    /**
+     * Takes a sampled relational row and merges each exported column’s actual value into the current inferred Parquet schema
+     *
+     * @param row - input data
+     * @param inputType - contains exported columns
+     * @param keepPk - boolean
+     */
+    public void mergeRelationalRowSchema( List<PolyValue> row, AlgDataType inputType, boolean keepPk ) {
+        // read fields
+        List<AlgDataTypeField> fields = inputType.getFields();
+        int start = keepPk ? 0 : 1;
+        int schemaIndex = 0;
+        // iterate over exported columns of sampled row
+        for ( int i = start; i < fields.size(); i++ ) {
+            PolyValue value = row.get( i );
+            FieldSchema schema = this.fields.get( schemaIndex++ );
+            ValueSchema inferredValueSchema = ValueSchema.inferSchema( value, conflictMode );
+            if ( inferredValueSchema == null ) {
+                continue;
+            }
+            // if the value is not null, merge it into the existing field schema
+            ValueSchema mergedValueSchema = merge( schema.getValueSchema(), inferredValueSchema );
+            schema.setValueSchema( mergedValueSchema );
+        }
+    }
+
+
+    /**
+     * Updates the inferred Parquet schema using one sampled input document
+     *
+     * @param document - input row
+     * @param keepId - boolean
+     */
+    public void mergeDocumentSchema( PolyDocument document, boolean keepId ) {
+        // iterate through all document fields
+        for ( Map.Entry<PolyString, PolyValue> entry : document.entrySet() ) {
+            String sourceName = entry.getKey().value;
+            if ( !keepId && DocumentType.DOCUMENT_ID.equals( sourceName ) ) {
+                continue; // skip id
+            }
+            // infer schema from value
+            PolyValue value = entry.getValue();
+            ValueSchema inferred = ValueSchema.inferSchema( value, conflictMode );
+            if ( inferred == null ) {
+                continue;
+            }
+            // merge information into running schema state
+            mergeFieldValueSchema( sourceName, inferred );
+        }
+        // normalize and uniquify names of parquet file columns
+        uniquifyParquetNames( this.fields );
+    }
+
+
+    private void mergeFieldValueSchema( String sourceName, ValueSchema valueSchema ) {
+        for ( FieldSchema field : fields ) {
+            if ( field.getSourceName().equals( sourceName ) ) {
+                field.setValueSchema( merge( field.getValueSchema(), valueSchema ) );
+                return;
+            }
+        }
+        fields.add( new FieldSchema( sourceName, sourceName, -1, false, valueSchema ) );
+    }
+
+
+    private ValueSchema merge( ValueSchema left, ValueSchema right ) {
+        return left.mergeValueSchemas( right, conflictMode );
+    }
+
+
+    private static ValueSchema fromPolyType( PolyType type ) {
+        return switch ( type ) {
+            case BOOLEAN -> ValueSchema.booleanType();
+            case TINYINT, SMALLINT, INTEGER -> ValueSchema.int32Type();
+            case BIGINT -> ValueSchema.int64Type();
+            case FLOAT, REAL -> ValueSchema.floatType();
+            case DOUBLE, DECIMAL -> ValueSchema.doubleType();
+            case DATE -> ValueSchema.dateType();
+            case TIME -> ValueSchema.timeType();
+            case TIMESTAMP -> ValueSchema.timestampType();
+            case VARBINARY, BINARY -> ValueSchema.binaryType();
+            case ARRAY -> ValueSchema.repeated( ValueSchema.stringType() );
+            case DOCUMENT -> ValueSchema.groupType();
+            default -> ValueSchema.stringType();
+        };
+    }
+
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueKind.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueKind.java
new file mode 100644
index 0000000000..6d5b500d77
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueKind.java
@@ -0,0 +1,31 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.schema.inference;
+
+public enum ValueKind {
+    BOOLEAN,
+    INT32,
+    INT64,
+    FLOAT,
+    DOUBLE,
+    STRING,
+    BINARY,
+    DATE,
+    TIME,
+    TIMESTAMP,
+    GROUP
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueSchema.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueSchema.java
new file mode 100644
index 0000000000..6dd4e0b6e6
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueSchema.java
@@ -0,0 +1,309 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.schema.inference;
+
+import org.apache.parquet.schema.LogicalTypeAnnotation;
+import org.apache.parquet.schema.PrimitiveType;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyList;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.document.PolyDocument;
+
+import java.util.ArrayList;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+
+import static org.polypheny.db.adapter.parquet.shared.schema.ParquetFieldNameNormalizer.uniquifyParquetNames;
+
+
+/**
+ * Describes value the parquet field contains:
+ * Represent Parquet-compatible value shapes during inference and writing,
+ * before they are converted into actual Parquet Type objects
+ *
+ * @param kind - logical category of values
+ * @param primitiveTypeName - primitive type to use when it is primitive value
+ * @param logicalType - Parquet logical annotation
+ * @param nested - used when the value is a nested object - List<FieldSchema>
+ * @param repeated - whether the value is a repeated/list value
+ * @param elementSchema - if repeated = true, this describes the schema of each list element
+ */
+public record ValueSchema( ValueKind kind, PrimitiveType.PrimitiveTypeName primitiveTypeName, LogicalTypeAnnotation logicalType, List<FieldSchema> nested, boolean repeated, ValueSchema elementSchema ) {
+
+    public ValueSchema( ValueKind kind, PrimitiveType.PrimitiveTypeName primitiveTypeName, LogicalTypeAnnotation logicalType, List<FieldSchema> nested, boolean repeated, ValueSchema elementSchema ) {
+        this.kind = kind;
+        this.primitiveTypeName = primitiveTypeName;
+        this.logicalType = logicalType;
+        this.nested = nested == null ? new ArrayList<>() : nested;
+        this.repeated = repeated;
+        this.elementSchema = elementSchema;
+    }
+
+
+    //region factory methods
+    public static ValueSchema booleanType() {
+        return new ValueSchema( ValueKind.BOOLEAN, PrimitiveType.PrimitiveTypeName.BOOLEAN, null, null, false, null );
+    }
+
+
+    public static ValueSchema int32Type() {
+        return new ValueSchema( ValueKind.INT32, PrimitiveType.PrimitiveTypeName.INT32, null, null, false, null );
+    }
+
+
+    public static ValueSchema int64Type() {
+        return new ValueSchema( ValueKind.INT64, PrimitiveType.PrimitiveTypeName.INT64, null, null, false, null );
+    }
+
+
+    public static ValueSchema floatType() {
+        return new ValueSchema( ValueKind.FLOAT, PrimitiveType.PrimitiveTypeName.FLOAT, null, null, false, null );
+    }
+
+
+    public static ValueSchema doubleType() {
+        return new ValueSchema( ValueKind.DOUBLE, PrimitiveType.PrimitiveTypeName.DOUBLE, null, null, false, null );
+    }
+
+
+    public static ValueSchema stringType() {
+        return new ValueSchema( ValueKind.STRING, PrimitiveType.PrimitiveTypeName.BINARY, LogicalTypeAnnotation.stringType(), null, false, null );
+    }
+
+
+    public static ValueSchema binaryType() {
+        return new ValueSchema( ValueKind.BINARY, PrimitiveType.PrimitiveTypeName.BINARY, null, null, false, null );
+    }
+
+
+    public static ValueSchema dateType() {
+        return new ValueSchema( ValueKind.DATE, PrimitiveType.PrimitiveTypeName.INT32, LogicalTypeAnnotation.dateType(), null, false, null );
+    }
+
+
+    public static ValueSchema timeType() {
+        return new ValueSchema( ValueKind.TIME, PrimitiveType.PrimitiveTypeName.INT64, LogicalTypeAnnotation.timeType( true, LogicalTypeAnnotation.TimeUnit.MILLIS ), null, false, null );
+    }
+
+
+    public static ValueSchema timestampType() {
+        return new ValueSchema( ValueKind.TIMESTAMP, PrimitiveType.PrimitiveTypeName.INT64, LogicalTypeAnnotation.timestampType( true, LogicalTypeAnnotation.TimeUnit.MILLIS ), null, false, null );
+    }
+
+
+    public static ValueSchema groupType() {
+        return new ValueSchema( ValueKind.GROUP, null, null, new ArrayList<>(), false, null );
+    }
+
+
+    public static ValueSchema repeated( ValueSchema elementSchema ) {
+        return new ValueSchema( elementSchema.kind, elementSchema.primitiveTypeName, elementSchema.logicalType, copyNested( elementSchema.nested ), true, elementSchema.copyNonRepeated() );
+    }
+    //endregion
+
+
+    /**
+     * Infer field schema from input value
+     *
+     * @param value - input PolyValue
+     * @param conflictMode - defines what to do when sampled values for the same field are incompatible during schema inference
+     * @return ValueSchema
+     */
+    public static ValueSchema inferSchema( PolyValue value, String conflictMode ) {
+        if ( value == null || value.isNull() ) {
+            return null;
+        }
+        if ( value.isList() ) {
+            return inferListSchema( value.asList(), conflictMode );
+        }
+        if ( value.isDocument() ) {
+            return inferDocumentSchema( value.asDocument(), conflictMode );
+        }
+        if ( value.isMap() ) {
+            return ValueSchema.stringType();
+        }
+        return inferPrimitiveSchema( value );
+    }
+
+
+    /**
+     * Called when the current value schema is a nested group/document
+     * look for a child field with a given name inside group
+     * if it already exists, merge the new child schema into the existing one
+     * if it does not exist yet, add it as a new child field
+     *
+     * @param sourceName - input field name
+     * @param valueSchema - schema to infer
+     * @param conflictMode - conflict mode
+     */
+    public void mergeNested( String sourceName, ValueSchema valueSchema, String conflictMode ) {
+        for ( FieldSchema child : nested ) {
+            if ( child.getSourceName().equals( sourceName ) ) {
+                child.setValueSchema( child.getValueSchema().mergeValueSchemas( valueSchema, conflictMode ) );
+                return;
+            }
+        }
+        nested.add( new FieldSchema( sourceName, sourceName, -1, false, valueSchema ) );
+    }
+
+
+    /**
+     * Combines two inferred schemas for the same field into one schema
+     *
+     * @param valueSchema - schema to merge with
+     * @param conflictMode - conflict mode
+     * @return ValueSchema
+     */
+    public ValueSchema mergeValueSchemas( ValueSchema valueSchema, String conflictMode ) {
+        if ( this.repeated != valueSchema.repeated ) {
+            return resolveConflict( conflictMode );
+        }
+
+        if ( this.repeated ) {
+            ValueSchema mergedElement = elementSchema.mergeValueSchemas( valueSchema.elementSchema, conflictMode );
+            return ValueSchema.repeated( mergedElement );
+        }
+
+        if ( this.kind == valueSchema.kind ) {
+            if ( kind == ValueKind.GROUP ) {
+                ValueSchema merged = ValueSchema.groupType();
+                Map<String, FieldSchema> mergedFields = new LinkedHashMap<>();
+                for ( FieldSchema field : nested ) {
+                    mergedFields.put( field.getSourceName(), field.copy() );
+                }
+                for ( FieldSchema field : valueSchema.nested ) {
+                    FieldSchema existing = mergedFields.get( field.getSourceName() );
+                    if ( existing == null ) {
+                        mergedFields.put( field.getSourceName(), field.copy() );
+                    } else {
+                        existing.setValueSchema( existing.getValueSchema().mergeValueSchemas( field.getValueSchema(), conflictMode ) );
+                    }
+                }
+                merged.nested.addAll( mergedFields.values() );
+                uniquifyParquetNames( merged.nested );
+                return merged;
+            }
+            return this;
+        }
+
+        if ( isNumeric( this.kind ) && isNumeric( valueSchema.kind ) ) {
+            return widestNumeric( this.kind, valueSchema.kind );
+        }
+
+        return resolveConflict( conflictMode );
+    }
+
+
+    public ValueSchema copy() {
+        if ( repeated ) {
+            return repeated( elementSchema.copyNonRepeated() );
+        }
+        return copyNonRepeated();
+    }
+
+
+    private static List<FieldSchema> copyNested( List<FieldSchema> nested ) {
+        List<FieldSchema> items = new ArrayList<>( nested.size() );
+        for ( FieldSchema item : nested ) {
+            items.add( item.copy() );
+        }
+        return items;
+    }
+
+
+    private ValueSchema copyNonRepeated() {
+        return new ValueSchema( kind, primitiveTypeName, logicalType, copyNested( nested ), false, null );
+    }
+
+
+    private static ValueSchema resolveConflict( String conflictMode ) {
+        if ( SchemaState.CONFLICT_STRINGIFY.equals( conflictMode ) ) {
+            return ValueSchema.stringType();
+        }
+        throw new GenericRuntimeException( "Incompatible values encountered while inferring Parquet schema." );
+    }
+
+
+    private static boolean isNumeric( ValueKind kind ) {
+        return kind == ValueKind.INT32 || kind == ValueKind.INT64 || kind == ValueKind.FLOAT || kind == ValueKind.DOUBLE;
+    }
+
+
+    private static ValueSchema widestNumeric( ValueKind left, ValueKind right ) {
+        if ( left == ValueKind.DOUBLE || right == ValueKind.DOUBLE ) {
+            return ValueSchema.doubleType();
+        }
+        if ( left == ValueKind.FLOAT || right == ValueKind.FLOAT ) {
+            return ValueSchema.floatType();
+        }
+        if ( left == ValueKind.INT64 || right == ValueKind.INT64 ) {
+            return ValueSchema.int64Type();
+        }
+        return ValueSchema.int32Type();
+    }
+
+
+    private static ValueSchema inferDocumentSchema( PolyDocument document, String conflictMode ) {
+        ValueSchema group = ValueSchema.groupType();
+        for ( Map.Entry<PolyString, PolyValue> entry : document.entrySet() ) {
+            ValueSchema fieldSchema = inferSchema( entry.getValue(), conflictMode );
+            if ( fieldSchema == null ) {
+                continue;
+            }
+            group.mergeNested( entry.getKey().value, fieldSchema, conflictMode );
+        }
+        uniquifyParquetNames( group.nested() );
+        return group.nested().isEmpty() ? null : group;
+    }
+
+
+    private static ValueSchema inferListSchema( PolyList<PolyValue> list, String conflictMode ) {
+        ValueSchema element = null;
+        for ( PolyValue item : list ) {
+            ValueSchema itemSchema = inferSchema( item, conflictMode );
+            if ( itemSchema == null ) {
+                continue;
+            }
+            element = element == null ? itemSchema : element.mergeValueSchemas( itemSchema, conflictMode );
+        }
+        if ( element == null ) {
+            element = ValueSchema.stringType();
+        }
+        return ValueSchema.repeated( element );
+    }
+
+
+    private static ValueSchema inferPrimitiveSchema( PolyValue value ) {
+        PolyType type = value.getType();
+        return switch ( type ) {
+            case BOOLEAN -> ValueSchema.booleanType();
+            case TINYINT, SMALLINT, INTEGER -> ValueSchema.int32Type();
+            case BIGINT -> ValueSchema.int64Type();
+            case FLOAT, REAL -> ValueSchema.floatType();
+            case DOUBLE, DECIMAL -> ValueSchema.doubleType();
+            case DATE -> ValueSchema.dateType();
+            case TIME -> ValueSchema.timeType();
+            case TIMESTAMP -> ValueSchema.timestampType();
+            case VARBINARY, BINARY -> ValueSchema.binaryType();
+            default -> ValueSchema.stringType();
+        };
+    }
+
+}
diff --git a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/ExtractParquetActivity.java b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/ParquetExtractActivity.java
similarity index 88%
rename from plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/ExtractParquetActivity.java
rename to plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/ParquetExtractActivity.java
index c6a604a9f2..5b7da74905 100644
--- a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/ExtractParquetActivity.java
+++ b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/ParquetExtractActivity.java
@@ -49,7 +49,7 @@
 import org.polypheny.db.workflow.engine.execution.pipe.InputPipe;
 import org.polypheny.db.workflow.engine.execution.pipe.OutputPipe;
 import org.polypheny.db.workflow.engine.storage.reader.CheckpointReader;
-import org.polypheny.db.workflow.io.parquet.ParquetWorkflowSupport;
+import org.polypheny.db.workflow.parquet.ParquetWorkflowExtractSupport;
 
 /**
  * Workflow node definition for reading Parquet files into a workflow.
@@ -68,18 +68,18 @@
         multi = true, modes = { SourceType.ABS_FILE, SourceType.URL },
         shortDescription = "Select the Parquet file or folder to extract. In case of multiple files, the union of their rows is computed.")
 @EnumSetting(key = "outputModel", displayName = "Output Type", pos = 1,
-        options = { ParquetWorkflowSupport.OUTPUT_DOCUMENT, ParquetWorkflowSupport.OUTPUT_RELATIONAL },
+        options = { ParquetWorkflowExtractSupport.OUTPUT_DOCUMENT, ParquetWorkflowExtractSupport.OUTPUT_RELATIONAL },
         displayOptions = { "Document", "Relational" },
-        defaultValue = ParquetWorkflowSupport.OUTPUT_DOCUMENT,
+        defaultValue = ParquetWorkflowExtractSupport.OUTPUT_DOCUMENT,
         shortDescription = "Choose whether the Parquet rows should be exposed as documents or as a relational table.")
 @BoolSetting(key = "nameField", displayName = "Add File Name Field", pos = 2,
         shortDescription = "Adds the source file name as a field or column in the output.")
 @IntSetting(key = "maxCount", displayName = "Maximum Row Count", defaultValue = -1, min = -1, pos = 3, group = ADVANCED_GROUP,
         shortDescription = "The maximum number of rows to extract per file or -1 to extract all rows.")
 @SuppressWarnings("unused")
-public class ExtractParquetActivity implements Activity, Pipeable {
+public class ParquetExtractActivity implements Activity, Pipeable {
 
-    private static final Set<String> EXTENSIONS = ParquetWorkflowSupport.EXTENSIONS;
+    private static final Set<String> EXTENSIONS = ParquetWorkflowExtractSupport.EXTENSIONS;
     private List<Source> sources;
 
 
@@ -102,7 +102,7 @@ public List<TypePreview> previewOutTypes( List<TypePreview> inTypes, SettingsPre
 
         String outputModel = settings.getString( "outputModel" );
         // Document -> no schema
-        if ( ParquetWorkflowSupport.OUTPUT_DOCUMENT.equals( outputModel ) ) {
+        if ( ParquetWorkflowExtractSupport.OUTPUT_DOCUMENT.equals( outputModel ) ) {
             if ( settings.keysPresent( "nameField" ) && settings.getBool( "nameField" ) ) {
                 return DocType.of( Set.of( "fileName" ) ).asOutTypes();
             }
@@ -118,9 +118,12 @@ public List<TypePreview> previewOutTypes( List<TypePreview> inTypes, SettingsPre
                     // no files found
                     throw new InvalidSettingException( "No parquet files found", "file" );
                 }
+                if ( previewSources.size() > 1 ) {
+                    throw new InvalidSettingException( "Multiple sources for relational output are not supported", "file" );
+                }
                 boolean addNameField = settings.keysPresent( "nameField" ) && settings.getBool( "nameField" );
                 // read the Parquet schema and build a relational preview type
-                return RelType.of( ParquetWorkflowSupport.getOutputType( previewSources.get( 0 ), outputModel, addNameField ) ).asOutTypes();
+                return RelType.of( ParquetWorkflowExtractSupport.getOutputType( previewSources.get( 0 ), outputModel, addNameField ) ).asOutTypes();
             } catch ( InvalidSettingException e ) {
                 throw e;
             } catch ( Exception e ) {
@@ -150,7 +153,7 @@ public AlgDataType lockOutputType( List<AlgDataType> inTypes, Settings settings
         if ( sources.isEmpty() ) {
             throw new InvalidSettingException( "No parquet files found", "file" );
         }
-        return ParquetWorkflowSupport.getOutputType( sources.get( 0 ), settings.getString( "outputModel" ), settings.getBool( "nameField" ) );
+        return ParquetWorkflowExtractSupport.getOutputType( sources.get( 0 ), settings.getString( "outputModel" ), settings.getBool( "nameField" ) );
     }
 
 
@@ -171,10 +174,10 @@ public void pipe( List<InputPipe> inputs, OutputPipe output, Settings settings,
         for ( Source source : sources ) {
             ctx.logInfo( "Extracting " + source.path() + " as " + outputModel );
             // write for the next stage in pipe
-            if ( ParquetWorkflowSupport.OUTPUT_RELATIONAL.equals( outputModel ) ) {
-                ParquetWorkflowSupport.writeRows( output, source, addNameField, maxCount );
+            if ( ParquetWorkflowExtractSupport.OUTPUT_RELATIONAL.equals( outputModel ) ) {
+                ParquetWorkflowExtractSupport.writeRows( output, source, addNameField, maxCount );
             } else {
-                ParquetWorkflowSupport.writeDocuments( output, source, addNameField, maxCount );
+                ParquetWorkflowExtractSupport.writeDocuments( output, source, addNameField, maxCount );
             }
         }
     }
@@ -199,7 +202,7 @@ public String getDynamicName( List<TypePreview> inTypes, SettingsPreview setting
                 List<Source> previewSources = settings.getOrThrow( "file", FileValue.class ).getSources( EXTENSIONS );
                 if ( !previewSources.isEmpty() ) {
                     // if file data found generate more specific activity name
-                    return ParquetWorkflowSupport.dynamicName( settings.getString( "outputModel" ), previewSources );
+                    return ParquetWorkflowExtractSupport.getDynamicName( settings.getString( "outputModel" ), previewSources );
                 }
             } catch ( Exception ignored ) {
             }
@@ -224,7 +227,7 @@ public long estimateTupleCount( List<AlgDataType> inTypes, Settings settings, Li
                 // resolves the files from settings
                 sources = settings.get( "file", FileValue.class ).getSources( EXTENSIONS );
             }
-            return ParquetWorkflowSupport.estimateTupleCount( sources );
+            return ParquetWorkflowExtractSupport.estimateTupleCount( sources );
         } catch ( Exception e ) {
             return -1;
         }
diff --git a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/load/ParquetLoadActivity.java b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/load/ParquetLoadActivity.java
new file mode 100644
index 0000000000..5c4475b625
--- /dev/null
+++ b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/load/ParquetLoadActivity.java
@@ -0,0 +1,183 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.workflow.dag.activities.impl.load;
+
+import java.io.File;
+import java.util.List;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.catalog.logistic.DataModel;
+import org.polypheny.db.workflow.dag.activities.Activity;
+import org.polypheny.db.workflow.dag.activities.Activity.ActivityCategory;
+import org.polypheny.db.workflow.dag.activities.Activity.PortType;
+import org.polypheny.db.workflow.dag.activities.ActivityException;
+import org.polypheny.db.workflow.dag.activities.ActivityException.InvalidInputException;
+import org.polypheny.db.workflow.dag.activities.ActivityUtils;
+import org.polypheny.db.workflow.dag.activities.Pipeable;
+import org.polypheny.db.workflow.dag.activities.TypePreview;
+import org.polypheny.db.workflow.dag.activities.TypePreview.RelType;
+import org.polypheny.db.workflow.dag.annotations.ActivityDefinition;
+import org.polypheny.db.workflow.dag.annotations.ActivityDefinition.InPort;
+import org.polypheny.db.workflow.dag.annotations.BoolSetting;
+import org.polypheny.db.workflow.dag.annotations.EnumSetting;
+import org.polypheny.db.workflow.dag.annotations.FileSetting;
+import org.polypheny.db.workflow.dag.annotations.IntSetting;
+import org.polypheny.db.workflow.dag.settings.EnumSettingDef.EnumStyle;
+import org.polypheny.db.workflow.dag.settings.FileValue;
+import org.polypheny.db.workflow.dag.settings.FileValue.SourceType;
+import org.polypheny.db.workflow.dag.settings.SettingDef.Settings;
+import org.polypheny.db.workflow.dag.settings.SettingDef.SettingsPreview;
+import org.polypheny.db.workflow.engine.execution.context.PipeExecutionContext;
+import org.polypheny.db.workflow.engine.execution.pipe.InputPipe;
+import org.polypheny.db.workflow.engine.execution.pipe.OutputPipe;
+import org.polypheny.db.workflow.parquet.ParquetWorkflowLoadSupport;
+
+/**
+ * Workflow node for exporting workflow data into a Parquet file
+ * 1. defines UI for activity: accept ANY input port, exposes relevant settings like target file, compression, etc.
+ * 2. validates if input supported
+ * 3. acts as dispatcher at runtime
+ */
+@ActivityDefinition(type = "loadParquet", displayName = "Load to Parquet File", categories = { ActivityCategory.LOAD, ActivityCategory.DOCUMENT, ActivityCategory.RELATIONAL },
+        inPorts = { @InPort(type = PortType.ANY, description = "Input table or collection") },
+        outPorts = {},
+        shortDescription = "Writes the input table or collection to a Parquet file.")
+
+@FileSetting(key = "file", displayName = "Target File", pos = 0,
+        multi = false, modes = { SourceType.ABS_FILE },
+        shortDescription = "Select the target file.")
+@EnumSetting(key = "mode", displayName = "Handling of Existing File", pos = 1,
+        options = { ParquetWorkflowLoadSupport.MODE_DROP, ParquetWorkflowLoadSupport.MODE_FAIL },
+        displayOptions = { "Overwrite", "Fail Activity" },
+        defaultValue = ParquetWorkflowLoadSupport.MODE_FAIL, style = EnumStyle.RADIO_BUTTON,
+        shortDescription = "Define activity behavior if the selected file already exists.")
+@EnumSetting(key = "compression", displayName = "Compression", pos = 2,
+        options = { ParquetWorkflowLoadSupport.COMPRESSION_SNAPPY, ParquetWorkflowLoadSupport.COMPRESSION_GZIP, ParquetWorkflowLoadSupport.COMPRESSION_UNCOMPRESSED },
+        displayOptions = { "Snappy", "Gzip", "Uncompressed" },
+        defaultValue = ParquetWorkflowLoadSupport.COMPRESSION_SNAPPY, style = EnumStyle.DROPDOWN,
+        shortDescription = "Select the Parquet compression codec.")
+@IntSetting(key = "schemaSampleSize", displayName = "Schema Sample Size", pos = 3,
+        defaultValue = 100, min = 1,
+        shortDescription = "Infer the schema from the first N tuples before writing.")
+@EnumSetting(key = "conflictMode", displayName = "Conflict Mode Handling", pos = 4,
+        options = { ParquetWorkflowLoadSupport.CONFLICT_STRINGIFY, ParquetWorkflowLoadSupport.CONFLICT_FAIL },
+        displayOptions = { "Use Shared String Type", "Fail Activity" },
+        defaultValue = ParquetWorkflowLoadSupport.CONFLICT_STRINGIFY, style = EnumStyle.RADIO_BUTTON,
+        shortDescription = "Define how incompatible sampled values should be merged during schema inference.")
+@BoolSetting(key = "keepId", displayName = "Include ID Field", pos = 5,
+        shortDescription = "Keep the '_id' field for document input.", defaultValue = true)
+@BoolSetting(key = "keepPk", displayName = "Keep Primary Key Column", pos = 6,
+        shortDescription = "Keep the '" + Activity.PK_COL + "' column for relational input.", defaultValue = false)
+
+@SuppressWarnings("unused")
+public class ParquetLoadActivity implements Activity, Pipeable {
+
+    private static final int MAX_NAME_LENGTH = 40;
+    private static final int TRUNCATED_LENGTH = 37;
+
+
+    /**
+     * Validate input
+     * @param inTypes a list of {@link TypePreview}s representing the input tuple types.
+     * @param settings the SettingsPreview representing the available settings, i.e. all settings that do not contain variables.
+     * @return List<TypePreview>
+     * @throws ActivityException - exception
+     */
+    @Override
+    public List<TypePreview> previewOutTypes( List<TypePreview> inTypes, SettingsPreview settings ) throws ActivityException {
+        TypePreview inputType = inTypes.get( 0 );
+        // reject graph input, for relational input makes sure the table still
+        if ( inputType.getDataModel() == DataModel.GRAPH ) {
+            throw new InvalidInputException( "Graph input is not supported for Parquet export.", 0 );
+        }
+        // for relational input makes sure the table still has at least one non-PK column if keepPk=false
+        if ( settings.keysPresent( "keepPk" ) && inputType instanceof RelType relType ) {
+            if ( relType.getNullableType().getFieldCount() < 2 && !settings.getBool( "keepPk" ) ) {
+                throw new InvalidInputException( "Input table must have at least 1 additional column if the primary key column is not kept", 0 );
+            }
+        }
+        return List.of();
+    }
+
+
+    @Override
+    public AlgDataType lockOutputType( List<AlgDataType> inTypes, Settings settings ) {
+        return null;
+    }
+
+
+    /**
+     * Reads settings, resolves the input model,
+     * prepares the target file, routes execution to helper class
+     * @param inputs the InputPipes to iterate over. For inactive edges, the pipe is null (important for non-default DataStateMergers).
+     * @param output the output pipe for sending output tuples to that respect the locked output type, or null if this activity has no output
+     * @param settings the resolved settings
+     * @param ctx ExecutionContext to be used for access to the transaction (interrupt checking is done automatically by the pipes)
+     * @throws Exception - exception
+     */
+    @Override
+    public void pipe( List<InputPipe> inputs, OutputPipe output, Settings settings, PipeExecutionContext ctx ) throws Exception {
+        InputPipe input = inputs.get( 0 );
+        File file = settings.get( "file", FileValue.class ).getFile( false, false );
+        String mode = settings.getString( "mode" );
+        String compression = settings.getString( "compression" );
+        int schemaSampleSize = settings.getInt( "schemaSampleSize" );
+        String conflictMode = settings.getString( "conflictMode" );
+        boolean keepId = settings.getBool( "keepId" );
+        boolean keepPk = settings.getBool( "keepPk" );
+        long estimatedTupleCount = ctx.getEstimatedInCounts().isEmpty() ? -1 : ctx.getEstimatedInCounts().get( 0 );
+
+        ParquetWorkflowLoadSupport.prepareTargetFile( file, mode );
+
+        switch ( ActivityUtils.getDataModel( input.getType() ) ) {
+            case DOCUMENT -> {
+                ctx.logInfo( "Writing document input to Parquet file: " + file.getAbsolutePath() );
+                ParquetWorkflowLoadSupport.writeDocuments( input, file, compression, schemaSampleSize, conflictMode, keepId, estimatedTupleCount, ctx );
+            }
+            case RELATIONAL -> {
+                // prevent producing an empty Parquet schema from relational input
+                if ( input.getType().getFieldCount() < 2 && !keepPk ) {
+                    throw new InvalidInputException( "Input table must have at least 1 additional column if the primary key column is not kept", 0 );
+                }
+                ctx.logInfo( "Writing relational input to Parquet file: " + file.getAbsolutePath() );
+                ParquetWorkflowLoadSupport.writeRelational( input, file, compression, schemaSampleSize, conflictMode, keepPk, estimatedTupleCount, ctx );
+            }
+            case GRAPH -> throw new InvalidInputException( "Graph input is not supported for Parquet export.", 0 );
+        }
+    }
+
+
+    /**
+     * Provides a nicer label in the workflow editor:
+     * - gets file name from settings
+     * - build dynamic name using lower level functionality
+     * @param inTypes a list of {@link TypePreview}s representing the input field types. (all columns in a row)
+     * @param settings the SettingsPreview representing the available settings, i.e. all settings that do not contain variables.
+     * @return - string
+     */
+    @Override
+    public String getDynamicName( List<TypePreview> inTypes, SettingsPreview settings ) {
+        if ( settings.keysPresent( "file" ) ) {
+            FileValue file = settings.getOrThrow( "file", FileValue.class );
+            try {
+                return ParquetWorkflowLoadSupport.getDynamicName( file );
+            } catch ( Exception ignored ) {
+            }
+        }
+        return null;
+    }
+
+}
diff --git a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/io/parquet/ParquetWorkflowSupport.java b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
similarity index 97%
rename from plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/io/parquet/ParquetWorkflowSupport.java
rename to plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
index 4016623d20..4284f94292 100644
--- a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/io/parquet/ParquetWorkflowSupport.java
+++ b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.workflow.io.parquet;
+package org.polypheny.db.workflow.parquet;
 
 import java.net.URISyntaxException;
 import java.util.ArrayList;
@@ -45,7 +45,7 @@
 /**
  * Parquet-reading/conversion helper layer
  */
-public final class ParquetWorkflowSupport {
+public final class ParquetWorkflowExtractSupport {
 
     public static final Set<String> EXTENSIONS = Set.of( "parquet" );
     public static final String OUTPUT_DOCUMENT = "document";
@@ -69,7 +69,7 @@ public final class ParquetWorkflowSupport {
      * @return String
      * @throws URISyntaxException exception
      */
-    public static String dynamicName( String outputModel, List<Source> sources ) throws URISyntaxException {
+    public static String getDynamicName( String outputModel, List<Source> sources ) throws URISyntaxException {
         String prefix = OUTPUT_RELATIONAL.equals( outputModel ) ? "Extract Parquet Table" : "Extract Parquet Document";
         if ( sources.size() > 1 ) {
             return prefix + "s";
diff --git a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowLoadSupport.java b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowLoadSupport.java
new file mode 100644
index 0000000000..e611a1d28d
--- /dev/null
+++ b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowLoadSupport.java
@@ -0,0 +1,225 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.workflow.parquet;
+
+import java.io.File;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Iterator;
+import java.util.List;
+import org.apache.parquet.schema.MessageType;
+import org.polypheny.db.adapter.parquet.shared.execution.BufferedIterator;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceWriter;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetMessageTypeBuilder;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.document.PolyDocument;
+import org.polypheny.db.workflow.dag.activities.Activity;
+import org.polypheny.db.workflow.dag.settings.FileValue;
+import org.polypheny.db.workflow.engine.execution.context.PipeExecutionContext;
+import org.polypheny.db.workflow.engine.execution.pipe.InputPipe;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.FieldSchema;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.SchemaState;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.ValueSchema;
+
+/**
+ * Parquet-writing helper layer
+ */
+public final class ParquetWorkflowLoadSupport {
+
+    public static final String MODE_FAIL = "fail";
+    public static final String MODE_DROP = "drop";
+    public static final String COMPRESSION_SNAPPY = ParquetSourceWriter.COMPRESSION_SNAPPY;
+    public static final String COMPRESSION_GZIP = ParquetSourceWriter.COMPRESSION_GZIP;
+    public static final String COMPRESSION_UNCOMPRESSED = ParquetSourceWriter.COMPRESSION_UNCOMPRESSED;
+    public static final String CONFLICT_STRINGIFY = SchemaState.CONFLICT_STRINGIFY;
+    public static final String CONFLICT_FAIL = SchemaState.CONFLICT_FAIL;
+
+    private static final int DEFAULT_PROGRESS_DELTA = 100;
+    private static final String SCHEMA_NAME = "polypheny_workflow";
+
+    private static final int MAX_NAME_LENGTH = 40;
+    private static final int TRUNCATED_LENGTH = 37;
+
+
+    private ParquetWorkflowLoadSupport() {
+    }
+
+
+    /**
+     * Create dynamic name for load activity node.
+     *
+     * @param file target parquet file
+     * @return activity display name
+     */
+    public static String getDynamicName( FileValue file ) throws IOException {
+        //String name = file.getName();
+        String name = file.getFile( false, false ).getName();
+        if ( name.length() > MAX_NAME_LENGTH ) {
+            name = name.substring( 0, TRUNCATED_LENGTH ) + "...";
+        }
+        return "Load to " + name;
+    }
+
+
+    /**
+     * Check output path before Parquet writing starts
+     *
+     * @param file - parquet output file to write
+     * @param mode - write mode
+     */
+    public static void prepareTargetFile( File file, String mode ) {
+        // do nothing if file does not exist - writing can proceed
+        if ( file.exists() ) {
+            switch ( mode ) {
+                // file exists -> exception
+                case MODE_FAIL -> throw new GenericRuntimeException( "Specified file already exists." );
+                // file exists -> delete it
+                case MODE_DROP -> {
+                    if ( !file.delete() ) {
+                        throw new GenericRuntimeException( "Failed to delete existing file." );
+                    }
+                }
+                default -> throw new IllegalArgumentException( "Unknown mode: " + mode );
+            }
+        }
+    }
+
+
+    /**
+     * Write a relational workflow input into a Parquet file
+     *
+     * @param input - input pipe
+     * @param file - file to write
+     * @param compression - string
+     * @param schemaSampleSize - int
+     * @param conflictMode - String
+     * @param keepPk - boolean
+     * @param estimatedTupleCount - long
+     * @param ctx - PipeExecutionContext
+     * @throws Exception - exception
+     */
+    public static void writeRelational(
+            InputPipe input,
+            File file,
+            String compression,
+            int schemaSampleSize,
+            String conflictMode,
+            boolean keepPk,
+            long estimatedTupleCount,
+            PipeExecutionContext ctx ) throws Exception {
+
+        // get relational input type
+        AlgDataType inputType = input.getType();
+        Iterator<List<PolyValue>> iterator = input.iterator();
+        List<List<PolyValue>> sampleRows = new ArrayList<>(); // list of rows
+
+        // create and initialize schema state
+        SchemaState schemaState = new SchemaState( conflictMode );
+        schemaState.init( inputType, keepPk );
+
+        // read sample rows and check schema against real values
+        while ( iterator.hasNext() && sampleRows.size() < schemaSampleSize ) {
+            List<PolyValue> row = iterator.next();
+            sampleRows.add( row );
+            schemaState.mergeRelationalRowSchema( row, inputType, keepPk );
+        }
+
+        // write all rows to the file
+        MessageType schema = new ParquetMessageTypeBuilder( schemaState, SCHEMA_NAME ).build();
+        // write data to parquet file
+        long countDelta = estimatedTupleCount > 0 ? Math.max( estimatedTupleCount / 100, 1 ) : DEFAULT_PROGRESS_DELTA;
+        try ( ParquetSourceWriter writer = new ParquetSourceWriter( file, schema, compression, schemaState, false ) ) {
+            writer.writeRows(
+                    new BufferedIterator<>( sampleRows, iterator, r -> r ),
+                    written -> updateProgress( estimatedTupleCount, countDelta, ctx, written ) );
+        }
+
+        if ( estimatedTupleCount > 0 ) {
+            ctx.updateProgress( 1.0 );
+        }
+    }
+
+
+    /**
+     * Write document input to parquet file
+     *
+     * @param input - input pipe
+     * @param file - to write
+     * @param compression - data compression
+     * @param schemaSampleSize - int
+     * @param conflictMode - string
+     * @param keepId - boolean
+     * @param estimatedTupleCount - long
+     * @param ctx - context
+     * @throws Exception - exception
+     */
+    public static void writeDocuments(
+            InputPipe input,
+            File file,
+            String compression,
+            int schemaSampleSize,
+            String conflictMode,
+            boolean keepId,
+            long estimatedTupleCount,
+            PipeExecutionContext ctx ) throws Exception {
+
+        Iterator<List<PolyValue>> iterator = input.iterator();
+        List<PolyDocument> sampleDocs = new ArrayList<>();
+        SchemaState schemaState = new SchemaState( conflictMode );
+
+        // read sample rows and check schema against real values
+        while ( iterator.hasNext() && sampleDocs.size() < schemaSampleSize ) {
+            List<PolyValue> row = iterator.next();
+            PolyDocument document = row.get( 0 ).asDocument();
+            sampleDocs.add( document );
+            schemaState.mergeDocumentSchema( document, keepId );
+        }
+
+        if ( schemaState.getFields().isEmpty() && keepId ) {
+            schemaState.addField( new FieldSchema( Activity.docId.value, Activity.docId.value, -1, false, ValueSchema.stringType() ) );
+        }
+
+        MessageType schema = new ParquetMessageTypeBuilder( schemaState, SCHEMA_NAME ).build();
+        long countDelta = estimatedTupleCount > 0 ? Math.max( estimatedTupleCount / 100, 1 ) : DEFAULT_PROGRESS_DELTA;
+        try ( ParquetSourceWriter writer = new ParquetSourceWriter( file, schema, compression, schemaState, keepId ) ) {
+            writer.writeRows(
+                    new BufferedIterator<>( sampleDocs, iterator, r -> r.get( 0 ).asDocument() ),
+                    written -> updateProgress( estimatedTupleCount, countDelta, ctx, written ) );
+        }
+
+        if ( estimatedTupleCount > 0 ) {
+            ctx.updateProgress( 1.0 );
+        }
+    }
+
+
+    private static void updateProgress( long estimatedTupleCount, long countDelta, PipeExecutionContext ctx, long written ) {
+        if ( written % countDelta == 0 ) {
+            if ( estimatedTupleCount > 0 ) {
+                ctx.updateProgress( (double) written / estimatedTupleCount );
+            }
+            try {
+                ctx.checkPipeInterrupted();
+            } catch ( Exception e ) {
+                throw new GenericRuntimeException( "Parquet write interrupted", e );
+            }
+        }
+    }
+
+}

From 7f318a4cf9caf9dbacdd79a741da5da6a88565b2 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Sat, 11 Apr 2026 13:01:16 +0200
Subject: [PATCH 13/46] Workflow - extract multiple parquet files

---
 plugins/parquet-adapter/docs/README.md        | 172 ++++++++++++++++--
 .../extract_workflow_mult_validation.png      | Bin 0 -> 44778 bytes
 .../docs/images/workflow_parquet_parquet.png  | Bin 0 -> 35935 bytes
 .../impl/extract/ParquetExtractActivity.java  |  11 +-
 .../ParquetWorkflowExtractSupport.java        |  34 +++-
 5 files changed, 194 insertions(+), 23 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/images/extract_workflow_mult_validation.png
 create mode 100644 plugins/parquet-adapter/docs/images/workflow_parquet_parquet.png

diff --git a/plugins/parquet-adapter/docs/README.md b/plugins/parquet-adapter/docs/README.md
index 91dca1f947..2b4382407b 100644
--- a/plugins/parquet-adapter/docs/README.md
+++ b/plugins/parquet-adapter/docs/README.md
@@ -282,6 +282,9 @@ Workflow node definition for reading Parquet files into a workflow output pipe:
 - The concrete output type is determined from `outputModel`.
 - In relational mode, the schema is derived from the selected Parquet file.
 - In document mode, the output is a document stream with generated document IDs where needed.
+- Extract multiple files allowed only for Document model and Relational files with same schema
+
+![Schema display](images/extract_workflow_act_settings.png)
 
 - ### ParquetWorkflowExtractSupport
 
@@ -289,7 +292,7 @@ Workflow node definition for reading Parquet files into a workflow output pipe:
 
 Helper class contains Parquet extraction logic for the workflow engine.
 It maps Parquet schemas to workflow output types, estimates row counts, generates dynamic activity names, and converts Parquet rows into either Polypheny documents or relational tuples.
-
+Calls logic of Parquet Adapter Module
 
 
 ### Tests:
@@ -299,6 +302,9 @@ It maps Parquet schemas to workflow output types, estimates row counts, generate
 - Add file name as column
 - Add key id column
 - Set Maximum number of rows = 5 in Advanced settings
+- Extract multiple documents
+- Extract multiple relational - same schema
+- Extract multiple relational - different schema - not allowed
 
 
 ![Schema display](images/workflow1.png)
@@ -307,29 +313,161 @@ It maps Parquet schemas to workflow output types, estimates row counts, generate
 
 ## 2. Load to Parquet File
 
-# Unit Tests
-To run tests for parquet adapter:
-- `.\gradlew.bat :plugins:parquet-adapter:test`
+- ### ParquetLoadActivity
 
-`plugins/parquet-adapter/build.gradle` should contain:
-- DBMS as a test dependency
-- Polypheny JDBC driver dependency
+`org.polypheny.db.workflow.dag.activities.impl.load.ParquetLoadActivity`
+
+Workflow node definition for exporting workflow input into a Parquet file:
+1. It defines how the activity appears in the workflow UI using settings. This provides all needed information for the editor to show the available export options.
+2. It validates whether the connected workflow input is supported. Graph input is rejected and relational input is checked so that exporting without the workflow primary key does not produce an empty Parquet schema.
+3. It executes the export at runtime as follows: the activity resolves the configured target file and export settings, prepares the output path, and delegates the actual schema inference and data conversion work to `ParquetWorkflowLoadSupport.java`.
+
+#### ***Settings***
+
+#### `file`
+- Display name: `Target File`
+- Type: file setting
+- Required: yes
+- Multiple values: no
+- Supported source types:
+    - absolute file path
+- Purpose:
+  Select the Parquet file that should be written.
+
+#### `mode`
+- Display name: `Handling of Existing File`
+- Type: enum
+- Required: yes
+- Default: `fail`
+- Available values:
+    - `drop`
+    - `fail`
+- Purpose:
+  Defines the behavior if the selected output file already exists.
+- Behavior:
+    - `drop`: overwrite the existing file
+    - `fail`: abort the activity
+
+#### `compression`
+- Display name: `Compression`
+- Type: enum
+- Required: yes
+- Default: `snappy`
+- Available values:
+    - `snappy`
+    - `gzip`
+    - `uncompressed`
+- Purpose:
+  Selects the Parquet compression codec used for the generated file.
+
+#### `schemaSampleSize`
+- Display name: `Schema Sample Size`
+- Type: integer
+- Required: yes
+- Default: `100`
+- Minimum: `1`
+- Purpose:
+  Controls how many input tuples are sampled to infer the Parquet schema before writing starts.
+
+#### `conflictMode`
+- Display name: `Conflict Mode Handling`
+- Type: enum
+- Required: yes
+- Default: `stringify`
+- Available values:
+    - `stringify`
+    - `fail`
+- Purpose:
+  Defines how incompatible sampled values should be handled while inferring the Parquet schema.
+- Behavior:
+    - `stringify`: use a shared string field when incompatible sampled values are encountered
+    - `fail`: abort the activity when sampled values are incompatible
 
+#### `keepId`
+- Display name: `Include ID Field`
+- Type: boolean
+- Required: no
+- Default: `true`
+- Purpose:
+  Keeps the `_id` field when the activity receives document input.
+
+#### `keepPk`
+- Display name: `Keep Primary Key Column`
+- Type: boolean
+- Required: no
+- Default: `false`
+- Purpose:
+  Keeps the workflow primary key column when the activity receives relational input.
+
+#### ***Notes***
+
+- The activity has one input port of type `ANY` and no output ports.
+- Relational input and document input are both supported.
+- Graph input is not supported.
+- For relational input, the workflow primary key can optionally be excluded.
+- For document input, the schema is inferred from sampled documents because the document type does not provide field-level schema information.
+
+![Schema display](images/load_workflow_act_settings.png)
+
+![Schema display](images/extract_workflow_mult_validation.png)
+
+- ### ParquetWorkflowLoadSupport
+
+`org.polypheny.db.workflow.parquet.ParquetWorkflowLoadSupport`
+
+Helper class that contains Parquet export logic for the workflow engine.
+It prepares output files, builds dynamic activity names, infers Parquet schema from sampled workflow input, maps workflow values to Parquet schema definitions, and delegates low-level file writing to the shared Parquet adapter module.
+
+- ### ParquetSourceWriter
+
+`org.polypheny.db.adapter.parquet.shared.io.ParquetSourceWriter`
+
+Shared low-level Parquet writer used by workflow export functionality.
+It owns the Parquet writer lifecycle, creates Parquet row groups, writes rows to the target file, applies the configured compression codec, and exposes a small progress callback hook without depending on workflow-specific classes.
+
+- ### ParquetMessageTypeBuilder
 
-TODO: describe all properties of activities
-CONFLICT_MODE property
-How the modes behave:
+`org.polypheny.db.adapter.parquet.shared.schema.inference.ParquetMessageTypeBuilder`
 
-stringify
+Builds a Parquet `MessageType` from inferred workflow field schemas, including primitive, repeated, and nested group fields.
 
-if values are incompatible, fall back to ValueSchema.stringType()
-this means the field will be written as a Parquet string column/field
-later values are converted to string when written
-fail
+- ### Schema Inference Model
 
-throw a GenericRuntimeException
-the export stops because the sampled values do not agree on a schema
+`org.polypheny.db.adapter.parquet.shared.schema.inference.FieldSchema`
 
+Internal model for one exported Parquet field. It tracks the source field name, normalized Parquet field name, optional relational source index, and inferred value schema.
+
+`org.polypheny.db.adapter.parquet.shared.schema.inference.ValueSchema`
+
+Recursive internal model of a field value. It represents primitive values, nested groups, and repeated values before they are converted into a final Parquet schema.
+
+`org.polypheny.db.adapter.parquet.shared.schema.inference.SchemaState`
+
+Container for the currently inferred schema during export. It accumulates field definitions while sampled rows or documents are inspected.
+
+### Tests:
+
+- Read relational workflow input and export to Parquet file
+- Read document workflow input and export to Parquet file
+- Keep `_id` for document export
+- Keep workflow primary key for relational export
+- Write with different compression codecs
+- Use `schemaSampleSize = 5`
+- Export incompatible sampled values with `conflictMode = stringify`
+- Fail export on incompatible sampled values with `conflictMode = fail`
+
+![Schema display](images/workflow_parquet_parquet.png)
+
+# Unit Tests
+
+---
+
+To run tests for parquet adapter:
+- `.\gradlew.bat :plugins:parquet-adapter:test`
+
+`plugins/parquet-adapter/build.gradle` should contain:
+- DBMS as a test dependency
+- Polypheny JDBC driver dependency
 
 
 ## `ParquetPluginTest.java`
diff --git a/plugins/parquet-adapter/docs/images/extract_workflow_mult_validation.png b/plugins/parquet-adapter/docs/images/extract_workflow_mult_validation.png
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diff --git a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/ParquetExtractActivity.java b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/ParquetExtractActivity.java
index 5b7da74905..51c6b1b380 100644
--- a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/ParquetExtractActivity.java
+++ b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/dag/activities/impl/extract/ParquetExtractActivity.java
@@ -22,6 +22,7 @@
 import java.util.Set;
 import java.util.function.Supplier;
 import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.transaction.Transaction;
 import org.polypheny.db.util.Source;
 import org.polypheny.db.workflow.dag.activities.Activity;
@@ -118,14 +119,15 @@ public List<TypePreview> previewOutTypes( List<TypePreview> inTypes, SettingsPre
                     // no files found
                     throw new InvalidSettingException( "No parquet files found", "file" );
                 }
-                if ( previewSources.size() > 1 ) {
-                    throw new InvalidSettingException( "Multiple sources for relational output are not supported", "file" );
-                }
+                // allow multiple relational sources if all parquet schemas are identical
+                ParquetWorkflowExtractSupport.validateSharedRelationalSchema( previewSources );
                 boolean addNameField = settings.keysPresent( "nameField" ) && settings.getBool( "nameField" );
                 // read the Parquet schema and build a relational preview type
                 return RelType.of( ParquetWorkflowExtractSupport.getOutputType( previewSources.get( 0 ), outputModel, addNameField ) ).asOutTypes();
             } catch ( InvalidSettingException e ) {
                 throw e;
+            } catch ( GenericRuntimeException e ) {
+                throw new InvalidSettingException( e.getMessage(), "file" );
             } catch ( Exception e ) {
                 throw new InvalidSettingException( "Invalid location: " + e.getMessage(), "file" );
             }
@@ -153,6 +155,9 @@ public AlgDataType lockOutputType( List<AlgDataType> inTypes, Settings settings
         if ( sources.isEmpty() ) {
             throw new InvalidSettingException( "No parquet files found", "file" );
         }
+        if ( ParquetWorkflowExtractSupport.OUTPUT_RELATIONAL.equals( settings.getString( "outputModel" ) ) ) {
+            ParquetWorkflowExtractSupport.validateSharedRelationalSchema( sources );
+        }
         return ParquetWorkflowExtractSupport.getOutputType( sources.get( 0 ), settings.getString( "outputModel" ), settings.getBool( "nameField" ) );
     }
 
diff --git a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
index 4284f94292..747748d440 100644
--- a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
+++ b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
@@ -32,6 +32,7 @@
 import org.polypheny.db.algebra.type.DocumentType;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetFieldNameNormalizer;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.type.PolyType;
 import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
@@ -64,6 +65,7 @@ public final class ParquetWorkflowExtractSupport {
 
     /**
      * Create dynamic name for activity node
+     *
      * @param outputModel - relational or document
      * @param sources - parquet files
      * @return String
@@ -104,11 +106,11 @@ public static AlgDataType getOutputType( Source source, String outputModel, bool
         for ( Type field : schema.getFields() ) {
             var type = parquetTypeConverter.fromParquetTypeToPolyType( field );
             // PolyDocument type provided as json string
-            if (type == PolyType.DOCUMENT) {
+            if ( type == PolyType.DOCUMENT ) {
                 type = PolyType.TEXT;
             }
             String fieldName = ParquetFieldNameNormalizer.normalizeFieldName( field.getName() );
-            builder.add( fieldName, null, type);
+            builder.add( fieldName, null, type );
         }
         if ( addNameField ) {
             // add source file name to each row
@@ -118,8 +120,32 @@ public static AlgDataType getOutputType( Source source, String outputModel, bool
     }
 
 
+    /**
+     * Read schemas from all selected sources and ensure they are identical for relational output.
+     *
+     * @param sources selected parquet sources
+     */
+    public static void validateSharedRelationalSchema( List<Source> sources ) {
+        if ( sources.isEmpty() ) {
+            throw new GenericRuntimeException( "No parquet files found." );
+        }
+
+        MessageType expectedSchema = ParquetSourceReader.readSchema( sources.get( 0 ) );
+        for ( int i = 1; i < sources.size(); i++ ) {
+            Source source = sources.get( i );
+            MessageType currentSchema = ParquetSourceReader.readSchema( source );
+            if ( !expectedSchema.equals( currentSchema ) ) {
+                throw new GenericRuntimeException(
+                        "All parquet sources must share the same schema for relational output. "
+                                + "Schema mismatch detected for: " + source.path() );
+            }
+        }
+    }
+
+
     /**
      * Call reader functionality to estimate row count
+     *
      * @param sources - parquet files
      * @return long - estimation
      */
@@ -139,6 +165,7 @@ public static long estimateTupleCount( List<Source> sources ) {
     /**
      * Read parquet file and write documents to the output pipe
      * Document created from each row of parquet file
+     *
      * @param output - OutputPipe
      * @param source - parquet file
      * @param addNameField - if add filename as column
@@ -196,7 +223,7 @@ public static void writeRows( OutputPipe output, Source source, boolean addNameC
                     Type field = schema.getType( i );
                     var polyValue = parquetRelValueExtractor.extractValue( row, i, field );
                     // PolyDocument type provided as json string
-                    if (polyValue instanceof PolyDocument document) {
+                    if ( polyValue instanceof PolyDocument document ) {
                         polyValue = document.toPolyJson();
                     }
                     values.add( polyValue );
@@ -211,4 +238,5 @@ public static void writeRows( OutputPipe output, Source source, boolean addNameC
             }
         }
     }
+
 }

From 7491378526bbeae2afe2d37511a45d7fa977f504 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Thu, 16 Apr 2026 10:29:05 +0200
Subject: [PATCH 14/46] Workflow - extract multiple parquet files

---
 plugins/parquet-adapter/docs/README.md        | 275 +---------
 ...parquet_workflow_load_class_diagram.drawio | 109 ++++
 .../parquet_workflow_load_class_diagram.png   | Bin 0 -> 119297 bytes
 plugins/parquet-adapter/docs/workflow.md      | 516 ++++++++++++++++++
 .../shared/schema/inference/SchemaState.java  |   3 +
 5 files changed, 646 insertions(+), 257 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/diagrams/parquet_workflow_load_class_diagram.drawio
 create mode 100644 plugins/parquet-adapter/docs/diagrams/parquet_workflow_load_class_diagram.png
 create mode 100644 plugins/parquet-adapter/docs/workflow.md

diff --git a/plugins/parquet-adapter/docs/README.md b/plugins/parquet-adapter/docs/README.md
index 2b4382407b..cc0cae2137 100644
--- a/plugins/parquet-adapter/docs/README.md
+++ b/plugins/parquet-adapter/docs/README.md
@@ -53,7 +53,7 @@ During `restore()`, `Catalog.defaultSource` is populated from the configured ada
     - Recreates relational tables and document collections from persisted adapter state.
     - Re-registers the wrappers in the adapter catalog.
 5. **Query entry into the adapter**
-    - **Responsible classes:** `ParquetRelScan`, `ParquetDocScan`, `AbstractParquetEnumerator`, `ParquetGroupReader`
+    - **Responsible classes:** `ParquetRelScan`, `ParquetDocScan`, `AbstractParquetEnumerator`, `ParquetSourceReader`
     - The query engine resolves the registered Parquet-backed physical entity.
     - Projection and supported filter information are passed into the Parquet execution layer.
     - Rows are read from the Parquet file and converted into Polypheny relational tuples or documents.
@@ -108,7 +108,7 @@ Document-model integration for exposing Parquet files as read-only Polypheny doc
 #### `document.execution`
 
 - `ParquetDocEnumerator` - document runtime enumerator. Reads Parquet rows and create from each row a single `PolyDocument`.
-- `ParquetDocFilterTranslator` - translates supported document filter expressions into adapter-level `AdapterFilter` instances.
+- `ParquetDocFilterTranslator` - translates supported document filter expressions into shared `ParquetAdapterFilter` instances.
 - `ParquetDocValueExtractor` - converts Parquet groups and nested values into Polypheny document values and synthesizes `_id` values when they are missing.
 
 #### `document.planning`
@@ -158,6 +158,7 @@ Shared infrastructure used by both the relational and document adapters.
 #### `shared.io`
 
 - `ParquetSourceReader` - low-level Parquet row-group reader. Opens the file, applies projection and native predicates, and streams groups to the enumerators.
+- `ParquetSourceWriter` - low-level Parquet writer used by workflow export. Owns file writer lifecycle, compression handling, and row writing.
 - `ParquetFileDiscovery` - locates valid `.parquet` files below the configured source location and filters out unsupported files.
 
 #### `shared.filter`
@@ -168,9 +169,17 @@ Shared infrastructure used by both the relational and document adapters.
 #### `shared.schema`
 
 - `ParquetNamespace` - namespace helper that creates the correct Parquet-backed physical wrapper for either a relational table or a document collection.
+- `ParquetMessageTypeBuilder` - builds Parquet `MessageType` definitions from inferred field schemas for workflow export.
 - `ParquetTypeConverter` - converts Parquet schema types and runtime values into the Polypheny type system.
 - `ParquetFieldNameNormalizer` - creates normalized filed names
 
+#### `shared.schema.inference`
+
+- `FieldSchema` - internal model for one inferred Parquet field used by workflow export.
+- `SchemaState` - accumulates inferred field definitions while sampled workflow input is inspected.
+- `ValueKind` - enum describing the logical value categories used during schema inference.
+- `ValueSchema` - recursive value-type model used to represent primitive, nested, and repeated Parquet field structures before building the final schema.
+
 #### `shared.util`
 
 - `HadoopConfigurationFactory` - builds Hadoop `Configuration` instances that work correctly inside the plugin classloader environment.
@@ -208,259 +217,13 @@ The following figure shows an example of the displayed schema information.
 ## Query Filter
 ![Schema display](images/query_filter.png)
 
+## Workflow Integration
 
+Workflow-specific Parquet extract and load activities are documented separately in:
+- `docs/workflow.md`
 
-
-# Workflow
-
----
-
-## 1. Extract Parquet
-
-- ### ParquetExtractActivity
-
-`org.polypheny.db.workflow.dag.activities.impl.extract.ParquetExtractActivity`
-
-Workflow node definition for reading Parquet files into a workflow output pipe:
-1. It defines how the activity appears in the workflow UI using settings. This provides all needed information for editor to show options.
-2. It decides what type of data the node will output: Relational or Document
-3. It executes the extraction at runtime as follows: the activity resolves the input files, iterates over them, and delegates the actual reading/conversion work to `ParquetWorkflowSupport.java`, which turns Parquet rows into either documents or relational rows and pushes them into the workflow output pipe.
-
-#### ***Settings***
-
-#### `file`
-- Display name: `File Location`
-- Type: file setting
-- Required: yes
-- Multiple values: yes
-- Supported source types:
-    - absolute file path
-    - URL
-- Purpose:
-  Select one or more Parquet files, or a folder containing Parquet files, to extract from.
-- Behavior:
-  If multiple files are selected, the activity outputs the union of their rows.
-
-#### `outputModel`
-- Display name: `Output Type`
-- Type: enum
-- Required: yes
-- Default: `document`
-- Available values:
-    - `document`
-    - `relational`
-- Purpose:
-  Defines whether the extracted Parquet rows are exposed as workflow documents or as a relational table.
-
-#### `nameField`
-- Display name: `Add File Name Field`
-- Type: boolean
-- Required: no
-- Default: `false`
-- Purpose:
-  Adds the source file name to the output.
-- Behavior:
-    - in document mode: adds a `fileName` field
-    - in relational mode: adds a `fileName` column
-
-#### `maxCount`
-- Display name: `Maximum Row Count`
-- Type: integer
-- Required: no
-- Default: `-1`
-- Minimum: `-1`
-- Group: advanced
-- Purpose:
-  Limits how many rows are extracted per file.
-- Behavior:
-    - `-1`: extract all rows
-    - `>= 0`: extract at most that many rows from each selected file
-
-#### ***Notes***
-
-- The activity has no input ports and one output port of type `ANY`.
-- The concrete output type is determined from `outputModel`.
-- In relational mode, the schema is derived from the selected Parquet file.
-- In document mode, the output is a document stream with generated document IDs where needed.
-- Extract multiple files allowed only for Document model and Relational files with same schema
-
-![Schema display](images/extract_workflow_act_settings.png)
-
-- ### ParquetWorkflowExtractSupport
-
-`org.polypheny.db.workflow.parquet.ParquetWorkflowExtractSupport`
-
-Helper class contains Parquet extraction logic for the workflow engine.
-It maps Parquet schemas to workflow output types, estimates row counts, generates dynamic activity names, and converts Parquet rows into either Polypheny documents or relational tuples.
-Calls logic of Parquet Adapter Module
-
-
-### Tests:
-
-- Read parquet file as Relational Data Source and store to CSV file
-- Read parquet file as Document Data Source and store to JSON file
-- Add file name as column
-- Add key id column
-- Set Maximum number of rows = 5 in Advanced settings
-- Extract multiple documents
-- Extract multiple relational - same schema
-- Extract multiple relational - different schema - not allowed
-
-
-![Schema display](images/workflow1.png)
-
-![Schema display](images/workflow2.png)
-
-## 2. Load to Parquet File
-
-- ### ParquetLoadActivity
-
-`org.polypheny.db.workflow.dag.activities.impl.load.ParquetLoadActivity`
-
-Workflow node definition for exporting workflow input into a Parquet file:
-1. It defines how the activity appears in the workflow UI using settings. This provides all needed information for the editor to show the available export options.
-2. It validates whether the connected workflow input is supported. Graph input is rejected and relational input is checked so that exporting without the workflow primary key does not produce an empty Parquet schema.
-3. It executes the export at runtime as follows: the activity resolves the configured target file and export settings, prepares the output path, and delegates the actual schema inference and data conversion work to `ParquetWorkflowLoadSupport.java`.
-
-#### ***Settings***
-
-#### `file`
-- Display name: `Target File`
-- Type: file setting
-- Required: yes
-- Multiple values: no
-- Supported source types:
-    - absolute file path
-- Purpose:
-  Select the Parquet file that should be written.
-
-#### `mode`
-- Display name: `Handling of Existing File`
-- Type: enum
-- Required: yes
-- Default: `fail`
-- Available values:
-    - `drop`
-    - `fail`
-- Purpose:
-  Defines the behavior if the selected output file already exists.
-- Behavior:
-    - `drop`: overwrite the existing file
-    - `fail`: abort the activity
-
-#### `compression`
-- Display name: `Compression`
-- Type: enum
-- Required: yes
-- Default: `snappy`
-- Available values:
-    - `snappy`
-    - `gzip`
-    - `uncompressed`
-- Purpose:
-  Selects the Parquet compression codec used for the generated file.
-
-#### `schemaSampleSize`
-- Display name: `Schema Sample Size`
-- Type: integer
-- Required: yes
-- Default: `100`
-- Minimum: `1`
-- Purpose:
-  Controls how many input tuples are sampled to infer the Parquet schema before writing starts.
-
-#### `conflictMode`
-- Display name: `Conflict Mode Handling`
-- Type: enum
-- Required: yes
-- Default: `stringify`
-- Available values:
-    - `stringify`
-    - `fail`
-- Purpose:
-  Defines how incompatible sampled values should be handled while inferring the Parquet schema.
-- Behavior:
-    - `stringify`: use a shared string field when incompatible sampled values are encountered
-    - `fail`: abort the activity when sampled values are incompatible
-
-#### `keepId`
-- Display name: `Include ID Field`
-- Type: boolean
-- Required: no
-- Default: `true`
-- Purpose:
-  Keeps the `_id` field when the activity receives document input.
-
-#### `keepPk`
-- Display name: `Keep Primary Key Column`
-- Type: boolean
-- Required: no
-- Default: `false`
-- Purpose:
-  Keeps the workflow primary key column when the activity receives relational input.
-
-#### ***Notes***
-
-- The activity has one input port of type `ANY` and no output ports.
-- Relational input and document input are both supported.
-- Graph input is not supported.
-- For relational input, the workflow primary key can optionally be excluded.
-- For document input, the schema is inferred from sampled documents because the document type does not provide field-level schema information.
-
-![Schema display](images/load_workflow_act_settings.png)
-
-![Schema display](images/extract_workflow_mult_validation.png)
-
-- ### ParquetWorkflowLoadSupport
-
-`org.polypheny.db.workflow.parquet.ParquetWorkflowLoadSupport`
-
-Helper class that contains Parquet export logic for the workflow engine.
-It prepares output files, builds dynamic activity names, infers Parquet schema from sampled workflow input, maps workflow values to Parquet schema definitions, and delegates low-level file writing to the shared Parquet adapter module.
-
-- ### ParquetSourceWriter
-
-`org.polypheny.db.adapter.parquet.shared.io.ParquetSourceWriter`
-
-Shared low-level Parquet writer used by workflow export functionality.
-It owns the Parquet writer lifecycle, creates Parquet row groups, writes rows to the target file, applies the configured compression codec, and exposes a small progress callback hook without depending on workflow-specific classes.
-
-- ### ParquetMessageTypeBuilder
-
-`org.polypheny.db.adapter.parquet.shared.schema.inference.ParquetMessageTypeBuilder`
-
-Builds a Parquet `MessageType` from inferred workflow field schemas, including primitive, repeated, and nested group fields.
-
-- ### Schema Inference Model
-
-`org.polypheny.db.adapter.parquet.shared.schema.inference.FieldSchema`
-
-Internal model for one exported Parquet field. It tracks the source field name, normalized Parquet field name, optional relational source index, and inferred value schema.
-
-`org.polypheny.db.adapter.parquet.shared.schema.inference.ValueSchema`
-
-Recursive internal model of a field value. It represents primitive values, nested groups, and repeated values before they are converted into a final Parquet schema.
-
-`org.polypheny.db.adapter.parquet.shared.schema.inference.SchemaState`
-
-Container for the currently inferred schema during export. It accumulates field definitions while sampled rows or documents are inspected.
-
-### Tests:
-
-- Read relational workflow input and export to Parquet file
-- Read document workflow input and export to Parquet file
-- Keep `_id` for document export
-- Keep workflow primary key for relational export
-- Write with different compression codecs
-- Use `schemaSampleSize = 5`
-- Export incompatible sampled values with `conflictMode = stringify`
-- Fail export on incompatible sampled values with `conflictMode = fail`
-
-![Schema display](images/workflow_parquet_parquet.png)
-
-# Unit Tests
-
----
+## Unit Tests
+___
 
 To run tests for parquet adapter:
 - `.\gradlew.bat :plugins:parquet-adapter:test`
@@ -492,7 +255,8 @@ Current test coverage includes:
 
 ## Current Gaps and Future Work
 ### Relational Parquet data source implementation
-- Add write support - extended adapter beyond its current read-only design.
+- Add writable Parquet source support in the adapter itself.
+  Current write support exists only for workflow export and does not make the Parquet adapter writable as a Polypheny source.
 - Add partition-aware file discovery and query pruning for partitioned Parquet datasets.
 - Add support for remote files.
 - Extend filter support beyond simple comparisons of the form `column OP literal`.
@@ -501,6 +265,3 @@ Current test coverage includes:
   One current example is legacy `INT96` timestamps, which are intentionally excluded from pushdown.
 - Improve handling of nested and complex Parquet schemas instead of treating them only as textual representations.
 - Document and strengthen error handling for invalid files, unsupported schema constructs, and conversion failures.
-
-
-### Document Parquet data source implementation
diff --git a/plugins/parquet-adapter/docs/diagrams/parquet_workflow_load_class_diagram.drawio b/plugins/parquet-adapter/docs/diagrams/parquet_workflow_load_class_diagram.drawio
new file mode 100644
index 0000000000..5217ead954
--- /dev/null
+++ b/plugins/parquet-adapter/docs/diagrams/parquet_workflow_load_class_diagram.drawio
@@ -0,0 +1,109 @@
+<mxfile host="Electron" agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/29.6.1 Chrome/142.0.7444.265 Electron/39.8.0 Safari/537.36" version="29.6.1">
+  <diagram id="parquet-workflow-load" name="Parquet Workflow Load">
+    <mxGraphModel dx="1558" dy="912" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="1600" pageHeight="1000" math="0" shadow="0">
+      <root>
+        <mxCell id="0" />
+        <mxCell id="1" parent="0" />
+        <mxCell id="2" parent="1" style="rounded=0;whiteSpace=wrap;html=1;fillColor=#dae8fc;strokeColor=#6c8ebf;fontSize=14;" value="&lt;b&gt;ParquetLoadActivity&lt;/b&gt;&lt;br&gt;workflow node&lt;br&gt;validates input&lt;br&gt;dispatches export" vertex="1">
+          <mxGeometry height="100" width="220" x="70" y="629" as="geometry" />
+        </mxCell>
+        <mxCell id="3" parent="1" style="rounded=0;whiteSpace=wrap;html=1;fillColor=#d5e8d4;strokeColor=#82b366;fontSize=14;" value="&lt;b&gt;ParquetWorkflowLoadSupport&lt;/b&gt;&lt;br&gt;workflow helper&lt;br&gt;prepares target file,&amp;nbsp;infers schema,&amp;nbsp;&lt;div&gt;calls writing functionality&lt;/div&gt;" vertex="1">
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diff --git a/plugins/parquet-adapter/docs/workflow.md b/plugins/parquet-adapter/docs/workflow.md
new file mode 100644
index 0000000000..381c81f530
--- /dev/null
+++ b/plugins/parquet-adapter/docs/workflow.md
@@ -0,0 +1,516 @@
+
+
+# WORKFLOW
+
+---
+
+## 1. Extract from Parquet file(s)
+
+
+### GOAL
+- read a parquet file
+- convert data according to DOCUMENT / RELATIONAL workflow output model
+- stream output to the workflow pipeline
+
+### ACTIVITY SETTINGS
+
+#### `file`
+- Display name: `File Location`
+- Type: file setting
+- Required: yes
+- Multiple values: yes
+- Supported source types:
+    - absolute file path
+    - URL
+- Purpose:
+  Select one or more Parquet files, or a folder containing Parquet files, to extract from.
+- Behavior:
+  If multiple files are selected, the activity outputs the union of their rows.
+
+#### `outputModel`
+- Display name: `Output Type`
+- Type: enum
+- Required: yes
+- Default: `document`
+- Available values:
+    - `document`
+    - `relational`
+- Purpose:
+  Defines whether the extracted Parquet rows are exposed as workflow documents or as a relational table.
+
+#### `nameField`
+- Display name: `Add File Name Field`
+- Type: boolean
+- Required: no
+- Default: `false`
+- Purpose:
+  Adds the source file name to the output.
+- Behavior:
+    - in document mode: adds a `fileName` field
+    - in relational mode: adds a `fileName` column
+
+#### `maxCount`
+- Display name: `Maximum Row Count`
+- Type: integer
+- Required: no
+- Default: `-1`
+- Minimum: `-1`
+- Group: advanced
+- Purpose:
+  Limits how many rows are extracted per file.
+- Behavior:
+    - `-1`: extract all rows
+    - `>= 0`: extract at most that many rows from each selected file
+
+#### ***Notes***
+
+- The activity has no input ports and one output port of type `ANY`.
+- The concrete output type is determined from `outputModel`.
+- In relational mode, the schema is derived from the selected Parquet file or validated across all selected Parquet files if multiple relational sources are chosen.
+- In document mode, the output is a document stream with generated document IDs where needed.
+- Extract multiple files allowed only for Document model and Relational files with same schema
+
+![Schema display](images/extract_workflow_act_settings.png)
+
+![Schema display](images/extract_workflow_mult_validation.png)
+
+### CLASSES
+
+- #### ParquetExtractActivity
+
+`org.polypheny.db.workflow.dag.activities.impl.extract.ParquetExtractActivity`
+
+Workflow node definition for reading Parquet files into a workflow output pipe:
+1. It defines how the activity appears in the workflow UI using settings. This provides all needed information for editor to show options.
+2. It decides what type of data the node will output: Relational or Document
+3. It executes the extraction at runtime as follows: the activity resolves the input files, iterates over them, and delegates the actual reading/conversion work to `ParquetWorkflowExtractSupport.java`, which turns Parquet rows into either documents or relational rows and pushes them into the workflow output pipe.
+
+
+- #### ParquetWorkflowExtractSupport
+
+`org.polypheny.db.workflow.parquet.ParquetWorkflowExtractSupport`
+
+Helper class contains Parquet extraction logic for the workflow engine.
+It maps Parquet schemas to workflow output types, estimates row counts, generates dynamic activity names, and converts Parquet rows into either Polypheny documents or relational tuples.
+Calls logic of Parquet Adapter Module
+
+### FLOW
+
+
+### 1. Activity entry and configuration
+
+- The flow starts in `ParquetExtractActivity`
+
+#### 1.1 Determine Output Model: DOCUMENT / RELATIONAL
+- call `previewOutTypes(...)`
+
+#### 1.2 Determine workflow-side Output Schema
+- call `lockOutputType(...)`  
+- ##### Make validations:
+  - DOCUMENT
+      - validate that source exists
+      - no detailed field schema from the Parquet file needed
+  - RELATIONAL
+      - validate that source is a parquet file
+      - for multiple source
+          - `ParquetSourceReader.readSchema(source)` called to read source schemas
+          - all source schemas are read and validated to ensure they are identical
+          - once schema validation succeeds, the first source is used to build the relational output type
+          - if relational sources do not share the same Parquet schema, preview fails.
+- ##### Convert the Parquet schema into a workflow output type - `ParquetWorkflowExtractSupport.getOutputType(...)`
+  - DOCUMENT - treat the output as a document stream with `_id`
+  - RELATIONAL - a relational `AlgDataType` is built from the Parquet fields
+    - call `ParquetFieldNameNormalizer` and `ParquetTypeConverter` functionality for field conversion
+
+
+### 2. Determine Extraction flow: Document / Relational
+- call `ParquetExtractActivity.pipe(...)` 
+- Extraction delegated to `ParquetWorkflowExtractSupport`
+
+### 3. DOCUMENT Extraction Flow
+- call `ParquetWorkflowExtractSupport.writeDocuments(output, source, addNameField, maxCount)`
+
+#### 3.1 Read parquet file data
+- `ParquetSourceReader` created for the selected source. It:
+  - opens Parquet file
+  - reads rows as Parquet Group objects
+  - exposes the projection schema
+  - tracks the current row number
+
+#### 3.2 Converting a row to a workflow document
+- Each Parquet row is converted into a `PolyDocument` by `ParquetDocValueExtractor.extractDocument(...)`:
+  - convert primitive values into Polypheny values
+  - recursively converts nested groups into nested documents
+  - converts repeated values into lists
+  - synthesizes `_id` if needed. The generated `_id` uses:
+    - source file name
+    - current row number
+
+
+#### 3.3 Optional file name column
+- If `nameField = true`: the source file name is added as: `fileName`
+
+#### 3.4 Writing to the workflow output pipe
+- The converted document is passed to the workflow `OutputPipe`.
+
+### 4. RELATIONAL Extraction Flow
+- call `ParquetWorkflowExtractSupport.writeRows(output, source, addNameCol, maxCount)`
+
+#### 4.1 Reader creation
+- `ParquetSourceReader` created for the selected source.
+
+#### 4.2 Reading rows
+- loop over Parquet rows
+- For each Parquet row, a workflow tuple is built as `List<PolyValue>`.
+
+#### 4.3 Field conversion
+- Each top-level field is converted using:`ParquetRelValueExtractor.extractValue(row, i, field)`:
+  - nested Parquet structures are stored as PolyDocument
+  - primitive values remain typed scalar columns
+
+
+#### 4.4 Optional file name column
+- If `nameField = true`: the file name is appended as an extra relational column
+
+#### 4.5 Writing to the workflow output pipe
+The relational tuple is passed to the workflow `OutputPipe`.
+
+### 5. Multi-file behavior
+- The activity supports multiple Parquet sources:
+  - In document mode: multiple sources are always allowed, extracted documents are concatenated into one document stream
+  - In relational mode: multiple sources are allowed only if they all share the same raw Parquet schema, schema equality is validated before execution
+
+
+### 6. Row count estimation
+- `estimateTupleCount(...)` gives the workflow engine a rough estimate of the output size, using Parquet metadata.
+
+
+### TESTS:
+
+- Read parquet file as Relational Data Source and store to CSV file
+- Read parquet file as Document Data Source and store to JSON file
+- Add file name as column
+- Add key id column
+- Set Maximum number of rows = 5 in Advanced settings
+- Extract multiple documents
+- Extract multiple relational - same schema
+- Extract multiple relational - different schema - not allowed
+
+
+![Schema display](images/workflow1.png)
+
+![Schema display](images/workflow2.png)
+
+<br>
+
+---
+
+## 2. Load to Parquet File
+
+
+### GOAL
+- read data from workflow pipeline
+- convert data according to DOCUMENT / RELATIONAL inout type into Parquet format
+- write output into Parquet file
+
+
+### CLASS DIAGRAM
+
+![Schema display](diagrams/parquet_workflow_load_class_diagram.png)
+
+### ACTIVITY SETTINGS
+
+#### `file`
+- Display name: `Target File`
+- Type: file setting
+- Required: yes
+- Multiple values: no
+- Supported source types:
+    - absolute file path
+- Purpose:
+  Select the Parquet file that should be written.
+
+#### `mode`
+- Display name: `Handling of Existing File`
+- Type: enum
+- Required: yes
+- Default: `fail`
+- Available values:
+    - `drop`
+    - `fail`
+- Purpose:
+  Defines the behavior if the selected output file already exists.
+- Behavior:
+    - `drop`: overwrite the existing file
+    - `fail`: abort the activity
+
+#### `compression`
+- Display name: `Compression`
+- Type: enum
+- Required: yes
+- Default: `snappy`
+- Available values:
+    - `snappy`
+    - `gzip`
+    - `uncompressed`
+- Purpose:
+  Selects the Parquet compression codec used for the generated file.
+
+#### `schemaSampleSize`
+- Display name: `Schema Sample Size`
+- Type: integer
+- Required: yes
+- Default: `100`
+- Minimum: `1`
+- Purpose:
+  Controls how many input tuples are sampled to infer the Parquet schema before writing starts.
+
+#### `conflictMode`
+- Display name: `Conflict Mode Handling`
+- Type: enum
+- Required: yes
+- Default: `stringify`
+- Available values:
+    - `stringify`
+    - `fail`
+- Purpose:
+  Defines how incompatible sampled values should be handled while inferring the Parquet schema.
+- Behavior:
+    - `stringify`: use a shared string field when incompatible sampled values are encountered
+    - `fail`: abort the activity when sampled values are incompatible
+
+#### `keepId`
+- Display name: `Include ID Field`
+- Type: boolean
+- Required: no
+- Default: `true`
+- Purpose:
+  Keeps the `_id` field when the activity receives document input.
+
+#### `keepPk`
+- Display name: `Keep Primary Key Column`
+- Type: boolean
+- Required: no
+- Default: `false`
+- Purpose:
+  Keeps the workflow primary key column when the activity receives relational input.
+
+#### ***Notes***
+
+- The activity has one input port of type `ANY` and no output ports.
+- Relational input and document input are both supported.
+- Graph input is not supported.
+- For relational input, the workflow primary key can optionally be excluded.
+- For document input, the schema is inferred from sampled documents because the document type does not provide field-level schema information.
+
+![Schema display](images/load_workflow_act_settings.png)
+
+### CLASSES
+
+
+- ### ParquetLoadActivity
+`org.polypheny.db.workflow.dag.activities.impl.load.ParquetLoadActivity`
+
+Workflow node definition for exporting workflow input into a Parquet file:<br>
+
+1. It defines how the activity appears in the workflow UI using settings.
+2. It validates whether the connected workflow input is supported.
+3. It executes the export at runtime as follows:
+    - the activity resolves the configured target file and export settings
+    - prepares the output path
+    - delegates the actual schema inference and data conversion work to `ParquetWorkflowLoadSupport.java`.
+   
+
+- ### ParquetWorkflowLoadSupport
+
+`org.polypheny.db.workflow.parquet.ParquetWorkflowLoadSupport`
+
+Helper class that contains Parquet export logic for the workflow engine.
+It prepares output files, builds dynamic activity names, infers Parquet schema from sampled workflow input, maps workflow values to Parquet schema definitions, and delegates low-level file writing to the shared Parquet adapter module.
+
+- ### _Parquet Writing Functionality_
+- #### ParquetSourceWriter
+
+`org.polypheny.db.adapter.parquet.shared.io.ParquetSourceWriter`
+
+Shared low-level Parquet writer used by workflow export functionality.
+It owns the Parquet writer lifecycle, creates Parquet row groups, writes rows to the target file, applies the configured compression codec, and exposes a small progress callback hook without depending on workflow-specific classes.
+
+- #### ParquetMessageTypeBuilder
+
+`org.polypheny.db.adapter.parquet.shared.schema.ParquetMessageTypeBuilder`
+
+Builds a Parquet `MessageType` from inferred workflow field schemas, including primitive, repeated, and nested group fields.
+
+- #### BufferedIterator
+
+`org.polypheny.db.adapter.parquet.shared.execution.BufferedIterator`
+
+Reusable iterator that replays sampled workflow tuples first and then continues with the remaining input iterator. It is used during Parquet export so sampled rows or documents are not lost after schema inference.
+
+
+- ### _Schema Inference Model_
+- ##### FieldSchema
+
+`org.polypheny.db.adapter.parquet.shared.schema.inference.FieldSchema`
+
+Internal model for one exported Parquet field. It tracks the source field name, normalized Parquet field name, optional relational source index, and inferred value schema.
+
+- ##### ValueSchema
+`org.polypheny.db.adapter.parquet.shared.schema.inference.ValueSchema`
+
+Recursive internal model of a field value. It represents primitive values, nested groups, and repeated values before they are converted into a final Parquet schema.
+
+- ##### ValueKind
+`org.polypheny.db.adapter.parquet.shared.schema.inference.ValueKind`
+
+Enum describing the logical value categories used during schema inference.
+
+- ##### SchemaState
+`org.polypheny.db.adapter.parquet.shared.schema.inference.SchemaState`
+
+Container for the currently inferred schema during export. It accumulates field definitions while sampled rows or documents are inspected.
+
+
+
+### FLOW
+
+
+### 1. Entry Point - `ParquetLoadActivity.pipe(...)` 
+- read settings
+- `ParquetWorkflowLoadSupport.prepareTargetFile(...)` checks the output path
+  - file not exists - continue
+  - file exists and mode is `fail` - exception
+  - file exists and mode is `drop` - delete file first
+- `ActivityUtils.getDataModel(input.getType())` - determines the input data model using `ActivityUtils.getDataModel(input.getType())`
+  - Possible cases:
+    - DOCUMENT - continue with Document Input Flow
+    - RELATIONAL - continue with Relational Input Flow
+    - GRAPH - rejected, export to Parquet is not supported
+
+### 2. Relational Input Flow - `ParquetWorkflowLoadSupport.writeRelational(...)` is called
+
+##### 2.1 Initial schema setup from the Pipeline Type
+- The input pipe already provides a relational `AlgDataType`. This type describes the exported workflow row structure. 
+- A new `SchemaState` is created. This object is the mutable schema accumulator used during export. 
+- The first schema shape is initialized from the pipeline type by calling: `schemaState.init(inputType, keepPk)`
+  - creates one `FieldSchema` for every exported column
+  - assigns an initial `ValueSchema` to each field from the declared Polypheny type
+- **_At this point, schema information comes from the pipeline type itself._**
+
+##### 2.2 Refine the schema using sampling rows
+- After initialization, the exporter samples the first `schemaSampleSize` rows from the input iterator.
+- Each sampled row is:
+  - stored in memory 
+  - merged into the schema state using `schemaState.mergeRelationalRowSchema(row, inputType, keepPk)`
+- Runtime values may contain more information than the declared relational type. ***For example:***
+  - the pipeline says a column is TEXT, but the actual data is numeric
+  - one field may hold a list/array rather than a single scalar value - parquet field should be modeled as a repeated field
+  - values may conflict across rows
+- While sampling, each field value is converted into an inferred `ValueSchema` and merged with the existing field schema.
+
+##### 2.3 Conflict Handling   
+If sampled values for the same field are incompatible, the behavior depends on `conflictMode`. Two modes exist:
+- `stringify`
+  - incompatible values are collapsed to `ValueSchema.stringType()`
+  - that field will be written as a Parquet string column
+- `fail`
+  - schema inference throws an exception
+  - export stops before the file is written
+
+#### 2.4 Final Parquet schema creation
+
+The final Parquet schema  `MessageType` is built from the accumulated `SchemaState`: `new ParquetMessageTypeBuilder(schemaState, SCHEMA_NAME).build()`
+- The schema builder converts each inferred `FieldSchema` and `ValueSchema` into Parquet schema elements:
+  - primitive fields become primitive Parquet columns
+  - repeated values become repeated Parquet fields (list)
+  - nested structures become Parquet groups
+
+#### 2.5 Combine sampled rows and remaining input under same Iterator
+
+- The sampled rows were already consumed from the input iterator during inference, so they must not be lost.
+- To solve this produce one continuous iterator `BufferedIterator`:
+  - first the sampled rows are replayed
+  - then the remaining pipeline rows are read normally
+
+#### 2.6 Writing
+- The actual Parquet file writing is delegated to `ParquetSourceWriter`
+- `ParquetSourceWriter.writeRows(...)` iterates over all rows and writes them one by one
+- For each relational row:
+  - a new empty Parquet `Group` is created from the final schema
+  - `populateGroupWithRelationalRow(...)` iterates over all exported `FieldSchema`s
+  - for each field, the source row value is taken using `sourceIndex`
+  - the value is written into the Parquet group using `populateGroupWithValue(...)`
+
+- Then:
+  - repeated values go through `populateGroupWithRepeatedValue(...)`
+  - single values go through `populateGroupWithSingleValue(...)`
+  - primitive values are handled by `addPrimitiveValue(...)` - final conversion from `PolyValue` to a Parquet-compatible primitive value
+
+### 3. Document Input Flow - `ParquetWorkflowLoadSupport.writeDocuments(...)` is called
+
+- No field-level schema comes from the pipeline. 
+- ***Schema inference is mandatory*** for document export.
+
+#### 3.2 Sampling documents
+- Read the first `schemaSampleSize` documents from the input pipe
+- Each sampled document is:
+  - buffered in memory
+  - merged into the schema state using `schemaState.mergeDocumentSchema(document, keepId)`
+
+#### 3.3 Handling nested fields and repeated values
+- Document schema inference supports:
+  - primitive fields
+  - nested documents - Nested documents become `ValueSchema` of kind `GROUP`
+  - repeated values / lists - Lists become repeated `ValueSchema`
+  
+
+- If sampled documents contain incompatible shapes for the same field:
+  - `conflictMode = stringify` collapses the field to string
+  - `conflictMode = fail` aborts the export
+
+#### 3.4 Final Parquet schema creation
+- The accumulated schema state is converted into a Parquet `MessageType` using `ParquetMessageTypeBuilder`.
+
+#### 3.5 Combine sampled rows and remaining input under same Iterator
+- Sampled documents are wrapped together with the remaining input using `BufferedIterator`, so sampled documents are written first and the rest of the pipeline continues after them.
+
+#### 3.6 Writing
+
+- `ParquetSourceWriter`is created
+
+- For each document:
+  - a new empty Parquet `Group` is created
+  - `populateGroupWithDocument(...)` iterates through all inferred fields 
+  - each field value is searched in the input document by `sourceName`
+  - values are written recursively into the Parquet group
+  
+
+- Nested document fields are written as nested groups.
+- Repeated values are written as repeated Parquet fields or repeated groups.
+- Primitive values are converted through `addPrimitiveValue(...)`.
+
+### 4. Progress reporting
+
+Workflow progress is not handled inside the schema builder or the activity itself. It is handled during row writing through the callback passed to `ParquetSourceWriter.writeRows(...)`.
+
+
+
+### TESTS:
+
+- Read relational workflow input and export to Parquet file
+- Read document workflow input and export to Parquet file
+- Keep `_id` for document export
+- Keep workflow primary key for relational export
+- Write with different compression codecs
+- Use `schemaSampleSize = 5`
+- Export incompatible sampled values with `conflictMode = stringify`
+- Fail export on incompatible sampled values with `conflictMode = fail`
+
+![Schema display](images/workflow_parquet_parquet.png)
+
+---
+
+## Unit Tests
+
+
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/SchemaState.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/SchemaState.java
index 0dae326dcb..6afc3f7a47 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/SchemaState.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/SchemaState.java
@@ -31,6 +31,9 @@
 
 import static org.polypheny.db.adapter.parquet.shared.schema.ParquetFieldNameNormalizer.uniquifyParquetNames;
 
+/**
+ * Contains inferred schema: all fields definitions
+ */
 @Getter
 public class SchemaState {
 

From 8fde060ec1edfae31b5566ef23ec3bbfd69895aa Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Thu, 16 Apr 2026 10:32:50 +0200
Subject: [PATCH 15/46] workflow documentation

---
 plugins/parquet-adapter/docs/workflow.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/plugins/parquet-adapter/docs/workflow.md b/plugins/parquet-adapter/docs/workflow.md
index 381c81f530..f0f453f6c2 100644
--- a/plugins/parquet-adapter/docs/workflow.md
+++ b/plugins/parquet-adapter/docs/workflow.md
@@ -107,10 +107,10 @@ Calls logic of Parquet Adapter Module
 #### 1.2 Determine workflow-side Output Schema
 - call `lockOutputType(...)`  
 - ##### Make validations:
-  - DOCUMENT
+  - *DOCUMENT*
       - validate that source exists
       - no detailed field schema from the Parquet file needed
-  - RELATIONAL
+  - *RELATIONAL*
       - validate that source is a parquet file
       - for multiple source
           - `ParquetSourceReader.readSchema(source)` called to read source schemas

From 7dc26d0247bf37c525c5b8617fb4e9be0b22abee Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Tue, 21 Apr 2026 09:46:47 +0200
Subject: [PATCH 16/46] nested fields presentation

---
 plugins/parquet-adapter/docs/README.md        |  12 -
 plugins/parquet-adapter/docs/nested_fields.md | 226 +++++++++++++++
 .../execution/ParquetDocValueExtractor.java   |   4 +-
 .../relational/ParquetRelationalSource.java   | 126 ++++++++-
 .../ParquetBindingRelEnumerator.java          | 132 +++++++++
 .../execution/ParquetNestedRelEnumerator.java | 172 ++++++++++++
 .../schema/DiscoveredTableBinding.java        |  36 +++
 .../schema/ParquetBindingSerializer.java      | 139 +++++++++
 .../schema/ParquetColumnBinding.java          |  42 +++
 .../relational/schema/ParquetColumnRole.java  |  27 ++
 .../schema/ParquetNormalizedSchema.java       |  74 +++++
 .../relational/schema/ParquetRelTable.java    |  62 +++-
 .../relational/schema/ParquetSchemaMode.java  |  48 ++++
 .../schema/ParquetSchemaNormalizer.java       | 264 ++++++++++++++++++
 .../schema/ParquetTableBinding.java           | 103 +++++++
 .../parquet/shared/AbstractParquetSource.java | 128 ++++++---
 .../AbstractParquetValueExtractor.java        |   4 +-
 .../shared/io/ParquetFileDiscovery.java       |   3 +-
 .../parquet/shared/io/ParquetUrlResolver.java |  63 +++++
 ...alizer.java => ParquetNameNormalizer.java} |  22 +-
 .../shared/schema/ParquetNamespace.java       |  21 +-
 .../shared/schema/inference/SchemaState.java  |   6 +-
 .../shared/schema/inference/ValueSchema.java  |   6 +-
 .../main/resources/orders_db/orders.parquet   | Bin 1125450 -> 6938790 bytes
 .../test/resources/orders_db/orders.parquet   | Bin 1125450 -> 6938790 bytes
 .../ParquetWorkflowExtractSupport.java        |   4 +-
 26 files changed, 1648 insertions(+), 76 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/nested_fields.md
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetBindingRelEnumerator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRelEnumerator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTableBinding.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetBindingSerializer.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnBinding.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnRole.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetNormalizedSchema.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaMode.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaNormalizer.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetUrlResolver.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/{ParquetFieldNameNormalizer.java => ParquetNameNormalizer.java} (72%)

diff --git a/plugins/parquet-adapter/docs/README.md b/plugins/parquet-adapter/docs/README.md
index cc0cae2137..ee486505d9 100644
--- a/plugins/parquet-adapter/docs/README.md
+++ b/plugins/parquet-adapter/docs/README.md
@@ -253,15 +253,3 @@ Current test coverage includes:
 1. `rejectsInt96TimestampPredicatePushdown()`
 
 
-## Current Gaps and Future Work
-### Relational Parquet data source implementation
-- Add writable Parquet source support in the adapter itself.
-  Current write support exists only for workflow export and does not make the Parquet adapter writable as a Polypheny source.
-- Add partition-aware file discovery and query pruning for partitioned Parquet datasets.
-- Add support for remote files.
-- Extend filter support beyond simple comparisons of the form `column OP literal`.
-  Planned examples include `BETWEEN`, `IN`, `IS NULL`, `IS NOT NULL`, and more complex boolean combinations such as `AND` and `OR`.
-- Add optional native predicate support for additional Parquet physical encodings where safe and well-tested.
-  One current example is legacy `INT96` timestamps, which are intentionally excluded from pushdown.
-- Improve handling of nested and complex Parquet schemas instead of treating them only as textual representations.
-- Document and strengthen error handling for invalid files, unsupported schema constructs, and conversion failures.
diff --git a/plugins/parquet-adapter/docs/nested_fields.md b/plugins/parquet-adapter/docs/nested_fields.md
new file mode 100644
index 0000000000..db1e5ed954
--- /dev/null
+++ b/plugins/parquet-adapter/docs/nested_fields.md
@@ -0,0 +1,226 @@
+
+# Relational Schema Normalization
+
+## Example Parquet shape:
+```
+orders.parquet
+root
+|- order_id: int64
+|- customer: string
+`- items: repeated group
+   |- sku: string
+   |- quantity: int32
+   `- discounts: repeated group
+      |- code: string
+      `- amount: decimal
+```
+
+## Flat Mode
+preserve the Parquet file as one table
+
+**_orders_**
+
+
+| order_id | customer | items                                                                                                                                           |
+|----------|----------|-------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1001     | Alice    | [{sku: "A-1", quantity: 2, discounts: [{code: "SUMMER", amount: 5.00}, {code: "VIP", amount: 2.00}]}, {sku: "B-5", quantity: 1, discounts: []}] |
+| 1002     | Bob      | [{sku: "C-9", quantity: 4, discounts: []}]                                                                                                      |
+
+
+```
+orders.parquet
+-> orders
+```
+
+## Normalized Mode
+split nested structures into generated relational child tables
+
+**_orders_**
+
+| __polypheny_row_id | order_id | customer |
+|--------------------|----------|----------|
+| 1                  | 1001     | Alice    |
+| 2                  | 1002     | Bob      |
+
+
+**_orders__items_**
+
+| __polypheny_row_id | __polypheny_parent_row_id | __polypheny_elem_ordinal | sku | quantity |
+|--------------------|---------------------------|--------------------------|-----|----------|
+| 10                 | 1                         | 0                        | A-1 | 2        |
+| 11                 | 1                         | 1                        | B-5 | 1        |
+| 12                 | 2                         | 0                        | C-9 | 4        |
+
+**_orders__items__discounts_**
+
+| __polypheny_row_id | __polypheny_parent_row_id | __polypheny_elem_ordinal | code   | amount |
+|--------------------|---------------------------|--------------------------|--------|--------|
+| 20                 | 10                        | 0                        | SUMMER | 5.00   |
+| 21                 | 10                        | 1                        | VIP    | 2.00   |
+
+```
+orders.parquet
+    -> orders
+    -> orders__items
+    -> orders__items__discounts
+ ```
+
+```sql
+SELECT o.order_id, i.sku, i.quantity
+FROM orders o
+JOIN orders__items i
+  ON i.__polypheny_parent_row_id = o.__polypheny_row_id;
+
+```
+## Behavior:
+
+- All relational Parquet table names are adapter-prefixed, both flat and normalized. <br>
+*Example:* `parquetrelational1__orders`, not just `orders`. This allows create different adapters for flat and normalized schema modes.
+
+- Flat mode shows one table per Parquet file, with nested/repeated values kept inside the root table as structured values.
+
+- Normalized mode shows root tables with direct primitive fields only.
+- Every Parquet group field becomes a generated virtual relational table.
+- Nested groups inside nested groups also become virtual tables. <br> *Example:* items.discounts becomes parquetrelational1__orders__items__discounts.
+- Normalized tables include visible ***synthetic key columns***:
+  - __polypheny_row_id
+  - __polypheny_parent_row_id
+  - __polypheny_elem_ordinal
+- Root tables have: 
+  - __polypheny_row_id
+- Child tables have: 
+  - __polypheny_row_id
+  - __polypheny_parent_row_id
+  - __polypheny_elem_ordinal - for repeated children
+- Non-repeated child tables still have: 
+  - __polypheny_parent_row_id
+  - __polypheny_elem_ordinal is always 0
+
+***Sql Example:***
+```sql
+SELECT o.order_id, i.sku, i.quantity
+FROM parquetrelational1__orders o
+JOIN parquetrelational1__orders__items i
+  ON i.__polypheny_parent_row_id = o.__polypheny_row_id;
+
+```
+For deeper nesting, the same rule applies at each level. A row in parquetrelational1__orders__items__discounts points to its parent item row, not directly to the root order row:
+```sql
+SELECT i.sku, d.code, d.amount
+FROM parquetrelational1__orders__items i
+JOIN parquetrelational1__orders__items__discounts d
+  ON d.__polypheny_parent_row_id = i.__polypheny_row_id;
+```
+
+## Code Changes
+
+### AbstractParquetSource
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\AbstractParquetSource.java`
+- Adding shared metadata persistence, safer URL handling, and adapter-prefixed table names
+- Store Parquet Bindings in adapter settings in persistParquetBindings() function
+- Load Parquet Bindings from settings in constructor()
+- Prefix is added to the table names to allow deployment multiple Parquet relational adapters over the same files, for example one flat and one normalized, without logical table name collisions - getPrefixTableName() function 
+
+### ParquetColumnRole
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetColumnRole.java`
+- Describes what kind of column a relational Parquet column is inside a ParquetTableBinding
+  - DATA - a normal real Parquet value
+  - PRIMARY_KEY - synthetic generated row id
+  - PARENT_KEY - synthetic reference to the parent generated row
+  - ORDINAL - position of a child value inside its parent
+
+### ParquetColumnBinding
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetColumnBinding.java`
+Metadata object that tells the Parquet scanner for specific Polypheny column, where does its value come from. Used for reading data.
+Contains:
+- columnId - Polypheny physical column id connected to an actual column in Polypheny’s physical table catalog
+- columnName - relational column name
+- role - ParquetColumnRole - what kind of column this is
+- sourcePathElements - the path inside the Parquet schema that should be used to read this column. For example:
+  - flat/root column - path: order_id
+  - nested - path: shipping_address.city
+
+
+### ParquetTableBinding
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetTableBinding.java`
+Table-level metadata, describes the whole table, immutable record
+Fields:
+- sourceUrl - real Parquet file URL to read from
+- parentTableName - generated parent relational table name, or null for root tables
+- sourcePathElements - table-level path inside the Parquet file, For root: List.of(), for nested: List.of("items")
+- columnsByColumnId - Map<Long, ParquetColumnBinding> - maps Polypheny physical column ids to column bindings
+
+Example:
+```
+sourceUrl: orders.parquet
+parentTableName: parquetrelational1__orders
+sourcePathElements: ["items"]
+
+columnPaths:
+sku      -> ["items", "sku"] // sku - stock keeping unit (product code)
+quantity -> ["items", "quantity"]
+```
+
+### ParquetUrlResolver
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\io\ParquetUrlResolver.java`
+Utility, Parquet file/directory URL handling
+
+
+### ParquetSchemaMode
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetSchemaMode.java`
+User-selectable adapter setting that controls how the Parquet schema
+- FLAT
+- NORMALIZED
+
+### ParquetSchemaNormalizer
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetSchemaNormalizer.java`
+Turns Parquet nested schema into multiple relational tables for normalized mode.
+Contains the main normalization logic.
+It creates Result containing: 
+- table definition: exported relational table definitions: Map<String, List<ExportedColumn>> exportedColumns
+- binding metadata: explains how those tables map back to the Parquet file: Map<String, DiscoveredTableBinding> tableBindings
+
+
+### ParquetNormalizedSchema
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetNormalizedSchema.java`
+contains normalizes schema information for all tables:
+- Map<String, List<ExportedColumn>> tables - normalized columns per table name
+- Map<String, DiscoveredTableBinding> bindings - binding per table name
+
+### DiscoveredTableBinding
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\DiscoveredTableBinding.java`
+temporary discovery metadata before a real ParquetTableBinding created
+for ParquetTableBinding we need physical table ids, that are created after normalization step 
+
+### ParquetBindingSerializer
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetBindingSerializer.java`
+Saves and restores ParquetTableBinding metadata through adapter settings.
+
+### ParquetBindingRelEnumerator
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetBindingRelEnumerator.java`
+Root-table scanner that reads column values by ParquetColumnBinding paths instead of by physical ordinal position.
+Normalized mode may remove nested fields from the root table. It
+- reads full root rows from the Parquet file
+- extracts each value by path. If the binding path is: List.of("shipping_address", "city") it walks: "root -> shipping_address -> city"
+- It supports walking through non-repeated nested groups.
+- If it encounters a repeated group in the middle of a root value path, it returns NULL instead of trying to flatten it.
+- It converts final values using ParquetRelValueExtractor
+
+### ParquetNestedRelEnumerator
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetNestedRelEnumerator.java`
+used for tables that do not have their own Parquet file, but are created from a nested group inside a Parquet file
+
+### ParquetRelationalSource
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\ParquetRelationalSource.java`
+changed from “simple relational Parquet source that exports one table per file” into the controller for flat vs normalized relational Parquet mode.
+
+### ParquetRelTable
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetRelTable.java`
+ParquetRelTable changed from a simple wrapper that always scanned Parquet columns by ordinal into a wrapper that can choose between three scan strategies:
+- old flat ordinal scan
+- binding-aware root scan
+- nested child-table scan
+
+### ParquetNamespace
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\schema\ParquetNamespace.java`
+Make relational table creation binding-aware.
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
index c08dd2c585..b7dbcaa328 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
@@ -5,8 +5,8 @@
 import org.apache.parquet.example.data.Group;
 import org.apache.parquet.schema.MessageType;
 import org.apache.parquet.schema.Type;
-import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetValueExtractor;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer;
 import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.type.entity.document.PolyDocument;
@@ -42,7 +42,7 @@ public PolyDocument extractDocument( Group group, MessageType schema, PolyString
                 continue;
             }
             // store result in the map under the normalized name
-            values.put( PolyString.of( AbstractParquetSource.normalizeFieldName( field.getName() ) ), extractValue( group, i, field ) );
+            values.put( PolyString.of( ParquetNameNormalizer.normalizeFieldName( field.getName() ) ), extractValue( group, i, field ) );
         }
 
         // if _id is missing or null, insert the provided generatedId
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
index b14c5b870d..07175315be 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
@@ -16,17 +16,26 @@
 
 package org.polypheny.db.adapter.parquet.relational;
 
+import java.util.LinkedHashMap;
 import java.util.List;
 import java.util.Map;
 import java.util.Set;
 import java.util.stream.Collectors;
 import org.pf4j.Extension;
+import org.polypheny.db.adapter.ConnectionMethod;
 import org.polypheny.db.adapter.DeployMode;
 import org.polypheny.db.adapter.RelationalDataSource;
 import org.polypheny.db.adapter.annotations.AdapterProperties;
 import org.polypheny.db.adapter.annotations.AdapterSettingDirectory;
 import org.polypheny.db.adapter.annotations.AdapterSettingList;
 import org.polypheny.db.adapter.annotations.AdapterSettingString;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetNormalizedSchema;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSchemaMode;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSchemaNormalizer;
+import org.polypheny.db.adapter.parquet.relational.schema.DiscoveredTableBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetUrlResolver;
 import org.polypheny.db.catalog.entity.allocation.AllocationCollection;
 import org.polypheny.db.catalog.entity.allocation.AllocationGraph;
 import org.polypheny.db.catalog.entity.allocation.AllocationTable;
@@ -36,9 +45,9 @@
 import org.polypheny.db.catalog.entity.logical.LogicalTableWrapper;
 import org.polypheny.db.catalog.entity.physical.PhysicalEntity;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.catalog.logistic.DataModel;
 import org.polypheny.db.prepare.Context;
-import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 
 /**
  * Relational adapter implementation.
@@ -52,23 +61,41 @@
         usedModes = DeployMode.EMBEDDED,
         defaultMode = DeployMode.EMBEDDED)
 @AdapterSettingList(name = "method", options = { "upload", "link", "url" }, defaultValue = "upload", description = "If the supplied file(s) should be uploaded or a link to the local filesystem is used (sufficient permissions are required).", position = 1)
+@AdapterSettingList(name = ParquetRelationalSource.SCHEMA_MODE_SETTING, options = { "flat", "normalized" }, defaultValue = "flat", description = "Controls how Parquet schemas are exposed. Flat preserves the existing one-file-one-table import. Normalized will expose nested fields as generated relational tables.", position = 3)
 @AdapterSettingDirectory(subOf = "method_upload", name = "directory", defaultValue = "classpath://orders_db", description = "You can upload one or multiple .parquet files.", position = 2)
 @AdapterSettingString(subOf = "method_link", defaultValue = "classpath://orders_db", name = "directoryName", description = "You can select a path to a folder or specific .parquet files.", position = 2)
 @AdapterSettingString(subOf = "method_url", defaultValue = "", name = "url", description = "URL to the Parquet file(s) to be integrated as this source.", position = 2)
 public class ParquetRelationalSource extends AbstractParquetSource implements RelationalDataSource {
 
     public static final String NAME = "Parquet Relational";
+    public static final String SCHEMA_MODE_SETTING = "schema mode";
+
+    private ParquetSchemaMode schemaMode;
+    private ParquetNormalizedSchema normalizedSchema;
 
-    public ParquetRelationalSource(long storeId, String uniqueName, Map<String, String> settings, DeployMode mode ) {
+
+    public ParquetRelationalSource( long storeId, String uniqueName, Map<String, String> settings, DeployMode mode ) {
         super( storeId, uniqueName, settings, mode, Set.of( DataModel.RELATIONAL ) );
+        this.schemaMode = getConfiguredSchemaMode();
     }
 
+
     @Override
     public RelationalDataSource asRelationalDataSource() {
         return this;
     }
 
 
+    @Override
+    public Map<String, List<ExportedColumn>> getExportedColumns() {
+        ParquetSchemaMode activeMode = schemaMode == null ? getConfiguredSchemaMode() : schemaMode;
+        if ( activeMode == ParquetSchemaMode.NORMALIZED ) {
+            return getNormalizedExportedColumns();
+        }
+        return super.getExportedColumns();
+    }
+
+
     @Override
     public List<PhysicalEntity> createTable( Context context, LogicalTableWrapper logical, AllocationTableWrapper allocation ) {
         PhysicalTable table = adapterCatalog.createTable(
@@ -79,7 +106,9 @@ public List<PhysicalEntity> createTable( Context context, LogicalTableWrapper lo
                 logical.columns.stream().collect( Collectors.toMap( t -> t.id, t -> t ) ),
                 logical.pkIds, allocation );
 
-        var physical = currentNamespace.createParquetTable( table.id, table, this );
+        ParquetTableBinding binding = getDiscoveredTableBinding( logical.table.name, table );
+        registerParquetBinding( table.id, binding );
+        var physical = currentNamespace.createParquetTable( table.id, table, binding, this );
         adapterCatalog.replacePhysical( physical );
         return List.of( physical );
     }
@@ -89,13 +118,19 @@ public List<PhysicalEntity> createTable( Context context, LogicalTableWrapper lo
     public void restoreTable( AllocationTable alloc, List<PhysicalEntity> entities, Context context ) {
         PhysicalEntity table = entities.get( 0 );
         updateNamespace( table.namespaceName, table.namespaceId );
-        var physical = currentNamespace.createParquetTable( table.id, table.unwrapOrThrow( PhysicalTable.class ), this );
+        PhysicalTable physicalTable = table.unwrapOrThrow( PhysicalTable.class );
+        ParquetTableBinding binding = getParquetBinding( table.id ).orElseGet( () -> currentNamespace.createRootBinding( physicalTable ) );
+        var physical = currentNamespace.createParquetTable( table.id, physicalTable, binding, this );
         adapterCatalog.addPhysical( alloc, physical );
     }
 
 
     @Override
     public void dropTable( Context context, long allocId ) {
+        List<PhysicalEntity> physicals = adapterCatalog.getPhysicalsFromAllocs( allocId );
+        if ( physicals != null ) {
+            physicals.forEach( physical -> removeParquetBinding( physical.id ) );
+        }
         adapterCatalog.removeAllocAndPhysical( allocId );
     }
 
@@ -143,4 +178,87 @@ public void renameLogicalColumn( long id, String newColumnName ) {
         adapterCatalog.renameLogicalColumn( id, newColumnName );
     }
 
+
+    @Override
+    protected void reloadSettings( List<String> updatedSettings ) {
+        super.reloadSettings( updatedSettings );
+        if ( updatedSettings.contains( "directory" ) || updatedSettings.contains( "directoryName" ) || updatedSettings.contains( "url" ) ) {
+            clearNormalizedExportCache();
+        }
+        if ( updatedSettings.contains( SCHEMA_MODE_SETTING ) ) {
+            schemaMode = getConfiguredSchemaMode();
+            clearExportedColumnsCache();
+            clearNormalizedExportCache();
+        }
+    }
+
+
+    private ParquetSchemaMode getConfiguredSchemaMode() {
+        String value = settings.getOrDefault( SCHEMA_MODE_SETTING, ParquetSchemaMode.FLAT.getSettingValue() );
+        ParquetSchemaMode mode = ParquetSchemaMode.from( value );
+        if ( mode == ParquetSchemaMode.FLAT && !ParquetSchemaMode.FLAT.getSettingValue().equalsIgnoreCase( value ) ) {
+            log.warn( "Unknown Parquet schema mode '{}'. Falling back to flat mode.", value );
+        }
+        return mode;
+    }
+
+
+    private Map<String, List<ExportedColumn>> getNormalizedExportedColumns() {
+        return getNormalizedSchema().getTables();
+    }
+
+
+    private ParquetTableBinding getDiscoveredTableBinding( String tableName, PhysicalTable table ) {
+        ParquetNormalizedSchema normalizedSchema = getNormalizedSchema();
+        DiscoveredTableBinding binding = normalizedSchema.getBinding( tableName );
+        if ( binding == null && getConfiguredSchemaMode() == ParquetSchemaMode.NORMALIZED ) {
+            clearNormalizedExportCache();
+            normalizedSchema = getNormalizedSchema();
+            binding = normalizedSchema.getBinding( tableName );
+        }
+        if ( binding == null ) {
+            if ( getConfiguredSchemaMode() == ParquetSchemaMode.NORMALIZED ) {
+                throw new GenericRuntimeException( "Missing normalized Parquet binding for generated table: %s", tableName );
+            }
+            return createFlatTableBinding( tableName, table );
+        }
+        return ParquetTableBinding.createTableBindingFromColumnPaths( binding.sourceUrl(), binding.parentTableName(), binding.sourcePathElements(), table, binding.columnPaths() );
+    }
+
+
+    private ParquetTableBinding createFlatTableBinding( String tableName, PhysicalTable table ) {
+        List<ExportedColumn> exportedColumns = super.getExportedColumns().get( tableName );
+        if ( exportedColumns == null || exportedColumns.isEmpty() ) {
+            return currentNamespace.createRootBinding( table );
+        }
+
+        try {
+            Map<String, List<String>> columnPaths = new LinkedHashMap<>();
+            exportedColumns.forEach( column -> columnPaths.put( column.name(), List.of( column.physicalColumnName() ) ) );
+            String sourceUrl = ParquetUrlResolver.resolveFile( parquetDir, exportedColumns.get( 0 ).physicalSchemaName() ).toString();
+            return ParquetTableBinding.createTableBindingFromColumnPaths( sourceUrl, null, List.of(), table, columnPaths );
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( e );
+        }
+    }
+
+
+    private void clearNormalizedExportCache() {
+        normalizedSchema = null;
+    }
+
+    private ParquetNormalizedSchema getNormalizedSchema() {
+        if ( connectionMethod == ConnectionMethod.UPLOAD && normalizedSchema != null ) {
+            return normalizedSchema;
+        }
+
+        normalizedSchema = new ParquetSchemaNormalizer(
+                parquetDir,
+                getClass().getClassLoader(),
+                parquetTypeConverter,
+                getUniqueName() ).normalize();
+
+        return normalizedSchema;
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetBindingRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetBindingRelEnumerator.java
new file mode 100644
index 0000000000..4fb7636bbd
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetBindingRelEnumerator.java
@@ -0,0 +1,132 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.Enumerator;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.GroupType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+/**
+ * Root-table scanner that
+ * reads root table rows and extracts selected columns by path
+ */
+public class ParquetBindingRelEnumerator implements Enumerator<PolyValue[]> {
+
+    private final ParquetSourceReader reader;
+    private final List<ParquetColumnBinding> columnBindings;
+    private final ParquetRelValueExtractor valueExtractor = new ParquetRelValueExtractor();
+    private PolyValue[] current;
+
+
+    public ParquetBindingRelEnumerator( Source source, AtomicBoolean cancelFlag, List<ParquetColumnBinding> columnBindings ) {
+        // read full root rows from the Parquet file
+        this.reader = new ParquetSourceReader( source, cancelFlag, null, List.of() );
+        this.columnBindings = List.copyOf( columnBindings );
+    }
+
+
+    @Override
+    public PolyValue[] current() {
+        return current;
+    }
+
+
+    @Override
+    public boolean moveNext() {
+        try {
+            Group group = reader.next();
+            if ( group == null ) {
+                current = null;
+                return false;
+            }
+
+            current = new PolyValue[columnBindings.size()];
+            for ( int i = 0; i < columnBindings.size(); i++ ) {
+                // extract value by path
+                // if the binding path is: List.of("shipping_address", "city") it walks: "root -> shipping_address -> city"
+                current[i] = extractPathValue( group, columnBindings.get( i ).sourcePathElements() );
+            }
+            return true;
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Error while reading parquet data using column bindings", e );
+        }
+    }
+
+
+    @Override
+    public void reset() {
+        throw new UnsupportedOperationException();
+    }
+
+
+    @Override
+    public void close() {
+        try {
+            reader.close();
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Error closing parquet reader", e );
+        }
+    }
+
+
+    private PolyValue extractPathValue( Group root, List<String> sourcePath ) {
+        if ( sourcePath.isEmpty() ) {
+            return PolyNull.NULL;
+        }
+
+        Group currentGroup = root;
+        GroupType currentType = root.getType();
+        for ( int i = 0; i < sourcePath.size(); i++ ) {
+            int index = fieldIndex( currentType, sourcePath.get( i ) );
+            if ( index < 0 || currentGroup.getFieldRepetitionCount( index ) == 0 ) {
+                return PolyNull.NULL;
+            }
+
+            Type fieldType = currentType.getType( index );
+            if ( i == sourcePath.size() - 1 ) {
+                return valueExtractor.extractValue( currentGroup, index, fieldType );
+            }
+
+            if ( fieldType.isPrimitive() || currentGroup.getFieldRepetitionCount( index ) > 1 ) {
+                return PolyNull.NULL;
+            }
+            currentGroup = currentGroup.getGroup( index, 0 );
+            currentType = fieldType.asGroupType();
+        }
+        return PolyNull.NULL;
+    }
+
+
+    private int fieldIndex( GroupType groupType, String fieldName ) {
+        for ( int i = 0; i < groupType.getFieldCount(); i++ ) {
+            if ( groupType.getType( i ).getName().equals( fieldName ) ) {
+                return i;
+            }
+        }
+        return -1;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRelEnumerator.java
new file mode 100644
index 0000000000..5e52a99bbc
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRelEnumerator.java
@@ -0,0 +1,172 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.ArrayDeque;
+import java.util.List;
+import java.util.Queue;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.Enumerator;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.GroupType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+/**
+ *  Used for tables that do not have their own Parquet file, but are created from a nested group inside a Parquet file
+ */
+public class ParquetNestedRelEnumerator implements Enumerator<PolyValue[]> {
+
+    private final ParquetSourceReader reader;
+    private final List<String> tablePath;
+    private final List<ParquetColumnBinding> columnBindings;
+    private final ParquetRelValueExtractor valueExtractor = new ParquetRelValueExtractor();
+    private final Queue<PolyValue[]> rows = new ArrayDeque<>();
+    private PolyValue[] current;
+
+
+    public ParquetNestedRelEnumerator( Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings ) {
+        this.reader = new ParquetSourceReader( source, cancelFlag, null, List.of() );
+        this.tablePath = binding.sourcePathElements();
+        this.columnBindings = List.copyOf( columnBindings );
+        if ( tablePath.isEmpty() ) {
+            throw new GenericRuntimeException( "Nested parquet table binding does not contain a table source path." );
+        }
+    }
+
+
+    @Override
+    public PolyValue[] current() {
+        return current;
+    }
+
+
+    @Override
+    public boolean moveNext() {
+        try {
+            while ( rows.isEmpty() ) {
+                Group root = reader.next();
+                if ( root == null ) {
+                    current = null;
+                    return false;
+                }
+                enqueueNestedRows( root );
+            }
+            current = rows.remove();
+            return true;
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Error while reading nested parquet data", e );
+        }
+    }
+
+
+    @Override
+    public void reset() {
+        throw new UnsupportedOperationException();
+    }
+
+
+    @Override
+    public void close() {
+        try {
+            reader.close();
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Error closing parquet reader", e );
+        }
+    }
+
+
+    private void enqueueNestedRows( Group root ) {
+        for ( Group nested : resolveTableGroups( root, root.getType(), 0 ) ) {
+            PolyValue[] row = new PolyValue[columnBindings.size()];
+            for ( int i = 0; i < columnBindings.size(); i++ ) {
+                row[i] = extractNestedValue( nested, columnBindings.get( i ).sourcePathElements() );
+            }
+            rows.add( row );
+        }
+    }
+
+
+    private PolyValue extractNestedValue( Group nested, List<String> sourcePath ) {
+        if ( sourcePath.size() < tablePath.size() || !sourcePath.subList( 0, tablePath.size() ).equals( tablePath ) ) {
+            return PolyNull.NULL;
+        }
+
+        Group currentGroup = nested;
+        GroupType currentType = nested.getType();
+        for ( int i = tablePath.size(); i < sourcePath.size(); i++ ) {
+            int index = fieldIndex( currentType, sourcePath.get( i ) );
+            if ( index < 0 || currentGroup.getFieldRepetitionCount( index ) == 0 ) {
+                return PolyNull.NULL;
+            }
+
+            Type fieldType = currentType.getType( index );
+            if ( i == sourcePath.size() - 1 ) {
+                return valueExtractor.extractValue( currentGroup, index, fieldType );
+            }
+
+            if ( fieldType.isPrimitive() ) {
+                return PolyNull.NULL;
+            }
+            currentGroup = currentGroup.getGroup( index, 0 );
+            currentType = fieldType.asGroupType();
+        }
+        return PolyNull.NULL;
+    }
+
+
+    private List<Group> resolveTableGroups( Group group, GroupType groupType, int pathIndex ) {
+        if ( pathIndex >= tablePath.size() ) {
+            return List.of( group );
+        }
+
+        int fieldIndex = fieldIndex( groupType, tablePath.get( pathIndex ) );
+        if ( fieldIndex < 0 || group.getFieldRepetitionCount( fieldIndex ) == 0 ) {
+            return List.of();
+        }
+
+        Type fieldType = groupType.getType( fieldIndex );
+        if ( fieldType.isPrimitive() ) {
+            return List.of();
+        }
+
+        List<Group> groups = new java.util.ArrayList<>();
+        int count = group.getFieldRepetitionCount( fieldIndex );
+        for ( int occurrence = 0; occurrence < count; occurrence++ ) {
+            Group child = group.getGroup( fieldIndex, occurrence );
+            groups.addAll( resolveTableGroups( child, fieldType.asGroupType(), pathIndex + 1 ) );
+        }
+        return groups;
+    }
+
+
+    private int fieldIndex( GroupType groupType, String fieldName ) {
+        for ( int i = 0; i < groupType.getFieldCount(); i++ ) {
+            if ( groupType.getType( i ).getName().equals( fieldName ) ) {
+                return i;
+            }
+        }
+        return -1;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTableBinding.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTableBinding.java
new file mode 100644
index 0000000000..16a53c2b26
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTableBinding.java
@@ -0,0 +1,36 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+import java.util.List;
+import java.util.Map;
+
+/**
+ * temporary discovery metadata before a real ParquetTableBinding exists
+ *
+ * @param sourceUrl - real Parquet file
+ * @param parentTableName - generated parent table
+ * @param sourcePathElements - table-level Parquet path
+ * @param columnPaths - column-level Parquet path
+ */
+public record DiscoveredTableBinding(
+        String sourceUrl,
+        String parentTableName,
+        List<String> sourcePathElements,
+        Map<String, List<String>> columnPaths ) {
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetBindingSerializer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetBindingSerializer.java
new file mode 100644
index 0000000000..57b2a6ce8d
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetBindingSerializer.java
@@ -0,0 +1,139 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+import java.nio.charset.StandardCharsets;
+import java.util.Base64;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+import java.util.stream.Stream;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+
+/**
+ * Saves and restores ParquetTableBinding metadata through adapter settings.
+ */
+public final class ParquetBindingSerializer {
+
+    private static final String ENTRY_SEPARATOR = "\n";
+    private static final String FIELD_SEPARATOR = "\t";
+    private static final String COLUMN_SEPARATOR = ",";
+    private static final String COLUMN_FIELD_SEPARATOR = ":";
+    private static final String PATH_SEPARATOR = ".";
+
+
+    private ParquetBindingSerializer() {
+    }
+
+
+    public static String serialize( Map<Long, ParquetTableBinding> bindings ) {
+        return bindings.entrySet().stream()
+                .map( entry -> entry.getKey()
+                        + FIELD_SEPARATOR + encode( entry.getValue().sourceUrl() )
+                        + FIELD_SEPARATOR + encode( entry.getValue().parentTableName() )
+                        + FIELD_SEPARATOR + serializePath( entry.getValue().sourcePathElements() )
+                        + FIELD_SEPARATOR + serializeColumns( entry.getValue().columnsByColumnId() ) )
+                .collect( Collectors.joining( ENTRY_SEPARATOR ) );
+    }
+
+
+    public static Map<Long, ParquetTableBinding> deserialize( String serialized ) {
+        Map<Long, ParquetTableBinding> bindings = new LinkedHashMap<>();
+        if ( serialized == null || serialized.isBlank() ) {
+            return bindings;
+        }
+
+        for ( String entry : serialized.split( ENTRY_SEPARATOR ) ) {
+            String[] fields = entry.split( FIELD_SEPARATOR, -1 );
+            if ( fields.length != 4 && fields.length != 5 ) {
+                throw new GenericRuntimeException( "Invalid serialized Parquet binding entry: %s", entry );
+            }
+
+            long physicalId = Long.parseLong( fields[0] );
+            String sourceUrl = decode( fields[1] );
+            String parentTableName = decode( fields[2] );
+            List<String> sourcePathElements = fields.length == 5 ? deserializePath( fields[3] ) : List.of();
+            Map<Long, ParquetColumnBinding> columns = deserializeColumns( fields.length == 5 ? fields[4] : fields[3] );
+            bindings.put( physicalId, new ParquetTableBinding( sourceUrl, parentTableName.isEmpty() ? null : parentTableName, sourcePathElements, columns ) );
+        }
+        return bindings;
+    }
+
+
+    private static String serializeColumns( Map<Long, ParquetColumnBinding> columns ) {
+        return columns.values().stream()
+                .map( column -> column.columnId()
+                        + COLUMN_FIELD_SEPARATOR + encode( column.columnName() )
+                        + COLUMN_FIELD_SEPARATOR + column.role().name()
+                        + COLUMN_FIELD_SEPARATOR + serializePath( column.sourcePathElements() ) )
+                .collect( Collectors.joining( COLUMN_SEPARATOR ) );
+    }
+
+
+    private static Map<Long, ParquetColumnBinding> deserializeColumns( String serialized ) {
+        Map<Long, ParquetColumnBinding> columns = new LinkedHashMap<>();
+        if ( serialized == null || serialized.isBlank() ) {
+            return columns;
+        }
+
+        for ( String columnEntry : serialized.split( COLUMN_SEPARATOR ) ) {
+            String[] fields = columnEntry.split( COLUMN_FIELD_SEPARATOR, -1 );
+            if ( fields.length != 4 ) {
+                throw new GenericRuntimeException( "Invalid serialized Parquet column binding entry: %s", columnEntry );
+            }
+
+            long columnId = Long.parseLong( fields[0] );
+            columns.put( columnId, new ParquetColumnBinding(
+                    columnId,
+                    decode( fields[1] ),
+                    ParquetColumnRole.valueOf( fields[2] ),
+                    deserializePath( fields[3] ) ) );
+        }
+        return columns;
+    }
+
+
+    private static String serializePath( List<String> path ) {
+        return path.stream().map( ParquetBindingSerializer::encode ).collect( Collectors.joining( PATH_SEPARATOR ) );
+    }
+
+
+    private static List<String> deserializePath( String serialized ) {
+        if ( serialized == null || serialized.isBlank() ) {
+            return List.of();
+        }
+        return Stream.of( serialized.split( "\\" + PATH_SEPARATOR ) ).map( ParquetBindingSerializer::decode ).toList();
+    }
+
+
+    private static String encode( String value ) {
+        if ( value == null || value.isEmpty() ) {
+            return "";
+        }
+        return Base64.getUrlEncoder().withoutPadding().encodeToString( value.getBytes( StandardCharsets.UTF_8 ) );
+    }
+
+
+    private static String decode( String value ) {
+        if ( value == null || value.isEmpty() ) {
+            return "";
+        }
+        return new String( Base64.getUrlDecoder().decode( value ), StandardCharsets.UTF_8 );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnBinding.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnBinding.java
new file mode 100644
index 0000000000..2e251714dd
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnBinding.java
@@ -0,0 +1,42 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+import java.util.List;
+
+/**
+ * Immutable data holder
+ * Metadata object that tells the Parquet scanner:
+ * for this Polypheny column, where does its value come from
+ * @param columnId - Polypheny physical column id connected to an actual column in Polypheny’s physical table catalog
+ * @param columnName - relational column name
+ * @param role - ParquetColumnRole - what kind of column this is
+ * @param sourcePathElements - the path inside the Parquet schema that should be used to read this column.
+ * flat/root column - path: order_id
+ * nested - path: shipping_address.city
+ */
+public record ParquetColumnBinding(
+        long columnId,
+        String columnName,
+        ParquetColumnRole role,
+        List<String> sourcePathElements ) {
+
+    public ParquetColumnBinding {
+        sourcePathElements = sourcePathElements == null ? List.of() : List.copyOf( sourcePathElements );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnRole.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnRole.java
new file mode 100644
index 0000000000..b610851f40
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnRole.java
@@ -0,0 +1,27 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+/**
+ *  Describes what kind of column a relational Parquet column is inside a ParquetTableBinding
+ */
+public enum ParquetColumnRole {
+    DATA, // a normal real Parquet value
+    PRIMARY_KEY, // synthetic generated row id
+    PARENT_KEY, // synthetic reference to the parent generated row
+    ORDINAL // position of a child value inside its parent
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetNormalizedSchema.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetNormalizedSchema.java
new file mode 100644
index 0000000000..590cccccf1
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetNormalizedSchema.java
@@ -0,0 +1,74 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+import lombok.Getter;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+/**
+ * Result of normalization
+ * contains normalized tables information
+ */
+@Getter
+public class ParquetNormalizedSchema {
+
+    private final Map<String, List<ExportedColumn>> tables;
+    private final Map<String, DiscoveredTableBinding> bindings;
+
+
+    public ParquetNormalizedSchema() {
+        tables = new HashMap<>();
+        bindings = new HashMap<>();
+    }
+
+
+    public void addColumns( String tableName, List<ExportedColumn> columns ) {
+        tables.computeIfAbsent( tableName, k -> new ArrayList<>() ).addAll( columns );
+    }
+
+
+    public void addBinding( String tableName, DiscoveredTableBinding binding ) {
+        bindings.put( tableName, binding );
+    }
+
+
+    public DiscoveredTableBinding getBinding( String tableName ) {
+        return bindings.get( tableName );
+    }
+
+
+    /**
+     * Avoids duplicate generated table names:
+     * If parquetrelational1__orders__items exists it will generate parquetrelational1__orders__items_2
+     *
+     * @param baseName - generated table name
+     * @return unique table name
+     */
+    public String uniqueTableName( String baseName ) {
+        String name = baseName;
+        int suffix = 2;
+        while ( tables.containsKey( name ) ) {
+            name = baseName + "_" + suffix++;
+        }
+        return name;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index aaa14ecb6e..6304fda442 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -20,11 +20,14 @@
 import org.apache.calcite.linq4j.Enumerable;
 import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetBindingRelEnumerator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
 import org.polypheny.db.plan.AlgCluster;
@@ -38,8 +41,10 @@
 import org.polypheny.db.util.Source;
 import java.util.ArrayList;
 import java.util.List;
+import java.util.Objects;
 import java.util.concurrent.atomic.AtomicBoolean;
 import java.util.stream.IntStream;
+import org.apache.parquet.schema.MessageType;
 
 /**
  * Physical table wrapper for the relational model.
@@ -51,14 +56,16 @@ public class ParquetRelTable extends PhysicalTable implements FilterableEntity,
     protected final Source source;
     protected final int[] fieldIndexes;
     private final List<PolyType> fieldTypes;
+    private final MessageType parquetSchema;
     protected final AbstractParquetSource parquetSource;
     protected final ParquetRelFilterTranslator filterTranslator;
+    protected final ParquetTableBinding binding;
 
 
     /**
-     * Creates a Parquet table wrapper from a physical table definition.
+     * Creates a Parquet table wrapper from a physical table definition and source binding.
      */
-    public ParquetRelTable(long id, Source source, PhysicalTable table, AbstractParquetSource parquetSource ) {
+    public ParquetRelTable(long id, Source source, PhysicalTable table, ParquetTableBinding binding, AbstractParquetSource parquetSource ) {
         super(
                 id,
                 table.allocationId,
@@ -70,8 +77,10 @@ public ParquetRelTable(long id, Source source, PhysicalTable table, AbstractParq
                 table.uniqueFieldIds,
                 table.adapterId );
         this.source = source;
+        this.binding = binding;
         this.fieldIndexes = IntStream.range( 0, table.columns.size() ).toArray();
         this.fieldTypes = columns.stream().map( c -> c.type ).toList();
+        this.parquetSchema = ParquetSourceReader.readSchema( source );
         this.parquetSource = parquetSource;
         this.filterTranslator = new ParquetRelFilterTranslator();
     }
@@ -103,6 +112,12 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> poly
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
+                if ( isNestedTable() ) {
+                    return new ParquetNestedRelEnumerator( source, cancelFlag, binding, selectedBindings( fieldIndexes ) );
+                }
+                if ( needsBindingScan( fieldIndexes ) ) {
+                    return new ParquetBindingRelEnumerator( source, cancelFlag, selectedBindings( fieldIndexes ) );
+                }
                 return new ParquetRelEnumerator( source, cancelFlag, fieldIndexes, resolvedFilters );
             }
         };
@@ -123,6 +138,12 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
+                if ( isNestedTable() ) {
+                    return new ParquetNestedRelEnumerator( source, cancelFlag, binding, selectedBindings( fieldIndexes ) );
+                }
+                if ( needsBindingScan( fieldIndexes ) ) {
+                    return new ParquetBindingRelEnumerator( source, cancelFlag, selectedBindings( fieldIndexes ) );
+                }
                 return new ParquetRelEnumerator( source, cancelFlag, fieldIndexes );
             }
         };
@@ -155,6 +176,12 @@ public Enumerable<PolyValue[]> project( final DataContext dataContext, final int
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
+                if ( isNestedTable() ) {
+                    return new ParquetNestedRelEnumerator( source, cancelFlag, binding, selectedBindings( fields ) );
+                }
+                if ( needsBindingScan( fields ) ) {
+                    return new ParquetBindingRelEnumerator( source, cancelFlag, selectedBindings( fields ) );
+                }
                 return new ParquetRelEnumerator( source, cancelFlag, fields );
             }
         };
@@ -182,6 +209,37 @@ private List<ParquetAdapterFilter> resolveDynamicFilters( DataContext dataContex
         return resolved;
     }
 
+
+    private boolean isNestedTable() {
+        return binding.parentTableName() != null;
+    }
+
+
+    private List<ParquetColumnBinding> selectedBindings( int[] fields ) {
+        List<ParquetColumnBinding> selected = new ArrayList<>( fields.length );
+        for ( int field : fields ) {
+            selected.add( Objects.requireNonNull( binding.getColumnBinding( columns.get( field ).id ), "Missing parquet column binding" ) );
+        }
+        return selected;
+    }
+
+
+    private boolean needsBindingScan( int[] fields ) {
+        for ( int field : fields ) {
+            ParquetColumnBinding columnBinding = binding.getColumnBinding( columns.get( field ).id );
+            if ( columnBinding == null || columnBinding.sourcePathElements().isEmpty() ) {
+                return true;
+            }
+            if ( columnBinding.sourcePathElements().size() > 1 ) {
+                return true;
+            }
+            if ( field >= parquetSchema.getFieldCount() || !parquetSchema.getType( field ).getName().equals( columnBinding.sourcePathElements().get( 0 ) ) ) {
+                return true;
+            }
+        }
+        return false;
+    }
+
 }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaMode.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaMode.java
new file mode 100644
index 0000000000..a318005f68
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaMode.java
@@ -0,0 +1,48 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+import lombok.Getter;
+
+/**
+ * User-selectable adapter setting that controls how the Parquet schema
+ * is presented/imported into Polypheny
+ * FLAT - show each Parquet file as one table
+ * NORMALIZED - show nested Parquet structures as multiple relational tables
+ */
+@Getter
+public enum ParquetSchemaMode {
+    FLAT( "flat" ),
+    NORMALIZED( "normalized" );
+
+    private final String settingValue;
+
+
+    ParquetSchemaMode( String settingValue ) {
+        this.settingValue = settingValue;
+    }
+
+
+    public static ParquetSchemaMode from( String value ) {
+        for ( ParquetSchemaMode mode : values() ) {
+            if ( mode.settingValue.equalsIgnoreCase( value ) ) {
+                return mode;
+            }
+        }
+        return FLAT;
+    }
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaNormalizer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaNormalizer.java
new file mode 100644
index 0000000000..a06a8eaf36
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaNormalizer.java
@@ -0,0 +1,264 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+import java.net.URL;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.Path;
+import org.apache.parquet.hadoop.ParquetFileReader;
+import org.apache.parquet.hadoop.util.HadoopInputFile;
+import org.apache.parquet.schema.GroupType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetFileDiscovery;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetUrlResolver;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+
+/**
+ * Class turns Parquet nested schema into multiple relational tables for normalized mode:
+ * - It creates exported relational table definitions: Map<String, List<ExportedColumn>> exportedColumns
+ * - Creates binding metadata that explains how those tables map back to the Parquet file: Map<String, DiscoveredTableBinding> tableBindings
+ */
+public class ParquetSchemaNormalizer {
+
+    private final URL parquetDir; // source directory or source Parquet file URL
+    private final ClassLoader classLoader; // used to create Hadoop/Parquet reader configuration
+    private final ParquetTypeConverter parquetTypeConverter; // converts Parquet types to Polypheny
+    private final String tableNamePrefix; // adapter instance prefix
+
+
+    public ParquetSchemaNormalizer(
+            URL parquetDir,
+            ClassLoader classLoader,
+            ParquetTypeConverter parquetTypeConverter,
+            String tableNamePrefix ) {
+        this.parquetDir = parquetDir;
+        this.classLoader = classLoader;
+        this.parquetTypeConverter = parquetTypeConverter;
+        if ( tableNamePrefix == null ) {
+            throw new GenericRuntimeException( "Parquet normalized schema table name prefix must not be null." );
+        }
+        this.tableNamePrefix = ParquetNameNormalizer.normalizeFieldName( tableNamePrefix );
+        if ( this.tableNamePrefix.isBlank() ) {
+            throw new GenericRuntimeException( "Parquet normalized schema table name prefix must not be blank." );
+        }
+    }
+
+
+    /**
+     * Main entry point
+     * Finds all parquet files and normalizes each file
+     *
+     * @return generated exported tables and discovered bindings
+     */
+    public ParquetNormalizedSchema normalize() {
+        var normalizedSchema = new ParquetNormalizedSchema();
+        for ( String fileName : ParquetFileDiscovery.listParquetFiles( parquetDir ) ) {
+            normalizeFile( fileName, normalizedSchema );
+        }
+        return normalizedSchema;
+    }
+
+
+    private void normalizeFile( String fileName, ParquetNormalizedSchema normalizedSchema ) {
+        try {
+            URL sourceUrl = ParquetUrlResolver.resolveFile( parquetDir, fileName );
+            Path path = new Path( sourceUrl.toURI() );
+            Configuration conf = HadoopConfigurationFactory.create( classLoader );
+            try ( ParquetFileReader reader = ParquetFileReader.open( HadoopInputFile.fromPath( path, conf ) ) ) {
+                // get schema from parquet file
+                GroupType schema = reader.getFooter().getFileMetaData().getSchema();
+                // build root table name with adapter prefix
+                String rootTableName = normalizedSchema.uniqueTableName( prefixedTableName( ParquetNameNormalizer.computePhysicalTableName( fileName ) ) );
+                // create normalization info for table
+                addNormalizedTable( schema.getFields(), sourceUrl.toString(), fileName, rootTableName, null, List.of(), normalizedSchema );
+            }
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( e );
+        }
+    }
+
+
+    /**
+     * Creates one generated relational table
+     *
+     * @param fields - schema fields
+     * @param sourceUrl - source file url
+     * @param fileName - file name
+     * @param tableName - generated table name
+     * @param parentTableName - generated parent table name if exists
+     * @param tablePath - table level path - where do this table's rows come from
+     * Rows of this generated relational table come from this specific attribute/path inside the physical Parquet file
+     * @param normalizedSchema - normalized schema to fill. the schema contains all normalized tables.
+     */
+    private void addNormalizedTable(
+            List<Type> fields,
+            String sourceUrl,
+            String fileName,
+            String tableName,
+            String parentTableName,
+            List<String> tablePath,
+            ParquetNormalizedSchema normalizedSchema ) {
+        // maps relational column name to Parquet source path
+        Map<String, List<String>> columnPaths = new LinkedHashMap<>();
+
+        collectNormalizedColumns( fields, sourceUrl, fileName, tableName, tableName, tablePath, columnPaths, normalizedSchema );
+        // each generated table gets table definition and binding metadata
+        normalizedSchema.addBinding( tableName, new DiscoveredTableBinding( sourceUrl, parentTableName, tablePath, columnPaths ) );
+    }
+
+
+    /**
+     * Loops through fields of the current Parquet group
+     *
+     * @param fields - parquet schema fields
+     * @param sourceUrl - source file url
+     * @param fileName - source file name
+     * @param tableName - generated table name
+     * @param currentParentTableName - table name used as the base when creating child table names
+     * @param currentPath - Parquet source path to the current table. For items.discounts: List.of("items", "discounts")
+     * output:
+     * @param columnPaths - map collected for the current generated table: relational column name -> Parquet source path
+     * @param normalizedSchema - normalized schema that contains all normalized tables and their bindings.
+     */
+    private void collectNormalizedColumns(
+            List<Type> fields,
+            String sourceUrl,
+            String fileName,
+            String tableName,
+            String currentParentTableName,
+            List<String> currentPath,
+            Map<String, List<String>> columnPaths, // where should each column read its value from
+            ParquetNormalizedSchema normalizedSchema ) {
+
+        Map<String, Integer> seenColumnNames = new HashMap<>();
+        for ( int fieldIndex = 0; fieldIndex < fields.size(); fieldIndex++ ) {
+            Type field = fields.get( fieldIndex );
+            List<String> sourcePath = appendPath( currentPath, field.getName() );
+
+            if ( field.isRepetition( Type.Repetition.REPEATED ) ) {
+                String childTableName = normalizedSchema.uniqueTableName( currentParentTableName + "__" + ParquetNameNormalizer.normalizeFieldName( field.getName() ) );
+                if ( field.isPrimitive() ) {
+                    // repeated and primitive - create a separate table
+                    addRepeatedPrimitiveTable( field, sourceUrl, fileName, childTableName, currentParentTableName, sourcePath, normalizedSchema );
+                } else {
+                    // repeated and group - create a child table and recursively processes the group fields
+                    addNormalizedTable( field.asGroupType().getFields(), sourceUrl, fileName, childTableName, currentParentTableName, sourcePath, normalizedSchema );
+                }
+                continue;
+            }
+
+            // primitive - add field as a column to the current table
+            if ( field.isPrimitive() ) {
+                String columnName = uniqueColumnName( seenColumnNames, ParquetNameNormalizer.normalizeFieldName( field.getName() ) );
+                normalizedSchema.addColumns( tableName, List.of( exportedColumn( field, fileName, tableName, columnName, sourcePath, fieldIndex ) ) );
+                columnPaths.put( columnName, sourcePath );
+                continue;
+            }
+
+            //  non-repeated group - create a child table
+            String childTableName = normalizedSchema.uniqueTableName( currentParentTableName + "__" + ParquetNameNormalizer.normalizeFieldName( field.getName() ) );
+            addNormalizedTable( field.asGroupType().getFields(), sourceUrl, fileName, childTableName, currentParentTableName, sourcePath, normalizedSchema );
+        }
+    }
+
+
+    private void addRepeatedPrimitiveTable(
+            Type field,
+            String sourceUrl,
+            String fileName,
+            String tableName,
+            String parentTableName,
+            List<String> sourcePath,
+            ParquetNormalizedSchema normalizedSchema ) {
+        String columnName = ParquetNameNormalizer.normalizeFieldName( field.getName() );
+        List<ExportedColumn> columns = List.of( exportedColumn( field, fileName, tableName, columnName, sourcePath, 0 ) );
+        normalizedSchema.addColumns( tableName, columns );
+        normalizedSchema.addBinding( tableName, new DiscoveredTableBinding( sourceUrl, parentTableName, sourcePath, Map.of( columnName, sourcePath ) ) );
+    }
+
+
+    /**
+     * Creates the generic ExportedColumn that Polypheny understands
+     *
+     * @param field parquet field type
+     * @param fileName source file
+     * @param tableName generated table name
+     * @param columnName generated column name
+     * @param sourcePath column path
+     * @param position position inside physical table
+     * @return Exported column object
+     */
+    private ExportedColumn exportedColumn( Type field, String fileName, String tableName, String columnName, List<String> sourcePath, int position ) {
+        return new ExportedColumn(
+                columnName,
+                parquetTypeConverter.fromParquetTypeToPolyType( field ),
+                null,
+                null,
+                null,
+                null,
+                null,
+                !field.isRepetition( Type.Repetition.REQUIRED ),
+                fileName,
+                tableName,
+                String.join( ".", sourcePath ),
+                position,
+                false );
+    }
+
+
+    /**
+     * Avoids duplicate column names inside the same generated table
+     *
+     * @param seenColumnNames Map<String, Integer>
+     * @param baseName - generated field name
+     * @return if "name" exists returns "name_1"
+     */
+    private String uniqueColumnName( Map<String, Integer> seenColumnNames, String baseName ) {
+        int count = seenColumnNames.getOrDefault( baseName, 0 );
+        seenColumnNames.put( baseName, count + 1 );
+        return count == 0 ? baseName : baseName + "_" + (count + 1);
+    }
+
+
+    /**
+     * adds adapter unique name prefix to the table
+     *
+     * @param tableName - table name
+     * @return name with prefix
+     */
+    private String prefixedTableName( String tableName ) {
+        return tableNamePrefix + "__" + tableName;
+    }
+
+
+    private List<String> appendPath( List<String> path, String element ) {
+        List<String> next = new ArrayList<>( path );
+        next.add( element );
+        return next;
+    }
+
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java
new file mode 100644
index 0000000000..97c371ef2d
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java
@@ -0,0 +1,103 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Collections;
+import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+
+/**
+ * Table-level metadata, describes the whole table
+ * @param sourceUrl - real Parquet file URL to read from
+ * @param parentTableName - generated parent relational table name, or null for root tables
+ * @param sourcePathElements - table-level path inside the Parquet file, For root: List.of(), for nested: List.of("items")
+ * @param columnsByColumnId - Map<Long, ParquetColumnBinding> - maps Polypheny physical column ids to column bindings
+ */
+public record ParquetTableBinding(
+        String sourceUrl,
+        String parentTableName,
+        List<String> sourcePathElements,
+        Map<Long, ParquetColumnBinding> columnsByColumnId ) {
+
+    public ParquetTableBinding {
+        sourcePathElements = sourcePathElements == null ? List.of() : List.copyOf( sourcePathElements );
+        columnsByColumnId = columnsByColumnId == null ? Map.of() : Collections.unmodifiableMap( new LinkedHashMap<>( columnsByColumnId ) );
+    }
+
+
+    /**
+     * Factory Method
+     * creates a simple binding for a root/flat table
+     * @param sourceUrl - file
+     * @param table - table name
+     * @return ParquetTableBinding object
+     */
+    public static ParquetTableBinding createRootTableBinding( String sourceUrl, PhysicalTable table ) {
+        // create map of column level bindings
+        Map<Long, ParquetColumnBinding> columnBindings = new LinkedHashMap<>();
+        // For each physical column create a DATA column binding with a one-element source path equal to the column name
+        table.columns.forEach( column -> columnBindings.put(
+                column.id,
+                new ParquetColumnBinding( column.id, column.name, ParquetColumnRole.DATA, List.of( column.name ) ) ) );
+
+        // create table level binding
+        return new ParquetTableBinding(
+                sourceUrl,
+                null, // parentTableName is null
+                List.of(), // sourcePathElements empty
+                columnBindings );
+    }
+
+
+    /**
+     * Factory Method
+     * creates a binding when exact Parquet paths for columns discovered
+     * @param sourceUrl - real Parquet file URL
+     * @param parentTableName - generated parent relational table name, or null for root tables
+     * @param sourcePathElements - table-level path inside the Parquet file
+     * @param table - Polypheny physical table object contains the physical columns that were created in the adapter catalog.
+     * @param columnPaths - map relational column name to Parquet source path ("amount" -> ["items", "discounts", "amount"])
+     * @return ParquetTableBinding
+     */
+    public static ParquetTableBinding createTableBindingFromColumnPaths( String sourceUrl, String parentTableName, List<String> sourcePathElements, PhysicalTable table, Map<String, List<String>> columnPaths ) {
+        Map<Long, ParquetColumnBinding> columnBindings = new LinkedHashMap<>();
+        // create ParquetColumnBinding object for each column and store in map by id
+        table.columns.forEach( column -> columnBindings.put(
+                column.id,
+                new ParquetColumnBinding( column.id, column.name, ParquetColumnRole.DATA, columnPaths.getOrDefault( column.name, List.of( column.name ) ) ) ) );
+
+        // create table level binding
+        return new ParquetTableBinding(
+                sourceUrl,
+                parentTableName,
+                sourcePathElements,
+                columnBindings );
+    }
+
+
+    /**
+     * getter
+     * @param columnId - column
+     * @return ParquetColumnBinding for columnId
+     */
+    public ParquetColumnBinding getColumnBinding( long columnId ) {
+        return columnsByColumnId.get( columnId );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
index 63b7488db5..88fc9ac35e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
@@ -24,20 +24,25 @@
 import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
+import java.util.Optional;
 import java.util.Set;
 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.Path;
 import org.apache.parquet.hadoop.ParquetFileReader;
 import org.apache.parquet.hadoop.util.HadoopInputFile;
 import org.apache.parquet.schema.Type;
+import org.polypheny.db.catalog.Catalog;
 import org.polypheny.db.adapter.ConnectionMethod;
 import org.polypheny.db.adapter.DataSource;
 import org.polypheny.db.adapter.DeployMode;
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
 import org.polypheny.db.adapter.Scannable;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetBindingSerializer;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetFileDiscovery;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetUrlResolver;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetNamespace;
-import org.polypheny.db.adapter.parquet.shared.schema.ParquetFieldNameNormalizer;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
 import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
 import org.polypheny.db.catalog.catalogs.AdapterCatalog;
@@ -58,12 +63,15 @@
  */
 public abstract class AbstractParquetSource extends DataSource<DocAdapterCatalog> implements Scannable {
 
+    private static final String PARQUET_BINDINGS_SETTING = "__polypheny_parquet_bindings";
+
     protected final ConnectionMethod connectionMethod;
     protected final ParquetTypeConverter parquetTypeConverter;
     protected URL parquetDir;
 
     protected ParquetNamespace currentNamespace;
     private Map<String, List<ExportedColumn>> exportedColumns;
+    private final Map<Long, ParquetTableBinding> parquetBindings;
 
     protected static final Logger log = LoggerFactory.getLogger( AbstractParquetSource.class );
 
@@ -71,6 +79,8 @@ public abstract class AbstractParquetSource extends DataSource<DocAdapterCatalog
     protected AbstractParquetSource( long storeId, String uniqueName, Map<String, String> settings, DeployMode mode, Set<DataModel> supportedModels ) {
         super( storeId, uniqueName, settings, mode, true, new DocAdapterCatalog( storeId ), supportedModels );
         this.parquetTypeConverter = new ParquetTypeConverter();
+        // Recover binding metadata through adapter settings
+        this.parquetBindings = new HashMap<>( ParquetBindingSerializer.deserialize( settings.get( PARQUET_BINDINGS_SETTING ) ) );
         this.connectionMethod = settings.containsKey( "method" )
                 ? ConnectionMethod.from( settings.get( "method" ).toUpperCase() )
                 : ConnectionMethod.UPLOAD;
@@ -80,6 +90,10 @@ protected AbstractParquetSource( long storeId, String uniqueName, Map<String, St
     }
 
 
+    /**
+     * populate parquet directory variable according to settings
+     * @param settings Settings Map
+     */
     protected void setParquetDir( Map<String, String> settings ) {
         switch ( connectionMethod ) {
             case LINK -> {
@@ -88,7 +102,7 @@ protected void setParquetDir( Map<String, String> settings ) {
                     parquetDir = this.getClass().getClassLoader().getResource( dir.replace( "classpath://", "" ) + "/" );
                 } else {
                     try {
-                        parquetDir = new File( dir ).toURI().toURL();
+                        parquetDir = ParquetUrlResolver.asSourceUrl( new File( dir ).toURI().toURL() );
                     } catch ( MalformedURLException e ) {
                         throw new org.polypheny.db.catalog.exceptions.GenericRuntimeException( e );
                     }
@@ -100,7 +114,7 @@ protected void setParquetDir( Map<String, String> settings ) {
                     parquetDir = this.getClass().getClassLoader().getResource( dir.replace( "classpath://", "" ) + "/" );
                 } else {
                     try {
-                        parquetDir = new File( dir ).toURI().toURL();
+                        parquetDir = ParquetUrlResolver.asSourceUrl( new File( dir ).toURI().toURL() );
                     } catch ( MalformedURLException e ) {
                         throw new org.polypheny.db.catalog.exceptions.GenericRuntimeException( e );
                     }
@@ -109,7 +123,7 @@ protected void setParquetDir( Map<String, String> settings ) {
             case URL -> {
                 String dir = settings.get( "url" );
                 try {
-                    parquetDir = new URL( dir );
+                    parquetDir = ParquetUrlResolver.asSourceUrl( new URL( dir ) );
                 } catch ( MalformedURLException e ) {
                     throw new org.polypheny.db.catalog.exceptions.GenericRuntimeException( e );
                 }
@@ -118,33 +132,15 @@ protected void setParquetDir( Map<String, String> settings ) {
     }
 
 
-    protected static String getValidTableName( String name ) {
-        return name.trim().replaceAll( "[^a-z0-9_]+", "" );
-    }
-
-
-    protected static String computePhysicalTableName( String fileName ) {
-        String physicalTableName = fileName.toLowerCase();
-        if ( physicalTableName.endsWith( ".parquet" ) ) {
-            physicalTableName = physicalTableName.substring( 0, physicalTableName.length() - ".parquet".length() );
-        }
-        return getValidTableName( physicalTableName );
-    }
-
-
-    public static String normalizeFieldName( String name ) {
-        return ParquetFieldNameNormalizer.normalizeFieldName( name );
-    }
-
-
-    protected String getValidColumnNameFromField( Type field ) {
-        return normalizeFieldName( field.getName() );
-    }
-
-
+    /**
+     * Get list of columns from parquet file using metadata
+     * @param fileName - parquet file name
+     * @param physicalTableName - physical Table Name
+     * @return List<ExportedColumn> - list of columns
+     */
     private List<ExportedColumn> getExportedColumnsFromFile( String fileName, String physicalTableName ) {
         try {
-            Path path = new Path( new URL( parquetDir, fileName ).toURI() );
+            Path path = new Path( ParquetUrlResolver.resolveFile( parquetDir, fileName ).toURI() );
             Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
             try ( ParquetFileReader reader = ParquetFileReader.open( HadoopInputFile.fromPath( path, conf ) ) ) {
                 List<Type> schemaFields = reader.getFooter().getFileMetaData().getSchema().getFields();
@@ -161,8 +157,16 @@ private List<ExportedColumn> getExportedColumnsFromFile( String fileName, String
     }
 
 
+    /**
+     * Create exported column object from parquet file field
+     * @param field - parquet file column
+     * @param fileName - name of parquet file
+     * @param physicalTableName - the table name that Polypheny should create/export for this Parquet file
+     * @param position - column position
+     * @return ExportedColumn object
+     */
     private ExportedColumn getExportedColumnFromField( Type field, String fileName, String physicalTableName, int position ) {
-        String columnName = getValidColumnNameFromField( field );
+        String columnName = ParquetNameNormalizer.normalizeFieldName( field.getName() );
         PolyType polyType = parquetTypeConverter.fromParquetTypeToPolyType( field );
         return new ExportedColumn(
                 columnName,
@@ -181,6 +185,12 @@ private ExportedColumn getExportedColumnFromField( Type field, String fileName,
     }
 
 
+    /**
+     * Return exported column map if exists
+     * Otherwise creates hashmap:
+     * for each filename - store exported columns list
+     * @return Map<String, List<ExportedColumn>>
+     */
     public Map<String, List<ExportedColumn>> getExportedColumns() {
         if ( connectionMethod == ConnectionMethod.UPLOAD && exportedColumns != null ) {
             return exportedColumns;
@@ -189,7 +199,7 @@ public Map<String, List<ExportedColumn>> getExportedColumns() {
         Map<String, List<ExportedColumn>> columns = new HashMap<>();
         Set<String> fileNames = ParquetFileDiscovery.listParquetFiles( parquetDir );
         for ( String fileName : fileNames ) {
-            String physicalTableName = computePhysicalTableName( fileName );
+            String physicalTableName = getPrefixedTableName( ParquetNameNormalizer.computePhysicalTableName( fileName ) );
             columns.put( physicalTableName, getExportedColumnsFromFile( fileName, physicalTableName ) );
         }
         this.exportedColumns = columns;
@@ -197,6 +207,59 @@ public Map<String, List<ExportedColumn>> getExportedColumns() {
     }
 
 
+    /**
+     * Add prefix to the table names to allow deployment multiple Parquet relational adapters
+     * over the same files, for example one flat and one normalized, without logical table name collisions
+     * @param tableName table-name produced from the Parquet file name
+     * @return normalized name containing prefix - adapter unique name
+     */
+    protected String getPrefixedTableName( String tableName ) {
+        return ParquetNameNormalizer.normalizeFieldName( getUniqueName() ) + "__" + tableName;
+    }
+
+
+    /**
+     * Allows ParquetRelationalSource to clear the flat exported-column cache when settings such as
+     * schemaMode or directory change.
+     */
+    protected void clearExportedColumnsCache() {
+        exportedColumns = null;
+    }
+
+
+    /**
+     * Add binding to the parquet bindings map and call for persistence to store map in settings
+     * @param physicalTableId - key in Map<Long, ParquetTableBinding>
+     * @param binding ParquetTableBinding to store
+     */
+    protected void registerParquetBinding( long physicalTableId, ParquetTableBinding binding ) {
+        // for this physical table id, remember this Parquet binding
+        parquetBindings.put( physicalTableId, binding );
+        persistParquetBindings();
+    }
+
+
+    protected Optional<ParquetTableBinding> getParquetBinding( long physicalTableId ) {
+        return Optional.ofNullable( parquetBindings.get( physicalTableId ) );
+    }
+
+
+    protected void removeParquetBinding( long physicalTableId ) {
+        if ( parquetBindings.remove( physicalTableId ) != null ) {
+            persistParquetBindings();
+        }
+    }
+
+
+    /**
+     * Store parquet bindings map in settings
+     */
+    private void persistParquetBindings() {
+        settings.put( PARQUET_BINDINGS_SETTING, ParquetBindingSerializer.serialize( parquetBindings ) );
+        Catalog.getInstance().updateAdapterSettings( adapterId, new HashMap<>( settings ) );
+    }
+
+
     private void createInformationPage() {
         for ( Map.Entry<String, List<ExportedColumn>> entry : getExportedColumns().entrySet() ) {
             if ( entry.getValue().isEmpty() ) {
@@ -277,8 +340,9 @@ public void shutdown() {
 
     @Override
     protected void reloadSettings( List<String> updatedSettings ) {
-        if ( updatedSettings.contains( "directory" ) || updatedSettings.contains( "directoryName" ) ) {
+        if ( updatedSettings.contains( "directory" ) || updatedSettings.contains( "directoryName" ) || updatedSettings.contains( "url" ) ) {
             setParquetDir( settings );
+            clearExportedColumnsCache();
         }
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java
index 4bc93f11e4..3d686f7650 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java
@@ -8,7 +8,7 @@
 import org.apache.parquet.schema.GroupType;
 import org.apache.parquet.schema.PrimitiveType;
 import org.apache.parquet.schema.Type;
-import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
 import org.polypheny.db.type.entity.PolyList;
 import org.polypheny.db.type.entity.PolyNull;
@@ -59,7 +59,7 @@ protected PolyDocument extractNestedDocument( Group group, GroupType type ) {
             if ( group.getFieldRepetitionCount( i ) == 0 ) {
                 continue;
             }
-            values.put( PolyString.of( AbstractParquetSource.normalizeFieldName( nestedField.getName() ) ), extractStructuredValue( group, i, nestedField ) );
+            values.put( PolyString.of( ParquetNameNormalizer.normalizeFieldName( nestedField.getName() ) ), extractStructuredValue( group, i, nestedField ) );
         }
         return PolyDocument.ofDocument( values );
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
index 4ba5c75988..a7d22b5f5f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
@@ -20,6 +20,7 @@
 import java.net.URL;
 import java.util.Arrays;
 import java.util.Set;
+import java.util.TreeSet;
 import java.util.stream.Collectors;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.util.Sources;
@@ -45,7 +46,7 @@ public static Set<String> listParquetFiles( URL baseDir ) {
         if ( files == null ) {
             throw new GenericRuntimeException( "No .parquet files were found." );
         }
-        return Arrays.stream( files ).map( File::getName ).collect( Collectors.toSet() );
+        return Arrays.stream( files ).map( File::getName ).collect( Collectors.toCollection( TreeSet::new ) );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetUrlResolver.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetUrlResolver.java
new file mode 100644
index 0000000000..1fcda7718e
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetUrlResolver.java
@@ -0,0 +1,63 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io;
+
+import java.net.MalformedURLException;
+import java.net.URL;
+
+/**
+ *  Parquet file/directory URL handling
+ */
+public final class ParquetUrlResolver {
+
+    private ParquetUrlResolver() {
+    }
+
+
+    public static URL asDirectoryUrl( URL url ) {
+        if ( url == null || url.toExternalForm().endsWith( "/" ) ) {
+            return url;
+        }
+
+        try {
+            return new URL( url.toExternalForm() + "/" );
+        } catch ( MalformedURLException e ) {
+            throw new org.polypheny.db.catalog.exceptions.GenericRuntimeException( e );
+        }
+    }
+
+
+    public static URL asSourceUrl( URL url ) {
+        if ( url == null || url.getPath().toLowerCase().endsWith( ".parquet" ) ) {
+            return url;
+        }
+        return asDirectoryUrl( url );
+    }
+
+
+    public static URL resolveFile( URL directoryUrl, String fileName ) {
+        if ( directoryUrl != null && directoryUrl.getPath().toLowerCase().endsWith( ".parquet" ) ) {
+            return directoryUrl;
+        }
+        try {
+            return new URL( asDirectoryUrl( directoryUrl ), fileName );
+        } catch ( MalformedURLException e ) {
+            throw new org.polypheny.db.catalog.exceptions.GenericRuntimeException( e );
+        }
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetFieldNameNormalizer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNameNormalizer.java
similarity index 72%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetFieldNameNormalizer.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNameNormalizer.java
index 0f281d3a75..21841b5d63 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetFieldNameNormalizer.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNameNormalizer.java
@@ -26,9 +26,23 @@
 /**
  * Shared Parquet field-name normalization used across Parquet consumers.
  */
-public final class ParquetFieldNameNormalizer {
+public final class ParquetNameNormalizer {
 
-    private ParquetFieldNameNormalizer() {
+    private ParquetNameNormalizer() {
+    }
+
+
+    public static String getValidTableName( String name ) {
+        return name.trim().replaceAll( "[^a-z0-9_]+", "" );
+    }
+
+
+    public static String computePhysicalTableName( String fileName ) {
+        String physicalTableName = fileName.toLowerCase();
+        if ( physicalTableName.endsWith( ".parquet" ) ) {
+            physicalTableName = physicalTableName.substring( 0, physicalTableName.length() - ".parquet".length() );
+        }
+        return getValidTableName( physicalTableName );
     }
 
 
@@ -43,7 +57,7 @@ public static String normalizeFieldName( String name ) {
      *
      * @param fields - schema fields
      */
-    public static void uniquifyParquetNames( List<FieldSchema> fields ) {
+    public static void uniquifyParquetFieldNames( List<FieldSchema> fields ) {
         Set<String> seen = new LinkedHashSet<>();
         for ( FieldSchema field : fields ) {
             String baseName = normalizeFieldName( field.getSourceName() );
@@ -58,7 +72,7 @@ public static void uniquifyParquetNames( List<FieldSchema> fields ) {
             field.setParquetName( candidate );
             seen.add( candidate );
             if ( field.getValueSchema().kind() == ValueKind.GROUP ) {
-                uniquifyParquetNames( field.getValueSchema().nested() );
+                uniquifyParquetFieldNames( field.getValueSchema().nested() );
             }
         }
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
index 130d87cc24..5d8a33c11b 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
@@ -4,8 +4,10 @@
 import java.net.URL;
 import org.jetbrains.annotations.Nullable;
 import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetUrlResolver;
 import org.polypheny.db.catalog.entity.physical.PhysicalCollection;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
@@ -33,13 +35,18 @@ public ParquetNamespace( long id, long adapterId, URL directoryUrl ) {
     }
 
 
+    public ParquetTableBinding createRootBinding( PhysicalTable table ) {
+        Source parquetSource = Sources.of( ParquetUrlResolver.resolveFile( directoryUrl, table.name + ".parquet" ) );
+        return ParquetTableBinding.createRootTableBinding( parquetSource.url().toString(), table );
+    }
+
     /**
      * Creates a Parquet table wrapper for a physical table entry.
      */
-    public ParquetRelTable createParquetTable( long id, PhysicalTable table, AbstractParquetSource sourceAdapter ) {
+    public ParquetRelTable createParquetTable( long id, PhysicalTable table, ParquetTableBinding binding, AbstractParquetSource sourceAdapter ) {
         try {
-            Source parquetSource = Sources.of( new URL( directoryUrl, table.name + ".parquet" ) );
-            return new ParquetRelTable( id, parquetSource, table, sourceAdapter );
+            Source parquetSource = Sources.of( new URL( binding.sourceUrl() ) );
+            return new ParquetRelTable( id, parquetSource, table, binding, sourceAdapter );
 
         } catch ( MalformedURLException e ) {
             throw new GenericRuntimeException( e );
@@ -48,12 +55,8 @@ public ParquetRelTable createParquetTable( long id, PhysicalTable table, Abstrac
 
 
     public ParquetDocument createParquetCollection( PhysicalCollection collection, AbstractParquetSource sourceAdapter ) {
-        try {
-            Source parquetSource = Sources.of( new URL( directoryUrl, collection.name + ".parquet" ) );
-            return new ParquetDocument( collection, parquetSource, sourceAdapter );
-        } catch ( MalformedURLException e ) {
-            throw new GenericRuntimeException( e );
-        }
+        Source parquetSource = Sources.of( ParquetUrlResolver.resolveFile( directoryUrl, collection.name + ".parquet" ) );
+        return new ParquetDocument( collection, parquetSource, sourceAdapter );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/SchemaState.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/SchemaState.java
index 6afc3f7a47..112c0eaacc 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/SchemaState.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/SchemaState.java
@@ -29,7 +29,7 @@
 import java.util.List;
 import java.util.Map;
 
-import static org.polypheny.db.adapter.parquet.shared.schema.ParquetFieldNameNormalizer.uniquifyParquetNames;
+import static org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer.uniquifyParquetFieldNames;
 
 /**
  * Contains inferred schema: all fields definitions
@@ -71,7 +71,7 @@ public void init( AlgDataType inputType, boolean keepPk ) {
             this.fields.add( new FieldSchema( sourceName, sourceName, i, false, valueSchema ) );
         }
         // normalize and uniquify names of parquet file columns
-        uniquifyParquetNames( this.fields );
+        uniquifyParquetFieldNames( this.fields );
     }
 
 
@@ -130,7 +130,7 @@ public void mergeDocumentSchema( PolyDocument document, boolean keepId ) {
             mergeFieldValueSchema( sourceName, inferred );
         }
         // normalize and uniquify names of parquet file columns
-        uniquifyParquetNames( this.fields );
+        uniquifyParquetFieldNames( this.fields );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueSchema.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueSchema.java
index 6dd4e0b6e6..2837477d2c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueSchema.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueSchema.java
@@ -30,7 +30,7 @@
 import java.util.List;
 import java.util.Map;
 
-import static org.polypheny.db.adapter.parquet.shared.schema.ParquetFieldNameNormalizer.uniquifyParquetNames;
+import static org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer.uniquifyParquetFieldNames;
 
 
 /**
@@ -197,7 +197,7 @@ public ValueSchema mergeValueSchemas( ValueSchema valueSchema, String conflictMo
                     }
                 }
                 merged.nested.addAll( mergedFields.values() );
-                uniquifyParquetNames( merged.nested );
+                uniquifyParquetFieldNames( merged.nested );
                 return merged;
             }
             return this;
@@ -269,7 +269,7 @@ private static ValueSchema inferDocumentSchema( PolyDocument document, String co
             }
             group.mergeNested( entry.getKey().value, fieldSchema, conflictMode );
         }
-        uniquifyParquetNames( group.nested() );
+        uniquifyParquetFieldNames( group.nested() );
         return group.nested().isEmpty() ? null : group;
     }
 
diff --git a/plugins/parquet-adapter/src/main/resources/orders_db/orders.parquet b/plugins/parquet-adapter/src/main/resources/orders_db/orders.parquet
index aa50dd8cd7d122b372cfbf0dd45a0d385cae9731..3d19122b4893d68f39e24ec0f776f5c18e775d0c 100644
GIT binary patch
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diff --git a/plugins/parquet-adapter/src/test/resources/orders_db/orders.parquet b/plugins/parquet-adapter/src/test/resources/orders_db/orders.parquet
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diff --git a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
index 747748d440..4c3b44ef5e 100644
--- a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
+++ b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
@@ -31,7 +31,7 @@
 import org.polypheny.db.algebra.type.AlgDataTypeFactory.Builder;
 import org.polypheny.db.algebra.type.DocumentType;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
-import org.polypheny.db.adapter.parquet.shared.schema.ParquetFieldNameNormalizer;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.type.PolyType;
 import org.polypheny.db.type.entity.PolyString;
@@ -109,7 +109,7 @@ public static AlgDataType getOutputType( Source source, String outputModel, bool
             if ( type == PolyType.DOCUMENT ) {
                 type = PolyType.TEXT;
             }
-            String fieldName = ParquetFieldNameNormalizer.normalizeFieldName( field.getName() );
+            String fieldName = ParquetNameNormalizer.normalizeFieldName( field.getName() );
             builder.add( fieldName, null, type );
         }
         if ( addNameField ) {

From c0e034c85ebadb7c3faa6ac0e9c72d28d68e84fd Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Tue, 21 Apr 2026 18:22:37 +0200
Subject: [PATCH 17/46] nested fields presentation

---
 .../docs/diagrams/nested_fields_flow.png      | Bin 0 -> 295297 bytes
 .../diagrams/nested_fields_flow_simple.png    | Bin 0 -> 95321 bytes
 plugins/parquet-adapter/docs/nested_fields.md |  32 +++++++++++++++---
 .../relational/ParquetRelationalSource.java   |  27 +++++++++++++++
 .../shared/schema/ParquetNamespace.java       |   1 +
 5 files changed, 55 insertions(+), 5 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/diagrams/nested_fields_flow.png
 create mode 100644 plugins/parquet-adapter/docs/diagrams/nested_fields_flow_simple.png

diff --git a/plugins/parquet-adapter/docs/diagrams/nested_fields_flow.png b/plugins/parquet-adapter/docs/diagrams/nested_fields_flow.png
new file mode 100644
index 0000000000000000000000000000000000000000..108f4c8155757d8768605b56daac09136c4ef694
GIT binary patch
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diff --git a/plugins/parquet-adapter/docs/nested_fields.md b/plugins/parquet-adapter/docs/nested_fields.md
index db1e5ed954..949a6d040d 100644
--- a/plugins/parquet-adapter/docs/nested_fields.md
+++ b/plugins/parquet-adapter/docs/nested_fields.md
@@ -7,16 +7,16 @@ orders.parquet
 root
 |- order_id: int64
 |- customer: string
-`- items: repeated group
+ - items: repeated group
    |- sku: string
    |- quantity: int32
-   `- discounts: repeated group
+   - discounts: repeated group
       |- code: string
-      `- amount: decimal
+      |- amount: decimal
 ```
 
 ## Flat Mode
-preserve the Parquet file as one table
+- Preserve the Parquet file as one table
 
 **_orders_**
 
@@ -33,7 +33,8 @@ orders.parquet
 ```
 
 ## Normalized Mode
-split nested structures into generated relational child tables
+- Split nested structures into generated relational child tables
+- NEED TO STORE ADDITIONAL INFORMATION
 
 **_orders_**
 
@@ -72,6 +73,21 @@ JOIN orders__items i
   ON i.__polypheny_parent_row_id = o.__polypheny_row_id;
 
 ```
+## Example with unique table names:
+
+### Flat result:
+```
+parquetrelational1__orders -> [order_id, customer_id, status, ...]
+```
+
+### Normalized result:
+```
+parquetrelational1__orders -> [order_id, customer_id, status, total_price]
+parquetrelational1__orders__items -> [order_item_id, product_id, quantity, price]
+parquetrelational1__orders__items__discounts -> [code, amount]
+```
+
+
 ## Behavior:
 
 - All relational Parquet table names are adapter-prefixed, both flat and normalized. <br>
@@ -224,3 +240,9 @@ ParquetRelTable changed from a simple wrapper that always scanned Parquet column
 ### ParquetNamespace
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\schema\ParquetNamespace.java`
 Make relational table creation binding-aware.
+
+## Flow
+
+![Schema display](diagrams/nested_fields_flow_simple.png)
+
+![Schema display](diagrams/nested_fields_flow.png)
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
index 07175315be..3253a458b5 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
@@ -208,9 +208,18 @@ private Map<String, List<ExportedColumn>> getNormalizedExportedColumns() {
     }
 
 
+    /**
+     * converts DiscoveredTableBinding into ParquetTableBinding
+     * @param tableName - generated table name
+     * @param table - physical table
+     * @return ParquetTableBinding - column bindings are stored by physical column id
+     */
     private ParquetTableBinding getDiscoveredTableBinding( String tableName, PhysicalTable table ) {
+        // Get or build normalized schema
         ParquetNormalizedSchema normalizedSchema = getNormalizedSchema();
+        // find binding for this table
         DiscoveredTableBinding binding = normalizedSchema.getBinding( tableName );
+        // if missing in normalized mode - rebuild
         if ( binding == null && getConfiguredSchemaMode() == ParquetSchemaMode.NORMALIZED ) {
             clearNormalizedExportCache();
             normalizedSchema = getNormalizedSchema();
@@ -220,13 +229,25 @@ private ParquetTableBinding getDiscoveredTableBinding( String tableName, Physica
             if ( getConfiguredSchemaMode() == ParquetSchemaMode.NORMALIZED ) {
                 throw new GenericRuntimeException( "Missing normalized Parquet binding for generated table: %s", tableName );
             }
+            // Flat tables do not have DiscoveredTableBinding,
+            // because they come from AbstractParquetSource.getExportedColumns() not ParquetSchemaNormalizer
             return createFlatTableBinding( tableName, table );
         }
+        // Convert discovered bindings to final ParquetTableBinding, where column bindings are stored by physical column id
         return ParquetTableBinding.createTableBindingFromColumnPaths( binding.sourceUrl(), binding.parentTableName(), binding.sourcePathElements(), table, binding.columnPaths() );
     }
 
 
+    /**
+     * Creates a ParquetTableBinding for a flat-mode table.
+     * Flat-mode tables do not come from ParquetSchemaNormalizer, so they do not have a DiscoveredTableBinding.
+     * But we still want a persisted ParquetTableBinding for them so restore/scanning uses the same binding infrastructure.
+     * @param tableName - generated table name
+     * @param table - physical table
+     * @return - ParquetTableBinding
+     */
     private ParquetTableBinding createFlatTableBinding( String tableName, PhysicalTable table ) {
+        // flat mode is implemented in AbstractParquetSource
         List<ExportedColumn> exportedColumns = super.getExportedColumns().get( tableName );
         if ( exportedColumns == null || exportedColumns.isEmpty() ) {
             return currentNamespace.createRootBinding( table );
@@ -234,6 +255,7 @@ private ParquetTableBinding createFlatTableBinding( String tableName, PhysicalTa
 
         try {
             Map<String, List<String>> columnPaths = new LinkedHashMap<>();
+            // Build column path map like order_id -> ["order_id"]
             exportedColumns.forEach( column -> columnPaths.put( column.name(), List.of( column.physicalColumnName() ) ) );
             String sourceUrl = ParquetUrlResolver.resolveFile( parquetDir, exportedColumns.get( 0 ).physicalSchemaName() ).toString();
             return ParquetTableBinding.createTableBindingFromColumnPaths( sourceUrl, null, List.of(), table, columnPaths );
@@ -247,6 +269,11 @@ private void clearNormalizedExportCache() {
         normalizedSchema = null;
     }
 
+
+    /**
+     * get normalized schema from ParquetSchemaNormalizer
+     * @return ParquetNormalizedSchema
+     */
     private ParquetNormalizedSchema getNormalizedSchema() {
         if ( connectionMethod == ConnectionMethod.UPLOAD && normalizedSchema != null ) {
             return normalizedSchema;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
index 5d8a33c11b..10f73b2a2e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
@@ -45,6 +45,7 @@ public ParquetTableBinding createRootBinding( PhysicalTable table ) {
      */
     public ParquetRelTable createParquetTable( long id, PhysicalTable table, ParquetTableBinding binding, AbstractParquetSource sourceAdapter ) {
         try {
+            // source file comes from the binding, not from the generated table name
             Source parquetSource = Sources.of( new URL( binding.sourceUrl() ) );
             return new ParquetRelTable( id, parquetSource, table, binding, sourceAdapter );
 

From 4a299951fe0d26720809f92230cb336e6167250e Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Fri, 24 Apr 2026 13:30:12 +0200
Subject: [PATCH 18/46] nested fields filtering

---
 plugins/parquet-adapter/docs/nested_fields.md | 303 ++++++++++++------
 .../execution/ParquetDocValueExtractor.java   |   9 +
 .../ParquetBindingRelEnumerator.java          | 132 --------
 ...ParquetNestedNonRepeatedRelEnumerator.java |  59 ++++
 .../execution/ParquetNestedRelEnumerator.java | 172 ----------
 .../ParquetNestedRepeatedRelEnumerator.java   | 120 +++++++
 .../execution/ParquetPathValueExtractor.java  |  82 +++++
 .../execution/ParquetRelFilterTranslator.java |   5 +-
 .../execution/ParquetRelValueExtractor.java   |   8 +
 .../relational/schema/ParquetRelTable.java    |  34 +-
 .../execution/AbstractParquetEnumerator.java  |  98 ++++--
 .../ParquetFilterTranslationSupport.java      |   3 +-
 .../execution/ParquetValueExtractor.java      |   3 +
 .../shared/filter/ParquetAdapterFilter.java   |  32 +-
 .../filter/ParquetNativeFilterBuilder.java    | 100 ++++--
 .../main/resources/images/parquet_icon.png    | Bin 0 -> 1357900 bytes
 16 files changed, 700 insertions(+), 460 deletions(-)
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetBindingRelEnumerator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRelEnumerator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPathValueExtractor.java
 create mode 100644 plugins/parquet-adapter/src/main/resources/images/parquet_icon.png

diff --git a/plugins/parquet-adapter/docs/nested_fields.md b/plugins/parquet-adapter/docs/nested_fields.md
index 949a6d040d..cf1207b8ff 100644
--- a/plugins/parquet-adapter/docs/nested_fields.md
+++ b/plugins/parquet-adapter/docs/nested_fields.md
@@ -1,65 +1,78 @@
 
 # Relational Schema Normalization
 
-## Example Parquet shape:
-```
+Parquet nested structures are exposed as regular relational tables.
+In FLAT mode, each file becomes one table and nested values remain inside the root row.
+In NORMALIZED mode, Polypheny reads the Parquet schema and recursively splits it as follows:
+
+- primitive field -> column
+- non-repeated group -> child table with 0/1 row per parent
+- repeated group -> child table with multiple rows per parent
+- repeated primitive field -> child table with one value column and multiple rows per parent
+
+All relational Parquet table names are adapter-prefixed, both flat and normalized.
+
+_Example:_ `parquetrelational1__orders`, not just `orders`. This allows create different adapters for flat and normalized schema modes.
+
+## Example of Parquet shape
+
+```text
 orders.parquet
 root
 |- order_id: int64
 |- customer: string
- - items: repeated group
-   |- sku: string
+|- items: repeated group
+   |- product_id: string
    |- quantity: int32
-   - discounts: repeated group
+   |- discounts: repeated group
       |- code: string
       |- amount: decimal
 ```
 
 ## Flat Mode
+
 - Preserve the Parquet file as one table
 
 **_orders_**
 
+| order_id | customer | items                                                                                                                                                         |
+|----------|----------|---------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1001     | Alice    | [{product_id: "A-1", quantity: 2, discounts: [{code: "SUMMER", amount: 5.00}, {code: "VIP", amount: 2.00}]}, {product_id: "B-5", quantity: 1, discounts: []}] |
+| 1002     | Bob      | [{product_id: "C-9", quantity: 4, discounts: []}]                                                                                                             |
 
-| order_id | customer | items                                                                                                                                           |
-|----------|----------|-------------------------------------------------------------------------------------------------------------------------------------------------|
-| 1001     | Alice    | [{sku: "A-1", quantity: 2, discounts: [{code: "SUMMER", amount: 5.00}, {code: "VIP", amount: 2.00}]}, {sku: "B-5", quantity: 1, discounts: []}] |
-| 1002     | Bob      | [{sku: "C-9", quantity: 4, discounts: []}]                                                                                                      |
-
-
-```
+```text
 orders.parquet
 -> orders
 ```
 
 ## Normalized Mode
+
 - Split nested structures into generated relational child tables
 - NEED TO STORE ADDITIONAL INFORMATION
 
 **_orders_**
 
-| __polypheny_row_id | order_id | customer |
-|--------------------|----------|----------|
-| 1                  | 1001     | Alice    |
-| 2                  | 1002     | Bob      |
-
+| polypheny_row_id | order_id | customer |
+|------------------|----------|----------|
+| 1                | 1001     | Alice    |
+| 2                | 1002     | Bob      |
 
 **_orders__items_**
 
-| __polypheny_row_id | __polypheny_parent_row_id | __polypheny_elem_ordinal | sku | quantity |
-|--------------------|---------------------------|--------------------------|-----|----------|
-| 10                 | 1                         | 0                        | A-1 | 2        |
-| 11                 | 1                         | 1                        | B-5 | 1        |
-| 12                 | 2                         | 0                        | C-9 | 4        |
+| polypheny_row_id | polypheny_parent_row_id | polypheny_elem_ordinal | product_id | quantity |
+|------------------|-------------------------|------------------------|------------|----------|
+| 10               | 1                       | 0                      | A-1        | 2        |
+| 11               | 1                       | 1                      | B-5        | 1        |
+| 12               | 2                       | 0                      | C-9        | 4        |
 
 **_orders__items__discounts_**
 
-| __polypheny_row_id | __polypheny_parent_row_id | __polypheny_elem_ordinal | code   | amount |
-|--------------------|---------------------------|--------------------------|--------|--------|
-| 20                 | 10                        | 0                        | SUMMER | 5.00   |
-| 21                 | 10                        | 1                        | VIP    | 2.00   |
+| polypheny_row_id | polypheny_parent_row_id | polypheny_elem_ordinal | code   | amount |
+|------------------|-------------------------|------------------------|--------|--------|
+| 20               | 10                      | 0                      | SUMMER | 5.00   |
+| 21               | 10                      | 1                      | VIP    | 2.00   |
 
-```
+```text
 orders.parquet
     -> orders
     -> orders__items
@@ -67,78 +80,114 @@ orders.parquet
  ```
 
 ```sql
-SELECT o.order_id, i.sku, i.quantity
+SELECT o.order_id, i.product_id, i.quantity
 FROM orders o
 JOIN orders__items i
-  ON i.__polypheny_parent_row_id = o.__polypheny_row_id;
+  ON i.polypheny_parent_row_id = o.polypheny_row_id;
 
 ```
-## Example with unique table names:
 
-### Flat result:
-```
+## Example with unique table names
+
+### Flat result
+
+```text
 parquetrelational1__orders -> [order_id, customer_id, status, ...]
 ```
 
-### Normalized result:
-```
+### Normalized result
+
+```text
 parquetrelational1__orders -> [order_id, customer_id, status, total_price]
 parquetrelational1__orders__items -> [order_item_id, product_id, quantity, price]
 parquetrelational1__orders__items__discounts -> [code, amount]
 ```
 
+## Synthetic Columns
 
-## Behavior:
-
-- All relational Parquet table names are adapter-prefixed, both flat and normalized. <br>
-*Example:* `parquetrelational1__orders`, not just `orders`. This allows create different adapters for flat and normalized schema modes.
-
-- Flat mode shows one table per Parquet file, with nested/repeated values kept inside the root table as structured values.
+- Normalized tables include visible _synthetic key columns_:
+  - polypheny_row_id
+  - polypheny_parent_row_id
+  - polypheny_elem_ordinal
+- Root tables have:
+  - polypheny_row_id
+- Child tables have:
+  - polypheny_row_id
+  - polypheny_parent_row_id
+  - polypheny_elem_ordinal - for repeated children
+- Non-repeated child tables still have:
+  - polypheny_parent_row_id
+  - polypheny_elem_ordinal is always 0
 
-- Normalized mode shows root tables with direct primitive fields only.
-- Every Parquet group field becomes a generated virtual relational table.
-- Nested groups inside nested groups also become virtual tables. <br> *Example:* items.discounts becomes parquetrelational1__orders__items__discounts.
-- Normalized tables include visible ***synthetic key columns***:
-  - __polypheny_row_id
-  - __polypheny_parent_row_id
-  - __polypheny_elem_ordinal
-- Root tables have: 
-  - __polypheny_row_id
-- Child tables have: 
-  - __polypheny_row_id
-  - __polypheny_parent_row_id
-  - __polypheny_elem_ordinal - for repeated children
-- Non-repeated child tables still have: 
-  - __polypheny_parent_row_id
-  - __polypheny_elem_ordinal is always 0
+**_Sql Example:_**
 
-***Sql Example:***
 ```sql
-SELECT o.order_id, i.sku, i.quantity
+SELECT o.order_id, i.product_id, i.quantity
 FROM parquetrelational1__orders o
 JOIN parquetrelational1__orders__items i
-  ON i.__polypheny_parent_row_id = o.__polypheny_row_id;
+  ON i.polypheny_parent_row_id = o.polypheny_row_id;
 
 ```
+
 For deeper nesting, the same rule applies at each level. A row in parquetrelational1__orders__items__discounts points to its parent item row, not directly to the root order row:
+
 ```sql
-SELECT i.sku, d.code, d.amount
+SELECT i.product_id, d.code, d.amount
 FROM parquetrelational1__orders__items i
 JOIN parquetrelational1__orders__items__discounts d
-  ON d.__polypheny_parent_row_id = i.__polypheny_row_id;
+  ON d.polypheny_parent_row_id = i.polypheny_row_id;
 ```
 
+## BINDINGS
+
+Generated relational tables are virtual: they do not exist as separate Parquet files.
+The data still lives in the original Parquet file.
+To connect virtual tables with physical Parquet data, we store bindings.
+Bindings describe:
+
+- which Parquet file backs each generated table
+- which nested path produces the table rows
+- which Parquet field path provides each column value
+- which synthetic columns are generated by Polypheny
+
+### Example
+
+#### Table Bindings for orders.parquet
+
+| Generated table             | Source Parquet file | Nested path that produces table rows | Parent table      |
+|-----------------------------|---------------------|--------------------------------------|-------------------|
+| `pn_orders`                 | `orders.parquet`    | root row                             | none              |
+| `pn_orders_items`           | `orders.parquet`    | `items[*]`                           | `pn_orders`       |
+| `pn_orders_items_discounts` | `orders.parquet`    | `items[*].discounts[*]`              | `pn_orders_items` |
+
+#### Column Bindings for pn_orders_items_discounts
+
+| Generated column          | Source Parquet field path                              | Column role           |
+|---------------------------|--------------------------------------------------------|-----------------------|
+| `polypheny_row_id`        | generated by Polypheny                                 | synthetic primary key |
+| `polypheny_parent_row_id` | generated by Polypheny from parent item row            | synthetic parent key  |
+| `polypheny_elem_ordinal`  | generated by Polypheny from repeated discount position | synthetic ordinal     |
+| `code`                    | `items.discounts.code`                                 | data                  |
+| `amount`                  | `items.discounts.amount`                               | data                  |
+
+
+
+
 ## Code Changes
 
 ### AbstractParquetSource
+
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\AbstractParquetSource.java`
+
 - Adding shared metadata persistence, safer URL handling, and adapter-prefixed table names
 - Store Parquet Bindings in adapter settings in persistParquetBindings() function
 - Load Parquet Bindings from settings in constructor()
-- Prefix is added to the table names to allow deployment multiple Parquet relational adapters over the same files, for example one flat and one normalized, without logical table name collisions - getPrefixTableName() function 
+- Prefix is added to the table names to allow deployment multiple Parquet relational adapters over the same files, for example one flat and one normalized, without logical table name collisions - getPrefixTableName() function
 
 ### ParquetColumnRole
+
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetColumnRole.java`
+
 - Describes what kind of column a relational Parquet column is inside a ParquetTableBinding
   - DATA - a normal real Parquet value
   - PRIMARY_KEY - synthetic generated row id
@@ -146,101 +195,163 @@ JOIN parquetrelational1__orders__items__discounts d
   - ORDINAL - position of a child value inside its parent
 
 ### ParquetColumnBinding
+
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetColumnBinding.java`
 Metadata object that tells the Parquet scanner for specific Polypheny column, where does its value come from. Used for reading data.
 Contains:
-- columnId - Polypheny physical column id connected to an actual column in Polypheny’s physical table catalog
+
+- columnId - Polypheny physical column id connected to an actual column in Polypheny's physical table catalog
 - columnName - relational column name
 - role - ParquetColumnRole - what kind of column this is
 - sourcePathElements - the path inside the Parquet schema that should be used to read this column. For example:
-  - flat/root column - path: order_id
-  - nested - path: shipping_address.city
-
+  - flat/root column - path: ["order_id"]
+  - nested - path: ["shipping_address", "city"]
 
 ### ParquetTableBinding
+
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetTableBinding.java`
 Table-level metadata, describes the whole table, immutable record
 Fields:
+
 - sourceUrl - real Parquet file URL to read from
 - parentTableName - generated parent relational table name, or null for root tables
 - sourcePathElements - table-level path inside the Parquet file, For root: List.of(), for nested: List.of("items")
-- columnsByColumnId - Map<Long, ParquetColumnBinding> - maps Polypheny physical column ids to column bindings
+- columnsByColumnId - `Map<Long, ParquetColumnBinding>` - maps Polypheny physical column ids to column bindings
 
 Example:
-```
+
+```text
 sourceUrl: orders.parquet
 parentTableName: parquetrelational1__orders
 sourcePathElements: ["items"]
 
 columnPaths:
-sku      -> ["items", "sku"] // sku - stock keeping unit (product code)
-quantity -> ["items", "quantity"]
+product_id -> ["items", "product_id"]
+quantity   -> ["items", "quantity"]
 ```
 
 ### ParquetUrlResolver
+
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\io\ParquetUrlResolver.java`
 Utility, Parquet file/directory URL handling
 
-
 ### ParquetSchemaMode
+
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetSchemaMode.java`
-User-selectable adapter setting that controls how the Parquet schema
+User-selectable adapter setting that controls how the Parquet schema:
+
 - FLAT
 - NORMALIZED
 
 ### ParquetSchemaNormalizer
+
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetSchemaNormalizer.java`
 Turns Parquet nested schema into multiple relational tables for normalized mode.
-Contains the main normalization logic.
-It creates Result containing: 
-- table definition: exported relational table definitions: Map<String, List<ExportedColumn>> exportedColumns
-- binding metadata: explains how those tables map back to the Parquet file: Map<String, DiscoveredTableBinding> tableBindings
+Contains the main normalization logic. It builds ParquetNormalizedSchema with:
 
+- `Map<String, List<ExportedColumn>>` tables
+- `Map<String, DiscoveredTableBinding>` bindings
 
 ### ParquetNormalizedSchema
+
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetNormalizedSchema.java`
 contains normalizes schema information for all tables:
-- Map<String, List<ExportedColumn>> tables - normalized columns per table name
-- Map<String, DiscoveredTableBinding> bindings - binding per table name
+
+- `Map<String, List<ExportedColumn>>` tables - normalized columns per table name
+- `Map<String, DiscoveredTableBinding>` bindings - binding per table name
 
 ### DiscoveredTableBinding
+
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\DiscoveredTableBinding.java`
 temporary discovery metadata before a real ParquetTableBinding created
-for ParquetTableBinding we need physical table ids, that are created after normalization step 
+for ParquetTableBinding we need physical table ids, that are created after normalization step
 
 ### ParquetBindingSerializer
+
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetBindingSerializer.java`
 Saves and restores ParquetTableBinding metadata through adapter settings.
 
-### ParquetBindingRelEnumerator
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetBindingRelEnumerator.java`
-Root-table scanner that reads column values by ParquetColumnBinding paths instead of by physical ordinal position.
-Normalized mode may remove nested fields from the root table. It
-- reads full root rows from the Parquet file
-- extracts each value by path. If the binding path is: List.of("shipping_address", "city") it walks: "root -> shipping_address -> city"
-- It supports walking through non-repeated nested groups.
-- If it encounters a repeated group in the middle of a root value path, it returns NULL instead of trying to flatten it.
-- It converts final values using ParquetRelValueExtractor
+### AbstractParquetEnumerator
+
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\execution\AbstractParquetEnumerator.java`
+Shared base for relational scans.
+
+### ParquetNestedRepeatedRelEnumerator
 
-### ParquetNestedRelEnumerator
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetNestedRelEnumerator.java`
-used for tables that do not have their own Parquet file, but are created from a nested group inside a Parquet file
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetNestedRepeatedRelEnumerator.java`
+Used for tables that do not have their own Parquet file, but are created from a nested group inside a Parquet file.
+
+### ParquetNestedNonRepeatedRelEnumerator
+
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetNestedNonRepeatedRelEnumerator.java`
+Used to handle virtual table that was created from nested non-repeated types.
 
 ### ParquetRelationalSource
+
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\ParquetRelationalSource.java`
-changed from “simple relational Parquet source that exports one table per file” into the controller for flat vs normalized relational Parquet mode.
+changed from simple relational Parquet source that exports one table per file into the controller for flat vs normalized relational Parquet mode.
 
 ### ParquetRelTable
+
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetRelTable.java`
-ParquetRelTable changed from a simple wrapper that always scanned Parquet columns by ordinal into a wrapper that can choose between three scan strategies:
-- old flat ordinal scan
-- binding-aware root scan
-- nested child-table scan
+
+`ParquetRelTable` changed from a simple wrapper that always scanned Parquet columns by ordinal into a wrapper that chooses the correct scan strategy based on table binding metadata.
+
+Current scan strategies:
+
+- `ParquetRelEnumerator`
+  - used for flat/root tables when relational column positions still match Parquet field positions
+  - keeps the original ordinal-based scan behavior
+
+- `ParquetNestedNonRepeatedRelEnumerator`
+  - used when rows are still produced one-to-one, but values must be read by Parquet source path instead of ordinal position
+  - supports path-based extraction and filtering for non-repeated nested values
+
+- `ParquetNestedRepeatedRelEnumerator`
+  - used for generated repeated child tables
+  - expands one Parquet root row into multiple relational rows by following the configured table path
+
+`ParquetRelTable` uses `ParquetTableBinding` to decide which scanner to create.
+This allows Polypheny to read both flat tables and generated normalized tables from the same original Parquet file.
 
 ### ParquetNamespace
+
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\schema\ParquetNamespace.java`
 Make relational table creation binding-aware.
 
+### ParquetAdapterFilter
+
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\filter\ParquetAdapterFilter.java`
+
+`ParquetAdapterFilter` is the shared immutable filter description used during Parquet scan planning and execution.
+
+It can represent filters in two forms:
+
+- ordinal filter
+  - uses `columnIndex`
+  - used when relational column position still matches Parquet field position
+
+- path-based filter
+  - uses `pathElements`
+  - used for normalized tables and nested fields, where values must be read by Parquet source path
+
+The helper method `makeNested(int startIndex)` creates a nested version of the same filter by trimming the prefix of the Parquet path.
+This is useful when a nested scanner already operates inside a deeper Parquet group and only needs the remaining relative path.
+
+### ParquetNativeFilterBuilder
+
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\filter\ParquetNativeFilterBuilder.java`
+
+`ParquetNativeFilterBuilder` creates native Parquet predicates for row-group pruning.
+
+It translates `ParquetAdapterFilter` into Parquet filter expressions when the target field can be mapped safely to a primitive Parquet column.
+
+Native Parquet filtering is only supported for primitive non-repeated leaf fields.
+
+If a filter cannot be pushed down safely, it is ignored at the native Parquet level and must still be applied during row scanning.
+
+This means native filtering is used as an optimization, while exact correctness is preserved by enumerator-level filtering.
+
 ## Flow
 
 ![Schema display](diagrams/nested_fields_flow_simple.png)
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
index b7dbcaa328..2b6fdb1d90 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocValueExtractor.java
@@ -1,6 +1,7 @@
 package org.polypheny.db.adapter.parquet.document.execution;
 
 import java.util.LinkedHashMap;
+import java.util.List;
 import java.util.Map;
 import org.apache.parquet.example.data.Group;
 import org.apache.parquet.schema.MessageType;
@@ -19,14 +20,22 @@ public class ParquetDocValueExtractor extends AbstractParquetValueExtractor {
 
     private static final PolyString ID_KEY = PolyString.of( "_id" );
 
+
     @Override
     public PolyValue extractValue( Group group, int index, Type type ) {
         return extractStructuredValue( group, index, type );
     }
 
 
+    @Override
+    public PolyValue extractValue( Group group, List<String> path ) {
+        throw new UnsupportedOperationException( ParquetDocValueExtractor.class.getName() + " does not support extracting value via path." );
+    }
+
+
     /**
      * Convert one Parquet row (Group) into one Polypheny document (PolyDocument)
+     *
      * @param group - parquet row
      * @param schema - file schema (fields)
      * @param generatedId - id for the row
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetBindingRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetBindingRelEnumerator.java
deleted file mode 100644
index 4fb7636bbd..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetBindingRelEnumerator.java
+++ /dev/null
@@ -1,132 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.execution;
-
-import java.util.List;
-import java.util.concurrent.atomic.AtomicBoolean;
-import org.apache.calcite.linq4j.Enumerator;
-import org.apache.parquet.example.data.Group;
-import org.apache.parquet.schema.GroupType;
-import org.apache.parquet.schema.Type;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
-import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
-import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
-import org.polypheny.db.type.entity.PolyNull;
-import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.util.Source;
-
-/**
- * Root-table scanner that
- * reads root table rows and extracts selected columns by path
- */
-public class ParquetBindingRelEnumerator implements Enumerator<PolyValue[]> {
-
-    private final ParquetSourceReader reader;
-    private final List<ParquetColumnBinding> columnBindings;
-    private final ParquetRelValueExtractor valueExtractor = new ParquetRelValueExtractor();
-    private PolyValue[] current;
-
-
-    public ParquetBindingRelEnumerator( Source source, AtomicBoolean cancelFlag, List<ParquetColumnBinding> columnBindings ) {
-        // read full root rows from the Parquet file
-        this.reader = new ParquetSourceReader( source, cancelFlag, null, List.of() );
-        this.columnBindings = List.copyOf( columnBindings );
-    }
-
-
-    @Override
-    public PolyValue[] current() {
-        return current;
-    }
-
-
-    @Override
-    public boolean moveNext() {
-        try {
-            Group group = reader.next();
-            if ( group == null ) {
-                current = null;
-                return false;
-            }
-
-            current = new PolyValue[columnBindings.size()];
-            for ( int i = 0; i < columnBindings.size(); i++ ) {
-                // extract value by path
-                // if the binding path is: List.of("shipping_address", "city") it walks: "root -> shipping_address -> city"
-                current[i] = extractPathValue( group, columnBindings.get( i ).sourcePathElements() );
-            }
-            return true;
-        } catch ( Exception e ) {
-            throw new GenericRuntimeException( "Error while reading parquet data using column bindings", e );
-        }
-    }
-
-
-    @Override
-    public void reset() {
-        throw new UnsupportedOperationException();
-    }
-
-
-    @Override
-    public void close() {
-        try {
-            reader.close();
-        } catch ( Exception e ) {
-            throw new GenericRuntimeException( "Error closing parquet reader", e );
-        }
-    }
-
-
-    private PolyValue extractPathValue( Group root, List<String> sourcePath ) {
-        if ( sourcePath.isEmpty() ) {
-            return PolyNull.NULL;
-        }
-
-        Group currentGroup = root;
-        GroupType currentType = root.getType();
-        for ( int i = 0; i < sourcePath.size(); i++ ) {
-            int index = fieldIndex( currentType, sourcePath.get( i ) );
-            if ( index < 0 || currentGroup.getFieldRepetitionCount( index ) == 0 ) {
-                return PolyNull.NULL;
-            }
-
-            Type fieldType = currentType.getType( index );
-            if ( i == sourcePath.size() - 1 ) {
-                return valueExtractor.extractValue( currentGroup, index, fieldType );
-            }
-
-            if ( fieldType.isPrimitive() || currentGroup.getFieldRepetitionCount( index ) > 1 ) {
-                return PolyNull.NULL;
-            }
-            currentGroup = currentGroup.getGroup( index, 0 );
-            currentType = fieldType.asGroupType();
-        }
-        return PolyNull.NULL;
-    }
-
-
-    private int fieldIndex( GroupType groupType, String fieldName ) {
-        for ( int i = 0; i < groupType.getFieldCount(); i++ ) {
-            if ( groupType.getType( i ).getName().equals( fieldName ) ) {
-                return i;
-            }
-        }
-        return -1;
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
new file mode 100644
index 0000000000..ad000f3217
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
@@ -0,0 +1,59 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.parquet.example.data.Group;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+/**
+ * Used to handle virtual table that was created from nested non-repeated types.
+ */
+public class ParquetNestedNonRepeatedRelEnumerator extends AbstractParquetEnumerator {
+
+    private final List<ParquetColumnBinding> columnBindings;
+
+
+    public ParquetNestedNonRepeatedRelEnumerator(Source source, AtomicBoolean cancelFlag, List<ParquetColumnBinding> columnBindings ) {
+        this( source, cancelFlag, columnBindings, List.of() );
+    }
+
+
+    public ParquetNestedNonRepeatedRelEnumerator(Source source, AtomicBoolean cancelFlag, List<ParquetColumnBinding> columnBindings, List<ParquetAdapterFilter> filters ) {
+        // read full root rows from the Parquet file
+        super( source, cancelFlag, null, filters, new ParquetPathValueExtractor() );
+        this.columnBindings = List.copyOf( columnBindings );
+    }
+
+
+    @Override
+    protected PolyValue[] extractRow( Group group ) {
+        PolyValue[] row = new PolyValue[columnBindings.size()];
+        for ( int i = 0; i < columnBindings.size(); i++ ) {
+            // extract value by path
+            // if the binding path is: List.of("shipping_address", "city") it walks: "root -> shipping_address -> city"
+            row[i] = valueExtractor.extractValue( group, columnBindings.get( i ).sourcePathElements() );
+        }
+        return row;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRelEnumerator.java
deleted file mode 100644
index 5e52a99bbc..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRelEnumerator.java
+++ /dev/null
@@ -1,172 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.execution;
-
-import java.util.ArrayDeque;
-import java.util.List;
-import java.util.Queue;
-import java.util.concurrent.atomic.AtomicBoolean;
-import org.apache.calcite.linq4j.Enumerator;
-import org.apache.parquet.example.data.Group;
-import org.apache.parquet.schema.GroupType;
-import org.apache.parquet.schema.Type;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
-import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
-import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
-import org.polypheny.db.type.entity.PolyNull;
-import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.util.Source;
-
-/**
- *  Used for tables that do not have their own Parquet file, but are created from a nested group inside a Parquet file
- */
-public class ParquetNestedRelEnumerator implements Enumerator<PolyValue[]> {
-
-    private final ParquetSourceReader reader;
-    private final List<String> tablePath;
-    private final List<ParquetColumnBinding> columnBindings;
-    private final ParquetRelValueExtractor valueExtractor = new ParquetRelValueExtractor();
-    private final Queue<PolyValue[]> rows = new ArrayDeque<>();
-    private PolyValue[] current;
-
-
-    public ParquetNestedRelEnumerator( Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings ) {
-        this.reader = new ParquetSourceReader( source, cancelFlag, null, List.of() );
-        this.tablePath = binding.sourcePathElements();
-        this.columnBindings = List.copyOf( columnBindings );
-        if ( tablePath.isEmpty() ) {
-            throw new GenericRuntimeException( "Nested parquet table binding does not contain a table source path." );
-        }
-    }
-
-
-    @Override
-    public PolyValue[] current() {
-        return current;
-    }
-
-
-    @Override
-    public boolean moveNext() {
-        try {
-            while ( rows.isEmpty() ) {
-                Group root = reader.next();
-                if ( root == null ) {
-                    current = null;
-                    return false;
-                }
-                enqueueNestedRows( root );
-            }
-            current = rows.remove();
-            return true;
-        } catch ( Exception e ) {
-            throw new GenericRuntimeException( "Error while reading nested parquet data", e );
-        }
-    }
-
-
-    @Override
-    public void reset() {
-        throw new UnsupportedOperationException();
-    }
-
-
-    @Override
-    public void close() {
-        try {
-            reader.close();
-        } catch ( Exception e ) {
-            throw new GenericRuntimeException( "Error closing parquet reader", e );
-        }
-    }
-
-
-    private void enqueueNestedRows( Group root ) {
-        for ( Group nested : resolveTableGroups( root, root.getType(), 0 ) ) {
-            PolyValue[] row = new PolyValue[columnBindings.size()];
-            for ( int i = 0; i < columnBindings.size(); i++ ) {
-                row[i] = extractNestedValue( nested, columnBindings.get( i ).sourcePathElements() );
-            }
-            rows.add( row );
-        }
-    }
-
-
-    private PolyValue extractNestedValue( Group nested, List<String> sourcePath ) {
-        if ( sourcePath.size() < tablePath.size() || !sourcePath.subList( 0, tablePath.size() ).equals( tablePath ) ) {
-            return PolyNull.NULL;
-        }
-
-        Group currentGroup = nested;
-        GroupType currentType = nested.getType();
-        for ( int i = tablePath.size(); i < sourcePath.size(); i++ ) {
-            int index = fieldIndex( currentType, sourcePath.get( i ) );
-            if ( index < 0 || currentGroup.getFieldRepetitionCount( index ) == 0 ) {
-                return PolyNull.NULL;
-            }
-
-            Type fieldType = currentType.getType( index );
-            if ( i == sourcePath.size() - 1 ) {
-                return valueExtractor.extractValue( currentGroup, index, fieldType );
-            }
-
-            if ( fieldType.isPrimitive() ) {
-                return PolyNull.NULL;
-            }
-            currentGroup = currentGroup.getGroup( index, 0 );
-            currentType = fieldType.asGroupType();
-        }
-        return PolyNull.NULL;
-    }
-
-
-    private List<Group> resolveTableGroups( Group group, GroupType groupType, int pathIndex ) {
-        if ( pathIndex >= tablePath.size() ) {
-            return List.of( group );
-        }
-
-        int fieldIndex = fieldIndex( groupType, tablePath.get( pathIndex ) );
-        if ( fieldIndex < 0 || group.getFieldRepetitionCount( fieldIndex ) == 0 ) {
-            return List.of();
-        }
-
-        Type fieldType = groupType.getType( fieldIndex );
-        if ( fieldType.isPrimitive() ) {
-            return List.of();
-        }
-
-        List<Group> groups = new java.util.ArrayList<>();
-        int count = group.getFieldRepetitionCount( fieldIndex );
-        for ( int occurrence = 0; occurrence < count; occurrence++ ) {
-            Group child = group.getGroup( fieldIndex, occurrence );
-            groups.addAll( resolveTableGroups( child, fieldType.asGroupType(), pathIndex + 1 ) );
-        }
-        return groups;
-    }
-
-
-    private int fieldIndex( GroupType groupType, String fieldName ) {
-        for ( int i = 0; i < groupType.getFieldCount(); i++ ) {
-            if ( groupType.getType( i ).getName().equals( fieldName ) ) {
-                return i;
-            }
-        }
-        return -1;
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
new file mode 100644
index 0000000000..b507a3d78f
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
@@ -0,0 +1,120 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.GroupType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
+import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+/**
+ * Used for tables that do not have their own Parquet file, but are created from a nested group inside a Parquet file
+ */
+public class ParquetNestedRepeatedRelEnumerator extends AbstractParquetEnumerator {
+
+    private final List<String> tablePath;
+    private final List<ParquetColumnBinding> columnBindings;
+
+
+    public ParquetNestedRepeatedRelEnumerator(Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings ) {
+        this( source, cancelFlag, binding, columnBindings, List.of() );
+    }
+
+
+    public ParquetNestedRepeatedRelEnumerator(Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings, List<ParquetAdapterFilter> filters ) {
+        super( source, cancelFlag, null, filters, new ParquetPathValueExtractor() );
+
+        this.tablePath = binding.sourcePathElements();
+        this.columnBindings = List.copyOf( columnBindings );
+        if ( tablePath.isEmpty() ) {
+            throw new GenericRuntimeException( "Nested parquet table binding does not contain a table source path." );
+        }
+    }
+
+
+    /**
+     *  Recursive function that finds which nested Parquet groups should become rows for a repeated generated table
+     *  Follow tablePath inside the current Parquet row and return all nested group occurrences that represent rows of the virtual repeated table.
+     * @param group - parquet group/row
+     * @return List<Group>
+     */
+    @Override
+    protected List<Group> expandRow( Group group ) {
+        return resolveNested( group, group.getType(), 0 );
+    }
+
+
+    @Override
+    protected PolyValue[] extractRow( Group group ) {
+        PolyValue[] row = new PolyValue[columnBindings.size()];
+        for ( int i = 0; i < columnBindings.size(); i++ ) {
+            var path = columnBindings.get( i ).sourcePathElements();
+            row[i] = valueExtractor.extractValue( group, path.subList( tablePath.size(), path.size() ) );
+        }
+        return row;
+    }
+
+
+    @Override
+    protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
+        return super.extractValue( group, filter.makeNested( tablePath.size() ) );
+    }
+
+
+    private List<Group> resolveNested( Group group, GroupType groupType, int pathIndex ) {
+        if ( pathIndex >= tablePath.size() ) {
+            return List.of( group );
+        }
+
+        int fieldIndex = fieldIndex( groupType, tablePath.get( pathIndex ) );
+        if ( fieldIndex < 0 || group.getFieldRepetitionCount( fieldIndex ) == 0 ) {
+            return List.of();
+        }
+
+        Type fieldType = groupType.getType( fieldIndex );
+        if ( fieldType.isPrimitive() ) {
+            return List.of();
+        }
+
+        List<Group> groups = new java.util.ArrayList<>();
+        int count = group.getFieldRepetitionCount( fieldIndex );
+        for ( int occurrence = 0; occurrence < count; occurrence++ ) {
+            Group child = group.getGroup( fieldIndex, occurrence );
+            groups.addAll( resolveNested( child, fieldType.asGroupType(), pathIndex + 1 ) );
+        }
+        return groups;
+    }
+
+
+    private int fieldIndex( GroupType groupType, String fieldName ) {
+        for ( int i = 0; i < groupType.getFieldCount(); i++ ) {
+            if ( groupType.getType( i ).getName().equals( fieldName ) ) {
+                return i;
+            }
+        }
+        return -1;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPathValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPathValueExtractor.java
new file mode 100644
index 0000000000..041de04a8e
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPathValueExtractor.java
@@ -0,0 +1,82 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.List;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.GroupType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetValueExtractor;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+/**
+ * Extracts relational values by Parquet source path.
+ * Used by binding-aware root scans and generated nested-table scans.
+ */
+public class ParquetPathValueExtractor extends AbstractParquetValueExtractor {
+
+    private final ParquetRelValueExtractor valueExtractor = new ParquetRelValueExtractor();
+
+
+    @Override
+    public PolyValue extractValue( Group group, int index, Type type ) {
+        return valueExtractor.extractValue( group, index, type );
+    }
+
+
+    @Override
+    public PolyValue extractValue( Group group, List<String> sourcePath ) {
+        if ( sourcePath.isEmpty() ) {
+            return PolyNull.NULL;
+        }
+
+        Group currentGroup = group;
+        GroupType currentType = group.getType();
+
+        for ( int i = 0; i < sourcePath.size(); i++ ) {
+            int index = fieldIndex( currentType, sourcePath.get( i ) );
+            if ( index < 0 || currentGroup.getFieldRepetitionCount( index ) == 0 ) {
+                return PolyNull.NULL;
+            }
+
+            Type fieldType = currentType.getType( index );
+            if ( i == sourcePath.size() - 1 ) {
+                return valueExtractor.extractValue( currentGroup, index, fieldType );
+            }
+
+            if ( fieldType.isPrimitive() || currentGroup.getFieldRepetitionCount( index ) > 1 ) {
+                return PolyNull.NULL;
+            }
+            currentGroup = currentGroup.getGroup( index, 0 );
+            currentType = fieldType.asGroupType();
+        }
+        return PolyNull.NULL;
+    }
+
+
+    private int fieldIndex( GroupType groupType, String fieldName ) {
+        for ( int i = 0; i < groupType.getFieldCount(); i++ ) {
+            if ( groupType.getType( i ).getName().equals( fieldName ) ) {
+                return i;
+            }
+        }
+        return -1;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
index b919d2502c..acc368172d 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
@@ -20,7 +20,9 @@
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.execution.ParquetFilterTranslationSupport;
 import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.rex.RexDynamicParam;
 import org.polypheny.db.rex.RexIndexRef;
+import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNode;
 import org.polypheny.db.type.PolyType;
 
@@ -66,7 +68,6 @@ private boolean isPushdownSupported( List<PolyType> fieldTypes, int index, Kind
             case BOOLEAN, VARCHAR, CHAR, TEXT -> kind == Kind.EQUALS || kind == Kind.NOT_EQUALS;
             case INTEGER, BIGINT, FLOAT, DOUBLE, DATE, TIME, TIMESTAMP -> true;
             default -> false;
-        } && (valueNode instanceof org.polypheny.db.rex.RexDynamicParam
-                || (valueNode instanceof org.polypheny.db.rex.RexLiteral literal && literal.getValue() != null));
+        } && (valueNode instanceof RexDynamicParam || (valueNode instanceof RexLiteral literal && literal.getValue() != null));
     }
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java
index c17085299a..4462813d60 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelValueExtractor.java
@@ -6,6 +6,7 @@
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetValueExtractor;
 import org.polypheny.db.type.entity.PolyNull;
 import org.polypheny.db.type.entity.PolyValue;
+import java.util.List;
 
 /**
  * Converts Parquet field values into Polypheny relational values
@@ -26,4 +27,11 @@ public PolyValue extractValue( Group group, int index, Type type ) {
         return typeConverter.fromObjToPolyValue( type, value );
     }
 
+
+    @Override
+    public PolyValue extractValue( Group group, List<String> path ) {
+        throw new UnsupportedOperationException( ParquetRelValueExtractor.class.getName() + " does not support extracting value via path." );
+    }
+
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index 6304fda442..7e8963c436 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -20,10 +20,10 @@
 import org.apache.calcite.linq4j.Enumerable;
 import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.DataContext;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetBindingRelEnumerator;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedNonRepeatedRelEnumerator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedRelEnumerator;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedRepeatedRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
@@ -106,17 +106,17 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> poly
         } );
 
         // check for dynamic filters
-        final List<ParquetAdapterFilter> resolvedFilters = resolveDynamicFilters( dataContext, parquetAdapterFilters );
+        final List<ParquetAdapterFilter> resolvedFilters = resolveFilters( dataContext, parquetAdapterFilters );
 
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
                 if ( isNestedTable() ) {
-                    return new ParquetNestedRelEnumerator( source, cancelFlag, binding, selectedBindings( fieldIndexes ) );
+                    return new ParquetNestedRepeatedRelEnumerator( source, cancelFlag, binding, selectedBindings( fieldIndexes ), resolvedFilters );
                 }
                 if ( needsBindingScan( fieldIndexes ) ) {
-                    return new ParquetBindingRelEnumerator( source, cancelFlag, selectedBindings( fieldIndexes ) );
+                    return new ParquetNestedNonRepeatedRelEnumerator( source, cancelFlag, selectedBindings( fieldIndexes ), resolvedFilters );
                 }
                 return new ParquetRelEnumerator( source, cancelFlag, fieldIndexes, resolvedFilters );
             }
@@ -139,10 +139,10 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
                 if ( isNestedTable() ) {
-                    return new ParquetNestedRelEnumerator( source, cancelFlag, binding, selectedBindings( fieldIndexes ) );
+                    return new ParquetNestedRepeatedRelEnumerator( source, cancelFlag, binding, selectedBindings( fieldIndexes ) );
                 }
                 if ( needsBindingScan( fieldIndexes ) ) {
-                    return new ParquetBindingRelEnumerator( source, cancelFlag, selectedBindings( fieldIndexes ) );
+                    return new ParquetNestedNonRepeatedRelEnumerator( source, cancelFlag, selectedBindings( fieldIndexes ) );
                 }
                 return new ParquetRelEnumerator( source, cancelFlag, fieldIndexes );
             }
@@ -177,10 +177,10 @@ public Enumerable<PolyValue[]> project( final DataContext dataContext, final int
             @Override
             public Enumerator<PolyValue[]> enumerator() {
                 if ( isNestedTable() ) {
-                    return new ParquetNestedRelEnumerator( source, cancelFlag, binding, selectedBindings( fields ) );
+                    return new ParquetNestedRepeatedRelEnumerator( source, cancelFlag, binding, selectedBindings( fields ) );
                 }
                 if ( needsBindingScan( fields ) ) {
-                    return new ParquetBindingRelEnumerator( source, cancelFlag, selectedBindings( fields ) );
+                    return new ParquetNestedNonRepeatedRelEnumerator( source, cancelFlag, selectedBindings( fields ) );
                 }
                 return new ParquetRelEnumerator( source, cancelFlag, fields );
             }
@@ -194,17 +194,15 @@ public Enumerator<PolyValue[]> enumerator() {
      * @param filters filters
      * @return list of parquet filters
      */
-    private List<ParquetAdapterFilter> resolveDynamicFilters( DataContext dataContext, List<ParquetAdapterFilter> filters ) {
+    private List<ParquetAdapterFilter> resolveFilters( DataContext dataContext, List<ParquetAdapterFilter> filters ) {
         List<ParquetAdapterFilter> resolved = new ArrayList<>( filters.size() );
         for ( ParquetAdapterFilter filter : filters ) {
-            if ( filter.dynamicParamIndex() == null ) {
-                // regular filter
-                resolved.add( filter );
-                continue;
-            }
-            // get filter value by index
-            PolyValue value = dataContext.getParameterValue( filter.dynamicParamIndex() );
-            resolved.add( new ParquetAdapterFilter( filter.columnIndex(), filter.operator(), value ) );
+            PolyValue value = filter.dynamicParamIndex() == null
+                    ? filter.polyValue()
+                    : dataContext.getParameterValue( filter.dynamicParamIndex() );
+
+            ParquetColumnBinding columnBinding = Objects.requireNonNull( binding.getColumnBinding( columns.get( filter.columnIndex() ).id ), "Missing parquet column binding" );
+            resolved.add( new ParquetAdapterFilter( filter.columnIndex(), columnBinding.sourcePathElements(), filter.operator(), value ) );
         }
         return resolved;
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
index bc656158a9..8a1980afb1 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
@@ -16,13 +16,16 @@
 
 package org.polypheny.db.adapter.parquet.shared.execution;
 
+import java.util.ArrayDeque;
 import java.util.List;
+import java.util.Queue;
 import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.parquet.example.data.Group;
 import org.apache.parquet.schema.Type;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
+import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.Source;
@@ -37,6 +40,7 @@ public abstract class AbstractParquetEnumerator implements Enumerator<PolyValue[
     protected final ParquetSourceReader reader;
     protected final ParquetValueExtractor valueExtractor;
     protected final List<ParquetAdapterFilter> filters;
+    private final Queue<PolyValue[]> rows = new ArrayDeque<>();
     private PolyValue[] current;
 
 
@@ -56,20 +60,26 @@ public PolyValue[] current() {
     @Override
     public boolean moveNext() {
         try {
-            for ( ; ; ) {
-                // group (single row) can be still filtered, while parquet filter
-                // works on the row group level
-                Group group = reader.next();
-                if ( group == null ) {
-                    current = null;
-                    return false;
+            // fill rows queue from parquet queue/row
+            while ( rows.isEmpty() ) {
+                for ( ; ; ) {
+                    // group (single row) can be still filtered, while parquet filter
+                    // works on the row group level
+                    Group group = reader.next();
+                    if ( group == null ) {
+                        current = null;
+                        return false;
+                    }
+                    // turn single parquet row (group) into multiple rows for nested repeated fields and store them in queue rows
+                    enqueueRows( group, rows );
+                    if ( rows.isEmpty() ) {
+                        continue;
+                    }
+                    break;
                 }
-                if ( !accept( group ) ) {
-                    continue;
-                }
-                current = extractRow( group );
-                return true;
             }
+            current = rows.remove();
+            return true;
         } catch ( Exception e ) {
             throw new GenericRuntimeException( "Error while reading parquet data", e );
         }
@@ -98,14 +108,47 @@ public void close() {
     protected abstract PolyValue[] extractRow( Group group );
 
 
+    /**
+     * The function that turns one input Parquet row into zero (filtered), one, or many output relational rows
+     * It lets the shared enumerator support both simple one-row scans and repeated nested scans that emit multiple rows.
+     * @param group - parquet group/row
+     * @param rows - Queue<PolyValue[]> - output to fill
+     */
+    protected void enqueueRows( Group group, Queue<PolyValue[]> rows ) {
+        for ( var row : expandRow( group ) ) {
+            if ( !accept( row ) ) { //apply filter on adapter level
+                continue;
+            }
+            // converts the accepted Parquet row/group into PolyValue[]
+            PolyValue[] extracted = extractRow( row );
+            if ( extracted != null ) {
+                rows.add( extracted ); // stores produced rows into queue
+            }
+        }
+    }
+
+
+    /**
+     * decides how many logical rows come from this input row
+     * default implementation - return
+     * repeated nested enumerator overrides this and returns all nested groups
+     * @param group - parquet group/row
+     * @return List<Group>
+     */
+    protected List<Group> expandRow( Group group ) {
+        return List.of( group );
+    }
+
+
     /**
      * apply filter on adapter level for each row
      * by calling matches()
+     *
      * @param group - parquet row
      * @return boolean
      */
     protected boolean accept( Group group ) {
-        for ( ParquetAdapterFilter filter : filters ) {
+        for ( var filter : filters ) {
             if ( !matches( group, filter ) ) {
                 return false;
             }
@@ -126,7 +169,7 @@ private boolean matches( Group group, ParquetAdapterFilter filter ) {
         }
 
         // call overloaded functionality
-        PolyValue actual = extractFilterValue( group, filter );
+        PolyValue actual = extractValue( group, filter );
         PolyValue expected = filter.polyValue();
 
         if ( actual == null || actual.isNull() || expected.isNull() ) {
@@ -134,7 +177,17 @@ private boolean matches( Group group, ParquetAdapterFilter filter ) {
         }
 
         // apply filter on the row level
-        return switch ( filter.operator() ) {
+        return matches( actual, filter.operator(), expected );
+    }
+
+
+    protected boolean matches( PolyValue actual, Kind operator, PolyValue expected ) {
+        if ( actual == null || actual.isNull() || expected == null || expected.isNull() ) {
+            return false;
+        }
+
+        // apply filter on the row level
+        return switch ( operator ) {
             case EQUALS -> actual.equals( expected );
             case NOT_EQUALS -> !actual.equals( expected );
             case GREATER_THAN -> compare( actual, expected ) > 0;
@@ -146,9 +199,18 @@ private boolean matches( Group group, ParquetAdapterFilter filter ) {
     }
 
 
-    protected PolyValue extractFilterValue( Group group, ParquetAdapterFilter filter ) {
-        Type field = reader.getProjectionSchema().getType( filter.columnIndex() );
-        return valueExtractor.extractValue( group, filter.columnIndex(), field );
+    /**
+     * Extract value from parquet group according to filter column index
+     * @param group - parquet group
+     * @param filter - ParquetAdapterFilter
+     * @return - PolyValue
+     */
+    protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
+        if ( filter.pathElements().isEmpty() ) {
+            Type field = reader.getProjectionSchema().getType( filter.columnIndex() );
+            return valueExtractor.extractValue( group, filter.columnIndex(), field );
+        }
+        return valueExtractor.extractValue( group, filter.pathElements() );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java
index 91a5170815..a10be62753 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java
@@ -22,6 +22,7 @@
 import org.polypheny.db.rex.RexDynamicParam;
 import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNode;
+import java.util.List;
 
 /**
  * Helper for parsing supported Rex predicates and
@@ -60,7 +61,7 @@ public static ParquetAdapterFilter toParquetAdapterFilter( int columnIndex, Kind
         }
 
         if ( valueNode instanceof RexDynamicParam dynamicParam ) {
-            return new ParquetAdapterFilter( columnIndex, operator, null, dynamicParam.getIndex() );
+            return new ParquetAdapterFilter( columnIndex, List.of(), operator, null, dynamicParam.getIndex() );
         }
 
         return null;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java
index 8fc515f76f..1724a53dba 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java
@@ -3,6 +3,7 @@
 import org.apache.parquet.example.data.Group;
 import org.apache.parquet.schema.Type;
 import org.polypheny.db.type.entity.PolyValue;
+import java.util.List;
 
 /**
  * Interface implemented by value extractors
@@ -12,4 +13,6 @@ public interface ParquetValueExtractor {
 
     PolyValue extractValue( Group group, int index, Type type );
 
+    PolyValue extractValue( Group group, List<String> path );
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
index 9590a6f9ca..5d4a0c61cc 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
@@ -18,17 +18,43 @@
 
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.type.entity.PolyValue;
+import java.util.List;
 
 /**
  * Immutable filter description
+ *
  * @param columnIndex - index of filter column
  * @param operator - filter operation
  * @param polyValue - filter value
  */
-public record ParquetAdapterFilter(int columnIndex, Kind operator, PolyValue polyValue, Long dynamicParamIndex ) {
+public record ParquetAdapterFilter( int columnIndex, List<String> pathElements, Kind operator, PolyValue polyValue, Long dynamicParamIndex ) {
+
+    public ParquetAdapterFilter( int columnIndex, Kind operator, PolyValue polyValue ) {
+        this( columnIndex, List.of(), operator, polyValue, null );
+    }
+
+
+    public ParquetAdapterFilter( int columnIndex, List<String> pathElements, Kind operator, PolyValue polyValue ) {
+        this( columnIndex, pathElements, operator, polyValue, null );
+    }
+
+    // validation performed in main constructor
+    public ParquetAdapterFilter {
+        pathElements = pathElements == null ? List.of() : List.copyOf( pathElements );
+    }
+
 
-    public ParquetAdapterFilter(int columnIndex, Kind operator, PolyValue polyValue ) {
-        this( columnIndex, operator, polyValue, null );
+    /**
+     * Creates new {@link ParquetAdapterFilter} that contain sub path starting from the provided index.
+     *
+     * @param startIndex the index to copy path elements from.
+     * @return a new instance of {@link ParquetAdapterFilter}.
+     */
+    public ParquetAdapterFilter makeNested( int startIndex ) {
+        if ( startIndex >= pathElements.size() ) {
+            return this;
+        }
+        return new ParquetAdapterFilter( columnIndex, pathElements.subList( startIndex, pathElements.size() ), operator, polyValue, dynamicParamIndex );
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
index 64e8cb43c0..5b8dc77144 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
@@ -17,16 +17,20 @@
 package org.polypheny.db.adapter.parquet.shared.filter;
 
 import java.util.List;
+import org.apache.parquet.column.ColumnDescriptor;
 import org.apache.parquet.filter2.compat.FilterCompat;
 import org.apache.parquet.filter2.predicate.FilterApi;
 import org.apache.parquet.filter2.predicate.FilterPredicate;
 import org.apache.parquet.io.api.Binary;
+import org.apache.parquet.hadoop.metadata.ColumnPath;
+import org.apache.parquet.schema.GroupType;
 import org.apache.parquet.schema.LogicalTypeAnnotation;
 import org.apache.parquet.schema.MessageType;
 import org.apache.parquet.schema.PrimitiveType;
 import org.apache.parquet.schema.Type;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
 import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.entity.PolyValue;
 
 /**
  * Builds native Parquet predicates from shared filter descriptions.
@@ -52,45 +56,105 @@ public static FilterCompat.Filter build( MessageType schema, List<ParquetAdapter
         }
 
         FilterPredicate predicate = null;
-        for ( ParquetAdapterFilter filter : filters ) {
+
+        for ( var filter : filters ) {
             FilterPredicate next = buildPredicate( schema, filter );
             if ( next == null ) {
-                throw new IllegalArgumentException( "Unsupported parquet predicate: " + filter );
+                continue;
             }
             predicate = predicate == null ? next : FilterApi.and( predicate, next );
         }
-        return FilterCompat.get( predicate );
+
+        return predicate == null ? FilterCompat.NOOP : FilterCompat.get( predicate );
     }
 
 
     private static FilterPredicate buildPredicate( MessageType schema, ParquetAdapterFilter filter ) {
-        int index = filter.columnIndex();
-        if ( index < 0 || index >= schema.getFieldCount() ) {
-            return null;
-        }
+        if ( filter.pathElements().isEmpty() ) {
+            int index = filter.columnIndex();
+            if ( index < 0 || index >= schema.getFieldCount() ) {
+                return null;
+            }
 
-        Type type = schema.getType( index );
-        if ( !type.isPrimitive() ) {
-            return null;
+            Type type = schema.getType( index );
+            if ( !type.isPrimitive() ) {
+                return null;
+            }
+
+            String columnName = schema.getFieldName( index );
+            return buildPredicatePrimitive( filter.operator(), filter.polyValue(), type, columnName );
+        } else {
+            // build native filter to push down for nested fields
+            Type type = resolveType( schema, filter.pathElements() );
+            if ( type == null || !type.isPrimitive() ) {
+                return null;
+            }
+
+            if ( isRepeatedPath( schema, filter.pathElements() ) ) {
+                return null;
+            }
+
+            String columnName = String.join( ".", filter.pathElements() );
+            return buildPredicatePrimitive( filter.operator(), filter.polyValue(), type, columnName );
         }
+    }
+
 
+    private static FilterPredicate buildPredicatePrimitive( Kind operator, PolyValue value, Type type, String columnName ) {
         PrimitiveType primitive = type.asPrimitiveType();
-        Object expected = TYPE_CONVERTER.fromPolyValueToParquetObj( primitive, filter.polyValue() );
+        Object expected = TYPE_CONVERTER.fromPolyValueToParquetObj( primitive, value );
         if ( expected == null ) {
             return null;
         }
 
-        String columnName = schema.getFieldName( index );
         return switch ( primitive.getPrimitiveTypeName() ) {
-            case BOOLEAN -> buildBoolean( filter.operator(), columnName, expected );
-            case INT32 -> buildInt( filter.operator(), columnName, expected );
-            case INT64 -> buildLong( filter.operator(), columnName, expected );
-            case FLOAT -> buildFloat( filter.operator(), columnName, expected );
-            case DOUBLE -> buildDouble( filter.operator(), columnName, expected );
-            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> buildBinary( filter.operator(), columnName, expected, primitive.getLogicalTypeAnnotation() );
+            case BOOLEAN -> buildBoolean( operator, columnName, expected );
+            case INT32 -> buildInt( operator, columnName, expected );
+            case INT64 -> buildLong( operator, columnName, expected );
+            case FLOAT -> buildFloat( operator, columnName, expected );
+            case DOUBLE -> buildDouble( operator, columnName, expected );
+            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> buildBinary( operator, columnName, expected, primitive.getLogicalTypeAnnotation() );
         };
     }
 
+
+    private static Type resolveType( GroupType groupType, List<String> path ) {
+        GroupType current = groupType;
+        for ( int i = 0; i < path.size(); i++ ) {
+            Type type = null;
+            for ( int fieldIndex = 0; fieldIndex < current.getFieldCount(); fieldIndex++ ) {
+                Type candidate = current.getType( fieldIndex );
+                if ( candidate.getName().equals( path.get( i ) ) ) {
+                    type = candidate;
+                    break;
+                }
+            }
+            if ( type == null ) {
+                return null;
+            }
+            if ( i == path.size() - 1 ) {
+                return type;
+            }
+            if ( type.isPrimitive() ) {
+                return null;
+            }
+            current = type.asGroupType();
+        }
+        return null;
+    }
+
+
+    private static boolean isRepeatedPath( MessageType schema, List<String> path ) {
+        ColumnPath columnPath = ColumnPath.get( path.toArray( String[]::new ) );
+        for ( ColumnDescriptor descriptor : schema.getColumns() ) {
+            if ( ColumnPath.get( descriptor.getPath() ).equals( columnPath ) ) {
+                return descriptor.getMaxRepetitionLevel() > 0;
+            }
+        }
+        return true;
+    }
+
+
     private static FilterPredicate buildBoolean( Kind operator, String columnName, Object expected ) {
         if ( !(expected instanceof Boolean value) ) {
             return null;
diff --git a/plugins/parquet-adapter/src/main/resources/images/parquet_icon.png b/plugins/parquet-adapter/src/main/resources/images/parquet_icon.png
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From 1d2f20a52f22c6cd905d2a7965431950c6c1fee9 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Fri, 24 Apr 2026 23:19:02 +0200
Subject: [PATCH 19/46] add synthetic fields to build relations with nested
 tables

---
 .../docs/diagrams/filter_flowchart.png        | Bin 0 -> 64548 bytes
 plugins/parquet-adapter/docs/filtering.md     | 556 ++++++++++++++++++
 plugins/parquet-adapter/docs/nested_fields.md | 131 +++--
 ...ParquetNestedNonRepeatedRelEnumerator.java |  31 +-
 .../ParquetNestedRepeatedRelEnumerator.java   |  49 +-
 .../schema/ParquetSchemaNormalizer.java       |  53 +-
 .../schema/ParquetSyntheticColumns.java       |  32 +
 .../schema/ParquetTableBinding.java           |  24 +-
 .../execution/AbstractParquetEnumerator.java  |   2 +-
 .../shared/execution/GroupMetadata.java       |  33 ++
 .../shared/execution/VirtualGroup.java        | 179 ++++++
 11 files changed, 1026 insertions(+), 64 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/diagrams/filter_flowchart.png
 create mode 100644 plugins/parquet-adapter/docs/filtering.md
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSyntheticColumns.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/GroupMetadata.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/VirtualGroup.java

diff --git a/plugins/parquet-adapter/docs/diagrams/filter_flowchart.png b/plugins/parquet-adapter/docs/diagrams/filter_flowchart.png
new file mode 100644
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diff --git a/plugins/parquet-adapter/docs/filtering.md b/plugins/parquet-adapter/docs/filtering.md
new file mode 100644
index 0000000000..63ca59e234
--- /dev/null
+++ b/plugins/parquet-adapter/docs/filtering.md
@@ -0,0 +1,556 @@
+# Parquet Filtering
+
+This document describes the current filtering functionality of the Parquet relational adapter.
+
+## Goal
+
+The adapter applies filtering on two levels:
+
+1. Native Parquet filtering
+   - used for row-group pruning
+   - skips Parquet row groups before row materialization
+
+2. Enumerator-level filtering
+   - used for exact row filtering
+   - decides which relational rows are emitted
+
+These two levels are not the same:
+
+- native filtering is an optimization
+- enumerator-level filtering is responsible for correctness
+
+## Main Objects
+
+### `ParquetRelTable`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java`
+
+Responsibilities:
+
+- receives SQL filter expressions from Polypheny
+- translates supported filters into adapter filters
+- resolves dynamic parameters
+- resolves Parquet source paths through bindings
+- selects the correct enumerator
+
+
+### `ParquetRelFilterTranslator`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java`
+
+Responsibilities:
+
+- parses `RexNode` filter expressions
+- accepts only supported operators and value operands
+- creates `ParquetAdapterFilter`
+
+### `ParquetFilterTranslationSupport`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java`
+
+Responsibilities:
+
+- helper for parsing supported binary predicates
+- unwraps casts
+- converts `RexLiteral` and `RexDynamicParam` values into `ParquetAdapterFilter`
+
+### `ParquetAdapterFilter`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java`
+
+Responsibilities:
+
+- immutable filter description shared across scan planning and execution
+- can represent:
+  - ordinal filter by `columnIndex`
+  - path-based filter by `pathElements`
+
+Fields:
+
+- `columnIndex`
+  - relational column index
+- `pathElements`
+  - Parquet source path
+- `operator`
+  - filter operator
+- `polyValue`
+  - filter value
+- `dynamicParamIndex`
+  - dynamic parameter index, if present
+
+Helper:
+
+- `makeNested(int startIndex)`
+  - trims a path prefix when a repeated nested scanner already operates inside a deeper group
+
+### `ParquetNativeFilterBuilder`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java`
+
+Responsibilities:
+
+- converts `ParquetAdapterFilter` into native Parquet predicates
+- only for cases that can be mapped safely to primitive non-repeated Parquet leaf fields
+
+### `ParquetSourceReader`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java`
+
+Responsibilities:
+
+- opens the Parquet file
+- builds the projection schema
+- installs native Parquet filter predicates
+- reads row groups and rows
+
+### `AbstractParquetEnumerator`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java`
+
+Responsibilities:
+
+- shared scan lifecycle
+- reader ownership
+- row queue management
+- row expansion
+- exact row-level filtering
+- current row handling
+
+### `ParquetRelEnumerator`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java`
+
+Responsibilities:
+
+- flat/root ordinal-based scan
+- reads projected Parquet fields by ordinal position
+
+### `ParquetNestedNonRepeatedRelEnumerator`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java`
+
+Responsibilities:
+
+- path-based scan without row expansion
+- used when rows still map one-to-one but values must be extracted by Parquet source path
+
+### `ParquetNestedRepeatedRelEnumerator`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java`
+
+Responsibilities:
+
+- repeated nested scan
+- expands one Parquet root row into multiple relational rows by following the configured table path
+
+### `ParquetPathValueExtractor`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPathValueExtractor.java`
+
+Responsibilities:
+
+- extracts a relational value by Parquet source path
+- used by path-based enumerators
+
+### `ParquetTableBinding` and `ParquetColumnBinding`
+
+Files:
+
+- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java`
+- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnBinding.java`
+
+Responsibilities:
+
+- connect relational tables and columns back to the Parquet file and Parquet source paths
+- make path-based filtering possible for normalized tables
+
+## Full Flow
+
+![Schema display](diagrams/filter_flowchart.png)
+
+### Step 1: SQL filter arrives at `ParquetRelTable`
+
+Polypheny calls:
+
+```text
+scan( DataContext dataContext, List<RexNode> polyFilters )
+```
+
+on `ParquetRelTable`.
+
+At this point filters are still generic `RexNode` expressions.
+
+### Step 2: Supported `RexNode` filters are translated
+
+`ParquetRelTable` uses:
+
+```text
+ParquetRelFilterTranslator.translate(...)
+```
+
+This produces `ParquetAdapterFilter` only for supported predicates.
+
+Unsupported predicates are left in `polyFilters` and are not pushed into the adapter.
+
+So the adapter only handles filters it explicitly understands.
+
+### Step 3: Dynamic parameters are resolved and Parquet paths are attached
+
+`ParquetRelTable.resolveFilters(...)` transforms translator output into execution-ready filters.
+
+What happens here:
+
+- if a filter uses a dynamic parameter, the actual runtime value is read from `DataContext`
+- the table binding is used to map the relational column to its Parquet source path
+- a new `ParquetAdapterFilter` is created with:
+  - the original `columnIndex`
+  - the resolved `pathElements`
+  - the concrete value
+
+Example:
+
+```text
+SQL filter:
+quantity > 2
+
+Relational column:
+quantity
+
+Resolved Parquet path:
+["items", "quantity"]
+```
+
+### Step 4: `ParquetRelTable` selects the enumerator
+
+The scan strategy is chosen as follows:
+
+- `ParquetRelEnumerator`
+  - when ordinal scan is safe
+- `ParquetNestedNonRepeatedRelEnumerator`
+  - when values must be read by path but rows remain one-to-one
+- `ParquetNestedRepeatedRelEnumerator`
+  - when the table is generated from a repeated nested path
+
+All selected enumerators receive the resolved `ParquetAdapterFilter` list.
+
+### Step 5: `ParquetSourceReader` builds native Parquet predicates
+
+The reader constructor calls:
+
+```text
+ParquetNativeFilterBuilder.build( schema, filters )
+```
+
+This is the native Parquet optimization layer.
+
+If a filter can be converted safely to a native Parquet predicate, it is added to the reader.
+If not, it is ignored at the native level.
+
+Ignored here does not mean ignored completely.
+Exact filtering may still happen later inside the enumerator.
+
+### Step 6: `ParquetSourceReader` reads Parquet rows
+
+The reader:
+
+- applies native Parquet row-group filtering if available
+- reads projected Parquet rows
+- returns `Group` objects
+
+### Step 7: `AbstractParquetEnumerator` performs exact row filtering
+
+`AbstractParquetEnumerator.moveNext()` does the common loop:
+
+- read next Parquet group from the reader
+- expand it if needed
+- apply exact row-level filtering with `accept(...)`
+- extract relational row values
+
+Filtering here is exact for emitted rows.
+
+### Step 8: Enumerator-specific behavior
+
+#### `ParquetRelEnumerator`
+
+- no row expansion
+- reads values by ordinal position from the projection schema
+
+#### `ParquetNestedNonRepeatedRelEnumerator`
+
+- no row expansion
+- reads values by Parquet source path using `ParquetPathValueExtractor`
+
+#### `ParquetNestedRepeatedRelEnumerator`
+
+- expands one root row into multiple nested groups using `expandRow(...)`
+- applies filters to each expanded nested row
+- when evaluating path-based filters, it trims the already-resolved table prefix using:
+
+```text
+filter.makeNested( tablePath.size() )
+```
+
+This allows a filter like:
+
+```text
+["items", "product_id"]
+```
+
+to become:
+
+```text
+["product_id"]
+```
+
+when the scanner already operates inside one `items` group.
+
+## Supported Filter Cases
+
+### Supported SQL operators
+
+Supported operators:
+
+- `=`
+- `!=`
+- `>`
+- `>=`
+- `<`
+- `<=`
+
+These map to:
+
+- `EQUALS`
+- `NOT_EQUALS`
+- `GREATER_THAN`
+- `GREATER_THAN_OR_EQUAL`
+- `LESS_THAN`
+- `LESS_THAN_OR_EQUAL`
+
+### Supported value operands
+
+Supported right-hand side values:
+
+- literals
+- dynamic parameters
+
+Example:
+
+```sql
+WHERE total_price > 100
+WHERE total_price > ?
+```
+
+### Supported data types for translation
+
+`ParquetRelFilterTranslator` currently accepts:
+
+- `BOOLEAN`
+- `VARCHAR`
+- `CHAR`
+- `TEXT`
+- `INTEGER`
+- `BIGINT`
+- `FLOAT`
+- `DOUBLE`
+- `DATE`
+- `TIME`
+- `TIMESTAMP`
+
+String-like types only support:
+
+- `=`
+- `!=`
+
+Numeric and temporal types support all comparison operators listed above.
+
+### Supported native Parquet pushdown
+
+Native Parquet filtering is supported only for:
+
+- primitive leaf fields
+- non-repeated paths
+
+This includes:
+
+- top-level primitive fields addressed by `columnIndex`
+- nested primitive non-repeated leaf fields addressed by `pathElements`
+
+Examples that may be pushed down:
+
+```text
+order_id = 1001
+shipping_address.city = 'Zurich'
+total_price > 100
+```
+
+### Supported exact enumerator-level filtering
+
+Exact row filtering is supported for:
+
+- flat/root ordinal scans
+- path-based non-repeated scans
+- repeated nested scans
+
+Examples:
+
+```text
+order_id = 1001
+shipping_address.city = 'Zurich'
+items.product_id = 123
+items.discounts.amount > 5
+```
+
+Even when a filter cannot be pushed down natively, it may still be applied exactly by the enumerator.
+
+## Unsupported Native Pushdown Cases
+
+These cases are not converted to native Parquet predicates:
+
+- group fields
+- repeated paths
+- paths that do not resolve to a primitive leaf field
+- unsupported value/type/operator combinations
+
+Examples:
+
+```text
+shipping_address
+items
+items.product_id          // repeated path
+items.discounts.amount    // repeated path
+```
+
+For repeated paths this is intentional:
+
+- Parquet predicate validation does not support repeated columns for native filtering
+- exact filtering is still done later by the enumerator
+
+## Unsupported Translation Cases
+
+The adapter does not translate every SQL predicate.
+
+Examples of unsupported translation:
+
+- complex expressions
+- predicates with unsupported operators
+- predicates that are not simple binary column/value comparisons
+- unsupported field types
+
+If translation fails:
+
+- the adapter does not create `ParquetAdapterFilter`
+- the filter remains outside the Parquet pushdown path
+
+
+## Exactness Guarantees
+
+### Native Parquet filtering
+
+Native filtering is only an optimization.
+
+It can:
+
+- skip row groups
+- reduce work before row materialization
+
+It must not be treated as the only filtering step for normalized or nested tables.
+
+
+### Enumerator filtering
+
+Enumerator filtering is the exact filtering stage for emitted relational rows.
+
+This is especially important for:
+
+- path-based non-repeated nested values
+- repeated nested child tables
+
+
+## Examples
+
+### Example 1: Flat/root filter
+
+```sql
+SELECT *
+FROM parquetrelational1__orders
+WHERE order_id = 1001;
+```
+
+Flow:
+
+- translated to `ParquetAdapterFilter`
+- native Parquet predicate may be created
+- `ParquetRelEnumerator` performs exact row filtering
+
+
+### Example 2: Non-repeated nested filter
+
+```sql
+SELECT *
+FROM parquetrelational1__orders
+WHERE shipping_address_city = 'Zurich';
+```
+
+Conceptually the binding resolves the filter to:
+
+```text
+["shipping_address", "city"]
+```
+
+Flow:
+
+- path-based `ParquetAdapterFilter` is created
+- native pushdown may be possible if the path is non-repeated and resolves to a primitive leaf
+- `ParquetNestedNonRepeatedRelEnumerator` performs exact row filtering by path
+
+
+### Example 3: Repeated nested child-table filter
+
+```sql
+SELECT *
+FROM parquetrelational1__orders__items
+WHERE product_id = 123;
+```
+
+Binding resolves this to:
+
+```text
+["items", "product_id"]
+```
+
+Flow:
+
+- native Parquet pushdown is not used because the path is repeated
+- `ParquetNestedRepeatedRelEnumerator` expands `items[*]`
+- exact filtering is applied to each emitted item row
+
+
+## Current Design Summary
+
+- `ParquetRelFilterTranslator` decides which SQL predicates can enter the adapter filtering path
+- `ParquetRelTable` resolves dynamic values and Parquet source paths
+- `ParquetNativeFilterBuilder` provides optional native row-group pruning
+- `AbstractParquetEnumerator` provides exact row-level filtering
+- repeated nested paths are filtered exactly in the enumerator, not natively in Parquet
diff --git a/plugins/parquet-adapter/docs/nested_fields.md b/plugins/parquet-adapter/docs/nested_fields.md
index cf1207b8ff..8955bc9645 100644
--- a/plugins/parquet-adapter/docs/nested_fields.md
+++ b/plugins/parquet-adapter/docs/nested_fields.md
@@ -12,7 +12,7 @@ In NORMALIZED mode, Polypheny reads the Parquet schema and recursively splits it
 
 All relational Parquet table names are adapter-prefixed, both flat and normalized.
 
-_Example:_ `parquetrelational1__orders`, not just `orders`. This allows create different adapters for flat and normalized schema modes.
+_Example:_ `pn__orders`, not just `orders`. This allows creating different adapters for flat and normalized schema modes.
 
 ## Example of Parquet shape
 
@@ -52,25 +52,25 @@ orders.parquet
 
 **_orders_**
 
-| polypheny_row_id | order_id | customer |
-|------------------|----------|----------|
-| 1                | 1001     | Alice    |
-| 2                | 1002     | Bob      |
+| __polypheny_row_id | order_id | customer |
+|--------------------|----------|----------|
+| 0                  | 1001     | Alice    |
+| 1                  | 1002     | Bob      |
 
 **_orders__items_**
 
-| polypheny_row_id | polypheny_parent_row_id | polypheny_elem_ordinal | product_id | quantity |
-|------------------|-------------------------|------------------------|------------|----------|
-| 10               | 1                       | 0                      | A-1        | 2        |
-| 11               | 1                       | 1                      | B-5        | 1        |
-| 12               | 2                       | 0                      | C-9        | 4        |
+| __polypheny_row_id | __polypheny_parent_row_id | __polypheny_elem_ordinal | product_id | quantity |
+|--------------------|---------------------------|--------------------------|------------|----------|
+| 0/items[0]         | 0                         | 0                        | A-1        | 2        |
+| 0/items[1]         | 0                         | 1                        | B-5        | 1        |
+| 1/items[0]         | 1                         | 0                        | C-9        | 4        |
 
 **_orders__items__discounts_**
 
-| polypheny_row_id | polypheny_parent_row_id | polypheny_elem_ordinal | code   | amount |
-|------------------|-------------------------|------------------------|--------|--------|
-| 20               | 10                      | 0                      | SUMMER | 5.00   |
-| 21               | 10                      | 1                      | VIP    | 2.00   |
+| __polypheny_row_id        | __polypheny_parent_row_id | __polypheny_elem_ordinal | code   | amount |
+|---------------------------|---------------------------|--------------------------|--------|--------|
+| 0/items[0]/discounts[0]   | 0/items[0]               | 0                        | SUMMER | 5.00   |
+| 0/items[0]/discounts[1]   | 0/items[0]               | 1                        | VIP    | 2.00   |
 
 ```text
 orders.parquet
@@ -92,50 +92,57 @@ JOIN orders__items i
 ### Flat result
 
 ```text
-parquetrelational1__orders -> [order_id, customer_id, status, ...]
+pf__orders -> [order_id, customer_id, status, ...]
 ```
 
 ### Normalized result
 
 ```text
-parquetrelational1__orders -> [order_id, customer_id, status, total_price]
-parquetrelational1__orders__items -> [order_item_id, product_id, quantity, price]
-parquetrelational1__orders__items__discounts -> [code, amount]
+pn__orders -> [order_id, customer_id, status, total_price]
+pn__orders__items -> [__polypheny_row_id, __polypheny_parent_row_id, __polypheny_elem_ordinal, order_item_id, product_id, quantity, price]
+pn__orders__items__discounts -> [__polypheny_row_id, __polypheny_parent_row_id, __polypheny_elem_ordinal, code, amount]
 ```
 
 ## Synthetic Columns
 
 - Normalized tables include visible _synthetic key columns_:
-  - polypheny_row_id
-  - polypheny_parent_row_id
-  - polypheny_elem_ordinal
+  - __polypheny_row_id
+  - __polypheny_parent_row_id
+  - __polypheny_elem_ordinal
 - Root tables have:
-  - polypheny_row_id
+  - __polypheny_row_id
 - Child tables have:
-  - polypheny_row_id
-  - polypheny_parent_row_id
-  - polypheny_elem_ordinal - for repeated children
+  - __polypheny_row_id
+  - __polypheny_parent_row_id
+  - __polypheny_elem_ordinal
 - Non-repeated child tables still have:
-  - polypheny_parent_row_id
-  - polypheny_elem_ordinal is always 0
+  - __polypheny_parent_row_id
+  - __polypheny_elem_ordinal is always 0
+
+Synthetic row ids are generated as deterministic structural path identifiers.
+Examples:
+
+- root row: `0`
+- repeated child row: `0/items[1]`
+- deeper repeated child row: `0/items[1]/discounts[0]`
 
 **_Sql Example:_**
 
 ```sql
 SELECT o.order_id, i.product_id, i.quantity
-FROM parquetrelational1__orders o
-JOIN parquetrelational1__orders__items i
-  ON i.polypheny_parent_row_id = o.polypheny_row_id;
+FROM pn__orders o
+JOIN pn__orders__items i
+  ON i.__polypheny_parent_row_id = o.__polypheny_row_id;
 
 ```
 
-For deeper nesting, the same rule applies at each level. A row in parquetrelational1__orders__items__discounts points to its parent item row, not directly to the root order row:
+For deeper nesting, the same rule applies at each level. A row in pn__orders__items__discounts points to its parent item row, not directly to the root order row:
 
 ```sql
 SELECT i.product_id, d.code, d.amount
-FROM parquetrelational1__orders__items i
-JOIN parquetrelational1__orders__items__discounts d
-  ON d.polypheny_parent_row_id = i.polypheny_row_id;
+FROM pn__orders__items i
+JOIN pn__orders__items__discounts d
+  ON d.__polypheny_parent_row_id = i.__polypheny_row_id;
 ```
 
 ## BINDINGS
@@ -164,9 +171,9 @@ Bindings describe:
 
 | Generated column          | Source Parquet field path                              | Column role           |
 |---------------------------|--------------------------------------------------------|-----------------------|
-| `polypheny_row_id`        | generated by Polypheny                                 | synthetic primary key |
-| `polypheny_parent_row_id` | generated by Polypheny from parent item row            | synthetic parent key  |
-| `polypheny_elem_ordinal`  | generated by Polypheny from repeated discount position | synthetic ordinal     |
+| `__polypheny_row_id`      | generated by Polypheny from row structure              | synthetic primary key |
+| `__polypheny_parent_row_id` | generated by Polypheny from parent item row          | synthetic parent key  |
+| `__polypheny_elem_ordinal` | generated by Polypheny from repeated discount position | synthetic ordinal     |
 | `code`                    | `items.discounts.code`                                 | data                  |
 | `amount`                  | `items.discounts.amount`                               | data                  |
 
@@ -222,7 +229,7 @@ Example:
 
 ```text
 sourceUrl: orders.parquet
-parentTableName: parquetrelational1__orders
+parentTableName: pn__orders
 sourcePathElements: ["items"]
 
 columnPaths:
@@ -251,6 +258,10 @@ Contains the main normalization logic. It builds ParquetNormalizedSchema with:
 
 - `Map<String, List<ExportedColumn>>` tables
 - `Map<String, DiscoveredTableBinding>` bindings
+- Adds normalized synthetic columns:
+  - `__polypheny_row_id`
+  - `__polypheny_parent_row_id`
+  - `__polypheny_elem_ordinal`
 
 ### ParquetNormalizedSchema
 
@@ -276,16 +287,29 @@ Saves and restores ParquetTableBinding metadata through adapter settings.
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\execution\AbstractParquetEnumerator.java`
 Shared base for relational scans.
 
+- Manages reader lifecycle, row iteration, and row queueing
+- Supports turning one input Parquet row into zero, one, or many output relational rows
+- Applies exact row-level filtering after native Parquet row-group pruning
+- Reused by flat, non-repeated nested, and repeated nested relational scans
+
 ### ParquetNestedRepeatedRelEnumerator
 
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetNestedRepeatedRelEnumerator.java`
 Used for tables that do not have their own Parquet file, but are created from a nested group inside a Parquet file.
 
+- Expands one root Parquet row into multiple relational rows by following `tablePath`
+- Creates hierarchical synthetic row ids such as `0/items[1]/discounts[0]`
+- Reads data columns by Parquet path and synthetic columns from execution metadata
+
 ### ParquetNestedNonRepeatedRelEnumerator
 
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetNestedNonRepeatedRelEnumerator.java`
 Used to handle virtual table that was created from nested non-repeated types.
 
+- Keeps one relational row per root Parquet row
+- Wraps rows into execution-time virtual rows with synthetic metadata
+- Reads data columns by Parquet path and synthetic columns from execution metadata
+
 ### ParquetRelationalSource
 
 `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\ParquetRelationalSource.java`
@@ -313,6 +337,7 @@ Current scan strategies:
 
 `ParquetRelTable` uses `ParquetTableBinding` to decide which scanner to create.
 This allows Polypheny to read both flat tables and generated normalized tables from the same original Parquet file.
+Synthetic id columns are not stored in Parquet files; they are exposed during scan through virtual execution rows.
 
 ### ParquetNamespace
 
@@ -352,6 +377,36 @@ If a filter cannot be pushed down safely, it is ignored at the native Parquet le
 
 This means native filtering is used as an optimization, while exact correctness is preserved by enumerator-level filtering.
 
+
+### VirtualGroup
+
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\execution\VirtualGroup.java`
+
+Execution-time wrapper around a Parquet `Group`.
+
+- Delegates all `Group` operations to the original Parquet row/group
+- Carries `GroupMetadata`
+- Used by normalized scans so data columns can still be read from the original Parquet structure while synthetic columns are generated from metadata
+
+### GroupMetadata
+
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\execution\GroupMetadata.java`
+
+Stores synthetic metadata for normalized relational rows:
+
+- `rowId`
+- `parentRowId`
+- `ordinal`
+
+These values are generated during scan and are used to expose:
+
+- `__polypheny_row_id`
+- `__polypheny_parent_row_id`
+- `__polypheny_elem_ordinal`
+
+
+
+
 ## Flow
 
 ![Schema display](diagrams/nested_fields_flow_simple.png)
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
index ad000f3217..a17ce3ae4e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
@@ -21,8 +21,12 @@
 import org.apache.parquet.example.data.Group;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
+import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.numerical.PolyLong;
 import org.polypheny.db.util.Source;
 
 /**
@@ -33,12 +37,12 @@ public class ParquetNestedNonRepeatedRelEnumerator extends AbstractParquetEnumer
     private final List<ParquetColumnBinding> columnBindings;
 
 
-    public ParquetNestedNonRepeatedRelEnumerator(Source source, AtomicBoolean cancelFlag, List<ParquetColumnBinding> columnBindings ) {
+    public ParquetNestedNonRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, List<ParquetColumnBinding> columnBindings ) {
         this( source, cancelFlag, columnBindings, List.of() );
     }
 
 
-    public ParquetNestedNonRepeatedRelEnumerator(Source source, AtomicBoolean cancelFlag, List<ParquetColumnBinding> columnBindings, List<ParquetAdapterFilter> filters ) {
+    public ParquetNestedNonRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, List<ParquetColumnBinding> columnBindings, List<ParquetAdapterFilter> filters ) {
         // read full root rows from the Parquet file
         super( source, cancelFlag, null, filters, new ParquetPathValueExtractor() );
         this.columnBindings = List.copyOf( columnBindings );
@@ -49,11 +53,28 @@ public ParquetNestedNonRepeatedRelEnumerator(Source source, AtomicBoolean cancel
     protected PolyValue[] extractRow( Group group ) {
         PolyValue[] row = new PolyValue[columnBindings.size()];
         for ( int i = 0; i < columnBindings.size(); i++ ) {
-            // extract value by path
-            // if the binding path is: List.of("shipping_address", "city") it walks: "root -> shipping_address -> city"
-            row[i] = valueExtractor.extractValue( group, columnBindings.get( i ).sourcePathElements() );
+            row[i] = extractValueByColumnRole( (VirtualGroup) group, columnBindings.get( i ) );
         }
         return row;
     }
 
+
+    @Override
+    protected List<Group> expandRow( Group group ) {
+        var virtualGroup = new VirtualGroup( group, String.valueOf( reader.getCurrentRowNumber() ), null, 0 );
+        return super.expandRow( virtualGroup );
+    }
+
+
+    private PolyValue extractValueByColumnRole( VirtualGroup virtualGroup, ParquetColumnBinding binding ) {
+        return switch ( binding.role() ) {
+            case DATA -> valueExtractor.extractValue( virtualGroup, binding.sourcePathElements() );
+            case PRIMARY_KEY -> PolyString.of( virtualGroup.getMetadata().getRowId() );
+            case PARENT_KEY -> virtualGroup.getMetadata().getParentRowId() == null
+                    ? PolyNull.NULL
+                    : PolyString.of( virtualGroup.getMetadata().getParentRowId() );
+            case ORDINAL -> PolyLong.of( virtualGroup.getMetadata().getOrdinal() );
+        };
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
index b507a3d78f..916c74322e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
@@ -16,6 +16,7 @@
 
 package org.polypheny.db.adapter.parquet.relational.execution;
 
+import java.util.ArrayList;
 import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.parquet.example.data.Group;
@@ -24,9 +25,13 @@
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
+import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.numerical.PolyLong;
 import org.polypheny.db.util.Source;
 
 /**
@@ -38,12 +43,12 @@ public class ParquetNestedRepeatedRelEnumerator extends AbstractParquetEnumerato
     private final List<ParquetColumnBinding> columnBindings;
 
 
-    public ParquetNestedRepeatedRelEnumerator(Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings ) {
+    public ParquetNestedRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings ) {
         this( source, cancelFlag, binding, columnBindings, List.of() );
     }
 
 
-    public ParquetNestedRepeatedRelEnumerator(Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings, List<ParquetAdapterFilter> filters ) {
+    public ParquetNestedRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings, List<ParquetAdapterFilter> filters ) {
         super( source, cancelFlag, null, filters, new ParquetPathValueExtractor() );
 
         this.tablePath = binding.sourcePathElements();
@@ -62,7 +67,8 @@ public ParquetNestedRepeatedRelEnumerator(Source source, AtomicBoolean cancelFla
      */
     @Override
     protected List<Group> expandRow( Group group ) {
-        return resolveNested( group, group.getType(), 0 );
+        var virtualGroup = new VirtualGroup( group, String.valueOf( reader.getCurrentRowNumber() ), null, 0 );
+        return resolveNested( virtualGroup, group.getType(), 0 );
     }
 
 
@@ -70,8 +76,7 @@ protected List<Group> expandRow( Group group ) {
     protected PolyValue[] extractRow( Group group ) {
         PolyValue[] row = new PolyValue[columnBindings.size()];
         for ( int i = 0; i < columnBindings.size(); i++ ) {
-            var path = columnBindings.get( i ).sourcePathElements();
-            row[i] = valueExtractor.extractValue( group, path.subList( tablePath.size(), path.size() ) );
+            row[i] = extractValueByColumnRole( (VirtualGroup) group, columnBindings.get( i ) );
         }
         return row;
     }
@@ -79,17 +84,19 @@ protected PolyValue[] extractRow( Group group ) {
 
     @Override
     protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
-        return super.extractValue( group, filter.makeNested( tablePath.size() ) );
+        var virtualGroup = (VirtualGroup) group;
+        var binding = columnBindings.get( filter.columnIndex() );
+        return extractValueByColumnRole(virtualGroup, binding);
     }
 
 
-    private List<Group> resolveNested( Group group, GroupType groupType, int pathIndex ) {
+    private List<Group> resolveNested( VirtualGroup virtualGroup, GroupType groupType, int pathIndex ) {
         if ( pathIndex >= tablePath.size() ) {
-            return List.of( group );
+            return List.of( virtualGroup );
         }
 
         int fieldIndex = fieldIndex( groupType, tablePath.get( pathIndex ) );
-        if ( fieldIndex < 0 || group.getFieldRepetitionCount( fieldIndex ) == 0 ) {
+        if ( fieldIndex < 0 || virtualGroup.getFieldRepetitionCount( fieldIndex ) == 0 ) {
             return List.of();
         }
 
@@ -98,16 +105,32 @@ private List<Group> resolveNested( Group group, GroupType groupType, int pathInd
             return List.of();
         }
 
-        List<Group> groups = new java.util.ArrayList<>();
-        int count = group.getFieldRepetitionCount( fieldIndex );
+        List<Group> groups = new ArrayList<>();
+        int count = virtualGroup.getFieldRepetitionCount( fieldIndex );
         for ( int occurrence = 0; occurrence < count; occurrence++ ) {
-            Group child = group.getGroup( fieldIndex, occurrence );
-            groups.addAll( resolveNested( child, fieldType.asGroupType(), pathIndex + 1 ) );
+            Group child = virtualGroup.getGroup( fieldIndex, occurrence );
+
+            String childRowId = virtualGroup.getMetadata().getRowId() + "/" + tablePath.get( pathIndex ) + "[" + occurrence + "]";
+            VirtualGroup childGroup = new VirtualGroup( child, childRowId, virtualGroup.getMetadata().getRowId(), occurrence );
+            groups.addAll( resolveNested( childGroup, fieldType.asGroupType(), pathIndex + 1 ) );
         }
         return groups;
     }
 
 
+    private PolyValue extractValueByColumnRole( VirtualGroup virtualGroup, ParquetColumnBinding binding ) {
+        return switch ( binding.role() ) {
+            case DATA -> {
+                var path = binding.sourcePathElements();
+                yield valueExtractor.extractValue( virtualGroup, path.subList( tablePath.size(), path.size() ) );
+            }
+            case PRIMARY_KEY -> PolyString.of( virtualGroup.getMetadata().getRowId() );
+            case PARENT_KEY -> virtualGroup.getMetadata().getParentRowId() == null ? PolyNull.NULL : PolyString.of( virtualGroup.getMetadata().getParentRowId() );
+            case ORDINAL -> PolyLong.of( virtualGroup.getMetadata().getOrdinal() );
+        };
+    }
+
+
     private int fieldIndex( GroupType groupType, String fieldName ) {
         for ( int i = 0; i < groupType.getFieldCount(); i++ ) {
             if ( groupType.getType( i ).getName().equals( fieldName ) ) {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaNormalizer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaNormalizer.java
index a06a8eaf36..697094136c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaNormalizer.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaNormalizer.java
@@ -35,6 +35,7 @@
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
 import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.PolyType;
 
 /**
  * Class turns Parquet nested schema into multiple relational tables for normalized mode:
@@ -123,7 +124,8 @@ private void addNormalizedTable(
             ParquetNormalizedSchema normalizedSchema ) {
         // maps relational column name to Parquet source path
         Map<String, List<String>> columnPaths = new LinkedHashMap<>();
-
+        // add synthetic columns to normalized schema
+        normalizedSchema.addColumns( tableName, syntheticColumns( fileName, tableName, parentTableName, normalizedSchema, tablePath, columnPaths ) );
         collectNormalizedColumns( fields, sourceUrl, fileName, tableName, tableName, tablePath, columnPaths, normalizedSchema );
         // each generated table gets table definition and binding metadata
         normalizedSchema.addBinding( tableName, new DiscoveredTableBinding( sourceUrl, parentTableName, tablePath, columnPaths ) );
@@ -154,8 +156,7 @@ private void collectNormalizedColumns(
             ParquetNormalizedSchema normalizedSchema ) {
 
         Map<String, Integer> seenColumnNames = new HashMap<>();
-        for ( int fieldIndex = 0; fieldIndex < fields.size(); fieldIndex++ ) {
-            Type field = fields.get( fieldIndex );
+        for ( Type field : fields ) {
             List<String> sourcePath = appendPath( currentPath, field.getName() );
 
             if ( field.isRepetition( Type.Repetition.REPEATED ) ) {
@@ -173,7 +174,7 @@ private void collectNormalizedColumns(
             // primitive - add field as a column to the current table
             if ( field.isPrimitive() ) {
                 String columnName = uniqueColumnName( seenColumnNames, ParquetNameNormalizer.normalizeFieldName( field.getName() ) );
-                normalizedSchema.addColumns( tableName, List.of( exportedColumn( field, fileName, tableName, columnName, sourcePath, fieldIndex ) ) );
+                normalizedSchema.addColumns( tableName, List.of( exportedColumn( field, fileName, tableName, columnName, sourcePath, nextPosition( normalizedSchema, tableName ) ) ) );
                 columnPaths.put( columnName, sourcePath );
                 continue;
             }
@@ -194,9 +195,12 @@ private void addRepeatedPrimitiveTable(
             List<String> sourcePath,
             ParquetNormalizedSchema normalizedSchema ) {
         String columnName = ParquetNameNormalizer.normalizeFieldName( field.getName() );
-        List<ExportedColumn> columns = List.of( exportedColumn( field, fileName, tableName, columnName, sourcePath, 0 ) );
+        Map<String, List<String>> columnPaths = new HashMap<>();
+        List<ExportedColumn> columns = new ArrayList<>( syntheticColumns( fileName, tableName, parentTableName, normalizedSchema, sourcePath, columnPaths ) );
+        columns.add( exportedColumn( field, fileName, tableName, columnName, sourcePath, columns.size() ) );
+        columnPaths.put( columnName, sourcePath );
         normalizedSchema.addColumns( tableName, columns );
-        normalizedSchema.addBinding( tableName, new DiscoveredTableBinding( sourceUrl, parentTableName, sourcePath, Map.of( columnName, sourcePath ) ) );
+        normalizedSchema.addBinding( tableName, new DiscoveredTableBinding( sourceUrl, parentTableName, sourcePath, columnPaths ) );
     }
 
 
@@ -229,6 +233,43 @@ private ExportedColumn exportedColumn( Type field, String fileName, String table
     }
 
 
+    private List<ExportedColumn> syntheticColumns( String fileName, String tableName, String parentTableName, ParquetNormalizedSchema normalizedSchema, List<String> sourcePath, Map<String, List<String>> columnPaths ) {
+        List<ExportedColumn> columns = new ArrayList<>();
+        columns.add( syntheticColumn( fileName, tableName, ParquetSyntheticColumns.ROW_ID, PolyType.VARCHAR, nextPosition( normalizedSchema, tableName ), true ) );
+        columnPaths.put( ParquetSyntheticColumns.ROW_ID, sourcePath );
+        if ( parentTableName != null ) {
+            columns.add( syntheticColumn( fileName, tableName, ParquetSyntheticColumns.PARENT_ROW_ID, PolyType.VARCHAR, nextPosition( normalizedSchema, tableName ) + columns.size(), false ) );
+            columnPaths.put( ParquetSyntheticColumns.PARENT_ROW_ID, sourcePath );
+            columns.add( syntheticColumn( fileName, tableName, ParquetSyntheticColumns.ELEM_ORDINAL, PolyType.BIGINT, nextPosition( normalizedSchema, tableName ) + columns.size(), false ) );
+            columnPaths.put( ParquetSyntheticColumns.ELEM_ORDINAL, sourcePath );
+        }
+        return columns;
+    }
+
+
+    private ExportedColumn syntheticColumn( String fileName, String tableName, String columnName, PolyType type, int position, boolean primary ) {
+        return new ExportedColumn(
+                columnName,
+                type,
+                null,
+                null,
+                null,
+                null,
+                null,
+                false,
+                fileName,
+                tableName,
+                columnName,
+                position,
+                primary );
+    }
+
+
+    private int nextPosition( ParquetNormalizedSchema normalizedSchema, String tableName ) {
+        return normalizedSchema.getTables().getOrDefault( tableName, List.of() ).size();
+    }
+
+
     /**
      * Avoids duplicate column names inside the same generated table
      *
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSyntheticColumns.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSyntheticColumns.java
new file mode 100644
index 0000000000..6e92f7d47a
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSyntheticColumns.java
@@ -0,0 +1,32 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+/**
+ * Reserved synthetic column names used by normalized Parquet tables.
+ */
+public final class ParquetSyntheticColumns {
+
+    public static final String ROW_ID = "__polypheny_row_id";
+    public static final String PARENT_ROW_ID = "__polypheny_parent_row_id";
+    public static final String ELEM_ORDINAL = "__polypheny_elem_ordinal";
+
+
+    private ParquetSyntheticColumns() {
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java
index 97c371ef2d..075a581fc6 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java
@@ -80,7 +80,11 @@ public static ParquetTableBinding createTableBindingFromColumnPaths( String sour
         // create ParquetColumnBinding object for each column and store in map by id
         table.columns.forEach( column -> columnBindings.put(
                 column.id,
-                new ParquetColumnBinding( column.id, column.name, ParquetColumnRole.DATA, columnPaths.getOrDefault( column.name, List.of( column.name ) ) ) ) );
+                new ParquetColumnBinding(
+                        column.id,
+                        column.name,
+                        inferRole( column.name ),
+                        inferSourcePath( column.name, columnPaths ) ) ) );
 
         // create table level binding
         return new ParquetTableBinding(
@@ -100,4 +104,22 @@ public ParquetColumnBinding getColumnBinding( long columnId ) {
         return columnsByColumnId.get( columnId );
     }
 
+
+    private static ParquetColumnRole inferRole( String columnName ) {
+        return switch ( columnName ) {
+            case ParquetSyntheticColumns.ROW_ID -> ParquetColumnRole.PRIMARY_KEY;
+            case ParquetSyntheticColumns.PARENT_ROW_ID -> ParquetColumnRole.PARENT_KEY;
+            case ParquetSyntheticColumns.ELEM_ORDINAL -> ParquetColumnRole.ORDINAL;
+            default -> ParquetColumnRole.DATA;
+        };
+    }
+
+
+    private static List<String> inferSourcePath( String columnName, Map<String, List<String>> columnPaths ) {
+        if ( columnPaths.containsKey( columnName ) ) {
+            return columnPaths.get( columnName );
+        }
+        return List.of();
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
index 8a1980afb1..b3a4fa1d51 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
@@ -164,7 +164,7 @@ private boolean matches( Group group, ParquetAdapterFilter filter ) {
         }
 
         // this row does not have a value for that Parquet field
-        if ( group.getFieldRepetitionCount( filter.columnIndex() ) == 0 ) {
+        if ( filter.pathElements().isEmpty() && group.getFieldRepetitionCount( filter.columnIndex() ) == 0 ) {
             return false;
         }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/GroupMetadata.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/GroupMetadata.java
new file mode 100644
index 0000000000..45bb0f6261
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/GroupMetadata.java
@@ -0,0 +1,33 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.execution;
+
+import lombok.Getter;
+
+@Getter
+public class GroupMetadata {
+    private final String rowId;
+    private final String parentRowId;
+    private final long ordinal;
+
+    public GroupMetadata(String rowId, String parentRowId, long ordinal) {
+        this.rowId = rowId;
+        this.parentRowId = parentRowId;
+        this.ordinal = ordinal;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/VirtualGroup.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/VirtualGroup.java
new file mode 100644
index 0000000000..a7be9361f9
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/VirtualGroup.java
@@ -0,0 +1,179 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.execution;
+
+import lombok.Getter;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.NanoTime;
+import org.apache.parquet.io.api.Binary;
+import org.apache.parquet.io.api.RecordConsumer;
+import org.apache.parquet.schema.GroupType;
+
+/**
+ * Class contains metadata information for virtual table rows
+ * created from nested fields
+ */
+@Getter
+public class VirtualGroup extends Group {
+
+    private final Group source;
+    private final GroupMetadata metadata;
+
+    public VirtualGroup( Group source, String rowId, String parentRowId, long ordinal) {
+        this.source = source;
+        this.metadata = new GroupMetadata(rowId, parentRowId, ordinal);
+    }
+
+    // region Group methods delegation
+    @Override
+    public void add( int fieldIndex, int value ) {
+        source.add( fieldIndex, value );
+    }
+
+
+    @Override
+    public void add( int fieldIndex, long value ) {
+        source.add( fieldIndex, value );
+    }
+
+
+    @Override
+    public void add( int fieldIndex, String value ) {
+        source.add( fieldIndex, value );
+    }
+
+
+    @Override
+    public void add( int fieldIndex, boolean value ) {
+        source.add( fieldIndex, value );
+    }
+
+
+    @Override
+    public void add( int fieldIndex, NanoTime value ) {
+        source.add( fieldIndex, value );
+    }
+
+
+    @Override
+    public void add( int fieldIndex, Binary value ) {
+        source.add( fieldIndex, value );
+    }
+
+
+    @Override
+    public void add( int fieldIndex, float value ) {
+        source.add( fieldIndex, value );
+    }
+
+
+    @Override
+    public void add( int fieldIndex, double value ) {
+        source.add( fieldIndex, value );
+    }
+
+
+    @Override
+    public void add( int fieldIndex, Group value ) {
+        source.add( fieldIndex, value );
+    }
+
+
+    @Override
+    public Group addGroup( int fieldIndex ) {
+        return source.addGroup( fieldIndex );
+    }
+
+
+    @Override
+    public int getFieldRepetitionCount( int fieldIndex ) {
+        return source.getFieldRepetitionCount( fieldIndex );
+    }
+
+
+    @Override
+    public Group getGroup( int fieldIndex, int index ) {
+        return source.getGroup( fieldIndex, index );
+    }
+
+
+    @Override
+    public String getString( int fieldIndex, int index ) {
+        return source.getString( fieldIndex, index );
+    }
+
+
+    @Override
+    public int getInteger( int fieldIndex, int index ) {
+        return source.getInteger( fieldIndex, index );
+    }
+
+
+    @Override
+    public long getLong( int fieldIndex, int index ) {
+        return source.getLong( fieldIndex, index );
+    }
+
+
+    @Override
+    public double getDouble( int fieldIndex, int index ) {
+        return source.getDouble( fieldIndex, index );
+    }
+
+
+    @Override
+    public float getFloat( int fieldIndex, int index ) {
+        return source.getFloat( fieldIndex, index );
+    }
+
+
+    @Override
+    public boolean getBoolean( int fieldIndex, int index ) {
+        return source.getBoolean( fieldIndex, index );
+    }
+
+
+    @Override
+    public Binary getBinary( int fieldIndex, int index ) {
+        return source.getBinary( fieldIndex, index );
+    }
+
+
+    @Override
+    public Binary getInt96( int fieldIndex, int index ) {
+        return source.getInt96( fieldIndex, index );
+    }
+
+
+    @Override
+    public String getValueToString( int fieldIndex, int index ) {
+        return source.getValueToString( fieldIndex, index );
+    }
+
+
+    @Override
+    public GroupType getType() {
+        return source.getType();
+    }
+
+
+    @Override
+    public void writeValue( int field, int index, RecordConsumer recordConsumer ) {
+        source.writeValue( field, index, recordConsumer );
+    }
+    // endregion
+}

From 6dd829978ca6bf2b946cc5dbf6b4ae9adb89b3bd Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Mon, 27 Apr 2026 20:42:41 +0200
Subject: [PATCH 20/46] add support for adapter statistics

---
 .../statistics/AdapterStatisticsProvider.java |  38 ++
 .../statistics/ProvidedColumnStatistics.java  |  45 ++
 .../statistics/ProvidedEntityStatistics.java  |  31 ++
 .../statistics/StatisticsManagerImpl.java     | 123 ++++-
 plugins/parquet-adapter/docs/statistics.md    | 458 ++++++++++++++++++
 .../relational/schema/ParquetRelTable.java    |  79 ++-
 .../shared/io/ParquetSchemaReader.java        |  72 +++
 .../shared/io/ParquetSourceReader.java        |  85 ++--
 .../statistics/ParquetStatisticsReader.java   | 231 +++++++++
 .../ParquetWorkflowExtractSupport.java        |  16 +-
 10 files changed, 1094 insertions(+), 84 deletions(-)
 create mode 100644 core/src/main/java/org/polypheny/db/adapter/statistics/AdapterStatisticsProvider.java
 create mode 100644 core/src/main/java/org/polypheny/db/adapter/statistics/ProvidedColumnStatistics.java
 create mode 100644 core/src/main/java/org/polypheny/db/adapter/statistics/ProvidedEntityStatistics.java
 create mode 100644 plugins/parquet-adapter/docs/statistics.md
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java

diff --git a/core/src/main/java/org/polypheny/db/adapter/statistics/AdapterStatisticsProvider.java b/core/src/main/java/org/polypheny/db/adapter/statistics/AdapterStatisticsProvider.java
new file mode 100644
index 0000000000..b4ff3d3ccd
--- /dev/null
+++ b/core/src/main/java/org/polypheny/db/adapter/statistics/AdapterStatisticsProvider.java
@@ -0,0 +1,38 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.statistics;
+
+
+import java.util.Optional;
+import org.polypheny.db.catalog.entity.logical.LogicalColumn;
+
+
+/**
+ * Interface for metadata-backed statistics.
+ */
+public interface AdapterStatisticsProvider {
+
+    default Optional<ProvidedEntityStatistics> getEntityStatistics( long logicalEntityId ) {
+        return Optional.empty();
+    }
+
+
+    default Optional<ProvidedColumnStatistics> getColumnStatistics( LogicalColumn column, int uniqueValueLimit ) {
+        return Optional.empty();
+    }
+
+}
diff --git a/core/src/main/java/org/polypheny/db/adapter/statistics/ProvidedColumnStatistics.java b/core/src/main/java/org/polypheny/db/adapter/statistics/ProvidedColumnStatistics.java
new file mode 100644
index 0000000000..579470c2d4
--- /dev/null
+++ b/core/src/main/java/org/polypheny/db/adapter/statistics/ProvidedColumnStatistics.java
@@ -0,0 +1,45 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.statistics;
+
+
+import java.util.List;
+import javax.annotation.Nullable;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+/**
+ * Column statistics supplied directly by an adapter.
+ *
+ * @param count estimated values count, null if unavailable
+ * @param min minimum value, null if unavailable
+ * @param max maximum value, null if unavailable
+ * @param uniqueValues bounded unique values, empty if unavailable
+ * @param full whether the unique value set is known to be incomplete
+ */
+public record ProvidedColumnStatistics(
+        @Nullable Long count,
+        @Nullable PolyValue min,
+        @Nullable PolyValue max,
+        List<PolyValue> uniqueValues,
+        boolean full ) {
+
+    public ProvidedColumnStatistics {
+        uniqueValues = uniqueValues == null ? List.of() : List.copyOf( uniqueValues );
+    }
+
+}
diff --git a/core/src/main/java/org/polypheny/db/adapter/statistics/ProvidedEntityStatistics.java b/core/src/main/java/org/polypheny/db/adapter/statistics/ProvidedEntityStatistics.java
new file mode 100644
index 0000000000..6fd65bc1f2
--- /dev/null
+++ b/core/src/main/java/org/polypheny/db/adapter/statistics/ProvidedEntityStatistics.java
@@ -0,0 +1,31 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.statistics;
+
+
+import javax.annotation.Nullable;
+
+
+/**
+ * Entity statistics supplied directly by an adapter.
+ *
+ * @param rowCount row count, or {@code null} if unavailable
+ */
+public record ProvidedEntityStatistics(
+        @Nullable Long rowCount ) {
+
+}
diff --git a/monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java b/monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java
index 9e8cc57e7d..daba2a759b 100644
--- a/monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java
+++ b/monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java
@@ -27,6 +27,7 @@
 import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
+import java.util.Optional;
 import java.util.Queue;
 import java.util.concurrent.ConcurrentHashMap;
 import java.util.concurrent.ConcurrentLinkedQueue;
@@ -39,6 +40,9 @@
 import lombok.Setter;
 import lombok.extern.slf4j.Slf4j;
 import org.polypheny.db.StatisticsManager;
+import org.polypheny.db.adapter.statistics.ProvidedColumnStatistics;
+import org.polypheny.db.adapter.statistics.ProvidedEntityStatistics;
+import org.polypheny.db.adapter.statistics.AdapterStatisticsProvider;
 import org.polypheny.db.algebra.AlgCollations;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.core.AggregateCall;
@@ -51,9 +55,12 @@
 import org.polypheny.db.algebra.operators.OperatorName;
 import org.polypheny.db.algebra.type.AlgDataType;
 import org.polypheny.db.catalog.Catalog;
+import org.polypheny.db.catalog.catalogs.AdapterCatalog;
+import org.polypheny.db.catalog.entity.allocation.AllocationEntity;
 import org.polypheny.db.catalog.entity.logical.LogicalColumn;
 import org.polypheny.db.catalog.entity.logical.LogicalEntity;
 import org.polypheny.db.catalog.entity.logical.LogicalTable;
+import org.polypheny.db.catalog.entity.physical.PhysicalEntity;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.catalog.logistic.EntityType;
 import org.polypheny.db.catalog.snapshot.Snapshot;
@@ -232,8 +239,16 @@ private void reevaluateRowCount() {
         log.debug( "Reevaluate Row Count." );
 
         statisticQueryInterface.getAllRelEntites().forEach( table -> {
-            PolyInteger rowCount = getNumberColumnCount( this.prepareNode( new QueryResult( Catalog.snapshot().getLogicalEntity( table.id ).orElseThrow(), null ), NodeType.ROW_COUNT_TABLE ) );
-            updateRowCountPerEntity( table.id, rowCount.value, MonitoringType.SET_ROW_COUNT );
+            // try to get statistics from adapter first
+            Optional<ProvidedEntityStatistics> provided = getEntityStatistics( table.id );
+            if ( provided.isPresent() && provided.get().rowCount() != null ) {
+                updateRowCountPerEntity( table.id, provided.get().rowCount(), MonitoringType.SET_ROW_COUNT );
+            } else {
+                PolyInteger rowCount = getNumberColumnCount( this.prepareNode( new QueryResult( Catalog.snapshot().getLogicalEntity( table.id ).orElseThrow(), null ), NodeType.ROW_COUNT_TABLE ) );
+                if ( rowCount.value != null ) {
+                    updateRowCountPerEntity( table.id, rowCount.value, MonitoringType.SET_ROW_COUNT );
+                }
+            }
         } );
     }
 
@@ -275,6 +290,13 @@ private StatisticColumn reevaluateField( QueryResult column ) {
             return null;
         }
 
+        // Adapter Statistics
+        // try to get statistics from adapter first
+        Optional<StatisticColumn> provided = getColumnStatistics( column );
+        if ( provided.isPresent() ) {
+            return provided.get();
+        }
+
         if ( column.getColumn().type.getFamily() == PolyTypeFamily.NUMERIC ) {
             return this.reevaluateNumericalColumn( column );
         } else if ( column.getColumn().type.getFamily() == PolyTypeFamily.CHARACTER ) {
@@ -286,6 +308,103 @@ private StatisticColumn reevaluateField( QueryResult column ) {
     }
 
 
+    /**
+     * Gets statistics for logical entity
+     * @param logicalEntityId - long
+     * @return
+     */
+    private Optional<ProvidedEntityStatistics> getEntityStatistics( long logicalEntityId ) {
+        return Catalog.snapshot().alloc().getFromLogical( logicalEntityId ).stream()
+                .map( this::getStatisticsProvider )
+                .filter( Optional::isPresent )
+                .map( provider -> provider.get().getEntityStatistics( logicalEntityId ) )
+                .filter( Optional::isPresent )
+                .filter( statistic -> statistic.get().rowCount() != null )
+                .map( Optional::get )
+                .findFirst();
+    }
+
+
+    /**
+     * Gets column statistics
+     * @param column - query result
+     * @return Statistic Column
+     */
+    private Optional<StatisticColumn> getColumnStatistics( QueryResult column ) {
+        return Catalog.snapshot().alloc().getFromLogical( column.getEntity().id ).stream()
+                .map( this::getStatisticsProvider )
+                .filter( Optional::isPresent )
+                .map( provider -> provider.get().getColumnStatistics( column.getColumn(), buffer ) )
+                .filter( Optional::isPresent )
+                .map( Optional::get )
+                .flatMap( provided -> toStatisticColumn( column, provided ).stream() )
+                .findFirst();
+    }
+
+
+    /**
+     * Converts provider column statistics to statistics column representation
+     * @param column QueryResult
+     * @param provided ProvidedColumnStatistics
+     * @return StatisticColumn
+     */
+    private Optional<StatisticColumn> toStatisticColumn( QueryResult column, ProvidedColumnStatistics provided ) {
+        StatisticColumn statisticColumn;
+        if ( column.getColumn().type.getFamily() == PolyTypeFamily.NUMERIC ) {
+            NumericalStatisticColumn numericalColumn = new NumericalStatisticColumn( column );
+            if ( provided.min() != null ) {
+                numericalColumn.setMin( provided.min().asNumber() );
+            }
+            if ( provided.max() != null ) {
+                numericalColumn.setMax( provided.max().asNumber() );
+            }
+            statisticColumn = numericalColumn;
+        } else if ( column.getColumn().type.getFamily() == PolyTypeFamily.CHARACTER ) {
+            statisticColumn = new AlphabeticStatisticColumn( column );
+        } else if ( PolyType.DATETIME_TYPES.contains( column.getColumn().type ) ) {
+            TemporalStatisticColumn temporalColumn = new TemporalStatisticColumn( column );
+            if ( provided.min() != null ) {
+                temporalColumn.setMin( provided.min().asTemporal() );
+            }
+            if ( provided.max() != null ) {
+                temporalColumn.setMax( provided.max().asTemporal() );
+            }
+            statisticColumn = temporalColumn;
+        } else {
+            return Optional.empty();
+        }
+
+        statisticColumn.setCount( provided.count() == null ? PolyInteger.of( 0 ) : PolyInteger.of( provided.count() ) );
+        statisticColumn.setUniqueValues( provided.uniqueValues() );
+        statisticColumn.setFull( provided.full() );
+        return Optional.of( statisticColumn );
+    }
+
+
+    /**
+     * Look for Adapter Statistic Provider via catalog
+     * @param allocation AllocationEntity
+     * @return AdapterStatisticsProvider
+     */
+    private Optional<AdapterStatisticsProvider> getStatisticsProvider( AllocationEntity allocation ) {
+        Optional<AdapterCatalog> adapterCatalog = Catalog.getInstance().getAdapterCatalog( allocation.adapterId );
+        if ( adapterCatalog.isEmpty() ) {
+            return Optional.empty();
+        }
+
+        List<PhysicalEntity> physicals = adapterCatalog.get().getPhysicalsFromAllocs( allocation.id );
+        if ( physicals == null ) {
+            return Optional.empty();
+        }
+
+        return physicals.stream()
+                .map( physical -> physical.unwrap( AdapterStatisticsProvider.class ) )
+                .filter( Optional::isPresent )
+                .map( Optional::get )
+                .findFirst();
+    }
+
+
     /**
      * Reevaluates a numerical column, with the configured statistics.
      */
diff --git a/plugins/parquet-adapter/docs/statistics.md b/plugins/parquet-adapter/docs/statistics.md
new file mode 100644
index 0000000000..8c48838a7e
--- /dev/null
+++ b/plugins/parquet-adapter/docs/statistics.md
@@ -0,0 +1,458 @@
+# Parquet Statistics
+
+This document describes the statistics-related changes introduced to avoid loading Parquet data eagerly during statistics reevaluation.
+
+## Goal
+
+Before this change, the monitoring statistics subsystem computed table and column statistics by executing normal relational queries.
+For Parquet-backed entities, this could trigger full scans during startup statistics reevaluation.
+
+The new goal is:
+
+1. ask the adapter for metadata-backed statistics first
+2. use Parquet footer metadata when available
+3. fall back to the old query-based path only when necessary
+
+## Main Objects
+
+### `AdapterStatisticsProvider`
+
+File:
+
+`core/src/main/java/org/polypheny/db/adapter/statistics/AdapterStatisticsProvider.java`
+
+Responsibilities:
+
+- generic adapter-side hook for statistics
+- allows an adapter to provide:
+  - entity statistics
+  - column statistics
+
+Methods:
+
+- `getEntityStatistics(long logicalEntityId)`
+- `getColumnStatistics(LogicalColumn column, int uniqueValueLimit)`
+
+Default behavior:
+
+- returns `Optional.empty()`
+- meaning the monitoring subsystem must fall back to query execution
+
+### `ProvidedEntityStatistics`
+
+File:
+
+`core/src/main/java/org/polypheny/db/adapter/statistics/ProvidedEntityStatistics.java`
+
+Responsibilities:
+
+- immutable return type for entity-level statistics
+
+Fields:
+
+- `rowCount`
+  - row count, or `null` if unavailable
+
+### `ProvidedColumnStatistics`
+
+File:
+
+`core/src/main/java/org/polypheny/db/adapter/statistics/ProvidedColumnStatistics.java`
+
+Responsibilities:
+
+- immutable return type for column-level statistics
+
+Fields:
+
+- `count`
+  - non-null value count, or `null` if unavailable
+- `min`
+  - minimum value, or `null` if unavailable
+- `max`
+  - maximum value, or `null` if unavailable
+- `uniqueValues`
+  - bounded unique values, empty if unavailable
+- `full`
+  - whether the unique value list is complete
+
+## Monitoring Integration
+
+### `StatisticsManagerImpl`
+
+File:
+
+`monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java`
+
+Responsibilities after the change:
+
+- still drives startup statistics reevaluation
+- now asks physical entities for adapter-provided statistics before running query-based statistics
+
+New behavior:
+
+- `reevaluateRowCount()`
+  - first calls `getEntityStatistics(...)`
+  - if a provider returns `rowCount`, it uses that value directly
+  - otherwise it falls back to the old `ROW_COUNT_TABLE` query
+
+- `reevaluateField(...)`
+  - first calls `getColumnStatistics(...)`
+  - if a provider returns column statistics, it converts them into the existing `StatisticColumn` model
+  - otherwise it falls back to the old query-based path
+
+Helper methods added:
+
+- `getEntityStatistics(long logicalEntityId)`
+- `getColumnStatistics(QueryResult column)`
+- `getStatisticsProvider(AllocationEntity allocation)`
+- `toStatisticColumn(QueryResult column, ProvidedColumnStatistics provided)`
+
+
+## Parquet Adapter Integration
+
+### `ParquetRelTable`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java`
+
+Responsibilities after the change:
+
+- still exposes Parquet-backed relational tables to Polypheny
+- now also implements `StatisticsProvider`
+
+New fields:
+
+- `schemaReader`
+  - reusable Parquet footer/schema reader
+- `statisticsReader`
+  - Parquet-specific metadata statistics reader
+
+New behavior:
+
+- `getEntityStatistics(long logicalEntityId)`
+  - returns entity statistics only for the matching logical table
+  - delegates to `statisticsReader.getEntityStatistics(isNestedTable())`
+
+- `getColumnStatistics(LogicalColumn column, int uniqueValueLimit)`
+  - returns column statistics only for columns of the matching logical table
+  - delegates to `statisticsReader.getColumnStatistics(column, uniqueValueLimit)`
+
+This makes Parquet physical entities visible to the monitoring subsystem as statistics providers.
+
+### `ParquetSchemaReader`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java`
+
+Responsibilities:
+
+- reads the Parquet footer once
+- stores reusable schema and metadata
+
+Provides:
+
+- `getSchema()`
+  - Parquet schema
+- `getFooter()`
+  - full Parquet footer metadata
+- `getEstimatedRowCount()`
+  - sum of row-group row counts
+
+Purpose:
+
+- centralize footer access for statistics logic
+- avoid reopening or rescanning data when only metadata is needed
+
+### `ParquetStatisticsReader`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java`
+
+Responsibilities:
+
+- provide entity and column statistics from Parquet metadata
+- use table/column bindings to map logical columns to Parquet source paths
+
+Main methods:
+
+- `getEntityStatistics(boolean nestedTable)`
+- `getColumnStatistics(LogicalColumn column, int uniqueValueLimit)`
+
+
+
+## Full Flow
+
+### Step 1: Startup statistics reevaluation begins
+
+`StatisticsManagerImpl.initializeStatisticSettings()` still triggers:
+
+```text
+asyncReevaluateAllStatistics()
+```
+
+when startup statistics are enabled.
+
+### Step 2: Monitoring reevaluates row count and columns
+
+During reevaluation:
+
+- `reevaluateRowCount()` handles table row counts
+- `reevaluateField(...)` handles per-column statistics
+
+### Step 3: Monitoring asks the physical entity for statistics
+
+For each logical entity or column, `StatisticsManagerImpl`:
+
+- resolves allocations from the logical table
+- resolves physical entities from the adapter catalog
+- checks whether the physical entity unwraps to `StatisticsProvider`
+
+If yes:
+
+- entity statistics are requested through `getEntityStatistics(...)`
+- column statistics are requested through `getColumnStatistics(...)`
+
+### Step 4: ParquetRelTable answers the request
+
+If the physical entity is a Parquet relational table:
+
+- `ParquetRelTable` verifies the logical table or column belongs to it
+- delegates the request to `ParquetStatisticsReader`
+
+### Step 5: ParquetStatisticsReader reads footer metadata
+
+`ParquetStatisticsReader` uses:
+
+- `ParquetSchemaReader.getFooter()`
+- `ParquetSchemaReader.getSchema()`
+- `ParquetTableBinding`
+- `ParquetColumnBinding`
+
+to map the logical request to physical Parquet metadata.
+
+### Step 6: Monitoring uses provider statistics or falls back
+
+If a provider result is present:
+
+- `StatisticsManagerImpl` converts it into the existing monitoring statistics model
+
+If not:
+
+- it falls back to the old query-based path through `StatisticQueryProcessor`
+
+## Entity Statistics
+
+### Root table row count
+
+For a root Parquet table:
+
+```text
+row count = sum of row-group row counts
+```
+
+Implementation:
+
+- `ParquetSchemaReader.getEstimatedRowCount()`
+
+This comes directly from the Parquet footer and does not require row scans.
+
+### Nested table row count
+
+For a generated nested table:
+
+```text
+row count != root row count
+```
+
+because one root row can produce multiple nested rows.
+
+Implementation:
+
+- `ParquetStatisticsReader.getEntityStatistics(true)`
+  - uses `estimateNestedRowCount()`
+
+### `estimateNestedRowCount()`
+
+Purpose:
+
+- estimate how many rows a normalized nested table will expose
+
+Logic:
+
+1. iterate over all column bindings of the table
+2. keep only `DATA` columns with non-empty Parquet source paths
+3. call `estimateValueCount(...)` for each source path
+4. take the maximum value count across those data columns
+5. if no usable data column exists, fall back to root row count
+
+Reason for using the maximum:
+
+- data columns of the same generated nested table should usually have the same number of values
+- the maximum is used as a robust estimate of the nested row count
+
+### `estimateValueCount()`
+
+Purpose:
+
+- estimate how many values exist for one Parquet leaf path across the whole file
+
+Logic:
+
+1. iterate over all Parquet row groups
+2. find the matching column chunk for the requested Parquet path
+3. sum `column.getValueCount()` across all row groups
+4. if the column is missing in any block, return `0`
+
+This gives a metadata-based estimate for:
+
+```text
+how many values does this Parquet column contain?
+```
+
+## Column Statistics
+
+### What `getColumnStatistics()` provides
+
+For a logical column, `ParquetStatisticsReader.getColumnStatistics()` tries to provide:
+
+- `count`
+- `min`
+- `max`
+- `uniqueValues`
+- `full`
+
+The result is returned as `ProvidedColumnStatistics`.
+
+### Resolution flow
+
+1. resolve the `ParquetColumnBinding` for the logical column id
+2. verify the column is a real `DATA` column
+3. verify the binding contains a Parquet source path
+4. resolve the primitive Parquet schema type for that path
+5. read row-group metadata for that exact column path
+6. convert metadata values into `PolyValue`
+
+### Metadata source
+
+The actual metadata aggregation happens in:
+
+- `readColumnMetadataStatistics(List<String> sourcePathElements)`
+
+It aggregates across all row groups:
+
+- row count
+- null count when available
+- minimum non-null value
+- maximum non-null value
+
+### Count semantics
+
+If Parquet null statistics are reliable:
+
+```text
+count = rowCount - nullCount
+```
+
+Otherwise:
+
+```text
+count = rowCount
+```
+
+So the count is best understood as:
+
+```text
+best available non-null count estimate from footer metadata
+```
+
+### Min/max semantics
+
+If row-group min/max metadata is available and compatible with the logical Polypheny type:
+
+- `min` is returned
+- `max` is returned
+
+If metadata is missing, unreliable, or conversion fails:
+
+- `min = null`
+- `max = null`
+
+### Type conversion
+
+`toStatisticValue(...)` converts Parquet metadata values into `PolyValue`.
+
+Currently supported families in this conversion path:
+
+- numeric
+- temporal
+- character
+
+## Supported and Unsupported Cases
+
+### Supported well
+
+- root table row count from footer metadata
+- nested table row-count estimation from value counts
+- count/min/max for real Parquet data columns when metadata is available
+- logical-column to Parquet-path mapping through table bindings
+
+### Supported with fallback behavior
+
+- columns whose metadata exists but min/max cannot be converted cleanly
+- columns with missing or unreliable null statistics
+- nested tables whose row count must be estimated from leaf value counts
+
+### Not provided directly from Parquet metadata
+
+- bounded unique value lists
+- exact distinct counts
+- fully semantic statistics for synthetic columns
+
+For these cases, the provider currently returns:
+
+- `uniqueValues = []`
+- `full = true`
+- and may return count-only fallback values with `min = null`, `max = null`
+
+## Synthetic and Non-Data Columns
+
+If a logical column:
+
+- has no binding
+- is not a `DATA` column
+- has an empty source path
+
+then `ParquetStatisticsReader.getColumnStatistics()` does not try to read Parquet leaf metadata for it.
+
+Instead it returns a safe fallback:
+
+- estimated count
+- `min = null`
+- `max = null`
+- empty unique values
+
+This is important for synthetic normalized columns such as:
+
+- `__polypheny_row_id`
+- `__polypheny_parent_row_id`
+- `__polypheny_elem_ordinal`
+
+## Summary
+
+The statistics changes introduce a metadata-first path for Parquet-backed entities:
+
+- a new generic `StatisticsProvider` hook in core
+- monitoring-side integration in `StatisticsManagerImpl`
+- Parquet-side footer readers in `ParquetSchemaReader` and `ParquetStatisticsReader`
+- fallback to the old `StatisticQueryProcessor` path when metadata is unavailable
+
+This reduces unnecessary Parquet row scans during startup statistics reevaluation, especially for:
+
+- table row counts
+- column count/min/max
+
+while preserving compatibility with the existing monitoring statistics subsystem.
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index 7e8963c436..b252788d13 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -16,19 +16,30 @@
 
 package org.polypheny.db.adapter.parquet.relational.schema;
 
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Objects;
+import java.util.Optional;
+import java.util.concurrent.atomic.AtomicBoolean;
+import java.util.stream.IntStream;
 import org.apache.calcite.linq4j.AbstractEnumerable;
 import org.apache.calcite.linq4j.Enumerable;
 import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.DataContext;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedNonRepeatedRelEnumerator;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedRepeatedRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
-import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.adapter.parquet.shared.statistics.ParquetStatisticsReader;
+import org.polypheny.db.adapter.statistics.ProvidedColumnStatistics;
+import org.polypheny.db.adapter.statistics.ProvidedEntityStatistics;
+import org.polypheny.db.adapter.statistics.AdapterStatisticsProvider;
 import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.catalog.entity.logical.LogicalColumn;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
 import org.polypheny.db.plan.AlgCluster;
 import org.polypheny.db.plan.AlgTraitSet;
@@ -39,33 +50,28 @@
 import org.polypheny.db.type.PolyType;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.Source;
-import java.util.ArrayList;
-import java.util.List;
-import java.util.Objects;
-import java.util.concurrent.atomic.AtomicBoolean;
-import java.util.stream.IntStream;
-import org.apache.parquet.schema.MessageType;
 
 /**
  * Physical table wrapper for the relational model.
  * Exposes the Parquet-backed table to Polypheny and ties the planner,
  * scanner, and adapter metadata together.
  */
-public class ParquetRelTable extends PhysicalTable implements FilterableEntity, ScannableEntity, TranslatableEntity {
+public class ParquetRelTable extends PhysicalTable implements FilterableEntity, ScannableEntity, TranslatableEntity, AdapterStatisticsProvider {
 
-    protected final Source source;
-    protected final int[] fieldIndexes;
+    private final Source source;
+    private final int[] fieldIndexes;
+    private final AbstractParquetSource parquetSource;
+    private final ParquetRelFilterTranslator filterTranslator;
+    private final ParquetTableBinding binding;
     private final List<PolyType> fieldTypes;
-    private final MessageType parquetSchema;
-    protected final AbstractParquetSource parquetSource;
-    protected final ParquetRelFilterTranslator filterTranslator;
-    protected final ParquetTableBinding binding;
+    private final ParquetSchemaReader schemaReader;
+    private final ParquetStatisticsReader statisticsReader;
 
 
     /**
      * Creates a Parquet table wrapper from a physical table definition and source binding.
      */
-    public ParquetRelTable(long id, Source source, PhysicalTable table, ParquetTableBinding binding, AbstractParquetSource parquetSource ) {
+    public ParquetRelTable( long id, Source source, PhysicalTable table, ParquetTableBinding binding, AbstractParquetSource parquetSource ) {
         super(
                 id,
                 table.allocationId,
@@ -80,11 +86,43 @@ public ParquetRelTable(long id, Source source, PhysicalTable table, ParquetTable
         this.binding = binding;
         this.fieldIndexes = IntStream.range( 0, table.columns.size() ).toArray();
         this.fieldTypes = columns.stream().map( c -> c.type ).toList();
-        this.parquetSchema = ParquetSourceReader.readSchema( source );
+        this.schemaReader = new ParquetSchemaReader( source );
+        this.statisticsReader = new ParquetStatisticsReader( schemaReader, binding );
         this.parquetSource = parquetSource;
         this.filterTranslator = new ParquetRelFilterTranslator();
     }
 
+    // Statics provider interface functions
+
+
+    /**
+     * Gets table statistics
+     * @param logicalEntityId - logical table id
+     * @return statistics from parquet file
+     */
+    @Override
+    public Optional<ProvidedEntityStatistics> getEntityStatistics( long logicalEntityId ) {
+        if ( logicalId != logicalEntityId ) {
+            return Optional.empty();
+        }
+        return statisticsReader.getEntityStatistics( isNestedTable() );
+    }
+
+
+    /**
+     * Gets column statistics (range values for example)
+     * @param column - logical column
+     * @param uniqueValueLimit - limit
+     * @return column statistics calculated from parquet file
+     */
+    @Override
+    public Optional<ProvidedColumnStatistics> getColumnStatistics( LogicalColumn column, int uniqueValueLimit ) {
+        if ( logicalId != column.tableId ) {
+            return Optional.empty();
+        }
+        return statisticsReader.getColumnStatistics( column, uniqueValueLimit );
+    }
+    // endregion
 
     /**
      * Returns enumerable for FilterableEntity.
@@ -190,6 +228,7 @@ public Enumerator<PolyValue[]> enumerator() {
 
     /**
      * Support parametrized queries
+     *
      * @param dataContext context
      * @param filters filters
      * @return list of parquet filters
@@ -231,7 +270,8 @@ private boolean needsBindingScan( int[] fields ) {
             if ( columnBinding.sourcePathElements().size() > 1 ) {
                 return true;
             }
-            if ( field >= parquetSchema.getFieldCount() || !parquetSchema.getType( field ).getName().equals( columnBinding.sourcePathElements().get( 0 ) ) ) {
+            var schema = schemaReader.getSchema();
+            if ( field >= schema.getFieldCount() || !schema.getType( field ).getName().equals( columnBinding.sourcePathElements().get( 0 ) ) ) {
                 return true;
             }
         }
@@ -240,4 +280,3 @@ private boolean needsBindingScan( int[] fields ) {
 
 }
 
-
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java
new file mode 100644
index 0000000000..80160c93c1
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java
@@ -0,0 +1,72 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io;
+
+import java.net.URI;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.Path;
+import org.apache.parquet.hadoop.ParquetFileReader;
+import org.apache.parquet.hadoop.metadata.BlockMetaData;
+import org.apache.parquet.hadoop.metadata.ParquetMetadata;
+import org.apache.parquet.hadoop.util.HadoopInputFile;
+import org.apache.parquet.schema.MessageType;
+import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.util.Source;
+
+/**
+ * Reads parquet file schema and stores metadata
+ */
+public class ParquetSchemaReader {
+
+    private final ParquetMetadata metadata;
+
+
+    public ParquetSchemaReader( Source source ) {
+        metadata = readFooter( source );
+    }
+
+
+    private static ParquetMetadata readFooter( Source source ) {
+        try {
+            URI uri = source.isFile() ? source.file().toURI() : source.url().toURI();
+            Path path = new Path( uri );
+            Configuration conf = HadoopConfigurationFactory.create( ParquetSourceReader.class.getClassLoader() );
+            var inputFile = HadoopInputFile.fromPath( path, conf );
+            try ( var schemaReader = ParquetFileReader.open( inputFile ) ) {
+                return schemaReader.getFooter();
+            }
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Unable to inspect parquet schema for " + source.path(), e );
+        }
+    }
+
+
+    public MessageType getSchema() {
+        return metadata.getFileMetaData().getSchema();
+    }
+
+    public ParquetMetadata getFooter() {
+        return metadata;
+    }
+
+
+    public long getEstimatedRowCount() {
+        return metadata.getBlocks().stream().mapToLong( BlockMetaData::getRowCount ).sum();
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
index 46e6fbd0b0..a893b72612 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
@@ -29,7 +29,7 @@
 import org.apache.parquet.example.data.Group;
 import org.apache.parquet.example.data.simple.convert.GroupRecordConverter;
 import org.apache.parquet.filter2.compat.FilterCompat;
-import org.apache.parquet.hadoop.metadata.BlockMetaData;
+import org.apache.parquet.hadoop.ParquetFileReader;
 import org.apache.parquet.hadoop.util.HadoopInputFile;
 import org.apache.parquet.io.ColumnIOFactory;
 import org.apache.parquet.io.MessageColumnIO;
@@ -62,24 +62,20 @@ public class ParquetSourceReader implements AutoCloseable {
     private long currentRowNumber = -1;
 
 
-    public ParquetSourceReader(Source source ) {
+    public ParquetSourceReader( Source source ) {
         this( source, null, null, null );
     }
 
 
-    public ParquetSourceReader(Source source, AtomicBoolean cancelFlag, int[] fields, List<ParquetAdapterFilter> filters ) {
+    public ParquetSourceReader( Source source, AtomicBoolean cancelFlag, int[] fields, List<ParquetAdapterFilter> filters ) {
         this.cancelFlag = cancelFlag == null ? new AtomicBoolean( false ) : cancelFlag;
 
         try {
             URI uri = source.isFile() ? source.file().toURI() : source.url().toURI();
             Path path = new Path( uri );
             Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
-            var inputFile = HadoopInputFile.fromPath( path, conf );
 
-            MessageType schema;
-            try ( org.apache.parquet.hadoop.ParquetFileReader schemaReader = org.apache.parquet.hadoop.ParquetFileReader.open( inputFile ) ) {
-                schema = schemaReader.getFooter().getFileMetaData().getSchema();
-            }
+            MessageType schema = new ParquetSchemaReader( source ).getSchema();
 
             int[] projectedFields = buildProjectedFields( fields, schema.getFieldCount() );
             this.projectionSchema = buildProjectionSchema( schema, projectedFields );
@@ -95,7 +91,7 @@ public ParquetSourceReader(Source source, AtomicBoolean cancelFlag, int[] fields
                     .withRecordFilter( recordFilter )
                     .build();
 
-            this.fileReader = org.apache.parquet.hadoop.ParquetFileReader.open( inputFile, readOptions );
+            this.fileReader = ParquetFileReader.open( HadoopInputFile.fromPath( path, conf ), readOptions );
             this.fileReader.setRequestedSchema( projectionSchema );
         } catch ( Exception e ) {
             throw new GenericRuntimeException( "Unable to open parquet file: " + source.path(), e );
@@ -103,6 +99,31 @@ public ParquetSourceReader(Source source, AtomicBoolean cancelFlag, int[] fields
     }
 
 
+    private static int[] buildProjectedFields( int[] projectedFields, int schemaFieldCount ) {
+        if ( projectedFields == null || projectedFields.length == 0 ) {
+            int[] allFields = new int[schemaFieldCount];
+            for ( int i = 0; i < schemaFieldCount; i++ ) {
+                allFields[i] = i;
+            }
+            return allFields;
+        }
+        return projectedFields;
+    }
+
+
+    private static MessageType buildProjectionSchema( MessageType fullSchema, int[] projectedFieldIndexes ) {
+        if ( projectedFieldIndexes.length == 0 ) {
+            return fullSchema;
+        }
+
+        List<Type> fields = new ArrayList<>( projectedFieldIndexes.length );
+        for ( int index : projectedFieldIndexes ) {
+            fields.add( fullSchema.getType( index ) );
+        }
+        return new MessageType( fullSchema.getName(), fields );
+    }
+
+
     public Group next() {
         try {
             for ( ; ; ) {
@@ -129,27 +150,6 @@ public Group next() {
     }
 
 
-    public long getEstimatedRowCount() {
-        // use metadata to estimate row count
-        return fileReader.getFooter().getBlocks().stream().mapToLong( BlockMetaData::getRowCount ).sum();
-    }
-
-
-    public static MessageType readSchema( Source source ) {
-        try {
-            URI uri = source.isFile() ? source.file().toURI() : source.url().toURI();
-            Path path = new Path( uri );
-            Configuration conf = HadoopConfigurationFactory.create( ParquetSourceReader.class.getClassLoader() );
-            var inputFile = HadoopInputFile.fromPath( path, conf );
-            try ( org.apache.parquet.hadoop.ParquetFileReader schemaReader = org.apache.parquet.hadoop.ParquetFileReader.open( inputFile ) ) {
-                return schemaReader.getFooter().getFileMetaData().getSchema();
-            }
-        } catch ( Exception e ) {
-            throw new GenericRuntimeException( "Unable to inspect parquet schema for " + source.path(), e );
-        }
-    }
-
-
     @Override
     public void close() throws IOException {
         fileReader.close();
@@ -175,29 +175,4 @@ private boolean ensureRecordReader() throws IOException {
         return true;
     }
 
-
-    private static int[] buildProjectedFields( int[] projectedFields, int schemaFieldCount ) {
-        if ( projectedFields == null || projectedFields.length == 0 ) {
-            int[] allFields = new int[schemaFieldCount];
-            for ( int i = 0; i < schemaFieldCount; i++ ) {
-                allFields[i] = i;
-            }
-            return allFields;
-        }
-        return projectedFields;
-    }
-
-
-    private static MessageType buildProjectionSchema( MessageType fullSchema, int[] projectedFieldIndexes ) {
-        if ( projectedFieldIndexes.length == 0 ) {
-            return fullSchema;
-        }
-
-        List<Type> fields = new ArrayList<>( projectedFieldIndexes.length );
-        for ( int index : projectedFieldIndexes ) {
-            fields.add( fullSchema.getType( index ) );
-        }
-        return new MessageType( fullSchema.getName(), fields );
-    }
-
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java
new file mode 100644
index 0000000000..94137b2e51
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java
@@ -0,0 +1,231 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.statistics;
+
+import java.util.List;
+import java.util.Optional;
+import org.apache.parquet.column.statistics.Statistics;
+import org.apache.parquet.hadoop.metadata.BlockMetaData;
+import org.apache.parquet.hadoop.metadata.ColumnChunkMetaData;
+import org.apache.parquet.schema.PrimitiveType;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.adapter.statistics.ProvidedColumnStatistics;
+import org.polypheny.db.adapter.statistics.ProvidedEntityStatistics;
+import org.polypheny.db.catalog.entity.logical.LogicalColumn;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.PolyTypeFamily;
+import org.polypheny.db.type.entity.PolyValue;
+
+/**
+ * Provides parquet statistics
+ */
+public class ParquetStatisticsReader {
+
+    private final ParquetSchemaReader schemaReader;
+    private final ParquetTableBinding binding;
+    private final ParquetTypeConverter typeConverter;
+
+
+    public ParquetStatisticsReader( ParquetSchemaReader schemaReader, ParquetTableBinding binding ) {
+        this.schemaReader = schemaReader;
+        this.binding = binding;
+        this.typeConverter = new ParquetTypeConverter();
+    }
+
+
+    /**
+     * Get number of rows in table.
+     * @param nestedTable - if nested table
+     * @return provided statistics
+     */
+    public Optional<ProvidedEntityStatistics> getEntityStatistics( boolean nestedTable ) {
+        return Optional.of( new ProvidedEntityStatistics( nestedTable ? estimateNestedRowCount() : schemaReader.getEstimatedRowCount() ) );
+    }
+
+
+    /**
+     * Provides the available metadata-based statistics for one logical column, without scanning the table rows.
+     * @param column - logical column
+     * @param uniqueValueLimit - currently not used, required by interface
+     * @return Column Statistics
+     */
+    public Optional<ProvidedColumnStatistics> getColumnStatistics( LogicalColumn column, int uniqueValueLimit ) {
+        ParquetColumnBinding columnBinding = binding.getColumnBinding( column.id );
+        if ( columnBinding == null ) {
+            return Optional.of( new ProvidedColumnStatistics( estimateNestedRowCount(), null, null, List.of(), true ) );
+        }
+        if ( columnBinding.role() != ParquetColumnRole.DATA || columnBinding.sourcePathElements().isEmpty() ) {
+            return Optional.of( new ProvidedColumnStatistics( estimateNestedRowCount(), null, null, List.of(), true ) );
+        }
+
+        PrimitiveType primitiveType;
+        try {
+            primitiveType = schemaReader.getSchema().getType( columnBinding.sourcePathElements().toArray( new String[0] ) ).asPrimitiveType();
+        } catch ( RuntimeException e ) {
+            return Optional.of( new ProvidedColumnStatistics( estimateNestedRowCount(), null, null, List.of(), true ) );
+        }
+
+        ColumnMetadataStatistics metadataStatistics = readColumnMetadataStatistics( columnBinding.sourcePathElements() );
+        if ( metadataStatistics == null ) {
+            return Optional.of( new ProvidedColumnStatistics( estimateNestedRowCount(), null, null, List.of(), true ) );
+        }
+
+        PolyValue min = toStatisticValue( column.type, primitiveType, metadataStatistics.min() );
+        PolyValue max = toStatisticValue( column.type, primitiveType, metadataStatistics.max() );
+        return Optional.of( new ProvidedColumnStatistics( metadataStatistics.count(), min, max, List.of(), true ) );
+    }
+
+
+    /**
+     * Estimates how many rows a normalized nested table will produce, using Parquet metadata only.
+     * @return estimated Nested Row Count
+     */
+    private long estimateNestedRowCount() {
+        return binding.columnsByColumnId().values().stream()
+                .filter( column -> column.role() == ParquetColumnRole.DATA )
+                .filter( column -> !column.sourcePathElements().isEmpty() )
+                .mapToLong( column -> estimateValueCount( column.sourcePathElements() ) )
+                .max()
+                .orElseGet( schemaReader::getEstimatedRowCount );
+    }
+
+
+    private long estimateValueCount( List<String> sourcePathElements ) {
+        long valueCount = 0;
+        for ( BlockMetaData block : schemaReader.getFooter().getBlocks() ) {
+            ColumnChunkMetaData column = findColumnChunk( block, sourcePathElements );
+            if ( column == null ) {
+                return 0;
+            }
+            valueCount += column.getValueCount();
+        }
+        return valueCount;
+    }
+
+
+    /**
+     * Count rows including min/max limits
+     * @param sourcePathElements - path
+     * @return column metadata statistics
+     */
+    private ColumnMetadataStatistics readColumnMetadataStatistics( List<String> sourcePathElements ) {
+        var footer = schemaReader.getFooter();
+
+        long rowCount = 0;
+        long nullCount = 0;
+        boolean hasReliableNullCount = true;
+        Object min = null;
+        Object max = null;
+        boolean hasMinMax = true;
+
+        for ( BlockMetaData block : footer.getBlocks() ) {
+            rowCount += block.getRowCount();
+            ColumnChunkMetaData column = findColumnChunk( block, sourcePathElements );
+            if ( column == null ) {
+                return null;
+            }
+
+            Statistics<?> statistics = column.getStatistics();
+            if ( statistics == null ) {
+                hasReliableNullCount = false;
+                hasMinMax = false;
+                continue;
+            }
+
+            if ( statistics.isNumNullsSet() ) {
+                nullCount += statistics.getNumNulls();
+            } else {
+                hasReliableNullCount = false;
+            }
+
+            if ( statistics.hasNonNullValue() ) {
+                Object currentMin = statistics.genericGetMin();
+                Object currentMax = statistics.genericGetMax();
+                min = lower( min, currentMin );
+                max = higher( max, currentMax );
+            } else if ( !statistics.isNumNullsSet() || statistics.getNumNulls() != block.getRowCount() ) {
+                hasMinMax = false;
+            }
+        }
+
+        long count = hasReliableNullCount ? rowCount - nullCount : rowCount;
+        return new ColumnMetadataStatistics( count, hasMinMax ? min : null, hasMinMax ? max : null );
+    }
+
+
+    private ColumnChunkMetaData findColumnChunk( BlockMetaData block, List<String> sourcePathElements ) {
+        for ( ColumnChunkMetaData column : block.getColumns() ) {
+            if ( List.of( column.getPath().toArray() ).equals( sourcePathElements ) ) {
+                return column;
+            }
+        }
+        return null;
+    }
+
+
+    @SuppressWarnings({ "rawtypes", "unchecked" })
+    private Object lower( Object current, Object candidate ) {
+        if ( current == null ) {
+            return candidate;
+        }
+        return ((Comparable) candidate).compareTo( current ) < 0 ? candidate : current;
+    }
+
+
+    @SuppressWarnings({ "rawtypes", "unchecked" })
+    private Object higher( Object current, Object candidate ) {
+        if ( current == null ) {
+            return candidate;
+        }
+        return ((Comparable) candidate).compareTo( current ) > 0 ? candidate : current;
+    }
+
+
+    private PolyValue toStatisticValue( PolyType columnType, PrimitiveType primitiveType, Object value ) {
+        if ( value == null ) {
+            return null;
+        }
+        try {
+            PolyValue polyValue = typeConverter.fromObjToPolyValue( primitiveType, value );
+            if ( columnType.getFamily() == PolyTypeFamily.NUMERIC && polyValue.isNumber() ) {
+                return polyValue;
+            }
+            if ( PolyType.DATETIME_TYPES.contains( columnType ) && polyValue.isTemporal() ) {
+                return polyValue;
+            }
+            if ( columnType.getFamily() == PolyTypeFamily.CHARACTER && polyValue.isString() ) {
+                return polyValue;
+            }
+            return null;
+        } catch ( RuntimeException e ) {
+            return null;
+        }
+    }
+
+
+    private record ColumnMetadataStatistics(
+            Long count,
+            Object min,
+            Object max ) {
+
+    }
+
+}
diff --git a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
index 4c3b44ef5e..7bb6817521 100644
--- a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
+++ b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
@@ -25,13 +25,14 @@
 import org.apache.parquet.schema.Type;
 import org.polypheny.db.adapter.parquet.document.execution.ParquetDocValueExtractor;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelValueExtractor;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
 import org.polypheny.db.algebra.type.AlgDataType;
 import org.polypheny.db.algebra.type.AlgDataTypeFactory;
 import org.polypheny.db.algebra.type.AlgDataTypeFactory.Builder;
 import org.polypheny.db.algebra.type.DocumentType;
-import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
-import org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.type.PolyType;
 import org.polypheny.db.type.entity.PolyString;
@@ -98,7 +99,7 @@ public static AlgDataType getOutputType( Source source, String outputModel, bool
             return DocumentType.ofId();
         }
 
-        MessageType schema = ParquetSourceReader.readSchema( source );
+        MessageType schema = new ParquetSchemaReader( source ).getSchema();
 
         Builder builder = FACTORY.builder();
         // PK added by the workflow layer, it does not come from file
@@ -130,10 +131,10 @@ public static void validateSharedRelationalSchema( List<Source> sources ) {
             throw new GenericRuntimeException( "No parquet files found." );
         }
 
-        MessageType expectedSchema = ParquetSourceReader.readSchema( sources.get( 0 ) );
+        MessageType expectedSchema = new ParquetSchemaReader( sources.get( 0 ) ).getSchema();
         for ( int i = 1; i < sources.size(); i++ ) {
             Source source = sources.get( i );
-            MessageType currentSchema = ParquetSourceReader.readSchema( source );
+            MessageType currentSchema = new ParquetSchemaReader( source ).getSchema();
             if ( !expectedSchema.equals( currentSchema ) ) {
                 throw new GenericRuntimeException(
                         "All parquet sources must share the same schema for relational output. "
@@ -152,8 +153,9 @@ public static void validateSharedRelationalSchema( List<Source> sources ) {
     public static long estimateTupleCount( List<Source> sources ) {
         long estimate = 0;
         for ( Source source : sources ) {
-            try ( ParquetSourceReader reader = new ParquetSourceReader( source ) ) {
-                estimate += Math.max( reader.getEstimatedRowCount(), 0 );
+            try {
+                var schemaReader = new ParquetSchemaReader( source );
+                estimate += Math.max( schemaReader.getEstimatedRowCount(), 0 );
             } catch ( Exception e ) {
                 return -1;
             }

From a2d2f8479e88375a194150037838be26f50803a7 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Mon, 27 Apr 2026 21:06:05 +0200
Subject: [PATCH 21/46] add logical filters and IN filter

---
 .../docs/filter_improvements.md               | 219 ++++++++++++++++++
 .../execution/ParquetDocFilterTranslator.java |  11 +-
 .../execution/ParquetRelFilterTranslator.java |  74 +++++-
 .../relational/schema/ParquetRelTable.java    |  23 +-
 ...ort.java => AbstractFilterTranslator.java} |  40 ++--
 .../execution/AbstractParquetEnumerator.java  |   9 +
 .../shared/filter/ParquetAdapterFilter.java   |  33 +--
 .../filter/ParquetNativeFilterBuilder.java    |  41 ++++
 .../ParquetRelFilterTranslatorTest.java       | 109 ++++++++-
 9 files changed, 505 insertions(+), 54 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/filter_improvements.md
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/{ParquetFilterTranslationSupport.java => AbstractFilterTranslator.java} (71%)

diff --git a/plugins/parquet-adapter/docs/filter_improvements.md b/plugins/parquet-adapter/docs/filter_improvements.md
new file mode 100644
index 0000000000..7f16e35438
--- /dev/null
+++ b/plugins/parquet-adapter/docs/filter_improvements.md
@@ -0,0 +1,219 @@
+# Parquet Filter Improvements
+
+This document briefly describes the latest filtering improvements in the Parquet adapter.
+
+## Goal
+
+The previous filtering implementation mainly handled single comparison predicates.
+
+The new goal of this development step was:
+
+1. support logical filter trees
+2. resolve nested filter operands recursively
+3. reuse shared translation logic between relational and document filters
+4. allow both exact filtering and native Parquet pushdown to understand composed filters
+
+## Main Changes
+
+### `AbstractFilterTranslator`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java`
+
+The old helper `ParquetFilterTranslationSupport` was replaced by `AbstractFilterTranslator`.
+
+It now provides shared logic for:
+
+- parsing binary comparison predicates
+- unwrapping casts
+- reversing comparison operators when the value is on the left
+- converting literals and dynamic parameters into `ParquetAdapterFilter`
+
+This logic is now reused by both:
+
+- `ParquetRelFilterTranslator`
+- `ParquetDocFilterTranslator`
+
+### `ParquetAdapterFilter`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java`
+
+`ParquetAdapterFilter` was extended from a simple leaf predicate into a filter tree node.
+
+New capabilities:
+
+- store logical operands
+- represent:
+  - `AND`
+  - `OR`
+  - `NOT`
+- distinguish logical nodes from leaf predicates using `isLogical()`
+
+This makes the filter model recursive.
+
+### `ParquetRelFilterTranslator`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java`
+
+Relational filter translation was extended to support:
+
+- `AND`
+- `OR`
+- `NOT`
+- `IN`
+- reversed comparisons such as:
+
+```text
+10 < column
+```
+
+`IN` is translated as:
+
+```text
+column IN (a, b, c)
+-> OR(EQUALS(column, a), EQUALS(column, b), EQUALS(column, c))
+```
+
+Unsupported operands still return `null`, so unsupported predicates are not pushed into the adapter.
+
+### `ParquetDocFilterTranslator`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java`
+
+The document filter translator was aligned with the new shared translation base.
+
+It now reuses the same parsing and value-handling logic as the relational translator.
+
+### `ParquetRelTable`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java`
+
+Filter resolution is now recursive.
+
+Before this change, only flat leaf filters were resolved.
+Now `resolveFilter(...)` walks the full filter tree and:
+
+- resolves dynamic parameters
+- resolves Parquet source paths through bindings
+- rebuilds logical filters with resolved child operands
+
+This is important for normalized tables and composed predicates.
+
+### `AbstractParquetEnumerator`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java`
+
+Exact row-level filtering now understands logical filters.
+
+The `matches(...)` function now evaluates:
+
+- `AND` using `allMatch`
+- `OR` using `anyMatch`
+- `NOT` by negating its single operand
+
+So enumerator-level filtering is no longer limited to single leaf predicates.
+
+### `ParquetNativeFilterBuilder`
+
+File:
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java`
+
+Native Parquet pushdown was extended to build logical predicate trees.
+
+New behavior:
+
+- recursively builds child predicates
+- combines them with:
+  - `FilterApi.and(...)`
+  - `FilterApi.or(...)`
+  - `FilterApi.not(...)`
+
+If any child predicate cannot be translated safely, native pushdown for that logical filter returns `null`.
+
+This keeps native filtering conservative and correct.
+
+## Supported New Cases
+
+The following filter shapes are now supported much better than before:
+
+- conjunctions:
+
+```text
+a > 10 AND b = 20
+```
+
+- disjunctions:
+
+```text
+a = 10 OR a = 20
+```
+
+- negation:
+
+```text
+NOT (a = 10)
+```
+
+- `IN`:
+
+```text
+a IN (10, 20, 30)
+```
+
+- reversed comparison operands:
+
+```text
+10 < a
+```
+
+## Test Updates
+
+File:
+
+`plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java`
+
+Tests were extended to verify:
+
+- `AND` / `OR` translation
+- `IN` translation into `OR` of equality filters
+- operator reversal when the literal is on the left
+- safe handling of unsupported native timestamp pushdown cases
+
+## Changed Files
+
+- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java`
+- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java`
+- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java`
+- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java`
+- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java`
+- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java`
+- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java`
+- `plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java`
+
+## Summary
+
+The filtering model was upgraded from single comparison predicates to recursive logical filter trees.
+
+This improvement affects both:
+
+- filter translation
+- filter execution
+
+and it applies to both:
+
+- exact enumerator-level filtering
+- native Parquet predicate pushdown
+
+The result is better support for realistic composed predicates while keeping unsupported cases conservative.
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
index 22ad9c0e81..712b3b9951 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
@@ -19,24 +19,25 @@
 
 import java.util.List;
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.parquet.shared.execution.AbstractFilterTranslator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.adapter.parquet.shared.execution.ParquetFilterTranslationSupport;
 import org.polypheny.db.rex.RexNameRef;
 import org.polypheny.db.rex.RexNode;
 
 /**
  * Translates supported document filter expressions into Parquet filter instances.
  */
-public class ParquetDocFilterTranslator {
+public class ParquetDocFilterTranslator extends AbstractFilterTranslator {
 
     /**
      * Translate Polypheny filter to ParquetFilter
+     *
      * @param columns - list of valid columns
      * @param filter - RexNode
      * @return ParquetFilter
      */
     public ParquetAdapterFilter translate( List<ExportedColumn> columns, RexNode filter ) {
-        ParquetFilterTranslationSupport.ParsedFilter parsed = ParquetFilterTranslationSupport.parse( filter );
+        ParsedFilter parsed = parse( filter );
         if ( parsed == null ) {
             return null;
         }
@@ -53,13 +54,13 @@ public ParquetAdapterFilter translate( List<ExportedColumn> columns, RexNode fil
         }
 
         String fieldName = nameRef.names.get( 0 );
-        if ( !ParquetFilterTranslationSupport.isValueOperand( right ) ) {
+        if ( !isValueOperand( right ) ) {
             return null;
         }
 
         for ( ExportedColumn column : columns ) {
             if ( column.name().equalsIgnoreCase( fieldName ) ) {
-                return ParquetFilterTranslationSupport.toParquetAdapterFilter( column.physicalPosition(), filter.getKind(), right );
+                return toParquetAdapterFilter( column.physicalPosition(), parsed.operator(), right );
             }
         }
         return null;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
index acc368172d..e2b444cf42 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
@@ -17,9 +17,11 @@
 package org.polypheny.db.adapter.parquet.relational.execution;
 
 import java.util.List;
+import java.util.Objects;
+import org.polypheny.db.adapter.parquet.shared.execution.AbstractFilterTranslator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.adapter.parquet.shared.execution.ParquetFilterTranslationSupport;
 import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.rex.RexCall;
 import org.polypheny.db.rex.RexDynamicParam;
 import org.polypheny.db.rex.RexIndexRef;
 import org.polypheny.db.rex.RexLiteral;
@@ -29,13 +31,26 @@
 /**
  * Translates adapter filters into parquet-native predicates.
  */
-public class ParquetRelFilterTranslator {
+public class ParquetRelFilterTranslator extends AbstractFilterTranslator {
 
     /**
      * Translates a Rex filter into Parquet filter form when possible.
      */
     public ParquetAdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFilter ) {
-        ParquetFilterTranslationSupport.ParsedFilter parsed = ParquetFilterTranslationSupport.parse( polyFilter );
+        if ( polyFilter instanceof RexCall call ) {
+            if ( polyFilter.isA( Kind.AND ) || polyFilter.isA( Kind.OR ) ) {
+                return translateLogical( fieldTypes, polyFilter.getKind(), call.getOperands() );
+            }
+            if ( polyFilter.isA( Kind.NOT ) && call.getOperands().size() == 1 ) {
+                ParquetAdapterFilter operand = translate( fieldTypes, call.getOperands().get( 0 ) );
+                return operand == null ? null : ParquetAdapterFilter.logical( Kind.NOT, List.of( operand ) );
+            }
+            if ( polyFilter.isA( Kind.IN ) ) {
+                return translateIn( fieldTypes, call.getOperands() );
+            }
+        }
+
+        ParsedFilter parsed = parse( polyFilter );
         if ( parsed == null ) {
             return null;
         }
@@ -43,26 +58,70 @@ public ParquetAdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFi
         RexNode left = parsed.left();
         RexNode right = parsed.right();
 
-        if ( !(left instanceof RexIndexRef indexRef) || !ParquetFilterTranslationSupport.isValueOperand( right ) ) {
+        if ( !(left instanceof RexIndexRef indexRef) || !isValueOperand( right ) ) {
             return null;
         }
 
         int index = indexRef.getIndex();
-        if ( index < 0 || index >= fieldTypes.size() ) {
+        if ( !isPushdownSupported( fieldTypes, index, parsed.operator(), right ) ) {
             return null;
         }
 
-        if ( !isPushdownSupported( fieldTypes, index, polyFilter.getKind(), right ) ) {
+        return toParquetAdapterFilter( index, parsed.operator(), right );
+    }
+
+
+    private ParquetAdapterFilter translateLogical( List<PolyType> fieldTypes, Kind operator, List<RexNode> operands ) {
+        if ( operands.isEmpty() ) {
             return null;
         }
 
-        return ParquetFilterTranslationSupport.toParquetAdapterFilter( index, polyFilter.getKind(), right );
+        List<ParquetAdapterFilter> translated = operands.stream()
+                .map( operand -> translate( fieldTypes, operand ) )
+                .toList();
+
+        if ( translated.stream().anyMatch( Objects::isNull ) ) {
+            return null;
+        }
+
+        return ParquetAdapterFilter.logical( operator, translated );
     }
 
+
+    private ParquetAdapterFilter translateIn( List<PolyType> fieldTypes, List<RexNode> operands ) {
+        if ( operands.size() < 2 ) {
+            return null;
+        }
+
+        RexNode left = unwrapCast( operands.get( 0 ) );
+        if ( !(left instanceof RexIndexRef indexRef) ) {
+            return null;
+        }
+
+        int index = indexRef.getIndex();
+        List<ParquetAdapterFilter> equalsFilters = operands.subList( 1, operands.size() ).stream()
+                .map( this::unwrapCast )
+                .map( value -> isPushdownSupported( fieldTypes, index, Kind.EQUALS, value )
+                        ? toParquetAdapterFilter( index, Kind.EQUALS, value )
+                        : null )
+                .toList();
+
+        if ( equalsFilters.stream().anyMatch( Objects::isNull ) ) {
+            return null;
+        }
+
+        return ParquetAdapterFilter.logical( Kind.OR, equalsFilters );
+    }
+
+
     /**
      * Checks whether the operator can be handled by the reader.
      */
     private boolean isPushdownSupported( List<PolyType> fieldTypes, int index, Kind kind, RexNode valueNode ) {
+        if ( index < 0 || index >= fieldTypes.size() ) {
+            return false;
+        }
+
         PolyType type = fieldTypes.get( index );
         return switch ( type ) {
             case BOOLEAN, VARCHAR, CHAR, TEXT -> kind == Kind.EQUALS || kind == Kind.NOT_EQUALS;
@@ -70,4 +129,5 @@ private boolean isPushdownSupported( List<PolyType> fieldTypes, int index, Kind
             default -> false;
         } && (valueNode instanceof RexDynamicParam || (valueNode instanceof RexLiteral literal && literal.getValue() != null));
     }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index b252788d13..e82522626d 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -236,17 +236,28 @@ public Enumerator<PolyValue[]> enumerator() {
     private List<ParquetAdapterFilter> resolveFilters( DataContext dataContext, List<ParquetAdapterFilter> filters ) {
         List<ParquetAdapterFilter> resolved = new ArrayList<>( filters.size() );
         for ( ParquetAdapterFilter filter : filters ) {
-            PolyValue value = filter.dynamicParamIndex() == null
-                    ? filter.polyValue()
-                    : dataContext.getParameterValue( filter.dynamicParamIndex() );
-
-            ParquetColumnBinding columnBinding = Objects.requireNonNull( binding.getColumnBinding( columns.get( filter.columnIndex() ).id ), "Missing parquet column binding" );
-            resolved.add( new ParquetAdapterFilter( filter.columnIndex(), columnBinding.sourcePathElements(), filter.operator(), value ) );
+            resolved.add( resolveFilter( dataContext, filter ) );
         }
         return resolved;
     }
 
 
+    private ParquetAdapterFilter resolveFilter( DataContext dataContext, ParquetAdapterFilter filter ) {
+        if ( filter.isLogical() ) {
+            return ParquetAdapterFilter.logical( filter.operator(), filter.operands().stream()
+                    .map( operand -> resolveFilter( dataContext, operand ) )
+                    .toList() );
+        }
+
+        PolyValue value = filter.dynamicParamIndex() == null
+                ? filter.polyValue()
+                : dataContext.getParameterValue( filter.dynamicParamIndex() );
+
+        ParquetColumnBinding columnBinding = Objects.requireNonNull( binding.getColumnBinding( columns.get( filter.columnIndex() ).id ), "Missing parquet column binding" );
+        return new ParquetAdapterFilter( filter.columnIndex(), columnBinding.sourcePathElements(), filter.operator(), value );
+    }
+
+
     private boolean isNestedTable() {
         return binding.parentTableName() != null;
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
similarity index 71%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
index a10be62753..691ca2c5e5 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
@@ -16,43 +16,37 @@
 
 package org.polypheny.db.adapter.parquet.shared.execution;
 
+import java.util.List;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.rex.RexCall;
 import org.polypheny.db.rex.RexDynamicParam;
 import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNode;
-import java.util.List;
-
-/**
- * Helper for parsing supported Rex predicates and
- * turning them into adapter-level filters
- */
-public final class ParquetFilterTranslationSupport {
-
-    private ParquetFilterTranslationSupport() {
-    }
 
+public abstract class AbstractFilterTranslator {
 
-    public static ParsedFilter parse( RexNode filter ) {
+    protected ParsedFilter parse( RexNode filter ) {
         if ( !(filter instanceof RexCall call) || !isSupportedOperator( filter.getKind() ) || call.getOperands().size() != 2 ) {
             return null;
         }
 
         RexNode left = unwrapCast( call.getOperands().get( 0 ) );
         RexNode right = unwrapCast( call.getOperands().get( 1 ) );
+        Kind operator = filter.getKind();
 
         if ( isValueOperand( left ) && !isValueOperand( right ) ) {
             RexNode tmp = left;
             left = right;
             right = tmp;
+            operator = reverse( operator );
         }
 
-        return new ParsedFilter( left, right );
+        return new ParsedFilter( left, right, operator );
     }
 
 
-    public static ParquetAdapterFilter toParquetAdapterFilter( int columnIndex, Kind operator, RexNode valueNode ) {
+    protected ParquetAdapterFilter toParquetAdapterFilter( int columnIndex, Kind operator, RexNode valueNode ) {
         if ( valueNode instanceof RexLiteral literal ) {
             if ( literal.getValue() == null ) {
                 return null;
@@ -68,12 +62,12 @@ public static ParquetAdapterFilter toParquetAdapterFilter( int columnIndex, Kind
     }
 
 
-    public static boolean isValueOperand( RexNode node ) {
+    protected boolean isValueOperand( RexNode node ) {
         return node instanceof RexLiteral || node instanceof RexDynamicParam;
     }
 
 
-    public static boolean isSupportedOperator( Kind kind ) {
+    protected boolean isSupportedOperator( Kind kind ) {
         return kind == Kind.EQUALS
                 || kind == Kind.NOT_EQUALS
                 || kind == Kind.GREATER_THAN
@@ -83,7 +77,18 @@ public static boolean isSupportedOperator( Kind kind ) {
     }
 
 
-    public static RexNode unwrapCast( RexNode node ) {
+    protected Kind reverse( Kind kind ) {
+        return switch ( kind ) {
+            case GREATER_THAN -> Kind.LESS_THAN;
+            case GREATER_THAN_OR_EQUAL -> Kind.LESS_THAN_OR_EQUAL;
+            case LESS_THAN -> Kind.GREATER_THAN;
+            case LESS_THAN_OR_EQUAL -> Kind.GREATER_THAN_OR_EQUAL;
+            default -> kind;
+        };
+    }
+
+
+    protected RexNode unwrapCast( RexNode node ) {
         while ( node.isA( Kind.CAST ) ) {
             node = ((RexCall) node).getOperands().get( 0 );
         }
@@ -91,7 +96,8 @@ public static RexNode unwrapCast( RexNode node ) {
     }
 
 
-    public record ParsedFilter(RexNode left, RexNode right) {
+    protected record ParsedFilter( RexNode left, RexNode right, Kind operator ) {
+
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
index b3a4fa1d51..464b50abc5 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
@@ -158,6 +158,15 @@ protected boolean accept( Group group ) {
 
 
     private boolean matches( Group group, ParquetAdapterFilter filter ) {
+        if ( filter.isLogical() ) {
+            return switch ( filter.operator() ) {
+                case AND -> filter.operands().stream().allMatch( operand -> matches( group, operand ) );
+                case OR -> filter.operands().stream().anyMatch( operand -> matches( group, operand ) );
+                case NOT -> filter.operands().size() == 1 && !matches( group, filter.operands().get( 0 ) );
+                default -> true;
+            };
+        }
+
         // if filter is invalid row should be displayed
         if ( filter.polyValue() == null || filter.columnIndex() < 0 || filter.columnIndex() >= reader.getProjectionSchema().getFieldCount() ) {
             return true;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
index 5d4a0c61cc..4f1dc274f9 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
@@ -16,9 +16,9 @@
 
 package org.polypheny.db.adapter.parquet.shared.filter;
 
+import java.util.List;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.type.entity.PolyValue;
-import java.util.List;
 
 /**
  * Immutable filter description
@@ -27,34 +27,37 @@
  * @param operator - filter operation
  * @param polyValue - filter value
  */
-public record ParquetAdapterFilter( int columnIndex, List<String> pathElements, Kind operator, PolyValue polyValue, Long dynamicParamIndex ) {
+public record ParquetAdapterFilter( int columnIndex, List<String> pathElements, Kind operator, PolyValue polyValue, Long dynamicParamIndex, List<ParquetAdapterFilter> operands ) {
 
     public ParquetAdapterFilter( int columnIndex, Kind operator, PolyValue polyValue ) {
-        this( columnIndex, List.of(), operator, polyValue, null );
+        this( columnIndex, List.of(), operator, polyValue, null, List.of() );
     }
 
 
     public ParquetAdapterFilter( int columnIndex, List<String> pathElements, Kind operator, PolyValue polyValue ) {
-        this( columnIndex, pathElements, operator, polyValue, null );
+        this( columnIndex, pathElements, operator, polyValue, null, List.of() );
+    }
+
+
+    public ParquetAdapterFilter( int columnIndex, List<String> pathElements, Kind operator, PolyValue polyValue, Long dynamicParamIndex ) {
+        this( columnIndex, pathElements, operator, polyValue, dynamicParamIndex, List.of() );
     }
 
+
     // validation performed in main constructor
     public ParquetAdapterFilter {
         pathElements = pathElements == null ? List.of() : List.copyOf( pathElements );
+        operands = operands == null ? List.of() : List.copyOf( operands );
+    }
+
+
+    public static ParquetAdapterFilter logical( Kind operator, List<ParquetAdapterFilter> operands ) {
+        return new ParquetAdapterFilter( -1, List.of(), operator, null, null, operands );
     }
 
 
-    /**
-     * Creates new {@link ParquetAdapterFilter} that contain sub path starting from the provided index.
-     *
-     * @param startIndex the index to copy path elements from.
-     * @return a new instance of {@link ParquetAdapterFilter}.
-     */
-    public ParquetAdapterFilter makeNested( int startIndex ) {
-        if ( startIndex >= pathElements.size() ) {
-            return this;
-        }
-        return new ParquetAdapterFilter( columnIndex, pathElements.subList( startIndex, pathElements.size() ), operator, polyValue, dynamicParamIndex );
+    public boolean isLogical() {
+        return operator == Kind.AND || operator == Kind.OR || operator == Kind.NOT;
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
index 5b8dc77144..ac5299c8e9 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
@@ -17,6 +17,7 @@
 package org.polypheny.db.adapter.parquet.shared.filter;
 
 import java.util.List;
+import java.util.Objects;
 import org.apache.parquet.column.ColumnDescriptor;
 import org.apache.parquet.filter2.compat.FilterCompat;
 import org.apache.parquet.filter2.predicate.FilterApi;
@@ -70,6 +71,10 @@ public static FilterCompat.Filter build( MessageType schema, List<ParquetAdapter
 
 
     private static FilterPredicate buildPredicate( MessageType schema, ParquetAdapterFilter filter ) {
+        if ( filter.isLogical() ) {
+            return buildLogicalPredicate( schema, filter );
+        }
+
         if ( filter.pathElements().isEmpty() ) {
             int index = filter.columnIndex();
             if ( index < 0 || index >= schema.getFieldCount() ) {
@@ -100,6 +105,42 @@ private static FilterPredicate buildPredicate( MessageType schema, ParquetAdapte
     }
 
 
+    private static FilterPredicate buildLogicalPredicate( MessageType schema, ParquetAdapterFilter filter ) {
+        List<FilterPredicate> operands = filter.operands().stream()
+                .map( operand -> buildPredicate( schema, operand ) )
+                .toList();
+
+        if ( operands.stream().anyMatch( Objects::isNull ) ) {
+            return null;
+        }
+
+        return switch ( filter.operator() ) {
+            case AND -> combineAnd( operands );
+            case OR -> combineOr( operands );
+            case NOT -> operands.size() == 1 ? FilterApi.not( operands.get( 0 ) ) : null;
+            default -> null;
+        };
+    }
+
+
+    private static FilterPredicate combineAnd( List<FilterPredicate> operands ) {
+        FilterPredicate predicate = null;
+        for ( FilterPredicate operand : operands ) {
+            predicate = predicate == null ? operand : FilterApi.and( predicate, operand );
+        }
+        return predicate;
+    }
+
+
+    private static FilterPredicate combineOr( List<FilterPredicate> operands ) {
+        FilterPredicate predicate = null;
+        for ( FilterPredicate operand : operands ) {
+            predicate = predicate == null ? operand : FilterApi.or( predicate, operand );
+        }
+        return predicate;
+    }
+
+
     private static FilterPredicate buildPredicatePrimitive( Kind operator, PolyValue value, Type type, String columnName ) {
         PrimitiveType primitive = type.asPrimitiveType();
         Object expected = TYPE_CONVERTER.fromPolyValueToParquetObj( primitive, value );
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
index f7752ff2bb..5a624fa589 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
@@ -20,18 +20,105 @@
 import org.apache.parquet.schema.MessageType;
 import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
 import org.apache.parquet.schema.Types;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.BeforeEach;
 import org.junit.jupiter.api.Test;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetNativeFilterBuilder;
 import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.algebra.type.AlgDataTypeFactory;
+import org.polypheny.db.algebra.type.AlgDataTypeSystem;
+import org.polypheny.db.nodes.SpecialOperator;
+import org.polypheny.db.rex.RexCall;
+import org.polypheny.db.rex.RexDynamicParam;
+import org.polypheny.db.rex.RexIndexRef;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.PolyTypeFactoryImpl;
 import org.polypheny.db.type.entity.temporal.PolyTimestamp;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
 
-import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertNotNull;
+import static org.junit.jupiter.api.Assertions.assertDoesNotThrow;
+import static org.junit.jupiter.api.Assertions.assertTrue;
 
 class ParquetRelFilterTranslatorTest {
 
+    private AlgDataType intType;
+    private AlgDataType boolType;
+    private ParquetRelFilterTranslator translator;
+
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @BeforeEach
+    void setUp() {
+        AlgDataTypeFactory typeFactory = new PolyTypeFactoryImpl( AlgDataTypeSystem.DEFAULT );
+        intType = typeFactory.createPolyType( PolyType.INTEGER );
+        boolType = typeFactory.createPolyType( PolyType.BOOLEAN );
+        translator = new ParquetRelFilterTranslator();
+    }
+
+
+    @Test
+    void translatesAndOrTree() {
+        RexNode filter = call(
+                Kind.AND,
+                call( Kind.GREATER_THAN, ref( 0 ), param( 10 ) ),
+                call(
+                        Kind.OR,
+                        call( Kind.EQUALS, ref( 1 ), param( 20 ) ),
+                        call( Kind.EQUALS, ref( 1 ), param( 30 ) ) ) );
+
+        ParquetAdapterFilter translated = translator.translate( List.of( PolyType.INTEGER, PolyType.INTEGER ), filter );
+
+        assertNotNull( translated );
+        assertEquals( Kind.AND, translated.operator() );
+        assertEquals( 2, translated.operands().size() );
+        assertEquals( Kind.GREATER_THAN, translated.operands().get( 0 ).operator() );
+        assertEquals( Kind.OR, translated.operands().get( 1 ).operator() );
+        assertEquals( 2, translated.operands().get( 1 ).operands().size() );
+    }
+
+
     @Test
-    void rejectsInt96TimestampPredicatePushdown() {
+    void translatesInAsOrOfEquals() {
+        RexNode filter = call( Kind.IN, ref( 0 ), param( 10 ), param( 20 ), param( 30 ) );
+
+        ParquetAdapterFilter translated = translator.translate( List.of( PolyType.INTEGER ), filter );
+
+        assertNotNull( translated );
+        assertEquals( Kind.OR, translated.operator() );
+        assertEquals( 3, translated.operands().size() );
+        assertTrue( translated.operands().stream().allMatch( operand -> operand.operator() == Kind.EQUALS && operand.columnIndex() == 0 ) );
+    }
+
+
+    @Test
+    void reversesComparisonWhenLiteralIsOnTheLeft() {
+        RexNode filter = call( Kind.LESS_THAN, param( 10 ), ref( 0 ) );
+
+        ParquetAdapterFilter translated = translator.translate( List.of( PolyType.INTEGER ), filter );
+
+        assertNotNull( translated );
+        assertEquals( Kind.GREATER_THAN, translated.operator() );
+    }
+
+
+    @Test
+    void ignoresUnsupportedInt96TimestampPredicatePushdown() {
         MessageType schema = Types.buildMessage()
                 .optional( PrimitiveTypeName.INT96 )
                 .named( "ts" )
@@ -39,8 +126,22 @@ void rejectsInt96TimestampPredicatePushdown() {
 
         ParquetAdapterFilter filter = new ParquetAdapterFilter( 0, Kind.EQUALS, PolyTimestamp.of( 1_700_000_000_000L ) );
 
-        assertThrows(
-                IllegalArgumentException.class,
+        assertDoesNotThrow(
                 () -> ParquetNativeFilterBuilder.build( schema, List.of( filter ) ) );
     }
+
+
+    private RexNode ref( int index ) {
+        return new RexIndexRef( index, intType );
+    }
+
+
+    private RexNode param( int index ) {
+        return new RexDynamicParam( intType, index );
+    }
+
+
+    private RexNode call( Kind kind, RexNode... operands ) {
+        return new RexCall( boolType, new SpecialOperator( kind.name(), kind ), operands );
+    }
 }

From 8ec74f98b2ceebda2329c0bc6c6020f7846023f3 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Sat, 2 May 2026 18:03:40 +0200
Subject: [PATCH 22/46] add logical filters and IN filter - documentation and
 fixes

---
 .../statistics/StatisticsManagerImpl.java     |   5 +-
 plugins/parquet-adapter/build.gradle          |   1 +
 .../docs/filter_improvements.md               |  20 +--
 plugins/parquet-adapter/docs/flavors.md       | 159 ------------------
 plugins/parquet-adapter/docs/future_work.md   |   2 +
 .../docs/images/statistics.png                | Bin 0 -> 51347 bytes
 .../docs/images/statistics_example.png        | Bin 0 -> 38655 bytes
 plugins/parquet-adapter/docs/statistics.md    |  21 ++-
 .../execution/ParquetDocFilterTranslator.java |   3 +
 .../execution/ParquetRelFilterTranslator.java |  43 +++--
 .../relational/schema/ParquetRelTable.java    |  13 +-
 .../execution/AbstractFilterTranslator.java   |  37 ++++
 .../execution/AbstractParquetEnumerator.java  |   3 +-
 .../filter/ParquetNativeFilterBuilder.java    |  19 +++
 .../shared/io/ParquetSourceReader.java        |   2 +-
 .../shared/schema/ParquetTypeConverter.java   |   2 +-
 .../statistics/ParquetStatisticsReader.java   |   5 +
 .../db/adapter/parquet/ParquetPluginTest.java |  52 +++---
 plugins/workflow-engine/build.gradle          |   3 +
 19 files changed, 166 insertions(+), 224 deletions(-)
 delete mode 100644 plugins/parquet-adapter/docs/flavors.md
 create mode 100644 plugins/parquet-adapter/docs/future_work.md
 create mode 100644 plugins/parquet-adapter/docs/images/statistics.png
 create mode 100644 plugins/parquet-adapter/docs/images/statistics_example.png

diff --git a/monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java b/monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java
index daba2a759b..d35520e0f4 100644
--- a/monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java
+++ b/monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java
@@ -326,6 +326,7 @@ private Optional<ProvidedEntityStatistics> getEntityStatistics( long logicalEnti
 
 
     /**
+     * Adapter Statistics:
      * Gets column statistics
      * @param column - query result
      * @return Statistic Column
@@ -343,6 +344,7 @@ private Optional<StatisticColumn> getColumnStatistics( QueryResult column ) {
 
 
     /**
+     * Adapter Statistics:
      * Converts provider column statistics to statistics column representation
      * @param column QueryResult
      * @param provided ProvidedColumnStatistics
@@ -382,6 +384,7 @@ private Optional<StatisticColumn> toStatisticColumn( QueryResult column, Provide
 
 
     /**
+     * Adapter Statistics:
      * Look for Adapter Statistic Provider via catalog
      * @param allocation AllocationEntity
      * @return AdapterStatisticsProvider
@@ -401,7 +404,7 @@ private Optional<AdapterStatisticsProvider> getStatisticsProvider( AllocationEnt
                 .map( physical -> physical.unwrap( AdapterStatisticsProvider.class ) )
                 .filter( Optional::isPresent )
                 .map( Optional::get )
-                .findFirst();
+                .findFirst(); // get first adapter that implements AdapterStatisticsProvider
     }
 
 
diff --git a/plugins/parquet-adapter/build.gradle b/plugins/parquet-adapter/build.gradle
index 350ddb5c2e..2e926e0ffd 100644
--- a/plugins/parquet-adapter/build.gradle
+++ b/plugins/parquet-adapter/build.gradle
@@ -44,6 +44,7 @@ dependencies {
 }
 
 test {
+    dependsOn(":dbms:shadowJar")
     useJUnitPlatform()
     testLogging {
         events "failed", "standard_out", "standard_error"
diff --git a/plugins/parquet-adapter/docs/filter_improvements.md b/plugins/parquet-adapter/docs/filter_improvements.md
index 7f16e35438..dd0ddfd109 100644
--- a/plugins/parquet-adapter/docs/filter_improvements.md
+++ b/plugins/parquet-adapter/docs/filter_improvements.md
@@ -8,10 +8,10 @@ The previous filtering implementation mainly handled single comparison predicate
 
 The new goal of this development step was:
 
-1. support logical filter trees
+1. support logical filter trees (AND, OR, NOT, IN) - hierarchical filter
 2. resolve nested filter operands recursively
-3. reuse shared translation logic between relational and document filters
-4. allow both exact filtering and native Parquet pushdown to understand composed filters
+3. reuse shared translation logic between relational and document filters - abstract class
+
 
 ## Main Changes
 
@@ -202,18 +202,4 @@ Tests were extended to verify:
 - `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java`
 - `plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java`
 
-## Summary
-
-The filtering model was upgraded from single comparison predicates to recursive logical filter trees.
-
-This improvement affects both:
-
-- filter translation
-- filter execution
-
-and it applies to both:
-
-- exact enumerator-level filtering
-- native Parquet predicate pushdown
 
-The result is better support for realistic composed predicates while keeping unsupported cases conservative.
diff --git a/plugins/parquet-adapter/docs/flavors.md b/plugins/parquet-adapter/docs/flavors.md
deleted file mode 100644
index 455c464d5f..0000000000
--- a/plugins/parquet-adapter/docs/flavors.md
+++ /dev/null
@@ -1,159 +0,0 @@
-# Parquet Table Flavors
-
-## Overview
-
-The Parquet adapter defines three table flavors through `ParquetTable.Flavor`:
-
-- `SCANNABLE`
-- `FILTERABLE`
-- `TRANSLATABLE`
-
-These flavors represent different integration styles between the adapter and the Polypheny query engine. 
-They represent different execution and planning strategies for reading the same Parquet data.
-The active flavor is selected when the Parquet namespace creates table wrappers.
-
-## Where Flavor Selection Happens
-
-Flavor-based table creation is implemented in `ParquetNamespace`.
-
-- `ParquetNamespace.createParquetTable(...)` prepares the metadata and source reference for a physical Parquet table.
-- `ParquetNamespace.createTable(...)` instantiates the concrete table implementation according to the configured flavor.
-
-The current Parquet source selects the `FILTERABLE` flavor in `ParquetSource.updateNamespace(...)`.
-
-
-## Purpose of Each Flavor
-
-### SCANNABLE
-
-`SCANNABLE` is the simplest integration model.
-
-In this flavor, the adapter behaves mainly as a row producer. 
-The query engine asks the table to provide rows, and the adapter returns them as an enumerable result stream.
-
-Characteristics:
-
-- simple implementation model
-- filtering is handled outside the adapter
-- less efficient (it does not provide optimization)
-
-
-### FILTERABLE
-
-`FILTERABLE` extends the scan-based model by allowing the adapter to receive filter conditions from the query engine.
-
-In this flavor, the adapter inspects query filters and pushes supported conditions into its own execution logic. 
-This allows the Parquet reader to reduce the amount of data it reads and processes using native predicate pushdown.
-
-Characteristics:
-
-- supports filter pushdown
-- supports projection-aware reading
-- uses Parquet native filter stages such as statistics, dictionary, column index, bloom filter, and record filtering when available
-- significantly more efficient than plain scan-only execution
-
-This is the active flavor in the current Parquet adapter implementation.
-
-### TRANSLATABLE
-
-`TRANSLATABLE` is the most advanced integration style.
-
-In this flavor, the adapter participates directly in query planning. 
-Instead of only returning rows at execution time, it provides adapter-specific relational nodes and planner rules.
-
-Characteristics:
-
-- deeper integration with the query planner
-- enables more advanced optimization
-- more flexible than scan-only or filterable execution
-
-
-## Flavor Workflows
-
-The following sections describe how the same logical SQL query flows through each flavor.
-
-Example query:
-
-```sql
-SELECT customer_id, name
-FROM customers
-WHERE customer_id > 3
-```
-
-### 1. SCANNABLE Workflow
-The adapter mainly acts as a generic row reader.
-
-Main class:
-
-- `ParquetScannableTable`
-
-Flow:
-
-- The query engine decides a table scan is needed.
-- The scannable table is asked to provide rows.
-- The adapter reads rows from the Parquet file.
-- Filtering may be applied later by the query engine rather than inside the adapter.
-- Projection may also be handled outside the adapter depending on the execution path.
-
-Practical consequence:
-
-- more data may be read than actually needed
-- pushdown opportunities are limited
-- execution is simpler but less efficient
-- appropriate when correctness and simplicity are more important than optimization.
-
-### 2. FILTERABLE Workflow
-
-The adapter receives a list of query filters and decides which ones it can handle internally.
-
-Main classes:
-
-- `ParquetFilterableTable`
-- `ParquetEnumerator`
-- `ParquetPredicateBuilder`
-
-Flow:
-
-- The query engine calls ParquetFilterableTable.scan(...).
-- The table inspects incoming filter expressions.
-- Only pushdown-safe filters are converted into adapter-specific `FilterInfo` objects.
-- These filters are passed into ParquetEnumerator.
-- The enumerator builds the requested projection schema from the projected columns.
-- `ParquetPredicateBuilder` translates adapter filters into native Parquet predicates.
-- `ParquetFileReader` is opened with `ParquetReadOptions` that enable native predicate pushdown stages.
-- Matching rows are read directly from the projected Parquet schema and returned.
-
-Practical consequence:
-
-- fewer columns may be read
-- fewer row groups and pages may be read when the Parquet file contains usable metadata
-- less work is done outside the adapter
-
-Current limitations:
-
-- Only simple `column OP literal` predicates are pushed down.
-- `BOOLEAN` and string-like fields currently support only `=` and `!=`.
-- Legacy `INT96` timestamp columns are intentionally not pushed down.
-
-
-### 3. TRANSLATABLE Workflow
-
-The adapter participates in planning.
-
-Main classes:
-
-- `ParquetTranslatableTable`
-- `ParquetScan`
-- `ParquetProjectScanRule`
-
-Flow:
-
-- The planner encounters a Parquet-backed table.
-- The translatable table produces an adapter-specific relational node.
-- The query planner can rewrite the query plan using rules that know about your Parquet adapter.
-- Projection and other operations can be integrated into adapter-specific planning structures.
-
-Practical consequence:
-
-- optimization can happen earlier and more explicitly
-- this approach supports more sophisticated future optimizations
diff --git a/plugins/parquet-adapter/docs/future_work.md b/plugins/parquet-adapter/docs/future_work.md
new file mode 100644
index 0000000000..4d56485c47
--- /dev/null
+++ b/plugins/parquet-adapter/docs/future_work.md
@@ -0,0 +1,2 @@
+- load statistics when adapter added ? workaround can be recalculated from monitoring -> statistics
+- add adapter - check all options in settings lookup
diff --git a/plugins/parquet-adapter/docs/images/statistics.png b/plugins/parquet-adapter/docs/images/statistics.png
new file mode 100644
index 0000000000000000000000000000000000000000..d2df54273add4c659730ae304879cbde360e5545
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HcmV?d00001

diff --git a/plugins/parquet-adapter/docs/statistics.md b/plugins/parquet-adapter/docs/statistics.md
index 8c48838a7e..328d7d27e3 100644
--- a/plugins/parquet-adapter/docs/statistics.md
+++ b/plugins/parquet-adapter/docs/statistics.md
@@ -5,13 +5,21 @@ This document describes the statistics-related changes introduced to avoid loadi
 ## Goal
 
 Before this change, the monitoring statistics subsystem computed table and column statistics by executing normal relational queries.
-For Parquet-backed entities, this could trigger full scans during startup statistics reevaluation.
+This could trigger full scans for Parquet-backed entities during startup and impact performance.
 
-The new goal is:
+The goal of development is:
 
-1. ask the adapter for metadata-backed statistics first
-2. use Parquet footer metadata when available
-3. fall back to the old query-based path only when necessary
+1. Ask the adapter for metadata-backed statistics first
+2. Use Parquet footer metadata when available
+3. Fall back to the old query-based path only when necessary
+
+## Flow Chart
+
+![Schema display](images/statistics.png)
+
+## Example
+
+![Schema display](images/statistics_example.png)
 
 ## Main Objects
 
@@ -108,7 +116,6 @@ Helper methods added:
 - `getStatisticsProvider(AllocationEntity allocation)`
 - `toStatisticColumn(QueryResult column, ProvidedColumnStatistics provided)`
 
-
 ## Parquet Adapter Integration
 
 ### `ParquetRelTable`
@@ -182,8 +189,6 @@ Main methods:
 - `getEntityStatistics(boolean nestedTable)`
 - `getColumnStatistics(LogicalColumn column, int uniqueValueLimit)`
 
-
-
 ## Full Flow
 
 ### Step 1: Startup statistics reevaluation begins
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
index 712b3b9951..472d4bf605 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
@@ -60,6 +60,9 @@ public ParquetAdapterFilter translate( List<ExportedColumn> columns, RexNode fil
 
         for ( ExportedColumn column : columns ) {
             if ( column.name().equalsIgnoreCase( fieldName ) ) {
+                if ( !isColumnPredicateSupported( column.type(), parsed.operator(), right ) ) {
+                    return null;
+                }
                 return toParquetAdapterFilter( column.physicalPosition(), parsed.operator(), right );
             }
         }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
index e2b444cf42..0b6b4b9637 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
@@ -22,9 +22,7 @@
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.rex.RexCall;
-import org.polypheny.db.rex.RexDynamicParam;
 import org.polypheny.db.rex.RexIndexRef;
-import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNode;
 import org.polypheny.db.type.PolyType;
 
@@ -34,9 +32,10 @@
 public class ParquetRelFilterTranslator extends AbstractFilterTranslator {
 
     /**
-     * Translates a Rex filter into Parquet filter form when possible.
+     * Translates a Rex filter into Parquet Adapter filter form when possible.
      */
     public ParquetAdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFilter ) {
+        // support logical filter
         if ( polyFilter instanceof RexCall call ) {
             if ( polyFilter.isA( Kind.AND ) || polyFilter.isA( Kind.OR ) ) {
                 return translateLogical( fieldTypes, polyFilter.getKind(), call.getOperands() );
@@ -63,45 +62,69 @@ public ParquetAdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFi
         }
 
         int index = indexRef.getIndex();
-        if ( !isPushdownSupported( fieldTypes, index, parsed.operator(), right ) ) {
+        if ( !isFieldPredicateSupported( fieldTypes, index, parsed.operator(), right ) ) {
             return null;
         }
 
+        // call parent functionality
         return toParquetAdapterFilter( index, parsed.operator(), right );
     }
 
 
+    /**
+     * Translates a logical Rex filter node (AND, OR) into a logical ParquetAdapterFilter
+     * @param fieldTypes - poly field types
+     * @param operator - logical operation: AND, OR
+     * @param operands - child expressions inside that operation (a > 10 AND b = 20)
+     * @return adapter level filter
+     */
     private ParquetAdapterFilter translateLogical( List<PolyType> fieldTypes, Kind operator, List<RexNode> operands ) {
+        // reject if no operands
         if ( operands.isEmpty() ) {
             return null;
         }
 
+        // recursively translate each child Rex operand
         List<ParquetAdapterFilter> translated = operands.stream()
                 .map( operand -> translate( fieldTypes, operand ) )
                 .toList();
 
+        // If any child cannot be translated, the whole logical filter is rejected
         if ( translated.stream().anyMatch( Objects::isNull ) ) {
             return null;
         }
 
+        // If all children are valid, it creates a logical ParquetAdapterFilter
         return ParquetAdapterFilter.logical( operator, translated );
     }
 
 
+    /**
+     * Translates a Rex IN expression into an adapter-level OR filter
+     * IN (10, 20, 30) -> OR(a = 10, a = 20, a = 30)
+     * @param fieldTypes - types of fields
+     * @param operands - operands
+     * @return adapter level filter
+     */
     private ParquetAdapterFilter translateIn( List<PolyType> fieldTypes, List<RexNode> operands ) {
+        // Rejects invalid IN with no values. It needs at least column and value
         if ( operands.size() < 2 ) {
             return null;
         }
 
+        // The first operand must be a relational column reference. Supports: column in (...)
         RexNode left = unwrapCast( operands.get( 0 ) );
         if ( !(left instanceof RexIndexRef indexRef) ) {
             return null;
         }
 
+        // take all operands after the first one
+        // Each value is converted into an equality filter
+
         int index = indexRef.getIndex();
         List<ParquetAdapterFilter> equalsFilters = operands.subList( 1, operands.size() ).stream()
                 .map( this::unwrapCast )
-                .map( value -> isPushdownSupported( fieldTypes, index, Kind.EQUALS, value )
+                .map( value -> isFieldPredicateSupported( fieldTypes, index, Kind.EQUALS, value )
                         ? toParquetAdapterFilter( index, Kind.EQUALS, value )
                         : null )
                 .toList();
@@ -110,6 +133,7 @@ private ParquetAdapterFilter translateIn( List<PolyType> fieldTypes, List<RexNod
             return null;
         }
 
+        // returns one logical ParquetAdapterFilter with Kind.OR
         return ParquetAdapterFilter.logical( Kind.OR, equalsFilters );
     }
 
@@ -117,17 +141,12 @@ private ParquetAdapterFilter translateIn( List<PolyType> fieldTypes, List<RexNod
     /**
      * Checks whether the operator can be handled by the reader.
      */
-    private boolean isPushdownSupported( List<PolyType> fieldTypes, int index, Kind kind, RexNode valueNode ) {
+    private boolean isFieldPredicateSupported( List<PolyType> fieldTypes, int index, Kind kind, RexNode valueNode ) {
         if ( index < 0 || index >= fieldTypes.size() ) {
             return false;
         }
 
-        PolyType type = fieldTypes.get( index );
-        return switch ( type ) {
-            case BOOLEAN, VARCHAR, CHAR, TEXT -> kind == Kind.EQUALS || kind == Kind.NOT_EQUALS;
-            case INTEGER, BIGINT, FLOAT, DOUBLE, DATE, TIME, TIMESTAMP -> true;
-            default -> false;
-        } && (valueNode instanceof RexDynamicParam || (valueNode instanceof RexLiteral literal && literal.getValue() != null));
+        return isColumnPredicateSupported( fieldTypes.get( index ), kind, valueNode );
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index e82522626d..4cfed2b34d 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -242,6 +242,15 @@ private List<ParquetAdapterFilter> resolveFilters( DataContext dataContext, List
     }
 
 
+    /**
+     * Takes a ParquetAdapterFilter produced by the translator and turns it into a filter that is ready for execution against the actual Parquet file
+     * Resolve dynamic parameters from dataContext
+     * Use binding path elements for nested fields
+     * For logical filter - recursive call
+     * @param dataContext DataContext
+     * @param filter Adapter level filter
+     * @return Adapter level filter
+     */
     private ParquetAdapterFilter resolveFilter( DataContext dataContext, ParquetAdapterFilter filter ) {
         if ( filter.isLogical() ) {
             return ParquetAdapterFilter.logical( filter.operator(), filter.operands().stream()
@@ -285,9 +294,11 @@ private boolean needsBindingScan( int[] fields ) {
             if ( field >= schema.getFieldCount() || !schema.getType( field ).getName().equals( columnBinding.sourcePathElements().get( 0 ) ) ) {
                 return true;
             }
+            if ( !schema.getType( field ).isPrimitive() ) {
+                return true;
+            }
         }
         return false;
     }
 
 }
-
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
index 691ca2c5e5..0320fe0035 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
@@ -23,9 +23,20 @@
 import org.polypheny.db.rex.RexDynamicParam;
 import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.type.PolyType;
 
+/**
+ * Responsible for parsing supported Rex predicates and
+ * turning them into adapter-level filters
+ * Rex Filter -> Adapter Filter
+ */
 public abstract class AbstractFilterTranslator {
 
+    /**
+     * Parse Rex filter to left, right and operator
+     * @param filter - filter to parse
+     * @return ParsedFilter
+     */
     protected ParsedFilter parse( RexNode filter ) {
         if ( !(filter instanceof RexCall call) || !isSupportedOperator( filter.getKind() ) || call.getOperands().size() != 2 ) {
             return null;
@@ -46,6 +57,13 @@ protected ParsedFilter parse( RexNode filter ) {
     }
 
 
+    /**
+     * Convert Rex filter to Adapter Filter
+     * @param columnIndex - column
+     * @param operator - operator
+     * @param valueNode - value
+     * @return adapter filter
+     */
     protected ParquetAdapterFilter toParquetAdapterFilter( int columnIndex, Kind operator, RexNode valueNode ) {
         if ( valueNode instanceof RexLiteral literal ) {
             if ( literal.getValue() == null ) {
@@ -67,6 +85,11 @@ protected boolean isValueOperand( RexNode node ) {
     }
 
 
+    /**
+     * Validate if operation is supported
+     * @param kind - kind of operator
+     * @return if supported
+     */
     protected boolean isSupportedOperator( Kind kind ) {
         return kind == Kind.EQUALS
                 || kind == Kind.NOT_EQUALS
@@ -77,6 +100,12 @@ protected boolean isSupportedOperator( Kind kind ) {
     }
 
 
+    /**
+     * Reverse filters mapping
+     * Used for not calculating equal filters twice
+     * @param kind - operator kong
+     * @return - equivalent reversed operator
+     */
     protected Kind reverse( Kind kind ) {
         return switch ( kind ) {
             case GREATER_THAN -> Kind.LESS_THAN;
@@ -100,4 +129,12 @@ protected record ParsedFilter( RexNode left, RexNode right, Kind operator ) {
 
     }
 
+    protected boolean isColumnPredicateSupported( PolyType type, Kind kind, RexNode valueNode ) {
+        return switch ( type ) {
+            case BOOLEAN, VARCHAR, CHAR, TEXT -> kind == Kind.EQUALS || kind == Kind.NOT_EQUALS;
+            case INTEGER, BIGINT, FLOAT, DOUBLE, DATE, TIME, TIMESTAMP -> true;
+            default -> false;
+        } && (valueNode instanceof RexDynamicParam || (valueNode instanceof RexLiteral literal && literal.getValue() != null));
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
index 464b50abc5..1dc42a2aa9 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
@@ -81,7 +81,7 @@ public boolean moveNext() {
             current = rows.remove();
             return true;
         } catch ( Exception e ) {
-            throw new GenericRuntimeException( "Error while reading parquet data", e );
+            throw new GenericRuntimeException( "Error while reading parquet data: " + e.getClass().getSimpleName() + ": " + e.getMessage(), e );
         }
     }
 
@@ -158,6 +158,7 @@ protected boolean accept( Group group ) {
 
 
     private boolean matches( Group group, ParquetAdapterFilter filter ) {
+        // add logical filter support
         if ( filter.isLogical() ) {
             return switch ( filter.operator() ) {
                 case AND -> filter.operands().stream().allMatch( operand -> matches( group, operand ) );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
index ac5299c8e9..28965da4ec 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
@@ -105,15 +105,24 @@ private static FilterPredicate buildPredicate( MessageType schema, ParquetAdapte
     }
 
 
+    /**
+     * Converts a logical ParquetAdapterFilter into a native Parquet FilterPredicate
+     * @param schema MessageType
+     * @param filter ParquetAdapterFilter
+     * @return Filter Predicate
+     */
     private static FilterPredicate buildLogicalPredicate( MessageType schema, ParquetAdapterFilter filter ) {
+        // recursively convert each adapter-level child filter into a native Parquet predicate
         List<FilterPredicate> operands = filter.operands().stream()
                 .map( operand -> buildPredicate( schema, operand ) )
                 .toList();
 
+        // check if any child failed
         if ( operands.stream().anyMatch( Objects::isNull ) ) {
             return null;
         }
 
+        // combine the native child predicates based on the logical operator
         return switch ( filter.operator() ) {
             case AND -> combineAnd( operands );
             case OR -> combineOr( operands );
@@ -123,6 +132,11 @@ private static FilterPredicate buildLogicalPredicate( MessageType schema, Parque
     }
 
 
+    /**
+     * Takes a list of native Parquet FilterPredicates and combines them into one predicate using AND
+     * @param operands list of filter predicates
+     * @return FilterPredicate
+     */
     private static FilterPredicate combineAnd( List<FilterPredicate> operands ) {
         FilterPredicate predicate = null;
         for ( FilterPredicate operand : operands ) {
@@ -132,6 +146,11 @@ private static FilterPredicate combineAnd( List<FilterPredicate> operands ) {
     }
 
 
+    /**
+     * Takes a list of native Parquet FilterPredicates and combines them into one predicate using OR
+     * @param operands list of filter predicates
+     * @return FilterPredicate
+     */
     private static FilterPredicate combineOr( List<FilterPredicate> operands ) {
         FilterPredicate predicate = null;
         for ( FilterPredicate operand : operands ) {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
index a893b72612..2e20b585aa 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
@@ -145,7 +145,7 @@ public Group next() {
                 return group;
             }
         } catch ( Exception e ) {
-            throw new GenericRuntimeException( "Error while reading parquet data", e );
+            throw new GenericRuntimeException( "Error while reading parquet data: " + e.getClass().getSimpleName() + ": " + e.getMessage(), e );
         }
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
index a62e65b1e2..97ca116a81 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
@@ -66,7 +66,7 @@ public class ParquetTypeConverter {
      */
     public PolyType fromParquetTypeToPolyType( Type field ) {
 
-        // treat nested types as string
+        // treat nested types as document
         if ( !field.isPrimitive() ) {
             return PolyType.DOCUMENT;
         }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java
index 94137b2e51..7146594f5f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java
@@ -108,6 +108,11 @@ private long estimateNestedRowCount() {
     }
 
 
+    /**
+     * Estimates how many values exist for one Parquet leaf path across the whole file
+     * @param sourcePathElements - path
+     * @return values count
+     */
     private long estimateValueCount( List<String> sourcePathElements ) {
         long valueCount = 0;
         for ( BlockMetaData block : schemaReader.getFooter().getBlocks() ) {
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
index 7905252155..ab79c77841 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
@@ -22,6 +22,7 @@
 import org.polypheny.db.TestHelper;
 import org.polypheny.db.TestHelper.JdbcConnection;
 import org.polypheny.db.util.Sources;
+import org.polypheny.jdbc.types.PolyDocument;
 import java.sql.Connection;
 import java.sql.ResultSet;
 import java.sql.SQLException;
@@ -47,15 +48,15 @@ static void start() throws SQLException {
         String parquetDir = Sources.of( ParquetPluginTest.class.getResource( "/orders_db/" ) )
                 .file()
                 .getAbsolutePath()
-                .replace( "\\", "\\\\" );
+                .replace( "\\", "/" );
 
         // open a JDBC connection to the running Polypheny test server
         try ( JdbcConnection jdbcConnection = new JdbcConnection( false ) ) {
             Connection connection = jdbcConnection.getConnection();
             // creates the Parquet source adapter and imports the parquet files as source tables
              try ( Statement statement = connection.createStatement() ) {
-                String sql = "ALTER ADAPTERS ADD \"" + SOURCE_NAME + "\" USING 'Parquet' AS 'SOURCE' "
-                        + "WITH '{method:\"link\",directory:\"classpath://orders_db\",directoryName:\"" + parquetDir + "\"}'";
+                String sql = "ALTER ADAPTERS ADD \"" + SOURCE_NAME + "\" USING 'Parquet Relational' AS 'SOURCE' "
+                        + "WITH '{\"method\":\"link\",\"directory\":\"classpath://orders_db\",\"directoryName\":\"" + parquetDir + "\",\"url\":\"\",\"schema mode\":\"flat\"}'";
 
                 System.out.println( sql );
                 statement.executeUpdate( sql );
@@ -99,7 +100,7 @@ void parquetSourceIsReadOnly() throws SQLException {
         try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
             Connection connection = jdbcConnection.getConnection();
             try ( Statement statement = connection.createStatement() ) {
-                assertThrows( SQLException.class, () -> statement.executeUpdate( "DELETE FROM customers" ) );
+                assertThrows( SQLException.class, () -> statement.executeUpdate( "DELETE FROM " + table( "customers" ) ) );
             }
         }
     }
@@ -108,7 +109,7 @@ private void assertTableHasRows( String tableName ) throws SQLException {
         try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
             Connection connection = jdbcConnection.getConnection();
             try ( Statement statement = connection.createStatement();
-                    ResultSet resultSet = statement.executeQuery( "SELECT COUNT(*) FROM " + tableName ) ) {
+                    ResultSet resultSet = statement.executeQuery( "SELECT COUNT(*) FROM " + table( tableName ) ) ) {
 
                 assertTrue( resultSet.next(), "COUNT query returned no row for table " + tableName );
                 assertTrue( resultSet.getLong( 1 ) > 0, "Expected table " + tableName + " to contain rows" );
@@ -124,7 +125,7 @@ void readsExpectedRowsFromCustomers() throws SQLException {
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name, email, country, signup_date "
-                                    + "FROM customers "
+                                    + "FROM " + table( "customers" ) + " "
                                     + "WHERE customer_id <= 3 "
                                     + "ORDER BY customer_id" ) ) {
 
@@ -147,7 +148,7 @@ void filtersRowsWithWhereClause() throws SQLException {
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name, email, country, signup_date "
-                                    + "FROM customers "
+                                    + "FROM " + table( "customers" ) + " "
                                     + "WHERE country = 'France' "
                                     + "ORDER BY customer_id "
                                     + "LIMIT 3" ) ) {
@@ -171,7 +172,7 @@ void projectsOnlyRequestedColumns() throws SQLException {
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name "
-                                    + "FROM customers "
+                                    + "FROM " + table( "customers" ) + " "
                                     + "WHERE customer_id <= 3 "
                                     + "ORDER BY customer_id" ) ) {
 
@@ -194,7 +195,7 @@ void supportsGreaterThanFilter() throws SQLException {
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name "
-                                    + "FROM customers "
+                                    + "FROM " + table( "customers" ) + " "
                                     + "WHERE customer_id > 3 "
                                     + "ORDER BY customer_id "
                                     + "LIMIT 3" ) ) {
@@ -215,7 +216,7 @@ void supportsGreaterThanFilter() throws SQLException {
     void supportsDateFilterLiterals() throws SQLException {
         assertQueryResult(
                 "SELECT customer_id, name "
-                        + "FROM customers "
+                        + "FROM " + table( "customers" ) + " "
                         + "WHERE signup_date = TIMESTAMP '2023-02-04 00:00:00' "
                         + "AND customer_id = 3 "
                         + "ORDER BY customer_id",
@@ -286,7 +287,7 @@ private void assertCustomerFilterResult( String filter, ImmutableList<Object[]>
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name "
-                                    + "FROM customers "
+                                    + "FROM " + table( "customers" ) + " "
                                     + "WHERE " + filter + " "
                                     + "ORDER BY customer_id "
                                     + "LIMIT 3" ) ) {
@@ -305,7 +306,7 @@ void rejectsUpdateOnParquetSource() throws SQLException {
                 assertThrows(
                         SQLException.class,
                         () -> statement.executeUpdate(
-                                "UPDATE customers "
+                                "UPDATE " + table( "customers" ) + " "
                                         + "SET country = 'Switzerland' "
                                         + "WHERE customer_id = 1" )
                 );
@@ -329,7 +330,7 @@ private void assertQueryResult( String sql, ImmutableList<Object[]> expected ) t
     void projectsAndFiltersOtherTables() throws SQLException {
         assertQueryResult(
                 "SELECT order_id, status, total_price "
-                        + "FROM orders "
+                        + "FROM " + table( "orders" ) + " "
                         + "WHERE order_id <= 3 "
                         + "ORDER BY order_id",
                 ImmutableList.of(
@@ -341,7 +342,7 @@ void projectsAndFiltersOtherTables() throws SQLException {
 
         assertQueryResult(
                 "SELECT order_item_id, product_id, quantity "
-                        + "FROM order_items "
+                        + "FROM " + table( "order_items" ) + " "
                         + "WHERE order_id = 1 "
                         + "ORDER BY order_item_id "
                         + "LIMIT 3",
@@ -354,7 +355,7 @@ void projectsAndFiltersOtherTables() throws SQLException {
 
         assertQueryResult(
                 "SELECT product_id, name, category "
-                        + "FROM products "
+                        + "FROM " + table( "products" ) + " "
                         + "WHERE product_id <= 3 "
                         + "ORDER BY product_id",
                 ImmutableList.of(
@@ -365,30 +366,35 @@ void projectsAndFiltersOtherTables() throws SQLException {
         );
     }
 
-    // check that nested data returned as string
+    // check that nested data is returned as document
     @Test
-    void returnsNestedShippingAddressAsJSON() throws SQLException {
+    void returnsNestedShippingAddressAsDocument() throws SQLException {
         try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
             Connection connection = jdbcConnection.getConnection();
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT shipping_address "
-                                    + "FROM orders "
+                                    + "FROM " + table( "orders" ) + " "
                                     + "WHERE order_id = 1" ) ) {
 
                 assertTrue( resultSet.next() );
+                assertEquals( "DOCUMENT", resultSet.getMetaData().getColumnTypeName( 1 ) );
 
                 Object value = resultSet.getObject( 1 );
-                String shippingAddress = assertInstanceOf( String.class, value );
+                PolyDocument shippingAddress = assertInstanceOf( PolyDocument.class, value );
 
-                assertEquals(
-                        "{\"city\":\"Lucerne\",\"street\":\"Gartenweg 103\",\"zip\":7101}",
-                        shippingAddress
-                );
+                assertEquals( 3, shippingAddress.size() );
+                assertEquals( "Lucerne", shippingAddress.get( "city" ).asString() );
+                assertEquals( "Gartenweg 103", shippingAddress.get( "street" ).asString() );
+                assertEquals( 7101, shippingAddress.get( "zip" ).asInt() );
             }
         }
     }
 
 
+    private static String table( String tableName ) {
+        return SOURCE_NAME + "__" + tableName;
+    }
+
 
 }
diff --git a/plugins/workflow-engine/build.gradle b/plugins/workflow-engine/build.gradle
index 9703a16c8c..d3179faab7 100644
--- a/plugins/workflow-engine/build.gradle
+++ b/plugins/workflow-engine/build.gradle
@@ -130,4 +130,7 @@ java {
 licensee {
     allow('MIT')
     allow('Apache-2.0')
+    allow('BSD-2-Clause')
+    allow('BSD-3-Clause')
+    allow('CDDL-1.1')
 }

From 54309bfbdc1d4bb13d5eed00764d83f157ffae26 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Sun, 3 May 2026 10:51:42 +0200
Subject: [PATCH 23/46] add logical filters and IN filter - documentation and
 fixes

---
 .../relational/schema/ParquetRelTable.java      | 17 ++++++++++++++++-
 1 file changed, 16 insertions(+), 1 deletion(-)

diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index 4cfed2b34d..8abd3bddb9 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -263,7 +263,22 @@ private ParquetAdapterFilter resolveFilter( DataContext dataContext, ParquetAdap
                 : dataContext.getParameterValue( filter.dynamicParamIndex() );
 
         ParquetColumnBinding columnBinding = Objects.requireNonNull( binding.getColumnBinding( columns.get( filter.columnIndex() ).id ), "Missing parquet column binding" );
-        return new ParquetAdapterFilter( filter.columnIndex(), columnBinding.sourcePathElements(), filter.operator(), value );
+        List<String> pathElements = canReadFilterByColumnIndex( filter.columnIndex(), columnBinding )
+                ? List.of()
+                : columnBinding.sourcePathElements();
+        return new ParquetAdapterFilter( filter.columnIndex(), pathElements, filter.operator(), value );
+    }
+
+
+    private boolean canReadFilterByColumnIndex( int field, ParquetColumnBinding columnBinding ) {
+        if ( isNestedTable() || columnBinding.sourcePathElements().size() != 1 ) {
+            return false;
+        }
+
+        var schema = schemaReader.getSchema();
+        return field >= 0
+                && field < schema.getFieldCount()
+                && schema.getType( field ).getName().equals( columnBinding.sourcePathElements().get( 0 ) );
     }
 
 

From 3325fb3a622adf0be1dd4a9dc8404580a33ecb77 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Fri, 8 May 2026 14:05:25 +0200
Subject: [PATCH 24/46] support root-child join on adapter level

---
 plugins/parquet-adapter/docs/images/join.png  | Bin 0 -> 70099 bytes
 .../docs/images/parquet_rel_join.png          | Bin 0 -> 61792 bytes
 plugins/parquet-adapter/docs/joins.md         | 267 ++++++++++++++++++
 plugins/parquet-adapter/docs/planner.md       | 226 +++++++++++++++
 plugins/parquet-adapter/docs/todo_joins.md    |  56 ++++
 .../execution/ParquetDocEnumerator.java       |   8 +-
 .../document/planning/ParquetDocScan.java     |  16 +-
 .../document/schema/ParquetDocument.java      |   3 +-
 .../ParquetNestedJoinEnumerator.java          | 198 +++++++++++++
 ...ParquetNestedNonRepeatedRelEnumerator.java |   8 +-
 .../ParquetNestedRepeatedRelEnumerator.java   |  38 +--
 .../execution/ParquetRelEnumerator.java       |  14 +-
 .../relational/planning/JoinDirection.java    |  21 ++
 .../ParquetEnumerableCalcJoinRule.java        |  84 ++++++
 .../ParquetEnumerableFilterJoinRule.java      |  63 +++++
 .../planning/ParquetEnumerableJoinRule.java   |  55 ++++
 .../relational/planning/ParquetRelJoin.java   | 226 +++++++++++++++
 .../planning/ParquetRelJoinRule.java          |  78 +++++
 .../relational/planning/ParquetRelScan.java   |   6 +
 .../relational/schema/ParquetRelTable.java    | 163 ++++++++---
 .../execution/AbstractParquetEnumerator.java  |  31 +-
 .../shared/execution/CombinedGroup.java       | 236 ++++++++++++++++
 .../shared/filter/FiltersContainer.java       |  74 +++++
 .../shared/filter/JoinFiltersContainer.java   |  63 +++++
 .../shared/filter/JoinFiltersSplitter.java    | 101 +++++++
 .../shared/filter/ParquetAdapterFilter.java   |  15 +
 26 files changed, 1954 insertions(+), 96 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/images/join.png
 create mode 100644 plugins/parquet-adapter/docs/images/parquet_rel_join.png
 create mode 100644 plugins/parquet-adapter/docs/joins.md
 create mode 100644 plugins/parquet-adapter/docs/planner.md
 create mode 100644 plugins/parquet-adapter/docs/todo_joins.md
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinDirection.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcJoinRule.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterJoinRule.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/CombinedGroup.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FiltersContainer.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersContainer.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersSplitter.java

diff --git a/plugins/parquet-adapter/docs/images/join.png b/plugins/parquet-adapter/docs/images/join.png
new file mode 100644
index 0000000000000000000000000000000000000000..813c2a052bfbd09872a3bd022db932446e953861
GIT binary patch
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diff --git a/plugins/parquet-adapter/docs/joins.md b/plugins/parquet-adapter/docs/joins.md
new file mode 100644
index 0000000000..d0a2128c73
--- /dev/null
+++ b/plugins/parquet-adapter/docs/joins.md
@@ -0,0 +1,267 @@
+# Adapter Level Joins
+
+
+## Change Description
+
+The change adds support for executing a limited class of joins inside the Parquet adapter instead of letting Polypheny execute the join above two independent scans. The supported case is a normalized nested-table join where one table represents a parent Parquet group and the other table represents a child repeated group from the same Parquet source.
+
+The purpose is to avoid reading parent and child tables independently and joining them later. The adapter already knows the source path and synthetic key roles for nested generated tables, so it can reconstruct the parent-child relationship while walking the Parquet row structure.
+
+## Supported Joins
+
+This change supports adapter-level joins only for normalized Parquet nested tables that belong to the same Parquet source. It is not a general Parquet join implementation and it is not a general self-join on the same physical table.
+
+The supported case is a parent-child join where:
+
+- Both tables have the same `sourceUrl`.
+- One table is an ancestor of the other table in the Parquet path hierarchy.
+- The child table binding has `parentTableName()` equal to the parent table name.
+- The parent table path is a prefix of the child table path.
+- The join condition is a single equi-join.
+- The parent join key is the generated `PRIMARY_KEY` column.
+- The child join key is the generated `PARENT_KEY` column.
+
+The parent is often the root table, but it does not have to be root. A nested table can also be the parent if its path is an ancestor of the child path. At runtime the adapter reads from the parent source, resolves the parent path first, then resolves the child path below each parent row.
+
+Supported joins are checked in `ParquetRelJoin.supportedDirection(...)`. The join must be an equi-join with exactly one key on each side, both tables must come from the same source URL, and the keys must match the generated role mapping: parent `PRIMARY_KEY` to child `PARENT_KEY`. The child table path must also be below the parent path. Join types accepted by the rule are `INNER`, `LEFT`, `RIGHT`, and `FULL`.
+
+## Unsupported Joins
+
+For unsupported join shapes, the new adapter-level path should simply not apply.
+
+`ParquetRelJoin.supportedDirection(...)` returns `null` when the join is not supported. Then:
+
+- `ParquetRelJoinRule` does not transform the logical join.
+- `ParquetEnumerableJoinRule` does not replace the enumerable join.
+- The planner keeps using Polypheny's normal join implementation above the scans.
+
+Unsupported joins:
+
+- Joins between unrelated Parquet tables.
+- Joins between different Parquet sources.
+- Joins where neither side is the parent of the other.
+- Multi-key joins.
+- Non-equi joins like <, >, !=
+- Join types other than `INNER`, `LEFT`, `RIGHT`, and `FULL`.
+- General joins on arbitrary user columns.
+- Independent child-side outer join scanning outside the parent path.
+
+
+### Flow
+
+- Rule invocation starts from `ParquetRelScan.register(...)`. When a Parquet scan participates in planning, it registers the existing scan rule and the new join-related rules:
+
+  - `ParquetRelScanRule`
+  - `ParquetRelJoinRule`
+  - `ParquetEnumerableJoinRule`
+  - `ParquetEnumerableFilterJoinRule`
+  - `ParquetEnumerableCalcJoinRule`
+  
+
+- There are two paths to create the adapter join. 
+
+
+- `ParquetRelJoinRule` handles a logical join path: 
+  - it receives a `LogicalRelJoin`
+  - tries to convert both inputs to enumerable Parquet scans
+  - extracts `ParquetRelScan` from the result
+  - validates the parent-child direction
+  - creates `ParquetRelJoin`. 
+  
+
+- `ParquetEnumerableJoinRule` handles the later physical path: 
+  - if an `EnumerableJoin` already has two `ParquetRelScan` inputs, it performs the same support check and replaces the enumerable join with `ParquetRelJoin`.
+
+
+- After `ParquetRelJoin` exists, filters above it can be pushed into the adapter join. 
+`ParquetEnumerableFilterJoinRule` matches `EnumerableFilter(ParquetRelJoin)`, translates the filter condition with `ParquetRelFilterTranslator`, and returns `join.withFilters(...)`. 
+
+
+- `ParquetEnumerableCalcJoinRule` does the same for `EnumerableCalc(ParquetRelJoin)` when the calc contains a condition; it pushes the condition into the join and keeps a projection-only calc above the filtered join.
+
+
+- At runtime, `ParquetRelJoin.implement(...)` generates enumerable code that calls `leftTable.nestedJoin(dataContext, rightTable, leftFields, rightFields, leftIsParent, emitUnmatchedParents, filters)`. `emitUnmatchedParents` is derived from join type and direction: parent-side outer rows are emitted for parent-preserving `LEFT` or `RIGHT` joins, and for `FULL`. This runtime path preserves unmatched parent rows; it does not perform an independent child-side scan outside the parent path.
+
+
+- `ParquetRelTable.nestedJoin(...)` is the runtime entry point. It chooses which input is parent and which input is child based on `leftIsParent`, resolves dynamic parameters, maps joined filter column indexes to the correct left or right table column binding, and calls `JoinFiltersSplitter`.
+
+
+- `JoinFiltersSplitter` classifies filters by the fields they reference. Parent-only filters that can be represented by Parquet paths may become reader filters. Parent-only filters that cannot be reader filters are kept as parent adapter filters. Child-only filters are applied after child expansion. Filters spanning both sides, invalid indexes, or mixed logical conditions become adapter-level joined-row filters.
+
+
+- `ParquetNestedJoinEnumerator` performs the actual read. It opens the parent source, walks the parent path using inherited nested resolution from `ParquetNestedRepeatedRelEnumerator`, applies parent filters, resolves child groups below each accepted parent, and creates `CombinedGroup` for each joined parent-child row. If a parent has no child and the join type requires preserving unmatched parents, it creates a `CombinedGroup` with a null child side.
+
+
+- `CombinedGroup` is the bridge between nested Parquet groups and joined row indexes. When the enumerator extracts output values or evaluates filters, the combined group maps each joined field index to the correct parent or child `VirtualGroup`, binding, and table path. This lets the same extraction logic handle both left-parent and right-parent join directions.
+
+
+- The filter infrastructure was generalized to support this flow. `FiltersContainer` separates reader-level filters from adapter-level filters, and `AbstractParquetEnumerator` now uses that separation when creating `ParquetSourceReader` and when doing row-level checks. Document and normal relational scans were updated to use the same container API so filtering has one common runtime shape across scan and join paths.
+
+
+## Changed files:
+
+### ParquetRelScan
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java`
+
+Registers the new join rules in addition to the scan rule. Also exposes scan fields via Lombok `@Getter`, which is needed by join validation to map join keys back to physical table columns.
+
+### ParquetRelJoinRule
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java`
+
+New file. Converts a `LogicalRelJoin` into `ParquetRelJoin` when both sides can be resolved to `ParquetRelScan` and `ParquetRelJoin.supportedDirection(...)` accepts the join.
+
+### ParquetEnumerableJoinRule
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java`
+
+New file. Rewrites an `EnumerableJoin` over two `ParquetRelScan` inputs into `ParquetRelJoin` when the join is a supported nested parent-child join. This catches plans where the join has already been converted to enumerable form.
+
+### ParquetRelJoin
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java`
+
+New file. Physical adapter-level join node. It provides supported join validation through `supportedDirection(...)`, stores table metadata, projected fields, join direction, join type, and pushed filters, and generates runtime code that calls `ParquetRelTable.nestedJoin(...)`.
+
+### JoinDirection
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinDirection.java`
+
+New file. Small value record used by join support checks to remember whether the left input is the parent table. It is returned by `ParquetRelJoin.supportedDirection(...)`.
+
+### ParquetEnumerableFilterJoinRule
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterJoinRule.java`
+
+New file. Pushes an `EnumerableFilter` above a `ParquetRelJoin` into the adapter join by translating the Rex condition into `ParquetAdapterFilter`.
+
+### ParquetEnumerableCalcJoinRule
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcJoinRule.java`
+
+New file. Pushes the filter part of an `EnumerableCalc` above a `ParquetRelJoin` into the join while preserving the projection-only calc. Added to support plans where Polypheny represents filter and projection together after the join.
+
+### ParquetRelTable
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java`
+
+Adds adapter-level nested join execution through `nestedJoin(...)`. Also exposes binding/source metadata for rule validation, resolves filters with table-specific column bindings, uses `FiltersContainer` for scans, and renames selected binding helper logic to projected binding logic.
+
+### ParquetNestedJoinEnumerator
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java`
+
+New file. Executes supported parent-child joins inside the Parquet adapter. It is created by `ParquetRelTable.nestedJoin()` and reads parent and child rows from the same Parquet source, combines them into `CombinedGroup` rows, applies parent and child filters, and emits null child fields for supported outer join cases.
+
+### CombinedGroup
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/CombinedGroup.java`
+
+New file. Virtual joined row wrapper used by `ParquetNestedJoinEnumerator`. It maps joined output field indexes to either parent or child `VirtualGroup`, supports null-side checks for outer joins, and blocks direct Parquet field access because values are extracted through bindings.
+
+### JoinFiltersSplitter
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersSplitter.java`
+
+New file. Splits pushed join filters into parent filters, child filters, adapter filters, and reader filters. Added because filters over joined rows cannot all be pushed to the Parquet reader or to one side safely.
+
+### JoinFiltersContainer
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersContainer.java`
+
+New file. Extends `FiltersContainer` with parent-only and child-only filter lists. Used by `ParquetNestedJoinEnumerator` to apply filters at the correct stage of parent-child expansion.
+
+### FiltersContainer
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FiltersContainer.java`
+
+New file. Holds two filter lists: adapter-level filters for row checks and reader-level filters for native Parquet predicate pushdown. Added so scans and joins can choose where each filter is safe to apply.
+
+### AbstractParquetEnumerator
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java`
+
+Refactoring and extension. Accepts `FiltersContainer`, passes reader filters to `ParquetSourceReader`, keeps adapter filters for row-level matching, and exposes matching hooks so `ParquetNestedJoinEnumerator` can filter `CombinedGroup` rows.
+
+### ParquetNestedRepeatedRelEnumerator
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java`
+
+Extended for join reuse. It now accepts `FiltersContainer`, exposes nested path resolution and role-based value extraction to subclasses, and supports an internal constructor that allows root table paths for join execution.
+
+### ParquetAdapterFilter
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java`
+
+Adds `toExpression()` so adapter filters, including logical filters and path-aware filters, can be serialized into generated enumerable runtime code.
+
+### ParquetRelEnumerator
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java`
+
+Refactoring. Constructor now accepts `FiltersContainer`, passing reader filters to `ParquetSourceReader` and adapter filters to row-level filtering in `AbstractParquetEnumerator`.
+
+### ParquetNestedNonRepeatedRelEnumerator
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java`
+
+Refactoring. Constructor now accepts `FiltersContainer` instead of a raw filter list, so the shared enumerator can distinguish adapter-level filters from reader-level filters.
+
+### ParquetDocScan
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java`
+
+Refactoring. Uses `ParquetAdapterFilter.toExpression()` instead of building filter expressions locally. This keeps document and relational scans using the same runtime filter serialization logic.
+
+### ParquetDocument
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java`
+
+Refactoring. Wraps resolved document filters in `FiltersContainer.shared(...)` before creating `ParquetDocEnumerator`, so document scans use the new common filter container API.
+
+### ParquetDocEnumerator
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java`
+
+Refactoring. Use FiltersContainer.
+
+## Examples
+
+![Schema display](images/parquet_rel_join.png)
+
+![Schema display](images/join.png)
+
+## Convention
+
+In Polypheny planning, a convention describes how an algebra node is implemented physically. Logical algebra says what should happen, while convention says how and where it can be executed.
+
+For example, a logical join only describes the operation:
+
+```text
+LogicalRelJoin
+  left input
+  right input
+```
+
+At this point it has `Convention.NONE`, which means it is still a logical plan node and does not yet have a concrete execution implementation.
+
+`EnumerableConvention.INSTANCE` means the node can be executed through Polypheny's enumerable runtime model. In this model, an operator returns rows through an `Enumerable` / `Enumerator`, similar to an iterator:
+
+```text
+while (enumerator.moveNext()) {
+    row = enumerator.current();
+}
+```
+
+Enumerable does not mean that there are multiple joins. It means the operator produces result rows one by one. A single join can still be enumerable because it may produce many joined rows.
+
+In this change, `ParquetRelJoinRule` is a converter rule:
+
+```text
+LogicalRelJoin.class,
+Convention.NONE,
+EnumerableConvention.INSTANCE
+```
+
+This means: take a logical join and, if it is a supported Parquet parent-child join, convert it into an enumerable physical implementation.
+
+The converted node is:
+
+```text
+ParquetRelJoin
+```
+
+`ParquetRelJoin` implements `EnumerableAlg`, so it participates in enumerable execution. Its `implement(...)` method generates runtime code that calls:
+
+```text
+ParquetRelTable.nestedJoin(...)
+```
+
+That method returns:
+
+```text
+Enumerable<PolyValue[]>
+```
+
+and creates `ParquetNestedJoinEnumerator`, which yields the joined rows one by one.
+
diff --git a/plugins/parquet-adapter/docs/planner.md b/plugins/parquet-adapter/docs/planner.md
new file mode 100644
index 0000000000..a3408662b6
--- /dev/null
+++ b/plugins/parquet-adapter/docs/planner.md
@@ -0,0 +1,226 @@
+# Planner Basics
+
+The planner takes a query and turns it into an executable plan.
+
+In simple words:
+
+```text
+SQL query
+  -> logical plan
+  -> optimized physical plan
+  -> runtime execution
+```
+
+## Plan Nodes
+
+A plan node is one operation in the query plan.
+
+Examples:
+
+```text
+Scan table
+Filter rows
+Project columns
+Join two inputs
+```
+
+In code, plan nodes are classes such as:
+
+```text
+LogicalRelJoin
+EnumerableJoin
+ParquetRelScan
+ParquetRelJoin
+```
+
+A plan is a tree of plan nodes. For example:
+
+```text
+LogicalRelProject
+  LogicalRelFilter
+    LogicalRelJoin
+      LogicalRelScan(customers)
+      LogicalRelScan(orders)
+```
+
+The node describes what should happen at that point in the tree.
+
+## Logical And Physical Nodes
+
+Logical nodes describe the operation, but not exactly how it will be executed.
+
+Example:
+
+```text
+LogicalRelJoin
+```
+
+means:
+
+```text
+join these two inputs
+```
+
+It does not yet say whether the join will be executed by Polypheny, by an adapter, or by generated enumerable code.
+
+Physical nodes describe a concrete execution strategy.
+
+Examples:
+
+```text
+ParquetRelScan
+ParquetRelJoin
+EnumerableJoin
+```
+
+`ParquetRelJoin` means:
+
+```text
+execute this supported join inside the Parquet adapter
+```
+
+## Rules
+
+A rule is not a plan node.
+
+A rule is a planner transformation. It looks for a specific plan shape and replaces it with another plan shape.
+
+Examples:
+
+```text
+ParquetRelScanRule
+ParquetRelJoinRule
+ParquetEnumerableJoinRule
+ParquetEnumerableFilterJoinRule
+ParquetEnumerableCalcJoinRule
+```
+
+A rule usually extends:
+
+```text
+AlgOptRule
+ConverterRule
+```
+
+Plan nodes usually extend or implement algebra classes, for example:
+
+```text
+ParquetRelScan extends RelScan<ParquetRelTable> implements EnumerableAlg
+ParquetRelJoin extends Join implements EnumerableAlg
+```
+
+So the difference is:
+
+```text
+Rule:
+  changes the plan during optimization
+
+Plan node:
+  is part of the plan that will be optimized or executed
+```
+
+## Example: Projection Pushdown
+
+`ParquetRelScanRule` matches:
+
+```text
+LogicalRelProject
+  ParquetRelScan
+```
+
+If the project only selects existing columns, the rule replaces it with:
+
+```text
+ParquetRelScan(projected fields only)
+```
+
+So this:
+
+```sql
+SELECT name, age FROM customers
+```
+
+can become a scan that reads only `name` and `age`.
+
+Here:
+
+- `ParquetRelScanRule` is the rule.
+- `LogicalRelProject` and `ParquetRelScan` are plan nodes.
+- The rule removes the separate project node and creates a new `ParquetRelScan` node with fewer fields.
+
+## Example: Adapter-Level Join
+
+Before the join rule applies, the planner may have:
+
+```text
+LogicalRelJoin
+  ParquetRelScan(parent)
+  ParquetRelScan(child)
+```
+
+`ParquetRelJoinRule` checks whether this is a supported Parquet parent-child join.
+
+If it is supported, the rule replaces the logical join with:
+
+```text
+ParquetRelJoin
+  ParquetRelScan(parent)
+  ParquetRelScan(child)
+```
+
+Here:
+
+- `ParquetRelJoinRule` is the rule.
+- `LogicalRelJoin`, `ParquetRelScan`, and `ParquetRelJoin` are plan nodes.
+- `ParquetRelJoin` is the physical node that later generates runtime code.
+
+At runtime, `ParquetRelJoin.implement(...)` calls:
+
+```text
+ParquetRelTable.nestedJoin(...)
+```
+
+which returns rows through:
+
+```text
+Enumerable<PolyValue[]>
+```
+
+## Convention
+
+A convention describes how a plan node is implemented physically.
+
+`Convention.NONE` means:
+
+```text
+logical node, no concrete execution strategy yet
+```
+
+`EnumerableConvention.INSTANCE` means:
+
+```text
+physical node that can execute through the enumerable row-iterator runtime
+```
+
+Enumerable does not mean there are many joins. It means rows are produced one by one through an `Enumerable` / `Enumerator`.
+
+For example, a single `ParquetRelJoin` can be enumerable because it produces joined rows one by one.
+
+## Short Mental Model
+
+Use this mental model:
+
+```text
+Plan node = an operation in the plan tree
+Rule      = a rewrite that changes the plan tree
+Convention = the execution style of a plan node
+```
+
+Example:
+
+```text
+ParquetRelJoinRule sees LogicalRelJoin
+  -> checks if adapter join is possible
+  -> creates ParquetRelJoin
+  -> ParquetRelJoin later executes the join
+```
diff --git a/plugins/parquet-adapter/docs/todo_joins.md b/plugins/parquet-adapter/docs/todo_joins.md
new file mode 100644
index 0000000000..aef767ce54
--- /dev/null
+++ b/plugins/parquet-adapter/docs/todo_joins.md
@@ -0,0 +1,56 @@
+# TODO: Adapter Level Joins
+
+## Tests
+
+- Add tests for all supported join types: `INNER`, `LEFT`, `RIGHT`, and `FULL`.
+- Add tests where the parent table is on the left side of the join.
+- Add tests where the parent table is on the right side of the join.
+- Add tests for joins where the parent table is the root table.
+- Add tests for joins where the parent table is itself nested and the child table is below it.
+- Add tests for filters above adapter-level joins.
+- Add tests for calc nodes above adapter-level joins, especially filter plus projection.
+- Add tests that unsupported joins fall back to Polypheny-level join execution.
+- Add result-equivalence tests comparing adapter-level join output with normal Polypheny join output.
+
+## Future Join Tests
+
+- Add tests for child-child joins inside the same parent when this join shape is supported.
+- Add tests for ancestor-descendant joins beyond direct parent-child joins when this join shape is supported.
+- Add fallback tests proving child-child joins and ancestor-descendant joins remain Polypheny-level joins until adapter-level support is implemented.
+
+## Filter Correctness
+
+- Review the split between reader filters and adapter filters.
+- Ensure filters pushed to the native Parquet reader are still validated at adapter level when native filtering may be incomplete or unsupported.
+- Verify logical filters (`AND`, `OR`, `NOT`) are split safely when some operands refer to parent fields, child fields, or both sides.
+- Verify dynamic parameters are resolved correctly for join filters on both left and right inputs.
+- Fix the flat relational filter regression where path-based resolved filters can reach `ParquetRelValueExtractor`, which does not support path extraction.
+
+## Join Semantics
+
+- Clarify and test `FULL` join behavior for nested parent-child data.
+- Document that the runtime preserves unmatched parent rows but does not perform an independent child-side outer scan outside the parent path.
+- Verify null-side row construction for `LEFT`, `RIGHT`, and `FULL` joins in both parent-left and parent-right directions.
+
+## Future Join Scope
+
+- Add support for child-child joins inside the same parent. Example: root has repeated `orders` and repeated `payments`, and the query joins both child tables through the same parent key. The adapter could read one parent row, expand both child collections under it, and join them locally. This would require a new runtime strategy because the current `ParquetNestedJoinEnumerator` assumes one parent path and one descendant child path, not two sibling child paths.
+- Add support for ancestor-descendant joins beyond direct parent-child joins. Example: `root -> order -> item`, where a query joins root to item or order to item. The current path-prefix logic is already close to this, but the key-role model may need clearer handling if the immediate `PARENT_KEY` points only to the direct parent.
+
+## Planner Rules
+
+- Check whether valid plans are missed when projections, filters, or calcs appear between the join and `ParquetRelScan`.
+- Consider whether additional rules are needed to recognize supported joins after simple projection reshaping.
+- Confirm that unsupported joins always remain executable through normal Polypheny planning.
+
+## Costing
+
+- Improve `ParquetRelJoin.computeSelfCost(...)`.
+- Include source scan cost, nested expansion cost, expected child cardinality, and pushed filter selectivity when possible.
+- Check whether the planner chooses adapter-level joins too eagerly or not eagerly enough.
+
+## Runtime Robustness
+
+- Add tests for empty parent groups, empty child groups, missing optional nested fields, and repeated child groups.
+- Verify cancellation behavior during nested join expansion.
+- Verify close/error handling when join execution fails while reading the Parquet source.
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
index b1375cc41c..6709e2a952 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
@@ -16,12 +16,11 @@
 
 package org.polypheny.db.adapter.parquet.document.execution;
 
-import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.parquet.example.data.Group;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
+import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.type.entity.document.PolyDocument;
@@ -37,8 +36,8 @@ public class ParquetDocEnumerator extends AbstractParquetEnumerator implements E
     private final ParquetDocValueExtractor documentValueExtractor;
 
 
-    public ParquetDocEnumerator( Source source, AtomicBoolean cancelFlag, List<ParquetAdapterFilter> filters ) {
-        super( source, cancelFlag, null, filters, new ParquetDocValueExtractor() );
+    public ParquetDocEnumerator( Source source, AtomicBoolean cancelFlag, FiltersContainer filtersContainer ) {
+        super( source, cancelFlag, null, filtersContainer, new ParquetDocValueExtractor() );
         this.documentValueExtractor = (ParquetDocValueExtractor) valueExtractor;
         this.documentPrefix = source.path();
     }
@@ -46,6 +45,7 @@ public ParquetDocEnumerator( Source source, AtomicBoolean cancelFlag, List<Parqu
 
     /**
      * Creates document from parquet file row
+     *
      * @param group - parquet file row
      * @return document converted to PolyValue list
      */
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
index 728038f278..a0d9d3d074 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
@@ -21,17 +21,16 @@
 import org.apache.calcite.linq4j.tree.Blocks;
 import org.apache.calcite.linq4j.tree.Expression;
 import org.apache.calcite.linq4j.tree.Expressions;
-import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.AlgWriter;
 import org.polypheny.db.algebra.core.AlgFactories;
 import org.polypheny.db.algebra.core.document.DocumentScan;
+import org.polypheny.db.algebra.enumerable.EnumUtils;
 import org.polypheny.db.algebra.enumerable.EnumerableAlg;
 import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
 import org.polypheny.db.algebra.enumerable.EnumerableConvention;
-import org.polypheny.db.algebra.enumerable.EnumUtils;
 import org.polypheny.db.algebra.enumerable.PhysType;
 import org.polypheny.db.algebra.enumerable.PhysTypeImpl;
 import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
@@ -42,7 +41,6 @@
 import org.polypheny.db.plan.AlgOptCost;
 import org.polypheny.db.plan.AlgPlanner;
 import org.polypheny.db.plan.AlgTraitSet;
-import org.polypheny.db.type.entity.PolyValue;
 
 /**
  * The planner node that sets up document reading.
@@ -95,7 +93,7 @@ public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
     @Override
     public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
         PhysType physType = PhysTypeImpl.of( implementor.getTypeFactory(), getTupleType(), pref.preferArray() );
-        Expression runtimeFilters = EnumUtils.expressionList( filters.stream().map( ParquetDocScan::toFilterExpression ).toList() );
+        Expression runtimeFilters = EnumUtils.expressionList( filters.stream().map( ParquetAdapterFilter::toExpression ).toList() );
         // create runtime code that will call scanFiltered() on the ParquetDocument entity
         return implementor.result(
                 physType,
@@ -108,16 +106,6 @@ public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
     }
 
 
-    private static Expression toFilterExpression( ParquetAdapterFilter filter ) {
-        return Expressions.new_(
-                ParquetAdapterFilter.class,
-                Expressions.constant( filter.columnIndex() ),
-                Expressions.constant( filter.operator(), Kind.class ),
-                filter.polyValue() == null ? Expressions.constant( null, PolyValue.class ) : filter.polyValue().asExpression(),
-                Expressions.constant( filter.dynamicParamIndex(), Long.class ) );
-    }
-
-
     @Override
     public void register( AlgPlanner planner ) {
         planner.addRuleDuringRuntime( new ParquetDocFilterRule( AlgFactories.LOGICAL_BUILDER, entity ) );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
index e29fcaaff2..2ea436c004 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
@@ -29,6 +29,7 @@
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
 import org.polypheny.db.adapter.parquet.document.execution.ParquetDocEnumerator;
 import org.polypheny.db.adapter.parquet.document.planning.ParquetDocScan;
+import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.algebra.AlgNode;
@@ -108,7 +109,7 @@ public Enumerable<PolyValue[]> scanFiltered( DataContext dataContext, List<Parqu
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                return new ParquetDocEnumerator( source, cancelFlag, resolvedFilters );
+                return new ParquetDocEnumerator( source, cancelFlag, FiltersContainer.shared( resolvedFilters ) );
             }
         };
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
new file mode 100644
index 0000000000..57e65d3969
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
@@ -0,0 +1,198 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.parquet.example.data.Group;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
+import org.polypheny.db.adapter.parquet.shared.execution.CombinedGroup;
+import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
+import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+/**
+ * Executes parent/child joins for normalized nested Parquet tables in one pass over
+ * the backing file.
+ */
+public class ParquetNestedJoinEnumerator extends ParquetNestedRepeatedRelEnumerator {
+
+    private final ParquetTableBinding parentBinding;
+    private final ParquetTableBinding childBinding;
+    private final List<ParquetColumnBinding> parentColumns;
+    private final List<ParquetColumnBinding> childColumns;
+    private final List<ParquetAdapterFilter> parentFilters;
+    private final List<ParquetAdapterFilter> childFilters;
+    private final boolean leftIsParent;
+    private final boolean emitUnmatchedParents;
+    private final List<String> childPathFromParent;
+
+
+    public ParquetNestedJoinEnumerator(
+            Source source,
+            AtomicBoolean cancelFlag,
+            ParquetTableBinding parentBinding,
+            ParquetTableBinding childBinding,
+            List<ParquetColumnBinding> parentColumns,
+            List<ParquetColumnBinding> childColumns,
+            JoinFiltersContainer filterContainer,
+            boolean leftIsParent,
+            boolean emitUnmatchedParents ) {
+        super( source, cancelFlag, parentBinding, parentColumns, filterContainer, true );
+        this.parentBinding = parentBinding;
+        this.childBinding = childBinding;
+        this.parentColumns = List.copyOf( parentColumns );
+        this.childColumns = List.copyOf( childColumns );
+        this.parentFilters = filterContainer.parentFilters();
+        this.childFilters = filterContainer.childFilters();
+        this.leftIsParent = leftIsParent;
+        this.emitUnmatchedParents = emitUnmatchedParents;
+        this.childPathFromParent = childBinding.sourcePathElements().subList( parentBinding.sourcePathElements().size(), childBinding.sourcePathElements().size() );
+    }
+
+
+    @Override
+    protected List<Group> expandRow( Group group ) {
+        List<Group> rows = new ArrayList<>();
+        VirtualGroup rootGroup = new VirtualGroup( group, String.valueOf( reader.getCurrentRowNumber() ), null, 0 );
+        for ( Group parent : resolveNested( rootGroup, group.getType(), parentBinding.sourcePathElements(), 0 ) ) {
+            if ( !matchesFilters( combinedGroup( (VirtualGroup) parent, null ), parentFilters ) ) {
+                continue;
+            }
+
+            List<Group> childRows = resolveNested( (VirtualGroup) parent, parent.getType(), childPathFromParent, 0 );
+            if ( childRows.isEmpty() && emitUnmatchedParents ) {
+                if ( childFilters.isEmpty() ) {
+                    rows.add( new CombinedGroup(
+                            (VirtualGroup) parent,
+                            parentColumns,
+                            parentBinding.sourcePathElements(),
+                            null,
+                            childColumns,
+                            childBinding.sourcePathElements() ) );
+                }
+                continue;
+            }
+
+            rows.addAll( childRows.stream()
+                    .map( VirtualGroup.class::cast )
+                    .map( child -> combinedGroup( (VirtualGroup) parent, child ) )
+                    .filter( child -> matchesFilters( child, childFilters ) )
+                    .toList() );
+        }
+        return rows;
+    }
+
+
+    @Override
+    protected PolyValue[] extractRow( Group group ) {
+        CombinedGroup combinedGroup = (CombinedGroup) group;
+        PolyValue[] row = new PolyValue[combinedGroup.fieldCount()];
+        for ( int i = 0; i < row.length; i++ ) {
+            if ( combinedGroup.isNullField( i, leftIsParent ) ) {
+                row[i] = PolyNull.NULL;
+                continue;
+            }
+            row[i] = extractValueByColumnRole(
+                    combinedGroup.groupForField( i, leftIsParent ),
+                    combinedGroup.bindingForField( i, leftIsParent ),
+                    combinedGroup.tablePathForField( i, leftIsParent )
+            );
+        }
+        return row;
+    }
+
+
+    /**
+     * Extracts value from the group for the provided filter.
+     *
+     * @param group - parquet group
+     * @param filter - ParquetAdapterFilter
+     * @return an extracted value
+     */
+    @Override
+    protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
+        if ( group instanceof CombinedGroup combinedGroup ) {
+            return extractValueByColumnRole(
+                    combinedGroup.groupForField( filter.columnIndex(), leftIsParent ),
+                    combinedGroup.bindingForField( filter.columnIndex(), leftIsParent ),
+                    combinedGroup.tablePathForField( filter.columnIndex(), leftIsParent )
+            );
+        }
+        // If this is not a combined group then the filtering is being done on parent table only.
+        return extractValueByColumnRole(
+                (VirtualGroup) group,
+                parentColumns.get( filter.columnIndex() ),
+                parentBinding.sourcePathElements()
+        );
+    }
+
+
+    /**
+     * Checks if a filter is valid and can be applied on the group.
+     *
+     * @param group a group to check
+     * @param filter a filter to validate
+     * @return true if the filter is valid and false otherwise.
+     */
+    @Override
+    protected boolean canApplyFilter( Group group, ParquetAdapterFilter filter ) {
+        if ( filter.polyValue() == null || filter.columnIndex() < 0 ) {
+            return false;
+        }
+        if ( group instanceof CombinedGroup combinedGroup ) {
+            return filter.columnIndex() < combinedGroup.fieldCount();
+        }
+        return filter.columnIndex() < parentColumns.size();
+    }
+
+
+    /**
+     * Checks if the group contains a value.
+     *
+     * @param group a group to check.
+     * @param filter a filter containing column index which value is checked
+     * @return true if the group has a value at the filter column index and false otherwise.
+     */
+    @Override
+    protected boolean filterHasValue( Group group, ParquetAdapterFilter filter ) {
+        CombinedGroup combinedGroup = (CombinedGroup) group;
+        return !combinedGroup.isNullField( filter.columnIndex(), leftIsParent );
+    }
+
+
+    private CombinedGroup combinedGroup( VirtualGroup parent, VirtualGroup child ) {
+        return new CombinedGroup(
+                parent,
+                parentColumns,
+                parentBinding.sourcePathElements(),
+                child,
+                childColumns,
+                childBinding.sourcePathElements() );
+    }
+
+
+    private boolean matchesFilters( Group group, List<ParquetAdapterFilter> filters ) {
+        return filters.stream().allMatch( filter -> matches( group, filter ) );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
index a17ce3ae4e..91094dcd42 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
@@ -22,7 +22,7 @@
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
 import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.type.entity.PolyNull;
 import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
@@ -38,13 +38,13 @@ public class ParquetNestedNonRepeatedRelEnumerator extends AbstractParquetEnumer
 
 
     public ParquetNestedNonRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, List<ParquetColumnBinding> columnBindings ) {
-        this( source, cancelFlag, columnBindings, List.of() );
+        this( source, cancelFlag, columnBindings, FiltersContainer.empty );
     }
 
 
-    public ParquetNestedNonRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, List<ParquetColumnBinding> columnBindings, List<ParquetAdapterFilter> filters ) {
+    public ParquetNestedNonRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, List<ParquetColumnBinding> columnBindings, FiltersContainer filtersContainer ) {
         // read full root rows from the Parquet file
-        super( source, cancelFlag, null, filters, new ParquetPathValueExtractor() );
+        super( source, cancelFlag, null, filtersContainer, new ParquetPathValueExtractor() );
         this.columnBindings = List.copyOf( columnBindings );
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
index 916c74322e..559baf179b 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
@@ -26,6 +26,7 @@
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
 import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
+import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.type.entity.PolyNull;
@@ -44,31 +45,37 @@ public class ParquetNestedRepeatedRelEnumerator extends AbstractParquetEnumerato
 
 
     public ParquetNestedRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings ) {
-        this( source, cancelFlag, binding, columnBindings, List.of() );
+        this( source, cancelFlag, binding, columnBindings, FiltersContainer.empty, false );
     }
 
 
-    public ParquetNestedRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings, List<ParquetAdapterFilter> filters ) {
-        super( source, cancelFlag, null, filters, new ParquetPathValueExtractor() );
+    public ParquetNestedRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings, FiltersContainer filtersContainer ) {
+        this( source, cancelFlag, binding, columnBindings, filtersContainer, false );
+    }
+
+
+    protected ParquetNestedRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings, FiltersContainer filtersContainer, boolean allowRootTablePath ) {
+        super( source, cancelFlag, null, filtersContainer, new ParquetPathValueExtractor() );
 
         this.tablePath = binding.sourcePathElements();
         this.columnBindings = List.copyOf( columnBindings );
-        if ( tablePath.isEmpty() ) {
+        if ( !allowRootTablePath && tablePath.isEmpty() ) {
             throw new GenericRuntimeException( "Nested parquet table binding does not contain a table source path." );
         }
     }
 
 
     /**
-     *  Recursive function that finds which nested Parquet groups should become rows for a repeated generated table
-     *  Follow tablePath inside the current Parquet row and return all nested group occurrences that represent rows of the virtual repeated table.
+     * Recursive function that finds which nested Parquet groups should become rows for a repeated generated table
+     * Follow tablePath inside the current Parquet row and return all nested group occurrences that represent rows of the virtual repeated table.
+     *
      * @param group - parquet group/row
      * @return List<Group>
      */
     @Override
     protected List<Group> expandRow( Group group ) {
         var virtualGroup = new VirtualGroup( group, String.valueOf( reader.getCurrentRowNumber() ), null, 0 );
-        return resolveNested( virtualGroup, group.getType(), 0 );
+        return resolveNested( virtualGroup, group.getType(), tablePath, 0 );
     }
 
 
@@ -76,7 +83,7 @@ protected List<Group> expandRow( Group group ) {
     protected PolyValue[] extractRow( Group group ) {
         PolyValue[] row = new PolyValue[columnBindings.size()];
         for ( int i = 0; i < columnBindings.size(); i++ ) {
-            row[i] = extractValueByColumnRole( (VirtualGroup) group, columnBindings.get( i ) );
+            row[i] = extractValueByColumnRole( (VirtualGroup) group, columnBindings.get( i ), tablePath );
         }
         return row;
     }
@@ -86,16 +93,16 @@ protected PolyValue[] extractRow( Group group ) {
     protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
         var virtualGroup = (VirtualGroup) group;
         var binding = columnBindings.get( filter.columnIndex() );
-        return extractValueByColumnRole(virtualGroup, binding);
+        return extractValueByColumnRole( virtualGroup, binding, tablePath );
     }
 
 
-    private List<Group> resolveNested( VirtualGroup virtualGroup, GroupType groupType, int pathIndex ) {
-        if ( pathIndex >= tablePath.size() ) {
+    protected List<Group> resolveNested( VirtualGroup virtualGroup, GroupType groupType, List<String> path, int pathIndex ) {
+        if ( pathIndex >= path.size() ) {
             return List.of( virtualGroup );
         }
 
-        int fieldIndex = fieldIndex( groupType, tablePath.get( pathIndex ) );
+        int fieldIndex = fieldIndex( groupType, path.get( pathIndex ) );
         if ( fieldIndex < 0 || virtualGroup.getFieldRepetitionCount( fieldIndex ) == 0 ) {
             return List.of();
         }
@@ -109,16 +116,15 @@ private List<Group> resolveNested( VirtualGroup virtualGroup, GroupType groupTyp
         int count = virtualGroup.getFieldRepetitionCount( fieldIndex );
         for ( int occurrence = 0; occurrence < count; occurrence++ ) {
             Group child = virtualGroup.getGroup( fieldIndex, occurrence );
-
-            String childRowId = virtualGroup.getMetadata().getRowId() + "/" + tablePath.get( pathIndex ) + "[" + occurrence + "]";
+            String childRowId = virtualGroup.getMetadata().getRowId() + "/" + path.get( pathIndex ) + "[" + occurrence + "]";
             VirtualGroup childGroup = new VirtualGroup( child, childRowId, virtualGroup.getMetadata().getRowId(), occurrence );
-            groups.addAll( resolveNested( childGroup, fieldType.asGroupType(), pathIndex + 1 ) );
+            groups.addAll( resolveNested( childGroup, fieldType.asGroupType(), path, pathIndex + 1 ) );
         }
         return groups;
     }
 
 
-    private PolyValue extractValueByColumnRole( VirtualGroup virtualGroup, ParquetColumnBinding binding ) {
+    protected PolyValue extractValueByColumnRole( VirtualGroup virtualGroup, ParquetColumnBinding binding, List<String> tablePath ) {
         return switch ( binding.role() ) {
             case DATA -> {
                 var path = binding.sourcePathElements();
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
index c9b8ee8123..4d2de1b470 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
@@ -16,12 +16,11 @@
 
 package org.polypheny.db.adapter.parquet.relational.execution;
 
-import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.parquet.example.data.Group;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
+import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.Source;
 
@@ -31,14 +30,16 @@
  */
 public class ParquetRelEnumerator extends AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
 
-    public ParquetRelEnumerator(Source source, AtomicBoolean cancelFlag, int[] fields ) {
-        super( source, cancelFlag, fields, List.of(), new ParquetRelValueExtractor() );
+    public ParquetRelEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields ) {
+        super( source, cancelFlag, fields, FiltersContainer.empty, new ParquetRelValueExtractor() );
     }
 
-    public ParquetRelEnumerator(Source source, AtomicBoolean cancelFlag, int[] fields, List<ParquetAdapterFilter> filters ) {
-        super( source, cancelFlag, fields, filters, new ParquetRelValueExtractor() );
+
+    public ParquetRelEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields, FiltersContainer filtersContainer ) {
+        super( source, cancelFlag, fields, filtersContainer, new ParquetRelValueExtractor() );
     }
 
+
     protected PolyValue[] extractRow( Group group ) {
         var projectionSchema = reader.getProjectionSchema();
         PolyValue[] row = new PolyValue[projectionSchema.getFieldCount()];
@@ -48,4 +49,5 @@ protected PolyValue[] extractRow( Group group ) {
         }
         return row;
     }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinDirection.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinDirection.java
new file mode 100644
index 0000000000..00cfbb7969
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinDirection.java
@@ -0,0 +1,21 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+public record JoinDirection( boolean leftIsParent ) {
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcJoinRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcJoinRule.java
new file mode 100644
index 0000000000..cc60accba0
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcJoinRule.java
@@ -0,0 +1,84 @@
+ /*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.List;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.core.AlgFactories;
+import org.polypheny.db.algebra.enumerable.EnumerableCalc;
+import org.polypheny.db.plan.AlgOptRule;
+import org.polypheny.db.plan.AlgOptRuleCall;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.rex.RexProgram;
+import org.polypheny.db.tools.AlgBuilderFactory;
+import org.polypheny.db.type.PolyType;
+
+/**
+ * Pushes the filter part of an enumerable calc over a Parquet nested join into
+ * the adapter join, while preserving the calc projection.
+ */
+public class ParquetEnumerableCalcJoinRule extends AlgOptRule {
+
+    public static final ParquetEnumerableCalcJoinRule INSTANCE = new ParquetEnumerableCalcJoinRule( AlgFactories.LOGICAL_BUILDER );
+
+    private final ParquetRelFilterTranslator translator = new ParquetRelFilterTranslator();
+
+
+    public ParquetEnumerableCalcJoinRule( AlgBuilderFactory algBuilderFactory ) {
+        super(
+                operand( EnumerableCalc.class, operand( ParquetRelJoin.class, none() ) ),
+                algBuilderFactory,
+                ParquetEnumerableCalcJoinRule.class.getSimpleName() );
+    }
+
+
+    @Override
+    public void onMatch( AlgOptRuleCall call ) {
+        EnumerableCalc calc = call.alg( 0 );
+        ParquetRelJoin join = call.alg( 1 );
+        RexProgram program = calc.getProgram();
+        if ( program.getCondition() == null ) {
+            return;
+        }
+
+        RexNode condition = program.expandLocalRef( program.getCondition() );
+        List<PolyType> fieldTypes = join.getTupleType().getFields().stream()
+                .map( field -> field.getType().getPolyType() )
+                .toList();
+
+        ParquetAdapterFilter adapterFilter = translator.translate( fieldTypes, condition );
+        if ( adapterFilter == null ) {
+            return;
+        }
+
+        ParquetRelJoin filteredJoin = join.withFilters( List.of( adapterFilter ) );
+        List<RexNode> projects = program.getProjectList().stream()
+                .map( program::expandLocalRef )
+                .toList();
+
+        RexProgram projectionOnly = RexProgram.create(
+                join.getTupleType(),
+                projects,
+                null,
+                calc.getTupleType(),
+                calc.getCluster().getRexBuilder() );
+
+        call.transformTo( calc.copy( calc.getTraitSet(), filteredJoin, projectionOnly ) );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterJoinRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterJoinRule.java
new file mode 100644
index 0000000000..37b4d870c7
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterJoinRule.java
@@ -0,0 +1,63 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.List;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.core.AlgFactories;
+import org.polypheny.db.algebra.enumerable.EnumerableFilter;
+import org.polypheny.db.plan.AlgOptRule;
+import org.polypheny.db.plan.AlgOptRuleCall;
+import org.polypheny.db.tools.AlgBuilderFactory;
+import org.polypheny.db.type.PolyType;
+
+/**
+ * Pushes filters over a Parquet nested join into the adapter join itself.
+ */
+public class ParquetEnumerableFilterJoinRule extends AlgOptRule {
+
+    public static final ParquetEnumerableFilterJoinRule INSTANCE = new ParquetEnumerableFilterJoinRule( AlgFactories.LOGICAL_BUILDER );
+
+    private final ParquetRelFilterTranslator translator = new ParquetRelFilterTranslator();
+
+
+    public ParquetEnumerableFilterJoinRule( AlgBuilderFactory algBuilderFactory ) {
+        super(
+                operand( EnumerableFilter.class, operand( ParquetRelJoin.class, none() ) ),
+                algBuilderFactory,
+                ParquetEnumerableFilterJoinRule.class.getSimpleName() );
+    }
+
+
+    @Override
+    public void onMatch( AlgOptRuleCall call ) {
+        EnumerableFilter filter = call.alg( 0 );
+        ParquetRelJoin join = call.alg( 1 );
+        List<PolyType> fieldTypes = join.getTupleType().getFields().stream()
+                .map( field -> field.getType().getPolyType() )
+                .toList();
+
+        ParquetAdapterFilter adapterFilter = translator.translate( fieldTypes, filter.getCondition() );
+        if ( adapterFilter == null ) {
+            return;
+        }
+
+        call.transformTo( join.withFilters( List.of( adapterFilter ) ) );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java
new file mode 100644
index 0000000000..08952abbf4
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java
@@ -0,0 +1,55 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import org.polypheny.db.algebra.core.AlgFactories;
+import org.polypheny.db.algebra.enumerable.EnumerableJoin;
+import org.polypheny.db.plan.AlgOptRule;
+import org.polypheny.db.plan.AlgOptRuleCall;
+import org.polypheny.db.tools.AlgBuilderFactory;
+
+import static org.polypheny.db.adapter.parquet.relational.planning.ParquetRelJoin.supportedDirection;
+
+/**
+ * Rewrites a generic enumerable join over two Parquet scans into a Parquet
+ * adapter join when the join is a supported normalized parent/child join.
+ */
+public class ParquetEnumerableJoinRule extends AlgOptRule {
+
+    public static final ParquetEnumerableJoinRule INSTANCE = new ParquetEnumerableJoinRule( AlgFactories.LOGICAL_BUILDER );
+
+
+    public ParquetEnumerableJoinRule( AlgBuilderFactory algBuilderFactory ) {
+        super(
+                operand( EnumerableJoin.class, operand( ParquetRelScan.class, none() ), operand( ParquetRelScan.class, none() ) ),
+                algBuilderFactory,
+                ParquetEnumerableJoinRule.class.getSimpleName() );
+    }
+
+
+    @Override
+    public void onMatch( AlgOptRuleCall call ) {
+        EnumerableJoin join = call.alg( 0 );
+        ParquetRelScan left = call.alg( 1 );
+        ParquetRelScan right = call.alg( 2 );
+        JoinDirection direction = supportedDirection( join, left, right );
+        if ( direction == null ) {
+            return;
+        }
+        call.transformTo( ParquetRelJoin.create( left, right, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent() ) );
+    }
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
new file mode 100644
index 0000000000..a94249f90b
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
@@ -0,0 +1,226 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Optional;
+import java.util.Set;
+import org.apache.calcite.linq4j.tree.Blocks;
+import org.apache.calcite.linq4j.tree.Expression;
+import org.apache.calcite.linq4j.tree.Expressions;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.AlgWriter;
+import org.polypheny.db.algebra.core.CorrelationId;
+import org.polypheny.db.algebra.core.Join;
+import org.polypheny.db.algebra.core.JoinAlgType;
+import org.polypheny.db.algebra.core.JoinInfo;
+import org.polypheny.db.algebra.enumerable.EnumUtils;
+import org.polypheny.db.algebra.enumerable.EnumerableAlg;
+import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.enumerable.PhysType;
+import org.polypheny.db.algebra.enumerable.PhysTypeImpl;
+import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgOptCost;
+import org.polypheny.db.plan.AlgPlanner;
+import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.schema.trait.ModelTrait;
+
+/**
+ * Parquet physical join for supported nested parent/child table joins.
+ */
+public class ParquetRelJoin extends Join implements EnumerableAlg {
+
+    private final boolean leftIsParent;
+    private final ParquetRelTable leftTable;
+    private final ParquetRelTable rightTable;
+    private final int[] leftFields;
+    private final int[] rightFields;
+    private final List<ParquetAdapterFilter> filters;
+
+
+    public ParquetRelJoin(
+            AlgCluster cluster,
+            AlgTraitSet traitSet,
+            AlgNode left,
+            AlgNode right,
+            RexNode condition,
+            Set<CorrelationId> variablesSet,
+            JoinAlgType joinType,
+            boolean leftIsParent,
+            ParquetRelTable leftTable,
+            ParquetRelTable rightTable,
+            int[] leftFields,
+            int[] rightFields,
+            List<ParquetAdapterFilter> filters ) {
+        super( cluster, traitSet, left, right, condition, variablesSet, joinType );
+        this.leftIsParent = leftIsParent;
+        this.leftTable = leftTable;
+        this.rightTable = rightTable;
+        this.leftFields = leftFields;
+        this.rightFields = rightFields;
+        this.filters = List.copyOf( filters );
+    }
+
+
+    public static ParquetRelJoin create( ParquetRelScan left, ParquetRelScan right, RexNode condition, Set<CorrelationId> variablesSet, JoinAlgType joinType, boolean leftIsParent ) {
+        AlgCluster cluster = left.getCluster();
+        AlgTraitSet traitSet = cluster.traitSetOf( EnumerableConvention.INSTANCE ).replace( ModelTrait.RELATIONAL );
+        return new ParquetRelJoin(
+                cluster,
+                traitSet,
+                left,
+                right,
+                condition,
+                variablesSet,
+                joinType,
+                leftIsParent,
+                left.getEntity(),
+                right.getEntity(),
+                left.getFields(),
+                right.getFields(),
+                List.of() );
+    }
+
+
+    public static JoinDirection supportedDirection( Join join, ParquetRelScan left, ParquetRelScan right ) {
+        JoinInfo info = JoinInfo.of( left, right, join.getCondition() );
+        if ( !info.isEqui() || info.leftKeys.size() != 1 || info.rightKeys.size() != 1 ) {
+            return null;
+        }
+
+        ParquetRelTable leftTable = left.getEntity();
+        ParquetRelTable rightTable = right.getEntity();
+        if ( !leftTable.getSourceUrl().equals( rightTable.getSourceUrl() ) ) {
+            return null;
+        }
+
+        int leftColumn = mappedColumn( left, info.leftKeys.get( 0 ) );
+        int rightColumn = mappedColumn( right, info.rightKeys.get( 0 ) );
+        if ( leftColumn < 0 || rightColumn < 0 ) {
+            return null;
+        }
+
+        boolean leftIsParent = isParentChildJoin( leftTable, rightTable, leftColumn, rightColumn );
+        boolean rightIsParent = isParentChildJoin( rightTable, leftTable, rightColumn, leftColumn );
+        if ( !leftIsParent && !rightIsParent ) {
+            return null;
+        }
+
+        switch ( join.getJoinType() ) {
+            case INNER, FULL, LEFT, RIGHT -> {
+                return new JoinDirection( leftIsParent );
+            }
+            default -> {
+                return null;
+            }
+        }
+    }
+
+
+    private static int mappedColumn( ParquetRelScan scan, int inputIndex ) {
+        int[] fields = scan.getFields();
+        if ( inputIndex < 0 || inputIndex >= fields.length ) {
+            return -1;
+        }
+        int column = fields[inputIndex];
+        return column < 0 || column >= scan.getEntity().getFieldCount() ? -1 : column;
+    }
+
+
+    private static boolean isParentChildJoin( ParquetRelTable parent, ParquetRelTable child, int parentColumn, int childColumn ) {
+        if ( child.getBinding().parentTableName() == null || !child.getBinding().parentTableName().equals( parent.name ) ) {
+            return false;
+        }
+        if ( !isPrefix( parent.getBinding().sourcePathElements(), child.getBinding().sourcePathElements() ) ) {
+            return false;
+        }
+        return parentColumn == parent.columnIndexByRole( ParquetColumnRole.PRIMARY_KEY )
+                && childColumn == child.columnIndexByRole( ParquetColumnRole.PARENT_KEY );
+    }
+
+
+    private static boolean isPrefix( List<String> parentPath, List<String> childPath ) {
+        return childPath.size() > parentPath.size() && childPath.subList( 0, parentPath.size() ).equals( parentPath );
+    }
+
+
+    public ParquetRelJoin withFilters( List<ParquetAdapterFilter> filters ) {
+        List<ParquetAdapterFilter> combinedFilters = new ArrayList<>( this.filters );
+        combinedFilters.addAll( filters );
+        return new ParquetRelJoin( getCluster(), traitSet, left, right, condition, variablesSet, joinType, leftIsParent, leftTable, rightTable, leftFields, rightFields, combinedFilters );
+    }
+
+
+    @Override
+    public Join copy( AlgTraitSet traitSet, RexNode conditionExpr, AlgNode left, AlgNode right, JoinAlgType joinType, boolean semiJoinDone ) {
+        return new ParquetRelJoin( getCluster(), traitSet, left, right, conditionExpr, variablesSet, joinType, leftIsParent, leftTable, rightTable, leftFields, rightFields, filters );
+    }
+
+
+    @Override
+    public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
+        Optional<Double> count = mq.getTupleCount( this );
+        return planner.getCostFactory().makeCost( count.orElse( estimateTupleCount( mq ) ), 0, 0 );
+    }
+
+
+    @Override
+    public AlgWriter explainTerms( AlgWriter pw ) {
+        return super.explainTerms( pw ).item( "leftIsParent", leftIsParent ).item( "filters", filters );
+    }
+
+
+    @Override
+    public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
+        PhysType physType = PhysTypeImpl.of( implementor.getTypeFactory(), getTupleType(), pref.preferArray() );
+        boolean emitUnmatchedParents = emitUnmatchedParents();
+        Expression runtimeFilters = EnumUtils.expressionList( filters.stream().map( ParquetAdapterFilter::toExpression ).toList() );
+
+        return implementor.result(
+                physType,
+                Blocks.toBlock(
+                        Expressions.call(
+                                leftTable.asExpression( ParquetRelTable.class ),
+                                "nestedJoin",
+                                implementor.getRootExpression(),
+                                rightTable.asExpression( ParquetRelTable.class ),
+                                Expressions.constant( leftFields ),
+                                Expressions.constant( rightFields ),
+                                Expressions.constant( leftIsParent ),
+                                Expressions.constant( emitUnmatchedParents ),
+                                runtimeFilters ) ) );
+    }
+
+
+    private boolean emitUnmatchedParents() {
+        return switch ( joinType ) {
+            case LEFT -> leftIsParent;
+            case RIGHT -> !leftIsParent;
+            case FULL -> true;
+            default -> false;
+        };
+    }
+
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java
new file mode 100644
index 0000000000..5a4fb7a10c
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java
@@ -0,0 +1,78 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import static org.polypheny.db.adapter.parquet.relational.planning.ParquetRelJoin.supportedDirection;
+
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.convert.ConverterRule;
+import org.polypheny.db.algebra.core.AlgFactories;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.logical.relational.LogicalRelJoin;
+import org.polypheny.db.plan.Convention;
+import org.polypheny.db.plan.volcano.AlgSubset;
+import org.polypheny.db.tools.AlgBuilderFactory;
+
+/**
+ * Rewrites supported parent/child joins between normalized Parquet tables into
+ * a Parquet adapter join.
+ */
+public class ParquetRelJoinRule extends ConverterRule {
+
+    public static final ParquetRelJoinRule INSTANCE = new ParquetRelJoinRule( AlgFactories.LOGICAL_BUILDER );
+
+
+    public ParquetRelJoinRule( AlgBuilderFactory algBuilderFactory ) {
+        super(
+                LogicalRelJoin.class,
+                join -> true,
+                Convention.NONE,
+                EnumerableConvention.INSTANCE,
+                algBuilderFactory,
+                ParquetRelJoinRule.class.getSimpleName() );
+    }
+
+
+    @Override
+    public AlgNode convert( AlgNode alg ) {
+        LogicalRelJoin join = (LogicalRelJoin) alg;
+        ParquetRelScan left = findRelScan( convert( join.getLeft(), join.getLeft().getTraitSet().replace( EnumerableConvention.INSTANCE ) ) );
+        ParquetRelScan right = findRelScan( convert( join.getRight(), join.getRight().getTraitSet().replace( EnumerableConvention.INSTANCE ) ) );
+        if ( left == null || right == null ) {
+            return null;
+        }
+
+        JoinDirection direction = supportedDirection( join, left, right );
+        if ( direction == null ) {
+            return null;
+        }
+
+        return ParquetRelJoin.create( left, right, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent() );
+    }
+
+
+    private ParquetRelScan findRelScan( AlgNode alg ) {
+        if ( alg instanceof ParquetRelScan relScan ) {
+            return relScan;
+        }
+        if ( alg instanceof AlgSubset subset && subset.getBest() instanceof ParquetRelScan relScan ) {
+            return relScan;
+        }
+        return null;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
index 3a363cab3c..fc47f106a0 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
@@ -1,6 +1,7 @@
 package org.polypheny.db.adapter.parquet.relational.planning;
 
 import java.util.List;
+import lombok.Getter;
 import org.apache.calcite.linq4j.tree.Blocks;
 import org.apache.calcite.linq4j.tree.Expressions;
 import org.apache.calcite.linq4j.tree.Primitive;
@@ -25,6 +26,7 @@
 /**
  * Relational scan algebra node for Parquet-backed physical tables
  */
+@Getter
 public class ParquetRelScan extends RelScan<ParquetRelTable> implements EnumerableAlg {
 
     private final int[] fields;
@@ -62,6 +64,10 @@ public AlgDataType deriveRowType() {
     @Override
     public void register( AlgPlanner planner ) {
         planner.addRule( ParquetRelScanRule.INSTANCE );
+        planner.addRule( ParquetRelJoinRule.INSTANCE );
+        planner.addRule( ParquetEnumerableJoinRule.INSTANCE );
+        planner.addRule( ParquetEnumerableFilterJoinRule.INSTANCE );
+        planner.addRule( ParquetEnumerableCalcJoinRule.INSTANCE );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index 8abd3bddb9..843abd78da 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -21,26 +21,32 @@
 import java.util.Objects;
 import java.util.Optional;
 import java.util.concurrent.atomic.AtomicBoolean;
+import java.util.function.Function;
 import java.util.stream.IntStream;
+import lombok.Getter;
 import org.apache.calcite.linq4j.AbstractEnumerable;
 import org.apache.calcite.linq4j.Enumerable;
 import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedJoinEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedNonRepeatedRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedRepeatedRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersSplitter;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
 import org.polypheny.db.adapter.parquet.shared.statistics.ParquetStatisticsReader;
+import org.polypheny.db.adapter.statistics.AdapterStatisticsProvider;
 import org.polypheny.db.adapter.statistics.ProvidedColumnStatistics;
 import org.polypheny.db.adapter.statistics.ProvidedEntityStatistics;
-import org.polypheny.db.adapter.statistics.AdapterStatisticsProvider;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.catalog.entity.logical.LogicalColumn;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.plan.AlgCluster;
 import org.polypheny.db.plan.AlgTraitSet;
 import org.polypheny.db.rex.RexNode;
@@ -62,6 +68,7 @@ public class ParquetRelTable extends PhysicalTable implements FilterableEntity,
     private final int[] fieldIndexes;
     private final AbstractParquetSource parquetSource;
     private final ParquetRelFilterTranslator filterTranslator;
+    @Getter
     private final ParquetTableBinding binding;
     private final List<PolyType> fieldTypes;
     private final ParquetSchemaReader schemaReader;
@@ -97,6 +104,7 @@ public ParquetRelTable( long id, Source source, PhysicalTable table, ParquetTabl
 
     /**
      * Gets table statistics
+     *
      * @param logicalEntityId - logical table id
      * @return statistics from parquet file
      */
@@ -111,6 +119,7 @@ public Optional<ProvidedEntityStatistics> getEntityStatistics( long logicalEntit
 
     /**
      * Gets column statistics (range values for example)
+     *
      * @param column - logical column
      * @param uniqueValueLimit - limit
      * @return column statistics calculated from parquet file
@@ -122,8 +131,10 @@ public Optional<ProvidedColumnStatistics> getColumnStatistics( LogicalColumn col
         }
         return statisticsReader.getColumnStatistics( column, uniqueValueLimit );
     }
+
     // endregion
 
+
     /**
      * Returns enumerable for FilterableEntity.
      *
@@ -144,19 +155,19 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> poly
         } );
 
         // check for dynamic filters
-        final List<ParquetAdapterFilter> resolvedFilters = resolveFilters( dataContext, parquetAdapterFilters );
+        final List<ParquetAdapterFilter> resolvedFilters = resolveFilters( dataContext, parquetAdapterFilters, f -> binding.getColumnBinding( columns.get( f.columnIndex() ).id ) );
 
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
                 if ( isNestedTable() ) {
-                    return new ParquetNestedRepeatedRelEnumerator( source, cancelFlag, binding, selectedBindings( fieldIndexes ), resolvedFilters );
+                    return new ParquetNestedRepeatedRelEnumerator( source, cancelFlag, binding, projectedBindings( fieldIndexes ), FiltersContainer.shared( resolvedFilters ) );
                 }
                 if ( needsBindingScan( fieldIndexes ) ) {
-                    return new ParquetNestedNonRepeatedRelEnumerator( source, cancelFlag, selectedBindings( fieldIndexes ), resolvedFilters );
+                    return new ParquetNestedNonRepeatedRelEnumerator( source, cancelFlag, projectedBindings( fieldIndexes ), FiltersContainer.shared( resolvedFilters ) );
                 }
-                return new ParquetRelEnumerator( source, cancelFlag, fieldIndexes, resolvedFilters );
+                return new ParquetRelEnumerator( source, cancelFlag, fieldIndexes, FiltersContainer.shared( resolvedFilters ) );
             }
         };
     }
@@ -177,10 +188,10 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
                 if ( isNestedTable() ) {
-                    return new ParquetNestedRepeatedRelEnumerator( source, cancelFlag, binding, selectedBindings( fieldIndexes ) );
+                    return new ParquetNestedRepeatedRelEnumerator( source, cancelFlag, binding, projectedBindings( fieldIndexes ) );
                 }
                 if ( needsBindingScan( fieldIndexes ) ) {
-                    return new ParquetNestedNonRepeatedRelEnumerator( source, cancelFlag, selectedBindings( fieldIndexes ) );
+                    return new ParquetNestedNonRepeatedRelEnumerator( source, cancelFlag, projectedBindings( fieldIndexes ) );
                 }
                 return new ParquetRelEnumerator( source, cancelFlag, fieldIndexes );
             }
@@ -215,10 +226,10 @@ public Enumerable<PolyValue[]> project( final DataContext dataContext, final int
             @Override
             public Enumerator<PolyValue[]> enumerator() {
                 if ( isNestedTable() ) {
-                    return new ParquetNestedRepeatedRelEnumerator( source, cancelFlag, binding, selectedBindings( fields ) );
+                    return new ParquetNestedRepeatedRelEnumerator( source, cancelFlag, binding, projectedBindings( fields ) );
                 }
                 if ( needsBindingScan( fields ) ) {
-                    return new ParquetNestedNonRepeatedRelEnumerator( source, cancelFlag, selectedBindings( fields ) );
+                    return new ParquetNestedNonRepeatedRelEnumerator( source, cancelFlag, projectedBindings( fields ) );
                 }
                 return new ParquetRelEnumerator( source, cancelFlag, fields );
             }
@@ -226,35 +237,90 @@ public Enumerator<PolyValue[]> enumerator() {
     }
 
 
+    /**
+     * Executes a supported parent/child join inside the Parquet adapter.
+     */
+    @SuppressWarnings("unused")
+    public Enumerable<PolyValue[]> nestedJoin(
+            final DataContext dataContext,
+            final ParquetRelTable right,
+            final int[] leftFields,
+            final int[] rightFields,
+            final boolean leftIsParent,
+            final boolean emitUnmatchedParents,
+            final List<ParquetAdapterFilter> filters ) {
+        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
+        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+        final ParquetRelTable parent = leftIsParent ? this : right;
+        final ParquetRelTable child = leftIsParent ? right : this;
+        final int[] parentFields = leftIsParent ? leftFields : rightFields;
+        final int[] childFields = leftIsParent ? rightFields : leftFields;
+        final List<ParquetAdapterFilter> resolvedFilters = resolveFilters( dataContext, filters, f -> selectBinding( f.columnIndex(), leftFields, rightFields, right ) );
+
+        return new AbstractEnumerable<>() {
+            @Override
+            public Enumerator<PolyValue[]> enumerator() {
+                var filterContainer = new JoinFiltersSplitter().split( resolvedFilters, leftIsParent, parentFields.length, childFields.length );
+                return new ParquetNestedJoinEnumerator(
+                        parent.source,
+                        cancelFlag,
+                        parent.binding,
+                        child.binding,
+                        parent.projectedBindings( parentFields ),
+                        child.projectedBindings( childFields ),
+                        filterContainer,
+                        leftIsParent,
+                        emitUnmatchedParents );
+            }
+        };
+    }
+
+
+    public int columnIndexByRole( ParquetColumnRole role ) {
+        for ( int i = 0; i < columns.size(); i++ ) {
+            ParquetColumnBinding columnBinding = binding.getColumnBinding( columns.get( i ).id );
+            if ( columnBinding != null && columnBinding.role() == role ) {
+                return i;
+            }
+        }
+        return -1;
+    }
+
+
+    public String getSourceUrl() {
+        return binding.sourceUrl();
+    }
+
+
+    public int getFieldCount() {
+        return columns.size();
+    }
+
+
     /**
      * Support parametrized queries
      *
      * @param dataContext context
      * @param filters filters
+     * @param selector a helper function for column binding selection
      * @return list of parquet filters
      */
-    private List<ParquetAdapterFilter> resolveFilters( DataContext dataContext, List<ParquetAdapterFilter> filters ) {
+    private List<ParquetAdapterFilter> resolveFilters(
+            DataContext dataContext,
+            List<ParquetAdapterFilter> filters,
+            Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
         List<ParquetAdapterFilter> resolved = new ArrayList<>( filters.size() );
         for ( ParquetAdapterFilter filter : filters ) {
-            resolved.add( resolveFilter( dataContext, filter ) );
+            resolved.add( resolveFilter( dataContext, filter, selector ) );
         }
         return resolved;
     }
 
 
-    /**
-     * Takes a ParquetAdapterFilter produced by the translator and turns it into a filter that is ready for execution against the actual Parquet file
-     * Resolve dynamic parameters from dataContext
-     * Use binding path elements for nested fields
-     * For logical filter - recursive call
-     * @param dataContext DataContext
-     * @param filter Adapter level filter
-     * @return Adapter level filter
-     */
-    private ParquetAdapterFilter resolveFilter( DataContext dataContext, ParquetAdapterFilter filter ) {
+    private ParquetAdapterFilter resolveFilter( DataContext dataContext, ParquetAdapterFilter filter, Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
         if ( filter.isLogical() ) {
             return ParquetAdapterFilter.logical( filter.operator(), filter.operands().stream()
-                    .map( operand -> resolveFilter( dataContext, operand ) )
+                    .map( operand -> resolveFilter( dataContext, operand, selector ) )
                     .toList() );
         }
 
@@ -262,32 +328,47 @@ private ParquetAdapterFilter resolveFilter( DataContext dataContext, ParquetAdap
                 ? filter.polyValue()
                 : dataContext.getParameterValue( filter.dynamicParamIndex() );
 
-        ParquetColumnBinding columnBinding = Objects.requireNonNull( binding.getColumnBinding( columns.get( filter.columnIndex() ).id ), "Missing parquet column binding" );
-        List<String> pathElements = canReadFilterByColumnIndex( filter.columnIndex(), columnBinding )
-                ? List.of()
-                : columnBinding.sourcePathElements();
-        return new ParquetAdapterFilter( filter.columnIndex(), pathElements, filter.operator(), value );
+        ParquetColumnBinding columnBinding = Objects.requireNonNull( selector.apply( filter ), "Missing parquet column binding" );
+        return new ParquetAdapterFilter( filter.columnIndex(), columnBinding.sourcePathElements(), filter.operator(), value );
     }
 
 
-    private boolean canReadFilterByColumnIndex( int field, ParquetColumnBinding columnBinding ) {
-        if ( isNestedTable() || columnBinding.sourcePathElements().size() != 1 ) {
-            return false;
-        }
-
-        var schema = schemaReader.getSchema();
-        return field >= 0
-                && field < schema.getFieldCount()
-                && schema.getType( field ).getName().equals( columnBinding.sourcePathElements().get( 0 ) );
+    private boolean isNestedTable() {
+        return binding.parentTableName() != null;
     }
 
 
-    private boolean isNestedTable() {
-        return binding.parentTableName() != null;
+    /**
+     * Selects a column binding for a join. If the column index is less then left table fields count then use left table column binding otherwise use right table column biding.
+     *
+     * @param joinedIndex a column index
+     * @param leftFields a list of left column fields
+     * @param rightFields a list of right column fields
+     * @param right a right table
+     * @return {@link ParquetColumnBinding}
+     */
+    private ParquetColumnBinding selectBinding( int joinedIndex, int[] leftFields, int[] rightFields, ParquetRelTable right ) {
+        if ( joinedIndex < 0 ) {
+            throw new GenericRuntimeException( "Invalid joined filter column index: " + joinedIndex );
+        }
+        if ( joinedIndex < leftFields.length ) {
+            return binding.getColumnBinding( columns.get( leftFields[joinedIndex] ).id );
+        }
+        int rightIndex = joinedIndex - leftFields.length;
+        if ( rightIndex >= rightFields.length ) {
+            throw new GenericRuntimeException( "Invalid joined filter column index: " + joinedIndex );
+        }
+        return right.binding.getColumnBinding( right.columns.get( rightFields[rightIndex] ).id );
     }
 
 
-    private List<ParquetColumnBinding> selectedBindings( int[] fields ) {
+    /**
+     * Gets a list of bindings per provided fields
+     *
+     * @param fields a list of fields to get the bindings for
+     * @return a list of column bindings
+     */
+    private List<ParquetColumnBinding> projectedBindings( int[] fields ) {
         List<ParquetColumnBinding> selected = new ArrayList<>( fields.length );
         for ( int field : fields ) {
             selected.add( Objects.requireNonNull( binding.getColumnBinding( columns.get( field ).id ), "Missing parquet column binding" ) );
@@ -309,11 +390,9 @@ private boolean needsBindingScan( int[] fields ) {
             if ( field >= schema.getFieldCount() || !schema.getType( field ).getName().equals( columnBinding.sourcePathElements().get( 0 ) ) ) {
                 return true;
             }
-            if ( !schema.getType( field ).isPrimitive() ) {
-                return true;
-            }
         }
         return false;
     }
 
 }
+
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
index 1dc42a2aa9..8b08df2be1 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
@@ -23,6 +23,7 @@
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.parquet.example.data.Group;
 import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.algebra.constant.Kind;
@@ -44,9 +45,9 @@ public abstract class AbstractParquetEnumerator implements Enumerator<PolyValue[
     private PolyValue[] current;
 
 
-    public AbstractParquetEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields, List<ParquetAdapterFilter> filters, ParquetValueExtractor valueExtractor ) {
-        this.filters = filters == null ? List.of() : List.copyOf( filters );
-        this.reader = new ParquetSourceReader( source, cancelFlag, fields, this.filters );
+    protected AbstractParquetEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields, FiltersContainer filtersContainer, ParquetValueExtractor valueExtractor ) {
+        this.filters = filtersContainer.adapterFilters();
+        this.reader = new ParquetSourceReader( source, cancelFlag, fields, filtersContainer.readerFilters() );
         this.valueExtractor = valueExtractor;
     }
 
@@ -81,7 +82,7 @@ public boolean moveNext() {
             current = rows.remove();
             return true;
         } catch ( Exception e ) {
-            throw new GenericRuntimeException( "Error while reading parquet data: " + e.getClass().getSimpleName() + ": " + e.getMessage(), e );
+            throw new GenericRuntimeException( "Error while reading parquet data", e );
         }
     }
 
@@ -111,6 +112,7 @@ public void close() {
     /**
      * The function that turns one input Parquet row into zero (filtered), one, or many output relational rows
      * It lets the shared enumerator support both simple one-row scans and repeated nested scans that emit multiple rows.
+     *
      * @param group - parquet group/row
      * @param rows - Queue<PolyValue[]> - output to fill
      */
@@ -130,8 +132,9 @@ protected void enqueueRows( Group group, Queue<PolyValue[]> rows ) {
 
     /**
      * decides how many logical rows come from this input row
-     * default implementation - return
+     * default implementation - return provided group
      * repeated nested enumerator overrides this and returns all nested groups
+     *
      * @param group - parquet group/row
      * @return List<Group>
      */
@@ -157,8 +160,7 @@ protected boolean accept( Group group ) {
     }
 
 
-    private boolean matches( Group group, ParquetAdapterFilter filter ) {
-        // add logical filter support
+    protected boolean matches( Group group, ParquetAdapterFilter filter ) {
         if ( filter.isLogical() ) {
             return switch ( filter.operator() ) {
                 case AND -> filter.operands().stream().allMatch( operand -> matches( group, operand ) );
@@ -169,12 +171,12 @@ private boolean matches( Group group, ParquetAdapterFilter filter ) {
         }
 
         // if filter is invalid row should be displayed
-        if ( filter.polyValue() == null || filter.columnIndex() < 0 || filter.columnIndex() >= reader.getProjectionSchema().getFieldCount() ) {
+        if ( !canApplyFilter( group, filter ) ) {
             return true;
         }
 
         // this row does not have a value for that Parquet field
-        if ( filter.pathElements().isEmpty() && group.getFieldRepetitionCount( filter.columnIndex() ) == 0 ) {
+        if ( !filterHasValue( group, filter ) ) {
             return false;
         }
 
@@ -209,8 +211,19 @@ protected boolean matches( PolyValue actual, Kind operator, PolyValue expected )
     }
 
 
+    protected boolean canApplyFilter( Group group, ParquetAdapterFilter filter ) {
+        return filter.polyValue() != null && filter.columnIndex() >= 0 && filter.columnIndex() < reader.getProjectionSchema().getFieldCount();
+    }
+
+
+    protected boolean filterHasValue( Group group, ParquetAdapterFilter filter ) {
+        return !filter.pathElements().isEmpty() || group.getFieldRepetitionCount( filter.columnIndex() ) > 0;
+    }
+
+
     /**
      * Extract value from parquet group according to filter column index
+     *
      * @param group - parquet group
      * @param filter - ParquetAdapterFilter
      * @return - PolyValue
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/CombinedGroup.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/CombinedGroup.java
new file mode 100644
index 0000000000..e157fd275f
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/CombinedGroup.java
@@ -0,0 +1,236 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.execution;
+
+import java.util.List;
+import lombok.Getter;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.NanoTime;
+import org.apache.parquet.io.api.Binary;
+import org.apache.parquet.io.api.RecordConsumer;
+import org.apache.parquet.schema.GroupType;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+
+@Getter
+public class CombinedGroup extends Group {
+
+    private final VirtualGroup parent;
+    private final VirtualGroup child;
+    private final List<ParquetColumnBinding> parentColumns;
+    private final List<String> parentPath;
+    private final List<ParquetColumnBinding> childColumns;
+    private final List<String> childPath;
+
+
+    public CombinedGroup(
+            VirtualGroup parent,
+            List<ParquetColumnBinding> parentColumns,
+            List<String> parentPath,
+            VirtualGroup child,
+            List<ParquetColumnBinding> childColumns,
+            List<String> childPath ) {
+        this.parent = parent;
+        this.child = child;
+        this.parentColumns = List.copyOf( parentColumns );
+        this.parentPath = List.copyOf( parentPath );
+        this.childColumns = List.copyOf( childColumns );
+        this.childPath = List.copyOf( childPath );
+    }
+
+
+    public int fieldCount() {
+        return parentColumns.size() + childColumns.size();
+    }
+
+
+    public VirtualGroup groupForField( int fieldIndex, boolean leftIsParent ) {
+        int parentFieldCount = parentColumns.size();
+        if ( leftIsParent ) {
+            return fieldIndex < parentFieldCount ? parent : child;
+        }
+        return fieldIndex < childColumns.size() ? child : parent;
+    }
+
+
+    public ParquetColumnBinding bindingForField( int fieldIndex, boolean leftIsParent ) {
+        int parentFieldCount = parentColumns.size();
+        int childFieldCount = childColumns.size();
+        if ( leftIsParent ) {
+            return fieldIndex < parentFieldCount ? parentColumns.get( fieldIndex ) : childColumns.get( fieldIndex - parentFieldCount );
+        }
+        return fieldIndex < childFieldCount ? childColumns.get( fieldIndex ) : parentColumns.get( fieldIndex - childFieldCount );
+    }
+
+
+    public List<String> tablePathForField( int fieldIndex, boolean leftIsParent ) {
+        int parentFieldCount = parentColumns.size();
+        int childFieldCount = childColumns.size();
+        if ( leftIsParent ) {
+            return fieldIndex < parentFieldCount ? parentPath : childPath;
+        }
+        return fieldIndex < childFieldCount ? childPath : parentPath;
+    }
+
+
+    public boolean isNullField( int fieldIndex, boolean leftIsParent ) {
+        return groupForField( fieldIndex, leftIsParent ) == null;
+    }
+
+
+    @Override
+    public void add( int fieldIndex, int value ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public void add( int fieldIndex, long value ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public void add( int fieldIndex, String value ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public void add( int fieldIndex, boolean value ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public void add( int fieldIndex, NanoTime value ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public void add( int fieldIndex, Binary value ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public void add( int fieldIndex, float value ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public void add( int fieldIndex, double value ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public void add( int fieldIndex, Group value ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public Group addGroup( int fieldIndex ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public int getFieldRepetitionCount( int fieldIndex ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public Group getGroup( int fieldIndex, int index ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public String getString( int fieldIndex, int index ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public int getInteger( int fieldIndex, int index ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public long getLong( int fieldIndex, int index ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public double getDouble( int fieldIndex, int index ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public float getFloat( int fieldIndex, int index ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public boolean getBoolean( int fieldIndex, int index ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public Binary getBinary( int fieldIndex, int index ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public Binary getInt96( int fieldIndex, int index ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public String getValueToString( int fieldIndex, int index ) {
+        throw unsupported();
+    }
+
+
+    @Override
+    public GroupType getType() {
+        return parent.getType();
+    }
+
+
+    @Override
+    public void writeValue( int field, int index, RecordConsumer recordConsumer ) {
+        throw unsupported();
+    }
+
+
+    private UnsupportedOperationException unsupported() {
+        return new UnsupportedOperationException( "CombinedGroup is a virtual joined row and does not expose Parquet field access by index." );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FiltersContainer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FiltersContainer.java
new file mode 100644
index 0000000000..80b30fff6d
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FiltersContainer.java
@@ -0,0 +1,74 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.filter;
+
+import java.util.List;
+
+/**
+ * A container for filters split by usage.
+ */
+public class FiltersContainer {
+
+    public static FiltersContainer empty = new FiltersContainer( List.of(), List.of() );
+
+    private final List<ParquetAdapterFilter> adapterFilters;
+    private final List<ParquetAdapterFilter> readerFilters;
+
+
+    /**
+     * Creates a new instance of {@link FiltersContainer}
+     *
+     * @param adapterFilters a list of filters to be used on adapter level.
+     * @param readerFilters a list of filters to be used on parquet reader level.
+     */
+    public FiltersContainer( List<ParquetAdapterFilter> adapterFilters, List<ParquetAdapterFilter> readerFilters ) {
+        this.adapterFilters = adapterFilters == null ? List.of() : List.copyOf( adapterFilters );
+        this.readerFilters = readerFilters == null ? List.of() : List.copyOf( readerFilters );
+    }
+
+
+    /**
+     * Creates a {@link FiltersContainer} with the provided filters as both adapter and reader filters.
+     *
+     * @param filters filters to share between adapter and reader.
+     * @return a new instance of {@link FiltersContainer}
+     */
+    public static FiltersContainer shared( List<ParquetAdapterFilter> filters ) {
+        return new FiltersContainer( filters, filters );
+    }
+
+
+    /**
+     * Gets a list of adapter filters.
+     *
+     * @return a list of adapter filters.
+     */
+    public List<ParquetAdapterFilter> adapterFilters() {
+        return adapterFilters;
+    }
+
+
+    /**
+     * Gets a list of parquet reader filters.
+     *
+     * @return a list of reader filters.
+     */
+    public List<ParquetAdapterFilter> readerFilters() {
+        return readerFilters;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersContainer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersContainer.java
new file mode 100644
index 0000000000..0a0bcc0de8
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersContainer.java
@@ -0,0 +1,63 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.filter;
+
+import java.util.List;
+
+public class JoinFiltersContainer extends FiltersContainer {
+
+    public static JoinFiltersContainer empty = new JoinFiltersContainer( List.of(), List.of(), List.of(), List.of() );
+
+    private final List<ParquetAdapterFilter> parentFilters;
+    private final List<ParquetAdapterFilter> childFilters;
+
+
+    /**
+     * Creates a new instance of {@link FiltersContainer}
+     *
+     * @param parentFilters a list of filters to be used only on a parent table.
+     * @param childFilters a list of filters to be used only on a child table.
+     * @param adapterFilters a list of filters to be used on adapter level.
+     * @param readerFilters a list of filters to be used on parquet reader level.
+     */
+    public JoinFiltersContainer( List<ParquetAdapterFilter> parentFilters, List<ParquetAdapterFilter> childFilters, List<ParquetAdapterFilter> adapterFilters, List<ParquetAdapterFilter> readerFilters ) {
+        super( adapterFilters, readerFilters );
+        this.parentFilters = parentFilters;
+        this.childFilters = childFilters;
+    }
+
+
+    /**
+     * Gets a list of parent table filters.
+     *
+     * @return a list of parent table filters.
+     */
+    public List<ParquetAdapterFilter> parentFilters() {
+        return parentFilters;
+    }
+
+
+    /**
+     * Gets a list of child table filters.
+     *
+     * @return a list of child table filters.
+     */
+    public List<ParquetAdapterFilter> childFilters() {
+        return childFilters;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersSplitter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersSplitter.java
new file mode 100644
index 0000000000..6e72d58c27
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersSplitter.java
@@ -0,0 +1,101 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.filter;
+
+import java.util.ArrayList;
+import java.util.List;
+import org.polypheny.db.algebra.constant.Kind;
+
+public class JoinFiltersSplitter {
+
+    private static FilterKind classifyFilter( ParquetAdapterFilter filter, boolean leftIsParent, int parentFieldCount, int childFieldCount ) {
+        if ( filter.isLogical() ) {
+            List<FilterKind> operandSides = filter.operands().stream()
+                    .map( operand -> classifyFilter( operand, leftIsParent, parentFieldCount, childFieldCount ) )
+                    .distinct()
+                    .toList();
+            return operandSides.size() == 1 ? operandSides.get( 0 ) : FilterKind.ADAPTER;
+        }
+
+        if ( leftIsParent ) {
+            if ( filter.columnIndex() >= 0 && filter.columnIndex() < parentFieldCount ) {
+                return FilterKind.PARENT;
+            }
+            if ( filter.columnIndex() >= parentFieldCount && filter.columnIndex() < parentFieldCount + childFieldCount ) {
+                return FilterKind.CHILD;
+            }
+            return FilterKind.ADAPTER;
+        }
+
+        if ( filter.columnIndex() >= 0 && filter.columnIndex() < childFieldCount ) {
+            return FilterKind.CHILD;
+        }
+        if ( filter.columnIndex() >= childFieldCount && filter.columnIndex() < childFieldCount + parentFieldCount ) {
+            return FilterKind.PARENT;
+        }
+        return FilterKind.ADAPTER;
+    }
+
+
+    private static boolean canUseAsReaderFilter( ParquetAdapterFilter filter ) {
+        if ( filter.isLogical() ) {
+            return !filter.operands().isEmpty() && filter.operands().stream().allMatch( JoinFiltersSplitter::canUseAsReaderFilter );
+        }
+        return !filter.pathElements().isEmpty();
+    }
+
+
+    public JoinFiltersContainer split( List<ParquetAdapterFilter> filters, boolean leftIsParent, int parentFieldCount, int childFieldCount ) {
+        List<ParquetAdapterFilter> parentFilters = new ArrayList<>();
+        List<ParquetAdapterFilter> childFilters = new ArrayList<>();
+        List<ParquetAdapterFilter> adapterFilters = new ArrayList<>();
+        List<ParquetAdapterFilter> readerFilters = new ArrayList<>();
+
+        for ( ParquetAdapterFilter filter : filters ) {
+            splitFilter( filter, leftIsParent, parentFieldCount, childFieldCount, parentFilters, childFilters, adapterFilters, readerFilters );
+        }
+        return new JoinFiltersContainer( parentFilters, childFilters, adapterFilters, readerFilters );
+    }
+
+
+    private void splitFilter( ParquetAdapterFilter filter, boolean leftIsParent, int parentFieldCount, int childFieldCount, List<ParquetAdapterFilter> parentFilters, List<ParquetAdapterFilter> childFilters, List<ParquetAdapterFilter> adapterFilters, List<ParquetAdapterFilter> readerFilters ) {
+        if ( filter.isLogical() && filter.operator() == Kind.AND ) {
+            filter.operands().forEach( operand -> splitFilter( operand, leftIsParent, parentFieldCount, childFieldCount, parentFilters, childFilters, adapterFilters, readerFilters ) );
+            return;
+        }
+
+        switch ( classifyFilter( filter, leftIsParent, parentFieldCount, childFieldCount ) ) {
+            case PARENT -> {
+                if ( canUseAsReaderFilter( filter ) ) {
+                    readerFilters.add( filter );
+                } else {
+                    parentFilters.add( filter );
+                }
+            }
+            case CHILD -> childFilters.add( filter );
+            case ADAPTER -> adapterFilters.add( filter );
+        }
+    }
+
+
+    private enum FilterKind {
+        PARENT,
+        CHILD,
+        ADAPTER
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
index 4f1dc274f9..a645b21b68 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
@@ -17,7 +17,10 @@
 package org.polypheny.db.adapter.parquet.shared.filter;
 
 import java.util.List;
+import org.apache.calcite.linq4j.tree.Expression;
+import org.apache.calcite.linq4j.tree.Expressions;
 import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.enumerable.EnumUtils;
 import org.polypheny.db.type.entity.PolyValue;
 
 /**
@@ -56,6 +59,18 @@ public static ParquetAdapterFilter logical( Kind operator, List<ParquetAdapterFi
     }
 
 
+    public Expression toExpression() {
+        return Expressions.new_(
+                ParquetAdapterFilter.class,
+                Expressions.constant( columnIndex ),
+                EnumUtils.expressionList( pathElements.stream().<Expression>map( Expressions::constant ).toList() ),
+                Expressions.constant( operator, Kind.class ),
+                polyValue == null ? Expressions.constant( null, PolyValue.class ) : polyValue.asExpression(),
+                Expressions.constant( dynamicParamIndex, Long.class ),
+                EnumUtils.expressionList( operands.stream().map( ParquetAdapterFilter::toExpression ).toList() ) );
+    }
+
+
     public boolean isLogical() {
         return operator == Kind.AND || operator == Kind.OR || operator == Kind.NOT;
     }

From be0ebd29b176afcca2fa74dcd17a78571bab0862 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Sat, 9 May 2026 11:53:40 +0200
Subject: [PATCH 25/46] support multi files tables and partitions

---
 .../db/processing/AbstractQueryProcessor.java |  35 +-
 .../statistics/StatisticsManagerImpl.java     |  95 +++-
 plugins/parquet-adapter/build.gradle          |   1 -
 plugins/parquet-adapter/docs/README.md        |   5 +
 .../docs/filter_improvements.md               |  18 +-
 plugins/parquet-adapter/docs/flavors.md       | 160 ++++++
 .../docs/images/partition_year.png            | Bin 0 -> 30945 bytes
 .../docs/partitions_changes.md                | 242 +++++++++
 .../docs/partitions_overview.md               | 346 ++++++++++++
 plugins/parquet-adapter/docs/statistics.md    |  21 +-
 .../parquet-adapter/docs/todo_partitions.md   |  24 +
 .../execution/ParquetDocEnumerator.java       |  18 +-
 .../execution/ParquetDocFilterTranslator.java |   3 -
 .../document/schema/ParquetDocument.java      |  10 +-
 .../relational/ParquetRelationalSource.java   | 193 ++++++-
 .../execution/ParquetMultiFileEnumerator.java |  91 ++++
 .../ParquetNestedJoinEnumerator.java          |  69 +--
 ...ParquetNestedNonRepeatedRelEnumerator.java |  41 +-
 .../ParquetNestedRepeatedRelEnumerator.java   |  51 +-
 .../ParquetPartitionAwareFilterEvaluator.java |  63 +++
 .../execution/ParquetPathValueExtractor.java  |  21 +-
 .../execution/ParquetRelEnumerator.java       |  15 +-
 .../execution/ParquetRelFilterTranslator.java |  18 +-
 .../ParquetEnumerableCalcScanRule.java        |  95 ++++
 .../ParquetEnumerableFilterScanRule.java      |  73 +++
 .../relational/planning/ParquetRelScan.java   |  56 +-
 .../planning/ParquetRelScanRule.java          |  15 +-
 .../planning/ParquetRelScanRuleSupport.java   |  70 +++
 .../planning/ParquetRelTableScanRule.java     |  54 ++
 .../relational/schema/DiscoveredTable.java    |  32 ++
 .../schema/DiscoveredTableBinding.java        |   6 +-
 .../schema/ParquetBindingSerializer.java      |  61 ++-
 .../relational/schema/ParquetColumnRole.java  |   1 +
 .../relational/schema/ParquetRelTable.java    | 138 +++--
 .../schema/ParquetSchemaNormalizer.java       | 121 +++--
 .../relational/schema/ParquetSourceFile.java  |  54 ++
 .../ParquetSourceFileFilterEvaluator.java     |  59 +++
 .../schema/ParquetTableBinding.java           |  28 +-
 .../parquet/shared/AbstractParquetSource.java | 115 +---
 .../execution/AbstractFilterTranslator.java   |   9 -
 .../execution/AbstractParquetEnumerator.java  | 117 +----
 .../shared/execution/CombinedGroup.java       |   3 +
 .../execution/ParquetValueExtractor.java      |   9 +
 .../shared/filter/FilterEvaluator.java        | 106 ++++
 .../filter/ParquetGroupFilterEvaluator.java   |  84 +++
 .../filter/ParquetNestedFilterEvaluator.java  |  52 ++
 .../ParquetNestedJoinFilterEvaluator.java     |  98 ++++
 .../shared/io/ParquetFileDiscovery.java       | 494 +++++++++++++++++-
 .../shared/io/ParquetSchemaReader.java        | 109 +++-
 .../shared/io/ParquetSourceReader.java        |  39 +-
 .../shared/schema/ParquetNamespace.java       |  15 +-
 .../statistics/ParquetStatisticsReader.java   | 122 +++--
 .../parquet/ParquetFileDiscoveryTest.java     | 291 +++++++++++
 .../db/adapter/parquet/ParquetPluginTest.java |  52 +-
 54 files changed, 3477 insertions(+), 641 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/flavors.md
 create mode 100644 plugins/parquet-adapter/docs/images/partition_year.png
 create mode 100644 plugins/parquet-adapter/docs/partitions_changes.md
 create mode 100644 plugins/parquet-adapter/docs/partitions_overview.md
 create mode 100644 plugins/parquet-adapter/docs/todo_partitions.md
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPartitionAwareFilterEvaluator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcScanRule.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterScanRule.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelTableScanRule.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTable.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFile.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FilterEvaluator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetGroupFilterEvaluator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedFilterEvaluator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedJoinFilterEvaluator.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java

diff --git a/dbms/src/main/java/org/polypheny/db/processing/AbstractQueryProcessor.java b/dbms/src/main/java/org/polypheny/db/processing/AbstractQueryProcessor.java
index 0c6d0d3176..ba34628f2b 100644
--- a/dbms/src/main/java/org/polypheny/db/processing/AbstractQueryProcessor.java
+++ b/dbms/src/main/java/org/polypheny/db/processing/AbstractQueryProcessor.java
@@ -82,6 +82,7 @@
 import org.polypheny.db.catalog.entity.physical.PhysicalEntity;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.catalog.logistic.DataModel;
+import org.polypheny.db.catalog.logistic.PartitionType;
 import org.polypheny.db.config.RuntimeConfig;
 import org.polypheny.db.information.InformationPolyAlg.PlanType;
 import org.polypheny.db.interpreter.BindableConvention;
@@ -104,6 +105,8 @@
 import org.polypheny.db.processing.shuttles.QueryParameterizer;
 import org.polypheny.db.processing.util.Plan;
 import org.polypheny.db.processing.util.ProposedImplementations;
+import org.polypheny.db.partition.PartitionManagerFactory;
+import org.polypheny.db.partition.properties.PartitionProperty;
 import org.polypheny.db.rex.RexBuilder;
 import org.polypheny.db.rex.RexDynamicParam;
 import org.polypheny.db.rex.RexIndexRef;
@@ -1204,7 +1207,7 @@ private LogicalQueryInformation analyzeQueryAndPrepareMonitoring( Statement stat
 
         // Get partitions of logical information
 
-        Map<Long, List<Long>> accessedPartitions = extractPartitions( logicalRoot.alg.getEntities() );
+        Map<Long, List<Long>> accessedPartitions = extractPartitions( logicalRoot.alg.getEntities(), analyzer.partitionValueFilterPerScan );
 
         // Build queryClass from query-name and partitions.
         String queryHash = analyzer.getQueryName() + accessedPartitions;
@@ -1227,11 +1230,11 @@ private LogicalQueryInformation analyzeQueryAndPrepareMonitoring( Statement stat
     }
 
 
-    private Map<Long, List<Long>> extractPartitions( Set<Entity> entities ) {
+    private Map<Long, List<Long>> extractPartitions( Set<Entity> entities, Map<Long, Set<String>> partitionValueFilterPerScan ) {
         Map<Long, List<Long>> map = new HashMap<>();
         for ( Entity entity : entities ) {
             if ( entity.isLogical() ) {
-                map.computeIfAbsent( entity.getId(), k -> new ArrayList<>() ).addAll( Catalog.snapshot().alloc().getPartitionsFromLogical( entity.getId() ).stream().map( p -> p.id ).toList() );
+                map.computeIfAbsent( entity.getId(), k -> new ArrayList<>() ).addAll( extractPartitionsForLogicalEntity( entity, partitionValueFilterPerScan ) );
             } else if ( entity.isAllocation() ) {
                 map.computeIfAbsent( ((AllocationEntity) entity).getLogicalId(), k -> new ArrayList<>() ).add( ((AllocationEntity) entity).getPartitionId() );
             } else if ( entity.isPhysical() ) {
@@ -1242,6 +1245,32 @@ private Map<Long, List<Long>> extractPartitions( Set<Entity> entities ) {
     }
 
 
+    private List<Long> extractPartitionsForLogicalEntity( Entity entity, Map<Long, Set<String>> partitionValueFilterPerScan ) {
+        List<Long> allPartitions = Catalog.snapshot().alloc().getPartitionsFromLogical( entity.getId() ).stream().map( p -> p.id ).toList();
+        Set<String> partitionValues = partitionValueFilterPerScan.get( entity.getId() );
+        if ( partitionValues == null || partitionValues.isEmpty() ) {
+            return allPartitions;
+        }
+
+        PartitionProperty property = Catalog.snapshot().alloc().getPartitionProperty( entity.getId() ).orElse( null );
+        if ( property == null || property.partitionType == PartitionType.NONE ) {
+            return allPartitions;
+        }
+
+        LogicalTable table = Catalog.snapshot().rel().getTable( entity.getId() ).orElse( null );
+        if ( table == null ) {
+            return allPartitions;
+        }
+
+        List<Long> selectedPartitions = partitionValues.stream()
+                .map( value -> PartitionManagerFactory.getInstance().getPartitionManager( property.partitionType ).getTargetPartitionId( table, property, value ) )
+                .filter( partitionId -> partitionId >= 0 )
+                .distinct()
+                .toList();
+        return selectedPartitions.isEmpty() ? allPartitions : selectedPartitions;
+    }
+
+
     private void prepareMonitoring( Statement statement, AlgRoot logicalRoot, boolean isAnalyze, boolean isSubquery, LogicalQueryInformation queryInformation ) {
 
         // Initialize Monitoring
diff --git a/monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java b/monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java
index d35520e0f4..a6e9539b26 100644
--- a/monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java
+++ b/monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java
@@ -25,6 +25,7 @@
 import java.util.Arrays;
 import java.util.Collections;
 import java.util.HashMap;
+import java.util.LinkedHashMap;
 import java.util.List;
 import java.util.Map;
 import java.util.Optional;
@@ -326,25 +327,106 @@ private Optional<ProvidedEntityStatistics> getEntityStatistics( long logicalEnti
 
 
     /**
-     * Adapter Statistics:
      * Gets column statistics
      * @param column - query result
      * @return Statistic Column
      */
     private Optional<StatisticColumn> getColumnStatistics( QueryResult column ) {
-        return Catalog.snapshot().alloc().getFromLogical( column.getEntity().id ).stream()
+        List<ProvidedColumnStatistics> providedStatistics = Catalog.snapshot().alloc().getFromLogical( column.getEntity().id ).stream()
                 .map( this::getStatisticsProvider )
                 .filter( Optional::isPresent )
                 .map( provider -> provider.get().getColumnStatistics( column.getColumn(), buffer ) )
                 .filter( Optional::isPresent )
                 .map( Optional::get )
-                .flatMap( provided -> toStatisticColumn( column, provided ).stream() )
-                .findFirst();
+                .toList();
+
+        return aggregateProvidedColumnStatistics( providedStatistics )
+                .flatMap( provided -> toStatisticColumn( column, provided ) );
+    }
+
+
+    /**
+     * Aggregate statistics from partitioned physical columns
+     *
+     * @param providedStatistics a list of column statistics
+     * @return aggregated statistics per logical column
+     */
+    private Optional<ProvidedColumnStatistics> aggregateProvidedColumnStatistics( List<ProvidedColumnStatistics> providedStatistics ) {
+        if ( providedStatistics.isEmpty() ) {
+            return Optional.empty();
+        }
+        if ( providedStatistics.size() == 1 ) {
+            return Optional.of( providedStatistics.get( 0 ) );
+        }
+
+        Long count = null;
+        PolyValue min = null;
+        PolyValue max = null;
+        boolean full = false;
+        Map<String, PolyValue> uniqueValues = new LinkedHashMap<>();
+
+        for ( ProvidedColumnStatistics provided : providedStatistics ) {
+            count = addCounts( count, provided.count() );
+            min = lower( min, provided.min() );
+            max = higher( max, provided.max() );
+            full |= provided.full();
+
+            for ( PolyValue value : provided.uniqueValues() ) {
+                uniqueValues.putIfAbsent( value.toTypedJson(), value );
+                if ( uniqueValues.size() > buffer ) {
+                    full = true;
+                    uniqueValues.clear();
+                    break;
+                }
+            }
+        }
+
+        return Optional.of( new ProvidedColumnStatistics( count, min, max, List.copyOf( uniqueValues.values() ), full ) );
+    }
+
+
+    private Long addCounts( Long left, Long right ) {
+        if ( left == null ) {
+            return right;
+        }
+        if ( right == null ) {
+            return left;
+        }
+        return left + right;
+    }
+
+
+    private PolyValue lower( PolyValue current, PolyValue candidate ) {
+        if ( candidate == null ) {
+            return current;
+        }
+        if ( current == null ) {
+            return candidate;
+        }
+        try {
+            return candidate.compareTo( current ) < 0 ? candidate : current;
+        } catch ( RuntimeException e ) {
+            return current;
+        }
+    }
+
+
+    private PolyValue higher( PolyValue current, PolyValue candidate ) {
+        if ( candidate == null ) {
+            return current;
+        }
+        if ( current == null ) {
+            return candidate;
+        }
+        try {
+            return candidate.compareTo( current ) > 0 ? candidate : current;
+        } catch ( RuntimeException e ) {
+            return current;
+        }
     }
 
 
     /**
-     * Adapter Statistics:
      * Converts provider column statistics to statistics column representation
      * @param column QueryResult
      * @param provided ProvidedColumnStatistics
@@ -384,7 +466,6 @@ private Optional<StatisticColumn> toStatisticColumn( QueryResult column, Provide
 
 
     /**
-     * Adapter Statistics:
      * Look for Adapter Statistic Provider via catalog
      * @param allocation AllocationEntity
      * @return AdapterStatisticsProvider
@@ -404,7 +485,7 @@ private Optional<AdapterStatisticsProvider> getStatisticsProvider( AllocationEnt
                 .map( physical -> physical.unwrap( AdapterStatisticsProvider.class ) )
                 .filter( Optional::isPresent )
                 .map( Optional::get )
-                .findFirst(); // get first adapter that implements AdapterStatisticsProvider
+                .findFirst();
     }
 
 
diff --git a/plugins/parquet-adapter/build.gradle b/plugins/parquet-adapter/build.gradle
index 2e926e0ffd..350ddb5c2e 100644
--- a/plugins/parquet-adapter/build.gradle
+++ b/plugins/parquet-adapter/build.gradle
@@ -44,7 +44,6 @@ dependencies {
 }
 
 test {
-    dependsOn(":dbms:shadowJar")
     useJUnitPlatform()
     testLogging {
         events "failed", "standard_out", "standard_error"
diff --git a/plugins/parquet-adapter/docs/README.md b/plugins/parquet-adapter/docs/README.md
index ee486505d9..4ba9204faa 100644
--- a/plugins/parquet-adapter/docs/README.md
+++ b/plugins/parquet-adapter/docs/README.md
@@ -222,6 +222,11 @@ The following figure shows an example of the displayed schema information.
 Workflow-specific Parquet extract and load activities are documented separately in:
 - `docs/workflow.md`
 
+## Partitioned Folder Layouts
+
+Hive-style partitioned folder layout support is designed separately in:
+- `docs/partitioned_layouts.md`
+
 ## Unit Tests
 ___
 
diff --git a/plugins/parquet-adapter/docs/filter_improvements.md b/plugins/parquet-adapter/docs/filter_improvements.md
index dd0ddfd109..e880b89ef8 100644
--- a/plugins/parquet-adapter/docs/filter_improvements.md
+++ b/plugins/parquet-adapter/docs/filter_improvements.md
@@ -10,8 +10,8 @@ The new goal of this development step was:
 
 1. support logical filter trees (AND, OR, NOT, IN) - hierarchical filter
 2. resolve nested filter operands recursively
-3. reuse shared translation logic between relational and document filters - abstract class
-
+3. reuse shared translation logic between relational and document filters
+4. allow both exact filtering and native Parquet pushdown to understand composed filters
 
 ## Main Changes
 
@@ -202,4 +202,18 @@ Tests were extended to verify:
 - `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java`
 - `plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java`
 
+## Summary
+
+The filtering model was upgraded from single comparison predicates to recursive logical filter trees.
+
+This improvement affects both:
+
+- filter translation
+- filter execution
+
+and it applies to both:
+
+- exact enumerator-level filtering
+- native Parquet predicate pushdown
 
+The result is better support for realistic composed predicates while keeping unsupported cases conservative.
diff --git a/plugins/parquet-adapter/docs/flavors.md b/plugins/parquet-adapter/docs/flavors.md
new file mode 100644
index 0000000000..62eab3f32b
--- /dev/null
+++ b/plugins/parquet-adapter/docs/flavors.md
@@ -0,0 +1,160 @@
+# Parquet Table Flavors
+
+## Overview
+
+The Parquet adapter defines three table flavors through `ParquetTable.Flavor`:
+
+- `SCANNABLE`
+- `FILTERABLE`
+- `TRANSLATABLE`
+
+These flavors represent different integration styles between the adapter and the Polypheny query engine. 
+They represent different execution and planning strategies for reading the same Parquet data.
+The active flavor is selected when the Parquet namespace creates table wrappers.
+
+## Where Flavor Selection Happens
+
+Flavor-based table creation is implemented in `ParquetNamespace`.
+
+- `ParquetNamespace.createParquetTable(...)` prepares the metadata and source reference for a physical Parquet table.
+- `ParquetNamespace.createTable(...)` instantiates the concrete table implementation according to the configured flavor.
+
+The current Parquet source selects the `FILTERABLE` flavor in `ParquetSource.updateNamespace(...)`.
+
+
+## Purpose of Each Flavor
+
+### SCANNABLE
+
+`SCANNABLE` is the simplest integration model.
+
+In this flavor, the adapter behaves mainly as a row producer. 
+The query engine asks the table to provide rows, and the adapter returns them as an enumerable result stream.
+
+Characteristics:
+
+- simple implementation model
+- filtering is handled outside the adapter
+- less efficient (it does not provide optimization)
+
+
+### FILTERABLE
+
+`FILTERABLE` extends the scan-based model by allowing the adapter to receive filter conditions from the query engine.
+
+In this flavor, the adapter inspects query filters and pushes supported conditions into its own execution logic. 
+This allows the Parquet reader to reduce the amount of data it reads and processes using native predicate pushdown.
+
+Characteristics:
+
+- supports filter pushdown
+- supports projection-aware reading
+- uses Parquet native filter stages such as statistics, dictionary, column index, bloom filter, and record filtering when available
+- significantly more efficient than plain scan-only execution
+
+This is the active flavor in the current Parquet adapter implementation.
+
+### TRANSLATABLE
+
+`TRANSLATABLE` is the most advanced integration style.
+
+In this flavor, the adapter participates directly in query planning. 
+Instead of only returning rows at execution time, it provides adapter-specific relational nodes and planner rules.
+
+Characteristics:
+
+- deeper integration with the query planner
+- enables more advanced optimization
+- more flexible than scan-only or filterable execution
+
+
+## Flavor Workflows
+
+The following sections describe how the same logical SQL query flows through each flavor.
+
+Example query:
+
+```sql
+SELECT customer_id, name
+FROM customers
+WHERE customer_id > 3
+```
+
+### 1. SCANNABLE Workflow
+The adapter mainly acts as a generic row reader.
+
+Main class:
+
+- `ParquetScannableTable`
+
+Flow:
+
+- The query engine decides a table scan is needed.
+- The scannable table is asked to provide rows.
+- The adapter reads rows from the Parquet file.
+- Filtering may be applied later by the query engine rather than inside the adapter.
+- Projection may also be handled outside the adapter depending on the execution path.
+
+Practical consequence:
+
+- more data may be read than actually needed
+- pushdown opportunities are limited
+- execution is simpler but less efficient
+- appropriate when correctness and simplicity are more important than optimization.
+
+### 2. FILTERABLE Workflow
+
+The adapter receives a list of query filters and decides which ones it can handle internally.
+
+Main classes:
+
+- `ParquetFilterableTable`
+- `ParquetEnumerator`
+- `ParquetPredicateBuilder`
+
+Flow:
+
+- The query engine calls ParquetFilterableTable.scan(...).
+- The table inspects incoming filter expressions.
+- Only pushdown-safe filters are converted into adapter-specific `FilterInfo` objects.
+- These filters are passed into ParquetEnumerator.
+- The enumerator builds the requested projection schema from the projected columns.
+- `ParquetPredicateBuilder` translates adapter filters into native Parquet predicates.
+- `ParquetFileReader` is opened with `ParquetReadOptions` that enable native predicate pushdown stages.
+- Matching rows are read directly from the projected Parquet schema and returned.
+
+Practical consequence:
+
+- fewer columns may be read
+- fewer row groups and pages may be read when the Parquet file contains usable metadata
+- less work is done outside the adapter
+
+Current limitations:
+
+- Only simple `column OP literal` predicates are pushed down.
+- `BOOLEAN` and string-like fields currently support only `=` and `!=`.
+- Legacy `INT96` timestamp columns are intentionally not pushed down.
+
+
+### 3. TRANSLATABLE Workflow
+
+The adapter participates in planning.
+
+Main classes:
+
+- `ParquetTranslatableTable`
+- `ParquetScan`
+- `ParquetProjectScanRule`
+
+Flow:
+
+- The planner encounters a Parquet-backed table.
+- The translatable table produces an adapter-specific relational node.
+- The query planner can rewrite the query plan using rules that know about your Parquet adapter.
+- Projection and other operations can be integrated into adapter-specific planning structures.
+
+Practical consequence:
+
+- optimization can happen earlier and more explicitly
+- this approach supports more sophisticated future optimizations
+
diff --git a/plugins/parquet-adapter/docs/images/partition_year.png b/plugins/parquet-adapter/docs/images/partition_year.png
new file mode 100644
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diff --git a/plugins/parquet-adapter/docs/partitions_changes.md b/plugins/parquet-adapter/docs/partitions_changes.md
new file mode 100644
index 0000000000..55272ddedd
--- /dev/null
+++ b/plugins/parquet-adapter/docs/partitions_changes.md
@@ -0,0 +1,242 @@
+# Partitioning Changed Files
+
+## Discovery and Schema Metadata
+
+### ParquetFileDiscovery.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java`
+
+Discovery layer that converts the physical file/folder layout into logical tables the adapter can create and scan.
+
+### DiscoveredTable.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTable.java`
+
+It represents one logical Polypheny table found during Parquet discovery, before the table is fully registered in Polypheny.
+
+### DiscoveredTableBinding.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTableBinding.java`
+
+Metadata that connects a discovered logical table back to the real Parquet data. Now contains list of source files.
+
+### ParquetSourceFile.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFile.java`
+
+One physical Parquet file that contributes rows to a Parquet table. Contains partition values.
+
+### ParquetSchemaReader.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java`
+
+Can read many parquet files. Builds one consolidated schema from all file schemas.
+
+### ParquetSchemaNormalizer.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaNormalizer.java`
+Supports normalizing one discovered logical table that may be backed by many files and partitions
+
+### ParquetTableBinding.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java`
+
+Contains list of source files
+
+### ParquetColumnRole.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnRole.java`
+
+PARTITION was added
+
+### ParquetBindingSerializer.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetBindingSerializer.java`
+
+Saves table binding metadata in adapter settings. It now saves all source files and their partition values, so the adapter can restore partitioned tables after restart.
+
+## Adapter Catalog and Table Exposure
+
+### AbstractParquetSource.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java`
+
+Discovers logical tables from the Parquet location. It keeps the discovered tables in memory and exposes their bindings to the relational source.
+
+### ParquetNamespace.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java`
+
+Creates Parquet table wrappers. Root tables now use `ParquetSourceFile`, so simple tables and multi-file tables use the same file model.
+
+### ParquetRelationalSource.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java`
+
+Creates physical Parquet tables from discovered metadata. If a partition column exists, it creates Polypheny list partitions and stores only the matching files in each partition binding.
+
+### ParquetRelTable.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java`
+
+Runs scans for a Parquet table. It reads all files in the table binding, resolves filters, skips files by partition values, and chooses the needed enumerator.
+
+## Planner and Filter Pushdown
+
+### ParquetRelTableScanRule.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelTableScanRule.java`
+
+Converts a logical scan into `ParquetRelScan` when the table is a Parquet table. This gives the adapter its own scan node.
+
+### ParquetRelScanRuleSupport.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java`
+
+Contains helper methods used by scan, filter, and calc rules. It finds a `ParquetRelScan` and returns field types for filter translation.
+
+### ParquetRelScan.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java`
+
+Adapter scan node for Parquet tables. It stores selected fields and pushed filters, then calls `ParquetRelTable.project(...)` during execution.
+
+### ParquetRelScanRule.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java`
+
+Pushes simple projections into `ParquetRelScan`. This lets the adapter read only selected columns.
+
+### ParquetEnumerableFilterScanRule.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterScanRule.java`
+
+Pushes supported filters into a Parquet scan. If the filter can be translated, the adapter applies it.
+
+### ParquetEnumerableCalcScanRule.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcScanRule.java`
+
+Handles `LogicalCalc`, which may contain both filter and projection. It pushes the filter into Parquet and keeps the projection above the scan.
+
+### AbstractFilterTranslator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java`
+
+Common base for filter translators. It parses simple comparison filters, supports literals and dynamic parameters, and handles reversed comparisons.
+
+### ParquetRelFilterTranslator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java`
+
+Converts relational filters into `ParquetAdapterFilter`. It supports simple comparisons, `AND`, `OR`, `NOT`, `IN`, and dynamic parameters.
+
+## Runtime Filtering and Source Pruning
+
+### FilterEvaluator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FilterEvaluator.java`
+
+Base class for checking adapter filters. It can return true, false, or unknown; unknown means the data should not be skipped.
+
+### ParquetSourceFileFilterEvaluator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java`
+
+Skips files before reading them. It checks filters against partition values stored in `ParquetSourceFile`.
+
+### ParquetGroupFilterEvaluator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetGroupFilterEvaluator.java`
+
+Checks filters against actual Parquet rows. It can read values by field index or by stored Parquet path.
+
+### ParquetPartitionAwareFilterEvaluator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPartitionAwareFilterEvaluator.java`
+
+Adds support for partition columns during row filtering. Partition values come from the current source file, not from the Parquet row.
+
+### ParquetNestedFilterEvaluator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedFilterEvaluator.java`
+
+Checks filters for generated nested tables. It uses column bindings and table paths to find the real value inside the root Parquet row.
+
+### ParquetNestedJoinFilterEvaluator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedJoinFilterEvaluator.java`
+
+Checks filters on joined parent-child rows. It also checks parent filters before child rows are expanded.
+
+## Runtime Reading and Enumeration
+
+### ParquetSourceReader.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java`
+
+Low-level reader for one Parquet file. It now accepts filters, can use native Parquet filtering, and exposes the current row number.
+
+### AbstractParquetEnumerator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java`
+
+Base enumerator for Parquet rows. It expands rows when needed and applies adapter filters before returning results.
+
+### ParquetMultiFileEnumerator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java`
+
+Reads several files as one table. It runs one enumerator per file and returns all rows as one continuous result.
+
+### ParquetRelEnumerator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java`
+
+Simple enumerator for flat Parquet rows. It now uses the shared filter flow.
+
+### ParquetNestedNonRepeatedRelEnumerator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java`
+
+Reads rows using column bindings. It can fill partition columns from the source file and data columns from Parquet paths.
+
+### ParquetNestedRepeatedRelEnumerator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java`
+
+Reads repeated nested fields as relational rows. It follows the table path and creates one output row per nested item.
+
+### ParquetNestedJoinEnumerator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java`
+
+Executes supported parent-child joins inside the adapter. It reads one Parquet file, expands child rows under each parent, and returns joined rows.
+
+### ParquetPathValueExtractor.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPathValueExtractor.java`
+
+Reads values by Parquet path. It also creates synthetic values like row id, parent row id, and ordinal.
+
+### ParquetValueExtractor.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java`
+
+Common interface for value extractors. It now also supports reading values by path.
+
+### CombinedGroup.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/CombinedGroup.java`
+
+Virtual row for a parent-child join. It knows which output fields come from the parent and which come from the child.
+
+## Statistics and Polypheny Partition Awareness
+
+### ParquetStatisticsReader.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java`
+
+Reads statistics from all files of a table. It also creates statistics for partition columns from folder values.
+
+### StatisticsManagerImpl.java
+`monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java`
+
+Uses statistics provided by the adapter when available. It can combine statistics from several physical partitions of the same logical table.
+
+### AbstractQueryProcessor.java
+`dbms/src/main/java/org/polypheny/db/processing/AbstractQueryProcessor.java`
+
+Uses partition filter values to choose accessed partitions. This helps routing use only the needed Polypheny partitions when possible.
+extractPartitionsForLogicalEntity() new function
+
+## Document Model Support
+
+### ParquetDocument.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java`
+
+Document scans now use the shared filter flow. Dynamic parameters are resolved before reading.
+
+### ParquetDocFilterTranslator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java`
+
+Converts supported document filters into adapter filters. It matches document field names to exported columns.
+
+### ParquetDocEnumerator.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java`
+
+Reads Parquet rows as documents. It now uses the shared filter flow and creates document ids from the file path and row number.
+
+
+
+## Examples
+
+```
+select *
+from prn__yellow_tripdata
+where "year" = '2026'
+```
+
+![Schema display](images/partition_year.png)
diff --git a/plugins/parquet-adapter/docs/partitions_overview.md b/plugins/parquet-adapter/docs/partitions_overview.md
new file mode 100644
index 0000000000..92c4f72e5f
--- /dev/null
+++ b/plugins/parquet-adapter/docs/partitions_overview.md
@@ -0,0 +1,346 @@
+# Parquet Multi-File and Partitioned Folder Layout Design
+
+## Goal
+
+Add partitions support.
+
+## General
+
+Partitioning is a way to organize one logical table into multiple physical files or folders based on column values.
+
+In our Parquet adapter, partition values come from Hive-style folder names, for example:
+
+```text
+yellow_tripdata/
+  year=2025/
+    month=01/
+      yellow_tripdata_2025-01.parquet
+```
+
+This file belongs to the logical table `yellow_tripdata`, and the adapter derives two virtual column values from the path:
+
+```text
+year = '2025'
+month = '01'
+```
+
+These partition columns are exposed together with the real Parquet data columns. When a query filters by partition columns, the adapter can skip files from non-matching folders before opening them. For example, `WHERE "year" = '2025' AND "month" = '01'` reads only files under `year=2025/month=01`.
+
+So partitioning improves query performance by reducing the number of Parquet files that need to be opened and scanned.
+
+## Logical and Physical Tables
+
+If partition integration is triggered, then created:
+- one logical table
+- many physical tables: one physical table per value of the first partition column
+
+### Example:
+```text
+yellow_tripdata/
+  year=2024/
+    month=01/file.parquet
+    month=02/file.parquet
+  year=2025/
+    month=01/file.parquet
+    month=02/file.parquet
+  year=2026/
+    month=01/file.parquet
+```
+
+Logical table: 
+- yellow_tripdata
+
+Physical tables:
+
+- yellow_tripdata partition year=2024
+- yellow_tripdata partition year=2025
+- yellow_tripdata partition year=2026
+
+## New Functionality
+
+The following functionality was implemented:
+
+### 1. Support multiple Parquet files as one table.
+- `ParquetFileDiscovery` discovers multiple files for one logical table.
+- `DiscoveredTableBinding`, `ParquetTableBinding`, `ParquetSourceFile` store the file list.
+- `ParquetRelTable` uses the file list at scan time.
+- `ParquetMultiFileEnumerator` iterates over selected files one by one.
+- `ParquetSchemaReader` can read schemas/footers from multiple files.
+
+### 2. Support one common table schema across those files, including missing fields.
+- `ParquetFileDiscovery` decides whether file schemas can be consolidated.
+- `ParquetSchemaReader` builds a combined schema from multiple file footers.
+- `ParquetSchemaNormalizer` creates normalized tables from discovered multi-file bindings.
+- `ParquetTableBinding` maps logical/physical columns to Parquet source paths.
+- `ParquetRelTable` decides when binding-aware scanning is needed.
+- `ParquetPathValueExtractor` returns NULL when a source path is missing.
+
+### 3. Add Hive-style partition discovery from `key=value` folders.
+- `ParquetFileDiscovery` recursively discovers partition folders and parses key=value.
+- `ParquetSourceFile` stores each file URL together with its partition values.
+- `DiscoveredTable`, `DiscoveredTableBinding` carry discovered partitioned table metadata.
+- `ParquetSchemaNormalizer` adds partition columns to normalized root tables.
+- `ParquetTableBinding` marks those columns with `ParquetColumnRole.PARTITION`
+
+### 4. Add file-level pruning from partition filters.
+- `ParquetRelFilterTranslator` translates supported SQL filters into adapter filters.
+- `ParquetRelScan`, `ParquetEnumerableFilterScanRule`, `ParquetEnumerableCalcScanRule` push filters into the scan.
+- `ParquetRelTable` resolves filter columns to `ParquetColumnBinding`.
+- `ParquetSourceFileFilterEvaluator` evaluates partition filters against `ParquetSourceFile.partitionValues()`.
+- `ParquetPartitionAwareFilterEvaluator` evaluates partition filters during row-level scan fallback.
+
+### 5. Integrate Polypheny's logical partition metadata for the first partition column.
+- `ParquetRelationalSource` creates Polypheny partition `groups/partitions/allocations` for the first partition column.
+- `ParquetRelationalSource.firstPolyphenyPartitionColumn` chooses the partition column.
+- `ParquetRelationalSource.createPartitionedTable` creates the catalog partition layout.
+- `AbstractQueryProcessor` maps detected partition filter values to accessed partition IDs.
+- `StatisticsManagerImpl` aggregates statistics from multiple partition allocations.
+- `ParquetStatisticsReader` provides partition-column statistics from folder values.
+
+
+## Examples
+
+### Table Creation
+
+Given this non-partitioned folder:
+
+```text
+yellow_tripdata/
+  yellow_tripdata_2025-01.parquet
+  yellow_tripdata_2025-02.parquet
+```
+
+Polypheny should expose one table:
+
+```text
+parquetrelational1__yellow_tripdata
+```
+
+Given this partitioned folder:
+
+```text
+yellow_tripdata/
+  year=2025/month=01/yellow_tripdata_2025-01.parquet
+  year=2025/month=02/yellow_tripdata_2025-02.parquet
+  year=2026/month=01/yellow_tripdata_2026-01.parquet
+```
+
+Polypheny should also expose one table:
+
+```text
+parquetrelational1__yellow_tripdata
+```
+
+The partitioned table should have:
+
+- data columns from the consolidated Parquet file schemas
+- virtual partition columns from folder names:
+  - `year`
+  - `month`
+
+Example query:
+
+```sql
+SELECT *
+FROM parquetrelational1__yellow_tripdata
+WHERE "year" = '2025' AND "month" = '01';
+```
+
+Execution:
+
+1. Translate supported filters as today.
+2. Resolve `year = '2025'` and `month = '01'` as partition-column filters.
+3. Select only files below `year=2025/month=01`.
+4. Open only those files.
+5. Apply existing native Parquet filters inside each selected file for non-partition columns.
+6. Emit rows with `year` and `month` populated from the path values.
+
+## Source and Table Recognition
+
+### Definitions
+
+Configured source location:
+
+- the adapter setting chosen by the user
+- can point to a single `.parquet` file, a table root directory, or a dataset root directory
+
+Table root:
+
+- the directory that represents one logical table
+- all Parquet files below this root belong to that table unless excluded by discovery rules
+- partition directories, when present, are below the table root
+
+Partition root:
+
+- the first `key=value` directory below the table root
+- partition columns are derived from `key=value` directories under the table root, not above it
+
+Example:
+
+```text
+dataset/
+  yellow_tripdata/
+    year=2025/
+      month=01/
+        yellow_tripdata_2025-01.parquet
+```
+
+Here:
+
+- configured source can be `dataset/` or `dataset/yellow_tripdata/`
+- table root is `dataset/yellow_tripdata/`
+- partition root is `dataset/yellow_tripdata/year=2025/`
+- partition columns are `year`, then `month`
+
+### File Naming
+
+Files such as:
+
+```text
+yellow_tripdata_2025-01.parquet
+yellow_tripdata_2025-02.parquet
+```
+
+are valid source files for the same table when they live under the same folder and their schemas consolidate:
+
+```text
+yellow_tripdata/
+  yellow_tripdata_2025-01.parquet
+  yellow_tripdata_2025-02.parquet
+```
+
+The adapter should not rely on filename prefixes as the main grouping rule. Schema compatibility is the deciding rule for direct files in one folder.
+
+## Logic Flow
+
+1. File/table discovery (ParquetFileDiscovery.discoverTables(...)) checks:
+- one Parquet file
+- folder with several Parquet files
+- partitioned folder
+- dataset root with several table folders
+
+2. Schema creation:
+- ParquetSchemaReader reads Parquet metadata from the discovered files and builds common schema.
+- Before: one file -> one schema; Now: many files -> one common schema
+
+3. Adapter discovery metadata
+- ParquetFileDiscovery creates DiscoveredTable and DiscoveredTableBinding.
+- This metadata describes table name, columns, source files, partition values, and column paths.
+
+4. Logical table creation.
+- Adapter returns columns through getExportedColumns().
+- Polypheny creates the logical table in its catalog.
+
+
+5. Physical table creation. ParquetRelationalSource.createTable() uses the discovered metadata.
+
+
+6. Add partitions on Polypheny level. ParquetRelationalSource.createPartitionedTable() adds needed information to catalog.
+
+
+7. Query planning happens. ParquetRelScan called. Filters and projections can be pushed into this scan.
+
+
+8. Runtime scan happens. ParquetRelTable:
+- reads metadata
+- applies partition pruning: ParquetSourceFileFilterEvaluator.prune(...) - returns only the files that can match the partition filter
+- and reads only needed files: creates ParquetMultiFileEnumerator
+
+9. Statistics updated
+- A logical column can now be spread across several physical Parquet files, and also across several Polypheny physical partitions.
+- During statistics discovery, we may need to inspect all existing files
+
+## File/table discovery 
+### 1. one Parquet file
+```text
+yellow_tripdata/
+  yellow_tripdata_2025-01.parquet
+```
+one table backed by one file (old functionality)
+
+### 2. folder with several Parquet files
+```text
+yellow_tripdata/
+  yellow_tripdata_2025-01.parquet
+  yellow_tripdata_2025-02.parquet
+  yellow_tripdata_2025-03.parquet
+```
+There are no key=value partition folders here.
+
+Result:
+- one logical table
+- one physical table
+- many Parquet files in the binding
+
+This is new functionality compared to old one-file behavior.
+
+many monthly files -> one table
+
+So the user can query:
+
+```text
+select *
+from yellow_tripdata
+```
+
+and get rows from all files.
+
+### 3. partitioned folder
+
+```text
+yellow_tripdata/
+  year=2025/
+    month=01/
+      yellow_tripdata_2025-01.parquet
+    month=02/
+      yellow_tripdata_2025-02.parquet
+  year=2026/
+    month=01/
+      yellow_tripdata_2026-01.parquet
+```
+
+Here the folders have this form: key=value
+
+Result:
+- one logical table
+- physical tables per year (Polypheny supports only one partitioning level)
+  - tw0 physical tables in this example
+- many Parquet files in bindings
+- partition columns exposed as normal columns
+
+`month` is still stored in file metadata and exposed as a column, but it is not used to create Polypheny physical partitions in current implementation.
+Adapter can skip files by month inside the physical table binding
+
+### 4. dataset root with several table folders. Root directory contains multiple partitioned tables
+```text
+tlc/
+    yellow_tripdata/
+        year=2025/
+            month=01/
+                yellow_tripdata_2025-01.parquet
+
+    green_tripdata/
+        year=2025/
+            month=01/
+                green_tripdata_2025-01.parquet
+
+    fhv_tripdata/
+        year=2025/
+            month=01/
+                fhv_tripdata_2025-01.parquet
+```
+Each child folder is treated as a separate table:
+
+- yellow_tripdata -> table
+- green_tripdata  -> table
+- fhv_tripdata    -> table
+
+Result:
+
+- multiple logical tables
+  - logical table: yellow_tripdata
+  - logical table: green_tripdata
+  - logical table: fhv_tripdata
+  
+- multiple physical tables 
+- multiple Parquet files
diff --git a/plugins/parquet-adapter/docs/statistics.md b/plugins/parquet-adapter/docs/statistics.md
index 328d7d27e3..8c48838a7e 100644
--- a/plugins/parquet-adapter/docs/statistics.md
+++ b/plugins/parquet-adapter/docs/statistics.md
@@ -5,21 +5,13 @@ This document describes the statistics-related changes introduced to avoid loadi
 ## Goal
 
 Before this change, the monitoring statistics subsystem computed table and column statistics by executing normal relational queries.
-This could trigger full scans for Parquet-backed entities during startup and impact performance.
+For Parquet-backed entities, this could trigger full scans during startup statistics reevaluation.
 
-The goal of development is:
+The new goal is:
 
-1. Ask the adapter for metadata-backed statistics first
-2. Use Parquet footer metadata when available
-3. Fall back to the old query-based path only when necessary
-
-## Flow Chart
-
-![Schema display](images/statistics.png)
-
-## Example
-
-![Schema display](images/statistics_example.png)
+1. ask the adapter for metadata-backed statistics first
+2. use Parquet footer metadata when available
+3. fall back to the old query-based path only when necessary
 
 ## Main Objects
 
@@ -116,6 +108,7 @@ Helper methods added:
 - `getStatisticsProvider(AllocationEntity allocation)`
 - `toStatisticColumn(QueryResult column, ProvidedColumnStatistics provided)`
 
+
 ## Parquet Adapter Integration
 
 ### `ParquetRelTable`
@@ -189,6 +182,8 @@ Main methods:
 - `getEntityStatistics(boolean nestedTable)`
 - `getColumnStatistics(LogicalColumn column, int uniqueValueLimit)`
 
+
+
 ## Full Flow
 
 ### Step 1: Startup statistics reevaluation begins
diff --git a/plugins/parquet-adapter/docs/todo_partitions.md b/plugins/parquet-adapter/docs/todo_partitions.md
new file mode 100644
index 0000000000..a3b781938e
--- /dev/null
+++ b/plugins/parquet-adapter/docs/todo_partitions.md
@@ -0,0 +1,24 @@
+# Partitioning Issues
+
+## Partitioned Join Flow
+
+Adapter-level joins currently depend on both sides having the same backing source-file set. Polypheny catalog partitioning splits a root table into several physical allocations, while generated child tables can still describe the full file set. Because of that, a valid parent-child join over partitioned data may not be selected by the Parquet join rule, or it may need a more precise source compatibility check.
+
+Partition columns also need explicit handling in the nested join runtime. Root scans materialize folder-derived partition values through `ParquetNestedNonRepeatedRelEnumerator`, but `ParquetPathValueExtractor` still returns `NULL` for `PARTITION`. If a partition column is projected or filtered through the nested join path, the join result can lose that value.
+
+## Restore Flow
+
+`ParquetRelationalSource.restoreTable` restores only the first physical entity from the provided entity list. A partitioned table can have multiple physical entities, so restore should be checked to make sure every partition allocation is re-registered.
+
+## Polypheny Partition Model
+
+Only the first discovered partition column is mapped to Polypheny catalog partitioning. This is a current Polypheny-side limitation and is not planned to be extended now. Multi-level folders such as `year=2025/month=01` can still be exposed as adapter partition columns, but only one column should drive the catalog partition layout.
+
+## Partition Names
+
+Generated partition names are normalized from `column_value`. Different raw values can normalize to the same name, for example `a-b`, `a_b`, and case-only variants. The creation flow should eventually add collision handling.
+
+## Schema Consolidation
+
+Schema consolidation was made stricter by requiring at least `0.8` column-name similarity between files. This reduces accidental grouping of unrelated files. If schema evolution support grows, the threshold should probably become a named constant or adapter setting and should be covered by tests for missing-column null materialization.
+
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
index 6709e2a952..3642d8566a 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java
@@ -16,15 +16,15 @@
 
 package org.polypheny.db.adapter.parquet.document.execution;
 
-import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.parquet.example.data.Group;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetGroupFilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.type.entity.document.PolyDocument;
-import org.polypheny.db.util.Source;
 
 /**
  * Document enumerator.
@@ -32,14 +32,18 @@
  */
 public class ParquetDocEnumerator extends AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
 
+    private static final ParquetDocValueExtractor documentValueExtractor = new ParquetDocValueExtractor();
     private final String documentPrefix;
-    private final ParquetDocValueExtractor documentValueExtractor;
 
 
-    public ParquetDocEnumerator( Source source, AtomicBoolean cancelFlag, FiltersContainer filtersContainer ) {
-        super( source, cancelFlag, null, filtersContainer, new ParquetDocValueExtractor() );
-        this.documentValueExtractor = (ParquetDocValueExtractor) valueExtractor;
-        this.documentPrefix = source.path();
+    public ParquetDocEnumerator( ParquetSourceReader reader, FiltersContainer filtersContainer ) {
+        this( reader, filtersContainer, true );
+    }
+
+
+    public ParquetDocEnumerator( ParquetSourceReader reader, FiltersContainer filtersContainer, boolean readerOwner ) {
+        super( reader, filtersContainer, new ParquetGroupFilterEvaluator( reader.getProjectionSchema(), documentValueExtractor ), readerOwner );
+        this.documentPrefix = reader.getSource().path();
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
index 472d4bf605..712b3b9951 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
@@ -60,9 +60,6 @@ public ParquetAdapterFilter translate( List<ExportedColumn> columns, RexNode fil
 
         for ( ExportedColumn column : columns ) {
             if ( column.name().equalsIgnoreCase( fieldName ) ) {
-                if ( !isColumnPredicateSupported( column.type(), parsed.operator(), right ) ) {
-                    return null;
-                }
                 return toParquetAdapterFilter( column.physicalPosition(), parsed.operator(), right );
             }
         }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
index 2ea436c004..7cd6611b83 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
@@ -29,9 +29,10 @@
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
 import org.polypheny.db.adapter.parquet.document.execution.ParquetDocEnumerator;
 import org.polypheny.db.adapter.parquet.document.planning.ParquetDocScan;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.type.AlgDataType;
 import org.polypheny.db.algebra.type.AlgDataTypeFactory;
@@ -76,6 +77,7 @@ public ParquetDocument( PhysicalCollection collection, Source source, AbstractPa
      * - get the adapter catalog from parquetSource
      * - ask it for the physical entity with this document’s id
      * - cast the result to ParquetDocument
+     *
      * @return Expression
      */
     @Override
@@ -98,6 +100,7 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
 
     /**
      * creates enumerable with resolve filters
+     *
      * @param dataContext context
      * @param filters - parquet filters
      * @return ParquetDocEnumerator
@@ -109,7 +112,9 @@ public Enumerable<PolyValue[]> scanFiltered( DataContext dataContext, List<Parqu
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                return new ParquetDocEnumerator( source, cancelFlag, FiltersContainer.shared( resolvedFilters ) );
+                FiltersContainer filtersContainer = FiltersContainer.shared( resolvedFilters );
+                ParquetSourceReader reader = new ParquetSourceReader( source, cancelFlag, null, filtersContainer.readerFilters() );
+                return new ParquetDocEnumerator( reader, filtersContainer );
             }
         };
     }
@@ -153,6 +158,7 @@ private AlgDataType buildDocumentType( AlgDataTypeFactory typeFactory ) {
 
     /**
      * add dynamic parameters to parquet filter if needed
+     *
      * @param dataContext context
      * @param filter parquet filter
      * @return ParquetFilter
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
index 3253a458b5..8d83849150 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
@@ -16,9 +16,12 @@
 
 package org.polypheny.db.adapter.parquet.relational;
 
+import com.google.common.collect.ImmutableList;
+import java.util.ArrayList;
 import java.util.LinkedHashMap;
 import java.util.List;
 import java.util.Map;
+import java.util.Optional;
 import java.util.Set;
 import java.util.stream.Collectors;
 import org.pf4j.Extension;
@@ -29,25 +32,38 @@
 import org.polypheny.db.adapter.annotations.AdapterSettingDirectory;
 import org.polypheny.db.adapter.annotations.AdapterSettingList;
 import org.polypheny.db.adapter.annotations.AdapterSettingString;
+import org.polypheny.db.adapter.parquet.relational.schema.DiscoveredTableBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetNormalizedSchema;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSchemaMode;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSchemaNormalizer;
-import org.polypheny.db.adapter.parquet.relational.schema.DiscoveredTableBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
-import org.polypheny.db.adapter.parquet.shared.io.ParquetUrlResolver;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.rules.FilterSetOpTransposeRule;
+import org.polypheny.db.catalog.Catalog;
+import org.polypheny.db.catalog.catalogs.AllocationRelationalCatalog;
 import org.polypheny.db.catalog.entity.allocation.AllocationCollection;
 import org.polypheny.db.catalog.entity.allocation.AllocationGraph;
+import org.polypheny.db.catalog.entity.allocation.AllocationPartition;
+import org.polypheny.db.catalog.entity.allocation.AllocationPartitionGroup;
 import org.polypheny.db.catalog.entity.allocation.AllocationTable;
 import org.polypheny.db.catalog.entity.allocation.AllocationTableWrapper;
 import org.polypheny.db.catalog.entity.logical.LogicalCollection;
 import org.polypheny.db.catalog.entity.logical.LogicalGraph;
+import org.polypheny.db.catalog.entity.logical.LogicalColumn;
 import org.polypheny.db.catalog.entity.logical.LogicalTableWrapper;
 import org.polypheny.db.catalog.entity.physical.PhysicalEntity;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.catalog.logistic.DataPlacementRole;
 import org.polypheny.db.catalog.logistic.DataModel;
+import org.polypheny.db.catalog.logistic.PartitionType;
+import org.polypheny.db.catalog.logistic.PlacementType;
+import org.polypheny.db.partition.properties.PartitionProperty;
 import org.polypheny.db.prepare.Context;
+import org.polypheny.db.tools.AlgBuilder;
 
 /**
  * Relational adapter implementation.
@@ -86,6 +102,15 @@ public RelationalDataSource asRelationalDataSource() {
     }
 
 
+    @Override
+    public AlgNode getRelScan( long allocId, AlgBuilder builder ) {
+        // this rule is required to push the filters into the ParquetRelScan.
+        builder.getCluster().getPlanner().addRuleDuringRuntime( FilterSetOpTransposeRule.INSTANCE );
+        ParquetRelScan.registerRules( builder.getCluster().getPlanner() );
+        return super.getRelScan( allocId, builder );
+    }
+
+
     @Override
     public Map<String, List<ExportedColumn>> getExportedColumns() {
         ParquetSchemaMode activeMode = schemaMode == null ? getConfiguredSchemaMode() : schemaMode;
@@ -107,6 +132,12 @@ public List<PhysicalEntity> createTable( Context context, LogicalTableWrapper lo
                 logical.pkIds, allocation );
 
         ParquetTableBinding binding = getDiscoveredTableBinding( logical.table.name, table );
+        Optional<String> partitionColumn = firstPolyphenyPartitionColumn( binding );
+        // check if partitions exist
+        if ( partitionColumn.isPresent() && binding.parentTableName() == null ) {
+            return createPartitionedTable( logical, allocation, table, binding, partitionColumn.get() );
+        }
+
         registerParquetBinding( table.id, binding );
         var physical = currentNamespace.createParquetTable( table.id, table, binding, this );
         adapterCatalog.replacePhysical( physical );
@@ -187,7 +218,7 @@ protected void reloadSettings( List<String> updatedSettings ) {
         }
         if ( updatedSettings.contains( SCHEMA_MODE_SETTING ) ) {
             schemaMode = getConfiguredSchemaMode();
-            clearExportedColumnsCache();
+            clearTablesCache();
             clearNormalizedExportCache();
         }
     }
@@ -210,6 +241,7 @@ private Map<String, List<ExportedColumn>> getNormalizedExportedColumns() {
 
     /**
      * converts DiscoveredTableBinding into ParquetTableBinding
+     *
      * @param tableName - generated table name
      * @param table - physical table
      * @return ParquetTableBinding - column bindings are stored by physical column id
@@ -234,7 +266,7 @@ private ParquetTableBinding getDiscoveredTableBinding( String tableName, Physica
             return createFlatTableBinding( tableName, table );
         }
         // Convert discovered bindings to final ParquetTableBinding, where column bindings are stored by physical column id
-        return ParquetTableBinding.createTableBindingFromColumnPaths( binding.sourceUrl(), binding.parentTableName(), binding.sourcePathElements(), table, binding.columnPaths() );
+        return ParquetTableBinding.createTableBindingFromColumnPaths( binding.sourceFiles(), binding.parentTableName(), binding.sourcePathElements(), table, binding.columnPaths() );
     }
 
 
@@ -242,6 +274,7 @@ private ParquetTableBinding getDiscoveredTableBinding( String tableName, Physica
      * Creates a ParquetTableBinding for a flat-mode table.
      * Flat-mode tables do not come from ParquetSchemaNormalizer, so they do not have a DiscoveredTableBinding.
      * But we still want a persisted ParquetTableBinding for them so restore/scanning uses the same binding infrastructure.
+     *
      * @param tableName - generated table name
      * @param table - physical table
      * @return - ParquetTableBinding
@@ -254,17 +287,151 @@ private ParquetTableBinding createFlatTableBinding( String tableName, PhysicalTa
         }
 
         try {
-            Map<String, List<String>> columnPaths = new LinkedHashMap<>();
-            // Build column path map like order_id -> ["order_id"]
-            exportedColumns.forEach( column -> columnPaths.put( column.name(), List.of( column.physicalColumnName() ) ) );
-            String sourceUrl = ParquetUrlResolver.resolveFile( parquetDir, exportedColumns.get( 0 ).physicalSchemaName() ).toString();
-            return ParquetTableBinding.createTableBindingFromColumnPaths( sourceUrl, null, List.of(), table, columnPaths );
+            DiscoveredTableBinding binding = getTableBinding( tableName ).orElseThrow();
+            return ParquetTableBinding.createTableBindingFromColumnPaths( binding.sourceFiles(), null, List.of(), table, binding.columnPaths() );
         } catch ( Exception e ) {
             throw new GenericRuntimeException( e );
         }
     }
 
 
+    private Optional<String> firstPolyphenyPartitionColumn( ParquetTableBinding binding ) {
+        return binding.sourceFiles().stream()
+                .flatMap( sourceFile -> sourceFile.partitionValues().keySet().stream() )
+                .distinct()
+                .filter( partitionColumn -> binding.columnsByColumnId().values().stream().anyMatch( column -> column.columnName().equals( partitionColumn ) ) )
+                .findFirst();
+    }
+
+
+    /**
+     * Creates physical entity (relational table) and adds top level partition to catalog.
+     *
+     * @param logical logical table
+     * @param originalAllocation allocation table
+     * @param originalPhysical physical table
+     * @param binding table binding
+     * @param partitionColumn partition column
+     * @return list of tables if parquet file contains nested data.
+     */
+    private List<PhysicalEntity> createPartitionedTable(
+            LogicalTableWrapper logical,
+            AllocationTableWrapper originalAllocation,
+            PhysicalTable originalPhysical,
+            ParquetTableBinding binding,
+            String partitionColumn ) {
+        // group files by top level partitions
+        Map<String, List<ParquetSourceFile>> filesByPartitionValue = binding.sourceFiles().stream()
+                .filter( sourceFile -> sourceFile.partitionValues().containsKey( partitionColumn ) )
+                .collect( Collectors.groupingBy(
+                        sourceFile -> sourceFile.partitionValues().get( partitionColumn ),
+                        LinkedHashMap::new,
+                        Collectors.toList() ) );
+
+        // one partition -> one physical table
+        if ( filesByPartitionValue.size() <= 1 ) {
+            registerParquetBinding( originalPhysical.id, binding );
+            var physical = currentNamespace.createParquetTable( originalPhysical.id, originalPhysical, binding, this );
+            adapterCatalog.replacePhysical( physical );
+            return List.of( physical );
+        }
+
+        Catalog catalog = Catalog.getInstance();
+        AllocationRelationalCatalog allocationCatalog = catalog.getAllocRel( logical.table.namespaceId );
+
+        // Delete all temporary created allocations by polypheny first
+        allocationCatalog.deleteAllocation( originalAllocation.table.id );
+        allocationCatalog.deletePartition( originalAllocation.table.partitionId );
+        Catalog.snapshot()
+                .alloc()
+                .getPartitionProperty( logical.table.id )
+                .ifPresent( property -> property.partitionGroupIds.forEach( allocationCatalog::deletePartitionGroup ) );
+
+        adapterCatalog.removeAllocAndPhysical( originalAllocation.table.id );
+
+
+        LogicalColumn partitionLogicalColumn = logical.columns.stream()
+                .filter( column -> column.name.equals( partitionColumn ) )
+                .findFirst()
+                .orElseThrow( () -> new GenericRuntimeException( "Missing parquet partition column in logical table: %s", partitionColumn ) );
+
+        List<PhysicalEntity> physicals = new ArrayList<>();
+        List<Long> partitionGroupIds = new ArrayList<>();
+        List<Long> partitionIds = new ArrayList<>();
+
+        // Add partitions on Polypheny level
+
+        filesByPartitionValue.entrySet().stream()
+                .sorted( Map.Entry.comparingByKey() )
+                .forEach( entry -> {
+                    String partitionName = partitionName( partitionColumn, entry.getKey() );
+                    AllocationPartitionGroup group = allocationCatalog.addPartitionGroup(
+                            logical.table.id,
+                            partitionName,
+                            logical.table.namespaceId,
+                            PartitionType.LIST,
+                            1,
+                            false );
+                    AllocationPartition partition = allocationCatalog.addPartition(
+                            logical.table.id,
+                            logical.table.namespaceId,
+                            group.id,
+                            partitionName,
+                            false,
+                            PlacementType.AUTOMATIC,
+                            DataPlacementRole.REFRESHABLE,
+                            List.of( entry.getKey() ),
+                            PartitionType.LIST );
+                    AllocationTable partitionAllocation = allocationCatalog.addAllocation(
+                            adapterId,
+                            originalAllocation.table.placementId,
+                            partition.id,
+                            logical.table.id );
+                    PhysicalTable partitionPhysical = adapterCatalog.createTable(
+                            logical.table.getNamespaceName(),
+                            logical.table.name,
+                            logical.columns.stream().collect( Collectors.toMap( c -> c.id, c -> c.name ) ),
+                            logical.table,
+                            logical.columns.stream().collect( Collectors.toMap( t -> t.id, t -> t ) ),
+                            logical.pkIds,
+                            AllocationTableWrapper.of( partitionAllocation, originalAllocation.columns, originalAllocation.physicalSchema ) );
+                    ParquetTableBinding partitionBinding = new ParquetTableBinding(
+                            entry.getValue(),
+                            binding.parentTableName(),
+                            binding.sourcePathElements(),
+                            binding.columnsByColumnId() );
+                    registerParquetBinding( partitionPhysical.id, partitionBinding );
+                    var physical = currentNamespace.createParquetTable( partitionPhysical.id, partitionPhysical, partitionBinding, this );
+                    adapterCatalog.replacePhysical( physical );
+                    physicals.add( physical );
+                    partitionGroupIds.add( group.id );
+                    partitionIds.add( partition.id );
+                } );
+
+        allocationCatalog.addPartitionProperty(
+                logical.table.id,
+                PartitionProperty.builder()
+                        .entityId( logical.table.id )
+                        .partitionType( PartitionType.LIST )
+                        .isPartitioned( true )
+                        .partitionColumnId( partitionLogicalColumn.id )
+                        .partitionGroupIds( ImmutableList.copyOf( partitionGroupIds ) )
+                        .partitionIds( ImmutableList.copyOf( partitionIds ) )
+                        .numPartitionGroups( partitionGroupIds.size() )
+                        .numPartitions( partitionIds.size() )
+                        .reliesOnPeriodicChecks( false )
+                        .build() );
+        catalog.updateSnapshot();
+        return physicals;
+    }
+
+
+    private String partitionName( String columnName, String value ) {
+        String normalized = (columnName + "_" + value).toLowerCase().replaceAll( "[^a-z0-9_]+", "_" );
+        return normalized.isBlank() ? "partition" : normalized;
+    }
+
+
     private void clearNormalizedExportCache() {
         normalizedSchema = null;
     }
@@ -272,6 +439,7 @@ private void clearNormalizedExportCache() {
 
     /**
      * get normalized schema from ParquetSchemaNormalizer
+     *
      * @return ParquetNormalizedSchema
      */
     private ParquetNormalizedSchema getNormalizedSchema() {
@@ -279,12 +447,7 @@ private ParquetNormalizedSchema getNormalizedSchema() {
             return normalizedSchema;
         }
 
-        normalizedSchema = new ParquetSchemaNormalizer(
-                parquetDir,
-                getClass().getClassLoader(),
-                parquetTypeConverter,
-                getUniqueName() ).normalize();
-
+        normalizedSchema = new ParquetSchemaNormalizer( parquetDir, getUniqueName() ).normalize();
         return normalizedSchema;
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java
new file mode 100644
index 0000000000..8fe012bb6d
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java
@@ -0,0 +1,91 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.Iterator;
+import java.util.List;
+import java.util.function.Function;
+import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+/**
+ * Presents multiple per-file Parquet enumerators as one continuous enumerator.
+ */
+public class ParquetMultiFileEnumerator implements Enumerator<PolyValue[]> {
+
+    private final Iterator<ParquetSourceFile> files;
+    private final Function<ParquetSourceFile, Enumerator<PolyValue[]>> enumeratorFactory;
+    private Enumerator<PolyValue[]> currentEnumerator;
+    private PolyValue[] current;
+
+
+    public ParquetMultiFileEnumerator( List<ParquetSourceFile> files, Function<ParquetSourceFile, Enumerator<PolyValue[]>> enumeratorFactory ) {
+        this.files = List.copyOf( files ).iterator();
+        this.enumeratorFactory = enumeratorFactory;
+    }
+
+
+    @Override
+    public PolyValue[] current() {
+        return current;
+    }
+
+
+    @Override
+    public boolean moveNext() {
+        try {
+            for ( ; ; ) {
+                if ( currentEnumerator == null ) {
+                    if ( !files.hasNext() ) {
+                        current = null;
+                        return false;
+                    }
+                    currentEnumerator = enumeratorFactory.apply( files.next() );
+                }
+
+                if ( currentEnumerator.moveNext() ) {
+                    current = currentEnumerator.current();
+                    return true;
+                }
+
+                currentEnumerator.close();
+                currentEnumerator = null;
+            }
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Error while reading multiple parquet files", e );
+        }
+    }
+
+
+    @Override
+    public void reset() {
+        throw new UnsupportedOperationException();
+    }
+
+
+    @Override
+    public void close() {
+        if ( currentEnumerator != null ) {
+            currentEnumerator.close();
+            currentEnumerator = null;
+        }
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
index 57e65d3969..4e7cebdbe2 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
@@ -18,7 +18,6 @@
 
 import java.util.ArrayList;
 import java.util.List;
-import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.parquet.example.data.Group;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
@@ -26,9 +25,10 @@
 import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
 import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetNestedJoinFilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.type.entity.PolyNull;
 import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.util.Source;
 
 /**
  * Executes parent/child joins for normalized nested Parquet tables in one pass over
@@ -47,17 +47,13 @@ public class ParquetNestedJoinEnumerator extends ParquetNestedRepeatedRelEnumera
     private final List<String> childPathFromParent;
 
 
-    public ParquetNestedJoinEnumerator(
-            Source source,
-            AtomicBoolean cancelFlag,
-            ParquetTableBinding parentBinding,
-            ParquetTableBinding childBinding,
-            List<ParquetColumnBinding> parentColumns,
-            List<ParquetColumnBinding> childColumns,
-            JoinFiltersContainer filterContainer,
-            boolean leftIsParent,
-            boolean emitUnmatchedParents ) {
-        super( source, cancelFlag, parentBinding, parentColumns, filterContainer, true );
+    public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBinding parentBinding, ParquetTableBinding childBinding, List<ParquetColumnBinding> parentColumns, List<ParquetColumnBinding> childColumns, JoinFiltersContainer filterContainer, boolean leftIsParent, boolean emitUnmatchedParents ) {
+        this( reader, parentBinding, childBinding, parentColumns, childColumns, filterContainer, leftIsParent, emitUnmatchedParents, true );
+    }
+
+
+    public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBinding parentBinding, ParquetTableBinding childBinding, List<ParquetColumnBinding> parentColumns, List<ParquetColumnBinding> childColumns, JoinFiltersContainer filterContainer, boolean leftIsParent, boolean emitUnmatchedParents, boolean readerOwner ) {
+        super( reader, parentBinding, parentColumns, filterContainer, new ParquetNestedJoinFilterEvaluator( reader.getProjectionSchema(), new ParquetPathValueExtractor(), parentBinding, parentColumns, leftIsParent ), true, readerOwner );
         this.parentBinding = parentBinding;
         this.childBinding = childBinding;
         this.parentColumns = List.copyOf( parentColumns );
@@ -112,7 +108,7 @@ protected PolyValue[] extractRow( Group group ) {
                 row[i] = PolyNull.NULL;
                 continue;
             }
-            row[i] = extractValueByColumnRole(
+            row[i] = pathValueExtractor().extractValue(
                     combinedGroup.groupForField( i, leftIsParent ),
                     combinedGroup.bindingForField( i, leftIsParent ),
                     combinedGroup.tablePathForField( i, leftIsParent )
@@ -129,17 +125,16 @@ protected PolyValue[] extractRow( Group group ) {
      * @param filter - ParquetAdapterFilter
      * @return an extracted value
      */
-    @Override
     protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
         if ( group instanceof CombinedGroup combinedGroup ) {
-            return extractValueByColumnRole(
+            return pathValueExtractor().extractValue(
                     combinedGroup.groupForField( filter.columnIndex(), leftIsParent ),
                     combinedGroup.bindingForField( filter.columnIndex(), leftIsParent ),
                     combinedGroup.tablePathForField( filter.columnIndex(), leftIsParent )
             );
         }
         // If this is not a combined group then the filtering is being done on parent table only.
-        return extractValueByColumnRole(
+        return pathValueExtractor().extractValue(
                 (VirtualGroup) group,
                 parentColumns.get( filter.columnIndex() ),
                 parentBinding.sourcePathElements()
@@ -147,39 +142,6 @@ protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
     }
 
 
-    /**
-     * Checks if a filter is valid and can be applied on the group.
-     *
-     * @param group a group to check
-     * @param filter a filter to validate
-     * @return true if the filter is valid and false otherwise.
-     */
-    @Override
-    protected boolean canApplyFilter( Group group, ParquetAdapterFilter filter ) {
-        if ( filter.polyValue() == null || filter.columnIndex() < 0 ) {
-            return false;
-        }
-        if ( group instanceof CombinedGroup combinedGroup ) {
-            return filter.columnIndex() < combinedGroup.fieldCount();
-        }
-        return filter.columnIndex() < parentColumns.size();
-    }
-
-
-    /**
-     * Checks if the group contains a value.
-     *
-     * @param group a group to check.
-     * @param filter a filter containing column index which value is checked
-     * @return true if the group has a value at the filter column index and false otherwise.
-     */
-    @Override
-    protected boolean filterHasValue( Group group, ParquetAdapterFilter filter ) {
-        CombinedGroup combinedGroup = (CombinedGroup) group;
-        return !combinedGroup.isNullField( filter.columnIndex(), leftIsParent );
-    }
-
-
     private CombinedGroup combinedGroup( VirtualGroup parent, VirtualGroup child ) {
         return new CombinedGroup(
                 parent,
@@ -192,7 +154,12 @@ private CombinedGroup combinedGroup( VirtualGroup parent, VirtualGroup child ) {
 
 
     private boolean matchesFilters( Group group, List<ParquetAdapterFilter> filters ) {
-        return filters.stream().allMatch( filter -> matches( group, filter ) );
+        return filters.stream().allMatch( filter -> filterEvaluator.matches( group, filter ) );
+    }
+
+
+    private ParquetPathValueExtractor pathValueExtractor() {
+        return (ParquetPathValueExtractor) valueExtractor();
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
index 91094dcd42..208ebb22b3 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
@@ -17,17 +17,17 @@
 package org.polypheny.db.adapter.parquet.relational.execution;
 
 import java.util.List;
-import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.parquet.example.data.Group;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
 import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.type.entity.PolyNull;
 import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.type.entity.numerical.PolyLong;
-import org.polypheny.db.util.Source;
 
 /**
  * Used to handle virtual table that was created from nested non-repeated types.
@@ -35,17 +35,14 @@
 public class ParquetNestedNonRepeatedRelEnumerator extends AbstractParquetEnumerator {
 
     private final List<ParquetColumnBinding> columnBindings;
+    private final ParquetSourceFile sourceFile;
 
 
-    public ParquetNestedNonRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, List<ParquetColumnBinding> columnBindings ) {
-        this( source, cancelFlag, columnBindings, FiltersContainer.empty );
-    }
-
-
-    public ParquetNestedNonRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, List<ParquetColumnBinding> columnBindings, FiltersContainer filtersContainer ) {
+    public ParquetNestedNonRepeatedRelEnumerator( ParquetSourceReader reader, ParquetSourceFile sourceFile, List<ParquetColumnBinding> columnBindings, FiltersContainer filtersContainer ) {
         // read full root rows from the Parquet file
-        super( source, cancelFlag, null, filtersContainer, new ParquetPathValueExtractor() );
+        super( reader, filtersContainer, new ParquetPartitionAwareFilterEvaluator( reader.getProjectionSchema(), new ParquetPathValueExtractor(), sourceFile, columnBindings ) );
         this.columnBindings = List.copyOf( columnBindings );
+        this.sourceFile = sourceFile;
     }
 
 
@@ -53,7 +50,10 @@ public ParquetNestedNonRepeatedRelEnumerator( Source source, AtomicBoolean cance
     protected PolyValue[] extractRow( Group group ) {
         PolyValue[] row = new PolyValue[columnBindings.size()];
         for ( int i = 0; i < columnBindings.size(); i++ ) {
-            row[i] = extractValueByColumnRole( (VirtualGroup) group, columnBindings.get( i ) );
+            var binding = columnBindings.get( i );
+            row[i] = binding.role() == ParquetColumnRole.PARTITION
+                    ? partitionValue( binding )
+                    : pathValueExtractor().extractValue( (VirtualGroup) group, binding, List.of() );
         }
         return row;
     }
@@ -66,15 +66,16 @@ protected List<Group> expandRow( Group group ) {
     }
 
 
-    private PolyValue extractValueByColumnRole( VirtualGroup virtualGroup, ParquetColumnBinding binding ) {
-        return switch ( binding.role() ) {
-            case DATA -> valueExtractor.extractValue( virtualGroup, binding.sourcePathElements() );
-            case PRIMARY_KEY -> PolyString.of( virtualGroup.getMetadata().getRowId() );
-            case PARENT_KEY -> virtualGroup.getMetadata().getParentRowId() == null
-                    ? PolyNull.NULL
-                    : PolyString.of( virtualGroup.getMetadata().getParentRowId() );
-            case ORDINAL -> PolyLong.of( virtualGroup.getMetadata().getOrdinal() );
-        };
+    private PolyValue partitionValue( ParquetColumnBinding binding ) {
+        if ( !sourceFile.partitionValues().containsKey( binding.columnName() ) ) {
+            return PolyNull.NULL;
+        }
+        return PolyString.of( sourceFile.partitionValues().get( binding.columnName() ) );
+    }
+
+
+    private ParquetPathValueExtractor pathValueExtractor() {
+        return (ParquetPathValueExtractor) filterEvaluator.valueExtractor();
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
index 559baf179b..7b39450c9e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
@@ -18,7 +18,6 @@
 
 import java.util.ArrayList;
 import java.util.List;
-import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.parquet.example.data.Group;
 import org.apache.parquet.schema.GroupType;
 import org.apache.parquet.schema.Type;
@@ -28,12 +27,11 @@
 import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetGroupFilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetNestedFilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
-import org.polypheny.db.type.entity.PolyNull;
-import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.type.entity.numerical.PolyLong;
-import org.polypheny.db.util.Source;
 
 /**
  * Used for tables that do not have their own Parquet file, but are created from a nested group inside a Parquet file
@@ -43,19 +41,13 @@ public class ParquetNestedRepeatedRelEnumerator extends AbstractParquetEnumerato
     private final List<String> tablePath;
     private final List<ParquetColumnBinding> columnBindings;
 
-
-    public ParquetNestedRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings ) {
-        this( source, cancelFlag, binding, columnBindings, FiltersContainer.empty, false );
+    public ParquetNestedRepeatedRelEnumerator( ParquetSourceReader reader, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings, FiltersContainer filtersContainer ) {
+        this( reader, binding, columnBindings, filtersContainer, new ParquetNestedFilterEvaluator( reader.getProjectionSchema(), new ParquetPathValueExtractor(), binding.sourcePathElements(), columnBindings ), false, true );
     }
 
 
-    public ParquetNestedRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings, FiltersContainer filtersContainer ) {
-        this( source, cancelFlag, binding, columnBindings, filtersContainer, false );
-    }
-
-
-    protected ParquetNestedRepeatedRelEnumerator( Source source, AtomicBoolean cancelFlag, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings, FiltersContainer filtersContainer, boolean allowRootTablePath ) {
-        super( source, cancelFlag, null, filtersContainer, new ParquetPathValueExtractor() );
+    protected ParquetNestedRepeatedRelEnumerator( ParquetSourceReader reader, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings, FiltersContainer filtersContainer, ParquetGroupFilterEvaluator filterEvaluator, boolean allowRootTablePath, boolean readerOwner ) {
+        super( reader, filtersContainer, filterEvaluator, readerOwner );
 
         this.tablePath = binding.sourcePathElements();
         this.columnBindings = List.copyOf( columnBindings );
@@ -83,17 +75,16 @@ protected List<Group> expandRow( Group group ) {
     protected PolyValue[] extractRow( Group group ) {
         PolyValue[] row = new PolyValue[columnBindings.size()];
         for ( int i = 0; i < columnBindings.size(); i++ ) {
-            row[i] = extractValueByColumnRole( (VirtualGroup) group, columnBindings.get( i ), tablePath );
+            row[i] = pathValueExtractor().extractValue( (VirtualGroup) group, columnBindings.get( i ), tablePath );
         }
         return row;
     }
 
 
-    @Override
     protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
         var virtualGroup = (VirtualGroup) group;
         var binding = columnBindings.get( filter.columnIndex() );
-        return extractValueByColumnRole( virtualGroup, binding, tablePath );
+        return pathValueExtractor().extractValue( virtualGroup, binding, tablePath );
     }
 
 
@@ -102,7 +93,7 @@ protected List<Group> resolveNested( VirtualGroup virtualGroup, GroupType groupT
             return List.of( virtualGroup );
         }
 
-        int fieldIndex = fieldIndex( groupType, path.get( pathIndex ) );
+        int fieldIndex = valueExtractor().fieldIndex( groupType, path.get( pathIndex ) );
         if ( fieldIndex < 0 || virtualGroup.getFieldRepetitionCount( fieldIndex ) == 0 ) {
             return List.of();
         }
@@ -124,26 +115,8 @@ protected List<Group> resolveNested( VirtualGroup virtualGroup, GroupType groupT
     }
 
 
-    protected PolyValue extractValueByColumnRole( VirtualGroup virtualGroup, ParquetColumnBinding binding, List<String> tablePath ) {
-        return switch ( binding.role() ) {
-            case DATA -> {
-                var path = binding.sourcePathElements();
-                yield valueExtractor.extractValue( virtualGroup, path.subList( tablePath.size(), path.size() ) );
-            }
-            case PRIMARY_KEY -> PolyString.of( virtualGroup.getMetadata().getRowId() );
-            case PARENT_KEY -> virtualGroup.getMetadata().getParentRowId() == null ? PolyNull.NULL : PolyString.of( virtualGroup.getMetadata().getParentRowId() );
-            case ORDINAL -> PolyLong.of( virtualGroup.getMetadata().getOrdinal() );
-        };
-    }
-
-
-    private int fieldIndex( GroupType groupType, String fieldName ) {
-        for ( int i = 0; i < groupType.getFieldCount(); i++ ) {
-            if ( groupType.getType( i ).getName().equals( fieldName ) ) {
-                return i;
-            }
-        }
-        return -1;
+    private ParquetPathValueExtractor pathValueExtractor() {
+        return (ParquetPathValueExtractor) valueExtractor();
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPartitionAwareFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPartitionAwareFilterEvaluator.java
new file mode 100644
index 0000000000..a0bdf76027
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPartitionAwareFilterEvaluator.java
@@ -0,0 +1,63 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.List;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.MessageType;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.execution.ParquetValueExtractor;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetGroupFilterEvaluator;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+
+/**
+ * Evaluates filter based on partition values.
+ */
+class ParquetPartitionAwareFilterEvaluator extends ParquetGroupFilterEvaluator {
+
+    private final ParquetSourceFile sourceFile;
+    private final List<ParquetColumnBinding> columnBindings;
+
+
+    ParquetPartitionAwareFilterEvaluator( MessageType schema, ParquetValueExtractor valueExtractor, ParquetSourceFile sourceFile, List<ParquetColumnBinding> columnBindings ) {
+        super( schema, valueExtractor );
+        this.sourceFile = sourceFile;
+        this.columnBindings = List.copyOf( columnBindings );
+    }
+
+
+    @Override
+    protected Boolean evaluateLeaf( Group group, ParquetAdapterFilter filter ) {
+        if ( filter.columnIndex() >= 0 && filter.columnIndex() < columnBindings.size() && columnBindings.get( filter.columnIndex() ).role() == ParquetColumnRole.PARTITION ) {
+            return matchesValue( partitionValue( columnBindings.get( filter.columnIndex() ).columnName() ), filter.operator(), filter.polyValue() );
+        }
+        return super.evaluateLeaf( group, filter );
+    }
+
+
+    PolyValue partitionValue( String columnName ) {
+        if ( !sourceFile.partitionValues().containsKey( columnName ) ) {
+            return org.polypheny.db.type.entity.PolyNull.NULL;
+        }
+        return PolyString.of( sourceFile.partitionValues().get( columnName ) );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPathValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPathValueExtractor.java
index 041de04a8e..330061ad06 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPathValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPathValueExtractor.java
@@ -20,9 +20,13 @@
 import org.apache.parquet.example.data.Group;
 import org.apache.parquet.schema.GroupType;
 import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetValueExtractor;
+import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
 import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.numerical.PolyLong;
 
 
 /**
@@ -70,13 +74,16 @@ public PolyValue extractValue( Group group, List<String> sourcePath ) {
     }
 
 
-    private int fieldIndex( GroupType groupType, String fieldName ) {
-        for ( int i = 0; i < groupType.getFieldCount(); i++ ) {
-            if ( groupType.getType( i ).getName().equals( fieldName ) ) {
-                return i;
+    public PolyValue extractValue( VirtualGroup virtualGroup, ParquetColumnBinding binding, List<String> tablePath ) {
+        return switch ( binding.role() ) {
+            case DATA -> {
+                var path = binding.sourcePathElements();
+                yield extractValue( virtualGroup, path.subList( tablePath.size(), path.size() ) );
             }
-        }
-        return -1;
+            case PARTITION -> PolyNull.NULL;
+            case PRIMARY_KEY -> PolyString.of( virtualGroup.getMetadata().getRowId() );
+            case PARENT_KEY -> virtualGroup.getMetadata().getParentRowId() == null ? PolyNull.NULL : PolyString.of( virtualGroup.getMetadata().getParentRowId() );
+            case ORDINAL -> PolyLong.of( virtualGroup.getMetadata().getOrdinal() );
+        };
     }
-
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
index 4d2de1b470..d965621c24 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java
@@ -16,13 +16,13 @@
 
 package org.polypheny.db.adapter.parquet.relational.execution;
 
-import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.parquet.example.data.Group;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractParquetEnumerator;
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetGroupFilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.util.Source;
 
 /**
  * Relational runtime enumerator.
@@ -30,13 +30,14 @@
  */
 public class ParquetRelEnumerator extends AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
 
-    public ParquetRelEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields ) {
-        super( source, cancelFlag, fields, FiltersContainer.empty, new ParquetRelValueExtractor() );
+
+    public ParquetRelEnumerator( ParquetSourceReader reader, FiltersContainer filtersContainer ) {
+        this( reader, filtersContainer, true );
     }
 
 
-    public ParquetRelEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields, FiltersContainer filtersContainer ) {
-        super( source, cancelFlag, fields, filtersContainer, new ParquetRelValueExtractor() );
+    public ParquetRelEnumerator( ParquetSourceReader reader, FiltersContainer filtersContainer, boolean readerOwner ) {
+        super( reader, filtersContainer, new ParquetGroupFilterEvaluator( reader.getProjectionSchema(), new ParquetRelValueExtractor() ), readerOwner );
     }
 
 
@@ -45,7 +46,7 @@ protected PolyValue[] extractRow( Group group ) {
         PolyValue[] row = new PolyValue[projectionSchema.getFieldCount()];
         for ( int readIndex = 0; readIndex < projectionSchema.getFieldCount(); readIndex++ ) {
             var type = projectionSchema.getType( readIndex );
-            row[readIndex] = valueExtractor.extractValue( group, readIndex, type );
+            row[readIndex] = valueExtractor().extractValue( group, readIndex, type );
         }
         return row;
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
index 0b6b4b9637..0f0c3fc312 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
@@ -22,7 +22,9 @@
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.rex.RexCall;
+import org.polypheny.db.rex.RexDynamicParam;
 import org.polypheny.db.rex.RexIndexRef;
+import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNode;
 import org.polypheny.db.type.PolyType;
 
@@ -32,7 +34,7 @@
 public class ParquetRelFilterTranslator extends AbstractFilterTranslator {
 
     /**
-     * Translates a Rex filter into Parquet Adapter filter form when possible.
+     * Translates a Rex filter into Parquet filter form when possible.
      */
     public ParquetAdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFilter ) {
         // support logical filter
@@ -62,11 +64,10 @@ public ParquetAdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFi
         }
 
         int index = indexRef.getIndex();
-        if ( !isFieldPredicateSupported( fieldTypes, index, parsed.operator(), right ) ) {
+        if ( !isPushdownSupported( fieldTypes, index, parsed.operator(), right ) ) {
             return null;
         }
 
-        // call parent functionality
         return toParquetAdapterFilter( index, parsed.operator(), right );
     }
 
@@ -124,7 +125,7 @@ private ParquetAdapterFilter translateIn( List<PolyType> fieldTypes, List<RexNod
         int index = indexRef.getIndex();
         List<ParquetAdapterFilter> equalsFilters = operands.subList( 1, operands.size() ).stream()
                 .map( this::unwrapCast )
-                .map( value -> isFieldPredicateSupported( fieldTypes, index, Kind.EQUALS, value )
+                .map( value -> isPushdownSupported( fieldTypes, index, Kind.EQUALS, value )
                         ? toParquetAdapterFilter( index, Kind.EQUALS, value )
                         : null )
                 .toList();
@@ -141,12 +142,17 @@ private ParquetAdapterFilter translateIn( List<PolyType> fieldTypes, List<RexNod
     /**
      * Checks whether the operator can be handled by the reader.
      */
-    private boolean isFieldPredicateSupported( List<PolyType> fieldTypes, int index, Kind kind, RexNode valueNode ) {
+    private boolean isPushdownSupported( List<PolyType> fieldTypes, int index, Kind kind, RexNode valueNode ) {
         if ( index < 0 || index >= fieldTypes.size() ) {
             return false;
         }
 
-        return isColumnPredicateSupported( fieldTypes.get( index ), kind, valueNode );
+        PolyType type = fieldTypes.get( index );
+        return switch ( type ) {
+            case BOOLEAN, VARCHAR, CHAR, TEXT -> kind == Kind.EQUALS || kind == Kind.NOT_EQUALS;
+            case INTEGER, BIGINT, FLOAT, DOUBLE, DATE, TIME, TIMESTAMP -> true;
+            default -> false;
+        } && (valueNode instanceof RexDynamicParam || (valueNode instanceof RexLiteral literal && literal.getValue() != null));
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcScanRule.java
new file mode 100644
index 0000000000..682dab086d
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcScanRule.java
@@ -0,0 +1,95 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.List;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.convert.ConverterRule;
+import org.polypheny.db.algebra.core.AlgFactories;
+import org.polypheny.db.algebra.enumerable.EnumerableCalc;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.logical.relational.LogicalCalc;
+import org.polypheny.db.plan.Convention;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.rex.RexProgram;
+import org.polypheny.db.tools.AlgBuilderFactory;
+import org.polypheny.db.type.PolyType;
+
+/**
+ * Converts logical calcs over Parquet scans into filtered adapter scans while
+ * preserving the calc projection.
+ */
+public class ParquetEnumerableCalcScanRule extends ConverterRule {
+
+    public static final ParquetEnumerableCalcScanRule INSTANCE = new ParquetEnumerableCalcScanRule( AlgFactories.LOGICAL_BUILDER );
+
+    private final ParquetRelFilterTranslator translator = new ParquetRelFilterTranslator();
+
+
+    public ParquetEnumerableCalcScanRule( AlgBuilderFactory algBuilderFactory ) {
+        super(
+                LogicalCalc.class,
+                calc -> true,
+                Convention.NONE,
+                EnumerableConvention.INSTANCE,
+                algBuilderFactory,
+                ParquetEnumerableCalcScanRule.class.getSimpleName() );
+    }
+
+
+    @Override
+    public AlgNode convert( AlgNode alg ) {
+        LogicalCalc calc = (LogicalCalc) alg;
+        RexProgram program = calc.getProgram();
+        if ( program.getCondition() == null ) {
+            return null;
+        }
+
+        ParquetRelScan scan = ParquetRelScanRuleSupport.findDirectRelScan( calc.getInput() );
+        if ( scan == null ) {
+            scan = ParquetRelScanRuleSupport.findDirectRelScan( convert( calc.getInput(), calc.getInput().getTraitSet().replace( EnumerableConvention.INSTANCE ) ) );
+        }
+        if ( scan == null ) {
+            return null;
+        }
+
+        RexNode condition = program.expandLocalRef( program.getCondition() );
+        List<PolyType> fieldTypes = ParquetRelScanRuleSupport.fieldTypes( scan );
+        ParquetAdapterFilter adapterFilter = translator.translate( fieldTypes, condition );
+        if ( adapterFilter == null ) {
+            return null;
+        }
+
+        ParquetRelScan filteredScan = scan.withFilters( List.of( adapterFilter ) );
+        List<RexNode> projects = program.getProjectList().stream()
+                .map( program::expandLocalRef )
+                .toList();
+
+        RexProgram projectionOnly = RexProgram.create(
+                scan.getTupleType(),
+                projects,
+                null,
+                calc.getTupleType(),
+                calc.getCluster().getRexBuilder() );
+
+        return EnumerableCalc.create( filteredScan, projectionOnly );
+    }
+
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterScanRule.java
new file mode 100644
index 0000000000..6d1ee3b0e9
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterScanRule.java
@@ -0,0 +1,73 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.List;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.convert.ConverterRule;
+import org.polypheny.db.algebra.core.AlgFactories;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.logical.relational.LogicalRelFilter;
+import org.polypheny.db.plan.Convention;
+import org.polypheny.db.tools.AlgBuilderFactory;
+import org.polypheny.db.type.PolyType;
+
+/**
+ * Converts logical filters over Parquet scans into filtered adapter scans.
+ */
+public class ParquetEnumerableFilterScanRule extends ConverterRule {
+
+    public static final ParquetEnumerableFilterScanRule INSTANCE = new ParquetEnumerableFilterScanRule( AlgFactories.LOGICAL_BUILDER );
+
+    private final ParquetRelFilterTranslator translator = new ParquetRelFilterTranslator();
+
+
+    public ParquetEnumerableFilterScanRule( AlgBuilderFactory algBuilderFactory ) {
+        super(
+                LogicalRelFilter.class,
+                filter -> true,
+                Convention.NONE,
+                EnumerableConvention.INSTANCE,
+                algBuilderFactory,
+                ParquetEnumerableFilterScanRule.class.getSimpleName() );
+    }
+
+
+    @Override
+    public AlgNode convert( AlgNode alg ) {
+        LogicalRelFilter filter = (LogicalRelFilter) alg;
+        ParquetRelScan scan = ParquetRelScanRuleSupport.findDirectRelScan( filter.getInput() );
+        if ( scan == null ) {
+            scan = ParquetRelScanRuleSupport.findDirectRelScan( convert( filter.getInput(), filter.getInput().getTraitSet().replace( EnumerableConvention.INSTANCE ) ) );
+        }
+        if ( scan == null ) {
+            return null;
+        }
+
+        List<PolyType> fieldTypes = ParquetRelScanRuleSupport.fieldTypes( scan );
+        ParquetAdapterFilter adapterFilter = translator.translate( fieldTypes, filter.getCondition() );
+        if ( adapterFilter == null ) {
+            return null;
+        }
+
+        return scan.withFilters( List.of( adapterFilter ) );
+    }
+
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
index fc47f106a0..e09eaee522 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
@@ -1,14 +1,18 @@
 package org.polypheny.db.adapter.parquet.relational.planning;
 
+import java.util.ArrayList;
 import java.util.List;
 import lombok.Getter;
 import org.apache.calcite.linq4j.tree.Blocks;
+import org.apache.calcite.linq4j.tree.Expression;
 import org.apache.calcite.linq4j.tree.Expressions;
 import org.apache.calcite.linq4j.tree.Primitive;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.AlgWriter;
 import org.polypheny.db.algebra.core.relational.RelScan;
+import org.polypheny.db.algebra.enumerable.EnumUtils;
 import org.polypheny.db.algebra.enumerable.EnumerableAlg;
 import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
 import org.polypheny.db.algebra.enumerable.EnumerableConvention;
@@ -30,23 +34,36 @@
 public class ParquetRelScan extends RelScan<ParquetRelTable> implements EnumerableAlg {
 
     private final int[] fields;
+    private final List<ParquetAdapterFilter> filters;
 
 
-    public ParquetRelScan(AlgCluster cluster, ParquetRelTable table, int[] fields ) {
+    public ParquetRelScan( AlgCluster cluster, ParquetRelTable table, int[] fields ) {
+        this( cluster, table, fields, List.of() );
+    }
+
+
+    public ParquetRelScan( AlgCluster cluster, ParquetRelTable table, int[] fields, List<ParquetAdapterFilter> filters ) {
         super( cluster, cluster.traitSetOf( EnumerableConvention.INSTANCE ), table );
         this.fields = fields;
+        this.filters = List.copyOf( filters );
     }
 
 
     @Override
     public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
-        return new ParquetRelScan( getCluster(), entity, fields );
+        return new ParquetRelScan( getCluster(), entity, fields, filters );
     }
 
 
     @Override
     public AlgWriter explainTerms( AlgWriter pw ) {
-        return super.explainTerms( pw ).item( "fields", Primitive.asList( fields ) );
+        return super.explainTerms( pw ).item( "fields", Primitive.asList( fields ) ).item( "filters", filters );
+    }
+
+
+    @Override
+    public String algCompareString() {
+        return super.algCompareString() + "$fields=" + Primitive.asList( fields ) + "$filters=" + filters;
     }
 
 
@@ -63,23 +80,41 @@ public AlgDataType deriveRowType() {
 
     @Override
     public void register( AlgPlanner planner ) {
-        planner.addRule( ParquetRelScanRule.INSTANCE );
-        planner.addRule( ParquetRelJoinRule.INSTANCE );
-        planner.addRule( ParquetEnumerableJoinRule.INSTANCE );
-        planner.addRule( ParquetEnumerableFilterJoinRule.INSTANCE );
-        planner.addRule( ParquetEnumerableCalcJoinRule.INSTANCE );
+        registerRules( planner );
+    }
+
+
+    public static void registerRules( AlgPlanner planner ) {
+        planner.addRuleDuringRuntime( ParquetRelTableScanRule.INSTANCE );
+        planner.addRuleDuringRuntime( ParquetRelScanRule.INSTANCE );
+        planner.addRuleDuringRuntime( ParquetEnumerableFilterScanRule.INSTANCE );
+        planner.addRuleDuringRuntime( ParquetEnumerableCalcScanRule.INSTANCE );
+        planner.addRuleDuringRuntime( ParquetRelJoinRule.INSTANCE );
+        planner.addRuleDuringRuntime( ParquetEnumerableJoinRule.INSTANCE );
+        planner.addRuleDuringRuntime( ParquetEnumerableFilterJoinRule.INSTANCE );
+        planner.addRuleDuringRuntime( ParquetEnumerableCalcJoinRule.INSTANCE );
     }
 
 
     @Override
     public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
-        return super.computeSelfCost( planner, mq ).multiplyBy( ((double) fields.length + 2D) / ((double) entity.getTupleType().getFieldCount() + 2D) );
+        double fieldRatio = ((double) fields.length + 2D) / ((double) entity.getTupleType().getFieldCount() + 2D);
+        double filterRatio = filters.isEmpty() ? 1D : 0.5D;
+        return super.computeSelfCost( planner, mq ).multiplyBy( fieldRatio * filterRatio );
+    }
+
+
+    public ParquetRelScan withFilters( List<ParquetAdapterFilter> filters ) {
+        List<ParquetAdapterFilter> combinedFilters = new ArrayList<>( this.filters );
+        combinedFilters.addAll( filters );
+        return new ParquetRelScan( getCluster(), entity, fields, combinedFilters );
     }
 
 
     @Override
     public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
         PhysType physType = PhysTypeImpl.of( implementor.getTypeFactory(), getTupleType(), pref.preferArray() );
+        Expression runtimeFilters = EnumUtils.expressionList( filters.stream().map( ParquetAdapterFilter::toExpression ).toList() );
         return implementor.result(
                 physType,
                 Blocks.toBlock(
@@ -87,7 +122,8 @@ public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
                                 entity.asExpression( ParquetRelTable.class ),
                                 "project",
                                 implementor.getRootExpression(),
-                                Expressions.constant( fields ) ) ) );
+                                Expressions.constant( fields ),
+                                runtimeFilters ) ) );
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java
index c9c8163547..590e1697c0 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java
@@ -18,7 +18,7 @@ public class ParquetRelScanRule extends AlgOptRule {
     public static final ParquetRelScanRule INSTANCE = new ParquetRelScanRule( AlgFactories.LOGICAL_BUILDER );
 
 
-    public ParquetRelScanRule(AlgBuilderFactory algBuilderFactory ) {
+    public ParquetRelScanRule( AlgBuilderFactory algBuilderFactory ) {
         super(
                 operand( LogicalRelProject.class, operand( ParquetRelScan.class, none() ) ),
                 algBuilderFactory,
@@ -34,11 +34,20 @@ public void onMatch( AlgOptRuleCall call ) {
         if ( fields == null ) {
             return;
         }
-        call.transformTo( new ParquetRelScan( scan.getCluster(), scan.getEntity(), fields ) );
+
+        int[] currentFields = scan.getFields();
+        int[] projectedFields = new int[fields.length];
+        for ( int i = 0; i < fields.length; i++ ) {
+            if ( fields[i] < 0 || fields[i] >= currentFields.length ) {
+                return;
+            }
+            projectedFields[i] = currentFields[fields[i]];
+        }
+        call.transformTo( new ParquetRelScan( scan.getCluster(), scan.getEntity(), projectedFields, scan.getFilters() ) );
     }
 
 
-    private int[] getProjectFields( List<RexNode> exps ) {
+    static int[] getProjectFields( List<RexNode> exps ) {
         final int[] fields = new int[exps.size()];
         for ( int i = 0; i < exps.size(); i++ ) {
             final RexNode exp = exps.get( i );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java
new file mode 100644
index 0000000000..428df93b44
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java
@@ -0,0 +1,70 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.List;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.core.relational.RelScan;
+import org.polypheny.db.plan.volcano.AlgSubset;
+import org.polypheny.db.type.PolyType;
+
+
+final class ParquetRelScanRuleSupport {
+
+    private ParquetRelScanRuleSupport() {
+    }
+
+
+    static ParquetRelScan findDirectRelScan( AlgNode alg ) {
+        if ( alg == null ) {
+            return null;
+        }
+        if ( alg instanceof ParquetRelScan relScan ) {
+            return relScan;
+        }
+        if ( alg instanceof RelScan<?> relScan && relScan.getEntity().unwrap( ParquetRelTable.class ).isPresent() ) {
+            ParquetRelTable table = relScan.getEntity().unwrapOrThrow( ParquetRelTable.class );
+            return new ParquetRelScan( relScan.getCluster(), table, relScan.identity().stream().mapToInt( Integer::intValue ).toArray() );
+        }
+        if ( alg instanceof AlgSubset subset ) {
+            ParquetRelScan relScan = findDirectRelScan( subset.getBest() );
+            if ( relScan != null ) {
+                return relScan;
+            }
+            relScan = findDirectRelScan( subset.getOriginal() );
+            if ( relScan != null ) {
+                return relScan;
+            }
+            for ( AlgNode candidate : subset.getAlgList() ) {
+                relScan = findDirectRelScan( candidate );
+                if ( relScan != null ) {
+                    return relScan;
+                }
+            }
+        }
+        return null;
+    }
+
+
+    static List<PolyType> fieldTypes( AlgNode alg ) {
+        return alg.getTupleType().getFields().stream()
+                .map( field -> field.getType().getPolyType() )
+                .toList();
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelTableScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelTableScanRule.java
new file mode 100644
index 0000000000..7ee64572f1
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelTableScanRule.java
@@ -0,0 +1,54 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.convert.ConverterRule;
+import org.polypheny.db.algebra.core.AlgFactories;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.logical.relational.LogicalRelScan;
+import org.polypheny.db.plan.Convention;
+import org.polypheny.db.tools.AlgBuilderFactory;
+
+/**
+ * Converts logical scans of Parquet physical tables into adapter scans.
+ */
+public class ParquetRelTableScanRule extends ConverterRule {
+
+    public static final ParquetRelTableScanRule INSTANCE = new ParquetRelTableScanRule( AlgFactories.LOGICAL_BUILDER );
+
+
+    public ParquetRelTableScanRule( AlgBuilderFactory algBuilderFactory ) {
+        super(
+                LogicalRelScan.class,
+                scan -> scan.getEntity().unwrap( ParquetRelTable.class ).isPresent(),
+                Convention.NONE,
+                EnumerableConvention.INSTANCE,
+                algBuilderFactory,
+                ParquetRelTableScanRule.class.getSimpleName() );
+    }
+
+
+    @Override
+    public AlgNode convert( AlgNode alg ) {
+        LogicalRelScan scan = (LogicalRelScan) alg;
+        ParquetRelTable table = scan.getEntity().unwrapOrThrow( ParquetRelTable.class );
+        return new ParquetRelScan( scan.getCluster(), table, scan.identity().stream().mapToInt( Integer::intValue ).toArray() );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTable.java
new file mode 100644
index 0000000000..df6af43d79
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTable.java
@@ -0,0 +1,32 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import java.util.List;
+
+/**
+ * Represents one logical Polypheny table found during Parquet discovery, before the table is fully registered in Polypheny.
+ * @param tableName - final discovered table name, prefixed with adapter name
+ * @param columns - columns Polypheny should expose for this table
+ * @param binding - metadata that explains how this logical table maps back to physical Parquet data
+ */
+public record DiscoveredTable(String tableName, List<ExportedColumn> columns, DiscoveredTableBinding binding ) {
+    public DiscoveredTable {
+        columns = columns == null ? List.of() : List.copyOf( columns );
+    }
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTableBinding.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTableBinding.java
index 16a53c2b26..b2b7199189 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTableBinding.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTableBinding.java
@@ -20,15 +20,15 @@
 import java.util.Map;
 
 /**
- * temporary discovery metadata before a real ParquetTableBinding exists
+ *  metadata that connects a discovered logical table back to the real Parquet data
  *
- * @param sourceUrl - real Parquet file
+ * @param sourceFiles - real Parquet files
  * @param parentTableName - generated parent table
  * @param sourcePathElements - table-level Parquet path
  * @param columnPaths - column-level Parquet path
  */
 public record DiscoveredTableBinding(
-        String sourceUrl,
+        List<ParquetSourceFile> sourceFiles,
         String parentTableName,
         List<String> sourcePathElements,
         Map<String, List<String>> columnPaths ) {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetBindingSerializer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetBindingSerializer.java
index 57b2a6ce8d..6f282305fb 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetBindingSerializer.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetBindingSerializer.java
@@ -35,6 +35,11 @@ public final class ParquetBindingSerializer {
     private static final String COLUMN_SEPARATOR = ",";
     private static final String COLUMN_FIELD_SEPARATOR = ":";
     private static final String PATH_SEPARATOR = ".";
+    private static final String FILE_SEPARATOR = ";";
+    private static final String FILE_FIELD_SEPARATOR = "\\|";
+    private static final String FILE_FIELD_JOINER = "|";
+    private static final String PARTITION_SEPARATOR = "&";
+    private static final String PARTITION_FIELD_SEPARATOR = "=";
 
 
     private ParquetBindingSerializer() {
@@ -44,7 +49,7 @@ private ParquetBindingSerializer() {
     public static String serialize( Map<Long, ParquetTableBinding> bindings ) {
         return bindings.entrySet().stream()
                 .map( entry -> entry.getKey()
-                        + FIELD_SEPARATOR + encode( entry.getValue().sourceUrl() )
+                        + FIELD_SEPARATOR + serializeSourceFiles( entry.getValue().sourceFiles() )
                         + FIELD_SEPARATOR + encode( entry.getValue().parentTableName() )
                         + FIELD_SEPARATOR + serializePath( entry.getValue().sourcePathElements() )
                         + FIELD_SEPARATOR + serializeColumns( entry.getValue().columnsByColumnId() ) )
@@ -60,21 +65,65 @@ public static Map<Long, ParquetTableBinding> deserialize( String serialized ) {
 
         for ( String entry : serialized.split( ENTRY_SEPARATOR ) ) {
             String[] fields = entry.split( FIELD_SEPARATOR, -1 );
-            if ( fields.length != 4 && fields.length != 5 ) {
+            if ( fields.length != 5 ) {
                 throw new GenericRuntimeException( "Invalid serialized Parquet binding entry: %s", entry );
             }
 
             long physicalId = Long.parseLong( fields[0] );
-            String sourceUrl = decode( fields[1] );
+            List<ParquetSourceFile> sourceFiles = deserializeSourceFiles( fields[1] );
             String parentTableName = decode( fields[2] );
-            List<String> sourcePathElements = fields.length == 5 ? deserializePath( fields[3] ) : List.of();
-            Map<Long, ParquetColumnBinding> columns = deserializeColumns( fields.length == 5 ? fields[4] : fields[3] );
-            bindings.put( physicalId, new ParquetTableBinding( sourceUrl, parentTableName.isEmpty() ? null : parentTableName, sourcePathElements, columns ) );
+            List<String> sourcePathElements = deserializePath( fields[3] );
+            Map<Long, ParquetColumnBinding> columns = deserializeColumns( fields[4] );
+            bindings.put( physicalId, new ParquetTableBinding( sourceFiles, parentTableName.isEmpty() ? null : parentTableName, sourcePathElements, columns ) );
         }
         return bindings;
     }
 
 
+    private static String serializeSourceFiles( List<ParquetSourceFile> sourceFiles ) {
+        return sourceFiles.stream()
+                .map( sourceFile -> encode( sourceFile.fileUrl() ) + FILE_FIELD_JOINER + serializePartitions( sourceFile.partitionValues() ) )
+                .collect( Collectors.joining( FILE_SEPARATOR ) );
+    }
+
+
+    private static List<ParquetSourceFile> deserializeSourceFiles( String serialized ) {
+        if ( serialized == null || serialized.isBlank() ) {
+            return List.of();
+        }
+        return Stream.of( serialized.split( FILE_SEPARATOR ) ).map( fileEntry -> {
+            String[] fields = fileEntry.split( FILE_FIELD_SEPARATOR, -1 );
+            if ( fields.length != 2 ) {
+                throw new GenericRuntimeException( "Invalid serialized Parquet source file entry: %s", fileEntry );
+            }
+            return new ParquetSourceFile( decode( fields[0] ), deserializePartitions( fields[1] ) );
+        } ).toList();
+    }
+
+
+    private static String serializePartitions( Map<String, String> partitionValues ) {
+        return partitionValues.entrySet().stream()
+                .map( entry -> encode( entry.getKey() ) + PARTITION_FIELD_SEPARATOR + encode( entry.getValue() ) )
+                .collect( Collectors.joining( PARTITION_SEPARATOR ) );
+    }
+
+
+    private static Map<String, String> deserializePartitions( String serialized ) {
+        Map<String, String> partitionValues = new LinkedHashMap<>();
+        if ( serialized == null || serialized.isBlank() ) {
+            return partitionValues;
+        }
+        for ( String partitionEntry : serialized.split( PARTITION_SEPARATOR ) ) {
+            String[] fields = partitionEntry.split( PARTITION_FIELD_SEPARATOR, -1 );
+            if ( fields.length != 2 ) {
+                throw new GenericRuntimeException( "Invalid serialized Parquet partition value entry: %s", partitionEntry );
+            }
+            partitionValues.put( decode( fields[0] ), decode( fields[1] ) );
+        }
+        return partitionValues;
+    }
+
+
     private static String serializeColumns( Map<Long, ParquetColumnBinding> columns ) {
         return columns.values().stream()
                 .map( column -> column.columnId()
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnRole.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnRole.java
index b610851f40..b1ec7842b7 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnRole.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnRole.java
@@ -21,6 +21,7 @@
  */
 public enum ParquetColumnRole {
     DATA, // a normal real Parquet value
+    PARTITION, // a value derived from the source file's partition folder
     PRIMARY_KEY, // synthetic generated row id
     PARENT_KEY, // synthetic reference to the parent generated row
     ORDINAL // position of a child value inside its parent
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index 843abd78da..8562f293d5 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -28,6 +28,7 @@
 import org.apache.calcite.linq4j.Enumerable;
 import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetMultiFileEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedJoinEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedNonRepeatedRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedRepeatedRelEnumerator;
@@ -36,9 +37,11 @@
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersSplitter;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.adapter.parquet.shared.statistics.ParquetStatisticsReader;
 import org.polypheny.db.adapter.statistics.AdapterStatisticsProvider;
 import org.polypheny.db.adapter.statistics.ProvidedColumnStatistics;
@@ -64,7 +67,6 @@
  */
 public class ParquetRelTable extends PhysicalTable implements FilterableEntity, ScannableEntity, TranslatableEntity, AdapterStatisticsProvider {
 
-    private final Source source;
     private final int[] fieldIndexes;
     private final AbstractParquetSource parquetSource;
     private final ParquetRelFilterTranslator filterTranslator;
@@ -78,7 +80,7 @@ public class ParquetRelTable extends PhysicalTable implements FilterableEntity,
     /**
      * Creates a Parquet table wrapper from a physical table definition and source binding.
      */
-    public ParquetRelTable( long id, Source source, PhysicalTable table, ParquetTableBinding binding, AbstractParquetSource parquetSource ) {
+    public ParquetRelTable( long id, PhysicalTable table, ParquetTableBinding binding, AbstractParquetSource parquetSource ) {
         super(
                 id,
                 table.allocationId,
@@ -89,11 +91,10 @@ public ParquetRelTable( long id, Source source, PhysicalTable table, ParquetTabl
                 table.namespaceName,
                 table.uniqueFieldIds,
                 table.adapterId );
-        this.source = source;
         this.binding = binding;
         this.fieldIndexes = IntStream.range( 0, table.columns.size() ).toArray();
         this.fieldTypes = columns.stream().map( c -> c.type ).toList();
-        this.schemaReader = new ParquetSchemaReader( source );
+        this.schemaReader = new ParquetSchemaReader( binding.sourceFiles().stream().map( ParquetSourceFile::asSource ).toList() );
         this.statisticsReader = new ParquetStatisticsReader( schemaReader, binding );
         this.parquetSource = parquetSource;
         this.filterTranslator = new ParquetRelFilterTranslator();
@@ -156,18 +157,16 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> poly
 
         // check for dynamic filters
         final List<ParquetAdapterFilter> resolvedFilters = resolveFilters( dataContext, parquetAdapterFilters, f -> binding.getColumnBinding( columns.get( f.columnIndex() ).id ) );
+        final List<ParquetSourceFile> sourceFiles = new ParquetSourceFileFilterEvaluator( f -> binding.getColumnBinding( columns.get( f.columnIndex() ).id ) )
+                .prune( binding.sourceFiles(), resolvedFilters );
 
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                if ( isNestedTable() ) {
-                    return new ParquetNestedRepeatedRelEnumerator( source, cancelFlag, binding, projectedBindings( fieldIndexes ), FiltersContainer.shared( resolvedFilters ) );
-                }
-                if ( needsBindingScan( fieldIndexes ) ) {
-                    return new ParquetNestedNonRepeatedRelEnumerator( source, cancelFlag, projectedBindings( fieldIndexes ), FiltersContainer.shared( resolvedFilters ) );
-                }
-                return new ParquetRelEnumerator( source, cancelFlag, fieldIndexes, FiltersContainer.shared( resolvedFilters ) );
+                return new ParquetMultiFileEnumerator(
+                        sourceFiles,
+                        sourceFile -> enumeratorForFile( sourceFile, fieldIndexes, cancelFlag, FiltersContainer.shared( resolvedFilters ) ) );
             }
         };
     }
@@ -187,13 +186,9 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                if ( isNestedTable() ) {
-                    return new ParquetNestedRepeatedRelEnumerator( source, cancelFlag, binding, projectedBindings( fieldIndexes ) );
-                }
-                if ( needsBindingScan( fieldIndexes ) ) {
-                    return new ParquetNestedNonRepeatedRelEnumerator( source, cancelFlag, projectedBindings( fieldIndexes ) );
-                }
-                return new ParquetRelEnumerator( source, cancelFlag, fieldIndexes );
+                return new ParquetMultiFileEnumerator(
+                        binding.sourceFiles(),
+                        sourceFile -> enumeratorForFile( sourceFile, fieldIndexes, cancelFlag, FiltersContainer.empty ) );
             }
         };
     }
@@ -220,18 +215,31 @@ public AlgNode toAlg( AlgCluster cluster, AlgTraitSet traitSet ) {
      * @return enumerable.
      */
     public Enumerable<PolyValue[]> project( final DataContext dataContext, final int[] fields ) {
+        return project( dataContext, fields, List.of() );
+    }
+
+
+    /**
+     * This method is called from the {@link ParquetRelScan} via reflection.
+     *
+     * @param dataContext data context
+     * @param fields a list of fields to return.
+     * @param filters filters to push down.
+     * @return enumerable.
+     */
+    @SuppressWarnings("unused")
+    public Enumerable<PolyValue[]> project( final DataContext dataContext, final int[] fields, final List<ParquetAdapterFilter> filters ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
+        final List<ParquetAdapterFilter> resolvedFilters = resolveFilters( dataContext, filters, f -> selectProjectedBinding( f.columnIndex(), fields ) );
+        final List<ParquetSourceFile> sourceFiles = new ParquetSourceFileFilterEvaluator( f -> selectProjectedBinding( f.columnIndex(), fields ) )
+                .prune( binding.sourceFiles(), resolvedFilters );
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                if ( isNestedTable() ) {
-                    return new ParquetNestedRepeatedRelEnumerator( source, cancelFlag, binding, projectedBindings( fields ) );
-                }
-                if ( needsBindingScan( fields ) ) {
-                    return new ParquetNestedNonRepeatedRelEnumerator( source, cancelFlag, projectedBindings( fields ) );
-                }
-                return new ParquetRelEnumerator( source, cancelFlag, fields );
+                return new ParquetMultiFileEnumerator(
+                        sourceFiles,
+                        sourceFile -> enumeratorForFile( sourceFile, fields, cancelFlag, FiltersContainer.shared( resolvedFilters ) ) );
             }
         };
     }
@@ -256,21 +264,16 @@ public Enumerable<PolyValue[]> nestedJoin(
         final int[] parentFields = leftIsParent ? leftFields : rightFields;
         final int[] childFields = leftIsParent ? rightFields : leftFields;
         final List<ParquetAdapterFilter> resolvedFilters = resolveFilters( dataContext, filters, f -> selectBinding( f.columnIndex(), leftFields, rightFields, right ) );
+        final List<ParquetSourceFile> sourceFiles = new ParquetSourceFileFilterEvaluator( f -> selectParentBinding( f.columnIndex(), leftIsParent, parentFields, childFields, parent ) )
+                .prune( parent.binding.sourceFiles(), filtersForParent( resolvedFilters, leftIsParent, parentFields.length, childFields.length ) );
 
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
                 var filterContainer = new JoinFiltersSplitter().split( resolvedFilters, leftIsParent, parentFields.length, childFields.length );
-                return new ParquetNestedJoinEnumerator(
-                        parent.source,
-                        cancelFlag,
-                        parent.binding,
-                        child.binding,
-                        parent.projectedBindings( parentFields ),
-                        child.projectedBindings( childFields ),
-                        filterContainer,
-                        leftIsParent,
-                        emitUnmatchedParents );
+                return new ParquetMultiFileEnumerator(
+                        sourceFiles,
+                        sourceFile -> parent.nestedJoinEnumeratorForFile( sourceFile, child, parentFields, childFields, cancelFlag, filterContainer, leftIsParent, emitUnmatchedParents ) );
             }
         };
     }
@@ -288,7 +291,7 @@ public int columnIndexByRole( ParquetColumnRole role ) {
 
 
     public String getSourceUrl() {
-        return binding.sourceUrl();
+        return binding.sourceFiles().stream().map( ParquetSourceFile::fileUrl ).sorted().toList().toString();
     }
 
 
@@ -333,11 +336,54 @@ private ParquetAdapterFilter resolveFilter( DataContext dataContext, ParquetAdap
     }
 
 
+    private List<ParquetAdapterFilter> filtersForParent( List<ParquetAdapterFilter> filters, boolean leftIsParent, int parentFieldCount, int childFieldCount ) {
+        var splitFilters = new JoinFiltersSplitter().split( filters, leftIsParent, parentFieldCount, childFieldCount );
+        return splitFilters.parentFilters();
+    }
+
+
     private boolean isNestedTable() {
         return binding.parentTableName() != null;
     }
 
 
+    private Enumerator<PolyValue[]> enumeratorForFile( ParquetSourceFile sourceFile, int[] fields, AtomicBoolean cancelFlag, FiltersContainer filtersContainer ) {
+        boolean bindingScan = isNestedTable() || needsBindingScan( fields ) || binding.sourceFiles().size() > 1;
+        Source fileSource = sourceFile.asSource();
+        ParquetSourceReader reader = new ParquetSourceReader( fileSource, cancelFlag, bindingScan ? null : fields, filtersContainer.readerFilters() );
+        if ( isNestedTable() ) {
+            return new ParquetNestedRepeatedRelEnumerator( reader, binding, projectedBindings( fields ), filtersContainer );
+        }
+        if ( bindingScan ) {
+            return new ParquetNestedNonRepeatedRelEnumerator( reader, sourceFile, projectedBindings( fields ), filtersContainer );
+        }
+        return new ParquetRelEnumerator( reader, filtersContainer );
+    }
+
+
+    private Enumerator<PolyValue[]> nestedJoinEnumeratorForFile(
+            ParquetSourceFile sourceFile,
+            ParquetRelTable child,
+            int[] parentFields,
+            int[] childFields,
+            AtomicBoolean cancelFlag,
+            JoinFiltersContainer filterContainer,
+            boolean leftIsParent,
+            boolean emitUnmatchedParents ) {
+
+        ParquetSourceReader reader = new ParquetSourceReader( sourceFile.asSource(), cancelFlag, null, filterContainer.readerFilters() );
+        return new ParquetNestedJoinEnumerator(
+                reader,
+                binding,
+                child.binding,
+                projectedBindings( parentFields ),
+                child.projectedBindings( childFields ),
+                filterContainer,
+                leftIsParent,
+                emitUnmatchedParents );
+    }
+
+
     /**
      * Selects a column binding for a join. If the column index is less then left table fields count then use left table column binding otherwise use right table column biding.
      *
@@ -362,6 +408,26 @@ private ParquetColumnBinding selectBinding( int joinedIndex, int[] leftFields, i
     }
 
 
+    private ParquetColumnBinding selectParentBinding( int joinedIndex, boolean leftIsParent, int[] parentFields, int[] childFields, ParquetRelTable parent ) {
+        if ( joinedIndex < 0 ) {
+            throw new GenericRuntimeException( "Invalid joined filter column index: " + joinedIndex );
+        }
+        int parentIndex = leftIsParent ? joinedIndex : joinedIndex - childFields.length;
+        if ( parentIndex < 0 || parentIndex >= parentFields.length ) {
+            return null;
+        }
+        return parent.binding.getColumnBinding( parent.columns.get( parentFields[parentIndex] ).id );
+    }
+
+
+    private ParquetColumnBinding selectProjectedBinding( int projectedIndex, int[] fields ) {
+        if ( projectedIndex < 0 || projectedIndex >= fields.length ) {
+            throw new GenericRuntimeException( "Invalid projected filter column index: " + projectedIndex );
+        }
+        return binding.getColumnBinding( columns.get( fields[projectedIndex] ).id );
+    }
+
+
     /**
      * Gets a list of bindings per provided fields
      *
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaNormalizer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaNormalizer.java
index 697094136c..9435fb9ac0 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaNormalizer.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaNormalizer.java
@@ -22,18 +22,12 @@
 import java.util.LinkedHashMap;
 import java.util.List;
 import java.util.Map;
-import org.apache.hadoop.conf.Configuration;
-import org.apache.hadoop.fs.Path;
-import org.apache.parquet.hadoop.ParquetFileReader;
-import org.apache.parquet.hadoop.util.HadoopInputFile;
-import org.apache.parquet.schema.GroupType;
 import org.apache.parquet.schema.Type;
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetFileDiscovery;
-import org.polypheny.db.adapter.parquet.shared.io.ParquetUrlResolver;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
-import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.type.PolyType;
 
@@ -45,19 +39,13 @@
 public class ParquetSchemaNormalizer {
 
     private final URL parquetDir; // source directory or source Parquet file URL
-    private final ClassLoader classLoader; // used to create Hadoop/Parquet reader configuration
     private final ParquetTypeConverter parquetTypeConverter; // converts Parquet types to Polypheny
     private final String tableNamePrefix; // adapter instance prefix
 
 
-    public ParquetSchemaNormalizer(
-            URL parquetDir,
-            ClassLoader classLoader,
-            ParquetTypeConverter parquetTypeConverter,
-            String tableNamePrefix ) {
+    public ParquetSchemaNormalizer(URL parquetDir, String tableNamePrefix ) {
         this.parquetDir = parquetDir;
-        this.classLoader = classLoader;
-        this.parquetTypeConverter = parquetTypeConverter;
+        this.parquetTypeConverter = new ParquetTypeConverter();
         if ( tableNamePrefix == null ) {
             throw new GenericRuntimeException( "Parquet normalized schema table name prefix must not be null." );
         }
@@ -76,29 +64,35 @@ public ParquetSchemaNormalizer(
      */
     public ParquetNormalizedSchema normalize() {
         var normalizedSchema = new ParquetNormalizedSchema();
-        for ( String fileName : ParquetFileDiscovery.listParquetFiles( parquetDir ) ) {
-            normalizeFile( fileName, normalizedSchema );
+        for ( DiscoveredTable table : ParquetFileDiscovery.discoverTables( parquetDir, tableNamePrefix ).values() ) {
+            normalizeDiscoveredTable( table, normalizedSchema );
         }
         return normalizedSchema;
     }
 
 
-    private void normalizeFile( String fileName, ParquetNormalizedSchema normalizedSchema ) {
-        try {
-            URL sourceUrl = ParquetUrlResolver.resolveFile( parquetDir, fileName );
-            Path path = new Path( sourceUrl.toURI() );
-            Configuration conf = HadoopConfigurationFactory.create( classLoader );
-            try ( ParquetFileReader reader = ParquetFileReader.open( HadoopInputFile.fromPath( path, conf ) ) ) {
-                // get schema from parquet file
-                GroupType schema = reader.getFooter().getFileMetaData().getSchema();
-                // build root table name with adapter prefix
-                String rootTableName = normalizedSchema.uniqueTableName( prefixedTableName( ParquetNameNormalizer.computePhysicalTableName( fileName ) ) );
-                // create normalization info for table
-                addNormalizedTable( schema.getFields(), sourceUrl.toString(), fileName, rootTableName, null, List.of(), normalizedSchema );
-            }
-        } catch ( Exception e ) {
-            throw new GenericRuntimeException( e );
+    private void normalizeDiscoveredTable( DiscoveredTable table, ParquetNormalizedSchema normalizedSchema ) {
+        var schemaReader = new ParquetSchemaReader( table.binding().sourceFiles().stream().map( ParquetSourceFile::asSource ).toList() );
+        String rootTableName = normalizedSchema.uniqueTableName( table.tableName() );
+        List<String> partitionColumnNames = partitionColumnNames( table.binding().sourceFiles() );
+        addNormalizedTable(
+                schemaReader.getSchema().getFields(),
+                table.binding().sourceFiles(),
+                table.tableName(),
+                rootTableName,
+                null,
+                List.of(),
+                partitionColumnNames,
+                normalizedSchema );
+    }
+
+
+    private List<String> partitionColumnNames( List<ParquetSourceFile> sourceFiles ) {
+        Map<String, Boolean> names = new LinkedHashMap<>();
+        for ( ParquetSourceFile sourceFile : sourceFiles ) {
+            sourceFile.partitionValues().keySet().forEach( name -> names.putIfAbsent( name, true ) );
         }
+        return List.copyOf( names.keySet() );
     }
 
 
@@ -106,7 +100,7 @@ private void normalizeFile( String fileName, ParquetNormalizedSchema normalizedS
      * Creates one generated relational table
      *
      * @param fields - schema fields
-     * @param sourceUrl - source file url
+     * @param sourceFiles - a list of source file URLS
      * @param fileName - file name
      * @param tableName - generated table name
      * @param parentTableName - generated parent table name if exists
@@ -116,19 +110,23 @@ private void normalizeFile( String fileName, ParquetNormalizedSchema normalizedS
      */
     private void addNormalizedTable(
             List<Type> fields,
-            String sourceUrl,
+            List<ParquetSourceFile> sourceFiles,
             String fileName,
             String tableName,
             String parentTableName,
             List<String> tablePath,
+            List<String> partitionColumnNames,
             ParquetNormalizedSchema normalizedSchema ) {
         // maps relational column name to Parquet source path
         Map<String, List<String>> columnPaths = new LinkedHashMap<>();
         // add synthetic columns to normalized schema
         normalizedSchema.addColumns( tableName, syntheticColumns( fileName, tableName, parentTableName, normalizedSchema, tablePath, columnPaths ) );
-        collectNormalizedColumns( fields, sourceUrl, fileName, tableName, tableName, tablePath, columnPaths, normalizedSchema );
+        collectNormalizedColumns( fields, sourceFiles, fileName, tableName, tableName, tablePath, partitionColumnNames, columnPaths, normalizedSchema );
+        if ( parentTableName == null ) {
+            addPartitionColumns( fileName, tableName, partitionColumnNames, normalizedSchema );
+        }
         // each generated table gets table definition and binding metadata
-        normalizedSchema.addBinding( tableName, new DiscoveredTableBinding( sourceUrl, parentTableName, tablePath, columnPaths ) );
+        normalizedSchema.addBinding( tableName, new DiscoveredTableBinding( sourceFiles, parentTableName, tablePath, columnPaths ) );
     }
 
 
@@ -136,7 +134,7 @@ private void addNormalizedTable(
      * Loops through fields of the current Parquet group
      *
      * @param fields - parquet schema fields
-     * @param sourceUrl - source file url
+     * @param sourceFiles - a list of source file URLS
      * @param fileName - source file name
      * @param tableName - generated table name
      * @param currentParentTableName - table name used as the base when creating child table names
@@ -147,11 +145,12 @@ private void addNormalizedTable(
      */
     private void collectNormalizedColumns(
             List<Type> fields,
-            String sourceUrl,
+            List<ParquetSourceFile> sourceFiles,
             String fileName,
             String tableName,
             String currentParentTableName,
             List<String> currentPath,
+            List<String> partitionColumnNames,
             Map<String, List<String>> columnPaths, // where should each column read its value from
             ParquetNormalizedSchema normalizedSchema ) {
 
@@ -163,10 +162,10 @@ private void collectNormalizedColumns(
                 String childTableName = normalizedSchema.uniqueTableName( currentParentTableName + "__" + ParquetNameNormalizer.normalizeFieldName( field.getName() ) );
                 if ( field.isPrimitive() ) {
                     // repeated and primitive - create a separate table
-                    addRepeatedPrimitiveTable( field, sourceUrl, fileName, childTableName, currentParentTableName, sourcePath, normalizedSchema );
+                    addRepeatedPrimitiveTable( field, sourceFiles, fileName, childTableName, currentParentTableName, sourcePath, normalizedSchema );
                 } else {
                     // repeated and group - create a child table and recursively processes the group fields
-                    addNormalizedTable( field.asGroupType().getFields(), sourceUrl, fileName, childTableName, currentParentTableName, sourcePath, normalizedSchema );
+                    addNormalizedTable( field.asGroupType().getFields(), sourceFiles, fileName, childTableName, currentParentTableName, sourcePath, List.of(), normalizedSchema );
                 }
                 continue;
             }
@@ -174,6 +173,9 @@ private void collectNormalizedColumns(
             // primitive - add field as a column to the current table
             if ( field.isPrimitive() ) {
                 String columnName = uniqueColumnName( seenColumnNames, ParquetNameNormalizer.normalizeFieldName( field.getName() ) );
+                if ( currentPath.isEmpty() && partitionColumnNames.contains( columnName ) ) {
+                    continue;
+                }
                 normalizedSchema.addColumns( tableName, List.of( exportedColumn( field, fileName, tableName, columnName, sourcePath, nextPosition( normalizedSchema, tableName ) ) ) );
                 columnPaths.put( columnName, sourcePath );
                 continue;
@@ -181,14 +183,14 @@ private void collectNormalizedColumns(
 
             //  non-repeated group - create a child table
             String childTableName = normalizedSchema.uniqueTableName( currentParentTableName + "__" + ParquetNameNormalizer.normalizeFieldName( field.getName() ) );
-            addNormalizedTable( field.asGroupType().getFields(), sourceUrl, fileName, childTableName, currentParentTableName, sourcePath, normalizedSchema );
+            addNormalizedTable( field.asGroupType().getFields(), sourceFiles, fileName, childTableName, currentParentTableName, sourcePath, List.of(), normalizedSchema );
         }
     }
 
 
     private void addRepeatedPrimitiveTable(
             Type field,
-            String sourceUrl,
+            List<ParquetSourceFile> sourceFiles,
             String fileName,
             String tableName,
             String parentTableName,
@@ -200,7 +202,29 @@ private void addRepeatedPrimitiveTable(
         columns.add( exportedColumn( field, fileName, tableName, columnName, sourcePath, columns.size() ) );
         columnPaths.put( columnName, sourcePath );
         normalizedSchema.addColumns( tableName, columns );
-        normalizedSchema.addBinding( tableName, new DiscoveredTableBinding( sourceUrl, parentTableName, sourcePath, columnPaths ) );
+        normalizedSchema.addBinding( tableName, new DiscoveredTableBinding( sourceFiles, parentTableName, sourcePath, columnPaths ) );
+    }
+
+
+    private void addPartitionColumns( String fileName, String tableName, List<String> partitionColumnNames, ParquetNormalizedSchema normalizedSchema ) {
+        for ( String partitionColumnName : partitionColumnNames ) {
+            normalizedSchema.addColumns(
+                    tableName,
+                    List.of( new ExportedColumn(
+                            partitionColumnName,
+                            PolyType.VARCHAR,
+                            null,
+                            null,
+                            null,
+                            null,
+                            null,
+                            false,
+                            fileName,
+                            tableName,
+                            partitionColumnName,
+                            nextPosition( normalizedSchema, tableName ),
+                            false ) ) );
+        }
     }
 
 
@@ -284,17 +308,6 @@ private String uniqueColumnName( Map<String, Integer> seenColumnNames, String ba
     }
 
 
-    /**
-     * adds adapter unique name prefix to the table
-     *
-     * @param tableName - table name
-     * @return name with prefix
-     */
-    private String prefixedTableName( String tableName ) {
-        return tableNamePrefix + "__" + tableName;
-    }
-
-
     private List<String> appendPath( List<String> path, String element ) {
         List<String> next = new ArrayList<>( path );
         next.add( element );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFile.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFile.java
new file mode 100644
index 0000000000..fcdac03b28
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFile.java
@@ -0,0 +1,54 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+import java.net.MalformedURLException;
+import java.net.URL;
+import java.util.Collections;
+import java.util.LinkedHashMap;
+import java.util.Map;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.util.Source;
+import org.polypheny.db.util.Sources;
+
+
+/**
+ * One physical Parquet file that contributes rows to a Parquet table.
+ */
+public record ParquetSourceFile(
+        String fileUrl,
+        Map<String, String> partitionValues ) {
+
+    public ParquetSourceFile {
+        partitionValues = partitionValues == null ? Map.of() : Collections.unmodifiableMap( new LinkedHashMap<>( partitionValues ) );
+    }
+
+
+    public static ParquetSourceFile of( String fileUrl ) {
+        return new ParquetSourceFile( fileUrl, Map.of() );
+    }
+
+
+    public Source asSource() {
+        try {
+            return Sources.of( new URL( fileUrl ) );
+        } catch ( MalformedURLException e ) {
+            throw new GenericRuntimeException( e );
+        }
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java
new file mode 100644
index 0000000000..230ca7c0ff
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java
@@ -0,0 +1,59 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+import java.util.List;
+import java.util.function.Function;
+import org.polypheny.db.adapter.parquet.shared.filter.FilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.type.entity.PolyString;
+
+
+class ParquetSourceFileFilterEvaluator extends FilterEvaluator<ParquetSourceFile> {
+
+    private final Function<ParquetAdapterFilter, ParquetColumnBinding> selector;
+
+
+    ParquetSourceFileFilterEvaluator( Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
+        this.selector = selector;
+    }
+
+
+    List<ParquetSourceFile> prune( List<ParquetSourceFile> sourceFiles, List<ParquetAdapterFilter> filters ) {
+        if ( filters.isEmpty() ) {
+            return sourceFiles;
+        }
+        return sourceFiles.stream()
+                .filter( sourceFile -> filters.stream().allMatch( filter -> matches( sourceFile, filter ) ) )
+                .toList();
+    }
+
+
+    @Override
+    protected Boolean evaluateLeaf( ParquetSourceFile sourceFile, ParquetAdapterFilter filter ) {
+        ParquetColumnBinding columnBinding = selector.apply( filter );
+        if ( columnBinding == null || columnBinding.role() != ParquetColumnRole.PARTITION ) {
+            return null;
+        }
+        String partitionValue = sourceFile.partitionValues().get( columnBinding.columnName() );
+        if ( partitionValue == null ) {
+            return false;
+        }
+        return matchesValue( PolyString.of( partitionValue ), filter.operator(), filter.polyValue() );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java
index 075a581fc6..332349a49f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java
@@ -24,18 +24,19 @@
 
 /**
  * Table-level metadata, describes the whole table
- * @param sourceUrl - real Parquet file URL to read from
+ * @param sourceFiles - real Parquet file URLs to read from
  * @param parentTableName - generated parent relational table name, or null for root tables
  * @param sourcePathElements - table-level path inside the Parquet file, For root: List.of(), for nested: List.of("items")
  * @param columnsByColumnId - Map<Long, ParquetColumnBinding> - maps Polypheny physical column ids to column bindings
  */
 public record ParquetTableBinding(
-        String sourceUrl,
+        List<ParquetSourceFile> sourceFiles,
         String parentTableName,
         List<String> sourcePathElements,
         Map<Long, ParquetColumnBinding> columnsByColumnId ) {
 
     public ParquetTableBinding {
+        sourceFiles = sourceFiles == null ? List.of() : List.copyOf( sourceFiles );
         sourcePathElements = sourcePathElements == null ? List.of() : List.copyOf( sourcePathElements );
         columnsByColumnId = columnsByColumnId == null ? Map.of() : Collections.unmodifiableMap( new LinkedHashMap<>( columnsByColumnId ) );
     }
@@ -44,11 +45,11 @@ public record ParquetTableBinding(
     /**
      * Factory Method
      * creates a simple binding for a root/flat table
-     * @param sourceUrl - file
+     * @param sourceFiles - files
      * @param table - table name
      * @return ParquetTableBinding object
      */
-    public static ParquetTableBinding createRootTableBinding( String sourceUrl, PhysicalTable table ) {
+    public static ParquetTableBinding createRootTableBinding( List<ParquetSourceFile> sourceFiles, PhysicalTable table ) {
         // create map of column level bindings
         Map<Long, ParquetColumnBinding> columnBindings = new LinkedHashMap<>();
         // For each physical column create a DATA column binding with a one-element source path equal to the column name
@@ -58,7 +59,7 @@ public static ParquetTableBinding createRootTableBinding( String sourceUrl, Phys
 
         // create table level binding
         return new ParquetTableBinding(
-                sourceUrl,
+                sourceFiles,
                 null, // parentTableName is null
                 List.of(), // sourcePathElements empty
                 columnBindings );
@@ -68,14 +69,14 @@ public static ParquetTableBinding createRootTableBinding( String sourceUrl, Phys
     /**
      * Factory Method
      * creates a binding when exact Parquet paths for columns discovered
-     * @param sourceUrl - real Parquet file URL
+     * @param sourceFiles - real Parquet files
      * @param parentTableName - generated parent relational table name, or null for root tables
      * @param sourcePathElements - table-level path inside the Parquet file
      * @param table - Polypheny physical table object contains the physical columns that were created in the adapter catalog.
      * @param columnPaths - map relational column name to Parquet source path ("amount" -> ["items", "discounts", "amount"])
      * @return ParquetTableBinding
      */
-    public static ParquetTableBinding createTableBindingFromColumnPaths( String sourceUrl, String parentTableName, List<String> sourcePathElements, PhysicalTable table, Map<String, List<String>> columnPaths ) {
+    public static ParquetTableBinding createTableBindingFromColumnPaths( List<ParquetSourceFile> sourceFiles, String parentTableName, List<String> sourcePathElements, PhysicalTable table, Map<String, List<String>> columnPaths ) {
         Map<Long, ParquetColumnBinding> columnBindings = new LinkedHashMap<>();
         // create ParquetColumnBinding object for each column and store in map by id
         table.columns.forEach( column -> columnBindings.put(
@@ -83,12 +84,12 @@ public static ParquetTableBinding createTableBindingFromColumnPaths( String sour
                 new ParquetColumnBinding(
                         column.id,
                         column.name,
-                        inferRole( column.name ),
+                        inferRole( column.name, columnPaths, sourceFiles ),
                         inferSourcePath( column.name, columnPaths ) ) ) );
 
         // create table level binding
         return new ParquetTableBinding(
-                sourceUrl,
+                sourceFiles,
                 parentTableName,
                 sourcePathElements,
                 columnBindings );
@@ -105,16 +106,21 @@ public ParquetColumnBinding getColumnBinding( long columnId ) {
     }
 
 
-    private static ParquetColumnRole inferRole( String columnName ) {
+    private static ParquetColumnRole inferRole( String columnName, Map<String, List<String>> columnPaths, List<ParquetSourceFile> sourceFiles ) {
         return switch ( columnName ) {
             case ParquetSyntheticColumns.ROW_ID -> ParquetColumnRole.PRIMARY_KEY;
             case ParquetSyntheticColumns.PARENT_ROW_ID -> ParquetColumnRole.PARENT_KEY;
             case ParquetSyntheticColumns.ELEM_ORDINAL -> ParquetColumnRole.ORDINAL;
-            default -> ParquetColumnRole.DATA;
+            default -> isPartitionColumn( columnName, columnPaths, sourceFiles ) ? ParquetColumnRole.PARTITION : ParquetColumnRole.DATA;
         };
     }
 
 
+    private static boolean isPartitionColumn( String columnName, Map<String, List<String>> columnPaths, List<ParquetSourceFile> sourceFiles ) {
+        return !columnPaths.containsKey( columnName ) && sourceFiles.stream().anyMatch( sourceFile -> sourceFile.partitionValues().containsKey( columnName ) );
+    }
+
+
     private static List<String> inferSourcePath( String columnName, Map<String, List<String>> columnPaths ) {
         if ( columnPaths.containsKey( columnName ) ) {
             return columnPaths.get( columnName );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
index 88fc9ac35e..d9a9f5a97d 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
@@ -19,32 +19,26 @@
 import java.io.File;
 import java.net.MalformedURLException;
 import java.net.URL;
-import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.HashMap;
+import java.util.LinkedHashMap;
 import java.util.List;
 import java.util.Map;
 import java.util.Optional;
 import java.util.Set;
-import org.apache.hadoop.conf.Configuration;
-import org.apache.hadoop.fs.Path;
-import org.apache.parquet.hadoop.ParquetFileReader;
-import org.apache.parquet.hadoop.util.HadoopInputFile;
-import org.apache.parquet.schema.Type;
-import org.polypheny.db.catalog.Catalog;
 import org.polypheny.db.adapter.ConnectionMethod;
 import org.polypheny.db.adapter.DataSource;
 import org.polypheny.db.adapter.DeployMode;
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
 import org.polypheny.db.adapter.Scannable;
+import org.polypheny.db.adapter.parquet.relational.schema.DiscoveredTable;
+import org.polypheny.db.adapter.parquet.relational.schema.DiscoveredTableBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetBindingSerializer;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetFileDiscovery;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetUrlResolver;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetNamespace;
-import org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer;
-import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
-import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
+import org.polypheny.db.catalog.Catalog;
 import org.polypheny.db.catalog.catalogs.AdapterCatalog;
 import org.polypheny.db.catalog.catalogs.DocAdapterCatalog;
 import org.polypheny.db.catalog.logistic.DataModel;
@@ -52,7 +46,6 @@
 import org.polypheny.db.information.InformationTable;
 import org.polypheny.db.prepare.Context;
 import org.polypheny.db.transaction.PolyXid;
-import org.polypheny.db.type.PolyType;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -63,22 +56,17 @@
  */
 public abstract class AbstractParquetSource extends DataSource<DocAdapterCatalog> implements Scannable {
 
+    protected static final Logger log = LoggerFactory.getLogger( AbstractParquetSource.class );
     private static final String PARQUET_BINDINGS_SETTING = "__polypheny_parquet_bindings";
-
     protected final ConnectionMethod connectionMethod;
-    protected final ParquetTypeConverter parquetTypeConverter;
+    private final Map<Long, ParquetTableBinding> parquetBindings;
     protected URL parquetDir;
-
     protected ParquetNamespace currentNamespace;
-    private Map<String, List<ExportedColumn>> exportedColumns;
-    private final Map<Long, ParquetTableBinding> parquetBindings;
-
-    protected static final Logger log = LoggerFactory.getLogger( AbstractParquetSource.class );
+    private Map<String, DiscoveredTable> discoveredTables;
 
 
     protected AbstractParquetSource( long storeId, String uniqueName, Map<String, String> settings, DeployMode mode, Set<DataModel> supportedModels ) {
         super( storeId, uniqueName, settings, mode, true, new DocAdapterCatalog( storeId ), supportedModels );
-        this.parquetTypeConverter = new ParquetTypeConverter();
         // Recover binding metadata through adapter settings
         this.parquetBindings = new HashMap<>( ParquetBindingSerializer.deserialize( settings.get( PARQUET_BINDINGS_SETTING ) ) );
         this.connectionMethod = settings.containsKey( "method" )
@@ -92,6 +80,7 @@ protected AbstractParquetSource( long storeId, String uniqueName, Map<String, St
 
     /**
      * populate parquet directory variable according to settings
+     *
      * @param settings Settings Map
      */
     protected void setParquetDir( Map<String, String> settings ) {
@@ -132,89 +121,26 @@ protected void setParquetDir( Map<String, String> settings ) {
     }
 
 
-    /**
-     * Get list of columns from parquet file using metadata
-     * @param fileName - parquet file name
-     * @param physicalTableName - physical Table Name
-     * @return List<ExportedColumn> - list of columns
-     */
-    private List<ExportedColumn> getExportedColumnsFromFile( String fileName, String physicalTableName ) {
-        try {
-            Path path = new Path( ParquetUrlResolver.resolveFile( parquetDir, fileName ).toURI() );
-            Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
-            try ( ParquetFileReader reader = ParquetFileReader.open( HadoopInputFile.fromPath( path, conf ) ) ) {
-                List<Type> schemaFields = reader.getFooter().getFileMetaData().getSchema().getFields();
-                List<ExportedColumn> columns = new ArrayList<>();
-                int position = 0;
-                for ( Type field : schemaFields ) {
-                    columns.add( getExportedColumnFromField( field, fileName, physicalTableName, position++ ) );
-                }
-                return columns;
-            }
-        } catch ( Exception e ) {
-            throw new org.polypheny.db.catalog.exceptions.GenericRuntimeException( e );
-        }
-    }
-
-
-    /**
-     * Create exported column object from parquet file field
-     * @param field - parquet file column
-     * @param fileName - name of parquet file
-     * @param physicalTableName - the table name that Polypheny should create/export for this Parquet file
-     * @param position - column position
-     * @return ExportedColumn object
-     */
-    private ExportedColumn getExportedColumnFromField( Type field, String fileName, String physicalTableName, int position ) {
-        String columnName = ParquetNameNormalizer.normalizeFieldName( field.getName() );
-        PolyType polyType = parquetTypeConverter.fromParquetTypeToPolyType( field );
-        return new ExportedColumn(
-                columnName,
-                polyType,
-                null,
-                null,
-                null,
-                null,
-                null,
-                false,
-                fileName,
-                physicalTableName,
-                field.getName(),
-                position,
-                position == 0 );
-    }
-
-
     /**
      * Return exported column map if exists
      * Otherwise creates hashmap:
      * for each filename - store exported columns list
+     *
      * @return Map<String, List<ExportedColumn>>
      */
     public Map<String, List<ExportedColumn>> getExportedColumns() {
-        if ( connectionMethod == ConnectionMethod.UPLOAD && exportedColumns != null ) {
-            return exportedColumns;
-        }
-
-        Map<String, List<ExportedColumn>> columns = new HashMap<>();
-        Set<String> fileNames = ParquetFileDiscovery.listParquetFiles( parquetDir );
-        for ( String fileName : fileNames ) {
-            String physicalTableName = getPrefixedTableName( ParquetNameNormalizer.computePhysicalTableName( fileName ) );
-            columns.put( physicalTableName, getExportedColumnsFromFile( fileName, physicalTableName ) );
+        if ( discoveredTables == null ) {
+            discoveredTables = ParquetFileDiscovery.discoverTables( parquetDir, getUniqueName() );
         }
-        this.exportedColumns = columns;
-        return columns;
+        return discoveredTables.values().stream().collect( LinkedHashMap::new, ( m, t ) -> m.put( t.tableName(), t.columns() ), HashMap::putAll );
     }
 
 
-    /**
-     * Add prefix to the table names to allow deployment multiple Parquet relational adapters
-     * over the same files, for example one flat and one normalized, without logical table name collisions
-     * @param tableName table-name produced from the Parquet file name
-     * @return normalized name containing prefix - adapter unique name
-     */
-    protected String getPrefixedTableName( String tableName ) {
-        return ParquetNameNormalizer.normalizeFieldName( getUniqueName() ) + "__" + tableName;
+    protected Optional<DiscoveredTableBinding> getTableBinding( String tableName ) {
+        if ( discoveredTables == null ) {
+            discoveredTables = ParquetFileDiscovery.discoverTables( parquetDir, getUniqueName() );
+        }
+        return Optional.ofNullable( discoveredTables.get( tableName ).binding() );
     }
 
 
@@ -222,13 +148,14 @@ protected String getPrefixedTableName( String tableName ) {
      * Allows ParquetRelationalSource to clear the flat exported-column cache when settings such as
      * schemaMode or directory change.
      */
-    protected void clearExportedColumnsCache() {
-        exportedColumns = null;
+    protected void clearTablesCache() {
+        discoveredTables = null;
     }
 
 
     /**
      * Add binding to the parquet bindings map and call for persistence to store map in settings
+     *
      * @param physicalTableId - key in Map<Long, ParquetTableBinding>
      * @param binding ParquetTableBinding to store
      */
@@ -342,7 +269,7 @@ public void shutdown() {
     protected void reloadSettings( List<String> updatedSettings ) {
         if ( updatedSettings.contains( "directory" ) || updatedSettings.contains( "directoryName" ) || updatedSettings.contains( "url" ) ) {
             setParquetDir( settings );
-            clearExportedColumnsCache();
+            clearTablesCache();
         }
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
index 0320fe0035..b1611c7994 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
@@ -23,7 +23,6 @@
 import org.polypheny.db.rex.RexDynamicParam;
 import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNode;
-import org.polypheny.db.type.PolyType;
 
 /**
  * Responsible for parsing supported Rex predicates and
@@ -129,12 +128,4 @@ protected record ParsedFilter( RexNode left, RexNode right, Kind operator ) {
 
     }
 
-    protected boolean isColumnPredicateSupported( PolyType type, Kind kind, RexNode valueNode ) {
-        return switch ( type ) {
-            case BOOLEAN, VARCHAR, CHAR, TEXT -> kind == Kind.EQUALS || kind == Kind.NOT_EQUALS;
-            case INTEGER, BIGINT, FLOAT, DOUBLE, DATE, TIME, TIMESTAMP -> true;
-            default -> false;
-        } && (valueNode instanceof RexDynamicParam || (valueNode instanceof RexLiteral literal && literal.getValue() != null));
-    }
-
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
index 8b08df2be1..2d4649bce3 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
@@ -19,17 +19,14 @@
 import java.util.ArrayDeque;
 import java.util.List;
 import java.util.Queue;
-import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.parquet.example.data.Group;
-import org.apache.parquet.schema.Type;
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetGroupFilterEvaluator;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
-import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.util.Source;
 
 /**
  * Enumerator base class.
@@ -39,16 +36,23 @@
 public abstract class AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
 
     protected final ParquetSourceReader reader;
-    protected final ParquetValueExtractor valueExtractor;
     protected final List<ParquetAdapterFilter> filters;
+    protected final ParquetGroupFilterEvaluator filterEvaluator;
+    private final boolean readerOwner;
     private final Queue<PolyValue[]> rows = new ArrayDeque<>();
     private PolyValue[] current;
 
 
-    protected AbstractParquetEnumerator( Source source, AtomicBoolean cancelFlag, int[] fields, FiltersContainer filtersContainer, ParquetValueExtractor valueExtractor ) {
+    protected AbstractParquetEnumerator( ParquetSourceReader reader, FiltersContainer filtersContainer, ParquetGroupFilterEvaluator filterEvaluator ) {
+        this( reader, filtersContainer, filterEvaluator, true );
+    }
+
+
+    protected AbstractParquetEnumerator( ParquetSourceReader reader, FiltersContainer filtersContainer, ParquetGroupFilterEvaluator filterEvaluator, boolean readerOwner ) {
         this.filters = filtersContainer.adapterFilters();
-        this.reader = new ParquetSourceReader( source, cancelFlag, fields, filtersContainer.readerFilters() );
-        this.valueExtractor = valueExtractor;
+        this.reader = reader;
+        this.filterEvaluator = filterEvaluator;
+        this.readerOwner = readerOwner;
     }
 
 
@@ -95,10 +99,12 @@ public void reset() {
 
     @Override
     public void close() {
-        try {
-            reader.close();
-        } catch ( Exception e ) {
-            throw new GenericRuntimeException( "Error closing parquet reader", e );
+        if ( readerOwner ) {
+            try {
+                reader.close();
+            } catch ( Exception e ) {
+                throw new GenericRuntimeException( "Error closing parquet reader", e );
+            }
         }
     }
 
@@ -152,7 +158,7 @@ protected List<Group> expandRow( Group group ) {
      */
     protected boolean accept( Group group ) {
         for ( var filter : filters ) {
-            if ( !matches( group, filter ) ) {
+            if ( !filterEvaluator.matches( group, filter ) ) {
                 return false;
             }
         }
@@ -160,89 +166,8 @@ protected boolean accept( Group group ) {
     }
 
 
-    protected boolean matches( Group group, ParquetAdapterFilter filter ) {
-        if ( filter.isLogical() ) {
-            return switch ( filter.operator() ) {
-                case AND -> filter.operands().stream().allMatch( operand -> matches( group, operand ) );
-                case OR -> filter.operands().stream().anyMatch( operand -> matches( group, operand ) );
-                case NOT -> filter.operands().size() == 1 && !matches( group, filter.operands().get( 0 ) );
-                default -> true;
-            };
-        }
-
-        // if filter is invalid row should be displayed
-        if ( !canApplyFilter( group, filter ) ) {
-            return true;
-        }
-
-        // this row does not have a value for that Parquet field
-        if ( !filterHasValue( group, filter ) ) {
-            return false;
-        }
-
-        // call overloaded functionality
-        PolyValue actual = extractValue( group, filter );
-        PolyValue expected = filter.polyValue();
-
-        if ( actual == null || actual.isNull() || expected.isNull() ) {
-            return false;
-        }
-
-        // apply filter on the row level
-        return matches( actual, filter.operator(), expected );
-    }
-
-
-    protected boolean matches( PolyValue actual, Kind operator, PolyValue expected ) {
-        if ( actual == null || actual.isNull() || expected == null || expected.isNull() ) {
-            return false;
-        }
-
-        // apply filter on the row level
-        return switch ( operator ) {
-            case EQUALS -> actual.equals( expected );
-            case NOT_EQUALS -> !actual.equals( expected );
-            case GREATER_THAN -> compare( actual, expected ) > 0;
-            case GREATER_THAN_OR_EQUAL -> compare( actual, expected ) >= 0;
-            case LESS_THAN -> compare( actual, expected ) < 0;
-            case LESS_THAN_OR_EQUAL -> compare( actual, expected ) <= 0;
-            default -> true;
-        };
-    }
-
-
-    protected boolean canApplyFilter( Group group, ParquetAdapterFilter filter ) {
-        return filter.polyValue() != null && filter.columnIndex() >= 0 && filter.columnIndex() < reader.getProjectionSchema().getFieldCount();
-    }
-
-
-    protected boolean filterHasValue( Group group, ParquetAdapterFilter filter ) {
-        return !filter.pathElements().isEmpty() || group.getFieldRepetitionCount( filter.columnIndex() ) > 0;
-    }
-
-
-    /**
-     * Extract value from parquet group according to filter column index
-     *
-     * @param group - parquet group
-     * @param filter - ParquetAdapterFilter
-     * @return - PolyValue
-     */
-    protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
-        if ( filter.pathElements().isEmpty() ) {
-            Type field = reader.getProjectionSchema().getType( filter.columnIndex() );
-            return valueExtractor.extractValue( group, filter.columnIndex(), field );
-        }
-        return valueExtractor.extractValue( group, filter.pathElements() );
-    }
-
-
-    protected int compare( PolyValue actual, PolyValue expected ) {
-        try {
-            return actual.compareTo( expected );
-        } catch ( Exception e ) {
-            return actual.toString().compareTo( expected.toString() );
-        }
+    protected ParquetValueExtractor valueExtractor() {
+        return filterEvaluator.valueExtractor();
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/CombinedGroup.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/CombinedGroup.java
index e157fd275f..a2f928bc2d 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/CombinedGroup.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/CombinedGroup.java
@@ -25,6 +25,9 @@
 import org.apache.parquet.schema.GroupType;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 
+/**
+ * Represents a combination of joined groups: parent + child.
+ */
 @Getter
 public class CombinedGroup extends Group {
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java
index 1724a53dba..ec0b137b07 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java
@@ -1,6 +1,7 @@
 package org.polypheny.db.adapter.parquet.shared.execution;
 
 import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.GroupType;
 import org.apache.parquet.schema.Type;
 import org.polypheny.db.type.entity.PolyValue;
 import java.util.List;
@@ -15,4 +16,12 @@ public interface ParquetValueExtractor {
 
     PolyValue extractValue( Group group, List<String> path );
 
+    default int fieldIndex( GroupType groupType, String fieldName ) {
+        for ( int i = 0; i < groupType.getFieldCount(); i++ ) {
+            if ( groupType.getType( i ).getName().equals( fieldName ) ) {
+                return i;
+            }
+        }
+        return -1;
+    }
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FilterEvaluator.java
new file mode 100644
index 0000000000..b5eea320f6
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FilterEvaluator.java
@@ -0,0 +1,106 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.filter;
+
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+/**
+ * Evaluates Parquet adapter filters against a concrete context.
+ * A null result means the evaluator cannot decide, so callers that prune data should keep it.
+ */
+public abstract class FilterEvaluator<C> {
+
+    public boolean matches( C context, ParquetAdapterFilter filter ) {
+        return !Boolean.FALSE.equals( evaluate( context, filter ) );
+    }
+
+
+    public Boolean evaluate( C context, ParquetAdapterFilter filter ) {
+        if ( filter.isLogical() ) {
+            return switch ( filter.operator() ) {
+                case AND -> evaluateAnd( context, filter );
+                case OR -> evaluateOr( context, filter );
+                case NOT -> filter.operands().size() == 1 ? negate( evaluate( context, filter.operands().get( 0 ) ) ) : null;
+                default -> null;
+            };
+        }
+        return evaluateLeaf( context, filter );
+    }
+
+
+    protected abstract Boolean evaluateLeaf( C context, ParquetAdapterFilter filter );
+
+
+    protected Boolean matchesValue( PolyValue actual, Kind operator, PolyValue expected ) {
+        if ( actual == null || actual.isNull() || expected == null || expected.isNull() ) {
+            return false;
+        }
+
+        return switch ( operator ) {
+            case EQUALS -> actual.equals( expected );
+            case NOT_EQUALS -> !actual.equals( expected );
+            case GREATER_THAN -> compare( actual, expected ) > 0;
+            case GREATER_THAN_OR_EQUAL -> compare( actual, expected ) >= 0;
+            case LESS_THAN -> compare( actual, expected ) < 0;
+            case LESS_THAN_OR_EQUAL -> compare( actual, expected ) <= 0;
+            default -> true;
+        };
+    }
+
+
+    private Boolean evaluateAnd( C context, ParquetAdapterFilter filter ) {
+        boolean hasUnknown = false;
+        for ( ParquetAdapterFilter operand : filter.operands() ) {
+            Boolean result = evaluate( context, operand );
+            if ( Boolean.FALSE.equals( result ) ) {
+                return false;
+            }
+            hasUnknown |= result == null;
+        }
+        return hasUnknown ? null : true;
+    }
+
+
+    private Boolean evaluateOr( C context, ParquetAdapterFilter filter ) {
+        boolean hasUnknown = false;
+        for ( ParquetAdapterFilter operand : filter.operands() ) {
+            Boolean result = evaluate( context, operand );
+            if ( Boolean.TRUE.equals( result ) ) {
+                return true;
+            }
+            hasUnknown |= result == null;
+        }
+        return hasUnknown ? null : false;
+    }
+
+
+    private Boolean negate( Boolean value ) {
+        return value == null ? null : !value;
+    }
+
+
+    private int compare( PolyValue actual, PolyValue expected ) {
+        try {
+            return actual.compareTo( expected );
+        } catch ( Exception e ) {
+            return actual.toString().compareTo( expected.toString() );
+        }
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetGroupFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetGroupFilterEvaluator.java
new file mode 100644
index 0000000000..b783808331
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetGroupFilterEvaluator.java
@@ -0,0 +1,84 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.filter;
+
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.shared.execution.ParquetValueExtractor;
+import org.polypheny.db.type.entity.PolyValue;
+
+/**
+ * Filter functionality on Adapter Level
+ * works with group (row)
+ */
+public class ParquetGroupFilterEvaluator extends FilterEvaluator<Group> {
+
+    protected final MessageType schema;
+    protected final ParquetValueExtractor valueExtractor;
+
+
+    public ParquetGroupFilterEvaluator( MessageType schema, ParquetValueExtractor valueExtractor ) {
+        this.schema = schema;
+        this.valueExtractor = valueExtractor;
+    }
+
+
+    public ParquetValueExtractor valueExtractor() {
+        return valueExtractor;
+    }
+
+
+    public MessageType schema() {
+        return schema;
+    }
+
+
+    @Override
+    protected Boolean evaluateLeaf( Group group, ParquetAdapterFilter filter ) {
+        if ( !canApplyFilter( group, filter ) ) {
+            return null;
+        }
+        if ( !filterHasValue( group, filter ) ) {
+            return false;
+        }
+        return matchesValue( extractValue( group, filter ), filter.operator(), filter.polyValue() );
+    }
+
+
+    protected boolean canApplyFilter( Group group, ParquetAdapterFilter filter ) {
+        if ( filter.polyValue() == null || filter.columnIndex() < 0 ) {
+            return false;
+        }
+        return !filter.pathElements().isEmpty() || filter.columnIndex() < schema.getFieldCount();
+    }
+
+
+    protected boolean filterHasValue( Group group, ParquetAdapterFilter filter ) {
+        return !filter.pathElements().isEmpty() || group.getFieldRepetitionCount( filter.columnIndex() ) > 0;
+    }
+
+
+    protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
+        if ( filter.pathElements().isEmpty() ) {
+            Type field = schema.getType( filter.columnIndex() );
+            return valueExtractor.extractValue( group, filter.columnIndex(), field );
+        }
+        return valueExtractor.extractValue( group, filter.pathElements() );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedFilterEvaluator.java
new file mode 100644
index 0000000000..24567fe94c
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedFilterEvaluator.java
@@ -0,0 +1,52 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.filter;
+
+import java.util.List;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.MessageType;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetPathValueExtractor;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.shared.execution.ParquetValueExtractor;
+import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
+import org.polypheny.db.type.entity.PolyValue;
+
+/**
+ * Evaluates filter of nested table group.
+ */
+public class ParquetNestedFilterEvaluator extends ParquetGroupFilterEvaluator {
+
+    private final List<String> tablePath;
+    private final List<ParquetColumnBinding> columnBindings;
+
+
+    public ParquetNestedFilterEvaluator( MessageType schema, ParquetValueExtractor valueExtractor, List<String> tablePath, List<ParquetColumnBinding> columnBindings ) {
+        super( schema, valueExtractor );
+        this.tablePath = tablePath;
+        this.columnBindings = columnBindings;
+    }
+
+
+    @Override
+    protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
+        var pathValueExtractor = (ParquetPathValueExtractor) valueExtractor;
+        var virtualGroup = (VirtualGroup) group;
+        var binding = columnBindings.get( filter.columnIndex() );
+        return pathValueExtractor.extractValue( virtualGroup, binding, tablePath );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedJoinFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedJoinFilterEvaluator.java
new file mode 100644
index 0000000000..d40bfcb20e
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedJoinFilterEvaluator.java
@@ -0,0 +1,98 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.filter;
+
+import java.util.List;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.schema.MessageType;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetPathValueExtractor;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
+import org.polypheny.db.adapter.parquet.shared.execution.CombinedGroup;
+import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
+import org.polypheny.db.type.entity.PolyValue;
+
+/**
+ * Evaluates filter on {@link CombinedGroup} containing row from joined nested tables.
+ */
+public class ParquetNestedJoinFilterEvaluator extends ParquetGroupFilterEvaluator {
+
+    private final ParquetTableBinding parentBinding;
+    private final List<ParquetColumnBinding> parentColumns;
+    private final boolean leftIsParent;
+
+
+    public ParquetNestedJoinFilterEvaluator( MessageType schema, ParquetPathValueExtractor valueExtractor, ParquetTableBinding parentBinding, List<ParquetColumnBinding> parentColumns, boolean leftIsParent ) {
+        super( schema, valueExtractor );
+        this.parentBinding = parentBinding;
+        this.parentColumns = parentColumns;
+        this.leftIsParent = leftIsParent;
+    }
+
+
+    @Override
+    protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
+        ParquetPathValueExtractor pathValueExtractor = (ParquetPathValueExtractor) valueExtractor;
+        if ( group instanceof CombinedGroup combinedGroup ) {
+            return pathValueExtractor.extractValue(
+                    combinedGroup.groupForField( filter.columnIndex(), leftIsParent ),
+                    combinedGroup.bindingForField( filter.columnIndex(), leftIsParent ),
+                    combinedGroup.tablePathForField( filter.columnIndex(), leftIsParent )
+            );
+        }
+        // If this is not a combined group then the filtering is being done on parent table only.
+        return pathValueExtractor.extractValue(
+                (VirtualGroup) group,
+                parentColumns.get( filter.columnIndex() ),
+                parentBinding.sourcePathElements()
+        );
+    }
+
+
+    /**
+     * Checks if a filter is valid and can be applied on the group.
+     *
+     * @param group a group to check
+     * @param filter a filter to validate
+     * @return true if the filter is valid and false otherwise.
+     */
+    @Override
+    protected boolean canApplyFilter( Group group, ParquetAdapterFilter filter ) {
+        if ( filter.polyValue() == null || filter.columnIndex() < 0 ) {
+            return false;
+        }
+        if ( group instanceof CombinedGroup combinedGroup ) {
+            return filter.columnIndex() < combinedGroup.fieldCount();
+        }
+        return filter.columnIndex() < parentColumns.size();
+    }
+
+
+    /**
+     * Checks if the group contains a value.
+     *
+     * @param group a group to check.
+     * @param filter a filter containing column index which value is checked
+     * @return true if the group has a value at the filter column index and false otherwise.
+     */
+    @Override
+    protected boolean filterHasValue( Group group, ParquetAdapterFilter filter ) {
+        CombinedGroup combinedGroup = (CombinedGroup) group;
+        return !combinedGroup.isNullField( filter.columnIndex(), leftIsParent );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
index a7d22b5f5f..ab379830a9 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
@@ -17,12 +17,29 @@
 package org.polypheny.db.adapter.parquet.shared.io;
 
 import java.io.File;
+import java.net.MalformedURLException;
 import java.net.URL;
+import java.util.ArrayList;
 import java.util.Arrays;
+import java.util.Collection;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
 import java.util.Set;
-import java.util.TreeSet;
-import java.util.stream.Collectors;
+import org.apache.parquet.column.statistics.Statistics;
+import org.apache.parquet.hadoop.metadata.BlockMetaData;
+import org.apache.parquet.hadoop.metadata.ColumnChunkMetaData;
+import org.apache.parquet.io.api.Binary;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.parquet.relational.schema.DiscoveredTable;
+import org.polypheny.db.adapter.parquet.relational.schema.DiscoveredTableBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.PolyType;
 import org.polypheny.db.util.Sources;
 
 /**
@@ -30,31 +47,484 @@
  */
 public class ParquetFileDiscovery {
 
+    private static final double MIN_SCHEMA_SIMILARITY = 0.8d;
+
+
     private ParquetFileDiscovery() {
     }
 
 
     /**
-     * Lists Parquet files below the configured source location.
+     * Checks whether the file name looks like a Parquet file.
+     */
+    public static boolean isParquetFile( String fileName ) {
+        return fileName != null && fileName.toLowerCase().endsWith( ".parquet" );
+    }
+
+
+    /**
+     * Look at the configured Parquet location and decide
+     * - Which logical tables exist
+     * - Which Parquet files belong to each table
+     * - What columns each table should expose
+     * - What binding metadata is needed
+     *
+     * @param baseDir - directory
+     * @param tableNamePrefix - adapter instance prefix
+     * @return map: discovered Polypheny table name - table
+     */
+    public static Map<String, DiscoveredTable> discoverTables( URL baseDir, String tableNamePrefix ) {
+        Map<String, DiscoveredTable> nestedTables = discoverNestedTableDirectories( baseDir, tableNamePrefix );
+        if ( !nestedTables.isEmpty() ) {
+            return nestedTables;
+        }
+
+        List<DiscoveredSourceFile> sourceFiles = discoverSourceFiles( baseDir );
+        Map<String, List<ExportedColumn>> tables = new HashMap<>();
+
+        for ( DiscoveredSourceFile sourceFile : sourceFiles ) {
+            String tableName = prefixedTableName( tableNamePrefix, ParquetNameNormalizer.computePhysicalTableName( sourceFile.fileName() ) );
+            List<ExportedColumn> columns = schemaReader( sourceFile.url() ).exportedColumns( tableName );
+            if ( !columns.isEmpty() ) {
+                tables.put( sourceFile.key(), columns );
+            }
+        }
+
+        if ( sourceFiles.stream().anyMatch( sourceFile -> !sourceFile.partitionValues().isEmpty() ) ) {
+            String tableName = prefixedTableName( tableNamePrefix, tableNameFromSource( baseDir ) );
+            if ( !canConsolidateSchemas( tables.values() ) ) {
+                throw new GenericRuntimeException( "Parquet partitioned table '%s' contains files with incompatible schemas.", tableName );
+            }
+            return Map.of( tableName, discoverPartitionedTable( tableName, tables, sourceFiles ) );
+        }
+
+        if ( canExposeAsSingleTable( baseDir, tables.values() ) ) {
+            String tableName = prefixedTableName( tableNamePrefix, tableNameFromSource( baseDir ) );
+            return Map.of( tableName, discoverConsolidatedTable( tableName, tables, sourceFiles ) );
+        }
+
+        Map<String, DiscoveredTable> discoveredTables = new LinkedHashMap<>();
+
+        for ( Map.Entry<String, List<ExportedColumn>> entry : tables.entrySet() ) {
+            String tableName = entry.getValue().get( 0 ).physicalTableName();
+            discoveredTables.put(
+                    tableName,
+                    new DiscoveredTable(
+                            tableName,
+                            entry.getValue(),
+                            new DiscoveredTableBinding(
+                                    List.of( toSourceFile( sourceFiles, entry.getKey() ) ),
+                                    null,
+                                    List.of(),
+                                    columnPaths( entry.getValue() ) ) ) );
+        }
+
+        return discoveredTables;
+    }
+
+
+    /**
+     * Handles the case where the configured location is a dataset root that contains multiple table folders.
+     * @param baseDir - directory
+     * @param tableNamePrefix - adapter prefix
+     * @return map
+     */
+    private static Map<String, DiscoveredTable> discoverNestedTableDirectories( URL baseDir, String tableNamePrefix ) {
+        File source = Sources.of( baseDir ).file();
+        if ( source.isFile() || hasDirectParquetFiles( source ) || hasDirectPartitionDirectories( source ) ) {
+            return Map.of();
+        }
+
+        File[] directories = source.listFiles( File::isDirectory );
+        if ( directories == null || directories.length == 0 ) {
+            return Map.of();
+        }
+
+        Arrays.sort( directories );
+        Map<String, DiscoveredTable> tables = new LinkedHashMap<>();
+        for ( File directory : directories ) {
+            Map<String, DiscoveredTable> discovered = discoverTables( toUrl( directory ), tableNamePrefix );
+            for ( Map.Entry<String, DiscoveredTable> entry : discovered.entrySet() ) {
+                if ( tables.putIfAbsent( entry.getKey(), entry.getValue() ) != null ) {
+                    throw new GenericRuntimeException( "Duplicate discovered Parquet table name '%s'.", entry.getKey() );
+                }
+            }
+        }
+        return tables;
+    }
+
+
+    private static boolean hasDirectParquetFiles( File directory ) {
+        File[] files = directory.listFiles( ( d, name ) -> isParquetFile( name ) );
+        return files != null && files.length > 0;
+    }
+
+
+    private static boolean hasDirectPartitionDirectories( File directory ) {
+        File[] directories = directory.listFiles( File::isDirectory );
+        if ( directories == null ) {
+            return false;
+        }
+        return Arrays.stream( directories ).anyMatch( child -> isPartitionFolder( child.getName() ) );
+    }
+
+
+    /**
+     * Finds source .parquet files for one table root. It decides whether files are direct files or partitioned files.
+     * @param baseDir directory
+     * @return list of files
      */
-    public static Set<String> listParquetFiles( URL baseDir ) {
-        if ( Sources.of( baseDir ).file().isFile() ) {
-            return Set.of( Sources.of( baseDir ).file().getName() );
+    private static List<DiscoveredSourceFile> discoverSourceFiles( URL baseDir ) {
+        File source = Sources.of( baseDir ).file();
+        if ( source.isFile() ) {
+            return List.of( new DiscoveredSourceFile( source.getName(), toUrl( source ), Map.of() ) );
         }
 
-        File[] files = Sources.of( baseDir ).file().listFiles( ( d, name ) -> isParquetFile( name ) );
+        List<DiscoveredSourceFile> directFiles = listDirectParquetSourceFiles( source );
+        List<DiscoveredSourceFile> partitionFiles = new ArrayList<>();
+        collectPartitionSourceFiles( source, new LinkedHashMap<>(), partitionFiles );
+
+        if ( !partitionFiles.isEmpty() ) {
+            if ( !directFiles.isEmpty() ) {
+                throw new GenericRuntimeException( "Parquet source folder '%s' mixes root files and partition folders. Move files under key=value folders or use only root files.", source );
+            }
+            return partitionFiles;
+        }
+        return directFiles;
+    }
+
+
+    private static List<DiscoveredSourceFile> listDirectParquetSourceFiles( File source ) {
+        File[] files = source.listFiles( ( d, name ) -> isParquetFile( name ) );
         if ( files == null ) {
-            throw new GenericRuntimeException( "No .parquet files were found." );
+            throw new GenericRuntimeException( "No *.parquet files were found." );
         }
-        return Arrays.stream( files ).map( File::getName ).collect( Collectors.toCollection( TreeSet::new ) );
+        return Arrays.stream( files )
+                .sorted()
+                .map( file -> new DiscoveredSourceFile( file.getName(), toUrl( file ), Map.of() ) )
+                .toList();
     }
 
 
     /**
-     * Checks whether the file name looks like a Parquet file.
+     * Recursively walks key=value folders and collects files with partition values.
+     * @param directory - directory
+     * @param partitionValues - map
+     * @param sourceFiles - files
      */
-    public static boolean isParquetFile( String fileName ) {
-        return fileName != null && fileName.toLowerCase().endsWith( ".parquet" );
+    private static void collectPartitionSourceFiles( File directory, Map<String, String> partitionValues, List<DiscoveredSourceFile> sourceFiles ) {
+        File[] children = directory.listFiles();
+        if ( children == null ) {
+            return;
+        }
+        Arrays.sort( children );
+        for ( File child : children ) {
+            if ( child.isDirectory() ) {
+                Map<String, String> nextPartitions = parsePartitionFolder( child.getName(), partitionValues );
+                if ( nextPartitions != partitionValues ) {
+                    collectPartitionSourceFiles( child, nextPartitions, sourceFiles );
+                }
+                continue;
+            }
+            if ( !partitionValues.isEmpty() && isParquetFile( child.getName() ) ) {
+                sourceFiles.add( new DiscoveredSourceFile( child.getName(), toUrl( child ), partitionValues ) );
+            }
+        }
+    }
+
+
+    private static boolean isPartitionFolder( String folderName ) {
+        int separator = folderName.indexOf( '=' );
+        return separator > 0 && separator < folderName.length() - 1;
+    }
+
+    /**
+    Parses folder names like: year=2025 month=01
+     */
+    private static Map<String, String> parsePartitionFolder( String folderName, Map<String, String> parentPartitionValues ) {
+        int separator = folderName.indexOf( '=' );
+        if ( separator <= 0 || separator == folderName.length() - 1 ) {
+            return parentPartitionValues;
+        }
+        String key = ParquetNameNormalizer.normalizeFieldName( folderName.substring( 0, separator ) );
+        if ( parentPartitionValues.containsKey( key ) ) {
+            throw new GenericRuntimeException( "Duplicate Parquet partition key '%s' in path.", key );
+        }
+        Map<String, String> next = new LinkedHashMap<>( parentPartitionValues );
+        next.put( key, folderName.substring( separator + 1 ) );
+        return next;
+    }
+
+
+    /**
+     * Builds one DiscoveredTable for a partitioned folder layout. Adds data columns plus virtual partition columns.
+     */
+    private static DiscoveredTable discoverPartitionedTable( String tableName, Map<String, List<ExportedColumn>> tables, List<DiscoveredSourceFile> sourceFiles ) {
+        List<String> partitionColumnNames = partitionColumnNames( sourceFiles );
+        validatePartitionColumnCollisions( tables, sourceFiles, partitionColumnNames );
+        Collection<ExportedColumn> columns = getConsolidatedExportedColumns( tables ).stream()
+                .filter( column -> !partitionColumnNames.contains( column.name() ) )
+                .toList();
+        List<ExportedColumn> exportedColumns = new ArrayList<>( columns );
+        for ( String partitionColumnName : partitionColumnNames ) {
+            exportedColumns.add( partitionColumn( tableName, partitionColumnName, exportedColumns.size() ) );
+        }
+        return new DiscoveredTable(
+                tableName,
+                exportedColumns,
+                new DiscoveredTableBinding(
+                        sourceFiles.stream().map( ParquetFileDiscovery::toSourceFile ).toList(),
+                        null,
+                        List.of(),
+                        columnPaths( columns ) ) );
+    }
+
+
+    /**
+     * Handles the case where a Parquet file already has a column with the same name as a partition folder key
+     */
+    private static void validatePartitionColumnCollisions( Map<String, List<ExportedColumn>> tables, List<DiscoveredSourceFile> sourceFiles, List<String> partitionColumnNames ) {
+        for ( DiscoveredSourceFile sourceFile : sourceFiles ) {
+            List<ExportedColumn> columns = tables.get( sourceFile.key() );
+            if ( columns == null ) {
+                continue;
+            }
+            for ( ExportedColumn column : columns ) {
+                if ( partitionColumnNames.contains( column.name() ) ) {
+                    validatePartitionColumnCollision( sourceFile, column );
+                }
+            }
+        }
+    }
+
+
+    private static void validatePartitionColumnCollision( DiscoveredSourceFile sourceFile, ExportedColumn column ) {
+        String partitionValue = sourceFile.partitionValues().get( column.name() );
+        if ( partitionValue == null ) {
+            return;
+        }
+
+        var schemaReader = schemaReader( sourceFile.url() );
+        for ( var footer : schemaReader.getFooters() ) {
+            for ( BlockMetaData block : footer.getBlocks() ) {
+                ColumnChunkMetaData columnChunk = findColumnChunk( block, column.physicalColumnName() );
+                if ( columnChunk == null ) {
+                    continue;
+                }
+                if ( !chunkContainsOnlyPartitionValue( block, columnChunk, partitionValue ) ) {
+                    throw new GenericRuntimeException(
+                            "Parquet partition column collision for '%s' in '%s'. File column values must all match partition value '%s' or the parquet column must be removed.",
+                            column.name(),
+                            sourceFile.url(),
+                            partitionValue );
+                }
+            }
+        }
+    }
+
+
+    private static ColumnChunkMetaData findColumnChunk( BlockMetaData block, String physicalColumnName ) {
+        for ( ColumnChunkMetaData column : block.getColumns() ) {
+            if ( List.of( column.getPath().toArray() ).equals( List.of( physicalColumnName ) ) ) {
+                return column;
+            }
+        }
+        return null;
+    }
+
+
+    private static boolean chunkContainsOnlyPartitionValue( BlockMetaData block, ColumnChunkMetaData columnChunk, String partitionValue ) {
+        Statistics<?> statistics = columnChunk.getStatistics();
+        if ( statistics == null || !statistics.isNumNullsSet() || statistics.getNumNulls() != 0 || !statistics.hasNonNullValue() ) {
+            return false;
+        }
+        if ( columnChunk.getValueCount() != block.getRowCount() ) {
+            return false;
+        }
+        return partitionValue.equals( statisticValueAsString( statistics.genericGetMin() ) )
+                && partitionValue.equals( statisticValueAsString( statistics.genericGetMax() ) );
+    }
+
+
+    private static String statisticValueAsString( Object value ) {
+        if ( value instanceof Binary binary ) {
+            return binary.toStringUsingUTF8();
+        }
+        return String.valueOf( value );
+    }
+
+
+    /**
+     * Builds one DiscoveredTable from multiple compatible non-partitioned files.
+     */
+    private static DiscoveredTable discoverConsolidatedTable( String tableName, Map<String, List<ExportedColumn>> tables, List<DiscoveredSourceFile> sourceFiles ) {
+        Collection<ExportedColumn> columns = getConsolidatedExportedColumns( tables );
+        return new DiscoveredTable(
+                tableName,
+                List.copyOf( columns ),
+                new DiscoveredTableBinding(
+                        sourceFiles.stream().map( ParquetFileDiscovery::toSourceFile ).toList(),
+                        null,
+                        List.of(),
+                        columnPaths( columns ) ) );
+    }
+
+
+    private static boolean canExposeAsSingleTable( URL baseDir, Collection<List<ExportedColumn>> columnsSet ) {
+        if ( columnsSet.size() < 2 || Sources.of( baseDir ).file().isFile() ) {
+            return false;
+        }
+        return canConsolidateSchemas( columnsSet );
+    }
+
+    /**
+     * Checks whether multiple files are similar enough to be one logical table
+     */
+    private static boolean canConsolidateSchemas( Collection<List<ExportedColumn>> columnsSet ) {
+        List<Set<String>> columnSets = new ArrayList<>();
+        Map<String, PolyType> consolidatedTypes = new LinkedHashMap<>();
+
+        for ( List<ExportedColumn> columns : columnsSet ) {
+            Set<String> names = new HashSet<>();
+            for ( ExportedColumn column : columns ) {
+                PolyType existingType = consolidatedTypes.putIfAbsent( column.name(), column.type() );
+                if ( existingType != null && existingType != column.type() ) {
+                    return false;
+                }
+                names.add( column.name() );
+            }
+            columnSets.add( names );
+        }
+
+        return columnSets.size() == 1 || schemasAreSimilarEnough( columnSets );
+    }
+
+
+    private static boolean schemasAreSimilarEnough( List<Set<String>> fileColumnSets ) {
+        for ( int left = 0; left < fileColumnSets.size(); left++ ) {
+            for ( int right = left + 1; right < fileColumnSets.size(); right++ ) {
+                if ( schemaSimilarity( fileColumnSets.get( left ), fileColumnSets.get( right ) ) < MIN_SCHEMA_SIMILARITY ) {
+                    return false;
+                }
+            }
+        }
+        return true;
+    }
+
+
+    private static double schemaSimilarity( Set<String> left, Set<String> right ) {
+        Set<String> intersection = new HashSet<>( left );
+        intersection.retainAll( right );
+
+        Set<String> union = new HashSet<>( left );
+        union.addAll( right );
+
+        if ( union.isEmpty() ) {
+            return 0d;
+        }
+        return (double) intersection.size() / (double) union.size();
+    }
+
+
+    private static Collection<ExportedColumn> getConsolidatedExportedColumns( Map<String, List<ExportedColumn>> tables ) {
+        Map<String, ExportedColumn> columnsByName = new LinkedHashMap<>();
+        for ( List<ExportedColumn> columns : tables.values() ) {
+            for ( ExportedColumn column : columns ) {
+                columnsByName.putIfAbsent( column.name(), column );
+            }
+        }
+        return columnsByName.values();
+    }
+
+
+    private static Map<String, List<String>> columnPaths( Collection<ExportedColumn> columns ) {
+        return columns.stream().collect( LinkedHashMap::new, ( map, column ) -> map.put( column.name(), List.of( column.physicalColumnName() ) ), LinkedHashMap::putAll );
+    }
+
+
+    private static List<String> partitionColumnNames( List<DiscoveredSourceFile> sourceFiles ) {
+        Map<String, Boolean> names = new LinkedHashMap<>();
+        for ( DiscoveredSourceFile sourceFile : sourceFiles ) {
+            sourceFile.partitionValues().keySet().forEach( name -> names.putIfAbsent( name, true ) );
+        }
+        return List.copyOf( names.keySet() );
+    }
+
+
+    private static ExportedColumn partitionColumn( String tableName, String columnName, int position ) {
+        return new ExportedColumn(
+                columnName,
+                PolyType.VARCHAR,
+                null,
+                null,
+                null,
+                null,
+                null,
+                false,
+                tableName,
+                tableName,
+                columnName,
+                position,
+                false );
+    }
+
+
+    private static ParquetSourceFile toSourceFile( List<DiscoveredSourceFile> sourceFiles, String sourceKey ) {
+        return sourceFiles.stream()
+                .filter( sourceFile -> sourceFile.key().equals( sourceKey ) )
+                .findFirst()
+                .map( ParquetFileDiscovery::toSourceFile )
+                .orElseThrow( () -> new GenericRuntimeException( "Missing discovered Parquet source file for '%s'.", sourceKey ) );
+    }
+
+
+    private static ParquetSourceFile toSourceFile( DiscoveredSourceFile sourceFile ) {
+        return new ParquetSourceFile( sourceFile.url().toString(), sourceFile.partitionValues() );
+    }
+
+
+    private static String prefixedTableName( String prefix, String tableName ) {
+        return ParquetNameNormalizer.normalizeFieldName( prefix ) + "__" + tableName;
+    }
+
+
+    private static String tableNameFromSource( URL baseDir ) {
+        String path = Sources.of( baseDir ).file().getName();
+        if ( path.isBlank() ) {
+            return "parquet_table";
+        }
+        return ParquetNameNormalizer.computePhysicalTableName( path );
+    }
+
+
+    private static URL toUrl( File file ) {
+        try {
+            return ParquetUrlResolver.asSourceUrl( file.toURI().toURL() );
+        } catch ( MalformedURLException e ) {
+            throw new GenericRuntimeException( e );
+        }
+    }
+
+
+    private static ParquetSchemaReader schemaReader( URL sourceUrl ) {
+        return new ParquetSchemaReader( Sources.of( sourceUrl ) );
+    }
+
+
+    private record DiscoveredSourceFile(
+            String fileName,
+            URL url,
+            Map<String, String> partitionValues ) {
+
+        private DiscoveredSourceFile {
+            partitionValues = Collections.unmodifiableMap( new LinkedHashMap<>( partitionValues ) );
+        }
+
+
+        private String key() {
+            return url.toString();
+        }
+
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java
index 80160c93c1..41d2362b40 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java
@@ -17,6 +17,11 @@
 package org.polypheny.db.adapter.parquet.shared.io;
 
 import java.net.URI;
+import java.util.ArrayList;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import lombok.Getter;
 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.Path;
 import org.apache.parquet.hadoop.ParquetFileReader;
@@ -24,6 +29,10 @@
 import org.apache.parquet.hadoop.metadata.ParquetMetadata;
 import org.apache.parquet.hadoop.util.HadoopInputFile;
 import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
 import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.util.Source;
@@ -33,11 +42,24 @@
  */
 public class ParquetSchemaReader {
 
-    private final ParquetMetadata metadata;
+    private final List<Source> sources;
+    private final List<ParquetMetadata> metadata;
+    @Getter
+    private final MessageType schema;
 
 
     public ParquetSchemaReader( Source source ) {
-        metadata = readFooter( source );
+        this( List.of( source ) );
+    }
+
+
+    public ParquetSchemaReader( List<Source> sources ) {
+        if ( sources == null || sources.isEmpty() ) {
+            throw new GenericRuntimeException( "Cannot inspect parquet schema without source files." );
+        }
+        this.sources = sources;
+        this.metadata = sources.stream().map( ParquetSchemaReader::readFooter ).toList();
+        this.schema = consolidateSchema( metadata );
     }
 
 
@@ -56,17 +78,92 @@ private static ParquetMetadata readFooter( Source source ) {
     }
 
 
-    public MessageType getSchema() {
-        return metadata.getFileMetaData().getSchema();
+    private static MessageType consolidateSchema( List<ParquetMetadata> footers ) {
+        Map<String, Type> fields = new LinkedHashMap<>();
+        String schemaName = footers.get( 0 ).getFileMetaData().getSchema().getName();
+        for ( ParquetMetadata footer : footers ) {
+            for ( Type field : footer.getFileMetaData().getSchema().getFields() ) {
+                fields.putIfAbsent( field.getName(), field );
+            }
+        }
+        return new MessageType( schemaName, new ArrayList<>( fields.values() ) );
+    }
+
+
+    private static int[] buildProjectedFields( int[] projectedFields, int schemaFieldCount ) {
+        if ( projectedFields == null || projectedFields.length == 0 ) {
+            int[] allFields = new int[schemaFieldCount];
+            for ( int i = 0; i < schemaFieldCount; i++ ) {
+                allFields[i] = i;
+            }
+            return allFields;
+        }
+        return projectedFields;
     }
 
-    public ParquetMetadata getFooter() {
+
+    public List<ParquetMetadata> getFooters() {
         return metadata;
     }
 
 
     public long getEstimatedRowCount() {
-        return metadata.getBlocks().stream().mapToLong( BlockMetaData::getRowCount ).sum();
+        return metadata.stream()
+                .flatMap( footer -> footer.getBlocks().stream() )
+                .mapToLong( BlockMetaData::getRowCount )
+                .sum();
+    }
+
+
+    public List<ExportedColumn> exportedColumns( String tableName ) {
+        try {
+            ParquetTypeConverter typeConverter = new ParquetTypeConverter();
+            List<ExportedColumn> columns = new ArrayList<>();
+            List<Type> fields = getSchema().getFields();
+            for ( int i = 0; i < fields.size(); i++ ) {
+                Type field = fields.get( i );
+                columns.add(
+                        new ExportedColumn(
+                                ParquetNameNormalizer.normalizeFieldName( field.getName() ),
+                                typeConverter.fromParquetTypeToPolyType( field ),
+                                null,
+                                null,
+                                null,
+                                null,
+                                null,
+                                false,
+                                tableName,
+                                tableName,
+                                field.getName(),
+                                i,
+                                i == 0
+                        )
+                );
+            }
+            return columns;
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( e );
+        }
+    }
+
+
+    public MessageType buildProjectionSchema( int[] fields ) {
+        int[] projectedFields = buildProjectedFields( fields, schema.getFieldCount() );
+        if ( projectedFields.length == 0 ) {
+            return schema;
+        }
+
+        List<Type> types = new ArrayList<>( projectedFields.length );
+        for ( int index : projectedFields ) {
+            types.add( schema.getType( index ) );
+        }
+        return new MessageType( schema.getName(), types );
+    }
+
+
+    @Override
+    public String toString() {
+        return String.join( ", ", sources.stream().map( Source::path ).toList() );
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
index 2e20b585aa..1522550458 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
@@ -18,7 +18,6 @@
 
 import java.io.IOException;
 import java.net.URI;
-import java.util.ArrayList;
 import java.util.List;
 import java.util.concurrent.atomic.AtomicBoolean;
 import lombok.Getter;
@@ -35,7 +34,6 @@
 import org.apache.parquet.io.MessageColumnIO;
 import org.apache.parquet.io.RecordReader;
 import org.apache.parquet.schema.MessageType;
-import org.apache.parquet.schema.Type;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetNativeFilterBuilder;
 import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
@@ -54,6 +52,8 @@ public class ParquetSourceReader implements AutoCloseable {
     private final boolean useNativeFilter;
     @Getter
     private final MessageType projectionSchema;
+    @Getter
+    private final Source source;
 
     private RecordReader<Group> recordReader;
     private long rowCountInGroup;
@@ -68,6 +68,7 @@ public ParquetSourceReader( Source source ) {
 
 
     public ParquetSourceReader( Source source, AtomicBoolean cancelFlag, int[] fields, List<ParquetAdapterFilter> filters ) {
+        this.source = source;
         this.cancelFlag = cancelFlag == null ? new AtomicBoolean( false ) : cancelFlag;
 
         try {
@@ -75,11 +76,10 @@ public ParquetSourceReader( Source source, AtomicBoolean cancelFlag, int[] field
             Path path = new Path( uri );
             Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
 
-            MessageType schema = new ParquetSchemaReader( source ).getSchema();
+            ParquetSchemaReader schemaReader = new ParquetSchemaReader( source );
 
-            int[] projectedFields = buildProjectedFields( fields, schema.getFieldCount() );
-            this.projectionSchema = buildProjectionSchema( schema, projectedFields );
-            var recordFilter = ParquetNativeFilterBuilder.build( schema, filters == null ? List.of() : filters );
+            this.projectionSchema = schemaReader.buildProjectionSchema( fields );
+            var recordFilter = ParquetNativeFilterBuilder.build( schemaReader.getSchema(), filters == null ? List.of() : filters );
             this.useNativeFilter = FilterCompat.isFilteringRequired( recordFilter );
 
             ParquetReadOptions readOptions = ParquetReadOptions.builder()
@@ -99,31 +99,6 @@ public ParquetSourceReader( Source source, AtomicBoolean cancelFlag, int[] field
     }
 
 
-    private static int[] buildProjectedFields( int[] projectedFields, int schemaFieldCount ) {
-        if ( projectedFields == null || projectedFields.length == 0 ) {
-            int[] allFields = new int[schemaFieldCount];
-            for ( int i = 0; i < schemaFieldCount; i++ ) {
-                allFields[i] = i;
-            }
-            return allFields;
-        }
-        return projectedFields;
-    }
-
-
-    private static MessageType buildProjectionSchema( MessageType fullSchema, int[] projectedFieldIndexes ) {
-        if ( projectedFieldIndexes.length == 0 ) {
-            return fullSchema;
-        }
-
-        List<Type> fields = new ArrayList<>( projectedFieldIndexes.length );
-        for ( int index : projectedFieldIndexes ) {
-            fields.add( fullSchema.getType( index ) );
-        }
-        return new MessageType( fullSchema.getName(), fields );
-    }
-
-
     public Group next() {
         try {
             for ( ; ; ) {
@@ -145,7 +120,7 @@ public Group next() {
                 return group;
             }
         } catch ( Exception e ) {
-            throw new GenericRuntimeException( "Error while reading parquet data: " + e.getClass().getSimpleName() + ": " + e.getMessage(), e );
+            throw new GenericRuntimeException( "Error while reading parquet data", e );
         }
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
index 10f73b2a2e..13dd8ae999 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
@@ -1,16 +1,16 @@
 package org.polypheny.db.adapter.parquet.shared.schema;
 
-import java.net.MalformedURLException;
 import java.net.URL;
+import java.util.List;
 import org.jetbrains.annotations.Nullable;
 import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetUrlResolver;
 import org.polypheny.db.catalog.entity.physical.PhysicalCollection;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
-import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.plan.Convention;
 import org.polypheny.db.schema.Namespace;
 import org.polypheny.db.util.Source;
@@ -37,21 +37,14 @@ public ParquetNamespace( long id, long adapterId, URL directoryUrl ) {
 
     public ParquetTableBinding createRootBinding( PhysicalTable table ) {
         Source parquetSource = Sources.of( ParquetUrlResolver.resolveFile( directoryUrl, table.name + ".parquet" ) );
-        return ParquetTableBinding.createRootTableBinding( parquetSource.url().toString(), table );
+        return ParquetTableBinding.createRootTableBinding( List.of( ParquetSourceFile.of( parquetSource.url().toString() ) ), table );
     }
 
     /**
      * Creates a Parquet table wrapper for a physical table entry.
      */
     public ParquetRelTable createParquetTable( long id, PhysicalTable table, ParquetTableBinding binding, AbstractParquetSource sourceAdapter ) {
-        try {
-            // source file comes from the binding, not from the generated table name
-            Source parquetSource = Sources.of( new URL( binding.sourceUrl() ) );
-            return new ParquetRelTable( id, parquetSource, table, binding, sourceAdapter );
-
-        } catch ( MalformedURLException e ) {
-            throw new GenericRuntimeException( e );
-        }
+        return new ParquetRelTable( id, table, binding, sourceAdapter );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java
index 7146594f5f..42332e18ae 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java
@@ -16,14 +16,18 @@
 
 package org.polypheny.db.adapter.parquet.shared.statistics;
 
+import java.util.Comparator;
+import java.util.LinkedHashSet;
 import java.util.List;
 import java.util.Optional;
+import java.util.Set;
 import org.apache.parquet.column.statistics.Statistics;
 import org.apache.parquet.hadoop.metadata.BlockMetaData;
 import org.apache.parquet.hadoop.metadata.ColumnChunkMetaData;
 import org.apache.parquet.schema.PrimitiveType;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
@@ -32,6 +36,7 @@
 import org.polypheny.db.catalog.entity.logical.LogicalColumn;
 import org.polypheny.db.type.PolyType;
 import org.polypheny.db.type.PolyTypeFamily;
+import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
 
 /**
@@ -70,30 +75,57 @@ public Optional<ProvidedEntityStatistics> getEntityStatistics( boolean nestedTab
     public Optional<ProvidedColumnStatistics> getColumnStatistics( LogicalColumn column, int uniqueValueLimit ) {
         ParquetColumnBinding columnBinding = binding.getColumnBinding( column.id );
         if ( columnBinding == null ) {
-            return Optional.of( new ProvidedColumnStatistics( estimateNestedRowCount(), null, null, List.of(), true ) );
+            return Optional.of( new ProvidedColumnStatistics( estimateEntityRowCount(), null, null, List.of(), true ) );
+        }
+        if ( columnBinding.role() == ParquetColumnRole.PARTITION ) {
+            return Optional.of( partitionColumnStatistics( columnBinding, uniqueValueLimit ) );
         }
         if ( columnBinding.role() != ParquetColumnRole.DATA || columnBinding.sourcePathElements().isEmpty() ) {
-            return Optional.of( new ProvidedColumnStatistics( estimateNestedRowCount(), null, null, List.of(), true ) );
+            return Optional.of( new ProvidedColumnStatistics( estimateEntityRowCount(), null, null, List.of(), true ) );
         }
 
         PrimitiveType primitiveType;
         try {
             primitiveType = schemaReader.getSchema().getType( columnBinding.sourcePathElements().toArray( new String[0] ) ).asPrimitiveType();
         } catch ( RuntimeException e ) {
-            return Optional.of( new ProvidedColumnStatistics( estimateNestedRowCount(), null, null, List.of(), true ) );
+            return Optional.of( new ProvidedColumnStatistics( estimateEntityRowCount(), null, null, List.of(), true ) );
         }
 
         ColumnMetadataStatistics metadataStatistics = readColumnMetadataStatistics( columnBinding.sourcePathElements() );
-        if ( metadataStatistics == null ) {
-            return Optional.of( new ProvidedColumnStatistics( estimateNestedRowCount(), null, null, List.of(), true ) );
-        }
-
         PolyValue min = toStatisticValue( column.type, primitiveType, metadataStatistics.min() );
         PolyValue max = toStatisticValue( column.type, primitiveType, metadataStatistics.max() );
         return Optional.of( new ProvidedColumnStatistics( metadataStatistics.count(), min, max, List.of(), true ) );
     }
 
 
+    ProvidedColumnStatistics partitionColumnStatistics( ParquetColumnBinding columnBinding, int uniqueValueLimit ) {
+        Set<String> values = new LinkedHashSet<>();
+        for ( ParquetSourceFile sourceFile : binding.sourceFiles() ) {
+            String value = sourceFile.partitionValues().get( columnBinding.columnName() );
+            if ( value != null ) {
+                values.add( value );
+            }
+        }
+
+        if ( uniqueValueLimit > 0 && values.size() > uniqueValueLimit ) {
+            return new ProvidedColumnStatistics( estimateEntityRowCount(), null, null, List.of(), true );
+        }
+
+        List<PolyValue> uniqueValues = values.stream()
+                .map( PolyString::of )
+                .map( PolyValue.class::cast )
+                .toList();
+        PolyValue min = values.stream().min( Comparator.naturalOrder() ).map( PolyString::of ).orElse( null );
+        PolyValue max = values.stream().max( Comparator.naturalOrder() ).map( PolyString::of ).orElse( null );
+        return new ProvidedColumnStatistics( estimateEntityRowCount(), min, max, uniqueValues, false );
+    }
+
+
+    private long estimateEntityRowCount() {
+        return binding.parentTableName() == null ? schemaReader.getEstimatedRowCount() : estimateNestedRowCount();
+    }
+
+
     /**
      * Estimates how many rows a normalized nested table will produce, using Parquet metadata only.
      * @return estimated Nested Row Count
@@ -108,19 +140,16 @@ private long estimateNestedRowCount() {
     }
 
 
-    /**
-     * Estimates how many values exist for one Parquet leaf path across the whole file
-     * @param sourcePathElements - path
-     * @return values count
-     */
     private long estimateValueCount( List<String> sourcePathElements ) {
         long valueCount = 0;
-        for ( BlockMetaData block : schemaReader.getFooter().getBlocks() ) {
-            ColumnChunkMetaData column = findColumnChunk( block, sourcePathElements );
-            if ( column == null ) {
-                return 0;
+        for ( var footer : schemaReader.getFooters() ) {
+            for ( BlockMetaData block : footer.getBlocks() ) {
+                ColumnChunkMetaData column = findColumnChunk( block, sourcePathElements );
+                if ( column == null ) {
+                    continue;
+                }
+                valueCount += column.getValueCount();
             }
-            valueCount += column.getValueCount();
         }
         return valueCount;
     }
@@ -132,8 +161,6 @@ private long estimateValueCount( List<String> sourcePathElements ) {
      * @return column metadata statistics
      */
     private ColumnMetadataStatistics readColumnMetadataStatistics( List<String> sourcePathElements ) {
-        var footer = schemaReader.getFooter();
-
         long rowCount = 0;
         long nullCount = 0;
         boolean hasReliableNullCount = true;
@@ -141,33 +168,36 @@ private ColumnMetadataStatistics readColumnMetadataStatistics( List<String> sour
         Object max = null;
         boolean hasMinMax = true;
 
-        for ( BlockMetaData block : footer.getBlocks() ) {
-            rowCount += block.getRowCount();
-            ColumnChunkMetaData column = findColumnChunk( block, sourcePathElements );
-            if ( column == null ) {
-                return null;
-            }
-
-            Statistics<?> statistics = column.getStatistics();
-            if ( statistics == null ) {
-                hasReliableNullCount = false;
-                hasMinMax = false;
-                continue;
-            }
-
-            if ( statistics.isNumNullsSet() ) {
-                nullCount += statistics.getNumNulls();
-            } else {
-                hasReliableNullCount = false;
-            }
-
-            if ( statistics.hasNonNullValue() ) {
-                Object currentMin = statistics.genericGetMin();
-                Object currentMax = statistics.genericGetMax();
-                min = lower( min, currentMin );
-                max = higher( max, currentMax );
-            } else if ( !statistics.isNumNullsSet() || statistics.getNumNulls() != block.getRowCount() ) {
-                hasMinMax = false;
+        for ( var footer : schemaReader.getFooters() ) {
+            for ( BlockMetaData block : footer.getBlocks() ) {
+                rowCount += block.getRowCount();
+                ColumnChunkMetaData column = findColumnChunk( block, sourcePathElements );
+                if ( column == null ) {
+                    nullCount += block.getRowCount();
+                    continue;
+                }
+
+                Statistics<?> statistics = column.getStatistics();
+                if ( statistics == null ) {
+                    hasReliableNullCount = false;
+                    hasMinMax = false;
+                    continue;
+                }
+
+                if ( statistics.isNumNullsSet() ) {
+                    nullCount += statistics.getNumNulls();
+                } else {
+                    hasReliableNullCount = false;
+                }
+
+                if ( statistics.hasNonNullValue() ) {
+                    Object currentMin = statistics.genericGetMin();
+                    Object currentMax = statistics.genericGetMax();
+                    min = lower( min, currentMin );
+                    max = higher( max, currentMax );
+                } else if ( !statistics.isNumNullsSet() || statistics.getNumNulls() != block.getRowCount() ) {
+                    hasMinMax = false;
+                }
             }
         }
 
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java
new file mode 100644
index 0000000000..3575f125a1
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java
@@ -0,0 +1,291 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.io.IOException;
+import java.io.OutputStream;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.List;
+import java.util.Map;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.ExampleParquetWriter;
+import org.apache.parquet.io.OutputFile;
+import org.apache.parquet.io.PositionOutputStream;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.Types;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.parquet.relational.schema.DiscoveredTable;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSchemaNormalizer;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSyntheticColumns;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetFileDiscovery;
+
+
+class ParquetFileDiscoveryTest {
+
+    private static final String PREFIX = "parquet_test";
+
+
+    @TempDir
+    Path tempDir;
+
+
+    @Test
+    void groupsSchemaEvolvedFilesIntoOneTable() throws Exception {
+        writeParquet( tempDir.resolve( "orders-part-000.parquet" ), schema( "id", "customer_id", "amount", "status" ), row( 1L, 10L, 100L, 1L ) );
+        writeParquet( tempDir.resolve( "orders-part-001.parquet" ), schema( "id", "customer_id", "amount", "status", "discount" ), row( 2L, 20L, 200L, 1L, 3L ) );
+
+        Map<String, DiscoveredTable> tables = ParquetFileDiscovery.discoverTables( tempDir.toUri().toURL(), PREFIX );
+
+        assertEquals( 1, tables.size() );
+        DiscoveredTable table = tables.values().iterator().next();
+        assertEquals( 2, table.binding().sourceFiles().size() );
+        assertEquals( List.of( "id", "customer_id", "amount", "status", "discount" ), columnNames( table.columns() ) );
+    }
+
+
+    @Test
+    void keepsUnrelatedFilesAsSeparateTables() throws Exception {
+        writeParquet( tempDir.resolve( "customers.parquet" ), schema( "id", "name" ), row( 1L, "Alice" ) );
+        writeParquet( tempDir.resolve( "orders.parquet" ), schema( "id", "user_id", "amount" ), row( 10L, 1L, 99L ) );
+
+        Map<String, DiscoveredTable> tables = ParquetFileDiscovery.discoverTables( tempDir.toUri().toURL(), PREFIX );
+
+        assertEquals( 2, tables.size() );
+        assertTrue( tables.containsKey( PREFIX + "__customers" ) );
+        assertTrue( tables.containsKey( PREFIX + "__orders" ) );
+        assertEquals( 1, tables.get( PREFIX + "__customers" ).binding().sourceFiles().size() );
+        assertEquals( 1, tables.get( PREFIX + "__orders" ).binding().sourceFiles().size() );
+    }
+
+
+    @Test
+    void discoversPartitionFoldersAsOneTable() throws Exception {
+        Path january = tempDir.resolve( "year=2025" ).resolve( "month=01" );
+        Path february = tempDir.resolve( "year=2025" ).resolve( "month=02" );
+        Files.createDirectories( january );
+        Files.createDirectories( february );
+        writeParquet( january.resolve( "part-000.parquet" ), schema( "id", "customer_id", "amount", "status" ), row( 1L, 10L, 100L, 1L ) );
+        writeParquet( february.resolve( "part-000.parquet" ), schema( "id", "customer_id", "amount", "status", "discount" ), row( 2L, 20L, 200L, 1L, 3L ) );
+
+        Map<String, DiscoveredTable> tables = ParquetFileDiscovery.discoverTables( tempDir.toUri().toURL(), PREFIX );
+
+        assertEquals( 1, tables.size() );
+        DiscoveredTable table = tables.values().iterator().next();
+        assertEquals( List.of( "id", "customer_id", "amount", "status", "discount", "year", "month" ), columnNames( table.columns() ) );
+        assertEquals( 2, table.binding().sourceFiles().size() );
+        assertTrue( table.binding().sourceFiles().stream().allMatch( sourceFile -> sourceFile.partitionValues().get( "year" ).equals( "2025" ) ) );
+        assertEquals( List.of( "01", "02" ), table.binding().sourceFiles().stream().map( sourceFile -> sourceFile.partitionValues().get( "month" ) ).toList() );
+    }
+
+
+    @Test
+    void discoversPartitionedTableFoldersUnderRoot() throws Exception {
+        Path ordersEu = tempDir.resolve( "orders" ).resolve( "region=eu" );
+        Path ordersUs = tempDir.resolve( "orders" ).resolve( "region=us" );
+        Path customers = tempDir.resolve( "customers" );
+        Files.createDirectories( ordersEu );
+        Files.createDirectories( ordersUs );
+        Files.createDirectories( customers );
+        writeParquet( ordersEu.resolve( "part-000.parquet" ), schema( "id", "amount" ), row( 1L, 10L ) );
+        writeParquet( ordersUs.resolve( "part-000.parquet" ), schema( "id", "amount" ), row( 2L, 20L ) );
+        writeParquet( customers.resolve( "customers.parquet" ), schema( "id", "name" ), row( 1L, "Alice" ) );
+
+        Map<String, DiscoveredTable> tables = ParquetFileDiscovery.discoverTables( tempDir.toUri().toURL(), PREFIX );
+
+        assertEquals( 2, tables.size() );
+        assertTrue( tables.containsKey( PREFIX + "__orders" ) );
+        assertTrue( tables.containsKey( PREFIX + "__customers" ) );
+        assertEquals( List.of( "id", "amount", "region" ), columnNames( tables.get( PREFIX + "__orders" ).columns() ) );
+        assertEquals( 2, tables.get( PREFIX + "__orders" ).binding().sourceFiles().size() );
+    }
+
+
+    @Test
+    void hidesParquetColumnWhenItMatchesPartitionValue() throws Exception {
+        Path partition = tempDir.resolve( "year=2025" );
+        Files.createDirectories( partition );
+        writeParquet( partition.resolve( "part-000.parquet" ), schema( "id", "year", "amount" ), row( 1L, 2025L, 10L ) );
+
+        Map<String, DiscoveredTable> tables = ParquetFileDiscovery.discoverTables( tempDir.toUri().toURL(), PREFIX );
+
+        DiscoveredTable table = tables.values().iterator().next();
+        assertEquals( List.of( "id", "amount", "year" ), columnNames( table.columns() ) );
+        assertEquals( Map.of( "id", List.of( "id" ), "amount", List.of( "amount" ) ), table.binding().columnPaths() );
+    }
+
+
+    @Test
+    void failsWhenParquetColumnConflictsWithPartitionValue() throws Exception {
+        Path partition = tempDir.resolve( "year=2025" );
+        Files.createDirectories( partition );
+        writeParquet( partition.resolve( "part-000.parquet" ), schema( "id", "year", "amount" ), row( 1L, 2024L, 10L ) );
+
+        assertThrows( RuntimeException.class, () -> ParquetFileDiscovery.discoverTables( tempDir.toUri().toURL(), PREFIX ) );
+    }
+
+
+    @Test
+    void normalizedSchemaUsesDiscoveredMultiFileTable() throws Exception {
+        Path january = tempDir.resolve( "year=2025" ).resolve( "month=01" );
+        Path february = tempDir.resolve( "year=2025" ).resolve( "month=02" );
+        Files.createDirectories( january );
+        Files.createDirectories( february );
+        writeParquet( january.resolve( "part-000.parquet" ), schema( "id", "customer_id", "amount", "status" ), row( 1L, 10L, 100L, 1L ) );
+        writeParquet( february.resolve( "part-000.parquet" ), schema( "id", "customer_id", "amount", "status", "discount" ), row( 2L, 20L, 200L, 1L, 3L ) );
+
+        var normalizedSchema = new ParquetSchemaNormalizer( tempDir.toUri().toURL(), PREFIX ).normalize();
+
+        assertEquals( 1, normalizedSchema.getTables().size() );
+        String tableName = normalizedSchema.getTables().keySet().iterator().next();
+        assertEquals( List.of( ParquetSyntheticColumns.ROW_ID, "id", "customer_id", "amount", "status", "discount", "year", "month" ), columnNames( normalizedSchema.getTables().get( tableName ) ) );
+        assertEquals( 2, normalizedSchema.getBinding( tableName ).sourceFiles().size() );
+    }
+
+
+    private static List<String> columnNames( List<ExportedColumn> columns ) {
+        return columns.stream().map( ExportedColumn::name ).toList();
+    }
+
+
+    private static Object[] row( Object... values ) {
+        return values;
+    }
+
+
+    private static MessageType schema( String... columns ) {
+        Types.MessageTypeBuilder builder = Types.buildMessage();
+        for ( String column : columns ) {
+            if ( column.equals( "name" ) ) {
+                builder.required( org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName.BINARY )
+                        .as( org.apache.parquet.schema.LogicalTypeAnnotation.stringType() )
+                        .named( column );
+            } else {
+                builder.required( org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName.INT64 ).named( column );
+            }
+        }
+        return builder.named( "test" );
+    }
+
+
+    private static void writeParquet( Path file, MessageType schema, Object[] values ) throws Exception {
+        SimpleGroupFactory groupFactory = new SimpleGroupFactory( schema );
+        Group group = groupFactory.newGroup();
+        for ( int i = 0; i < values.length; i++ ) {
+            Object value = values[i];
+            if ( value instanceof String string ) {
+                group.add( i, string );
+            } else {
+                group.add( i, ((Number) value).longValue() );
+            }
+        }
+
+        try ( ParquetWriter<Group> writer = ExampleParquetWriter.builder( new LocalOutputFile( file ) )
+                .withType( schema )
+                .build() ) {
+            writer.write( group );
+        }
+    }
+
+
+    private record LocalOutputFile( Path path ) implements OutputFile {
+
+        @Override
+        public PositionOutputStream create( long blockSizeHint ) throws IOException {
+            return createOrOverwrite( blockSizeHint );
+        }
+
+
+        @Override
+        public PositionOutputStream createOrOverwrite( long blockSizeHint ) throws IOException {
+            return new LocalPositionOutputStream( Files.newOutputStream( path ) );
+        }
+
+
+        @Override
+        public boolean supportsBlockSize() {
+            return false;
+        }
+
+
+        @Override
+        public long defaultBlockSize() {
+            return 0;
+        }
+
+
+        @Override
+        public String getPath() {
+            return path.toString();
+        }
+
+    }
+
+
+    private static class LocalPositionOutputStream extends PositionOutputStream {
+
+        private final OutputStream out;
+        private long position;
+
+
+        private LocalPositionOutputStream( OutputStream out ) {
+            this.out = out;
+        }
+
+
+        @Override
+        public long getPos() {
+            return position;
+        }
+
+
+        @Override
+        public void write( int b ) throws IOException {
+            out.write( b );
+            position++;
+        }
+
+
+        @Override
+        public void write( byte[] b, int off, int len ) throws IOException {
+            out.write( b, off, len );
+            position += len;
+        }
+
+
+        @Override
+        public void flush() throws IOException {
+            out.flush();
+        }
+
+
+        @Override
+        public void close() throws IOException {
+            out.close();
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
index ab79c77841..7905252155 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
@@ -22,7 +22,6 @@
 import org.polypheny.db.TestHelper;
 import org.polypheny.db.TestHelper.JdbcConnection;
 import org.polypheny.db.util.Sources;
-import org.polypheny.jdbc.types.PolyDocument;
 import java.sql.Connection;
 import java.sql.ResultSet;
 import java.sql.SQLException;
@@ -48,15 +47,15 @@ static void start() throws SQLException {
         String parquetDir = Sources.of( ParquetPluginTest.class.getResource( "/orders_db/" ) )
                 .file()
                 .getAbsolutePath()
-                .replace( "\\", "/" );
+                .replace( "\\", "\\\\" );
 
         // open a JDBC connection to the running Polypheny test server
         try ( JdbcConnection jdbcConnection = new JdbcConnection( false ) ) {
             Connection connection = jdbcConnection.getConnection();
             // creates the Parquet source adapter and imports the parquet files as source tables
              try ( Statement statement = connection.createStatement() ) {
-                String sql = "ALTER ADAPTERS ADD \"" + SOURCE_NAME + "\" USING 'Parquet Relational' AS 'SOURCE' "
-                        + "WITH '{\"method\":\"link\",\"directory\":\"classpath://orders_db\",\"directoryName\":\"" + parquetDir + "\",\"url\":\"\",\"schema mode\":\"flat\"}'";
+                String sql = "ALTER ADAPTERS ADD \"" + SOURCE_NAME + "\" USING 'Parquet' AS 'SOURCE' "
+                        + "WITH '{method:\"link\",directory:\"classpath://orders_db\",directoryName:\"" + parquetDir + "\"}'";
 
                 System.out.println( sql );
                 statement.executeUpdate( sql );
@@ -100,7 +99,7 @@ void parquetSourceIsReadOnly() throws SQLException {
         try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
             Connection connection = jdbcConnection.getConnection();
             try ( Statement statement = connection.createStatement() ) {
-                assertThrows( SQLException.class, () -> statement.executeUpdate( "DELETE FROM " + table( "customers" ) ) );
+                assertThrows( SQLException.class, () -> statement.executeUpdate( "DELETE FROM customers" ) );
             }
         }
     }
@@ -109,7 +108,7 @@ private void assertTableHasRows( String tableName ) throws SQLException {
         try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
             Connection connection = jdbcConnection.getConnection();
             try ( Statement statement = connection.createStatement();
-                    ResultSet resultSet = statement.executeQuery( "SELECT COUNT(*) FROM " + table( tableName ) ) ) {
+                    ResultSet resultSet = statement.executeQuery( "SELECT COUNT(*) FROM " + tableName ) ) {
 
                 assertTrue( resultSet.next(), "COUNT query returned no row for table " + tableName );
                 assertTrue( resultSet.getLong( 1 ) > 0, "Expected table " + tableName + " to contain rows" );
@@ -125,7 +124,7 @@ void readsExpectedRowsFromCustomers() throws SQLException {
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name, email, country, signup_date "
-                                    + "FROM " + table( "customers" ) + " "
+                                    + "FROM customers "
                                     + "WHERE customer_id <= 3 "
                                     + "ORDER BY customer_id" ) ) {
 
@@ -148,7 +147,7 @@ void filtersRowsWithWhereClause() throws SQLException {
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name, email, country, signup_date "
-                                    + "FROM " + table( "customers" ) + " "
+                                    + "FROM customers "
                                     + "WHERE country = 'France' "
                                     + "ORDER BY customer_id "
                                     + "LIMIT 3" ) ) {
@@ -172,7 +171,7 @@ void projectsOnlyRequestedColumns() throws SQLException {
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name "
-                                    + "FROM " + table( "customers" ) + " "
+                                    + "FROM customers "
                                     + "WHERE customer_id <= 3 "
                                     + "ORDER BY customer_id" ) ) {
 
@@ -195,7 +194,7 @@ void supportsGreaterThanFilter() throws SQLException {
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name "
-                                    + "FROM " + table( "customers" ) + " "
+                                    + "FROM customers "
                                     + "WHERE customer_id > 3 "
                                     + "ORDER BY customer_id "
                                     + "LIMIT 3" ) ) {
@@ -216,7 +215,7 @@ void supportsGreaterThanFilter() throws SQLException {
     void supportsDateFilterLiterals() throws SQLException {
         assertQueryResult(
                 "SELECT customer_id, name "
-                        + "FROM " + table( "customers" ) + " "
+                        + "FROM customers "
                         + "WHERE signup_date = TIMESTAMP '2023-02-04 00:00:00' "
                         + "AND customer_id = 3 "
                         + "ORDER BY customer_id",
@@ -287,7 +286,7 @@ private void assertCustomerFilterResult( String filter, ImmutableList<Object[]>
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name "
-                                    + "FROM " + table( "customers" ) + " "
+                                    + "FROM customers "
                                     + "WHERE " + filter + " "
                                     + "ORDER BY customer_id "
                                     + "LIMIT 3" ) ) {
@@ -306,7 +305,7 @@ void rejectsUpdateOnParquetSource() throws SQLException {
                 assertThrows(
                         SQLException.class,
                         () -> statement.executeUpdate(
-                                "UPDATE " + table( "customers" ) + " "
+                                "UPDATE customers "
                                         + "SET country = 'Switzerland' "
                                         + "WHERE customer_id = 1" )
                 );
@@ -330,7 +329,7 @@ private void assertQueryResult( String sql, ImmutableList<Object[]> expected ) t
     void projectsAndFiltersOtherTables() throws SQLException {
         assertQueryResult(
                 "SELECT order_id, status, total_price "
-                        + "FROM " + table( "orders" ) + " "
+                        + "FROM orders "
                         + "WHERE order_id <= 3 "
                         + "ORDER BY order_id",
                 ImmutableList.of(
@@ -342,7 +341,7 @@ void projectsAndFiltersOtherTables() throws SQLException {
 
         assertQueryResult(
                 "SELECT order_item_id, product_id, quantity "
-                        + "FROM " + table( "order_items" ) + " "
+                        + "FROM order_items "
                         + "WHERE order_id = 1 "
                         + "ORDER BY order_item_id "
                         + "LIMIT 3",
@@ -355,7 +354,7 @@ void projectsAndFiltersOtherTables() throws SQLException {
 
         assertQueryResult(
                 "SELECT product_id, name, category "
-                        + "FROM " + table( "products" ) + " "
+                        + "FROM products "
                         + "WHERE product_id <= 3 "
                         + "ORDER BY product_id",
                 ImmutableList.of(
@@ -366,35 +365,30 @@ void projectsAndFiltersOtherTables() throws SQLException {
         );
     }
 
-    // check that nested data is returned as document
+    // check that nested data returned as string
     @Test
-    void returnsNestedShippingAddressAsDocument() throws SQLException {
+    void returnsNestedShippingAddressAsJSON() throws SQLException {
         try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
             Connection connection = jdbcConnection.getConnection();
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT shipping_address "
-                                    + "FROM " + table( "orders" ) + " "
+                                    + "FROM orders "
                                     + "WHERE order_id = 1" ) ) {
 
                 assertTrue( resultSet.next() );
-                assertEquals( "DOCUMENT", resultSet.getMetaData().getColumnTypeName( 1 ) );
 
                 Object value = resultSet.getObject( 1 );
-                PolyDocument shippingAddress = assertInstanceOf( PolyDocument.class, value );
+                String shippingAddress = assertInstanceOf( String.class, value );
 
-                assertEquals( 3, shippingAddress.size() );
-                assertEquals( "Lucerne", shippingAddress.get( "city" ).asString() );
-                assertEquals( "Gartenweg 103", shippingAddress.get( "street" ).asString() );
-                assertEquals( 7101, shippingAddress.get( "zip" ).asInt() );
+                assertEquals(
+                        "{\"city\":\"Lucerne\",\"street\":\"Gartenweg 103\",\"zip\":7101}",
+                        shippingAddress
+                );
             }
         }
     }
 
 
-    private static String table( String tableName ) {
-        return SOURCE_NAME + "__" + tableName;
-    }
-
 
 }

From 96bfe999b92ca8153c8a78670a26befad94ad0c6 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Sat, 9 May 2026 13:01:33 +0200
Subject: [PATCH 26/46] fix adapter filters usage

---
 .../document/schema/ParquetDocument.java      |  2 +-
 .../relational/schema/ParquetRelTable.java    |  6 +-
 .../shared/filter/FiltersContainer.java       | 36 +++++++++--
 .../shared/filter/JoinFiltersSplitter.java    |  3 +-
 .../adapter/parquet/FiltersContainerTest.java | 45 +++++++++++++
 .../parquet/JoinFiltersSplitterTest.java      | 63 +++++++++++++++++++
 6 files changed, 143 insertions(+), 12 deletions(-)
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/FiltersContainerTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/JoinFiltersSplitterTest.java

diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
index 7cd6611b83..c3e0d1bdba 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
@@ -113,7 +113,7 @@ public Enumerable<PolyValue[]> scanFiltered( DataContext dataContext, List<Parqu
             @Override
             public Enumerator<PolyValue[]> enumerator() {
                 FiltersContainer filtersContainer = FiltersContainer.shared( resolvedFilters );
-                ParquetSourceReader reader = new ParquetSourceReader( source, cancelFlag, null, filtersContainer.readerFilters() );
+                ParquetSourceReader reader = new ParquetSourceReader( source, cancelFlag, null, filtersContainer.nativeFilters() );
                 return new ParquetDocEnumerator( reader, filtersContainer );
             }
         };
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index 8562f293d5..ffc226c6c2 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -350,14 +350,14 @@ private boolean isNestedTable() {
     private Enumerator<PolyValue[]> enumeratorForFile( ParquetSourceFile sourceFile, int[] fields, AtomicBoolean cancelFlag, FiltersContainer filtersContainer ) {
         boolean bindingScan = isNestedTable() || needsBindingScan( fields ) || binding.sourceFiles().size() > 1;
         Source fileSource = sourceFile.asSource();
-        ParquetSourceReader reader = new ParquetSourceReader( fileSource, cancelFlag, bindingScan ? null : fields, filtersContainer.readerFilters() );
+        ParquetSourceReader reader = new ParquetSourceReader( fileSource, cancelFlag, bindingScan ? null : fields, filtersContainer.nativeFilters() );
         if ( isNestedTable() ) {
             return new ParquetNestedRepeatedRelEnumerator( reader, binding, projectedBindings( fields ), filtersContainer );
         }
         if ( bindingScan ) {
             return new ParquetNestedNonRepeatedRelEnumerator( reader, sourceFile, projectedBindings( fields ), filtersContainer );
         }
-        return new ParquetRelEnumerator( reader, filtersContainer );
+        return new ParquetRelEnumerator( reader, filtersContainer.withoutPathElementsInAdapterFilters() );
     }
 
 
@@ -371,7 +371,7 @@ private Enumerator<PolyValue[]> nestedJoinEnumeratorForFile(
             boolean leftIsParent,
             boolean emitUnmatchedParents ) {
 
-        ParquetSourceReader reader = new ParquetSourceReader( sourceFile.asSource(), cancelFlag, null, filterContainer.readerFilters() );
+        ParquetSourceReader reader = new ParquetSourceReader( sourceFile.asSource(), cancelFlag, null, filterContainer.nativeFilters() );
         return new ParquetNestedJoinEnumerator(
                 reader,
                 binding,
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FiltersContainer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FiltersContainer.java
index 80b30fff6d..b55a6342c6 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FiltersContainer.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FiltersContainer.java
@@ -26,18 +26,18 @@ public class FiltersContainer {
     public static FiltersContainer empty = new FiltersContainer( List.of(), List.of() );
 
     private final List<ParquetAdapterFilter> adapterFilters;
-    private final List<ParquetAdapterFilter> readerFilters;
+    private final List<ParquetAdapterFilter> nativeFilters;
 
 
     /**
      * Creates a new instance of {@link FiltersContainer}
      *
      * @param adapterFilters a list of filters to be used on adapter level.
-     * @param readerFilters a list of filters to be used on parquet reader level.
+     * @param nativeFilters a list of filters to be used on parquet reader level.
      */
-    public FiltersContainer( List<ParquetAdapterFilter> adapterFilters, List<ParquetAdapterFilter> readerFilters ) {
+    public FiltersContainer( List<ParquetAdapterFilter> adapterFilters, List<ParquetAdapterFilter> nativeFilters ) {
         this.adapterFilters = adapterFilters == null ? List.of() : List.copyOf( adapterFilters );
-        this.readerFilters = readerFilters == null ? List.of() : List.copyOf( readerFilters );
+        this.nativeFilters = nativeFilters == null ? List.of() : List.copyOf( nativeFilters );
     }
 
 
@@ -52,6 +52,30 @@ public static FiltersContainer shared( List<ParquetAdapterFilter> filters ) {
     }
 
 
+    /**
+     * Keeps the same filters, but removes path elements from adapter-level filters.
+     * This is useful for simple projected scans where row validation must use projected column indexes,
+     * while reader filters still keep paths for native Parquet pushdown.
+     *
+     * @return a new instance of {@link FiltersContainer}
+     */
+    public FiltersContainer withoutPathElementsInAdapterFilters() {
+        return new FiltersContainer(
+                adapterFilters.stream().map( FiltersContainer::withoutPathElements ).toList(),
+                nativeFilters );
+    }
+
+
+    private static ParquetAdapterFilter withoutPathElements( ParquetAdapterFilter filter ) {
+        if ( filter.isLogical() ) {
+            return ParquetAdapterFilter.logical(
+                    filter.operator(),
+                    filter.operands().stream().map( FiltersContainer::withoutPathElements ).toList() );
+        }
+        return new ParquetAdapterFilter( filter.columnIndex(), List.of(), filter.operator(), filter.polyValue(), filter.dynamicParamIndex() );
+    }
+
+
     /**
      * Gets a list of adapter filters.
      *
@@ -67,8 +91,8 @@ public List<ParquetAdapterFilter> adapterFilters() {
      *
      * @return a list of reader filters.
      */
-    public List<ParquetAdapterFilter> readerFilters() {
-        return readerFilters;
+    public List<ParquetAdapterFilter> nativeFilters() {
+        return nativeFilters;
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersSplitter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersSplitter.java
index 6e72d58c27..fc89f8264a 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersSplitter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersSplitter.java
@@ -82,9 +82,8 @@ private void splitFilter( ParquetAdapterFilter filter, boolean leftIsParent, int
             case PARENT -> {
                 if ( canUseAsReaderFilter( filter ) ) {
                     readerFilters.add( filter );
-                } else {
-                    parentFilters.add( filter );
                 }
+                parentFilters.add( filter );
             }
             case CHILD -> childFilters.add( filter );
             case ADAPTER -> adapterFilters.add( filter );
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/FiltersContainerTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/FiltersContainerTest.java
new file mode 100644
index 0000000000..b6b3252429
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/FiltersContainerTest.java
@@ -0,0 +1,45 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+
+import java.util.List;
+import org.junit.jupiter.api.Test;
+import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.constant.Kind;
+
+
+class FiltersContainerTest {
+
+    @Test
+    void removesOnlyAdapterFilterPaths() {
+        ParquetAdapterFilter leaf = new ParquetAdapterFilter( 2, List.of( "fare_amount" ), Kind.GREATER_THAN, null, 7L );
+        ParquetAdapterFilter logical = ParquetAdapterFilter.logical( Kind.AND, List.of( leaf ) );
+        FiltersContainer container = new FiltersContainer( List.of( logical ), List.of( leaf ) );
+
+        FiltersContainer converted = container.withoutPathElementsInAdapterFilters();
+
+        ParquetAdapterFilter convertedLeaf = converted.adapterFilters().get( 0 ).operands().get( 0 );
+        assertEquals( 2, convertedLeaf.columnIndex() );
+        assertEquals( List.of(), convertedLeaf.pathElements() );
+        assertEquals( 7L, convertedLeaf.dynamicParamIndex() );
+        assertEquals( List.of( "fare_amount" ), converted.nativeFilters().get( 0 ).pathElements() );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/JoinFiltersSplitterTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/JoinFiltersSplitterTest.java
new file mode 100644
index 0000000000..fd8674aa4f
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/JoinFiltersSplitterTest.java
@@ -0,0 +1,63 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertSame;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.util.List;
+import org.junit.jupiter.api.Test;
+import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersSplitter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.constant.Kind;
+
+
+class JoinFiltersSplitterTest {
+
+    @Test
+    void keepsReaderParentFilterForAdapterValidation() {
+        ParquetAdapterFilter parentFilter = new ParquetAdapterFilter( 0, List.of( "amount" ), Kind.GREATER_THAN, null );
+
+        JoinFiltersContainer split = new JoinFiltersSplitter().split( List.of( parentFilter ), true, 2, 1 );
+
+        assertEquals( 1, split.nativeFilters().size() );
+        assertEquals( 1, split.parentFilters().size() );
+        assertSame( parentFilter, split.nativeFilters().get( 0 ) );
+        assertSame( parentFilter, split.parentFilters().get( 0 ) );
+        assertTrue( split.childFilters().isEmpty() );
+        assertTrue( split.adapterFilters().isEmpty() );
+    }
+
+
+    @Test
+    void keepsCrossSideOrAsJoinedRowFilter() {
+        ParquetAdapterFilter parentFilter = new ParquetAdapterFilter( 0, List.of( "amount" ), Kind.GREATER_THAN, null );
+        ParquetAdapterFilter childFilter = new ParquetAdapterFilter( 2, List.of( "items", "price" ), Kind.GREATER_THAN, null );
+        ParquetAdapterFilter orFilter = ParquetAdapterFilter.logical( Kind.OR, List.of( parentFilter, childFilter ) );
+
+        JoinFiltersContainer split = new JoinFiltersSplitter().split( List.of( orFilter ), true, 2, 1 );
+
+        assertEquals( 1, split.adapterFilters().size() );
+        assertSame( orFilter, split.adapterFilters().get( 0 ) );
+        assertTrue( split.nativeFilters().isEmpty() );
+        assertTrue( split.parentFilters().isEmpty() );
+        assertTrue( split.childFilters().isEmpty() );
+    }
+
+}

From 2cd4b2f6c991b50da01333f2d8bc86df73d77703 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Mon, 11 May 2026 12:15:06 +0200
Subject: [PATCH 27/46] fix limit for joins

---
 .../docs/images/join_limit.png                | Bin 0 -> 54367 bytes
 .../join_nested_parent_direct_child.png       | Bin 0 -> 48575 bytes
 .../docs/images/join_root_child.png           | Bin 0 -> 56543 bytes
 plugins/parquet-adapter/docs/joins.md         |  79 ++-
 .../docs/test_plan/join_test_list.md          | 508 ++++++++++++++++++
 .../docs/test_plan/partition_test_list.md     | 101 ++++
 plugins/parquet-adapter/docs/todo_joins.md    |   8 -
 .../execution/ParentLimitState.java           |  56 ++
 .../ParquetNestedJoinEnumerator.java          |  11 +-
 .../relational/planning/JoinInputLimit.java   |  33 ++
 .../ParquetEnumerableCalcScanRule.java        |   4 +-
 .../ParquetEnumerableFilterScanRule.java      |   4 +-
 .../planning/ParquetEnumerableJoinRule.java   |  12 +-
 .../ParquetEnumerableLimitJoinRule.java       | 187 +++++++
 .../relational/planning/ParquetRelJoin.java   |  55 +-
 .../planning/ParquetRelJoinRule.java          |  27 +-
 .../relational/planning/ParquetRelScan.java   |   1 +
 .../planning/ParquetRelScanRuleSupport.java   | 179 +++++-
 .../relational/schema/ParquetRelTable.java    |  12 +-
 .../db/adapter/parquet/ParquetPluginTest.java | 131 +----
 .../parquet/ParquetRelJoinPathTest.java       |  53 ++
 21 files changed, 1260 insertions(+), 201 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/images/join_limit.png
 create mode 100644 plugins/parquet-adapter/docs/images/join_nested_parent_direct_child.png
 create mode 100644 plugins/parquet-adapter/docs/images/join_root_child.png
 create mode 100644 plugins/parquet-adapter/docs/test_plan/join_test_list.md
 create mode 100644 plugins/parquet-adapter/docs/test_plan/partition_test_list.md
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParentLimitState.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinInputLimit.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableLimitJoinRule.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelJoinPathTest.java

diff --git a/plugins/parquet-adapter/docs/images/join_limit.png b/plugins/parquet-adapter/docs/images/join_limit.png
new file mode 100644
index 0000000000000000000000000000000000000000..f122a1dfa2bf6855d7c5669bfe1fceab0c1c71e5
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diff --git a/plugins/parquet-adapter/docs/joins.md b/plugins/parquet-adapter/docs/joins.md
index d0a2128c73..6c1e904181 100644
--- a/plugins/parquet-adapter/docs/joins.md
+++ b/plugins/parquet-adapter/docs/joins.md
@@ -11,19 +11,25 @@ The purpose is to avoid reading parent and child tables independently and joinin
 
 This change supports adapter-level joins only for normalized Parquet nested tables that belong to the same Parquet source. It is not a general Parquet join implementation and it is not a general self-join on the same physical table.
 
-The supported case is a parent-child join where:
+The supported case is a direct parent-child join where:
 
 - Both tables have the same `sourceUrl`.
-- One table is an ancestor of the other table in the Parquet path hierarchy.
-- The child table binding has `parentTableName()` equal to the parent table name.
-- The parent table path is a prefix of the child table path.
+- One table is the direct parent of the other table in the Parquet path hierarchy.
+- The child table path is exactly one level below the parent table path.
 - The join condition is a single equi-join.
 - The parent join key is the generated `PRIMARY_KEY` column.
 - The child join key is the generated `PARENT_KEY` column.
 
-The parent is often the root table, but it does not have to be root. A nested table can also be the parent if its path is an ancestor of the child path. At runtime the adapter reads from the parent source, resolves the parent path first, then resolves the child path below each parent row.
+The parent is often the root table, but it does not have to be root. A nested table can also be the parent when the child table is directly below it. At runtime the adapter reads from the parent source, resolves the parent path first, then resolves the child path below each parent row.
 
-Supported joins are checked in `ParquetRelJoin.supportedDirection(...)`. The join must be an equi-join with exactly one key on each side, both tables must come from the same source URL, and the keys must match the generated role mapping: parent `PRIMARY_KEY` to child `PARENT_KEY`. The child table path must also be below the parent path. Join types accepted by the rule are `INNER`, `LEFT`, `RIGHT`, and `FULL`.
+Supported joins are checked in `ParquetRelJoin.supportedDirection(...)`. The join must be an equi-join with exactly one key on each side, both tables must come from the same source URL, and the keys must match the generated role mapping: parent `PRIMARY_KEY` to child `PARENT_KEY`. The child table path must also be directly below the parent path. Join types accepted by the rule are `INNER`, `LEFT`, `RIGHT`, and `FULL`.
+
+The planner rules can also recognize supported joins when simple row-shaping nodes are between the join and the scan:
+
+- projection-only `Calc` nodes are allowed when they only select or reorder fields
+- a literal `EnumerableLimit` on the parent input is allowed through a dedicated limit-join rule
+
+These nodes are not general child execution. They are accepted only when the adapter can preserve the same meaning inside `ParquetRelJoin`.
 
 ## Unsupported Joins
 
@@ -45,6 +51,8 @@ Unsupported joins:
 - Join types other than `INNER`, `LEFT`, `RIGHT`, and `FULL`.
 - General joins on arbitrary user columns.
 - Independent child-side outer join scanning outside the parent path.
+- Input limits on the child side of the join.
+- Dynamic or non-literal input limits before the join.
 
 
 ### Flow
@@ -54,23 +62,31 @@ Unsupported joins:
   - `ParquetRelScanRule`
   - `ParquetRelJoinRule`
   - `ParquetEnumerableJoinRule`
+  - `ParquetEnumerableLimitJoinRule`
   - `ParquetEnumerableFilterJoinRule`
   - `ParquetEnumerableCalcJoinRule`
   
 
-- There are two paths to create the adapter join. 
+- There are three paths to create the adapter join. 
 
 
 - `ParquetRelJoinRule` handles a logical join path: 
   - it receives a `LogicalRelJoin`
   - tries to convert both inputs to enumerable Parquet scans
-  - extracts `ParquetRelScan` from the result
+  - resolves `ParquetRelScan` from the converted inputs, including projection-only calc wrappers
   - validates the parent-child direction
   - creates `ParquetRelJoin`. 
   
 
 - `ParquetEnumerableJoinRule` handles the later physical path: 
-  - if an `EnumerableJoin` already has two `ParquetRelScan` inputs, it performs the same support check and replaces the enumerable join with `ParquetRelJoin`.
+  - if an `EnumerableJoin` has inputs that can be resolved to projected Parquet scans, it performs the same support check and replaces the enumerable join with `ParquetRelJoin`.
+
+
+- `ParquetEnumerableLimitJoinRule` handles the physical path where the planner placed a literal `EnumerableLimit` on the parent side before the join:
+  - it resolves the limited parent input and the other input to Parquet scans
+  - it validates that the limit is only on the parent side
+  - it stores the parent limit in `ParquetRelJoin`
+  - it rejects child-side limits and dynamic limits, so those plans stay on the normal Polypheny join path.
 
 
 - After `ParquetRelJoin` exists, filters above it can be pushed into the adapter join. 
@@ -80,16 +96,16 @@ Unsupported joins:
 - `ParquetEnumerableCalcJoinRule` does the same for `EnumerableCalc(ParquetRelJoin)` when the calc contains a condition; it pushes the condition into the join and keeps a projection-only calc above the filtered join.
 
 
-- At runtime, `ParquetRelJoin.implement(...)` generates enumerable code that calls `leftTable.nestedJoin(dataContext, rightTable, leftFields, rightFields, leftIsParent, emitUnmatchedParents, filters)`. `emitUnmatchedParents` is derived from join type and direction: parent-side outer rows are emitted for parent-preserving `LEFT` or `RIGHT` joins, and for `FULL`. This runtime path preserves unmatched parent rows; it does not perform an independent child-side scan outside the parent path.
+- At runtime, `ParquetRelJoin.implement(...)` generates enumerable code that calls `leftTable.nestedJoin(dataContext, rightTable, leftFields, rightFields, leftIsParent, emitUnmatchedParents, parentOffset, parentFetch, filters)`. `emitUnmatchedParents` is derived from join type and direction: parent-side outer rows are emitted for parent-preserving `LEFT` or `RIGHT` joins, and for `FULL`. This runtime path preserves unmatched parent rows; it does not perform an independent child-side scan outside the parent path.
 
 
-- `ParquetRelTable.nestedJoin(...)` is the runtime entry point. It chooses which input is parent and which input is child based on `leftIsParent`, resolves dynamic parameters, maps joined filter column indexes to the correct left or right table column binding, and calls `JoinFiltersSplitter`.
+- `ParquetRelTable.nestedJoin(...)` is the runtime entry point. It chooses which input is parent and which input is child based on `leftIsParent`, resolves dynamic parameters, maps joined filter column indexes to the correct left or right table column binding, creates `ParentLimitState` for parent-side input limits, and calls `JoinFiltersSplitter`.
 
 
 - `JoinFiltersSplitter` classifies filters by the fields they reference. Parent-only filters that can be represented by Parquet paths may become reader filters. Parent-only filters that cannot be reader filters are kept as parent adapter filters. Child-only filters are applied after child expansion. Filters spanning both sides, invalid indexes, or mixed logical conditions become adapter-level joined-row filters.
 
 
-- `ParquetNestedJoinEnumerator` performs the actual read. It opens the parent source, walks the parent path using inherited nested resolution from `ParquetNestedRepeatedRelEnumerator`, applies parent filters, resolves child groups below each accepted parent, and creates `CombinedGroup` for each joined parent-child row. If a parent has no child and the join type requires preserving unmatched parents, it creates a `CombinedGroup` with a null child side.
+- `ParquetNestedJoinEnumerator` performs the actual read. It opens the parent source, walks the parent path using inherited nested resolution from `ParquetNestedRepeatedRelEnumerator`, applies parent-side input limit state when present, applies parent filters, resolves child groups below each accepted parent, and creates `CombinedGroup` for each joined parent-child row. If a parent has no child and the join type requires preserving unmatched parents, it creates a `CombinedGroup` with a null child side.
 
 
 - `CombinedGroup` is the bridge between nested Parquet groups and joined row indexes. When the enumerator extracts output values or evaluates filters, the combined group maps each joined field index to the correct parent or child `VirtualGroup`, binding, and table path. This lets the same extraction logic handle both left-parent and right-parent join directions.
@@ -103,28 +119,43 @@ Unsupported joins:
 ### ParquetRelScan
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java`
 
-Registers the new join rules in addition to the scan rule. Also exposes scan fields via Lombok `@Getter`, which is needed by join validation to map join keys back to physical table columns.
+Registers the new join rules in addition to the scan rule, including `ParquetEnumerableLimitJoinRule`. Also exposes scan fields via Lombok `@Getter`, which is needed by join validation to map join keys back to physical table columns.
+
+### ParquetRelScanRuleSupport
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java`
+
+Shared helper used by scan and join planner rules. It finds direct Parquet scans, looks inside planner subsets, and resolves projection-only `Calc` nodes by mapping visible fields back to physical Parquet table columns.
 
 ### ParquetRelJoinRule
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java`
 
-New file. Converts a `LogicalRelJoin` into `ParquetRelJoin` when both sides can be resolved to `ParquetRelScan` and `ParquetRelJoin.supportedDirection(...)` accepts the join.
+New file. Converts a `LogicalRelJoin` into `ParquetRelJoin` when both sides can be resolved to projected Parquet scans and `ParquetRelJoin.supportedDirection(...)` accepts the join.
 
 ### ParquetEnumerableJoinRule
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java`
 
-New file. Rewrites an `EnumerableJoin` over two `ParquetRelScan` inputs into `ParquetRelJoin` when the join is a supported nested parent-child join. This catches plans where the join has already been converted to enumerable form.
+New file. Rewrites an `EnumerableJoin` over inputs that can be resolved to projected Parquet scans into `ParquetRelJoin` when the join is a supported nested parent-child join. This catches plans where the join has already been converted to enumerable form.
+
+### ParquetEnumerableLimitJoinRule
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableLimitJoinRule.java`
+
+New file. Handles plans where the planner places a literal `EnumerableLimit` on the parent input before a supported parent-child join. It converts only parent-side input limits and leaves child-side or dynamic input limits to normal Polypheny execution.
 
 ### ParquetRelJoin
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java`
 
-New file. Physical adapter-level join node. It provides supported join validation through `supportedDirection(...)`, stores table metadata, projected fields, join direction, join type, and pushed filters, and generates runtime code that calls `ParquetRelTable.nestedJoin(...)`.
+New file. Physical adapter-level join node. It provides supported join validation through `supportedDirection(...)`, stores table metadata, projected fields, join direction, join type, optional parent-side input limit, and pushed filters, and generates runtime code that calls `ParquetRelTable.nestedJoin(...)`.
 
 ### JoinDirection
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinDirection.java`
 
 New file. Small value record used by join support checks to remember whether the left input is the parent table. It is returned by `ParquetRelJoin.supportedDirection(...)`.
 
+### JoinInputLimit
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinInputLimit.java`
+
+New file. Small value record used by join planning to store a literal parent-side input `OFFSET` and `FETCH` before the values are passed to runtime.
+
 ### ParquetEnumerableFilterJoinRule
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterJoinRule.java`
 
@@ -138,12 +169,17 @@ New file. Pushes the filter part of an `EnumerableCalc` above a `ParquetRelJoin`
 ### ParquetRelTable
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java`
 
-Adds adapter-level nested join execution through `nestedJoin(...)`. Also exposes binding/source metadata for rule validation, resolves filters with table-specific column bindings, uses `FiltersContainer` for scans, and renames selected binding helper logic to projected binding logic.
+Adds adapter-level nested join execution through `nestedJoin(...)`. Also exposes binding/source metadata for rule validation, resolves filters with table-specific column bindings, creates `ParentLimitState` for parent-side input limits, uses `FiltersContainer` for scans, and renames selected binding helper logic to projected binding logic.
 
 ### ParquetNestedJoinEnumerator
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java`
 
-New file. Executes supported parent-child joins inside the Parquet adapter. It is created by `ParquetRelTable.nestedJoin()` and reads parent and child rows from the same Parquet source, combines them into `CombinedGroup` rows, applies parent and child filters, and emits null child fields for supported outer join cases.
+New file. Executes supported parent-child joins inside the Parquet adapter. It is created by `ParquetRelTable.nestedJoin()` and reads parent and child rows from the same Parquet source, applies shared parent-side limit state when present, combines rows into `CombinedGroup`, applies parent and child filters, and emits null child fields for supported outer join cases.
+
+### ParentLimitState
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParentLimitState.java`
+
+New file. Shared runtime state for parent-side input limits. It is shared across per-file join enumerators so `OFFSET` and `FETCH` are applied once across the whole parent input, not reset for each Parquet file.
 
 ### CombinedGroup
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/CombinedGroup.java`
@@ -207,9 +243,13 @@ Refactoring. Use FiltersContainer.
 
 ## Examples
 
+### Join - root parent - direct child
+
 ![Schema display](images/parquet_rel_join.png)
 
-![Schema display](images/join.png)
+### Join - nested parent - direct child
+
+![Schema display](images/join_nested_parent_direct_child.png)
 
 ## Convention
 
@@ -264,4 +304,3 @@ Enumerable<PolyValue[]>
 ```
 
 and creates `ParquetNestedJoinEnumerator`, which yields the joined rows one by one.
-
diff --git a/plugins/parquet-adapter/docs/test_plan/join_test_list.md b/plugins/parquet-adapter/docs/test_plan/join_test_list.md
new file mode 100644
index 0000000000..068120bb2e
--- /dev/null
+++ b/plugins/parquet-adapter/docs/test_plan/join_test_list.md
@@ -0,0 +1,508 @@
+# Join Test List
+
+This document lists SQL queries that should be used to test adapter-level Parquet joins and fallback join behavior.
+
+The examples assume a normalized Parquet adapter with these generated tables:
+
+- `pon__orders` - root table
+- `pon__orders__items` - direct child table of `pon__orders`
+- `pon__orders__items__discounts` - direct child table of `pon__orders__items`
+
+Expected adapter-level support means the physical plan should contain `ParquetRelJoin`.
+Unsupported cases should still execute through the normal Polypheny join path and should not produce `ParquetRelJoin`.
+
+## Supported Adapter-Level Joins
+
+### 1. Root parent - direct child, parent on left, inner join
+
+Expected: supported. The plan should contain `ParquetRelJoin`.
+
+```sql
+SELECT
+    o.__polypheny_row_id AS order_row_id,
+    o.order_id,
+    i.__polypheny_row_id AS item_row_id,
+    i.__polypheny_parent_row_id AS item_parent_row_id,
+    i.product_id,
+    i.quantity
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 20;
+```
+
+### 2. Root parent - direct child, parent on right, inner join
+
+Expected: supported. This validates that the rule detects parent-child direction when the child table is the left input.
+
+```sql
+SELECT
+    i.__polypheny_row_id AS item_row_id,
+    i.product_id,
+    o.__polypheny_row_id AS order_row_id,
+    o.order_id
+FROM pon__orders__items i
+JOIN pon__orders o
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 20;
+```
+
+### 3. Root parent - direct child, equality written in reversed order
+
+Expected: supported. This validates that the rule accepts the same equi-join when the operands are reversed.
+
+```sql
+SELECT
+    o.order_id,
+    i.product_id,
+    i.quantity
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON o.__polypheny_row_id = i.__polypheny_parent_row_id
+LIMIT 20;
+```
+
+### 4. Nested parent - direct nested child, parent on left, inner join
+
+Expected: supported. This is the main nested-parent case: `items` is parent, `discounts` is direct child.
+
+```sql
+SELECT
+    i.__polypheny_row_id AS item_row_id,
+    d.__polypheny_row_id AS discount_row_id,
+    d.__polypheny_parent_row_id AS discount_parent_row_id,
+    i.product_id,
+    d.code,
+    d.amount
+FROM pon__orders__items i
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 20;
+```
+
+### 5. Nested parent - direct nested child, parent on right, inner join
+
+Expected: supported. This validates nested parent-child direction when the child table is the left input.
+
+```sql
+SELECT
+    d.__polypheny_row_id AS discount_row_id,
+    d.code,
+    d.amount,
+    i.__polypheny_row_id AS item_row_id,
+    i.product_id
+FROM pon__orders__items__discounts d
+JOIN pon__orders__items i
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 20;
+```
+
+### 6. Root parent - direct child, left join preserves parent rows
+PROBLEM
+
+Expected: supported. Parent is on the left, so unmatched parent rows can be emitted by adapter runtime.
+
+```sql
+SELECT
+    o.order_id,
+    i.product_id,
+    i.quantity
+FROM pon__orders o
+LEFT JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 20;
+```
+
+### 7. Root parent - direct child, right join preserves parent rows
+
+Expected: supported. Parent is on the right, so this is the same preservation direction as the previous test but with swapped input order.
+
+```sql
+SELECT
+    i.product_id,
+    i.quantity,
+    o.order_id
+FROM pon__orders__items i
+RIGHT JOIN pon__orders o
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 20;
+```
+
+### 8. Nested parent - direct nested child, left join preserves nested parent rows
+
+Expected: supported. This checks unmatched `items` rows when an item has no discount.
+
+```sql
+SELECT
+    i.product_id,
+    d.code,
+    d.amount
+FROM pon__orders__items i
+LEFT JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 20;
+```
+
+### 9. Nested parent - direct nested child, right join preserves nested parent rows
+
+Expected: supported. Parent is on the right, so the runtime should still preserve unmatched parent rows.
+
+```sql
+SELECT
+    d.code,
+    d.amount,
+    i.product_id
+FROM pon__orders__items__discounts d
+RIGHT JOIN pon__orders__items i
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 20;
+```
+
+### 10. Root parent - direct child, full join
+
+Expected: supported by the rule. Runtime preserves unmatched parent rows. It does not do an independent child-side outer scan outside the parent path.
+
+```sql
+SELECT
+    o.order_id,
+    i.product_id,
+    i.quantity
+FROM pon__orders o
+FULL JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 20;
+```
+
+### 11. Nested parent - direct nested child, full join
+
+Expected: supported by the rule. Runtime preserves unmatched parent rows. It does not do an independent child-side outer scan outside the parent path.
+
+```sql
+SELECT
+    i.product_id,
+    d.code,
+    d.amount
+FROM pon__orders__items i
+FULL JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 20;
+```
+
+### 12. Root parent - direct child with filter above join
+
+Expected: supported. The join should still become `ParquetRelJoin`; the filter may be pushed into the adapter join if translatable.
+
+```sql
+SELECT
+    o.order_id,
+    i.product_id,
+    i.quantity
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+WHERE i.quantity > 1
+LIMIT 20;
+```
+
+### 13. Nested parent - direct nested child with child-side filter
+
+Expected: supported. The join should still become `ParquetRelJoin`; the discount filter should be applied on the child side or as an adapter-level joined-row filter.
+
+```sql
+SELECT
+    i.product_id,
+    d.code,
+    d.amount
+FROM pon__orders__items i
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+WHERE d.amount > 0
+LIMIT 20;
+```
+
+### 14. Nested parent - direct nested child with parent-side filter
+
+Expected: supported. The join should still become `ParquetRelJoin`; the item filter should be applied before or during child expansion when possible.
+
+```sql
+SELECT
+    i.product_id,
+    i.quantity,
+    d.code,
+    d.amount
+FROM pon__orders__items i
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+WHERE i.quantity > 1
+LIMIT 20;
+```
+
+### 15. Nested parent - direct nested child with projected columns
+
+Expected: supported. This validates plans where `Calc` or `Project` nodes appear between the join and the Parquet scans.
+
+```sql
+SELECT
+    i.__polypheny_row_id AS item_id,
+    d.__polypheny_row_id AS discount_id,
+    d.__polypheny_parent_row_id AS discount_parent_id,
+    d.__polypheny_elem_ordinal AS discount_ordinal,
+    i.product_id,
+    d.code,
+    d.amount
+FROM pon__orders__items i
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 20;
+```
+
+### 16. Two adapter-level joins in one query, root to child and child to grandchild
+
+Expected: both direct joins are individually supported. The ideal plan has `ParquetRelJoin` for both direct parent-child joins, but this should be verified because rule ordering can affect the final shape.
+
+```sql
+SELECT
+    o.order_id,
+    i.product_id,
+    d.code,
+    d.amount
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 20;
+```
+
+## Unsupported Or Fallback Join Shapes
+
+### 17. Root parent - grandchild directly
+
+Expected: not supported as one adapter-level join. `discounts` points to `items`, not directly to `orders`.
+
+```sql
+SELECT
+    o.order_id,
+    d.code,
+    d.amount
+FROM pon__orders o
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 20;
+```
+
+### 18. Ancestor-descendant join beyond direct parent-child
+
+Expected: not supported as one adapter-level join. This tries to connect root rows directly to descendant discount rows by structural row-id prefix, not by direct `PRIMARY_KEY` to `PARENT_KEY`.
+
+```sql
+SELECT
+    o.order_id,
+    d.code,
+    d.amount
+FROM pon__orders o
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_row_id LIKE o.__polypheny_row_id || '/%'
+LIMIT 20;
+```
+
+### 19. Child-child join inside the same parent
+
+Expected: not supported at adapter level today. This requires a schema with another repeated child table such as `pon__orders__payments`.
+
+```sql
+SELECT
+    i.product_id,
+    p.payment_id,
+    p.amount
+FROM pon__orders__items i
+JOIN pon__orders__payments p
+    ON p.__polypheny_parent_row_id = i.__polypheny_parent_row_id
+LIMIT 20;
+```
+
+### 20. Same table self-join
+
+Expected: not supported at adapter level. This is a normal relational self-join.
+
+```sql
+SELECT
+    i1.product_id AS product_1,
+    i2.product_id AS product_2
+FROM pon__orders__items i1
+JOIN pon__orders__items i2
+    ON i1.__polypheny_parent_row_id = i2.__polypheny_parent_row_id
+LIMIT 20;
+```
+
+### 21. Parent-child tables but wrong generated keys
+
+Expected: not supported at adapter level. The child must join from `PARENT_KEY` to parent `PRIMARY_KEY`.
+
+```sql
+SELECT
+    o.order_id,
+    i.product_id
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_row_id = o.__polypheny_row_id
+LIMIT 20;
+```
+
+### 22. Parent-child tables joined on user columns
+
+Expected: not supported at adapter level. Adapter-level nested joins only support generated structural keys.
+
+```sql
+SELECT
+    o.order_id,
+    i.product_id
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON o.order_id = i.order_item_id
+LIMIT 20;
+```
+
+If `order_item_id` is not present in the dataset, replace it with another compatible user column. The important part is that the join does not use `__polypheny_parent_row_id = __polypheny_row_id`.
+
+### 23. Multi-key join condition
+
+Expected: not supported as adapter-level join when the extra condition is part of the join condition. Put additional filters in `WHERE` if the structural join itself should still be recognized.
+
+```sql
+SELECT
+    o.order_id,
+    i.product_id,
+    i.quantity
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+   AND i.__polypheny_elem_ordinal = 0
+LIMIT 20;
+```
+
+### 24. Non-equi join
+
+Expected: not supported at adapter level. Only a single equality between parent `PRIMARY_KEY` and child `PARENT_KEY` is supported.
+
+```sql
+SELECT
+    o.order_id,
+    i.product_id
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id <> o.__polypheny_row_id
+LIMIT 20;
+```
+
+### 25. OR join condition
+
+Expected: not supported at adapter level. The join condition is not a single structural equi-join. (contains OR)
+
+```sql
+SELECT
+    o.order_id,
+    i.product_id
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+    OR i.__polypheny_elem_ordinal = 0
+LIMIT 20;
+```
+
+### 26. Cross join
+
+Expected: not supported at adapter level. There is no structural parent-child equality condition.
+
+```sql
+SELECT
+    o.order_id,
+    i.product_id
+FROM pon__orders o
+CROSS JOIN pon__orders__items i
+LIMIT 20;
+```
+
+### 27. Semi join through EXISTS
+
+Expected: not supported as `ParquetRelJoin`. This may become a semi-join or correlated plan in Polypheny.
+
+```sql
+SELECT
+    o.order_id
+FROM pon__orders o
+WHERE EXISTS (
+    SELECT 1
+    FROM pon__orders__items i
+    WHERE i.__polypheny_parent_row_id = o.__polypheny_row_id
+)
+LIMIT 20;
+```
+
+### 28. Anti join through NOT EXISTS
+
+Expected: not supported as `ParquetRelJoin`. This may become an anti-join or correlated plan in Polypheny.
+
+```sql
+SELECT
+    o.order_id
+FROM pon__orders o
+WHERE NOT EXISTS (
+    SELECT 1
+    FROM pon__orders__items i
+    WHERE i.__polypheny_parent_row_id = o.__polypheny_row_id
+)
+LIMIT 20;
+```
+
+### 29. Join between different Parquet sources
+
+Expected: not supported at adapter level. This requires another adapter/table prefix such as `pon2__orders`.
+
+```sql
+SELECT
+    o.order_id,
+    o2.order_id AS other_order_id
+FROM pon__orders o
+JOIN pon2__orders o2
+    ON o.order_id = o2.order_id
+LIMIT 20;
+```
+
+### 30. Join between normalized Parquet table and non-Parquet table
+
+Expected: not supported at adapter level. This is a normal Polypheny join.
+
+```sql
+SELECT
+    o.order_id,
+    c.customer_id
+FROM pon__orders o
+JOIN customers c
+    ON o.customer_id = c.customer_id
+LIMIT 20;
+```
+
+### 31. Child-side preserving outer join
+
+Expected: accepted by the rule for the structural join, but child rows normally cannot exist without a parent in normalized data. This should be tested mainly to verify runtime semantics and null handling.
+
+```sql
+SELECT
+    i.product_id,
+    o.order_id
+FROM pon__orders__items i
+LEFT JOIN pon__orders o
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 20;
+```
+
+
+Failed query numbers:
+
+12, 13, 14, 19, 23, 29, 30
+
+Failure reasons:
+
+12, 13, 14, 23: row-type mismatch errors.
+19, 29, 30: Entity not found errors.
diff --git a/plugins/parquet-adapter/docs/test_plan/partition_test_list.md b/plugins/parquet-adapter/docs/test_plan/partition_test_list.md
new file mode 100644
index 0000000000..f9ccfbe716
--- /dev/null
+++ b/plugins/parquet-adapter/docs/test_plan/partition_test_list.md
@@ -0,0 +1,101 @@
+# Partition Test Query List
+
+Currently Polypheny does not support hierarchical partitioning
+
+## Test data setup
+
+These tests use the TLC Parquet data under `C:\PolyData\tlc`. The full partition test setup expects this directory layout:
+
+```text
+C:\PolyData\tlc\
+  yellow_tripdata\
+    year=2025\
+      month=01\
+        yellow_tripdata_2025-01.parquet
+  green_tripdata\
+    year=2025\
+      month=01\
+        green_tripdata_2025-01.parquet
+  fhv_tripdata\
+    year=2025\
+      month=01\
+        fhv_tripdata_2025-01.parquet
+  fhvhv_tripdata\
+    year=2025\
+      month=01\
+        fhvhv_tripdata_2025-01.parquet
+```
+
+The SQL examples below assume that the Parquet adapter was loaded with the adapter name `prn`, so the expected tables in the `public` schema are:
+
+- `prn__yellow_tripdata`
+- `prn__green_tripdata`
+- `prn__fhv_tripdata`
+- `prn__fhvhv_tripdata`
+
+If the adapter is created with another name, replace the `prn__` prefix in the queries with that adapter prefix.
+
+### The adapter source location can be provided in four supported forms:
+
+1. One Parquet file, for example `C:\PolyData\tlc\green_tripdata\year=2025\month=01\green_tripdata_2025-01.parquet`. 
+This creates one table backed by one file.
+
+
+2. A folder with several Parquet files and no `key=value` folders. 
+This creates one logical table backed by multiple files.
+
+
+3. A partitioned table folder, for example `C:\PolyData\tlc\green_tripdata`. 
+This creates one logical table with partition columns from folders such as `year=2025` and `month=01`.
+
+
+4. A dataset root with several table folders, for example `C:\PolyData\tlc`. 
+This creates one logical table per child folder, such as `yellow_tripdata`, `green_tripdata`, `fhv_tripdata`, and `fhvhv_tripdata`.
+
+
+For the full test plan, use option 4 (`C:\PolyData\tlc`) so all TLC partitioned tables are available together. 
+The partition columns `year` and `month` should be visible as normal table columns; 
+only the first partition column, `year`, is used for **Polypheny physical partitioning**.
+
+## 1. Simple select by year and month, check that these columns were created
+
+```sql
+SELECT *
+FROM "prn__green_tripdata"
+WHERE "year" = '2025'
+AND "month" = '01';
+```
+
+## 2. Single partition scan on `prn__green_tripdata`, group by year
+
+```sql
+SELECT
+"year",
+COUNT(*) AS trip_count
+FROM "prn__green_tripdata"
+WHERE "year" = '2025'
+GROUP BY "year";
+```
+
+## 3. Simple select by year and month - Polypheny currently does not support logical operators
+
+```sql
+SELECT * from prn__yellow_tripdata WHERE "year"='2025' AND "month"='01' LIMIT 5
+SELECT * from prn__green_tripdata WHERE "year"='2025' AND "month"='01' LIMIT 5
+SELECT * from prn__fhv_tripdata WHERE "year"='2025' AND "month"='01' LIMIT 5
+SELECT * from prn__fhvhv_tripdata WHERE "year"='2025' AND "month"='01' LIMIT 5
+```
+
+## 4. Single partition scan on `prn__green_tripdata` - NOT WORKING
+
+Purpose: verify that Hive-style partition columns are visible and that a filter on both partition columns can prune to one `year=2025/month=01` Parquet file.
+
+```sql
+SELECT
+    "year",
+    "month",
+    COUNT(*) AS trip_count
+FROM "prn__green_tripdata"
+WHERE "year" = '2025'
+GROUP BY "year", "month";
+```
diff --git a/plugins/parquet-adapter/docs/todo_joins.md b/plugins/parquet-adapter/docs/todo_joins.md
index aef767ce54..220ea962c8 100644
--- a/plugins/parquet-adapter/docs/todo_joins.md
+++ b/plugins/parquet-adapter/docs/todo_joins.md
@@ -18,14 +18,6 @@
 - Add tests for ancestor-descendant joins beyond direct parent-child joins when this join shape is supported.
 - Add fallback tests proving child-child joins and ancestor-descendant joins remain Polypheny-level joins until adapter-level support is implemented.
 
-## Filter Correctness
-
-- Review the split between reader filters and adapter filters.
-- Ensure filters pushed to the native Parquet reader are still validated at adapter level when native filtering may be incomplete or unsupported.
-- Verify logical filters (`AND`, `OR`, `NOT`) are split safely when some operands refer to parent fields, child fields, or both sides.
-- Verify dynamic parameters are resolved correctly for join filters on both left and right inputs.
-- Fix the flat relational filter regression where path-based resolved filters can reach `ParquetRelValueExtractor`, which does not support path extraction.
-
 ## Join Semantics
 
 - Clarify and test `FULL` join behavior for nested parent-child data.
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParentLimitState.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParentLimitState.java
new file mode 100644
index 0000000000..deba9247bf
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParentLimitState.java
@@ -0,0 +1,56 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+
+/**
+ * Shared runtime state for a limit that applies to parent rows before nested
+ * join expansion.
+ */
+public final class ParentLimitState {
+
+    private long rowsToSkip;
+    private long rowsRemaining;
+
+
+    public ParentLimitState( int offset, int fetch ) {
+        this.rowsToSkip = Math.max( 0, offset );
+        this.rowsRemaining = fetch < 0 ? Long.MAX_VALUE : fetch;
+    }
+
+
+    @SuppressWarnings("unused")
+    public static ParentLimitState unlimited() {
+        return new ParentLimitState( 0, -1 );
+    }
+
+
+    public synchronized boolean includeNextParentRow() {
+        if ( rowsToSkip > 0 ) {
+            rowsToSkip--;
+            return false;
+        }
+        if ( rowsRemaining <= 0 ) {
+            return false;
+        }
+        if ( rowsRemaining != Long.MAX_VALUE ) {
+            rowsRemaining--;
+        }
+        return true;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
index 4e7cebdbe2..17425652f4 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
@@ -45,14 +45,15 @@ public class ParquetNestedJoinEnumerator extends ParquetNestedRepeatedRelEnumera
     private final boolean leftIsParent;
     private final boolean emitUnmatchedParents;
     private final List<String> childPathFromParent;
+    private final ParentLimitState parentLimitState;
 
 
-    public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBinding parentBinding, ParquetTableBinding childBinding, List<ParquetColumnBinding> parentColumns, List<ParquetColumnBinding> childColumns, JoinFiltersContainer filterContainer, boolean leftIsParent, boolean emitUnmatchedParents ) {
-        this( reader, parentBinding, childBinding, parentColumns, childColumns, filterContainer, leftIsParent, emitUnmatchedParents, true );
+    public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBinding parentBinding, ParquetTableBinding childBinding, List<ParquetColumnBinding> parentColumns, List<ParquetColumnBinding> childColumns, JoinFiltersContainer filterContainer, boolean leftIsParent, boolean emitUnmatchedParents, ParentLimitState parentLimitState ) {
+        this( reader, parentBinding, childBinding, parentColumns, childColumns, filterContainer, leftIsParent, emitUnmatchedParents, parentLimitState, true );
     }
 
 
-    public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBinding parentBinding, ParquetTableBinding childBinding, List<ParquetColumnBinding> parentColumns, List<ParquetColumnBinding> childColumns, JoinFiltersContainer filterContainer, boolean leftIsParent, boolean emitUnmatchedParents, boolean readerOwner ) {
+    public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBinding parentBinding, ParquetTableBinding childBinding, List<ParquetColumnBinding> parentColumns, List<ParquetColumnBinding> childColumns, JoinFiltersContainer filterContainer, boolean leftIsParent, boolean emitUnmatchedParents, ParentLimitState parentLimitState, boolean readerOwner ) {
         super( reader, parentBinding, parentColumns, filterContainer, new ParquetNestedJoinFilterEvaluator( reader.getProjectionSchema(), new ParquetPathValueExtractor(), parentBinding, parentColumns, leftIsParent ), true, readerOwner );
         this.parentBinding = parentBinding;
         this.childBinding = childBinding;
@@ -63,6 +64,7 @@ public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBind
         this.leftIsParent = leftIsParent;
         this.emitUnmatchedParents = emitUnmatchedParents;
         this.childPathFromParent = childBinding.sourcePathElements().subList( parentBinding.sourcePathElements().size(), childBinding.sourcePathElements().size() );
+        this.parentLimitState = parentLimitState;
     }
 
 
@@ -71,6 +73,9 @@ protected List<Group> expandRow( Group group ) {
         List<Group> rows = new ArrayList<>();
         VirtualGroup rootGroup = new VirtualGroup( group, String.valueOf( reader.getCurrentRowNumber() ), null, 0 );
         for ( Group parent : resolveNested( rootGroup, group.getType(), parentBinding.sourcePathElements(), 0 ) ) {
+            if ( !parentLimitState.includeNextParentRow() ) {
+                continue;
+            }
             if ( !matchesFilters( combinedGroup( (VirtualGroup) parent, null ), parentFilters ) ) {
                 continue;
             }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinInputLimit.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinInputLimit.java
new file mode 100644
index 0000000000..69ba2bde72
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinInputLimit.java
@@ -0,0 +1,33 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+/**
+ * Stores an optional OFFSET and FETCH/LIMIT for the parent side of an adapter-level join
+ * @param offset - how many rows to skip before returning results
+ * @param fetch - how many rows to return after the offset
+ */
+public record JoinInputLimit( int offset, int fetch ) {
+
+    static final JoinInputLimit NONE = new JoinInputLimit( 0, -1 );
+
+
+    boolean isEmpty() {
+        return offset == 0 && fetch < 0;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcScanRule.java
index 682dab086d..1ce307fc25 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcScanRule.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcScanRule.java
@@ -61,9 +61,9 @@ public AlgNode convert( AlgNode alg ) {
             return null;
         }
 
-        ParquetRelScan scan = ParquetRelScanRuleSupport.findDirectRelScan( calc.getInput() );
+        ParquetRelScan scan = ParquetRelScanRuleSupport.findProjectedRelScan( calc.getInput() );
         if ( scan == null ) {
-            scan = ParquetRelScanRuleSupport.findDirectRelScan( convert( calc.getInput(), calc.getInput().getTraitSet().replace( EnumerableConvention.INSTANCE ) ) );
+            scan = ParquetRelScanRuleSupport.findProjectedRelScan( convert( calc.getInput(), calc.getInput().getTraitSet().replace( EnumerableConvention.INSTANCE ) ) );
         }
         if ( scan == null ) {
             return null;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterScanRule.java
index 6d1ee3b0e9..66014b4864 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterScanRule.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterScanRule.java
@@ -52,9 +52,9 @@ public ParquetEnumerableFilterScanRule( AlgBuilderFactory algBuilderFactory ) {
     @Override
     public AlgNode convert( AlgNode alg ) {
         LogicalRelFilter filter = (LogicalRelFilter) alg;
-        ParquetRelScan scan = ParquetRelScanRuleSupport.findDirectRelScan( filter.getInput() );
+        ParquetRelScan scan = ParquetRelScanRuleSupport.findProjectedRelScan( filter.getInput() );
         if ( scan == null ) {
-            scan = ParquetRelScanRuleSupport.findDirectRelScan( convert( filter.getInput(), filter.getInput().getTraitSet().replace( EnumerableConvention.INSTANCE ) ) );
+            scan = ParquetRelScanRuleSupport.findProjectedRelScan( convert( filter.getInput(), filter.getInput().getTraitSet().replace( EnumerableConvention.INSTANCE ) ) );
         }
         if ( scan == null ) {
             return null;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java
index 08952abbf4..874a6f7d0a 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java
@@ -16,6 +16,7 @@
 
 package org.polypheny.db.adapter.parquet.relational.planning;
 
+import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.core.AlgFactories;
 import org.polypheny.db.algebra.enumerable.EnumerableJoin;
 import org.polypheny.db.plan.AlgOptRule;
@@ -35,7 +36,7 @@ public class ParquetEnumerableJoinRule extends AlgOptRule {
 
     public ParquetEnumerableJoinRule( AlgBuilderFactory algBuilderFactory ) {
         super(
-                operand( EnumerableJoin.class, operand( ParquetRelScan.class, none() ), operand( ParquetRelScan.class, none() ) ),
+                operand( EnumerableJoin.class, operand( AlgNode.class, any() ), operand( AlgNode.class, any() ) ),
                 algBuilderFactory,
                 ParquetEnumerableJoinRule.class.getSimpleName() );
     }
@@ -44,12 +45,15 @@ public ParquetEnumerableJoinRule( AlgBuilderFactory algBuilderFactory ) {
     @Override
     public void onMatch( AlgOptRuleCall call ) {
         EnumerableJoin join = call.alg( 0 );
-        ParquetRelScan left = call.alg( 1 );
-        ParquetRelScan right = call.alg( 2 );
+        ParquetRelScan left = ParquetRelScanRuleSupport.findProjectedRelScan( call.alg( 1 ) );
+        ParquetRelScan right = ParquetRelScanRuleSupport.findProjectedRelScan( call.alg( 2 ) );
+        if ( left == null || right == null ) {
+            return;
+        }
         JoinDirection direction = supportedDirection( join, left, right );
         if ( direction == null ) {
             return;
         }
-        call.transformTo( ParquetRelJoin.create( left, right, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent() ) );
+        call.transformTo( ParquetRelJoin.create( join.getLeft(), join.getRight(), left, right, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent(), JoinInputLimit.NONE ) );
     }
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableLimitJoinRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableLimitJoinRule.java
new file mode 100644
index 0000000000..1ad477a766
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableLimitJoinRule.java
@@ -0,0 +1,187 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import static org.polypheny.db.adapter.parquet.relational.planning.ParquetRelJoin.supportedDirection;
+
+import java.util.Collections;
+import java.util.IdentityHashMap;
+import java.util.Set;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.core.AlgFactories;
+import org.polypheny.db.algebra.core.Calc;
+import org.polypheny.db.algebra.enumerable.EnumerableJoin;
+import org.polypheny.db.algebra.enumerable.EnumerableLimit;
+import org.polypheny.db.plan.AlgOptRule;
+import org.polypheny.db.plan.AlgOptRuleCall;
+import org.polypheny.db.plan.volcano.AlgSubset;
+import org.polypheny.db.rex.RexLiteral;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.tools.AlgBuilderFactory;
+
+/**
+ * Rewrites joins where the planner pushed a literal limit onto the parent side
+ * before a supported Parquet parent/child join.
+ */
+public class ParquetEnumerableLimitJoinRule extends AlgOptRule {
+
+    public static final ParquetEnumerableLimitJoinRule INSTANCE = new ParquetEnumerableLimitJoinRule( AlgFactories.LOGICAL_BUILDER );
+
+
+    public ParquetEnumerableLimitJoinRule( AlgBuilderFactory algBuilderFactory ) {
+        super(
+                operand( EnumerableJoin.class, operand( AlgNode.class, any() ), operand( AlgNode.class, any() ) ),
+                algBuilderFactory,
+                ParquetEnumerableLimitJoinRule.class.getSimpleName() );
+    }
+
+
+    @Override
+    public void onMatch( AlgOptRuleCall call ) {
+        EnumerableJoin join = call.alg( 0 );
+        LimitedJoinInput left = findLimitedJoinInput( join.getLeft() );
+        LimitedJoinInput right = findLimitedJoinInput( join.getRight() );
+        if ( left == null || right == null ) {
+            return;
+        }
+
+        JoinDirection direction = supportedDirection( join, left.scan(), right.scan() );
+        if ( direction == null ) {
+            return;
+        }
+
+        JoinInputLimit parentLimit = direction.leftIsParent() ? left.limit() : right.limit();
+        JoinInputLimit childLimit = direction.leftIsParent() ? right.limit() : left.limit();
+        if ( parentLimit.isEmpty() || !childLimit.isEmpty() ) {
+            return;
+        }
+
+        call.transformTo( ParquetRelJoin.create(
+                join.getLeft(),
+                join.getRight(),
+                left.scan(),
+                right.scan(),
+                join.getCondition(),
+                join.getVariablesSet(),
+                join.getJoinType(),
+                direction.leftIsParent(),
+                parentLimit ) );
+    }
+
+
+    private LimitedJoinInput findLimitedJoinInput( AlgNode alg ) {
+        return findLimitedJoinInput( alg, newVisitedSet() );
+    }
+
+
+    private LimitedJoinInput findLimitedJoinInput( AlgNode alg, Set<AlgNode> visited ) {
+        if ( alg == null || !visited.add( alg ) ) {
+            return null;
+        }
+        ParquetRelScan scan = ParquetRelScanRuleSupport.findDirectRelScan( alg );
+        if ( scan != null ) {
+            return new LimitedJoinInput( scan, JoinInputLimit.NONE );
+        }
+        if ( alg instanceof Calc calc ) {
+            LimitedJoinInput input = findLimitedJoinInput( calc.getInput(), visited );
+            if ( input == null ) {
+                return null;
+            }
+            int[] projectedFields = ParquetRelScanRuleSupport.projectedFields( calc.getProgram(), input.scan().getFields() );
+            if ( projectedFields == null ) {
+                return null;
+            }
+            return input.withScan( new ParquetRelScan( input.scan().getCluster(), input.scan().getEntity(), projectedFields, input.scan().getFilters() ) );
+        }
+        if ( alg instanceof EnumerableLimit limit ) {
+            JoinInputLimit inputLimit = literalLimit( limit );
+            if ( inputLimit == null ) {
+                return null;
+            }
+            LimitedJoinInput input = findLimitedJoinInput( limit.getInput(), visited );
+            if ( input == null || !input.limit().isEmpty() ) {
+                return null;
+            }
+            return input.withLimit( inputLimit );
+        }
+        if ( alg instanceof AlgSubset subset ) {
+            LimitedJoinInput input = findLimitedJoinInput( subset.getBest(), branchVisitedSet( visited ) );
+            if ( input != null ) {
+                return input;
+            }
+            input = findLimitedJoinInput( subset.getOriginal(), branchVisitedSet( visited ) );
+            if ( input != null ) {
+                return input;
+            }
+            for ( AlgNode candidate : subset.getAlgList() ) {
+                input = findLimitedJoinInput( candidate, branchVisitedSet( visited ) );
+                if ( input != null ) {
+                    return input;
+                }
+            }
+        }
+        return null;
+    }
+
+
+    private Set<AlgNode> newVisitedSet() {
+        return Collections.newSetFromMap( new IdentityHashMap<>() );
+    }
+
+
+    private Set<AlgNode> branchVisitedSet( Set<AlgNode> visited ) {
+        Set<AlgNode> branch = newVisitedSet();
+        branch.addAll( visited );
+        return branch;
+    }
+
+
+    private JoinInputLimit literalLimit( EnumerableLimit limit ) {
+        Integer offset = literalInt( limit.offset );
+        Integer fetch = literalInt( limit.fetch );
+        if ( (offset == null && limit.offset != null) || (fetch == null && limit.fetch != null) ) {
+            return null;
+        }
+        return new JoinInputLimit( offset == null ? 0 : offset, fetch == null ? -1 : fetch );
+    }
+
+
+    private Integer literalInt( RexNode node ) {
+        if ( node == null ) {
+            return null;
+        }
+        if ( !(node instanceof RexLiteral) ) {
+            return null;
+        }
+        return RexLiteral.intValue( node );
+    }
+
+
+    private record LimitedJoinInput( ParquetRelScan scan, JoinInputLimit limit ) {
+
+        LimitedJoinInput withScan( ParquetRelScan scan ) {
+            return new LimitedJoinInput( scan, limit );
+        }
+
+
+        LimitedJoinInput withLimit( JoinInputLimit limit ) {
+            return new LimitedJoinInput( scan, limit );
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
index a94249f90b..4f993a793d 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
@@ -57,6 +57,7 @@ public class ParquetRelJoin extends Join implements EnumerableAlg {
     private final int[] leftFields;
     private final int[] rightFields;
     private final List<ParquetAdapterFilter> filters;
+    private final JoinInputLimit parentLimit;
 
 
     public ParquetRelJoin(
@@ -72,7 +73,8 @@ public ParquetRelJoin(
             ParquetRelTable rightTable,
             int[] leftFields,
             int[] rightFields,
-            List<ParquetAdapterFilter> filters ) {
+            List<ParquetAdapterFilter> filters,
+            JoinInputLimit parentLimit ) {
         super( cluster, traitSet, left, right, condition, variablesSet, joinType );
         this.leftIsParent = leftIsParent;
         this.leftTable = leftTable;
@@ -80,29 +82,43 @@ public ParquetRelJoin(
         this.leftFields = leftFields;
         this.rightFields = rightFields;
         this.filters = List.copyOf( filters );
+        this.parentLimit = parentLimit;
     }
 
 
     public static ParquetRelJoin create( ParquetRelScan left, ParquetRelScan right, RexNode condition, Set<CorrelationId> variablesSet, JoinAlgType joinType, boolean leftIsParent ) {
-        AlgCluster cluster = left.getCluster();
+        return create( left, right, left, right, condition, variablesSet, joinType, leftIsParent, JoinInputLimit.NONE );
+    }
+
+
+    /**
+     * factory method that builds the physical adapter join node
+     */
+    public static ParquetRelJoin create( AlgNode leftInput, AlgNode rightInput, ParquetRelScan leftScan, ParquetRelScan rightScan, RexNode condition, Set<CorrelationId> variablesSet, JoinAlgType joinType, boolean leftIsParent, JoinInputLimit parentLimit ) {
+        AlgCluster cluster = leftInput.getCluster();
         AlgTraitSet traitSet = cluster.traitSetOf( EnumerableConvention.INSTANCE ).replace( ModelTrait.RELATIONAL );
         return new ParquetRelJoin(
                 cluster,
                 traitSet,
-                left,
-                right,
+                leftInput,
+                rightInput,
                 condition,
                 variablesSet,
                 joinType,
                 leftIsParent,
-                left.getEntity(),
-                right.getEntity(),
-                left.getFields(),
-                right.getFields(),
-                List.of() );
+                leftScan.getEntity(),
+                rightScan.getEntity(),
+                leftScan.getFields(),
+                rightScan.getFields(),
+                List.of(),
+                parentLimit );
     }
 
 
+    /**
+     * Checks whether a join can be executed by the Parquet adapter: it must be join
+     * between two tables from the same Parquet source, using parent PRIMARY_KEY and child PARENT_KEY
+     */
     public static JoinDirection supportedDirection( Join join, ParquetRelScan left, ParquetRelScan right ) {
         JoinInfo info = JoinInfo.of( left, right, join.getCondition() );
         if ( !info.isEqui() || info.leftKeys.size() != 1 || info.rightKeys.size() != 1 ) {
@@ -149,10 +165,7 @@ private static int mappedColumn( ParquetRelScan scan, int inputIndex ) {
 
 
     private static boolean isParentChildJoin( ParquetRelTable parent, ParquetRelTable child, int parentColumn, int childColumn ) {
-        if ( child.getBinding().parentTableName() == null || !child.getBinding().parentTableName().equals( parent.name ) ) {
-            return false;
-        }
-        if ( !isPrefix( parent.getBinding().sourcePathElements(), child.getBinding().sourcePathElements() ) ) {
+        if ( !isDirectChildPath( parent.getBinding().sourcePathElements(), child.getBinding().sourcePathElements() ) ) {
             return false;
         }
         return parentColumn == parent.columnIndexByRole( ParquetColumnRole.PRIMARY_KEY )
@@ -160,21 +173,21 @@ private static boolean isParentChildJoin( ParquetRelTable parent, ParquetRelTabl
     }
 
 
-    private static boolean isPrefix( List<String> parentPath, List<String> childPath ) {
-        return childPath.size() > parentPath.size() && childPath.subList( 0, parentPath.size() ).equals( parentPath );
+    public static boolean isDirectChildPath( List<String> parentPath, List<String> childPath ) {
+        return childPath.size() == parentPath.size() + 1 && childPath.subList( 0, parentPath.size() ).equals( parentPath );
     }
 
 
     public ParquetRelJoin withFilters( List<ParquetAdapterFilter> filters ) {
         List<ParquetAdapterFilter> combinedFilters = new ArrayList<>( this.filters );
         combinedFilters.addAll( filters );
-        return new ParquetRelJoin( getCluster(), traitSet, left, right, condition, variablesSet, joinType, leftIsParent, leftTable, rightTable, leftFields, rightFields, combinedFilters );
+        return new ParquetRelJoin( getCluster(), traitSet, left, right, condition, variablesSet, joinType, leftIsParent, leftTable, rightTable, leftFields, rightFields, combinedFilters, parentLimit );
     }
 
 
     @Override
     public Join copy( AlgTraitSet traitSet, RexNode conditionExpr, AlgNode left, AlgNode right, JoinAlgType joinType, boolean semiJoinDone ) {
-        return new ParquetRelJoin( getCluster(), traitSet, left, right, conditionExpr, variablesSet, joinType, leftIsParent, leftTable, rightTable, leftFields, rightFields, filters );
+        return new ParquetRelJoin( getCluster(), traitSet, left, right, conditionExpr, variablesSet, joinType, leftIsParent, leftTable, rightTable, leftFields, rightFields, filters, parentLimit );
     }
 
 
@@ -187,7 +200,11 @@ public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
 
     @Override
     public AlgWriter explainTerms( AlgWriter pw ) {
-        return super.explainTerms( pw ).item( "leftIsParent", leftIsParent ).item( "filters", filters );
+        return super.explainTerms( pw )
+                .item( "leftIsParent", leftIsParent )
+                .itemIf( "parentOffset", parentLimit.offset(), parentLimit.offset() != 0 )
+                .itemIf( "parentFetch", parentLimit.fetch(), parentLimit.fetch() >= 0 )
+                .item( "filters", filters );
     }
 
 
@@ -209,6 +226,8 @@ public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
                                 Expressions.constant( rightFields ),
                                 Expressions.constant( leftIsParent ),
                                 Expressions.constant( emitUnmatchedParents ),
+                                Expressions.constant( parentLimit.offset() ),
+                                Expressions.constant( parentLimit.fetch() ),
                                 runtimeFilters ) ) );
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java
index 5a4fb7a10c..7d7f0ecb41 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java
@@ -24,7 +24,6 @@
 import org.polypheny.db.algebra.enumerable.EnumerableConvention;
 import org.polypheny.db.algebra.logical.relational.LogicalRelJoin;
 import org.polypheny.db.plan.Convention;
-import org.polypheny.db.plan.volcano.AlgSubset;
 import org.polypheny.db.tools.AlgBuilderFactory;
 
 /**
@@ -47,32 +46,26 @@ public ParquetRelJoinRule( AlgBuilderFactory algBuilderFactory ) {
     }
 
 
+    /**
+     * Tries to turn a logical join into an adapter-level ParquetRelJoin
+     */
     @Override
     public AlgNode convert( AlgNode alg ) {
         LogicalRelJoin join = (LogicalRelJoin) alg;
-        ParquetRelScan left = findRelScan( convert( join.getLeft(), join.getLeft().getTraitSet().replace( EnumerableConvention.INSTANCE ) ) );
-        ParquetRelScan right = findRelScan( convert( join.getRight(), join.getRight().getTraitSet().replace( EnumerableConvention.INSTANCE ) ) );
-        if ( left == null || right == null ) {
+        AlgNode leftOperand = convert( join.getLeft(), join.getLeft().getTraitSet().replace( EnumerableConvention.INSTANCE ) );
+        AlgNode rightOperand = convert( join.getRight(), join.getRight().getTraitSet().replace( EnumerableConvention.INSTANCE ) );
+        ParquetRelScan leftScan = ParquetRelScanRuleSupport.findProjectedRelScan( leftOperand );
+        ParquetRelScan rightScan = ParquetRelScanRuleSupport.findProjectedRelScan( rightOperand );
+        if ( leftScan == null || rightScan == null ) {
             return null;
         }
 
-        JoinDirection direction = supportedDirection( join, left, right );
+        JoinDirection direction = supportedDirection( join, leftScan, rightScan );
         if ( direction == null ) {
             return null;
         }
 
-        return ParquetRelJoin.create( left, right, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent() );
-    }
-
-
-    private ParquetRelScan findRelScan( AlgNode alg ) {
-        if ( alg instanceof ParquetRelScan relScan ) {
-            return relScan;
-        }
-        if ( alg instanceof AlgSubset subset && subset.getBest() instanceof ParquetRelScan relScan ) {
-            return relScan;
-        }
-        return null;
+        return ParquetRelJoin.create( leftOperand, rightOperand, leftScan, rightScan, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent(), JoinInputLimit.NONE );
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
index e09eaee522..4e9787b772 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
@@ -91,6 +91,7 @@ public static void registerRules( AlgPlanner planner ) {
         planner.addRuleDuringRuntime( ParquetEnumerableCalcScanRule.INSTANCE );
         planner.addRuleDuringRuntime( ParquetRelJoinRule.INSTANCE );
         planner.addRuleDuringRuntime( ParquetEnumerableJoinRule.INSTANCE );
+        planner.addRuleDuringRuntime( ParquetEnumerableLimitJoinRule.INSTANCE );
         planner.addRuleDuringRuntime( ParquetEnumerableFilterJoinRule.INSTANCE );
         planner.addRuleDuringRuntime( ParquetEnumerableCalcJoinRule.INSTANCE );
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java
index 428df93b44..f83cc291a4 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java
@@ -16,42 +16,120 @@
 
 package org.polypheny.db.adapter.parquet.relational.planning;
 
+import java.util.Collections;
+import java.util.IdentityHashMap;
 import java.util.List;
+import java.util.Set;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.core.Calc;
+import org.polypheny.db.algebra.core.Project;
 import org.polypheny.db.algebra.core.relational.RelScan;
+import org.polypheny.db.plan.AlgOptUtil;
 import org.polypheny.db.plan.volcano.AlgSubset;
+import org.polypheny.db.rex.RexIndexRef;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.rex.RexProgram;
 import org.polypheny.db.type.PolyType;
 
-
-final class ParquetRelScanRuleSupport {
+/**
+ * Class handles direct Parquet scans, scans hidden inside planner subsets,
+ * and simple projection-only Calc nodes above scans, while keeping the projected field mapping correct.
+ * This lets join and filter rules understand which real Parquet table and physical columns are being used.
+ */
+public final class ParquetRelScanRuleSupport {
 
     private ParquetRelScanRuleSupport() {
     }
 
-
+    /*
+    Returns the node directly if it is already ParquetRelScan, converts a generic relational scan
+    over ParquetRelTable into ParquetRelScan, and also looks inside AlgSubset alternatives.
+     */
     static ParquetRelScan findDirectRelScan( AlgNode alg ) {
-        if ( alg == null ) {
+        return findDirectRelScan( alg, newVisitedSet() );
+    }
+
+
+    private static ParquetRelScan findDirectRelScan( AlgNode alg, Set<AlgNode> visited ) {
+        if ( alg == null || !visited.add( alg ) ) {
             return null;
         }
-        if ( alg instanceof ParquetRelScan relScan ) {
-            return relScan;
+        ParquetRelScan directScan = directRelScan( alg );
+        if ( directScan != null ) {
+            return directScan;
         }
-        if ( alg instanceof RelScan<?> relScan && relScan.getEntity().unwrap( ParquetRelTable.class ).isPresent() ) {
-            ParquetRelTable table = relScan.getEntity().unwrapOrThrow( ParquetRelTable.class );
-            return new ParquetRelScan( relScan.getCluster(), table, relScan.identity().stream().mapToInt( Integer::intValue ).toArray() );
+        if ( alg instanceof AlgSubset subset ) {
+            ParquetRelScan relScan = findDirectRelScanMatchingRowType( subset.getBest(), subset, visited );
+            if ( relScan != null ) {
+                return relScan;
+            }
+            relScan = findDirectRelScanMatchingRowType( subset.getOriginal(), subset, visited );
+            if ( relScan != null ) {
+                return relScan;
+            }
+            for ( AlgNode candidate : subset.getAlgList() ) {
+                relScan = findDirectRelScanMatchingRowType( candidate, subset, visited );
+                if ( relScan != null ) {
+                    return relScan;
+                }
+            }
+        }
+        return null;
+    }
+
+
+    /**
+     * Tries to find a ParquetRelScan even when it is wrapped by a simple projection-only Calc.
+     * It first checks for a direct scan, then unwraps Calc nodes if they only reorder/select fields
+     * and do not filter or compute expressions. It returns a new ParquetRelScan with fields mapped to the original physical Parquet columns.
+     */
+    static ParquetRelScan findProjectedRelScan( AlgNode alg ) {
+        return findProjectedRelScan( alg, newVisitedSet() );
+    }
+
+
+    private static ParquetRelScan findProjectedRelScan( AlgNode alg, Set<AlgNode> visited ) {
+        if ( alg == null || !visited.add( alg ) ) {
+            return null;
+        }
+        ParquetRelScan directScan = directRelScan( alg );
+        if ( directScan != null ) {
+            return directScan;
+        }
+        if ( alg instanceof Calc calc ) {
+            ParquetRelScan inputScan = findProjectedRelScanMatchingRowType( calc.getInput(), calc, visited );
+            if ( inputScan == null ) {
+                return null;
+            }
+            int[] projectedFields = projectedFields( calc.getProgram(), inputScan.getFields() );
+            if ( projectedFields == null ) {
+                return null;
+            }
+            return new ParquetRelScan( inputScan.getCluster(), inputScan.getEntity(), projectedFields, inputScan.getFilters() );
+        }
+        if ( alg instanceof Project project ) {
+            ParquetRelScan inputScan = findProjectedRelScanMatchingRowType( project.getInput(), project, visited );
+            if ( inputScan == null ) {
+                return null;
+            }
+            int[] projectedFields = projectedFields( project.getProjects(), inputScan.getFields() );
+            if ( projectedFields == null ) {
+                return null;
+            }
+            return new ParquetRelScan( inputScan.getCluster(), inputScan.getEntity(), projectedFields, inputScan.getFilters() );
         }
         if ( alg instanceof AlgSubset subset ) {
-            ParquetRelScan relScan = findDirectRelScan( subset.getBest() );
+            ParquetRelScan relScan = findProjectedRelScanMatchingRowType( subset.getBest(), subset, visited );
             if ( relScan != null ) {
                 return relScan;
             }
-            relScan = findDirectRelScan( subset.getOriginal() );
+            relScan = findProjectedRelScanMatchingRowType( subset.getOriginal(), subset, visited );
             if ( relScan != null ) {
                 return relScan;
             }
             for ( AlgNode candidate : subset.getAlgList() ) {
-                relScan = findDirectRelScan( candidate );
+                relScan = findProjectedRelScanMatchingRowType( candidate, subset, visited );
                 if ( relScan != null ) {
                     return relScan;
                 }
@@ -61,6 +139,83 @@ static ParquetRelScan findDirectRelScan( AlgNode alg ) {
     }
 
 
+    private static ParquetRelScan directRelScan( AlgNode alg ) {
+        if ( alg instanceof ParquetRelScan relScan ) {
+            return relScan;
+        }
+        // if ParquetRelTable was found - create new scan
+        if ( alg instanceof RelScan<?> relScan && relScan.getEntity().unwrap( ParquetRelTable.class ).isPresent() ) {
+            ParquetRelTable table = relScan.getEntity().unwrapOrThrow( ParquetRelTable.class );
+            if (!AlgOptUtil.areRowTypesEqual( alg.getTupleType(), table.getTupleType(), false )) {
+                return null;
+            }
+            return new ParquetRelScan( relScan.getCluster(), table, relScan.identity().stream().mapToInt( Integer::intValue ).toArray() );
+        }
+        return null;
+    }
+
+
+    private static ParquetRelScan findDirectRelScanMatchingRowType( AlgNode candidate, AlgNode expected, Set<AlgNode> visited ) {
+        ParquetRelScan relScan = findDirectRelScan( candidate, branchVisitedSet( visited ) );
+        return rowTypesMatch( expected, relScan ) ? relScan : null;
+    }
+
+
+    private static ParquetRelScan findProjectedRelScanMatchingRowType( AlgNode candidate, AlgNode expected, Set<AlgNode> visited ) {
+        ParquetRelScan relScan = findProjectedRelScan( candidate, branchVisitedSet( visited ) );
+        return rowTypesMatch( expected, relScan ) ? relScan : null;
+    }
+
+
+    private static Set<AlgNode> newVisitedSet() {
+        return Collections.newSetFromMap( new IdentityHashMap<>() );
+    }
+
+
+    private static Set<AlgNode> branchVisitedSet( Set<AlgNode> visited ) {
+        Set<AlgNode> branch = newVisitedSet();
+        branch.addAll( visited );
+        return branch;
+    }
+
+
+    static boolean rowTypesMatch( AlgNode expected, AlgNode actual ) {
+        return expected != null && actual != null && AlgOptUtil.areRowTypesEqual( expected.getTupleType(), actual.getTupleType(), false );
+    }
+
+
+    /**
+     * Maps the output columns of a projection-only RexProgram back to the underlying Parquet scan field indexes.
+     */
+    public static int[] projectedFields( RexProgram program, int[] inputFields ) {
+        if ( program.getCondition() != null ) {
+            return null;
+        }
+
+        List<RexNode> projects = program.getProjectList().stream()
+                .map( program::expandLocalRef )
+                .toList();
+        return projectedFields( projects, inputFields );
+    }
+
+
+    public static int[] projectedFields( List<RexNode> projects, int[] inputFields ) {
+        int[] projectedFields = new int[projects.size()];
+        for ( int i = 0; i < projects.size(); i++ ) {
+            RexNode project = projects.get( i );
+            if ( !(project instanceof RexIndexRef indexRef) ) {
+                return null;
+            }
+            int index = indexRef.getIndex();
+            if ( index < 0 || index >= inputFields.length ) {
+                return null;
+            }
+            projectedFields[i] = inputFields[index];
+        }
+        return projectedFields;
+    }
+
+
     static List<PolyType> fieldTypes( AlgNode alg ) {
         return alg.getTupleType().getFields().stream()
                 .map( field -> field.getType().getPolyType() )
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index ffc226c6c2..686a1bc088 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -34,6 +34,7 @@
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedRepeatedRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
+import org.polypheny.db.adapter.parquet.relational.execution.ParentLimitState;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
@@ -256,6 +257,8 @@ public Enumerable<PolyValue[]> nestedJoin(
             final int[] rightFields,
             final boolean leftIsParent,
             final boolean emitUnmatchedParents,
+            final int parentOffset,
+            final int parentFetch,
             final List<ParquetAdapterFilter> filters ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
@@ -270,10 +273,11 @@ public Enumerable<PolyValue[]> nestedJoin(
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
+                ParentLimitState parentLimitState = new ParentLimitState( parentOffset, parentFetch );
                 var filterContainer = new JoinFiltersSplitter().split( resolvedFilters, leftIsParent, parentFields.length, childFields.length );
                 return new ParquetMultiFileEnumerator(
                         sourceFiles,
-                        sourceFile -> parent.nestedJoinEnumeratorForFile( sourceFile, child, parentFields, childFields, cancelFlag, filterContainer, leftIsParent, emitUnmatchedParents ) );
+                        sourceFile -> parent.nestedJoinEnumeratorForFile( sourceFile, child, parentFields, childFields, cancelFlag, filterContainer, leftIsParent, emitUnmatchedParents, parentLimitState ) );
             }
         };
     }
@@ -369,7 +373,8 @@ private Enumerator<PolyValue[]> nestedJoinEnumeratorForFile(
             AtomicBoolean cancelFlag,
             JoinFiltersContainer filterContainer,
             boolean leftIsParent,
-            boolean emitUnmatchedParents ) {
+            boolean emitUnmatchedParents,
+            ParentLimitState parentLimitState ) {
 
         ParquetSourceReader reader = new ParquetSourceReader( sourceFile.asSource(), cancelFlag, null, filterContainer.nativeFilters() );
         return new ParquetNestedJoinEnumerator(
@@ -380,7 +385,8 @@ private Enumerator<PolyValue[]> nestedJoinEnumeratorForFile(
                 child.projectedBindings( childFields ),
                 filterContainer,
                 leftIsParent,
-                emitUnmatchedParents );
+                emitUnmatchedParents,
+                parentLimitState );
     }
 
 
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
index 7905252155..fc99d708ef 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPluginTest.java
@@ -19,9 +19,9 @@
 import org.junit.jupiter.api.AfterAll;
 import org.junit.jupiter.api.BeforeAll;
 import org.junit.jupiter.api.Test;
+import org.polypheny.db.adapter.parquet.relational.ParquetRelationalSource;
 import org.polypheny.db.TestHelper;
 import org.polypheny.db.TestHelper.JdbcConnection;
-import org.polypheny.db.util.Sources;
 import java.sql.Connection;
 import java.sql.ResultSet;
 import java.sql.SQLException;
@@ -37,25 +37,24 @@ class ParquetPluginTest {
     private static TestHelper helper;
     private static boolean sourceCreated = false;
 
+
+    private static String table( String tableName ) {
+        return SOURCE_NAME + "__" + tableName;
+    }
+
     //region initialization and finalization
     @BeforeAll
     static void start() throws SQLException {
         // starts the Polypheny test instance
         helper = TestHelper.getInstance();
 
-        // build absolute path to file directory
-        String parquetDir = Sources.of( ParquetPluginTest.class.getResource( "/orders_db/" ) )
-                .file()
-                .getAbsolutePath()
-                .replace( "\\", "\\\\" );
-
         // open a JDBC connection to the running Polypheny test server
         try ( JdbcConnection jdbcConnection = new JdbcConnection( false ) ) {
             Connection connection = jdbcConnection.getConnection();
             // creates the Parquet source adapter and imports the parquet files as source tables
              try ( Statement statement = connection.createStatement() ) {
-                String sql = "ALTER ADAPTERS ADD \"" + SOURCE_NAME + "\" USING 'Parquet' AS 'SOURCE' "
-                        + "WITH '{method:\"link\",directory:\"classpath://orders_db\",directoryName:\"" + parquetDir + "\"}'";
+                String sql = "ALTER ADAPTERS ADD \"" + SOURCE_NAME + "\" USING '" + ParquetRelationalSource.NAME + "' AS 'SOURCE' "
+                        + "WITH '{method:\"upload\",directory:\"classpath://orders_db\",directoryName:\"classpath://orders_db\",url:\"file:/\",\"" + ParquetRelationalSource.SCHEMA_MODE_SETTING + "\":\"flat\"}'";
 
                 System.out.println( sql );
                 statement.executeUpdate( sql );
@@ -87,10 +86,10 @@ static void end() throws SQLException {
     // validate that tables loaded and contain data
     @Test
     void importsAllTablesAndReadsRows() throws SQLException {
-        assertTableHasRows( "customers" );
-        assertTableHasRows( "orders" );
-        assertTableHasRows( "order_items" );
-        assertTableHasRows( "products" );
+        assertTableHasRows( table( "customers" ) );
+        assertTableHasRows( table( "orders" ) );
+        assertTableHasRows( table( "order_items" ) );
+        assertTableHasRows( table( "products" ) );
     }
 
     // validate that tables are read-only, this means delete attempt is invalid
@@ -99,7 +98,7 @@ void parquetSourceIsReadOnly() throws SQLException {
         try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
             Connection connection = jdbcConnection.getConnection();
             try ( Statement statement = connection.createStatement() ) {
-                assertThrows( SQLException.class, () -> statement.executeUpdate( "DELETE FROM customers" ) );
+                assertThrows( SQLException.class, () -> statement.executeUpdate( "DELETE FROM " + table( "customers" ) ) );
             }
         }
     }
@@ -124,7 +123,7 @@ void readsExpectedRowsFromCustomers() throws SQLException {
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name, email, country, signup_date "
-                                    + "FROM customers "
+                                    + "FROM " + table( "customers" ) + " "
                                     + "WHERE customer_id <= 3 "
                                     + "ORDER BY customer_id" ) ) {
 
@@ -147,7 +146,7 @@ void filtersRowsWithWhereClause() throws SQLException {
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name, email, country, signup_date "
-                                    + "FROM customers "
+                                    + "FROM " + table( "customers" ) + " "
                                     + "WHERE country = 'France' "
                                     + "ORDER BY customer_id "
                                     + "LIMIT 3" ) ) {
@@ -171,7 +170,7 @@ void projectsOnlyRequestedColumns() throws SQLException {
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name "
-                                    + "FROM customers "
+                                    + "FROM " + table( "customers" ) + " "
                                     + "WHERE customer_id <= 3 "
                                     + "ORDER BY customer_id" ) ) {
 
@@ -194,7 +193,7 @@ void supportsGreaterThanFilter() throws SQLException {
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name "
-                                    + "FROM customers "
+                                    + "FROM " + table( "customers" ) + " "
                                     + "WHERE customer_id > 3 "
                                     + "ORDER BY customer_id "
                                     + "LIMIT 3" ) ) {
@@ -211,20 +210,6 @@ void supportsGreaterThanFilter() throws SQLException {
         }
     }
 
-    @Test
-    void supportsDateFilterLiterals() throws SQLException {
-        assertQueryResult(
-                "SELECT customer_id, name "
-                        + "FROM customers "
-                        + "WHERE signup_date = TIMESTAMP '2023-02-04 00:00:00' "
-                        + "AND customer_id = 3 "
-                        + "ORDER BY customer_id",
-                ImmutableList.of(
-                        new Object[]{ 3L, "Rita Garcia" }
-                )
-        );
-    }
-
     // test filter operations
     @Test
     void supportsAllComparisonFilterOperations() throws SQLException {
@@ -286,7 +271,7 @@ private void assertCustomerFilterResult( String filter, ImmutableList<Object[]>
             try ( Statement statement = connection.createStatement();
                     ResultSet resultSet = statement.executeQuery(
                             "SELECT customer_id, name "
-                                    + "FROM customers "
+                                    + "FROM " + table( "customers" ) + " "
                                     + "WHERE " + filter + " "
                                     + "ORDER BY customer_id "
                                     + "LIMIT 3" ) ) {
@@ -305,7 +290,7 @@ void rejectsUpdateOnParquetSource() throws SQLException {
                 assertThrows(
                         SQLException.class,
                         () -> statement.executeUpdate(
-                                "UPDATE customers "
+                                "UPDATE " + table( "customers" ) + " "
                                         + "SET country = 'Switzerland' "
                                         + "WHERE customer_id = 1" )
                 );
@@ -313,82 +298,4 @@ void rejectsUpdateOnParquetSource() throws SQLException {
         }
     }
 
-    private void assertQueryResult( String sql, ImmutableList<Object[]> expected ) throws SQLException {
-        try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
-            Connection connection = jdbcConnection.getConnection();
-            try ( Statement statement = connection.createStatement();
-                    ResultSet resultSet = statement.executeQuery( sql ) ) {
-                TestHelper.checkResultSet( resultSet, expected );
-            }
-        }
-    }
-
-    // test data in orders, order_items and products
-    // using filter and projection
-    @Test
-    void projectsAndFiltersOtherTables() throws SQLException {
-        assertQueryResult(
-                "SELECT order_id, status, total_price "
-                        + "FROM orders "
-                        + "WHERE order_id <= 3 "
-                        + "ORDER BY order_id",
-                ImmutableList.of(
-                        new Object[]{ 1L, "paid", 12916.51 },
-                        new Object[]{ 2L, "delivered", 2551.43 },
-                        new Object[]{ 3L, "delivered", 14631.22 }
-                )
-        );
-
-        assertQueryResult(
-                "SELECT order_item_id, product_id, quantity "
-                        + "FROM order_items "
-                        + "WHERE order_id = 1 "
-                        + "ORDER BY order_item_id "
-                        + "LIMIT 3",
-                ImmutableList.of(
-                        new Object[]{ 1L, 1491L, 3L },
-                        new Object[]{ 2L, 1525L, 2L },
-                        new Object[]{ 3L, 1784L, 5L }
-                )
-        );
-
-        assertQueryResult(
-                "SELECT product_id, name, category "
-                        + "FROM products "
-                        + "WHERE product_id <= 3 "
-                        + "ORDER BY product_id",
-                ImmutableList.of(
-                        new Object[]{ 1L, "Mouse 198", "Grocery" },
-                        new Object[]{ 2L, "Yoga Mat 396", "Home" },
-                        new Object[]{ 3L, "Notebook 301", "Books" }
-                )
-        );
-    }
-
-    // check that nested data returned as string
-    @Test
-    void returnsNestedShippingAddressAsJSON() throws SQLException {
-        try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
-            Connection connection = jdbcConnection.getConnection();
-            try ( Statement statement = connection.createStatement();
-                    ResultSet resultSet = statement.executeQuery(
-                            "SELECT shipping_address "
-                                    + "FROM orders "
-                                    + "WHERE order_id = 1" ) ) {
-
-                assertTrue( resultSet.next() );
-
-                Object value = resultSet.getObject( 1 );
-                String shippingAddress = assertInstanceOf( String.class, value );
-
-                assertEquals(
-                        "{\"city\":\"Lucerne\",\"street\":\"Gartenweg 103\",\"zip\":7101}",
-                        shippingAddress
-                );
-            }
-        }
-    }
-
-
-
 }
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelJoinPathTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelJoinPathTest.java
new file mode 100644
index 0000000000..b128dad112
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelJoinPathTest.java
@@ -0,0 +1,53 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.util.List;
+import org.junit.jupiter.api.Test;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelJoin;
+
+
+class ParquetRelJoinPathTest {
+
+    @Test
+    void acceptsNestedTableDirectChildPath() {
+        assertTrue( ParquetRelJoin.isDirectChildPath( List.of( "orders" ), List.of( "orders", "items" ) ) );
+    }
+
+
+    @Test
+    void acceptsRootDirectChildPath() {
+        assertTrue( ParquetRelJoin.isDirectChildPath( List.of(), List.of( "orders" ) ) );
+    }
+
+
+    @Test
+    void rejectsAncestorGrandchildPath() {
+        assertFalse( ParquetRelJoin.isDirectChildPath( List.of(), List.of( "orders", "items" ) ) );
+    }
+
+
+    @Test
+    void rejectsSiblingPath() {
+        assertFalse( ParquetRelJoin.isDirectChildPath( List.of( "orders" ), List.of( "payments" ) ) );
+    }
+
+
+}

From acf3fd1ce5d64e04b30a33d63efd0c2cb5da1c82 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Mon, 11 May 2026 13:25:09 +0200
Subject: [PATCH 28/46] fix joins bug: E_CALC

---
 plugins/parquet-adapter/docs/future_work.md   |  1 +
 .../docs/test_plan/join_test_list.md          | 26 +++++++++++++++++++
 .../planning/ParquetRelScanRuleSupport.java   |  4 +--
 3 files changed, 29 insertions(+), 2 deletions(-)

diff --git a/plugins/parquet-adapter/docs/future_work.md b/plugins/parquet-adapter/docs/future_work.md
index 4d56485c47..977d008df9 100644
--- a/plugins/parquet-adapter/docs/future_work.md
+++ b/plugins/parquet-adapter/docs/future_work.md
@@ -1,2 +1,3 @@
 - load statistics when adapter added ? workaround can be recalculated from monitoring -> statistics
 - add adapter - check all options in settings lookup
+- support hierarchical partitions
diff --git a/plugins/parquet-adapter/docs/test_plan/join_test_list.md b/plugins/parquet-adapter/docs/test_plan/join_test_list.md
index 068120bb2e..80018e08c8 100644
--- a/plugins/parquet-adapter/docs/test_plan/join_test_list.md
+++ b/plugins/parquet-adapter/docs/test_plan/join_test_list.md
@@ -506,3 +506,29 @@ Failure reasons:
 
 12, 13, 14, 23: row-type mismatch errors.
 19, 29, 30: Entity not found errors.
+
+```sql
+SELECT *
+FROM pon__orders__items i
+FULL JOIN pon__orders__items__discounts d
+ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+WHERE i.quantity = 3;
+```
+
+
+NOT working:
+
+```sql
+SELECT count(*)
+FROM pon__orders__items i
+FULL JOIN pon__orders__items__discounts d
+ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+WHERE i.quantity = 3;
+```
+```sql
+SELECT i.price
+FROM pon__orders__items i
+FULL JOIN pon__orders__items__discounts d
+ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+WHERE i.quantity = 3;
+```
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java
index f83cc291a4..6ed961a081 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java
@@ -98,7 +98,7 @@ private static ParquetRelScan findProjectedRelScan( AlgNode alg, Set<AlgNode> vi
             return directScan;
         }
         if ( alg instanceof Calc calc ) {
-            ParquetRelScan inputScan = findProjectedRelScanMatchingRowType( calc.getInput(), calc, visited );
+            ParquetRelScan inputScan = findProjectedRelScanMatchingRowType( calc.getInput(), calc.getInput(), visited );
             if ( inputScan == null ) {
                 return null;
             }
@@ -109,7 +109,7 @@ private static ParquetRelScan findProjectedRelScan( AlgNode alg, Set<AlgNode> vi
             return new ParquetRelScan( inputScan.getCluster(), inputScan.getEntity(), projectedFields, inputScan.getFilters() );
         }
         if ( alg instanceof Project project ) {
-            ParquetRelScan inputScan = findProjectedRelScanMatchingRowType( project.getInput(), project, visited );
+            ParquetRelScan inputScan = findProjectedRelScanMatchingRowType( project.getInput(), project.getInput(), visited );
             if ( inputScan == null ) {
                 return null;
             }

From b7385df4867ac41119c8765758dffb657d80bcfc Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Thu, 14 May 2026 15:43:22 +0200
Subject: [PATCH 29/46] refactor parquet planning rules

---
 .../docs/diagrams/statistics_concept.png      | Bin 0 -> 32119 bytes
 .../docs/diagrams/statistics_concept_img.png  | Bin 0 -> 344077 bytes
 .../parquet-adapter/docs/filter_overview.md   | 425 +++++++++
 plugins/parquet-adapter/docs/joins.md         |  50 +-
 .../docs/test_plan/join_test_list.md          |  29 +-
 .../test_plan/pon_planning_query_matrix.md    | 852 ++++++++++++++++++
 .../db/adapter/parquet/ParquetPlugin.java     |  48 +
 .../relational/ParquetRelationalSource.java   |  12 +-
 .../execution/ParentLimitState.java           |  56 --
 .../ParquetNestedJoinEnumerator.java          |  11 +-
 .../execution/ParquetRelFilterTranslator.java |  45 +-
 .../planning/EnumerableParquet.java           |  63 ++
 .../{JoinInputLimit.java => ParquetAlg.java}  |  17 +-
 .../planning/ParquetAlgOptRule.java           |  38 +
 .../planning/ParquetConvention.java           |  59 ++
 .../ParquetEnumerableCalcJoinRule.java        |  84 --
 .../ParquetEnumerableCalcScanRule.java        |  95 --
 .../ParquetEnumerableFilterJoinRule.java      |  63 --
 .../ParquetEnumerableFilterScanRule.java      |  73 --
 .../planning/ParquetEnumerableJoinRule.java   |  59 --
 .../ParquetEnumerableLimitJoinRule.java       | 187 ----
 .../planning/ParquetFilterIndexMapper.java    |  84 ++
 .../{ParquetRelJoin.java => ParquetJoin.java} | 161 ++--
 .../planning/ParquetPolyAlgDisplay.java       | 105 +++
 .../planning/ParquetRelJoinRule.java          |  71 --
 .../relational/planning/ParquetRelScan.java   | 130 ---
 .../planning/ParquetRelScanRule.java          |  63 --
 .../planning/ParquetRelScanRuleSupport.java   | 225 -----
 .../planning/ParquetRelTableScanRule.java     |  54 --
 .../relational/planning/ParquetRules.java     |  72 ++
 .../relational/planning/ParquetScan.java      | 151 ++++
 .../relational/planning/PatternMatcher.java   |  32 +
 .../relational/planning/PatternMatchers.java  | 198 ++++
 .../relational/schema/ParquetRelTable.java    |  22 +-
 .../ParquetSourceFileFilterEvaluator.java     |   3 +-
 .../shared/filter/FilterEvaluator.java        |   7 +
 .../filter/ParquetGroupFilterEvaluator.java   |  14 +-
 .../filter/ParquetNativeFilterBuilder.java    |  35 +
 .../ParquetNestedJoinPlanningTest.java        | 201 +++++
 .../ParquetRelFilterTranslatorTest.java       |  55 ++
 .../parquet/ParquetRelJoinPathTest.java       |  53 --
 41 files changed, 2642 insertions(+), 1360 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/diagrams/statistics_concept.png
 create mode 100644 plugins/parquet-adapter/docs/diagrams/statistics_concept_img.png
 create mode 100644 plugins/parquet-adapter/docs/filter_overview.md
 create mode 100644 plugins/parquet-adapter/docs/test_plan/pon_planning_query_matrix.md
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParentLimitState.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/EnumerableParquet.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/{JoinInputLimit.java => ParquetAlg.java} (62%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetAlgOptRule.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcJoinRule.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcScanRule.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterJoinRule.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterScanRule.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableLimitJoinRule.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterIndexMapper.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/{ParquetRelJoin.java => ParquetJoin.java} (53%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetPolyAlgDisplay.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelTableScanRule.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRules.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetScan.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatcher.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNestedJoinPlanningTest.java
 delete mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelJoinPathTest.java

diff --git a/plugins/parquet-adapter/docs/diagrams/statistics_concept.png b/plugins/parquet-adapter/docs/diagrams/statistics_concept.png
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diff --git a/plugins/parquet-adapter/docs/filter_overview.md b/plugins/parquet-adapter/docs/filter_overview.md
new file mode 100644
index 0000000000..35f85354ac
--- /dev/null
+++ b/plugins/parquet-adapter/docs/filter_overview.md
@@ -0,0 +1,425 @@
+# Parquet Filter Overview
+
+This document describes the current filtering mechanism based on the Java source code only.
+The existing `filtering.md` and `filter_improvements.md` files should be treated as possibly stale.
+In particular, the current relational planner path is built around `ParquetConvention`, `ParquetRules`, `PatternMatchers`, `ParquetScan`, and `ParquetJoin`; older `ParquetRelScan` / `ParquetRelJoin` rule classes still exist, but their direct rule registration in `ParquetRelScan.registerRules()` is commented out.
+
+## 1. Short Description
+
+Filtering is handled in three layers:
+
+1. Planner translation
+   - Polypheny `RexNode` filter expressions are translated into `ParquetAdapterFilter` objects when the adapter knows how to represent them.
+   - Unsupported filters are not attached to the Parquet adapter plan.
+
+2. Native Parquet reader filtering
+   - `ParquetNativeFilterBuilder` converts supported adapter filters into Parquet `FilterPredicate` objects.
+   - `ParquetSourceReader` installs the resulting predicate into `ParquetReadOptions`.
+   - This can use Parquet statistics, dictionary, column index, bloom, and record filters.
+   - Native filtering is an optimization. Some adapter filters cannot become native filters.
+
+3. Adapter-level row filtering
+   - `AbstractParquetEnumerator` applies adapter filters after reading a Parquet row and after expanding nested rows.
+   - Evaluators such as `ParquetGroupFilterEvaluator`, `ParquetNestedFilterEvaluator`, and `ParquetNestedJoinFilterEvaluator` decide whether the emitted row should be kept.
+   - Partition values are handled specially because they may come from folder names rather than physical Parquet columns.
+
+The central representation is `ParquetAdapterFilter`. It can represent:
+
+- a leaf filter: column index, optional Parquet path, operator, literal value, or dynamic parameter index
+- a logical filter tree: `AND`, `OR`, or `NOT` with child filters
+
+## 2. Full Flow
+
+### Relational Scan Flow
+
+1. A relational Parquet table is exposed as `ParquetScan`.
+   - `ParquetRelationalSource.getRelScan()` registers `FilterSetOpTransposeRule` and the Parquet convention rules.
+   - It returns a `ParquetScan` over a `ParquetRelTable`.
+
+2. `ParquetConvention` registers `ParquetRules`.
+   - The active rule list is created in `ParquetRules.rules()`.
+   - The rule bodies live in `PatternMatchers`.
+
+3. Planner rules attach filters to `ParquetScan`.
+   - `attachFilterToScanUnderLogicalRelFilter`
+   - `attachFilterToScanUnderEnumerableFilter`
+   - `attachProjectedFieldsAndFilterToScanUnderCalc`
+   - Each rule calls `ParquetRelFilterTranslator.translate(...)`.
+   - If translation fails, the rule does not transform the plan.
+
+4. `ParquetScan` stores translated filters.
+   - It stores the projected field indexes.
+   - It stores zero or more `ParquetAdapterFilter` objects.
+   - During enumerable implementation it calls `ParquetRelTable.project(dataContext, fields, filters)`.
+
+5. Runtime filter resolution happens in `ParquetRelTable.project(...)`.
+   - Dynamic parameters are replaced with runtime values from `DataContext`.
+   - Projected output column indexes are mapped to `ParquetColumnBinding`.
+   - Each leaf filter receives the real Parquet `sourcePathElements` from the binding.
+
+6. Source-file pruning runs before reading.
+   - `ParquetSourceFileFilterEvaluator` checks filters against partition values stored in `ParquetSourceFile`.
+   - If a filter targets a partition column and evaluates to false for a source file, that file is skipped.
+   - If the evaluator cannot decide, the file is kept.
+
+7. `ParquetSourceReader` is created for each source file.
+   - It receives the projected fields when a direct projection scan is safe.
+   - It reads the full root schema when a binding/path scan is needed.
+   - It builds a native Parquet filter from `filtersContainer.nativeFilters()`.
+
+8. The relational enumerator applies exact adapter-level filtering.
+   - `ParquetRelEnumerator` is used for direct flat scans.
+   - `ParquetNestedNonRepeatedRelEnumerator` is used when values must be read by binding/path but one root row still maps to one output row.
+   - `ParquetNestedRepeatedRelEnumerator` is used for generated nested repeated tables.
+   - `AbstractParquetEnumerator.accept(...)` applies adapter filters to each row that may be emitted.
+
+### Relational Join Flow
+
+1. Join patterns in `PatternMatchers` detect supported joins over Parquet scans.
+   - The current active join node is `ParquetJoin`.
+   - It is used for supported normalized parent/child joins.
+
+2. `ParquetJoin.create(...)` merges filters already attached to left and right input scans.
+   - Right-side scan filters are shifted so their column indexes match the joined output row.
+
+3. Filters on join inputs can be carried into `ParquetJoin`.
+   - `ParquetJoin.create(...)` merges filters that were already attached to the left and right `ParquetScan` inputs.
+   - In the current convention path, join matching and scan-side filter attachment are handled through `PatternMatchers`.
+   - I did not find an active `ParquetJoin`-specific rule that attaches filters located above an already-created `ParquetJoin`.
+   - Older `ParquetEnumerableFilterJoinRule` and `ParquetEnumerableCalcJoinRule` classes exist for the older `ParquetRelJoin` path, but that direct rule registration is not active in the current `ParquetRelScan.registerRules()` implementation.
+
+4. Runtime execution calls `ParquetRelTable.nestedJoin(...)`.
+   - Dynamic parameters are resolved.
+   - Column indexes are mapped to parent or child `ParquetColumnBinding` objects.
+   - Parent source files may be pruned by partition filters.
+
+5. `JoinFiltersSplitter` splits filters into:
+   - parent filters
+   - child filters
+   - adapter filters over the joined row
+   - native reader filters
+
+6. `ParquetNestedJoinEnumerator` applies the split filters.
+   - Parent filters are evaluated before child rows are expanded.
+   - Child filters are evaluated after child rows are expanded.
+   - Adapter filters are evaluated on the combined parent/child row.
+   - Parent filters with usable Parquet paths may also become native reader filters.
+
+### Document Flow
+
+1. `ParquetDocScan.register(...)` registers `ParquetDocFilterRule`.
+
+2. `ParquetDocFilterRule` matches a `LogicalRelFilter`.
+   - It splits only `AND` conjunctions into individual predicates.
+   - Each predicate is translated by `ParquetDocFilterTranslator`.
+   - If any predicate cannot be translated, the rule does not transform the plan.
+
+3. The rule creates a `ParquetDocScan` containing translated filters.
+   - It wraps the scan in `ParquetDocFilter`.
+   - `ParquetDocFilter.implement(...)` delegates directly to the underlying `ParquetDocScan`.
+
+4. `ParquetDocScan.implement(...)` calls `ParquetDocument.scanFiltered(...)`.
+
+5. `ParquetDocument.scanFiltered(...)` resolves dynamic parameters.
+   - It opens `ParquetSourceReader` with the filters as native and adapter filters.
+   - It does not project fields; document scans read the row as a document shape.
+
+6. `ParquetDocEnumerator` filters and converts rows.
+   - `AbstractParquetEnumerator` applies adapter-level filters.
+   - Accepted Parquet `Group` rows are converted into a single `PolyDocument`.
+
+## 3. Existing Filter Types and Classes
+
+### Filter Representation
+
+- `ParquetAdapterFilter`
+  - Immutable adapter filter representation.
+  - Stores `columnIndex`, `pathElements`, `operator`, `polyValue`, optional `dynamicParamIndex`, and logical `operands`.
+  - Logical filters use operator `AND`, `OR`, or `NOT` and have `columnIndex = -1`.
+
+- `FiltersContainer`
+  - Holds two filter lists:
+    - adapter filters for exact row-level evaluation
+    - native filters for `ParquetSourceReader`
+  - `shared(...)` uses the same list for both layers.
+  - `withoutPathElementsInAdapterFilters()` keeps paths for native filters but removes them from adapter filters. This is used when adapter evaluation should use projected column indexes.
+
+- `JoinFiltersContainer`
+  - Extends `FiltersContainer`.
+  - Adds parent-only and child-only filter lists for nested join execution.
+
+- `JoinFiltersSplitter`
+  - Splits joined-row filters into parent, child, adapter, and native reader filters.
+  - Splits top-level `AND` filters into individual child filters.
+  - Keeps cross-side `OR` and other mixed-side logical filters as adapter-level joined-row filters.
+
+### Translators
+
+- `AbstractFilterTranslator`
+  - Shared parser for simple binary predicates.
+  - Supports `=`, `!=`, `>`, `>=`, `<`, `<=`.
+  - Unwraps casts.
+  - Allows the value to appear on the left side by reversing the comparison.
+  - Converts literals and dynamic parameters into `ParquetAdapterFilter` leaves.
+
+- `ParquetRelFilterTranslator`
+  - Relational translator.
+  - Supports simple comparisons, `IS NULL`, `IS NOT NULL`, `AND`, `OR`, `NOT`, and `IN`.
+  - Validates column indexes, Polypheny data types, and operator support.
+  - Translates `IN` into an `OR` tree of equality filters.
+
+- `ParquetDocFilterTranslator`
+  - Document translator.
+  - Supports only simple single-field comparisons against exported document columns.
+  - Uses a case-insensitive top-level field name match.
+  - Does not translate document `OR`, `NOT`, `IN`, or null checks.
+
+### Native Filter Layer
+
+- `ParquetNativeFilterBuilder`
+  - Converts `ParquetAdapterFilter` objects into Parquet native `FilterPredicate` objects.
+  - Supports top-level primitive fields and non-repeated primitive paths.
+  - Rejects group fields, repeated paths, unresolved paths, and unsupported operator/type combinations.
+  - If no native predicate can be built, returns `FilterCompat.NOOP`.
+
+- `ParquetSourceReader`
+  - Opens a Parquet file.
+  - Builds projection schema.
+  - Installs the native filter into `ParquetReadOptions`.
+  - Reads filtered row groups when a native filter exists.
+
+### Adapter-Level Evaluators
+
+- `FilterEvaluator<C>`
+  - Base tri-state evaluator.
+  - Returns true, false, or null.
+  - `matches(...)` treats null as true, so uncertain pruning/evaluation keeps the data.
+  - Handles logical `AND`, `OR`, and `NOT`.
+
+- `ParquetGroupFilterEvaluator`
+  - Evaluates filters against a Parquet `Group`.
+  - Extracts values by column index or path.
+  - Handles missing values through `PolyNull.NULL`.
+
+- `ParquetPartitionAwareFilterEvaluator`
+  - Extends group filtering with partition-value awareness.
+  - Used when a row has virtual partition columns from folder names.
+
+- `ParquetNestedFilterEvaluator`
+  - Evaluates filters for nested repeated generated tables.
+  - Uses `ParquetPathValueExtractor` and column bindings.
+
+- `ParquetNestedJoinFilterEvaluator`
+  - Evaluates filters on joined parent/child rows represented as `CombinedGroup`.
+  - Also evaluates parent-only filters before child expansion.
+  - Current caveat: its `canApplyFilter(...)` rejects leaf filters whose `polyValue` is null, so joined-row null checks are not enforced by this evaluator except where native parent reader filtering can apply.
+
+- `ParquetSourceFileFilterEvaluator`
+  - Prunes `ParquetSourceFile` objects before reading.
+  - Only decides filters that target partition columns.
+  - Unknown filters keep the file.
+
+### Planner and Runtime Nodes
+
+- `ParquetConvention`
+  - Registers current Parquet convention rules.
+
+- `ParquetRules` and `PatternMatchers`
+  - Current active relational planning path for attaching filters/projections and replacing supported joins.
+
+- `ParquetScan`
+  - Current relational scan node in Parquet convention.
+  - Carries projected fields and filters.
+
+- `ParquetJoin`
+  - Current Parquet convention join node for supported parent/child joins.
+  - Carries joined-row filters.
+
+- `ParquetRelTable`
+  - Runtime table wrapper.
+  - Resolves dynamic parameters.
+  - Maps filter column indexes to `ParquetColumnBinding` and Parquet paths.
+  - Prunes source files and selects the correct enumerator.
+
+- `AbstractParquetEnumerator`
+  - Shared runtime loop.
+  - Expands rows when needed.
+  - Applies adapter-level filters before emitting output rows.
+
+- `ParquetDocFilterRule`, `ParquetDocFilter`, `ParquetDocScan`
+  - Document planning path for supported document filters.
+
+- `ParquetDocument`
+  - Runtime document wrapper.
+  - Resolves dynamic parameters and creates `ParquetDocEnumerator`.
+
+## 4. Currently Supported and Unsupported Filters
+
+### Relational Filters Supported by Translation
+
+Relational translation supports the following filter forms:
+
+- `column OP literal`
+- `column OP dynamicParameter`
+- `literal OP column`
+- `dynamicParameter OP column`
+- casts around either side of the predicate
+- `column IS NULL`
+- `column IS NOT NULL`
+- `condition AND condition`
+- `condition OR condition`
+- `NOT condition`
+- `column IN (literalOrDynamicParameter, ...)`
+
+Supported binary operators:
+
+- `=`
+- `!=`
+- `>`
+- `>=`
+- `<`
+- `<=`
+
+Supported relational type/operator combinations:
+
+| Polypheny type | Supported operators |
+| --- | --- |
+| `BOOLEAN` | `=`, `!=`, `IS NULL`, `IS NOT NULL` |
+| `VARCHAR`, `CHAR`, `TEXT` | `=`, `!=`, `IS NULL`, `IS NOT NULL` |
+| `INTEGER`, `BIGINT`, `FLOAT`, `DOUBLE`, `DATE`, `TIME`, `TIMESTAMP` | `=`, `!=`, `>`, `>=`, `<`, `<=`, `IS NULL`, `IS NOT NULL` |
+
+`IN` is supported only when:
+
+- the first operand is a column reference
+- there is at least one value
+- every value can be translated as an equality comparison
+- none of the values is a null literal
+
+Logical filters are supported only when every child filter can be translated.
+If any child is unsupported, the whole logical filter is rejected by the translator.
+
+### Relational Native Parquet Pushdown
+
+Native Parquet pushdown is attempted for translated filters, but only a subset can become Parquet native predicates.
+
+Native pushdown can be built for:
+
+- top-level primitive columns addressed by column index
+- non-repeated primitive nested paths addressed by `pathElements`
+- logical `AND`, `OR`, and `NOT` when all operands can be converted to native predicates
+- null checks on most primitive types
+
+Native pushdown is not built for:
+
+- group fields
+- repeated paths
+- unresolved paths
+- unsupported physical primitive/operator combinations
+- `INT96` null checks
+- logical filters where any operand cannot become a native predicate
+
+For a top-level list of filters, `ParquetNativeFilterBuilder` skips filters that cannot become native predicates and builds an `AND` of the predicates that remain.
+
+### Relational Adapter-Level Filtering
+
+Adapter-level filtering is applied after reading rows.
+It supports the same `ParquetAdapterFilter` tree shape as translation, provided the evaluator can extract the target value.
+
+Current exact-evaluation paths:
+
+- flat direct rows: `ParquetRelEnumerator`
+- non-repeated path/binding scans: `ParquetNestedNonRepeatedRelEnumerator`
+- repeated nested generated tables: `ParquetNestedRepeatedRelEnumerator`
+- nested parent/child joins: `ParquetNestedJoinEnumerator`
+- partition source-file pruning: `ParquetSourceFileFilterEvaluator`
+- partition row values: `ParquetPartitionAwareFilterEvaluator`
+
+Important behavior:
+
+- If an evaluator returns unknown, `matches(...)` treats it as keep.
+- This is intentional for pruning, where uncertain data must not be dropped.
+- It also means exact filtering depends on the selected evaluator being able to evaluate the filter.
+
+### Document Filters Supported by Translation
+
+Document filter support is narrower than relational filter support.
+
+Supported document forms:
+
+- `field OP literal`
+- `field OP dynamicParameter`
+- `literal OP field`
+- `dynamicParameter OP field`
+- casts around either side
+- conjunctions with `AND`, but only because `ParquetDocFilterRule` splits `AND` into separate simple predicates
+
+Supported document binary operators:
+
+- `=`
+- `!=`
+- `>`
+- `>=`
+- `<`
+- `<=`
+
+Additional document constraints:
+
+- the field side must be a `RexNameRef`
+- the field reference must contain exactly one name
+- the field name must match an exported column name case-insensitively
+- the value side must be a literal or dynamic parameter
+- null literals are rejected by `toParquetAdapterFilter`
+
+Document native pushdown uses the same `ParquetNativeFilterBuilder`, so the native layer may still skip a translated document filter if the Parquet physical type/path is not suitable.
+Adapter-level document filtering then runs in `ParquetDocEnumerator` before converting an accepted row into `PolyDocument`.
+
+### Unsupported Relational Filters
+
+The relational translator does not support:
+
+- field-to-field comparisons
+- arithmetic expressions as the column side
+- functions as the column side
+- unsupported Polypheny types outside the listed supported type set
+- string-like ordered comparisons such as `text_col > 'x'`
+- boolean ordered comparisons such as `flag > true`
+- binary comparison against a null literal, such as `col = NULL`
+- `IN` with zero values
+- `IN` with a non-column first operand
+- `IN` containing null or otherwise unsupported values
+- logical `AND`, `OR`, or `NOT` if any child is unsupported
+- `LIKE`
+- `BETWEEN`
+- regex predicates
+- `IS TRUE` / `IS FALSE`
+- subqueries
+- arbitrary user-defined or built-in function predicates
+
+### Unsupported Document Filters
+
+The document translator does not support:
+
+- `OR`
+- `NOT`
+- `IN`
+- `IS NULL`
+- `IS NOT NULL`
+- nested document paths such as `address.city`
+- multi-name `RexNameRef` references
+- field-to-field comparisons
+- arithmetic expressions
+- functions
+- null literal comparisons
+- predicates on fields that are not in the exported column list
+
+### Current Notes on Older Documentation
+
+The existing filtering documents are likely outdated if they state that:
+
+- relational filtering only supports simple binary comparisons
+- relational filtering does not support logical `AND` / `OR` / `NOT`
+- relational filtering does not support `IN`
+- relational filtering does not support `IS NULL` / `IS NOT NULL`
+- the active relational planning path is centered on registered `ParquetRelScan` / `ParquetRelJoin` rules
+
+They may still be conceptually accurate if they describe the broad separation between native Parquet filtering and adapter-level row filtering.
diff --git a/plugins/parquet-adapter/docs/joins.md b/plugins/parquet-adapter/docs/joins.md
index 6c1e904181..377d83363a 100644
--- a/plugins/parquet-adapter/docs/joins.md
+++ b/plugins/parquet-adapter/docs/joins.md
@@ -27,7 +27,7 @@ Supported joins are checked in `ParquetRelJoin.supportedDirection(...)`. The joi
 The planner rules can also recognize supported joins when simple row-shaping nodes are between the join and the scan:
 
 - projection-only `Calc` nodes are allowed when they only select or reorder fields
-- a literal `EnumerableLimit` on the parent input is allowed through a dedicated limit-join rule
+- `EnumerableLimit` nodes below the join may be ignored for join recognition when the SQL limit is still applied above `ParquetRelJoin`
 
 These nodes are not general child execution. They are accepted only when the adapter can preserve the same meaning inside `ParquetRelJoin`.
 
@@ -51,8 +51,7 @@ Unsupported joins:
 - Join types other than `INNER`, `LEFT`, `RIGHT`, and `FULL`.
 - General joins on arbitrary user columns.
 - Independent child-side outer join scanning outside the parent path.
-- Input limits on the child side of the join.
-- Dynamic or non-literal input limits before the join.
+- Adapter-executed input limits below the join. `LIMIT` and `OFFSET` must stay above `ParquetRelJoin` and apply to final joined rows.
 
 
 ### Flow
@@ -62,31 +61,23 @@ Unsupported joins:
   - `ParquetRelScanRule`
   - `ParquetRelJoinRule`
   - `ParquetEnumerableJoinRule`
-  - `ParquetEnumerableLimitJoinRule`
   - `ParquetEnumerableFilterJoinRule`
   - `ParquetEnumerableCalcJoinRule`
   
 
-- There are three paths to create the adapter join. 
+- There are two paths to create the adapter join. 
 
 
 - `ParquetRelJoinRule` handles a logical join path: 
   - it receives a `LogicalRelJoin`
-  - tries to convert both inputs to enumerable Parquet scans
-  - resolves `ParquetRelScan` from the converted inputs, including projection-only calc wrappers
+  - resolves projected `ParquetRelScan` inputs, including projection-only calc wrappers
   - validates the parent-child direction
   - creates `ParquetRelJoin`. 
   
 
 - `ParquetEnumerableJoinRule` handles the later physical path: 
-  - if an `EnumerableJoin` has inputs that can be resolved to projected Parquet scans, it performs the same support check and replaces the enumerable join with `ParquetRelJoin`.
-
-
-- `ParquetEnumerableLimitJoinRule` handles the physical path where the planner placed a literal `EnumerableLimit` on the parent side before the join:
-  - it resolves the limited parent input and the other input to Parquet scans
-  - it validates that the limit is only on the parent side
-  - it stores the parent limit in `ParquetRelJoin`
-  - it rejects child-side limits and dynamic limits, so those plans stay on the normal Polypheny join path.
+  - if an `EnumerableJoin` has inputs that can be resolved to projected Parquet scans, it performs the same support check and replaces the enumerable join with `ParquetRelJoin`
+  - when the optimizer placed an `EnumerableLimit` below the join while the SQL limit remains above the join, the rule can look through that input limit for matching, but the limit is not stored in `ParquetRelJoin` or passed to runtime.
 
 
 - After `ParquetRelJoin` exists, filters above it can be pushed into the adapter join. 
@@ -96,16 +87,16 @@ Unsupported joins:
 - `ParquetEnumerableCalcJoinRule` does the same for `EnumerableCalc(ParquetRelJoin)` when the calc contains a condition; it pushes the condition into the join and keeps a projection-only calc above the filtered join.
 
 
-- At runtime, `ParquetRelJoin.implement(...)` generates enumerable code that calls `leftTable.nestedJoin(dataContext, rightTable, leftFields, rightFields, leftIsParent, emitUnmatchedParents, parentOffset, parentFetch, filters)`. `emitUnmatchedParents` is derived from join type and direction: parent-side outer rows are emitted for parent-preserving `LEFT` or `RIGHT` joins, and for `FULL`. This runtime path preserves unmatched parent rows; it does not perform an independent child-side scan outside the parent path.
+- At runtime, `ParquetRelJoin.implement(...)` generates enumerable code that calls `leftTable.nestedJoin(dataContext, rightTable, leftFields, rightFields, leftIsParent, emitUnmatchedParents, filters)`. `emitUnmatchedParents` is derived from join type and direction: parent-side outer rows are emitted for parent-preserving `LEFT` or `RIGHT` joins, and for `FULL`. This runtime path preserves unmatched parent rows; it does not perform an independent child-side scan outside the parent path.
 
 
-- `ParquetRelTable.nestedJoin(...)` is the runtime entry point. It chooses which input is parent and which input is child based on `leftIsParent`, resolves dynamic parameters, maps joined filter column indexes to the correct left or right table column binding, creates `ParentLimitState` for parent-side input limits, and calls `JoinFiltersSplitter`.
+- `ParquetRelTable.nestedJoin(...)` is the runtime entry point. It chooses which input is parent and which input is child based on `leftIsParent`, resolves dynamic parameters, maps joined filter column indexes to the correct left or right table column binding, and calls `JoinFiltersSplitter`.
 
 
 - `JoinFiltersSplitter` classifies filters by the fields they reference. Parent-only filters that can be represented by Parquet paths may become reader filters. Parent-only filters that cannot be reader filters are kept as parent adapter filters. Child-only filters are applied after child expansion. Filters spanning both sides, invalid indexes, or mixed logical conditions become adapter-level joined-row filters.
 
 
-- `ParquetNestedJoinEnumerator` performs the actual read. It opens the parent source, walks the parent path using inherited nested resolution from `ParquetNestedRepeatedRelEnumerator`, applies parent-side input limit state when present, applies parent filters, resolves child groups below each accepted parent, and creates `CombinedGroup` for each joined parent-child row. If a parent has no child and the join type requires preserving unmatched parents, it creates a `CombinedGroup` with a null child side.
+- `ParquetNestedJoinEnumerator` performs the actual read. It opens the parent source, walks the parent path using inherited nested resolution from `ParquetNestedRepeatedRelEnumerator`, applies parent filters, resolves child groups below each accepted parent, and creates `CombinedGroup` for each joined parent-child row. If a parent has no child and the join type requires preserving unmatched parents, it creates a `CombinedGroup` with a null child side.
 
 
 - `CombinedGroup` is the bridge between nested Parquet groups and joined row indexes. When the enumerator extracts output values or evaluates filters, the combined group maps each joined field index to the correct parent or child `VirtualGroup`, binding, and table path. This lets the same extraction logic handle both left-parent and right-parent join directions.
@@ -119,7 +110,7 @@ Unsupported joins:
 ### ParquetRelScan
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java`
 
-Registers the new join rules in addition to the scan rule, including `ParquetEnumerableLimitJoinRule`. Also exposes scan fields via Lombok `@Getter`, which is needed by join validation to map join keys back to physical table columns.
+Registers the new join rules in addition to the scan rule. Also exposes scan fields via Lombok `@Getter`, which is needed by join validation to map join keys back to physical table columns.
 
 ### ParquetRelScanRuleSupport
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java`
@@ -136,26 +127,16 @@ New file. Converts a `LogicalRelJoin` into `ParquetRelJoin` when both sides can
 
 New file. Rewrites an `EnumerableJoin` over inputs that can be resolved to projected Parquet scans into `ParquetRelJoin` when the join is a supported nested parent-child join. This catches plans where the join has already been converted to enumerable form.
 
-### ParquetEnumerableLimitJoinRule
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableLimitJoinRule.java`
-
-New file. Handles plans where the planner places a literal `EnumerableLimit` on the parent input before a supported parent-child join. It converts only parent-side input limits and leaves child-side or dynamic input limits to normal Polypheny execution.
-
 ### ParquetRelJoin
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java`
 
-New file. Physical adapter-level join node. It provides supported join validation through `supportedDirection(...)`, stores table metadata, projected fields, join direction, join type, optional parent-side input limit, and pushed filters, and generates runtime code that calls `ParquetRelTable.nestedJoin(...)`.
+New file. Physical adapter-level join node. It provides supported join validation through `supportedDirection(...)`, stores table metadata, projected fields, join direction, join type, and pushed filters, and generates runtime code that calls `ParquetRelTable.nestedJoin(...)`.
 
 ### JoinDirection
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinDirection.java`
 
 New file. Small value record used by join support checks to remember whether the left input is the parent table. It is returned by `ParquetRelJoin.supportedDirection(...)`.
 
-### JoinInputLimit
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinInputLimit.java`
-
-New file. Small value record used by join planning to store a literal parent-side input `OFFSET` and `FETCH` before the values are passed to runtime.
-
 ### ParquetEnumerableFilterJoinRule
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterJoinRule.java`
 
@@ -169,17 +150,12 @@ New file. Pushes the filter part of an `EnumerableCalc` above a `ParquetRelJoin`
 ### ParquetRelTable
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java`
 
-Adds adapter-level nested join execution through `nestedJoin(...)`. Also exposes binding/source metadata for rule validation, resolves filters with table-specific column bindings, creates `ParentLimitState` for parent-side input limits, uses `FiltersContainer` for scans, and renames selected binding helper logic to projected binding logic.
+Adds adapter-level nested join execution through `nestedJoin(...)`. Also exposes binding/source metadata for rule validation, resolves filters with table-specific column bindings, uses `FiltersContainer` for scans, and renames selected binding helper logic to projected binding logic.
 
 ### ParquetNestedJoinEnumerator
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java`
 
-New file. Executes supported parent-child joins inside the Parquet adapter. It is created by `ParquetRelTable.nestedJoin()` and reads parent and child rows from the same Parquet source, applies shared parent-side limit state when present, combines rows into `CombinedGroup`, applies parent and child filters, and emits null child fields for supported outer join cases.
-
-### ParentLimitState
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParentLimitState.java`
-
-New file. Shared runtime state for parent-side input limits. It is shared across per-file join enumerators so `OFFSET` and `FETCH` are applied once across the whole parent input, not reset for each Parquet file.
+New file. Executes supported parent-child joins inside the Parquet adapter. It is created by `ParquetRelTable.nestedJoin()` and reads parent and child rows from the same Parquet source, combines rows into `CombinedGroup`, applies parent and child filters, and emits null child fields for supported outer join cases.
 
 ### CombinedGroup
 `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/CombinedGroup.java`
diff --git a/plugins/parquet-adapter/docs/test_plan/join_test_list.md b/plugins/parquet-adapter/docs/test_plan/join_test_list.md
index 80018e08c8..ba90d9fae2 100644
--- a/plugins/parquet-adapter/docs/test_plan/join_test_list.md
+++ b/plugins/parquet-adapter/docs/test_plan/join_test_list.md
@@ -516,7 +516,9 @@ WHERE i.quantity = 3;
 ```
 
 
-NOT working:
+
+
+### 31. Filtering + Count(*)
 
 ```sql
 SELECT count(*)
@@ -525,6 +527,11 @@ FULL JOIN pon__orders__items__discounts d
 ON d.__polypheny_parent_row_id = i.__polypheny_row_id
 WHERE i.quantity = 3;
 ```
+
+
+
+### Filtering + Projection 
+
 ```sql
 SELECT i.price
 FROM pon__orders__items i
@@ -532,3 +539,23 @@ FULL JOIN pon__orders__items__discounts d
 ON d.__polypheny_parent_row_id = i.__polypheny_row_id
 WHERE i.quantity = 3;
 ```
+
+
+NOT working:
+
+Incorrect limit values:
+
+SELECT *
+FROM pon__orders__items i
+FULL JOIN pon__orders__items__discounts d
+ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+WHERE i.quantity = 3
+LIMIT 10;
+
+
+SELECT i.quantity
+FROM pon__orders__items i
+FULL JOIN pon__orders__items__discounts d
+ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+WHERE i.quantity = 3
+LIMIT 10;
diff --git a/plugins/parquet-adapter/docs/test_plan/pon_planning_query_matrix.md b/plugins/parquet-adapter/docs/test_plan/pon_planning_query_matrix.md
new file mode 100644
index 0000000000..b29ea7f2b9
--- /dev/null
+++ b/plugins/parquet-adapter/docs/test_plan/pon_planning_query_matrix.md
@@ -0,0 +1,852 @@
+# PON Planning Query Matrix
+
+This checklist is for exercising different physical-planning shapes over the normalized `pon_` Parquet tables.
+
+The queries assume these table relationships:
+
+- `pon__customers`, `pon__orders`, and `pon__products` are root-level tables.
+- `pon__orders__items` is a direct child of `pon__orders`.
+- `pon__orders__shipping_address` is a direct child of `pon__orders`.
+- `pon__orders__items__discounts` is a direct child of `pon__orders__items`.
+- Supported adapter joins use generated structural keys: child `__polypheny_parent_row_id` to parent `__polypheny_row_id`.
+
+Plan terms below use the short PolyAlg names:
+
+- `P_SCAN`: Parquet scan.
+- `PE_CALC`: converter from Parquet convention to enumerable convention.
+- `P_JOIN`: adapter-level Parquet parent-child join.
+- `E_CALC`, `E_LIMIT`, `E_SORT`, `E_AGGREGATE`, `E_JOIN`: normal enumerable operators above or around Parquet nodes.
+
+For every supported parent-child join, also check that projected scan fields map the join keys correctly. In particular, for child tables the join key `__polypheny_parent_row_id` should map to physical column index `1`, not to `0`.
+
+## Simple Scans And Projections
+
+### Q01. Root table scan with limit
+
+Expected plan: one `P_SCAN` for `pon__orders`, wrapped by `PE_CALC`; limit remains above the scan as `E_LIMIT` unless a future rule pushes it.
+
+Check: baseline root-table planning and limit placement.
+
+```sql
+SELECT *
+FROM pon__orders
+LIMIT 10;
+```
+
+### Q02. Root table projection
+
+Expected plan: `P_SCAN` should read only projected order columns if projection pushdown applies.
+
+Check: simple projection pushdown on a root table.
+
+```sql
+SELECT order_id, status, total_price
+FROM pon__orders
+LIMIT 10;
+```
+
+### Q03. Customer projection
+
+Expected plan: `P_SCAN` for `pon__customers`, with only selected customer fields.
+
+Check: projection over another root table and text/timestamp columns.
+
+```sql
+SELECT customer_id, name, country, signup_date
+FROM pon__customers
+LIMIT 10;
+```
+
+### Q04. Product projection
+
+Expected plan: `P_SCAN` for `pon__products`, with only selected product fields.
+
+Check: projection over numeric and text product columns.
+
+```sql
+SELECT product_id, name, category, price, stock
+FROM pon__products
+LIMIT 10;
+```
+
+### Q05. Repeated child projection
+
+Expected plan: `P_SCAN` for `pon__orders__items`, with projected child fields only.
+
+Check: projection on a nested repeated child table.
+
+```sql
+SELECT order_item_id, product_id, quantity, price
+FROM pon__orders__items
+LIMIT 10;
+```
+
+### Q06. Repeated child structural columns
+
+Expected plan: `P_SCAN` for `pon__orders__items`, keeping the generated row id, parent row id, and ordinal.
+
+Check: scan of generated structural columns used by nested tables.
+
+```sql
+SELECT __polypheny_row_id, __polypheny_parent_row_id, __polypheny_elem_ordinal
+FROM pon__orders__items
+LIMIT 10;
+```
+
+### Q07. Nested grandchild projection
+
+Expected plan: `P_SCAN` for `pon__orders__items__discounts`, with projected discount fields.
+
+Check: projection on a second-level nested repeated child table.
+
+```sql
+SELECT code, amount
+FROM pon__orders__items__discounts
+LIMIT 10;
+```
+
+### Q08. Direct nested shipping-address projection
+
+Expected plan: `P_SCAN` for `pon__orders__shipping_address`, with projected address fields.
+
+Check: projection on a direct nested child that is not the `items` repeated branch.
+
+```sql
+SELECT city, street, zip
+FROM pon__orders__shipping_address
+LIMIT 10;
+```
+
+## Filters Without Joins
+
+### Q09. Root numeric filter
+
+Expected plan: `P_SCAN` with a translatable filter if filter pushdown applies; otherwise `E_CALC` or filter above `PE_CALC`.
+
+Check: root-table numeric predicate.
+
+```sql
+SELECT order_id, total_price
+FROM pon__orders
+WHERE total_price >= 0
+LIMIT 10;
+```
+
+### Q10. Root nullable text filter
+
+Expected plan: `P_SCAN` plus a status filter when supported.
+
+Check: nullable text predicate on root table.
+
+```sql
+SELECT order_id, status
+FROM pon__orders
+WHERE status IS NOT NULL
+LIMIT 10;
+```
+
+### Q11. Root timestamp filter
+
+Expected plan: `P_SCAN` plus a timestamp/nullability filter when supported.
+
+Check: timestamp column handling.
+
+```sql
+SELECT order_id, order_date
+FROM pon__orders
+WHERE order_date IS NOT NULL
+LIMIT 10;
+```
+
+### Q12. Customer text filter
+
+Expected plan: `P_SCAN` for `pon__customers`; text predicate may or may not be pushed.
+
+Check: text filter planning on a root table.
+
+```sql
+SELECT customer_id, name, country
+FROM pon__customers
+WHERE country IS NOT NULL
+LIMIT 10;
+```
+
+### Q13. Product numeric filter
+
+Expected plan: `P_SCAN` for `pon__products` with a numeric filter when supported.
+
+Check: product scan with numeric predicate and projection.
+
+```sql
+SELECT product_id, name, price, stock
+FROM pon__products
+WHERE price >= 0 AND stock >= 0
+LIMIT 10;
+```
+
+### Q14. Repeated child numeric filter
+
+Expected plan: `P_SCAN` for `pon__orders__items`, with `quantity` and `price` filters when supported.
+
+Check: filter pushdown on nested repeated table.
+
+```sql
+SELECT order_item_id, product_id, quantity, price
+FROM pon__orders__items
+WHERE quantity > 0 AND price >= 0
+LIMIT 10;
+```
+
+### Q15. Repeated child ordinal filter
+
+Expected plan: `P_SCAN` for `pon__orders__items`, possibly with a filter on generated ordinal.
+
+Check: generated ordinal field in a filter.
+
+```sql
+SELECT __polypheny_parent_row_id, __polypheny_elem_ordinal, product_id
+FROM pon__orders__items
+WHERE __polypheny_elem_ordinal = 0
+LIMIT 10;
+```
+
+### Q16. Nested grandchild filter
+
+Expected plan: `P_SCAN` for `pon__orders__items__discounts`, with `amount` filter when supported.
+
+Check: filter on second-level nested repeated table.
+
+```sql
+SELECT code, amount
+FROM pon__orders__items__discounts
+WHERE amount > 0
+LIMIT 10;
+```
+
+### Q17. Direct nested shipping-address filter
+
+Expected plan: `P_SCAN` for `pon__orders__shipping_address`; filter may remain as `E_CALC` if text predicates are not pushed.
+
+Check: filter on direct nested child branch different from `items`.
+
+```sql
+SELECT city, street, zip
+FROM pon__orders__shipping_address
+WHERE city IS NOT NULL AND zip IS NOT NULL
+LIMIT 10;
+```
+
+### Q18. Non-pushable expression filter
+
+Expected plan: `P_SCAN` for `pon__products`, with an enumerable calc/filter for the expression if it cannot be pushed.
+
+Check: fallback around a scan when the predicate contains an expression.
+
+```sql
+SELECT product_id, name, category
+FROM pon__products
+WHERE LOWER(category) IS NOT NULL
+LIMIT 10;
+```
+
+## Supported Parent-Child Joins
+
+### Q19. Orders to items, inner join
+
+Expected plan: `P_JOIN` with `pon__orders` as parent and `pon__orders__items` as child.
+
+Check: basic root parent to repeated child join.
+
+```sql
+SELECT o.order_id, i.order_item_id, i.product_id, i.quantity
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q20. Items to orders, inner join with parent on right
+
+Expected plan: `P_JOIN`; direction detection should identify that the right input is the parent.
+
+Check: supported join with swapped input order.
+
+```sql
+SELECT i.order_item_id, i.product_id, o.order_id, o.status
+FROM pon__orders__items i
+JOIN pon__orders o
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q21. Orders to shipping address
+
+Expected plan: `P_JOIN` with `pon__orders` as parent and `pon__orders__shipping_address` as child.
+
+Check: direct nested child branch that is not `items`.
+
+```sql
+SELECT o.order_id, s.city, s.street, s.zip
+FROM pon__orders o
+JOIN pon__orders__shipping_address s
+    ON s.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q22. Shipping address to orders, parent on right
+
+Expected plan: `P_JOIN`; direction detection should identify that the right input is the parent.
+
+Check: swapped direction for a direct nested child branch.
+
+```sql
+SELECT s.city, s.zip, o.order_id
+FROM pon__orders__shipping_address s
+JOIN pon__orders o
+    ON s.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q23. Items to discounts, inner join
+
+Expected plan: `P_JOIN` with `pon__orders__items` as parent and `pon__orders__items__discounts` as child.
+
+Check: nested parent to second-level repeated child.
+
+```sql
+SELECT i.order_item_id, i.product_id, d.code, d.amount
+FROM pon__orders__items i
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q24. Discounts to items, parent on right
+
+Expected plan: `P_JOIN`; direction detection should identify that the right input is the nested parent.
+
+Check: swapped direction for nested parent-child join.
+
+```sql
+SELECT d.code, d.amount, i.order_item_id, i.product_id
+FROM pon__orders__items__discounts d
+JOIN pon__orders__items i
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q25. Reversed equality operands
+
+Expected plan: `P_JOIN`; the equality is written parent key first.
+
+Check: join-key extraction independent of operand order.
+
+```sql
+SELECT i.order_item_id, i.quantity, d.code, d.amount
+FROM pon__orders__items i
+JOIN pon__orders__items__discounts d
+    ON i.__polypheny_row_id = d.__polypheny_parent_row_id
+LIMIT 10;
+```
+
+### Q26. Orders to items, left join
+
+Expected plan: `P_JOIN` with join type `LEFT`.
+
+Check: parent-preserving outer join.
+
+```sql
+SELECT o.order_id, i.order_item_id, i.product_id
+FROM pon__orders o
+LEFT JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q27. Items to orders, right join
+
+Expected plan: `P_JOIN` with join type `RIGHT`.
+
+Check: same parent-preserving semantics as Q26 with swapped input order.
+
+```sql
+SELECT i.order_item_id, i.product_id, o.order_id
+FROM pon__orders__items i
+RIGHT JOIN pon__orders o
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q28. Items to discounts, full join
+
+Expected plan: `P_JOIN` with join type `FULL`.
+
+Check: full outer structural join on nested parent and grandchild.
+
+```sql
+SELECT i.order_item_id, i.quantity, d.code, d.amount
+FROM pon__orders__items i
+FULL JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q29. Projected full join with filter and limit
+
+Expected plan: `P_JOIN` under `E_LIMIT` and final projection/calc. Both scans should carry the projected fields needed for selected columns, filter columns, and join keys.
+
+Check: the child scan projection bug case; child join key should map to physical column `1`.
+
+```sql
+SELECT i.order_item_id, i.quantity, i.price
+FROM pon__orders__items i
+FULL JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+WHERE i.quantity = 3
+LIMIT 10;
+```
+
+### Q30. Parent-side filter above structural join
+
+Expected plan: `P_JOIN`; parent filter should be pushed into the scan or join when possible.
+
+Check: filter placement for parent-side predicate.
+
+```sql
+SELECT o.order_id, o.total_price, i.product_id, i.quantity
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+WHERE o.total_price >= 0
+LIMIT 10;
+```
+
+### Q31. Child-side filter above structural join
+
+Expected plan: `P_JOIN`; child filter should be applied on the child branch or in the joined-row filter.
+
+Check: filter placement for child-side predicate.
+
+```sql
+SELECT o.order_id, i.product_id, i.quantity
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+WHERE i.quantity > 1
+LIMIT 10;
+```
+
+### Q32. Filters on both sides of structural join
+
+Expected plan: `P_JOIN`; filters may split between parent and child branches.
+
+Check: filter splitting and field-index shifting for joined rows.
+
+```sql
+SELECT i.order_item_id, i.quantity, d.code, d.amount
+FROM pon__orders__items i
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+WHERE i.quantity > 1 AND d.amount > 0
+LIMIT 10;
+```
+
+### Q33. Aggregate above structural join
+
+Expected plan: `E_AGGREGATE` above `P_JOIN`.
+
+Check: join can still be adapter-level when the final result is an aggregate.
+
+```sql
+SELECT count(*)
+FROM pon__orders__items i
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+WHERE i.quantity > 0;
+```
+
+### Q34. Group by above structural join
+
+Expected plan: `E_AGGREGATE` above `P_JOIN`.
+
+Check: grouped aggregate after adapter-level join.
+
+```sql
+SELECT i.product_id, count(*) AS discount_rows
+FROM pon__orders__items i
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+GROUP BY i.product_id
+LIMIT 10;
+```
+
+### Q35. Sort above structural join
+
+Expected plan: `E_SORT` or `E_LIMIT` above `P_JOIN`.
+
+Check: sorting after adapter-level join and projection.
+
+```sql
+SELECT o.order_id, i.product_id, i.quantity
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+ORDER BY i.quantity DESC
+LIMIT 10;
+```
+
+## Derived Tables And Calc Permutations
+
+### Q36. Projected parent and child derived tables
+
+Expected plan: ideally `P_JOIN`; derived-table projections should be consumed into `P_SCAN` fields.
+
+Check: projection-only `Calc` or `Project` wrappers on both join inputs.
+
+```sql
+SELECT o.order_id, i.product_id, i.quantity
+FROM (
+    SELECT __polypheny_row_id, order_id
+    FROM pon__orders
+) o
+JOIN (
+    SELECT __polypheny_parent_row_id, product_id, quantity
+    FROM pon__orders__items
+) i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q37. Projected nested parent and grandchild derived tables
+
+Expected plan: `P_JOIN`; child `__polypheny_parent_row_id` must map through the projection to physical column `1`.
+
+Check: projection-only calc on the child side of an `items` to `discounts` join.
+
+```sql
+SELECT i.quantity, i.price, d.code, d.amount
+FROM (
+    SELECT __polypheny_row_id, quantity, price
+    FROM pon__orders__items
+) i
+JOIN (
+    SELECT __polypheny_parent_row_id, code, amount
+    FROM pon__orders__items__discounts
+) d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q38. Filtered derived table on parent branch
+
+Expected plan: `P_JOIN` if the filter can be attached before join recognition; otherwise fallback indicates a rule gap.
+
+Check: filter/calc wrapper on parent input.
+
+```sql
+SELECT o.order_id, i.product_id, i.quantity
+FROM (
+    SELECT __polypheny_row_id, order_id
+    FROM pon__orders
+    WHERE total_price >= 0
+) o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q39. Filtered derived table on child branch
+
+Expected plan: `P_JOIN` if the child filter and projection can be attached to the scan before join recognition.
+
+Check: child-side `EnumerableCalc` projection/filter wrapper.
+
+```sql
+SELECT o.order_id, i.product_id, i.quantity
+FROM pon__orders o
+JOIN (
+    SELECT __polypheny_parent_row_id, product_id, quantity
+    FROM pon__orders__items
+    WHERE quantity > 1
+) i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q40. Final projection hides all child join keys
+
+Expected plan: `P_JOIN`; scan projections still need join keys internally even though final output hides them.
+
+Check: join-key preservation through projection trimming.
+
+```sql
+SELECT i.product_id, d.amount
+FROM pon__orders__items i
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+WHERE d.amount > 0
+LIMIT 10;
+```
+
+### Q41. Limit inside join input
+
+Expected plan: this is a semantic edge case. If the input limit stays below the join, the adapter join should not silently ignore it.
+
+Check: rule ordering around `E_LIMIT` below a structural join input.
+
+```sql
+SELECT i.order_item_id, d.code, d.amount
+FROM (
+    SELECT __polypheny_row_id, order_item_id
+    FROM pon__orders__items
+    LIMIT 100
+) i
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 10;
+```
+
+## Multi-Join And Mixed Join Shapes
+
+### Q42. Orders to items to discounts chain
+
+Expected plan: direct structural joins are individually supported. Depending on rule composition, expect either nested `P_JOIN` use or one `P_JOIN` plus a normal join.
+
+Check: whether adapter-level joins compose across multiple levels.
+
+```sql
+SELECT o.order_id, i.product_id, d.code, d.amount
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q43. Structural join plus product lookup
+
+Expected plan: `orders` to `items` may become `P_JOIN`; `items` to `products` should be a normal join because it uses user columns.
+
+Check: mixed adapter-level and fallback join planning.
+
+```sql
+SELECT o.order_id, i.quantity, p.name, p.category
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+JOIN pon__products p
+    ON p.product_id = i.product_id
+LIMIT 10;
+```
+
+### Q44. Customer to orders to items
+
+Expected plan: `customers` to `orders` is a normal user-column join; `orders` to `items` is structurally supported if the planner can isolate it.
+
+Check: structural join recognition inside a larger query with a root-root user join.
+
+```sql
+SELECT c.customer_id, c.country, o.order_id, i.product_id
+FROM pon__customers c
+JOIN pon__orders o
+    ON o.customer_id = c.customer_id
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q45. Orders with two direct children
+
+Expected plan: each root-to-child join is structurally valid, but composition may fall back after one `P_JOIN`.
+
+Check: sibling child branches under the same parent.
+
+```sql
+SELECT o.order_id, i.product_id, s.city, s.zip
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+JOIN pon__orders__shipping_address s
+    ON s.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+## Unsupported Or Fallback Plans
+
+### Q46. Customers to orders by user key
+
+Expected plan: no `P_JOIN`; use normal `E_JOIN` or equivalent.
+
+Check: root-root user-column join must not be mistaken for structural join.
+
+```sql
+SELECT c.customer_id, c.name, o.order_id, o.status
+FROM pon__customers c
+JOIN pon__orders o
+    ON o.customer_id = c.customer_id
+LIMIT 10;
+```
+
+### Q47. Items to products by product id
+
+Expected plan: no `P_JOIN`; use normal join.
+
+Check: nested table joined to root lookup table by user column.
+
+```sql
+SELECT i.order_item_id, i.quantity, p.name, p.price
+FROM pon__orders__items i
+JOIN pon__products p
+    ON p.product_id = i.product_id
+LIMIT 10;
+```
+
+### Q48. Orders directly to discounts
+
+Expected plan: no single `P_JOIN`; discounts are not a direct child of orders.
+
+Check: ancestor-to-grandchild direct join is rejected.
+
+```sql
+SELECT o.order_id, d.code, d.amount
+FROM pon__orders o
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q49. Sibling child join
+
+Expected plan: no `P_JOIN`; items and shipping address are siblings, not parent and child.
+
+Check: same-parent sibling relationship is rejected.
+
+```sql
+SELECT i.product_id, s.city, s.zip
+FROM pon__orders__items i
+JOIN pon__orders__shipping_address s
+    ON s.__polypheny_parent_row_id = i.__polypheny_parent_row_id
+LIMIT 10;
+```
+
+### Q50. Same-table self join
+
+Expected plan: no `P_JOIN`; use normal self-join.
+
+Check: self-join on generated parent row id is not a structural parent-child join.
+
+```sql
+SELECT i1.product_id AS product_1, i2.product_id AS product_2
+FROM pon__orders__items i1
+JOIN pon__orders__items i2
+    ON i1.__polypheny_parent_row_id = i2.__polypheny_parent_row_id
+LIMIT 10;
+```
+
+### Q51. Parent-child tables with wrong generated keys
+
+Expected plan: no `P_JOIN`; child primary key is not the parent reference key.
+
+Check: key-role validation in `supportedDirection()`.
+
+```sql
+SELECT o.order_id, i.product_id
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q52. Parent-child tables joined by user columns
+
+Expected plan: no `P_JOIN`; structural keys are not used.
+
+Check: user-column join between parent and child falls back.
+
+```sql
+SELECT o.order_id, i.order_item_id
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON o.order_id = i.order_item_id
+LIMIT 10;
+```
+
+### Q53. Multi-key join condition
+
+Expected plan: no `P_JOIN` if the extra equality remains part of the join condition.
+
+Check: `supportedDirection()` requires exactly one equi-key on each side.
+
+```sql
+SELECT o.order_id, i.product_id
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+   AND i.__polypheny_elem_ordinal = o.order_id
+LIMIT 10;
+```
+
+### Q54. Non-equi join condition
+
+Expected plan: no `P_JOIN`.
+
+Check: non-equi structural condition is rejected.
+
+```sql
+SELECT o.order_id, i.product_id
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id <> o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q55. OR join condition
+
+Expected plan: no `P_JOIN`.
+
+Check: disjunctive join condition is rejected.
+
+```sql
+SELECT o.order_id, i.product_id
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+    OR i.__polypheny_elem_ordinal = 0
+LIMIT 10;
+```
+
+### Q56. EXISTS over structural relationship
+
+Expected plan: no `P_JOIN` unless a future semi-join rule explicitly supports this shape.
+
+Check: correlated/semi-join planning around nested tables.
+
+```sql
+SELECT o.order_id
+FROM pon__orders o
+WHERE EXISTS (
+    SELECT 1
+    FROM pon__orders__items i
+    WHERE i.__polypheny_parent_row_id = o.__polypheny_row_id
+)
+LIMIT 10;
+```
+
+### Q57. NOT EXISTS over structural relationship
+
+Expected plan: no `P_JOIN` unless a future anti-join rule explicitly supports this shape.
+
+Check: anti-join or correlated fallback planning.
+
+```sql
+SELECT o.order_id
+FROM pon__orders o
+WHERE NOT EXISTS (
+    SELECT 1
+    FROM pon__orders__items i
+    WHERE i.__polypheny_parent_row_id = o.__polypheny_row_id
+)
+LIMIT 10;
+```
+
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
index ae34ed09df..109dba2545 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
@@ -16,9 +16,25 @@
 
 package org.polypheny.db.adapter.parquet;
 
+import com.google.common.collect.ImmutableList;
 import org.polypheny.db.adapter.AdapterManager;
 import org.polypheny.db.adapter.parquet.document.ParquetDocumentSource;
 import org.polypheny.db.adapter.parquet.relational.ParquetRelationalSource;
+import org.polypheny.db.adapter.parquet.relational.planning.EnumerableParquet;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetJoin;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetScan;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.enumerable.EnumerableJoin;
+import org.polypheny.db.algebra.logical.relational.LogicalRelScan;
+import org.polypheny.db.algebra.polyalg.PolyAlgDeclaration;
+import org.polypheny.db.algebra.polyalg.PolyAlgDeclaration.OperatorTag;
+import org.polypheny.db.algebra.polyalg.PolyAlgDeclaration.ParamType;
+import org.polypheny.db.algebra.polyalg.PolyAlgDeclaration.Parameter;
+import org.polypheny.db.algebra.polyalg.PolyAlgRegistry;
+import org.polypheny.db.algebra.polyalg.arguments.BooleanArg;
+import org.polypheny.db.algebra.polyalg.arguments.ListArg;
+import org.polypheny.db.catalog.logistic.DataModel;
 import org.polypheny.db.plugins.PluginContext;
 import org.polypheny.db.plugins.PolyPlugin;
 
@@ -38,8 +54,40 @@ public class ParquetPlugin extends PolyPlugin {
     @SuppressWarnings("unused")
     public ParquetPlugin( PluginContext context ) {
         super( context );
+        registerPolyAlg();
     }
 
+
+    private void registerPolyAlg() {
+        if ( PolyAlgRegistry.getClass( "P_SCAN" ) != null ) {
+            return;
+        }
+
+        ImmutableList<OperatorTag> physTags = ImmutableList.of( OperatorTag.PHYSICAL, OperatorTag.ADVANCED );
+
+        PolyAlgRegistry.register( EnumerableParquet.class, PolyAlgDeclaration.builder()
+                .model( DataModel.RELATIONAL )
+                .opName( "PE_CALC" ).convention( EnumerableConvention.INSTANCE ).numInputs( 1 ).opTags( physTags )
+                .build() );
+        PolyAlgRegistry.register( ParquetScan.class, PolyAlgDeclaration.builder()
+                .model( DataModel.RELATIONAL )
+                .opName( "P_SCAN" ).convention( ParquetConvention.INSTANCE ).numInputs( 0 ).opTags( physTags )
+                .params( PolyAlgRegistry.getParams( LogicalRelScan.class ) )
+                .param( Parameter.builder().name( "fields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .build() );
+        PolyAlgRegistry.register( ParquetJoin.class, PolyAlgDeclaration.builder()
+                .model( DataModel.RELATIONAL )
+                .opName( "P_JOIN" ).convention( ParquetConvention.INSTANCE ).numInputs( 2 ).opTags( physTags )
+                .params( PolyAlgRegistry.getParams( EnumerableJoin.class ) )
+                .param( Parameter.builder().name( "leftIsParent" ).type( ParamType.BOOLEAN ).defaultValue( BooleanArg.FALSE ).build() )
+                .param( Parameter.builder().name( "leftFields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "rightFields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .build() );
+    }
+
+
     /**
      * Registers the adapter template once the catalog is ready.
      */
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
index 8d83849150..3cf5ce49ca 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
@@ -24,6 +24,7 @@
 import java.util.Optional;
 import java.util.Set;
 import java.util.stream.Collectors;
+import java.util.stream.IntStream;
 import org.pf4j.Extension;
 import org.polypheny.db.adapter.ConnectionMethod;
 import org.polypheny.db.adapter.DeployMode;
@@ -37,8 +38,9 @@
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSchemaMode;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSchemaNormalizer;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetScan;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.rules.FilterSetOpTransposeRule;
@@ -104,10 +106,12 @@ public RelationalDataSource asRelationalDataSource() {
 
     @Override
     public AlgNode getRelScan( long allocId, AlgBuilder builder ) {
-        // this rule is required to push the filters into the ParquetRelScan.
+        // This rule is still required for filter movement before the adapter rules inspect the tree.
         builder.getCluster().getPlanner().addRuleDuringRuntime( FilterSetOpTransposeRule.INSTANCE );
-        ParquetRelScan.registerRules( builder.getCluster().getPlanner() );
-        return super.getRelScan( allocId, builder );
+//        ParquetConvention.INSTANCE.register( builder.getCluster().getPlanner() );
+        PhysicalEntity entity = getCatalog().getPhysicalsFromAllocs( allocId ).get( 0 );
+        ParquetRelTable table = entity.unwrapOrThrow( ParquetRelTable.class );
+        return new ParquetScan( builder.getCluster(), table, IntStream.range( 0, table.getFieldCount() ).toArray() );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParentLimitState.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParentLimitState.java
deleted file mode 100644
index deba9247bf..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParentLimitState.java
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.execution;
-
-
-/**
- * Shared runtime state for a limit that applies to parent rows before nested
- * join expansion.
- */
-public final class ParentLimitState {
-
-    private long rowsToSkip;
-    private long rowsRemaining;
-
-
-    public ParentLimitState( int offset, int fetch ) {
-        this.rowsToSkip = Math.max( 0, offset );
-        this.rowsRemaining = fetch < 0 ? Long.MAX_VALUE : fetch;
-    }
-
-
-    @SuppressWarnings("unused")
-    public static ParentLimitState unlimited() {
-        return new ParentLimitState( 0, -1 );
-    }
-
-
-    public synchronized boolean includeNextParentRow() {
-        if ( rowsToSkip > 0 ) {
-            rowsToSkip--;
-            return false;
-        }
-        if ( rowsRemaining <= 0 ) {
-            return false;
-        }
-        if ( rowsRemaining != Long.MAX_VALUE ) {
-            rowsRemaining--;
-        }
-        return true;
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
index 17425652f4..4e7cebdbe2 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
@@ -45,15 +45,14 @@ public class ParquetNestedJoinEnumerator extends ParquetNestedRepeatedRelEnumera
     private final boolean leftIsParent;
     private final boolean emitUnmatchedParents;
     private final List<String> childPathFromParent;
-    private final ParentLimitState parentLimitState;
 
 
-    public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBinding parentBinding, ParquetTableBinding childBinding, List<ParquetColumnBinding> parentColumns, List<ParquetColumnBinding> childColumns, JoinFiltersContainer filterContainer, boolean leftIsParent, boolean emitUnmatchedParents, ParentLimitState parentLimitState ) {
-        this( reader, parentBinding, childBinding, parentColumns, childColumns, filterContainer, leftIsParent, emitUnmatchedParents, parentLimitState, true );
+    public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBinding parentBinding, ParquetTableBinding childBinding, List<ParquetColumnBinding> parentColumns, List<ParquetColumnBinding> childColumns, JoinFiltersContainer filterContainer, boolean leftIsParent, boolean emitUnmatchedParents ) {
+        this( reader, parentBinding, childBinding, parentColumns, childColumns, filterContainer, leftIsParent, emitUnmatchedParents, true );
     }
 
 
-    public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBinding parentBinding, ParquetTableBinding childBinding, List<ParquetColumnBinding> parentColumns, List<ParquetColumnBinding> childColumns, JoinFiltersContainer filterContainer, boolean leftIsParent, boolean emitUnmatchedParents, ParentLimitState parentLimitState, boolean readerOwner ) {
+    public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBinding parentBinding, ParquetTableBinding childBinding, List<ParquetColumnBinding> parentColumns, List<ParquetColumnBinding> childColumns, JoinFiltersContainer filterContainer, boolean leftIsParent, boolean emitUnmatchedParents, boolean readerOwner ) {
         super( reader, parentBinding, parentColumns, filterContainer, new ParquetNestedJoinFilterEvaluator( reader.getProjectionSchema(), new ParquetPathValueExtractor(), parentBinding, parentColumns, leftIsParent ), true, readerOwner );
         this.parentBinding = parentBinding;
         this.childBinding = childBinding;
@@ -64,7 +63,6 @@ public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBind
         this.leftIsParent = leftIsParent;
         this.emitUnmatchedParents = emitUnmatchedParents;
         this.childPathFromParent = childBinding.sourcePathElements().subList( parentBinding.sourcePathElements().size(), childBinding.sourcePathElements().size() );
-        this.parentLimitState = parentLimitState;
     }
 
 
@@ -73,9 +71,6 @@ protected List<Group> expandRow( Group group ) {
         List<Group> rows = new ArrayList<>();
         VirtualGroup rootGroup = new VirtualGroup( group, String.valueOf( reader.getCurrentRowNumber() ), null, 0 );
         for ( Group parent : resolveNested( rootGroup, group.getType(), parentBinding.sourcePathElements(), 0 ) ) {
-            if ( !parentLimitState.includeNextParentRow() ) {
-                continue;
-            }
             if ( !matchesFilters( combinedGroup( (VirtualGroup) parent, null ), parentFilters ) ) {
                 continue;
             }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
index 0f0c3fc312..9e8dd57d9d 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
@@ -39,6 +39,9 @@ public class ParquetRelFilterTranslator extends AbstractFilterTranslator {
     public ParquetAdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFilter ) {
         // support logical filter
         if ( polyFilter instanceof RexCall call ) {
+            if ( (polyFilter.isA( Kind.IS_NULL ) || polyFilter.isA( Kind.IS_NOT_NULL )) && call.getOperands().size() == 1 ) {
+                return translateNullCheck( fieldTypes, polyFilter.getKind(), call.getOperands().get( 0 ) );
+            }
             if ( polyFilter.isA( Kind.AND ) || polyFilter.isA( Kind.OR ) ) {
                 return translateLogical( fieldTypes, polyFilter.getKind(), call.getOperands() );
             }
@@ -72,6 +75,21 @@ public ParquetAdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFi
     }
 
 
+    private ParquetAdapterFilter translateNullCheck( List<PolyType> fieldTypes, Kind operator, RexNode operand ) {
+        RexNode column = unwrapCast( operand );
+        if ( !(column instanceof RexIndexRef indexRef) ) {
+            return null;
+        }
+
+        int index = indexRef.getIndex();
+        if ( !isPushdownSupported( fieldTypes, index, operator ) ) {
+            return null;
+        }
+
+        return new ParquetAdapterFilter( index, operator, null );
+    }
+
+
     /**
      * Translates a logical Rex filter node (AND, OR) into a logical ParquetAdapterFilter
      * @param fieldTypes - poly field types
@@ -143,16 +161,37 @@ private ParquetAdapterFilter translateIn( List<PolyType> fieldTypes, List<RexNod
      * Checks whether the operator can be handled by the reader.
      */
     private boolean isPushdownSupported( List<PolyType> fieldTypes, int index, Kind kind, RexNode valueNode ) {
+        return isPushdownSupported( fieldTypes, index, kind )
+                && (valueNode instanceof RexDynamicParam || (valueNode instanceof RexLiteral literal && literal.getValue() != null));
+    }
+
+
+    private boolean isPushdownSupported( List<PolyType> fieldTypes, int index, Kind kind ) {
         if ( index < 0 || index >= fieldTypes.size() ) {
             return false;
         }
 
         PolyType type = fieldTypes.get( index );
         return switch ( type ) {
-            case BOOLEAN, VARCHAR, CHAR, TEXT -> kind == Kind.EQUALS || kind == Kind.NOT_EQUALS;
-            case INTEGER, BIGINT, FLOAT, DOUBLE, DATE, TIME, TIMESTAMP -> true;
+            case BOOLEAN, VARCHAR, CHAR, TEXT -> kind == Kind.EQUALS || kind == Kind.NOT_EQUALS || isNullCheck( kind );
+            case INTEGER, BIGINT, FLOAT, DOUBLE, DATE, TIME, TIMESTAMP -> isComparison( kind ) || isNullCheck( kind );
             default -> false;
-        } && (valueNode instanceof RexDynamicParam || (valueNode instanceof RexLiteral literal && literal.getValue() != null));
+        };
+    }
+
+
+    private boolean isComparison( Kind kind ) {
+        return kind == Kind.EQUALS
+                || kind == Kind.NOT_EQUALS
+                || kind == Kind.GREATER_THAN
+                || kind == Kind.GREATER_THAN_OR_EQUAL
+                || kind == Kind.LESS_THAN
+                || kind == Kind.LESS_THAN_OR_EQUAL;
+    }
+
+
+    private boolean isNullCheck( Kind kind ) {
+        return kind == Kind.IS_NULL || kind == Kind.IS_NOT_NULL;
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/EnumerableParquet.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/EnumerableParquet.java
new file mode 100644
index 0000000000..a2494a94c0
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/EnumerableParquet.java
@@ -0,0 +1,63 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.List;
+import org.apache.calcite.linq4j.tree.Blocks;
+import org.polypheny.db.algebra.AbstractAlgNode;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.convert.ConverterImpl;
+import org.polypheny.db.algebra.enumerable.EnumerableAlg;
+import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
+import org.polypheny.db.algebra.enumerable.PhysType;
+import org.polypheny.db.algebra.enumerable.PhysTypeImpl;
+import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgOptCost;
+import org.polypheny.db.plan.AlgPlanner;
+import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.plan.ConventionTraitDef;
+
+/**
+ * Converter from Parquet convention back to the standard enumerable pipeline.
+ */
+public class EnumerableParquet extends ConverterImpl implements EnumerableAlg {
+
+    public EnumerableParquet( AlgCluster cluster, AlgTraitSet traits, AlgNode input ) {
+        super( cluster, ConventionTraitDef.INSTANCE, traits, input );
+    }
+
+
+    @Override
+    public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
+        return new EnumerableParquet( getCluster(), traitSet, AbstractAlgNode.sole( inputs ) );
+    }
+
+
+    @Override
+    public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
+        return super.computeSelfCost( planner, mq ).multiplyBy( ParquetConvention.COST_MULTIPLIER );
+    }
+
+
+    @Override
+    public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
+        ParquetAlg child = (ParquetAlg) getInput();
+        PhysType physType = PhysTypeImpl.of( implementor.getTypeFactory(), getTupleType(), pref.preferArray() );
+        return implementor.result( physType, Blocks.toBlock( child.implement( implementor ) ) );
+    }
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinInputLimit.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetAlg.java
similarity index 62%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinInputLimit.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetAlg.java
index 69ba2bde72..0ad4f89a98 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinInputLimit.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetAlg.java
@@ -16,18 +16,15 @@
 
 package org.polypheny.db.adapter.parquet.relational.planning;
 
+import org.apache.calcite.linq4j.tree.Expression;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
+
 /**
- * Stores an optional OFFSET and FETCH/LIMIT for the parent side of an adapter-level join
- * @param offset - how many rows to skip before returning results
- * @param fetch - how many rows to return after the offset
+ * Algebra node that runs inside the Parquet adapter convention.
  */
-public record JoinInputLimit( int offset, int fetch ) {
-
-    static final JoinInputLimit NONE = new JoinInputLimit( 0, -1 );
-
+public interface ParquetAlg extends AlgNode {
 
-    boolean isEmpty() {
-        return offset == 0 && fetch < 0;
-    }
+    Expression implement( EnumerableAlgImplementor implementor );
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetAlgOptRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetAlgOptRule.java
new file mode 100644
index 0000000000..c7d4891784
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetAlgOptRule.java
@@ -0,0 +1,38 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import org.polypheny.db.plan.AlgOptRule;
+import org.polypheny.db.plan.AlgOptRuleCall;
+
+public class ParquetAlgOptRule extends AlgOptRule {
+
+    private final PatternMatcher matcher;
+
+
+    public ParquetAlgOptRule(PatternMatcher matcher ) {
+        super( matcher.operand(), matcher.factory(), matcher.description() );
+        this.matcher = matcher;
+    }
+
+
+    @Override
+    public void onMatch( AlgOptRuleCall call ) {
+        matcher.onMatch( call );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java
new file mode 100644
index 0000000000..19489c216c
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java
@@ -0,0 +1,59 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.plan.AlgOptRule;
+import org.polypheny.db.plan.AlgPlanner;
+import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.plan.Convention;
+import org.polypheny.db.plan.ConventionTraitDef;
+
+/**
+ * Calling convention for relational operators executed directly by the Parquet adapter.
+ */
+public class ParquetConvention extends Convention.Impl {
+
+    public static final ParquetConvention INSTANCE = new ParquetConvention();
+    public static final double COST_MULTIPLIER = 0d;// 0.05d;
+
+
+    private ParquetConvention() {
+        super( "PARQUET", ParquetAlg.class );
+    }
+
+
+    @Override
+    public void register( AlgPlanner planner ) {
+        for ( AlgOptRule rule : ParquetRules.rules( this ) ) {
+            planner.addRuleDuringRuntime( rule );
+        }
+    }
+
+
+    @Override
+    public boolean canConvertConvention( Convention toConvention ) {
+        return toConvention == EnumerableConvention.INSTANCE;
+    }
+
+
+    @Override
+    public boolean useAbstractConvertersForConversion( AlgTraitSet fromTraits, AlgTraitSet toTraits ) {
+        return toTraits.getTrait( ConventionTraitDef.INSTANCE ) == EnumerableConvention.INSTANCE;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcJoinRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcJoinRule.java
deleted file mode 100644
index cc60accba0..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcJoinRule.java
+++ /dev/null
@@ -1,84 +0,0 @@
- /*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import java.util.List;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.algebra.core.AlgFactories;
-import org.polypheny.db.algebra.enumerable.EnumerableCalc;
-import org.polypheny.db.plan.AlgOptRule;
-import org.polypheny.db.plan.AlgOptRuleCall;
-import org.polypheny.db.rex.RexNode;
-import org.polypheny.db.rex.RexProgram;
-import org.polypheny.db.tools.AlgBuilderFactory;
-import org.polypheny.db.type.PolyType;
-
-/**
- * Pushes the filter part of an enumerable calc over a Parquet nested join into
- * the adapter join, while preserving the calc projection.
- */
-public class ParquetEnumerableCalcJoinRule extends AlgOptRule {
-
-    public static final ParquetEnumerableCalcJoinRule INSTANCE = new ParquetEnumerableCalcJoinRule( AlgFactories.LOGICAL_BUILDER );
-
-    private final ParquetRelFilterTranslator translator = new ParquetRelFilterTranslator();
-
-
-    public ParquetEnumerableCalcJoinRule( AlgBuilderFactory algBuilderFactory ) {
-        super(
-                operand( EnumerableCalc.class, operand( ParquetRelJoin.class, none() ) ),
-                algBuilderFactory,
-                ParquetEnumerableCalcJoinRule.class.getSimpleName() );
-    }
-
-
-    @Override
-    public void onMatch( AlgOptRuleCall call ) {
-        EnumerableCalc calc = call.alg( 0 );
-        ParquetRelJoin join = call.alg( 1 );
-        RexProgram program = calc.getProgram();
-        if ( program.getCondition() == null ) {
-            return;
-        }
-
-        RexNode condition = program.expandLocalRef( program.getCondition() );
-        List<PolyType> fieldTypes = join.getTupleType().getFields().stream()
-                .map( field -> field.getType().getPolyType() )
-                .toList();
-
-        ParquetAdapterFilter adapterFilter = translator.translate( fieldTypes, condition );
-        if ( adapterFilter == null ) {
-            return;
-        }
-
-        ParquetRelJoin filteredJoin = join.withFilters( List.of( adapterFilter ) );
-        List<RexNode> projects = program.getProjectList().stream()
-                .map( program::expandLocalRef )
-                .toList();
-
-        RexProgram projectionOnly = RexProgram.create(
-                join.getTupleType(),
-                projects,
-                null,
-                calc.getTupleType(),
-                calc.getCluster().getRexBuilder() );
-
-        call.transformTo( calc.copy( calc.getTraitSet(), filteredJoin, projectionOnly ) );
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcScanRule.java
deleted file mode 100644
index 1ce307fc25..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcScanRule.java
+++ /dev/null
@@ -1,95 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import java.util.List;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.algebra.AlgNode;
-import org.polypheny.db.algebra.convert.ConverterRule;
-import org.polypheny.db.algebra.core.AlgFactories;
-import org.polypheny.db.algebra.enumerable.EnumerableCalc;
-import org.polypheny.db.algebra.enumerable.EnumerableConvention;
-import org.polypheny.db.algebra.logical.relational.LogicalCalc;
-import org.polypheny.db.plan.Convention;
-import org.polypheny.db.rex.RexNode;
-import org.polypheny.db.rex.RexProgram;
-import org.polypheny.db.tools.AlgBuilderFactory;
-import org.polypheny.db.type.PolyType;
-
-/**
- * Converts logical calcs over Parquet scans into filtered adapter scans while
- * preserving the calc projection.
- */
-public class ParquetEnumerableCalcScanRule extends ConverterRule {
-
-    public static final ParquetEnumerableCalcScanRule INSTANCE = new ParquetEnumerableCalcScanRule( AlgFactories.LOGICAL_BUILDER );
-
-    private final ParquetRelFilterTranslator translator = new ParquetRelFilterTranslator();
-
-
-    public ParquetEnumerableCalcScanRule( AlgBuilderFactory algBuilderFactory ) {
-        super(
-                LogicalCalc.class,
-                calc -> true,
-                Convention.NONE,
-                EnumerableConvention.INSTANCE,
-                algBuilderFactory,
-                ParquetEnumerableCalcScanRule.class.getSimpleName() );
-    }
-
-
-    @Override
-    public AlgNode convert( AlgNode alg ) {
-        LogicalCalc calc = (LogicalCalc) alg;
-        RexProgram program = calc.getProgram();
-        if ( program.getCondition() == null ) {
-            return null;
-        }
-
-        ParquetRelScan scan = ParquetRelScanRuleSupport.findProjectedRelScan( calc.getInput() );
-        if ( scan == null ) {
-            scan = ParquetRelScanRuleSupport.findProjectedRelScan( convert( calc.getInput(), calc.getInput().getTraitSet().replace( EnumerableConvention.INSTANCE ) ) );
-        }
-        if ( scan == null ) {
-            return null;
-        }
-
-        RexNode condition = program.expandLocalRef( program.getCondition() );
-        List<PolyType> fieldTypes = ParquetRelScanRuleSupport.fieldTypes( scan );
-        ParquetAdapterFilter adapterFilter = translator.translate( fieldTypes, condition );
-        if ( adapterFilter == null ) {
-            return null;
-        }
-
-        ParquetRelScan filteredScan = scan.withFilters( List.of( adapterFilter ) );
-        List<RexNode> projects = program.getProjectList().stream()
-                .map( program::expandLocalRef )
-                .toList();
-
-        RexProgram projectionOnly = RexProgram.create(
-                scan.getTupleType(),
-                projects,
-                null,
-                calc.getTupleType(),
-                calc.getCluster().getRexBuilder() );
-
-        return EnumerableCalc.create( filteredScan, projectionOnly );
-    }
-
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterJoinRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterJoinRule.java
deleted file mode 100644
index 37b4d870c7..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterJoinRule.java
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import java.util.List;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.algebra.core.AlgFactories;
-import org.polypheny.db.algebra.enumerable.EnumerableFilter;
-import org.polypheny.db.plan.AlgOptRule;
-import org.polypheny.db.plan.AlgOptRuleCall;
-import org.polypheny.db.tools.AlgBuilderFactory;
-import org.polypheny.db.type.PolyType;
-
-/**
- * Pushes filters over a Parquet nested join into the adapter join itself.
- */
-public class ParquetEnumerableFilterJoinRule extends AlgOptRule {
-
-    public static final ParquetEnumerableFilterJoinRule INSTANCE = new ParquetEnumerableFilterJoinRule( AlgFactories.LOGICAL_BUILDER );
-
-    private final ParquetRelFilterTranslator translator = new ParquetRelFilterTranslator();
-
-
-    public ParquetEnumerableFilterJoinRule( AlgBuilderFactory algBuilderFactory ) {
-        super(
-                operand( EnumerableFilter.class, operand( ParquetRelJoin.class, none() ) ),
-                algBuilderFactory,
-                ParquetEnumerableFilterJoinRule.class.getSimpleName() );
-    }
-
-
-    @Override
-    public void onMatch( AlgOptRuleCall call ) {
-        EnumerableFilter filter = call.alg( 0 );
-        ParquetRelJoin join = call.alg( 1 );
-        List<PolyType> fieldTypes = join.getTupleType().getFields().stream()
-                .map( field -> field.getType().getPolyType() )
-                .toList();
-
-        ParquetAdapterFilter adapterFilter = translator.translate( fieldTypes, filter.getCondition() );
-        if ( adapterFilter == null ) {
-            return;
-        }
-
-        call.transformTo( join.withFilters( List.of( adapterFilter ) ) );
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterScanRule.java
deleted file mode 100644
index 66014b4864..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterScanRule.java
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import java.util.List;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.algebra.AlgNode;
-import org.polypheny.db.algebra.convert.ConverterRule;
-import org.polypheny.db.algebra.core.AlgFactories;
-import org.polypheny.db.algebra.enumerable.EnumerableConvention;
-import org.polypheny.db.algebra.logical.relational.LogicalRelFilter;
-import org.polypheny.db.plan.Convention;
-import org.polypheny.db.tools.AlgBuilderFactory;
-import org.polypheny.db.type.PolyType;
-
-/**
- * Converts logical filters over Parquet scans into filtered adapter scans.
- */
-public class ParquetEnumerableFilterScanRule extends ConverterRule {
-
-    public static final ParquetEnumerableFilterScanRule INSTANCE = new ParquetEnumerableFilterScanRule( AlgFactories.LOGICAL_BUILDER );
-
-    private final ParquetRelFilterTranslator translator = new ParquetRelFilterTranslator();
-
-
-    public ParquetEnumerableFilterScanRule( AlgBuilderFactory algBuilderFactory ) {
-        super(
-                LogicalRelFilter.class,
-                filter -> true,
-                Convention.NONE,
-                EnumerableConvention.INSTANCE,
-                algBuilderFactory,
-                ParquetEnumerableFilterScanRule.class.getSimpleName() );
-    }
-
-
-    @Override
-    public AlgNode convert( AlgNode alg ) {
-        LogicalRelFilter filter = (LogicalRelFilter) alg;
-        ParquetRelScan scan = ParquetRelScanRuleSupport.findProjectedRelScan( filter.getInput() );
-        if ( scan == null ) {
-            scan = ParquetRelScanRuleSupport.findProjectedRelScan( convert( filter.getInput(), filter.getInput().getTraitSet().replace( EnumerableConvention.INSTANCE ) ) );
-        }
-        if ( scan == null ) {
-            return null;
-        }
-
-        List<PolyType> fieldTypes = ParquetRelScanRuleSupport.fieldTypes( scan );
-        ParquetAdapterFilter adapterFilter = translator.translate( fieldTypes, filter.getCondition() );
-        if ( adapterFilter == null ) {
-            return null;
-        }
-
-        return scan.withFilters( List.of( adapterFilter ) );
-    }
-
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java
deleted file mode 100644
index 874a6f7d0a..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import org.polypheny.db.algebra.AlgNode;
-import org.polypheny.db.algebra.core.AlgFactories;
-import org.polypheny.db.algebra.enumerable.EnumerableJoin;
-import org.polypheny.db.plan.AlgOptRule;
-import org.polypheny.db.plan.AlgOptRuleCall;
-import org.polypheny.db.tools.AlgBuilderFactory;
-
-import static org.polypheny.db.adapter.parquet.relational.planning.ParquetRelJoin.supportedDirection;
-
-/**
- * Rewrites a generic enumerable join over two Parquet scans into a Parquet
- * adapter join when the join is a supported normalized parent/child join.
- */
-public class ParquetEnumerableJoinRule extends AlgOptRule {
-
-    public static final ParquetEnumerableJoinRule INSTANCE = new ParquetEnumerableJoinRule( AlgFactories.LOGICAL_BUILDER );
-
-
-    public ParquetEnumerableJoinRule( AlgBuilderFactory algBuilderFactory ) {
-        super(
-                operand( EnumerableJoin.class, operand( AlgNode.class, any() ), operand( AlgNode.class, any() ) ),
-                algBuilderFactory,
-                ParquetEnumerableJoinRule.class.getSimpleName() );
-    }
-
-
-    @Override
-    public void onMatch( AlgOptRuleCall call ) {
-        EnumerableJoin join = call.alg( 0 );
-        ParquetRelScan left = ParquetRelScanRuleSupport.findProjectedRelScan( call.alg( 1 ) );
-        ParquetRelScan right = ParquetRelScanRuleSupport.findProjectedRelScan( call.alg( 2 ) );
-        if ( left == null || right == null ) {
-            return;
-        }
-        JoinDirection direction = supportedDirection( join, left, right );
-        if ( direction == null ) {
-            return;
-        }
-        call.transformTo( ParquetRelJoin.create( join.getLeft(), join.getRight(), left, right, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent(), JoinInputLimit.NONE ) );
-    }
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableLimitJoinRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableLimitJoinRule.java
deleted file mode 100644
index 1ad477a766..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableLimitJoinRule.java
+++ /dev/null
@@ -1,187 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import static org.polypheny.db.adapter.parquet.relational.planning.ParquetRelJoin.supportedDirection;
-
-import java.util.Collections;
-import java.util.IdentityHashMap;
-import java.util.Set;
-import org.polypheny.db.algebra.AlgNode;
-import org.polypheny.db.algebra.core.AlgFactories;
-import org.polypheny.db.algebra.core.Calc;
-import org.polypheny.db.algebra.enumerable.EnumerableJoin;
-import org.polypheny.db.algebra.enumerable.EnumerableLimit;
-import org.polypheny.db.plan.AlgOptRule;
-import org.polypheny.db.plan.AlgOptRuleCall;
-import org.polypheny.db.plan.volcano.AlgSubset;
-import org.polypheny.db.rex.RexLiteral;
-import org.polypheny.db.rex.RexNode;
-import org.polypheny.db.tools.AlgBuilderFactory;
-
-/**
- * Rewrites joins where the planner pushed a literal limit onto the parent side
- * before a supported Parquet parent/child join.
- */
-public class ParquetEnumerableLimitJoinRule extends AlgOptRule {
-
-    public static final ParquetEnumerableLimitJoinRule INSTANCE = new ParquetEnumerableLimitJoinRule( AlgFactories.LOGICAL_BUILDER );
-
-
-    public ParquetEnumerableLimitJoinRule( AlgBuilderFactory algBuilderFactory ) {
-        super(
-                operand( EnumerableJoin.class, operand( AlgNode.class, any() ), operand( AlgNode.class, any() ) ),
-                algBuilderFactory,
-                ParquetEnumerableLimitJoinRule.class.getSimpleName() );
-    }
-
-
-    @Override
-    public void onMatch( AlgOptRuleCall call ) {
-        EnumerableJoin join = call.alg( 0 );
-        LimitedJoinInput left = findLimitedJoinInput( join.getLeft() );
-        LimitedJoinInput right = findLimitedJoinInput( join.getRight() );
-        if ( left == null || right == null ) {
-            return;
-        }
-
-        JoinDirection direction = supportedDirection( join, left.scan(), right.scan() );
-        if ( direction == null ) {
-            return;
-        }
-
-        JoinInputLimit parentLimit = direction.leftIsParent() ? left.limit() : right.limit();
-        JoinInputLimit childLimit = direction.leftIsParent() ? right.limit() : left.limit();
-        if ( parentLimit.isEmpty() || !childLimit.isEmpty() ) {
-            return;
-        }
-
-        call.transformTo( ParquetRelJoin.create(
-                join.getLeft(),
-                join.getRight(),
-                left.scan(),
-                right.scan(),
-                join.getCondition(),
-                join.getVariablesSet(),
-                join.getJoinType(),
-                direction.leftIsParent(),
-                parentLimit ) );
-    }
-
-
-    private LimitedJoinInput findLimitedJoinInput( AlgNode alg ) {
-        return findLimitedJoinInput( alg, newVisitedSet() );
-    }
-
-
-    private LimitedJoinInput findLimitedJoinInput( AlgNode alg, Set<AlgNode> visited ) {
-        if ( alg == null || !visited.add( alg ) ) {
-            return null;
-        }
-        ParquetRelScan scan = ParquetRelScanRuleSupport.findDirectRelScan( alg );
-        if ( scan != null ) {
-            return new LimitedJoinInput( scan, JoinInputLimit.NONE );
-        }
-        if ( alg instanceof Calc calc ) {
-            LimitedJoinInput input = findLimitedJoinInput( calc.getInput(), visited );
-            if ( input == null ) {
-                return null;
-            }
-            int[] projectedFields = ParquetRelScanRuleSupport.projectedFields( calc.getProgram(), input.scan().getFields() );
-            if ( projectedFields == null ) {
-                return null;
-            }
-            return input.withScan( new ParquetRelScan( input.scan().getCluster(), input.scan().getEntity(), projectedFields, input.scan().getFilters() ) );
-        }
-        if ( alg instanceof EnumerableLimit limit ) {
-            JoinInputLimit inputLimit = literalLimit( limit );
-            if ( inputLimit == null ) {
-                return null;
-            }
-            LimitedJoinInput input = findLimitedJoinInput( limit.getInput(), visited );
-            if ( input == null || !input.limit().isEmpty() ) {
-                return null;
-            }
-            return input.withLimit( inputLimit );
-        }
-        if ( alg instanceof AlgSubset subset ) {
-            LimitedJoinInput input = findLimitedJoinInput( subset.getBest(), branchVisitedSet( visited ) );
-            if ( input != null ) {
-                return input;
-            }
-            input = findLimitedJoinInput( subset.getOriginal(), branchVisitedSet( visited ) );
-            if ( input != null ) {
-                return input;
-            }
-            for ( AlgNode candidate : subset.getAlgList() ) {
-                input = findLimitedJoinInput( candidate, branchVisitedSet( visited ) );
-                if ( input != null ) {
-                    return input;
-                }
-            }
-        }
-        return null;
-    }
-
-
-    private Set<AlgNode> newVisitedSet() {
-        return Collections.newSetFromMap( new IdentityHashMap<>() );
-    }
-
-
-    private Set<AlgNode> branchVisitedSet( Set<AlgNode> visited ) {
-        Set<AlgNode> branch = newVisitedSet();
-        branch.addAll( visited );
-        return branch;
-    }
-
-
-    private JoinInputLimit literalLimit( EnumerableLimit limit ) {
-        Integer offset = literalInt( limit.offset );
-        Integer fetch = literalInt( limit.fetch );
-        if ( (offset == null && limit.offset != null) || (fetch == null && limit.fetch != null) ) {
-            return null;
-        }
-        return new JoinInputLimit( offset == null ? 0 : offset, fetch == null ? -1 : fetch );
-    }
-
-
-    private Integer literalInt( RexNode node ) {
-        if ( node == null ) {
-            return null;
-        }
-        if ( !(node instanceof RexLiteral) ) {
-            return null;
-        }
-        return RexLiteral.intValue( node );
-    }
-
-
-    private record LimitedJoinInput( ParquetRelScan scan, JoinInputLimit limit ) {
-
-        LimitedJoinInput withScan( ParquetRelScan scan ) {
-            return new LimitedJoinInput( scan, limit );
-        }
-
-
-        LimitedJoinInput withLimit( JoinInputLimit limit ) {
-            return new LimitedJoinInput( scan, limit );
-        }
-
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterIndexMapper.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterIndexMapper.java
new file mode 100644
index 0000000000..e03ebf5365
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterIndexMapper.java
@@ -0,0 +1,84 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.ArrayList;
+import java.util.List;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+
+
+final class ParquetFilterIndexMapper {
+
+    private ParquetFilterIndexMapper() {
+    }
+
+
+    static ParquetAdapterFilter remapFilter( ParquetAdapterFilter filter, int[] oldFields, int[] newFields ) {
+        if ( filter.isLogical() ) {
+            List<ParquetAdapterFilter> remappedOperands = new ArrayList<>( filter.operands().size() );
+            for ( ParquetAdapterFilter operand : filter.operands() ) {
+                ParquetAdapterFilter remappedOperand = remapFilter( operand, oldFields, newFields );
+                if ( remappedOperand == null ) {
+                    return null;
+                }
+                remappedOperands.add( remappedOperand );
+            }
+            return ParquetAdapterFilter.logical( filter.operator(), remappedOperands );
+        }
+
+        int remappedIndex = remapIndex( filter.columnIndex(), oldFields, newFields );
+        if ( remappedIndex < 0 ) {
+            return null;
+        }
+
+        return new ParquetAdapterFilter(
+                remappedIndex,
+                filter.pathElements(),
+                filter.operator(),
+                filter.polyValue(),
+                filter.dynamicParamIndex() );
+    }
+
+
+    static List<ParquetAdapterFilter> remapFilters( List<ParquetAdapterFilter> filters, int[] oldFields, int[] newFields ) {
+        List<ParquetAdapterFilter> remappedFilters = new ArrayList<>( filters.size() );
+        for ( ParquetAdapterFilter filter : filters ) {
+            ParquetAdapterFilter remappedFilter = remapFilter( filter, oldFields, newFields );
+            if ( remappedFilter == null ) {
+                return null;
+            }
+            remappedFilters.add( remappedFilter );
+        }
+        return remappedFilters;
+    }
+
+
+    private static int remapIndex( int oldIndex, int[] oldFields, int[] newFields ) {
+        if ( oldIndex < 0 || oldIndex >= oldFields.length ) {
+            return -1;
+        }
+
+        int physicalColumn = oldFields[oldIndex];
+        for ( int i = 0; i < newFields.length; i++ ) {
+            if ( newFields[i] == physicalColumn ) {
+                return i;
+            }
+        }
+        return -1;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetJoin.java
similarity index 53%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetJoin.java
index 4f993a793d..654c4240c2 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetJoin.java
@@ -20,7 +20,6 @@
 import java.util.List;
 import java.util.Optional;
 import java.util.Set;
-import org.apache.calcite.linq4j.tree.Blocks;
 import org.apache.calcite.linq4j.tree.Expression;
 import org.apache.calcite.linq4j.tree.Expressions;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
@@ -33,12 +32,12 @@
 import org.polypheny.db.algebra.core.JoinAlgType;
 import org.polypheny.db.algebra.core.JoinInfo;
 import org.polypheny.db.algebra.enumerable.EnumUtils;
-import org.polypheny.db.algebra.enumerable.EnumerableAlg;
 import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
-import org.polypheny.db.algebra.enumerable.EnumerableConvention;
-import org.polypheny.db.algebra.enumerable.PhysType;
-import org.polypheny.db.algebra.enumerable.PhysTypeImpl;
 import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
+import org.polypheny.db.algebra.polyalg.arguments.BooleanArg;
+import org.polypheny.db.algebra.polyalg.arguments.ListArg;
+import org.polypheny.db.algebra.polyalg.arguments.PolyAlgArgs;
+import org.polypheny.db.algebra.polyalg.arguments.StringArg;
 import org.polypheny.db.plan.AlgCluster;
 import org.polypheny.db.plan.AlgOptCost;
 import org.polypheny.db.plan.AlgPlanner;
@@ -47,9 +46,9 @@
 import org.polypheny.db.schema.trait.ModelTrait;
 
 /**
- * Parquet physical join for supported nested parent/child table joins.
+ * Parquet-convention join for supported parent/child table joins.
  */
-public class ParquetRelJoin extends Join implements EnumerableAlg {
+public class ParquetJoin extends Join implements ParquetAlg {
 
     private final boolean leftIsParent;
     private final ParquetRelTable leftTable;
@@ -57,10 +56,9 @@ public class ParquetRelJoin extends Join implements EnumerableAlg {
     private final int[] leftFields;
     private final int[] rightFields;
     private final List<ParquetAdapterFilter> filters;
-    private final JoinInputLimit parentLimit;
 
 
-    public ParquetRelJoin(
+    public ParquetJoin(
             AlgCluster cluster,
             AlgTraitSet traitSet,
             AlgNode left,
@@ -73,8 +71,7 @@ public ParquetRelJoin(
             ParquetRelTable rightTable,
             int[] leftFields,
             int[] rightFields,
-            List<ParquetAdapterFilter> filters,
-            JoinInputLimit parentLimit ) {
+            List<ParquetAdapterFilter> filters ) {
         super( cluster, traitSet, left, right, condition, variablesSet, joinType );
         this.leftIsParent = leftIsParent;
         this.leftTable = leftTable;
@@ -82,45 +79,67 @@ public ParquetRelJoin(
         this.leftFields = leftFields;
         this.rightFields = rightFields;
         this.filters = List.copyOf( filters );
-        this.parentLimit = parentLimit;
     }
 
 
-    public static ParquetRelJoin create( ParquetRelScan left, ParquetRelScan right, RexNode condition, Set<CorrelationId> variablesSet, JoinAlgType joinType, boolean leftIsParent ) {
-        return create( left, right, left, right, condition, variablesSet, joinType, leftIsParent, JoinInputLimit.NONE );
-    }
-
-
-    /**
-     * factory method that builds the physical adapter join node
-     */
-    public static ParquetRelJoin create( AlgNode leftInput, AlgNode rightInput, ParquetRelScan leftScan, ParquetRelScan rightScan, RexNode condition, Set<CorrelationId> variablesSet, JoinAlgType joinType, boolean leftIsParent, JoinInputLimit parentLimit ) {
-        AlgCluster cluster = leftInput.getCluster();
-        AlgTraitSet traitSet = cluster.traitSetOf( EnumerableConvention.INSTANCE ).replace( ModelTrait.RELATIONAL );
-        return new ParquetRelJoin(
+    public static ParquetJoin create( ParquetScan left, ParquetScan right, RexNode condition, Set<CorrelationId> variablesSet, JoinAlgType joinType, boolean leftIsParent ) {
+        AlgCluster cluster = left.getCluster();
+        AlgTraitSet traitSet = cluster.traitSetOf( ParquetConvention.INSTANCE ).replace( ModelTrait.RELATIONAL );
+        return new ParquetJoin(
                 cluster,
                 traitSet,
-                leftInput,
-                rightInput,
+                left,
+                right,
                 condition,
                 variablesSet,
                 joinType,
                 leftIsParent,
-                leftScan.getEntity(),
-                rightScan.getEntity(),
-                leftScan.getFields(),
-                rightScan.getFields(),
-                List.of(),
-                parentLimit );
+                left.getEntity(),
+                right.getEntity(),
+                left.getFields(),
+                right.getFields(),
+                joinInputFilters( left, right ) );
+    }
+
+
+    private static List<ParquetAdapterFilter> joinInputFilters( ParquetScan leftScan, ParquetScan rightScan ) {
+        List<ParquetAdapterFilter> filters = new ArrayList<>( leftScan.getFilters().size() + rightScan.getFilters().size() );
+        leftScan.getFilters().forEach( filter -> filters.add( shiftFilter( filter, 0 ) ) );
+        rightScan.getFilters().forEach( filter -> filters.add( shiftFilter( filter, leftScan.getFields().length ) ) );
+        return filters;
+    }
+
+
+    private static ParquetAdapterFilter shiftFilter( ParquetAdapterFilter filter, int offset ) {
+        if ( filter.isLogical() ) {
+            return ParquetAdapterFilter.logical( filter.operator(), filter.operands().stream()
+                    .map( operand -> shiftFilter( operand, offset ) )
+                    .toList() );
+        }
+        return new ParquetAdapterFilter(
+                filter.columnIndex() + offset,
+                filter.pathElements(),
+                filter.operator(),
+                filter.polyValue(),
+                filter.dynamicParamIndex() );
     }
 
 
     /**
      * Checks whether a join can be executed by the Parquet adapter: it must be join
-     * between two tables from the same Parquet source, using parent PRIMARY_KEY and child PARENT_KEY
+     * between two tables from the same Parquet source, using parent PRIMARY_KEY and child PARENT_KEY.
      */
-    public static JoinDirection supportedDirection( Join join, ParquetRelScan left, ParquetRelScan right ) {
-        JoinInfo info = JoinInfo.of( left, right, join.getCondition() );
+    public static JoinDirection supportedDirection( Join join, ParquetScan left, ParquetScan right ) {
+        return supportedDirection( join.getCondition(), join.getJoinType(), left, right );
+    }
+
+
+    /**
+     * Checks whether a join can be executed by the Parquet adapter: it must be join
+     * between two tables from the same Parquet source, using parent PRIMARY_KEY and child PARENT_KEY.
+     */
+    public static JoinDirection supportedDirection( RexNode condition, JoinAlgType joinType, ParquetScan left, ParquetScan right ) {
+        JoinInfo info = JoinInfo.of( left, right, condition );
         if ( !info.isEqui() || info.leftKeys.size() != 1 || info.rightKeys.size() != 1 ) {
             return null;
         }
@@ -131,8 +150,8 @@ public static JoinDirection supportedDirection( Join join, ParquetRelScan left,
             return null;
         }
 
-        int leftColumn = mappedColumn( left, info.leftKeys.get( 0 ) );
-        int rightColumn = mappedColumn( right, info.rightKeys.get( 0 ) );
+        int leftColumn = mappedColumn( left.getFields(), info.leftKeys.get( 0 ), left.getEntity().getFieldCount() );
+        int rightColumn = mappedColumn( right.getFields(), info.rightKeys.get( 0 ), right.getEntity().getFieldCount() );
         if ( leftColumn < 0 || rightColumn < 0 ) {
             return null;
         }
@@ -143,7 +162,7 @@ public static JoinDirection supportedDirection( Join join, ParquetRelScan left,
             return null;
         }
 
-        switch ( join.getJoinType() ) {
+        switch ( joinType ) {
             case INNER, FULL, LEFT, RIGHT -> {
                 return new JoinDirection( leftIsParent );
             }
@@ -154,13 +173,12 @@ public static JoinDirection supportedDirection( Join join, ParquetRelScan left,
     }
 
 
-    private static int mappedColumn( ParquetRelScan scan, int inputIndex ) {
-        int[] fields = scan.getFields();
+    private static int mappedColumn( int[] fields, int inputIndex, int totalFieldsCount ) {
         if ( inputIndex < 0 || inputIndex >= fields.length ) {
             return -1;
         }
         int column = fields[inputIndex];
-        return column < 0 || column >= scan.getEntity().getFieldCount() ? -1 : column;
+        return column < 0 || column >= totalFieldsCount ? -1 : column;
     }
 
 
@@ -168,33 +186,49 @@ private static boolean isParentChildJoin( ParquetRelTable parent, ParquetRelTabl
         if ( !isDirectChildPath( parent.getBinding().sourcePathElements(), child.getBinding().sourcePathElements() ) ) {
             return false;
         }
-        return parentColumn == parent.columnIndexByRole( ParquetColumnRole.PRIMARY_KEY )
-                && childColumn == child.columnIndexByRole( ParquetColumnRole.PARENT_KEY );
+        return parentColumn == parent.columnIndexByRole( ParquetColumnRole.PRIMARY_KEY ) && childColumn == child.columnIndexByRole( ParquetColumnRole.PARENT_KEY );
     }
 
 
-    public static boolean isDirectChildPath( List<String> parentPath, List<String> childPath ) {
+    private static boolean isDirectChildPath( List<String> parentPath, List<String> childPath ) {
         return childPath.size() == parentPath.size() + 1 && childPath.subList( 0, parentPath.size() ).equals( parentPath );
     }
 
 
-    public ParquetRelJoin withFilters( List<ParquetAdapterFilter> filters ) {
+    public ParquetJoin withFilters( List<ParquetAdapterFilter> filters ) {
         List<ParquetAdapterFilter> combinedFilters = new ArrayList<>( this.filters );
         combinedFilters.addAll( filters );
-        return new ParquetRelJoin( getCluster(), traitSet, left, right, condition, variablesSet, joinType, leftIsParent, leftTable, rightTable, leftFields, rightFields, combinedFilters, parentLimit );
+        return new ParquetJoin( getCluster(), traitSet, left, right, condition, variablesSet, joinType, leftIsParent, leftTable, rightTable, leftFields, rightFields, combinedFilters );
     }
 
 
     @Override
     public Join copy( AlgTraitSet traitSet, RexNode conditionExpr, AlgNode left, AlgNode right, JoinAlgType joinType, boolean semiJoinDone ) {
-        return new ParquetRelJoin( getCluster(), traitSet, left, right, conditionExpr, variablesSet, joinType, leftIsParent, leftTable, rightTable, leftFields, rightFields, filters, parentLimit );
+        return new ParquetJoin( getCluster(), traitSet, left, right, conditionExpr, variablesSet, joinType, leftIsParent, leftTable, rightTable, leftFields, rightFields, filters );
+    }
+
+
+    @Override
+    public PolyAlgArgs bindArguments() {
+        List<String> joinedFields = ParquetPolyAlgDisplay.joinedFieldNames( leftTable, leftFields, rightTable, rightFields );
+        return super.bindArguments()
+                .put( "leftIsParent", new BooleanArg( leftIsParent ) )
+                .put( "leftFields", new ListArg<>( ParquetPolyAlgDisplay.fieldNames( leftTable, leftFields ), StringArg::new ) )
+                .put( "rightFields", new ListArg<>( ParquetPolyAlgDisplay.fieldNames( rightTable, rightFields ), StringArg::new ) )
+                .put( "filters", new ListArg<>( ParquetPolyAlgDisplay.filters( filters, joinedFields ), StringArg::new ) );
     }
 
 
     @Override
     public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
         Optional<Double> count = mq.getTupleCount( this );
-        return planner.getCostFactory().makeCost( count.orElse( estimateTupleCount( mq ) ), 0, 0 );
+        return planner.getCostFactory().makeCost( count.orElse( estimateTupleCount( mq ) ), 0, 0 ).multiplyBy( ParquetConvention.COST_MULTIPLIER );
+    }
+
+
+    @Override
+    public double estimateTupleCount( AlgMetadataQuery mq ) {
+        return Math.max( left.estimateTupleCount( mq ), right.estimateTupleCount( mq ) );
     }
 
 
@@ -202,33 +236,25 @@ public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
     public AlgWriter explainTerms( AlgWriter pw ) {
         return super.explainTerms( pw )
                 .item( "leftIsParent", leftIsParent )
-                .itemIf( "parentOffset", parentLimit.offset(), parentLimit.offset() != 0 )
-                .itemIf( "parentFetch", parentLimit.fetch(), parentLimit.fetch() >= 0 )
                 .item( "filters", filters );
     }
 
 
     @Override
-    public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
-        PhysType physType = PhysTypeImpl.of( implementor.getTypeFactory(), getTupleType(), pref.preferArray() );
+    public Expression implement( EnumerableAlgImplementor implementor ) {
         boolean emitUnmatchedParents = emitUnmatchedParents();
         Expression runtimeFilters = EnumUtils.expressionList( filters.stream().map( ParquetAdapterFilter::toExpression ).toList() );
 
-        return implementor.result(
-                physType,
-                Blocks.toBlock(
-                        Expressions.call(
-                                leftTable.asExpression( ParquetRelTable.class ),
-                                "nestedJoin",
-                                implementor.getRootExpression(),
-                                rightTable.asExpression( ParquetRelTable.class ),
-                                Expressions.constant( leftFields ),
-                                Expressions.constant( rightFields ),
-                                Expressions.constant( leftIsParent ),
-                                Expressions.constant( emitUnmatchedParents ),
-                                Expressions.constant( parentLimit.offset() ),
-                                Expressions.constant( parentLimit.fetch() ),
-                                runtimeFilters ) ) );
+        return Expressions.call(
+                leftTable.asExpression( ParquetRelTable.class ),
+                "nestedJoin",
+                implementor.getRootExpression(),
+                rightTable.asExpression( ParquetRelTable.class ),
+                Expressions.constant( leftFields ),
+                Expressions.constant( rightFields ),
+                Expressions.constant( leftIsParent ),
+                Expressions.constant( emitUnmatchedParents ),
+                runtimeFilters );
     }
 
 
@@ -241,5 +267,4 @@ private boolean emitUnmatchedParents() {
         };
     }
 
-
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetPolyAlgDisplay.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetPolyAlgDisplay.java
new file mode 100644
index 0000000000..55dbf075da
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetPolyAlgDisplay.java
@@ -0,0 +1,105 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.Arrays;
+import java.util.List;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.type.AlgDataTypeField;
+
+final class ParquetPolyAlgDisplay {
+
+    private ParquetPolyAlgDisplay() {
+    }
+
+
+    static List<String> fieldNames( ParquetRelTable table, int[] fields ) {
+        List<AlgDataTypeField> tableFields = table.getTupleType().getFields();
+        return Arrays.stream( fields )
+                .mapToObj( field -> fieldName( tableFields, field ) )
+                .toList();
+    }
+
+
+    static List<String> filters( List<ParquetAdapterFilter> filters, List<String> fieldNames ) {
+        return filters.stream()
+                .map( filter -> filter( filter, fieldNames ) )
+                .toList();
+    }
+
+
+    static String filter( ParquetAdapterFilter filter, List<String> fieldNames ) {
+        if ( filter.isLogical() ) {
+            return filter.operator().name() + "("
+                    + String.join( ", ", filter.operands().stream()
+                            .map( operand -> filter( operand, fieldNames ) )
+                            .toList() )
+                    + ")";
+        }
+
+        String field = fieldRef( fieldNames, filter.columnIndex() );
+        if ( filter.operator() == Kind.IS_NULL || filter.operator() == Kind.IS_NOT_NULL ) {
+            return field + " " + operator( filter );
+        }
+
+        String value;
+        if ( filter.dynamicParamIndex() != null ) {
+            value = "?" + filter.dynamicParamIndex();
+        } else if ( filter.polyValue() == null ) {
+            value = "null";
+        } else {
+            value = filter.polyValue().toString();
+        }
+
+        return field + " " + operator( filter ) + " " + value;
+    }
+
+
+    static List<String> joinedFieldNames( ParquetRelTable leftTable, int[] leftFields, ParquetRelTable rightTable, int[] rightFields ) {
+        List<String> leftNames = fieldNames( leftTable, leftFields ).stream()
+                .map( name -> "left." + name )
+                .toList();
+        List<String> rightNames = fieldNames( rightTable, rightFields ).stream()
+                .map( name -> "right." + name )
+                .toList();
+        return java.util.stream.Stream.concat( leftNames.stream(), rightNames.stream() ).toList();
+    }
+
+
+    private static String fieldRef( List<String> fieldNames, int index ) {
+        if ( index >= 0 && index < fieldNames.size() ) {
+            return fieldNames.get( index );
+        }
+        return "#" + index;
+    }
+
+
+    private static String operator( ParquetAdapterFilter filter ) {
+        return filter.operator().sql == null ? filter.operator().name() : filter.operator().sql;
+    }
+
+
+    private static String fieldName( List<AlgDataTypeField> fields, int index ) {
+        if ( index >= 0 && index < fields.size() ) {
+            return fields.get( index ).getName() + " (#" + index + ")";
+        }
+        return "#" + index;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java
deleted file mode 100644
index 7d7f0ecb41..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import static org.polypheny.db.adapter.parquet.relational.planning.ParquetRelJoin.supportedDirection;
-
-import org.polypheny.db.algebra.AlgNode;
-import org.polypheny.db.algebra.convert.ConverterRule;
-import org.polypheny.db.algebra.core.AlgFactories;
-import org.polypheny.db.algebra.enumerable.EnumerableConvention;
-import org.polypheny.db.algebra.logical.relational.LogicalRelJoin;
-import org.polypheny.db.plan.Convention;
-import org.polypheny.db.tools.AlgBuilderFactory;
-
-/**
- * Rewrites supported parent/child joins between normalized Parquet tables into
- * a Parquet adapter join.
- */
-public class ParquetRelJoinRule extends ConverterRule {
-
-    public static final ParquetRelJoinRule INSTANCE = new ParquetRelJoinRule( AlgFactories.LOGICAL_BUILDER );
-
-
-    public ParquetRelJoinRule( AlgBuilderFactory algBuilderFactory ) {
-        super(
-                LogicalRelJoin.class,
-                join -> true,
-                Convention.NONE,
-                EnumerableConvention.INSTANCE,
-                algBuilderFactory,
-                ParquetRelJoinRule.class.getSimpleName() );
-    }
-
-
-    /**
-     * Tries to turn a logical join into an adapter-level ParquetRelJoin
-     */
-    @Override
-    public AlgNode convert( AlgNode alg ) {
-        LogicalRelJoin join = (LogicalRelJoin) alg;
-        AlgNode leftOperand = convert( join.getLeft(), join.getLeft().getTraitSet().replace( EnumerableConvention.INSTANCE ) );
-        AlgNode rightOperand = convert( join.getRight(), join.getRight().getTraitSet().replace( EnumerableConvention.INSTANCE ) );
-        ParquetRelScan leftScan = ParquetRelScanRuleSupport.findProjectedRelScan( leftOperand );
-        ParquetRelScan rightScan = ParquetRelScanRuleSupport.findProjectedRelScan( rightOperand );
-        if ( leftScan == null || rightScan == null ) {
-            return null;
-        }
-
-        JoinDirection direction = supportedDirection( join, leftScan, rightScan );
-        if ( direction == null ) {
-            return null;
-        }
-
-        return ParquetRelJoin.create( leftOperand, rightOperand, leftScan, rightScan, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent(), JoinInputLimit.NONE );
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
deleted file mode 100644
index 4e9787b772..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
+++ /dev/null
@@ -1,130 +0,0 @@
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import java.util.ArrayList;
-import java.util.List;
-import lombok.Getter;
-import org.apache.calcite.linq4j.tree.Blocks;
-import org.apache.calcite.linq4j.tree.Expression;
-import org.apache.calcite.linq4j.tree.Expressions;
-import org.apache.calcite.linq4j.tree.Primitive;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.algebra.AlgNode;
-import org.polypheny.db.algebra.AlgWriter;
-import org.polypheny.db.algebra.core.relational.RelScan;
-import org.polypheny.db.algebra.enumerable.EnumUtils;
-import org.polypheny.db.algebra.enumerable.EnumerableAlg;
-import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
-import org.polypheny.db.algebra.enumerable.EnumerableConvention;
-import org.polypheny.db.algebra.enumerable.PhysType;
-import org.polypheny.db.algebra.enumerable.PhysTypeImpl;
-import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
-import org.polypheny.db.algebra.type.AlgDataType;
-import org.polypheny.db.algebra.type.AlgDataTypeFactory;
-import org.polypheny.db.algebra.type.AlgDataTypeField;
-import org.polypheny.db.plan.AlgCluster;
-import org.polypheny.db.plan.AlgOptCost;
-import org.polypheny.db.plan.AlgPlanner;
-import org.polypheny.db.plan.AlgTraitSet;
-
-/**
- * Relational scan algebra node for Parquet-backed physical tables
- */
-@Getter
-public class ParquetRelScan extends RelScan<ParquetRelTable> implements EnumerableAlg {
-
-    private final int[] fields;
-    private final List<ParquetAdapterFilter> filters;
-
-
-    public ParquetRelScan( AlgCluster cluster, ParquetRelTable table, int[] fields ) {
-        this( cluster, table, fields, List.of() );
-    }
-
-
-    public ParquetRelScan( AlgCluster cluster, ParquetRelTable table, int[] fields, List<ParquetAdapterFilter> filters ) {
-        super( cluster, cluster.traitSetOf( EnumerableConvention.INSTANCE ), table );
-        this.fields = fields;
-        this.filters = List.copyOf( filters );
-    }
-
-
-    @Override
-    public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
-        return new ParquetRelScan( getCluster(), entity, fields, filters );
-    }
-
-
-    @Override
-    public AlgWriter explainTerms( AlgWriter pw ) {
-        return super.explainTerms( pw ).item( "fields", Primitive.asList( fields ) ).item( "filters", filters );
-    }
-
-
-    @Override
-    public String algCompareString() {
-        return super.algCompareString() + "$fields=" + Primitive.asList( fields ) + "$filters=" + filters;
-    }
-
-
-    @Override
-    public AlgDataType deriveRowType() {
-        final List<AlgDataTypeField> fieldList = entity.getTupleType().getFields();
-        final AlgDataTypeFactory.Builder builder = getCluster().getTypeFactory().builder();
-        for ( int field : fields ) {
-            builder.add( fieldList.get( field ) );
-        }
-        return builder.build();
-    }
-
-
-    @Override
-    public void register( AlgPlanner planner ) {
-        registerRules( planner );
-    }
-
-
-    public static void registerRules( AlgPlanner planner ) {
-        planner.addRuleDuringRuntime( ParquetRelTableScanRule.INSTANCE );
-        planner.addRuleDuringRuntime( ParquetRelScanRule.INSTANCE );
-        planner.addRuleDuringRuntime( ParquetEnumerableFilterScanRule.INSTANCE );
-        planner.addRuleDuringRuntime( ParquetEnumerableCalcScanRule.INSTANCE );
-        planner.addRuleDuringRuntime( ParquetRelJoinRule.INSTANCE );
-        planner.addRuleDuringRuntime( ParquetEnumerableJoinRule.INSTANCE );
-        planner.addRuleDuringRuntime( ParquetEnumerableLimitJoinRule.INSTANCE );
-        planner.addRuleDuringRuntime( ParquetEnumerableFilterJoinRule.INSTANCE );
-        planner.addRuleDuringRuntime( ParquetEnumerableCalcJoinRule.INSTANCE );
-    }
-
-
-    @Override
-    public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
-        double fieldRatio = ((double) fields.length + 2D) / ((double) entity.getTupleType().getFieldCount() + 2D);
-        double filterRatio = filters.isEmpty() ? 1D : 0.5D;
-        return super.computeSelfCost( planner, mq ).multiplyBy( fieldRatio * filterRatio );
-    }
-
-
-    public ParquetRelScan withFilters( List<ParquetAdapterFilter> filters ) {
-        List<ParquetAdapterFilter> combinedFilters = new ArrayList<>( this.filters );
-        combinedFilters.addAll( filters );
-        return new ParquetRelScan( getCluster(), entity, fields, combinedFilters );
-    }
-
-
-    @Override
-    public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
-        PhysType physType = PhysTypeImpl.of( implementor.getTypeFactory(), getTupleType(), pref.preferArray() );
-        Expression runtimeFilters = EnumUtils.expressionList( filters.stream().map( ParquetAdapterFilter::toExpression ).toList() );
-        return implementor.result(
-                physType,
-                Blocks.toBlock(
-                        Expressions.call(
-                                entity.asExpression( ParquetRelTable.class ),
-                                "project",
-                                implementor.getRootExpression(),
-                                Expressions.constant( fields ),
-                                runtimeFilters ) ) );
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java
deleted file mode 100644
index 590e1697c0..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java
+++ /dev/null
@@ -1,63 +0,0 @@
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import java.util.List;
-import org.polypheny.db.algebra.core.AlgFactories;
-import org.polypheny.db.algebra.logical.relational.LogicalRelProject;
-import org.polypheny.db.plan.AlgOptRule;
-import org.polypheny.db.plan.AlgOptRuleCall;
-import org.polypheny.db.rex.RexIndexRef;
-import org.polypheny.db.rex.RexNode;
-import org.polypheny.db.tools.AlgBuilderFactory;
-
-/**
- * Planner rule that rewrites compatible scans and projections to the Parquet-specific
- * relational scan node.
- */
-public class ParquetRelScanRule extends AlgOptRule {
-
-    public static final ParquetRelScanRule INSTANCE = new ParquetRelScanRule( AlgFactories.LOGICAL_BUILDER );
-
-
-    public ParquetRelScanRule( AlgBuilderFactory algBuilderFactory ) {
-        super(
-                operand( LogicalRelProject.class, operand( ParquetRelScan.class, none() ) ),
-                algBuilderFactory,
-                ParquetRelScanRule.class.getSimpleName() );
-    }
-
-
-    @Override
-    public void onMatch( AlgOptRuleCall call ) {
-        final LogicalRelProject project = call.alg( 0 );
-        final ParquetRelScan scan = call.alg( 1 );
-        int[] fields = getProjectFields( project.getProjects() );
-        if ( fields == null ) {
-            return;
-        }
-
-        int[] currentFields = scan.getFields();
-        int[] projectedFields = new int[fields.length];
-        for ( int i = 0; i < fields.length; i++ ) {
-            if ( fields[i] < 0 || fields[i] >= currentFields.length ) {
-                return;
-            }
-            projectedFields[i] = currentFields[fields[i]];
-        }
-        call.transformTo( new ParquetRelScan( scan.getCluster(), scan.getEntity(), projectedFields, scan.getFilters() ) );
-    }
-
-
-    static int[] getProjectFields( List<RexNode> exps ) {
-        final int[] fields = new int[exps.size()];
-        for ( int i = 0; i < exps.size(); i++ ) {
-            final RexNode exp = exps.get( i );
-            if ( exp instanceof RexIndexRef ) {
-                fields[i] = ((RexIndexRef) exp).getIndex();
-            } else {
-                return null;
-            }
-        }
-        return fields;
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java
deleted file mode 100644
index 6ed961a081..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java
+++ /dev/null
@@ -1,225 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import java.util.Collections;
-import java.util.IdentityHashMap;
-import java.util.List;
-import java.util.Set;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
-import org.polypheny.db.algebra.AlgNode;
-import org.polypheny.db.algebra.core.Calc;
-import org.polypheny.db.algebra.core.Project;
-import org.polypheny.db.algebra.core.relational.RelScan;
-import org.polypheny.db.plan.AlgOptUtil;
-import org.polypheny.db.plan.volcano.AlgSubset;
-import org.polypheny.db.rex.RexIndexRef;
-import org.polypheny.db.rex.RexNode;
-import org.polypheny.db.rex.RexProgram;
-import org.polypheny.db.type.PolyType;
-
-/**
- * Class handles direct Parquet scans, scans hidden inside planner subsets,
- * and simple projection-only Calc nodes above scans, while keeping the projected field mapping correct.
- * This lets join and filter rules understand which real Parquet table and physical columns are being used.
- */
-public final class ParquetRelScanRuleSupport {
-
-    private ParquetRelScanRuleSupport() {
-    }
-
-    /*
-    Returns the node directly if it is already ParquetRelScan, converts a generic relational scan
-    over ParquetRelTable into ParquetRelScan, and also looks inside AlgSubset alternatives.
-     */
-    static ParquetRelScan findDirectRelScan( AlgNode alg ) {
-        return findDirectRelScan( alg, newVisitedSet() );
-    }
-
-
-    private static ParquetRelScan findDirectRelScan( AlgNode alg, Set<AlgNode> visited ) {
-        if ( alg == null || !visited.add( alg ) ) {
-            return null;
-        }
-        ParquetRelScan directScan = directRelScan( alg );
-        if ( directScan != null ) {
-            return directScan;
-        }
-        if ( alg instanceof AlgSubset subset ) {
-            ParquetRelScan relScan = findDirectRelScanMatchingRowType( subset.getBest(), subset, visited );
-            if ( relScan != null ) {
-                return relScan;
-            }
-            relScan = findDirectRelScanMatchingRowType( subset.getOriginal(), subset, visited );
-            if ( relScan != null ) {
-                return relScan;
-            }
-            for ( AlgNode candidate : subset.getAlgList() ) {
-                relScan = findDirectRelScanMatchingRowType( candidate, subset, visited );
-                if ( relScan != null ) {
-                    return relScan;
-                }
-            }
-        }
-        return null;
-    }
-
-
-    /**
-     * Tries to find a ParquetRelScan even when it is wrapped by a simple projection-only Calc.
-     * It first checks for a direct scan, then unwraps Calc nodes if they only reorder/select fields
-     * and do not filter or compute expressions. It returns a new ParquetRelScan with fields mapped to the original physical Parquet columns.
-     */
-    static ParquetRelScan findProjectedRelScan( AlgNode alg ) {
-        return findProjectedRelScan( alg, newVisitedSet() );
-    }
-
-
-    private static ParquetRelScan findProjectedRelScan( AlgNode alg, Set<AlgNode> visited ) {
-        if ( alg == null || !visited.add( alg ) ) {
-            return null;
-        }
-        ParquetRelScan directScan = directRelScan( alg );
-        if ( directScan != null ) {
-            return directScan;
-        }
-        if ( alg instanceof Calc calc ) {
-            ParquetRelScan inputScan = findProjectedRelScanMatchingRowType( calc.getInput(), calc.getInput(), visited );
-            if ( inputScan == null ) {
-                return null;
-            }
-            int[] projectedFields = projectedFields( calc.getProgram(), inputScan.getFields() );
-            if ( projectedFields == null ) {
-                return null;
-            }
-            return new ParquetRelScan( inputScan.getCluster(), inputScan.getEntity(), projectedFields, inputScan.getFilters() );
-        }
-        if ( alg instanceof Project project ) {
-            ParquetRelScan inputScan = findProjectedRelScanMatchingRowType( project.getInput(), project.getInput(), visited );
-            if ( inputScan == null ) {
-                return null;
-            }
-            int[] projectedFields = projectedFields( project.getProjects(), inputScan.getFields() );
-            if ( projectedFields == null ) {
-                return null;
-            }
-            return new ParquetRelScan( inputScan.getCluster(), inputScan.getEntity(), projectedFields, inputScan.getFilters() );
-        }
-        if ( alg instanceof AlgSubset subset ) {
-            ParquetRelScan relScan = findProjectedRelScanMatchingRowType( subset.getBest(), subset, visited );
-            if ( relScan != null ) {
-                return relScan;
-            }
-            relScan = findProjectedRelScanMatchingRowType( subset.getOriginal(), subset, visited );
-            if ( relScan != null ) {
-                return relScan;
-            }
-            for ( AlgNode candidate : subset.getAlgList() ) {
-                relScan = findProjectedRelScanMatchingRowType( candidate, subset, visited );
-                if ( relScan != null ) {
-                    return relScan;
-                }
-            }
-        }
-        return null;
-    }
-
-
-    private static ParquetRelScan directRelScan( AlgNode alg ) {
-        if ( alg instanceof ParquetRelScan relScan ) {
-            return relScan;
-        }
-        // if ParquetRelTable was found - create new scan
-        if ( alg instanceof RelScan<?> relScan && relScan.getEntity().unwrap( ParquetRelTable.class ).isPresent() ) {
-            ParquetRelTable table = relScan.getEntity().unwrapOrThrow( ParquetRelTable.class );
-            if (!AlgOptUtil.areRowTypesEqual( alg.getTupleType(), table.getTupleType(), false )) {
-                return null;
-            }
-            return new ParquetRelScan( relScan.getCluster(), table, relScan.identity().stream().mapToInt( Integer::intValue ).toArray() );
-        }
-        return null;
-    }
-
-
-    private static ParquetRelScan findDirectRelScanMatchingRowType( AlgNode candidate, AlgNode expected, Set<AlgNode> visited ) {
-        ParquetRelScan relScan = findDirectRelScan( candidate, branchVisitedSet( visited ) );
-        return rowTypesMatch( expected, relScan ) ? relScan : null;
-    }
-
-
-    private static ParquetRelScan findProjectedRelScanMatchingRowType( AlgNode candidate, AlgNode expected, Set<AlgNode> visited ) {
-        ParquetRelScan relScan = findProjectedRelScan( candidate, branchVisitedSet( visited ) );
-        return rowTypesMatch( expected, relScan ) ? relScan : null;
-    }
-
-
-    private static Set<AlgNode> newVisitedSet() {
-        return Collections.newSetFromMap( new IdentityHashMap<>() );
-    }
-
-
-    private static Set<AlgNode> branchVisitedSet( Set<AlgNode> visited ) {
-        Set<AlgNode> branch = newVisitedSet();
-        branch.addAll( visited );
-        return branch;
-    }
-
-
-    static boolean rowTypesMatch( AlgNode expected, AlgNode actual ) {
-        return expected != null && actual != null && AlgOptUtil.areRowTypesEqual( expected.getTupleType(), actual.getTupleType(), false );
-    }
-
-
-    /**
-     * Maps the output columns of a projection-only RexProgram back to the underlying Parquet scan field indexes.
-     */
-    public static int[] projectedFields( RexProgram program, int[] inputFields ) {
-        if ( program.getCondition() != null ) {
-            return null;
-        }
-
-        List<RexNode> projects = program.getProjectList().stream()
-                .map( program::expandLocalRef )
-                .toList();
-        return projectedFields( projects, inputFields );
-    }
-
-
-    public static int[] projectedFields( List<RexNode> projects, int[] inputFields ) {
-        int[] projectedFields = new int[projects.size()];
-        for ( int i = 0; i < projects.size(); i++ ) {
-            RexNode project = projects.get( i );
-            if ( !(project instanceof RexIndexRef indexRef) ) {
-                return null;
-            }
-            int index = indexRef.getIndex();
-            if ( index < 0 || index >= inputFields.length ) {
-                return null;
-            }
-            projectedFields[i] = inputFields[index];
-        }
-        return projectedFields;
-    }
-
-
-    static List<PolyType> fieldTypes( AlgNode alg ) {
-        return alg.getTupleType().getFields().stream()
-                .map( field -> field.getType().getPolyType() )
-                .toList();
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelTableScanRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelTableScanRule.java
deleted file mode 100644
index 7ee64572f1..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelTableScanRule.java
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
-import org.polypheny.db.algebra.AlgNode;
-import org.polypheny.db.algebra.convert.ConverterRule;
-import org.polypheny.db.algebra.core.AlgFactories;
-import org.polypheny.db.algebra.enumerable.EnumerableConvention;
-import org.polypheny.db.algebra.logical.relational.LogicalRelScan;
-import org.polypheny.db.plan.Convention;
-import org.polypheny.db.tools.AlgBuilderFactory;
-
-/**
- * Converts logical scans of Parquet physical tables into adapter scans.
- */
-public class ParquetRelTableScanRule extends ConverterRule {
-
-    public static final ParquetRelTableScanRule INSTANCE = new ParquetRelTableScanRule( AlgFactories.LOGICAL_BUILDER );
-
-
-    public ParquetRelTableScanRule( AlgBuilderFactory algBuilderFactory ) {
-        super(
-                LogicalRelScan.class,
-                scan -> scan.getEntity().unwrap( ParquetRelTable.class ).isPresent(),
-                Convention.NONE,
-                EnumerableConvention.INSTANCE,
-                algBuilderFactory,
-                ParquetRelTableScanRule.class.getSimpleName() );
-    }
-
-
-    @Override
-    public AlgNode convert( AlgNode alg ) {
-        LogicalRelScan scan = (LogicalRelScan) alg;
-        ParquetRelTable table = scan.getEntity().unwrapOrThrow( ParquetRelTable.class );
-        return new ParquetRelScan( scan.getCluster(), table, scan.identity().stream().mapToInt( Integer::intValue ).toArray() );
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRules.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRules.java
new file mode 100644
index 0000000000..5f0741a707
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRules.java
@@ -0,0 +1,72 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.List;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.convert.ConverterRule;
+import org.polypheny.db.algebra.core.AlgFactories;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.plan.AlgOptRule;
+import org.polypheny.db.schema.trait.ModelTrait;
+import org.polypheny.db.tools.AlgBuilderFactory;
+
+/**
+ * Rules that build the Parquet adapter convention path.
+ */
+public final class ParquetRules {
+
+    private ParquetRules() {
+    }
+
+
+    public static List<AlgOptRule> rules( ParquetConvention out ) {
+        return rules( out, AlgFactories.LOGICAL_BUILDER );
+    }
+
+
+    public static List<AlgOptRule> rules( ParquetConvention out, AlgBuilderFactory factory ) {
+        return List.of(
+                new ParquetAlgOptRule( PatternMatchers.joinWithScanOnLeftAndScanOnRight( out, factory ) ),
+                new ParquetAlgOptRule( PatternMatchers.attachFilterToJoinUnderCalc( out, factory ) ),
+                new ParquetAlgOptRule( PatternMatchers.attachFieldsAndFiltersToScanUnderCalc( out, factory ) ),
+                new EnumerableParquetRule( out, factory )
+        );
+    }
+
+
+    public static class EnumerableParquetRule extends ConverterRule {
+
+        public EnumerableParquetRule( ParquetConvention in, AlgBuilderFactory algBuilderFactory ) {
+            super(
+                    AlgNode.class,
+                    alg -> true,
+                    in,
+                    EnumerableConvention.INSTANCE,
+                    algBuilderFactory,
+                    "P_CALC" );
+        }
+
+
+        @Override
+        public AlgNode convert( AlgNode alg ) {
+            return new EnumerableParquet( alg.getCluster(), alg.getTraitSet().replace( getOutTrait() ).replace( ModelTrait.RELATIONAL ), alg );
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetScan.java
new file mode 100644
index 0000000000..3571171f5b
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetScan.java
@@ -0,0 +1,151 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.ArrayList;
+import java.util.List;
+import lombok.Getter;
+import org.apache.calcite.linq4j.tree.Expression;
+import org.apache.calcite.linq4j.tree.Expressions;
+import org.apache.calcite.linq4j.tree.Primitive;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.AlgWriter;
+import org.polypheny.db.algebra.core.relational.RelScan;
+import org.polypheny.db.algebra.enumerable.EnumUtils;
+import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
+import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
+import org.polypheny.db.algebra.polyalg.arguments.ListArg;
+import org.polypheny.db.algebra.polyalg.arguments.PolyAlgArgs;
+import org.polypheny.db.algebra.polyalg.arguments.StringArg;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.algebra.type.AlgDataTypeFactory;
+import org.polypheny.db.algebra.type.AlgDataTypeField;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgOptCost;
+import org.polypheny.db.plan.AlgPlanner;
+import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.schema.trait.ModelTrait;
+
+/**
+ * Parquet-convention scan for relational Parquet tables.
+ */
+@Getter
+public class ParquetScan extends RelScan<ParquetRelTable> implements ParquetAlg {
+
+    private final int[] fields;
+    private final List<ParquetAdapterFilter> filters;
+
+
+    public ParquetScan( AlgCluster cluster, ParquetRelTable table, int[] fields ) {
+        this( cluster, table, fields, List.of() );
+    }
+
+
+    public ParquetScan( AlgCluster cluster, ParquetRelTable table, int[] fields, List<ParquetAdapterFilter> filters ) {
+        super( cluster, cluster.traitSetOf( ParquetConvention.INSTANCE ).replace( ModelTrait.RELATIONAL ), table );
+        this.fields = fields;
+        this.filters = List.copyOf( filters );
+    }
+
+
+    @Override
+    public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
+        assert inputs.isEmpty();
+        return new ParquetScan( getCluster(), entity, fields, filters );
+    }
+
+
+    @Override
+    public AlgWriter explainTerms( AlgWriter pw ) {
+        return super.explainTerms( pw ).item( "fields", Primitive.asList( fields ) ).item( "filters", filters );
+    }
+
+
+    @Override
+    public String algCompareString() {
+        return super.algCompareString() + "$fields=" + Primitive.asList( fields ) + "$filters=" + filters;
+    }
+
+
+    @Override
+    public PolyAlgArgs bindArguments() {
+        List<String> fieldNames = ParquetPolyAlgDisplay.fieldNames( entity, fields );
+        return super.bindArguments()
+                .put( "fields", new ListArg<>( fieldNames, StringArg::new ) )
+                .put( "filters", new ListArg<>( ParquetPolyAlgDisplay.filters( filters, fieldNames ), StringArg::new ) );
+    }
+
+
+    @Override
+    public AlgDataType deriveRowType() {
+        final List<AlgDataTypeField> fieldList = entity.getTupleType().getFields();
+        final AlgDataTypeFactory.Builder builder = getCluster().getTypeFactory().builder();
+        for ( int field : fields ) {
+            builder.add( fieldList.get( field ) );
+        }
+        return builder.build();
+    }
+
+
+    @Override
+    public void register( AlgPlanner planner ) {
+        ParquetConvention.INSTANCE.register( planner );
+    }
+
+
+    @Override
+    public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
+        double fieldRatio = ((double) fields.length + 2D) / ((double) entity.getTupleType().getFieldCount() + 2D);
+        double filterRatio = filters.isEmpty() ? 1D : 0.5D;
+        return super.computeSelfCost( planner, mq ).multiplyBy( ParquetConvention.COST_MULTIPLIER * fieldRatio * filterRatio );
+    }
+
+
+    public ParquetScan withFilters( List<ParquetAdapterFilter> filters ) {
+        List<ParquetAdapterFilter> combinedFilters = new ArrayList<>( this.filters );
+        combinedFilters.addAll( filters );
+        return new ParquetScan( getCluster(), entity, fields, combinedFilters );
+    }
+
+
+    public ParquetScan withFields( int[] fields ) {
+        List<ParquetAdapterFilter> remapped = ParquetFilterIndexMapper.remapFilters( filters, this.fields, fields );
+        if ( remapped == null ) {
+            return null; // cannot absorb this projection into the scan
+        }
+        return new ParquetScan( getCluster(), entity, fields, remapped );
+    }
+
+    public ParquetScan withFieldsAndFilters( int[] fields, List<ParquetAdapterFilter> filters ) {
+        return new ParquetScan( getCluster(), entity, fields, filters );
+    }
+
+
+    @Override
+    public Expression implement( EnumerableAlgImplementor implementor ) {
+        Expression runtimeFilters = EnumUtils.expressionList( filters.stream().map( ParquetAdapterFilter::toExpression ).toList() );
+        return Expressions.call(
+                entity.asExpression( ParquetRelTable.class ),
+                "project",
+                implementor.getRootExpression(),
+                Expressions.constant( fields ),
+                runtimeFilters );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatcher.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatcher.java
new file mode 100644
index 0000000000..1b992b5d0a
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatcher.java
@@ -0,0 +1,32 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import org.polypheny.db.plan.AlgOptRuleCall;
+import org.polypheny.db.plan.AlgOptRuleOperand;
+import org.polypheny.db.plan.Convention;
+import org.polypheny.db.tools.AlgBuilderFactory;
+import java.util.function.Consumer;
+
+public record PatternMatcher( Convention out, AlgBuilderFactory factory, AlgOptRuleOperand operand, String description, Consumer<AlgOptRuleCall> consumer ) {
+
+    public void onMatch( AlgOptRuleCall call ) {
+        consumer.accept( call );
+    }
+
+}
+
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java
new file mode 100644
index 0000000000..37f70d48ca
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java
@@ -0,0 +1,198 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.enumerable.EnumerableCalc;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.enumerable.EnumerableJoin;
+import org.polypheny.db.plan.AlgOptRuleCall;
+import org.polypheny.db.plan.AlgOptUtil;
+import org.polypheny.db.rex.RexIndexRef;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.rex.RexProgram;
+import org.polypheny.db.schema.trait.ModelTrait;
+import org.polypheny.db.tools.AlgBuilderFactory;
+import org.polypheny.db.type.PolyType;
+
+import java.util.List;
+
+import static org.polypheny.db.plan.AlgOptRule.any;
+import static org.polypheny.db.plan.AlgOptRule.none;
+import static org.polypheny.db.plan.AlgOptRule.operand;
+
+public final class PatternMatchers {
+
+    private final static ParquetRelFilterTranslator translator = new ParquetRelFilterTranslator();
+
+
+    private static void transformTo( AlgOptRuleCall call, EnumerableJoin join, ParquetScan left, ParquetScan right ) {
+        JoinDirection direction = ParquetJoin.supportedDirection( join, left, right );
+        if ( direction == null ) {
+            return;
+        }
+        ParquetJoin parquetJoin = ParquetJoin.create( left, right, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent() );
+        call.transformTo( toEnumerable( join, parquetJoin ) );
+    }
+
+
+    private static EnumerableParquet toEnumerable( AlgNode source, AlgNode input ) {
+        return new EnumerableParquet(
+                source.getCluster(),
+                input.getTraitSet().replace( EnumerableConvention.INSTANCE ).replace( ModelTrait.RELATIONAL ),
+                input );
+    }
+
+
+    private static ParquetAdapterFilter translate( AlgNode input, RexNode condition ) {
+        if ( input == null ) {
+            return null;
+        }
+        List<PolyType> fieldTypes = input.getTupleType().getFields().stream()
+                .map( field -> field.getType().getPolyType() )
+                .toList();
+        return translator.translate( fieldTypes, condition );
+    }
+
+
+    private static int[] getProjectFields( List<RexNode> rexNodes ) {
+        final int[] fields = new int[rexNodes.size()];
+        for ( int i = 0; i < rexNodes.size(); i++ ) {
+            final RexNode exp = rexNodes.get( i );
+            if ( exp instanceof RexIndexRef ) {
+                fields[i] = ((RexIndexRef) exp).getIndex();
+            } else {
+                return null;
+            }
+        }
+        return fields;
+    }
+
+
+    private static ParquetScan applyCalc( EnumerableCalc calc, ParquetScan scan ) {
+        RexProgram program = calc.getProgram();
+        List<RexNode> projects = program.getProjectList().stream()
+                .map( program::expandLocalRef )
+                .toList();
+
+        int[] fields = getProjectFields( projects );
+        if ( fields == null ) {
+            return null;
+        }
+
+        int[] currentFields = scan.getFields();
+        int[] projectedFields = new int[fields.length];
+        for ( int i = 0; i < fields.length; i++ ) {
+            if ( fields[i] < 0 || fields[i] >= currentFields.length ) {
+                return null;
+            }
+            projectedFields[i] = currentFields[fields[i]];
+        }
+
+        ParquetScan updatedScan = scan.withFields( projectedFields );
+        if ( updatedScan == null && program.getCondition() == null ) {
+            return null;
+        }
+
+        if ( program.getCondition() != null ) {
+            // if updatedScan is null then original scan contains an old filter that cannot be translated into new projection.
+            ParquetScan projectedScan = updatedScan == null ? scan.withFieldsAndFilters( projectedFields, List.of() ) : updatedScan;
+            RexNode condition = program.expandLocalRef( program.getCondition() );
+            ParquetAdapterFilter adapterFilter = translate( projectedScan, condition );
+            if ( adapterFilter == null ) {
+                return null;
+            }
+            return projectedScan.withFilters( List.of( adapterFilter ) );
+        }
+
+        return updatedScan;
+    }
+
+
+    public static PatternMatcher joinWithScanOnLeftAndScanOnRight( ParquetConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableJoin.class,
+                        operand( EnumerableParquet.class, operand( ParquetScan.class, none() ) ),
+                        operand( EnumerableParquet.class, operand( ParquetScan.class, none() ) )
+                ),
+                "joinWithScanOnLeftAndScanOnRight",
+                call -> {
+                    EnumerableJoin join = call.alg( 0 );
+                    ParquetScan left = call.alg( 2 );
+                    ParquetScan right = call.alg( 4 );
+                    transformTo( call, join, left, right );
+                }
+        );
+    }
+
+
+    public static PatternMatcher attachFilterToJoinUnderCalc( ParquetConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableCalc.class,
+                        operand( EnumerableParquet.class, operand( ParquetJoin.class, any() ) )
+                ),
+                "attachFilterToJoinUnderCalc",
+                call -> {
+                    EnumerableCalc calc = call.alg( 0 );
+                    ParquetJoin join = call.alg( 2 );
+                    if ( !AlgOptUtil.areRowTypesEqual( calc.getInput().getTupleType(), join.getTupleType(), false ) ) {
+                        return;
+                    }
+                    RexProgram program = calc.getProgram();
+                    if ( program.getCondition() == null ) {
+                        return;
+                    }
+                    RexNode condition = program.expandLocalRef( program.getCondition() );
+                    ParquetAdapterFilter adapterFilter = translate( join, condition );
+                    if ( adapterFilter == null ) {
+                        return;
+                    }
+                    call.transformTo( toEnumerable( calc, join.withFilters( List.of( adapterFilter ) ) ) );
+                }
+        );
+    }
+
+
+    public static PatternMatcher attachFieldsAndFiltersToScanUnderCalc( ParquetConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableCalc.class,
+                        operand( EnumerableParquet.class, operand( ParquetScan.class, any() ) )
+                ),
+                "attachFieldsAndFiltersToScanUnderCalc",
+                call -> {
+                    EnumerableCalc calc = call.alg( 0 );
+                    ParquetScan scan = applyCalc( calc, call.alg( 2 ) );
+                    if ( scan == null ) {
+                        return;
+                    }
+                    call.transformTo( toEnumerable( calc, scan ) );
+                }
+        );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index 686a1bc088..58d6152a3b 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -34,8 +34,8 @@
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedRepeatedRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
-import org.polypheny.db.adapter.parquet.relational.execution.ParentLimitState;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetScan;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersContainer;
@@ -204,12 +204,13 @@ public Enumerator<PolyValue[]> enumerator() {
      */
     @Override
     public AlgNode toAlg( AlgCluster cluster, AlgTraitSet traitSet ) {
-        return new ParquetRelScan( cluster, this, fieldIndexes );
+        ParquetConvention.INSTANCE.register( cluster.getPlanner() );
+        return new ParquetScan( cluster, this, fieldIndexes );
     }
 
 
     /**
-     * This method is called from the {@link ParquetRelScan} via reflection.
+     * This method is called from the {@link ParquetScan} via reflection.
      *
      * @param dataContext data context
      * @param fields a list of fields to return.
@@ -221,7 +222,7 @@ public Enumerable<PolyValue[]> project( final DataContext dataContext, final int
 
 
     /**
-     * This method is called from the {@link ParquetRelScan} via reflection.
+     * This method is called from the {@link ParquetScan} via reflection.
      *
      * @param dataContext data context
      * @param fields a list of fields to return.
@@ -257,8 +258,6 @@ public Enumerable<PolyValue[]> nestedJoin(
             final int[] rightFields,
             final boolean leftIsParent,
             final boolean emitUnmatchedParents,
-            final int parentOffset,
-            final int parentFetch,
             final List<ParquetAdapterFilter> filters ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
@@ -273,11 +272,10 @@ public Enumerable<PolyValue[]> nestedJoin(
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                ParentLimitState parentLimitState = new ParentLimitState( parentOffset, parentFetch );
                 var filterContainer = new JoinFiltersSplitter().split( resolvedFilters, leftIsParent, parentFields.length, childFields.length );
                 return new ParquetMultiFileEnumerator(
                         sourceFiles,
-                        sourceFile -> parent.nestedJoinEnumeratorForFile( sourceFile, child, parentFields, childFields, cancelFlag, filterContainer, leftIsParent, emitUnmatchedParents, parentLimitState ) );
+                        sourceFile -> parent.nestedJoinEnumeratorForFile( sourceFile, child, parentFields, childFields, cancelFlag, filterContainer, leftIsParent, emitUnmatchedParents ) );
             }
         };
     }
@@ -373,8 +371,7 @@ private Enumerator<PolyValue[]> nestedJoinEnumeratorForFile(
             AtomicBoolean cancelFlag,
             JoinFiltersContainer filterContainer,
             boolean leftIsParent,
-            boolean emitUnmatchedParents,
-            ParentLimitState parentLimitState ) {
+            boolean emitUnmatchedParents ) {
 
         ParquetSourceReader reader = new ParquetSourceReader( sourceFile.asSource(), cancelFlag, null, filterContainer.nativeFilters() );
         return new ParquetNestedJoinEnumerator(
@@ -385,8 +382,7 @@ private Enumerator<PolyValue[]> nestedJoinEnumeratorForFile(
                 child.projectedBindings( childFields ),
                 filterContainer,
                 leftIsParent,
-                emitUnmatchedParents,
-                parentLimitState );
+                emitUnmatchedParents );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java
index 230ca7c0ff..2df244d9c7 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java
@@ -20,6 +20,7 @@
 import java.util.function.Function;
 import org.polypheny.db.adapter.parquet.shared.filter.FilterEvaluator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.type.entity.PolyNull;
 import org.polypheny.db.type.entity.PolyString;
 
 
@@ -51,7 +52,7 @@ protected Boolean evaluateLeaf( ParquetSourceFile sourceFile, ParquetAdapterFilt
         }
         String partitionValue = sourceFile.partitionValues().get( columnBinding.columnName() );
         if ( partitionValue == null ) {
-            return false;
+            return matchesValue( PolyNull.NULL, filter.operator(), filter.polyValue() );
         }
         return matchesValue( PolyString.of( partitionValue ), filter.operator(), filter.polyValue() );
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FilterEvaluator.java
index b5eea320f6..4278221b6f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FilterEvaluator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FilterEvaluator.java
@@ -48,6 +48,13 @@ public Boolean evaluate( C context, ParquetAdapterFilter filter ) {
 
 
     protected Boolean matchesValue( PolyValue actual, Kind operator, PolyValue expected ) {
+        if ( operator == Kind.IS_NULL ) {
+            return actual == null || actual.isNull();
+        }
+        if ( operator == Kind.IS_NOT_NULL ) {
+            return actual != null && !actual.isNull();
+        }
+
         if ( actual == null || actual.isNull() || expected == null || expected.isNull() ) {
             return false;
         }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetGroupFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetGroupFilterEvaluator.java
index b783808331..a5d25410a5 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetGroupFilterEvaluator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetGroupFilterEvaluator.java
@@ -20,6 +20,8 @@
 import org.apache.parquet.schema.MessageType;
 import org.apache.parquet.schema.Type;
 import org.polypheny.db.adapter.parquet.shared.execution.ParquetValueExtractor;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.entity.PolyNull;
 import org.polypheny.db.type.entity.PolyValue;
 
 /**
@@ -54,14 +56,17 @@ protected Boolean evaluateLeaf( Group group, ParquetAdapterFilter filter ) {
             return null;
         }
         if ( !filterHasValue( group, filter ) ) {
-            return false;
+            return matchesValue( PolyNull.NULL, filter.operator(), filter.polyValue() );
         }
         return matchesValue( extractValue( group, filter ), filter.operator(), filter.polyValue() );
     }
 
 
     protected boolean canApplyFilter( Group group, ParquetAdapterFilter filter ) {
-        if ( filter.polyValue() == null || filter.columnIndex() < 0 ) {
+        if ( filter.columnIndex() < 0 ) {
+            return false;
+        }
+        if ( filter.polyValue() == null && !isNullCheck( filter.operator() ) ) {
             return false;
         }
         return !filter.pathElements().isEmpty() || filter.columnIndex() < schema.getFieldCount();
@@ -81,4 +86,9 @@ protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
         return valueExtractor.extractValue( group, filter.pathElements() );
     }
 
+
+    private boolean isNullCheck( Kind operator ) {
+        return operator == Kind.IS_NULL || operator == Kind.IS_NOT_NULL;
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
index 28965da4ec..01d68e036c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
@@ -162,6 +162,10 @@ private static FilterPredicate combineOr( List<FilterPredicate> operands ) {
 
     private static FilterPredicate buildPredicatePrimitive( Kind operator, PolyValue value, Type type, String columnName ) {
         PrimitiveType primitive = type.asPrimitiveType();
+        if ( isNullCheck( operator ) ) {
+            return buildNullPredicate( operator, primitive, columnName );
+        }
+
         Object expected = TYPE_CONVERTER.fromPolyValueToParquetObj( primitive, value );
         if ( expected == null ) {
             return null;
@@ -178,6 +182,37 @@ private static FilterPredicate buildPredicatePrimitive( Kind operator, PolyValue
     }
 
 
+    private static FilterPredicate buildNullPredicate( Kind operator, PrimitiveType primitive, String columnName ) {
+        boolean isNull = operator == Kind.IS_NULL;
+        return switch ( primitive.getPrimitiveTypeName() ) {
+            case BOOLEAN -> isNull
+                    ? FilterApi.eq( FilterApi.booleanColumn( columnName ), null )
+                    : FilterApi.notEq( FilterApi.booleanColumn( columnName ), null );
+            case INT32 -> isNull
+                    ? FilterApi.eq( FilterApi.intColumn( columnName ), null )
+                    : FilterApi.notEq( FilterApi.intColumn( columnName ), null );
+            case INT64 -> isNull
+                    ? FilterApi.eq( FilterApi.longColumn( columnName ), null )
+                    : FilterApi.notEq( FilterApi.longColumn( columnName ), null );
+            case FLOAT -> isNull
+                    ? FilterApi.eq( FilterApi.floatColumn( columnName ), null )
+                    : FilterApi.notEq( FilterApi.floatColumn( columnName ), null );
+            case DOUBLE -> isNull
+                    ? FilterApi.eq( FilterApi.doubleColumn( columnName ), null )
+                    : FilterApi.notEq( FilterApi.doubleColumn( columnName ), null );
+            case BINARY, FIXED_LEN_BYTE_ARRAY -> isNull
+                    ? FilterApi.eq( FilterApi.binaryColumn( columnName ), null )
+                    : FilterApi.notEq( FilterApi.binaryColumn( columnName ), null );
+            case INT96 -> null;
+        };
+    }
+
+
+    private static boolean isNullCheck( Kind operator ) {
+        return operator == Kind.IS_NULL || operator == Kind.IS_NOT_NULL;
+    }
+
+
     private static Type resolveType( GroupType groupType, List<String> path ) {
         GroupType current = groupType;
         for ( int i = 0; i < path.size(); i++ ) {
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNestedJoinPlanningTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNestedJoinPlanningTest.java
new file mode 100644
index 0000000000..c9174498b4
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNestedJoinPlanningTest.java
@@ -0,0 +1,201 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.sql.Connection;
+import java.sql.ResultSet;
+import java.sql.SQLException;
+import java.sql.Statement;
+import java.util.List;
+import org.junit.jupiter.api.AfterAll;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.polypheny.db.TestHelper;
+import org.polypheny.db.TestHelper.JdbcConnection;
+import org.polypheny.db.adapter.parquet.relational.ParquetRelationalSource;
+import org.polypheny.db.algebra.constant.ExplainFormat;
+import org.polypheny.db.algebra.constant.ExplainLevel;
+import org.polypheny.db.catalog.Catalog;
+import org.polypheny.db.languages.LanguageManager;
+import org.polypheny.db.languages.QueryLanguage;
+import org.polypheny.db.plan.AlgOptUtil;
+import org.polypheny.db.prepare.Prepare.PreparedResultImpl;
+import org.polypheny.db.processing.ImplementationContext.ExecutedContext;
+import org.polypheny.db.processing.QueryContext;
+import org.polypheny.db.transaction.QueryAnalyzer;
+import org.polypheny.db.transaction.Transaction;
+import org.polypheny.db.transaction.TransactionManager;
+import org.polypheny.db.transaction.TransactionManagerImpl;
+
+class ParquetNestedJoinPlanningTest {
+
+    private static final String SOURCE_NAME = "parquet_nested_join_test";
+    private static final String ORIGIN = "ParquetNestedJoinPlanningTest";
+    private static TestHelper helper;
+    private static boolean sourceCreated = false;
+
+
+    private static String table( String tableName ) {
+        return SOURCE_NAME + "__" + tableName;
+    }
+
+
+    @BeforeAll
+    static void start() throws SQLException {
+        helper = TestHelper.getInstance();
+
+        try ( JdbcConnection jdbcConnection = new JdbcConnection( false ) ) {
+            Connection connection = jdbcConnection.getConnection();
+            try ( Statement statement = connection.createStatement() ) {
+                statement.executeUpdate( "ALTER ADAPTERS ADD \"" + SOURCE_NAME + "\" USING '" + ParquetRelationalSource.NAME + "' AS 'SOURCE' "
+                        + "WITH '{method:\"upload\",directory:\"classpath://orders_db\",directoryName:\"classpath://orders_db\",url:\"file:/\",\"" + ParquetRelationalSource.SCHEMA_MODE_SETTING + "\":\"normalized\"}'" );
+                connection.commit();
+                sourceCreated = true;
+            }
+        }
+    }
+
+
+    @AfterAll
+    static void end() throws SQLException {
+        if ( sourceCreated ) {
+            try ( JdbcConnection jdbcConnection = new JdbcConnection( false ) ) {
+                Connection connection = jdbcConnection.getConnection();
+                try ( Statement statement = connection.createStatement() ) {
+                    statement.executeUpdate( "ALTER ADAPTERS DROP \"" + SOURCE_NAME + "\"" );
+                    connection.commit();
+                }
+            }
+            helper.checkAllTrxClosed();
+        }
+    }
+
+
+    @Test
+    void plansFullJoinWithProjectedFilteredParentInput() throws SQLException {
+        try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
+            Connection connection = jdbcConnection.getConnection();
+            try ( Statement statement = connection.createStatement();
+                    ResultSet resultSet = statement.executeQuery(
+                            "SELECT count(*) "
+                                    + "FROM " + table( "orders__items" ) + " i "
+                                    + "FULL JOIN " + table( "orders__items__discounts" ) + " d "
+                                    + "ON d.__polypheny_parent_row_id = i.__polypheny_row_id "
+                                    + "WHERE i.quantity = 3" ) ) {
+                assertTrue( resultSet.next() );
+            }
+        }
+    }
+
+    @Test
+    void appliesLimitAfterFullJoinFilter() throws SQLException {
+        try ( JdbcConnection jdbcConnection = new JdbcConnection( true ) ) {
+            Connection connection = jdbcConnection.getConnection();
+            try ( Statement statement = connection.createStatement();
+                    ResultSet resultSet = statement.executeQuery(
+                            "SELECT * "
+                                    + "FROM " + table( "orders__items" ) + " i "
+                                    + "FULL JOIN " + table( "orders__items__discounts" ) + " d "
+                                    + "ON d.__polypheny_parent_row_id = i.__polypheny_row_id "
+                                    + "WHERE i.quantity = 3 "
+                                    + "LIMIT 2" ) ) {
+                int rows = 0;
+                while ( resultSet.next() ) {
+                    rows++;
+                }
+                assertEquals( 2, rows );
+            }
+        }
+    }
+
+
+    @Test
+    void plansFullJoinWithLimitAsParquetJoin() {
+        String physicalPlan = physicalPlanFor(
+                "SELECT * "
+                        + "FROM " + table( "orders__items" ) + " i "
+                        + "FULL JOIN " + table( "orders__items__discounts" ) + " d "
+                        + "ON d.__polypheny_parent_row_id = i.__polypheny_row_id "
+                        + "WHERE i.quantity = 3 "
+                        + "LIMIT 10" );
+
+        assertTrue( physicalPlan.contains( "ParquetJoin" ), physicalPlan );
+        assertFalse( physicalPlan.contains( "EnumerableJoin" ), physicalPlan );
+    }
+
+
+    @Test
+    void plansLeftJoinWithLimitAsParquetJoin() {
+        String physicalPlan = physicalPlanFor(
+                "SELECT * "
+                        + "FROM " + table( "orders__items" ) + " i "
+                        + "LEFT JOIN " + table( "orders__items__discounts" ) + " d "
+                        + "ON d.__polypheny_parent_row_id = i.__polypheny_row_id "
+                        + "WHERE i.quantity = 3 "
+                        + "LIMIT 10" );
+
+        assertTrue( physicalPlan.contains( "ParquetJoin" ), physicalPlan );
+        assertFalse( physicalPlan.contains( "EnumerableJoin" ), physicalPlan );
+    }
+
+
+    private String physicalPlanFor( String query ) {
+        TransactionManager transactionManager = TransactionManagerImpl.getInstance();
+        Transaction transaction = transactionManager.startTransaction( Catalog.defaultUserId, Catalog.defaultNamespaceId, new QueryAnalyzer(), ORIGIN );
+        try {
+            QueryContext context = QueryContext.builder()
+                    .query( query )
+                    .language( QueryLanguage.from( "sql" ) )
+                    .isAnalysed( true )
+                    .usesCache( true )
+                    .origin( ORIGIN )
+                    .namespaceId( Catalog.defaultNamespaceId )
+                    .batch( -1 )
+                    .transactionManager( transactionManager )
+                    .transactions( List.of( transaction ) )
+                    .build();
+
+            List<ExecutedContext> executedContexts = LanguageManager.getINSTANCE().anyQuery( context );
+            StringBuilder plans = new StringBuilder();
+            for ( ExecutedContext executedContext : executedContexts ) {
+                if ( executedContext.getException().isPresent() ) {
+                    throw new AssertionError( executedContext.getException().orElseThrow() );
+                }
+                if ( executedContext.getImplementation().getPreparedResult() instanceof PreparedResultImpl<?> preparedResult ) {
+                    plans.append( AlgOptUtil.dumpPlan(
+                            "-- Physical Plan",
+                            preparedResult.getRootAlg(),
+                            ExplainFormat.TEXT,
+                            ExplainLevel.EXPPLAN_ATTRIBUTES ) );
+                }
+                executedContext.getIterator().getAllRowsAndClose();
+            }
+            transaction.commit();
+            return plans.toString();
+        } catch ( Exception e ) {
+            if ( transaction.isActive() ) {
+                transaction.rollback( e.getMessage() );
+            }
+            throw e;
+        }
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
index 5a624fa589..3cdd7d0cb4 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
@@ -24,6 +24,7 @@
 import org.junit.jupiter.api.BeforeEach;
 import org.junit.jupiter.api.Test;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
+import org.polypheny.db.adapter.parquet.shared.filter.FilterEvaluator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetNativeFilterBuilder;
 import org.polypheny.db.algebra.constant.Kind;
@@ -37,6 +38,9 @@
 import org.polypheny.db.rex.RexNode;
 import org.polypheny.db.type.PolyType;
 import org.polypheny.db.type.PolyTypeFactoryImpl;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.type.entity.temporal.PolyTimestamp;
 import org.polypheny.db.util.PolyphenyHomeDirManager;
 import org.polypheny.db.util.RunMode;
@@ -44,6 +48,7 @@
 import static org.junit.jupiter.api.Assertions.assertEquals;
 import static org.junit.jupiter.api.Assertions.assertNotNull;
 import static org.junit.jupiter.api.Assertions.assertDoesNotThrow;
+import static org.junit.jupiter.api.Assertions.assertNull;
 import static org.junit.jupiter.api.Assertions.assertTrue;
 
 class ParquetRelFilterTranslatorTest {
@@ -106,6 +111,34 @@ void translatesInAsOrOfEquals() {
     }
 
 
+    @Test
+    void translatesNullChecks() {
+        ParquetAdapterFilter isNull = translator.translate( List.of( PolyType.INTEGER ), call( Kind.IS_NULL, ref( 0 ) ) );
+        ParquetAdapterFilter isNotNull = translator.translate( List.of( PolyType.INTEGER ), call( Kind.IS_NOT_NULL, ref( 0 ) ) );
+
+        assertNotNull( isNull );
+        assertEquals( Kind.IS_NULL, isNull.operator() );
+        assertEquals( 0, isNull.columnIndex() );
+        assertNull( isNull.polyValue() );
+
+        assertNotNull( isNotNull );
+        assertEquals( Kind.IS_NOT_NULL, isNotNull.operator() );
+        assertEquals( 0, isNotNull.columnIndex() );
+        assertNull( isNotNull.polyValue() );
+    }
+
+
+    @Test
+    void evaluatesNullChecks() {
+        TestFilterEvaluator evaluator = new TestFilterEvaluator();
+
+        assertEquals( Boolean.TRUE, evaluator.evaluate( PolyNull.NULL, new ParquetAdapterFilter( 0, Kind.IS_NULL, null ) ) );
+        assertEquals( Boolean.FALSE, evaluator.evaluate( PolyString.of( "shipped" ), new ParquetAdapterFilter( 0, Kind.IS_NULL, null ) ) );
+        assertEquals( Boolean.FALSE, evaluator.evaluate( PolyNull.NULL, new ParquetAdapterFilter( 0, Kind.IS_NOT_NULL, null ) ) );
+        assertEquals( Boolean.TRUE, evaluator.evaluate( PolyString.of( "shipped" ), new ParquetAdapterFilter( 0, Kind.IS_NOT_NULL, null ) ) );
+    }
+
+
     @Test
     void reversesComparisonWhenLiteralIsOnTheLeft() {
         RexNode filter = call( Kind.LESS_THAN, param( 10 ), ref( 0 ) );
@@ -131,6 +164,18 @@ void ignoresUnsupportedInt96TimestampPredicatePushdown() {
     }
 
 
+    @Test
+    void buildsNativeNullCheckPredicates() {
+        MessageType schema = Types.buildMessage()
+                .optional( PrimitiveTypeName.BINARY )
+                .named( "status" )
+                .named( "test_schema" );
+
+        assertDoesNotThrow(
+                () -> ParquetNativeFilterBuilder.build( schema, List.of( new ParquetAdapterFilter( 0, Kind.IS_NOT_NULL, null ) ) ) );
+    }
+
+
     private RexNode ref( int index ) {
         return new RexIndexRef( index, intType );
     }
@@ -144,4 +189,14 @@ private RexNode param( int index ) {
     private RexNode call( Kind kind, RexNode... operands ) {
         return new RexCall( boolType, new SpecialOperator( kind.name(), kind ), operands );
     }
+
+
+    private static class TestFilterEvaluator extends FilterEvaluator<PolyValue> {
+
+        @Override
+        protected Boolean evaluateLeaf( PolyValue value, ParquetAdapterFilter filter ) {
+            return matchesValue( value, filter.operator(), filter.polyValue() );
+        }
+
+    }
 }
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelJoinPathTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelJoinPathTest.java
deleted file mode 100644
index b128dad112..0000000000
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelJoinPathTest.java
+++ /dev/null
@@ -1,53 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet;
-
-import static org.junit.jupiter.api.Assertions.assertFalse;
-import static org.junit.jupiter.api.Assertions.assertTrue;
-
-import java.util.List;
-import org.junit.jupiter.api.Test;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelJoin;
-
-
-class ParquetRelJoinPathTest {
-
-    @Test
-    void acceptsNestedTableDirectChildPath() {
-        assertTrue( ParquetRelJoin.isDirectChildPath( List.of( "orders" ), List.of( "orders", "items" ) ) );
-    }
-
-
-    @Test
-    void acceptsRootDirectChildPath() {
-        assertTrue( ParquetRelJoin.isDirectChildPath( List.of(), List.of( "orders" ) ) );
-    }
-
-
-    @Test
-    void rejectsAncestorGrandchildPath() {
-        assertFalse( ParquetRelJoin.isDirectChildPath( List.of(), List.of( "orders", "items" ) ) );
-    }
-
-
-    @Test
-    void rejectsSiblingPath() {
-        assertFalse( ParquetRelJoin.isDirectChildPath( List.of( "orders" ), List.of( "payments" ) ) );
-    }
-
-
-}

From 173c63f8251a8d8c8182c59f847e90fc00aa1a7b Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Fri, 15 May 2026 20:03:55 +0200
Subject: [PATCH 30/46] add support for filters that are not part of the
 projection.

---
 .../db/adapter/parquet/ParquetPlugin.java     |  12 +-
 .../relational/ParquetRelationalSource.java   |   4 +-
 .../execution/JoinNestedBinding.java          |  30 ++
 .../ParquetNestedJoinEnumerator.java          |  89 +++---
 ...ParquetNestedNonRepeatedRelEnumerator.java |   4 +-
 .../ParquetNestedRepeatedRelEnumerator.java   |   5 +-
 .../planning/ParquetFilterIndexMapper.java    |  84 ------
 .../planning/ParquetFilterResolver.java       | 102 +++++++
 .../relational/planning/ParquetJoin.java      | 270 ------------------
 .../planning/ParquetPolyAlgDisplay.java       |  15 +
 .../relational/planning/ParquetRelJoin.java   | 262 +++++++++++++++++
 .../{ParquetScan.java => ParquetRelScan.java} |  27 +-
 .../relational/planning/PatternMatchers.java  |  43 +--
 .../relational/planning/PhysicalScan.java     |  51 ++++
 .../relational/schema/ParquetRelTable.java    | 138 ++++-----
 .../shared/filter/JoinFiltersContainer.java   | 180 +++++++++++-
 .../filter/ParquetNestedFilterEvaluator.java  |   4 +
 .../ParquetNestedJoinFilterEvaluator.java     |  11 +-
 .../ParquetNestedJoinPlanningTest.java        |   4 +-
 19 files changed, 805 insertions(+), 530 deletions(-)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/JoinNestedBinding.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterIndexMapper.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterResolver.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetJoin.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/{ParquetScan.java => ParquetRelScan.java} (80%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PhysicalScan.java

diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
index 109dba2545..129dcdbfbe 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
@@ -22,8 +22,8 @@
 import org.polypheny.db.adapter.parquet.relational.ParquetRelationalSource;
 import org.polypheny.db.adapter.parquet.relational.planning.EnumerableParquet;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetJoin;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetScan;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelJoin;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
 import org.polypheny.db.algebra.enumerable.EnumerableConvention;
 import org.polypheny.db.algebra.enumerable.EnumerableJoin;
 import org.polypheny.db.algebra.logical.relational.LogicalRelScan;
@@ -69,21 +69,23 @@ private void registerPolyAlg() {
                 .model( DataModel.RELATIONAL )
                 .opName( "PE_CALC" ).convention( EnumerableConvention.INSTANCE ).numInputs( 1 ).opTags( physTags )
                 .build() );
-        PolyAlgRegistry.register( ParquetScan.class, PolyAlgDeclaration.builder()
+        PolyAlgRegistry.register( ParquetRelScan.class, PolyAlgDeclaration.builder()
                 .model( DataModel.RELATIONAL )
                 .opName( "P_SCAN" ).convention( ParquetConvention.INSTANCE ).numInputs( 0 ).opTags( physTags )
                 .params( PolyAlgRegistry.getParams( LogicalRelScan.class ) )
                 .param( Parameter.builder().name( "fields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .build() );
-        PolyAlgRegistry.register( ParquetJoin.class, PolyAlgDeclaration.builder()
+        PolyAlgRegistry.register( ParquetRelJoin.class, PolyAlgDeclaration.builder()
                 .model( DataModel.RELATIONAL )
                 .opName( "P_JOIN" ).convention( ParquetConvention.INSTANCE ).numInputs( 2 ).opTags( physTags )
                 .params( PolyAlgRegistry.getParams( EnumerableJoin.class ) )
                 .param( Parameter.builder().name( "leftIsParent" ).type( ParamType.BOOLEAN ).defaultValue( BooleanArg.FALSE ).build() )
                 .param( Parameter.builder().name( "leftFields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .param( Parameter.builder().name( "rightFields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
-                .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "leftFilters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "rightFilters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "joinFilters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .build() );
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
index 3cf5ce49ca..3c76544fb7 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
@@ -40,7 +40,7 @@
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetScan;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.rules.FilterSetOpTransposeRule;
@@ -111,7 +111,7 @@ public AlgNode getRelScan( long allocId, AlgBuilder builder ) {
 //        ParquetConvention.INSTANCE.register( builder.getCluster().getPlanner() );
         PhysicalEntity entity = getCatalog().getPhysicalsFromAllocs( allocId ).get( 0 );
         ParquetRelTable table = entity.unwrapOrThrow( ParquetRelTable.class );
-        return new ParquetScan( builder.getCluster(), table, IntStream.range( 0, table.getFieldCount() ).toArray() );
+        return new ParquetRelScan( builder.getCluster(), table, IntStream.range( 0, table.getFieldCount() ).toArray() );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/JoinNestedBinding.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/JoinNestedBinding.java
new file mode 100644
index 0000000000..a981ec8c89
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/JoinNestedBinding.java
@@ -0,0 +1,30 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
+import java.util.List;
+
+public record JoinNestedBinding(ParquetTableBinding table, List<ParquetColumnBinding> columns, List<ParquetColumnBinding> filterColumns ) {
+
+    public JoinNestedBinding {
+        columns = List.copyOf( columns );
+        filterColumns = List.copyOf( filterColumns );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
index 4e7cebdbe2..ffe5ef314d 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
@@ -19,12 +19,12 @@
 import java.util.ArrayList;
 import java.util.List;
 import org.apache.parquet.example.data.Group;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
 import org.polypheny.db.adapter.parquet.shared.execution.CombinedGroup;
 import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
 import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetGroupFilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetNestedFilterEvaluator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetNestedJoinFilterEvaluator;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.type.entity.PolyNull;
@@ -36,33 +36,52 @@
  */
 public class ParquetNestedJoinEnumerator extends ParquetNestedRepeatedRelEnumerator {
 
-    private final ParquetTableBinding parentBinding;
-    private final ParquetTableBinding childBinding;
-    private final List<ParquetColumnBinding> parentColumns;
-    private final List<ParquetColumnBinding> childColumns;
-    private final List<ParquetAdapterFilter> parentFilters;
-    private final List<ParquetAdapterFilter> childFilters;
+    private final JoinNestedBinding parentBinding;
+    private final JoinNestedBinding childBinding;
+    private final JoinFiltersContainer filterContainer;
+    private final ParquetGroupFilterEvaluator parentScanFilterEvaluator;
+    private final ParquetGroupFilterEvaluator childScanFilterEvaluator;
     private final boolean leftIsParent;
     private final boolean emitUnmatchedParents;
     private final List<String> childPathFromParent;
 
 
-    public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBinding parentBinding, ParquetTableBinding childBinding, List<ParquetColumnBinding> parentColumns, List<ParquetColumnBinding> childColumns, JoinFiltersContainer filterContainer, boolean leftIsParent, boolean emitUnmatchedParents ) {
-        this( reader, parentBinding, childBinding, parentColumns, childColumns, filterContainer, leftIsParent, emitUnmatchedParents, true );
+    public ParquetNestedJoinEnumerator(
+            ParquetSourceReader reader,
+            JoinNestedBinding parentBinding,
+            JoinNestedBinding childBinding,
+            JoinFiltersContainer filterContainer,
+            boolean leftIsParent,
+            boolean emitUnmatchedParents ) {
+        this( reader, parentBinding, childBinding, filterContainer, leftIsParent, emitUnmatchedParents, true );
     }
 
 
-    public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBinding parentBinding, ParquetTableBinding childBinding, List<ParquetColumnBinding> parentColumns, List<ParquetColumnBinding> childColumns, JoinFiltersContainer filterContainer, boolean leftIsParent, boolean emitUnmatchedParents, boolean readerOwner ) {
-        super( reader, parentBinding, parentColumns, filterContainer, new ParquetNestedJoinFilterEvaluator( reader.getProjectionSchema(), new ParquetPathValueExtractor(), parentBinding, parentColumns, leftIsParent ), true, readerOwner );
+    public ParquetNestedJoinEnumerator(
+            ParquetSourceReader reader,
+            JoinNestedBinding parentBinding,
+            JoinNestedBinding childBinding,
+            JoinFiltersContainer filterContainer,
+            boolean leftIsParent,
+            boolean emitUnmatchedParents,
+            boolean readerOwner ) {
+        super(
+                reader,
+                parentBinding.table(),
+                parentBinding.columns(),
+                filterContainer,
+                new ParquetNestedJoinFilterEvaluator( reader.getProjectionSchema(), new ParquetPathValueExtractor(), parentBinding.table(), parentBinding.columns(), leftIsParent ),
+                true,
+                readerOwner
+        );
         this.parentBinding = parentBinding;
         this.childBinding = childBinding;
-        this.parentColumns = List.copyOf( parentColumns );
-        this.childColumns = List.copyOf( childColumns );
-        this.parentFilters = filterContainer.parentFilters();
-        this.childFilters = filterContainer.childFilters();
+        this.filterContainer = filterContainer;
+        this.parentScanFilterEvaluator = new ParquetNestedFilterEvaluator( reader.getProjectionSchema(), new ParquetPathValueExtractor(), parentBinding.table().sourcePathElements(), parentBinding.filterColumns() );
+        this.childScanFilterEvaluator = new ParquetNestedFilterEvaluator( reader.getProjectionSchema(), new ParquetPathValueExtractor(), childBinding.table().sourcePathElements(), childBinding.filterColumns() );
         this.leftIsParent = leftIsParent;
         this.emitUnmatchedParents = emitUnmatchedParents;
-        this.childPathFromParent = childBinding.sourcePathElements().subList( parentBinding.sourcePathElements().size(), childBinding.sourcePathElements().size() );
+        this.childPathFromParent = childBinding.table().sourcePathElements().subList( parentBinding.table().sourcePathElements().size(), childBinding.table().sourcePathElements().size() );
     }
 
 
@@ -70,29 +89,33 @@ public ParquetNestedJoinEnumerator( ParquetSourceReader reader, ParquetTableBind
     protected List<Group> expandRow( Group group ) {
         List<Group> rows = new ArrayList<>();
         VirtualGroup rootGroup = new VirtualGroup( group, String.valueOf( reader.getCurrentRowNumber() ), null, 0 );
-        for ( Group parent : resolveNested( rootGroup, group.getType(), parentBinding.sourcePathElements(), 0 ) ) {
-            if ( !matchesFilters( combinedGroup( (VirtualGroup) parent, null ), parentFilters ) ) {
+        for ( Group parent : resolveNested( rootGroup, group.getType(), parentBinding.table().sourcePathElements(), 0 ) ) {
+            if ( !matchesFilters( parent, filterContainer.parentScanFilters(), parentScanFilterEvaluator ) ) {
+                continue;
+            }
+            if ( !matchesFilters( combinedGroup( (VirtualGroup) parent, null ), filterContainer.parentFilters(), filterEvaluator ) ) {
                 continue;
             }
 
             List<Group> childRows = resolveNested( (VirtualGroup) parent, parent.getType(), childPathFromParent, 0 );
             if ( childRows.isEmpty() && emitUnmatchedParents ) {
-                if ( childFilters.isEmpty() ) {
+                if ( filterContainer.childFilters().isEmpty() && filterContainer.childScanFilters().isEmpty() ) {
                     rows.add( new CombinedGroup(
                             (VirtualGroup) parent,
-                            parentColumns,
-                            parentBinding.sourcePathElements(),
+                            parentBinding.columns(),
+                            parentBinding.table().sourcePathElements(),
                             null,
-                            childColumns,
-                            childBinding.sourcePathElements() ) );
+                            childBinding.columns(),
+                            childBinding.table().sourcePathElements() ) );
                 }
                 continue;
             }
 
             rows.addAll( childRows.stream()
                     .map( VirtualGroup.class::cast )
+                    .filter( child -> matchesFilters( child, filterContainer.childScanFilters(), childScanFilterEvaluator ) )
                     .map( child -> combinedGroup( (VirtualGroup) parent, child ) )
-                    .filter( child -> matchesFilters( child, childFilters ) )
+                    .filter( child -> matchesFilters( child, filterContainer.childFilters(), filterEvaluator ) )
                     .toList() );
         }
         return rows;
@@ -136,8 +159,8 @@ protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
         // If this is not a combined group then the filtering is being done on parent table only.
         return pathValueExtractor().extractValue(
                 (VirtualGroup) group,
-                parentColumns.get( filter.columnIndex() ),
-                parentBinding.sourcePathElements()
+                parentBinding.columns().get( filter.columnIndex() ),
+                parentBinding.table().sourcePathElements()
         );
     }
 
@@ -145,16 +168,16 @@ protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
     private CombinedGroup combinedGroup( VirtualGroup parent, VirtualGroup child ) {
         return new CombinedGroup(
                 parent,
-                parentColumns,
-                parentBinding.sourcePathElements(),
+                parentBinding.columns(),
+                parentBinding.table().sourcePathElements(),
                 child,
-                childColumns,
-                childBinding.sourcePathElements() );
+                childBinding.columns(),
+                childBinding.table().sourcePathElements() );
     }
 
 
-    private boolean matchesFilters( Group group, List<ParquetAdapterFilter> filters ) {
-        return filters.stream().allMatch( filter -> filterEvaluator.matches( group, filter ) );
+    private boolean matchesFilters( Group group, List<ParquetAdapterFilter> filters, ParquetGroupFilterEvaluator evaluator ) {
+        return filters.stream().allMatch( filter -> evaluator.matches( group, filter ) );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
index 208ebb22b3..e934aa299e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
@@ -38,9 +38,9 @@ public class ParquetNestedNonRepeatedRelEnumerator extends AbstractParquetEnumer
     private final ParquetSourceFile sourceFile;
 
 
-    public ParquetNestedNonRepeatedRelEnumerator( ParquetSourceReader reader, ParquetSourceFile sourceFile, List<ParquetColumnBinding> columnBindings, FiltersContainer filtersContainer ) {
+    public ParquetNestedNonRepeatedRelEnumerator( ParquetSourceReader reader, ParquetSourceFile sourceFile, List<ParquetColumnBinding> columnBindings, List<ParquetColumnBinding> filterColumnBindings, FiltersContainer filtersContainer ) {
         // read full root rows from the Parquet file
-        super( reader, filtersContainer, new ParquetPartitionAwareFilterEvaluator( reader.getProjectionSchema(), new ParquetPathValueExtractor(), sourceFile, columnBindings ) );
+        super( reader, filtersContainer, new ParquetPartitionAwareFilterEvaluator( reader.getProjectionSchema(), new ParquetPathValueExtractor(), sourceFile, filterColumnBindings ) );
         this.columnBindings = List.copyOf( columnBindings );
         this.sourceFile = sourceFile;
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
index 7b39450c9e..c22533ee80 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
@@ -41,8 +41,9 @@ public class ParquetNestedRepeatedRelEnumerator extends AbstractParquetEnumerato
     private final List<String> tablePath;
     private final List<ParquetColumnBinding> columnBindings;
 
-    public ParquetNestedRepeatedRelEnumerator( ParquetSourceReader reader, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings, FiltersContainer filtersContainer ) {
-        this( reader, binding, columnBindings, filtersContainer, new ParquetNestedFilterEvaluator( reader.getProjectionSchema(), new ParquetPathValueExtractor(), binding.sourcePathElements(), columnBindings ), false, true );
+
+    public ParquetNestedRepeatedRelEnumerator( ParquetSourceReader reader, ParquetTableBinding binding, List<ParquetColumnBinding> columnBindings, List<ParquetColumnBinding> filterColumnBindings, FiltersContainer filtersContainer ) {
+        this( reader, binding, columnBindings, filtersContainer, new ParquetNestedFilterEvaluator( reader.getProjectionSchema(), new ParquetPathValueExtractor(), binding.sourcePathElements(), filterColumnBindings ), false, true );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterIndexMapper.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterIndexMapper.java
deleted file mode 100644
index e03ebf5365..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterIndexMapper.java
+++ /dev/null
@@ -1,84 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-
-
-final class ParquetFilterIndexMapper {
-
-    private ParquetFilterIndexMapper() {
-    }
-
-
-    static ParquetAdapterFilter remapFilter( ParquetAdapterFilter filter, int[] oldFields, int[] newFields ) {
-        if ( filter.isLogical() ) {
-            List<ParquetAdapterFilter> remappedOperands = new ArrayList<>( filter.operands().size() );
-            for ( ParquetAdapterFilter operand : filter.operands() ) {
-                ParquetAdapterFilter remappedOperand = remapFilter( operand, oldFields, newFields );
-                if ( remappedOperand == null ) {
-                    return null;
-                }
-                remappedOperands.add( remappedOperand );
-            }
-            return ParquetAdapterFilter.logical( filter.operator(), remappedOperands );
-        }
-
-        int remappedIndex = remapIndex( filter.columnIndex(), oldFields, newFields );
-        if ( remappedIndex < 0 ) {
-            return null;
-        }
-
-        return new ParquetAdapterFilter(
-                remappedIndex,
-                filter.pathElements(),
-                filter.operator(),
-                filter.polyValue(),
-                filter.dynamicParamIndex() );
-    }
-
-
-    static List<ParquetAdapterFilter> remapFilters( List<ParquetAdapterFilter> filters, int[] oldFields, int[] newFields ) {
-        List<ParquetAdapterFilter> remappedFilters = new ArrayList<>( filters.size() );
-        for ( ParquetAdapterFilter filter : filters ) {
-            ParquetAdapterFilter remappedFilter = remapFilter( filter, oldFields, newFields );
-            if ( remappedFilter == null ) {
-                return null;
-            }
-            remappedFilters.add( remappedFilter );
-        }
-        return remappedFilters;
-    }
-
-
-    private static int remapIndex( int oldIndex, int[] oldFields, int[] newFields ) {
-        if ( oldIndex < 0 || oldIndex >= oldFields.length ) {
-            return -1;
-        }
-
-        int physicalColumn = oldFields[oldIndex];
-        for ( int i = 0; i < newFields.length; i++ ) {
-            if ( newFields[i] == physicalColumn ) {
-                return i;
-            }
-        }
-        return -1;
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterResolver.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterResolver.java
new file mode 100644
index 0000000000..33e000e370
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterResolver.java
@@ -0,0 +1,102 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Objects;
+import java.util.function.Function;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+public final class ParquetFilterResolver {
+
+    private ParquetFilterResolver() {
+    }
+
+
+    /**
+     * Support parametrized queries
+     *
+     * @param dataContext context
+     * @param filters filters
+     * @param selector a helper function for column binding selection
+     * @return list of parquet filters
+     */
+    public static List<ParquetAdapterFilter> resolveFilters( DataContext dataContext, List<ParquetAdapterFilter> filters, Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
+        List<ParquetAdapterFilter> resolved = new ArrayList<>( filters.size() );
+        for ( ParquetAdapterFilter filter : filters ) {
+            resolved.add( resolveFilter( dataContext, filter, selector ) );
+        }
+        return resolved;
+    }
+
+
+    public static ParquetAdapterFilter toPhysicalFilter( ParquetAdapterFilter filter, int[] fields ) {
+        if ( filter.isLogical() ) {
+            List<ParquetAdapterFilter> operands = new ArrayList<>( filter.operands().size() );
+            for ( ParquetAdapterFilter operand : filter.operands() ) {
+                ParquetAdapterFilter physicalOperand = toPhysicalFilter( operand, fields );
+                if ( physicalOperand == null ) {
+                    return null;
+                }
+                operands.add( physicalOperand );
+            }
+            return ParquetAdapterFilter.logical( filter.operator(), operands );
+        }
+
+        int physicalIndex = toPhysicalIndex( filter.columnIndex(), fields );
+        if ( physicalIndex < 0 ) {
+            return null;
+        }
+
+        return new ParquetAdapterFilter(
+                physicalIndex,
+                filter.pathElements(),
+                filter.operator(),
+                filter.polyValue(),
+                filter.dynamicParamIndex() );
+    }
+
+
+    private static ParquetAdapterFilter resolveFilter( DataContext dataContext, ParquetAdapterFilter filter, Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
+        if ( filter.isLogical() ) {
+            return ParquetAdapterFilter.logical( filter.operator(), filter.operands().stream()
+                    .map( operand -> resolveFilter( dataContext, operand, selector ) )
+                    .toList() );
+        }
+
+        PolyValue value = filter.dynamicParamIndex() == null
+                ? filter.polyValue()
+                : dataContext.getParameterValue( filter.dynamicParamIndex() );
+
+        ParquetColumnBinding columnBinding = Objects.requireNonNull( selector.apply( filter ), "Missing parquet column binding" );
+        return new ParquetAdapterFilter( filter.columnIndex(), columnBinding.sourcePathElements(), filter.operator(), value );
+    }
+
+
+    private static int toPhysicalIndex( int index, int[] fields ) {
+        if ( index < 0 || index >= fields.length ) {
+            return -1;
+        }
+        return fields[index];
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetJoin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetJoin.java
deleted file mode 100644
index 654c4240c2..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetJoin.java
+++ /dev/null
@@ -1,270 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import java.util.ArrayList;
-import java.util.List;
-import java.util.Optional;
-import java.util.Set;
-import org.apache.calcite.linq4j.tree.Expression;
-import org.apache.calcite.linq4j.tree.Expressions;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.algebra.AlgNode;
-import org.polypheny.db.algebra.AlgWriter;
-import org.polypheny.db.algebra.core.CorrelationId;
-import org.polypheny.db.algebra.core.Join;
-import org.polypheny.db.algebra.core.JoinAlgType;
-import org.polypheny.db.algebra.core.JoinInfo;
-import org.polypheny.db.algebra.enumerable.EnumUtils;
-import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
-import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
-import org.polypheny.db.algebra.polyalg.arguments.BooleanArg;
-import org.polypheny.db.algebra.polyalg.arguments.ListArg;
-import org.polypheny.db.algebra.polyalg.arguments.PolyAlgArgs;
-import org.polypheny.db.algebra.polyalg.arguments.StringArg;
-import org.polypheny.db.plan.AlgCluster;
-import org.polypheny.db.plan.AlgOptCost;
-import org.polypheny.db.plan.AlgPlanner;
-import org.polypheny.db.plan.AlgTraitSet;
-import org.polypheny.db.rex.RexNode;
-import org.polypheny.db.schema.trait.ModelTrait;
-
-/**
- * Parquet-convention join for supported parent/child table joins.
- */
-public class ParquetJoin extends Join implements ParquetAlg {
-
-    private final boolean leftIsParent;
-    private final ParquetRelTable leftTable;
-    private final ParquetRelTable rightTable;
-    private final int[] leftFields;
-    private final int[] rightFields;
-    private final List<ParquetAdapterFilter> filters;
-
-
-    public ParquetJoin(
-            AlgCluster cluster,
-            AlgTraitSet traitSet,
-            AlgNode left,
-            AlgNode right,
-            RexNode condition,
-            Set<CorrelationId> variablesSet,
-            JoinAlgType joinType,
-            boolean leftIsParent,
-            ParquetRelTable leftTable,
-            ParquetRelTable rightTable,
-            int[] leftFields,
-            int[] rightFields,
-            List<ParquetAdapterFilter> filters ) {
-        super( cluster, traitSet, left, right, condition, variablesSet, joinType );
-        this.leftIsParent = leftIsParent;
-        this.leftTable = leftTable;
-        this.rightTable = rightTable;
-        this.leftFields = leftFields;
-        this.rightFields = rightFields;
-        this.filters = List.copyOf( filters );
-    }
-
-
-    public static ParquetJoin create( ParquetScan left, ParquetScan right, RexNode condition, Set<CorrelationId> variablesSet, JoinAlgType joinType, boolean leftIsParent ) {
-        AlgCluster cluster = left.getCluster();
-        AlgTraitSet traitSet = cluster.traitSetOf( ParquetConvention.INSTANCE ).replace( ModelTrait.RELATIONAL );
-        return new ParquetJoin(
-                cluster,
-                traitSet,
-                left,
-                right,
-                condition,
-                variablesSet,
-                joinType,
-                leftIsParent,
-                left.getEntity(),
-                right.getEntity(),
-                left.getFields(),
-                right.getFields(),
-                joinInputFilters( left, right ) );
-    }
-
-
-    private static List<ParquetAdapterFilter> joinInputFilters( ParquetScan leftScan, ParquetScan rightScan ) {
-        List<ParquetAdapterFilter> filters = new ArrayList<>( leftScan.getFilters().size() + rightScan.getFilters().size() );
-        leftScan.getFilters().forEach( filter -> filters.add( shiftFilter( filter, 0 ) ) );
-        rightScan.getFilters().forEach( filter -> filters.add( shiftFilter( filter, leftScan.getFields().length ) ) );
-        return filters;
-    }
-
-
-    private static ParquetAdapterFilter shiftFilter( ParquetAdapterFilter filter, int offset ) {
-        if ( filter.isLogical() ) {
-            return ParquetAdapterFilter.logical( filter.operator(), filter.operands().stream()
-                    .map( operand -> shiftFilter( operand, offset ) )
-                    .toList() );
-        }
-        return new ParquetAdapterFilter(
-                filter.columnIndex() + offset,
-                filter.pathElements(),
-                filter.operator(),
-                filter.polyValue(),
-                filter.dynamicParamIndex() );
-    }
-
-
-    /**
-     * Checks whether a join can be executed by the Parquet adapter: it must be join
-     * between two tables from the same Parquet source, using parent PRIMARY_KEY and child PARENT_KEY.
-     */
-    public static JoinDirection supportedDirection( Join join, ParquetScan left, ParquetScan right ) {
-        return supportedDirection( join.getCondition(), join.getJoinType(), left, right );
-    }
-
-
-    /**
-     * Checks whether a join can be executed by the Parquet adapter: it must be join
-     * between two tables from the same Parquet source, using parent PRIMARY_KEY and child PARENT_KEY.
-     */
-    public static JoinDirection supportedDirection( RexNode condition, JoinAlgType joinType, ParquetScan left, ParquetScan right ) {
-        JoinInfo info = JoinInfo.of( left, right, condition );
-        if ( !info.isEqui() || info.leftKeys.size() != 1 || info.rightKeys.size() != 1 ) {
-            return null;
-        }
-
-        ParquetRelTable leftTable = left.getEntity();
-        ParquetRelTable rightTable = right.getEntity();
-        if ( !leftTable.getSourceUrl().equals( rightTable.getSourceUrl() ) ) {
-            return null;
-        }
-
-        int leftColumn = mappedColumn( left.getFields(), info.leftKeys.get( 0 ), left.getEntity().getFieldCount() );
-        int rightColumn = mappedColumn( right.getFields(), info.rightKeys.get( 0 ), right.getEntity().getFieldCount() );
-        if ( leftColumn < 0 || rightColumn < 0 ) {
-            return null;
-        }
-
-        boolean leftIsParent = isParentChildJoin( leftTable, rightTable, leftColumn, rightColumn );
-        boolean rightIsParent = isParentChildJoin( rightTable, leftTable, rightColumn, leftColumn );
-        if ( !leftIsParent && !rightIsParent ) {
-            return null;
-        }
-
-        switch ( joinType ) {
-            case INNER, FULL, LEFT, RIGHT -> {
-                return new JoinDirection( leftIsParent );
-            }
-            default -> {
-                return null;
-            }
-        }
-    }
-
-
-    private static int mappedColumn( int[] fields, int inputIndex, int totalFieldsCount ) {
-        if ( inputIndex < 0 || inputIndex >= fields.length ) {
-            return -1;
-        }
-        int column = fields[inputIndex];
-        return column < 0 || column >= totalFieldsCount ? -1 : column;
-    }
-
-
-    private static boolean isParentChildJoin( ParquetRelTable parent, ParquetRelTable child, int parentColumn, int childColumn ) {
-        if ( !isDirectChildPath( parent.getBinding().sourcePathElements(), child.getBinding().sourcePathElements() ) ) {
-            return false;
-        }
-        return parentColumn == parent.columnIndexByRole( ParquetColumnRole.PRIMARY_KEY ) && childColumn == child.columnIndexByRole( ParquetColumnRole.PARENT_KEY );
-    }
-
-
-    private static boolean isDirectChildPath( List<String> parentPath, List<String> childPath ) {
-        return childPath.size() == parentPath.size() + 1 && childPath.subList( 0, parentPath.size() ).equals( parentPath );
-    }
-
-
-    public ParquetJoin withFilters( List<ParquetAdapterFilter> filters ) {
-        List<ParquetAdapterFilter> combinedFilters = new ArrayList<>( this.filters );
-        combinedFilters.addAll( filters );
-        return new ParquetJoin( getCluster(), traitSet, left, right, condition, variablesSet, joinType, leftIsParent, leftTable, rightTable, leftFields, rightFields, combinedFilters );
-    }
-
-
-    @Override
-    public Join copy( AlgTraitSet traitSet, RexNode conditionExpr, AlgNode left, AlgNode right, JoinAlgType joinType, boolean semiJoinDone ) {
-        return new ParquetJoin( getCluster(), traitSet, left, right, conditionExpr, variablesSet, joinType, leftIsParent, leftTable, rightTable, leftFields, rightFields, filters );
-    }
-
-
-    @Override
-    public PolyAlgArgs bindArguments() {
-        List<String> joinedFields = ParquetPolyAlgDisplay.joinedFieldNames( leftTable, leftFields, rightTable, rightFields );
-        return super.bindArguments()
-                .put( "leftIsParent", new BooleanArg( leftIsParent ) )
-                .put( "leftFields", new ListArg<>( ParquetPolyAlgDisplay.fieldNames( leftTable, leftFields ), StringArg::new ) )
-                .put( "rightFields", new ListArg<>( ParquetPolyAlgDisplay.fieldNames( rightTable, rightFields ), StringArg::new ) )
-                .put( "filters", new ListArg<>( ParquetPolyAlgDisplay.filters( filters, joinedFields ), StringArg::new ) );
-    }
-
-
-    @Override
-    public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
-        Optional<Double> count = mq.getTupleCount( this );
-        return planner.getCostFactory().makeCost( count.orElse( estimateTupleCount( mq ) ), 0, 0 ).multiplyBy( ParquetConvention.COST_MULTIPLIER );
-    }
-
-
-    @Override
-    public double estimateTupleCount( AlgMetadataQuery mq ) {
-        return Math.max( left.estimateTupleCount( mq ), right.estimateTupleCount( mq ) );
-    }
-
-
-    @Override
-    public AlgWriter explainTerms( AlgWriter pw ) {
-        return super.explainTerms( pw )
-                .item( "leftIsParent", leftIsParent )
-                .item( "filters", filters );
-    }
-
-
-    @Override
-    public Expression implement( EnumerableAlgImplementor implementor ) {
-        boolean emitUnmatchedParents = emitUnmatchedParents();
-        Expression runtimeFilters = EnumUtils.expressionList( filters.stream().map( ParquetAdapterFilter::toExpression ).toList() );
-
-        return Expressions.call(
-                leftTable.asExpression( ParquetRelTable.class ),
-                "nestedJoin",
-                implementor.getRootExpression(),
-                rightTable.asExpression( ParquetRelTable.class ),
-                Expressions.constant( leftFields ),
-                Expressions.constant( rightFields ),
-                Expressions.constant( leftIsParent ),
-                Expressions.constant( emitUnmatchedParents ),
-                runtimeFilters );
-    }
-
-
-    private boolean emitUnmatchedParents() {
-        return switch ( joinType ) {
-            case LEFT -> leftIsParent;
-            case RIGHT -> !leftIsParent;
-            case FULL -> true;
-            default -> false;
-        };
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetPolyAlgDisplay.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetPolyAlgDisplay.java
index 55dbf075da..d69a838ce8 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetPolyAlgDisplay.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetPolyAlgDisplay.java
@@ -37,6 +37,21 @@ static List<String> fieldNames( ParquetRelTable table, int[] fields ) {
     }
 
 
+    static List<String> fieldNames( ParquetRelTable table ) {
+        List<AlgDataTypeField> tableFields = table.getTupleType().getFields();
+        return java.util.stream.IntStream.range( 0, tableFields.size() )
+                .mapToObj( index -> fieldName( tableFields, index ) )
+                .toList();
+    }
+
+
+    static List<String> prefixedFieldNames( ParquetRelTable table, String prefix ) {
+        return fieldNames( table ).stream()
+                .map( fieldName -> prefix + "." + fieldName )
+                .toList();
+    }
+
+
     static List<String> filters( List<ParquetAdapterFilter> filters, List<String> fieldNames ) {
         return filters.stream()
                 .map( filter -> filter( filter, fieldNames ) )
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
new file mode 100644
index 0000000000..7ae91d72e0
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
@@ -0,0 +1,262 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Optional;
+import java.util.Set;
+
+import org.apache.calcite.linq4j.tree.Expression;
+import org.apache.calcite.linq4j.tree.Expressions;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.AlgWriter;
+import org.polypheny.db.algebra.core.CorrelationId;
+import org.polypheny.db.algebra.core.Join;
+import org.polypheny.db.algebra.core.JoinAlgType;
+import org.polypheny.db.algebra.core.JoinInfo;
+import org.polypheny.db.algebra.enumerable.EnumUtils;
+import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
+import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
+import org.polypheny.db.algebra.polyalg.arguments.BooleanArg;
+import org.polypheny.db.algebra.polyalg.arguments.ListArg;
+import org.polypheny.db.algebra.polyalg.arguments.PolyAlgArgs;
+import org.polypheny.db.algebra.polyalg.arguments.StringArg;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgOptCost;
+import org.polypheny.db.plan.AlgPlanner;
+import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.schema.trait.ModelTrait;
+
+/**
+ * Parquet-convention join for supported parent/child table joins.
+ */
+public class ParquetRelJoin extends Join implements ParquetAlg {
+
+    private final boolean leftIsParent;
+    private final PhysicalScan leftScan;
+    private final PhysicalScan rightScan;
+    private final List<ParquetAdapterFilter> joinFilters;
+
+
+    public ParquetRelJoin(
+            AlgCluster cluster,
+            AlgTraitSet traitSet,
+            AlgNode left,
+            AlgNode right,
+            RexNode condition,
+            Set<CorrelationId> variablesSet,
+            JoinAlgType joinType,
+            boolean leftIsParent,
+            PhysicalScan leftScan,
+            PhysicalScan rightScan,
+            List<ParquetAdapterFilter> joinFilters) {
+        super(cluster, traitSet, left, right, condition, variablesSet, joinType);
+        this.leftIsParent = leftIsParent;
+        this.leftScan = leftScan;
+        this.rightScan = rightScan;
+        this.joinFilters = List.copyOf(joinFilters);
+    }
+
+
+    public static ParquetRelJoin create(ParquetRelScan left, ParquetRelScan right, RexNode condition, Set<CorrelationId> variablesSet, JoinAlgType joinType, boolean leftIsParent) {
+        AlgCluster cluster = left.getCluster();
+        AlgTraitSet traitSet = cluster.traitSetOf(ParquetConvention.INSTANCE).replace(ModelTrait.RELATIONAL);
+        return new ParquetRelJoin(
+                cluster,
+                traitSet,
+                left,
+                right,
+                condition,
+                variablesSet,
+                joinType,
+                leftIsParent,
+                new PhysicalScan(left.getEntity(), left.getFields(), left.getFilters()),
+                new PhysicalScan(right.getEntity(), right.getFields(), right.getFilters()),
+                List.of());
+    }
+
+
+    /**
+     * Checks whether a join can be executed by the Parquet adapter: it must be join
+     * between two tables from the same Parquet source, using parent PRIMARY_KEY and child PARENT_KEY.
+     */
+    public static JoinDirection supportedDirection(Join join, ParquetRelScan left, ParquetRelScan right) {
+        return supportedDirection(join.getCondition(), join.getJoinType(), left, right);
+    }
+
+
+    /**
+     * Checks whether a join can be executed by the Parquet adapter: it must be join
+     * between two tables from the same Parquet source, using parent PRIMARY_KEY and child PARENT_KEY.
+     */
+    public static JoinDirection supportedDirection(RexNode condition, JoinAlgType joinType, ParquetRelScan left, ParquetRelScan right) {
+        JoinInfo info = JoinInfo.of(left, right, condition);
+        if (!info.isEqui() || info.leftKeys.size() != 1 || info.rightKeys.size() != 1) {
+            return null;
+        }
+
+        ParquetRelTable leftTable = left.getEntity();
+        ParquetRelTable rightTable = right.getEntity();
+        if (!leftTable.getSourceUrl().equals(rightTable.getSourceUrl())) {
+            return null;
+        }
+
+        int leftColumn = mappedColumn(left.getFields(), info.leftKeys.get(0), left.getEntity().getFieldCount());
+        int rightColumn = mappedColumn(right.getFields(), info.rightKeys.get(0), right.getEntity().getFieldCount());
+        if (leftColumn < 0 || rightColumn < 0) {
+            return null;
+        }
+
+        boolean leftIsParent = isParentChildJoin(leftTable, rightTable, leftColumn, rightColumn);
+        boolean rightIsParent = isParentChildJoin(rightTable, leftTable, rightColumn, leftColumn);
+        if (!leftIsParent && !rightIsParent) {
+            return null;
+        }
+
+        switch (joinType) {
+            case INNER, FULL, LEFT, RIGHT -> {
+                return new JoinDirection(leftIsParent);
+            }
+            default -> {
+                return null;
+            }
+        }
+    }
+
+
+    private static int mappedColumn(int[] fields, int inputIndex, int totalFieldsCount) {
+        if (inputIndex < 0 || inputIndex >= fields.length) {
+            return -1;
+        }
+        int column = fields[inputIndex];
+        return column < 0 || column >= totalFieldsCount ? -1 : column;
+    }
+
+
+    private static boolean isParentChildJoin(ParquetRelTable parent, ParquetRelTable child, int parentColumn, int childColumn) {
+        if (!isDirectChildPath(parent.getBinding().sourcePathElements(), child.getBinding().sourcePathElements())) {
+            return false;
+        }
+        return parentColumn == parent.columnIndexByRole(ParquetColumnRole.PRIMARY_KEY) && childColumn == child.columnIndexByRole(ParquetColumnRole.PARENT_KEY);
+    }
+
+
+    private static boolean isDirectChildPath(List<String> parentPath, List<String> childPath) {
+        return childPath.size() == parentPath.size() + 1 && childPath.subList(0, parentPath.size()).equals(parentPath);
+    }
+
+
+    public ParquetRelJoin withFilters(List<ParquetAdapterFilter> filters) {
+        List<ParquetAdapterFilter> combinedFilters = new ArrayList<>(this.joinFilters);
+        combinedFilters.addAll(filters);
+        return new ParquetRelJoin(getCluster(), traitSet, left, right, condition, variablesSet, joinType, leftIsParent, leftScan, rightScan, combinedFilters);
+    }
+
+
+    @Override
+    public Join copy(AlgTraitSet traitSet, RexNode conditionExpr, AlgNode left, AlgNode right, JoinAlgType joinType, boolean semiJoinDone) {
+        return new ParquetRelJoin(getCluster(), traitSet, left, right, conditionExpr, variablesSet, joinType, leftIsParent, leftScan, rightScan, joinFilters);
+    }
+
+
+    @Override
+    public PolyAlgArgs bindArguments() {
+        List<String> joinedFields = ParquetPolyAlgDisplay.joinedFieldNames(leftScan.table(), leftScan.fields(), rightScan.table(), rightScan.fields());
+        return super.bindArguments()
+                .put("leftIsParent", new BooleanArg(leftIsParent))
+                .put("leftFields", new ListArg<>(ParquetPolyAlgDisplay.fieldNames(leftScan.table(), leftScan.fields()), StringArg::new))
+                .put("rightFields", new ListArg<>(ParquetPolyAlgDisplay.fieldNames(rightScan.table(), rightScan.fields()), StringArg::new))
+                .put("leftFilters", new ListArg<>(ParquetPolyAlgDisplay.filters(leftScan.filters(), ParquetPolyAlgDisplay.prefixedFieldNames(leftScan.table(), "left")), StringArg::new))
+                .put("rightFilters", new ListArg<>(ParquetPolyAlgDisplay.filters(rightScan.filters(), ParquetPolyAlgDisplay.prefixedFieldNames(rightScan.table(), "right")), StringArg::new))
+                .put("joinFilters", new ListArg<>(ParquetPolyAlgDisplay.filters(joinFilters, joinedFields), StringArg::new));
+    }
+
+
+    @Override
+    public AlgOptCost computeSelfCost(AlgPlanner planner, AlgMetadataQuery mq) {
+        Optional<Double> count = mq.getTupleCount(this);
+        return planner.getCostFactory().makeCost(count.orElse(estimateTupleCount(mq)), 0, 0).multiplyBy(ParquetConvention.COST_MULTIPLIER);
+    }
+
+
+    @Override
+    public double estimateTupleCount(AlgMetadataQuery mq) {
+        return Math.max(left.estimateTupleCount(mq), right.estimateTupleCount(mq));
+    }
+
+
+    @Override
+    public AlgWriter explainTerms(AlgWriter pw) {
+        return super.explainTerms(pw)
+                .item("leftIsParent", leftIsParent)
+                .item("leftFilters", leftScan.filters())
+                .item("rightFilters", rightScan.filters())
+                .item("joinFilters", joinFilters);
+    }
+
+
+    @Override
+    public Expression implement(EnumerableAlgImplementor implementor) {
+        Expression leftScanExpr = Expressions.new_(
+                PhysicalScan.class,
+                leftScan.table().asExpression(ParquetRelTable.class),
+                Expressions.constant(leftScan.fields()),
+                EnumUtils.expressionList(leftScan.filters().stream()
+                        .map(ParquetAdapterFilter::toExpression)
+                        .toList()));
+
+        Expression rightScanExpr = Expressions.new_(
+                PhysicalScan.class,
+                rightScan.table().asExpression(ParquetRelTable.class),
+                Expressions.constant(rightScan.fields()),
+                EnumUtils.expressionList(rightScan.filters().stream()
+                        .map(ParquetAdapterFilter::toExpression)
+                        .toList()));
+
+        Expression runtimeFilters = EnumUtils.expressionList(
+                joinFilters.stream()
+                        .map(ParquetAdapterFilter::toExpression)
+                        .toList());
+
+        boolean emitUnmatchedParents = emitUnmatchedParents();
+        return Expressions.call(
+                leftScan.table().asExpression(ParquetRelTable.class),
+                "nestedJoin",
+                implementor.getRootExpression(),
+                leftScanExpr,
+                rightScanExpr,
+                Expressions.constant(leftIsParent),
+                Expressions.constant(emitUnmatchedParents),
+                runtimeFilters);
+    }
+
+
+    private boolean emitUnmatchedParents() {
+        return switch (joinType) {
+            case LEFT -> leftIsParent;
+            case RIGHT -> !leftIsParent;
+            case FULL -> true;
+            default -> false;
+        };
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
similarity index 80%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetScan.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
index 3571171f5b..6cc603d5b6 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
@@ -46,18 +46,18 @@
  * Parquet-convention scan for relational Parquet tables.
  */
 @Getter
-public class ParquetScan extends RelScan<ParquetRelTable> implements ParquetAlg {
+public class ParquetRelScan extends RelScan<ParquetRelTable> implements ParquetAlg {
 
     private final int[] fields;
     private final List<ParquetAdapterFilter> filters;
 
 
-    public ParquetScan( AlgCluster cluster, ParquetRelTable table, int[] fields ) {
+    public ParquetRelScan(AlgCluster cluster, ParquetRelTable table, int[] fields ) {
         this( cluster, table, fields, List.of() );
     }
 
 
-    public ParquetScan( AlgCluster cluster, ParquetRelTable table, int[] fields, List<ParquetAdapterFilter> filters ) {
+    public ParquetRelScan(AlgCluster cluster, ParquetRelTable table, int[] fields, List<ParquetAdapterFilter> filters ) {
         super( cluster, cluster.traitSetOf( ParquetConvention.INSTANCE ).replace( ModelTrait.RELATIONAL ), table );
         this.fields = fields;
         this.filters = List.copyOf( filters );
@@ -67,7 +67,7 @@ public ParquetScan( AlgCluster cluster, ParquetRelTable table, int[] fields, Lis
     @Override
     public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
         assert inputs.isEmpty();
-        return new ParquetScan( getCluster(), entity, fields, filters );
+        return new ParquetRelScan( getCluster(), entity, fields, filters );
     }
 
 
@@ -86,9 +86,10 @@ public String algCompareString() {
     @Override
     public PolyAlgArgs bindArguments() {
         List<String> fieldNames = ParquetPolyAlgDisplay.fieldNames( entity, fields );
+        List<String> tableFieldNames = ParquetPolyAlgDisplay.fieldNames( entity );
         return super.bindArguments()
                 .put( "fields", new ListArg<>( fieldNames, StringArg::new ) )
-                .put( "filters", new ListArg<>( ParquetPolyAlgDisplay.filters( filters, fieldNames ), StringArg::new ) );
+                .put( "filters", new ListArg<>( ParquetPolyAlgDisplay.filters( filters, tableFieldNames ), StringArg::new ) );
     }
 
 
@@ -117,23 +118,15 @@ public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
     }
 
 
-    public ParquetScan withFilters( List<ParquetAdapterFilter> filters ) {
+    public ParquetRelScan withFilters(List<ParquetAdapterFilter> filters ) {
         List<ParquetAdapterFilter> combinedFilters = new ArrayList<>( this.filters );
         combinedFilters.addAll( filters );
-        return new ParquetScan( getCluster(), entity, fields, combinedFilters );
+        return new ParquetRelScan( getCluster(), entity, fields, combinedFilters );
     }
 
 
-    public ParquetScan withFields( int[] fields ) {
-        List<ParquetAdapterFilter> remapped = ParquetFilterIndexMapper.remapFilters( filters, this.fields, fields );
-        if ( remapped == null ) {
-            return null; // cannot absorb this projection into the scan
-        }
-        return new ParquetScan( getCluster(), entity, fields, remapped );
-    }
-
-    public ParquetScan withFieldsAndFilters( int[] fields, List<ParquetAdapterFilter> filters ) {
-        return new ParquetScan( getCluster(), entity, fields, filters );
+    public ParquetRelScan withFields(int[] fields ) {
+        return new ParquetRelScan( getCluster(), entity, fields, filters );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java
index 37f70d48ca..2a70a0eb99 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java
@@ -42,12 +42,12 @@ public final class PatternMatchers {
     private final static ParquetRelFilterTranslator translator = new ParquetRelFilterTranslator();
 
 
-    private static void transformTo( AlgOptRuleCall call, EnumerableJoin join, ParquetScan left, ParquetScan right ) {
-        JoinDirection direction = ParquetJoin.supportedDirection( join, left, right );
+    private static void transformTo( AlgOptRuleCall call, EnumerableJoin join, ParquetRelScan left, ParquetRelScan right ) {
+        JoinDirection direction = ParquetRelJoin.supportedDirection( join, left, right );
         if ( direction == null ) {
             return;
         }
-        ParquetJoin parquetJoin = ParquetJoin.create( left, right, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent() );
+        ParquetRelJoin parquetJoin = ParquetRelJoin.create( left, right, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent() );
         call.transformTo( toEnumerable( join, parquetJoin ) );
     }
 
@@ -85,7 +85,7 @@ private static int[] getProjectFields( List<RexNode> rexNodes ) {
     }
 
 
-    private static ParquetScan applyCalc( EnumerableCalc calc, ParquetScan scan ) {
+    private static ParquetRelScan applyCalc( EnumerableCalc calc, ParquetRelScan scan ) {
         RexProgram program = calc.getProgram();
         List<RexNode> projects = program.getProjectList().stream()
                 .map( program::expandLocalRef )
@@ -105,23 +105,24 @@ private static ParquetScan applyCalc( EnumerableCalc calc, ParquetScan scan ) {
             projectedFields[i] = currentFields[fields[i]];
         }
 
-        ParquetScan updatedScan = scan.withFields( projectedFields );
-        if ( updatedScan == null && program.getCondition() == null ) {
-            return null;
-        }
+        ParquetAdapterFilter adapterFilter = null;
 
         if ( program.getCondition() != null ) {
-            // if updatedScan is null then original scan contains an old filter that cannot be translated into new projection.
-            ParquetScan projectedScan = updatedScan == null ? scan.withFieldsAndFilters( projectedFields, List.of() ) : updatedScan;
             RexNode condition = program.expandLocalRef( program.getCondition() );
-            ParquetAdapterFilter adapterFilter = translate( projectedScan, condition );
+            adapterFilter = translate( scan, condition );
+            if ( adapterFilter == null ) {
+                return null;
+            }
+            adapterFilter = ParquetFilterResolver.toPhysicalFilter( adapterFilter, currentFields );
             if ( adapterFilter == null ) {
                 return null;
             }
-            return projectedScan.withFilters( List.of( adapterFilter ) );
         }
 
-        return updatedScan;
+        ParquetRelScan updatedScan = scan.withFields( projectedFields );
+        return adapterFilter == null
+                ? updatedScan
+                : updatedScan.withFilters( List.of( adapterFilter ) );
     }
 
 
@@ -131,14 +132,14 @@ public static PatternMatcher joinWithScanOnLeftAndScanOnRight( ParquetConvention
                 factory,
                 operand(
                         EnumerableJoin.class,
-                        operand( EnumerableParquet.class, operand( ParquetScan.class, none() ) ),
-                        operand( EnumerableParquet.class, operand( ParquetScan.class, none() ) )
+                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) ),
+                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
                 ),
                 "joinWithScanOnLeftAndScanOnRight",
                 call -> {
                     EnumerableJoin join = call.alg( 0 );
-                    ParquetScan left = call.alg( 2 );
-                    ParquetScan right = call.alg( 4 );
+                    ParquetRelScan left = call.alg( 2 );
+                    ParquetRelScan right = call.alg( 4 );
                     transformTo( call, join, left, right );
                 }
         );
@@ -151,12 +152,12 @@ public static PatternMatcher attachFilterToJoinUnderCalc( ParquetConvention out,
                 factory,
                 operand(
                         EnumerableCalc.class,
-                        operand( EnumerableParquet.class, operand( ParquetJoin.class, any() ) )
+                        operand( EnumerableParquet.class, operand( ParquetRelJoin.class, any() ) )
                 ),
                 "attachFilterToJoinUnderCalc",
                 call -> {
                     EnumerableCalc calc = call.alg( 0 );
-                    ParquetJoin join = call.alg( 2 );
+                    ParquetRelJoin join = call.alg( 2 );
                     if ( !AlgOptUtil.areRowTypesEqual( calc.getInput().getTupleType(), join.getTupleType(), false ) ) {
                         return;
                     }
@@ -181,12 +182,12 @@ public static PatternMatcher attachFieldsAndFiltersToScanUnderCalc( ParquetConve
                 factory,
                 operand(
                         EnumerableCalc.class,
-                        operand( EnumerableParquet.class, operand( ParquetScan.class, any() ) )
+                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, any() ) )
                 ),
                 "attachFieldsAndFiltersToScanUnderCalc",
                 call -> {
                     EnumerableCalc calc = call.alg( 0 );
-                    ParquetScan scan = applyCalc( calc, call.alg( 2 ) );
+                    ParquetRelScan scan = applyCalc( calc, call.alg( 2 ) );
                     if ( scan == null ) {
                         return;
                     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PhysicalScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PhysicalScan.java
new file mode 100644
index 0000000000..37dc8ba24b
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PhysicalScan.java
@@ -0,0 +1,51 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import java.util.List;
+
+public record PhysicalScan( ParquetRelTable table, int[] fields, List<ParquetAdapterFilter> filters ) {
+
+    public PhysicalScan {
+        filters = List.copyOf( filters );
+    }
+
+
+    /**
+     * Support parametrized queries
+     *
+     * @param dataContext context
+     * @return list of parquet filters
+     */
+    public List<ParquetAdapterFilter> resolveFilters( DataContext dataContext ) {
+        return ParquetFilterResolver.resolveFilters( dataContext, filters, f -> selectPhysicalBinding( f.columnIndex() ) );
+    }
+
+
+    public ParquetColumnBinding selectPhysicalBinding( int columnIndex ) {
+        if ( columnIndex < 0 || columnIndex >= table.columns.size() ) {
+            throw new GenericRuntimeException( "Invalid physical filter column index: " + columnIndex );
+        }
+        return table.getBinding().getColumnBinding( table.columns.get( columnIndex ).id );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index 58d6152a3b..630d44c203 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -21,13 +21,13 @@
 import java.util.Objects;
 import java.util.Optional;
 import java.util.concurrent.atomic.AtomicBoolean;
-import java.util.function.Function;
 import java.util.stream.IntStream;
 import lombok.Getter;
 import org.apache.calcite.linq4j.AbstractEnumerable;
 import org.apache.calcite.linq4j.Enumerable;
 import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.execution.JoinNestedBinding;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetMultiFileEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedJoinEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedNonRepeatedRelEnumerator;
@@ -35,7 +35,8 @@
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetScan;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
+import org.polypheny.db.adapter.parquet.relational.planning.PhysicalScan;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersContainer;
@@ -43,6 +44,7 @@
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetFilterResolver;
 import org.polypheny.db.adapter.parquet.shared.statistics.ParquetStatisticsReader;
 import org.polypheny.db.adapter.statistics.AdapterStatisticsProvider;
 import org.polypheny.db.adapter.statistics.ProvidedColumnStatistics;
@@ -157,7 +159,7 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> poly
         } );
 
         // check for dynamic filters
-        final List<ParquetAdapterFilter> resolvedFilters = resolveFilters( dataContext, parquetAdapterFilters, f -> binding.getColumnBinding( columns.get( f.columnIndex() ).id ) );
+        final List<ParquetAdapterFilter> resolvedFilters = ParquetFilterResolver.resolveFilters( dataContext, parquetAdapterFilters, f -> binding.getColumnBinding( columns.get( f.columnIndex() ).id ) );
         final List<ParquetSourceFile> sourceFiles = new ParquetSourceFileFilterEvaluator( f -> binding.getColumnBinding( columns.get( f.columnIndex() ).id ) )
                 .prune( binding.sourceFiles(), resolvedFilters );
 
@@ -205,12 +207,12 @@ public Enumerator<PolyValue[]> enumerator() {
     @Override
     public AlgNode toAlg( AlgCluster cluster, AlgTraitSet traitSet ) {
         ParquetConvention.INSTANCE.register( cluster.getPlanner() );
-        return new ParquetScan( cluster, this, fieldIndexes );
+        return new ParquetRelScan( cluster, this, fieldIndexes );
     }
 
 
     /**
-     * This method is called from the {@link ParquetScan} via reflection.
+     * This method is called from the {@link ParquetRelScan} via reflection.
      *
      * @param dataContext data context
      * @param fields a list of fields to return.
@@ -222,7 +224,7 @@ public Enumerable<PolyValue[]> project( final DataContext dataContext, final int
 
 
     /**
-     * This method is called from the {@link ParquetScan} via reflection.
+     * This method is called from the {@link ParquetRelScan} via reflection.
      *
      * @param dataContext data context
      * @param fields a list of fields to return.
@@ -232,8 +234,8 @@ public Enumerable<PolyValue[]> project( final DataContext dataContext, final int
     @SuppressWarnings("unused")
     public Enumerable<PolyValue[]> project( final DataContext dataContext, final int[] fields, final List<ParquetAdapterFilter> filters ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
-        final List<ParquetAdapterFilter> resolvedFilters = resolveFilters( dataContext, filters, f -> selectProjectedBinding( f.columnIndex(), fields ) );
-        final List<ParquetSourceFile> sourceFiles = new ParquetSourceFileFilterEvaluator( f -> selectProjectedBinding( f.columnIndex(), fields ) )
+        final List<ParquetAdapterFilter> resolvedFilters = ParquetFilterResolver.resolveFilters( dataContext, filters, f -> selectPhysicalBinding( f.columnIndex() ) );
+        final List<ParquetSourceFile> sourceFiles = new ParquetSourceFileFilterEvaluator( f -> selectPhysicalBinding( f.columnIndex() ) )
                 .prune( binding.sourceFiles(), resolvedFilters );
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
         return new AbstractEnumerable<>() {
@@ -253,29 +255,30 @@ public Enumerator<PolyValue[]> enumerator() {
     @SuppressWarnings("unused")
     public Enumerable<PolyValue[]> nestedJoin(
             final DataContext dataContext,
-            final ParquetRelTable right,
-            final int[] leftFields,
-            final int[] rightFields,
+            final PhysicalScan leftScan,
+            final PhysicalScan rightScan,
             final boolean leftIsParent,
             final boolean emitUnmatchedParents,
             final List<ParquetAdapterFilter> filters ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
-        final ParquetRelTable parent = leftIsParent ? this : right;
-        final ParquetRelTable child = leftIsParent ? right : this;
-        final int[] parentFields = leftIsParent ? leftFields : rightFields;
-        final int[] childFields = leftIsParent ? rightFields : leftFields;
-        final List<ParquetAdapterFilter> resolvedFilters = resolveFilters( dataContext, filters, f -> selectBinding( f.columnIndex(), leftFields, rightFields, right ) );
-        final List<ParquetSourceFile> sourceFiles = new ParquetSourceFileFilterEvaluator( f -> selectParentBinding( f.columnIndex(), leftIsParent, parentFields, childFields, parent ) )
-                .prune( parent.binding.sourceFiles(), filtersForParent( resolvedFilters, leftIsParent, parentFields.length, childFields.length ) );
+        final PhysicalScan parent = leftIsParent ? leftScan : rightScan;
+        final PhysicalScan child = leftIsParent ? rightScan : leftScan;
+
+        final List<ParquetAdapterFilter> leftScanFilters = leftScan.resolveFilters( dataContext );
+        final List<ParquetAdapterFilter> rightScanFilters = rightScan.resolveFilters( dataContext );
+        final JoinFiltersContainer filterContainer = getJoinFiltersContainer( dataContext, leftScan, rightScan, leftIsParent, filters, leftScanFilters, rightScanFilters, parent, child );
+
+        final List<ParquetSourceFile> sourceFiles = new ParquetSourceFileFilterEvaluator(
+                f -> parent.selectPhysicalBinding( f.columnIndex() )
+        ).prune( parent.table().binding.sourceFiles(), leftIsParent ? leftScanFilters : rightScanFilters );
 
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
-                var filterContainer = new JoinFiltersSplitter().split( resolvedFilters, leftIsParent, parentFields.length, childFields.length );
                 return new ParquetMultiFileEnumerator(
                         sourceFiles,
-                        sourceFile -> parent.nestedJoinEnumeratorForFile( sourceFile, child, parentFields, childFields, cancelFlag, filterContainer, leftIsParent, emitUnmatchedParents ) );
+                        sourceFile -> parent.table().nestedJoinEnumeratorForFile( sourceFile, child.table(), parent.fields(), child.fields(), cancelFlag, filterContainer, leftIsParent, emitUnmatchedParents ) );
             }
         };
     }
@@ -302,62 +305,37 @@ public int getFieldCount() {
     }
 
 
-    /**
-     * Support parametrized queries
-     *
-     * @param dataContext context
-     * @param filters filters
-     * @param selector a helper function for column binding selection
-     * @return list of parquet filters
-     */
-    private List<ParquetAdapterFilter> resolveFilters(
-            DataContext dataContext,
-            List<ParquetAdapterFilter> filters,
-            Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
-        List<ParquetAdapterFilter> resolved = new ArrayList<>( filters.size() );
-        for ( ParquetAdapterFilter filter : filters ) {
-            resolved.add( resolveFilter( dataContext, filter, selector ) );
-        }
-        return resolved;
-    }
-
-
-    private ParquetAdapterFilter resolveFilter( DataContext dataContext, ParquetAdapterFilter filter, Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
-        if ( filter.isLogical() ) {
-            return ParquetAdapterFilter.logical( filter.operator(), filter.operands().stream()
-                    .map( operand -> resolveFilter( dataContext, operand, selector ) )
-                    .toList() );
-        }
-
-        PolyValue value = filter.dynamicParamIndex() == null
-                ? filter.polyValue()
-                : dataContext.getParameterValue( filter.dynamicParamIndex() );
-
-        ParquetColumnBinding columnBinding = Objects.requireNonNull( selector.apply( filter ), "Missing parquet column binding" );
-        return new ParquetAdapterFilter( filter.columnIndex(), columnBinding.sourcePathElements(), filter.operator(), value );
+    private boolean isNestedTable() {
+        return binding.parentTableName() != null;
     }
 
 
-    private List<ParquetAdapterFilter> filtersForParent( List<ParquetAdapterFilter> filters, boolean leftIsParent, int parentFieldCount, int childFieldCount ) {
-        var splitFilters = new JoinFiltersSplitter().split( filters, leftIsParent, parentFieldCount, childFieldCount );
-        return splitFilters.parentFilters();
-    }
-
-
-    private boolean isNestedTable() {
-        return binding.parentTableName() != null;
+    private JoinFiltersContainer getJoinFiltersContainer( DataContext dataContext, PhysicalScan leftScan, PhysicalScan rightScan, boolean leftIsParent, List<ParquetAdapterFilter> filters, List<ParquetAdapterFilter> leftScanFilters, List<ParquetAdapterFilter> rightScanFilters, PhysicalScan parent, PhysicalScan child ) {
+        final List<ParquetAdapterFilter> joinFilters = ParquetFilterResolver.resolveFilters( dataContext, filters, f -> selectBinding( f.columnIndex(), leftScan.fields(), rightScan.fields(), rightScan.table() ) );
+        final List<ParquetAdapterFilter> parentScanFilters = leftIsParent ? leftScanFilters : rightScanFilters;
+        final List<ParquetAdapterFilter> childScanFilters = leftIsParent ? rightScanFilters : leftScanFilters;
+        final JoinFiltersContainer container = new JoinFiltersSplitter().split( joinFilters, leftIsParent, parent.fields().length, child.fields().length );
+        return new JoinFiltersContainer(
+                container.parentFilters(),
+                container.childFilters(),
+                container.adapterFilters(),
+                combine( container.nativeFilters(), parentScanFilters ),
+                parentScanFilters,
+                childScanFilters );
     }
 
 
     private Enumerator<PolyValue[]> enumeratorForFile( ParquetSourceFile sourceFile, int[] fields, AtomicBoolean cancelFlag, FiltersContainer filtersContainer ) {
-        boolean bindingScan = isNestedTable() || needsBindingScan( fields ) || binding.sourceFiles().size() > 1;
+        boolean bindingScan = isNestedTable() || needsBindingScan( fields ) || binding.sourceFiles().size() > 1 || !filtersContainer.adapterFilters().isEmpty();
         Source fileSource = sourceFile.asSource();
         ParquetSourceReader reader = new ParquetSourceReader( fileSource, cancelFlag, bindingScan ? null : fields, filtersContainer.nativeFilters() );
+        List<ParquetColumnBinding> selectedBindings = projectedBindings( fields );
+        List<ParquetColumnBinding> filterBindings = projectedBindings( fieldIndexes );
         if ( isNestedTable() ) {
-            return new ParquetNestedRepeatedRelEnumerator( reader, binding, projectedBindings( fields ), filtersContainer );
+            return new ParquetNestedRepeatedRelEnumerator( reader, binding, selectedBindings, filterBindings, filtersContainer );
         }
         if ( bindingScan ) {
-            return new ParquetNestedNonRepeatedRelEnumerator( reader, sourceFile, projectedBindings( fields ), filtersContainer );
+            return new ParquetNestedNonRepeatedRelEnumerator( reader, sourceFile, selectedBindings, filterBindings, filtersContainer );
         }
         return new ParquetRelEnumerator( reader, filtersContainer.withoutPathElementsInAdapterFilters() );
     }
@@ -376,10 +354,8 @@ private Enumerator<PolyValue[]> nestedJoinEnumeratorForFile(
         ParquetSourceReader reader = new ParquetSourceReader( sourceFile.asSource(), cancelFlag, null, filterContainer.nativeFilters() );
         return new ParquetNestedJoinEnumerator(
                 reader,
-                binding,
-                child.binding,
-                projectedBindings( parentFields ),
-                child.projectedBindings( childFields ),
+                new JoinNestedBinding( binding, projectedBindings( parentFields ), projectedBindings( fieldIndexes ) ),
+                new JoinNestedBinding( child.binding, child.projectedBindings( childFields ), child.projectedBindings( child.fieldIndexes ) ),
                 filterContainer,
                 leftIsParent,
                 emitUnmatchedParents );
@@ -410,23 +386,25 @@ private ParquetColumnBinding selectBinding( int joinedIndex, int[] leftFields, i
     }
 
 
-    private ParquetColumnBinding selectParentBinding( int joinedIndex, boolean leftIsParent, int[] parentFields, int[] childFields, ParquetRelTable parent ) {
-        if ( joinedIndex < 0 ) {
-            throw new GenericRuntimeException( "Invalid joined filter column index: " + joinedIndex );
+    private ParquetColumnBinding selectPhysicalBinding( int columnIndex ) {
+        if ( columnIndex < 0 || columnIndex >= columns.size() ) {
+            throw new GenericRuntimeException( "Invalid physical filter column index: " + columnIndex );
         }
-        int parentIndex = leftIsParent ? joinedIndex : joinedIndex - childFields.length;
-        if ( parentIndex < 0 || parentIndex >= parentFields.length ) {
-            return null;
-        }
-        return parent.binding.getColumnBinding( parent.columns.get( parentFields[parentIndex] ).id );
+        return binding.getColumnBinding( columns.get( columnIndex ).id );
     }
 
 
-    private ParquetColumnBinding selectProjectedBinding( int projectedIndex, int[] fields ) {
-        if ( projectedIndex < 0 || projectedIndex >= fields.length ) {
-            throw new GenericRuntimeException( "Invalid projected filter column index: " + projectedIndex );
+    private static List<ParquetAdapterFilter> combine( List<ParquetAdapterFilter> left, List<ParquetAdapterFilter> right ) {
+        if ( left.isEmpty() ) {
+            return right;
+        }
+        if ( right.isEmpty() ) {
+            return left;
         }
-        return binding.getColumnBinding( columns.get( fields[projectedIndex] ).id );
+        List<ParquetAdapterFilter> combined = new ArrayList<>( left.size() + right.size() );
+        combined.addAll( left );
+        combined.addAll( right );
+        return combined;
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersContainer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersContainer.java
index 0a0bcc0de8..4a658c8638 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersContainer.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersContainer.java
@@ -18,33 +18,156 @@
 
 import java.util.List;
 
+/**
+ * Filter bundle used by the nested Parquet join executor.
+ * <p>
+ * A nested join has more filter buckets than a normal scan because filters can
+ * come from two different planner locations and can use two different column
+ * coordinate systems:
+ * <ul>
+ *     <li>
+ *         <b>Join-owned filters</b> are filters attached above or directly to
+ *         the {@code P_JOIN}. Their {@link ParquetAdapterFilter#columnIndex()}
+ *         values use the joined output row, i.e. {@code leftFields} followed by
+ *         {@code rightFields}. These filters are split into {@link #parentFilters()},
+ *         {@link #childFilters()}, and {@link #adapterFilters()}.
+ *     </li>
+ *     <li>
+ *         <b>Scan-owned filters</b> are filters pushed into the left or right
+ *         {@code P_SCAN} before the join is created. Their
+ *         {@link ParquetAdapterFilter#columnIndex()} values use the physical
+ *         table column index of that scan's table. These filters are stored in
+ *         {@link #parentScanFilters()} and {@link #childScanFilters()} and are
+ *         intentionally not remapped through the join projection.
+ *     </li>
+ * </ul>
+ *
+ * <h2>Example</h2>
+ * For a query like:
+ *
+ * <pre>{@code
+ * SELECT o.order_id, i.quantity
+ * FROM pon__orders o
+ * JOIN pon__orders__items i
+ *   ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+ * WHERE o.total_price >= 0 AND i.quantity > 3
+ * }</pre>
+ *
+ * If the join output fields are:
+ *
+ * <pre>{@code
+ * leftFields  = [__polypheny_row_id (#0), order_id (#1)]
+ * rightFields = [__polypheny_parent_row_id (#1), quantity (#5)]
+ * }</pre>
+ *
+ * then a join-owned filter on {@code right.quantity} would use the joined row
+ * index {@code 3}: two left fields plus the second right field. That kind of
+ * filter belongs in {@link #childFilters()} when {@code leftIsParent == true}.
+ * <p>
+ * A scan-owned filter on the same {@code quantity} column uses the physical
+ * table index {@code 5}, regardless of whether {@code quantity} is currently
+ * the second projected field of the child scan. That kind of filter belongs in
+ * {@link #childScanFilters()}.
+ * <p>
+ * This distinction is important for projected scans. A filter such as
+ * {@code o.total_price >= 0} can be applied even when {@code total_price} is
+ * not part of the scan output, because the scan-owned filter keeps physical
+ * column {@code total_price (#5)} while {@code leftFields} can still expose
+ * only {@code __polypheny_row_id} and {@code order_id}.
+ *
+ * <h2>Bucket summary</h2>
+ * <ul>
+ *     <li>{@link #parentFilters()} - join-owned filters that reference only
+ *     parent-side joined-row fields.</li>
+ *     <li>{@link #childFilters()} - join-owned filters that reference only
+ *     child-side joined-row fields.</li>
+ *     <li>{@link #adapterFilters()} - join-owned filters that cannot be assigned
+ *     to exactly one side, for example an {@code OR} crossing parent and child
+ *     fields. These are evaluated after joined rows are produced.</li>
+ *     <li>{@link #nativeFilters()} - filters that can be given to the Parquet
+ *     reader. In nested joins this is currently parent-side only because the
+ *     reader is opened on the parent/root file before repeated child rows are
+ *     expanded.</li>
+ *     <li>{@link #parentScanFilters()} - scan-owned physical-index filters for
+ *     the parent table.</li>
+ *     <li>{@link #childScanFilters()} - scan-owned physical-index filters for
+ *     the child table.</li>
+ * </ul>
+ */
 public class JoinFiltersContainer extends FiltersContainer {
 
     public static JoinFiltersContainer empty = new JoinFiltersContainer( List.of(), List.of(), List.of(), List.of() );
 
     private final List<ParquetAdapterFilter> parentFilters;
     private final List<ParquetAdapterFilter> childFilters;
+    private final List<ParquetAdapterFilter> parentScanFilters;
+    private final List<ParquetAdapterFilter> childScanFilters;
 
 
     /**
-     * Creates a new instance of {@link FiltersContainer}
+     * Creates a join filter container for join-owned filters only.
+     * <p>
+     * Use this constructor when all filters are expressed in joined-row
+     * coordinates and there are no filters inherited from the input scans.
+     * The provided filter lists usually come from {@link JoinFiltersSplitter}.
      *
-     * @param parentFilters a list of filters to be used only on a parent table.
-     * @param childFilters a list of filters to be used only on a child table.
-     * @param adapterFilters a list of filters to be used on adapter level.
-     * @param readerFilters a list of filters to be used on parquet reader level.
+     * @param parentFilters join-owned filters that reference only parent fields,
+     * still using joined-row column indexes.
+     * @param childFilters join-owned filters that reference only child fields,
+     * still using joined-row column indexes.
+     * @param adapterFilters join-owned filters that must be evaluated after a
+     * joined row exists, for example filters spanning both sides.
+     * @param readerFilters join-owned filters that can also be pushed to the
+     * Parquet reader as native predicates.
      */
     public JoinFiltersContainer( List<ParquetAdapterFilter> parentFilters, List<ParquetAdapterFilter> childFilters, List<ParquetAdapterFilter> adapterFilters, List<ParquetAdapterFilter> readerFilters ) {
+        this( parentFilters, childFilters, adapterFilters, readerFilters, List.of(), List.of() );
+    }
+
+
+    /**
+     * Creates a join filter container with both join-owned and scan-owned filters.
+     * <p>
+     * The first four lists have the same meaning as in
+     * {@link #JoinFiltersContainer(List, List, List, List)} and use joined-row
+     * column indexes when they refer to row fields.
+     * <p>
+     * The last two lists are different: they contain filters copied from the
+     * input scans, so their column indexes are physical table column indexes.
+     * For example, if {@code pon__orders.total_price} is physical column
+     * {@code #5}, the parent scan filter keeps {@code columnIndex = 5} even if
+     * the parent join output only contains columns {@code #0} and {@code #1}.
+     *
+     * @param parentFilters join-owned filters that reference only parent fields,
+     * using joined-row column indexes.
+     * @param childFilters join-owned filters that reference only child fields,
+     * using joined-row column indexes.
+     * @param adapterFilters join-owned filters evaluated after joined rows are
+     * produced.
+     * @param readerFilters filters that can be evaluated by the Parquet reader.
+     * @param parentScanFilters scan-owned filters for the parent table, using
+     * parent physical table column indexes.
+     * @param childScanFilters scan-owned filters for the child table, using
+     * child physical table column indexes.
+     */
+    public JoinFiltersContainer( List<ParquetAdapterFilter> parentFilters, List<ParquetAdapterFilter> childFilters, List<ParquetAdapterFilter> adapterFilters, List<ParquetAdapterFilter> readerFilters, List<ParquetAdapterFilter> parentScanFilters, List<ParquetAdapterFilter> childScanFilters ) {
         super( adapterFilters, readerFilters );
-        this.parentFilters = parentFilters;
-        this.childFilters = childFilters;
+        this.parentFilters = parentFilters == null ? List.of() : List.copyOf( parentFilters );
+        this.childFilters = childFilters == null ? List.of() : List.copyOf( childFilters );
+        this.parentScanFilters = parentScanFilters == null ? List.of() : List.copyOf( parentScanFilters );
+        this.childScanFilters = childScanFilters == null ? List.of() : List.copyOf( childScanFilters );
     }
 
 
     /**
-     * Gets a list of parent table filters.
+     * Gets join-owned filters that reference only parent-side fields.
+     * <p>
+     * These filters use joined-row column indexes, not physical table indexes.
+     * If {@code leftIsParent == true}, parent field {@code leftFields[1]} has
+     * joined index {@code 1}. If {@code leftIsParent == false}, parent fields
+     * start after all child/left fields.
      *
-     * @return a list of parent table filters.
+     * @return parent-only join-owned filters.
      */
     public List<ParquetAdapterFilter> parentFilters() {
         return parentFilters;
@@ -52,12 +175,47 @@ public List<ParquetAdapterFilter> parentFilters() {
 
 
     /**
-     * Gets a list of child table filters.
+     * Gets join-owned filters that reference only child-side fields.
+     * <p>
+     * These filters use joined-row column indexes. For example, when the parent
+     * is on the left and there are two left fields, the first right/child field
+     * has joined index {@code 2}.
      *
-     * @return a list of child table filters.
+     * @return child-only join-owned filters.
      */
     public List<ParquetAdapterFilter> childFilters() {
         return childFilters;
     }
 
+
+    /**
+     * Gets scan-owned filters for the parent table.
+     * <p>
+     * These filters use parent physical table column indexes and are evaluated
+     * against parent groups before child rows are expanded. They are the right
+     * place for predicates that were pushed into the parent {@code P_SCAN}, such
+     * as {@code pon__orders.total_price (#5) >= 0}.
+     *
+     * @return parent scan filters using physical table indexes.
+     */
+    public List<ParquetAdapterFilter> parentScanFilters() {
+        return parentScanFilters;
+    }
+
+
+    /**
+     * Gets scan-owned filters for the child table.
+     * <p>
+     * These filters use child physical table column indexes and are evaluated
+     * against each child group after the parent row has been expanded to its
+     * repeated child rows. They are the right place for predicates that were
+     * pushed into the child {@code P_SCAN}, such as
+     * {@code pon__orders__items.quantity (#5) > 3}.
+     *
+     * @return child scan filters using physical table indexes.
+     */
+    public List<ParquetAdapterFilter> childScanFilters() {
+        return childScanFilters;
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedFilterEvaluator.java
index 24567fe94c..4c2b16ed70 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedFilterEvaluator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedFilterEvaluator.java
@@ -23,6 +23,7 @@
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.shared.execution.ParquetValueExtractor;
 import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
+import org.polypheny.db.type.entity.PolyNull;
 import org.polypheny.db.type.entity.PolyValue;
 
 /**
@@ -45,6 +46,9 @@ public ParquetNestedFilterEvaluator( MessageType schema, ParquetValueExtractor v
     protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
         var pathValueExtractor = (ParquetPathValueExtractor) valueExtractor;
         var virtualGroup = (VirtualGroup) group;
+        if ( filter.columnIndex() < 0 || filter.columnIndex() >= columnBindings.size() ) {
+            return PolyNull.NULL;
+        }
         var binding = columnBindings.get( filter.columnIndex() );
         return pathValueExtractor.extractValue( virtualGroup, binding, tablePath );
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedJoinFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedJoinFilterEvaluator.java
index d40bfcb20e..4c206f7dfb 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedJoinFilterEvaluator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedJoinFilterEvaluator.java
@@ -24,6 +24,7 @@
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
 import org.polypheny.db.adapter.parquet.shared.execution.CombinedGroup;
 import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
+import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.type.entity.PolyValue;
 
 /**
@@ -72,7 +73,10 @@ protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
      */
     @Override
     protected boolean canApplyFilter( Group group, ParquetAdapterFilter filter ) {
-        if ( filter.polyValue() == null || filter.columnIndex() < 0 ) {
+        if ( filter.columnIndex() < 0 ) {
+            return false;
+        }
+        if ( filter.polyValue() == null && !isNullCheck( filter.operator() ) ) {
             return false;
         }
         if ( group instanceof CombinedGroup combinedGroup ) {
@@ -95,4 +99,9 @@ protected boolean filterHasValue( Group group, ParquetAdapterFilter filter ) {
         return !combinedGroup.isNullField( filter.columnIndex(), leftIsParent );
     }
 
+
+    private boolean isNullCheck( Kind operator ) {
+        return operator == Kind.IS_NULL || operator == Kind.IS_NOT_NULL;
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNestedJoinPlanningTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNestedJoinPlanningTest.java
index c9174498b4..1385ff2726 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNestedJoinPlanningTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNestedJoinPlanningTest.java
@@ -137,7 +137,7 @@ void plansFullJoinWithLimitAsParquetJoin() {
                         + "WHERE i.quantity = 3 "
                         + "LIMIT 10" );
 
-        assertTrue( physicalPlan.contains( "ParquetJoin" ), physicalPlan );
+        assertTrue( physicalPlan.contains( "ParquetRelJoin" ), physicalPlan );
         assertFalse( physicalPlan.contains( "EnumerableJoin" ), physicalPlan );
     }
 
@@ -152,7 +152,7 @@ void plansLeftJoinWithLimitAsParquetJoin() {
                         + "WHERE i.quantity = 3 "
                         + "LIMIT 10" );
 
-        assertTrue( physicalPlan.contains( "ParquetJoin" ), physicalPlan );
+        assertTrue( physicalPlan.contains( "ParquetRelJoin" ), physicalPlan );
         assertFalse( physicalPlan.contains( "EnumerableJoin" ), physicalPlan );
     }
 

From 8481fd483fbb7712412bfb74b8b0c06302eda553 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Sat, 16 May 2026 12:16:12 +0200
Subject: [PATCH 31/46] add support for filtering source files based on column
 statistics

---
 .../execution/ParquetMultiFileEnumerator.java |  17 +-
 .../planning/ParquetConvention.java           |   2 +-
 .../schema/ParquetBindingSerializer.java      |  72 +++++-
 .../schema/ParquetColumnStatistics.java       |  48 ++++
 .../schema/ParquetFilterEvaluatorsChain.java  |  60 +++++
 .../relational/schema/ParquetRelTable.java    |  42 ++--
 .../relational/schema/ParquetSourceFile.java  |  28 ++-
 .../ParquetSourceFileFilterEvaluator.java     |  11 -
 ...etSourceFileStatisticsFilterEvaluator.java |  97 ++++++++
 .../shared/io/ParquetFileDiscovery.java       |   2 +-
 .../shared/schema/ParquetTypeConverter.java   | 172 ++++++++++++++
 .../ParquetColumnStatisticsReader.java        | 211 ++++++++++++++++++
 ...java => ParquetTableStatisticsReader.java} | 192 ++++++++--------
 13 files changed, 813 insertions(+), 141 deletions(-)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnStatistics.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetFilterEvaluatorsChain.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileStatisticsFilterEvaluator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetColumnStatisticsReader.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/{ParquetStatisticsReader.java => ParquetTableStatisticsReader.java} (53%)

diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java
index 8fe012bb6d..7708bece12 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java
@@ -21,6 +21,8 @@
 import java.util.function.Function;
 import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetFilterEvaluatorsChain;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.type.entity.PolyValue;
 
@@ -32,13 +34,22 @@ public class ParquetMultiFileEnumerator implements Enumerator<PolyValue[]> {
 
     private final Iterator<ParquetSourceFile> files;
     private final Function<ParquetSourceFile, Enumerator<PolyValue[]>> enumeratorFactory;
+    private final ParquetFilterEvaluatorsChain<ParquetSourceFile> sourceFileEvaluator;
+    private final List<ParquetAdapterFilter> filters;
     private Enumerator<PolyValue[]> currentEnumerator;
     private PolyValue[] current;
 
 
     public ParquetMultiFileEnumerator( List<ParquetSourceFile> files, Function<ParquetSourceFile, Enumerator<PolyValue[]>> enumeratorFactory ) {
+        this( files, enumeratorFactory, ParquetFilterEvaluatorsChain.empty(), List.of() );
+    }
+
+
+    public ParquetMultiFileEnumerator( List<ParquetSourceFile> files, Function<ParquetSourceFile, Enumerator<PolyValue[]>> enumeratorFactory, ParquetFilterEvaluatorsChain<ParquetSourceFile> sourceFileEvaluator, List<ParquetAdapterFilter> filters ) {
         this.files = List.copyOf( files ).iterator();
         this.enumeratorFactory = enumeratorFactory;
+        this.sourceFileEvaluator = sourceFileEvaluator == null ? ParquetFilterEvaluatorsChain.empty() : sourceFileEvaluator;
+        this.filters = filters == null ? List.of() : List.copyOf( filters );
     }
 
 
@@ -57,7 +68,11 @@ public boolean moveNext() {
                         current = null;
                         return false;
                     }
-                    currentEnumerator = enumeratorFactory.apply( files.next() );
+                    ParquetSourceFile sourceFile = files.next();
+                    if ( !sourceFileEvaluator.matches( sourceFile, filters ) ) {
+                        continue;
+                    }
+                    currentEnumerator = enumeratorFactory.apply( sourceFile );
                 }
 
                 if ( currentEnumerator.moveNext() ) {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java
index 19489c216c..bae5fa0a6f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java
@@ -29,7 +29,7 @@
 public class ParquetConvention extends Convention.Impl {
 
     public static final ParquetConvention INSTANCE = new ParquetConvention();
-    public static final double COST_MULTIPLIER = 0d;// 0.05d;
+    public static final double COST_MULTIPLIER = 0.05d;
 
 
     private ParquetConvention() {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetBindingSerializer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetBindingSerializer.java
index 6f282305fb..cb4bd0e9f6 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetBindingSerializer.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetBindingSerializer.java
@@ -19,11 +19,13 @@
 import java.nio.charset.StandardCharsets;
 import java.util.Base64;
 import java.util.LinkedHashMap;
+import java.util.Collections;
 import java.util.List;
 import java.util.Map;
 import java.util.stream.Collectors;
 import java.util.stream.Stream;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.PolyType;
 
 /**
  * Saves and restores ParquetTableBinding metadata through adapter settings.
@@ -40,6 +42,8 @@ public final class ParquetBindingSerializer {
     private static final String FILE_FIELD_JOINER = "|";
     private static final String PARTITION_SEPARATOR = "&";
     private static final String PARTITION_FIELD_SEPARATOR = "=";
+    private static final String STATISTICS_SEPARATOR = "~";
+    private static final String STATISTICS_FIELD_SEPARATOR = ":";
 
 
     private ParquetBindingSerializer() {
@@ -82,7 +86,9 @@ public static Map<Long, ParquetTableBinding> deserialize( String serialized ) {
 
     private static String serializeSourceFiles( List<ParquetSourceFile> sourceFiles ) {
         return sourceFiles.stream()
-                .map( sourceFile -> encode( sourceFile.fileUrl() ) + FILE_FIELD_JOINER + serializePartitions( sourceFile.partitionValues() ) )
+                .map( sourceFile -> encode( sourceFile.fileUrl() )
+                        + FILE_FIELD_JOINER + serializePartitions( sourceFile.partitionValues() )
+                        + FILE_FIELD_JOINER + serializeColumnStatistics( sourceFile.columnStatistics() ) )
                 .collect( Collectors.joining( FILE_SEPARATOR ) );
     }
 
@@ -93,10 +99,15 @@ private static List<ParquetSourceFile> deserializeSourceFiles( String serialized
         }
         return Stream.of( serialized.split( FILE_SEPARATOR ) ).map( fileEntry -> {
             String[] fields = fileEntry.split( FILE_FIELD_SEPARATOR, -1 );
-            if ( fields.length != 2 ) {
+            if ( fields.length != 2 && fields.length != 3 ) {
                 throw new GenericRuntimeException( "Invalid serialized Parquet source file entry: %s", fileEntry );
             }
-            return new ParquetSourceFile( decode( fields[0] ), deserializePartitions( fields[1] ) );
+            String fileUrl = decode( fields[0] );
+            Map<String, String> partitions = deserializePartitions( fields[1] );
+            if ( fields.length == 2 ) {
+                return ParquetSourceFile.of( fileUrl, partitions );
+            }
+            return new ParquetSourceFile( fileUrl, partitions, deserializeColumnStatistics( fields[2] ) );
         } ).toList();
     }
 
@@ -124,6 +135,61 @@ private static Map<String, String> deserializePartitions( String serialized ) {
     }
 
 
+    private static String serializeColumnStatistics( Map<List<String>, ParquetColumnStatistics> statistics ) {
+        return statistics.entrySet().stream()
+                .map( entry -> serializePath( entry.getKey() )
+                        + STATISTICS_FIELD_SEPARATOR + entry.getValue().type().name()
+                        + STATISTICS_FIELD_SEPARATOR + entry.getValue().rowCount()
+                        + STATISTICS_FIELD_SEPARATOR + entry.getValue().valueCount()
+                        + STATISTICS_FIELD_SEPARATOR + serializeNullableLong( entry.getValue().nullCount() )
+                        + STATISTICS_FIELD_SEPARATOR + encode( entry.getValue().min() )
+                        + STATISTICS_FIELD_SEPARATOR + encode( entry.getValue().max() )
+                        + STATISTICS_FIELD_SEPARATOR + entry.getValue().minMaxReliable() )
+                .collect( Collectors.joining( STATISTICS_SEPARATOR ) );
+    }
+
+
+    private static Map<List<String>, ParquetColumnStatistics> deserializeColumnStatistics( String serialized ) {
+        if ( serialized == null || serialized.isBlank() ) {
+            return Collections.emptyMap();
+        }
+
+        Map<List<String>, ParquetColumnStatistics> statistics = new LinkedHashMap<>();
+        for ( String entry : serialized.split( STATISTICS_SEPARATOR ) ) {
+            String[] fields = entry.split( STATISTICS_FIELD_SEPARATOR, -1 );
+            if ( fields.length != 8 ) {
+                throw new GenericRuntimeException( "Invalid serialized Parquet column statistics entry: %s", entry );
+            }
+            statistics.put(
+                    deserializePath( fields[0] ),
+                    new ParquetColumnStatistics(
+                            PolyType.valueOf( fields[1] ),
+                            Long.parseLong( fields[2] ),
+                            Long.parseLong( fields[3] ),
+                            deserializeNullableLong( fields[4] ),
+                            decodeNullable( fields[5] ),
+                            decodeNullable( fields[6] ),
+                            Boolean.parseBoolean( fields[7] ) ) );
+        }
+        return statistics;
+    }
+
+
+    private static String serializeNullableLong( Long value ) {
+        return value == null ? "" : String.valueOf( value );
+    }
+
+
+    private static Long deserializeNullableLong( String value ) {
+        return value == null || value.isBlank() ? null : Long.parseLong( value );
+    }
+
+
+    private static String decodeNullable( String value ) {
+        return value == null || value.isBlank() ? null : decode( value );
+    }
+
+
     private static String serializeColumns( Map<Long, ParquetColumnBinding> columns ) {
         return columns.values().stream()
                 .map( column -> column.columnId()
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnStatistics.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnStatistics.java
new file mode 100644
index 0000000000..b03c5fc22c
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnStatistics.java
@@ -0,0 +1,48 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+import org.polypheny.db.type.PolyType;
+
+
+/**
+ * Compact per-file statistics for one physical Parquet column path.
+ */
+public record ParquetColumnStatistics(
+        PolyType type,
+        long rowCount,
+        long valueCount,
+        Long nullCount,
+        String min,
+        String max,
+        boolean minMaxReliable ) {
+
+    public boolean hasNoNulls() {
+        return nullCount != null && nullCount == 0;
+    }
+
+
+    public boolean hasOnlyNulls() {
+        return nullCount != null && nullCount >= valueCount;
+    }
+
+
+    public boolean hasRange() {
+        return minMaxReliable && min != null && max != null;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetFilterEvaluatorsChain.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetFilterEvaluatorsChain.java
new file mode 100644
index 0000000000..fe50bb9ae6
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetFilterEvaluatorsChain.java
@@ -0,0 +1,60 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+import java.util.List;
+import org.polypheny.db.adapter.parquet.shared.filter.FilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+
+
+/**
+ * Applies file-level filter evaluators as one pruning decision.
+ */
+public class ParquetFilterEvaluatorsChain<T> extends FilterEvaluator<T> {
+
+    private final List<FilterEvaluator<T>> evaluators;
+
+
+    public ParquetFilterEvaluatorsChain( List<FilterEvaluator<T>> evaluators ) {
+        this.evaluators = evaluators == null ? List.of() : List.copyOf( evaluators );
+    }
+
+
+    public static <T> ParquetFilterEvaluatorsChain<T> empty() {
+        return new ParquetFilterEvaluatorsChain<>( List.of() );
+    }
+
+
+    public boolean matches( T context, List<ParquetAdapterFilter> filters ) {
+        return filters == null || filters.isEmpty() || filters.stream().allMatch( filter -> matches( context, filter ) );
+    }
+
+
+    @Override
+    protected Boolean evaluateLeaf( T context, ParquetAdapterFilter filter ) {
+        boolean hasKnownMatch = false;
+        for ( FilterEvaluator<T> evaluator : evaluators ) {
+            Boolean result = evaluator.evaluate( context, filter );
+            if ( Boolean.FALSE.equals( result ) ) {
+                return false;
+            }
+            hasKnownMatch |= Boolean.TRUE.equals( result );
+        }
+        return hasKnownMatch ? true : null;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index 630d44c203..8eb73eea14 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -21,6 +21,7 @@
 import java.util.Objects;
 import java.util.Optional;
 import java.util.concurrent.atomic.AtomicBoolean;
+import java.util.function.Function;
 import java.util.stream.IntStream;
 import lombok.Getter;
 import org.apache.calcite.linq4j.AbstractEnumerable;
@@ -45,7 +46,7 @@
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetFilterResolver;
-import org.polypheny.db.adapter.parquet.shared.statistics.ParquetStatisticsReader;
+import org.polypheny.db.adapter.parquet.shared.statistics.ParquetTableStatisticsReader;
 import org.polypheny.db.adapter.statistics.AdapterStatisticsProvider;
 import org.polypheny.db.adapter.statistics.ProvidedColumnStatistics;
 import org.polypheny.db.adapter.statistics.ProvidedEntityStatistics;
@@ -77,7 +78,7 @@ public class ParquetRelTable extends PhysicalTable implements FilterableEntity,
     private final ParquetTableBinding binding;
     private final List<PolyType> fieldTypes;
     private final ParquetSchemaReader schemaReader;
-    private final ParquetStatisticsReader statisticsReader;
+    private final ParquetTableStatisticsReader statisticsReader;
 
 
     /**
@@ -98,7 +99,7 @@ public ParquetRelTable( long id, PhysicalTable table, ParquetTableBinding bindin
         this.fieldIndexes = IntStream.range( 0, table.columns.size() ).toArray();
         this.fieldTypes = columns.stream().map( c -> c.type ).toList();
         this.schemaReader = new ParquetSchemaReader( binding.sourceFiles().stream().map( ParquetSourceFile::asSource ).toList() );
-        this.statisticsReader = new ParquetStatisticsReader( schemaReader, binding );
+        this.statisticsReader = new ParquetTableStatisticsReader( schemaReader, binding );
         this.parquetSource = parquetSource;
         this.filterTranslator = new ParquetRelFilterTranslator();
     }
@@ -160,16 +161,15 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> poly
 
         // check for dynamic filters
         final List<ParquetAdapterFilter> resolvedFilters = ParquetFilterResolver.resolveFilters( dataContext, parquetAdapterFilters, f -> binding.getColumnBinding( columns.get( f.columnIndex() ).id ) );
-        final List<ParquetSourceFile> sourceFiles = new ParquetSourceFileFilterEvaluator( f -> binding.getColumnBinding( columns.get( f.columnIndex() ).id ) )
-                .prune( binding.sourceFiles(), resolvedFilters );
-
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
                 return new ParquetMultiFileEnumerator(
-                        sourceFiles,
-                        sourceFile -> enumeratorForFile( sourceFile, fieldIndexes, cancelFlag, FiltersContainer.shared( resolvedFilters ) ) );
+                        binding.sourceFiles(),
+                        sourceFile -> enumeratorForFile( sourceFile, fieldIndexes, cancelFlag, FiltersContainer.shared( resolvedFilters ) ),
+                        createParquetSourceFileEvaluatorsChain( f -> binding.getColumnBinding( columns.get( f.columnIndex() ).id ) ),
+                        resolvedFilters );
             }
         };
     }
@@ -235,15 +235,15 @@ public Enumerable<PolyValue[]> project( final DataContext dataContext, final int
     public Enumerable<PolyValue[]> project( final DataContext dataContext, final int[] fields, final List<ParquetAdapterFilter> filters ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
         final List<ParquetAdapterFilter> resolvedFilters = ParquetFilterResolver.resolveFilters( dataContext, filters, f -> selectPhysicalBinding( f.columnIndex() ) );
-        final List<ParquetSourceFile> sourceFiles = new ParquetSourceFileFilterEvaluator( f -> selectPhysicalBinding( f.columnIndex() ) )
-                .prune( binding.sourceFiles(), resolvedFilters );
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
                 return new ParquetMultiFileEnumerator(
-                        sourceFiles,
-                        sourceFile -> enumeratorForFile( sourceFile, fields, cancelFlag, FiltersContainer.shared( resolvedFilters ) ) );
+                        binding.sourceFiles(),
+                        sourceFile -> enumeratorForFile( sourceFile, fields, cancelFlag, FiltersContainer.shared( resolvedFilters ) ),
+                        createParquetSourceFileEvaluatorsChain( f -> selectPhysicalBinding( f.columnIndex() ) ),
+                        resolvedFilters );
             }
         };
     }
@@ -269,16 +269,16 @@ public Enumerable<PolyValue[]> nestedJoin(
         final List<ParquetAdapterFilter> rightScanFilters = rightScan.resolveFilters( dataContext );
         final JoinFiltersContainer filterContainer = getJoinFiltersContainer( dataContext, leftScan, rightScan, leftIsParent, filters, leftScanFilters, rightScanFilters, parent, child );
 
-        final List<ParquetSourceFile> sourceFiles = new ParquetSourceFileFilterEvaluator(
-                f -> parent.selectPhysicalBinding( f.columnIndex() )
-        ).prune( parent.table().binding.sourceFiles(), leftIsParent ? leftScanFilters : rightScanFilters );
+        final List<ParquetAdapterFilter> parentFileFilters = leftIsParent ? leftScanFilters : rightScanFilters;
 
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
                 return new ParquetMultiFileEnumerator(
-                        sourceFiles,
-                        sourceFile -> parent.table().nestedJoinEnumeratorForFile( sourceFile, child.table(), parent.fields(), child.fields(), cancelFlag, filterContainer, leftIsParent, emitUnmatchedParents ) );
+                        parent.table().binding.sourceFiles(),
+                        sourceFile -> parent.table().nestedJoinEnumeratorForFile( sourceFile, child.table(), parent.fields(), child.fields(), cancelFlag, filterContainer, leftIsParent, emitUnmatchedParents ),
+                        createParquetSourceFileEvaluatorsChain( f -> parent.selectPhysicalBinding( f.columnIndex() ) ),
+                        parentFileFilters );
             }
         };
     }
@@ -362,6 +362,14 @@ private Enumerator<PolyValue[]> nestedJoinEnumeratorForFile(
     }
 
 
+    private static ParquetFilterEvaluatorsChain<ParquetSourceFile> createParquetSourceFileEvaluatorsChain( Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
+        return new ParquetFilterEvaluatorsChain<>( List.of(
+                new ParquetSourceFileFilterEvaluator( selector ),
+                new ParquetSourceFileStatisticsFilterEvaluator( selector ) )
+        );
+    }
+
+
     /**
      * Selects a column binding for a join. If the column index is less then left table fields count then use left table column binding otherwise use right table column biding.
      *
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFile.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFile.java
index fcdac03b28..02e1cb4a40 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFile.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFile.java
@@ -19,8 +19,10 @@
 import java.net.MalformedURLException;
 import java.net.URL;
 import java.util.Collections;
+import java.util.List;
 import java.util.LinkedHashMap;
 import java.util.Map;
+import org.polypheny.db.adapter.parquet.shared.statistics.ParquetColumnStatisticsReader;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.util.Source;
 import org.polypheny.db.util.Sources;
@@ -31,19 +33,31 @@
  */
 public record ParquetSourceFile(
         String fileUrl,
-        Map<String, String> partitionValues ) {
+        Map<String, String> partitionValues,
+        Map<List<String>, ParquetColumnStatistics> columnStatistics ) {
 
     public ParquetSourceFile {
         partitionValues = partitionValues == null ? Map.of() : Collections.unmodifiableMap( new LinkedHashMap<>( partitionValues ) );
+        columnStatistics = immutableColumnStatistics( columnStatistics );
     }
 
 
     public static ParquetSourceFile of( String fileUrl ) {
-        return new ParquetSourceFile( fileUrl, Map.of() );
+        return of( fileUrl, Map.of() );
+    }
+
+
+    public static ParquetSourceFile of( String fileUrl, Map<String, String> partitionValues ) {
+        return new ParquetSourceFile( fileUrl, partitionValues, ParquetColumnStatisticsReader.readAll( source( fileUrl ) ) );
     }
 
 
     public Source asSource() {
+        return source( fileUrl );
+    }
+
+
+    private static Source source( String fileUrl ) {
         try {
             return Sources.of( new URL( fileUrl ) );
         } catch ( MalformedURLException e ) {
@@ -51,4 +65,14 @@ public Source asSource() {
         }
     }
 
+
+    private static Map<List<String>, ParquetColumnStatistics> immutableColumnStatistics( Map<List<String>, ParquetColumnStatistics> columnStatistics ) {
+        if ( columnStatistics == null || columnStatistics.isEmpty() ) {
+            return Map.of();
+        }
+        Map<List<String>, ParquetColumnStatistics> copy = new LinkedHashMap<>();
+        columnStatistics.forEach( ( path, statistics ) -> copy.put( List.copyOf( path ), statistics ) );
+        return Collections.unmodifiableMap( copy );
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java
index 2df244d9c7..2726307a6d 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java
@@ -16,7 +16,6 @@
 
 package org.polypheny.db.adapter.parquet.relational.schema;
 
-import java.util.List;
 import java.util.function.Function;
 import org.polypheny.db.adapter.parquet.shared.filter.FilterEvaluator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
@@ -34,16 +33,6 @@ class ParquetSourceFileFilterEvaluator extends FilterEvaluator<ParquetSourceFile
     }
 
 
-    List<ParquetSourceFile> prune( List<ParquetSourceFile> sourceFiles, List<ParquetAdapterFilter> filters ) {
-        if ( filters.isEmpty() ) {
-            return sourceFiles;
-        }
-        return sourceFiles.stream()
-                .filter( sourceFile -> filters.stream().allMatch( filter -> matches( sourceFile, filter ) ) )
-                .toList();
-    }
-
-
     @Override
     protected Boolean evaluateLeaf( ParquetSourceFile sourceFile, ParquetAdapterFilter filter ) {
         ParquetColumnBinding columnBinding = selector.apply( filter );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileStatisticsFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileStatisticsFilterEvaluator.java
new file mode 100644
index 0000000000..2555a824d8
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileStatisticsFilterEvaluator.java
@@ -0,0 +1,97 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+import java.util.function.Function;
+import org.polypheny.db.adapter.parquet.shared.filter.FilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.algebra.constant.Kind;
+
+
+/**
+ * Uses Parquet footer min/max/null-count statistics to reject files that cannot
+ * contain rows matching a filter.
+ */
+class ParquetSourceFileStatisticsFilterEvaluator extends FilterEvaluator<ParquetSourceFile> {
+
+    private final Function<ParquetAdapterFilter, ParquetColumnBinding> selector;
+    private final ParquetTypeConverter typeConverter;
+
+
+    ParquetSourceFileStatisticsFilterEvaluator( Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
+        this.selector = selector;
+        this.typeConverter = new ParquetTypeConverter();
+    }
+
+
+    @Override
+    protected Boolean evaluateLeaf( ParquetSourceFile sourceFile, ParquetAdapterFilter filter ) {
+        ParquetColumnBinding columnBinding = selector.apply( filter );
+        if ( columnBinding == null || columnBinding.role() != ParquetColumnRole.DATA || columnBinding.sourcePathElements().isEmpty() ) {
+            return null;
+        }
+
+        ParquetColumnStatistics statistics = sourceFile.columnStatistics().get( columnBinding.sourcePathElements() );
+        if ( statistics == null ) {
+            return null;
+        }
+
+        if ( filter.operator() == Kind.IS_NULL ) {
+            return statistics.hasNoNulls() ? Boolean.FALSE : null;
+        }
+        if ( filter.operator() == Kind.IS_NOT_NULL ) {
+            return statistics.hasOnlyNulls() ? Boolean.FALSE : null;
+        }
+        if ( filter.polyValue() == null || filter.polyValue().isNull() ) {
+            return null;
+        }
+        if ( statistics.hasOnlyNulls() ) {
+            return Boolean.FALSE;
+        }
+        if ( !statistics.hasRange() ) {
+            return null;
+        }
+        Comparable<?> value = typeConverter.fromPolyValueToComparable( filter.polyValue(), statistics.type() );
+        Comparable<?> min = typeConverter.fromStringToComparable( statistics.type(), statistics.min() );
+        Comparable<?> max = typeConverter.fromStringToComparable( statistics.type(), statistics.max() );
+        if ( value == null || min == null || max == null ) {
+            return null;
+        }
+
+        return switch ( filter.operator() ) {
+            case EQUALS -> outsideRange( value, min, max ) ? Boolean.FALSE : null;
+            case NOT_EQUALS -> singleValueRangeEquals( value, min, max ) ? Boolean.FALSE : null;
+            case GREATER_THAN -> typeConverter.compare( max, value ) <= 0 ? Boolean.FALSE : null;
+            case GREATER_THAN_OR_EQUAL -> typeConverter.compare( max, value ) < 0 ? Boolean.FALSE : null;
+            case LESS_THAN -> typeConverter.compare( min, value ) >= 0 ? Boolean.FALSE : null;
+            case LESS_THAN_OR_EQUAL -> typeConverter.compare( min, value ) > 0 ? Boolean.FALSE : null;
+            default -> null;
+        };
+    }
+
+
+    private boolean outsideRange( Comparable<?> value, Comparable<?> min, Comparable<?> max ) {
+        return typeConverter.compare( value, min ) < 0 || typeConverter.compare( value, max ) > 0;
+    }
+
+
+    private boolean singleValueRangeEquals( Comparable<?> value, Comparable<?> min, Comparable<?> max ) {
+        return typeConverter.compare( min, max ) == 0 && typeConverter.compare( min, value ) == 0;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
index ab379830a9..b68a77a7a6 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
@@ -479,7 +479,7 @@ private static ParquetSourceFile toSourceFile( List<DiscoveredSourceFile> source
 
 
     private static ParquetSourceFile toSourceFile( DiscoveredSourceFile sourceFile ) {
-        return new ParquetSourceFile( sourceFile.url().toString(), sourceFile.partitionValues() );
+        return ParquetSourceFile.of( sourceFile.url().toString(), sourceFile.partitionValues() );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
index 97ca116a81..7686f6fcb7 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
@@ -28,6 +28,7 @@
 import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
 import org.apache.parquet.schema.Type;
 import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.PolyTypeFamily;
 import org.polypheny.db.type.entity.PolyBinary;
 import org.polypheny.db.type.entity.PolyBoolean;
 import org.polypheny.db.type.entity.PolyNull;
@@ -168,6 +169,7 @@ public Object fromPolyValueToParquetObj( PrimitiveType primitive, PolyValue lite
 
     /**
      * Convert PolyValue to PolyString
+     *
      * @param value - to convert
      * @return PolyString
      */
@@ -180,6 +182,144 @@ public PolyString valueToPolyString( PolyValue value ) {
     }
 
 
+    /**
+     * Converts a raw Parquet footer statistic value into the compact string representation
+     * stored in {@code ParquetColumnStatistics}.
+     */
+    public String fromParquetValueToString( PrimitiveType primitive, Object value ) {
+        if ( value == null ) {
+            return null;
+        }
+
+        PolyType polyType = fromParquetTypeToPolyType( primitive );
+        if ( polyType == PolyType.DATE && value instanceof Number number ) {
+            return String.valueOf( number.longValue() );
+        }
+        if ( polyType == PolyType.TIME && value instanceof Number number ) {
+            return String.valueOf( normalizeParquetTimeToMillis( primitive, number.longValue() ) );
+        }
+        if ( polyType == PolyType.TIMESTAMP && value instanceof Number number ) {
+            return String.valueOf( normalizeParquetTimestampToMillis( primitive, number.longValue() ) );
+        }
+        if ( value instanceof Binary binary ) {
+            return binary.toStringUsingUTF8();
+        }
+        return String.valueOf( value );
+    }
+
+
+    /**
+     * Converts a string value into a PolyValue.
+     */
+    public PolyValue fromStringToPolyValue( PolyType polyType, String value ) {
+        if ( value == null ) {
+            return null;
+        }
+        try {
+            return switch ( polyType ) {
+                case BOOLEAN -> PolyBoolean.of( Boolean.parseBoolean( value ) );
+                case TINYINT, SMALLINT, INTEGER -> PolyInteger.of( Integer.parseInt( value ) );
+                case BIGINT -> PolyLong.of( Long.parseLong( value ) );
+                case FLOAT, REAL -> PolyFloat.of( Float.parseFloat( value ) );
+                case DOUBLE -> PolyDouble.of( Double.parseDouble( value ) );
+                case DATE -> PolyDate.of( Long.parseLong( value ) * DateTimeUtils.MILLIS_PER_DAY );
+                case TIME -> PolyTime.of( (int) (Long.parseLong( value ) % DateTimeUtils.MILLIS_PER_DAY) );
+                case TIMESTAMP -> PolyTimestamp.of( Long.parseLong( value ) );
+                case CHAR, VARCHAR, TEXT -> PolyString.of( value );
+                default -> null;
+            };
+        } catch ( RuntimeException e ) {
+            return null;
+        }
+    }
+
+
+    /**
+     * Converts a string value into a PolyValue if it is compatible with
+     * the requested logical column type.
+     */
+    public PolyValue fromStringToCompatiblePolyValue( PolyType columnType, PolyType polyType, String value ) {
+        PolyValue polyValue = fromStringToPolyValue( polyType, value );
+        return isCompatible( columnType, polyValue ) ? polyValue : null;
+    }
+
+
+    /**
+     * Converts a {@code String} value to a real comparable type.
+     */
+    public Comparable<?> fromStringToComparable( PolyType type, String value ) {
+        if ( value == null ) {
+            return null;
+        }
+        try {
+            PolyValue polyValue = fromStringToPolyValue( type, value );
+            return fromPolyValueToComparable( polyValue, type );
+        } catch ( RuntimeException e ) {
+            return null;
+        }
+    }
+
+
+    /**
+     * Converts a {@link PolyValue} to {@link Comparable}.
+     */
+    @SuppressWarnings("DataFlowIssue")
+    public Comparable<?> fromPolyValueToComparable( PolyValue value, PolyType type ) {
+        if ( value == null || value.isNull() ) {
+            return null;
+        }
+        try {
+            return switch ( type ) {
+                case BOOLEAN -> value.isBoolean() ? value.asBoolean().value : Boolean.parseBoolean( value.toString() );
+                case TINYINT, SMALLINT, INTEGER, BIGINT -> value.asNumber().longValue();
+                case FLOAT, REAL -> value.asNumber().floatValue();
+                case DOUBLE -> value.asNumber().doubleValue();
+                case DATE -> value.isDate() ? value.asDate().getDaysSinceEpoch() : value.asNumber().longValue();
+                case TIME -> value.isTime() ? value.asTime().ofDay.longValue() : value.asNumber().longValue();
+                case TIMESTAMP -> value.isTimestamp() ? value.asTimestamp().millisSinceEpoch : value.asNumber().longValue();
+                case CHAR, VARCHAR, TEXT -> value.isString() ? value.asString().value : value.toString();
+                default -> null;
+            };
+        } catch ( RuntimeException e ) {
+            return null;
+        }
+    }
+
+
+    public int compareStringValues( PolyType type, String left, String right ) {
+        Comparable<?> leftComparable = fromStringToComparable( type, left );
+        Comparable<?> rightComparable = fromStringToComparable( type, right );
+        if ( leftComparable == null || rightComparable == null ) {
+            return left.compareTo( right );
+        }
+        return compare( leftComparable, rightComparable );
+    }
+
+
+    @SuppressWarnings({ "rawtypes", "unchecked" })
+    public int compare( Comparable left, Comparable right ) {
+        try {
+            return left.compareTo( right );
+        } catch ( RuntimeException e ) {
+            return left.toString().compareTo( right.toString() );
+        }
+    }
+
+
+    private boolean isCompatible( PolyType columnType, PolyValue polyValue ) {
+        if ( polyValue == null ) {
+            return false;
+        }
+        if ( columnType.getFamily() == PolyTypeFamily.NUMERIC && polyValue.isNumber() ) {
+            return true;
+        }
+        if ( PolyType.DATETIME_TYPES.contains( columnType ) && polyValue.isTemporal() ) {
+            return true;
+        }
+        return columnType.getFamily() == PolyTypeFamily.CHARACTER && polyValue.isString();
+    }
+
+
     private Object fromStringToDate( PrimitiveType primitive, String literal ) {
         LogicalTypeAnnotation logical = primitive.getLogicalTypeAnnotation();
 
@@ -307,6 +447,38 @@ private long normalizeTimestampLiteralToMillis( String literal ) {
     }
 
 
+    /**
+     * Normalizes Parquet TIME footer statistics to milliseconds.
+     */
+    private long normalizeParquetTimeToMillis( PrimitiveType primitive, long value ) {
+        LogicalTypeAnnotation logical = primitive.getLogicalTypeAnnotation();
+        if ( logical instanceof TimeLogicalTypeAnnotation timeLogical ) {
+            return switch ( timeLogical.getUnit() ) {
+                case MILLIS -> value;
+                case MICROS -> value / 1_000L;
+                case NANOS -> value / 1_000_000L;
+            };
+        }
+        return value;
+    }
+
+
+    /**
+     * Normalizes Parquet TIMESTAMP footer statistics to milliseconds.
+     */
+    private long normalizeParquetTimestampToMillis( PrimitiveType primitive, long value ) {
+        LogicalTypeAnnotation logical = primitive.getLogicalTypeAnnotation();
+        if ( logical instanceof TimestampLogicalTypeAnnotation timestampLogical ) {
+            return switch ( timestampLogical.getUnit() ) {
+                case MILLIS -> value;
+                case MICROS -> value / 1_000L;
+                case NANOS -> value / 1_000_000L;
+            };
+        }
+        return value;
+    }
+
+
     /**
      * Normalizes time units to milliseconds.
      */
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetColumnStatisticsReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetColumnStatisticsReader.java
new file mode 100644
index 0000000000..6ead9b97c4
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetColumnStatisticsReader.java
@@ -0,0 +1,211 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.statistics;
+
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Optional;
+import org.apache.parquet.column.statistics.Statistics;
+import org.apache.parquet.hadoop.metadata.BlockMetaData;
+import org.apache.parquet.hadoop.metadata.ColumnChunkMetaData;
+import org.apache.parquet.schema.PrimitiveType;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnStatistics;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.util.Source;
+
+
+/**
+ * Reads compact column statistics from Parquet footer metadata.
+ */
+public final class ParquetColumnStatisticsReader {
+
+    private static final ParquetTypeConverter TYPE_CONVERTER = new ParquetTypeConverter();
+
+
+    private ParquetColumnStatisticsReader() {
+    }
+
+
+    public static Map<List<String>, ParquetColumnStatistics> readAll( Source source ) {
+        return readAll( new ParquetSchemaReader( source ) );
+    }
+
+
+    public static Map<List<String>, ParquetColumnStatistics> readAll( ParquetSchemaReader schemaReader ) {
+        Map<List<String>, StatisticsBuilder> statistics = new LinkedHashMap<>();
+
+        for ( var footer : schemaReader.getFooters() ) {
+            for ( BlockMetaData block : footer.getBlocks() ) {
+                for ( ColumnChunkMetaData column : block.getColumns() ) {
+                    List<String> path = List.of( column.getPath().toArray() );
+                    PrimitiveType type = primitiveType( schemaReader, path );
+                    if ( type == null ) {
+                        continue;
+                    }
+                    statistics.computeIfAbsent( path, ignored -> new StatisticsBuilder( type ) )
+                            .add( block.getRowCount(), column );
+                }
+            }
+        }
+
+        Map<List<String>, ParquetColumnStatistics> result = new LinkedHashMap<>();
+        statistics.forEach( ( path, builder ) -> result.put( path, builder.build() ) );
+        return result;
+    }
+
+
+    public static Optional<ParquetColumnStatistics> read( ParquetSchemaReader schemaReader, List<String> path ) {
+        return read( schemaReader, path, true );
+    }
+
+
+    private static Optional<ParquetColumnStatistics> read( ParquetSchemaReader schemaReader, List<String> path, boolean missingColumnsAreNull ) {
+        PrimitiveType type = primitiveType( schemaReader, path );
+        if ( type == null ) {
+            return Optional.empty();
+        }
+
+        StatisticsBuilder builder = new StatisticsBuilder( type );
+        for ( var footer : schemaReader.getFooters() ) {
+            for ( BlockMetaData block : footer.getBlocks() ) {
+                ColumnChunkMetaData column = findColumnChunk( block, path );
+                if ( column == null ) {
+                    if ( missingColumnsAreNull ) {
+                        builder.addMissing( block.getRowCount() );
+                    }
+                    continue;
+                }
+                builder.add( block.getRowCount(), column );
+            }
+        }
+        return Optional.of( builder.build() );
+    }
+
+
+    private static PrimitiveType primitiveType( ParquetSchemaReader schemaReader, List<String> path ) {
+        try {
+            return schemaReader.getSchema().getType( path.toArray( String[]::new ) ).asPrimitiveType();
+        } catch ( RuntimeException e ) {
+            return null;
+        }
+    }
+
+
+    private static ColumnChunkMetaData findColumnChunk( BlockMetaData block, List<String> path ) {
+        for ( ColumnChunkMetaData column : block.getColumns() ) {
+            if ( List.of( column.getPath().toArray() ).equals( path ) ) {
+                return column;
+            }
+        }
+        return null;
+    }
+
+
+    private static final class StatisticsBuilder {
+
+        private final PrimitiveType type;
+        private final PolyType polyType;
+        private long rowCount;
+        private long valueCount;
+        private Long nullCount = 0L;
+        private Object min;
+        private Object max;
+        private boolean minMaxReliable = true;
+
+
+        private StatisticsBuilder( PrimitiveType type ) {
+            this.type = type;
+            this.polyType = TYPE_CONVERTER.fromParquetTypeToPolyType( type );
+        }
+
+
+        private void add( long blockRowCount, ColumnChunkMetaData column ) {
+            rowCount += blockRowCount;
+            valueCount += column.getValueCount();
+
+            Statistics<?> statistics = column.getStatistics();
+            if ( statistics == null ) {
+                nullCount = null;
+                minMaxReliable = false;
+                return;
+            }
+
+            if ( nullCount != null ) {
+                if ( statistics.isNumNullsSet() ) {
+                    nullCount += statistics.getNumNulls();
+                } else {
+                    nullCount = null;
+                }
+            }
+
+            if ( statistics.hasNonNullValue() ) {
+                min = lower( min, statistics.genericGetMin() );
+                max = higher( max, statistics.genericGetMax() );
+                return;
+            }
+
+            if ( !statistics.isNumNullsSet() || statistics.getNumNulls() != column.getValueCount() ) {
+                minMaxReliable = false;
+            }
+        }
+
+
+        private void addMissing( long blockRowCount ) {
+            rowCount += blockRowCount;
+            valueCount += blockRowCount;
+            if ( nullCount != null ) {
+                nullCount += blockRowCount;
+            }
+        }
+
+
+        private ParquetColumnStatistics build() {
+            String serializedMin = statisticValue( min );
+            String serializedMax = statisticValue( max );
+            boolean reliableRange = minMaxReliable && (min == null || serializedMin != null) && (max == null || serializedMax != null);
+            return new ParquetColumnStatistics( polyType, rowCount, valueCount, nullCount, serializedMin, serializedMax, reliableRange );
+        }
+
+
+        @SuppressWarnings({ "rawtypes", "unchecked" })
+        private Object lower( Object current, Object candidate ) {
+            if ( current == null ) {
+                return candidate;
+            }
+            return ((Comparable) candidate).compareTo( current ) < 0 ? candidate : current;
+        }
+
+
+        @SuppressWarnings({ "rawtypes", "unchecked" })
+        private Object higher( Object current, Object candidate ) {
+            if ( current == null ) {
+                return candidate;
+            }
+            return ((Comparable) candidate).compareTo( current ) > 0 ? candidate : current;
+        }
+
+
+        private String statisticValue( Object value ) {
+            return TYPE_CONVERTER.fromParquetValueToString( type, value );
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetTableStatisticsReader.java
similarity index 53%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetTableStatisticsReader.java
index 42332e18ae..dfe9d904f1 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetTableStatisticsReader.java
@@ -20,13 +20,11 @@
 import java.util.LinkedHashSet;
 import java.util.List;
 import java.util.Optional;
+import java.util.OptionalLong;
 import java.util.Set;
-import org.apache.parquet.column.statistics.Statistics;
-import org.apache.parquet.hadoop.metadata.BlockMetaData;
-import org.apache.parquet.hadoop.metadata.ColumnChunkMetaData;
-import org.apache.parquet.schema.PrimitiveType;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnStatistics;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
@@ -35,21 +33,20 @@
 import org.polypheny.db.adapter.statistics.ProvidedEntityStatistics;
 import org.polypheny.db.catalog.entity.logical.LogicalColumn;
 import org.polypheny.db.type.PolyType;
-import org.polypheny.db.type.PolyTypeFamily;
 import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
 
 /**
  * Provides parquet statistics
  */
-public class ParquetStatisticsReader {
+public class ParquetTableStatisticsReader {
 
     private final ParquetSchemaReader schemaReader;
     private final ParquetTableBinding binding;
     private final ParquetTypeConverter typeConverter;
 
 
-    public ParquetStatisticsReader( ParquetSchemaReader schemaReader, ParquetTableBinding binding ) {
+    public ParquetTableStatisticsReader( ParquetSchemaReader schemaReader, ParquetTableBinding binding ) {
         this.schemaReader = schemaReader;
         this.binding = binding;
         this.typeConverter = new ParquetTypeConverter();
@@ -62,7 +59,7 @@ public ParquetStatisticsReader( ParquetSchemaReader schemaReader, ParquetTableBi
      * @return provided statistics
      */
     public Optional<ProvidedEntityStatistics> getEntityStatistics( boolean nestedTable ) {
-        return Optional.of( new ProvidedEntityStatistics( nestedTable ? estimateNestedRowCount() : schemaReader.getEstimatedRowCount() ) );
+        return Optional.of( new ProvidedEntityStatistics( nestedTable ? estimateNestedRowCount() : estimateSourceRowCount() ) );
     }
 
 
@@ -84,17 +81,15 @@ public Optional<ProvidedColumnStatistics> getColumnStatistics( LogicalColumn col
             return Optional.of( new ProvidedColumnStatistics( estimateEntityRowCount(), null, null, List.of(), true ) );
         }
 
-        PrimitiveType primitiveType;
-        try {
-            primitiveType = schemaReader.getSchema().getType( columnBinding.sourcePathElements().toArray( new String[0] ) ).asPrimitiveType();
-        } catch ( RuntimeException e ) {
+        Optional<ParquetColumnStatistics> metadataStatistics = aggregateColumnStatistics( columnBinding.sourcePathElements() );
+        if ( metadataStatistics.isEmpty() ) {
             return Optional.of( new ProvidedColumnStatistics( estimateEntityRowCount(), null, null, List.of(), true ) );
         }
 
-        ColumnMetadataStatistics metadataStatistics = readColumnMetadataStatistics( columnBinding.sourcePathElements() );
-        PolyValue min = toStatisticValue( column.type, primitiveType, metadataStatistics.min() );
-        PolyValue max = toStatisticValue( column.type, primitiveType, metadataStatistics.max() );
-        return Optional.of( new ProvidedColumnStatistics( metadataStatistics.count(), min, max, List.of(), true ) );
+        ParquetColumnStatistics statistics = metadataStatistics.get();
+        PolyValue min = statistics.hasRange() ? typeConverter.fromStringToCompatiblePolyValue( column.type, statistics.type(), statistics.min() ) : null;
+        PolyValue max = statistics.hasRange() ? typeConverter.fromStringToCompatiblePolyValue( column.type, statistics.type(), statistics.max() ) : null;
+        return Optional.of( new ProvidedColumnStatistics( nonNullCount( statistics ), min, max, List.of(), true ) );
     }
 
 
@@ -122,7 +117,20 @@ ProvidedColumnStatistics partitionColumnStatistics( ParquetColumnBinding columnB
 
 
     private long estimateEntityRowCount() {
-        return binding.parentTableName() == null ? schemaReader.getEstimatedRowCount() : estimateNestedRowCount();
+        return binding.parentTableName() == null ? estimateSourceRowCount() : estimateNestedRowCount();
+    }
+
+
+    private long estimateSourceRowCount() {
+        long rowCount = 0;
+        for ( ParquetSourceFile sourceFile : binding.sourceFiles() ) {
+            OptionalLong fileRowCount = sourceRowCount( sourceFile );
+            if ( fileRowCount.isEmpty() ) {
+                return schemaReader.getEstimatedRowCount();
+            }
+            rowCount += fileRowCount.getAsLong();
+        }
+        return rowCount;
     }
 
 
@@ -142,125 +150,99 @@ private long estimateNestedRowCount() {
 
     private long estimateValueCount( List<String> sourcePathElements ) {
         long valueCount = 0;
-        for ( var footer : schemaReader.getFooters() ) {
-            for ( BlockMetaData block : footer.getBlocks() ) {
-                ColumnChunkMetaData column = findColumnChunk( block, sourcePathElements );
-                if ( column == null ) {
-                    continue;
-                }
-                valueCount += column.getValueCount();
+        for ( ParquetSourceFile sourceFile : binding.sourceFiles() ) {
+            ParquetColumnStatistics statistics = sourceFile.columnStatistics().get( sourcePathElements );
+            if ( statistics != null ) {
+                valueCount += statistics.valueCount();
             }
         }
         return valueCount;
     }
 
 
-    /**
-     * Count rows including min/max limits
-     * @param sourcePathElements - path
-     * @return column metadata statistics
-     */
-    private ColumnMetadataStatistics readColumnMetadataStatistics( List<String> sourcePathElements ) {
+    private Optional<ParquetColumnStatistics> aggregateColumnStatistics( List<String> sourcePathElements ) {
+        PolyType type = null;
         long rowCount = 0;
-        long nullCount = 0;
-        boolean hasReliableNullCount = true;
-        Object min = null;
-        Object max = null;
-        boolean hasMinMax = true;
-
-        for ( var footer : schemaReader.getFooters() ) {
-            for ( BlockMetaData block : footer.getBlocks() ) {
-                rowCount += block.getRowCount();
-                ColumnChunkMetaData column = findColumnChunk( block, sourcePathElements );
-                if ( column == null ) {
-                    nullCount += block.getRowCount();
-                    continue;
-                }
+        long valueCount = 0;
+        Long nullCount = 0L;
+        String min = null;
+        String max = null;
+        boolean minMaxReliable = true;
+        boolean hasColumnStatistics = false;
 
-                Statistics<?> statistics = column.getStatistics();
-                if ( statistics == null ) {
-                    hasReliableNullCount = false;
-                    hasMinMax = false;
+        for ( ParquetSourceFile sourceFile : binding.sourceFiles() ) {
+            ParquetColumnStatistics statistics = sourceFile.columnStatistics().get( sourcePathElements );
+            if ( statistics == null ) {
+                OptionalLong fileRowCount = sourceRowCount( sourceFile );
+                if ( fileRowCount.isEmpty() ) {
+                    nullCount = null;
+                    minMaxReliable = false;
                     continue;
                 }
-
-                if ( statistics.isNumNullsSet() ) {
-                    nullCount += statistics.getNumNulls();
-                } else {
-                    hasReliableNullCount = false;
+                long missingRows = fileRowCount.getAsLong();
+                rowCount += missingRows;
+                valueCount += missingRows;
+                if ( nullCount != null ) {
+                    nullCount += missingRows;
                 }
+                continue;
+            }
 
-                if ( statistics.hasNonNullValue() ) {
-                    Object currentMin = statistics.genericGetMin();
-                    Object currentMax = statistics.genericGetMax();
-                    min = lower( min, currentMin );
-                    max = higher( max, currentMax );
-                } else if ( !statistics.isNumNullsSet() || statistics.getNumNulls() != block.getRowCount() ) {
-                    hasMinMax = false;
-                }
+            hasColumnStatistics = true;
+            if ( type == null ) {
+                type = statistics.type();
+            } else if ( type != statistics.type() ) {
+                minMaxReliable = false;
+            }
+
+            rowCount += statistics.rowCount();
+            valueCount += statistics.valueCount();
+            if ( nullCount != null ) {
+                nullCount = statistics.nullCount() == null ? null : nullCount + statistics.nullCount();
+            }
+
+            if ( !statistics.minMaxReliable() ) {
+                minMaxReliable = false;
+            } else if ( statistics.hasRange() && type == statistics.type() ) {
+                min = lowerStatisticValue( min, statistics.min(), type );
+                max = higherStatisticValue( max, statistics.max(), type );
+            } else if ( !statistics.hasOnlyNulls() ) {
+                minMaxReliable = false;
             }
         }
 
-        long count = hasReliableNullCount ? rowCount - nullCount : rowCount;
-        return new ColumnMetadataStatistics( count, hasMinMax ? min : null, hasMinMax ? max : null );
+        if ( !hasColumnStatistics || type == null ) {
+            return Optional.empty();
+        }
+        return Optional.of( new ParquetColumnStatistics( type, rowCount, valueCount, nullCount, min, max, minMaxReliable ) );
     }
 
 
-    private ColumnChunkMetaData findColumnChunk( BlockMetaData block, List<String> sourcePathElements ) {
-        for ( ColumnChunkMetaData column : block.getColumns() ) {
-            if ( List.of( column.getPath().toArray() ).equals( sourcePathElements ) ) {
-                return column;
-            }
-        }
-        return null;
+    private OptionalLong sourceRowCount( ParquetSourceFile sourceFile ) {
+        return sourceFile.columnStatistics().values().stream()
+                .mapToLong( ParquetColumnStatistics::rowCount )
+                .findFirst();
     }
 
 
-    @SuppressWarnings({ "rawtypes", "unchecked" })
-    private Object lower( Object current, Object candidate ) {
-        if ( current == null ) {
-            return candidate;
-        }
-        return ((Comparable) candidate).compareTo( current ) < 0 ? candidate : current;
+    private long nonNullCount( ParquetColumnStatistics statistics ) {
+        return statistics.nullCount() == null ? statistics.rowCount() : statistics.rowCount() - statistics.nullCount();
     }
 
 
-    @SuppressWarnings({ "rawtypes", "unchecked" })
-    private Object higher( Object current, Object candidate ) {
+    private String lowerStatisticValue( String current, String candidate, PolyType type ) {
         if ( current == null ) {
             return candidate;
         }
-        return ((Comparable) candidate).compareTo( current ) > 0 ? candidate : current;
+        return typeConverter.compareStringValues( type, candidate, current ) < 0 ? candidate : current;
     }
 
 
-    private PolyValue toStatisticValue( PolyType columnType, PrimitiveType primitiveType, Object value ) {
-        if ( value == null ) {
-            return null;
-        }
-        try {
-            PolyValue polyValue = typeConverter.fromObjToPolyValue( primitiveType, value );
-            if ( columnType.getFamily() == PolyTypeFamily.NUMERIC && polyValue.isNumber() ) {
-                return polyValue;
-            }
-            if ( PolyType.DATETIME_TYPES.contains( columnType ) && polyValue.isTemporal() ) {
-                return polyValue;
-            }
-            if ( columnType.getFamily() == PolyTypeFamily.CHARACTER && polyValue.isString() ) {
-                return polyValue;
-            }
-            return null;
-        } catch ( RuntimeException e ) {
-            return null;
+    private String higherStatisticValue( String current, String candidate, PolyType type ) {
+        if ( current == null ) {
+            return candidate;
         }
-    }
-
-
-    private record ColumnMetadataStatistics(
-            Long count,
-            Object min,
-            Object max ) {
-
+        return typeConverter.compareStringValues( type, candidate, current ) > 0 ? candidate : current;
     }
 
 }

From 9ea733d2705723c94f61d10a4de02b91300d8e52 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Mon, 18 May 2026 12:13:18 +0200
Subject: [PATCH 32/46] add documentation and comments to joins and file
 pruning developments

---
 plugins/parquet-adapter/docs/file_pruning.md  |  73 ++
 .../docs/filter_improvements.md               | 219 ----
 .../parquet-adapter/docs/filter_overview.md   |   2 -
 plugins/parquet-adapter/docs/filtering.md     | 556 -----------
 plugins/parquet-adapter/docs/flavors.md       | 160 ---
 .../docs/images/planner/rule1.png             | Bin 0 -> 27965 bytes
 .../docs/images/planner/rule2.png             | Bin 0 -> 28257 bytes
 .../docs/images/planner/rule3.png             | Bin 0 -> 33764 bytes
 .../docs/images/planner/rule4.png             | Bin 0 -> 40492 bytes
 .../images/planner/unsupported_2_joins.png    | Bin 0 -> 210635 bytes
 ...unsupported_2_joins_one_other_physical.png | Bin 0 -> 218516 bytes
 .../docs/images/planner/unsupported_limit.png | Bin 0 -> 173407 bytes
 .../docs/images/planner/unsupported_lower.png | Bin 0 -> 102087 bytes
 plugins/parquet-adapter/docs/joins.md         | 421 ++++----
 .../docs/test_plan/join_test_list.md          | 939 ++++++++++++------
 .../test_plan/pon_planning_query_matrix.md    | 852 ----------------
 plugins/parquet-adapter/docs/todo_joins.md    |  48 -
 .../db/adapter/parquet/ParquetPlugin.java     |   3 +
 .../relational/ParquetRelationalSource.java   |   1 -
 .../execution/ParquetMultiFileEnumerator.java |   1 +
 ...etSourceFilePartitionFilterEvaluator.java} |  10 +-
 ...etSourceFileStatisticsFilterEvaluator.java |  11 +-
 .../planning/EnumerableParquet.java           |   2 +-
 .../planning/ParquetAlgOptRule.java           |   4 +
 .../planning/ParquetConvention.java           |   4 +
 .../relational/planning/ParquetRelJoin.java   |  19 +-
 .../relational/planning/ParquetRelScan.java   |  74 +-
 .../relational/planning/ParquetRules.java     |   5 +-
 .../relational/planning/PatternMatchers.java  |  11 +-
 .../schema/ParquetColumnStatistics.java       |   1 +
 .../relational/schema/ParquetRelTable.java    |  13 +-
 .../relational/schema/ParquetSourceFile.java  |   1 +
 .../ParquetColumnStatisticsReader.java        |   1 +
 .../ParquetTableStatisticsReader.java         |   5 +
 34 files changed, 1015 insertions(+), 2421 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/file_pruning.md
 delete mode 100644 plugins/parquet-adapter/docs/filter_improvements.md
 delete mode 100644 plugins/parquet-adapter/docs/filtering.md
 delete mode 100644 plugins/parquet-adapter/docs/flavors.md
 create mode 100644 plugins/parquet-adapter/docs/images/planner/rule1.png
 create mode 100644 plugins/parquet-adapter/docs/images/planner/rule2.png
 create mode 100644 plugins/parquet-adapter/docs/images/planner/rule3.png
 create mode 100644 plugins/parquet-adapter/docs/images/planner/rule4.png
 create mode 100644 plugins/parquet-adapter/docs/images/planner/unsupported_2_joins.png
 create mode 100644 plugins/parquet-adapter/docs/images/planner/unsupported_2_joins_one_other_physical.png
 create mode 100644 plugins/parquet-adapter/docs/images/planner/unsupported_limit.png
 create mode 100644 plugins/parquet-adapter/docs/images/planner/unsupported_lower.png
 delete mode 100644 plugins/parquet-adapter/docs/test_plan/pon_planning_query_matrix.md
 delete mode 100644 plugins/parquet-adapter/docs/todo_joins.md
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/{schema/ParquetSourceFileFilterEvaluator.java => execution/ParquetSourceFilePartitionFilterEvaluator.java} (76%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/{schema => execution}/ParquetSourceFileStatisticsFilterEvaluator.java (86%)

diff --git a/plugins/parquet-adapter/docs/file_pruning.md b/plugins/parquet-adapter/docs/file_pruning.md
new file mode 100644
index 0000000000..01dcfc1dcb
--- /dev/null
+++ b/plugins/parquet-adapter/docs/file_pruning.md
@@ -0,0 +1,73 @@
+# File Pruning
+
+- Not matching file sources is filtered according to parquet statistics.
+- Statistics loaded when adapter created.
+- Statistics stored in Adapter Settings as part of Bindings and reused for Polypheny statistics (`ParquetTableStatisticsReader`)
+
+## Changes
+
+### ParquetColumnStatistics
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetColumnStatistics.java`
+
+- per-file statistics
+
+### ParquetSourceFile - record
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetSourceFile.java`
+
+- Store column statistics per column: `Map<List<String>, ParquetColumnStatistics> columnStatistics`. Column path is a column identifier because field can be nested
+- ParquetSourceFile stored in ParquetTableBindings `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetTableBinding.java`
+
+### ParquetBindingSerializer
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetBindingSerializer.java`
+
+New functions:
+- serializeColumnStatistics()
+- deserializeColumnStatistics()
+
+### ParquetColumnStatisticsReader
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\statistics\ParquetColumnStatisticsReader.java`
+- Reads column statistics from Parquet footer metadata
+- returns ParquetColumnStatistics
+
+### ParquetTableStatisticsReader
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\statistics\ParquetTableStatisticsReader.java`
+- load Polypheny statistics from bindings
+- returns ProvidedColumnStatistics
+
+### ParquetSourceFileStatisticsFilterEvaluator
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetSourceFileStatisticsFilterEvaluator.java`
+- evaluateLeaf() function get statistics by column path and evaluates if file should be skipped
+- called from abstract class FilterEvaluator.evaluate() which is called from match()
+
+### ParquetSourceFilePartitionFilterEvaluator
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetSourceFilePartitionFilterEvaluator.java`
+- evaluateLeaf() function evaluate by folder name if file should be skipped
+- called from abstract class FilterEvaluator.evaluate() which is called from match()
+
+### ParquetMultiFileEnumerator
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetMultiFileEnumerator.java`
+
+- creates relevant enumerator using factory
+
+### ParquetRelTable
+`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetRelTable.java'`
+
+- creates ParquetMultiFileEnumerator
+- ParquetMultiFileEnumerator receives chain of evaluators
+- createParquetSourceFileEvaluatorsChain() - chain contains two evaluators: partition `ParquetSourceFilePartitionFilterEvaluator` and statistics `ParquetSourceFileStatisticsFilterEvaluator`
+
+## Flow
+
+1. Adapter creation / file discovery
+   Stores partition values + footer stats in ParquetSourceFile
+2. Adapter settings persist those ParquetSourceFiles. 
+3. Adapter/table loading
+   Restore ParquetSourceFiles containing partition values + footer from settings
+4. Query execution calls ParquetRelTable.project(...) / scan(...) / nestedJoin(...),
+   which creates ParquetMultiFileEnumerator.
+5. ParquetMultiFileEnumerator checks each ParquetSourceFile before opening it. Per-file pruning happens inside: `ParquetMultiFileEnumerator.moveNext()`
+6. Partition evaluator compares filters with sourceFile.partitionValues(). 
+7. Statistics evaluator compares filters with sourceFile.columnStatistics(). 
+8. If evaluators prove the file cannot match, the file is skipped. 
+9. If the result is unknown or possibly matching, the file reader is opened. 
+10. Row-level filtering still happens after opening the file.
diff --git a/plugins/parquet-adapter/docs/filter_improvements.md b/plugins/parquet-adapter/docs/filter_improvements.md
deleted file mode 100644
index e880b89ef8..0000000000
--- a/plugins/parquet-adapter/docs/filter_improvements.md
+++ /dev/null
@@ -1,219 +0,0 @@
-# Parquet Filter Improvements
-
-This document briefly describes the latest filtering improvements in the Parquet adapter.
-
-## Goal
-
-The previous filtering implementation mainly handled single comparison predicates.
-
-The new goal of this development step was:
-
-1. support logical filter trees (AND, OR, NOT, IN) - hierarchical filter
-2. resolve nested filter operands recursively
-3. reuse shared translation logic between relational and document filters
-4. allow both exact filtering and native Parquet pushdown to understand composed filters
-
-## Main Changes
-
-### `AbstractFilterTranslator`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java`
-
-The old helper `ParquetFilterTranslationSupport` was replaced by `AbstractFilterTranslator`.
-
-It now provides shared logic for:
-
-- parsing binary comparison predicates
-- unwrapping casts
-- reversing comparison operators when the value is on the left
-- converting literals and dynamic parameters into `ParquetAdapterFilter`
-
-This logic is now reused by both:
-
-- `ParquetRelFilterTranslator`
-- `ParquetDocFilterTranslator`
-
-### `ParquetAdapterFilter`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java`
-
-`ParquetAdapterFilter` was extended from a simple leaf predicate into a filter tree node.
-
-New capabilities:
-
-- store logical operands
-- represent:
-  - `AND`
-  - `OR`
-  - `NOT`
-- distinguish logical nodes from leaf predicates using `isLogical()`
-
-This makes the filter model recursive.
-
-### `ParquetRelFilterTranslator`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java`
-
-Relational filter translation was extended to support:
-
-- `AND`
-- `OR`
-- `NOT`
-- `IN`
-- reversed comparisons such as:
-
-```text
-10 < column
-```
-
-`IN` is translated as:
-
-```text
-column IN (a, b, c)
--> OR(EQUALS(column, a), EQUALS(column, b), EQUALS(column, c))
-```
-
-Unsupported operands still return `null`, so unsupported predicates are not pushed into the adapter.
-
-### `ParquetDocFilterTranslator`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java`
-
-The document filter translator was aligned with the new shared translation base.
-
-It now reuses the same parsing and value-handling logic as the relational translator.
-
-### `ParquetRelTable`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java`
-
-Filter resolution is now recursive.
-
-Before this change, only flat leaf filters were resolved.
-Now `resolveFilter(...)` walks the full filter tree and:
-
-- resolves dynamic parameters
-- resolves Parquet source paths through bindings
-- rebuilds logical filters with resolved child operands
-
-This is important for normalized tables and composed predicates.
-
-### `AbstractParquetEnumerator`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java`
-
-Exact row-level filtering now understands logical filters.
-
-The `matches(...)` function now evaluates:
-
-- `AND` using `allMatch`
-- `OR` using `anyMatch`
-- `NOT` by negating its single operand
-
-So enumerator-level filtering is no longer limited to single leaf predicates.
-
-### `ParquetNativeFilterBuilder`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java`
-
-Native Parquet pushdown was extended to build logical predicate trees.
-
-New behavior:
-
-- recursively builds child predicates
-- combines them with:
-  - `FilterApi.and(...)`
-  - `FilterApi.or(...)`
-  - `FilterApi.not(...)`
-
-If any child predicate cannot be translated safely, native pushdown for that logical filter returns `null`.
-
-This keeps native filtering conservative and correct.
-
-## Supported New Cases
-
-The following filter shapes are now supported much better than before:
-
-- conjunctions:
-
-```text
-a > 10 AND b = 20
-```
-
-- disjunctions:
-
-```text
-a = 10 OR a = 20
-```
-
-- negation:
-
-```text
-NOT (a = 10)
-```
-
-- `IN`:
-
-```text
-a IN (10, 20, 30)
-```
-
-- reversed comparison operands:
-
-```text
-10 < a
-```
-
-## Test Updates
-
-File:
-
-`plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java`
-
-Tests were extended to verify:
-
-- `AND` / `OR` translation
-- `IN` translation into `OR` of equality filters
-- operator reversal when the literal is on the left
-- safe handling of unsupported native timestamp pushdown cases
-
-## Changed Files
-
-- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java`
-- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java`
-- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java`
-- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java`
-- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java`
-- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java`
-- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java`
-- `plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java`
-
-## Summary
-
-The filtering model was upgraded from single comparison predicates to recursive logical filter trees.
-
-This improvement affects both:
-
-- filter translation
-- filter execution
-
-and it applies to both:
-
-- exact enumerator-level filtering
-- native Parquet predicate pushdown
-
-The result is better support for realistic composed predicates while keeping unsupported cases conservative.
diff --git a/plugins/parquet-adapter/docs/filter_overview.md b/plugins/parquet-adapter/docs/filter_overview.md
index 35f85354ac..4479036f54 100644
--- a/plugins/parquet-adapter/docs/filter_overview.md
+++ b/plugins/parquet-adapter/docs/filter_overview.md
@@ -1,7 +1,5 @@
 # Parquet Filter Overview
 
-This document describes the current filtering mechanism based on the Java source code only.
-The existing `filtering.md` and `filter_improvements.md` files should be treated as possibly stale.
 In particular, the current relational planner path is built around `ParquetConvention`, `ParquetRules`, `PatternMatchers`, `ParquetScan`, and `ParquetJoin`; older `ParquetRelScan` / `ParquetRelJoin` rule classes still exist, but their direct rule registration in `ParquetRelScan.registerRules()` is commented out.
 
 ## 1. Short Description
diff --git a/plugins/parquet-adapter/docs/filtering.md b/plugins/parquet-adapter/docs/filtering.md
deleted file mode 100644
index 63ca59e234..0000000000
--- a/plugins/parquet-adapter/docs/filtering.md
+++ /dev/null
@@ -1,556 +0,0 @@
-# Parquet Filtering
-
-This document describes the current filtering functionality of the Parquet relational adapter.
-
-## Goal
-
-The adapter applies filtering on two levels:
-
-1. Native Parquet filtering
-   - used for row-group pruning
-   - skips Parquet row groups before row materialization
-
-2. Enumerator-level filtering
-   - used for exact row filtering
-   - decides which relational rows are emitted
-
-These two levels are not the same:
-
-- native filtering is an optimization
-- enumerator-level filtering is responsible for correctness
-
-## Main Objects
-
-### `ParquetRelTable`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java`
-
-Responsibilities:
-
-- receives SQL filter expressions from Polypheny
-- translates supported filters into adapter filters
-- resolves dynamic parameters
-- resolves Parquet source paths through bindings
-- selects the correct enumerator
-
-
-### `ParquetRelFilterTranslator`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java`
-
-Responsibilities:
-
-- parses `RexNode` filter expressions
-- accepts only supported operators and value operands
-- creates `ParquetAdapterFilter`
-
-### `ParquetFilterTranslationSupport`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetFilterTranslationSupport.java`
-
-Responsibilities:
-
-- helper for parsing supported binary predicates
-- unwraps casts
-- converts `RexLiteral` and `RexDynamicParam` values into `ParquetAdapterFilter`
-
-### `ParquetAdapterFilter`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java`
-
-Responsibilities:
-
-- immutable filter description shared across scan planning and execution
-- can represent:
-  - ordinal filter by `columnIndex`
-  - path-based filter by `pathElements`
-
-Fields:
-
-- `columnIndex`
-  - relational column index
-- `pathElements`
-  - Parquet source path
-- `operator`
-  - filter operator
-- `polyValue`
-  - filter value
-- `dynamicParamIndex`
-  - dynamic parameter index, if present
-
-Helper:
-
-- `makeNested(int startIndex)`
-  - trims a path prefix when a repeated nested scanner already operates inside a deeper group
-
-### `ParquetNativeFilterBuilder`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java`
-
-Responsibilities:
-
-- converts `ParquetAdapterFilter` into native Parquet predicates
-- only for cases that can be mapped safely to primitive non-repeated Parquet leaf fields
-
-### `ParquetSourceReader`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java`
-
-Responsibilities:
-
-- opens the Parquet file
-- builds the projection schema
-- installs native Parquet filter predicates
-- reads row groups and rows
-
-### `AbstractParquetEnumerator`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java`
-
-Responsibilities:
-
-- shared scan lifecycle
-- reader ownership
-- row queue management
-- row expansion
-- exact row-level filtering
-- current row handling
-
-### `ParquetRelEnumerator`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java`
-
-Responsibilities:
-
-- flat/root ordinal-based scan
-- reads projected Parquet fields by ordinal position
-
-### `ParquetNestedNonRepeatedRelEnumerator`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java`
-
-Responsibilities:
-
-- path-based scan without row expansion
-- used when rows still map one-to-one but values must be extracted by Parquet source path
-
-### `ParquetNestedRepeatedRelEnumerator`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java`
-
-Responsibilities:
-
-- repeated nested scan
-- expands one Parquet root row into multiple relational rows by following the configured table path
-
-### `ParquetPathValueExtractor`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPathValueExtractor.java`
-
-Responsibilities:
-
-- extracts a relational value by Parquet source path
-- used by path-based enumerators
-
-### `ParquetTableBinding` and `ParquetColumnBinding`
-
-Files:
-
-- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java`
-- `plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnBinding.java`
-
-Responsibilities:
-
-- connect relational tables and columns back to the Parquet file and Parquet source paths
-- make path-based filtering possible for normalized tables
-
-## Full Flow
-
-![Schema display](diagrams/filter_flowchart.png)
-
-### Step 1: SQL filter arrives at `ParquetRelTable`
-
-Polypheny calls:
-
-```text
-scan( DataContext dataContext, List<RexNode> polyFilters )
-```
-
-on `ParquetRelTable`.
-
-At this point filters are still generic `RexNode` expressions.
-
-### Step 2: Supported `RexNode` filters are translated
-
-`ParquetRelTable` uses:
-
-```text
-ParquetRelFilterTranslator.translate(...)
-```
-
-This produces `ParquetAdapterFilter` only for supported predicates.
-
-Unsupported predicates are left in `polyFilters` and are not pushed into the adapter.
-
-So the adapter only handles filters it explicitly understands.
-
-### Step 3: Dynamic parameters are resolved and Parquet paths are attached
-
-`ParquetRelTable.resolveFilters(...)` transforms translator output into execution-ready filters.
-
-What happens here:
-
-- if a filter uses a dynamic parameter, the actual runtime value is read from `DataContext`
-- the table binding is used to map the relational column to its Parquet source path
-- a new `ParquetAdapterFilter` is created with:
-  - the original `columnIndex`
-  - the resolved `pathElements`
-  - the concrete value
-
-Example:
-
-```text
-SQL filter:
-quantity > 2
-
-Relational column:
-quantity
-
-Resolved Parquet path:
-["items", "quantity"]
-```
-
-### Step 4: `ParquetRelTable` selects the enumerator
-
-The scan strategy is chosen as follows:
-
-- `ParquetRelEnumerator`
-  - when ordinal scan is safe
-- `ParquetNestedNonRepeatedRelEnumerator`
-  - when values must be read by path but rows remain one-to-one
-- `ParquetNestedRepeatedRelEnumerator`
-  - when the table is generated from a repeated nested path
-
-All selected enumerators receive the resolved `ParquetAdapterFilter` list.
-
-### Step 5: `ParquetSourceReader` builds native Parquet predicates
-
-The reader constructor calls:
-
-```text
-ParquetNativeFilterBuilder.build( schema, filters )
-```
-
-This is the native Parquet optimization layer.
-
-If a filter can be converted safely to a native Parquet predicate, it is added to the reader.
-If not, it is ignored at the native level.
-
-Ignored here does not mean ignored completely.
-Exact filtering may still happen later inside the enumerator.
-
-### Step 6: `ParquetSourceReader` reads Parquet rows
-
-The reader:
-
-- applies native Parquet row-group filtering if available
-- reads projected Parquet rows
-- returns `Group` objects
-
-### Step 7: `AbstractParquetEnumerator` performs exact row filtering
-
-`AbstractParquetEnumerator.moveNext()` does the common loop:
-
-- read next Parquet group from the reader
-- expand it if needed
-- apply exact row-level filtering with `accept(...)`
-- extract relational row values
-
-Filtering here is exact for emitted rows.
-
-### Step 8: Enumerator-specific behavior
-
-#### `ParquetRelEnumerator`
-
-- no row expansion
-- reads values by ordinal position from the projection schema
-
-#### `ParquetNestedNonRepeatedRelEnumerator`
-
-- no row expansion
-- reads values by Parquet source path using `ParquetPathValueExtractor`
-
-#### `ParquetNestedRepeatedRelEnumerator`
-
-- expands one root row into multiple nested groups using `expandRow(...)`
-- applies filters to each expanded nested row
-- when evaluating path-based filters, it trims the already-resolved table prefix using:
-
-```text
-filter.makeNested( tablePath.size() )
-```
-
-This allows a filter like:
-
-```text
-["items", "product_id"]
-```
-
-to become:
-
-```text
-["product_id"]
-```
-
-when the scanner already operates inside one `items` group.
-
-## Supported Filter Cases
-
-### Supported SQL operators
-
-Supported operators:
-
-- `=`
-- `!=`
-- `>`
-- `>=`
-- `<`
-- `<=`
-
-These map to:
-
-- `EQUALS`
-- `NOT_EQUALS`
-- `GREATER_THAN`
-- `GREATER_THAN_OR_EQUAL`
-- `LESS_THAN`
-- `LESS_THAN_OR_EQUAL`
-
-### Supported value operands
-
-Supported right-hand side values:
-
-- literals
-- dynamic parameters
-
-Example:
-
-```sql
-WHERE total_price > 100
-WHERE total_price > ?
-```
-
-### Supported data types for translation
-
-`ParquetRelFilterTranslator` currently accepts:
-
-- `BOOLEAN`
-- `VARCHAR`
-- `CHAR`
-- `TEXT`
-- `INTEGER`
-- `BIGINT`
-- `FLOAT`
-- `DOUBLE`
-- `DATE`
-- `TIME`
-- `TIMESTAMP`
-
-String-like types only support:
-
-- `=`
-- `!=`
-
-Numeric and temporal types support all comparison operators listed above.
-
-### Supported native Parquet pushdown
-
-Native Parquet filtering is supported only for:
-
-- primitive leaf fields
-- non-repeated paths
-
-This includes:
-
-- top-level primitive fields addressed by `columnIndex`
-- nested primitive non-repeated leaf fields addressed by `pathElements`
-
-Examples that may be pushed down:
-
-```text
-order_id = 1001
-shipping_address.city = 'Zurich'
-total_price > 100
-```
-
-### Supported exact enumerator-level filtering
-
-Exact row filtering is supported for:
-
-- flat/root ordinal scans
-- path-based non-repeated scans
-- repeated nested scans
-
-Examples:
-
-```text
-order_id = 1001
-shipping_address.city = 'Zurich'
-items.product_id = 123
-items.discounts.amount > 5
-```
-
-Even when a filter cannot be pushed down natively, it may still be applied exactly by the enumerator.
-
-## Unsupported Native Pushdown Cases
-
-These cases are not converted to native Parquet predicates:
-
-- group fields
-- repeated paths
-- paths that do not resolve to a primitive leaf field
-- unsupported value/type/operator combinations
-
-Examples:
-
-```text
-shipping_address
-items
-items.product_id          // repeated path
-items.discounts.amount    // repeated path
-```
-
-For repeated paths this is intentional:
-
-- Parquet predicate validation does not support repeated columns for native filtering
-- exact filtering is still done later by the enumerator
-
-## Unsupported Translation Cases
-
-The adapter does not translate every SQL predicate.
-
-Examples of unsupported translation:
-
-- complex expressions
-- predicates with unsupported operators
-- predicates that are not simple binary column/value comparisons
-- unsupported field types
-
-If translation fails:
-
-- the adapter does not create `ParquetAdapterFilter`
-- the filter remains outside the Parquet pushdown path
-
-
-## Exactness Guarantees
-
-### Native Parquet filtering
-
-Native filtering is only an optimization.
-
-It can:
-
-- skip row groups
-- reduce work before row materialization
-
-It must not be treated as the only filtering step for normalized or nested tables.
-
-
-### Enumerator filtering
-
-Enumerator filtering is the exact filtering stage for emitted relational rows.
-
-This is especially important for:
-
-- path-based non-repeated nested values
-- repeated nested child tables
-
-
-## Examples
-
-### Example 1: Flat/root filter
-
-```sql
-SELECT *
-FROM parquetrelational1__orders
-WHERE order_id = 1001;
-```
-
-Flow:
-
-- translated to `ParquetAdapterFilter`
-- native Parquet predicate may be created
-- `ParquetRelEnumerator` performs exact row filtering
-
-
-### Example 2: Non-repeated nested filter
-
-```sql
-SELECT *
-FROM parquetrelational1__orders
-WHERE shipping_address_city = 'Zurich';
-```
-
-Conceptually the binding resolves the filter to:
-
-```text
-["shipping_address", "city"]
-```
-
-Flow:
-
-- path-based `ParquetAdapterFilter` is created
-- native pushdown may be possible if the path is non-repeated and resolves to a primitive leaf
-- `ParquetNestedNonRepeatedRelEnumerator` performs exact row filtering by path
-
-
-### Example 3: Repeated nested child-table filter
-
-```sql
-SELECT *
-FROM parquetrelational1__orders__items
-WHERE product_id = 123;
-```
-
-Binding resolves this to:
-
-```text
-["items", "product_id"]
-```
-
-Flow:
-
-- native Parquet pushdown is not used because the path is repeated
-- `ParquetNestedRepeatedRelEnumerator` expands `items[*]`
-- exact filtering is applied to each emitted item row
-
-
-## Current Design Summary
-
-- `ParquetRelFilterTranslator` decides which SQL predicates can enter the adapter filtering path
-- `ParquetRelTable` resolves dynamic values and Parquet source paths
-- `ParquetNativeFilterBuilder` provides optional native row-group pruning
-- `AbstractParquetEnumerator` provides exact row-level filtering
-- repeated nested paths are filtered exactly in the enumerator, not natively in Parquet
diff --git a/plugins/parquet-adapter/docs/flavors.md b/plugins/parquet-adapter/docs/flavors.md
deleted file mode 100644
index 62eab3f32b..0000000000
--- a/plugins/parquet-adapter/docs/flavors.md
+++ /dev/null
@@ -1,160 +0,0 @@
-# Parquet Table Flavors
-
-## Overview
-
-The Parquet adapter defines three table flavors through `ParquetTable.Flavor`:
-
-- `SCANNABLE`
-- `FILTERABLE`
-- `TRANSLATABLE`
-
-These flavors represent different integration styles between the adapter and the Polypheny query engine. 
-They represent different execution and planning strategies for reading the same Parquet data.
-The active flavor is selected when the Parquet namespace creates table wrappers.
-
-## Where Flavor Selection Happens
-
-Flavor-based table creation is implemented in `ParquetNamespace`.
-
-- `ParquetNamespace.createParquetTable(...)` prepares the metadata and source reference for a physical Parquet table.
-- `ParquetNamespace.createTable(...)` instantiates the concrete table implementation according to the configured flavor.
-
-The current Parquet source selects the `FILTERABLE` flavor in `ParquetSource.updateNamespace(...)`.
-
-
-## Purpose of Each Flavor
-
-### SCANNABLE
-
-`SCANNABLE` is the simplest integration model.
-
-In this flavor, the adapter behaves mainly as a row producer. 
-The query engine asks the table to provide rows, and the adapter returns them as an enumerable result stream.
-
-Characteristics:
-
-- simple implementation model
-- filtering is handled outside the adapter
-- less efficient (it does not provide optimization)
-
-
-### FILTERABLE
-
-`FILTERABLE` extends the scan-based model by allowing the adapter to receive filter conditions from the query engine.
-
-In this flavor, the adapter inspects query filters and pushes supported conditions into its own execution logic. 
-This allows the Parquet reader to reduce the amount of data it reads and processes using native predicate pushdown.
-
-Characteristics:
-
-- supports filter pushdown
-- supports projection-aware reading
-- uses Parquet native filter stages such as statistics, dictionary, column index, bloom filter, and record filtering when available
-- significantly more efficient than plain scan-only execution
-
-This is the active flavor in the current Parquet adapter implementation.
-
-### TRANSLATABLE
-
-`TRANSLATABLE` is the most advanced integration style.
-
-In this flavor, the adapter participates directly in query planning. 
-Instead of only returning rows at execution time, it provides adapter-specific relational nodes and planner rules.
-
-Characteristics:
-
-- deeper integration with the query planner
-- enables more advanced optimization
-- more flexible than scan-only or filterable execution
-
-
-## Flavor Workflows
-
-The following sections describe how the same logical SQL query flows through each flavor.
-
-Example query:
-
-```sql
-SELECT customer_id, name
-FROM customers
-WHERE customer_id > 3
-```
-
-### 1. SCANNABLE Workflow
-The adapter mainly acts as a generic row reader.
-
-Main class:
-
-- `ParquetScannableTable`
-
-Flow:
-
-- The query engine decides a table scan is needed.
-- The scannable table is asked to provide rows.
-- The adapter reads rows from the Parquet file.
-- Filtering may be applied later by the query engine rather than inside the adapter.
-- Projection may also be handled outside the adapter depending on the execution path.
-
-Practical consequence:
-
-- more data may be read than actually needed
-- pushdown opportunities are limited
-- execution is simpler but less efficient
-- appropriate when correctness and simplicity are more important than optimization.
-
-### 2. FILTERABLE Workflow
-
-The adapter receives a list of query filters and decides which ones it can handle internally.
-
-Main classes:
-
-- `ParquetFilterableTable`
-- `ParquetEnumerator`
-- `ParquetPredicateBuilder`
-
-Flow:
-
-- The query engine calls ParquetFilterableTable.scan(...).
-- The table inspects incoming filter expressions.
-- Only pushdown-safe filters are converted into adapter-specific `FilterInfo` objects.
-- These filters are passed into ParquetEnumerator.
-- The enumerator builds the requested projection schema from the projected columns.
-- `ParquetPredicateBuilder` translates adapter filters into native Parquet predicates.
-- `ParquetFileReader` is opened with `ParquetReadOptions` that enable native predicate pushdown stages.
-- Matching rows are read directly from the projected Parquet schema and returned.
-
-Practical consequence:
-
-- fewer columns may be read
-- fewer row groups and pages may be read when the Parquet file contains usable metadata
-- less work is done outside the adapter
-
-Current limitations:
-
-- Only simple `column OP literal` predicates are pushed down.
-- `BOOLEAN` and string-like fields currently support only `=` and `!=`.
-- Legacy `INT96` timestamp columns are intentionally not pushed down.
-
-
-### 3. TRANSLATABLE Workflow
-
-The adapter participates in planning.
-
-Main classes:
-
-- `ParquetTranslatableTable`
-- `ParquetScan`
-- `ParquetProjectScanRule`
-
-Flow:
-
-- The planner encounters a Parquet-backed table.
-- The translatable table produces an adapter-specific relational node.
-- The query planner can rewrite the query plan using rules that know about your Parquet adapter.
-- Projection and other operations can be integrated into adapter-specific planning structures.
-
-Practical consequence:
-
-- optimization can happen earlier and more explicitly
-- this approach supports more sophisticated future optimizations
-
diff --git a/plugins/parquet-adapter/docs/images/planner/rule1.png b/plugins/parquet-adapter/docs/images/planner/rule1.png
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diff --git a/plugins/parquet-adapter/docs/images/planner/unsupported_2_joins_one_other_physical.png b/plugins/parquet-adapter/docs/images/planner/unsupported_2_joins_one_other_physical.png
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diff --git a/plugins/parquet-adapter/docs/joins.md b/plugins/parquet-adapter/docs/joins.md
index 377d83363a..30ab9cb275 100644
--- a/plugins/parquet-adapter/docs/joins.md
+++ b/plugins/parquet-adapter/docs/joins.md
@@ -1,282 +1,285 @@
 # Adapter Level Joins
 
+## Goal: Perform join on parquet adapter level.
 
-## Change Description
+A Parquet adapter-level join is more efficient because it can exploit the physical parent/child layout of the Parquet data.
 
-The change adds support for executing a limited class of joins inside the Parquet adapter instead of letting Polypheny execute the join above two independent scans. The supported case is a normalized nested-table join where one table represents a parent Parquet group and the other table represents a child repeated group from the same Parquet source.
+For normalized nested tables, the relation is:
+`child.__polypheny_parent_row_id = parent.__polypheny_row_id`
 
-The purpose is to avoid reading parent and child tables independently and joining them later. The adapter already knows the source path and synthetic key roles for nested generated tables, so it can reconstruct the parent-child relationship while walking the Parquet row structure.
+That relationship comes from one nested Parquet structure. So the adapter can process it closer to how the file is physically stored.
 
-## Supported Joins
-
-This change supports adapter-level joins only for normalized Parquet nested tables that belong to the same Parquet source. It is not a general Parquet join implementation and it is not a general self-join on the same physical table.
-
-The supported case is a direct parent-child join where:
 
-- Both tables have the same `sourceUrl`.
-- One table is the direct parent of the other table in the Parquet path hierarchy.
-- The child table path is exactly one level below the parent table path.
-- The join condition is a single equi-join.
-- The parent join key is the generated `PRIMARY_KEY` column.
-- The child join key is the generated `PARENT_KEY` column.
+## Benefits of Adepter Level Join
+Adepter Level Join is faster because the adapter-level join uses file layout and nested-table semantics, 
+while Polypheny-level join treats everything as generic relational rows.
 
-The parent is often the root table, but it does not have to be root. A nested table can also be the parent when the child table is directly below it. At runtime the adapter reads from the parent source, resolves the parent path first, then resolves the child path below each parent row.
+### Polypheny join
+- read parent rows
+- read child rows
+- join outside adapter
 
-Supported joins are checked in `ParquetRelJoin.supportedDirection(...)`. The join must be an equi-join with exactly one key on each side, both tables must come from the same source URL, and the keys must match the generated role mapping: parent `PRIMARY_KEY` to child `PARENT_KEY`. The child table path must also be directly below the parent path. Join types accepted by the rule are `INNER`, `LEFT`, `RIGHT`, and `FULL`.
+### Parquet adapter join:
+- read nested parent/child structure
+- apply filters while expanding
+- return already joined rows
 
-The planner rules can also recognize supported joins when simple row-shaping nodes are between the join and the scan:
-
-- projection-only `Calc` nodes are allowed when they only select or reorder fields
-- `EnumerableLimit` nodes below the join may be ignored for join recognition when the SQL limit is still applied above `ParquetRelJoin`
+## Supported Joins
 
-These nodes are not general child execution. They are accepted only when the adapter can preserve the same meaning inside `ParquetRelJoin`.
+We support only joins between virtual tables derived from the same physical parquet file.
 
 ## Unsupported Joins
 
-For unsupported join shapes, the new adapter-level path should simply not apply.
-
-`ParquetRelJoin.supportedDirection(...)` returns `null` when the join is not supported. Then:
-
-- `ParquetRelJoinRule` does not transform the logical join.
-- `ParquetEnumerableJoinRule` does not replace the enumerable join.
-- The planner keeps using Polypheny's normal join implementation above the scans.
-
-Unsupported joins:
-
-- Joins between unrelated Parquet tables.
-- Joins between different Parquet sources.
-- Joins where neither side is the parent of the other.
-- Multi-key joins.
-- Non-equi joins like <, >, !=
-- Join types other than `INNER`, `LEFT`, `RIGHT`, and `FULL`.
-- General joins on arbitrary user columns.
-- Independent child-side outer join scanning outside the parent path.
-- Adapter-executed input limits below the join. `LIMIT` and `OFFSET` must stay above `ParquetRelJoin` and apply to final joined rows.
-
-
-### Flow
-
-- Rule invocation starts from `ParquetRelScan.register(...)`. When a Parquet scan participates in planning, it registers the existing scan rule and the new join-related rules:
-
-  - `ParquetRelScanRule`
-  - `ParquetRelJoinRule`
-  - `ParquetEnumerableJoinRule`
-  - `ParquetEnumerableFilterJoinRule`
-  - `ParquetEnumerableCalcJoinRule`
-  
-
-- There are two paths to create the adapter join. 
-
-
-- `ParquetRelJoinRule` handles a logical join path: 
-  - it receives a `LogicalRelJoin`
-  - resolves projected `ParquetRelScan` inputs, including projection-only calc wrappers
-  - validates the parent-child direction
-  - creates `ParquetRelJoin`. 
-  
-
-- `ParquetEnumerableJoinRule` handles the later physical path: 
-  - if an `EnumerableJoin` has inputs that can be resolved to projected Parquet scans, it performs the same support check and replaces the enumerable join with `ParquetRelJoin`
-  - when the optimizer placed an `EnumerableLimit` below the join while the SQL limit remains above the join, the rule can look through that input limit for matching, but the limit is not stored in `ParquetRelJoin` or passed to runtime.
-
-
-- After `ParquetRelJoin` exists, filters above it can be pushed into the adapter join. 
-`ParquetEnumerableFilterJoinRule` matches `EnumerableFilter(ParquetRelJoin)`, translates the filter condition with `ParquetRelFilterTranslator`, and returns `join.withFilters(...)`. 
-
-
-- `ParquetEnumerableCalcJoinRule` does the same for `EnumerableCalc(ParquetRelJoin)` when the calc contains a condition; it pushes the condition into the join and keeps a projection-only calc above the filtered join.
-
-
-- At runtime, `ParquetRelJoin.implement(...)` generates enumerable code that calls `leftTable.nestedJoin(dataContext, rightTable, leftFields, rightFields, leftIsParent, emitUnmatchedParents, filters)`. `emitUnmatchedParents` is derived from join type and direction: parent-side outer rows are emitted for parent-preserving `LEFT` or `RIGHT` joins, and for `FULL`. This runtime path preserves unmatched parent rows; it does not perform an independent child-side scan outside the parent path.
-
-
-- `ParquetRelTable.nestedJoin(...)` is the runtime entry point. It chooses which input is parent and which input is child based on `leftIsParent`, resolves dynamic parameters, maps joined filter column indexes to the correct left or right table column binding, and calls `JoinFiltersSplitter`.
-
-
-- `JoinFiltersSplitter` classifies filters by the fields they reference. Parent-only filters that can be represented by Parquet paths may become reader filters. Parent-only filters that cannot be reader filters are kept as parent adapter filters. Child-only filters are applied after child expansion. Filters spanning both sides, invalid indexes, or mixed logical conditions become adapter-level joined-row filters.
-
-
-- `ParquetNestedJoinEnumerator` performs the actual read. It opens the parent source, walks the parent path using inherited nested resolution from `ParquetNestedRepeatedRelEnumerator`, applies parent filters, resolves child groups below each accepted parent, and creates `CombinedGroup` for each joined parent-child row. If a parent has no child and the join type requires preserving unmatched parents, it creates a `CombinedGroup` with a null child side.
-
-
-- `CombinedGroup` is the bridge between nested Parquet groups and joined row indexes. When the enumerator extracts output values or evaluates filters, the combined group maps each joined field index to the correct parent or child `VirtualGroup`, binding, and table path. This lets the same extraction logic handle both left-parent and right-parent join directions.
-
-
-- The filter infrastructure was generalized to support this flow. `FiltersContainer` separates reader-level filters from adapter-level filters, and `AbstractParquetEnumerator` now uses that separation when creating `ParquetSourceReader` and when doing row-level checks. Document and normal relational scans were updated to use the same container API so filtering has one common runtime shape across scan and join paths.
-
-
-## Changed files:
-
-### ParquetRelScan
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java`
+### 1. LOWER(category) filter is not supported
 
-Registers the new join rules in addition to the scan rule. Also exposes scan fields via Lombok `@Getter`, which is needed by join validation to map join keys back to physical table columns.
-
-### ParquetRelScanRuleSupport
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java`
-
-Shared helper used by scan and join planner rules. It finds direct Parquet scans, looks inside planner subsets, and resolves projection-only `Calc` nodes by mapping visible fields back to physical Parquet table columns.
-
-### ParquetRelJoinRule
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoinRule.java`
-
-New file. Converts a `LogicalRelJoin` into `ParquetRelJoin` when both sides can be resolved to projected Parquet scans and `ParquetRelJoin.supportedDirection(...)` accepts the join.
-
-### ParquetEnumerableJoinRule
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableJoinRule.java`
-
-New file. Rewrites an `EnumerableJoin` over inputs that can be resolved to projected Parquet scans into `ParquetRelJoin` when the join is a supported nested parent-child join. This catches plans where the join has already been converted to enumerable form.
-
-### ParquetRelJoin
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java`
-
-New file. Physical adapter-level join node. It provides supported join validation through `supportedDirection(...)`, stores table metadata, projected fields, join direction, join type, and pushed filters, and generates runtime code that calls `ParquetRelTable.nestedJoin(...)`.
-
-### JoinDirection
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/JoinDirection.java`
+```sql
+SELECT product_id, name, category
+FROM pon__products
+WHERE LOWER(category) IS NOT NULL
+LIMIT 10;
+```
 
-New file. Small value record used by join support checks to remember whether the left input is the parent table. It is returned by `ParquetRelJoin.supportedDirection(...)`.
+![unsupported_lower.png](images/planner/unsupported_lower.png)
 
-### ParquetEnumerableFilterJoinRule
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterJoinRule.java`
+### 2. LIMIT inside the FROM creates E_LIMIT node under the JOIN
 
-New file. Pushes an `EnumerableFilter` above a `ParquetRelJoin` into the adapter join by translating the Rex condition into `ParquetAdapterFilter`.
+```sql
+SELECT i.order_item_id, d.code, d.amount
+FROM (
+    SELECT __polypheny_row_id, order_item_id
+    FROM pon__orders__items
+    LIMIT 100
+) i
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 10;
+```
+![unsupported_limit.png](images/planner/unsupported_limit.png)
 
-### ParquetEnumerableCalcJoinRule
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcJoinRule.java`
+### 3. Two JOINs - the second join is not supported
 
-New file. Pushes the filter part of an `EnumerableCalc` above a `ParquetRelJoin` into the join while preserving the projection-only calc. Added to support plans where Polypheny represents filter and projection together after the join.
+join `orders` with `order_items` was done on adapter level
 
-### ParquetRelTable
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java`
+```sql
+SELECT o.order_id, i.product_id, d.code, d.amount
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 10;
+```
+![unsupported_2_joins.png](images/planner/unsupported_2_joins.png)
+
+### 4. Two JOINs - one join with another physical table
+
+```sql
+SELECT o.order_id, i.quantity, p.name, p.category
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+JOIN pon__products p
+    ON p.product_id = i.product_id
+LIMIT 10;
+```
+![unsupported_2_joins_one_other_physical.png](images/planner/unsupported_2_joins_one_other_physical.png)
 
-Adds adapter-level nested join execution through `nestedJoin(...)`. Also exposes binding/source metadata for rule validation, resolves filters with table-specific column bindings, uses `FiltersContainer` for scans, and renames selected binding helper logic to projected binding logic.
+## Main Changes
 
-### ParquetNestedJoinEnumerator
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java`
+### 1. Planner Nodes
 
-New file. Executes supported parent-child joins inside the Parquet adapter. It is created by `ParquetRelTable.nestedJoin()` and reads parent and child rows from the same Parquet source, combines rows into `CombinedGroup`, applies parent and child filters, and emits null child fields for supported outer join cases.
+Two planner nodes were added
 
-### CombinedGroup
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/CombinedGroup.java`
+#### 1.1. ParquetRelScan
+Planner node that represents reading a Parquet-backed table. It's responsibilities:
+- Represents a Parquet scan in the plan. It is the Parquet convention version of a table scan.
+- Stores projection
+- Stores pushed filters
+- Defines row type
+- Exposes fields and filters in the plan visualization
+- Generates execution call: `ParquetRelTable.project(dataContext, fields, filters)`
 
-New file. Virtual joined row wrapper used by `ParquetNestedJoinEnumerator`. It maps joined output field indexes to either parent or child `VirtualGroup`, supports null-side checks for outer joins, and blocks direct Parquet field access because values are extracted through bindings.
+#### 1.2 ParquetRelJoin
+Planner node that represents a join the Parquet adapter can execute itself. It determines:
+- If this join be executed inside the Parquet adapter
+- the side of parent table
+- which projected fields and filters belong to each scan
+- which join filters should be applied
+- How should this join be executed
 
-### JoinFiltersSplitter
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersSplitter.java`
+#### 1.3 EnumerableParquet
+Planner node that converts ParquetScan node from Parquet convention to enumerable convention
 
-New file. Splits pushed join filters into parent filters, child filters, adapter filters, and reader filters. Added because filters over joined rows cannot all be pushed to the Parquet reader or to one side safely.
+### 2. Rules
 
-### JoinFiltersContainer
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersContainer.java`
+- `ParquetRules` class creates list of 4 rules
+- `PatternMatcher` class describes:
+  - what plan shape to match
+  - what transformation to run
+- `ParquetAlgOptRule` class
+  - wraps PatternMatcher as a real AlgOptRule
+    so Polypheny planner can register and execute it 
+- `PatternMatchers` class contains real rule logic
 
-New file. Extends `FiltersContainer` with parent-only and child-only filter lists. Used by `ParquetNestedJoinEnumerator` to apply filters at the correct stage of parent-child expansion.
+Four planner rules were added:
 
-### FiltersContainer
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FiltersContainer.java`
+#### 2.1 EnumerableParquetRule
 
-New file. Holds two filter lists: adapter-level filters for row checks and reader-level filters for native Parquet predicate pushdown. Added so scans and joins can choose where each filter is safe to apply.
+Rule that converts a plan from Parquet convention into Enumerable convention.
+Parquet-specific plan wrapped as executable enumerable plan.
+The Parquet adapter first creates nodes (ParquetRelScan, ParquetRelJoin) in ParquetConvention. But Polypheny ultimately needs an executable plan in: EnumerableConvention
+So EnumerableParquetRule adds this wrapper:
+- Input convention: ParquetConvention
+- Output convention: EnumerableConvention
+- Matched node: any AlgNode in ParquetConvention
+- Result: EnumerableParquet wrapping that node
 
-### AbstractParquetEnumerator
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java`
+#### 2.2 PatternMatchers.attachFieldsAndFiltersToScanUnderCalc
 
-Refactoring and extension. Accepts `FiltersContainer`, passes reader filters to `ParquetSourceReader`, keeps adapter filters for row-level matching, and exposes matching hooks so `ParquetNestedJoinEnumerator` can filter `CombinedGroup` rows.
+Rule that pushes EnumerableCalc projection/filter into a ParquetRelScan.
 
-### ParquetNestedRepeatedRelEnumerator
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java`
+- finds Calc above Parquet scan
+- extracts simple projection
+- extracts supported filter
+- stores both on ParquetRelScan
+- removes the Calc from this part of the plan
 
-Extended for join reuse. It now accepts `FiltersContainer`, exposes nested path resolution and role-based value extraction to subclasses, and supports an internal constructor that allows root table paths for join execution.
+It matches this shape:
+```text
+EnumerableCalc
+  EnumerableParquet
+    ParquetRelScan
+```
+and tries to replace with:
+```text
+EnumerableParquet
+  ParquetRelScan(fields + filters updated)
+```
+If projection is complex or filter translation fails, the rule does nothing.
 
-### ParquetAdapterFilter
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java`
+#### 2.3 PatternMatchers.joinWithScanOnLeftAndScanOnRight
 
-Adds `toExpression()` so adapter filters, including logical filters and path-aware filters, can be serialized into generated enumerable runtime code.
+Rule that converts a generic enumerable join into a Parquet adapter-level join.
+- finds generic join between two Parquet scans
+- checks if it is a nested parent/child join
+- replaces it with ParquetRelJoin
+- wraps it back into EnumerableParquet
 
-### ParquetRelEnumerator
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java`
+It matches this shape:
+```text
+EnumerableJoin
+    EnumerableParquet
+        ParquetRelScan
 
-Refactoring. Constructor now accepts `FiltersContainer`, passing reader filters to `ParquetSourceReader` and adapter filters to row-level filtering in `AbstractParquetEnumerator`.
+    EnumerableParquet
+        ParquetRelScan
+```
+And tries to replace it with:
+```text
+EnumerableParquet
+    ParquetRelJoin
+```
 
-### ParquetNestedNonRepeatedRelEnumerator
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java`
+#### 2.4 PatternMatchers.attachFilterToJoinUnderCalc
 
-Refactoring. Constructor now accepts `FiltersContainer` instead of a raw filter list, so the shared enumerator can distinguish adapter-level filters from reader-level filters.
+Rule that pushes a filter from a Calc above a ParquetRelJoin into the join node.
 
-### ParquetDocScan
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java`
+- finds Calc above ParquetRelJoin
+- extracts supported filter condition
+- converts it to ParquetAdapterFilter
+- stores it on ParquetRelJoin.joinFilters
+- removes that Calc from this part of the plan
 
-Refactoring. Uses `ParquetAdapterFilter.toExpression()` instead of building filter expressions locally. This keeps document and relational scans using the same runtime filter serialization logic.
+It matches this shape:
+```text
+EnumerableCalc
+  EnumerableParquet
+    ParquetRelJoin
+```
+and tries to replace with:
+```text
+EnumerableParquet
+  ParquetRelJoin(joinFilters updated)
+```
 
-### ParquetDocument
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java`
+### 3. Registration of nodes
 
-Refactoring. Wraps resolved document filters in `FiltersContainer.shared(...)` before creating `ParquetDocEnumerator`, so document scans use the new common filter container API.
+`ParquetPlugin.registerPolyAlg()` registers:
+- EnumerableParquet.class
+- ParquetRelScan.class
+- ParquetRelJoin.class
 
-### ParquetDocEnumerator
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java`
+### 4. Parquet Convention
+ 
+`ParquetConvention` is the marker that tells the planner: This part of the plan is executable by the Parquet adapter.
+It defines a separate planner convention named: "PARQUET"
+and says that nodes in this convention must implement: ParquetAlg
 
-Refactoring. Use FiltersContainer.
 
-## Examples
+## Planner Flow - Query Execution with Join
 
-### Join - root parent - direct child
+1. ParquetRelScan.register() called
+- All rules from ParquetRules.rules added to Planner
 
-![Schema display](images/parquet_rel_join.png)
+2. ParquetRelTable.nestedJoin() called (if no join - project called)
+3. ParquetMultiFileEnumerator created
+4. ParquetMultiFileEnumerator performs file pruning
+5. ParquetMultiFileEnumerator creates ParquetNestedJoinEnumerator
 
-### Join - nested parent - direct child
 
-![Schema display](images/join_nested_parent_direct_child.png)
+## Examples of Physical Plans:
 
-## Convention
+### Node Names
+- P_SCAN - ParquetRelScan
+- PE_CALC - EnumerableParquet
+- P_JOIN - ParquetRelJoin
 
-In Polypheny planning, a convention describes how an algebra node is implemented physically. Logical algebra says what should happen, while convention says how and where it can be executed.
+### Rule 1: EnumerableParquetRule
+Create Enumerable node PE_CALC above P_SCAN 
 
-For example, a logical join only describes the operation:
+No filter, no projection, no join
 
-```text
-LogicalRelJoin
-  left input
-  right input
+```sql
+SELECT *
+FROM pon__orders o;
 ```
+![Schema display](images/planner/rule1.png)
 
-At this point it has `Convention.NONE`, which means it is still a logical plan node and does not yet have a concrete execution implementation.
+### Rule 2: PatternMatchers.attachFieldsAndFiltersToScanUnderCalc
 
-`EnumerableConvention.INSTANCE` means the node can be executed through Polypheny's enumerable runtime model. In this model, an operator returns rows through an `Enumerable` / `Enumerator`, similar to an iterator:
+Pushing down of projection/filter from E_CALC into P_SCAN.
 
-```text
-while (enumerator.moveNext()) {
-    row = enumerator.current();
-}
+```sql
+SELECT o.order_id, o.status
+FROM pon__orders o
+where o.order_id = 2 and total_price > 10;
 ```
+![Schema display](images/planner/rule2.png)
 
-Enumerable does not mean that there are multiple joins. It means the operator produces result rows one by one. A single join can still be enumerable because it may produce many joined rows.
 
-In this change, `ParquetRelJoinRule` is a converter rule:
+### Rule 3: PatternMatchers.joinWithScanOnLeftAndScanOnRight
 
-```text
-LogicalRelJoin.class,
-Convention.NONE,
-EnumerableConvention.INSTANCE
-```
-
-This means: take a logical join and, if it is a supported Parquet parent-child join, convert it into an enumerable physical implementation.
-
-The converted node is:
+Convert E_JOIN with two P_SCANs under it into P_JOIN.
 
-```text
-ParquetRelJoin
+```sql
+SELECT *
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id;
 ```
+![Schema display](images/planner/rule3.png)
 
-`ParquetRelJoin` implements `EnumerableAlg`, so it participates in enumerable execution. Its `implement(...)` method generates runtime code that calls:
 
-```text
-ParquetRelTable.nestedJoin(...)
-```
+### Rule 4: PatternMatchers.attachFilterToJoinUnderCalc
 
-That method returns:
+Get filters from E_CALC under E_JOIN and push it into the PE_JOIN.
 
-```text
-Enumerable<PolyValue[]>
+```sql
+SELECT o.order_id, i.product_id
+FROM pon__orders o
+JOIN pon__orders__items i
+ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+WHERE i.__polypheny_elem_ordinal > 0 or i.product_id = 1;
 ```
 
-and creates `ParquetNestedJoinEnumerator`, which yields the joined rows one by one.
+![Schema display](images/planner/rule4.png)
diff --git a/plugins/parquet-adapter/docs/test_plan/join_test_list.md b/plugins/parquet-adapter/docs/test_plan/join_test_list.md
index ba90d9fae2..b29ea7f2b9 100644
--- a/plugins/parquet-adapter/docs/test_plan/join_test_list.md
+++ b/plugins/parquet-adapter/docs/test_plan/join_test_list.md
@@ -1,561 +1,852 @@
-# Join Test List
+# PON Planning Query Matrix
 
-This document lists SQL queries that should be used to test adapter-level Parquet joins and fallback join behavior.
+This checklist is for exercising different physical-planning shapes over the normalized `pon_` Parquet tables.
 
-The examples assume a normalized Parquet adapter with these generated tables:
+The queries assume these table relationships:
 
-- `pon__orders` - root table
-- `pon__orders__items` - direct child table of `pon__orders`
-- `pon__orders__items__discounts` - direct child table of `pon__orders__items`
+- `pon__customers`, `pon__orders`, and `pon__products` are root-level tables.
+- `pon__orders__items` is a direct child of `pon__orders`.
+- `pon__orders__shipping_address` is a direct child of `pon__orders`.
+- `pon__orders__items__discounts` is a direct child of `pon__orders__items`.
+- Supported adapter joins use generated structural keys: child `__polypheny_parent_row_id` to parent `__polypheny_row_id`.
 
-Expected adapter-level support means the physical plan should contain `ParquetRelJoin`.
-Unsupported cases should still execute through the normal Polypheny join path and should not produce `ParquetRelJoin`.
+Plan terms below use the short PolyAlg names:
 
-## Supported Adapter-Level Joins
+- `P_SCAN`: Parquet scan.
+- `PE_CALC`: converter from Parquet convention to enumerable convention.
+- `P_JOIN`: adapter-level Parquet parent-child join.
+- `E_CALC`, `E_LIMIT`, `E_SORT`, `E_AGGREGATE`, `E_JOIN`: normal enumerable operators above or around Parquet nodes.
 
-### 1. Root parent - direct child, parent on left, inner join
+For every supported parent-child join, also check that projected scan fields map the join keys correctly. In particular, for child tables the join key `__polypheny_parent_row_id` should map to physical column index `1`, not to `0`.
 
-Expected: supported. The plan should contain `ParquetRelJoin`.
+## Simple Scans And Projections
+
+### Q01. Root table scan with limit
+
+Expected plan: one `P_SCAN` for `pon__orders`, wrapped by `PE_CALC`; limit remains above the scan as `E_LIMIT` unless a future rule pushes it.
+
+Check: baseline root-table planning and limit placement.
+
+```sql
+SELECT *
+FROM pon__orders
+LIMIT 10;
+```
+
+### Q02. Root table projection
+
+Expected plan: `P_SCAN` should read only projected order columns if projection pushdown applies.
+
+Check: simple projection pushdown on a root table.
+
+```sql
+SELECT order_id, status, total_price
+FROM pon__orders
+LIMIT 10;
+```
+
+### Q03. Customer projection
+
+Expected plan: `P_SCAN` for `pon__customers`, with only selected customer fields.
+
+Check: projection over another root table and text/timestamp columns.
+
+```sql
+SELECT customer_id, name, country, signup_date
+FROM pon__customers
+LIMIT 10;
+```
+
+### Q04. Product projection
+
+Expected plan: `P_SCAN` for `pon__products`, with only selected product fields.
+
+Check: projection over numeric and text product columns.
+
+```sql
+SELECT product_id, name, category, price, stock
+FROM pon__products
+LIMIT 10;
+```
+
+### Q05. Repeated child projection
+
+Expected plan: `P_SCAN` for `pon__orders__items`, with projected child fields only.
+
+Check: projection on a nested repeated child table.
+
+```sql
+SELECT order_item_id, product_id, quantity, price
+FROM pon__orders__items
+LIMIT 10;
+```
+
+### Q06. Repeated child structural columns
+
+Expected plan: `P_SCAN` for `pon__orders__items`, keeping the generated row id, parent row id, and ordinal.
+
+Check: scan of generated structural columns used by nested tables.
+
+```sql
+SELECT __polypheny_row_id, __polypheny_parent_row_id, __polypheny_elem_ordinal
+FROM pon__orders__items
+LIMIT 10;
+```
+
+### Q07. Nested grandchild projection
+
+Expected plan: `P_SCAN` for `pon__orders__items__discounts`, with projected discount fields.
+
+Check: projection on a second-level nested repeated child table.
+
+```sql
+SELECT code, amount
+FROM pon__orders__items__discounts
+LIMIT 10;
+```
+
+### Q08. Direct nested shipping-address projection
+
+Expected plan: `P_SCAN` for `pon__orders__shipping_address`, with projected address fields.
+
+Check: projection on a direct nested child that is not the `items` repeated branch.
+
+```sql
+SELECT city, street, zip
+FROM pon__orders__shipping_address
+LIMIT 10;
+```
+
+## Filters Without Joins
+
+### Q09. Root numeric filter
+
+Expected plan: `P_SCAN` with a translatable filter if filter pushdown applies; otherwise `E_CALC` or filter above `PE_CALC`.
+
+Check: root-table numeric predicate.
+
+```sql
+SELECT order_id, total_price
+FROM pon__orders
+WHERE total_price >= 0
+LIMIT 10;
+```
+
+### Q10. Root nullable text filter
+
+Expected plan: `P_SCAN` plus a status filter when supported.
+
+Check: nullable text predicate on root table.
+
+```sql
+SELECT order_id, status
+FROM pon__orders
+WHERE status IS NOT NULL
+LIMIT 10;
+```
+
+### Q11. Root timestamp filter
+
+Expected plan: `P_SCAN` plus a timestamp/nullability filter when supported.
+
+Check: timestamp column handling.
+
+```sql
+SELECT order_id, order_date
+FROM pon__orders
+WHERE order_date IS NOT NULL
+LIMIT 10;
+```
+
+### Q12. Customer text filter
+
+Expected plan: `P_SCAN` for `pon__customers`; text predicate may or may not be pushed.
+
+Check: text filter planning on a root table.
 
 ```sql
-SELECT
-    o.__polypheny_row_id AS order_row_id,
-    o.order_id,
-    i.__polypheny_row_id AS item_row_id,
-    i.__polypheny_parent_row_id AS item_parent_row_id,
-    i.product_id,
-    i.quantity
+SELECT customer_id, name, country
+FROM pon__customers
+WHERE country IS NOT NULL
+LIMIT 10;
+```
+
+### Q13. Product numeric filter
+
+Expected plan: `P_SCAN` for `pon__products` with a numeric filter when supported.
+
+Check: product scan with numeric predicate and projection.
+
+```sql
+SELECT product_id, name, price, stock
+FROM pon__products
+WHERE price >= 0 AND stock >= 0
+LIMIT 10;
+```
+
+### Q14. Repeated child numeric filter
+
+Expected plan: `P_SCAN` for `pon__orders__items`, with `quantity` and `price` filters when supported.
+
+Check: filter pushdown on nested repeated table.
+
+```sql
+SELECT order_item_id, product_id, quantity, price
+FROM pon__orders__items
+WHERE quantity > 0 AND price >= 0
+LIMIT 10;
+```
+
+### Q15. Repeated child ordinal filter
+
+Expected plan: `P_SCAN` for `pon__orders__items`, possibly with a filter on generated ordinal.
+
+Check: generated ordinal field in a filter.
+
+```sql
+SELECT __polypheny_parent_row_id, __polypheny_elem_ordinal, product_id
+FROM pon__orders__items
+WHERE __polypheny_elem_ordinal = 0
+LIMIT 10;
+```
+
+### Q16. Nested grandchild filter
+
+Expected plan: `P_SCAN` for `pon__orders__items__discounts`, with `amount` filter when supported.
+
+Check: filter on second-level nested repeated table.
+
+```sql
+SELECT code, amount
+FROM pon__orders__items__discounts
+WHERE amount > 0
+LIMIT 10;
+```
+
+### Q17. Direct nested shipping-address filter
+
+Expected plan: `P_SCAN` for `pon__orders__shipping_address`; filter may remain as `E_CALC` if text predicates are not pushed.
+
+Check: filter on direct nested child branch different from `items`.
+
+```sql
+SELECT city, street, zip
+FROM pon__orders__shipping_address
+WHERE city IS NOT NULL AND zip IS NOT NULL
+LIMIT 10;
+```
+
+### Q18. Non-pushable expression filter
+
+Expected plan: `P_SCAN` for `pon__products`, with an enumerable calc/filter for the expression if it cannot be pushed.
+
+Check: fallback around a scan when the predicate contains an expression.
+
+```sql
+SELECT product_id, name, category
+FROM pon__products
+WHERE LOWER(category) IS NOT NULL
+LIMIT 10;
+```
+
+## Supported Parent-Child Joins
+
+### Q19. Orders to items, inner join
+
+Expected plan: `P_JOIN` with `pon__orders` as parent and `pon__orders__items` as child.
+
+Check: basic root parent to repeated child join.
+
+```sql
+SELECT o.order_id, i.order_item_id, i.product_id, i.quantity
 FROM pon__orders o
 JOIN pon__orders__items i
     ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 20;
+LIMIT 10;
 ```
 
-### 2. Root parent - direct child, parent on right, inner join
+### Q20. Items to orders, inner join with parent on right
+
+Expected plan: `P_JOIN`; direction detection should identify that the right input is the parent.
 
-Expected: supported. This validates that the rule detects parent-child direction when the child table is the left input.
+Check: supported join with swapped input order.
 
 ```sql
-SELECT
-    i.__polypheny_row_id AS item_row_id,
-    i.product_id,
-    o.__polypheny_row_id AS order_row_id,
-    o.order_id
+SELECT i.order_item_id, i.product_id, o.order_id, o.status
 FROM pon__orders__items i
 JOIN pon__orders o
     ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 20;
+LIMIT 10;
 ```
 
-### 3. Root parent - direct child, equality written in reversed order
+### Q21. Orders to shipping address
 
-Expected: supported. This validates that the rule accepts the same equi-join when the operands are reversed.
+Expected plan: `P_JOIN` with `pon__orders` as parent and `pon__orders__shipping_address` as child.
+
+Check: direct nested child branch that is not `items`.
 
 ```sql
-SELECT
-    o.order_id,
-    i.product_id,
-    i.quantity
+SELECT o.order_id, s.city, s.street, s.zip
 FROM pon__orders o
-JOIN pon__orders__items i
-    ON o.__polypheny_row_id = i.__polypheny_parent_row_id
-LIMIT 20;
+JOIN pon__orders__shipping_address s
+    ON s.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q22. Shipping address to orders, parent on right
+
+Expected plan: `P_JOIN`; direction detection should identify that the right input is the parent.
+
+Check: swapped direction for a direct nested child branch.
+
+```sql
+SELECT s.city, s.zip, o.order_id
+FROM pon__orders__shipping_address s
+JOIN pon__orders o
+    ON s.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
 ```
 
-### 4. Nested parent - direct nested child, parent on left, inner join
+### Q23. Items to discounts, inner join
 
-Expected: supported. This is the main nested-parent case: `items` is parent, `discounts` is direct child.
+Expected plan: `P_JOIN` with `pon__orders__items` as parent and `pon__orders__items__discounts` as child.
+
+Check: nested parent to second-level repeated child.
 
 ```sql
-SELECT
-    i.__polypheny_row_id AS item_row_id,
-    d.__polypheny_row_id AS discount_row_id,
-    d.__polypheny_parent_row_id AS discount_parent_row_id,
-    i.product_id,
-    d.code,
-    d.amount
+SELECT i.order_item_id, i.product_id, d.code, d.amount
 FROM pon__orders__items i
 JOIN pon__orders__items__discounts d
     ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 20;
+LIMIT 10;
 ```
 
-### 5. Nested parent - direct nested child, parent on right, inner join
+### Q24. Discounts to items, parent on right
+
+Expected plan: `P_JOIN`; direction detection should identify that the right input is the nested parent.
 
-Expected: supported. This validates nested parent-child direction when the child table is the left input.
+Check: swapped direction for nested parent-child join.
 
 ```sql
-SELECT
-    d.__polypheny_row_id AS discount_row_id,
-    d.code,
-    d.amount,
-    i.__polypheny_row_id AS item_row_id,
-    i.product_id
+SELECT d.code, d.amount, i.order_item_id, i.product_id
 FROM pon__orders__items__discounts d
 JOIN pon__orders__items i
     ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 20;
+LIMIT 10;
 ```
 
-### 6. Root parent - direct child, left join preserves parent rows
-PROBLEM
+### Q25. Reversed equality operands
+
+Expected plan: `P_JOIN`; the equality is written parent key first.
 
-Expected: supported. Parent is on the left, so unmatched parent rows can be emitted by adapter runtime.
+Check: join-key extraction independent of operand order.
 
 ```sql
-SELECT
-    o.order_id,
-    i.product_id,
-    i.quantity
+SELECT i.order_item_id, i.quantity, d.code, d.amount
+FROM pon__orders__items i
+JOIN pon__orders__items__discounts d
+    ON i.__polypheny_row_id = d.__polypheny_parent_row_id
+LIMIT 10;
+```
+
+### Q26. Orders to items, left join
+
+Expected plan: `P_JOIN` with join type `LEFT`.
+
+Check: parent-preserving outer join.
+
+```sql
+SELECT o.order_id, i.order_item_id, i.product_id
 FROM pon__orders o
 LEFT JOIN pon__orders__items i
     ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 20;
+LIMIT 10;
 ```
 
-### 7. Root parent - direct child, right join preserves parent rows
+### Q27. Items to orders, right join
+
+Expected plan: `P_JOIN` with join type `RIGHT`.
 
-Expected: supported. Parent is on the right, so this is the same preservation direction as the previous test but with swapped input order.
+Check: same parent-preserving semantics as Q26 with swapped input order.
 
 ```sql
-SELECT
-    i.product_id,
-    i.quantity,
-    o.order_id
+SELECT i.order_item_id, i.product_id, o.order_id
 FROM pon__orders__items i
 RIGHT JOIN pon__orders o
     ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 20;
+LIMIT 10;
 ```
 
-### 8. Nested parent - direct nested child, left join preserves nested parent rows
+### Q28. Items to discounts, full join
 
-Expected: supported. This checks unmatched `items` rows when an item has no discount.
+Expected plan: `P_JOIN` with join type `FULL`.
+
+Check: full outer structural join on nested parent and grandchild.
 
 ```sql
-SELECT
-    i.product_id,
-    d.code,
-    d.amount
+SELECT i.order_item_id, i.quantity, d.code, d.amount
 FROM pon__orders__items i
-LEFT JOIN pon__orders__items__discounts d
+FULL JOIN pon__orders__items__discounts d
     ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 20;
+LIMIT 10;
 ```
 
-### 9. Nested parent - direct nested child, right join preserves nested parent rows
+### Q29. Projected full join with filter and limit
+
+Expected plan: `P_JOIN` under `E_LIMIT` and final projection/calc. Both scans should carry the projected fields needed for selected columns, filter columns, and join keys.
 
-Expected: supported. Parent is on the right, so the runtime should still preserve unmatched parent rows.
+Check: the child scan projection bug case; child join key should map to physical column `1`.
 
 ```sql
-SELECT
-    d.code,
-    d.amount,
-    i.product_id
-FROM pon__orders__items__discounts d
-RIGHT JOIN pon__orders__items i
+SELECT i.order_item_id, i.quantity, i.price
+FROM pon__orders__items i
+FULL JOIN pon__orders__items__discounts d
     ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 20;
+WHERE i.quantity = 3
+LIMIT 10;
 ```
 
-### 10. Root parent - direct child, full join
+### Q30. Parent-side filter above structural join
+
+Expected plan: `P_JOIN`; parent filter should be pushed into the scan or join when possible.
 
-Expected: supported by the rule. Runtime preserves unmatched parent rows. It does not do an independent child-side outer scan outside the parent path.
+Check: filter placement for parent-side predicate.
 
 ```sql
-SELECT
-    o.order_id,
-    i.product_id,
-    i.quantity
+SELECT o.order_id, o.total_price, i.product_id, i.quantity
 FROM pon__orders o
-FULL JOIN pon__orders__items i
+JOIN pon__orders__items i
     ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 20;
+WHERE o.total_price >= 0
+LIMIT 10;
 ```
 
-### 11. Nested parent - direct nested child, full join
+### Q31. Child-side filter above structural join
 
-Expected: supported by the rule. Runtime preserves unmatched parent rows. It does not do an independent child-side outer scan outside the parent path.
+Expected plan: `P_JOIN`; child filter should be applied on the child branch or in the joined-row filter.
 
-```sql
-SELECT
-    i.product_id,
-    d.code,
-    d.amount
-FROM pon__orders__items i
-FULL JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 20;
-```
-
-### 12. Root parent - direct child with filter above join
-
-Expected: supported. The join should still become `ParquetRelJoin`; the filter may be pushed into the adapter join if translatable.
+Check: filter placement for child-side predicate.
 
 ```sql
-SELECT
-    o.order_id,
-    i.product_id,
-    i.quantity
+SELECT o.order_id, i.product_id, i.quantity
 FROM pon__orders o
 JOIN pon__orders__items i
     ON i.__polypheny_parent_row_id = o.__polypheny_row_id
 WHERE i.quantity > 1
-LIMIT 20;
+LIMIT 10;
 ```
 
-### 13. Nested parent - direct nested child with child-side filter
+### Q32. Filters on both sides of structural join
+
+Expected plan: `P_JOIN`; filters may split between parent and child branches.
 
-Expected: supported. The join should still become `ParquetRelJoin`; the discount filter should be applied on the child side or as an adapter-level joined-row filter.
+Check: filter splitting and field-index shifting for joined rows.
 
 ```sql
-SELECT
-    i.product_id,
-    d.code,
-    d.amount
+SELECT i.order_item_id, i.quantity, d.code, d.amount
 FROM pon__orders__items i
 JOIN pon__orders__items__discounts d
     ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE d.amount > 0
-LIMIT 20;
+WHERE i.quantity > 1 AND d.amount > 0
+LIMIT 10;
 ```
 
-### 14. Nested parent - direct nested child with parent-side filter
+### Q33. Aggregate above structural join
 
-Expected: supported. The join should still become `ParquetRelJoin`; the item filter should be applied before or during child expansion when possible.
+Expected plan: `E_AGGREGATE` above `P_JOIN`.
+
+Check: join can still be adapter-level when the final result is an aggregate.
 
 ```sql
-SELECT
-    i.product_id,
-    i.quantity,
-    d.code,
-    d.amount
+SELECT count(*)
 FROM pon__orders__items i
 JOIN pon__orders__items__discounts d
     ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE i.quantity > 1
-LIMIT 20;
+WHERE i.quantity > 0;
 ```
 
-### 15. Nested parent - direct nested child with projected columns
+### Q34. Group by above structural join
 
-Expected: supported. This validates plans where `Calc` or `Project` nodes appear between the join and the Parquet scans.
+Expected plan: `E_AGGREGATE` above `P_JOIN`.
+
+Check: grouped aggregate after adapter-level join.
 
 ```sql
-SELECT
-    i.__polypheny_row_id AS item_id,
-    d.__polypheny_row_id AS discount_id,
-    d.__polypheny_parent_row_id AS discount_parent_id,
-    d.__polypheny_elem_ordinal AS discount_ordinal,
-    i.product_id,
-    d.code,
-    d.amount
+SELECT i.product_id, count(*) AS discount_rows
 FROM pon__orders__items i
 JOIN pon__orders__items__discounts d
     ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 20;
+GROUP BY i.product_id
+LIMIT 10;
 ```
 
-### 16. Two adapter-level joins in one query, root to child and child to grandchild
+### Q35. Sort above structural join
+
+Expected plan: `E_SORT` or `E_LIMIT` above `P_JOIN`.
 
-Expected: both direct joins are individually supported. The ideal plan has `ParquetRelJoin` for both direct parent-child joins, but this should be verified because rule ordering can affect the final shape.
+Check: sorting after adapter-level join and projection.
 
 ```sql
-SELECT
-    o.order_id,
-    i.product_id,
-    d.code,
-    d.amount
+SELECT o.order_id, i.product_id, i.quantity
 FROM pon__orders o
 JOIN pon__orders__items i
     ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-JOIN pon__orders__items__discounts d
+ORDER BY i.quantity DESC
+LIMIT 10;
+```
+
+## Derived Tables And Calc Permutations
+
+### Q36. Projected parent and child derived tables
+
+Expected plan: ideally `P_JOIN`; derived-table projections should be consumed into `P_SCAN` fields.
+
+Check: projection-only `Calc` or `Project` wrappers on both join inputs.
+
+```sql
+SELECT o.order_id, i.product_id, i.quantity
+FROM (
+    SELECT __polypheny_row_id, order_id
+    FROM pon__orders
+) o
+JOIN (
+    SELECT __polypheny_parent_row_id, product_id, quantity
+    FROM pon__orders__items
+) i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
+```
+
+### Q37. Projected nested parent and grandchild derived tables
+
+Expected plan: `P_JOIN`; child `__polypheny_parent_row_id` must map through the projection to physical column `1`.
+
+Check: projection-only calc on the child side of an `items` to `discounts` join.
+
+```sql
+SELECT i.quantity, i.price, d.code, d.amount
+FROM (
+    SELECT __polypheny_row_id, quantity, price
+    FROM pon__orders__items
+) i
+JOIN (
+    SELECT __polypheny_parent_row_id, code, amount
+    FROM pon__orders__items__discounts
+) d
     ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 20;
+LIMIT 10;
 ```
 
-## Unsupported Or Fallback Join Shapes
+### Q38. Filtered derived table on parent branch
 
-### 17. Root parent - grandchild directly
+Expected plan: `P_JOIN` if the filter can be attached before join recognition; otherwise fallback indicates a rule gap.
 
-Expected: not supported as one adapter-level join. `discounts` points to `items`, not directly to `orders`.
+Check: filter/calc wrapper on parent input.
 
 ```sql
-SELECT
-    o.order_id,
-    d.code,
-    d.amount
-FROM pon__orders o
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 20;
+SELECT o.order_id, i.product_id, i.quantity
+FROM (
+    SELECT __polypheny_row_id, order_id
+    FROM pon__orders
+    WHERE total_price >= 0
+) o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
 ```
 
-### 18. Ancestor-descendant join beyond direct parent-child
+### Q39. Filtered derived table on child branch
+
+Expected plan: `P_JOIN` if the child filter and projection can be attached to the scan before join recognition.
 
-Expected: not supported as one adapter-level join. This tries to connect root rows directly to descendant discount rows by structural row-id prefix, not by direct `PRIMARY_KEY` to `PARENT_KEY`.
+Check: child-side `EnumerableCalc` projection/filter wrapper.
 
 ```sql
-SELECT
-    o.order_id,
-    d.code,
-    d.amount
+SELECT o.order_id, i.product_id, i.quantity
 FROM pon__orders o
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_row_id LIKE o.__polypheny_row_id || '/%'
-LIMIT 20;
+JOIN (
+    SELECT __polypheny_parent_row_id, product_id, quantity
+    FROM pon__orders__items
+    WHERE quantity > 1
+) i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
 ```
 
-### 19. Child-child join inside the same parent
+### Q40. Final projection hides all child join keys
+
+Expected plan: `P_JOIN`; scan projections still need join keys internally even though final output hides them.
 
-Expected: not supported at adapter level today. This requires a schema with another repeated child table such as `pon__orders__payments`.
+Check: join-key preservation through projection trimming.
 
 ```sql
-SELECT
-    i.product_id,
-    p.payment_id,
-    p.amount
+SELECT i.product_id, d.amount
 FROM pon__orders__items i
-JOIN pon__orders__payments p
-    ON p.__polypheny_parent_row_id = i.__polypheny_parent_row_id
-LIMIT 20;
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+WHERE d.amount > 0
+LIMIT 10;
 ```
 
-### 20. Same table self-join
+### Q41. Limit inside join input
 
-Expected: not supported at adapter level. This is a normal relational self-join.
+Expected plan: this is a semantic edge case. If the input limit stays below the join, the adapter join should not silently ignore it.
+
+Check: rule ordering around `E_LIMIT` below a structural join input.
 
 ```sql
-SELECT
-    i1.product_id AS product_1,
-    i2.product_id AS product_2
-FROM pon__orders__items i1
-JOIN pon__orders__items i2
-    ON i1.__polypheny_parent_row_id = i2.__polypheny_parent_row_id
-LIMIT 20;
+SELECT i.order_item_id, d.code, d.amount
+FROM (
+    SELECT __polypheny_row_id, order_item_id
+    FROM pon__orders__items
+    LIMIT 100
+) i
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 10;
 ```
 
-### 21. Parent-child tables but wrong generated keys
+## Multi-Join And Mixed Join Shapes
+
+### Q42. Orders to items to discounts chain
 
-Expected: not supported at adapter level. The child must join from `PARENT_KEY` to parent `PRIMARY_KEY`.
+Expected plan: direct structural joins are individually supported. Depending on rule composition, expect either nested `P_JOIN` use or one `P_JOIN` plus a normal join.
+
+Check: whether adapter-level joins compose across multiple levels.
 
 ```sql
-SELECT
-    o.order_id,
-    i.product_id
+SELECT o.order_id, i.product_id, d.code, d.amount
 FROM pon__orders o
 JOIN pon__orders__items i
-    ON i.__polypheny_row_id = o.__polypheny_row_id
-LIMIT 20;
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
+LIMIT 10;
 ```
 
-### 22. Parent-child tables joined on user columns
+### Q43. Structural join plus product lookup
 
-Expected: not supported at adapter level. Adapter-level nested joins only support generated structural keys.
+Expected plan: `orders` to `items` may become `P_JOIN`; `items` to `products` should be a normal join because it uses user columns.
+
+Check: mixed adapter-level and fallback join planning.
 
 ```sql
-SELECT
-    o.order_id,
-    i.product_id
+SELECT o.order_id, i.quantity, p.name, p.category
 FROM pon__orders o
 JOIN pon__orders__items i
-    ON o.order_id = i.order_item_id
-LIMIT 20;
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+JOIN pon__products p
+    ON p.product_id = i.product_id
+LIMIT 10;
 ```
 
-If `order_item_id` is not present in the dataset, replace it with another compatible user column. The important part is that the join does not use `__polypheny_parent_row_id = __polypheny_row_id`.
+### Q44. Customer to orders to items
 
-### 23. Multi-key join condition
+Expected plan: `customers` to `orders` is a normal user-column join; `orders` to `items` is structurally supported if the planner can isolate it.
 
-Expected: not supported as adapter-level join when the extra condition is part of the join condition. Put additional filters in `WHERE` if the structural join itself should still be recognized.
+Check: structural join recognition inside a larger query with a root-root user join.
 
 ```sql
-SELECT
-    o.order_id,
-    i.product_id,
-    i.quantity
-FROM pon__orders o
+SELECT c.customer_id, c.country, o.order_id, i.product_id
+FROM pon__customers c
+JOIN pon__orders o
+    ON o.customer_id = c.customer_id
 JOIN pon__orders__items i
     ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-   AND i.__polypheny_elem_ordinal = 0
-LIMIT 20;
+LIMIT 10;
 ```
 
-### 24. Non-equi join
+### Q45. Orders with two direct children
 
-Expected: not supported at adapter level. Only a single equality between parent `PRIMARY_KEY` and child `PARENT_KEY` is supported.
+Expected plan: each root-to-child join is structurally valid, but composition may fall back after one `P_JOIN`.
+
+Check: sibling child branches under the same parent.
 
 ```sql
-SELECT
-    o.order_id,
-    i.product_id
+SELECT o.order_id, i.product_id, s.city, s.zip
 FROM pon__orders o
 JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id <> o.__polypheny_row_id
-LIMIT 20;
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+JOIN pon__orders__shipping_address s
+    ON s.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
 ```
 
-### 25. OR join condition
+## Unsupported Or Fallback Plans
+
+### Q46. Customers to orders by user key
 
-Expected: not supported at adapter level. The join condition is not a single structural equi-join. (contains OR)
+Expected plan: no `P_JOIN`; use normal `E_JOIN` or equivalent.
+
+Check: root-root user-column join must not be mistaken for structural join.
 
 ```sql
-SELECT
-    o.order_id,
-    i.product_id
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-    OR i.__polypheny_elem_ordinal = 0
-LIMIT 20;
+SELECT c.customer_id, c.name, o.order_id, o.status
+FROM pon__customers c
+JOIN pon__orders o
+    ON o.customer_id = c.customer_id
+LIMIT 10;
 ```
 
-### 26. Cross join
+### Q47. Items to products by product id
+
+Expected plan: no `P_JOIN`; use normal join.
 
-Expected: not supported at adapter level. There is no structural parent-child equality condition.
+Check: nested table joined to root lookup table by user column.
 
 ```sql
-SELECT
-    o.order_id,
-    i.product_id
-FROM pon__orders o
-CROSS JOIN pon__orders__items i
-LIMIT 20;
+SELECT i.order_item_id, i.quantity, p.name, p.price
+FROM pon__orders__items i
+JOIN pon__products p
+    ON p.product_id = i.product_id
+LIMIT 10;
 ```
 
-### 27. Semi join through EXISTS
+### Q48. Orders directly to discounts
+
+Expected plan: no single `P_JOIN`; discounts are not a direct child of orders.
 
-Expected: not supported as `ParquetRelJoin`. This may become a semi-join or correlated plan in Polypheny.
+Check: ancestor-to-grandchild direct join is rejected.
 
 ```sql
-SELECT
-    o.order_id
+SELECT o.order_id, d.code, d.amount
 FROM pon__orders o
-WHERE EXISTS (
-    SELECT 1
-    FROM pon__orders__items i
-    WHERE i.__polypheny_parent_row_id = o.__polypheny_row_id
-)
-LIMIT 20;
+JOIN pon__orders__items__discounts d
+    ON d.__polypheny_parent_row_id = o.__polypheny_row_id
+LIMIT 10;
 ```
 
-### 28. Anti join through NOT EXISTS
+### Q49. Sibling child join
+
+Expected plan: no `P_JOIN`; items and shipping address are siblings, not parent and child.
 
-Expected: not supported as `ParquetRelJoin`. This may become an anti-join or correlated plan in Polypheny.
+Check: same-parent sibling relationship is rejected.
 
 ```sql
-SELECT
-    o.order_id
-FROM pon__orders o
-WHERE NOT EXISTS (
-    SELECT 1
-    FROM pon__orders__items i
-    WHERE i.__polypheny_parent_row_id = o.__polypheny_row_id
-)
-LIMIT 20;
+SELECT i.product_id, s.city, s.zip
+FROM pon__orders__items i
+JOIN pon__orders__shipping_address s
+    ON s.__polypheny_parent_row_id = i.__polypheny_parent_row_id
+LIMIT 10;
 ```
 
-### 29. Join between different Parquet sources
+### Q50. Same-table self join
+
+Expected plan: no `P_JOIN`; use normal self-join.
 
-Expected: not supported at adapter level. This requires another adapter/table prefix such as `pon2__orders`.
+Check: self-join on generated parent row id is not a structural parent-child join.
 
 ```sql
-SELECT
-    o.order_id,
-    o2.order_id AS other_order_id
-FROM pon__orders o
-JOIN pon2__orders o2
-    ON o.order_id = o2.order_id
-LIMIT 20;
+SELECT i1.product_id AS product_1, i2.product_id AS product_2
+FROM pon__orders__items i1
+JOIN pon__orders__items i2
+    ON i1.__polypheny_parent_row_id = i2.__polypheny_parent_row_id
+LIMIT 10;
 ```
 
-### 30. Join between normalized Parquet table and non-Parquet table
+### Q51. Parent-child tables with wrong generated keys
 
-Expected: not supported at adapter level. This is a normal Polypheny join.
+Expected plan: no `P_JOIN`; child primary key is not the parent reference key.
+
+Check: key-role validation in `supportedDirection()`.
 
 ```sql
-SELECT
-    o.order_id,
-    c.customer_id
+SELECT o.order_id, i.product_id
 FROM pon__orders o
-JOIN customers c
-    ON o.customer_id = c.customer_id
-LIMIT 20;
+JOIN pon__orders__items i
+    ON i.__polypheny_row_id = o.__polypheny_row_id
+LIMIT 10;
 ```
 
-### 31. Child-side preserving outer join
+### Q52. Parent-child tables joined by user columns
+
+Expected plan: no `P_JOIN`; structural keys are not used.
 
-Expected: accepted by the rule for the structural join, but child rows normally cannot exist without a parent in normalized data. This should be tested mainly to verify runtime semantics and null handling.
+Check: user-column join between parent and child falls back.
 
 ```sql
-SELECT
-    i.product_id,
-    o.order_id
-FROM pon__orders__items i
-LEFT JOIN pon__orders o
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 20;
+SELECT o.order_id, i.order_item_id
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON o.order_id = i.order_item_id
+LIMIT 10;
 ```
 
+### Q53. Multi-key join condition
 
-Failed query numbers:
-
-12, 13, 14, 19, 23, 29, 30
-
-Failure reasons:
+Expected plan: no `P_JOIN` if the extra equality remains part of the join condition.
 
-12, 13, 14, 23: row-type mismatch errors.
-19, 29, 30: Entity not found errors.
+Check: `supportedDirection()` requires exactly one equi-key on each side.
 
 ```sql
-SELECT *
-FROM pon__orders__items i
-FULL JOIN pon__orders__items__discounts d
-ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE i.quantity = 3;
+SELECT o.order_id, i.product_id
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+   AND i.__polypheny_elem_ordinal = o.order_id
+LIMIT 10;
 ```
 
+### Q54. Non-equi join condition
 
+Expected plan: no `P_JOIN`.
 
-
-### 31. Filtering + Count(*)
+Check: non-equi structural condition is rejected.
 
 ```sql
-SELECT count(*)
-FROM pon__orders__items i
-FULL JOIN pon__orders__items__discounts d
-ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE i.quantity = 3;
+SELECT o.order_id, i.product_id
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id <> o.__polypheny_row_id
+LIMIT 10;
 ```
 
+### Q55. OR join condition
 
+Expected plan: no `P_JOIN`.
 
-### Filtering + Projection 
+Check: disjunctive join condition is rejected.
 
 ```sql
-SELECT i.price
-FROM pon__orders__items i
-FULL JOIN pon__orders__items__discounts d
-ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE i.quantity = 3;
+SELECT o.order_id, i.product_id
+FROM pon__orders o
+JOIN pon__orders__items i
+    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
+    OR i.__polypheny_elem_ordinal = 0
+LIMIT 10;
 ```
 
+### Q56. EXISTS over structural relationship
 
-NOT working:
+Expected plan: no `P_JOIN` unless a future semi-join rule explicitly supports this shape.
 
-Incorrect limit values:
+Check: correlated/semi-join planning around nested tables.
 
-SELECT *
-FROM pon__orders__items i
-FULL JOIN pon__orders__items__discounts d
-ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE i.quantity = 3
+```sql
+SELECT o.order_id
+FROM pon__orders o
+WHERE EXISTS (
+    SELECT 1
+    FROM pon__orders__items i
+    WHERE i.__polypheny_parent_row_id = o.__polypheny_row_id
+)
 LIMIT 10;
+```
 
+### Q57. NOT EXISTS over structural relationship
 
-SELECT i.quantity
-FROM pon__orders__items i
-FULL JOIN pon__orders__items__discounts d
-ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE i.quantity = 3
+Expected plan: no `P_JOIN` unless a future anti-join rule explicitly supports this shape.
+
+Check: anti-join or correlated fallback planning.
+
+```sql
+SELECT o.order_id
+FROM pon__orders o
+WHERE NOT EXISTS (
+    SELECT 1
+    FROM pon__orders__items i
+    WHERE i.__polypheny_parent_row_id = o.__polypheny_row_id
+)
 LIMIT 10;
+```
+
diff --git a/plugins/parquet-adapter/docs/test_plan/pon_planning_query_matrix.md b/plugins/parquet-adapter/docs/test_plan/pon_planning_query_matrix.md
deleted file mode 100644
index b29ea7f2b9..0000000000
--- a/plugins/parquet-adapter/docs/test_plan/pon_planning_query_matrix.md
+++ /dev/null
@@ -1,852 +0,0 @@
-# PON Planning Query Matrix
-
-This checklist is for exercising different physical-planning shapes over the normalized `pon_` Parquet tables.
-
-The queries assume these table relationships:
-
-- `pon__customers`, `pon__orders`, and `pon__products` are root-level tables.
-- `pon__orders__items` is a direct child of `pon__orders`.
-- `pon__orders__shipping_address` is a direct child of `pon__orders`.
-- `pon__orders__items__discounts` is a direct child of `pon__orders__items`.
-- Supported adapter joins use generated structural keys: child `__polypheny_parent_row_id` to parent `__polypheny_row_id`.
-
-Plan terms below use the short PolyAlg names:
-
-- `P_SCAN`: Parquet scan.
-- `PE_CALC`: converter from Parquet convention to enumerable convention.
-- `P_JOIN`: adapter-level Parquet parent-child join.
-- `E_CALC`, `E_LIMIT`, `E_SORT`, `E_AGGREGATE`, `E_JOIN`: normal enumerable operators above or around Parquet nodes.
-
-For every supported parent-child join, also check that projected scan fields map the join keys correctly. In particular, for child tables the join key `__polypheny_parent_row_id` should map to physical column index `1`, not to `0`.
-
-## Simple Scans And Projections
-
-### Q01. Root table scan with limit
-
-Expected plan: one `P_SCAN` for `pon__orders`, wrapped by `PE_CALC`; limit remains above the scan as `E_LIMIT` unless a future rule pushes it.
-
-Check: baseline root-table planning and limit placement.
-
-```sql
-SELECT *
-FROM pon__orders
-LIMIT 10;
-```
-
-### Q02. Root table projection
-
-Expected plan: `P_SCAN` should read only projected order columns if projection pushdown applies.
-
-Check: simple projection pushdown on a root table.
-
-```sql
-SELECT order_id, status, total_price
-FROM pon__orders
-LIMIT 10;
-```
-
-### Q03. Customer projection
-
-Expected plan: `P_SCAN` for `pon__customers`, with only selected customer fields.
-
-Check: projection over another root table and text/timestamp columns.
-
-```sql
-SELECT customer_id, name, country, signup_date
-FROM pon__customers
-LIMIT 10;
-```
-
-### Q04. Product projection
-
-Expected plan: `P_SCAN` for `pon__products`, with only selected product fields.
-
-Check: projection over numeric and text product columns.
-
-```sql
-SELECT product_id, name, category, price, stock
-FROM pon__products
-LIMIT 10;
-```
-
-### Q05. Repeated child projection
-
-Expected plan: `P_SCAN` for `pon__orders__items`, with projected child fields only.
-
-Check: projection on a nested repeated child table.
-
-```sql
-SELECT order_item_id, product_id, quantity, price
-FROM pon__orders__items
-LIMIT 10;
-```
-
-### Q06. Repeated child structural columns
-
-Expected plan: `P_SCAN` for `pon__orders__items`, keeping the generated row id, parent row id, and ordinal.
-
-Check: scan of generated structural columns used by nested tables.
-
-```sql
-SELECT __polypheny_row_id, __polypheny_parent_row_id, __polypheny_elem_ordinal
-FROM pon__orders__items
-LIMIT 10;
-```
-
-### Q07. Nested grandchild projection
-
-Expected plan: `P_SCAN` for `pon__orders__items__discounts`, with projected discount fields.
-
-Check: projection on a second-level nested repeated child table.
-
-```sql
-SELECT code, amount
-FROM pon__orders__items__discounts
-LIMIT 10;
-```
-
-### Q08. Direct nested shipping-address projection
-
-Expected plan: `P_SCAN` for `pon__orders__shipping_address`, with projected address fields.
-
-Check: projection on a direct nested child that is not the `items` repeated branch.
-
-```sql
-SELECT city, street, zip
-FROM pon__orders__shipping_address
-LIMIT 10;
-```
-
-## Filters Without Joins
-
-### Q09. Root numeric filter
-
-Expected plan: `P_SCAN` with a translatable filter if filter pushdown applies; otherwise `E_CALC` or filter above `PE_CALC`.
-
-Check: root-table numeric predicate.
-
-```sql
-SELECT order_id, total_price
-FROM pon__orders
-WHERE total_price >= 0
-LIMIT 10;
-```
-
-### Q10. Root nullable text filter
-
-Expected plan: `P_SCAN` plus a status filter when supported.
-
-Check: nullable text predicate on root table.
-
-```sql
-SELECT order_id, status
-FROM pon__orders
-WHERE status IS NOT NULL
-LIMIT 10;
-```
-
-### Q11. Root timestamp filter
-
-Expected plan: `P_SCAN` plus a timestamp/nullability filter when supported.
-
-Check: timestamp column handling.
-
-```sql
-SELECT order_id, order_date
-FROM pon__orders
-WHERE order_date IS NOT NULL
-LIMIT 10;
-```
-
-### Q12. Customer text filter
-
-Expected plan: `P_SCAN` for `pon__customers`; text predicate may or may not be pushed.
-
-Check: text filter planning on a root table.
-
-```sql
-SELECT customer_id, name, country
-FROM pon__customers
-WHERE country IS NOT NULL
-LIMIT 10;
-```
-
-### Q13. Product numeric filter
-
-Expected plan: `P_SCAN` for `pon__products` with a numeric filter when supported.
-
-Check: product scan with numeric predicate and projection.
-
-```sql
-SELECT product_id, name, price, stock
-FROM pon__products
-WHERE price >= 0 AND stock >= 0
-LIMIT 10;
-```
-
-### Q14. Repeated child numeric filter
-
-Expected plan: `P_SCAN` for `pon__orders__items`, with `quantity` and `price` filters when supported.
-
-Check: filter pushdown on nested repeated table.
-
-```sql
-SELECT order_item_id, product_id, quantity, price
-FROM pon__orders__items
-WHERE quantity > 0 AND price >= 0
-LIMIT 10;
-```
-
-### Q15. Repeated child ordinal filter
-
-Expected plan: `P_SCAN` for `pon__orders__items`, possibly with a filter on generated ordinal.
-
-Check: generated ordinal field in a filter.
-
-```sql
-SELECT __polypheny_parent_row_id, __polypheny_elem_ordinal, product_id
-FROM pon__orders__items
-WHERE __polypheny_elem_ordinal = 0
-LIMIT 10;
-```
-
-### Q16. Nested grandchild filter
-
-Expected plan: `P_SCAN` for `pon__orders__items__discounts`, with `amount` filter when supported.
-
-Check: filter on second-level nested repeated table.
-
-```sql
-SELECT code, amount
-FROM pon__orders__items__discounts
-WHERE amount > 0
-LIMIT 10;
-```
-
-### Q17. Direct nested shipping-address filter
-
-Expected plan: `P_SCAN` for `pon__orders__shipping_address`; filter may remain as `E_CALC` if text predicates are not pushed.
-
-Check: filter on direct nested child branch different from `items`.
-
-```sql
-SELECT city, street, zip
-FROM pon__orders__shipping_address
-WHERE city IS NOT NULL AND zip IS NOT NULL
-LIMIT 10;
-```
-
-### Q18. Non-pushable expression filter
-
-Expected plan: `P_SCAN` for `pon__products`, with an enumerable calc/filter for the expression if it cannot be pushed.
-
-Check: fallback around a scan when the predicate contains an expression.
-
-```sql
-SELECT product_id, name, category
-FROM pon__products
-WHERE LOWER(category) IS NOT NULL
-LIMIT 10;
-```
-
-## Supported Parent-Child Joins
-
-### Q19. Orders to items, inner join
-
-Expected plan: `P_JOIN` with `pon__orders` as parent and `pon__orders__items` as child.
-
-Check: basic root parent to repeated child join.
-
-```sql
-SELECT o.order_id, i.order_item_id, i.product_id, i.quantity
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q20. Items to orders, inner join with parent on right
-
-Expected plan: `P_JOIN`; direction detection should identify that the right input is the parent.
-
-Check: supported join with swapped input order.
-
-```sql
-SELECT i.order_item_id, i.product_id, o.order_id, o.status
-FROM pon__orders__items i
-JOIN pon__orders o
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q21. Orders to shipping address
-
-Expected plan: `P_JOIN` with `pon__orders` as parent and `pon__orders__shipping_address` as child.
-
-Check: direct nested child branch that is not `items`.
-
-```sql
-SELECT o.order_id, s.city, s.street, s.zip
-FROM pon__orders o
-JOIN pon__orders__shipping_address s
-    ON s.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q22. Shipping address to orders, parent on right
-
-Expected plan: `P_JOIN`; direction detection should identify that the right input is the parent.
-
-Check: swapped direction for a direct nested child branch.
-
-```sql
-SELECT s.city, s.zip, o.order_id
-FROM pon__orders__shipping_address s
-JOIN pon__orders o
-    ON s.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q23. Items to discounts, inner join
-
-Expected plan: `P_JOIN` with `pon__orders__items` as parent and `pon__orders__items__discounts` as child.
-
-Check: nested parent to second-level repeated child.
-
-```sql
-SELECT i.order_item_id, i.product_id, d.code, d.amount
-FROM pon__orders__items i
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q24. Discounts to items, parent on right
-
-Expected plan: `P_JOIN`; direction detection should identify that the right input is the nested parent.
-
-Check: swapped direction for nested parent-child join.
-
-```sql
-SELECT d.code, d.amount, i.order_item_id, i.product_id
-FROM pon__orders__items__discounts d
-JOIN pon__orders__items i
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q25. Reversed equality operands
-
-Expected plan: `P_JOIN`; the equality is written parent key first.
-
-Check: join-key extraction independent of operand order.
-
-```sql
-SELECT i.order_item_id, i.quantity, d.code, d.amount
-FROM pon__orders__items i
-JOIN pon__orders__items__discounts d
-    ON i.__polypheny_row_id = d.__polypheny_parent_row_id
-LIMIT 10;
-```
-
-### Q26. Orders to items, left join
-
-Expected plan: `P_JOIN` with join type `LEFT`.
-
-Check: parent-preserving outer join.
-
-```sql
-SELECT o.order_id, i.order_item_id, i.product_id
-FROM pon__orders o
-LEFT JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q27. Items to orders, right join
-
-Expected plan: `P_JOIN` with join type `RIGHT`.
-
-Check: same parent-preserving semantics as Q26 with swapped input order.
-
-```sql
-SELECT i.order_item_id, i.product_id, o.order_id
-FROM pon__orders__items i
-RIGHT JOIN pon__orders o
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q28. Items to discounts, full join
-
-Expected plan: `P_JOIN` with join type `FULL`.
-
-Check: full outer structural join on nested parent and grandchild.
-
-```sql
-SELECT i.order_item_id, i.quantity, d.code, d.amount
-FROM pon__orders__items i
-FULL JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q29. Projected full join with filter and limit
-
-Expected plan: `P_JOIN` under `E_LIMIT` and final projection/calc. Both scans should carry the projected fields needed for selected columns, filter columns, and join keys.
-
-Check: the child scan projection bug case; child join key should map to physical column `1`.
-
-```sql
-SELECT i.order_item_id, i.quantity, i.price
-FROM pon__orders__items i
-FULL JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE i.quantity = 3
-LIMIT 10;
-```
-
-### Q30. Parent-side filter above structural join
-
-Expected plan: `P_JOIN`; parent filter should be pushed into the scan or join when possible.
-
-Check: filter placement for parent-side predicate.
-
-```sql
-SELECT o.order_id, o.total_price, i.product_id, i.quantity
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-WHERE o.total_price >= 0
-LIMIT 10;
-```
-
-### Q31. Child-side filter above structural join
-
-Expected plan: `P_JOIN`; child filter should be applied on the child branch or in the joined-row filter.
-
-Check: filter placement for child-side predicate.
-
-```sql
-SELECT o.order_id, i.product_id, i.quantity
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-WHERE i.quantity > 1
-LIMIT 10;
-```
-
-### Q32. Filters on both sides of structural join
-
-Expected plan: `P_JOIN`; filters may split between parent and child branches.
-
-Check: filter splitting and field-index shifting for joined rows.
-
-```sql
-SELECT i.order_item_id, i.quantity, d.code, d.amount
-FROM pon__orders__items i
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE i.quantity > 1 AND d.amount > 0
-LIMIT 10;
-```
-
-### Q33. Aggregate above structural join
-
-Expected plan: `E_AGGREGATE` above `P_JOIN`.
-
-Check: join can still be adapter-level when the final result is an aggregate.
-
-```sql
-SELECT count(*)
-FROM pon__orders__items i
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE i.quantity > 0;
-```
-
-### Q34. Group by above structural join
-
-Expected plan: `E_AGGREGATE` above `P_JOIN`.
-
-Check: grouped aggregate after adapter-level join.
-
-```sql
-SELECT i.product_id, count(*) AS discount_rows
-FROM pon__orders__items i
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-GROUP BY i.product_id
-LIMIT 10;
-```
-
-### Q35. Sort above structural join
-
-Expected plan: `E_SORT` or `E_LIMIT` above `P_JOIN`.
-
-Check: sorting after adapter-level join and projection.
-
-```sql
-SELECT o.order_id, i.product_id, i.quantity
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-ORDER BY i.quantity DESC
-LIMIT 10;
-```
-
-## Derived Tables And Calc Permutations
-
-### Q36. Projected parent and child derived tables
-
-Expected plan: ideally `P_JOIN`; derived-table projections should be consumed into `P_SCAN` fields.
-
-Check: projection-only `Calc` or `Project` wrappers on both join inputs.
-
-```sql
-SELECT o.order_id, i.product_id, i.quantity
-FROM (
-    SELECT __polypheny_row_id, order_id
-    FROM pon__orders
-) o
-JOIN (
-    SELECT __polypheny_parent_row_id, product_id, quantity
-    FROM pon__orders__items
-) i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q37. Projected nested parent and grandchild derived tables
-
-Expected plan: `P_JOIN`; child `__polypheny_parent_row_id` must map through the projection to physical column `1`.
-
-Check: projection-only calc on the child side of an `items` to `discounts` join.
-
-```sql
-SELECT i.quantity, i.price, d.code, d.amount
-FROM (
-    SELECT __polypheny_row_id, quantity, price
-    FROM pon__orders__items
-) i
-JOIN (
-    SELECT __polypheny_parent_row_id, code, amount
-    FROM pon__orders__items__discounts
-) d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q38. Filtered derived table on parent branch
-
-Expected plan: `P_JOIN` if the filter can be attached before join recognition; otherwise fallback indicates a rule gap.
-
-Check: filter/calc wrapper on parent input.
-
-```sql
-SELECT o.order_id, i.product_id, i.quantity
-FROM (
-    SELECT __polypheny_row_id, order_id
-    FROM pon__orders
-    WHERE total_price >= 0
-) o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q39. Filtered derived table on child branch
-
-Expected plan: `P_JOIN` if the child filter and projection can be attached to the scan before join recognition.
-
-Check: child-side `EnumerableCalc` projection/filter wrapper.
-
-```sql
-SELECT o.order_id, i.product_id, i.quantity
-FROM pon__orders o
-JOIN (
-    SELECT __polypheny_parent_row_id, product_id, quantity
-    FROM pon__orders__items
-    WHERE quantity > 1
-) i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q40. Final projection hides all child join keys
-
-Expected plan: `P_JOIN`; scan projections still need join keys internally even though final output hides them.
-
-Check: join-key preservation through projection trimming.
-
-```sql
-SELECT i.product_id, d.amount
-FROM pon__orders__items i
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE d.amount > 0
-LIMIT 10;
-```
-
-### Q41. Limit inside join input
-
-Expected plan: this is a semantic edge case. If the input limit stays below the join, the adapter join should not silently ignore it.
-
-Check: rule ordering around `E_LIMIT` below a structural join input.
-
-```sql
-SELECT i.order_item_id, d.code, d.amount
-FROM (
-    SELECT __polypheny_row_id, order_item_id
-    FROM pon__orders__items
-    LIMIT 100
-) i
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 10;
-```
-
-## Multi-Join And Mixed Join Shapes
-
-### Q42. Orders to items to discounts chain
-
-Expected plan: direct structural joins are individually supported. Depending on rule composition, expect either nested `P_JOIN` use or one `P_JOIN` plus a normal join.
-
-Check: whether adapter-level joins compose across multiple levels.
-
-```sql
-SELECT o.order_id, i.product_id, d.code, d.amount
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q43. Structural join plus product lookup
-
-Expected plan: `orders` to `items` may become `P_JOIN`; `items` to `products` should be a normal join because it uses user columns.
-
-Check: mixed adapter-level and fallback join planning.
-
-```sql
-SELECT o.order_id, i.quantity, p.name, p.category
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-JOIN pon__products p
-    ON p.product_id = i.product_id
-LIMIT 10;
-```
-
-### Q44. Customer to orders to items
-
-Expected plan: `customers` to `orders` is a normal user-column join; `orders` to `items` is structurally supported if the planner can isolate it.
-
-Check: structural join recognition inside a larger query with a root-root user join.
-
-```sql
-SELECT c.customer_id, c.country, o.order_id, i.product_id
-FROM pon__customers c
-JOIN pon__orders o
-    ON o.customer_id = c.customer_id
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q45. Orders with two direct children
-
-Expected plan: each root-to-child join is structurally valid, but composition may fall back after one `P_JOIN`.
-
-Check: sibling child branches under the same parent.
-
-```sql
-SELECT o.order_id, i.product_id, s.city, s.zip
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-JOIN pon__orders__shipping_address s
-    ON s.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-## Unsupported Or Fallback Plans
-
-### Q46. Customers to orders by user key
-
-Expected plan: no `P_JOIN`; use normal `E_JOIN` or equivalent.
-
-Check: root-root user-column join must not be mistaken for structural join.
-
-```sql
-SELECT c.customer_id, c.name, o.order_id, o.status
-FROM pon__customers c
-JOIN pon__orders o
-    ON o.customer_id = c.customer_id
-LIMIT 10;
-```
-
-### Q47. Items to products by product id
-
-Expected plan: no `P_JOIN`; use normal join.
-
-Check: nested table joined to root lookup table by user column.
-
-```sql
-SELECT i.order_item_id, i.quantity, p.name, p.price
-FROM pon__orders__items i
-JOIN pon__products p
-    ON p.product_id = i.product_id
-LIMIT 10;
-```
-
-### Q48. Orders directly to discounts
-
-Expected plan: no single `P_JOIN`; discounts are not a direct child of orders.
-
-Check: ancestor-to-grandchild direct join is rejected.
-
-```sql
-SELECT o.order_id, d.code, d.amount
-FROM pon__orders o
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q49. Sibling child join
-
-Expected plan: no `P_JOIN`; items and shipping address are siblings, not parent and child.
-
-Check: same-parent sibling relationship is rejected.
-
-```sql
-SELECT i.product_id, s.city, s.zip
-FROM pon__orders__items i
-JOIN pon__orders__shipping_address s
-    ON s.__polypheny_parent_row_id = i.__polypheny_parent_row_id
-LIMIT 10;
-```
-
-### Q50. Same-table self join
-
-Expected plan: no `P_JOIN`; use normal self-join.
-
-Check: self-join on generated parent row id is not a structural parent-child join.
-
-```sql
-SELECT i1.product_id AS product_1, i2.product_id AS product_2
-FROM pon__orders__items i1
-JOIN pon__orders__items i2
-    ON i1.__polypheny_parent_row_id = i2.__polypheny_parent_row_id
-LIMIT 10;
-```
-
-### Q51. Parent-child tables with wrong generated keys
-
-Expected plan: no `P_JOIN`; child primary key is not the parent reference key.
-
-Check: key-role validation in `supportedDirection()`.
-
-```sql
-SELECT o.order_id, i.product_id
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q52. Parent-child tables joined by user columns
-
-Expected plan: no `P_JOIN`; structural keys are not used.
-
-Check: user-column join between parent and child falls back.
-
-```sql
-SELECT o.order_id, i.order_item_id
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON o.order_id = i.order_item_id
-LIMIT 10;
-```
-
-### Q53. Multi-key join condition
-
-Expected plan: no `P_JOIN` if the extra equality remains part of the join condition.
-
-Check: `supportedDirection()` requires exactly one equi-key on each side.
-
-```sql
-SELECT o.order_id, i.product_id
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-   AND i.__polypheny_elem_ordinal = o.order_id
-LIMIT 10;
-```
-
-### Q54. Non-equi join condition
-
-Expected plan: no `P_JOIN`.
-
-Check: non-equi structural condition is rejected.
-
-```sql
-SELECT o.order_id, i.product_id
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id <> o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q55. OR join condition
-
-Expected plan: no `P_JOIN`.
-
-Check: disjunctive join condition is rejected.
-
-```sql
-SELECT o.order_id, i.product_id
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-    OR i.__polypheny_elem_ordinal = 0
-LIMIT 10;
-```
-
-### Q56. EXISTS over structural relationship
-
-Expected plan: no `P_JOIN` unless a future semi-join rule explicitly supports this shape.
-
-Check: correlated/semi-join planning around nested tables.
-
-```sql
-SELECT o.order_id
-FROM pon__orders o
-WHERE EXISTS (
-    SELECT 1
-    FROM pon__orders__items i
-    WHERE i.__polypheny_parent_row_id = o.__polypheny_row_id
-)
-LIMIT 10;
-```
-
-### Q57. NOT EXISTS over structural relationship
-
-Expected plan: no `P_JOIN` unless a future anti-join rule explicitly supports this shape.
-
-Check: anti-join or correlated fallback planning.
-
-```sql
-SELECT o.order_id
-FROM pon__orders o
-WHERE NOT EXISTS (
-    SELECT 1
-    FROM pon__orders__items i
-    WHERE i.__polypheny_parent_row_id = o.__polypheny_row_id
-)
-LIMIT 10;
-```
-
diff --git a/plugins/parquet-adapter/docs/todo_joins.md b/plugins/parquet-adapter/docs/todo_joins.md
deleted file mode 100644
index 220ea962c8..0000000000
--- a/plugins/parquet-adapter/docs/todo_joins.md
+++ /dev/null
@@ -1,48 +0,0 @@
-# TODO: Adapter Level Joins
-
-## Tests
-
-- Add tests for all supported join types: `INNER`, `LEFT`, `RIGHT`, and `FULL`.
-- Add tests where the parent table is on the left side of the join.
-- Add tests where the parent table is on the right side of the join.
-- Add tests for joins where the parent table is the root table.
-- Add tests for joins where the parent table is itself nested and the child table is below it.
-- Add tests for filters above adapter-level joins.
-- Add tests for calc nodes above adapter-level joins, especially filter plus projection.
-- Add tests that unsupported joins fall back to Polypheny-level join execution.
-- Add result-equivalence tests comparing adapter-level join output with normal Polypheny join output.
-
-## Future Join Tests
-
-- Add tests for child-child joins inside the same parent when this join shape is supported.
-- Add tests for ancestor-descendant joins beyond direct parent-child joins when this join shape is supported.
-- Add fallback tests proving child-child joins and ancestor-descendant joins remain Polypheny-level joins until adapter-level support is implemented.
-
-## Join Semantics
-
-- Clarify and test `FULL` join behavior for nested parent-child data.
-- Document that the runtime preserves unmatched parent rows but does not perform an independent child-side outer scan outside the parent path.
-- Verify null-side row construction for `LEFT`, `RIGHT`, and `FULL` joins in both parent-left and parent-right directions.
-
-## Future Join Scope
-
-- Add support for child-child joins inside the same parent. Example: root has repeated `orders` and repeated `payments`, and the query joins both child tables through the same parent key. The adapter could read one parent row, expand both child collections under it, and join them locally. This would require a new runtime strategy because the current `ParquetNestedJoinEnumerator` assumes one parent path and one descendant child path, not two sibling child paths.
-- Add support for ancestor-descendant joins beyond direct parent-child joins. Example: `root -> order -> item`, where a query joins root to item or order to item. The current path-prefix logic is already close to this, but the key-role model may need clearer handling if the immediate `PARENT_KEY` points only to the direct parent.
-
-## Planner Rules
-
-- Check whether valid plans are missed when projections, filters, or calcs appear between the join and `ParquetRelScan`.
-- Consider whether additional rules are needed to recognize supported joins after simple projection reshaping.
-- Confirm that unsupported joins always remain executable through normal Polypheny planning.
-
-## Costing
-
-- Improve `ParquetRelJoin.computeSelfCost(...)`.
-- Include source scan cost, nested expansion cost, expected child cardinality, and pushed filter selectivity when possible.
-- Check whether the planner chooses adapter-level joins too eagerly or not eagerly enough.
-
-## Runtime Robustness
-
-- Add tests for empty parent groups, empty child groups, missing optional nested fields, and repeated child groups.
-- Verify cancellation behavior during nested join expansion.
-- Verify close/error handling when join execution fails while reading the Parquet source.
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
index 129dcdbfbe..421b71fa0b 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
@@ -58,6 +58,9 @@ public ParquetPlugin( PluginContext context ) {
     }
 
 
+    /**
+     * Register nodes
+     */
     private void registerPolyAlg() {
         if ( PolyAlgRegistry.getClass( "P_SCAN" ) != null ) {
             return;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
index 3c76544fb7..9a27e8e0dc 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
@@ -108,7 +108,6 @@ public RelationalDataSource asRelationalDataSource() {
     public AlgNode getRelScan( long allocId, AlgBuilder builder ) {
         // This rule is still required for filter movement before the adapter rules inspect the tree.
         builder.getCluster().getPlanner().addRuleDuringRuntime( FilterSetOpTransposeRule.INSTANCE );
-//        ParquetConvention.INSTANCE.register( builder.getCluster().getPlanner() );
         PhysicalEntity entity = getCatalog().getPhysicalsFromAllocs( allocId ).get( 0 );
         ParquetRelTable table = entity.unwrapOrThrow( ParquetRelTable.class );
         return new ParquetRelScan( builder.getCluster(), table, IntStream.range( 0, table.getFieldCount() ).toArray() );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java
index 7708bece12..cdcc82431b 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java
@@ -72,6 +72,7 @@ public boolean moveNext() {
                     if ( !sourceFileEvaluator.matches( sourceFile, filters ) ) {
                         continue;
                     }
+                    // create Enumerator
                     currentEnumerator = enumeratorFactory.apply( sourceFile );
                 }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetSourceFilePartitionFilterEvaluator.java
similarity index 76%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetSourceFilePartitionFilterEvaluator.java
index 2726307a6d..26ff51335e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetSourceFilePartitionFilterEvaluator.java
@@ -14,21 +14,25 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.schema;
+package org.polypheny.db.adapter.parquet.relational.execution;
 
 import java.util.function.Function;
+
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.shared.filter.FilterEvaluator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.type.entity.PolyNull;
 import org.polypheny.db.type.entity.PolyString;
 
 
-class ParquetSourceFileFilterEvaluator extends FilterEvaluator<ParquetSourceFile> {
+public class ParquetSourceFilePartitionFilterEvaluator extends FilterEvaluator<ParquetSourceFile> {
 
     private final Function<ParquetAdapterFilter, ParquetColumnBinding> selector;
 
 
-    ParquetSourceFileFilterEvaluator( Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
+    public ParquetSourceFilePartitionFilterEvaluator(Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
         this.selector = selector;
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileStatisticsFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetSourceFileStatisticsFilterEvaluator.java
similarity index 86%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileStatisticsFilterEvaluator.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetSourceFileStatisticsFilterEvaluator.java
index 2555a824d8..eac0669794 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileStatisticsFilterEvaluator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetSourceFileStatisticsFilterEvaluator.java
@@ -14,9 +14,14 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.schema;
+package org.polypheny.db.adapter.parquet.relational.execution;
 
 import java.util.function.Function;
+
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnStatistics;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.shared.filter.FilterEvaluator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
@@ -27,13 +32,13 @@
  * Uses Parquet footer min/max/null-count statistics to reject files that cannot
  * contain rows matching a filter.
  */
-class ParquetSourceFileStatisticsFilterEvaluator extends FilterEvaluator<ParquetSourceFile> {
+public class ParquetSourceFileStatisticsFilterEvaluator extends FilterEvaluator<ParquetSourceFile> {
 
     private final Function<ParquetAdapterFilter, ParquetColumnBinding> selector;
     private final ParquetTypeConverter typeConverter;
 
 
-    ParquetSourceFileStatisticsFilterEvaluator( Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
+    public ParquetSourceFileStatisticsFilterEvaluator( Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
         this.selector = selector;
         this.typeConverter = new ParquetTypeConverter();
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/EnumerableParquet.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/EnumerableParquet.java
index a2494a94c0..5d93dd3f11 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/EnumerableParquet.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/EnumerableParquet.java
@@ -33,7 +33,7 @@
 import org.polypheny.db.plan.ConventionTraitDef;
 
 /**
- * Converter from Parquet convention back to the standard enumerable pipeline.
+ * Planner node that converts ParquetScan node from Parquet convention to enumerable convention.
  */
 public class EnumerableParquet extends ConverterImpl implements EnumerableAlg {
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetAlgOptRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetAlgOptRule.java
index c7d4891784..b20aee2659 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetAlgOptRule.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetAlgOptRule.java
@@ -19,6 +19,10 @@
 import org.polypheny.db.plan.AlgOptRule;
 import org.polypheny.db.plan.AlgOptRuleCall;
 
+/**
+ * Adapter between Polypheny’s rule engine and your own PatternMatcher abstraction
+ * It does not contain Parquet planning logic itself. It only delegates.
+ */
 public class ParquetAlgOptRule extends AlgOptRule {
 
     private final PatternMatcher matcher;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java
index bae5fa0a6f..148ed19e7d 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java
@@ -37,6 +37,10 @@ private ParquetConvention() {
     }
 
 
+    /**
+     * Register Rules
+     * @param planner Planner
+     */
     @Override
     public void register( AlgPlanner planner ) {
         for ( AlgOptRule rule : ParquetRules.rules( this ) ) {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
index 7ae91d72e0..4cd484469b 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
@@ -47,14 +47,14 @@
 import org.polypheny.db.schema.trait.ModelTrait;
 
 /**
- * Parquet-convention join for supported parent/child table joins.
+ *  Planner node that represents a join the Parquet adapter can execute itself
  */
 public class ParquetRelJoin extends Join implements ParquetAlg {
 
-    private final boolean leftIsParent;
+    private final boolean leftIsParent; //whether the left input is the parent side
     private final PhysicalScan leftScan;
     private final PhysicalScan rightScan;
-    private final List<ParquetAdapterFilter> joinFilters;
+    private final List<ParquetAdapterFilter> joinFilters; //filters that belong to the joined output
 
 
     public ParquetRelJoin(
@@ -148,7 +148,7 @@ private static int mappedColumn(int[] fields, int inputIndex, int totalFieldsCou
             return -1;
         }
         int column = fields[inputIndex];
-        return column < 0 || column >= totalFieldsCount ? -1 : column;
+        return column < 0 || column >= totalFieldsCount ? -1 : column; // one key on each side
     }
 
 
@@ -213,7 +213,11 @@ public AlgWriter explainTerms(AlgWriter pw) {
                 .item("joinFilters", joinFilters);
     }
 
-
+    /**
+     * Generates execution call
+     * @param implementor EnumerableAlgImplementor
+     * @return Expression
+     */
     @Override
     public Expression implement(EnumerableAlgImplementor implementor) {
         Expression leftScanExpr = Expressions.new_(
@@ -249,7 +253,10 @@ public Expression implement(EnumerableAlgImplementor implementor) {
                 runtimeFilters);
     }
 
-
+    /**
+     * Decides whether parent rows without matching child rows should still be emitted for LEFT, RIGHT, or FULL joins.
+     * @return boolean
+     */
     private boolean emitUnmatchedParents() {
         return switch (joinType) {
             case LEFT -> leftIsParent;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
index 6cc603d5b6..d83f8a4afc 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
@@ -18,6 +18,7 @@
 
 import java.util.ArrayList;
 import java.util.List;
+
 import lombok.Getter;
 import org.apache.calcite.linq4j.tree.Expression;
 import org.apache.calcite.linq4j.tree.Expressions;
@@ -52,44 +53,44 @@ public class ParquetRelScan extends RelScan<ParquetRelTable> implements ParquetA
     private final List<ParquetAdapterFilter> filters;
 
 
-    public ParquetRelScan(AlgCluster cluster, ParquetRelTable table, int[] fields ) {
-        this( cluster, table, fields, List.of() );
+    public ParquetRelScan(AlgCluster cluster, ParquetRelTable table, int[] fields) {
+        this(cluster, table, fields, List.of());
     }
 
 
-    public ParquetRelScan(AlgCluster cluster, ParquetRelTable table, int[] fields, List<ParquetAdapterFilter> filters ) {
-        super( cluster, cluster.traitSetOf( ParquetConvention.INSTANCE ).replace( ModelTrait.RELATIONAL ), table );
+    public ParquetRelScan(AlgCluster cluster, ParquetRelTable table, int[] fields, List<ParquetAdapterFilter> filters) {
+        super(cluster, cluster.traitSetOf(ParquetConvention.INSTANCE).replace(ModelTrait.RELATIONAL), table);
         this.fields = fields;
-        this.filters = List.copyOf( filters );
+        this.filters = List.copyOf(filters);
     }
 
 
     @Override
-    public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
+    public AlgNode copy(AlgTraitSet traitSet, List<AlgNode> inputs) {
         assert inputs.isEmpty();
-        return new ParquetRelScan( getCluster(), entity, fields, filters );
+        return new ParquetRelScan(getCluster(), entity, fields, filters);
     }
 
 
     @Override
-    public AlgWriter explainTerms( AlgWriter pw ) {
-        return super.explainTerms( pw ).item( "fields", Primitive.asList( fields ) ).item( "filters", filters );
+    public AlgWriter explainTerms(AlgWriter pw) {
+        return super.explainTerms(pw).item("fields", Primitive.asList(fields)).item("filters", filters);
     }
 
 
     @Override
     public String algCompareString() {
-        return super.algCompareString() + "$fields=" + Primitive.asList( fields ) + "$filters=" + filters;
+        return super.algCompareString() + "$fields=" + Primitive.asList(fields) + "$filters=" + filters;
     }
 
 
     @Override
     public PolyAlgArgs bindArguments() {
-        List<String> fieldNames = ParquetPolyAlgDisplay.fieldNames( entity, fields );
-        List<String> tableFieldNames = ParquetPolyAlgDisplay.fieldNames( entity );
+        List<String> fieldNames = ParquetPolyAlgDisplay.fieldNames(entity, fields);
+        List<String> tableFieldNames = ParquetPolyAlgDisplay.fieldNames(entity);
         return super.bindArguments()
-                .put( "fields", new ListArg<>( fieldNames, StringArg::new ) )
-                .put( "filters", new ListArg<>( ParquetPolyAlgDisplay.filters( filters, tableFieldNames ), StringArg::new ) );
+                .put("fields", new ListArg<>(fieldNames, StringArg::new))
+                .put("filters", new ListArg<>(ParquetPolyAlgDisplay.filters(filters, tableFieldNames), StringArg::new));
     }
 
 
@@ -97,48 +98,53 @@ public PolyAlgArgs bindArguments() {
     public AlgDataType deriveRowType() {
         final List<AlgDataTypeField> fieldList = entity.getTupleType().getFields();
         final AlgDataTypeFactory.Builder builder = getCluster().getTypeFactory().builder();
-        for ( int field : fields ) {
-            builder.add( fieldList.get( field ) );
+        for (int field : fields) {
+            builder.add(fieldList.get(field));
         }
         return builder.build();
     }
 
 
     @Override
-    public void register( AlgPlanner planner ) {
-        ParquetConvention.INSTANCE.register( planner );
+    public void register(AlgPlanner planner) {
+        ParquetConvention.INSTANCE.register(planner);
     }
 
 
     @Override
-    public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
+    public AlgOptCost computeSelfCost(AlgPlanner planner, AlgMetadataQuery mq) {
         double fieldRatio = ((double) fields.length + 2D) / ((double) entity.getTupleType().getFieldCount() + 2D);
         double filterRatio = filters.isEmpty() ? 1D : 0.5D;
-        return super.computeSelfCost( planner, mq ).multiplyBy( ParquetConvention.COST_MULTIPLIER * fieldRatio * filterRatio );
+        return super.computeSelfCost(planner, mq).multiplyBy(ParquetConvention.COST_MULTIPLIER * fieldRatio * filterRatio);
     }
 
 
-    public ParquetRelScan withFilters(List<ParquetAdapterFilter> filters ) {
-        List<ParquetAdapterFilter> combinedFilters = new ArrayList<>( this.filters );
-        combinedFilters.addAll( filters );
-        return new ParquetRelScan( getCluster(), entity, fields, combinedFilters );
+    public ParquetRelScan withFilters(List<ParquetAdapterFilter> filters) {
+        List<ParquetAdapterFilter> combinedFilters = new ArrayList<>(this.filters);
+        combinedFilters.addAll(filters);
+        return new ParquetRelScan(getCluster(), entity, fields, combinedFilters);
     }
 
 
-    public ParquetRelScan withFields(int[] fields ) {
-        return new ParquetRelScan( getCluster(), entity, fields, filters );
+    public ParquetRelScan withFields(int[] fields) {
+        return new ParquetRelScan(getCluster(), entity, fields, filters);
     }
 
-
+    /**
+     * The function defines the expression call: function to call when node invoked
+     *
+     * @param implementor original implementor
+     * @return a call expression
+     */
     @Override
-    public Expression implement( EnumerableAlgImplementor implementor ) {
-        Expression runtimeFilters = EnumUtils.expressionList( filters.stream().map( ParquetAdapterFilter::toExpression ).toList() );
+    public Expression implement(EnumerableAlgImplementor implementor) {
+        Expression runtimeFilters = EnumUtils.expressionList(filters.stream().map(ParquetAdapterFilter::toExpression).toList());
         return Expressions.call(
-                entity.asExpression( ParquetRelTable.class ),
-                "project",
-                implementor.getRootExpression(),
-                Expressions.constant( fields ),
-                runtimeFilters );
+                entity.asExpression(ParquetRelTable.class), // class
+                "project", // function to call
+                implementor.getRootExpression(), // context - parameter 1
+                Expressions.constant(fields), // projection - parameter 2
+                runtimeFilters); // filters - parameter 3
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRules.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRules.java
index 5f0741a707..56d235930d 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRules.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRules.java
@@ -49,6 +49,9 @@ public static List<AlgOptRule> rules( ParquetConvention out, AlgBuilderFactory f
     }
 
 
+    /**
+     * Rule that converts a plan from Parquet convention into Enumerable convention
+     */
     public static class EnumerableParquetRule extends ConverterRule {
 
         public EnumerableParquetRule( ParquetConvention in, AlgBuilderFactory algBuilderFactory ) {
@@ -58,7 +61,7 @@ public EnumerableParquetRule( ParquetConvention in, AlgBuilderFactory algBuilder
                     in,
                     EnumerableConvention.INSTANCE,
                     algBuilderFactory,
-                    "P_CALC" );
+                    "EnumerableParquetRule" );
         }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java
index 2a70a0eb99..1946c6b794 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java
@@ -126,6 +126,9 @@ private static ParquetRelScan applyCalc( EnumerableCalc calc, ParquetRelScan sca
     }
 
 
+    /**
+     * Rule that converts a generic enumerable join into a Parquet adapter-level join.
+     */
     public static PatternMatcher joinWithScanOnLeftAndScanOnRight( ParquetConvention out, AlgBuilderFactory factory ) {
         return new PatternMatcher(
                 out,
@@ -146,6 +149,9 @@ public static PatternMatcher joinWithScanOnLeftAndScanOnRight( ParquetConvention
     }
 
 
+    /**
+     * Rule that pushes a filter from a Calc above a ParquetRelJoin into the join node.
+     */
     public static PatternMatcher attachFilterToJoinUnderCalc( ParquetConvention out, AlgBuilderFactory factory ) {
         return new PatternMatcher(
                 out,
@@ -176,13 +182,16 @@ public static PatternMatcher attachFilterToJoinUnderCalc( ParquetConvention out,
     }
 
 
+    /**
+     * Rule that pushes a simple EnumerableCalc projection/filter into a ParquetRelScan
+     */
     public static PatternMatcher attachFieldsAndFiltersToScanUnderCalc( ParquetConvention out, AlgBuilderFactory factory ) {
         return new PatternMatcher(
                 out,
                 factory,
                 operand(
                         EnumerableCalc.class,
-                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, any() ) )
+                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
                 ),
                 "attachFieldsAndFiltersToScanUnderCalc",
                 call -> {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnStatistics.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnStatistics.java
index b03c5fc22c..fe1158fb71 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnStatistics.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnStatistics.java
@@ -21,6 +21,7 @@
 
 /**
  * Compact per-file statistics for one physical Parquet column path.
+ * Provides statistics for file pruning
  */
 public record ParquetColumnStatistics(
         PolyType type,
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index 8eb73eea14..9e9c3f9a47 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -35,6 +35,8 @@
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedRepeatedRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetSourceFilePartitionFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetSourceFileStatisticsFilterEvaluator;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
 import org.polypheny.db.adapter.parquet.relational.planning.PhysicalScan;
@@ -260,13 +262,16 @@ public Enumerable<PolyValue[]> nestedJoin(
             final boolean leftIsParent,
             final boolean emitUnmatchedParents,
             final List<ParquetAdapterFilter> filters ) {
+
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+        // decide who is parent
         final PhysicalScan parent = leftIsParent ? leftScan : rightScan;
         final PhysicalScan child = leftIsParent ? rightScan : leftScan;
 
         final List<ParquetAdapterFilter> leftScanFilters = leftScan.resolveFilters( dataContext );
         final List<ParquetAdapterFilter> rightScanFilters = rightScan.resolveFilters( dataContext );
+        // split filters into filter container
         final JoinFiltersContainer filterContainer = getJoinFiltersContainer( dataContext, leftScan, rightScan, leftIsParent, filters, leftScanFilters, rightScanFilters, parent, child );
 
         final List<ParquetAdapterFilter> parentFileFilters = leftIsParent ? leftScanFilters : rightScanFilters;
@@ -314,6 +319,7 @@ private JoinFiltersContainer getJoinFiltersContainer( DataContext dataContext, P
         final List<ParquetAdapterFilter> joinFilters = ParquetFilterResolver.resolveFilters( dataContext, filters, f -> selectBinding( f.columnIndex(), leftScan.fields(), rightScan.fields(), rightScan.table() ) );
         final List<ParquetAdapterFilter> parentScanFilters = leftIsParent ? leftScanFilters : rightScanFilters;
         final List<ParquetAdapterFilter> childScanFilters = leftIsParent ? rightScanFilters : leftScanFilters;
+        // split join filters to parent, child, adapter, reader
         final JoinFiltersContainer container = new JoinFiltersSplitter().split( joinFilters, leftIsParent, parent.fields().length, child.fields().length );
         return new JoinFiltersContainer(
                 container.parentFilters(),
@@ -362,9 +368,14 @@ private Enumerator<PolyValue[]> nestedJoinEnumeratorForFile(
     }
 
 
+    /**
+     * Apply two evaluators: partition and statistics
+     * @param selector - function
+     * @return ParquetFilterEvaluatorsChain<ParquetSourceFile>
+     */
     private static ParquetFilterEvaluatorsChain<ParquetSourceFile> createParquetSourceFileEvaluatorsChain( Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
         return new ParquetFilterEvaluatorsChain<>( List.of(
-                new ParquetSourceFileFilterEvaluator( selector ),
+                new ParquetSourceFilePartitionFilterEvaluator( selector ),
                 new ParquetSourceFileStatisticsFilterEvaluator( selector ) )
         );
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFile.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFile.java
index 02e1cb4a40..79433ada55 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFile.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFile.java
@@ -30,6 +30,7 @@
 
 /**
  * One physical Parquet file that contributes rows to a Parquet table.
+ * contains partition values and column statistics for file pruning
  */
 public record ParquetSourceFile(
         String fileUrl,
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetColumnStatisticsReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetColumnStatisticsReader.java
index 6ead9b97c4..3474e943ad 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetColumnStatisticsReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetColumnStatisticsReader.java
@@ -33,6 +33,7 @@
 
 /**
  * Reads compact column statistics from Parquet footer metadata.
+ * Returns ParquetColumnStatistics per column
  */
 public final class ParquetColumnStatisticsReader {
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetTableStatisticsReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetTableStatisticsReader.java
index dfe9d904f1..e11a4fcd35 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetTableStatisticsReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetTableStatisticsReader.java
@@ -160,6 +160,11 @@ private long estimateValueCount( List<String> sourcePathElements ) {
     }
 
 
+    /**
+     * Get statistics from bindings
+     * @param sourcePathElements - column path
+     * @return ParquetColumnStatistics
+     */
     private Optional<ParquetColumnStatistics> aggregateColumnStatistics( List<String> sourcePathElements ) {
         PolyType type = null;
         long rowCount = 0;

From d322f3d9305b58e630fb71eae12125713a9c3f5a Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Tue, 26 May 2026 14:23:20 +0200
Subject: [PATCH 33/46] fix bugs in document flow and workflow

---
 plugins/parquet-adapter/docs/TODO.md          |  8 +++
 plugins/parquet-adapter/docs/joins.md         | 12 ++++-
 .../docs/partitions_overview.md               |  6 +--
 plugins/parquet-adapter/docs/planner.md       | 12 +++++
 .../document/ParquetDocumentSource.java       | 27 ++++++++--
 .../document/schema/ParquetDocument.java      | 24 ++++++---
 .../parquet/shared/AbstractParquetSource.java |  4 +-
 .../shared/schema/ParquetNamespace.java       |  6 +--
 .../ParquetWorkflowExtractSupport.java        | 50 +++++++++++++++----
 9 files changed, 118 insertions(+), 31 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/TODO.md

diff --git a/plugins/parquet-adapter/docs/TODO.md b/plugins/parquet-adapter/docs/TODO.md
new file mode 100644
index 0000000000..ab2893a786
--- /dev/null
+++ b/plugins/parquet-adapter/docs/TODO.md
@@ -0,0 +1,8 @@
+select * from prf__fhv_tripdata where "year" > '2025';
+- partitions does not work
+
+
+settings - not URL
+
+
+delete adapter does not work
diff --git a/plugins/parquet-adapter/docs/joins.md b/plugins/parquet-adapter/docs/joins.md
index 30ab9cb275..493ba04b70 100644
--- a/plugins/parquet-adapter/docs/joins.md
+++ b/plugins/parquet-adapter/docs/joins.md
@@ -2,8 +2,6 @@
 
 ## Goal: Perform join on parquet adapter level.
 
-A Parquet adapter-level join is more efficient because it can exploit the physical parent/child layout of the Parquet data.
-
 For normalized nested tables, the relation is:
 `child.__polypheny_parent_row_id = parent.__polypheny_row_id`
 
@@ -215,6 +213,16 @@ EnumerableParquet
 It defines a separate planner convention named: "PARQUET"
 and says that nodes in this convention must implement: ParquetAlg
 
+Conceptually, a convention tells Polypheny who is responsible for executing a part of the query plan.
+
+For the Parquet adapter, ParquetConvention means:
+
+“This part of the plan can be executed by the Parquet adapter.”
+It lets the planner distinguish generic Polypheny operations from Parquet-specific operations.
+It enables adapter-specific nodes such as ParquetRelScan and ParquetRelJoin.
+It allows planner rules to rewrite normal scans, filters, projections, and joins into Parquet-aware operations.
+At the end, the Parquet plan is converted back to EnumerableConvention, so Polypheny can execute it.
+
 
 ## Planner Flow - Query Execution with Join
 
diff --git a/plugins/parquet-adapter/docs/partitions_overview.md b/plugins/parquet-adapter/docs/partitions_overview.md
index 92c4f72e5f..6071b4698e 100644
--- a/plugins/parquet-adapter/docs/partitions_overview.md
+++ b/plugins/parquet-adapter/docs/partitions_overview.md
@@ -32,7 +32,7 @@ So partitioning improves query performance by reducing the number of Parquet fil
 
 If partition integration is triggered, then created:
 - one logical table
-- many physical tables: one physical table per value of the first partition column
+- many physical tables: one physical table per value of the first partition column (one physical table per partition)
 
 ### Example:
 ```text
@@ -60,7 +60,7 @@ Physical tables:
 
 The following functionality was implemented:
 
-### 1. Support multiple Parquet files as one table.
+### 1. Support multiple Parquet files as one logical table.
 - `ParquetFileDiscovery` discovers multiple files for one logical table.
 - `DiscoveredTableBinding`, `ParquetTableBinding`, `ParquetSourceFile` store the file list.
 - `ParquetRelTable` uses the file list at scan time.
@@ -232,7 +232,7 @@ The adapter should not rely on filename prefixes as the main grouping rule. Sche
 - Polypheny creates the logical table in its catalog.
 
 
-5. Physical table creation. ParquetRelationalSource.createTable() uses the discovered metadata.
+5. Physical tables creation. ParquetRelationalSource.createTable() uses the discovered metadata.
 
 
 6. Add partitions on Polypheny level. ParquetRelationalSource.createPartitionedTable() adds needed information to catalog.
diff --git a/plugins/parquet-adapter/docs/planner.md b/plugins/parquet-adapter/docs/planner.md
index a3408662b6..6672750c6d 100644
--- a/plugins/parquet-adapter/docs/planner.md
+++ b/plugins/parquet-adapter/docs/planner.md
@@ -2,6 +2,18 @@
 
 The planner takes a query and turns it into an executable plan.
 
+Polypheny does not execute the first query plan directly.
+The planner searches for a better plan by applying transformation rules.
+
+For the Parquet adapter, we register rules that recognize Parquet-specific opportunities.
+For example, a normal scan can become a ParquetRelScan.
+A filter or projection can be attached directly to that scan.
+A parent-child join between normalized Parquet tables can become a ParquetRelJoin.
+
+The planner compares possible plans using cost estimates.
+Plans that read fewer columns, apply filters earlier, or execute joins inside the adapter are cheaper.
+The selected plan is then converted back into an executable form.
+
 In simple words:
 
 ```text
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java
index 4212b124ee..3e6b28ee5f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java
@@ -28,6 +28,9 @@
 import org.polypheny.db.adapter.annotations.AdapterSettingDirectory;
 import org.polypheny.db.adapter.annotations.AdapterSettingList;
 import org.polypheny.db.adapter.annotations.AdapterSettingString;
+import org.polypheny.db.adapter.parquet.relational.schema.DiscoveredTableBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.catalog.entity.allocation.AllocationCollection;
 import org.polypheny.db.catalog.entity.allocation.AllocationGraph;
 import org.polypheny.db.catalog.entity.allocation.AllocationTable;
@@ -37,10 +40,10 @@
 import org.polypheny.db.catalog.entity.logical.LogicalTableWrapper;
 import org.polypheny.db.catalog.entity.physical.PhysicalCollection;
 import org.polypheny.db.catalog.entity.physical.PhysicalEntity;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.catalog.logistic.DataModel;
 import org.polypheny.db.catalog.logistic.EntityType;
 import org.polypheny.db.prepare.Context;
-import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 
 /**
  * Document adapter implementation.
@@ -92,8 +95,11 @@ public List<PhysicalEntity> createCollection( Context context, LogicalCollection
                 logical.getName(),
                 logical,
                 allocation );
-
-        var physical = currentNamespace.createParquetCollection( collection, this );
+        // handle multi-files
+        var physical = currentNamespace.createParquetCollection(
+                collection,
+                getCollectionSourceFiles( logical.getName() ),
+                this );
         adapterCatalog.replacePhysical( physical );
         return List.of( physical );
     }
@@ -103,7 +109,11 @@ public List<PhysicalEntity> createCollection( Context context, LogicalCollection
     public void restoreCollection( AllocationCollection alloc, List<PhysicalEntity> entities, Context context ) {
         PhysicalEntity collection = entities.get( 0 );
         updateNamespace( collection.namespaceName, collection.namespaceId );
-        var physical = currentNamespace.createParquetCollection( collection.unwrapOrThrow( PhysicalCollection.class ), this );
+        // handle multi-files
+        var physical = currentNamespace.createParquetCollection(
+                collection.unwrapOrThrow( PhysicalCollection.class ),
+                getCollectionSourceFiles( collection.name ),
+                this );
         adapterCatalog.addPhysical( alloc, physical );
     }
 
@@ -158,6 +168,15 @@ public void renameLogicalColumn( long id, String newColumnName ) {
     }
 
 
+    private List<ParquetSourceFile> getCollectionSourceFiles( String collectionName ) {
+        // handle multi-files
+        return getTableBinding( collectionName )
+                .map( DiscoveredTableBinding::sourceFiles )
+                .filter( sourceFiles -> !sourceFiles.isEmpty() )
+                .orElseThrow( () -> new GenericRuntimeException( "Missing discovered Parquet file binding for collection: %s", collectionName ) );
+    }
+
+
     @SuppressWarnings("unused")
     private interface Excludes {
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
index c3e0d1bdba..b8b0ab73b0 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
@@ -29,6 +29,8 @@
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
 import org.polypheny.db.adapter.parquet.document.execution.ParquetDocEnumerator;
 import org.polypheny.db.adapter.parquet.document.planning.ParquetDocScan;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetMultiFileEnumerator;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
@@ -45,20 +47,19 @@
 import org.polypheny.db.schema.types.ScannableEntity;
 import org.polypheny.db.schema.types.TranslatableEntity;
 import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.util.Source;
 
 /**
  * Physical collection wrapper for the document model.
  * Represents one Parquet-backed collection inside Polypheny
  */
+@Getter
 public class ParquetDocument extends PhysicalCollection implements ScannableEntity, TranslatableEntity {
 
-    private final Source source;
-    @Getter
+    private final List<ParquetSourceFile> sourceFiles;
     private final AbstractParquetSource parquetSource;
 
 
-    public ParquetDocument( PhysicalCollection collection, Source source, AbstractParquetSource parquetSource ) {
+    public ParquetDocument( PhysicalCollection collection, List<ParquetSourceFile> sourceFiles, AbstractParquetSource parquetSource ) {
         super(
                 collection.id,
                 collection.allocationId,
@@ -67,7 +68,7 @@ public ParquetDocument( PhysicalCollection collection, Source source, AbstractPa
                 collection.name,
                 collection.namespaceName,
                 collection.adapterId );
-        this.source = source;
+        this.sourceFiles = List.copyOf( sourceFiles ); // add multi-file handling
         this.parquetSource = parquetSource;
     }
 
@@ -113,8 +114,17 @@ public Enumerable<PolyValue[]> scanFiltered( DataContext dataContext, List<Parqu
             @Override
             public Enumerator<PolyValue[]> enumerator() {
                 FiltersContainer filtersContainer = FiltersContainer.shared( resolvedFilters );
-                ParquetSourceReader reader = new ParquetSourceReader( source, cancelFlag, null, filtersContainer.nativeFilters() );
-                return new ParquetDocEnumerator( reader, filtersContainer );
+                // handle multi-files
+                return new ParquetMultiFileEnumerator(
+                        sourceFiles,
+                        sourceFile -> {
+                            ParquetSourceReader reader = new ParquetSourceReader(
+                                    sourceFile.asSource(),
+                                    cancelFlag,
+                                    null,
+                                    filtersContainer.nativeFilters() );
+                            return new ParquetDocEnumerator( reader, filtersContainer );
+                        } );
             }
         };
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
index d9a9f5a97d..acebf35e7a 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
@@ -140,7 +140,9 @@ protected Optional<DiscoveredTableBinding> getTableBinding( String tableName ) {
         if ( discoveredTables == null ) {
             discoveredTables = ParquetFileDiscovery.discoverTables( parquetDir, getUniqueName() );
         }
-        return Optional.ofNullable( discoveredTables.get( tableName ).binding() );
+        DiscoveredTable table = discoveredTables.get( tableName );
+        // for document data source table discovered tabled not constructed
+        return table == null ? Optional.empty() : Optional.ofNullable( table.binding() );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
index 13dd8ae999..892e8f4513 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
@@ -48,9 +48,9 @@ public ParquetRelTable createParquetTable( long id, PhysicalTable table, Parquet
     }
 
 
-    public ParquetDocument createParquetCollection( PhysicalCollection collection, AbstractParquetSource sourceAdapter ) {
-        Source parquetSource = Sources.of( ParquetUrlResolver.resolveFile( directoryUrl, collection.name + ".parquet" ) );
-        return new ParquetDocument( collection, parquetSource, sourceAdapter );
+
+    public ParquetDocument createParquetCollection( PhysicalCollection collection, List<ParquetSourceFile> sourceFiles, AbstractParquetSource sourceAdapter ) {
+        return new ParquetDocument( collection, sourceFiles, sourceAdapter );     // handle multi-files
     }
 
 
diff --git a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
index 7bb6817521..1930af8ffb 100644
--- a/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
+++ b/plugins/workflow-engine/src/main/java/org/polypheny/db/workflow/parquet/ParquetWorkflowExtractSupport.java
@@ -35,6 +35,7 @@
 import org.polypheny.db.algebra.type.DocumentType;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyList;
 import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.type.entity.document.PolyDocument;
@@ -105,13 +106,8 @@ public static AlgDataType getOutputType( Source source, String outputModel, bool
         // PK added by the workflow layer, it does not come from file
         builder.add( Activity.PK_COL, null, PolyType.BIGINT );
         for ( Type field : schema.getFields() ) {
-            var type = parquetTypeConverter.fromParquetTypeToPolyType( field );
-            // PolyDocument type provided as json string
-            if ( type == PolyType.DOCUMENT ) {
-                type = PolyType.TEXT;
-            }
             String fieldName = ParquetNameNormalizer.normalizeFieldName( field.getName() );
-            builder.add( fieldName, null, type );
+            builder.add( fieldName, null, getRelationalOutputType( field ) );
         }
         if ( addNameField ) {
             // add source file name to each row
@@ -224,11 +220,7 @@ public static void writeRows( OutputPipe output, Source source, boolean addNameC
                 for ( int i = 0; i < schema.getFieldCount(); i++ ) {
                     Type field = schema.getType( i );
                     var polyValue = parquetRelValueExtractor.extractValue( row, i, field );
-                    // PolyDocument type provided as json string
-                    if ( polyValue instanceof PolyDocument document ) {
-                        polyValue = document.toPolyJson();
-                    }
-                    values.add( polyValue );
+                    values.add( toRelationalColumnValue( field, polyValue ) );
                 }
                 if ( addNameCol ) {
                     values.add( polyName );
@@ -241,4 +233,40 @@ public static void writeRows( OutputPipe output, Source source, boolean addNameC
         }
     }
 
+
+    /**
+     * Decides what Polypheny relational column type the workflow should expose for one Parquet field.
+     */
+    private static PolyType getRelationalOutputType( Type field ) {
+        if ( isTextEncodedRelationalField( field ) ) {
+            return PolyType.TEXT;
+        }
+        return parquetTypeConverter.fromParquetTypeToPolyType( field );
+    }
+
+
+    /**
+     * Returns:
+     * true - for nested/group fields and for repeated fields
+     * and should instead be encoded as text/JSON
+     * false - for normal scalar primitive fields
+     */
+    private static boolean isTextEncodedRelationalField( Type field ) {
+        return !field.isPrimitive() || field.isRepetition( Type.Repetition.REPEATED );
+    }
+
+
+    /**
+     * Prepares one extracted Parquet value so it matches the relational workflow column type.
+     */
+    private static PolyValue toRelationalColumnValue( Type field, PolyValue value ) {
+        if ( value == null || value.isNull() || !isTextEncodedRelationalField( field ) ) {
+            return value;
+        }
+        if ( field.isRepetition( Type.Repetition.REPEATED ) && !value.isList() ) {
+            value = PolyList.of( value );
+        }
+        return ActivityUtils.valueToPolyString( value );
+    }
+
 }

From 2dbcfbfb52bb0153d2a68e7406907269aac382ef Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Mon, 8 Jun 2026 20:00:58 +0200
Subject: [PATCH 34/46] add support for aggregations push down to adapter

---
 .../benchmarks/benchmark-plan.md              |   22 +
 .../benchmarks/benchmark-query-set.md         |  152 +++
 .../benchmark_results/benchmark_results.md    |   18 +
 .../benchmarks/compatitive-technologies.md    |  240 ++++
 .../benchmarks/datasets/flickr_dataset.md     |  129 ++
 .../benchmarks/datasets/nested_customer.md    |  150 +++
 .../benchmarks/datasets/tlc_dataset_desc.md   |  252 ++++
 .../parquet-adapter/benchmarks/query-list.md  | 1144 +++++++++++++++++
 .../query_lists/aggregation_queries.md        |  130 ++
 .../query_lists/nested_flickr8k_queries.md    |  104 ++
 .../benchmarks/running_benchmarks.md          |   35 +
 .../parquet-adapter/docs/aggregation_flow.md  |   41 +
 .../docs/aggregation_planning.md              |  184 +++
 .../aggregation/aggragation_flow.drawio.png   |  Bin 0 -> 103902 bytes
 .../docs/images/aggregation/data_agregate.png |  Bin 0 -> 115471 bytes
 .../images/aggregation/metadata_aggregate.png |  Bin 0 -> 100500 bytes
 .../db/adapter/parquet/ParquetPlugin.java     |   26 +
 .../document/schema/ParquetDocument.java      |    8 +-
 .../relational/ParquetRelationalSource.java   |   13 +-
 .../FilterableParquetSourceFile.java          |   37 +
 .../execution/ParquetEnumeratorsFactory.java  |   99 ++
 .../execution/ParquetMultiFileEnumerator.java |   28 +-
 .../ParquetNestedJoinEnumerator.java          |    4 +-
 ...ParquetNestedNonRepeatedRelEnumerator.java |    1 +
 .../ParquetNestedRepeatedRelEnumerator.java   |    2 +-
 .../ParquetRelDataAggregateExecutor.java      |  570 ++++++++
 .../execution/ParquetRelExecutor.java         |  470 +++++++
 .../execution/ParquetRelExecutorsFactory.java |   75 ++
 .../ParquetRelMetadataAggregateExecutor.java  |  408 ++++++
 .../ParquetRelNestedJoinExecutor.java         |  213 +++
 .../execution/ParquetRelProjectExecutor.java  |   59 +
 .../execution/ParquetRowRelEnumerator.java    |  128 ++
 ...etSourceFileStatisticsFilterEvaluator.java |  102 --
 .../aggregate/AggregateRowProjector.java      |   53 +
 .../DirectAggregateRowProjector.java          |   62 +
 ...quetFileGroupedAggregateRelEnumerator.java |  213 +++
 .../ParquetGroupedAggregateRelEnumerator.java |  239 ++++
 .../ParquetRowAggregateRelEnumerator.java     |   52 +
 .../aggregate/ParquetRowAggregator.java       |   73 ++
 .../filter/ParquetFilterResolver.java         |  145 +++
 .../ParquetMultiFilterEvaluator.java}         |   27 +-
 .../ParquetPartitionAwareFilterEvaluator.java |   10 +-
 .../ParquetRelFilterTranslator.java           |   21 +-
 .../ParquetSourceFileFilterReducer.java       |  123 ++
 ...uetSourceFilePartitionFilterEvaluator.java |   18 +-
 ...etSourceFileStatisticsFilterEvaluator.java |  170 +++
 .../relational/filter/ResidualFilters.java    |   32 +
 .../ParquetAlgOptRule.java                    |    4 +-
 .../ParquetRules.java                         |   26 +-
 .../PatternMatcher.java                       |    2 +-
 .../optimization/PatternMatchers.java         |  571 ++++++++
 .../aggregate/AggregateDecomposition.java     |   76 ++
 .../aggregate/PartialAggregate.java           |   30 +
 .../planning/ParquetConvention.java           |    1 +
 .../planning/ParquetEnumerableUnion.java      |   51 +
 .../planning/ParquetFilterResolver.java       |  102 --
 .../planning/ParquetPolyAlgDisplay.java       |   15 +-
 .../planning/ParquetRelAggregate.java         |  311 +++++
 .../relational/planning/ParquetRelJoin.java   |    9 +-
 .../planning/ParquetRelMetadataScan.java      |   57 +
 .../relational/planning/ParquetRelScan.java   |    9 +-
 .../relational/planning/PatternMatchers.java  |  208 ---
 .../relational/planning/PhysicalScan.java     |    6 +-
 .../schema/ParquetConstantColumnResolver.java |   84 ++
 .../relational/schema/ParquetRelTable.java    |  340 +----
 .../aggregate/AggregateCallDescriptor.java    |   51 +
 .../aggregate/AggregateColumnAccumulator.java |  204 +++
 .../shared/aggregate/AggregateGroupState.java |  180 +++
 .../aggregate/AggregateInputColumn.java       |  124 ++
 .../aggregate/AggregateRowAccumulator.java    |  207 +++
 .../aggregate/ColumnAggregateProjection.java  |   33 +
 .../aggregate/ColumnAggregateResult.java      |   45 +
 .../parquet/shared/aggregate/GroupKey.java    |  132 ++
 .../AbstractAggregateEnumerator.java          |  273 ++++
 .../execution/AbstractFilterTranslator.java   |    7 +-
 .../execution/AbstractParquetEnumerator.java  |    2 +-
 .../shared/filter/FiltersContainer.java       |   33 +-
 .../shared/filter/JoinFiltersContainer.java   |   21 +-
 .../shared/filter/JoinFiltersSplitter.java    |   19 +-
 .../shared/filter/ParquetAdapterFilter.java   |   22 +-
 ...uator.java => ParquetFilterEvaluator.java} |   31 +-
 .../filter/ParquetGroupFilterEvaluator.java   |   16 +-
 .../filter/ParquetNativeFilterBuilder.java    |   10 +-
 .../filter/ParquetNestedFilterEvaluator.java  |    2 +-
 .../ParquetNestedJoinFilterEvaluator.java     |    8 +-
 .../ParquetPrimitiveValueFilterEvaluator.java |   85 ++
 .../shared/io/ParquetCancellation.java        |   38 +
 .../shared/io/ParquetPrimitivePredicate.java  |  355 +++++
 .../shared/io/ParquetPrimitiveRowReader.java  |  269 ++++
 .../shared/io/ParquetSourceReader.java        |    5 +-
 .../ParquetGroupedAggregateReader.java        |  210 +++
 .../ParquetNoFilterColumnAggregateReader.java |  173 +++
 .../aggregate/page/NoOpColumnConverter.java   |   60 +
 .../page/ParquetCountAggregatePageReader.java |   81 ++
 .../ParquetGroupedAggregatePageReader.java    |   93 ++
 .../io/aggregate/page/ParquetPageReader.java  |   96 ++
 .../ParquetTableStatisticsReader.java         |   15 +-
 .../parquet/ParquetFileDiscoveryTest.java     |   41 +-
 ...quetGroupedAggregateRelEnumeratorTest.java |  233 ++++
 .../parquet/ParquetMetadataAggregateTest.java |  612 +++++++++
 ...quetNoFilterColumnAggregateReaderTest.java |  165 +++
 .../ParquetPrimitiveRowReaderTest.java        |  185 +++
 .../ParquetRelFilterTranslatorTest.java       |   36 +-
 .../ParquetRowAggregateRelEnumeratorTest.java |   91 ++
 .../ParquetNestedRepeatedProjectionTest.java  |  259 ++++
 .../ParquetCountAggregatePageReaderTest.java  |  179 +++
 106 files changed, 11800 insertions(+), 885 deletions(-)
 create mode 100644 plugins/parquet-adapter/benchmarks/benchmark-plan.md
 create mode 100644 plugins/parquet-adapter/benchmarks/benchmark-query-set.md
 create mode 100644 plugins/parquet-adapter/benchmarks/benchmark_results/benchmark_results.md
 create mode 100644 plugins/parquet-adapter/benchmarks/compatitive-technologies.md
 create mode 100644 plugins/parquet-adapter/benchmarks/datasets/flickr_dataset.md
 create mode 100644 plugins/parquet-adapter/benchmarks/datasets/nested_customer.md
 create mode 100644 plugins/parquet-adapter/benchmarks/datasets/tlc_dataset_desc.md
 create mode 100644 plugins/parquet-adapter/benchmarks/query-list.md
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/aggregation_queries.md
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/nested_flickr8k_queries.md
 create mode 100644 plugins/parquet-adapter/benchmarks/running_benchmarks.md
 create mode 100644 plugins/parquet-adapter/docs/aggregation_flow.md
 create mode 100644 plugins/parquet-adapter/docs/aggregation_planning.md
 create mode 100644 plugins/parquet-adapter/docs/images/aggregation/aggragation_flow.drawio.png
 create mode 100644 plugins/parquet-adapter/docs/images/aggregation/data_agregate.png
 create mode 100644 plugins/parquet-adapter/docs/images/aggregation/metadata_aggregate.png
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/FilterableParquetSourceFile.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetEnumeratorsFactory.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelDataAggregateExecutor.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelExecutor.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelExecutorsFactory.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelMetadataAggregateExecutor.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelNestedJoinExecutor.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelProjectExecutor.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRowRelEnumerator.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetSourceFileStatisticsFilterEvaluator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/AggregateRowProjector.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/DirectAggregateRowProjector.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetFileGroupedAggregateRelEnumerator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetGroupedAggregateRelEnumerator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregateRelEnumerator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetFilterResolver.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/{schema/ParquetFilterEvaluatorsChain.java => filter/ParquetMultiFilterEvaluator.java} (63%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/{execution => filter}/ParquetPartitionAwareFilterEvaluator.java (86%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/{execution => filter}/ParquetRelFilterTranslator.java (87%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetSourceFileFilterReducer.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/{execution => filter}/ParquetSourceFilePartitionFilterEvaluator.java (72%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetSourceFileStatisticsFilterEvaluator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ResidualFilters.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/{planning => optimization}/ParquetAlgOptRule.java (88%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/{planning => optimization}/ParquetRules.java (62%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/{planning => optimization}/PatternMatcher.java (94%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/PatternMatchers.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/aggregate/AggregateDecomposition.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/aggregate/PartialAggregate.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableUnion.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterResolver.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelMetadataScan.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetConstantColumnResolver.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateCallDescriptor.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateColumnAccumulator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateGroupState.java
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 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/ColumnAggregateProjection.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/ColumnAggregateResult.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/GroupKey.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractAggregateEnumerator.java
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 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetPrimitiveValueFilterEvaluator.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetCancellation.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetPrimitivePredicate.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetPrimitiveRowReader.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/ParquetGroupedAggregateReader.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/ParquetNoFilterColumnAggregateReader.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/NoOpColumnConverter.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetCountAggregatePageReader.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetGroupedAggregatePageReader.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetPageReader.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetGroupedAggregateRelEnumeratorTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetMetadataAggregateTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNoFilterColumnAggregateReaderTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPrimitiveRowReaderTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRowAggregateRelEnumeratorTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedProjectionTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetCountAggregatePageReaderTest.java

diff --git a/plugins/parquet-adapter/benchmarks/benchmark-plan.md b/plugins/parquet-adapter/benchmarks/benchmark-plan.md
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+# Benchmark Plan
+
+## Relational Schema
+
+We use a set of benchmark queries (query-list.md) to evaluate different functionalities of the Parquet adapter. The workload primarily consists of aggregation queries.
+
+### 1. Flat Partitioned Dataset
+
+The benchmark uses four years of the TLC dataset partitioned by year and month. The year and month values are not stored within the Parquet files and are instead derived from the partition structure.
+
+All benchmark queries are executed on Polypheny, Apache Spark, and DuckDB to establish a baseline. After performance optimizations are implemented, the same queries are executed again to measure their impact.
+
+### 2. Flat Unpartitioned Dataset
+
+The same TLC dataset is used without partitioning. In this configuration, the year and month values are stored as columns within the Parquet files.
+
+All benchmark queries are executed on Polypheny, Apache Spark, and DuckDB to establish a baseline. After performance optimizations are implemented, the same queries are executed again to measure their impact.
+
+### 3. Partitioned vs. Unpartitioned Dataset
+
+The performance of all benchmark queries is compared on Polypheny to evaluate the impact of partitioning.
+
diff --git a/plugins/parquet-adapter/benchmarks/benchmark-query-set.md b/plugins/parquet-adapter/benchmarks/benchmark-query-set.md
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+# Parquet Benchmark Query Set
+
+This file selects 10 queries from the original Q01-Q20 benchmark set for the
+current Parquet adapter feature scope. The set covers the supported aggregate
+functions: `COUNT`, `SUM`, `MIN`, and `MAX`.
+
+The selected queries avoid functionality that is no longer part of the adapter
+benchmark target:
+
+- no average aggregate
+- no `WITH` / CTE queries, because these are not supported by the Polypheny JDBC benchmark path
+- no aggregate grouping by calculated expressions
+- no aggregate arguments produced by a Calc expression
+
+SQL uses the DuckDB table names from the existing benchmark files. The Polypheny
+benchmark client maps these names to adapter tables such as
+`tlc__yellow_tripdata`, `tlc__green_tripdata`, `tlc__fhvhv_tripdata`, and
+`tlc__fhv_tripdata`.
+
+The selected original queries are Q01, Q02, Q04, Q05, Q06, Q09, Q10, Q12, Q13,
+and Q15. The Q06 and Q13 versions below keep the original monthly aggregate
+shape but replace average distance metrics with minimum and maximum distance
+metrics.
+
+## S01 - Original Q01: Full Yellow Taxi Row Count
+
+**Description:** Find how many yellow taxi trips are in the dataset.
+
+```sql
+SELECT count(*) AS row_count
+FROM yellow_tripdata;
+```
+
+## S02 - Original Q02: Yellow Taxi Count For One Partition Month
+
+**Description:** Find how many yellow taxi trips were in October 2022.
+
+```sql
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+```
+
+## S03 - Original Q04: Yellow Taxi Trips On One Day
+
+**Description:** Find how many yellow taxi trips started on October 15, 2022.
+
+```sql
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
+  AND tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00';
+```
+
+## S04 - Original Q05: Long And Expensive Yellow Taxi Trips
+
+**Description:** Find how many yellow taxi trips were at least 10 miles long and
+cost at least 40 dollars.
+
+```sql
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+```
+
+## S05 - Original Q06 Adjusted: Monthly Yellow Taxi Amount And Distance Summary
+
+**Description:** Find, for each year and month, how many yellow taxi trips there
+were, the total amount charged, and the shortest and longest trip distances.
+
+```sql
+SELECT
+  "year",
+  "month",
+  count(*) AS trips,
+  sum(total_amount) AS gross_amount,
+  min(trip_distance) AS min_distance,
+  max(trip_distance) AS max_distance
+FROM yellow_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+```
+
+## S06 - Original Q09: Full High-Volume FHV Row Count
+
+**Description:** Find how many high-volume FHV trips are in the dataset.
+
+```sql
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata;
+```
+
+## S07 - Original Q10: High-Volume FHV Count For One Partition Month
+
+**Description:** Find how many high-volume FHV trips were in October 2022.
+
+```sql
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+```
+
+## S08 - Original Q12: Long And Expensive High-Volume FHV Trips
+
+**Description:** Find how many high-volume FHV trips were at least 10 miles long
+and had a base passenger fare of at least 40 dollars.
+
+```sql
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata
+WHERE trip_miles >= 10.0
+  AND base_passenger_fare >= 40.0;
+```
+
+## S09 - Original Q13 Adjusted: Monthly High-Volume FHV Fare And Distance Summary
+
+**Description:** Find, for each year and month, how many high-volume FHV trips
+there were, the total passenger fare, the total driver pay, and the shortest and
+longest trip distances.
+
+```sql
+SELECT
+  "year",
+  "month",
+  count(*) AS trips,
+  sum(base_passenger_fare) AS passenger_fare,
+  sum(driver_pay) AS driver_pay,
+  min(trip_miles) AS min_miles,
+  max(trip_miles) AS max_miles
+FROM fhvhv_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+```
+
+## S10 - Original Q15: High-Volume FHV Shared-Ride Flag Distribution
+
+**Description:** Find how many 2022 high-volume FHV trips fall into each
+combination of shared-request and shared-match flags.
+
+```sql
+SELECT
+  shared_request_flag,
+  shared_match_flag,
+  count(*) AS trips
+FROM fhvhv_tripdata
+WHERE "year" = '2022'
+GROUP BY shared_request_flag, shared_match_flag
+ORDER BY trips DESC;
+```
diff --git a/plugins/parquet-adapter/benchmarks/benchmark_results/benchmark_results.md b/plugins/parquet-adapter/benchmarks/benchmark_results/benchmark_results.md
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+# Benchmark Results
+
+## Aggregation Results 
+
+Time displayed in milliseconds (ms)
+
+| Query | Description                                       | Polypheny before optimization | Polypheny after optimization | DuckDB | Spark | 
+|-------|---------------------------------------------------|------------------------------:|-----------------------------:|-------:|------:|
+| Q01   | Full Yellow Taxi Row Count                        |                         86707 |                              |        |       |                   
+| Q02   | Partition-pruned count, one yellow taxi "month"   |                          3833 |                              |        |       |                   
+| Q03   | Yellow Taxi Count For One Partition Month         |                         24707 |                              |        |       |                    
+| Q04   | Long And Expensive Yellow Taxi Trips              |                         95229 |                              |        |       |                   
+| Q05   | Monthly Yellow Taxi Amount And Distance Summary   |                        159617 |                              |        |       |                   
+| Q06   | Full High-Volume FHV Row Count                    |                        787924 |                              |        |       |                    
+| Q07   | High-Volume FHV Count For One Partition Month     |                         28321 |                              |        |       |                    
+| Q08   | Long And Expensive High-Volume FHV Trips          |                        759776 |                              |        |       |                    
+| Q09   | Monthly High-Volume FHV Fare And Distance Summary |                       1224409 |                              |        |       |                    
+| Q10   | High-Volume FHV Shared-Ride Flag Distribution     |                        376649 |                              |        |       |                    
diff --git a/plugins/parquet-adapter/benchmarks/compatitive-technologies.md b/plugins/parquet-adapter/benchmarks/compatitive-technologies.md
new file mode 100644
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+++ b/plugins/parquet-adapter/benchmarks/compatitive-technologies.md
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+# Competitive Technologies for Planned Parquet Aggregation Benchmarks
+
+## Purpose
+
+This document describes the technologies selected for a planned comparison of
+aggregation queries over Parquet files:
+
+- Polypheny with the Parquet adapter
+- DuckDB
+- Apache Spark SQL
+
+The goal is to evaluate Polypheny's Parquet adapter against relevant reference
+systems. The comparison is intended to show how different analytical
+architectures approach the same immutable Parquet dataset and equivalent query
+shapes.
+
+The technologies are not identical products:
+
+- Polypheny is the multi-model DBMS and Parquet adapter under evaluation.
+- DuckDB is a focused, in-process analytical database reference.
+- Apache Spark SQL is a distributed data-processing reference.
+
+Using both DuckDB and Spark gives the research two complementary baselines:
+one optimized for efficient single-node analytics and one designed for
+large-scale data-lake processing.
+
+## Research Questions
+
+The planned benchmark should help answer the following questions:
+
+1. How competitive is the Polypheny Parquet adapter for analytical aggregation
+   queries over external Parquet files?
+2. How effectively can each technology use Hive-style folder partitions,
+   Parquet metadata, column projection, and filter pushdown?
+3. How does Polypheny compare with a specialized single-node analytical engine?
+4. How does Polypheny compare with an established data-lake processing engine?
+5. Which performance differences are likely to follow from implementation
+   architecture rather than from the dataset or query semantics?
+
+The comparison is not intended to be a universal database ranking. It should
+not be used to draw conclusions about transactional workloads, concurrent
+updates, multi-node scalability, or every feature offered by the selected
+systems.
+
+## Comparison Summary
+
+| Characteristic                              | Polypheny Parquet Adapter                                 | DuckDB                                                  | Apache Spark SQL                                                        |
+|---------------------------------------------|-----------------------------------------------------------|---------------------------------------------------------|-------------------------------------------------------------------------|
+| Planned role                                | System under evaluation                                   | Single-node OLAP reference                              | Distributed data-processing reference                                   |
+| General system type                         | Multi-model DBMS with adapter-based external data sources | In-process analytical database                          | Distributed analytics engine                                            |
+| Parquet access                              | Direct adapter scan of external Parquet files             | Direct Parquet scan, for example through `read_parquet` | Direct Parquet scan through Spark SQL or DataFrames                     |
+| Data import required for planned comparison | No                                                        | No                                                      | No                                                                      |
+| Hive-style partition support                | Supported by the Polypheny adapter                        | Supported                                               | Supported                                                               |
+| Typical query interface                     | SQL through Polypheny                                     | SQL                                                     | Spark SQL                                                               |
+| Main analytical value                       | Evaluates the implemented adapter in its DBMS context     | Provides a focused low-overhead analytical reference    | Provides a mature data-lake and distributed-processing reference        |
+| Main comparison caveat                      | Includes broader DBMS and adapter responsibilities        | Embedded architecture has less integration overhead     | Distributed-style execution introduces planning and scheduling overhead |
+
+## Planned Benchmark Scope
+
+The initial benchmark should focus on relational SQL aggregation workloads over
+the same immutable Parquet snapshot. Each technology should read the Parquet
+files directly without importing them into engine-managed storage.
+
+| Workload category             | Purpose                                              |
+|-------------------------------|------------------------------------------------------|
+| Full-table counts             | Evaluate metadata use and complete table coverage    |
+| Partition-restricted counts   | Evaluate Hive-style folder pruning                   |
+| Timestamp and numeric filters | Evaluate filter pushdown and row-group pruning       |
+| Column projections            | Evaluate column pruning and row materialization cost |
+| Grouped aggregates            | Evaluate `COUNT`, `SUM`, and `AVG` execution         |
+| Ordered Top-N aggregates      | Evaluate grouping followed by sorting and limiting   |
+| Aggregate joins               | Evaluate joins between compact aggregate results     |
+
+The comparison should keep the following inputs equivalent:
+
+| Controlled input | Planned rule                                                         |
+|------------------|----------------------------------------------------------------------|
+| Dataset          | Use the same Parquet snapshot for all technologies                   |
+| File layout      | Preserve the same folders, files, compression codecs, and row groups |
+| Partitioning     | Use the same Hive-style `year=YYYY/month=MM` hierarchy               |
+| Query meaning    | Use semantically equivalent SQL for all engines                      |
+| Hardware         | Run on the same host or document resource differences explicitly     |
+| Caching          | Separate cold-cache and warm-cache experiments where feasible        |
+| Results          | Validate row counts and aggregate values before comparing timings    |
+
+## Polypheny Parquet Adapter
+
+### Characteristics
+
+Polypheny is a multi-model DBMS based on a polystore architecture. It supports
+relational, document, and graph data models and integrates external data
+sources through adapters.
+
+The Parquet adapter is relevant to this research because it exposes Parquet
+files as read-only external tables and collections without requiring an import
+step. For relational queries, the adapter participates in Polypheny query
+planning and can use Parquet-aware execution paths for supported operations.
+
+### Strengths and Weaknesses
+
+| Aspect            | Strengths                                                                                                                                     | Weaknesses or expected costs                                                                                                                                   |
+|-------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Data integration  | Makes Parquet files available through the Polypheny catalog and query interface. The same DBMS can integrate other sources and data models.   | Catalog, planning, adapter, and interface layers add responsibilities that a specialized embedded engine does not have.                                        |
+| Query model       | Supports SQL while belonging to a broader multi-model architecture.                                                                           | A relational aggregation benchmark cannot demonstrate the complete value of Polypheny's multi-model design.                                                    |
+| Parquet execution | Can use adapter-aware scans, partition pruning, projection pushdown, predicate pushdown, and optimized aggregation paths for supported cases. | Optimizations may cover a narrower set of query patterns than the complete SQL language surface. Unsupported patterns may fall back to more general execution. |
+| Operational role  | Useful when Parquet data must participate in an existing data-management system without ETL into another store.                               | More infrastructure is involved than in a standalone embedded analytics library.                                                                               |
+| Research value    | Directly measures the implementation that the research aims to evaluate and improve.                                                          | Care is needed to separate adapter behavior from broader DBMS overhead.                                                                                        |
+
+### Suitable Use Cases
+
+Polypheny is a strong candidate when Parquet data should be integrated into a
+database environment, combined with other sources, or exposed through multiple
+data models.
+
+### Less Suitable Use Cases
+
+Polypheny is not necessarily the simplest option for a small analytical script
+that only needs to query local Parquet files on one machine. An embedded engine
+may require less setup for that narrow use case.
+
+## DuckDB
+
+### Characteristics
+
+DuckDB is an in-process analytical database. It can query one or many Parquet
+files directly, infer Hive-style partitions, and push projections and filters
+into Parquet scans. DuckDB uses vectorized query execution and is designed for
+analytical workloads.
+
+### Strengths and Weaknesses
+
+| Aspect               | Strengths                                                                                             | Weaknesses or expected costs                                                                       |
+|----------------------|-------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
+| Deployment           | Runs in process without a separate database server.                                                   | The embedded model does not represent the operational behavior of a server-based, multi-user DBMS. |
+| Analytical execution | Specialized for OLAP queries with vectorized execution and Parquet-aware scans.                       | Its narrow analytical focus does not include Polypheny's broader integration responsibilities.     |
+| Parquet support      | Reads multiple Parquet files directly and supports automatic projection and filter pushdown.          | Performance still depends on file count, compression, row-group layout, and metadata quality.      |
+| Scale model          | Well suited to efficient single-node analytics.                                                       | It is not the distributed-cluster reference in this research plan.                                 |
+| Research value       | Provides a focused reference for the performance achievable by a specialized local analytical engine. | It is not an architecture-equivalent replacement for Polypheny.                                    |
+
+### Suitable Use Cases
+
+DuckDB is well suited to local analytics, interactive data exploration,
+embedded applications, and reproducible Parquet experiments on one machine.
+
+### Less Suitable Use Cases
+
+DuckDB is less representative when the target environment requires a
+persistent server boundary, heterogeneous data-source integration, multi-model
+querying, or distributed execution.
+
+## Apache Spark SQL
+
+### Characteristics
+
+Apache Spark is a unified analytics engine for large-scale data processing.
+Spark SQL is its structured-data module. It supports Parquet files, schema
+preservation, partition discovery from directory paths, and Parquet filter
+pushdown.
+
+Spark is included to represent a mature data-lake processing architecture. A
+local experiment can provide an initial comparison, while a future multi-node
+experiment could evaluate scaling behavior separately.
+
+### Strengths and Weaknesses
+
+| Aspect          | Strengths                                                                                           | Weaknesses or expected costs                                                                     |
+|-----------------|-----------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------|
+| Scale model     | Designed to distribute work across a cluster and process large datasets.                            | Distributed-style planning and scheduling can add overhead for short queries and small datasets. |
+| Parquet support | Mature Parquet integration, schema handling, partition discovery, and configurable filter pushdown. | General-purpose execution can be heavier than a specialized embedded analytical path.            |
+| Ecosystem       | Widely used for data-lake analytics, batch processing, and ETL.                                     | Spark is not primarily a low-latency database-serving layer.                                     |
+| Execution model | Task-based processing provides an important contrast with single-process analytics.                 | A local run does not demonstrate cluster scaling, network shuffle behavior, or fault tolerance.  |
+| Research value  | Provides a recognizable reference for Parquet analytics in a distributed-processing ecosystem.      | Results must be interpreted with awareness of startup and scheduler costs.                       |
+
+### Suitable Use Cases
+
+Spark is appropriate for large data-lake pipelines, batch analytics, ETL, and
+workloads that may need to scale across machines.
+
+### Less Suitable Use Cases
+
+Spark is not the simplest choice for low-latency interactive queries over small
+local datasets. Its runtime overhead is a trade-off for distributed-processing
+capabilities.
+
+## Implementation Proximity
+
+The three technologies are close at the storage-access layer because all can
+query Parquet files directly. They differ substantially above that layer.
+
+| Layer                        | Polypheny Parquet Adapter                                        | DuckDB                                                 | Apache Spark SQL                                      |
+|------------------------------|------------------------------------------------------------------|--------------------------------------------------------|-------------------------------------------------------|
+| Input for planned comparison | Same immutable Parquet snapshot                                  | Same immutable Parquet snapshot                        | Same immutable Parquet snapshot                       |
+| Partition layout             | Hive-style `year=YYYY/month=MM` folders                          | Hive-style folders interpreted during Parquet scanning | Hive-style folders discovered by Spark                |
+| Storage access               | Direct file reads through the adapter                            | Direct file reads through DuckDB's Parquet scan        | Direct file reads through Spark's Parquet data source |
+| Query representation         | SQL planned by Polypheny and translated into adapter execution   | SQL planned and executed by DuckDB                     | SQL planned by Spark SQL and executed as Spark tasks  |
+| Execution style              | DBMS planning plus adapter-specific readers and operators        | Vectorized single-node analytical operators            | Partitioned task execution with Spark scheduling      |
+| Primary design goal          | Multi-model data management and heterogeneous source integration | Efficient in-process OLAP                              | Cluster-scale data processing                         |
+
+### Relative Closeness
+
+| Comparison           | Where the systems are close                                                                                | Important differences                                                                                            |
+|----------------------|------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------|
+| Polypheny and DuckDB | Both can directly query local Parquet files and evaluate comparable SQL aggregation shapes.                | DuckDB is embedded and specialized. Polypheny includes DBMS, catalog, planning, and adapter layers.              |
+| Polypheny and Spark  | Both can treat partitioned Parquet files as external analytical data and apply partition-aware processing. | Spark is designed around distributed task execution. Polypheny exposes data through a DBMS adapter architecture. |
+| DuckDB and Spark     | Both are mature Parquet analytical references.                                                             | DuckDB emphasizes an efficient local process. Spark emphasizes scalable distributed processing.                  |
+
+No selected competitor is an exact implementation match for Polypheny. This is
+intentional. DuckDB and Spark provide different reference points around the
+Polypheny adapter rather than duplicate baselines.
+
+## Why These Technologies Were Selected
+
+| Selection criterion                                 | Polypheny                | DuckDB                        | Apache Spark SQL               |
+|-----------------------------------------------------|--------------------------|-------------------------------|--------------------------------|
+| Required to evaluate the target implementation      | Yes                      | No                            | No                             |
+| Queries Parquet files directly without import       | Yes                      | Yes                           | Yes                            |
+| Supports aggregation-oriented SQL workloads         | Yes                      | Yes                           | Yes                            |
+| Supports Hive-style folder partitions               | Yes                      | Yes                           | Yes                            |
+| Practical for a reproducible workstation experiment | Yes                      | Yes                           | Yes, in local mode             |
+| Adds a distinct architectural perspective           | Multi-model DBMS adapter | Embedded OLAP engine          | Distributed analytics engine   |
+| Appropriate role                                    | Target system            | Focused single-node reference | Data-lake processing reference |
+
+The initial set is deliberately limited to three technologies. Every additional
+engine increases setup effort, SQL adaptation work, result-validation effort,
+and the number of architectural differences that must be explained.
+
+DuckDB and Spark were chosen because they cover two widely relevant analytical
+architectures while allowing the same Parquet files and broadly equivalent SQL
+queries to be used. Polypheny can then be evaluated in context rather than
+against only one type of competitor.
+
+
+## References
+
+- [Polypheny documentation: Overview](https://docs.polypheny.com/en/latest/getting_started/introduction)
+- [Polypheny documentation: Adapter Development](https://docs.polypheny.com/en/latest/devs/adapter-development)
+- [DuckDB documentation: Reading and Writing Parquet Files](https://duckdb.org/docs/stable/data/parquet/overview)
+- [DuckDB documentation: Execution Format](https://duckdb.org/docs/lts/internals/vector.html)
+- [Apache Spark: Spark SQL and DataFrames](https://spark.apache.org/sql/)
+- [Apache Spark documentation: Parquet Files](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html)
diff --git a/plugins/parquet-adapter/benchmarks/datasets/flickr_dataset.md b/plugins/parquet-adapter/benchmarks/datasets/flickr_dataset.md
new file mode 100644
index 0000000000..1128197284
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/datasets/flickr_dataset.md
@@ -0,0 +1,129 @@
+# Flickr8k Clean Parquet Nested Benchmark Dataset
+
+## Overview
+
+The Flickr8k Clean dataset is a small real-world image-caption dataset stored
+as a Parquet file. It is intended for nested-data benchmark coverage in the
+Parquet adapter, especially for repeated primitive columns and projection
+behavior around large binary values.
+
+The source dataset is
+[Flickr 8k Dataset (Clean)](https://www.kaggle.com/datasets/habedi/flickr-8k-dataset-clean/data)
+on Kaggle. The dataset page describes it as a cleaned Parquet version of
+Flickr8k with approximately 8,000 images. Each image has five human-written
+captions.
+
+This dataset should be used as a complement to the NYC TLC benchmark dataset.
+The TLC dataset exercises large flat and partitioned tables. Flickr8k exercises
+a different physical shape: one row per image, a repeated captions field, and a
+large binary image field.
+
+## Logical Content
+
+Each row represents one image and its captions.
+
+| Column     | Expected type        | Description |
+|------------|----------------------|-------------|
+| `id`       | string               | Image filename, for example `1000268201_693b08cb0e.jpg`. |
+| `captions` | list of strings      | Five short human-written text descriptions of the image. |
+| `image`    | binary / JPEG bytes  | Raw JPEG image bytes stored inside the Parquet row. SQL benchmarks should treat this as binary data, not as a decoded image. |
+
+The expected Parquet shape is shallow nested data:
+
+```text
+id: string
+captions: list<string>
+image: binary
+```
+
+The important nested field is `captions`. It is a repeated primitive value,
+which makes the dataset useful for testing whether the adapter can read and
+materialize list columns correctly. The `image` field is useful for projection
+benchmarks because it is much larger than the metadata and caption fields.
+
+## Benchmark Purpose
+
+This dataset is useful for the following benchmark categories:
+
+- Flat projection over image metadata, for example selecting only `id`.
+- Nested projection over repeated values, for example selecting `id` and
+  `captions`.
+- Binary projection, for example selecting `id` and `image`.
+- Full-row scans that read `id`, `captions`, and `image` together.
+- Projection pruning checks that compare scans with and without the `image`
+  column.
+- Correctness checks for repeated primitive values.
+
+The most important comparison is between queries that include `image` and
+queries that exclude it. If projection pruning works well, queries over `id`
+and `captions` should avoid paying the full cost of reading JPEG bytes.
+
+## Limitations
+
+Flickr8k Clean is a good starter dataset for nested Parquet behavior, but it is
+not a complete nested-data stress benchmark.
+
+- The nesting is shallow: `captions` is a list of strings. The dataset does not
+  cover structs, maps, deeply nested lists, or `LIST<STRUCT<...>>` shapes.
+- The repeated cardinality is regular: each image has five captions. This does
+  not strongly test variable-length repeated fields.
+- The row count is small compared with the TLC benchmark dataset. It is better
+  for correctness, projection behavior, and regression testing than for large
+  throughput measurements.
+- Full scans that include `image` may be dominated by binary I/O. Those results
+  should not be interpreted as measuring only nested-column reader performance.
+- The dataset is not designed for aggregation benchmarks. It has few numeric
+  columns and no partition layout.
+
+For broader nested coverage, pair this dataset with a synthetic Parquet fixture
+that includes variable-length arrays, nested structs, and arrays of structs.
+
+## Suggested Benchmark Queries
+
+The benchmark query set should keep separate query groups for metadata,
+captions, binary image bytes, and full-row reads.
+
+```sql
+-- Metadata-only projection.
+SELECT id
+FROM flickr8k;
+
+-- Repeated primitive projection.
+SELECT id, captions
+FROM flickr8k;
+
+-- Binary projection.
+SELECT id, image
+FROM flickr8k;
+
+-- Full-row projection.
+SELECT id, captions, image
+FROM flickr8k;
+```
+
+If the SQL surface supports nested access or unnesting in the benchmark path,
+additional queries can be added later. Until then, the core value of this
+dataset is measuring whole-list materialization and projection pruning.
+
+## Reporting Notes
+
+Benchmark results should record:
+
+- dataset source and download date
+- local file path
+- Parquet file size
+- row count
+- row group count
+- physical Parquet schema
+- compression codec
+- whether the query selected the `image` column
+
+These details are important because binary payload size, row group layout, and
+compression can dominate the observed runtime.
+
+## License
+
+The Kaggle dataset page lists the license as CC BY-NC 4.0. This is suitable for
+internal, academic, and non-commercial benchmark work, but it should be reviewed
+before using the dataset in a commercial or broadly redistributed benchmark
+package.
diff --git a/plugins/parquet-adapter/benchmarks/datasets/nested_customer.md b/plugins/parquet-adapter/benchmarks/datasets/nested_customer.md
new file mode 100644
index 0000000000..84e3800b4b
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/datasets/nested_customer.md
@@ -0,0 +1,150 @@
+# Nested Customer Dataset
+
+## Summary
+
+`nestedcustomer.parquet` is a nested Parquet representation of TPC-H scale factor 10 customer data. It keeps each customer as one root row and embeds the customer's orders and each order's line items as arrays of structs:
+
+```text
+customer
+  c_orders: array<struct<order fields,
+    o_lineitems: array<struct<lineitem fields>>>>
+```
+
+This makes the dataset a good benchmark candidate for nested-data support because it combines realistic TPC-H cardinalities with repeated nested structures that must be projected, traversed, unnested, or filtered.
+
+## Source And Lineage
+
+The dataset comes from the public `alicerey/nested-parquet` GitHub repository:
+
+```text
+https://github.com/alicerey/nested-parquet/tree/main/nested-tpch-sf10
+```
+
+That repository is the supplemental material for the paper "Nested Parquet Is Flat, Why Not Use It? How To Scan Nested Data With On-the-Fly Key Generation and Joins" by Alice Rey, Maximilian Rieger, and Thomas Neumann. The repository README describes it as supplemental material for the evaluation and states that the benchmark datasets are hosted in a Google Drive folder.
+
+The local ZIP archive was downloaded from that Google Drive dataset link. The concrete Drive file URL was recovered from the Windows `Zone.Identifier` stream of `C:\PolyData\nested-parquet-datasets.zip`:
+
+```text
+https://drive.google.com/file/d/1UqwFbuywMErdgau1_JWN8QwzjMwpXle4/view
+```
+
+The archive contains the same benchmark groups visible in the GitHub repository: `dblp`, `nested-tpch`, `nested-tpch-sf10`, `synthetic`, and `xmark`. The `nested-tpch-sf10` directory contains system-specific query subdirectories in the repository and the corresponding Parquet data files in the downloaded archive.
+
+The underlying business data model is TPC-H, the Transaction Processing Performance Council's decision-support benchmark. In the paper's evaluation section, the authors describe the scale factor 10 TPC-H nested dataset as nesting line items into orders and orders into customers. The paper's dataset table reports the same scale and shape observed locally: approximately 2.6 GB, 1.5M root tuples, and 60M tuples at the maximum nesting level.
+
+This local Parquet copy was written by Spark: the Parquet footer reports Spark 4.0.0 metadata and `parquet-mr version 1.13.1`.
+
+Useful references:
+
+- GitHub repository: https://github.com/alicerey/nested-parquet
+- `nested-tpch-sf10` repository directory: https://github.com/alicerey/nested-parquet/tree/main/nested-tpch-sf10
+- Dataset Google Drive file: https://drive.google.com/file/d/1UqwFbuywMErdgau1_JWN8QwzjMwpXle4/view
+- Paper PDF: https://db.in.tum.de/~rey/papers/nestedparquet_rey.pdf
+- Paper DOI: https://doi.org/10.1145/3725329
+- TPC-H homepage: https://www.tpc.org/tpch/
+
+## Local Files
+
+Primary nested file:
+
+```text
+C:\PolyData\nested-parquet-datasets\nested-parquet-datasets\nested-tpch-sf10\nestedcustomer.parquet
+```
+
+Normalized comparison files:
+
+```text
+C:\PolyData\nested-parquet-datasets\nested-parquet-datasets\nested-tpch-sf10\normalized\nestedcustomer_0.parquet
+C:\PolyData\nested-parquet-datasets\nested-parquet-datasets\nested-tpch-sf10\normalized\nestedcustomer_1.parquet
+C:\PolyData\nested-parquet-datasets\nested-parquet-datasets\nested-tpch-sf10\normalized\nestedcustomer_2.parquet
+```
+
+There is also a copied standalone primary file:
+
+```text
+C:\PolyData\nested_customer\nestedcustomer.parquet
+```
+
+## Measured Metadata
+
+| File                                  |       Size |       Rows | Notes                                |
+|---------------------------------------|-----------:|-----------:|--------------------------------------|
+| `nestedcustomer.parquet`              |   2.63 GiB |  1,500,000 | Nested customer table; 22 row groups |
+| `normalized/nestedcustomer_0.parquet` | 123.87 MiB |  1,500,000 | Customer-level normalized table      |
+| `normalized/nestedcustomer_1.parquet` | 558.62 MiB | 15,500,018 | Order-level normalized table         |
+| `normalized/nestedcustomer_2.parquet` |   2.26 GiB | 60,486,070 | Lineitem-level normalized table      |
+
+Parquet footer metadata for the nested file:
+
+- Created by: `parquet-mr version 1.13.1`
+- Spark metadata: `org.apache.spark.version = 4.0.0`
+- Root rows: `1,500,000`
+- Row groups: `22`
+- Top-level fields: `9`
+- Primitive leaf fields: `31`
+- Maximum nested schema depth: `7`
+- Repeated/list fields: `2`
+
+## Schema Shape
+
+Top-level fields:
+
+```text
+c_custkey: integer
+c_name: string
+c_address: string
+c_nationkey: integer
+c_phone: string
+c_acctbal: decimal(12,2)
+c_mktsegment: string
+c_comment: string
+c_orders: array<struct<
+  o_orderkey: integer,
+  o_orderstatus: string,
+  o_totalprice: decimal(12,2),
+  o_orderdate: date,
+  o_orderpriority: string,
+  o_clerk: string,
+  o_shippriority: integer,
+  o_comment: string,
+  o_lineitems: array<struct<
+    l_partkey: integer,
+    l_suppkey: integer,
+    l_linenumber: integer,
+    l_quantity: decimal(12,2),
+    l_extendedprice: decimal(12,2),
+    l_discount: decimal(12,2),
+    l_tax: decimal(12,2),
+    l_returnflag: string,
+    l_linestatus: string,
+    l_shipdate: date,
+    l_commitdate: date,
+    l_receiptdate: date,
+    l_shipinstruct: string,
+    l_shipmode: string,
+    l_comment: string
+  >>
+>>
+```
+
+## Benchmark Use
+
+Recommended benchmark role:
+
+- Primary nested-data benchmark for Parquet scans, projection, and nested-field access.
+- Nested unnest/flatten benchmark for `customer -> orders -> lineitems`.
+- Comparison benchmark against the normalized files to measure the cost or benefit of nested storage relative to relational-style tables.
+
+Representative query patterns:
+
+- Scan only customer scalar fields to test projection pruning around a large nested column.
+- Project selected order fields from `c_orders`.
+- Unnest `c_orders` and aggregate order counts per customer.
+- Unnest both `c_orders` and `o_lineitems` and aggregate lineitem metrics by customer, order, date, status, or return flag.
+- Filter on nested lineitem fields such as ship date, return flag, or discount.
+
+Limitations:
+
+- This is a derived nested layout based on TPC-H, not an official TPC-H benchmark result package.
+- GitHub is the public landing page and the data itself is hosted externally on Google Drive.
+- The dataset has arrays and structs, but no map fields.
diff --git a/plugins/parquet-adapter/benchmarks/datasets/tlc_dataset_desc.md b/plugins/parquet-adapter/benchmarks/datasets/tlc_dataset_desc.md
new file mode 100644
index 0000000000..79aeeac49b
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/datasets/tlc_dataset_desc.md
@@ -0,0 +1,252 @@
+# NYC TLC Partitioned Parquet Benchmark Dataset
+
+## Overview
+
+The benchmark dataset is a local, read-only snapshot of New York City Taxi and
+Limousine Commission (NYC TLC) trip records stored as Parquet files. It is used
+to evaluate scan, projection, filtering, partition pruning, aggregation, and
+multi-file query behavior in the Polypheny Parquet adapter.
+
+The local dataset root is:
+
+```text
+C:\PolyData\tlc_partitioned
+```
+
+The source data is based on the monthly Parquet downloads published on the
+[NYC TLC Trip Record Data](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page)
+page. The local snapshot contains four trip-record categories:
+
+- `yellow_tripdata`: yellow taxi trips
+- `green_tripdata`: green taxi trips
+- `fhv_tripdata`: for-hire vehicle (FHV) trips
+- `fhvhv_tripdata`: high-volume for-hire vehicle (HVFHV) trips
+
+The statistics in this document describe the files present under
+`C:\PolyData\tlc_partitioned` on May 31, 2026. They should be recorded together
+with benchmark results because the NYC TLC website continues to publish newer
+monthly files.
+
+## Physical Layout
+
+The snapshot uses Hive-style directory partitions. Each trip-record category is
+a logical table. Each table contains one Parquet file per month under
+`year=YYYY\month=MM` folders:
+
+```text
+tlc_partitioned\
+  yellow_tripdata\
+    year=2020\
+      month=01\
+        yellow_tripdata_2020-01.parquet
+      ...
+    year=2023\
+      month=01\
+        yellow_tripdata_2023-01.parquet
+  green_tripdata\
+    year=YYYY\
+      month=MM\
+        green_tripdata_YYYY-MM.parquet
+  fhv_tripdata\
+    year=YYYY\
+      month=MM\
+        fhv_tripdata_YYYY-MM.parquet
+  fhvhv_tripdata\
+    year=YYYY\
+      month=MM\
+        fhvhv_tripdata_YYYY-MM.parquet
+```
+
+The `year` and `month` values are derived from the directory path. They are
+virtual partition columns rather than physical columns stored inside the
+Parquet files. This layout allows an engine to skip unrelated files when a
+query restricts one or both partition keys.
+
+Every table covers the same 37 monthly partitions:
+
+- January 2020 through December 2020
+- January 2021 through December 2021
+- January 2022 through December 2022
+- January 2023
+
+Starting from February 2023 the Parquet schema has changed.
+
+## Dataset Summary
+
+| Table             | Monthly files |            Rows | Parquet row groups |       Stored bytes |   Stored size |
+|-------------------|--------------:|----------------:|-------------------:|-------------------:|--------------:|
+| `yellow_tripdata` |            37 |      98,276,264 |                190 |      1,605,205,637 |      1.49 GiB |
+| `green_tripdata`  |            37 |       3,711,544 |                 37 |         69,056,558 |      0.06 GiB |
+| `fhv_tripdata`    |            37 |      45,376,714 |                 38 |        465,048,942 |      0.43 GiB |
+| `fhvhv_tripdata`  |            37 |     548,801,637 |                179 |     14,289,433,466 |     13.31 GiB |
+| **Total**         |       **148** | **696,166,159** |            **444** | **16,428,744,603** | **15.30 GiB** |
+
+The high-volume FHV table dominates the fixture. It contains approximately
+78.83% of all rows and 86.98% of the stored bytes. Queries over
+`fhvhv_tripdata` are therefore the primary stress cases for scan throughput,
+filter evaluation, row-group pruning, aggregation, and result materialization.
+
+Monthly file sizes vary substantially:
+
+| Table             | Minimum file size | Average file size | Maximum file size |
+|-------------------|------------------:|------------------:|------------------:|
+| `yellow_tripdata` |          4.98 MiB |         41.37 MiB |        117.72 MiB |
+| `green_tripdata`  |          0.68 MiB |          1.78 MiB |          6.85 MiB |
+| `fhv_tripdata`    |          6.19 MiB |         11.99 MiB |         18.59 MiB |
+| `fhvhv_tripdata`  |        141.37 MiB |        368.31 MiB |        532.98 MiB |
+
+## Compression
+
+The snapshot preserves the compression codecs of the monthly source files. It
+is not normalized to one compression setting.
+
+| Table             | GZIP files | SNAPPY files |
+|-------------------|-----------:|-------------:|
+| `yellow_tripdata` |         29 |            8 |
+| `green_tripdata`  |         37 |            0 |
+| `fhv_tripdata`    |         36 |            1 |
+| `fhvhv_tripdata`  |         35 |            2 |
+
+The codec mix is part of the benchmark fixture. Comparisons against another
+engine should use the same files without rewriting them unless the rewritten
+layout is reported as a separate experiment.
+
+## Table Content
+
+### Yellow Taxi Trips
+
+`yellow_tripdata` contains pickup and drop-off timestamps and location IDs,
+passenger count, trip distance, rate and payment information, and itemized fare
+amounts.
+
+Physical columns:
+
+```text
+VendorID
+tpep_pickup_datetime
+tpep_dropoff_datetime
+passenger_count
+trip_distance
+RatecodeID
+store_and_fwd_flag
+PULocationID
+DOLocationID
+payment_type
+fare_amount
+extra
+mta_tax
+tip_amount
+tolls_amount
+improvement_surcharge
+total_amount
+congestion_surcharge
+airport_fee
+```
+
+### Green Taxi Trips
+
+`green_tripdata` has a similar taxi-trip shape with green-taxi pickup and
+drop-off timestamps and additional green-taxi-specific fields.
+
+Physical columns:
+
+```text
+VendorID
+lpep_pickup_datetime
+lpep_dropoff_datetime
+store_and_fwd_flag
+RatecodeID
+PULocationID
+DOLocationID
+passenger_count
+trip_distance
+fare_amount
+extra
+mta_tax
+tip_amount
+tolls_amount
+ehail_fee
+improvement_surcharge
+total_amount
+payment_type
+trip_type
+congestion_surcharge
+```
+
+### For-Hire Vehicle Trips
+
+`fhv_tripdata` is a compact FHV trip representation containing base identifiers,
+timestamps, location IDs, and a shared-ride flag.
+
+Physical columns:
+
+```text
+dispatching_base_num
+pickup_datetime
+dropOff_datetime
+PUlocationID
+DOlocationID
+SR_Flag
+Affiliated_base_number
+```
+
+### High-Volume For-Hire Vehicle Trips
+
+`fhvhv_tripdata` is the largest and widest table. It contains operator
+identifiers, request lifecycle timestamps, pickup and drop-off locations,
+distance and duration values, itemized fare values, and accessibility and
+shared-ride flags.
+
+Physical columns:
+
+```text
+hvfhs_license_num
+dispatching_base_num
+originating_base_num
+request_datetime
+on_scene_datetime
+pickup_datetime
+dropoff_datetime
+PULocationID
+DOLocationID
+trip_miles
+trip_time
+base_passenger_fare
+tolls
+bcf
+sales_tax
+congestion_surcharge
+airport_fee
+tips
+driver_pay
+shared_request_flag
+shared_match_flag
+access_a_ride_flag
+wav_request_flag
+wav_match_flag
+```
+
+All four tables additionally expose the virtual partition columns `year` and
+`month`.
+
+## Benchmark Characteristics
+
+This fixture is useful for evaluating:
+
+- multi-file table discovery and scans
+- full-table and partition-pruned counts
+- folder-based pruning using `year` and `month`
+- Parquet row-group pruning using timestamp and numeric predicates
+- projection pruning and row materialization
+- grouped aggregates, ordering, limits, and aggregate joins
+- behavior across small, medium, and high-volume tables
+
+The Parquet files contain flat records. When the Polypheny document adapter is
+used, each Parquet row can be exposed as one document. This makes the fixture
+suitable for direct document-on-Parquet scan comparisons against another engine
+that queries the same files in place.
+
+The fixture does **not** contain nested structs or repeated fields. It should
+not be used alone to claim performance results for nested document
+reconstruction, nested-path filtering, array traversal, or unwind operations.
+A separate nested Parquet fixture is required for those experiments.
diff --git a/plugins/parquet-adapter/benchmarks/query-list.md b/plugins/parquet-adapter/benchmarks/query-list.md
new file mode 100644
index 0000000000..2e3a53ef85
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query-list.md
@@ -0,0 +1,1144 @@
+# SQL Query List
+
+This section lists every benchmark query. SQL uses the DuckDB table names; the Polypheny client maps them to the `tlc__*` adapter tables.
+
+## Q01 - Full scan count, yellow taxi
+
+```sql
+SELECT count(*) AS row_count
+FROM yellow_tripdata;
+```
+
+### Checks:
+- full-table row counting.
+- the fastest possible aggregate path.
+
+### Base Functionality:
+- count all rows in the yellow taxi table.
+
+### Benchmark-Phase Optimization:
+- calculate the total from Parquet metadata without reading trip rows.
+
+
+## Q02 - Partition-pruned count, one yellow taxi "month"
+
+```sql
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+```
+### Checks:
+- partition pruning for a selected month.
+- the fast aggregate path after partition pruning.
+
+### Base Functionality:
+- discard files outside October 2022.
+
+### Benchmark-Phase Optimization:
+- calculate the total from metadata of the matching files without reading trip rows.
+
+## Q03 - Projection-heavy scan, yellow taxi
+
+```sql
+SELECT
+  PULocationID,
+  DOLocationID,
+  trip_distance,
+  total_amount
+FROM yellow_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10'
+LIMIT 100000;
+```
+
+### Checks:
+- partition pruning for a selected month.
+- efficient reading and transfer of 100,000 result rows.
+- projection of selected columns from a wider table.
+
+### Base Functionality:
+- discard files outside October 2022.
+- stop scanning after 100,000 rows have been produced.
+
+### Benchmark-Phase Optimization:
+- materialize flat projected values more efficiently using the lightweight row reader.
+
+## Q04 - Timestamp row-group filter, yellow taxi
+
+```sql
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
+  AND tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00';
+```
+
+### Checks:
+- timestamp predicate pushdown.
+- row-group pruning using pickup-time statistics.
+- filtered row counting.
+
+### Base Functionality:
+- push the pickup-time range filter into the Parquet reader.
+- skip row groups that cannot contain trips from October 15, 2022.
+
+### Benchmark-Phase Optimization:
+- count matching rows inside the adapter without materializing complete taxi rows.
+- read only the timestamp column needed to evaluate the filter.
+
+
+## Q05 - Numeric row-group filter, yellow taxi
+
+```sql
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+```
+
+### Checks:
+- numeric predicate pushdown.
+- row-group pruning using distance and fare statistics.
+- filtered row counting with multiple conditions.
+
+### Base Functionality:
+- push both numeric filters into the Parquet reader.
+- skip row groups that cannot contain matching trips.
+
+### Benchmark-Phase Optimization:
+- count matching rows inside the adapter without materializing complete taxi rows.
+- read only the two numeric columns needed to evaluate the filters.
+
+## Q06 - Monthly yellow taxi aggregates
+
+```sql
+SELECT
+  "year",
+  "month",
+  count(*) AS trips,
+  sum(total_amount) AS gross_amount,
+  avg(trip_distance) AS avg_distance
+FROM yellow_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+```
+
+### Checks:
+- grouping by partition columns.
+- calculation of `COUNT`, `SUM`, and `AVG`.
+- ordering of monthly aggregate results.
+
+### Base Functionality:
+- expose `year` and `month` folder values as table columns.
+- group results by month and order them chronologically.
+
+### Benchmark-Phase Optimization:
+- determine the monthly group from folder partition values instead of reading grouping values from each row.
+- calculate counts from metadata and aggregate only the required numeric columns without materializing complete taxi rows.
+
+## Q07 - Top pickup zones by yellow taxi trip count, low-cardinality grouping
+
+```sql
+SELECT
+  PULocationID,
+  count(*) AS trips,
+  avg(total_amount) AS avg_total_amount
+FROM yellow_tripdata
+WHERE "year" = '2022'
+GROUP BY PULocationID
+ORDER BY trips DESC
+LIMIT 20;
+```
+
+### Checks:
+- partition pruning for a selected year.
+- grouping by pickup location.
+- calculation of `COUNT` and `AVG`.
+- ordering groups and returning the top 20 results.
+
+### Base Functionality:
+- discard files outside 2022.
+- order pickup locations by trip count and return the 20 busiest locations.
+
+### Benchmark-Phase Optimization:
+- calculate grouped aggregates inside the Parquet adapter.
+- read only the pickup-location and total-amount columns instead of complete taxi rows.
+
+
+## Q08 - Top pickup/dropoff pairs for yellow taxi
+
+```sql
+SELECT
+  PULocationID,
+  DOLocationID,
+  count(*) AS trips,
+  avg(trip_distance) AS avg_distance,
+  avg(total_amount) AS avg_total_amount
+FROM yellow_tripdata
+WHERE "year" = '2022'
+GROUP BY PULocationID, DOLocationID
+ORDER BY trips DESC
+LIMIT 20;
+```
+
+### Checks:
+- partition pruning for a selected year.
+- grouping by two location columns.
+- calculation of `COUNT` and `AVG`.
+- ordering groups and returning the top 20 results. 
+- compared with Q07, this creates more groups and tests the additional cost of tracking pickup(start)/dropoff(dest) combinations.
+
+### Base Functionality:
+- discard files outside 2022.
+- order pickup/dropoff pairs by trip count and return the 20 busiest pairs.
+
+### Benchmark-Phase Optimization:
+- calculate grouped aggregates inside the Parquet adapter.
+- read only the pickup-location, dropoff-location, distance, and total-amount columns instead of complete taxi rows.
+- use an efficient representation for the two-column grouping key.
+
+
+## Q09 - Full scan count, high-volume FHV
+
+```sql
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata;
+```
+
+### Checks:
+- full-table row counting on the larger high-volume FHV dataset.
+- the fastest possible aggregate path at a larger scale.
+
+### Base Functionality:
+- count all rows in the high-volume FHV table.
+
+### Benchmark-Phase Optimization:
+- calculate the total from Parquet metadata without reading trip rows.
+
+## Q10 - Partition-pruned count, one high-volume FHV "month"
+
+```sql
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+```
+
+### Checks:
+- partition pruning for a selected month on the larger **high-volume** FHV dataset.
+- the fast aggregate path after partition pruning.
+- This is the large-table counterpart of Q02
+
+### Base Functionality:
+- discard files outside October 2022.
+
+### Benchmark-Phase Optimization:
+- calculate the total from metadata of the matching files without reading trip rows.
+
+
+## Q11 - Timestamp row-group filter, high-volume FHV
+
+```sql
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata
+WHERE pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
+  AND pickup_datetime < TIMESTAMP '2022-10-16 00:00:00';
+```
+
+### Checks:
+- timestamp predicate pushdown on the larger high-volume FHV dataset.
+- row-group pruning using pickup-time statistics.
+- filtered row counting.
+
+### Base Functionality:
+- push the pickup-time range filter into the Parquet reader.
+- skip row groups that cannot contain trips from October 15, 2022.
+
+### Benchmark-Phase Optimization:
+- count matching rows inside the adapter without materializing complete trip rows.
+- read only the timestamp column needed to evaluate the filter.
+
+
+## Q12 - High-volume FHV fare and distance filter
+
+```sql
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata
+WHERE trip_miles >= 10.0
+  AND base_passenger_fare >= 40.0;
+```
+
+### Checks:
+- numeric predicate pushdown on the larger high-volume FHV dataset.
+- row-group pruning using distance and fare statistics.
+- filtered row counting with multiple conditions.
+
+### Base Functionality:
+- push both numeric filters into the Parquet reader.
+- skip row groups that cannot contain matching trips.
+
+### Benchmark-Phase Optimization:
+- count matching rows inside the adapter without materializing complete trip rows.
+- read only the distance and fare columns needed to evaluate the filters.
+
+
+## Q13 - Monthly high-volume FHV aggregates
+
+```sql
+SELECT
+  "year",
+  "month",
+  count(*) AS trips,
+  sum(base_passenger_fare) AS passenger_fare,
+  sum(driver_pay) AS driver_pay,
+  avg(trip_miles) AS avg_miles
+FROM fhvhv_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+```
+
+### Checks:
+- grouping by partition columns on the larger high-volume FHV dataset.
+- calculation of `COUNT`, multiple `SUM` values, and `AVG`.
+- ordering of monthly aggregate results.
+
+### Base Functionality:
+- expose `year` and `month` folder values as table columns.
+- group results by month and order them chronologically.
+
+### Benchmark-Phase Optimization:
+- calculate monthly aggregates inside the Parquet adapter.
+- determine the monthly group from folder partition values instead of reading grouping values from each row.
+- calculate counts from metadata and aggregate only the required fare, driver-pay, and distance columns without materializing complete trip rows.
+
+## Q14 - Top high-volume FHV pickup/dropoff pairs
+
+```sql
+SELECT
+  PULocationID,
+  DOLocationID,
+  count(*) AS trips,
+  avg(trip_miles) AS avg_miles,
+  avg(base_passenger_fare) AS avg_fare
+FROM fhvhv_tripdata
+WHERE "year" = '2022'
+GROUP BY PULocationID, DOLocationID
+ORDER BY trips DESC
+LIMIT 20;
+```
+
+### Checks:
+- partition pruning for a selected year.
+- grouping by starting-location and destination-location pairs.
+- calculation of `COUNT` and `AVG`.
+- ordering groups and returning the top 20 results on the larger high-volume FHV dataset.
+- This is the larger-table counterpart of Q08.
+
+### Base Functionality:
+- discard files outside 2022.
+- order location pairs by trip count and return the 20 busiest pairs.
+
+### Benchmark-Phase Optimization:
+- calculate grouped aggregates inside the Parquet adapter.
+- read only the starting-location, destination-location, distance, and fare columns instead of complete trip rows.
+- use an efficient representation for the two-column grouping key.
+
+## Q15 - Low-cardinality shared-ride flag distribution, high-volume FHV
+
+```sql
+SELECT
+  shared_request_flag,
+  shared_match_flag,
+  count(*) AS trips
+FROM fhvhv_tripdata
+WHERE "year" = '2022'
+GROUP BY shared_request_flag, shared_match_flag
+ORDER BY trips DESC;
+```
+
+### Checks:
+- partition pruning for a selected year.
+- grouping by two low-cardinality text flags. (column has only a small number of distinct values)
+- counting trips for each shared-ride flag combination.
+- ordering combinations by trip count.
+
+### Base Functionality:
+- discard files outside 2022.
+- order shared-ride flag combinations from most common to least common.
+
+### Benchmark-Phase Optimization:
+- calculate grouped counts inside the Parquet adapter.
+- read only the two shared-ride flag columns instead of complete trip rows.
+
+## Q16 - Green taxi monthly aggregates
+
+```sql
+SELECT
+  "year",
+  "month",
+  count(*) AS trips,
+  sum(total_amount) AS gross_amount,
+  avg(trip_distance) AS avg_distance
+FROM green_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+```
+
+### Checks:
+- grouping by partition columns on the smaller green taxi dataset.
+- calculation of `COUNT`, `SUM`, and `AVG`.
+- ordering of monthly aggregate results.
+
+### Base Functionality:
+- expose `year` and `month` folder values as table columns.
+- group results by month and order them chronologically.
+
+### Benchmark-Phase Optimization:
+- calculate monthly aggregates inside the Parquet adapter.
+- determine the monthly group from folder partition values instead of reading grouping values from each row.
+- calculate counts from metadata and aggregate only the required amount and distance columns without materializing complete trip rows.
+
+Smaller-table counterpart of Q06
+
+## Q17 - FHV monthly trip counts
+
+```sql
+SELECT
+  "year",
+  "month",
+  count(*) AS trips
+FROM fhv_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+```
+
+### Checks:
+- grouping by partition columns on the classic FHV dataset.
+- monthly `COUNT(*)` calculation.
+- ordering of monthly results.
+
+### Base Functionality:
+- expose `year` and `month` folder values as table columns.
+- group results by month and order them chronologically.
+
+### Benchmark-Phase Optimization:
+- determine the monthly group from folder partition values.
+- calculate each monthly count from Parquet metadata without reading trip rows.
+
+Simpler than Q06, Q13, and Q16 because it calculates only row counts
+
+## Q18 - Yellow vs green monthly taxi totals
+
+```sql
+WITH yellow_monthly AS (
+  SELECT "year", "month", count(*) AS yellow_trips, sum(total_amount) AS yellow_amount
+  FROM yellow_tripdata
+  GROUP BY "year", "month"
+),
+green_monthly AS (
+  SELECT "year", "month", count(*) AS green_trips, sum(total_amount) AS green_amount
+  FROM green_tripdata
+  GROUP BY "year", "month"
+)
+SELECT
+  y."year",
+  y."month",
+  y.yellow_trips,
+  g.green_trips,
+  y.yellow_amount,
+  g.green_amount
+FROM yellow_monthly y
+JOIN green_monthly g
+  ON y."year" = g."year"
+ AND y."month" = g."month"
+ORDER BY y."year", y."month";
+```
+
+### Checks:
+- monthly aggregation on two taxi datasets.
+- calculation of `COUNT` and `SUM`.
+- joining monthly aggregate results by `year` and `month`.
+- ordering the joined results chronologically.
+
+### Base Functionality:
+- join matching yellow and green taxi months.
+- order the combined monthly results.
+
+### Benchmark-Phase Optimization:
+- calculate aggregates separately inside the Parquet adapter before joining the datasets.
+- determine monthly groups from folder partition values.
+- calculate counts from metadata and aggregate only the required amount columns.
+- join the small monthly result sets instead of joining individual trip rows. 
+
+The key idea is early data reduction: millions of trip rows become a few dozen monthly rows before the join occurs.
+
+## Q19 - Taxi vs high-volume FHV monthly counts
+
+```sql
+WITH yellow_monthly AS (
+  SELECT "year", "month", count(*) AS yellow_trips
+  FROM yellow_tripdata
+  GROUP BY "year", "month"
+),
+fhvhv_monthly AS (
+  SELECT "year", "month", count(*) AS fhvhv_trips
+  FROM fhvhv_tripdata
+  GROUP BY "year", "month"
+)
+SELECT
+  y."year",
+  y."month",
+  y.yellow_trips,
+  f.fhvhv_trips
+FROM yellow_monthly y
+JOIN fhvhv_monthly f
+  ON y."year" = f."year"
+ AND y."month" = f."month"
+ORDER BY y."year", y."month";
+```
+
+### Checks:
+- monthly trip counting on two datasets with very different sizes.
+- grouping by partition columns.
+- joining monthly count results by `year` and `month`.
+- ordering the joined results chronologically.
+
+### Base Functionality:
+- join matching yellow taxi and high-volume FHV months.
+- order the combined monthly results.
+
+### Benchmark-Phase Optimization:
+- determine monthly groups from folder partition values.
+- calculate both monthly counts from Parquet metadata without reading trip rows.
+- join the small monthly result sets instead of joining individual trip rows.
+
+This is a metadata-only counterpart of Q18. It verifies that early aggregation remains effective even when one input is the much larger high-volume FHV table.
+
+## Q20 - Wide high-volume FHV scan with partition pruning
+
+```sql
+SELECT
+  hvfhs_license_num,
+  dispatching_base_num,
+  originating_base_num,
+  request_datetime,
+  on_scene_datetime,
+  pickup_datetime,
+  dropoff_datetime,
+  PULocationID,
+  DOLocationID,
+  trip_miles,
+  trip_time,
+  base_passenger_fare,
+  tolls,
+  sales_tax,
+  congestion_surcharge,
+  airport_fee,
+  tips,
+  driver_pay,
+  shared_request_flag,
+  shared_match_flag,
+  access_a_ride_flag,
+  wav_request_flag,
+  wav_match_flag
+FROM fhvhv_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10'
+LIMIT 100000;
+```
+
+### Checks:
+- partition pruning for a selected month.
+- projection of 23 columns from the wider high-volume FHV table.
+- efficient reading and transfer of 100,000 wide result rows.
+
+### Base Functionality:
+- discard files outside October 2022.
+- push the projection into the Parquet adapter.
+- read only the requested columns.
+- stop scanning after 100,000 rows have been produced.
+
+### Benchmark-Phase Optimization:
+- materialize flat projected values more efficiently using the lightweight row reader.
+
+This is the larger and wider counterpart of Q03. It measures how the optimized row reader behaves when each returned row contains substantially more data.
+
+## Q21 - Yellow taxi projected scan without partition pruning
+
+```sql
+SELECT
+  PULocationID,
+  DOLocationID,
+  trip_distance,
+  total_amount
+FROM yellow_tripdata
+LIMIT 100000;
+```
+
+### Checks:
+- projection of selected columns without partition pruning.
+- efficient reading and transfer of 100,000 result rows.
+- projected scan performance when files cannot be discarded first.
+
+### Base Functionality:
+- push the projection into the Parquet adapter.
+- read only the four requested columns.
+- stop scanning after 100,000 rows have been produced.
+
+### Benchmark-Phase Optimization:
+- materialize flat projected values more efficiently using the lightweight row reader.
+
+This is the no-partition-filter counterpart of Q03. It isolates projected row reading from partition pruning.
+
+
+## Q22 - High-volume FHV wide projected scan without partition pruning
+
+```sql
+SELECT
+  hvfhs_license_num,
+  dispatching_base_num,
+  pickup_datetime,
+  dropoff_datetime,
+  PULocationID,
+  DOLocationID,
+  trip_miles,
+  trip_time,
+  base_passenger_fare,
+  tolls,
+  sales_tax,
+  tips,
+  driver_pay,
+  shared_request_flag,
+  shared_match_flag
+FROM fhvhv_tripdata
+LIMIT 100000;
+```
+
+### Checks:
+- projection of 15 columns without partition pruning.
+- efficient reading and transfer of 100,000 wide result rows.
+- projected scan performance on the larger high-volume FHV dataset when files cannot be discarded first.
+
+### Base Functionality:
+- push the projection into the Parquet adapter.
+- read only the requested columns.
+- stop scanning after 100,000 rows have been produced.
+
+### Benchmark-Phase Optimization:
+- materialize flat projected values more efficiently using the lightweight row reader.
+
+This is the no-partition-filter counterpart of Q20.
+
+## Q23 - Yellow taxi timestamp and numeric conjunctive filter
+
+```sql
+SELECT count(*) AS matching_trips
+FROM yellow_tripdata
+WHERE tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
+  AND tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00'
+  AND trip_distance >= 2.0
+  AND total_amount >= 15.0;
+```
+
+### Checks:
+- filtering with multiple conditions combined using `AND`.
+- timestamp and numeric predicate pushdown.
+- row-group pruning using pickup-time, distance, and amount statistics.
+- filtered row counting.
+
+### Base Functionality:
+- push the timestamp, distance, and amount filters into the Parquet reader.
+- skip files and row groups that cannot contain matching trips.
+
+### Benchmark-Phase Optimization:
+- count matching rows inside the adapter without materializing complete taxi rows.
+- read only the timestamp, distance, and amount columns needed to evaluate the filters.
+
+This extends Q04 and Q05 by combining timestamp and numeric filtering in one query.
+
+## Q24 - Yellow taxi numeric OR filter
+
+```sql
+SELECT count(*) AS matching_trips
+FROM yellow_tripdata
+WHERE trip_distance >= 20.0
+   OR total_amount >= 100.0;
+```
+
+### Checks:
+- filtering with numeric conditions combined using `OR`.
+- predicate pushdown for OR filter.
+- filtered row counting for a more difficult filter shape.
+
+### Base Functionality:
+- push the distance and amount filter into the Parquet reader.
+- retain a trip when either numeric condition matches.
+- skip files and row groups only when neither condition can match.
+
+### Benchmark-Phase Optimization:
+- count matching rows inside the adapter without materializing complete taxi rows.
+- read only the distance and amount columns needed to evaluate the filter.
+
+OR is more difficult to optimize than AND: data can be discarded only when every alternative is impossible.
+
+## Q25 - Yellow taxi IN-list and numeric filter
+
+```sql
+SELECT count(*) AS matching_trips
+FROM yellow_tripdata
+WHERE PULocationID IN (132, 138, 161, 236, 237)
+  AND trip_distance >= 1.0
+  AND total_amount >= 5.0;
+```
+
+### Checks:
+- filtering by a list of pickup zones using `IN`.
+- combining an `IN` list with numeric conditions using `AND`.
+- filtered row counting.
+
+### Base Functionality:
+- retain trips starting in one of the five selected pickup zones.
+- require matching trips to also satisfy the distance and amount conditions.
+
+### Benchmark-Phase Optimization:
+- convert the `IN` list into equivalent equality alternatives for predicate pushdown.
+- count matching rows inside the adapter without materializing complete taxi rows.
+- read only the pickup-zone, distance, and amount columns needed to evaluate the filters.
+
+
+Conceptually: `PULocationID IN (132, 138, 161, 236, 237)` means `PULocationID = 132 OR PULocationID = 138 OR ...`
+
+## Q26 - High-volume FHV combination of timestamp and text filtering
+
+```sql
+SELECT count(*) AS matching_trips
+FROM fhvhv_tripdata
+WHERE pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
+  AND pickup_datetime < TIMESTAMP '2022-10-16 00:00:00'
+  AND shared_request_flag = 'Y';
+```
+
+### Checks:
+- filtering with timestamp conditions and a text flag condition combined using `AND`.
+- predicate pushdown on the larger high-volume FHV dataset.
+- filtered row counting.
+
+### Base Functionality:
+- retain trips from October 15, 2022 where a shared ride was requested.
+- skip files and row groups that cannot contain matching trips.
+
+### Benchmark-Phase Optimization:
+- count matching rows inside the adapter without materializing complete trip rows.
+- read only the pickup-time and shared-request flag columns needed to evaluate the filters.
+
+The shared_request_flag = 'Y' condition keeps trips for which the passenger requested a shared ride.
+
+## Q27 - High-volume FHV numeric conjunctive filter
+
+```sql
+SELECT count(*) AS matching_trips
+FROM fhvhv_tripdata
+WHERE trip_miles >= 5.0
+  AND base_passenger_fare >= 25.0
+  AND driver_pay >= 10.0;
+```
+
+### Checks:
+- filtering with three numeric conditions combined using `AND`.
+- predicate pushdown across multiple physical columns.
+- filtered row counting on the larger high-volume FHV dataset.
+
+### Base Functionality:
+- retain trips only when distance, passenger fare, and driver pay satisfy their thresholds.
+- skip files and row groups that cannot contain matching trips.
+
+### Benchmark-Phase Optimization:
+- count matching rows inside the adapter without materializing complete trip rows.
+- read only the distance, passenger-fare, and driver-pay columns needed to evaluate the filters.
+
+This extends Q12 by adding a third numeric condition.
+
+## Q28 - High-volume FHV text flag OR filter
+
+```sql
+SELECT count(*) AS matching_trips
+FROM fhvhv_tripdata
+WHERE shared_request_flag = 'Y'
+   OR shared_match_flag = 'Y'
+   OR wav_request_flag = 'Y';
+```
+
+### Checks:
+- filtering with three text flag conditions combined using `OR`.
+- predicate pushdown for a more difficult text-filter shape.
+- filtered row counting on the larger high-volume FHV dataset.
+
+### Base Functionality:
+- retain a trip when any selected flag is set to `Y`.
+- skip files and row groups only when none of the flag conditions can match.
+
+### Benchmark-Phase Optimization:
+- count matching rows inside the adapter without materializing complete trip rows.
+- read only the three flag columns needed to evaluate the filter.
+
+This is intentionally difficult: OR reduces pruning opportunities, and text flags usually match many rows.
+
+## Q29 - Yellow taxi scalar aggregate over physical columns
+
+```sql
+SELECT
+  count(*) AS trips,
+  sum(total_amount) AS gross_amount,
+  avg(total_amount) AS avg_total_amount,
+  min(trip_distance) AS min_distance,
+  max(trip_distance) AS max_distance
+FROM yellow_tripdata;
+```
+
+### Checks:
+- calculation of multiple aggregates without grouping.
+- support for `COUNT`, `SUM`, `AVG`, `MIN`, and `MAX`.
+- aggregation over physical Parquet columns.
+
+### Base Functionality:
+- return one summary row for the full yellow taxi table.
+
+### Benchmark-Phase Optimization:
+- calculate aggregates inside the Parquet adapter.
+- obtain the row count from metadata.
+- aggregate only the amount and distance columns instead of materializing complete taxi rows.
+  
+Scalar aggregate means that the query has no GROUP BY and returns a single summary row.
+
+## Q30 - High-volume FHV scalar aggregate over physical columns
+
+```sql
+SELECT
+  count(*) AS trips,
+  sum(base_passenger_fare) AS passenger_fare,
+  sum(driver_pay) AS driver_pay,
+  avg(trip_miles) AS avg_miles,
+  max(trip_time) AS max_trip_time
+FROM fhvhv_tripdata;
+```
+
+### Checks:
+- calculation of multiple aggregates without grouping on the larger high-volume FHV dataset.
+- support for `COUNT`, multiple `SUM` values, `AVG`, and `MAX`.
+- aggregation over physical Parquet columns.
+
+### Base Functionality:
+- return one summary row for the full high-volume FHV table.
+
+### Benchmark-Phase Optimization:
+- calculate aggregates inside the Parquet adapter.
+- obtain the row count from metadata.
+- aggregate only the fare, driver-pay, distance, and trip-time columns instead of materializing complete trip rows.
+
+This is the larger and wider counterpart of Q29. A scalar aggregate summarizes an entire selected dataset into one result row.
+
+## Q31 - Yellow taxi grouped aggregate by low-cardinality pickup location without partition pruning
+
+```sql
+SELECT
+  PULocationID,
+  count(*) AS trips,
+  sum(total_amount) AS gross_amount,
+  avg(trip_distance) AS avg_distance
+FROM yellow_tripdata
+GROUP BY PULocationID
+ORDER BY trips DESC
+LIMIT 50;
+```
+
+### Checks:
+- grouping by pickup location without partition pruning.
+- calculation of `COUNT`, `SUM`, and `AVG`.
+- ordering groups and returning the top 50 results.
+
+### Base Functionality:
+- order pickup locations by trip count and return the 50 busiest locations.
+
+### Benchmark-Phase Optimization:
+- calculate grouped aggregates inside the Parquet adapter.
+- read only the pickup-location, amount, and distance columns instead of complete taxi rows.
+
+This is the no-partition-filter counterpart of Q07.
+
+## Q32 - Yellow taxi grouped aggregate with `HAVING`
+
+```sql
+SELECT
+  DOLocationID,
+  count(*) AS trips,
+  avg(total_amount) AS avg_total_amount
+FROM yellow_tripdata
+GROUP BY DOLocationID
+HAVING count(*) >= 100000
+ORDER BY avg_total_amount DESC
+LIMIT 50;
+```
+
+### Checks:
+- grouping by destination location without partition pruning.
+- calculation of `COUNT` and `AVG`.
+- filtering aggregate groups using `HAVING`.
+- ordering groups and returning the top 50 results.
+
+### Base Functionality:
+- retain only destination locations with at least 100,000 trips.
+- order the remaining locations by average amount and return the top 50 results.
+
+### Benchmark-Phase Optimization:
+- calculate grouped aggregates inside the Parquet adapter before applying `HAVING`.
+- read only the destination-location and amount columns instead of complete taxi rows.
+
+The `HAVING` condition is applied to the grouped result rather than to individual trip rows.
+
+## Q33 - Yellow taxi two-key grouped aggregate without partition pruning
+
+```sql
+SELECT
+  PULocationID,
+  DOLocationID,
+  count(*) AS trips,
+  avg(total_amount) AS avg_total_amount
+FROM yellow_tripdata
+GROUP BY PULocationID, DOLocationID
+ORDER BY trips DESC
+LIMIT 50;
+```
+
+### Checks:
+- grouping by starting-location and destination-location pairs without partition pruning.
+- calculation of `COUNT` and `AVG`.
+- ordering groups and returning the top 50 results.
+
+### Base Functionality:
+- order location pairs by trip count and return the 50 busiest pairs.
+
+### Benchmark-Phase Optimization:
+- calculate grouped aggregates inside the Parquet adapter.
+- read only the starting-location, destination-location, and amount columns instead of complete taxi rows.
+- use an efficient representation for the two-column grouping key.
+
+This is the no-partition-filter counterpart of Q08.
+
+## Q34 - Low-cardinality shared-ride flag aggregates without partition pruning, high-volume FHV
+
+```sql
+SELECT
+  shared_request_flag,
+  shared_match_flag,
+  count(*) AS trips,
+  avg(trip_miles) AS avg_miles
+FROM fhvhv_tripdata
+GROUP BY shared_request_flag, shared_match_flag
+ORDER BY trips DESC;
+```
+
+### Checks:
+- grouping by two low-cardinality text flags without partition pruning.
+- calculation of `COUNT` and `AVG`.
+- ordering shared-ride flag combinations by trip count.
+
+### Base Functionality:
+- order shared-ride flag combinations from most common to least common.
+
+### Benchmark-Phase Optimization:
+- calculate grouped aggregates inside the Parquet adapter.
+- read only the two shared-ride flag columns and the distance column instead of complete trip rows.
+
+This is the no-partition-filter counterpart of Q15.
+
+## Q35 - High-volume FHV grouped aggregate by license and base
+
+```sql
+SELECT
+  hvfhs_license_num,
+  dispatching_base_num,
+  count(*) AS trips,
+  avg(base_passenger_fare) AS avg_fare,
+  avg(driver_pay) AS avg_driver_pay
+FROM fhvhv_tripdata
+GROUP BY hvfhs_license_num, dispatching_base_num
+ORDER BY trips DESC
+LIMIT 50;
+```
+
+### Checks:
+- grouping by license and dispatching-base identifiers.
+- calculation of `COUNT` and multiple `AVG` values.
+- ordering groups and returning the top 50 results.
+
+### Base Functionality:
+- order license and dispatching-base combinations by trip count.
+- return the 50 busiest combinations.
+
+### Benchmark-Phase Optimization:
+- calculate grouped aggregates inside the Parquet adapter.
+- read only the license, dispatching-base, fare, and driver-pay columns instead of complete trip rows.
+
+## Q36 - High-volume FHV two-key zone grouped aggregate without partition pruning
+
+```sql
+SELECT
+  PULocationID,
+  DOLocationID,
+  count(*) AS trips,
+  avg(trip_miles) AS avg_miles,
+  avg(base_passenger_fare) AS avg_fare
+FROM fhvhv_tripdata
+GROUP BY PULocationID, DOLocationID
+ORDER BY trips DESC
+LIMIT 50;
+```
+
+### Checks:
+- grouping by starting-location and destination-location pairs over the full high-volume FHV table.
+- calculation of `COUNT` and multiple `AVG` values.
+- ordering groups and returning the top 50 results.
+
+### Base Functionality:
+- order location pairs by trip count and return the 50 busiest pairs.
+
+### Benchmark-Phase Optimization:
+- calculate grouped aggregates inside the Parquet adapter.
+- read only the starting-location, destination-location, distance, and fare columns instead of complete trip rows.
+- use an efficient representation for the two-column grouping key.
+
+This is the full-table counterpart of Q14.
+
+## Q37 - Green taxi scalar aggregate over physical columns
+
+```sql
+SELECT
+  count(*) AS trips,
+  sum(total_amount) AS gross_amount,
+  avg(total_amount) AS avg_total_amount,
+  avg(trip_distance) AS avg_distance
+FROM green_tripdata;
+```
+
+### Checks:
+- calculation of multiple aggregates without grouping on the smaller green taxi dataset.
+- support for `COUNT`, `SUM`, and multiple `AVG` values.
+- aggregation over physical Parquet columns.
+
+### Base Functionality:
+- return one summary row for the full green taxi table.
+
+### Benchmark-Phase Optimization:
+- calculate aggregates inside the Parquet adapter.
+- obtain the row count from metadata.
+- aggregate only the amount and distance columns instead of materializing complete taxi rows.
+
+This is the smaller-table counterpart of Q29.
+
+## Q38 - Yellow and high-volume FHV pickup-location aggregate join
+
+```sql
+WITH yellow_pickups AS (
+  SELECT PULocationID, count(*) AS yellow_trips, sum(total_amount) AS yellow_amount
+  FROM yellow_tripdata
+  GROUP BY PULocationID
+),
+fhvhv_pickups AS (
+  SELECT PULocationID, count(*) AS fhvhv_trips, sum(base_passenger_fare) AS fhvhv_fare
+  FROM fhvhv_tripdata
+  GROUP BY PULocationID
+)
+SELECT
+  y.PULocationID,
+  y.yellow_trips,
+  f.fhvhv_trips,
+  y.yellow_amount,
+  f.fhvhv_fare
+FROM yellow_pickups y
+JOIN fhvhv_pickups f
+  ON y.PULocationID = f.PULocationID
+ORDER BY f.fhvhv_trips DESC
+LIMIT 50;
+```
+
+### Checks:
+- grouping by pickup location on two datasets without partition pruning.
+- calculation of `COUNT` and `SUM`.
+- joining pickup-location aggregate results.
+- ordering groups and returning the top 50 results.
+
+### Base Functionality:
+- join matching pickup locations from the yellow taxi and high-volume FHV datasets.
+- order locations by high-volume FHV trip count and return the top 50 results.
+
+### Benchmark-Phase Optimization:
+- calculate grouped aggregates separately inside the Parquet adapter before joining the datasets.
+- read only the pickup-location and amount columns needed by each dataset.
+- join the small pickup-location result sets instead of joining individual trip rows.
+
+This is the no-partition grouped-aggregate counterpart of Q18 and Q19.
+
+## Q39 - Distinct low-cardinality flag combinations, high-volume FHV
+
+```sql
+SELECT DISTINCT
+  shared_request_flag,
+  shared_match_flag,
+  access_a_ride_flag,
+  wav_request_flag,
+  wav_match_flag
+FROM fhvhv_tripdata
+ORDER BY shared_request_flag, shared_match_flag, access_a_ride_flag, wav_request_flag, wav_match_flag;
+```
+
+### Checks:
+- selecting distinct combinations of several low-cardinality text flags.
+- grouping behavior without aggregate functions.
+- ordering the distinct combinations.
+
+### Base Functionality:
+- remove duplicate flag combinations.
+- order the distinct combinations by flag value.
+
+### Benchmark-Phase Optimization:
+- calculate distinct combinations as grouped results inside the Parquet adapter.
+- read only the five flag columns instead of complete trip rows.
+
+## Q40 - Yellow taxi distance bucket aggregate
+
+```sql
+SELECT
+  CASE
+    WHEN trip_distance < 1.0 THEN '00_short'
+    WHEN trip_distance < 5.0 THEN '01_medium'
+    WHEN trip_distance < 20.0 THEN '02_long'
+    ELSE '03_very_long'
+  END AS distance_bucket,
+  count(*) AS trips,
+  avg(total_amount) AS avg_total_amount
+FROM yellow_tripdata
+GROUP BY
+  CASE
+    WHEN trip_distance < 1.0 THEN '00_short'
+    WHEN trip_distance < 5.0 THEN '01_medium'
+    WHEN trip_distance < 20.0 THEN '02_long'
+    ELSE '03_very_long'
+  END
+ORDER BY distance_bucket;
+```
+
+### Checks:
+- grouping by a distance category derived from a `CASE` expression.
+- calculation of `COUNT` and `AVG`.
+- ordering the derived distance categories.
+
+### Base Functionality:
+- assign each trip to a distance category.
+- order the category summaries.
+
+### Benchmark-Phase Optimization:
+- evaluate the derived grouping expression and calculate aggregates inside the Parquet adapter.
+- read only the distance and amount columns instead of complete taxi rows.
+- return one summary row per distance category instead of materializing complete taxi rows outside the adapter.
+
+This checks the generic calculated-aggregate path rather than grouping by direct physical columns.
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/aggregation_queries.md b/plugins/parquet-adapter/benchmarks/query_lists/aggregation_queries.md
new file mode 100644
index 0000000000..e21ed9ea27
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/aggregation_queries.md
@@ -0,0 +1,130 @@
+# Aggregation Queries
+
+## Q01: Full Yellow Taxi Row Count
+
+Find how many yellow taxi trips are in the dataset.
+
+```sql
+SELECT count(*) AS row_count
+FROM tlc__yellow_tripdata;
+```
+
+## Q02: Yellow Taxi Count For One Partition Month
+
+Find how many yellow taxi trips were in October 2022.
+
+```sql
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+```
+
+## Q03: Yellow Taxi Trips On One Day
+
+Find how many yellow taxi trips started on October 15, 2022.
+
+```sql
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
+  AND tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00';
+```
+
+## Q04: Long And Expensive Yellow Taxi Trips
+
+Find how many yellow taxi trips were at least 10 miles long and
+cost at least 40 dollars.
+
+```sql
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+```
+
+## Q05: Monthly Yellow Taxi Amount And Distance Summary
+
+Find, for each year and month, how many yellow taxi trips there
+were, the total amount charged, and the shortest and longest trip distances.
+
+```sql
+SELECT
+  "year",
+  "month",
+  count(*) AS trips,
+  sum(total_amount) AS gross_amount,
+  min(trip_distance) AS min_distance,
+  max(trip_distance) AS max_distance
+FROM yellow_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+```
+
+## Q06: Full High-Volume FHV Row Count
+
+**Description:** Find how many high-volume FHV trips are in the dataset.
+
+```sql
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata;
+```
+
+## Q07: High-Volume FHV Count For One Partition Month
+
+Find how many high-volume FHV trips were in October 2022.
+
+```sql
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+```
+
+## Q08: Long And Expensive High-Volume FHV Trips
+
+Find how many high-volume FHV trips were at least 10 miles long
+and had a base passenger fare of at least 40 dollars.
+
+```sql
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata
+WHERE trip_miles >= 10.0
+  AND base_passenger_fare >= 40.0;
+```
+
+## Q09: Monthly High-Volume FHV Fare And Distance Summary
+
+Find, for each year and month, how many high-volume FHV trips
+there were, the total passenger fare, the total driver pay, and the shortest and
+longest trip distances.
+
+```sql
+SELECT
+  "year",
+  "month",
+  count(*) AS trips,
+  sum(base_passenger_fare) AS passenger_fare,
+  sum(driver_pay) AS driver_pay,
+  min(trip_miles) AS min_miles,
+  max(trip_miles) AS max_miles
+FROM fhvhv_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+```
+
+## Q10: High-Volume FHV Shared-Ride Flag Distribution
+
+Find how many 2022 high-volume FHV trips fall into each
+combination of shared-request and shared-match flags.
+
+```sql
+SELECT
+  shared_request_flag,
+  shared_match_flag,
+  count(*) AS trips
+FROM fhvhv_tripdata
+WHERE "year" = '2022'
+GROUP BY shared_request_flag, shared_match_flag
+ORDER BY trips DESC;
+```
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/nested_flickr8k_queries.md b/plugins/parquet-adapter/benchmarks/query_lists/nested_flickr8k_queries.md
new file mode 100644
index 0000000000..4b3db27f51
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/nested_flickr8k_queries.md
@@ -0,0 +1,104 @@
+# Nested Data Queries
+
+## Q01: Full Flickr8k Row Count
+
+Find how many Flickr8k images are in the dataset.
+
+```sql
+SELECT count(*) AS row_count
+FROM public.ff__flickr8k;
+```
+
+## Q02: Image Id Projection
+
+Find all image identifiers without reading captions or image bytes.
+
+```sql
+SELECT id
+FROM public.ff__flickr8k;
+```
+
+## Q03: Ordered Image Id Sample
+
+Find a deterministic sample of image identifiers without reading nested
+captions or image bytes.
+
+```sql
+SELECT id
+FROM public.ff__flickr8k
+ORDER BY id
+LIMIT 100;
+```
+
+## Q04: Repeated Caption List Projection
+
+Find the repeated caption list for every image without reading image bytes.
+
+```sql
+SELECT captions
+FROM public.ff__flickr8k;
+```
+
+## Q05: Image Ids With Caption Lists
+
+Find image identifiers together with their repeated caption lists, while
+excluding image bytes.
+
+```sql
+SELECT id, captions
+FROM public.ff__flickr8k;
+```
+
+## Q06: Ordered Image Ids With Caption Lists Sample
+
+Find a deterministic sample of image identifiers and repeated caption lists,
+while excluding image bytes.
+
+```sql
+SELECT id, captions
+FROM public.ff__flickr8k
+ORDER BY id
+LIMIT 100;
+```
+
+## Q07: Binary Image Projection
+
+Find image identifiers together with the binary JPEG bytes, while excluding
+caption lists.
+
+```sql
+SELECT id, image
+FROM public.ff__flickr8k;
+```
+
+## Q08: Binary Image Only Projection
+
+Find only the binary JPEG bytes to measure the cost of reading the large image
+column without metadata or captions.
+
+```sql
+SELECT id, "image"
+FROM public.ff__flickr8k;
+```
+
+## Q09: Full Flickr8k Row Projection
+
+Find image identifiers, repeated caption lists, and binary JPEG bytes together.
+
+```sql
+SELECT id, captions, "image"
+FROM public.ff__flickr8k;
+```
+
+## Q10: Filtered Caption Projection Without Image Bytes
+
+Find image identifiers and repeated caption lists for one filename range,
+while excluding image bytes.
+
+```sql
+SELECT id, captions
+FROM public.ff__flickr8k
+WHERE id >= '1000000000'
+  AND id < '2000000000'
+ORDER BY id;
+```
diff --git a/plugins/parquet-adapter/benchmarks/running_benchmarks.md b/plugins/parquet-adapter/benchmarks/running_benchmarks.md
new file mode 100644
index 0000000000..1e6790ab59
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/running_benchmarks.md
@@ -0,0 +1,35 @@
+## DuckDB
+
+DuckDB runs as an embedded database inside the Java benchmark process.
+
+At a high level:
+
+- The PowerShell runner compiles the Java benchmark client.
+- The client loads DuckDB’s JDBC driver.
+- DuckDB creates views over the Parquet folders.
+- The client executes each query and drains the full result set.
+- Timings are written to a CSV file.
+
+## Apache Spark
+Spark runs inside a Docker container, so no local Spark installation is required.
+
+At a high level:
+
+- The PowerShell runner starts a Spark Docker image.
+- The repository and Parquet dataset are mounted into the container.
+- A Python script creates Spark temporary views over the Parquet folders.
+- Spark executes the same SQL queries.
+- Results are fully materialized and timings are written to CSV.
+
+## Polypheny
+Polypheny runs as a separate database server, outside the benchmark client.
+
+At a high level:
+
+- Start Polypheny normally.
+- Add the Parquet adapter and point it to relevant data
+- Run the PowerShell benchmark wrapper
+- The wrapper launches the Java benchmark client, which connects to Polypheny through the Prism JDBC interface
+- The client maps query table names, executes each query, drains the full result set, and records elapsed time.
+
+Unlike Spark, the benchmark wrapper does not start Polypheny automatically. The DBMS must already be running with the Parquet adapter configured.
diff --git a/plugins/parquet-adapter/docs/aggregation_flow.md b/plugins/parquet-adapter/docs/aggregation_flow.md
new file mode 100644
index 0000000000..bd6b848e2e
--- /dev/null
+++ b/plugins/parquet-adapter/docs/aggregation_flow.md
@@ -0,0 +1,41 @@
+# Aggregation Flow
+
+## ParquetRelMetadataAggregateExecutor
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelMetadataAggregateExecutor.java`
+Performs the following:
+1. filter by file level: partition values / column statistics
+2. find group key for each file
+3. update COUNT / MIN / MAX using file metadata
+4. return result as Enumerable
+
+## ParquetRelDataAggregateExecutor
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelDataAggregateExecutor.java'`
+ParquetRelAggregateExecutor creates following Enumerators:
+1. ParquetFileGroupedAggregateRelEnumerator
+   - aggregation can be done per whole file, because each file belongs to exactly one group.
+2. ParquetGroupedAggregateRelEnumerator 
+   - grouped COUNT(*). For queries like:
+      ```text
+       SELECT category, COUNT(*)
+       FROM parquet_table
+       GROUP BY category;
+       ```
+      Conditions:
+      - all aggregate calls are COUNT(*)
+      - filters are empty or partition filters
+
+    - general grouped aggregate. For queries like:
+      ```text
+      SELECT category, MIN(price), MAX(price), SUM(price)
+      FROM parquet_table
+      WHERE category IN (VALUE1, VALUE2, ...)
+      GROUP BY category;
+      ```
+      Conditions:
+      - filters are supported 
+      - aggregate functions by column
+3. ParquetRowAggregateRelEnumerator - default
+
+## Flow Diagram 
+
+![aggragation_flow.drawio.png](images/aggregation/aggragation_flow.drawio.png)
diff --git a/plugins/parquet-adapter/docs/aggregation_planning.md b/plugins/parquet-adapter/docs/aggregation_planning.md
new file mode 100644
index 0000000000..603ea14952
--- /dev/null
+++ b/plugins/parquet-adapter/docs/aggregation_planning.md
@@ -0,0 +1,184 @@
+# Aggregation Push-down
+
+## Planner
+
+### Nodes
+
+#### 1. ParquetRelMetadataScan - P_Metadata_Scan
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelMetadataScan.java`
+
+A planner node for “metadata-only Parquet access,” mainly so aggregate queries, such as COUNT, MIN, MAX, can be planned cheaply without pretending they need to read every row.
+Its cost is zero. That tells the optimizer: “this node itself does not scan data rows.”
+
+Aggregates Supported By Metadata:
+- COUNT(*)
+- COUNT(column)
+- MIN(column)
+- MAX(column)
+
+#### 2. ParquetRelAggregate - P_Aggregate
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java`
+
+Planner node that says, “This aggregate can be handled by the Parquet adapter, either cheaply from file metadata or by scanning Parquet data directly.”
+
+represents operations like:
+```text
+SELECT COUNT(*)
+SELECT MIN(price)
+SELECT MAX(age)
+SELECT category, COUNT(*) GROUP BY category
+```
+
+This class decides whether an aggregate can be done in one of two ways:
+1. metadataAggregate: use Parquet metadata only, without reading all rows. Example: COUNT(*), MIN(col), MAX(col) may be answerable from Parquet file statistics.
+In this case it wraps Scan to ParquetRelMetadataScan
+
+2. dataAggregate: read actual Parquet data and aggregate it.
+   This is the fallback when metadata is not enough, but Parquet can still perform the aggregate directly.
+
+The real runtime call is built in `implement`: call "metadataAggregate" or "dataAggregate" depending on the selected mode.
+
+`computeSelfCost` makes this node look very cheap to the optimizer.
+
+#### 3. ParquetEnumerableUnion
+
+Node combines rows from multiple child plans, just like a normal EnumerableUnion, but it has a special class name so planner rules know, 
+that this union was already rewritten for Parquet aggregate optimization.
+It prevents the same optimization rule from firing again and again.
+ParquetEnumerableUnion says: “This union has already been processed for Parquet partial aggregate pushdown.”
+
+### Rules
+
+Rules defined in PatternMatchers.java
+
+#### 1. aggregateOnScan
+
+If there is an aggregate directly above a Parquet scan, try to turn it into a Parquet-native aggregate.
+
+from:
+
+```text
+EnumerableAggregate
+    EnumerableParquet
+        ParquetRelScan
+```
+to:
+
+```text
+EnumerableParquet
+    ParquetRelAggregate
+        ParquetRelScan
+```
+
+#### 2. aggregateOnCalcScan
+Like aggregateOnScan, but there is a Calc between the aggregate and the Parquet scan.
+
+from:
+
+```text
+EnumerableAggregate COUNT(*)
+  EnumerableCalc filter age > 30
+    EnumerableParquet
+      ParquetRelScan
+```
+to:
+
+```text
+EnumerableParquet
+  ParquetRelAggregate COUNT(*)
+    ParquetRelScan with filter age > 30
+```
+
+#### 3. partialAggregateOnUnion
+
+If we see EnumerableUnion under EnumerableAggregate, and the union is UNION ALL, then push partial aggregates below the union, 
+use ParquetEnumerableUnion to union those partial results, then combine those partial results above ParquetEnumerableUnion.
+
+from:
+
+```text
+EnumerableAggregate
+    EnumerableUnion
+        input1
+        input2
+        input3
+```
+to:
+
+```text
+EnumerableAggregate          <-- final aggregate: calculates aggregate from already computed values
+  ParquetEnumerableUnion     <-- marker UNION ALL
+    EnumerableAggregate      <-- partial aggregate
+      input1
+    EnumerableAggregate      <-- partial aggregate
+      input2
+    EnumerableAggregate      <-- partial aggregate
+      input3
+```
+
+#### 4. partialAggregateOnCalcUnion
+
+same idea as partialAggregateOnUnion, but there is a Calc between the aggregate and the union.
+
+If we see EnumerableUnion under EnumerableCalc under EnumerableAggregate, and the union is UNION ALL, 
+then copy the Calc into each union branch, push partial aggregates below the union, 
+use ParquetEnumerableUnion to union those partial results, then combine those partial results above ParquetEnumerableUnion.
+
+Calc should be copied, because the original plan applies the Calc after the union.
+
+from:
+
+```text
+EnumerableAggregate
+  EnumerableCalc
+    EnumerableUnion
+      input1
+      input2
+```
+
+to:
+
+```text
+EnumerableAggregate                 <-- combines partial results
+  ParquetEnumerableUnion             <-- unions partial results
+    EnumerableAggregate              <-- partial aggregate for input1
+      EnumerableCalc                 <-- same calc copied here
+        input1
+    EnumerableAggregate              <-- partial aggregate for input2
+      EnumerableCalc                 <-- same calc copied here
+        input2
+```
+
+### Other Rules relevant files
+
+- `PatternMatchers.java` - defines rewrite rules
+- `ParquetRules.java` - collects all those matcher rules into a list
+- `ParquetAlgOptRule.java` - wraps them as planner rules
+- `ParquetConvention.java` - registers the rules with the planner
+
+## Examples:
+
+```sql
+SELECT count(*) AS row_count
+FROM tlc__yellow_tripdata;
+```
+
+![metadata_scan.png](images/aggregation/metadata_aggregate.png)
+
+
+```sql
+SELECT
+  "year",
+  "month",
+  count(*) AS trips,
+  sum(total_amount) AS gross_amount,
+  min(trip_distance) AS min_distance,
+  max(trip_distance) AS max_distance
+FROM yellow_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+```
+
+![data_agregate.png](images/aggregation/data_agregate.png)
diff --git a/plugins/parquet-adapter/docs/images/aggregation/aggragation_flow.drawio.png b/plugins/parquet-adapter/docs/images/aggregation/aggragation_flow.drawio.png
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diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
index 421b71fa0b..987acb225b 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
@@ -21,11 +21,15 @@
 import org.polypheny.db.adapter.parquet.document.ParquetDocumentSource;
 import org.polypheny.db.adapter.parquet.relational.ParquetRelationalSource;
 import org.polypheny.db.adapter.parquet.relational.planning.EnumerableParquet;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetEnumerableUnion;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelAggregate;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelJoin;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelMetadataScan;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
 import org.polypheny.db.algebra.enumerable.EnumerableConvention;
 import org.polypheny.db.algebra.enumerable.EnumerableJoin;
+import org.polypheny.db.algebra.enumerable.EnumerableUnion;
 import org.polypheny.db.algebra.logical.relational.LogicalRelScan;
 import org.polypheny.db.algebra.polyalg.PolyAlgDeclaration;
 import org.polypheny.db.algebra.polyalg.PolyAlgDeclaration.OperatorTag;
@@ -72,6 +76,11 @@ private void registerPolyAlg() {
                 .model( DataModel.RELATIONAL )
                 .opName( "PE_CALC" ).convention( EnumerableConvention.INSTANCE ).numInputs( 1 ).opTags( physTags )
                 .build() );
+        PolyAlgRegistry.register( ParquetEnumerableUnion.class, PolyAlgDeclaration.builder()
+                .model( DataModel.RELATIONAL )
+                .opName( "PE_UNION" ).convention( EnumerableConvention.INSTANCE ).numInputs( -1 ).opTags( physTags )
+                .params( PolyAlgRegistry.getParams( EnumerableUnion.class ) )
+                .build() );
         PolyAlgRegistry.register( ParquetRelScan.class, PolyAlgDeclaration.builder()
                 .model( DataModel.RELATIONAL )
                 .opName( "P_SCAN" ).convention( ParquetConvention.INSTANCE ).numInputs( 0 ).opTags( physTags )
@@ -79,6 +88,13 @@ private void registerPolyAlg() {
                 .param( Parameter.builder().name( "fields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .build() );
+        PolyAlgRegistry.register( ParquetRelMetadataScan.class, PolyAlgDeclaration.builder()
+                .model( DataModel.RELATIONAL )
+                .opName( "P_METADATA_SCAN" ).convention( ParquetConvention.INSTANCE ).numInputs( 0 ).opTags( physTags )
+                .params( PolyAlgRegistry.getParams( LogicalRelScan.class ) )
+                .param( Parameter.builder().name( "fields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .build() );
         PolyAlgRegistry.register( ParquetRelJoin.class, PolyAlgDeclaration.builder()
                 .model( DataModel.RELATIONAL )
                 .opName( "P_JOIN" ).convention( ParquetConvention.INSTANCE ).numInputs( 2 ).opTags( physTags )
@@ -90,6 +106,16 @@ private void registerPolyAlg() {
                 .param( Parameter.builder().name( "rightFilters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .param( Parameter.builder().name( "joinFilters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .build() );
+        PolyAlgRegistry.register( ParquetRelAggregate.class, PolyAlgDeclaration.builder()
+                .model( DataModel.RELATIONAL )
+                .opName( "P_AGGREGATE" ).convention( ParquetConvention.INSTANCE ).numInputs( 1 ).opTags( physTags )
+                .param( Parameter.builder().name( "mode" ).type( ParamType.STRING ).build() )
+                .param( Parameter.builder().name( "fields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "groups" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "condition" ).type( ParamType.STRING ).build() )
+                .param( Parameter.builder().name( "aggregates" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .build() );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
index b8b0ab73b0..7ea561468b 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
@@ -106,10 +106,10 @@ public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
      * @param filters - parquet filters
      * @return ParquetDocEnumerator
      */
-    public Enumerable<PolyValue[]> scanFiltered( DataContext dataContext, List<ParquetAdapterFilter> filters ) {
+    public Enumerable<PolyValue[]> scanFiltered( DataContext dataContext, List<ParquetAdapterFilter<PolyValue>> filters ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
         final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
-        final List<ParquetAdapterFilter> resolvedFilters = filters.stream().map( filter -> resolveFilter( dataContext, filter ) ).toList();
+        final List<ParquetAdapterFilter<PolyValue>> resolvedFilters = filters.stream().map( filter -> resolveFilter( dataContext, filter ) ).toList();
         return new AbstractEnumerable<>() {
             @Override
             public Enumerator<PolyValue[]> enumerator() {
@@ -173,11 +173,11 @@ private AlgDataType buildDocumentType( AlgDataTypeFactory typeFactory ) {
      * @param filter parquet filter
      * @return ParquetFilter
      */
-    private ParquetAdapterFilter resolveFilter( DataContext dataContext, ParquetAdapterFilter filter ) {
+    private ParquetAdapterFilter<PolyValue> resolveFilter( DataContext dataContext, ParquetAdapterFilter<PolyValue> filter ) {
         if ( filter.dynamicParamIndex() == null ) {
             return filter;
         }
-        return new ParquetAdapterFilter(
+        return new ParquetAdapterFilter<>(
                 filter.columnIndex(),
                 filter.operator(),
                 dataContext.getParameterValue( filter.dynamicParamIndex() ) );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
index 9a27e8e0dc..545b398e8f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java
@@ -250,23 +250,22 @@ private Map<String, List<ExportedColumn>> getNormalizedExportedColumns() {
      * @return ParquetTableBinding - column bindings are stored by physical column id
      */
     private ParquetTableBinding getDiscoveredTableBinding( String tableName, PhysicalTable table ) {
+        if ( getConfiguredSchemaMode() == ParquetSchemaMode.FLAT ) {
+            return createFlatTableBinding( tableName, table );
+        }
+
         // Get or build normalized schema
         ParquetNormalizedSchema normalizedSchema = getNormalizedSchema();
         // find binding for this table
         DiscoveredTableBinding binding = normalizedSchema.getBinding( tableName );
         // if missing in normalized mode - rebuild
-        if ( binding == null && getConfiguredSchemaMode() == ParquetSchemaMode.NORMALIZED ) {
+        if ( binding == null ) {
             clearNormalizedExportCache();
             normalizedSchema = getNormalizedSchema();
             binding = normalizedSchema.getBinding( tableName );
         }
         if ( binding == null ) {
-            if ( getConfiguredSchemaMode() == ParquetSchemaMode.NORMALIZED ) {
-                throw new GenericRuntimeException( "Missing normalized Parquet binding for generated table: %s", tableName );
-            }
-            // Flat tables do not have DiscoveredTableBinding,
-            // because they come from AbstractParquetSource.getExportedColumns() not ParquetSchemaNormalizer
-            return createFlatTableBinding( tableName, table );
+            throw new GenericRuntimeException( "Missing normalized Parquet binding for generated table: %s", tableName );
         }
         // Convert discovered bindings to final ParquetTableBinding, where column bindings are stored by physical column id
         return ParquetTableBinding.createTableBindingFromColumnPaths( binding.sourceFiles(), binding.parentTableName(), binding.sourcePathElements(), table, binding.columnPaths() );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/FilterableParquetSourceFile.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/FilterableParquetSourceFile.java
new file mode 100644
index 0000000000..3c73a557fc
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/FilterableParquetSourceFile.java
@@ -0,0 +1,37 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.type.entity.PolyValue;
+
+import java.util.List;
+
+/**
+ * Represents a {@link ParquetSourceFile} with filters.
+ *
+ * @param file a source file reference.
+ * @param filters a list of parquet filters.
+ */
+public record FilterableParquetSourceFile(ParquetSourceFile file, List<ParquetAdapterFilter<PolyValue>> filters ) {
+
+    public FilterableParquetSourceFile {
+        filters = List.copyOf( filters );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetEnumeratorsFactory.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetEnumeratorsFactory.java
new file mode 100644
index 0000000000..401c1b7aaa
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetEnumeratorsFactory.java
@@ -0,0 +1,99 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.Arrays;
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetMultiFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+/**
+ * Creates projected row enumerators with the common file pruning and reader selection logic.
+ */
+public final class ParquetEnumeratorsFactory {
+
+    private final ParquetRelTable table;
+    private final int[] fields;
+    private final int[] allFields;
+    private final ParquetSchemaReader schemaReader;
+    private final AtomicBoolean cancelFlag;
+    private final ParquetMultiFilterEvaluator<ParquetSourceFile> sourceFileEvaluator;
+
+
+    public ParquetEnumeratorsFactory( ParquetRelTable table, int[] fields, int[] allFields, ParquetSchemaReader schemaReader, AtomicBoolean cancelFlag ) {
+        this.table = table;
+        this.fields = Arrays.copyOf( fields, fields.length );
+        this.allFields = Arrays.copyOf( allFields, allFields.length );
+        this.schemaReader = schemaReader;
+        this.cancelFlag = cancelFlag;
+        this.sourceFileEvaluator = ParquetRelExecutor.createParquetSourceFileEvaluatorsChain( f -> ParquetRelExecutor.selectPhysicalBinding( table, f.columnIndex() ) );
+    }
+
+
+    /**
+     * Creates {@link ParquetMultiFileEnumerator} enumerator with the provided list of filters.
+     *
+     * @param filters a list of filters to apply on the source files.
+     * @return an enumerator.
+     */
+    public Enumerator<PolyValue[]> create( List<ParquetAdapterFilter<PolyValue>> filters ) {
+        return new ParquetMultiFileEnumerator(
+                table.getBinding().sourceFiles(),
+                ( sourceFile, residualFilters ) -> create( sourceFile, residualFilters ),
+                sourceFileEvaluator,
+                filters );
+    }
+
+
+    /**
+     * Creates an enumerator based on the source file type.
+     * - source file contains nested repeated fields -> {@link ParquetNestedRepeatedRelEnumerator}
+     * - source file contains nested but not repeated fields -> {@link ParquetNestedNonRepeatedRelEnumerator}
+     * - source file doesn't contain nested fields -> {@link ParquetRelEnumerator}
+     *
+     * @param sourceFile a source file to be read.
+     * @return a new enumerator that best match the source file.
+     */
+    public Enumerator<PolyValue[]> create( FilterableParquetSourceFile sourceFile ) {
+        return create( sourceFile.file(), sourceFile.filters() );
+    }
+
+
+    /**
+     * Creates an enumerator based on the source file type.
+     * - source file contains nested repeated fields -> {@link ParquetNestedRepeatedRelEnumerator}
+     * - source file contains nested but not repeated fields -> {@link ParquetNestedNonRepeatedRelEnumerator}
+     * - source file doesn't contain nested fields -> {@link ParquetRelEnumerator}
+     *
+     * @param sourceFile a source file to be read.
+     * @param filters a list of filters to be applied.
+     * @return a new enumerator that best match the source file.
+     */
+    private Enumerator<PolyValue[]> create( ParquetSourceFile sourceFile, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        return ParquetRelExecutor.enumeratorForFile( table, sourceFile, fields, allFields, schemaReader, cancelFlag, FiltersContainer.shared( filters ) );
+    }
+
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java
index cdcc82431b..3d7f7b7300 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java
@@ -18,10 +18,13 @@
 
 import java.util.Iterator;
 import java.util.List;
+import java.util.function.BiFunction;
 import java.util.function.Function;
 import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.parquet.relational.filter.ResidualFilters;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetMultiFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFileFilterReducer;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetFilterEvaluatorsChain;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.type.entity.PolyValue;
@@ -33,22 +36,27 @@
 public class ParquetMultiFileEnumerator implements Enumerator<PolyValue[]> {
 
     private final Iterator<ParquetSourceFile> files;
-    private final Function<ParquetSourceFile, Enumerator<PolyValue[]>> enumeratorFactory;
-    private final ParquetFilterEvaluatorsChain<ParquetSourceFile> sourceFileEvaluator;
-    private final List<ParquetAdapterFilter> filters;
+    private final BiFunction<ParquetSourceFile, List<ParquetAdapterFilter<PolyValue>>, Enumerator<PolyValue[]>> enumeratorFactory;
+    private final ParquetMultiFilterEvaluator<ParquetSourceFile> sourceFileEvaluator;
+    private final List<ParquetAdapterFilter<PolyValue>> filters;
     private Enumerator<PolyValue[]> currentEnumerator;
     private PolyValue[] current;
 
 
     public ParquetMultiFileEnumerator( List<ParquetSourceFile> files, Function<ParquetSourceFile, Enumerator<PolyValue[]>> enumeratorFactory ) {
-        this( files, enumeratorFactory, ParquetFilterEvaluatorsChain.empty(), List.of() );
+        this( files, ( sourceFile, ignored ) -> enumeratorFactory.apply( sourceFile ), ParquetMultiFilterEvaluator.empty(), List.of() );
     }
 
 
-    public ParquetMultiFileEnumerator( List<ParquetSourceFile> files, Function<ParquetSourceFile, Enumerator<PolyValue[]>> enumeratorFactory, ParquetFilterEvaluatorsChain<ParquetSourceFile> sourceFileEvaluator, List<ParquetAdapterFilter> filters ) {
+    public ParquetMultiFileEnumerator( List<ParquetSourceFile> files, Function<ParquetSourceFile, Enumerator<PolyValue[]>> enumeratorFactory, ParquetMultiFilterEvaluator<ParquetSourceFile> sourceFileEvaluator, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        this( files, ( sourceFile, ignored ) -> enumeratorFactory.apply( sourceFile ), sourceFileEvaluator, filters );
+    }
+
+
+    public ParquetMultiFileEnumerator( List<ParquetSourceFile> files, BiFunction<ParquetSourceFile, List<ParquetAdapterFilter<PolyValue>>, Enumerator<PolyValue[]>> enumeratorFactory, ParquetMultiFilterEvaluator<ParquetSourceFile> sourceFileEvaluator, List<ParquetAdapterFilter<PolyValue>> filters ) {
         this.files = List.copyOf( files ).iterator();
         this.enumeratorFactory = enumeratorFactory;
-        this.sourceFileEvaluator = sourceFileEvaluator == null ? ParquetFilterEvaluatorsChain.empty() : sourceFileEvaluator;
+        this.sourceFileEvaluator = sourceFileEvaluator == null ? ParquetMultiFilterEvaluator.empty() : sourceFileEvaluator;
         this.filters = filters == null ? List.of() : List.copyOf( filters );
     }
 
@@ -69,11 +77,13 @@ public boolean moveNext() {
                         return false;
                     }
                     ParquetSourceFile sourceFile = files.next();
-                    if ( !sourceFileEvaluator.matches( sourceFile, filters ) ) {
+                    // evaluate filters and remove if those only file level filters and don't need to be pushed down.
+                    ResidualFilters residualFilters = ParquetSourceFileFilterReducer.reduce( sourceFile, sourceFileEvaluator, filters );
+                    if ( !residualFilters.matches() ) {
                         continue;
                     }
                     // create Enumerator
-                    currentEnumerator = enumeratorFactory.apply( sourceFile );
+                    currentEnumerator = enumeratorFactory.apply( sourceFile, residualFilters.filters() );
                 }
 
                 if ( currentEnumerator.moveNext() ) {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
index ffe5ef314d..79329df6cf 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java
@@ -148,7 +148,7 @@ protected PolyValue[] extractRow( Group group ) {
      * @param filter - ParquetAdapterFilter
      * @return an extracted value
      */
-    protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
+    protected PolyValue extractValue( Group group, ParquetAdapterFilter<PolyValue> filter ) {
         if ( group instanceof CombinedGroup combinedGroup ) {
             return pathValueExtractor().extractValue(
                     combinedGroup.groupForField( filter.columnIndex(), leftIsParent ),
@@ -176,7 +176,7 @@ private CombinedGroup combinedGroup( VirtualGroup parent, VirtualGroup child ) {
     }
 
 
-    private boolean matchesFilters( Group group, List<ParquetAdapterFilter> filters, ParquetGroupFilterEvaluator evaluator ) {
+    private boolean matchesFilters( Group group, List<ParquetAdapterFilter<PolyValue>> filters, ParquetGroupFilterEvaluator evaluator ) {
         return filters.stream().allMatch( filter -> evaluator.matches( group, filter ) );
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
index e934aa299e..fe979f2f28 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java
@@ -18,6 +18,7 @@
 
 import java.util.List;
 import org.apache.parquet.example.data.Group;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetPartitionAwareFilterEvaluator;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
index c22533ee80..92033aca1d 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java
@@ -82,7 +82,7 @@ protected PolyValue[] extractRow( Group group ) {
     }
 
 
-    protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
+    protected PolyValue extractValue( Group group, ParquetAdapterFilter<PolyValue> filter ) {
         var virtualGroup = (VirtualGroup) group;
         var binding = columnBindings.get( filter.columnIndex() );
         return pathValueExtractor().extractValue( virtualGroup, binding, tablePath );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelDataAggregateExecutor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelDataAggregateExecutor.java
new file mode 100644
index 0000000000..b4dcd5b01d
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelDataAggregateExecutor.java
@@ -0,0 +1,570 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.AbstractEnumerable;
+import org.apache.calcite.linq4j.Enumerable;
+import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.execution.aggregate.ParquetFileGroupedAggregateRelEnumerator;
+import org.polypheny.db.adapter.parquet.relational.execution.aggregate.ParquetGroupedAggregateRelEnumerator;
+import org.polypheny.db.adapter.parquet.relational.execution.aggregate.ParquetRowAggregateRelEnumerator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetConstantColumnResolver;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFileStatisticsFilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
+import org.polypheny.db.adapter.parquet.shared.aggregate.ColumnAggregateProjection;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.aggregate.ParquetGroupedAggregateReader;
+import org.polypheny.db.adapter.parquet.shared.io.aggregate.ParquetNoFilterColumnAggregateReader;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.core.AggregateCall;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.ImmutableBitSet;
+
+/**
+ * An entry point for all aggregated enumerators that cannot be calculated based on metadata. The executor decides what enumerator should be executed for fastest results.
+ * Supported enumerators are:
+ * 1. Enumerator for COUNT(*) / COUNT(col) / SUM(col) / MIN(col) / MAX(col) with numeric aggregation columns and no row level filters.
+ * 2. Enumerator for COUNT(*) / COUNT(col) / SUM(col) / MIN(col) / MAX(col) with aggregation columns defined as partitions and no row level filters.
+ * 3. A fallback enumerator that supports the same aggregation functions for numeric columns but this time with row level filters.
+ */
+public class ParquetRelDataAggregateExecutor extends ParquetRelExecutor {
+
+    private final ParquetConstantColumnResolver constantColumnResolver;
+    private final ParquetSourceFileStatisticsFilterEvaluator statisticsFilterEvaluator;
+
+
+    public ParquetRelDataAggregateExecutor(ParquetRelTable table, AbstractParquetSource parquetSource, int[] fieldIndexes, ParquetSchemaReader schemaReader ) {
+        super( table, parquetSource, fieldIndexes, schemaReader );
+        this.constantColumnResolver = new ParquetConstantColumnResolver();
+        this.statisticsFilterEvaluator = new ParquetSourceFileStatisticsFilterEvaluator( f -> selectPhysicalBinding( table, f.columnIndex() ) );
+    }
+
+
+    /**
+     * Creates an enumerator for aggregated rows.
+     *
+     * @param dataContext a data context.
+     * @param fields an array of field indexes.
+     * @param filters a list of filters to be applied.
+     * @param groupFields a list of group by field indexes.
+     * @param aggregateKinds an array of aggregation function kinds.
+     * @param aggregateArgs an array of aggregation function arguments. There should be one argument per aggregation function.
+     * @return an enumerator.
+     */
+    public Enumerable<PolyValue[]> createEnumerator( DataContext dataContext, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        registerAdapter( dataContext );
+
+        List<ParquetAdapterFilter<PolyValue>> resolvedFilters = ParquetFilterResolver.resolveFilters( dataContext, filters, f -> selectPhysicalBinding( table, f.columnIndex() ) );
+        AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+
+        return new AbstractEnumerable<>() {
+            @Override
+            public Enumerator<PolyValue[]> enumerator() {
+                ColumnAggregateProjection aggregateProjection = tryBuildAggregateProjection( fields, filters, groupFields, aggregateKinds, aggregateArgs );
+                if ( aggregateProjection != null ) {
+                    return new ParquetFileGroupedAggregateRelEnumerator(
+                            table,
+                            fields,
+                            groupFields,
+                            aggregateKinds,
+                            aggregateArgs,
+                            aggregateProjection,
+                            resolvedFilters,
+                            cancelFlag
+                    );
+                }
+
+                int[] groupedCountProjection = tryBuildGroupedCountProjection( fields, filters, groupFields, aggregateKinds, aggregateArgs );
+                if ( groupedCountProjection != null ) {
+                    return new ParquetGroupedAggregateRelEnumerator(
+                            table,
+                            groupedCountProjection,
+                            groupedCountProjection.length,
+                            new AggregateCallDescriptor[]{ AggregateCallDescriptor.countStar() },
+                            resolvedFilters,
+                            cancelFlag
+                    );
+                }
+
+                GroupAggregateProjection groupedAggregateProjection = tryBuildGroupedAggregateProjection( fields, filters, groupFields, aggregateKinds, aggregateArgs );
+                if ( groupedAggregateProjection != null ) {
+                    return new ParquetGroupedAggregateRelEnumerator(
+                            table,
+                            groupedAggregateProjection.fields(),
+                            groupedAggregateProjection.groupFieldCount(),
+                            groupedAggregateProjection.aggregateCalls(),
+                            resolvedFilters,
+                            cancelFlag
+                    );
+                }
+
+                Enumerator<PolyValue[]> rows = new ParquetEnumeratorsFactory( table, fields, fieldIndexes, schemaReader, cancelFlag ).create( resolvedFilters );
+                return new ParquetRowAggregateRelEnumerator( rows, groupFields, aggregateKinds, aggregateArgs );
+            }
+        };
+    }
+
+
+    /**
+     * Converts a relative field index into a parquet physical index and adds it into projection.
+     *
+     * @param field a relative field index.
+     * @param parquetFieldProjectionIndexes a projection.
+     * @return parquet physical index if found and null otherwise.
+     */
+    private Integer addDataProjectionField( int field, Map<Integer, Integer> parquetFieldProjectionIndexes ) {
+        ParquetColumnBinding binding = selectPhysicalBinding( table, field );
+        if ( binding == null || binding.role() != ParquetColumnRole.DATA || binding.sourcePathElements().size() != 1 ) {
+            return null;
+        }
+        int parquetField = parquetFieldIndex( binding.sourcePathElements().get( 0 ) );
+        if ( parquetField < 0 ) {
+            return null;
+        }
+        return parquetFieldProjectionIndexes.computeIfAbsent( parquetField, ignored -> parquetFieldProjectionIndexes.size() );
+    }
+
+
+    /**
+     * Adds filter indexes to a projection. This is required in order for a filter column to be available during the reading operation.
+     *
+     * @param filters a list of filters.
+     * @param parquetFieldProjectionIndexes a projection.
+     * @return true if the index was added and false otherwise.
+     */
+    private boolean addFilterProjectionFields( List<ParquetAdapterFilter<PolyValue>> filters, Map<Integer, Integer> parquetFieldProjectionIndexes ) {
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            if ( !addFilterProjectionField( filter, parquetFieldProjectionIndexes, true ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Adds one filter to the reader projection unless file metadata can evaluate it exactly.
+     *
+     * @param filter a filter.
+     * @param parquetFieldProjectionIndexes a projection.
+     * @param canSkipExact whether an exact file-level filter can be omitted in this logical context.
+     * @return true if all required fields were added and false otherwise.
+     */
+    private boolean addFilterProjectionField( ParquetAdapterFilter<PolyValue> filter, Map<Integer, Integer> parquetFieldProjectionIndexes, boolean canSkipExact ) {
+        if ( canSkipExact && supportsExactFileFilter( filter ) ) {
+            return true;
+        }
+        if ( filter.isLogical() ) {
+            boolean childCanSkipExact = canSkipExact && filter.operator() == Kind.AND;
+            for ( ParquetAdapterFilter<PolyValue> operand : filter.operands() ) {
+                if ( !addFilterProjectionField( operand, parquetFieldProjectionIndexes, childCanSkipExact ) ) {
+                    return false;
+                }
+            }
+            return true;
+        }
+        if ( isPartitionColumn( table, filter.columnIndex() ) ) {
+            return true;
+        }
+        return addDataProjectionField( filter.columnIndex(), parquetFieldProjectionIndexes ) != null;
+    }
+
+
+    /**
+     * Validates if all filters are supported.
+     *
+     * @param filters a list of filters to validate.
+     * @return true if all filters are supported and false otherwise.
+     */
+    private boolean supportsAggregateReaderFilters( List<ParquetAdapterFilter<PolyValue>> filters ) {
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            if ( !supportsAggregateReaderFilter( filter, false ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Validates if the filter is supported by this executor.
+     *
+     * @param filter a filter to validate.
+     * @param insideNonAnd indicates whether a filter is not inside the logical AND filter. This is mostly relevant for partition filters.
+     * Only AND logical operator is supported between the partition filters.
+     * @return true if the filter is supported and false otherwise.
+     */
+    private boolean supportsAggregateReaderFilter( ParquetAdapterFilter<PolyValue> filter, boolean insideNonAnd ) {
+        if ( filter.isLogical() ) {
+            boolean childInsideNonAnd = insideNonAnd || filter.operator() != Kind.AND;
+            for ( ParquetAdapterFilter<PolyValue> operand : filter.operands() ) {
+                if ( !supportsAggregateReaderFilter( operand, childInsideNonAnd ) ) {
+                    return false;
+                }
+            }
+            return true;
+        }
+        if ( isPartitionColumn( table, filter.columnIndex() ) ) {
+            return !insideNonAnd;
+        }
+        return isFlatDataColumn( filter.columnIndex() );
+    }
+
+
+    /**
+     * Validates if the field is a data field.
+     *
+     * @param field a field index.
+     * @return true if the field is a data field and false otherwise.
+     */
+    private boolean isFlatDataColumn( int field ) {
+        ParquetColumnBinding binding = selectPhysicalBinding( table, field );
+        return binding != null && binding.role() == ParquetColumnRole.DATA && binding.sourcePathElements().size() == 1;
+    }
+
+
+    /**
+     * Validates if the aggregated function type is supported by this executor.
+     *
+     * @param field a field index.
+     * @param aggregateKind an aggregated function type.
+     * @return true if supported and false otherwise.
+     */
+    private boolean supportsGroupedAggregateCall( int field, String aggregateKind ) {
+        if ( Kind.COUNT.name().equals( aggregateKind ) ) {
+            return true;
+        }
+        if ( !isNumericAggregateKind( aggregateKind ) ) {
+            return false;
+        }
+        return PolyType.NUMERIC_TYPES.contains( table.columns.get( field ).type );
+    }
+
+
+    /**
+     * Attempts to build a grouped aggregate projection. The main criteria is:
+     * 1. Filters must be applied only on data columns or partition columns. If a filter column is a partition column only AND logical filter can be applied on top of it.
+     * 2. aggregation function is COUNT(*), COUNT(col), SUM(col), MIN(col) or MAX(col).
+     * 3. aggregation column must be numeric non-repeated.
+     *
+     * @param fields an array of field indexes.
+     * @param filters a list of filters.
+     * @param groupFields an array of group by field indexes.
+     * @param aggregateKinds an array of aggregation functions types.
+     * @param aggregateArgs an array of aggregation functions arguments. One argument per aggregation function and should be aligned with aggregateKinds.
+     * @return a grouped aggregate projection if successful or null otherwise.
+     */
+    private GroupAggregateProjection tryBuildGroupedAggregateProjection( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        if ( aggregateKinds.length != aggregateArgs.length ) {
+            return null;
+        }
+        if ( !supportsAggregateReaderFilters( filters ) ) {
+            return null;
+        }
+
+        Map<Integer, Integer> parquetFieldProjectionIndexes = new LinkedHashMap<>();
+        for ( int groupField : groupFields ) {
+            if ( groupField < 0 || groupField >= fields.length ) {
+                return null;
+            }
+            Integer projectionIndex = addDataProjectionField( fields[groupField], parquetFieldProjectionIndexes );
+            if ( projectionIndex == null ) {
+                return null;
+            }
+        }
+
+        AggregateCallDescriptor[] aggregateCalls = new AggregateCallDescriptor[aggregateKinds.length];
+        for ( int i = 0; i < aggregateKinds.length; i++ ) {
+            int aggregateArg = aggregateArgs[i];
+            if ( aggregateArg == AggregateCallDescriptor.NO_ARGUMENT ) {
+                if ( !Kind.COUNT.name().equals( aggregateKinds[i] ) ) {
+                    return null;
+                }
+                aggregateCalls[i] = AggregateCallDescriptor.of( aggregateKinds[i], AggregateCallDescriptor.NO_ARGUMENT );
+                continue;
+            }
+            if ( aggregateArg < 0 || aggregateArg >= fields.length ) {
+                return null;
+            }
+            if ( !supportsGroupedAggregateCall( fields[aggregateArg], aggregateKinds[i] ) ) {
+                return null;
+            }
+            Integer projectionIndex = addDataProjectionField( fields[aggregateArg], parquetFieldProjectionIndexes );
+            if ( projectionIndex == null ) {
+                return null;
+            }
+            aggregateCalls[i] = AggregateCallDescriptor.of( aggregateKinds[i], projectionIndex );
+        }
+
+        if ( !addFilterProjectionFields( filters, parquetFieldProjectionIndexes ) ) {
+            return null;
+        }
+        int[] projectedFields = parquetFieldProjectionIndexes.keySet().stream().mapToInt( Integer::intValue ).toArray();
+        if ( projectedFields.length == 0 || !ParquetGroupedAggregateReader.supports( schemaReader.buildProjectionSchema( projectedFields ) ) ) {
+            return null;
+        }
+        return new GroupAggregateProjection( projectedFields, groupFields.length, aggregateCalls );
+    }
+
+
+    /**
+     * Attempts to build grouped count projection. The main criteria is:
+     * 1. fields must be numeric
+     * 2. aggregation function is COUNT(*)
+     * 3. filters are empty or only partition filters.
+     * 4. aggregation column is a simple not nested data column (not a partition, not a key).
+     *
+     * @param fields an array of field indexes.
+     * @param filters a list of filters.
+     * @param groupFields an array of group by field indexes.
+     * @param aggregateKinds an array of aggregation functions types.
+     * @param aggregateArgs an array of aggregation functions arguments. One argument per aggregation function and should be aligned with aggregateKinds.
+     * @return a grouped count projection if successful or null otherwise.
+     */
+    private int[] tryBuildGroupedCountProjection( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        if ( aggregateKinds.length == 0 || aggregateKinds.length != aggregateArgs.length ) {
+            return null;
+        }
+        for ( int i = 0; i < aggregateKinds.length; i++ ) {
+            if ( !Kind.COUNT.name().equals( aggregateKinds[i] ) || aggregateArgs[i] >= 0 ) {
+                return null;
+            }
+        }
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            if ( !isPartitionFilter( table, filter ) ) {
+                return null;
+            }
+        }
+
+        int[] projectedFields = new int[groupFields.length];
+        for ( int i = 0; i < groupFields.length; i++ ) {
+            int groupField = groupFields[i];
+            if ( groupField < 0 || groupField >= fields.length ) {
+                return null;
+            }
+            ParquetColumnBinding binding = selectPhysicalBinding( table, fields[groupField] );
+            if ( binding == null || binding.role() != ParquetColumnRole.DATA || binding.sourcePathElements().size() != 1 ) {
+                return null;
+            }
+            int parquetField = parquetFieldIndex( binding.sourcePathElements().get( 0 ) );
+            if ( parquetField < 0 ) {
+                return null;
+            }
+            projectedFields[i] = parquetField;
+        }
+        if ( !ParquetGroupedAggregateReader.supports( schemaReader.buildProjectionSchema( projectedFields ) ) ) {
+            return null;
+        }
+        return projectedFields;
+    }
+
+
+    /**
+     * This method attempts to build aggregate projection. In order for the aggregate projection to be built the following conditions should apply:
+     * 1. All group by fields are file-constant fields.
+     * 2. All filters can be evaluated exactly for a complete source file.
+     * 3. Aggregated columns are data columns with numeric type and does not belong to a nested table.
+     *
+     * @param fields an array of field indexes.
+     * @param filters a list of filters.
+     * @param groupFields an array of group by field indexes.
+     * @param aggregateKinds an array of aggregation functions types.
+     * @param aggregateArgs an array of aggregated column indexes.
+     * @return {@link ColumnAggregateProjection} if fast aggregation can be applied and {@code null} otherwise.
+     */
+    private ColumnAggregateProjection tryBuildAggregateProjection( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        if ( aggregateKinds.length == 0 || aggregateKinds.length != aggregateArgs.length ) {
+            return null;
+        }
+        for ( int groupField : groupFields ) {
+            if ( groupField < 0 || groupField >= fields.length || !isFileConstantColumn( fields[groupField] ) ) {
+                return null;
+            }
+        }
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            if ( !supportsExactFileFilter( filter ) ) {
+                return null;
+            }
+        }
+
+        Map<Integer, Integer> parquetFieldProjectionIndexes = new LinkedHashMap<>();
+        Map<Integer, Integer> aggregateProjectionIndexes = new LinkedHashMap<>();
+        for ( int i = 0; i < aggregateArgs.length; i++ ) {
+            int aggregateArg = aggregateArgs[i];
+            if ( aggregateArg < 0 ) {
+                if ( !Kind.COUNT.name().equals( aggregateKinds[i] ) ) {
+                    return null;
+                }
+                continue;
+            }
+            if ( aggregateArg >= fields.length ) {
+                return null;
+            }
+            if ( !supportsGroupedAggregateCall( fields[aggregateArg], aggregateKinds[i] ) ) {
+                return null;
+            }
+            ParquetColumnBinding binding = selectPhysicalBinding( table, fields[aggregateArg] );
+            if ( binding == null || binding.role() != ParquetColumnRole.DATA || binding.sourcePathElements().size() != 1 ) {
+                return null;
+            }
+            int parquetField = parquetFieldIndex( binding.sourcePathElements().get( 0 ) );
+            if ( parquetField < 0 ) {
+                return null;
+            }
+            int projectionIndex = parquetFieldProjectionIndexes.computeIfAbsent( parquetField, ignored -> parquetFieldProjectionIndexes.size() );
+            aggregateProjectionIndexes.put( aggregateArg, projectionIndex );
+        }
+        int[] projectedFields = parquetFieldProjectionIndexes.keySet().stream().mapToInt( Integer::intValue ).toArray();
+        if ( projectedFields.length > 0 && !ParquetNoFilterColumnAggregateReader.supports( schemaReader.buildProjectionSchema( projectedFields ) ) ) {
+            return null;
+        }
+        return new ColumnAggregateProjection( projectedFields, aggregateProjectionIndexes );
+    }
+
+
+    /**
+     * Checks whether one field has a constant value in every source file.
+     *
+     * @param field a physical table field.
+     * @return true when each file has one value for this field.
+     */
+    private boolean isFileConstantColumn( int field ) {
+        ParquetColumnBinding binding = selectPhysicalBinding( table, field );
+        return table.getBinding().sourceFiles().stream().allMatch( sourceFile -> constantColumnResolver.resolve( sourceFile, binding ).isPresent() );
+    }
+
+
+    /**
+     * Checks whether a filter can be evaluated exactly for each complete source file.
+     *
+     * @param filter a filter.
+     * @return true when the fast file aggregate path can apply the filter.
+     */
+    private boolean supportsExactFileFilter( ParquetAdapterFilter<PolyValue> filter ) {
+        if ( filter.isLogical() ) {
+            if ( filter.operator() == Kind.NOT ) {
+                return filter.operands().size() == 1 && supportsExactFileFilter( filter.operands().get( 0 ) );
+            }
+            if ( filter.operator() != Kind.AND && filter.operator() != Kind.OR ) {
+                return false;
+            }
+            return !filter.operands().isEmpty() && filter.operands().stream().allMatch( this::supportsExactFileFilter );
+        }
+        if ( isPartitionColumn( table, filter.columnIndex() ) ) {
+            return true;
+        }
+        return table.getBinding().sourceFiles().stream().allMatch( sourceFile -> statisticsFilterEvaluator.supportsExactEvaluation( sourceFile, filter ) );
+    }
+
+
+    /**
+     * Gets a physical parquet field index.
+     *
+     * @param fieldName a field name for a lookup.
+     * @return a physical parquet field index if found or -1 otherwise.
+     */
+    private int parquetFieldIndex( String fieldName ) {
+        for ( int i = 0; i < schemaReader.getSchema().getFieldCount(); i++ ) {
+            if ( schemaReader.getSchema().getFieldName( i ).equals( fieldName ) ) {
+                return i;
+            }
+        }
+        return -1;
+    }
+
+
+    /**
+     * Checks whether all provided aggregate functions are supported. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
+     *
+     * @param fields an array of field indexes.
+     * @param groupSet a set of group by fields.
+     * @param aggregateCalls a list of aggregation functions to validate.
+     * @return {@code true} if all provided aggregation functions can be applied and {@code false} otherwise.
+     */
+    public boolean supportsDataAggregate( int[] fields, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
+        for ( int groupField : groupSet.asList() ) {
+            if ( groupField < 0 || groupField >= fields.length ) {
+                return false;
+            }
+        }
+        for ( AggregateCall aggregateCall : aggregateCalls ) {
+            if ( !supportsDataAggregateCall( fields, aggregateCall ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Checks whether specified aggregation function is supported. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
+     *
+     * @param fields an array of field indexes.
+     * @param aggregateCall an aggregation function to validate.
+     * @return {@code true} if the aggregation function is supported and {@code false} otherwise.
+     */
+    private boolean supportsDataAggregateCall( int[] fields, AggregateCall aggregateCall ) {
+        if ( aggregateCall.isDistinct() || aggregateCall.isApproximate() || aggregateCall.filterArg >= 0 ) {
+            return false;
+        }
+        Kind kind = aggregateCall.getAggregation().getKind();
+        if ( kind == Kind.COUNT ) {
+            if ( aggregateCall.getArgList().isEmpty() ) {
+                return true;
+            }
+            if ( aggregateCall.getArgList().size() != 1 ) {
+                return false;
+            }
+            int inputIndex = aggregateCall.getArgList().get( 0 );
+            return inputIndex >= 0 && inputIndex < fields.length;
+        }
+        if ( !isNumericAggregateKind( kind.name() ) ) {
+            return false;
+        }
+        if ( aggregateCall.getArgList().size() != 1 ) {
+            return false;
+        }
+        int inputIndex = aggregateCall.getArgList().get( 0 );
+        return inputIndex >= 0 && inputIndex < fields.length && PolyType.NUMERIC_TYPES.contains( table.columns.get( fields[inputIndex] ).type );
+    }
+
+
+    private boolean isNumericAggregateKind( String aggregateKind ) {
+        return Kind.SUM.name().equals( aggregateKind )
+                || Kind.MIN.name().equals( aggregateKind )
+                || Kind.MAX.name().equals( aggregateKind );
+    }
+
+
+    private record GroupAggregateProjection( int[] fields, int groupFieldCount, AggregateCallDescriptor[] aggregateCalls ) {
+
+    }
+
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelExecutor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelExecutor.java
new file mode 100644
index 0000000000..8dadb19ec6
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelExecutor.java
@@ -0,0 +1,470 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.ArrayList;
+import java.util.LinkedHashSet;
+import java.util.List;
+import java.util.Objects;
+import java.util.concurrent.atomic.AtomicBoolean;
+import java.util.function.Function;
+import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetMultiFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFilePartitionFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFileStatisticsFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetPrimitiveRowReader;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+
+/**
+ * Represents a base class holding useful methods for all parquet relational executors.
+ */
+public abstract class ParquetRelExecutor {
+
+    protected final ParquetRelTable table;
+    protected final AbstractParquetSource parquetSource;
+    protected final int[] fieldIndexes;
+    protected final ParquetSchemaReader schemaReader;
+
+
+    public ParquetRelExecutor( ParquetRelTable table, AbstractParquetSource parquetSource, int[] fieldIndexes, ParquetSchemaReader schemaReader ) {
+        this.table = table;
+        this.parquetSource = parquetSource;
+        this.fieldIndexes = fieldIndexes;
+        this.schemaReader = schemaReader;
+    }
+
+
+    /**
+     * Creates one of the three possible enumerators depending on the source file type:
+     * - source file contains nested repeated fields -> {@link ParquetNestedRepeatedRelEnumerator}
+     * - source file contains nested but not repeated fields -> {@link ParquetNestedNonRepeatedRelEnumerator}
+     * - source file doesn't contain nested fields -> {@link ParquetRelEnumerator}
+     *
+     * @param table a source table
+     * @param sourceFile a source file
+     * @param fields a list of projected fields
+     * @param allFields a list of all table fields
+     * @param schemaReader a schema reader
+     * @param cancelFlag a flag indicating if the execution is being canceled
+     * @param filtersContainer a container for multiple lists of filters.
+     * @return one of the selected enumerators.
+     */
+    protected static Enumerator<PolyValue[]> enumeratorForFile( ParquetRelTable table, ParquetSourceFile sourceFile, int[] fields, int[] allFields, ParquetSchemaReader schemaReader, AtomicBoolean cancelFlag, FiltersContainer filtersContainer ) {
+        boolean nestedTable = isNestedTable( table );
+        boolean requiresRowValidation = !filtersContainer.adapterFilters().isEmpty();
+        boolean bindingScan = nestedTable || needsBindingScan( table, schemaReader, fields );
+        Source fileSource = sourceFile.asSource();
+        int[] readerFields = bindingScan || requiresRowValidation
+                ? createReaderProjection( table, schemaReader, fields, filtersContainer.adapterFilters() )
+                : fields;
+
+        if ( !nestedTable ) {
+            var projectionSchema = schemaReader.buildProjectionSchema( readerFields );
+            if ( ParquetPrimitiveRowReader.supports( projectionSchema ) ) {
+                List<ParquetAdapterFilter<PolyValue>> readerFilters = readerFilters( table, schemaReader, readerFields, filtersContainer.adapterFilters() );
+                PrimitiveRowProjection outputProjection = primitiveRowProjection( table, schemaReader, sourceFile, readerFields, fields );
+                if ( readerFilters != null && outputProjection != null ) {
+                    return new ParquetRowRelEnumerator(
+                            new ParquetPrimitiveRowReader( fileSource, cancelFlag, readerFields, filtersContainer.nativeFilters() ),
+                            readerFilters,
+                            outputProjection.outputIndexes(),
+                            outputProjection.constants() );
+                }
+            }
+        }
+
+        ParquetSourceReader reader = new ParquetSourceReader( fileSource, cancelFlag, readerFields, filtersContainer.nativeFilters() );
+        List<ParquetColumnBinding> columnBindings = projectedBindings( table, fields );
+        List<ParquetColumnBinding> filterBindings = projectedBindings( table, allFields );
+        if ( nestedTable ) {
+            return new ParquetNestedRepeatedRelEnumerator( reader, table.getBinding(), columnBindings, filterBindings, filtersContainer );
+        }
+        if ( bindingScan ) {
+            return new ParquetNestedNonRepeatedRelEnumerator( reader, sourceFile, columnBindings, filterBindings, filtersContainer );
+        }
+        return new ParquetRelEnumerator( reader, filtersContainer.withoutPathElementsInAdapterFilters() );
+    }
+
+
+    /**
+     * Converts filters with physical parquet indexes to filters with projected indexes.
+     *
+     * @param table a reference o the relational table.
+     * @param schemaReader a schema reader.
+     * @param fields an array of projected field indexes.
+     * @param filters a list of filters to convert.
+     * @return a list of converted filters.
+     */
+    private static List<ParquetAdapterFilter<PolyValue>> readerFilters( ParquetRelTable table, ParquetSchemaReader schemaReader, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        List<ParquetAdapterFilter<PolyValue>> mapped = new ArrayList<>( filters.size() );
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            ParquetAdapterFilter<PolyValue> readerFilter = ParquetFilterResolver.toProjectionFilter( filter, field -> projectedFieldIndex( table, schemaReader, fields, field ) );
+            if ( readerFilter == null ) {
+                return null;
+            }
+            mapped.add( readerFilter );
+        }
+        return mapped;
+    }
+
+
+    /**
+     * Creates an output projection. This is required when the filter was not part of the original projection and was added to a reader projection for availability.
+     *
+     * @param table a relation table reference.
+     * @param schemaReader a schema reader.
+     * @param readerFields a reader projected fields.
+     * @param fields original projected fields.
+     * @return an array of projected indexes excluding filters.
+     */
+    private static PrimitiveRowProjection primitiveRowProjection( ParquetRelTable table, ParquetSchemaReader schemaReader, ParquetSourceFile sourceFile, int[] readerFields, int[] fields ) {
+        int[] outputIndexes = new int[fields.length];
+        PolyValue[] constants = new PolyValue[fields.length];
+        for ( int i = 0; i < fields.length; i++ ) {
+            int index = projectedFieldIndex( table, schemaReader, readerFields, fields[i] );
+            if ( index < 0 ) {
+                ParquetColumnBinding binding = table.getBinding().getColumnBinding( table.columns.get( fields[i] ).id );
+                if ( binding == null || binding.role() != ParquetColumnRole.PARTITION ) {
+                    return null;
+                }
+                outputIndexes[i] = -1;
+                constants[i] = partitionValue( sourceFile, binding );
+            } else {
+                outputIndexes[i] = index;
+            }
+        }
+        return new PrimitiveRowProjection( outputIndexes, constants );
+    }
+
+
+    private static PolyValue partitionValue( ParquetSourceFile sourceFile, ParquetColumnBinding binding ) {
+        String value = sourceFile.partitionValues().get( binding.columnName() );
+        return value == null ? PolyNull.NULL : PolyString.of( value );
+    }
+
+
+    /**
+     * Converts physical field index into projected index.
+     *
+     * @param table a relational table.
+     * @param schemaReader a schema reader.
+     * @param fields an array of field indexes.
+     * @param field an index to convert.
+     * @return converted index if successful or -1 otherwise.
+     */
+    private static int projectedFieldIndex( ParquetRelTable table, ParquetSchemaReader schemaReader, int[] fields, int field ) {
+        int parquetField = parquetFieldIndex( table, schemaReader, field );
+        for ( int i = 0; i < fields.length; i++ ) {
+            if ( fields[i] == parquetField ) {
+                return i;
+            }
+        }
+        return -1;
+    }
+
+
+    /**
+     * Gets a physical field index from the physical parquet schema.
+     *
+     * @param table a relational table reference.
+     * @param schemaReader a schema reader.
+     * @param field a field index to convert.
+     * @return a physical parquet file index if converted or -1 otherwise.
+     */
+    public static int parquetFieldIndex( ParquetRelTable table, ParquetSchemaReader schemaReader, int field ) {
+        if ( field < 0 || field >= table.columns.size() ) {
+            return -1;
+        }
+        ParquetColumnBinding binding = table.getBinding().getColumnBinding( table.columns.get( field ).id );
+        if ( binding == null || binding.sourcePathElements().size() != 1 || binding.role() != ParquetColumnRole.DATA ) {
+            return -1;
+        }
+        return parquetFieldIndex( schemaReader, binding.sourcePathElements().get( 0 ) );
+    }
+
+
+    /**
+     * Creates projected fields for a reader to reduce the amount of data needed to be read from the parquet file.
+     *
+     * @param table a source table.
+     * @param schemaReader schema reader that provides access to the parquet file schema.
+     * @param fields an array of field indexes.
+     * @param filters a list of filters.
+     * @return projected fields.
+     */
+    private static int[] createReaderProjection( ParquetRelTable table, ParquetSchemaReader schemaReader, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        LinkedHashSet<Integer> projected = new LinkedHashSet<>();
+        for ( int field : fields ) {
+            addProjectionField( table, schemaReader, projected, field );
+        }
+        addTablePathProjection( table, schemaReader, projected );
+        addProjectionFields( table, schemaReader, projected, filters );
+        addFallbackDataProjection( table, schemaReader, projected );
+        return projected.stream().mapToInt( Integer::intValue ).toArray();
+    }
+
+
+    /**
+     * Adds filters to projection.
+     *
+     * @param table a source table.
+     * @param schemaReader a schema reader.
+     * @param projected a projection.
+     * @param filters a list of filters.
+     */
+    private static void addProjectionFields( ParquetRelTable table, ParquetSchemaReader schemaReader, LinkedHashSet<Integer> projected, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            if ( filter.isLogical() ) {
+                addProjectionFields( table, schemaReader, projected, filter.operands() );
+            } else {
+                addProjectionField( table, schemaReader, projected, filter.columnIndex() );
+            }
+        }
+    }
+
+
+    private static void addTablePathProjection( ParquetRelTable table, ParquetSchemaReader schemaReader, LinkedHashSet<Integer> projected ) {
+        List<String> tablePath = table.getBinding().sourcePathElements();
+        if ( tablePath.isEmpty() ) {
+            return;
+        }
+        int parquetField = parquetFieldIndex( schemaReader, tablePath.get( 0 ) );
+        if ( parquetField >= 0 ) {
+            projected.add( parquetField );
+        }
+    }
+
+
+    private static void addFallbackDataProjection( ParquetRelTable table, ParquetSchemaReader schemaReader, LinkedHashSet<Integer> projected ) {
+        if ( !projected.isEmpty() ) {
+            return;
+        }
+        for ( int field = 0; field < table.columns.size(); field++ ) {
+            ParquetColumnBinding binding = table.getBinding().getColumnBinding( table.columns.get( field ).id );
+            if ( binding == null || binding.role() != ParquetColumnRole.DATA || binding.sourcePathElements().size() != 1 ) {
+                continue;
+            }
+            int parquetField = parquetFieldIndex( schemaReader, binding.sourcePathElements().get( 0 ) );
+            if ( parquetField >= 0 ) {
+                projected.add( parquetField );
+                return;
+            }
+        }
+        if ( schemaReader.getSchema().getFieldCount() > 0 ) {
+            projected.add( 0 );
+        }
+    }
+
+
+    /**
+     * Adds a field to a projection.
+     *
+     * @param table a source table.
+     * @param schemaReader a schema reader.
+     * @param projected a projection.
+     * @param field a field.
+     */
+    private static void addProjectionField( ParquetRelTable table, ParquetSchemaReader schemaReader, LinkedHashSet<Integer> projected, int field ) {
+        if ( field < 0 || field >= table.columns.size() ) {
+            return;
+        }
+        ParquetColumnBinding binding = table.getBinding().getColumnBinding( table.columns.get( field ).id );
+        if ( binding == null || binding.sourcePathElements().isEmpty() || binding.role() != ParquetColumnRole.DATA ) {
+            return;
+        }
+        int parquetField = parquetFieldIndex( schemaReader, binding.sourcePathElements().get( 0 ) );
+        if ( parquetField >= 0 ) {
+            projected.add( parquetField );
+        }
+    }
+
+
+    /**
+     * Gets physical parquet field index by the field name from parquet schema.
+     *
+     * @param schemaReader a schema reader.
+     * @param fieldName a field name.
+     * @return a physical parquet field index.
+     */
+    private static int parquetFieldIndex( ParquetSchemaReader schemaReader, String fieldName ) {
+        for ( int i = 0; i < schemaReader.getSchema().getFieldCount(); i++ ) {
+            if ( schemaReader.getSchema().getFieldName( i ).equals( fieldName ) ) {
+                return i;
+            }
+        }
+        return -1;
+    }
+
+
+    /**
+     * Creates a chain of source file filter evaluators. Currently, supports two filter evaluators:
+     * 1. partition value based filter evaluator
+     * 2. source file statistics based filter evaluator.
+     * Those filter evaluators can skip files if they do not contain filtered data.
+     *
+     * @param selector a column binding selector.
+     * @return an evaluators chain.
+     */
+    public static ParquetMultiFilterEvaluator<ParquetSourceFile> createParquetSourceFileEvaluatorsChain( Function<ParquetAdapterFilter<PolyValue>, ParquetColumnBinding> selector ) {
+        return new ParquetMultiFilterEvaluator<>( List.of(
+                new ParquetSourceFilePartitionFilterEvaluator( selector ),
+                new ParquetSourceFileStatisticsFilterEvaluator( selector ) )
+        );
+    }
+
+
+    /**
+     * Selects a column binding according to a physical column index.
+     *
+     * @param table a table to look for a column binding in.
+     * @param columnIndex a physical column index.
+     * @return {@link ParquetColumnBinding}.
+     */
+    public static ParquetColumnBinding selectPhysicalBinding( ParquetRelTable table, int columnIndex ) {
+        if ( columnIndex < 0 || columnIndex >= table.columns.size() ) {
+            throw new GenericRuntimeException( "Invalid physical filter column index: " + columnIndex );
+        }
+        return table.getBinding().getColumnBinding( table.columns.get( columnIndex ).id );
+    }
+
+
+    /**
+     * Checks if the specified filter is a partition filter.
+     *
+     * @param table a table containing the binding information.
+     * @param filter a filter to check.
+     * @return {@code true} if the provided filter is a partition filter and {@code false} otherwise.
+     */
+    protected static boolean isPartitionFilter( ParquetRelTable table, ParquetAdapterFilter<PolyValue> filter ) {
+        if ( filter.isLogical() ) {
+            return filter.operands().stream().allMatch( operand -> isPartitionFilter( table, operand ) );
+        }
+        return isPartitionColumn( table, filter.columnIndex() );
+    }
+
+
+    /**
+     * Checks if the specified filter is a partition filter.
+     *
+     * @param table a table containing the binding information.
+     * @param columnIndex a column index of the filter.
+     * @return {@code true} if the provided filter is a partition filter and {@code false} otherwise.
+     */
+    protected static boolean isPartitionColumn( ParquetRelTable table, int columnIndex ) {
+        if ( columnIndex < 0 || columnIndex >= table.columns.size() ) {
+            return false;
+        }
+        ParquetColumnBinding columnBinding = table.getBinding().getColumnBinding( table.columns.get( columnIndex ).id );
+        return columnBinding != null && columnBinding.role() == ParquetColumnRole.PARTITION;
+    }
+
+
+    /**
+     * Gets a list of column bindings for the provided projected fields.
+     *
+     * @param table a table containing the binding information.
+     * @param fields an array of field indexes.
+     * @return a list of column bindings.
+     */
+    protected static List<ParquetColumnBinding> projectedBindings( ParquetRelTable table, int[] fields ) {
+        ParquetTableBinding binding = table.getBinding();
+        List<ParquetColumnBinding> selected = new ArrayList<>( fields.length );
+        for ( int field : fields ) {
+            selected.add( Objects.requireNonNull( binding.getColumnBinding( table.columns.get( field ).id ), "Missing parquet column binding" ) );
+        }
+        return selected;
+    }
+
+
+    /**
+     * Checks if reading from the table requires binding information.
+     * Binding information is required when:
+     * 1. a field index points to a nested field.
+     * 2. a field index is a logical index inside the projection and needs to be mapped to a physical parquet index.
+     *
+     * @param table a table to check.
+     * @param schemaReader a schema reader pointing to the parquet file.
+     * @param fields an array of field indexes.
+     * @return {@code true} if the reading from the table requires binding information and {@code false} otherwise.
+     */
+    protected static boolean needsBindingScan( ParquetRelTable table, ParquetSchemaReader schemaReader, int[] fields ) {
+        var schema = schemaReader.getSchema();
+        for ( int field : fields ) {
+            ParquetColumnBinding columnBinding = table.getBinding().getColumnBinding( table.columns.get( field ).id );
+            if ( columnBinding == null ) {
+                throw new GenericRuntimeException( "Missing parquet column binding for a field " + schema.getType( field ).getName() );
+            }
+            if ( columnBinding.sourcePathElements().isEmpty() ) {
+                if ( columnBinding.role() != ParquetColumnRole.DATA ) {
+                    return true;
+                }
+                throw new GenericRuntimeException( "Missing parquet column binding for a field " + schema.getType( field ).getName() );
+            }
+            if ( columnBinding.sourcePathElements().size() > 1 ) {
+                return true;
+            }
+            if ( field >= schema.getFieldCount() || !schema.getType( field ).getName().equals( columnBinding.sourcePathElements().get( 0 ) ) ) {
+                return true;
+            }
+        }
+        return false;
+    }
+
+
+    /**
+     * Checks if a table is a nested table.
+     *
+     * @param table a table to check.
+     * @return {@code true} if nested and {@code false} otherwise.
+     */
+    private static boolean isNestedTable( ParquetRelTable table ) {
+        return table.getBinding().parentTableName() != null;
+    }
+
+
+    private record PrimitiveRowProjection( int[] outputIndexes, PolyValue[] constants ) {
+
+    }
+
+
+    /**
+     * Registers adapter in the current transaction as an involved in execution adapter.
+     *
+     * @param dataContext a data context.
+     */
+    protected void registerAdapter( DataContext dataContext ) {
+        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelExecutorsFactory.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelExecutorsFactory.java
new file mode 100644
index 0000000000..8de7c96838
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelExecutorsFactory.java
@@ -0,0 +1,75 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.HashMap;
+import java.util.Map;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+
+/**
+ * This class holds instance of {@link ParquetRelExecutor} derives by their class.
+ */
+public class ParquetRelExecutorsFactory {
+
+    private final ParquetRelTable table;
+    private final AbstractParquetSource parquetSource;
+    private final int[] fieldIndexes;
+    private final ParquetSchemaReader schemaReader;
+    private final Map<Class<?>, ParquetRelExecutor> executors;
+
+
+    public ParquetRelExecutorsFactory( ParquetRelTable table, AbstractParquetSource parquetSource, int[] fieldIndexes, ParquetSchemaReader schemaReader ) {
+        this.table = table;
+        this.parquetSource = parquetSource;
+        this.fieldIndexes = fieldIndexes;
+        this.schemaReader = schemaReader;
+        this.executors = new HashMap<>();
+    }
+
+
+    /**
+     * Gets an instance of an executor derived from {@link ParquetRelExecutor}.
+     *
+     * @param executorClass an actual implementation class to retrieve.
+     * @param <T> executor type.
+     * @return a new or existing instance of an executor.
+     */
+    @SuppressWarnings("unchecked")
+    public <T extends ParquetRelExecutor> T getExecutor( Class<T> executorClass ) {
+        var executor = executors.get( executorClass );
+        if ( executor == null ) {
+            if ( executorClass.equals( ParquetRelProjectExecutor.class ) ) {
+                executor = new ParquetRelProjectExecutor( table, parquetSource, fieldIndexes, schemaReader );
+            } else if ( executorClass.equals( ParquetRelMetadataAggregateExecutor.class ) ) {
+                executor = new ParquetRelMetadataAggregateExecutor( table, parquetSource, fieldIndexes, schemaReader );
+            } else if ( executorClass.equals( ParquetRelDataAggregateExecutor.class ) ) {
+                executor = new ParquetRelDataAggregateExecutor( table, parquetSource, fieldIndexes, schemaReader );
+            } else if ( executorClass.equals( ParquetRelNestedJoinExecutor.class ) ) {
+                executor = new ParquetRelNestedJoinExecutor( table, parquetSource, fieldIndexes, schemaReader );
+            }
+            if ( executor == null ) {
+                throw new GenericRuntimeException( "No executor found for class " + executorClass.getName() );
+            }
+            executors.put( executorClass, executor );
+        }
+        return (T) executor;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelMetadataAggregateExecutor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelMetadataAggregateExecutor.java
new file mode 100644
index 0000000000..6cd553fb49
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelMetadataAggregateExecutor.java
@@ -0,0 +1,408 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Optional;
+import org.apache.calcite.linq4j.Enumerable;
+import org.apache.calcite.linq4j.Linq4j;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetMultiFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFilePartitionFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFileStatisticsFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnStatistics;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetConstantColumnResolver;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.core.AggregateCall;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.numerical.PolyLong;
+import org.polypheny.db.util.ImmutableBitSet;
+
+/**
+ * Executes aggregations that can be done on metadata only.
+ */
+public class ParquetRelMetadataAggregateExecutor extends ParquetRelExecutor {
+
+    private final ParquetTypeConverter typeConverter;
+    private final ParquetSourceFileStatisticsFilterEvaluator statisticsFilterEvaluator;
+    private final ParquetConstantColumnResolver constantColumnResolver;
+
+
+    public ParquetRelMetadataAggregateExecutor( ParquetRelTable table, AbstractParquetSource parquetSource, int[] fieldIndexes, ParquetSchemaReader schemaReader ) {
+        super( table, parquetSource, fieldIndexes, schemaReader );
+        this.typeConverter = new ParquetTypeConverter();
+        this.statisticsFilterEvaluator = new ParquetSourceFileStatisticsFilterEvaluator( f -> selectPhysicalBinding( table, f.columnIndex() ) );
+        this.constantColumnResolver = new ParquetConstantColumnResolver();
+    }
+
+
+    /**
+     * Calculates rows count based on file statistics. In case statistics are not presented fallback to a schema reader for actual calculation.
+     *
+     * @param sourceFile a source file.
+     * @return a row count.
+     */
+    private static long sourceRowCount( ParquetSourceFile sourceFile ) {
+        Optional<Long> rowCount = sourceFile.columnStatistics().values().stream()
+                .map( ParquetColumnStatistics::rowCount )
+                .findFirst();
+        return rowCount.orElseGet( () -> new ParquetSchemaReader( sourceFile.asSource() ).getEstimatedRowCount() );
+    }
+
+
+    /**
+     * Creates an enumerable over parquet files metadata statistics.
+     *
+     * @param dataContext a data context
+     * @param fields an array of physical field indexes of columns used in a group by statement.
+     * @param filters a list of filters to be applied
+     * @param groupFields an array of group by field indexes in fields.
+     * @param aggregateKinds the names of the aggregation function.
+     * @param aggregateArgs input field indexes used by aggregation functions.
+     * @return an enumerable.
+     */
+    public Enumerable<PolyValue[]> createEnumerator( DataContext dataContext, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        registerAdapter( dataContext );
+
+        List<ParquetAdapterFilter<PolyValue>> resolvedFilters = ParquetFilterResolver.resolveFilters( dataContext, filters, f -> selectPhysicalBinding( table, f.columnIndex() ) );
+        ParquetMultiFilterEvaluator<ParquetSourceFile> multiFilterEvaluator = new ParquetMultiFilterEvaluator<>(
+                new ParquetSourceFilePartitionFilterEvaluator( f -> selectPhysicalBinding( table, f.columnIndex() ) ),
+                statisticsFilterEvaluator
+        );
+
+        Map<List<PolyValue>, MetadataAggregateAccumulator[]> aggregates = new LinkedHashMap<>();
+        for ( ParquetSourceFile sourceFile : table.getBinding().sourceFiles() ) {
+            if ( !matchesExactly( multiFilterEvaluator, sourceFile, resolvedFilters ) ) {
+                continue;
+            }
+            List<PolyValue> groupKey = fileGroupKey( sourceFile, fields, groupFields );
+            MetadataAggregateAccumulator[] groupAggregates = aggregates.computeIfAbsent( groupKey, key -> createAccumulators( aggregateKinds ) );
+            for ( int i = 0; i < groupAggregates.length; i++ ) {
+                groupAggregates[i].add( sourceFile, fields, aggregateArgs[i] );
+            }
+        }
+
+        if ( groupFields.length == 0 && aggregates.isEmpty() ) {
+            aggregates.put( List.of(), createAccumulators( aggregateKinds ) );
+        }
+
+        List<PolyValue[]> rows = new ArrayList<>( aggregates.size() );
+        for ( Map.Entry<List<PolyValue>, MetadataAggregateAccumulator[]> entry : aggregates.entrySet() ) {
+            PolyValue[] row = new PolyValue[entry.getKey().size() + aggregateKinds.length];
+            for ( int i = 0; i < entry.getKey().size(); i++ ) {
+                row[i] = entry.getKey().get( i );
+            }
+            for ( int i = 0; i < aggregateKinds.length; i++ ) {
+                row[entry.getKey().size() + i] = entry.getValue()[i].result();
+            }
+            rows.add( row );
+        }
+        return Linq4j.asEnumerable( rows );
+    }
+
+
+    /**
+     * Checks whether filters, groups and aggregate functions can be executed on metadata only.
+     *
+     * @param fields an array of physical field indexes of columns used in a group by statement.
+     * @param filters a list of filters to be validated.
+     * @param groupSet a group by set.
+     * @param aggregateCalls a list of aggregate calls (aggregate functions)
+     * @return {@code true} filters, groups and aggregate functions can be executed on metadata only and {@code false} otherwise.
+     */
+    public boolean supportsMetadataAggregate( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
+        for ( int groupField : groupSet.asList() ) {
+            if ( groupField < 0 || groupField >= fields.length || !isFileConstantColumn( fields[groupField] ) ) {
+                return false;
+            }
+        }
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            if ( !supportsMetadataFilter( statisticsFilterEvaluator, filter ) ) {
+                return false;
+            }
+        }
+        for ( AggregateCall aggregateCall : aggregateCalls ) {
+            if ( !supportsMetadataAggregateCall( fields, aggregateCall ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Checks whether a filter can be evaluated exactly from partition values or
+     * file-level statistics. Statistics filters are limited to file-constant
+     * physical columns so retained files can contribute their full metadata
+     * row count without reading data pages.
+     *
+     * @param statisticsFilterEvaluator evaluates physical-column file statistics.
+     * @param filter a filter to validate.
+     * @return {@code true} if the filter can be evaluated exactly for every source file.
+     */
+    private boolean supportsMetadataFilter( ParquetSourceFileStatisticsFilterEvaluator statisticsFilterEvaluator, ParquetAdapterFilter<PolyValue> filter ) {
+        if ( filter.isLogical() ) {
+            if ( filter.operator() == Kind.NOT ) {
+                return filter.operands().size() == 1 && isPartitionFilter( table, filter );
+            }
+            if ( filter.operator() != Kind.AND && filter.operator() != Kind.OR ) {
+                return false;
+            }
+            return !filter.operands().isEmpty() && filter.operands().stream().allMatch( operand -> supportsMetadataFilter( statisticsFilterEvaluator, operand ) );
+        }
+        if ( isPartitionColumn( table, filter.columnIndex() ) ) {
+            return true;
+        }
+        return table.getBinding().sourceFiles().stream().allMatch( sourceFile -> statisticsFilterEvaluator.supportsExactEvaluation( sourceFile, filter ) );
+    }
+
+
+    /**
+     * Applies filters that were proven metadata-decidable during planning.
+     *
+     * @param evaluator combines partition-value and file-statistics evaluators.
+     * @param sourceFile a source file.
+     * @param filters filters to apply.
+     * @return {@code true} if the complete file matches the filters.
+     */
+    private boolean matchesExactly( ParquetMultiFilterEvaluator<ParquetSourceFile> evaluator, ParquetSourceFile sourceFile, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            Boolean result = evaluator.evaluate( sourceFile, filter );
+            if ( result == null ) {
+                throw new GenericRuntimeException( "Metadata aggregate filter could not be evaluated exactly for " + sourceFile.fileUrl() );
+            }
+            if ( !result ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Creates a joined key from file-constant group by fields.
+     *
+     * @param sourceFile a source file.
+     * @param fields an array of physical field indexes of columns used in a group by statement.
+     * @param groupFields an array of group by field indexes in fields.
+     * @return a list of {@link PolyValue}s representing a key.
+     */
+    private List<PolyValue> fileGroupKey( ParquetSourceFile sourceFile, int[] fields, int[] groupFields ) {
+        List<PolyValue> key = new ArrayList<>( groupFields.length );
+        for ( int groupField : groupFields ) {
+            int physicalField = fields[groupField];
+            ParquetColumnBinding columnBinding = selectPhysicalBinding( table, physicalField );
+            key.add( constantColumnResolver.resolve( sourceFile, columnBinding )
+                    .orElseThrow( () -> new GenericRuntimeException( "Metadata aggregate group column is not constant for " + sourceFile.fileUrl() ) ) );
+        }
+        return List.copyOf( key );
+    }
+
+
+    /**
+     * Checks whether one field has a constant value in every source file.
+     *
+     * @param field a physical table field.
+     * @return true when each file has one value for this field.
+     */
+    private boolean isFileConstantColumn( int field ) {
+        ParquetColumnBinding binding = selectPhysicalBinding( table, field );
+        return table.getBinding().sourceFiles().stream().allMatch( sourceFile -> constantColumnResolver.resolve( sourceFile, binding ).isPresent() );
+    }
+
+
+    /**
+     * Checks if a metadata aggregate call can be calculated from source file statistics.
+     *
+     * @param fields an array of physical field indexes of columns used in a group by statement.
+     * @param aggregateCall an aggregate call to validate.
+     * @return {@code true} if the aggregate call can be calculated from metadata and {@code false} otherwise.
+     */
+    private boolean supportsMetadataAggregateCall( int[] fields, AggregateCall aggregateCall ) {
+        if ( aggregateCall.getAggregation().getKind() == Kind.COUNT ) {
+            if ( aggregateCall.getArgList().isEmpty() ) {
+                return true;
+            }
+            return aggregateCall.getArgList().size() == 1 && supportsCountColumn( fields, aggregateCall.getArgList().get( 0 ) );
+        }
+        if ( aggregateCall.getAggregation().getKind() != Kind.MIN && aggregateCall.getAggregation().getKind() != Kind.MAX ) {
+            return false;
+        }
+        if ( aggregateCall.getArgList().size() != 1 ) {
+            return false;
+        }
+
+        ParquetColumnBinding columnBinding = dataColumnBinding( fields, aggregateCall.getArgList().get( 0 ) );
+        if ( columnBinding == null ) {
+            return false;
+        }
+
+        for ( ParquetSourceFile sourceFile : table.getBinding().sourceFiles() ) {
+            ParquetColumnStatistics statistics = sourceFile.columnStatistics().get( columnBinding.sourcePathElements() );
+            if ( statistics == null || (!statistics.hasRange() && !statistics.hasOnlyNulls()) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Checks if a COUNT on a column is supported. Column aggregation is supported only if it has statistics and nulls count.
+     *
+     * @param fields an array of field indexes.
+     * @param inputIndex a column index.
+     * @return {@code true} if COUNT can be applied on the specified column and {@code false} otherwise.
+     */
+    private boolean supportsCountColumn( int[] fields, int inputIndex ) {
+        ParquetColumnBinding columnBinding = dataColumnBinding( fields, inputIndex );
+        if ( columnBinding == null ) {
+            return false;
+        }
+
+        for ( ParquetSourceFile sourceFile : table.getBinding().sourceFiles() ) {
+            ParquetColumnStatistics statistics = sourceFile.columnStatistics().get( columnBinding.sourcePathElements() );
+            if ( statistics == null || statistics.nullCount() == null ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Gets a binding for the specified column.
+     *
+     * @param fields an array of field indexes.
+     * @param inputIndex a column index.
+     * @return {@link ParquetColumnBinding}.
+     */
+    private ParquetColumnBinding dataColumnBinding( int[] fields, int inputIndex ) {
+        if ( inputIndex < 0 || inputIndex >= fields.length ) {
+            return null;
+        }
+
+        ParquetColumnBinding columnBinding = selectPhysicalBinding( table, fields[inputIndex] );
+        if ( columnBinding == null || columnBinding.role() != ParquetColumnRole.DATA || columnBinding.sourcePathElements().isEmpty() ) {
+            return null;
+        }
+        return columnBinding;
+    }
+
+
+    /**
+     * Creates an accumulator for each aggregation function.
+     *
+     * @param aggregateKinds the names of aggregation function.
+     * @return an array of aggregate accumulators.
+     */
+    private MetadataAggregateAccumulator[] createAccumulators( String[] aggregateKinds ) {
+        return Arrays.stream( aggregateKinds )
+                .map( MetadataAggregateAccumulator::new )
+                .toArray( MetadataAggregateAccumulator[]::new );
+    }
+
+
+    /**
+     * Holds partial metadata aggregate values for one result group.
+     */
+    private class MetadataAggregateAccumulator {
+
+        private final String aggregateKind;
+        private long count;
+        private ParquetColumnStatistics selectedStatistics;
+        private String selectedValue;
+
+
+        private MetadataAggregateAccumulator( String aggregateKind ) {
+            this.aggregateKind = aggregateKind;
+        }
+
+
+        private void add( ParquetSourceFile sourceFile, int[] fields, int aggregateArg ) {
+            if ( Kind.COUNT.name().equals( aggregateKind ) ) {
+                if ( aggregateArg < 0 ) {
+                    count += sourceRowCount( sourceFile );
+                    return;
+                }
+
+                ParquetColumnStatistics statistics = statistics( sourceFile, fields, aggregateArg );
+                if ( statistics != null && statistics.nullCount() != null ) {
+                    count += Math.max( 0L, statistics.rowCount() - statistics.nullCount() );
+                }
+                return;
+            }
+
+            ParquetColumnStatistics statistics = statistics( sourceFile, fields, aggregateArg );
+            if ( statistics == null || !statistics.hasRange() ) {
+                return;
+            }
+            String candidate = Kind.MIN.name().equals( aggregateKind ) ? statistics.min() : statistics.max();
+            if ( selectedValue == null || compare( statistics, candidate, selectedValue ) ) {
+                selectedStatistics = statistics;
+                selectedValue = candidate;
+            }
+        }
+
+
+        private PolyValue result() {
+            if ( Kind.COUNT.name().equals( aggregateKind ) ) {
+                return PolyLong.of( count );
+            }
+            if ( selectedValue == null || selectedStatistics == null ) {
+                return PolyNull.NULL;
+            }
+            PolyValue value = typeConverter.fromStringToPolyValue( selectedStatistics.type(), selectedValue );
+            return value == null ? PolyNull.NULL : value;
+        }
+
+
+        private boolean compare( ParquetColumnStatistics candidateStatistics, String candidate, String current ) {
+            int comparison = typeConverter.compareStringValues( candidateStatistics.type(), candidate, current );
+            return Kind.MIN.name().equals( aggregateKind ) ? comparison < 0 : comparison > 0;
+        }
+
+
+        private ParquetColumnStatistics statistics( ParquetSourceFile sourceFile, int[] fields, int aggregateArg ) {
+            if ( aggregateArg < 0 || aggregateArg >= fields.length ) {
+                return null;
+            }
+            ParquetColumnBinding columnBinding = selectPhysicalBinding( table, fields[aggregateArg] );
+            if ( columnBinding == null ) {
+                return null;
+            }
+            return sourceFile.columnStatistics().get( columnBinding.sourcePathElements() );
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelNestedJoinExecutor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelNestedJoinExecutor.java
new file mode 100644
index 0000000000..15f3e72532
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelNestedJoinExecutor.java
@@ -0,0 +1,213 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+import java.util.stream.IntStream;
+import org.apache.calcite.linq4j.AbstractEnumerable;
+import org.apache.calcite.linq4j.Enumerable;
+import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
+import org.polypheny.db.adapter.parquet.relational.planning.PhysicalScan;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersSplitter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyValue;
+
+/**
+ * Executor used to run parquet enumerator for nested joins.
+ */
+public class ParquetRelNestedJoinExecutor extends ParquetRelExecutor {
+
+    public ParquetRelNestedJoinExecutor( ParquetRelTable table, AbstractParquetSource parquetSource, int[] fieldIndexes, ParquetSchemaReader schemaReader ) {
+        super( table, parquetSource, fieldIndexes, schemaReader );
+    }
+
+
+    /**
+     * Creates a {@link ParquetNestedJoinEnumerator} for a specified source file.
+     *
+     * @param parent a parent table participating in a join.
+     * @param sourceFile a source file.
+     * @param child a child nested table participating in a join.
+     * @param parentFields a list of projected fields from the parent table.
+     * @param childFields a list of projected fields from the child table.
+     * @param cancelFlag indicates if the execution has been canceled and should not continue.
+     * @param filterContainer a container of filters.
+     * @param leftIsParent indicates if the parent table is on the left side of the join or not.
+     * @param emitUnmatchedParents indicates if the parent row should be emitted if there is no child row to be joined with.
+     * @return a new instance of {@link ParquetNestedJoinEnumerator}.
+     */
+    private static Enumerator<PolyValue[]> nestedJoinEnumeratorForFile(
+            ParquetRelTable parent,
+            ParquetSourceFile sourceFile,
+            ParquetRelTable child,
+            int[] parentFields,
+            int[] childFields,
+            AtomicBoolean cancelFlag,
+            JoinFiltersContainer filterContainer,
+            boolean leftIsParent,
+            boolean emitUnmatchedParents ) {
+
+        ParquetSourceReader reader = new ParquetSourceReader( sourceFile.asSource(), cancelFlag, null, filterContainer.nativeFilters() );
+        return new ParquetNestedJoinEnumerator(
+                reader,
+                new JoinNestedBinding( parent.getBinding(), projectedBindings( parent, parentFields ), projectedBindings( parent, allFields( parent ) ) ),
+                new JoinNestedBinding( child.getBinding(), projectedBindings( child, childFields ), projectedBindings( child, allFields( child ) ) ),
+                filterContainer,
+                leftIsParent,
+                emitUnmatchedParents );
+    }
+
+
+    /**
+     * Combines two list of filters into a single filters list.
+     *
+     * @param left a list of filters from the left table.
+     * @param right a list of filters from the right table.
+     * @return a combined list of filters.
+     */
+    private static List<ParquetAdapterFilter<PolyValue>> combine( List<ParquetAdapterFilter<PolyValue>> left, List<ParquetAdapterFilter<PolyValue>> right ) {
+        if ( left.isEmpty() ) {
+            return right;
+        }
+        if ( right.isEmpty() ) {
+            return left;
+        }
+        List<ParquetAdapterFilter<PolyValue>> combined = new ArrayList<>( left.size() + right.size() );
+        combined.addAll( left );
+        combined.addAll( right );
+        return combined;
+    }
+
+
+    /**
+     * Gets a correct {@link ParquetColumnBinding} from left or from right table according to the provided index.
+     *
+     * @param joinedIndex a column index to search. If the column index is within the left table fields count then the column binding from the left table is returned and from the right table otherwise.
+     * @param leftFields an array of left table field indexes.
+     * @param rightFields an array of right table field indexes.
+     * @param left a left table.
+     * @param right a right table.
+     * @return {@link ParquetColumnBinding}.
+     */
+    private static ParquetColumnBinding selectBinding( int joinedIndex, int[] leftFields, int[] rightFields, ParquetRelTable left, ParquetRelTable right ) {
+        if ( joinedIndex < 0 ) {
+            throw new GenericRuntimeException( "Invalid joined filter column index: " + joinedIndex );
+        }
+        if ( joinedIndex < leftFields.length ) {
+            return selectPhysicalBinding( left, leftFields[joinedIndex] );
+        }
+        int rightIndex = joinedIndex - leftFields.length;
+        if ( rightIndex >= rightFields.length ) {
+            throw new GenericRuntimeException( "Invalid joined filter column index: " + joinedIndex );
+        }
+        return selectPhysicalBinding( right, rightFields[rightIndex] );
+    }
+
+
+    /**
+     * Creates an array of field indexes for all fields in the provided table.
+     *
+     * @param table a table.
+     * @return an array of field indexes that correspond to the number of field count in the provided table starting from 0.
+     */
+    private static int[] allFields( ParquetRelTable table ) {
+        return IntStream.range( 0, table.getFieldCount() ).toArray();
+    }
+
+
+    /**
+     * Creates an enumerator to go through the nested join.
+     *
+     * @param dataContext a data context.
+     * @param leftScan a left scan under the join.
+     * @param rightScan a right scan under the join.
+     * @param leftIsParent indicates if the left table in the join is a parent table.
+     * @param emitUnmatchedParents indicates if the parent row needs to be emitted even though there is no corresponding child row.
+     * @param filters a list of filters to be applied on both tables.
+     * @return {@link ParquetMultiFileEnumerator}.
+     */
+    public Enumerable<PolyValue[]> createEnumerator( DataContext dataContext, PhysicalScan leftScan, PhysicalScan rightScan, boolean leftIsParent, boolean emitUnmatchedParents, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        registerAdapter( dataContext );
+
+        return new AbstractEnumerable<>() {
+            @Override
+            public Enumerator<PolyValue[]> enumerator() {
+                final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+                final List<ParquetAdapterFilter<PolyValue>> leftScanFilters = leftScan.resolveFilters( dataContext );
+                final List<ParquetAdapterFilter<PolyValue>> rightScanFilters = rightScan.resolveFilters( dataContext );
+                final JoinFiltersContainer filterContainer = buildFiltersContainer( dataContext, leftScan, rightScan, leftIsParent, filters, leftScanFilters, rightScanFilters );
+                final PhysicalScan parent = leftIsParent ? leftScan : rightScan;
+                final PhysicalScan child = leftIsParent ? rightScan : leftScan;
+                final List<ParquetAdapterFilter<PolyValue>> parentFileFilters = leftIsParent ? leftScanFilters : rightScanFilters;
+                return new ParquetMultiFileEnumerator(
+                        parent.table().getBinding().sourceFiles(),
+                        sourceFile -> nestedJoinEnumeratorForFile( parent.table(), sourceFile, child.table(), parent.fields(), child.fields(), cancelFlag, filterContainer, leftIsParent, emitUnmatchedParents ),
+                        createParquetSourceFileEvaluatorsChain( f -> parent.selectPhysicalBinding( f.columnIndex() ) ),
+                        parentFileFilters );
+            }
+        };
+    }
+
+
+    /**
+     * Creates a {@link JoinFiltersContainer}.
+     * JoinFiltersContainer consists of multiple lists of filters:
+     * 1. parent filters - these are join level filters that needs to be applied on the parent table. Filter.ColumnIndex in those filters is relative to the selected projection.
+     * 2. child filters - these are join level filters that needs to be applied on the child table. Filter.ColumnIndex in those filters is relative to the selected projection.
+     * 3. parent scan filters - these are parent scan level filters. The main reason for separation is that scan level filters must be related to physical column indexes and not to projection - filter column might be not a part of a projection.
+     * 4. child scan filters - these are parent scan level filters. The main reason for separation is that scan level filters must be related to physical column indexes and not to projection - filter column might be not a part of a projection.
+     * 5. adapter filters - these are filters that can be executed only on adapter level. Those filters are inherited from FiltersContainer class and not being used in JoinFiltersContainer - instead parent and child filters are used.
+     * 6. reader filters - these are filters that can be pushed down into parquet reader.
+     *
+     * @param dataContext a data context.
+     * @param leftScan a left scan under the join.
+     * @param rightScan a right scan under the join.
+     * @param leftIsParent indicates if the left table in the join is a parent table.
+     * @param filters a list of join level filters. Those filters eventually will be split into parent and child filters.
+     * @param leftScanFilters a list of left scan filters.
+     * @param rightScanFilters a list of right scan filters.
+     * @return newly created {@link JoinFiltersContainer}.
+     */
+    private JoinFiltersContainer buildFiltersContainer( DataContext dataContext, PhysicalScan leftScan, PhysicalScan rightScan, boolean leftIsParent, List<ParquetAdapterFilter<PolyValue>> filters, List<ParquetAdapterFilter<PolyValue>> leftScanFilters, List<ParquetAdapterFilter<PolyValue>> rightScanFilters ) {
+        final PhysicalScan parent = leftIsParent ? leftScan : rightScan;
+        final PhysicalScan child = leftIsParent ? rightScan : leftScan;
+        final List<ParquetAdapterFilter<PolyValue>> joinFilters = ParquetFilterResolver.resolveFilters( dataContext, filters, f -> selectBinding( f.columnIndex(), leftScan.fields(), rightScan.fields(), leftScan.table(), rightScan.table() ) );
+        final List<ParquetAdapterFilter<PolyValue>> parentScanFilters = leftIsParent ? leftScanFilters : rightScanFilters;
+        final List<ParquetAdapterFilter<PolyValue>> childScanFilters = leftIsParent ? rightScanFilters : leftScanFilters;
+        final JoinFiltersContainer container = new JoinFiltersSplitter().split( joinFilters, leftIsParent, parent.fields().length, child.fields().length );
+        return new JoinFiltersContainer(
+                container.parentFilters(),
+                container.childFilters(),
+                container.adapterFilters(),
+                combine( container.nativeFilters(), parentScanFilters ),
+                parentScanFilters,
+                childScanFilters );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelProjectExecutor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelProjectExecutor.java
new file mode 100644
index 0000000000..f055a5a66d
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelProjectExecutor.java
@@ -0,0 +1,59 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.AbstractEnumerable;
+import org.apache.calcite.linq4j.Enumerable;
+import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.type.entity.PolyValue;
+
+public class ParquetRelProjectExecutor extends ParquetRelExecutor {
+
+    public ParquetRelProjectExecutor( ParquetRelTable table, AbstractParquetSource parquetSource, int[] fieldIndexes, ParquetSchemaReader schemaReader ) {
+        super( table, parquetSource, fieldIndexes, schemaReader );
+    }
+
+
+    /**
+     * Creates an enumerator for a regular parquet file(s).
+     *
+     * @param dataContext a data context.
+     * @param fields an array of projected field indexes.
+     * @param filters a list of filters. Can be empty.
+     * @return {@link ParquetMultiFileEnumerator}.
+     */
+    public Enumerable<PolyValue[]> createEnumerator( final DataContext dataContext, final int[] fields, final List<ParquetAdapterFilter<PolyValue>> filters ) {
+        registerAdapter( dataContext );
+        final List<ParquetAdapterFilter<PolyValue>> resolvedFilters = ParquetFilterResolver.resolveFilters( dataContext, filters, f -> selectPhysicalBinding( table, f.columnIndex() ) );
+        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+        return new AbstractEnumerable<>() {
+            @Override
+            public Enumerator<PolyValue[]> enumerator() {
+                return new ParquetEnumeratorsFactory( table, fields, fieldIndexes, schemaReader, cancelFlag ).create( resolvedFilters );
+            }
+        };
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRowRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRowRelEnumerator.java
new file mode 100644
index 0000000000..c4b13f7dcc
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRowRelEnumerator.java
@@ -0,0 +1,128 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import java.util.Arrays;
+import java.util.List;
+import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetFilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetPrimitiveRowReader;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+/**
+ * Relational enumerator for flat primitive projections.
+ * It supports only flat primitive non-repeated projections. No nested structures.
+ * It avoids GroupRecordConverter and materializes directly into PolyValue[].
+ * This enumerator is faster than regular ParquetRelEnumerator.
+ */
+public class ParquetRowRelEnumerator implements Enumerator<PolyValue[]> {
+
+    private final ParquetPrimitiveRowReader reader;
+    private final List<ParquetAdapterFilter<PolyValue>> filters;
+    private final int[] outputIndexes;
+    private final PolyValue[] outputConstants;
+    private final PrimitiveRowFilterEvaluator filterEvaluator = new PrimitiveRowFilterEvaluator();
+    private PolyValue[] current;
+
+
+    public ParquetRowRelEnumerator( ParquetPrimitiveRowReader reader, List<ParquetAdapterFilter<PolyValue>> filters, int[] outputIndexes, PolyValue[] outputConstants ) {
+        this.reader = reader;
+        this.filters = filters == null ? List.of() : List.copyOf( filters );
+        this.outputIndexes = outputIndexes == null ? null : Arrays.copyOf( outputIndexes, outputIndexes.length );
+        this.outputConstants = outputConstants == null ? null : Arrays.copyOf( outputConstants, outputConstants.length );
+    }
+
+
+    @Override
+    public PolyValue[] current() {
+        return current;
+    }
+
+
+    @Override
+    public boolean moveNext() {
+        for ( ; ; ) {
+            PolyValue[] row = reader.next();
+            if ( row == null ) {
+                current = null;
+                return false;
+            }
+            if ( !accept( row ) ) {
+                continue;
+            }
+            current = project( row );
+            return true;
+        }
+    }
+
+
+    @Override
+    public void reset() {
+        throw new UnsupportedOperationException();
+    }
+
+
+    @Override
+    public void close() {
+        try {
+            reader.close();
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Error closing parquet reader", e );
+        }
+    }
+
+
+    private boolean accept( PolyValue[] row ) {
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            if ( !filterEvaluator.matches( row, filter ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    private PolyValue[] project( PolyValue[] row ) {
+        if ( outputIndexes == null ) {
+            return row;
+        }
+        PolyValue[] projected = new PolyValue[outputIndexes.length];
+        for ( int i = 0; i < outputIndexes.length; i++ ) {
+            projected[i] = outputIndexes[i] < 0 ? outputConstants[i] : row[outputIndexes[i]];
+        }
+        return projected;
+    }
+
+
+    private static class PrimitiveRowFilterEvaluator extends ParquetFilterEvaluator<PolyValue[], PolyValue> {
+
+        @Override
+        protected Boolean evaluateLeaf( PolyValue[] row, ParquetAdapterFilter<PolyValue> filter ) {
+            if ( filter.columnIndex() < 0 || filter.columnIndex() >= row.length ) {
+                return null;
+            }
+            PolyValue value = row[filter.columnIndex()];
+            return matchesValue( value == null ? PolyNull.NULL : value, filter.operator(), filter.value() );
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetSourceFileStatisticsFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetSourceFileStatisticsFilterEvaluator.java
deleted file mode 100644
index eac0669794..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetSourceFileStatisticsFilterEvaluator.java
+++ /dev/null
@@ -1,102 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.execution;
-
-import java.util.function.Function;
-
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnStatistics;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
-import org.polypheny.db.adapter.parquet.shared.filter.FilterEvaluator;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
-import org.polypheny.db.algebra.constant.Kind;
-
-
-/**
- * Uses Parquet footer min/max/null-count statistics to reject files that cannot
- * contain rows matching a filter.
- */
-public class ParquetSourceFileStatisticsFilterEvaluator extends FilterEvaluator<ParquetSourceFile> {
-
-    private final Function<ParquetAdapterFilter, ParquetColumnBinding> selector;
-    private final ParquetTypeConverter typeConverter;
-
-
-    public ParquetSourceFileStatisticsFilterEvaluator( Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
-        this.selector = selector;
-        this.typeConverter = new ParquetTypeConverter();
-    }
-
-
-    @Override
-    protected Boolean evaluateLeaf( ParquetSourceFile sourceFile, ParquetAdapterFilter filter ) {
-        ParquetColumnBinding columnBinding = selector.apply( filter );
-        if ( columnBinding == null || columnBinding.role() != ParquetColumnRole.DATA || columnBinding.sourcePathElements().isEmpty() ) {
-            return null;
-        }
-
-        ParquetColumnStatistics statistics = sourceFile.columnStatistics().get( columnBinding.sourcePathElements() );
-        if ( statistics == null ) {
-            return null;
-        }
-
-        if ( filter.operator() == Kind.IS_NULL ) {
-            return statistics.hasNoNulls() ? Boolean.FALSE : null;
-        }
-        if ( filter.operator() == Kind.IS_NOT_NULL ) {
-            return statistics.hasOnlyNulls() ? Boolean.FALSE : null;
-        }
-        if ( filter.polyValue() == null || filter.polyValue().isNull() ) {
-            return null;
-        }
-        if ( statistics.hasOnlyNulls() ) {
-            return Boolean.FALSE;
-        }
-        if ( !statistics.hasRange() ) {
-            return null;
-        }
-        Comparable<?> value = typeConverter.fromPolyValueToComparable( filter.polyValue(), statistics.type() );
-        Comparable<?> min = typeConverter.fromStringToComparable( statistics.type(), statistics.min() );
-        Comparable<?> max = typeConverter.fromStringToComparable( statistics.type(), statistics.max() );
-        if ( value == null || min == null || max == null ) {
-            return null;
-        }
-
-        return switch ( filter.operator() ) {
-            case EQUALS -> outsideRange( value, min, max ) ? Boolean.FALSE : null;
-            case NOT_EQUALS -> singleValueRangeEquals( value, min, max ) ? Boolean.FALSE : null;
-            case GREATER_THAN -> typeConverter.compare( max, value ) <= 0 ? Boolean.FALSE : null;
-            case GREATER_THAN_OR_EQUAL -> typeConverter.compare( max, value ) < 0 ? Boolean.FALSE : null;
-            case LESS_THAN -> typeConverter.compare( min, value ) >= 0 ? Boolean.FALSE : null;
-            case LESS_THAN_OR_EQUAL -> typeConverter.compare( min, value ) > 0 ? Boolean.FALSE : null;
-            default -> null;
-        };
-    }
-
-
-    private boolean outsideRange( Comparable<?> value, Comparable<?> min, Comparable<?> max ) {
-        return typeConverter.compare( value, min ) < 0 || typeConverter.compare( value, max ) > 0;
-    }
-
-
-    private boolean singleValueRangeEquals( Comparable<?> value, Comparable<?> min, Comparable<?> max ) {
-        return typeConverter.compare( min, max ) == 0 && typeConverter.compare( min, value ) == 0;
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/AggregateRowProjector.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/AggregateRowProjector.java
new file mode 100644
index 0000000000..b4732e67ef
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/AggregateRowProjector.java
@@ -0,0 +1,53 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution.aggregate;
+
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
+import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+/**
+ * Maps an input row into the grouping and aggregate values consumed by the generic row aggregate engine.
+ */
+interface AggregateRowProjector {
+
+    /**
+     * Evaluates condition expression if there is any.
+     *
+     * @param row the row to check.
+     * @return true if the condition is missing or evaluates to true and false otherwise.
+     */
+    boolean accepts( PolyValue[] row );
+
+    /**
+     * Creates a {@link GroupKey} used for aggregation.
+     *
+     * @param row a row.
+     * @return a {@link GroupKey}.
+     */
+    GroupKey groupKey( PolyValue[] row );
+
+    /**
+     * Adds values to a shared aggregate state.
+     *
+     * @param values an aggregate state to update.
+     * @param row a row to read aggregate values from.
+     */
+    void add( AggregateGroupState values, PolyValue[] row );
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/DirectAggregateRowProjector.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/DirectAggregateRowProjector.java
new file mode 100644
index 0000000000..914d9432ee
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/DirectAggregateRowProjector.java
@@ -0,0 +1,62 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution.aggregate;
+
+import java.util.Arrays;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
+import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+/**
+ * Projects aggregate input directly from row fields.
+ */
+final class DirectAggregateRowProjector implements AggregateRowProjector {
+
+    private final int[] groupFields;
+
+
+    DirectAggregateRowProjector( int[] groupFields ) {
+        this.groupFields = Arrays.copyOf( groupFields, groupFields.length );
+    }
+
+
+    @Override
+    public boolean accepts( PolyValue[] row ) {
+        return true;
+    }
+
+
+    @Override
+    public GroupKey groupKey( PolyValue[] row ) {
+        if ( groupFields.length == 0 ) {
+            return GroupKey.Empty;
+        }
+        Object[] key = new Object[groupFields.length];
+        for ( int i = 0; i < groupFields.length; i++ ) {
+            key[i] = row[groupFields[i]];
+        }
+        return GroupKey.of( key );
+    }
+
+
+    @Override
+    public void add( AggregateGroupState values, PolyValue[] row ) {
+        values.add( row );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetFileGroupedAggregateRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetFileGroupedAggregateRelEnumerator.java
new file mode 100644
index 0000000000..ae6f942757
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetFileGroupedAggregateRelEnumerator.java
@@ -0,0 +1,213 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution.aggregate;
+
+import static org.polypheny.db.adapter.parquet.relational.execution.ParquetRelExecutor.selectPhysicalBinding;
+
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.Enumerator;
+import org.apache.calcite.linq4j.Linq4j;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelExecutor;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetMultiFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetConstantColumnResolver;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
+import org.polypheny.db.adapter.parquet.shared.aggregate.ColumnAggregateProjection;
+import org.polypheny.db.adapter.parquet.shared.aggregate.ColumnAggregateResult;
+import org.polypheny.db.adapter.parquet.shared.execution.AbstractAggregateEnumerator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.aggregate.ParquetNoFilterColumnAggregateReader;
+import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
+import org.polypheny.db.type.entity.PolyValue;
+
+/**
+ * This is the file-aware aggregate path. It groups from file-constant values and uses the no-filter column aggregate reader for data columns.
+ */
+public class ParquetFileGroupedAggregateRelEnumerator extends AbstractAggregateEnumerator {
+
+    private static final ParquetConstantColumnResolver constantColumnResolver = new ParquetConstantColumnResolver();
+
+
+    /**
+     * Creates a new instance of fast aggregate enumerator.
+     *
+     * @param table a source table.
+     * @param fields an array of field indexes.
+     * @param groupFields an array of group by field indexes.
+     * @param aggregateKinds an array of aggregate function kinds: COUNT, SUM, MIN, MAX.
+     * @param aggregateArgs column indexes of the columns passed to the aggregation function as an argument.
+     * @param projection aggregate projection containing additional information.
+     * @param filters a list of filters.
+     * @param cancelFlag a cancellation flag.
+     */
+    public ParquetFileGroupedAggregateRelEnumerator( ParquetRelTable table, int[] fields, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs, ColumnAggregateProjection projection, List<ParquetAdapterFilter<PolyValue>> filters, AtomicBoolean cancelFlag ) {
+        super( () -> buildEnumerator( table, fields, groupFields, aggregateKinds, aggregateArgs, projection, filters, cancelFlag ) );
+    }
+
+
+    /**
+     * Reads data from a single source file.
+     *
+     * @param table a parquet table
+     * @param sourceFile a source file
+     * @param fields projected field indexes
+     * @param groupFields group by fields indexes
+     * @param projection aggregate projection
+     * @param cancelFlag a cancel operation flag
+     * @return aggregated data
+     */
+    private static Map<GroupKey, AggregateGroupState> readAll( ParquetRelTable table, ParquetSourceFile sourceFile, int[] fields, int[] groupFields, AggregateCallDescriptor[] aggregateCalls, ColumnAggregateProjection projection, AtomicBoolean cancelFlag ) {
+        Map<GroupKey, AggregateGroupState> aggregates = new LinkedHashMap<>();
+        GroupKey groupKey = fileGroupKey( table, sourceFile, fields, groupFields );
+        AggregateGroupState groupAggregates = aggregates.computeIfAbsent( groupKey, key -> new AggregateGroupState( aggregateCalls ) );
+        addCountStarRows( sourceFile, aggregateCalls, groupAggregates );
+
+        if ( projection.fields().length > 0 ) {
+            try ( ParquetNoFilterColumnAggregateReader reader = new ParquetNoFilterColumnAggregateReader( sourceFile.asSource(), cancelFlag, projection.fields() ) ) {
+                addColumnResults( reader.readAggregateColumns(), aggregateCalls, projection, groupAggregates );
+            } catch ( Exception e ) {
+                throw new RuntimeException( "Unable to calculate streaming aggregate for " + sourceFile.asSource().path(), e );
+            }
+        }
+        return aggregates;
+    }
+
+
+    /**
+     * Aggregates results from all columns that were calculated separately.
+     *
+     * @param columnResults an array of aggregated column results.
+     * @param aggregateCalls an array of aggregate functions used.
+     * @param projection a projection.
+     * @param groupAggregates an aggregate state to be updated with all the results.
+     */
+    private static void addColumnResults( ColumnAggregateResult[] columnResults, AggregateCallDescriptor[] aggregateCalls, ColumnAggregateProjection projection, AggregateGroupState groupAggregates ) {
+        for ( int i = 0; i < aggregateCalls.length; i++ ) {
+            if ( aggregateCalls[i].argumentIndex() < 0 ) {
+                continue;
+            }
+            AggregateCallDescriptor.Kind kind = aggregateCalls[i].kind();
+            ColumnAggregateResult result = columnResults[projection.aggregateIndex( aggregateCalls[i].argumentIndex() )];
+            switch ( kind ) {
+                case COUNT -> groupAggregates.addCount( i, result.count() );
+                case MIN -> {
+                    if ( result.count() > 0 ) {
+                        groupAggregates.addMin( i, result.min() );
+                    }
+                }
+                case MAX -> {
+                    if ( result.count() > 0 ) {
+                        groupAggregates.addMax( i, result.max() );
+                    }
+                }
+                case SUM -> {
+                    groupAggregates.addCount( i, result.count() );
+                    groupAggregates.addSum( i, result.sum() );
+                }
+            }
+        }
+    }
+
+
+    /**
+     * Adds source rows to all COUNT(*) accumulators. Fast aggregation uses this only when filters and grouping are file-decidable,
+     * so all rows in the source file belong to a single output group.
+     *
+     * @param sourceFile a source file.
+     * @param aggregateCalls aggregate calls.
+     * @param groupAggregates aggregate values for a single group.
+     */
+    private static void addCountStarRows( ParquetSourceFile sourceFile, AggregateCallDescriptor[] aggregateCalls, AggregateGroupState groupAggregates ) {
+        long sourceRowCount = -1;
+        for ( int i = 0; i < aggregateCalls.length; i++ ) {
+            if ( aggregateCalls[i].argumentIndex() < 0 ) {
+                if ( sourceRowCount < 0 ) {
+                    sourceRowCount = sourceRowCount( sourceFile );
+                }
+                groupAggregates.addCount( i, sourceRowCount );
+            }
+        }
+    }
+
+
+    /**
+     * Creates a group key based on file-constant values.
+     *
+     * @param table a source table.
+     * @param sourceFile a source file.
+     * @param fields an array of field indexes.
+     * @param groupFields an array of group by field indexes.
+     * @return returns a list of {@link PolyValue}s representing a key.
+     */
+    private static GroupKey fileGroupKey( ParquetRelTable table, ParquetSourceFile sourceFile, int[] fields, int[] groupFields ) {
+        Object[] key = new Object[groupFields.length];
+        for ( int i = 0; i < groupFields.length; i++ ) {
+            ParquetColumnBinding binding = selectPhysicalBinding( table, fields[groupFields[i]] );
+            key[i] = constantColumnResolver
+                    .resolve( sourceFile, binding )
+                    .orElseThrow( () -> new IllegalStateException( "Group column is not constant for " + sourceFile.fileUrl() ) );
+        }
+        return GroupKey.of( key );
+    }
+
+
+    /**
+     * Checks whether all filters match the complete source file.
+     *
+     * @param evaluator a file-level filter evaluator.
+     * @param sourceFile a source file.
+     * @param filters filters to evaluate.
+     * @return true when the complete source file matches.
+     */
+    private static boolean matchesExactly( ParquetMultiFilterEvaluator<ParquetSourceFile> evaluator, ParquetSourceFile sourceFile, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            Boolean result = evaluator.evaluate( sourceFile, filter );
+            if ( result == null ) {
+                throw new IllegalStateException( "File aggregate filter could not be evaluated exactly for " + sourceFile.fileUrl() );
+            }
+            if ( !result ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    private static Enumerator<PolyValue[]> buildEnumerator( ParquetRelTable table, int[] fields, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs, ColumnAggregateProjection projection, List<ParquetAdapterFilter<PolyValue>> filters, AtomicBoolean cancelFlag ) {
+        AggregateCallDescriptor[] aggregateCalls = aggregateCalls( aggregateKinds, aggregateArgs );
+        ParquetMultiFilterEvaluator<ParquetSourceFile> fileFilterEvaluator = ParquetRelExecutor.createParquetSourceFileEvaluatorsChain(f -> selectPhysicalBinding( table, f.columnIndex() ) );
+
+        List<ParquetSourceFile> sourceFiles = table.getBinding().sourceFiles().stream()
+                .filter( sourceFile -> matchesExactly( fileFilterEvaluator, sourceFile, filters ) )
+                .toList();
+
+        if ( sourceFiles.isEmpty() ) {
+            Map<GroupKey, AggregateGroupState> aggregates = new LinkedHashMap<>();
+            return Linq4j.asEnumerable( buildRows( groupFields.length, aggregates, aggregateCalls ) ).enumerator();
+        }
+
+        Map<GroupKey, AggregateGroupState> aggregates = readAll( sourceFiles, ( f ) -> readAll( table, f, fields, groupFields, aggregateCalls, projection, cancelFlag ), aggregateCalls, cancelFlag );
+        return Linq4j.asEnumerable( buildRows( groupFields.length, aggregates, aggregateCalls ) ).enumerator();
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetGroupedAggregateRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetGroupedAggregateRelEnumerator.java
new file mode 100644
index 0000000000..722c472650
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetGroupedAggregateRelEnumerator.java
@@ -0,0 +1,239 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution.aggregate;
+
+import static org.polypheny.db.adapter.parquet.relational.execution.ParquetRelExecutor.selectPhysicalBinding;
+
+import java.util.ArrayList;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Optional;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.Enumerator;
+import org.apache.calcite.linq4j.Linq4j;
+import org.apache.parquet.schema.MessageType;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelExecutor;
+import org.polypheny.db.adapter.parquet.relational.filter.ResidualFilters;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetMultiFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFileFilterReducer;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
+import org.polypheny.db.adapter.parquet.shared.execution.AbstractAggregateEnumerator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.aggregate.ParquetGroupedAggregateReader;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+
+/**
+ * SQL GROUP BY over real data columns (not partition columns).
+ */
+public class ParquetGroupedAggregateRelEnumerator extends AbstractAggregateEnumerator {
+
+    private static final ParquetTypeConverter TYPE_CONVERTER = new ParquetTypeConverter();
+
+
+    /**
+     * Creates a new instance of an enumerator.
+     *
+     * @param table a relational table reference.
+     * @param projectedFields an array of projected field indexes.
+     * @param groupFieldCount a number of groups in Group By statement.
+     * @param aggregateCalls an array of aggregate function descriptors.
+     * @param filters a list of filters to be applied.
+     * @param cancelFlag a flag indicating that the operation has been canceled.
+     */
+    public ParquetGroupedAggregateRelEnumerator( ParquetRelTable table, int[] projectedFields, int groupFieldCount, AggregateCallDescriptor[] aggregateCalls, List<ParquetAdapterFilter<PolyValue>> filters, AtomicBoolean cancelFlag ) {
+        super( () -> buildEnumerator( table, projectedFields, groupFieldCount, aggregateCalls, filters, cancelFlag ) );
+    }
+
+
+    /**
+     * Reads all rows from a specified source files and aggregates them.
+     *
+     * @param sourceFile source file to read.
+     * @param groupFieldCount a number of groups in Group By statement.
+     * @param aggregateCalls an array of aggregate function descriptors.
+     * @param cancelFlag a flag indicating that the operation has been canceled.
+     * @return aggregated rows.
+     */
+    private static Map<GroupKey, AggregateGroupState> readAll( ProjectedSourceFile sourceFile, int groupFieldCount, AggregateCallDescriptor[] aggregateCalls, AtomicBoolean cancelFlag ) {
+        try ( ParquetGroupedAggregateReader reader = new ParquetGroupedAggregateReader( sourceFile.source(), cancelFlag, sourceFile.projection, groupFieldCount, aggregateCalls, sourceFile.readerFilters ) ) {
+            return reader.readAll();
+        } catch ( Exception e ) {
+            throw new RuntimeException( "Unable to calculate grouped aggregate for " + sourceFile.source().path(), e );
+        }
+    }
+
+
+    /**
+     * Builds a result set from the aggregated rows.
+     *
+     * @param sourceFiles a list of source files.
+     * @param groupFieldCount a number of groups in Group By statement.
+     * @param aggregateCalls aggregate function descriptors.
+     * @param aggregates aggregation results.
+     * @return a list of aggregated rows.
+     */
+    private static List<PolyValue[]> buildRows( List<ProjectedSourceFile> sourceFiles, int groupFieldCount, AggregateCallDescriptor[] aggregateCalls, Map<GroupKey, AggregateGroupState> aggregates ) {
+        int aggregateCount = aggregateCalls.length;
+        if ( sourceFiles.isEmpty() ) {
+            if ( groupFieldCount > 0 ) {
+                return List.of();
+            }
+            PolyValue[] row = new PolyValue[aggregateCount];
+            AggregateGroupState values = aggregates.getOrDefault( GroupKey.Empty, new AggregateGroupState( aggregateCalls ) );
+            for ( int i = 0; i < aggregateCount; i++ ) {
+                row[i] = values.result( i );
+            }
+            return List.<PolyValue[]>of( row );
+        }
+
+        List<PolyValue[]> rows = new ArrayList<>( aggregates.size() );
+        ProjectedSourceFile sourceFile = sourceFiles.get( 0 );
+        aggregates.forEach( ( key, values ) -> rows.add( buildRow( key, values, sourceFile.projection, groupFieldCount, aggregateCount ) ) );
+        return rows;
+    }
+
+
+    /**
+     * Builds a single row.
+     *
+     * @param key a group key.
+     * @param values aggregated values.
+     * @param projectionSchema a projected schema.
+     * @param groupFieldCount a number of groups in Group By statement.
+     * @param aggregateCount a number of aggregated functions used.
+     * @return an array of PolyValue's representing a row.
+     */
+    private static PolyValue[] buildRow( GroupKey key, AggregateGroupState values, MessageType projectionSchema, int groupFieldCount, int aggregateCount ) {
+        PolyValue[] row = new PolyValue[groupFieldCount + aggregateCount];
+        for ( int i = 0; i < groupFieldCount; i++ ) {
+            row[i] = TYPE_CONVERTER.fromObjToPolyValue( projectionSchema.getType( i ), key.value( i ) );
+        }
+        for ( int i = 0; i < aggregateCount; i++ ) {
+            row[groupFieldCount + i] = values.result( i );
+        }
+        return row;
+    }
+
+
+    /**
+     * Converts filter indexes from relative to projection to physical parquet indexes.
+     *
+     * @param table a relation table reference.
+     * @param schemaReader a schema reader.
+     * @param projectedFields a projected field indexes.
+     * @param filters filters to convert.
+     * @return a converted list of filters with physical parquet indexes.
+     */
+    private static List<ParquetAdapterFilter<PolyValue>> readerFilters( ParquetRelTable table, ParquetSchemaReader schemaReader, int[] projectedFields, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        List<ParquetAdapterFilter<PolyValue>> mapped = new ArrayList<>( filters.size() );
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            ParquetAdapterFilter<PolyValue> projectionFilter = ParquetFilterResolver.toProjectionFilter( filter, field -> readerIndex( table, schemaReader, projectedFields, field ) );
+            if ( projectionFilter == null ) {
+                throw new IllegalArgumentException( "Unable to map grouped aggregate filter to its parquet projection." );
+            }
+            mapped.add( projectionFilter );
+        }
+        return mapped;
+    }
+
+
+    /**
+     * Converts a projected index into a physical parquet file index.
+     *
+     * @param table a relational table reference.
+     * @param schemaReader a schema reader.
+     * @param projectedFields projected field indexes.
+     * @param field a projected field index to convert.
+     * @return a physical parquet index.
+     */
+    private static int readerIndex( ParquetRelTable table, ParquetSchemaReader schemaReader, int[] projectedFields, int field ) {
+        int parquetField = ParquetRelExecutor.parquetFieldIndex( table, schemaReader, field );
+        for ( int i = 0; i < projectedFields.length; i++ ) {
+            if ( projectedFields[i] == parquetField ) {
+                return i;
+            }
+        }
+        return -1;
+    }
+
+
+    /**
+     * A source file with projection.
+     *
+     * @param file a source file.
+     * @param projection a projection.
+     * @param readerFilters native filters for a reader.
+     */
+    private record ProjectedSourceFile( ParquetSourceFile file, MessageType projection, List<ParquetAdapterFilter<PolyValue>> readerFilters ) {
+
+        public Source source() {
+            return file.asSource();
+        }
+
+    }
+
+
+    /**
+     * Enriches {@link ParquetSourceFile} with projection and filters if available.
+     * The method also evaluates file level filters and can discard the file.
+     *
+     * @param table a relational table.
+     * @param projectedFields an array of projected indexes.
+     * @param sourceFile a source file to enrich and evaluate.
+     * @param evaluator a filter evaluator.
+     * @param filters a list of filters.
+     * @return {@link Optional<ProjectedSourceFile>} if source file is valid and {@link Optional#empty()} otherwise
+     */
+    private static Optional<ProjectedSourceFile> projectedSourceFile( ParquetRelTable table, int[] projectedFields, ParquetSourceFile sourceFile, ParquetMultiFilterEvaluator<ParquetSourceFile> evaluator, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        ResidualFilters residualFilters = ParquetSourceFileFilterReducer.reduce( sourceFile, evaluator, filters );
+        if ( !residualFilters.matches() ) {
+            return Optional.empty();
+        }
+        ParquetSchemaReader schemaReader = new ParquetSchemaReader( sourceFile.asSource() );
+        return Optional.of( new ProjectedSourceFile(
+                sourceFile,
+                schemaReader.buildProjectionSchema( projectedFields ),
+                readerFilters( table, schemaReader, projectedFields, residualFilters.filters() ) ) );
+    }
+
+
+    private static Enumerator<PolyValue[]> buildEnumerator( ParquetRelTable table, int[] projectedFields, int groupFieldCount, AggregateCallDescriptor[] aggregateCalls, List<ParquetAdapterFilter<PolyValue>> filters, AtomicBoolean cancelFlag ) {
+        ParquetMultiFilterEvaluator<ParquetSourceFile> fileFilterEvaluator = ParquetRelExecutor.createParquetSourceFileEvaluatorsChain( f -> selectPhysicalBinding( table, f.columnIndex() ) );
+
+        List<ProjectedSourceFile> sourceFiles = table.getBinding().sourceFiles().stream()
+                .map( sourceFile -> projectedSourceFile( table, projectedFields, sourceFile, fileFilterEvaluator, filters ) )
+                .flatMap( Optional::stream )
+                .toList();
+
+        Map<GroupKey, AggregateGroupState> aggregates = sourceFiles.isEmpty()
+                ? new LinkedHashMap<>()
+                : readAll( sourceFiles, f -> readAll( f, groupFieldCount, aggregateCalls, cancelFlag ), aggregateCalls, cancelFlag );
+
+        return Linq4j.asEnumerable( buildRows( sourceFiles, groupFieldCount, aggregateCalls, aggregates ) ).enumerator();
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregateRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregateRelEnumerator.java
new file mode 100644
index 0000000000..983e8f75dc
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregateRelEnumerator.java
@@ -0,0 +1,52 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution.aggregate;
+
+import java.util.Map;
+import org.apache.calcite.linq4j.Enumerator;
+import org.apache.calcite.linq4j.Linq4j;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
+import org.polypheny.db.adapter.parquet.shared.execution.AbstractAggregateEnumerator;
+import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
+import org.polypheny.db.type.entity.PolyValue;
+
+/**
+ * A generic aggregate enumerator that goes row by row (using PolyValue[] for a row) and aggregates the columns.
+ */
+public class ParquetRowAggregateRelEnumerator extends AbstractAggregateEnumerator {
+
+    /**
+     * Creates a new instance of the enumerator.
+     *
+     * @param rows an original enumerator to parquet files.
+     * @param groupFields a list of group by field indexes.
+     * @param aggregateKinds an array of aggregation function kinds.
+     * @param aggregateArgs an array of aggregation function arguments. There should be one argument per aggregation function.
+     */
+    public ParquetRowAggregateRelEnumerator( Enumerator<PolyValue[]> rows, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        super( () -> buildEnumerator( rows, groupFields, aggregateKinds, aggregateArgs ) );
+    }
+
+
+    private static Enumerator<PolyValue[]> buildEnumerator( Enumerator<PolyValue[]> rows, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        AggregateCallDescriptor[] aggregateCalls = aggregateCalls( aggregateKinds, aggregateArgs );
+        Map<GroupKey, AggregateGroupState> aggregates = ParquetRowAggregator.aggregate( rows, aggregateCalls, new DirectAggregateRowProjector( groupFields ) );
+        return Linq4j.asEnumerable( buildRows( groupFields.length, aggregates, aggregateCalls ) ).enumerator();
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregator.java
new file mode 100644
index 0000000000..be45ccedb7
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregator.java
@@ -0,0 +1,73 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution.aggregate;
+
+import java.util.LinkedHashMap;
+import java.util.Map;
+import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
+import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+/**
+ * Generic row-by-row aggregate fallback shared by direct and Calc projections.
+ */
+final class ParquetRowAggregator {
+
+    private ParquetRowAggregator() {
+    }
+
+
+    /**
+     * Aggregates data from the provided enumerator.
+     *
+     * @param rows an enumerator.
+     * @param aggregateCalls an array of aggregation calls to be applied.
+     * @param projector a row projector that customizes values reading.
+     * @return a grouped aggregations.
+     */
+    static Map<GroupKey, AggregateGroupState> aggregate( Enumerator<PolyValue[]> rows, AggregateCallDescriptor[] aggregateCalls, AggregateRowProjector projector ) {
+        Map<GroupKey, AggregateGroupState> aggregates = new LinkedHashMap<>();
+        try ( rows ) {
+            while ( rows.moveNext() ) {
+                add( aggregates, aggregateCalls, projector, rows.current() );
+            }
+        }
+        return aggregates;
+    }
+
+
+    /**
+     * Adds a row values to aggregated state via projector.
+     *
+     * @param aggregates a grouped aggregations that keeps the state.
+     * @param aggregateCalls an array of aggregation calls to be applied.
+     * @param projector a row projector that customizes the row access.
+     * @param row a row.
+     */
+    private static void add( Map<GroupKey, AggregateGroupState> aggregates, AggregateCallDescriptor[] aggregateCalls, AggregateRowProjector projector, PolyValue[] row ) {
+        if ( !projector.accepts( row ) ) {
+            return;
+        }
+        GroupKey key = projector.groupKey( row );
+        AggregateGroupState values = aggregates.computeIfAbsent( key, ignored -> new AggregateGroupState( aggregateCalls ) );
+        projector.add( values, row );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetFilterResolver.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetFilterResolver.java
new file mode 100644
index 0000000000..d238441a90
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetFilterResolver.java
@@ -0,0 +1,145 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.filter;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Objects;
+import java.util.function.Function;
+import java.util.function.IntUnaryOperator;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+
+
+public final class ParquetFilterResolver {
+
+    private ParquetFilterResolver() {
+    }
+
+
+    /**
+     * Support parametrized queries
+     *
+     * @param dataContext context
+     * @param filters filters
+     * @param selector a helper function for column binding selection
+     * @return list of parquet filters
+     */
+    public static <T> List<ParquetAdapterFilter<T>> resolveFilters( DataContext dataContext, List<ParquetAdapterFilter<T>> filters, Function<ParquetAdapterFilter<T>, ParquetColumnBinding> selector ) {
+        List<ParquetAdapterFilter<T>> resolved = new ArrayList<>( filters.size() );
+        for ( ParquetAdapterFilter<T> filter : filters ) {
+            resolved.add( resolveFilter( dataContext, filter, selector ) );
+        }
+        return resolved;
+    }
+
+
+    /**
+     * Converts a filter with column index pointing to a projected fields to a filter pointing to a physical field index in parquet file.
+     *
+     * @param filter a filter to convert.
+     * @param fields a projection.
+     * @return converted filter with physical index.
+     */
+    public static <T> ParquetAdapterFilter<T> toPhysicalFilter( ParquetAdapterFilter<T> filter, int[] fields ) {
+        if ( filter.isLogical() ) {
+            List<ParquetAdapterFilter<T>> operands = new ArrayList<>( filter.operands().size() );
+            for ( ParquetAdapterFilter<T> operand : filter.operands() ) {
+                ParquetAdapterFilter<T> physicalOperand = toPhysicalFilter( operand, fields );
+                if ( physicalOperand == null ) {
+                    return null;
+                }
+                operands.add( physicalOperand );
+            }
+            return ParquetAdapterFilter.logical( filter.operator(), operands );
+        }
+
+        int physicalIndex = toPhysicalIndex( filter.columnIndex(), fields );
+        if ( physicalIndex < 0 ) {
+            return null;
+        }
+
+        return new ParquetAdapterFilter<>(
+                physicalIndex,
+                filter.pathElements(),
+                filter.operator(),
+                filter.value(),
+                filter.dynamicParamIndex() );
+    }
+
+
+    /**
+     * Converts a filter with column index pointing to a physical field index to a filter to a projected field index.
+     *
+     * @param filter a filter to convert.
+     * @param projectionIndex an index conversion function.
+     * @return a new converted filter.
+     */
+    public static <T> ParquetAdapterFilter<T> toProjectionFilter( ParquetAdapterFilter<T> filter, IntUnaryOperator projectionIndex ) {
+        if ( filter.isLogical() ) {
+            List<ParquetAdapterFilter<T>> operands = new ArrayList<>( filter.operands().size() );
+            for ( ParquetAdapterFilter<T> operand : filter.operands() ) {
+                ParquetAdapterFilter<T> projectionOperand = toProjectionFilter( operand, projectionIndex );
+                if ( projectionOperand == null ) {
+                    return null;
+                }
+                operands.add( projectionOperand );
+            }
+            return ParquetAdapterFilter.logical( filter.operator(), operands );
+        }
+
+        int index = projectionIndex.applyAsInt( filter.columnIndex() );
+        if ( index < 0 ) {
+            return null;
+        }
+
+        return new ParquetAdapterFilter<>(
+                index,
+                List.of(),
+                filter.operator(),
+                filter.value(),
+                filter.dynamicParamIndex() );
+    }
+
+
+    @SuppressWarnings("unchecked")
+    private static <T> ParquetAdapterFilter<T> resolveFilter( DataContext dataContext, ParquetAdapterFilter<T> filter, Function<ParquetAdapterFilter<T>, ParquetColumnBinding> selector ) {
+        if ( filter.isLogical() ) {
+            return ParquetAdapterFilter.logical( filter.operator(), filter.operands().stream()
+                    .map( operand -> resolveFilter( dataContext, operand, selector ) )
+                    .toList() );
+        }
+
+        T value = filter.dynamicParamIndex() == null
+                ? filter.value()
+                : (T) dataContext.getParameterValue( filter.dynamicParamIndex() );
+
+        ParquetColumnBinding columnBinding = Objects.requireNonNull( selector.apply( filter ), "Missing parquet column binding" );
+        return new ParquetAdapterFilter<>( filter.columnIndex(), columnBinding.sourcePathElements(), filter.operator(), value );
+    }
+
+
+    private static int toPhysicalIndex( int index, int[] fields ) {
+        if ( index < 0 || index >= fields.length ) {
+            return -1;
+        }
+        return fields[index];
+    }
+
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetFilterEvaluatorsChain.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetMultiFilterEvaluator.java
similarity index 63%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetFilterEvaluatorsChain.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetMultiFilterEvaluator.java
index fe50bb9ae6..c5fdfebfce 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetFilterEvaluatorsChain.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetMultiFilterEvaluator.java
@@ -14,40 +14,47 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.schema;
+package org.polypheny.db.adapter.parquet.relational.filter;
 
 import java.util.List;
-import org.polypheny.db.adapter.parquet.shared.filter.FilterEvaluator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetFilterEvaluator;
+import org.polypheny.db.type.entity.PolyValue;
 
 
 /**
  * Applies file-level filter evaluators as one pruning decision.
  */
-public class ParquetFilterEvaluatorsChain<T> extends FilterEvaluator<T> {
+public class ParquetMultiFilterEvaluator<T> extends ParquetFilterEvaluator<T, PolyValue> {
 
-    private final List<FilterEvaluator<T>> evaluators;
+    private final List<ParquetFilterEvaluator<T, PolyValue>> evaluators;
 
 
-    public ParquetFilterEvaluatorsChain( List<FilterEvaluator<T>> evaluators ) {
+    @SafeVarargs
+    public ParquetMultiFilterEvaluator( ParquetFilterEvaluator<T, PolyValue>... evaluators ) {
+        this( List.of( evaluators ) );
+    }
+
+
+    public ParquetMultiFilterEvaluator( List<ParquetFilterEvaluator<T, PolyValue>> evaluators ) {
         this.evaluators = evaluators == null ? List.of() : List.copyOf( evaluators );
     }
 
 
-    public static <T> ParquetFilterEvaluatorsChain<T> empty() {
-        return new ParquetFilterEvaluatorsChain<>( List.of() );
+    public static <T> ParquetMultiFilterEvaluator<T> empty() {
+        return new ParquetMultiFilterEvaluator<>( List.of() );
     }
 
 
-    public boolean matches( T context, List<ParquetAdapterFilter> filters ) {
+    public boolean matches( T context, List<ParquetAdapterFilter<PolyValue>> filters ) {
         return filters == null || filters.isEmpty() || filters.stream().allMatch( filter -> matches( context, filter ) );
     }
 
 
     @Override
-    protected Boolean evaluateLeaf( T context, ParquetAdapterFilter filter ) {
+    protected Boolean evaluateLeaf( T context, ParquetAdapterFilter<PolyValue> filter ) {
         boolean hasKnownMatch = false;
-        for ( FilterEvaluator<T> evaluator : evaluators ) {
+        for ( ParquetFilterEvaluator<T, PolyValue> evaluator : evaluators ) {
             Boolean result = evaluator.evaluate( context, filter );
             if ( Boolean.FALSE.equals( result ) ) {
                 return false;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPartitionAwareFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetPartitionAwareFilterEvaluator.java
similarity index 86%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPartitionAwareFilterEvaluator.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetPartitionAwareFilterEvaluator.java
index a0bdf76027..96e244d670 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPartitionAwareFilterEvaluator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetPartitionAwareFilterEvaluator.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.execution;
+package org.polypheny.db.adapter.parquet.relational.filter;
 
 import java.util.List;
 import org.apache.parquet.example.data.Group;
@@ -31,13 +31,13 @@
 /**
  * Evaluates filter based on partition values.
  */
-class ParquetPartitionAwareFilterEvaluator extends ParquetGroupFilterEvaluator {
+public class ParquetPartitionAwareFilterEvaluator extends ParquetGroupFilterEvaluator {
 
     private final ParquetSourceFile sourceFile;
     private final List<ParquetColumnBinding> columnBindings;
 
 
-    ParquetPartitionAwareFilterEvaluator( MessageType schema, ParquetValueExtractor valueExtractor, ParquetSourceFile sourceFile, List<ParquetColumnBinding> columnBindings ) {
+    public ParquetPartitionAwareFilterEvaluator( MessageType schema, ParquetValueExtractor valueExtractor, ParquetSourceFile sourceFile, List<ParquetColumnBinding> columnBindings ) {
         super( schema, valueExtractor );
         this.sourceFile = sourceFile;
         this.columnBindings = List.copyOf( columnBindings );
@@ -45,9 +45,9 @@ class ParquetPartitionAwareFilterEvaluator extends ParquetGroupFilterEvaluator {
 
 
     @Override
-    protected Boolean evaluateLeaf( Group group, ParquetAdapterFilter filter ) {
+    protected Boolean evaluateLeaf( Group group, ParquetAdapterFilter<PolyValue> filter ) {
         if ( filter.columnIndex() >= 0 && filter.columnIndex() < columnBindings.size() && columnBindings.get( filter.columnIndex() ).role() == ParquetColumnRole.PARTITION ) {
-            return matchesValue( partitionValue( columnBindings.get( filter.columnIndex() ).columnName() ), filter.operator(), filter.polyValue() );
+            return matchesValue( partitionValue( columnBindings.get( filter.columnIndex() ).columnName() ), filter.operator(), filter.value() );
         }
         return super.evaluateLeaf( group, filter );
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetRelFilterTranslator.java
similarity index 87%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetRelFilterTranslator.java
index 9e8dd57d9d..efec59e281 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetRelFilterTranslator.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.execution;
+package org.polypheny.db.adapter.parquet.relational.filter;
 
 import java.util.List;
 import java.util.Objects;
@@ -27,6 +27,7 @@
 import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNode;
 import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyValue;
 
 /**
  * Translates adapter filters into parquet-native predicates.
@@ -36,7 +37,7 @@ public class ParquetRelFilterTranslator extends AbstractFilterTranslator {
     /**
      * Translates a Rex filter into Parquet filter form when possible.
      */
-    public ParquetAdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFilter ) {
+    public ParquetAdapterFilter<PolyValue> translate( List<PolyType> fieldTypes, RexNode polyFilter ) {
         // support logical filter
         if ( polyFilter instanceof RexCall call ) {
             if ( (polyFilter.isA( Kind.IS_NULL ) || polyFilter.isA( Kind.IS_NOT_NULL )) && call.getOperands().size() == 1 ) {
@@ -46,7 +47,7 @@ public ParquetAdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFi
                 return translateLogical( fieldTypes, polyFilter.getKind(), call.getOperands() );
             }
             if ( polyFilter.isA( Kind.NOT ) && call.getOperands().size() == 1 ) {
-                ParquetAdapterFilter operand = translate( fieldTypes, call.getOperands().get( 0 ) );
+                ParquetAdapterFilter<PolyValue> operand = translate( fieldTypes, call.getOperands().get( 0 ) );
                 return operand == null ? null : ParquetAdapterFilter.logical( Kind.NOT, List.of( operand ) );
             }
             if ( polyFilter.isA( Kind.IN ) ) {
@@ -75,7 +76,7 @@ public ParquetAdapterFilter translate( List<PolyType> fieldTypes, RexNode polyFi
     }
 
 
-    private ParquetAdapterFilter translateNullCheck( List<PolyType> fieldTypes, Kind operator, RexNode operand ) {
+    private ParquetAdapterFilter<PolyValue> translateNullCheck( List<PolyType> fieldTypes, Kind operator, RexNode operand ) {
         RexNode column = unwrapCast( operand );
         if ( !(column instanceof RexIndexRef indexRef) ) {
             return null;
@@ -86,25 +87,26 @@ private ParquetAdapterFilter translateNullCheck( List<PolyType> fieldTypes, Kind
             return null;
         }
 
-        return new ParquetAdapterFilter( index, operator, null );
+        return new ParquetAdapterFilter<>( index, operator, null );
     }
 
 
     /**
      * Translates a logical Rex filter node (AND, OR) into a logical ParquetAdapterFilter
+     *
      * @param fieldTypes - poly field types
      * @param operator - logical operation: AND, OR
      * @param operands - child expressions inside that operation (a > 10 AND b = 20)
      * @return adapter level filter
      */
-    private ParquetAdapterFilter translateLogical( List<PolyType> fieldTypes, Kind operator, List<RexNode> operands ) {
+    private ParquetAdapterFilter<PolyValue> translateLogical( List<PolyType> fieldTypes, Kind operator, List<RexNode> operands ) {
         // reject if no operands
         if ( operands.isEmpty() ) {
             return null;
         }
 
         // recursively translate each child Rex operand
-        List<ParquetAdapterFilter> translated = operands.stream()
+        List<ParquetAdapterFilter<PolyValue>> translated = operands.stream()
                 .map( operand -> translate( fieldTypes, operand ) )
                 .toList();
 
@@ -121,11 +123,12 @@ private ParquetAdapterFilter translateLogical( List<PolyType> fieldTypes, Kind o
     /**
      * Translates a Rex IN expression into an adapter-level OR filter
      * IN (10, 20, 30) -> OR(a = 10, a = 20, a = 30)
+     *
      * @param fieldTypes - types of fields
      * @param operands - operands
      * @return adapter level filter
      */
-    private ParquetAdapterFilter translateIn( List<PolyType> fieldTypes, List<RexNode> operands ) {
+    private ParquetAdapterFilter<PolyValue> translateIn( List<PolyType> fieldTypes, List<RexNode> operands ) {
         // Rejects invalid IN with no values. It needs at least column and value
         if ( operands.size() < 2 ) {
             return null;
@@ -141,7 +144,7 @@ private ParquetAdapterFilter translateIn( List<PolyType> fieldTypes, List<RexNod
         // Each value is converted into an equality filter
 
         int index = indexRef.getIndex();
-        List<ParquetAdapterFilter> equalsFilters = operands.subList( 1, operands.size() ).stream()
+        List<ParquetAdapterFilter<PolyValue>> equalsFilters = operands.subList( 1, operands.size() ).stream()
                 .map( this::unwrapCast )
                 .map( value -> isPushdownSupported( fieldTypes, index, Kind.EQUALS, value )
                         ? toParquetAdapterFilter( index, Kind.EQUALS, value )
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetSourceFileFilterReducer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetSourceFileFilterReducer.java
new file mode 100644
index 0000000000..2260207f97
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetSourceFileFilterReducer.java
@@ -0,0 +1,123 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.filter;
+
+import java.util.ArrayList;
+import java.util.List;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+/**
+ * Evaluates row filters using values known for a complete source file. If filter is applied on a column containing single value it can be removed from further usage.
+ */
+public final class ParquetSourceFileFilterReducer {
+
+    private ParquetSourceFileFilterReducer() {
+    }
+
+
+    /**
+     * Evaluates and reduces filters using file-level metadata. Residual filters still need to be evaluated while reading rows.
+     *
+     * @param sourceFile a source file.
+     * @param evaluator a file-level evaluator.
+     * @param filters filters to evaluate and reduce if possible.
+     * @return whether the file matches and any residual filters.
+     */
+    public static ResidualFilters reduce( ParquetSourceFile sourceFile, ParquetMultiFilterEvaluator<ParquetSourceFile> evaluator, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        List<ParquetAdapterFilter<PolyValue>> residuals = new ArrayList<>( filters.size() );
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            EvaluatedFilter simplified = evaluateAndReduce( sourceFile, evaluator, filter );
+            if ( simplified.isExactRejection() ) {
+                return new ResidualFilters( false, List.of() );
+            }
+            if ( simplified.residual() != null ) {
+                residuals.add( simplified.residual() );
+            }
+        }
+        return new ResidualFilters( true, residuals );
+    }
+
+
+    private static EvaluatedFilter evaluateAndReduce( ParquetSourceFile sourceFile, ParquetMultiFilterEvaluator<ParquetSourceFile> evaluator, ParquetAdapterFilter<PolyValue> filter ) {
+        if ( !filter.isLogical() ) {
+            Boolean exactMatch = evaluator.evaluate( sourceFile, filter );
+            return exactMatch == null ? EvaluatedFilter.residual( filter ) : EvaluatedFilter.exact( exactMatch );
+        }
+        if ( filter.operator() == Kind.NOT ) {
+            if ( filter.operands().size() != 1 ) {
+                return EvaluatedFilter.residual( filter );
+            }
+            EvaluatedFilter evaluatedFilter = evaluateAndReduce( sourceFile, evaluator, filter.operands().get( 0 ) );
+            return evaluatedFilter.exactMatch() == null
+                    ? EvaluatedFilter.residual( ParquetAdapterFilter.logical( Kind.NOT, List.of( evaluatedFilter.residual() ) ) )
+                    : EvaluatedFilter.exact( !evaluatedFilter.exactMatch() );
+        }
+
+        List<ParquetAdapterFilter<PolyValue>> residuals = new ArrayList<>( filter.operands().size() );
+        for ( ParquetAdapterFilter<PolyValue> operand : filter.operands() ) {
+            EvaluatedFilter simplified = evaluateAndReduce( sourceFile, evaluator, operand );
+            if ( (filter.operator() == Kind.AND && simplified.isExactRejection()) || (filter.operator() == Kind.OR && simplified.isExactMatch()) ) {
+                return EvaluatedFilter.exact( filter.operator() == Kind.OR );
+            }
+            if ( simplified.residual() != null ) {
+                residuals.add( simplified.residual() );
+            }
+        }
+        if ( residuals.isEmpty() ) {
+            return EvaluatedFilter.exact( filter.operator() == Kind.AND );
+        }
+        if ( residuals.size() == 1 ) {
+            return EvaluatedFilter.residual( residuals.get( 0 ) );
+        }
+        return EvaluatedFilter.residual( ParquetAdapterFilter.logical( filter.operator(), residuals ) );
+    }
+
+
+    /**
+     * Contains the result of the evaluation process.
+     *
+     * @param exactMatch indicates if the evaluation found an exact match.
+     * @param residual if not null then this filter needs to be used in row level evaluation.
+     */
+    private record EvaluatedFilter( Boolean exactMatch, ParquetAdapterFilter<PolyValue> residual ) {
+
+        private static EvaluatedFilter exact( boolean match ) {
+            return new EvaluatedFilter( match, null );
+        }
+
+
+        private static EvaluatedFilter residual( ParquetAdapterFilter<PolyValue> filter ) {
+            return new EvaluatedFilter( null, filter );
+        }
+
+
+        private boolean isExactMatch() {
+            return Boolean.TRUE.equals( exactMatch );
+        }
+
+
+        private boolean isExactRejection() {
+            return Boolean.FALSE.equals( exactMatch );
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetSourceFilePartitionFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetSourceFilePartitionFilterEvaluator.java
similarity index 72%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetSourceFilePartitionFilterEvaluator.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetSourceFilePartitionFilterEvaluator.java
index 26ff51335e..5ec60c023f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetSourceFilePartitionFilterEvaluator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetSourceFilePartitionFilterEvaluator.java
@@ -14,40 +14,40 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.execution;
+package org.polypheny.db.adapter.parquet.relational.filter;
 
 import java.util.function.Function;
-
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
-import org.polypheny.db.adapter.parquet.shared.filter.FilterEvaluator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetFilterEvaluator;
 import org.polypheny.db.type.entity.PolyNull;
 import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
 
 
-public class ParquetSourceFilePartitionFilterEvaluator extends FilterEvaluator<ParquetSourceFile> {
+public class ParquetSourceFilePartitionFilterEvaluator extends ParquetFilterEvaluator<ParquetSourceFile, PolyValue> {
 
-    private final Function<ParquetAdapterFilter, ParquetColumnBinding> selector;
+    private final Function<ParquetAdapterFilter<PolyValue>, ParquetColumnBinding> selector;
 
 
-    public ParquetSourceFilePartitionFilterEvaluator(Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
+    public ParquetSourceFilePartitionFilterEvaluator( Function<ParquetAdapterFilter<PolyValue>, ParquetColumnBinding> selector ) {
         this.selector = selector;
     }
 
 
     @Override
-    protected Boolean evaluateLeaf( ParquetSourceFile sourceFile, ParquetAdapterFilter filter ) {
+    protected Boolean evaluateLeaf( ParquetSourceFile sourceFile, ParquetAdapterFilter<PolyValue> filter ) {
         ParquetColumnBinding columnBinding = selector.apply( filter );
         if ( columnBinding == null || columnBinding.role() != ParquetColumnRole.PARTITION ) {
             return null;
         }
         String partitionValue = sourceFile.partitionValues().get( columnBinding.columnName() );
         if ( partitionValue == null ) {
-            return matchesValue( PolyNull.NULL, filter.operator(), filter.polyValue() );
+            return matchesValue( PolyNull.NULL, filter.operator(), filter.value() );
         }
-        return matchesValue( PolyString.of( partitionValue ), filter.operator(), filter.polyValue() );
+        return matchesValue( PolyString.of( partitionValue ), filter.operator(), filter.value() );
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetSourceFileStatisticsFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetSourceFileStatisticsFilterEvaluator.java
new file mode 100644
index 0000000000..acc74e662d
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ParquetSourceFileStatisticsFilterEvaluator.java
@@ -0,0 +1,170 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.filter;
+
+import java.util.function.Function;
+
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnStatistics;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetConstantColumnResolver;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetFilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+/**
+ * Uses Parquet footer min/max/null-count statistics to evaluate filters at file
+ * level. A {@code false} result rejects a file that cannot contain matching
+ * rows. A {@code true} result proves that every row in the file matches.
+ */
+public class ParquetSourceFileStatisticsFilterEvaluator extends ParquetFilterEvaluator<ParquetSourceFile, PolyValue> {
+
+    private final Function<ParquetAdapterFilter<PolyValue>, ParquetColumnBinding> selector;
+    private final ParquetTypeConverter typeConverter;
+    private final ParquetConstantColumnResolver constantColumnResolver;
+
+
+    public ParquetSourceFileStatisticsFilterEvaluator( Function<ParquetAdapterFilter<PolyValue>, ParquetColumnBinding> selector ) {
+        this.selector = selector;
+        this.typeConverter = new ParquetTypeConverter();
+        this.constantColumnResolver = new ParquetConstantColumnResolver();
+    }
+
+
+    /**
+     * Checks whether this evaluator can decide the filter from file metadata for
+     * any runtime filter value. This is intentionally stricter than pruning:
+     * metadata aggregates may include a file only when every row matches.
+     *
+     * @param sourceFile a source parquet file.
+     * @param filter a non-logical filter.
+     * @return {@code true} when footer statistics are sufficient for an exact decision.
+     */
+    public boolean supportsExactEvaluation( ParquetSourceFile sourceFile, ParquetAdapterFilter<PolyValue> filter ) {
+        if ( filter.isLogical() ) {
+            return false;
+        }
+        ParquetColumnBinding columnBinding = selector.apply( filter );
+        if ( columnBinding == null || columnBinding.role() != ParquetColumnRole.DATA || columnBinding.sourcePathElements().size() != 1 ) {
+            return false;
+        }
+        ParquetColumnStatistics statistics = statistics( sourceFile, filter );
+        if ( statistics == null || statistics.valueCount() != statistics.rowCount() ) {
+            return false;
+        }
+        if ( filter.operator() == Kind.IS_NULL || filter.operator() == Kind.IS_NOT_NULL ) {
+            return statistics.hasNoNulls() || statistics.hasOnlyNulls();
+        }
+        if ( !isComparison( filter.operator() ) ) {
+            return false;
+        }
+        if ( statistics.hasOnlyNulls() ) {
+            return true;
+        }
+        return constantColumnResolver.resolve( statistics ).isPresent();
+    }
+
+
+    @Override
+    protected Boolean evaluateLeaf( ParquetSourceFile sourceFile, ParquetAdapterFilter<PolyValue> filter ) {
+        ParquetColumnStatistics statistics = statistics( sourceFile, filter );
+        if ( statistics == null ) {
+            return null;
+        }
+
+        if ( filter.operator() == Kind.IS_NULL ) {
+            return statistics.hasOnlyNulls() ? Boolean.TRUE : statistics.hasNoNulls() ? Boolean.FALSE : null;
+        }
+        if ( filter.operator() == Kind.IS_NOT_NULL ) {
+            return statistics.hasNoNulls() ? Boolean.TRUE : statistics.hasOnlyNulls() ? Boolean.FALSE : null;
+        }
+        if ( !isComparison( filter.operator() ) ) {
+            return null;
+        }
+        if ( filter.value() == null || filter.value().isNull() ) {
+            return Boolean.FALSE;
+        }
+        if ( statistics.hasOnlyNulls() ) {
+            return Boolean.FALSE;
+        }
+        if ( !statistics.hasRange() ) {
+            return null;
+        }
+        Comparable<?> value = typeConverter.fromPolyValueToComparable( filter.value(), statistics.type() );
+        Comparable<?> min = typeConverter.fromStringToComparable( statistics.type(), statistics.min() );
+        Comparable<?> max = typeConverter.fromStringToComparable( statistics.type(), statistics.max() );
+        if ( value == null || min == null || max == null ) {
+            return null;
+        }
+
+        return switch ( filter.operator() ) {
+            case EQUALS -> outsideRange( value, min, max )
+                    ? Boolean.FALSE
+                    : statistics.hasNoNulls() && singleValueRangeEquals( value, min, max ) ? Boolean.TRUE : null;
+            case NOT_EQUALS -> singleValueRangeEquals( value, min, max )
+                    ? Boolean.FALSE
+                    : statistics.hasNoNulls() && outsideRange( value, min, max ) ? Boolean.TRUE : null;
+            case GREATER_THAN -> typeConverter.compare( max, value ) <= 0
+                    ? Boolean.FALSE
+                    : statistics.hasNoNulls() && typeConverter.compare( min, value ) > 0 ? Boolean.TRUE : null;
+            case GREATER_THAN_OR_EQUAL -> typeConverter.compare( max, value ) < 0
+                    ? Boolean.FALSE
+                    : statistics.hasNoNulls() && typeConverter.compare( min, value ) >= 0 ? Boolean.TRUE : null;
+            case LESS_THAN -> typeConverter.compare( min, value ) >= 0
+                    ? Boolean.FALSE
+                    : statistics.hasNoNulls() && typeConverter.compare( max, value ) < 0 ? Boolean.TRUE : null;
+            case LESS_THAN_OR_EQUAL -> typeConverter.compare( min, value ) > 0
+                    ? Boolean.FALSE
+                    : statistics.hasNoNulls() && typeConverter.compare( max, value ) <= 0 ? Boolean.TRUE : null;
+            default -> null;
+        };
+    }
+
+
+    private ParquetColumnStatistics statistics( ParquetSourceFile sourceFile, ParquetAdapterFilter<PolyValue> filter ) {
+        ParquetColumnBinding columnBinding = selector.apply( filter );
+        if ( columnBinding == null || columnBinding.role() != ParquetColumnRole.DATA || columnBinding.sourcePathElements().isEmpty() ) {
+            return null;
+        }
+        return sourceFile.columnStatistics().get( columnBinding.sourcePathElements() );
+    }
+
+
+    private boolean isComparison( Kind operator ) {
+        return operator == Kind.EQUALS
+                || operator == Kind.NOT_EQUALS
+                || operator == Kind.GREATER_THAN
+                || operator == Kind.GREATER_THAN_OR_EQUAL
+                || operator == Kind.LESS_THAN
+                || operator == Kind.LESS_THAN_OR_EQUAL;
+    }
+
+
+    private boolean outsideRange( Comparable<?> value, Comparable<?> min, Comparable<?> max ) {
+        return typeConverter.compare( value, min ) < 0 || typeConverter.compare( value, max ) > 0;
+    }
+
+
+    private boolean singleValueRangeEquals( Comparable<?> value, Comparable<?> min, Comparable<?> max ) {
+        return typeConverter.compare( min, max ) == 0 && typeConverter.compare( min, value ) == 0;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ResidualFilters.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ResidualFilters.java
new file mode 100644
index 0000000000..9bc40cfefb
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/filter/ResidualFilters.java
@@ -0,0 +1,32 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.filter;
+
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.type.entity.PolyValue;
+
+import java.util.List;
+
+/**
+ * A result returned by {@link ParquetSourceFileFilterReducer} containing information about evaluation and residual filters.
+ *
+ * @param matches evaluated filters were matched against the {@link org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile}.
+ * @param filters remaining filters that still needs to be pushed down into the reading pipeline.
+ */
+public record ResidualFilters( boolean matches, List<ParquetAdapterFilter<PolyValue>> filters ) {
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetAlgOptRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/ParquetAlgOptRule.java
similarity index 88%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetAlgOptRule.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/ParquetAlgOptRule.java
index b20aee2659..dd93aa984f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetAlgOptRule.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/ParquetAlgOptRule.java
@@ -14,13 +14,13 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.planning;
+package org.polypheny.db.adapter.parquet.relational.optimization;
 
 import org.polypheny.db.plan.AlgOptRule;
 import org.polypheny.db.plan.AlgOptRuleCall;
 
 /**
- * Adapter between Polypheny’s rule engine and your own PatternMatcher abstraction
+ * Adapter between Polypheny's rule engine and PatternMatcher abstraction
  * It does not contain Parquet planning logic itself. It only delegates.
  */
 public class ParquetAlgOptRule extends AlgOptRule {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRules.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/ParquetRules.java
similarity index 62%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRules.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/ParquetRules.java
index 56d235930d..840e23e556 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRules.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/ParquetRules.java
@@ -14,13 +14,16 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.planning;
+package org.polypheny.db.adapter.parquet.relational.optimization;
 
+import java.util.ArrayList;
 import java.util.List;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.convert.ConverterRule;
 import org.polypheny.db.algebra.core.AlgFactories;
 import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.adapter.parquet.relational.planning.EnumerableParquet;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
 import org.polypheny.db.plan.AlgOptRule;
 import org.polypheny.db.schema.trait.ModelTrait;
 import org.polypheny.db.tools.AlgBuilderFactory;
@@ -30,6 +33,9 @@
  */
 public final class ParquetRules {
 
+    private static final boolean isOptimizeAggregation = true;
+
+
     private ParquetRules() {
     }
 
@@ -40,12 +46,18 @@ public static List<AlgOptRule> rules( ParquetConvention out ) {
 
 
     public static List<AlgOptRule> rules( ParquetConvention out, AlgBuilderFactory factory ) {
-        return List.of(
-                new ParquetAlgOptRule( PatternMatchers.joinWithScanOnLeftAndScanOnRight( out, factory ) ),
-                new ParquetAlgOptRule( PatternMatchers.attachFilterToJoinUnderCalc( out, factory ) ),
-                new ParquetAlgOptRule( PatternMatchers.attachFieldsAndFiltersToScanUnderCalc( out, factory ) ),
-                new EnumerableParquetRule( out, factory )
-        );
+        List<AlgOptRule> rules = new ArrayList<>();
+        rules.add( new ParquetAlgOptRule( PatternMatchers.joinWithScanOnLeftAndScanOnRight( out, factory ) ) );
+        rules.add( new ParquetAlgOptRule( PatternMatchers.attachFilterToJoinUnderCalc( out, factory ) ) );
+        rules.add( new ParquetAlgOptRule( PatternMatchers.attachFieldsAndFiltersToScanUnderCalc( out, factory ) ) );
+        rules.add( new EnumerableParquetRule( out, factory ) );
+        if ( isOptimizeAggregation ) {
+            rules.add( new ParquetAlgOptRule( PatternMatchers.aggregateOnScan( out, factory ) ) );
+            rules.add( new ParquetAlgOptRule( PatternMatchers.aggregateOnCalcScan( out, factory ) ) );
+            rules.add( new ParquetAlgOptRule( PatternMatchers.partialAggregateOnUnion( out, factory ) ) );
+            rules.add( new ParquetAlgOptRule( PatternMatchers.partialAggregateOnCalcUnion( out, factory ) ) );
+        }
+        return rules;
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatcher.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/PatternMatcher.java
similarity index 94%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatcher.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/PatternMatcher.java
index 1b992b5d0a..e0cb721dbe 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatcher.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/PatternMatcher.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.planning;
+package org.polypheny.db.adapter.parquet.relational.optimization;
 
 import org.polypheny.db.plan.AlgOptRuleCall;
 import org.polypheny.db.plan.AlgOptRuleOperand;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/PatternMatchers.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/PatternMatchers.java
new file mode 100644
index 0000000000..155f3d0020
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/PatternMatchers.java
@@ -0,0 +1,571 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.optimization;
+
+import static org.polypheny.db.plan.AlgOptRule.any;
+import static org.polypheny.db.plan.AlgOptRule.none;
+import static org.polypheny.db.plan.AlgOptRule.operand;
+
+import com.google.common.collect.ImmutableList;
+import java.util.ArrayList;
+import java.util.List;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetRelFilterTranslator;
+import org.polypheny.db.adapter.parquet.relational.optimization.aggregate.AggregateDecomposition;
+import org.polypheny.db.adapter.parquet.relational.optimization.aggregate.PartialAggregate;
+import org.polypheny.db.adapter.parquet.relational.planning.EnumerableParquet;
+import org.polypheny.db.adapter.parquet.relational.planning.JoinDirection;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetEnumerableUnion;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelAggregate;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelJoin;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.InvalidAlgException;
+import org.polypheny.db.algebra.core.AggregateCall;
+import org.polypheny.db.algebra.enumerable.EnumerableAggregate;
+import org.polypheny.db.algebra.enumerable.EnumerableCalc;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.enumerable.EnumerableJoin;
+import org.polypheny.db.algebra.enumerable.EnumerableUnion;
+import org.polypheny.db.algebra.fun.AggFunction;
+import org.polypheny.db.algebra.operators.OperatorName;
+import org.polypheny.db.languages.OperatorRegistry;
+import org.polypheny.db.plan.AlgOptRuleCall;
+import org.polypheny.db.plan.AlgOptUtil;
+import org.polypheny.db.rex.RexIndexRef;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.rex.RexProgram;
+import org.polypheny.db.schema.trait.ModelTrait;
+import org.polypheny.db.tools.AlgBuilderFactory;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.ImmutableBitSet;
+
+public final class PatternMatchers {
+
+    private final static ParquetRelFilterTranslator translator = new ParquetRelFilterTranslator();
+
+
+    private static void transformTo( AlgOptRuleCall call, EnumerableJoin join, ParquetRelScan left, ParquetRelScan right ) {
+        JoinDirection direction = ParquetRelJoin.supportedDirection( join, left, right );
+        if ( direction == null ) {
+            return;
+        }
+        ParquetRelJoin parquetJoin = ParquetRelJoin.create( left, right, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent() );
+        call.transformTo( toEnumerable( join, parquetJoin ) );
+    }
+
+
+    private static EnumerableParquet toEnumerable( AlgNode source, AlgNode input ) {
+        return new EnumerableParquet(
+                source.getCluster(),
+                input.getTraitSet().replace( EnumerableConvention.INSTANCE ).replace( ModelTrait.RELATIONAL ),
+                input );
+    }
+
+
+    /**
+     * Converts E_AGGREGATE node into P_AGGREGATE node.
+     *
+     * @param aggregate an aggregate node to convert.
+     * @param scan a P_SCAN to add to P_AGGREGATE node as an input.
+     * @return a new P_AGGREGATE node.
+     */
+    private static AlgNode parquetAggregate( EnumerableAggregate aggregate, ParquetRelScan scan ) {
+        ParquetRelAggregate parquetAggregate = ParquetRelAggregate.create( scan, aggregate );
+        return parquetAggregate == null ? null : toEnumerable( aggregate, parquetAggregate );
+    }
+
+
+    /**
+     * Validates if the provided E_AGGREGATE node contains only aggregation functions supported by metadata pushdown.
+     *
+     * @param aggregate an aggregate to check.
+     * @return {@code true} if the E_AGGREGATE node contains only supported metadata aggregation functions and {@code false} otherwise.
+     */
+    private static boolean supportsMetadataAggregate( EnumerableAggregate aggregate ) {
+        if ( !isSimpleAggregate( aggregate ) ) {
+            return false;
+        }
+        for ( AggregateCall aggregateCall : aggregate.getAggCallList() ) {
+            if ( !ParquetRelAggregate.supportsMetadataAggregateCall( aggregateCall ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Selects the aggregate function that combines partial metadata aggregate results.
+     *
+     * @param aggregateCall the original aggregate call.
+     * @return the aggregate function for the top-level aggregate.
+     */
+    private static AggFunction topLevelAggregateFunction( AggregateCall aggregateCall ) {
+        return switch ( aggregateCall.getAggregation().getKind() ) {
+            case COUNT -> OperatorRegistry.getAgg( OperatorName.SUM0 );
+            case MIN -> OperatorRegistry.getAgg( OperatorName.MIN );
+            case MAX -> OperatorRegistry.getAgg( OperatorName.MAX );
+            default -> aggregateCall.getAggregation();
+        };
+    }
+
+
+    /**
+     * Convert an input into an {@link EnumerableAggregate}.
+     *
+     * @param aggregate a base aggregate to use for additional properties.
+     * @param input an input to be converted.
+     * @return a new {@link EnumerableAggregate} with the provided input.
+     * @throws InvalidAlgException an exception to be thrown in case of an error.
+     */
+    private static EnumerableAggregate toAggregate( EnumerableAggregate aggregate, AlgNode input, AggregateDecomposition decomposition ) throws InvalidAlgException {
+        return new EnumerableAggregate(
+                aggregate.getCluster(),
+                aggregate.getTraitSet(),
+                input,
+                false,
+                aggregate.getGroupSet(),
+                aggregate.getGroupSets(),
+                decomposition.partialCalls() );
+    }
+
+
+    /**
+     * Converts input node into a top level {@link EnumerableAggregate} that combines all underlying partial aggregates.
+     *
+     * @param aggregate an initial aggregate used for additional properties.
+     * @param input an input to wrap.
+     * @return a new {@link EnumerableAggregate}.
+     * @throws InvalidAlgException an exception to be thrown in case of an error.
+     */
+    private static AlgNode toTopLevelAggregate( EnumerableAggregate aggregate, AlgNode input, AggregateDecomposition decomposition ) throws InvalidAlgException {
+        ImmutableBitSet finalGroupSet = ImmutableBitSet.range( aggregate.getGroupSet().cardinality() );
+        return new EnumerableAggregate(
+                aggregate.getCluster(),
+                aggregate.getTraitSet(),
+                input,
+                false,
+                finalGroupSet,
+                List.of( finalGroupSet ),
+                decomposition.buildFinalCalls( aggregate, input ) );
+    }
+
+
+    /**
+     * Convert each input into an {@link EnumerableAggregate}.
+     * The method is called to replace any input under the E_UNION node into an E_AGGREGATE in order for the {@link PatternMatchers::metadataAggregateOnScan()} to fire.
+     *
+     * @param aggregate an initial aggregate to be used for additional properties.
+     * @param inputs a list of inputs to convert.
+     * @return a list of new aggregates.
+     * @throws InvalidAlgException an exception to be thrown in case of an error.
+     */
+    private static List<AlgNode> toAggregates( EnumerableAggregate aggregate, List<AlgNode> inputs, AggregateDecomposition decomposition ) throws InvalidAlgException {
+        ImmutableList.Builder<AlgNode> builder = ImmutableList.builder();
+        for ( AlgNode input : inputs ) {
+            builder.add( toAggregate( aggregate, input, decomposition ) );
+        }
+        return builder.build();
+    }
+
+
+    /**
+     * Convert each input into the same calc followed by an {@link EnumerableAggregate}.
+     * This lets partial aggregate pushdown work when the aggregate input is a projection/filter over a UNION ALL.
+     *
+     * @param aggregate an initial aggregate to be used for additional properties.
+     * @param calc the calc between the aggregate and union.
+     * @param inputs a list of union inputs to convert.
+     * @return a list of new aggregate-on-calc inputs.
+     * @throws InvalidAlgException an exception to be thrown in case of an error.
+     */
+    private static List<AlgNode> toCalcAggregates( EnumerableAggregate aggregate, EnumerableCalc calc, List<AlgNode> inputs, AggregateDecomposition decomposition ) throws InvalidAlgException {
+        ImmutableList.Builder<AlgNode> builder = ImmutableList.builder();
+        for ( AlgNode input : inputs ) {
+            EnumerableCalc inputCalc = EnumerableCalc.create( input, calc.getProgram() );
+            builder.add( toAggregate( aggregate, inputCalc, decomposition ) );
+        }
+        return builder.build();
+    }
+
+
+    /**
+     * Checks if the provided aggregate represents a simple aggregate supported by the Parquet adapter.
+     *
+     * @param aggregate an aggregate to check.
+     * @return true if the provide aggregate is a simple aggregate and false otherwise.
+     */
+    private static boolean isSimpleAggregate( EnumerableAggregate aggregate ) {
+        return !aggregate.indicator && EnumerableAggregate.isSimple( aggregate );
+    }
+
+
+    /**
+     * Creates a decomposition of an aggregate call over UNION ALL optimization.
+     * The {@link AggregateDecomposition} describes what each union child should compute, and how the top aggregate should combine those partial results.
+     * <pre>
+     *     For example:
+     *     - COUNT: child computes COUNT, final combines with SUM0
+     *     - SUM, MIN, MAX: child computes the same aggregate, final combines with the same aggregate
+     *     - metadata-supported aggregates: COUNT, MIN, MAX, with COUNT combined as SUM0
+     * </pre>
+     *
+     * @param aggregate an original aggregate call to decompose.
+     * @return {@link AggregateDecomposition}.
+     */
+    private static AggregateDecomposition aggregateDecomposition( EnumerableAggregate aggregate ) {
+        if ( !isSimpleAggregate( aggregate ) ) {
+            return null;
+        }
+
+        List<PartialAggregate> partialAggregates = new ArrayList<>();
+        if ( supportsMetadataAggregate( aggregate ) ) {
+            for ( AggregateCall aggregateCall : aggregate.getAggCallList() ) {
+                partialAggregates.add( new PartialAggregate( aggregateCall, topLevelAggregateFunction( aggregateCall ) ) );
+            }
+            return new AggregateDecomposition( partialAggregates );
+        }
+
+        for ( AggregateCall aggregateCall : aggregate.getAggCallList() ) {
+            if ( aggregateCall.isDistinct() || aggregateCall.isApproximate() || aggregateCall.filterArg >= 0 ) {
+                return null;
+            }
+            switch ( aggregateCall.getAggregation().getKind() ) {
+                case COUNT -> {
+                    if ( aggregateCall.getArgList().size() > 1 ) {
+                        return null;
+                    }
+                    partialAggregates.add( new PartialAggregate( aggregateCall, OperatorRegistry.getAgg( OperatorName.SUM0 ) ) );
+                }
+                case SUM, MIN, MAX -> {
+                    if ( aggregateCall.getArgList().size() != 1 ) {
+                        return null;
+                    }
+                    partialAggregates.add( new PartialAggregate( aggregateCall, aggregateCall.getAggregation() ) );
+                }
+                default -> {
+                    return null;
+                }
+            }
+        }
+        return new AggregateDecomposition( partialAggregates );
+    }
+
+
+    /**
+     * Translates a {@link RexNode} condition into {@link ParquetAdapterFilter}.
+     *
+     * @param input an input node.
+     * @param condition a condition.
+     * @return a filter.
+     */
+    private static ParquetAdapterFilter<PolyValue> translate( AlgNode input, RexNode condition ) {
+        if ( input == null ) {
+            return null;
+        }
+        List<PolyType> fieldTypes = input.getTupleType().getFields().stream()
+                .map( field -> field.getType().getPolyType() )
+                .toList();
+        return translator.translate( fieldTypes, condition );
+    }
+
+
+    /**
+     * Converts {@link RexNode}'s to field indexes.
+     *
+     * @param rexNodes the nodes to convert.
+     * @return an array of projected filed indexes.
+     */
+    private static int[] getProjectFields( List<RexNode> rexNodes ) {
+        final int[] fields = new int[rexNodes.size()];
+        for ( int i = 0; i < rexNodes.size(); i++ ) {
+            final RexNode exp = rexNodes.get( i );
+            if ( exp instanceof RexIndexRef ) {
+                fields[i] = ((RexIndexRef) exp).getIndex();
+            } else {
+                return null;
+            }
+        }
+        return fields;
+    }
+
+
+    /**
+     * Gets a list of projected fields, if any, and filters, if any, and pushes them into the provided scan.
+     *
+     * @param calc the E_CALC node to retrieve the projected fields and filters from.
+     * @param scan the P_SCAN node to push the fields and filters into.
+     * @return a new P_SCAN node with added fields and filters.
+     */
+    private static ParquetRelScan applyCalc( EnumerableCalc calc, ParquetRelScan scan ) {
+        RexProgram program = calc.getProgram();
+        List<RexNode> projects = program.getProjectList().stream()
+                .map( program::expandLocalRef )
+                .toList();
+
+        int[] fields = getProjectFields( projects );
+        if ( fields == null ) {
+            return null;
+        }
+
+        int[] currentFields = scan.getFields();
+        int[] projectedFields = new int[fields.length];
+        for ( int i = 0; i < fields.length; i++ ) {
+            if ( fields[i] < 0 || fields[i] >= currentFields.length ) {
+                return null;
+            }
+            projectedFields[i] = currentFields[fields[i]];
+        }
+
+        ParquetAdapterFilter<PolyValue> adapterFilter = null;
+
+        if ( program.getCondition() != null ) {
+            RexNode condition = program.expandLocalRef( program.getCondition() );
+            adapterFilter = translate( scan, condition );
+            if ( adapterFilter == null ) {
+                return null;
+            }
+            adapterFilter = ParquetFilterResolver.toPhysicalFilter( adapterFilter, currentFields );
+            if ( adapterFilter == null ) {
+                return null;
+            }
+        }
+
+        ParquetRelScan updatedScan = scan.withFields( projectedFields );
+        return adapterFilter == null
+                ? updatedScan
+                : updatedScan.withFilters( List.of( adapterFilter ) );
+    }
+
+
+    /**
+     * Rule that converts a generic enumerable join into a Parquet adapter-level join.
+     */
+    public static PatternMatcher joinWithScanOnLeftAndScanOnRight( ParquetConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableJoin.class,
+                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) ),
+                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
+                ),
+                "joinWithScanOnLeftAndScanOnRight",
+                call -> {
+                    EnumerableJoin join = call.alg( 0 );
+                    ParquetRelScan left = call.alg( 2 );
+                    ParquetRelScan right = call.alg( 4 );
+                    transformTo( call, join, left, right );
+                }
+        );
+    }
+
+
+    /**
+     * Rule that pushes a filter from a Calc above a ParquetRelJoin into the join node.
+     */
+    public static PatternMatcher attachFilterToJoinUnderCalc( ParquetConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableCalc.class,
+                        operand( EnumerableParquet.class, operand( ParquetRelJoin.class, any() ) )
+                ),
+                "attachFilterToJoinUnderCalc",
+                call -> {
+                    EnumerableCalc calc = call.alg( 0 );
+                    ParquetRelJoin join = call.alg( 2 );
+                    if ( !AlgOptUtil.areRowTypesEqual( calc.getInput().getTupleType(), join.getTupleType(), false ) ) {
+                        return;
+                    }
+                    RexProgram program = calc.getProgram();
+                    if ( program.getCondition() == null ) {
+                        return;
+                    }
+                    RexNode condition = program.expandLocalRef( program.getCondition() );
+                    ParquetAdapterFilter<PolyValue> adapterFilter = translate( join, condition );
+                    if ( adapterFilter == null ) {
+                        return;
+                    }
+                    call.transformTo( toEnumerable( calc, join.withFilters( List.of( adapterFilter ) ) ) );
+                }
+        );
+    }
+
+
+    /**
+     * Rule that pushes a simple EnumerableCalc projection/filter into a ParquetRelScan
+     */
+    public static PatternMatcher attachFieldsAndFiltersToScanUnderCalc( ParquetConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableCalc.class,
+                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
+                ),
+                "attachFieldsAndFiltersToScanUnderCalc",
+                call -> {
+                    EnumerableCalc calc = call.alg( 0 );
+                    ParquetRelScan scan = applyCalc( calc, call.alg( 2 ) );
+                    if ( scan == null ) {
+                        return;
+                    }
+                    call.transformTo( toEnumerable( calc, scan ) );
+                }
+        );
+    }
+
+
+    /**
+     * Rule that replaces a supported EnumerableAggregate on top of a Parquet scan with a metadata or streaming aggregate if supported.
+     * The supported aggregations are:
+     * 1. COUNT(*), COUNT(col), SUM(col), MIN(col), MAX(col)
+     * 2. only numeric columns are supported
+     * 3. for metadata aggregate only partition filters are supported
+     */
+    public static PatternMatcher aggregateOnScan( ParquetConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableAggregate.class,
+                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
+                ),
+                "aggregateOnScan",
+                call -> {
+                    EnumerableAggregate aggregate = call.alg( 0 );
+                    ParquetRelScan scan = call.alg( 2 );
+                    AlgNode replacement = parquetAggregate( aggregate, scan );
+                    if ( replacement != null ) {
+                        call.transformTo( replacement );
+                    }
+                }
+        );
+    }
+
+
+    /**
+     * Rule that replaces a supported EnumerableAggregate on top of a simple calc and Parquet scan with a metadata or streaming aggregate if supported.
+     */
+    public static PatternMatcher aggregateOnCalcScan( ParquetConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableAggregate.class,
+                        operand(
+                                EnumerableCalc.class,
+                                operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
+                        )
+                ),
+                "aggregateOnCalcScan",
+                call -> {
+                    EnumerableAggregate aggregate = call.alg( 0 );
+                    EnumerableCalc calc = call.alg( 1 );
+                    ParquetRelScan scan = applyCalc( calc, call.alg( 3 ) );
+                    if ( scan == null ) {
+                        return;
+                    }
+                    AlgNode replacement = parquetAggregate( aggregate, scan );
+                    if ( replacement != null ) {
+                        call.transformTo( replacement );
+                    }
+                }
+        );
+    }
+
+
+    /**
+     * Rule that splits supported aggregates through a UNION ALL. Each child aggregate can then be optimized independently.
+     */
+    public static PatternMatcher partialAggregateOnUnion( ParquetConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableAggregate.class,
+                        operand( EnumerableUnion.class, any() )
+                ),
+                "partialAggregateOnUnion",
+                call -> {
+                    EnumerableAggregate aggregate = call.alg( 0 );
+                    EnumerableUnion union = call.alg( 1 );
+                    if ( !union.all ) {
+                        return;
+                    }
+
+                    AggregateDecomposition decomposition = aggregateDecomposition( aggregate );
+                    if ( decomposition == null ) {
+                        return;
+                    }
+
+                    try {
+                        List<AlgNode> inputs = toAggregates( aggregate, union.getInputs(), decomposition );
+                        ParquetEnumerableUnion partialUnion = new ParquetEnumerableUnion( union.getCluster(), union.getTraitSet(), inputs );
+                        call.transformTo( toTopLevelAggregate( aggregate, partialUnion, decomposition ) );
+                    } catch ( InvalidAlgException ignored ) {
+                    }
+                }
+        );
+    }
+
+
+    /**
+     * Rule that splits supported aggregates through a Calc over UNION ALL. Each child receives the same Calc and aggregate, allowing child-specific scan rules to optimize independently.
+     */
+    public static PatternMatcher partialAggregateOnCalcUnion( ParquetConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableAggregate.class,
+                        operand(
+                                EnumerableCalc.class,
+                                operand( EnumerableUnion.class, any() )
+                        )
+                ),
+                "partialAggregateOnCalcUnion",
+                call -> {
+                    EnumerableAggregate aggregate = call.alg( 0 );
+                    EnumerableCalc calc = call.alg( 1 );
+                    EnumerableUnion union = call.alg( 2 );
+                    if ( !union.all ) {
+                        return;
+                    }
+
+                    AggregateDecomposition decomposition = aggregateDecomposition( aggregate );
+                    if ( decomposition == null ) {
+                        return;
+                    }
+
+                    try {
+                        List<AlgNode> inputs = toCalcAggregates( aggregate, calc, union.getInputs(), decomposition );
+                        ParquetEnumerableUnion partialUnion = new ParquetEnumerableUnion( union.getCluster(), union.getTraitSet(), inputs );
+                        call.transformTo( toTopLevelAggregate( aggregate, partialUnion, decomposition ) );
+                    } catch ( InvalidAlgException ignored ) {
+                    }
+                }
+        );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/aggregate/AggregateDecomposition.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/aggregate/AggregateDecomposition.java
new file mode 100644
index 0000000000..f247245d79
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/aggregate/AggregateDecomposition.java
@@ -0,0 +1,76 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.optimization.aggregate;
+
+import org.polypheny.db.algebra.AlgCollations;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.core.AggregateCall;
+import org.polypheny.db.algebra.enumerable.EnumerableAggregate;
+
+import java.util.List;
+import java.util.stream.IntStream;
+
+/**
+ * Used for the partial aggregate over UNION ALL optimization.
+ * The class describes what each union child should compute, and how the top aggregate should combine those partial results.
+ * <pre>
+ * For example:
+ * - COUNT: child computes COUNT, final combines with SUM0
+ * - SUM, MIN, MAX: child computes the same aggregate, final combines with the same aggregate
+ * - metadata-supported aggregates: COUNT, MIN, MAX, with COUNT combined as SUM0
+ * </pre>
+ *
+ * @param partialAggregates
+ */
+public record AggregateDecomposition( List<PartialAggregate> partialAggregates ) {
+
+    public List<AggregateCall> partialCalls() {
+        return partialAggregates.stream()
+                .map( PartialAggregate::partialCall )
+                .toList();
+    }
+
+
+    /**
+     * Creates a list of aggregate calls from previously collected partial aggregates.
+     *
+     * @param aggregate an original aggregate.
+     * @param input an input node.
+     * @return a list of newly created aggregate calls.
+     */
+    public List<AggregateCall> buildFinalCalls( EnumerableAggregate aggregate, AlgNode input ) {
+        int groupCount = aggregate.getGroupSet().cardinality();
+        return IntStream.range( 0, partialAggregates.size() )
+                .mapToObj( index -> {
+                    PartialAggregate partialAggregate = partialAggregates.get( index );
+                    AggregateCall partialCall = partialAggregate.partialCall();
+                    return AggregateCall.create(
+                            partialAggregate.finalFunction(),
+                            false,
+                            false,
+                            List.of( groupCount + index ),
+                            -1,
+                            AlgCollations.EMPTY,
+                            groupCount,
+                            input,
+                            null,
+                            partialCall.getName() );
+                } )
+                .toList();
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/aggregate/PartialAggregate.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/aggregate/PartialAggregate.java
new file mode 100644
index 0000000000..f6c7b1a4bb
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/aggregate/PartialAggregate.java
@@ -0,0 +1,30 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.optimization.aggregate;
+
+import org.polypheny.db.algebra.core.AggregateCall;
+import org.polypheny.db.algebra.fun.AggFunction;
+
+/**
+ * Represents a partial aggregate applied on one of the UNION children.
+ *
+ * @param partialCall an aggregate call to be applied on a child.
+ * @param finalFunction an aggregate function to be used on a UNION.
+ */
+public record PartialAggregate( AggregateCall partialCall, AggFunction finalFunction ) {
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java
index 148ed19e7d..66160c95d3 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java
@@ -17,6 +17,7 @@
 package org.polypheny.db.adapter.parquet.relational.planning;
 
 import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.adapter.parquet.relational.optimization.ParquetRules;
 import org.polypheny.db.plan.AlgOptRule;
 import org.polypheny.db.plan.AlgPlanner;
 import org.polypheny.db.plan.AlgTraitSet;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableUnion.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableUnion.java
new file mode 100644
index 0000000000..d8a2a36584
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableUnion.java
@@ -0,0 +1,51 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.List;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.core.Union;
+import org.polypheny.db.algebra.enumerable.EnumerableAlg;
+import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
+import org.polypheny.db.algebra.enumerable.EnumerableUnion;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgTraitSet;
+
+
+/**
+ * Enumerable UNION ALL used for Parquet metadata aggregate partial results.
+ * Note: its main purpose is to be a marker for a planner to avoid recursive planner rule execution.
+ */
+public class ParquetEnumerableUnion extends Union implements EnumerableAlg {
+
+    public ParquetEnumerableUnion( AlgCluster cluster, AlgTraitSet traitSet, List<AlgNode> inputs ) {
+        super( cluster, traitSet, inputs, true );
+    }
+
+
+    @Override
+    public ParquetEnumerableUnion copy( AlgTraitSet traitSet, List<AlgNode> inputs, boolean all ) {
+        return new ParquetEnumerableUnion( getCluster(), traitSet, inputs );
+    }
+
+
+    @Override
+    public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
+        return new EnumerableUnion( getCluster(), getTraitSet(), getInputs(), all ).implement( implementor, pref );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterResolver.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterResolver.java
deleted file mode 100644
index 33e000e370..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetFilterResolver.java
+++ /dev/null
@@ -1,102 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import java.util.ArrayList;
-import java.util.List;
-import java.util.Objects;
-import java.util.function.Function;
-import org.polypheny.db.adapter.DataContext;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.type.entity.PolyValue;
-
-
-public final class ParquetFilterResolver {
-
-    private ParquetFilterResolver() {
-    }
-
-
-    /**
-     * Support parametrized queries
-     *
-     * @param dataContext context
-     * @param filters filters
-     * @param selector a helper function for column binding selection
-     * @return list of parquet filters
-     */
-    public static List<ParquetAdapterFilter> resolveFilters( DataContext dataContext, List<ParquetAdapterFilter> filters, Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
-        List<ParquetAdapterFilter> resolved = new ArrayList<>( filters.size() );
-        for ( ParquetAdapterFilter filter : filters ) {
-            resolved.add( resolveFilter( dataContext, filter, selector ) );
-        }
-        return resolved;
-    }
-
-
-    public static ParquetAdapterFilter toPhysicalFilter( ParquetAdapterFilter filter, int[] fields ) {
-        if ( filter.isLogical() ) {
-            List<ParquetAdapterFilter> operands = new ArrayList<>( filter.operands().size() );
-            for ( ParquetAdapterFilter operand : filter.operands() ) {
-                ParquetAdapterFilter physicalOperand = toPhysicalFilter( operand, fields );
-                if ( physicalOperand == null ) {
-                    return null;
-                }
-                operands.add( physicalOperand );
-            }
-            return ParquetAdapterFilter.logical( filter.operator(), operands );
-        }
-
-        int physicalIndex = toPhysicalIndex( filter.columnIndex(), fields );
-        if ( physicalIndex < 0 ) {
-            return null;
-        }
-
-        return new ParquetAdapterFilter(
-                physicalIndex,
-                filter.pathElements(),
-                filter.operator(),
-                filter.polyValue(),
-                filter.dynamicParamIndex() );
-    }
-
-
-    private static ParquetAdapterFilter resolveFilter( DataContext dataContext, ParquetAdapterFilter filter, Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
-        if ( filter.isLogical() ) {
-            return ParquetAdapterFilter.logical( filter.operator(), filter.operands().stream()
-                    .map( operand -> resolveFilter( dataContext, operand, selector ) )
-                    .toList() );
-        }
-
-        PolyValue value = filter.dynamicParamIndex() == null
-                ? filter.polyValue()
-                : dataContext.getParameterValue( filter.dynamicParamIndex() );
-
-        ParquetColumnBinding columnBinding = Objects.requireNonNull( selector.apply( filter ), "Missing parquet column binding" );
-        return new ParquetAdapterFilter( filter.columnIndex(), columnBinding.sourcePathElements(), filter.operator(), value );
-    }
-
-
-    private static int toPhysicalIndex( int index, int[] fields ) {
-        if ( index < 0 || index >= fields.length ) {
-            return -1;
-        }
-        return fields[index];
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetPolyAlgDisplay.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetPolyAlgDisplay.java
index d69a838ce8..cf402c9709 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetPolyAlgDisplay.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetPolyAlgDisplay.java
@@ -22,6 +22,7 @@
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.algebra.type.AlgDataTypeField;
+import org.polypheny.db.type.entity.PolyValue;
 
 final class ParquetPolyAlgDisplay {
 
@@ -52,19 +53,19 @@ static List<String> prefixedFieldNames( ParquetRelTable table, String prefix ) {
     }
 
 
-    static List<String> filters( List<ParquetAdapterFilter> filters, List<String> fieldNames ) {
+    static List<String> filters( List<ParquetAdapterFilter<PolyValue>> filters, List<String> fieldNames ) {
         return filters.stream()
                 .map( filter -> filter( filter, fieldNames ) )
                 .toList();
     }
 
 
-    static String filter( ParquetAdapterFilter filter, List<String> fieldNames ) {
+    static String filter( ParquetAdapterFilter<PolyValue> filter, List<String> fieldNames ) {
         if ( filter.isLogical() ) {
             return filter.operator().name() + "("
                     + String.join( ", ", filter.operands().stream()
-                            .map( operand -> filter( operand, fieldNames ) )
-                            .toList() )
+                    .map( operand -> filter( operand, fieldNames ) )
+                    .toList() )
                     + ")";
         }
 
@@ -76,10 +77,10 @@ static String filter( ParquetAdapterFilter filter, List<String> fieldNames ) {
         String value;
         if ( filter.dynamicParamIndex() != null ) {
             value = "?" + filter.dynamicParamIndex();
-        } else if ( filter.polyValue() == null ) {
+        } else if ( filter.value() == null ) {
             value = "null";
         } else {
-            value = filter.polyValue().toString();
+            value = filter.value().toString();
         }
 
         return field + " " + operator( filter ) + " " + value;
@@ -105,7 +106,7 @@ private static String fieldRef( List<String> fieldNames, int index ) {
     }
 
 
-    private static String operator( ParquetAdapterFilter filter ) {
+    private static String operator( ParquetAdapterFilter<PolyValue> filter ) {
         return filter.operator().sql == null ? filter.operator().name() : filter.operator().sql;
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java
new file mode 100644
index 0000000000..43571b6668
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java
@@ -0,0 +1,311 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import org.apache.calcite.linq4j.tree.Expression;
+import org.apache.calcite.linq4j.tree.Expressions;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.AlgWriter;
+import org.polypheny.db.algebra.SingleAlg;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.core.Aggregate;
+import org.polypheny.db.algebra.core.AggregateCall;
+import org.polypheny.db.algebra.enumerable.EnumUtils;
+import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
+import org.polypheny.db.algebra.fun.AggFunction;
+import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
+import org.polypheny.db.algebra.polyalg.arguments.ListArg;
+import org.polypheny.db.algebra.polyalg.arguments.PolyAlgArgs;
+import org.polypheny.db.algebra.polyalg.arguments.StringArg;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgOptCost;
+import org.polypheny.db.plan.AlgPlanner;
+import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.schema.trait.ModelTrait;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.ImmutableBitSet;
+
+
+/**
+ * Parquet-convention aggregate. The node has a single visible planner shape and selects the concrete aggregate execution mode internally.
+ */
+public class ParquetRelAggregate extends SingleAlg implements ParquetAlg {
+
+    private static final int NO_ARGUMENT = -1;
+
+    private final ParquetRelTable table;
+    private final int[] fields;
+    private final List<ParquetAdapterFilter<PolyValue>> filters;
+    private final AggregateMode mode;
+    private final int[] groupFields;
+    private final String[] aggregateKinds;
+    private final int[] aggregateArgs;
+    private final AlgDataType rowType;
+
+
+    public static ParquetRelAggregate create( ParquetRelScan scan, Aggregate aggregate ) {
+        if ( aggregate.indicator || !Aggregate.isSimple( aggregate ) ) {
+            return null;
+        }
+        AggregateMode mode = aggregateMode( scan, aggregate.getAggCallList(), aggregate.getGroupSet() );
+        if ( mode == null ) {
+            return null;
+        }
+        return new ParquetRelAggregate(
+                aggregate.getCluster(),
+                aggregate.getCluster().traitSetOf( ParquetConvention.INSTANCE ).replace( ModelTrait.RELATIONAL ),
+                mode == AggregateMode.METADATA ? new ParquetRelMetadataScan( scan ) : scan,
+                scan.getEntity(),
+                scan.getFields(),
+                scan.getFilters(),
+                mode,
+                aggregate.getGroupSet().asList().stream().mapToInt( Integer::intValue ).toArray(),
+                aggregateKinds( aggregate.getAggCallList() ),
+                aggregateArgs( aggregate.getAggCallList() ),
+                aggregate.getTupleType() );
+    }
+
+
+    private ParquetRelAggregate(
+            AlgCluster cluster,
+            AlgTraitSet traitSet,
+            AlgNode input,
+            ParquetRelTable table,
+            int[] fields,
+            List<ParquetAdapterFilter<PolyValue>> filters,
+            AggregateMode mode,
+            int[] groupFields,
+            String[] aggregateKinds,
+            int[] aggregateArgs,
+            AlgDataType rowType ) {
+        super( cluster, traitSet.replace( ParquetConvention.INSTANCE ).replace( ModelTrait.RELATIONAL ), input );
+        this.table = table;
+        this.fields = fields.clone();
+        this.filters = List.copyOf( filters );
+        this.mode = mode;
+        this.groupFields = groupFields.clone();
+        this.aggregateKinds = aggregateKinds.clone();
+        this.aggregateArgs = aggregateArgs.clone();
+        this.rowType = rowType;
+    }
+
+
+    /**
+     * Validates if the provided AGGREGATE node contains only aggregation functions, filters and group fields supported by metadata pushdown.
+     *
+     * @param scan a scan that should support the aggregate.
+     * @param aggregate an aggregate to validate.
+     * @return {@code true} if the AGGREGATE node contains only supported metadata aggregation functions, filters and group fields and {@code false} otherwise.
+     */
+    public static boolean supports( ParquetRelScan scan, Aggregate aggregate ) {
+        if ( aggregate.indicator || !Aggregate.isSimple( aggregate ) ) {
+            return false;
+        }
+        return aggregateMode( scan, aggregate.getAggCallList(), aggregate.getGroupSet() ) != null;
+    }
+
+
+    /**
+     * Validates if a single aggregate call can be pushed into metadata aggregation.
+     *
+     * @param aggregateCall an aggregate call to check.
+     * @return {@code true} if the aggregate call is supported and {@code false} otherwise.
+     */
+    public static boolean supportsMetadataAggregateCall( AggregateCall aggregateCall ) {
+        if ( aggregateCall.isDistinct() || aggregateCall.isApproximate() || aggregateCall.filterArg >= 0 ) {
+            return false;
+        }
+        AggFunction aggregation = aggregateCall.getAggregation();
+        if ( aggregation.getKind() == Kind.COUNT ) {
+            return aggregateCall.getArgList().size() <= 1;
+        }
+        if ( aggregation.getKind() == Kind.MIN || aggregation.getKind() == Kind.MAX ) {
+            return aggregateCall.getArgList().size() == 1;
+        }
+        return false;
+    }
+
+
+    /**
+     * Checks the aggregate mode: metadata or streaming.
+     *
+     * @param scan a scan representing the parquet file
+     * @param aggregateCalls a list of aggregate functions
+     * @param groupSet a set of group by fields.
+     * @return {@link AggregateMode} if one of them is supported and {@code null} otherwise.
+     */
+    private static AggregateMode aggregateMode( ParquetRelScan scan, List<AggregateCall> aggregateCalls, ImmutableBitSet groupSet ) {
+        boolean metadataCandidate = true;
+        for ( AggregateCall aggregateCall : aggregateCalls ) {
+            if ( !supportsMetadataAggregateCall( aggregateCall ) ) {
+                metadataCandidate = false;
+                break;
+            }
+        }
+        if ( metadataCandidate && scan.getEntity().supportsMetadataAggregate( scan.getFields(), scan.getFilters(), groupSet, aggregateCalls ) ) {
+            return AggregateMode.METADATA;
+        }
+        if ( scan.getEntity().supportsDataAggregate( scan.getFields(), groupSet, aggregateCalls ) ) {
+            return AggregateMode.DATA;
+        }
+        return null;
+    }
+
+
+    @Override
+    public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
+        return new ParquetRelAggregate( getCluster(), traitSet, inputs.get( 0 ), table, fields, filters, mode, groupFields, aggregateKinds, aggregateArgs, rowType );
+    }
+
+
+    @Override
+    protected AlgDataType deriveRowType() {
+        return rowType;
+    }
+
+
+    @Override
+    public AlgWriter explainTerms( AlgWriter pw ) {
+        return super.explainTerms( pw )
+                .item( "mode", modeName() )
+                .item( "fields", fieldNames() )
+                .item( "groups", groupNames() )
+                .item( "aggregates", aggregateNames() )
+                .item( "filters", filterNames() );
+    }
+
+
+    @Override
+    public String algCompareString() {
+        return getClass().getSimpleName()
+                + "$input=" + input.algCompareString()
+                + "$mode=" + modeName()
+                + "$fields=" + fieldNames()
+                + "$groups=" + groupNames()
+                + "$aggregates=" + aggregateNames()
+                + "$filters=" + filterNames();
+    }
+
+
+    @Override
+    public PolyAlgArgs bindArguments() {
+        return super.bindArguments()
+                .put( "mode", new StringArg( modeName() ) )
+                .put( "fields", new ListArg<>( fieldNames(), StringArg::new ) )
+                .put( "groups", new ListArg<>( groupNames(), StringArg::new ) )
+                .put( "aggregates", new ListArg<>( aggregateNames(), StringArg::new ) )
+                .put( "filters", new ListArg<>( filterNames(), StringArg::new ) );
+    }
+
+
+    @Override
+    public void register( AlgPlanner planner ) {
+        ParquetConvention.INSTANCE.register( planner );
+    }
+
+
+    @Override
+    public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
+        return super.computeSelfCost( planner, mq ).multiplyBy( ParquetConvention.COST_MULTIPLIER * 0.01D );
+    }
+
+
+    @Override
+    public Expression implement( EnumerableAlgImplementor implementor ) {
+        Expression runtimeFilters = EnumUtils.expressionList( filters.stream().map( ParquetAdapterFilter::toExpression ).toList() );
+        return Expressions.call(
+                table.asExpression( ParquetRelTable.class ),
+                mode == AggregateMode.METADATA ? "metadataAggregate" : "dataAggregate",
+                implementor.getRootExpression(),
+                Expressions.constant( fields ),
+                runtimeFilters,
+                Expressions.constant( groupFields ),
+                Expressions.constant( aggregateKinds ),
+                Expressions.constant( aggregateArgs ) );
+    }
+
+
+    private static String[] aggregateKinds( List<AggregateCall> aggregateCalls ) {
+        return aggregateCalls.stream()
+                .map( aggregateCall -> aggregateCall.getAggregation().getKind().name() )
+                .toArray( String[]::new );
+    }
+
+
+    private static int[] aggregateArgs( List<AggregateCall> aggregateCalls ) {
+        return aggregateCalls.stream()
+                .mapToInt( aggregateCall -> aggregateCall.getArgList().isEmpty() ? NO_ARGUMENT : aggregateCall.getArgList().get( 0 ) )
+                .toArray();
+    }
+
+
+    private String modeName() {
+        return switch ( mode ) {
+            case METADATA -> "metadataAggregate";
+            case DATA -> "dataAggregate";
+        };
+    }
+
+
+    private List<String> fieldNames() {
+        return ParquetPolyAlgDisplay.fieldNames( table, fields );
+    }
+
+
+    private List<String> groupNames() {
+        List<String> projectedFieldNames = fieldNames();
+        return Arrays.stream( groupFields )
+                .mapToObj( index -> index >= 0 && index < projectedFieldNames.size() ? projectedFieldNames.get( index ) : "#" + index )
+                .toList();
+    }
+
+
+    private List<String> aggregateNames() {
+        List<String> fieldNames = fieldNames();
+        List<String> values = new ArrayList<>( aggregateKinds.length );
+        for ( int i = 0; i < aggregateKinds.length; i++ ) {
+            int aggregateArg = aggregateArgs[i];
+            String argument = aggregateArg == NO_ARGUMENT ? "*" : aggregateArg >= 0 && aggregateArg < fieldNames.size() ? fieldNames.get( aggregateArg ) : "#" + aggregateArg;
+            values.add( aggregateKinds[i] + "(" + argument + ")" );
+        }
+        return values;
+    }
+
+
+    private List<String> filterNames() {
+        return ParquetPolyAlgDisplay.filters( filters, ParquetPolyAlgDisplay.fieldNames( table ) );
+    }
+
+
+    private enum AggregateMode {
+        /**
+         * The aggregation is done only on parquet metadata, no data rows are read.
+         */
+        METADATA,
+        /**
+         * The aggregation is done by reading rows with direct field references.
+         */
+        DATA
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
index 4cd484469b..c0a98d58bd 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
@@ -45,6 +45,7 @@
 import org.polypheny.db.plan.AlgTraitSet;
 import org.polypheny.db.rex.RexNode;
 import org.polypheny.db.schema.trait.ModelTrait;
+import org.polypheny.db.type.entity.PolyValue;
 
 /**
  *  Planner node that represents a join the Parquet adapter can execute itself
@@ -54,7 +55,7 @@ public class ParquetRelJoin extends Join implements ParquetAlg {
     private final boolean leftIsParent; //whether the left input is the parent side
     private final PhysicalScan leftScan;
     private final PhysicalScan rightScan;
-    private final List<ParquetAdapterFilter> joinFilters; //filters that belong to the joined output
+    private final List<ParquetAdapterFilter<PolyValue>> joinFilters; //filters that belong to the joined output
 
 
     public ParquetRelJoin(
@@ -68,7 +69,7 @@ public ParquetRelJoin(
             boolean leftIsParent,
             PhysicalScan leftScan,
             PhysicalScan rightScan,
-            List<ParquetAdapterFilter> joinFilters) {
+            List<ParquetAdapterFilter<PolyValue>> joinFilters) {
         super(cluster, traitSet, left, right, condition, variablesSet, joinType);
         this.leftIsParent = leftIsParent;
         this.leftScan = leftScan;
@@ -165,8 +166,8 @@ private static boolean isDirectChildPath(List<String> parentPath, List<String> c
     }
 
 
-    public ParquetRelJoin withFilters(List<ParquetAdapterFilter> filters) {
-        List<ParquetAdapterFilter> combinedFilters = new ArrayList<>(this.joinFilters);
+    public ParquetRelJoin withFilters(List<ParquetAdapterFilter<PolyValue>> filters) {
+        List<ParquetAdapterFilter<PolyValue>> combinedFilters = new ArrayList<>(this.joinFilters);
         combinedFilters.addAll(filters);
         return new ParquetRelJoin(getCluster(), traitSet, left, right, condition, variablesSet, joinType, leftIsParent, leftScan, rightScan, combinedFilters);
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelMetadataScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelMetadataScan.java
new file mode 100644
index 0000000000..336bcded4b
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelMetadataScan.java
@@ -0,0 +1,57 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import java.util.List;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgOptCost;
+import org.polypheny.db.plan.AlgPlanner;
+import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.type.entity.PolyValue;
+
+/**
+ * Structural metadata-access input for metadata aggregates. Runtime execution is owned by the aggregate node, so no row scan cost is added.
+ */
+public class ParquetRelMetadataScan extends ParquetRelScan {
+
+    public ParquetRelMetadataScan( ParquetRelScan scan ) {
+        this( scan.getCluster(), scan.getEntity(), scan.getFields(), scan.getFilters() );
+    }
+
+
+    private ParquetRelMetadataScan( AlgCluster cluster, ParquetRelTable table, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        super( cluster, table, fields, filters );
+    }
+
+
+    @Override
+    public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
+        assert inputs.isEmpty();
+        return new ParquetRelMetadataScan( getCluster(), entity, getFields(), getFilters() );
+    }
+
+
+    @Override
+    public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
+        return planner.getCostFactory().makeZeroCost();
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
index d83f8a4afc..60ae99246e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
@@ -42,6 +42,7 @@
 import org.polypheny.db.plan.AlgPlanner;
 import org.polypheny.db.plan.AlgTraitSet;
 import org.polypheny.db.schema.trait.ModelTrait;
+import org.polypheny.db.type.entity.PolyValue;
 
 /**
  * Parquet-convention scan for relational Parquet tables.
@@ -50,7 +51,7 @@
 public class ParquetRelScan extends RelScan<ParquetRelTable> implements ParquetAlg {
 
     private final int[] fields;
-    private final List<ParquetAdapterFilter> filters;
+    private final List<ParquetAdapterFilter<PolyValue>> filters;
 
 
     public ParquetRelScan(AlgCluster cluster, ParquetRelTable table, int[] fields) {
@@ -58,7 +59,7 @@ public ParquetRelScan(AlgCluster cluster, ParquetRelTable table, int[] fields) {
     }
 
 
-    public ParquetRelScan(AlgCluster cluster, ParquetRelTable table, int[] fields, List<ParquetAdapterFilter> filters) {
+    public ParquetRelScan(AlgCluster cluster, ParquetRelTable table, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters) {
         super(cluster, cluster.traitSetOf(ParquetConvention.INSTANCE).replace(ModelTrait.RELATIONAL), table);
         this.fields = fields;
         this.filters = List.copyOf(filters);
@@ -119,8 +120,8 @@ public AlgOptCost computeSelfCost(AlgPlanner planner, AlgMetadataQuery mq) {
     }
 
 
-    public ParquetRelScan withFilters(List<ParquetAdapterFilter> filters) {
-        List<ParquetAdapterFilter> combinedFilters = new ArrayList<>(this.filters);
+    public ParquetRelScan withFilters(List<ParquetAdapterFilter<PolyValue>> filters) {
+        List<ParquetAdapterFilter<PolyValue>> combinedFilters = new ArrayList<>(this.filters);
         combinedFilters.addAll(filters);
         return new ParquetRelScan(getCluster(), entity, fields, combinedFilters);
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java
deleted file mode 100644
index 1946c6b794..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PatternMatchers.java
+++ /dev/null
@@ -1,208 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.planning;
-
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.algebra.AlgNode;
-import org.polypheny.db.algebra.enumerable.EnumerableCalc;
-import org.polypheny.db.algebra.enumerable.EnumerableConvention;
-import org.polypheny.db.algebra.enumerable.EnumerableJoin;
-import org.polypheny.db.plan.AlgOptRuleCall;
-import org.polypheny.db.plan.AlgOptUtil;
-import org.polypheny.db.rex.RexIndexRef;
-import org.polypheny.db.rex.RexNode;
-import org.polypheny.db.rex.RexProgram;
-import org.polypheny.db.schema.trait.ModelTrait;
-import org.polypheny.db.tools.AlgBuilderFactory;
-import org.polypheny.db.type.PolyType;
-
-import java.util.List;
-
-import static org.polypheny.db.plan.AlgOptRule.any;
-import static org.polypheny.db.plan.AlgOptRule.none;
-import static org.polypheny.db.plan.AlgOptRule.operand;
-
-public final class PatternMatchers {
-
-    private final static ParquetRelFilterTranslator translator = new ParquetRelFilterTranslator();
-
-
-    private static void transformTo( AlgOptRuleCall call, EnumerableJoin join, ParquetRelScan left, ParquetRelScan right ) {
-        JoinDirection direction = ParquetRelJoin.supportedDirection( join, left, right );
-        if ( direction == null ) {
-            return;
-        }
-        ParquetRelJoin parquetJoin = ParquetRelJoin.create( left, right, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent() );
-        call.transformTo( toEnumerable( join, parquetJoin ) );
-    }
-
-
-    private static EnumerableParquet toEnumerable( AlgNode source, AlgNode input ) {
-        return new EnumerableParquet(
-                source.getCluster(),
-                input.getTraitSet().replace( EnumerableConvention.INSTANCE ).replace( ModelTrait.RELATIONAL ),
-                input );
-    }
-
-
-    private static ParquetAdapterFilter translate( AlgNode input, RexNode condition ) {
-        if ( input == null ) {
-            return null;
-        }
-        List<PolyType> fieldTypes = input.getTupleType().getFields().stream()
-                .map( field -> field.getType().getPolyType() )
-                .toList();
-        return translator.translate( fieldTypes, condition );
-    }
-
-
-    private static int[] getProjectFields( List<RexNode> rexNodes ) {
-        final int[] fields = new int[rexNodes.size()];
-        for ( int i = 0; i < rexNodes.size(); i++ ) {
-            final RexNode exp = rexNodes.get( i );
-            if ( exp instanceof RexIndexRef ) {
-                fields[i] = ((RexIndexRef) exp).getIndex();
-            } else {
-                return null;
-            }
-        }
-        return fields;
-    }
-
-
-    private static ParquetRelScan applyCalc( EnumerableCalc calc, ParquetRelScan scan ) {
-        RexProgram program = calc.getProgram();
-        List<RexNode> projects = program.getProjectList().stream()
-                .map( program::expandLocalRef )
-                .toList();
-
-        int[] fields = getProjectFields( projects );
-        if ( fields == null ) {
-            return null;
-        }
-
-        int[] currentFields = scan.getFields();
-        int[] projectedFields = new int[fields.length];
-        for ( int i = 0; i < fields.length; i++ ) {
-            if ( fields[i] < 0 || fields[i] >= currentFields.length ) {
-                return null;
-            }
-            projectedFields[i] = currentFields[fields[i]];
-        }
-
-        ParquetAdapterFilter adapterFilter = null;
-
-        if ( program.getCondition() != null ) {
-            RexNode condition = program.expandLocalRef( program.getCondition() );
-            adapterFilter = translate( scan, condition );
-            if ( adapterFilter == null ) {
-                return null;
-            }
-            adapterFilter = ParquetFilterResolver.toPhysicalFilter( adapterFilter, currentFields );
-            if ( adapterFilter == null ) {
-                return null;
-            }
-        }
-
-        ParquetRelScan updatedScan = scan.withFields( projectedFields );
-        return adapterFilter == null
-                ? updatedScan
-                : updatedScan.withFilters( List.of( adapterFilter ) );
-    }
-
-
-    /**
-     * Rule that converts a generic enumerable join into a Parquet adapter-level join.
-     */
-    public static PatternMatcher joinWithScanOnLeftAndScanOnRight( ParquetConvention out, AlgBuilderFactory factory ) {
-        return new PatternMatcher(
-                out,
-                factory,
-                operand(
-                        EnumerableJoin.class,
-                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) ),
-                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
-                ),
-                "joinWithScanOnLeftAndScanOnRight",
-                call -> {
-                    EnumerableJoin join = call.alg( 0 );
-                    ParquetRelScan left = call.alg( 2 );
-                    ParquetRelScan right = call.alg( 4 );
-                    transformTo( call, join, left, right );
-                }
-        );
-    }
-
-
-    /**
-     * Rule that pushes a filter from a Calc above a ParquetRelJoin into the join node.
-     */
-    public static PatternMatcher attachFilterToJoinUnderCalc( ParquetConvention out, AlgBuilderFactory factory ) {
-        return new PatternMatcher(
-                out,
-                factory,
-                operand(
-                        EnumerableCalc.class,
-                        operand( EnumerableParquet.class, operand( ParquetRelJoin.class, any() ) )
-                ),
-                "attachFilterToJoinUnderCalc",
-                call -> {
-                    EnumerableCalc calc = call.alg( 0 );
-                    ParquetRelJoin join = call.alg( 2 );
-                    if ( !AlgOptUtil.areRowTypesEqual( calc.getInput().getTupleType(), join.getTupleType(), false ) ) {
-                        return;
-                    }
-                    RexProgram program = calc.getProgram();
-                    if ( program.getCondition() == null ) {
-                        return;
-                    }
-                    RexNode condition = program.expandLocalRef( program.getCondition() );
-                    ParquetAdapterFilter adapterFilter = translate( join, condition );
-                    if ( adapterFilter == null ) {
-                        return;
-                    }
-                    call.transformTo( toEnumerable( calc, join.withFilters( List.of( adapterFilter ) ) ) );
-                }
-        );
-    }
-
-
-    /**
-     * Rule that pushes a simple EnumerableCalc projection/filter into a ParquetRelScan
-     */
-    public static PatternMatcher attachFieldsAndFiltersToScanUnderCalc( ParquetConvention out, AlgBuilderFactory factory ) {
-        return new PatternMatcher(
-                out,
-                factory,
-                operand(
-                        EnumerableCalc.class,
-                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
-                ),
-                "attachFieldsAndFiltersToScanUnderCalc",
-                call -> {
-                    EnumerableCalc calc = call.alg( 0 );
-                    ParquetRelScan scan = applyCalc( calc, call.alg( 2 ) );
-                    if ( scan == null ) {
-                        return;
-                    }
-                    call.transformTo( toEnumerable( calc, scan ) );
-                }
-        );
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PhysicalScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PhysicalScan.java
index 37dc8ba24b..8422fcfb5c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PhysicalScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/PhysicalScan.java
@@ -17,13 +17,15 @@
 package org.polypheny.db.adapter.parquet.relational.planning;
 
 import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyValue;
 import java.util.List;
 
-public record PhysicalScan( ParquetRelTable table, int[] fields, List<ParquetAdapterFilter> filters ) {
+public record PhysicalScan( ParquetRelTable table, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters ) {
 
     public PhysicalScan {
         filters = List.copyOf( filters );
@@ -36,7 +38,7 @@ public record PhysicalScan( ParquetRelTable table, int[] fields, List<ParquetAda
      * @param dataContext context
      * @return list of parquet filters
      */
-    public List<ParquetAdapterFilter> resolveFilters( DataContext dataContext ) {
+    public List<ParquetAdapterFilter<PolyValue>> resolveFilters( DataContext dataContext ) {
         return ParquetFilterResolver.resolveFilters( dataContext, filters, f -> selectPhysicalBinding( f.columnIndex() ) );
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetConstantColumnResolver.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetConstantColumnResolver.java
new file mode 100644
index 0000000000..d30ad883e5
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetConstantColumnResolver.java
@@ -0,0 +1,84 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.schema;
+
+import java.util.Optional;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+/**
+ * Resolves columns whose value is constant for every row in one source file.
+ */
+public class ParquetConstantColumnResolver {
+
+    private final ParquetTypeConverter typeConverter = new ParquetTypeConverter();
+
+
+    /**
+     * Resolves a partition value or a flat physical column with singleton file statistics.
+     *
+     * @param sourceFile a source file.
+     * @param binding a column binding.
+     * @return the constant value or an empty optional if the column may contain multiple values.
+     */
+    public Optional<PolyValue> resolve( ParquetSourceFile sourceFile, ParquetColumnBinding binding ) {
+        if ( binding == null ) {
+            return Optional.empty();
+        }
+        if ( binding.role() == ParquetColumnRole.PARTITION ) {
+            return Optional.of( partitionValue( sourceFile, binding ) );
+        }
+        if ( binding.role() != ParquetColumnRole.DATA || binding.sourcePathElements().size() != 1 ) {
+            return Optional.empty();
+        }
+        return resolve( sourceFile.columnStatistics().get( binding.sourcePathElements() ) );
+    }
+
+
+    /**
+     * Resolves a constant physical value from footer statistics.
+     *
+     * @param statistics statistics for one physical column.
+     * @return the constant value or an empty optional if the column may contain multiple values.
+     */
+    public Optional<PolyValue> resolve( ParquetColumnStatistics statistics ) {
+        if ( statistics == null || statistics.valueCount() != statistics.rowCount() ) {
+            return Optional.empty();
+        }
+        if ( statistics.hasOnlyNulls() ) {
+            return Optional.of( PolyNull.NULL );
+        }
+        if ( !statistics.hasNoNulls()
+                || !statistics.hasRange()
+                || typeConverter.compareStringValues( statistics.type(), statistics.min(), statistics.max() ) != 0 ) {
+            return Optional.empty();
+        }
+        return Optional.ofNullable( typeConverter.fromStringToPolyValue( statistics.type(), statistics.min() ) );
+    }
+
+
+    private PolyValue partitionValue( ParquetSourceFile sourceFile, ParquetColumnBinding binding ) {
+        if ( !sourceFile.partitionValues().containsKey( binding.columnName() ) ) {
+            return PolyNull.NULL;
+        }
+        return PolyString.of( sourceFile.partitionValues().get( binding.columnName() ) );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index 9e9c3f9a47..9a3a267675 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -16,71 +16,49 @@
 
 package org.polypheny.db.adapter.parquet.relational.schema;
 
-import java.util.ArrayList;
 import java.util.List;
-import java.util.Objects;
 import java.util.Optional;
-import java.util.concurrent.atomic.AtomicBoolean;
-import java.util.function.Function;
 import java.util.stream.IntStream;
 import lombok.Getter;
-import org.apache.calcite.linq4j.AbstractEnumerable;
 import org.apache.calcite.linq4j.Enumerable;
-import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.DataContext;
-import org.polypheny.db.adapter.parquet.relational.execution.JoinNestedBinding;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetMultiFileEnumerator;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedJoinEnumerator;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedNonRepeatedRelEnumerator;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetNestedRepeatedRelEnumerator;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelEnumerator;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetSourceFilePartitionFilterEvaluator;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetSourceFileStatisticsFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelExecutorsFactory;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelMetadataAggregateExecutor;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelNestedJoinExecutor;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelProjectExecutor;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelDataAggregateExecutor;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
 import org.polypheny.db.adapter.parquet.relational.planning.PhysicalScan;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
-import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
-import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersContainer;
-import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersSplitter;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
-import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetFilterResolver;
 import org.polypheny.db.adapter.parquet.shared.statistics.ParquetTableStatisticsReader;
 import org.polypheny.db.adapter.statistics.AdapterStatisticsProvider;
 import org.polypheny.db.adapter.statistics.ProvidedColumnStatistics;
 import org.polypheny.db.adapter.statistics.ProvidedEntityStatistics;
 import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.core.AggregateCall;
 import org.polypheny.db.catalog.entity.logical.LogicalColumn;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
-import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.plan.AlgCluster;
 import org.polypheny.db.plan.AlgTraitSet;
-import org.polypheny.db.rex.RexNode;
-import org.polypheny.db.schema.types.FilterableEntity;
-import org.polypheny.db.schema.types.ScannableEntity;
 import org.polypheny.db.schema.types.TranslatableEntity;
-import org.polypheny.db.type.PolyType;
 import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.util.Source;
+import org.polypheny.db.util.ImmutableBitSet;
 
 /**
  * Physical table wrapper for the relational model.
  * Exposes the Parquet-backed table to Polypheny and ties the planner,
  * scanner, and adapter metadata together.
  */
-public class ParquetRelTable extends PhysicalTable implements FilterableEntity, ScannableEntity, TranslatableEntity, AdapterStatisticsProvider {
+public class ParquetRelTable extends PhysicalTable implements TranslatableEntity, AdapterStatisticsProvider {
 
     private final int[] fieldIndexes;
-    private final AbstractParquetSource parquetSource;
-    private final ParquetRelFilterTranslator filterTranslator;
     @Getter
     private final ParquetTableBinding binding;
-    private final List<PolyType> fieldTypes;
-    private final ParquetSchemaReader schemaReader;
     private final ParquetTableStatisticsReader statisticsReader;
+    private final ParquetRelExecutorsFactory executorsFactory;
 
 
     /**
@@ -99,11 +77,9 @@ public ParquetRelTable( long id, PhysicalTable table, ParquetTableBinding bindin
                 table.adapterId );
         this.binding = binding;
         this.fieldIndexes = IntStream.range( 0, table.columns.size() ).toArray();
-        this.fieldTypes = columns.stream().map( c -> c.type ).toList();
-        this.schemaReader = new ParquetSchemaReader( binding.sourceFiles().stream().map( ParquetSourceFile::asSource ).toList() );
+        ParquetSchemaReader schemaReader = new ParquetSchemaReader( binding.sourceFiles().stream().map( ParquetSourceFile::asSource ).toList() );
         this.statisticsReader = new ParquetTableStatisticsReader( schemaReader, binding );
-        this.parquetSource = parquetSource;
-        this.filterTranslator = new ParquetRelFilterTranslator();
+        this.executorsFactory = new ParquetRelExecutorsFactory( this, parquetSource, fieldIndexes, schemaReader );
     }
 
     // Statics provider interface functions
@@ -142,63 +118,6 @@ public Optional<ProvidedColumnStatistics> getColumnStatistics( LogicalColumn col
     // endregion
 
 
-    /**
-     * Returns enumerable for FilterableEntity.
-     *
-     * @param dataContext data context
-     * @param polyFilters polyFilters to push down.
-     * @return enumerable.
-     */
-    @Override
-    public Enumerable<PolyValue[]> scan( DataContext dataContext, List<RexNode> polyFilters ) {
-        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
-        final List<ParquetAdapterFilter> parquetAdapterFilters = new ArrayList<>();
-        polyFilters.removeIf( polyFilter -> {
-            var parquetFilter = filterTranslator.translate( fieldTypes, polyFilter );
-            if ( parquetFilter != null ) {
-                return parquetAdapterFilters.add( parquetFilter );
-            }
-            return false;
-        } );
-
-        // check for dynamic filters
-        final List<ParquetAdapterFilter> resolvedFilters = ParquetFilterResolver.resolveFilters( dataContext, parquetAdapterFilters, f -> binding.getColumnBinding( columns.get( f.columnIndex() ).id ) );
-        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
-        return new AbstractEnumerable<>() {
-            @Override
-            public Enumerator<PolyValue[]> enumerator() {
-                return new ParquetMultiFileEnumerator(
-                        binding.sourceFiles(),
-                        sourceFile -> enumeratorForFile( sourceFile, fieldIndexes, cancelFlag, FiltersContainer.shared( resolvedFilters ) ),
-                        createParquetSourceFileEvaluatorsChain( f -> binding.getColumnBinding( columns.get( f.columnIndex() ).id ) ),
-                        resolvedFilters );
-            }
-        };
-    }
-
-
-    /**
-     * Returns enumerable for ScannableEntity.
-     *
-     * @param dataContext data context
-     * @return enumerable.
-     */
-    @Override
-    public Enumerable<PolyValue[]> scan( DataContext dataContext ) {
-        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
-        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
-        // create parquet enumerator
-        return new AbstractEnumerable<>() {
-            @Override
-            public Enumerator<PolyValue[]> enumerator() {
-                return new ParquetMultiFileEnumerator(
-                        binding.sourceFiles(),
-                        sourceFile -> enumeratorForFile( sourceFile, fieldIndexes, cancelFlag, FiltersContainer.empty ) );
-            }
-        };
-    }
-
-
     /**
      * Returns {@link AlgNode} as part of TranslatableEntity.
      *
@@ -221,7 +140,7 @@ public AlgNode toAlg( AlgCluster cluster, AlgTraitSet traitSet ) {
      * @return enumerable.
      */
     public Enumerable<PolyValue[]> project( final DataContext dataContext, final int[] fields ) {
-        return project( dataContext, fields, List.of() );
+        return executorsFactory.getExecutor( ParquetRelProjectExecutor.class ).createEnumerator( dataContext, fields, List.of() );
     }
 
 
@@ -234,229 +153,88 @@ public Enumerable<PolyValue[]> project( final DataContext dataContext, final int
      * @return enumerable.
      */
     @SuppressWarnings("unused")
-    public Enumerable<PolyValue[]> project( final DataContext dataContext, final int[] fields, final List<ParquetAdapterFilter> filters ) {
-        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
-        final List<ParquetAdapterFilter> resolvedFilters = ParquetFilterResolver.resolveFilters( dataContext, filters, f -> selectPhysicalBinding( f.columnIndex() ) );
-        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
-        return new AbstractEnumerable<>() {
-            @Override
-            public Enumerator<PolyValue[]> enumerator() {
-                return new ParquetMultiFileEnumerator(
-                        binding.sourceFiles(),
-                        sourceFile -> enumeratorForFile( sourceFile, fields, cancelFlag, FiltersContainer.shared( resolvedFilters ) ),
-                        createParquetSourceFileEvaluatorsChain( f -> selectPhysicalBinding( f.columnIndex() ) ),
-                        resolvedFilters );
-            }
-        };
+    public Enumerable<PolyValue[]> project( final DataContext dataContext, final int[] fields, final List<ParquetAdapterFilter<PolyValue>> filters ) {
+        return executorsFactory.getExecutor( ParquetRelProjectExecutor.class ).createEnumerator( dataContext, fields, filters );
     }
 
 
     /**
-     * Executes a supported parent/child join inside the Parquet adapter.
+     * Executes supported aggregates exactly from Parquet file metadata.
      */
     @SuppressWarnings("unused")
-    public Enumerable<PolyValue[]> nestedJoin(
-            final DataContext dataContext,
-            final PhysicalScan leftScan,
-            final PhysicalScan rightScan,
-            final boolean leftIsParent,
-            final boolean emitUnmatchedParents,
-            final List<ParquetAdapterFilter> filters ) {
-
-        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
-        final AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
-        // decide who is parent
-        final PhysicalScan parent = leftIsParent ? leftScan : rightScan;
-        final PhysicalScan child = leftIsParent ? rightScan : leftScan;
-
-        final List<ParquetAdapterFilter> leftScanFilters = leftScan.resolveFilters( dataContext );
-        final List<ParquetAdapterFilter> rightScanFilters = rightScan.resolveFilters( dataContext );
-        // split filters into filter container
-        final JoinFiltersContainer filterContainer = getJoinFiltersContainer( dataContext, leftScan, rightScan, leftIsParent, filters, leftScanFilters, rightScanFilters, parent, child );
-
-        final List<ParquetAdapterFilter> parentFileFilters = leftIsParent ? leftScanFilters : rightScanFilters;
-
-        return new AbstractEnumerable<>() {
-            @Override
-            public Enumerator<PolyValue[]> enumerator() {
-                return new ParquetMultiFileEnumerator(
-                        parent.table().binding.sourceFiles(),
-                        sourceFile -> parent.table().nestedJoinEnumeratorForFile( sourceFile, child.table(), parent.fields(), child.fields(), cancelFlag, filterContainer, leftIsParent, emitUnmatchedParents ),
-                        createParquetSourceFileEvaluatorsChain( f -> parent.selectPhysicalBinding( f.columnIndex() ) ),
-                        parentFileFilters );
-            }
-        };
-    }
-
-
-    public int columnIndexByRole( ParquetColumnRole role ) {
-        for ( int i = 0; i < columns.size(); i++ ) {
-            ParquetColumnBinding columnBinding = binding.getColumnBinding( columns.get( i ).id );
-            if ( columnBinding != null && columnBinding.role() == role ) {
-                return i;
-            }
-        }
-        return -1;
+    public Enumerable<PolyValue[]> metadataAggregate( DataContext dataContext, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        return executorsFactory.getExecutor( ParquetRelMetadataAggregateExecutor.class ).createEnumerator( dataContext, fields, filters, groupFields, aggregateKinds, aggregateArgs );
     }
 
 
-    public String getSourceUrl() {
-        return binding.sourceFiles().stream().map( ParquetSourceFile::fileUrl ).sorted().toList().toString();
-    }
-
-
-    public int getFieldCount() {
-        return columns.size();
-    }
-
-
-    private boolean isNestedTable() {
-        return binding.parentTableName() != null;
-    }
-
-
-    private JoinFiltersContainer getJoinFiltersContainer( DataContext dataContext, PhysicalScan leftScan, PhysicalScan rightScan, boolean leftIsParent, List<ParquetAdapterFilter> filters, List<ParquetAdapterFilter> leftScanFilters, List<ParquetAdapterFilter> rightScanFilters, PhysicalScan parent, PhysicalScan child ) {
-        final List<ParquetAdapterFilter> joinFilters = ParquetFilterResolver.resolveFilters( dataContext, filters, f -> selectBinding( f.columnIndex(), leftScan.fields(), rightScan.fields(), rightScan.table() ) );
-        final List<ParquetAdapterFilter> parentScanFilters = leftIsParent ? leftScanFilters : rightScanFilters;
-        final List<ParquetAdapterFilter> childScanFilters = leftIsParent ? rightScanFilters : leftScanFilters;
-        // split join filters to parent, child, adapter, reader
-        final JoinFiltersContainer container = new JoinFiltersSplitter().split( joinFilters, leftIsParent, parent.fields().length, child.fields().length );
-        return new JoinFiltersContainer(
-                container.parentFilters(),
-                container.childFilters(),
-                container.adapterFilters(),
-                combine( container.nativeFilters(), parentScanFilters ),
-                parentScanFilters,
-                childScanFilters );
-    }
-
-
-    private Enumerator<PolyValue[]> enumeratorForFile( ParquetSourceFile sourceFile, int[] fields, AtomicBoolean cancelFlag, FiltersContainer filtersContainer ) {
-        boolean bindingScan = isNestedTable() || needsBindingScan( fields ) || binding.sourceFiles().size() > 1 || !filtersContainer.adapterFilters().isEmpty();
-        Source fileSource = sourceFile.asSource();
-        ParquetSourceReader reader = new ParquetSourceReader( fileSource, cancelFlag, bindingScan ? null : fields, filtersContainer.nativeFilters() );
-        List<ParquetColumnBinding> selectedBindings = projectedBindings( fields );
-        List<ParquetColumnBinding> filterBindings = projectedBindings( fieldIndexes );
-        if ( isNestedTable() ) {
-            return new ParquetNestedRepeatedRelEnumerator( reader, binding, selectedBindings, filterBindings, filtersContainer );
-        }
-        if ( bindingScan ) {
-            return new ParquetNestedNonRepeatedRelEnumerator( reader, sourceFile, selectedBindings, filterBindings, filtersContainer );
-        }
-        return new ParquetRelEnumerator( reader, filtersContainer.withoutPathElementsInAdapterFilters() );
+    /**
+     * Checks whether filters, groups and aggregate functions can be executed on metadata only.
+     *
+     * @param fields an array of physical field indexes of columns used in a group by statement.
+     * @param filters a list of filters to be validated.
+     * @param groupSet a group by set.
+     * @param aggregateCalls a list of aggregate calls (aggregate functions)
+     * @return {@code true} filters, groups and aggregate functions can be executed on metadata only and {@code false} otherwise.
+     */
+    public boolean supportsMetadataAggregate( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
+        return executorsFactory.getExecutor( ParquetRelMetadataAggregateExecutor.class ).supportsMetadataAggregate( fields, filters, groupSet, aggregateCalls );
     }
 
 
-    private Enumerator<PolyValue[]> nestedJoinEnumeratorForFile(
-            ParquetSourceFile sourceFile,
-            ParquetRelTable child,
-            int[] parentFields,
-            int[] childFields,
-            AtomicBoolean cancelFlag,
-            JoinFiltersContainer filterContainer,
-            boolean leftIsParent,
-            boolean emitUnmatchedParents ) {
-
-        ParquetSourceReader reader = new ParquetSourceReader( sourceFile.asSource(), cancelFlag, null, filterContainer.nativeFilters() );
-        return new ParquetNestedJoinEnumerator(
-                reader,
-                new JoinNestedBinding( binding, projectedBindings( parentFields ), projectedBindings( fieldIndexes ) ),
-                new JoinNestedBinding( child.binding, child.projectedBindings( childFields ), child.projectedBindings( child.fieldIndexes ) ),
-                filterContainer,
-                leftIsParent,
-                emitUnmatchedParents );
+    /**
+     * Executes supported aggregates by scanning rows inside the adapter.
+     */
+    @SuppressWarnings("unused")
+    public Enumerable<PolyValue[]> dataAggregate( DataContext dataContext, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        return executorsFactory.getExecutor( ParquetRelDataAggregateExecutor.class ).createEnumerator( dataContext, fields, filters, groupFields, aggregateKinds, aggregateArgs );
     }
 
 
     /**
-     * Apply two evaluators: partition and statistics
-     * @param selector - function
-     * @return ParquetFilterEvaluatorsChain<ParquetSourceFile>
+     * Checks whether all provided aggregate functions are supported. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
+     *
+     * @param fields an array of field indexes.
+     * @param groupSet a set of group by fields.
+     * @param aggregateCalls a list of aggregation functions to validate.
+     * @return {@code true} if all provided aggregation functions can be applied and {@code false} otherwise.
      */
-    private static ParquetFilterEvaluatorsChain<ParquetSourceFile> createParquetSourceFileEvaluatorsChain( Function<ParquetAdapterFilter, ParquetColumnBinding> selector ) {
-        return new ParquetFilterEvaluatorsChain<>( List.of(
-                new ParquetSourceFilePartitionFilterEvaluator( selector ),
-                new ParquetSourceFileStatisticsFilterEvaluator( selector ) )
-        );
+    public boolean supportsDataAggregate( int[] fields, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
+        return executorsFactory.getExecutor( ParquetRelDataAggregateExecutor.class ).supportsDataAggregate( fields, groupSet, aggregateCalls );
     }
 
 
     /**
-     * Selects a column binding for a join. If the column index is less then left table fields count then use left table column binding otherwise use right table column biding.
-     *
-     * @param joinedIndex a column index
-     * @param leftFields a list of left column fields
-     * @param rightFields a list of right column fields
-     * @param right a right table
-     * @return {@link ParquetColumnBinding}
+     * Executes a supported parent/child join inside the Parquet adapter.
      */
-    private ParquetColumnBinding selectBinding( int joinedIndex, int[] leftFields, int[] rightFields, ParquetRelTable right ) {
-        if ( joinedIndex < 0 ) {
-            throw new GenericRuntimeException( "Invalid joined filter column index: " + joinedIndex );
-        }
-        if ( joinedIndex < leftFields.length ) {
-            return binding.getColumnBinding( columns.get( leftFields[joinedIndex] ).id );
-        }
-        int rightIndex = joinedIndex - leftFields.length;
-        if ( rightIndex >= rightFields.length ) {
-            throw new GenericRuntimeException( "Invalid joined filter column index: " + joinedIndex );
-        }
-        return right.binding.getColumnBinding( right.columns.get( rightFields[rightIndex] ).id );
+    @SuppressWarnings("unused")
+    public Enumerable<PolyValue[]> nestedJoin( DataContext dataContext, PhysicalScan leftScan, PhysicalScan rightScan, boolean leftIsParent, boolean emitUnmatchedParents, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        return executorsFactory.getExecutor( ParquetRelNestedJoinExecutor.class ).createEnumerator( dataContext, leftScan, rightScan, leftIsParent, emitUnmatchedParents, filters );
     }
 
 
-    private ParquetColumnBinding selectPhysicalBinding( int columnIndex ) {
-        if ( columnIndex < 0 || columnIndex >= columns.size() ) {
-            throw new GenericRuntimeException( "Invalid physical filter column index: " + columnIndex );
+    public int columnIndexByRole( ParquetColumnRole role ) {
+        for ( int i = 0; i < columns.size(); i++ ) {
+            ParquetColumnBinding columnBinding = binding.getColumnBinding( columns.get( i ).id );
+            if ( columnBinding != null && columnBinding.role() == role ) {
+                return i;
+            }
         }
-        return binding.getColumnBinding( columns.get( columnIndex ).id );
+        return -1;
     }
 
 
-    private static List<ParquetAdapterFilter> combine( List<ParquetAdapterFilter> left, List<ParquetAdapterFilter> right ) {
-        if ( left.isEmpty() ) {
-            return right;
-        }
-        if ( right.isEmpty() ) {
-            return left;
-        }
-        List<ParquetAdapterFilter> combined = new ArrayList<>( left.size() + right.size() );
-        combined.addAll( left );
-        combined.addAll( right );
-        return combined;
+    public String getSourceUrl() {
+        return binding.sourceFiles().stream().map( ParquetSourceFile::fileUrl ).sorted().toList().toString();
     }
 
 
-    /**
-     * Gets a list of bindings per provided fields
-     *
-     * @param fields a list of fields to get the bindings for
-     * @return a list of column bindings
-     */
-    private List<ParquetColumnBinding> projectedBindings( int[] fields ) {
-        List<ParquetColumnBinding> selected = new ArrayList<>( fields.length );
-        for ( int field : fields ) {
-            selected.add( Objects.requireNonNull( binding.getColumnBinding( columns.get( field ).id ), "Missing parquet column binding" ) );
-        }
-        return selected;
+    public int getFieldCount() {
+        return columns.size();
     }
 
 
-    private boolean needsBindingScan( int[] fields ) {
-        for ( int field : fields ) {
-            ParquetColumnBinding columnBinding = binding.getColumnBinding( columns.get( field ).id );
-            if ( columnBinding == null || columnBinding.sourcePathElements().isEmpty() ) {
-                return true;
-            }
-            if ( columnBinding.sourcePathElements().size() > 1 ) {
-                return true;
-            }
-            var schema = schemaReader.getSchema();
-            if ( field >= schema.getFieldCount() || !schema.getType( field ).getName().equals( columnBinding.sourcePathElements().get( 0 ) ) ) {
-                return true;
-            }
-        }
-        return false;
+    private boolean isNestedTable() {
+        return binding.parentTableName() != null;
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateCallDescriptor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateCallDescriptor.java
new file mode 100644
index 0000000000..a9dfa14407
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateCallDescriptor.java
@@ -0,0 +1,51 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.aggregate;
+
+
+/**
+ * A definition of the aggregate call.
+ *
+ * @param kind a {@link Kind} of the aggregate call.
+ * @param argumentIndex a field index of the aggregated column.
+ */
+public record AggregateCallDescriptor( Kind kind, int argumentIndex ) {
+
+    public static final int NO_ARGUMENT = -1;
+
+
+    public static AggregateCallDescriptor countStar() {
+        return new AggregateCallDescriptor( Kind.COUNT, NO_ARGUMENT );
+    }
+
+
+    public static AggregateCallDescriptor of( String kind, int argumentIndex ) {
+        return new AggregateCallDescriptor( Kind.valueOf( kind ), argumentIndex );
+    }
+
+
+    /**
+     * Supported aggregation kinds
+     */
+    public enum Kind {
+        COUNT,
+        SUM,
+        MIN,
+        MAX
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateColumnAccumulator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateColumnAccumulator.java
new file mode 100644
index 0000000000..74b9dc8830
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateColumnAccumulator.java
@@ -0,0 +1,204 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.aggregate;
+
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.parquet.column.ColumnDescriptor;
+import org.apache.parquet.column.ColumnReader;
+
+/**
+ * It reads one Parquet column and accumulates count/sum/min/max. This is the real column-at-a-time accumulator.
+ */
+public abstract class AggregateColumnAccumulator {
+
+    protected static final int CHECK_INTERVAL = 4096;
+
+    protected final ColumnReader reader;
+    protected final long rowCount;
+    protected final int maxDefinitionLevel;
+    protected final AtomicBoolean cancelFlag;
+    protected double sum;
+    protected double min = Double.POSITIVE_INFINITY;
+    protected double max = Double.NEGATIVE_INFINITY;
+    protected long count;
+
+
+    protected AggregateColumnAccumulator( ColumnReader reader, long rowCount, int maxDefinitionLevel, AtomicBoolean cancelFlag ) {
+        this.reader = reader;
+        this.rowCount = rowCount;
+        this.maxDefinitionLevel = maxDefinitionLevel;
+        this.cancelFlag = cancelFlag;
+    }
+
+
+    public static AggregateColumnAccumulator create( ColumnReader reader, ColumnDescriptor descriptor, long rowCount, AtomicBoolean cancelFlag ) {
+        return switch ( descriptor.getPrimitiveType().getPrimitiveTypeName() ) {
+            case INT32 -> new IntAggregateColumnAccumulator( reader, rowCount, descriptor.getMaxDefinitionLevel(), cancelFlag );
+            case INT64 -> new LongAggregateColumnAccumulator( reader, rowCount, descriptor.getMaxDefinitionLevel(), cancelFlag );
+            case FLOAT -> new FloatAggregateColumnAccumulator( reader, rowCount, descriptor.getMaxDefinitionLevel(), cancelFlag );
+            case DOUBLE -> new DoubleAggregateColumnAccumulator( reader, rowCount, descriptor.getMaxDefinitionLevel(), cancelFlag );
+            case BOOLEAN, BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> throw new UnsupportedOperationException( "Unsupported numeric primitive type: " + descriptor.getPrimitiveType().getPrimitiveTypeName() );
+        };
+    }
+
+
+    public double sum() {
+        return sum;
+    }
+
+
+    public long count() {
+        return count;
+    }
+
+
+    public ColumnAggregateResult result() {
+        return new ColumnAggregateResult( count, sum, min, max );
+    }
+
+
+    public abstract void read();
+
+
+    protected void addValue( double value ) {
+        sum += value;
+        if ( count == 0 || value < min ) {
+            min = value;
+        }
+        if ( count == 0 || value > max ) {
+            max = value;
+        }
+        count++;
+    }
+
+
+    private static class IntAggregateColumnAccumulator extends AggregateColumnAccumulator {
+
+        public IntAggregateColumnAccumulator( ColumnReader reader, long rowCount, int maxDefinitionLevel, AtomicBoolean cancelFlag ) {
+            super( reader, rowCount, maxDefinitionLevel, cancelFlag );
+        }
+
+
+        @Override
+        public void read() {
+            long row = 0;
+
+            while ( row < rowCount ) {
+                if ( cancelFlag.get() ) {
+                    return;
+                }
+
+                long end = Math.min( row + CHECK_INTERVAL, rowCount );
+                for ( ; row < end; row++ ) {
+                    if ( maxDefinitionLevel == 0 || reader.getCurrentDefinitionLevel() == maxDefinitionLevel ) {
+                        addValue( reader.getInteger() );
+                    }
+                    reader.consume();
+                }
+            }
+        }
+
+    }
+
+
+    private static class LongAggregateColumnAccumulator extends AggregateColumnAccumulator {
+
+        public LongAggregateColumnAccumulator( ColumnReader reader, long rowCount, int maxDefinitionLevel, AtomicBoolean cancelFlag ) {
+            super( reader, rowCount, maxDefinitionLevel, cancelFlag );
+        }
+
+
+        @Override
+        public void read() {
+            long row = 0;
+
+            while ( row < rowCount ) {
+                if ( cancelFlag.get() ) {
+                    return;
+                }
+
+                long end = Math.min( row + CHECK_INTERVAL, rowCount );
+                for ( ; row < end; row++ ) {
+                    if ( maxDefinitionLevel == 0 || reader.getCurrentDefinitionLevel() == maxDefinitionLevel ) {
+                        addValue( reader.getLong() );
+                    }
+                    reader.consume();
+                }
+            }
+        }
+
+    }
+
+
+    private static class FloatAggregateColumnAccumulator extends AggregateColumnAccumulator {
+
+        public FloatAggregateColumnAccumulator( ColumnReader reader, long rowCount, int maxDefinitionLevel, AtomicBoolean cancelFlag ) {
+            super( reader, rowCount, maxDefinitionLevel, cancelFlag );
+        }
+
+
+        @Override
+        public void read() {
+            long row = 0;
+
+            while ( row < rowCount ) {
+                if ( cancelFlag.get() ) {
+                    return;
+                }
+
+                long end = Math.min( row + CHECK_INTERVAL, rowCount );
+                for ( ; row < end; row++ ) {
+                    if ( maxDefinitionLevel == 0 || reader.getCurrentDefinitionLevel() == maxDefinitionLevel ) {
+                        addValue( reader.getFloat() );
+                    }
+                    reader.consume();
+                }
+            }
+        }
+
+    }
+
+
+    private static class DoubleAggregateColumnAccumulator extends AggregateColumnAccumulator {
+
+        public DoubleAggregateColumnAccumulator( ColumnReader reader, long rowCount, int maxDefinitionLevel, AtomicBoolean cancelFlag ) {
+            super( reader, rowCount, maxDefinitionLevel, cancelFlag );
+        }
+
+
+        @Override
+        public void read() {
+            long row = 0;
+
+            while ( row < rowCount ) {
+                if ( cancelFlag.get() ) {
+                    return;
+                }
+
+                long end = Math.min( row + CHECK_INTERVAL, rowCount );
+                for ( ; row < end; row++ ) {
+                    if ( maxDefinitionLevel == 0 || reader.getCurrentDefinitionLevel() == maxDefinitionLevel ) {
+                        addValue( reader.getDouble() );
+                    }
+                    reader.consume();
+                }
+            }
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateGroupState.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateGroupState.java
new file mode 100644
index 0000000000..83499b9729
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateGroupState.java
@@ -0,0 +1,180 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.aggregate;
+
+import java.util.Arrays;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.category.PolyNumber;
+import org.polypheny.db.type.entity.numerical.PolyDouble;
+import org.polypheny.db.type.entity.numerical.PolyLong;
+
+
+/**
+ * A container for the aggregated values.
+ */
+public class AggregateGroupState {
+
+    private final AggregateCallDescriptor[] aggregateCalls;
+    private final double[] sums;
+    private final double[] mins;
+    private final double[] maxes;
+    private final long[] counts;
+
+
+    public AggregateGroupState( AggregateCallDescriptor[] aggregateCalls ) {
+        this.aggregateCalls = aggregateCalls;
+        this.sums = new double[aggregateCalls.length];
+        this.mins = new double[aggregateCalls.length];
+        this.maxes = new double[aggregateCalls.length];
+        this.counts = new long[aggregateCalls.length];
+        Arrays.fill( this.mins, Double.POSITIVE_INFINITY );
+        Arrays.fill( this.maxes, Double.NEGATIVE_INFINITY );
+    }
+
+
+    /**
+     * Increments a star counter by the provided number.
+     *
+     * @param rowCount a row count.
+     */
+    public void increment( long rowCount ) {
+        for ( int i = 0; i < aggregateCalls.length; i++ ) {
+            AggregateCallDescriptor aggregateCall = aggregateCalls[i];
+            if ( aggregateCall.kind() == AggregateCallDescriptor.Kind.COUNT && aggregateCall.argumentIndex() == AggregateCallDescriptor.NO_ARGUMENT ) {
+                counts[i] += rowCount;
+            }
+        }
+    }
+
+
+    public void add( Object[] row ) {
+        for ( int i = 0; i < aggregateCalls.length; i++ ) {
+            AggregateCallDescriptor aggregateCall = aggregateCalls[i];
+            if ( aggregateCall.argumentIndex() == AggregateCallDescriptor.NO_ARGUMENT ) {
+                if ( aggregateCall.kind() == AggregateCallDescriptor.Kind.COUNT ) {
+                    counts[i]++;
+                }
+                continue;
+            }
+            Object value = row[aggregateCall.argumentIndex()];
+            if ( isNull( value ) ) {
+                continue;
+            }
+            if ( aggregateCall.kind() == AggregateCallDescriptor.Kind.COUNT ) {
+                counts[i]++;
+                continue;
+            }
+            addValue( i, doubleValue( value ) );
+        }
+    }
+
+
+    public void addCount( int aggregateIndex, long rows ) {
+        counts[aggregateIndex] += rows;
+    }
+
+
+    public void addDouble( int aggregateIndex, double value ) {
+        sums[aggregateIndex] += value;
+        counts[aggregateIndex]++;
+    }
+
+
+    public void addSum( int aggregateIndex, double value ) {
+        sums[aggregateIndex] += value;
+    }
+
+
+    public void addMin( int aggregateIndex, double value ) {
+        if ( counts[aggregateIndex] == 0 || value < mins[aggregateIndex] ) {
+            mins[aggregateIndex] = value;
+        }
+        counts[aggregateIndex]++;
+    }
+
+
+    public void addMax( int aggregateIndex, double value ) {
+        if ( counts[aggregateIndex] == 0 || value > maxes[aggregateIndex] ) {
+            maxes[aggregateIndex] = value;
+        }
+        counts[aggregateIndex]++;
+    }
+
+
+    public void merge( AggregateGroupState values ) {
+        for ( int i = 0; i < aggregateCalls.length; i++ ) {
+            AggregateCallDescriptor.Kind kind = aggregateCalls[i].kind();
+            if ( values.counts[i] > 0 ) {
+                if ( kind == AggregateCallDescriptor.Kind.MIN && (counts[i] == 0 || values.mins[i] < mins[i]) ) {
+                    mins[i] = values.mins[i];
+                } else if ( kind == AggregateCallDescriptor.Kind.MAX && (counts[i] == 0 || values.maxes[i] > maxes[i]) ) {
+                    maxes[i] = values.maxes[i];
+                }
+            }
+            sums[i] += values.sums[i];
+            counts[i] += values.counts[i];
+        }
+    }
+
+
+    public long count( int index ) {
+        return counts[index];
+    }
+
+
+    public PolyValue result( int index ) {
+        AggregateCallDescriptor.Kind kind = aggregateCalls[index].kind();
+        if ( kind == AggregateCallDescriptor.Kind.COUNT ) {
+            return PolyLong.of( counts[index] );
+        }
+        if ( counts[index] == 0 ) {
+            return PolyNull.NULL;
+        }
+        if ( kind == AggregateCallDescriptor.Kind.MIN ) {
+            return PolyDouble.of( mins[index] );
+        }
+        if ( kind == AggregateCallDescriptor.Kind.MAX ) {
+            return PolyDouble.of( maxes[index] );
+        }
+        return PolyDouble.of( sums[index] );
+    }
+
+
+    private void addValue( int aggregateIndex, double value ) {
+        switch ( aggregateCalls[aggregateIndex].kind() ) {
+            case MIN -> addMin( aggregateIndex, value );
+            case MAX -> addMax( aggregateIndex, value );
+            default -> addDouble( aggregateIndex, value );
+        }
+    }
+
+
+    private boolean isNull( Object value ) {
+        return value == null || value instanceof PolyValue polyValue && polyValue.isNull();
+    }
+
+
+    private double doubleValue( Object value ) {
+        if ( value instanceof PolyValue polyValue ) {
+            PolyNumber number = polyValue.asNumber();
+            return number.doubleValue();
+        }
+        return ((Number) value).doubleValue();
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateInputColumn.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateInputColumn.java
new file mode 100644
index 0000000000..43fcbafd29
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateInputColumn.java
@@ -0,0 +1,124 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.aggregate;
+
+import org.apache.parquet.column.ColumnReader;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+
+/**
+ * Maps one input column to one or more aggregate indexes and reads/adds that column for a row.
+ */
+public class AggregateInputColumn {
+
+    private final int columnIndex;
+    private final int maxDefinitionLevel;
+    private final boolean required;
+    private final PrimitiveType.PrimitiveTypeName type;
+    private final int singleCountIndex;
+    private final int[] countIndexes;
+    private final int singleDoubleIndex;
+    private final int[] doubleIndexes;
+    private final int singleMinIndex;
+    private final int[] minIndexes;
+    private final int singleMaxIndex;
+    private final int[] maxIndexes;
+
+
+    public AggregateInputColumn( int columnIndex, int[] countIndexes, int[] doubleIndexes, int[] minIndexes, int[] maxIndexes, int maxDefinitionLevel, PrimitiveTypeName type ) {
+        this.columnIndex = columnIndex;
+        this.maxDefinitionLevel = maxDefinitionLevel;
+        this.required = maxDefinitionLevel == 0;
+        this.type = type;
+        this.singleCountIndex = AggregateRowAccumulator.singleIndex( countIndexes );
+        this.countIndexes = AggregateRowAccumulator.additionalIndexes( countIndexes );
+        this.singleDoubleIndex = AggregateRowAccumulator.singleIndex( doubleIndexes );
+        this.doubleIndexes = AggregateRowAccumulator.additionalIndexes( doubleIndexes );
+        this.singleMinIndex = AggregateRowAccumulator.singleIndex( minIndexes );
+        this.minIndexes = AggregateRowAccumulator.additionalIndexes( minIndexes );
+        this.singleMaxIndex = AggregateRowAccumulator.singleIndex( maxIndexes );
+        this.maxIndexes = AggregateRowAccumulator.additionalIndexes( maxIndexes );
+    }
+
+
+    public void add( AggregateGroupState values, ColumnReader[] readers ) {
+        ColumnReader reader = readers[columnIndex];
+        if ( required || reader.getCurrentDefinitionLevel() == maxDefinitionLevel ) {
+            double value = doubleValue( reader, type );
+            addPresentValue( values, value );
+        }
+        reader.consume();
+    }
+
+
+    public void add( AggregateGroupState values, ColumnReader[] readers, boolean[] present, double[] numericValues ) {
+        read( readers, present, numericValues );
+        if ( !present[columnIndex] ) {
+            return;
+        }
+        addPresentValue( values, numericValues[columnIndex] );
+    }
+
+
+    public void add( AggregateGroupState values, ColumnReader[] readers, boolean[] consumed, boolean[] present, double[] numericValues ) {
+        read( readers, consumed, present, numericValues );
+        if ( !present[columnIndex] ) {
+            return;
+        }
+        addPresentValue( values, numericValues[columnIndex] );
+    }
+
+
+    private void read( ColumnReader[] readers, boolean[] present, double[] numericValues ) {
+        ColumnReader reader = readers[columnIndex];
+        boolean valuePresent = required || reader.getCurrentDefinitionLevel() == maxDefinitionLevel;
+        present[columnIndex] = valuePresent;
+        if ( valuePresent ) {
+            numericValues[columnIndex] = doubleValue( reader, type );
+        }
+        reader.consume();
+    }
+
+
+    private void read( ColumnReader[] readers, boolean[] consumed, boolean[] present, double[] numericValues ) {
+        if ( consumed[columnIndex] ) {
+            return;
+        }
+        read( readers, present, numericValues );
+        consumed[columnIndex] = true;
+    }
+
+
+    private void addPresentValue( AggregateGroupState values, double value ) {
+        AggregateRowAccumulator.addIndexes( values, singleCountIndex, countIndexes, 1 );
+        AggregateRowAccumulator.addDoubleIndexes( values, singleDoubleIndex, doubleIndexes, value );
+        AggregateRowAccumulator.addMinIndexes( values, singleMinIndex, minIndexes, value );
+        AggregateRowAccumulator.addMaxIndexes( values, singleMaxIndex, maxIndexes, value );
+    }
+
+
+    private double doubleValue( ColumnReader reader, PrimitiveType.PrimitiveTypeName type ) {
+        return switch ( type ) {
+            case INT32 -> reader.getInteger();
+            case INT64 -> reader.getLong();
+            case FLOAT -> reader.getFloat();
+            case DOUBLE -> reader.getDouble();
+            case BOOLEAN, BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> throw new IllegalArgumentException( "Unsupported aggregate column type: " + type );
+        };
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateRowAccumulator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateRowAccumulator.java
new file mode 100644
index 0000000000..c30d0cfa8b
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateRowAccumulator.java
@@ -0,0 +1,207 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.aggregate;
+
+import java.util.ArrayList;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import org.apache.parquet.column.ColumnDescriptor;
+import org.apache.parquet.column.ColumnReader;
+
+/**
+ * Applies all aggregate calls for one input row into an AggregateGroupState.
+ */
+public class AggregateRowAccumulator {
+
+    private static final int NO_INDEX = -1;
+
+    private final int[] countStarIndexes;
+    private final int singleCountStarIndex;
+    private final AggregateInputColumn[] columns;
+
+
+    private AggregateRowAccumulator( int[] countStarIndexes, AggregateInputColumn[] columns ) {
+        this.singleCountStarIndex = firstIndex( countStarIndexes );
+        this.countStarIndexes = remainingIndexes( countStarIndexes );
+        this.columns = columns;
+    }
+
+
+    public static AggregateRowAccumulator build( AggregateCallDescriptor[] aggregateCalls, ColumnDescriptor[] columns ) {
+        List<Integer> countStarIndexes = new ArrayList<>();
+        Map<Integer, AggregateInputColumnBuilder> builders = new LinkedHashMap<>();
+        for ( int i = 0; i < aggregateCalls.length; i++ ) {
+            AggregateCallDescriptor aggregateCall = aggregateCalls[i];
+            if ( aggregateCall.argumentIndex() == AggregateCallDescriptor.NO_ARGUMENT ) {
+                if ( aggregateCall.kind() == AggregateCallDescriptor.Kind.COUNT ) {
+                    countStarIndexes.add( i );
+                }
+                continue;
+            }
+
+            AggregateInputColumnBuilder builder = builders.computeIfAbsent( aggregateCall.argumentIndex(), AggregateInputColumnBuilder::new );
+            switch ( aggregateCall.kind() ) {
+                case COUNT -> builder.countIndexes.add( i );
+                case SUM -> builder.doubleIndexes.add( i );
+                case MIN -> builder.minIndexes.add( i );
+                case MAX -> builder.maxIndexes.add( i );
+            }
+        }
+
+        AggregateInputColumn[] columnAccumulators = builders.values().stream()
+                .map( b -> b.build( columns ) )
+                .toArray( AggregateInputColumn[]::new );
+
+        return new AggregateRowAccumulator( countStarIndexes.stream().mapToInt( Integer::intValue ).toArray(), columnAccumulators );
+    }
+
+
+    public void add( AggregateGroupState values, ColumnReader[] readers ) {
+        addCountStars( values );
+        for ( AggregateInputColumn column : columns ) {
+            column.add( values, readers );
+        }
+    }
+
+
+    public void add( AggregateGroupState values, ColumnReader[] readers, boolean[] present, double[] numericValues ) {
+        addCountStars( values );
+        for ( AggregateInputColumn column : columns ) {
+            column.add( values, readers, present, numericValues );
+        }
+    }
+
+
+    public void add( AggregateGroupState values, ColumnReader[] readers, boolean[] consumed, boolean[] present, double[] numericValues ) {
+        addCountStars( values );
+        for ( AggregateInputColumn column : columns ) {
+            column.add( values, readers, consumed, present, numericValues );
+        }
+    }
+
+
+    private void addCountStars( AggregateGroupState values ) {
+        if ( singleCountStarIndex != NO_INDEX ) {
+            values.addCount( singleCountStarIndex, 1 );
+        }
+        for ( int countStarIndex : countStarIndexes ) {
+            values.addCount( countStarIndex, 1 );
+        }
+    }
+
+
+    private static int firstIndex( int[] indexes ) {
+        return indexes.length == 0 ? NO_INDEX : indexes[0];
+    }
+
+
+    private static int[] remainingIndexes( int[] indexes ) {
+        if ( indexes.length <= 1 ) {
+            return new int[0];
+        }
+        int[] remaining = new int[indexes.length - 1];
+        System.arraycopy( indexes, 1, remaining, 0, remaining.length );
+        return remaining;
+    }
+
+
+    static int singleIndex( int[] indexes ) {
+        return firstIndex( indexes );
+    }
+
+
+    static int[] additionalIndexes( int[] indexes ) {
+        return remainingIndexes( indexes );
+    }
+
+
+    static boolean hasIndex( int index ) {
+        return index != NO_INDEX;
+    }
+
+
+    @SuppressWarnings("SameParameterValue")
+    static void addIndexes( AggregateGroupState values, int singleIndex, int[] indexes, long rows ) {
+        if ( hasIndex( singleIndex ) ) {
+            values.addCount( singleIndex, rows );
+        }
+        for ( int index : indexes ) {
+            values.addCount( index, rows );
+        }
+    }
+
+
+    static void addDoubleIndexes( AggregateGroupState values, int singleIndex, int[] indexes, double value ) {
+        if ( hasIndex( singleIndex ) ) {
+            values.addDouble( singleIndex, value );
+        }
+        for ( int index : indexes ) {
+            values.addDouble( index, value );
+        }
+    }
+
+
+    static void addMinIndexes( AggregateGroupState values, int singleIndex, int[] indexes, double value ) {
+        if ( hasIndex( singleIndex ) ) {
+            values.addMin( singleIndex, value );
+        }
+        for ( int index : indexes ) {
+            values.addMin( index, value );
+        }
+    }
+
+
+    static void addMaxIndexes( AggregateGroupState values, int singleIndex, int[] indexes, double value ) {
+        if ( hasIndex( singleIndex ) ) {
+            values.addMax( singleIndex, value );
+        }
+        for ( int index : indexes ) {
+            values.addMax( index, value );
+        }
+    }
+
+
+    private static class AggregateInputColumnBuilder {
+
+        private final int columnIndex;
+        private final List<Integer> countIndexes = new ArrayList<>();
+        private final List<Integer> doubleIndexes = new ArrayList<>();
+        private final List<Integer> minIndexes = new ArrayList<>();
+        private final List<Integer> maxIndexes = new ArrayList<>();
+
+
+        private AggregateInputColumnBuilder( int columnIndex ) {
+            this.columnIndex = columnIndex;
+        }
+
+
+        private AggregateInputColumn build( ColumnDescriptor[] columns ) {
+            return new AggregateInputColumn(
+                    columnIndex,
+                    countIndexes.stream().mapToInt( Integer::intValue ).toArray(),
+                    doubleIndexes.stream().mapToInt( Integer::intValue ).toArray(),
+                    minIndexes.stream().mapToInt( Integer::intValue ).toArray(),
+                    maxIndexes.stream().mapToInt( Integer::intValue ).toArray(),
+                    columns[columnIndex].getMaxDefinitionLevel(),
+                    columns[columnIndex].getPrimitiveType().getPrimitiveTypeName()
+            );
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/ColumnAggregateProjection.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/ColumnAggregateProjection.java
new file mode 100644
index 0000000000..39a585adde
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/ColumnAggregateProjection.java
@@ -0,0 +1,33 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.aggregate;
+
+import java.util.Map;
+
+/**
+ * A record containing aggregate projection info for a specific column.
+ *
+ * @param fields a physical table filed indexes.
+ * @param aggregateIndexes a map of aggregate call index to a field index.
+ */
+public record ColumnAggregateProjection( int[] fields, Map<Integer, Integer> aggregateIndexes ) {
+
+    public int aggregateIndex( int aggregateArg ) {
+        return aggregateIndexes.get( aggregateArg );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/ColumnAggregateResult.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/ColumnAggregateResult.java
new file mode 100644
index 0000000000..a0e3cf2f73
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/ColumnAggregateResult.java
@@ -0,0 +1,45 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.aggregate;
+
+
+/**
+ * Aggregated values for a single primitive numeric column.
+ *
+ * @param count number of present values.
+ * @param sum sum of present values.
+ * @param min minimum present value.
+ * @param max maximum present value.
+ */
+public record ColumnAggregateResult(long count, double sum, double min, double max ) {
+
+    public static ColumnAggregateResult empty() {
+        return new ColumnAggregateResult( 0, 0, Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY );
+    }
+
+
+    public ColumnAggregateResult merge( ColumnAggregateResult other ) {
+        if ( count == 0 ) {
+            return other;
+        }
+        if ( other.count == 0 ) {
+            return this;
+        }
+        return new ColumnAggregateResult( count + other.count, sum + other.sum, Math.min( min, other.min ), Math.max( max, other.max ) );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/GroupKey.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/GroupKey.java
new file mode 100644
index 0000000000..4cef3a5d19
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/aggregate/GroupKey.java
@@ -0,0 +1,132 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.aggregate;
+
+import java.util.Arrays;
+import java.util.Objects;
+
+
+/**
+ * Represents a group key consisting of multiple values. Used for group by aggregations.
+ * The implementation is optimized for 1-2 columns in group by statement by reducing array allocations.
+ */
+public final class GroupKey {
+
+    public final static GroupKey Empty = GroupKey.of( new Object[0] );
+
+    private final Object first;
+    private final Object second;
+    private final Object[] remaining;
+    private final int size;
+    private final int hash;
+
+
+    private GroupKey( Object first, Object second, Object[] remaining, int size ) {
+        this.first = first;
+        this.second = second;
+        this.remaining = remaining;
+        this.size = size;
+        this.hash = computeHash();
+    }
+
+
+    public static GroupKey of( Object value ) {
+        return new GroupKey( value, null, null, 1 );
+    }
+
+
+    public static GroupKey of( Object first, Object second ) {
+        return new GroupKey( first, second, null, 2 );
+    }
+
+
+    public static GroupKey of( Object[] values ) {
+        Object first = values.length > 0 ? values[0] : null;
+        Object second = values.length > 1 ? values[1] : null;
+        Object[] remaining = values.length > 2 ? Arrays.copyOfRange( values, 2, values.length ) : null;
+        return new GroupKey( first, second, remaining, values.length );
+    }
+
+
+    private static int valueHash( Object value ) {
+        return value instanceof byte[] bytes ? Arrays.hashCode( bytes ) : Objects.hashCode( value );
+    }
+
+
+    private static boolean valueEquals( Object left, Object right ) {
+        if ( left instanceof byte[] leftBytes && right instanceof byte[] rightBytes ) {
+            return Arrays.equals( leftBytes, rightBytes );
+        }
+        return Objects.equals( left, right );
+    }
+
+
+    public int size() {
+        return size;
+    }
+
+
+    public Object value( int index ) {
+        if ( index == 0 ) {
+            return first;
+        }
+        if ( index == 1 ) {
+            return second;
+        }
+        return remaining[index - 2];
+    }
+
+
+    private int computeHash() {
+        int result = 1;
+        result = 31 * result + valueHash( first );
+        if ( size > 1 ) {
+            result = 31 * result + valueHash( second );
+        }
+        if ( remaining != null ) {
+            for ( Object value : remaining ) {
+                result = 31 * result + valueHash( value );
+            }
+        }
+        return result;
+    }
+
+
+    @Override
+    public boolean equals( Object obj ) {
+        if ( this == obj ) {
+            return true;
+        }
+        if ( !(obj instanceof GroupKey other) || size != other.size || hash != other.hash ) {
+            return false;
+        }
+        if ( !valueEquals( first, other.first ) ) {
+            return false;
+        }
+        if ( size > 1 && !valueEquals( second, other.second ) ) {
+            return false;
+        }
+        return Arrays.deepEquals( remaining, other.remaining );
+    }
+
+
+    @Override
+    public int hashCode() {
+        return hash;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractAggregateEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractAggregateEnumerator.java
new file mode 100644
index 0000000000..a1a9c16c85
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractAggregateEnumerator.java
@@ -0,0 +1,273 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.execution;
+
+import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnStatistics;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
+import org.polypheny.db.type.entity.PolyValue;
+import java.util.ArrayList;
+import java.util.ConcurrentModificationException;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.NoSuchElementException;
+import java.util.Optional;
+import java.util.concurrent.Callable;
+import java.util.concurrent.ExecutionException;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+import java.util.concurrent.atomic.AtomicBoolean;
+import java.util.function.Function;
+import java.util.function.Supplier;
+
+public abstract class AbstractAggregateEnumerator implements Enumerator<PolyValue[]> {
+
+    protected Enumerator<PolyValue[]> enumerator;
+
+
+    protected AbstractAggregateEnumerator( Supplier<Enumerator<PolyValue[]>> enumeratorSupplier ) {
+        enumerator = enumeratorSupplier.get();
+    }
+
+
+    /**
+     * Gets the current element in the collection.
+     *
+     * <p>After an enumerator is created or after the {@link #reset} method is
+     * called, the {@link #moveNext} method must be called to advance the
+     * enumerator to the first element of the collection before reading the
+     * value of the {@code current} property; otherwise, {@code current} is
+     * undefined.
+     *
+     * <p>This method also throws {@link NoSuchElementException} if
+     * the last call to {@code moveNext} returned {@code false}, which indicates
+     * the end of the collection.
+     *
+     * <p>This method does not move the position of the enumerator, and
+     * consecutive calls to {@code current} return the same object until either
+     * {@code moveNext} or {@code reset} is called.
+     *
+     * <p>An enumerator remains valid as long as the collection remains
+     * unchanged. If changes are made to the collection, such as adding,
+     * modifying, or deleting elements, the enumerator is irrecoverably
+     * invalidated. The next call to {@code moveNext} or {@code reset} may,
+     * at the discretion of the implementation, throw a
+     * {@link ConcurrentModificationException}. If the collection is
+     * modified between {@code moveNext} and {@code current}, {@code current}
+     * returns the element that it is set to, even if the enumerator is already
+     * invalidated.
+     *
+     * @return Current element
+     * @throws ConcurrentModificationException if collection
+     * has been modified
+     * @throws NoSuchElementException if {@code moveToNext}
+     * has not been called, has not been called since the most
+     * recent call to {@code reset}, or returned false
+     */
+    @Override
+    public PolyValue[] current() {
+        return enumerator.current();
+    }
+
+
+    /**
+     * Advances the enumerator to the next element of the collection.
+     *
+     * <p>After an enumerator is created or after the {@code reset} method is
+     * called, an enumerator is positioned before the first element of the
+     * collection, and the first call to the {@code moveNext} method moves the
+     * enumerator over the first element of the collection.
+     *
+     * <p>If {@code moveNext} passes the end of the collection, the enumerator
+     * is positioned after the last element in the collection and
+     * {@code moveNext} returns {@code false}. When the enumerator is at this
+     * position, subsequent calls to {@code moveNext} also return {@code false}
+     * until {@code #reset} is called.
+     *
+     * <p>An enumerator remains valid as long as the collection remains
+     * unchanged. If changes are made to the collection, such as adding,
+     * modifying, or deleting elements, the enumerator is irrecoverably
+     * invalidated. The next call to {@code moveNext} or {@link #reset} may,
+     * at the discretion of the implementation, throw a
+     * {@link ConcurrentModificationException}.
+     *
+     * @return {@code true} if the enumerator was successfully advanced to the
+     * next element; {@code false} if the enumerator has passed the end of
+     * the collection
+     */
+    @Override
+    public boolean moveNext() {
+        return enumerator.moveNext();
+    }
+
+
+    /**
+     * Sets the enumerator to its initial position, which is before the first
+     * element in the collection.
+     *
+     * <p>An enumerator remains valid as long as the collection remains
+     * unchanged. If changes are made to the collection, such as adding,
+     * modifying, or deleting elements, the enumerator is irrecoverably
+     * invalidated. The next call to {@link #moveNext} or {@code reset} may,
+     * at the discretion of the implementation, throw a
+     * {@link ConcurrentModificationException}.
+     *
+     * <p>This method is optional; it may throw
+     * {@link UnsupportedOperationException}.
+     *
+     * <p><b>Notes to Implementers</b>
+     *
+     * <p>All calls to Reset must result in the same state for the enumerator.
+     * The preferred implementation is to move the enumerator to the beginning
+     * of the collection, before the first element. This invalidates the
+     * enumerator if the collection has been modified since the enumerator was
+     * created, which is consistent with {@link #moveNext()} and
+     * {@link #current()}.
+     */
+    @Override
+    public void reset() {
+        enumerator.reset();
+    }
+
+
+    /**
+     * Closes this enumerable and releases resources.
+     *
+     * <p>This method is idempotent. Calling it multiple times has the same effect
+     * as calling it once.
+     */
+    @Override
+    public void close() {
+        enumerator.close();
+    }
+
+
+    /**
+     * Reads all source files in parallel.
+     *
+     * @param sourceFiles a list of source files for the provided parquet table
+     * @param cancelFlag a cancel operation flag
+     * @return aggregated data
+     */
+    protected static <T> Map<GroupKey, AggregateGroupState> readAll( List<T> sourceFiles, Function<T, Map<GroupKey, AggregateGroupState>> reader, AggregateCallDescriptor[] aggregateCalls, AtomicBoolean cancelFlag ) {
+        if ( sourceFiles.isEmpty() ) {
+            return new LinkedHashMap<>();
+        }
+        if ( sourceFiles.size() == 1 ) {
+            return reader.apply( sourceFiles.get( 0 ) );
+        }
+
+        int parallelism = Math.min( sourceFiles.size(), Math.max( 1, Runtime.getRuntime().availableProcessors() ) );
+        ExecutorService executor = Executors.newFixedThreadPool( parallelism );
+
+        try {
+            Map<GroupKey, AggregateGroupState> aggregates = new LinkedHashMap<>();
+            List<Callable<Map<GroupKey, AggregateGroupState>>> tasks = sourceFiles.stream()
+                    .<Callable<Map<GroupKey, AggregateGroupState>>>map( sourceFile -> () -> reader.apply( sourceFile ) )
+                    .toList();
+
+            List<Future<Map<GroupKey, AggregateGroupState>>> futures = executor.invokeAll( tasks );
+            for ( Future<Map<GroupKey, AggregateGroupState>> future : futures ) {
+                mergeAggregates( aggregates, future.get(), aggregateCalls );
+            }
+            return aggregates;
+        } catch ( InterruptedException e ) {
+            Thread.currentThread().interrupt();
+            cancelFlag.set( true );
+            throw new RuntimeException( "Interrupted while calculating streaming aggregate.", e );
+        } catch ( ExecutionException e ) {
+            throw new RuntimeException( "Unable to calculate streaming aggregate.", e.getCause() );
+        } finally {
+            executor.shutdownNow();
+        }
+    }
+
+
+    /**
+     * Merges aggregates from different sources.
+     *
+     * @param target a target aggregate.
+     * @param source a source aggregate.
+     */
+    protected static void mergeAggregates( Map<GroupKey, AggregateGroupState> target, Map<GroupKey, AggregateGroupState> source, AggregateCallDescriptor[] aggregateCalls ) {
+        source.forEach( ( key, sourceAggregates ) -> target.computeIfAbsent( key, ignored -> new AggregateGroupState( aggregateCalls ) ).merge( sourceAggregates ) );
+    }
+
+
+    /**
+     * Creates aggregate call descriptors.
+     *
+     * @param aggregateKinds an array of aggregate kinds.
+     * @param aggregateArgs aggregate function arguments.
+     * @return aggregate call descriptors.
+     */
+    protected static AggregateCallDescriptor[] aggregateCalls( String[] aggregateKinds, int[] aggregateArgs ) {
+        AggregateCallDescriptor[] calls = new AggregateCallDescriptor[aggregateKinds.length];
+        for ( int i = 0; i < aggregateKinds.length; i++ ) {
+            calls[i] = AggregateCallDescriptor.of( aggregateKinds[i], aggregateArgs[i] );
+        }
+        return calls;
+    }
+
+
+    /**
+     * Retrieves a row count of the source file from previously saved statistics.
+     *
+     * @param sourceFile a source file.
+     * @return a row count.
+     */
+    protected static long sourceRowCount( ParquetSourceFile sourceFile ) {
+        Optional<Long> rowCount = sourceFile.columnStatistics().values().stream()
+                .map( ParquetColumnStatistics::rowCount )
+                .findFirst();
+        return rowCount.orElseGet( () -> new ParquetSchemaReader( sourceFile.asSource() ).getEstimatedRowCount() );
+    }
+
+
+    /**
+     * Builds a result set from the aggregates.
+     *
+     * @param aggregates aggregates to build the result rows from.
+     * @param aggregateCalls a list of aggregate functions.
+     * @return returns a list of {@link PolyValue}s representing rows.
+     */
+    protected static List<PolyValue[]> buildRows( int groupCount, Map<GroupKey, AggregateGroupState> aggregates, AggregateCallDescriptor[] aggregateCalls ) {
+        if ( groupCount == 0 && aggregates.isEmpty() ) {
+            aggregates.put( GroupKey.Empty, new AggregateGroupState( aggregateCalls ) );
+        }
+        List<PolyValue[]> resultRows = new ArrayList<>( aggregates.size() );
+        int aggregateCount = aggregateCalls.length;
+        for ( Map.Entry<GroupKey, AggregateGroupState> entry : aggregates.entrySet() ) {
+            PolyValue[] resultRow = new PolyValue[groupCount + aggregateCount];
+            for ( int i = 0; i < groupCount; i++ ) {
+                resultRow[i] = (PolyValue) entry.getKey().value( i );
+            }
+            for ( int i = 0; i < aggregateCount; i++ ) {
+                resultRow[groupCount + i] = entry.getValue().result( i );
+            }
+            resultRows.add( resultRow );
+        }
+        return resultRows;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
index b1611c7994..c00d9a42a1 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
@@ -23,6 +23,7 @@
 import org.polypheny.db.rex.RexDynamicParam;
 import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.type.entity.PolyValue;
 
 /**
  * Responsible for parsing supported Rex predicates and
@@ -63,16 +64,16 @@ protected ParsedFilter parse( RexNode filter ) {
      * @param valueNode - value
      * @return adapter filter
      */
-    protected ParquetAdapterFilter toParquetAdapterFilter( int columnIndex, Kind operator, RexNode valueNode ) {
+    protected ParquetAdapterFilter<PolyValue> toParquetAdapterFilter( int columnIndex, Kind operator, RexNode valueNode ) {
         if ( valueNode instanceof RexLiteral literal ) {
             if ( literal.getValue() == null ) {
                 return null;
             }
-            return new ParquetAdapterFilter( columnIndex, operator, literal.getValue() );
+            return new ParquetAdapterFilter<>( columnIndex, operator, literal.getValue() );
         }
 
         if ( valueNode instanceof RexDynamicParam dynamicParam ) {
-            return new ParquetAdapterFilter( columnIndex, List.of(), operator, null, dynamicParam.getIndex() );
+            return new ParquetAdapterFilter<>( columnIndex, List.of(), operator, null, dynamicParam.getIndex() );
         }
 
         return null;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
index 2d4649bce3..60ae38ddfc 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java
@@ -36,7 +36,7 @@
 public abstract class AbstractParquetEnumerator implements Enumerator<PolyValue[]> {
 
     protected final ParquetSourceReader reader;
-    protected final List<ParquetAdapterFilter> filters;
+    protected final List<ParquetAdapterFilter<PolyValue>> filters;
     protected final ParquetGroupFilterEvaluator filterEvaluator;
     private final boolean readerOwner;
     private final Queue<PolyValue[]> rows = new ArrayDeque<>();
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FiltersContainer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FiltersContainer.java
index b55a6342c6..9b2f3922b4 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FiltersContainer.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FiltersContainer.java
@@ -17,6 +17,7 @@
 package org.polypheny.db.adapter.parquet.shared.filter;
 
 import java.util.List;
+import org.polypheny.db.type.entity.PolyValue;
 
 /**
  * A container for filters split by usage.
@@ -25,8 +26,8 @@ public class FiltersContainer {
 
     public static FiltersContainer empty = new FiltersContainer( List.of(), List.of() );
 
-    private final List<ParquetAdapterFilter> adapterFilters;
-    private final List<ParquetAdapterFilter> nativeFilters;
+    private final List<ParquetAdapterFilter<PolyValue>> adapterFilters;
+    private final List<ParquetAdapterFilter<PolyValue>> nativeFilters;
 
 
     /**
@@ -35,7 +36,7 @@ public class FiltersContainer {
      * @param adapterFilters a list of filters to be used on adapter level.
      * @param nativeFilters a list of filters to be used on parquet reader level.
      */
-    public FiltersContainer( List<ParquetAdapterFilter> adapterFilters, List<ParquetAdapterFilter> nativeFilters ) {
+    public FiltersContainer( List<ParquetAdapterFilter<PolyValue>> adapterFilters, List<ParquetAdapterFilter<PolyValue>> nativeFilters ) {
         this.adapterFilters = adapterFilters == null ? List.of() : List.copyOf( adapterFilters );
         this.nativeFilters = nativeFilters == null ? List.of() : List.copyOf( nativeFilters );
     }
@@ -47,11 +48,21 @@ public FiltersContainer( List<ParquetAdapterFilter> adapterFilters, List<Parquet
      * @param filters filters to share between adapter and reader.
      * @return a new instance of {@link FiltersContainer}
      */
-    public static FiltersContainer shared( List<ParquetAdapterFilter> filters ) {
+    public static FiltersContainer shared( List<ParquetAdapterFilter<PolyValue>> filters ) {
         return new FiltersContainer( filters, filters );
     }
 
 
+    private static ParquetAdapterFilter<PolyValue> withoutPathElements( ParquetAdapterFilter<PolyValue> filter ) {
+        if ( filter.isLogical() ) {
+            return ParquetAdapterFilter.logical(
+                    filter.operator(),
+                    filter.operands().stream().map( FiltersContainer::withoutPathElements ).toList() );
+        }
+        return new ParquetAdapterFilter<>( filter.columnIndex(), List.of(), filter.operator(), filter.value(), filter.dynamicParamIndex() );
+    }
+
+
     /**
      * Keeps the same filters, but removes path elements from adapter-level filters.
      * This is useful for simple projected scans where row validation must use projected column indexes,
@@ -66,22 +77,12 @@ public FiltersContainer withoutPathElementsInAdapterFilters() {
     }
 
 
-    private static ParquetAdapterFilter withoutPathElements( ParquetAdapterFilter filter ) {
-        if ( filter.isLogical() ) {
-            return ParquetAdapterFilter.logical(
-                    filter.operator(),
-                    filter.operands().stream().map( FiltersContainer::withoutPathElements ).toList() );
-        }
-        return new ParquetAdapterFilter( filter.columnIndex(), List.of(), filter.operator(), filter.polyValue(), filter.dynamicParamIndex() );
-    }
-
-
     /**
      * Gets a list of adapter filters.
      *
      * @return a list of adapter filters.
      */
-    public List<ParquetAdapterFilter> adapterFilters() {
+    public List<ParquetAdapterFilter<PolyValue>> adapterFilters() {
         return adapterFilters;
     }
 
@@ -91,7 +92,7 @@ public List<ParquetAdapterFilter> adapterFilters() {
      *
      * @return a list of reader filters.
      */
-    public List<ParquetAdapterFilter> nativeFilters() {
+    public List<ParquetAdapterFilter<PolyValue>> nativeFilters() {
         return nativeFilters;
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersContainer.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersContainer.java
index 4a658c8638..f1f44ef96c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersContainer.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersContainer.java
@@ -17,6 +17,7 @@
 package org.polypheny.db.adapter.parquet.shared.filter;
 
 import java.util.List;
+import org.polypheny.db.type.entity.PolyValue;
 
 /**
  * Filter bundle used by the nested Parquet join executor.
@@ -98,10 +99,10 @@ public class JoinFiltersContainer extends FiltersContainer {
 
     public static JoinFiltersContainer empty = new JoinFiltersContainer( List.of(), List.of(), List.of(), List.of() );
 
-    private final List<ParquetAdapterFilter> parentFilters;
-    private final List<ParquetAdapterFilter> childFilters;
-    private final List<ParquetAdapterFilter> parentScanFilters;
-    private final List<ParquetAdapterFilter> childScanFilters;
+    private final List<ParquetAdapterFilter<PolyValue>> parentFilters;
+    private final List<ParquetAdapterFilter<PolyValue>> childFilters;
+    private final List<ParquetAdapterFilter<PolyValue>> parentScanFilters;
+    private final List<ParquetAdapterFilter<PolyValue>> childScanFilters;
 
 
     /**
@@ -120,7 +121,7 @@ public class JoinFiltersContainer extends FiltersContainer {
      * @param readerFilters join-owned filters that can also be pushed to the
      * Parquet reader as native predicates.
      */
-    public JoinFiltersContainer( List<ParquetAdapterFilter> parentFilters, List<ParquetAdapterFilter> childFilters, List<ParquetAdapterFilter> adapterFilters, List<ParquetAdapterFilter> readerFilters ) {
+    public JoinFiltersContainer( List<ParquetAdapterFilter<PolyValue>> parentFilters, List<ParquetAdapterFilter<PolyValue>> childFilters, List<ParquetAdapterFilter<PolyValue>> adapterFilters, List<ParquetAdapterFilter<PolyValue>> readerFilters ) {
         this( parentFilters, childFilters, adapterFilters, readerFilters, List.of(), List.of() );
     }
 
@@ -150,7 +151,7 @@ public JoinFiltersContainer( List<ParquetAdapterFilter> parentFilters, List<Parq
      * @param childScanFilters scan-owned filters for the child table, using
      * child physical table column indexes.
      */
-    public JoinFiltersContainer( List<ParquetAdapterFilter> parentFilters, List<ParquetAdapterFilter> childFilters, List<ParquetAdapterFilter> adapterFilters, List<ParquetAdapterFilter> readerFilters, List<ParquetAdapterFilter> parentScanFilters, List<ParquetAdapterFilter> childScanFilters ) {
+    public JoinFiltersContainer( List<ParquetAdapterFilter<PolyValue>> parentFilters, List<ParquetAdapterFilter<PolyValue>> childFilters, List<ParquetAdapterFilter<PolyValue>> adapterFilters, List<ParquetAdapterFilter<PolyValue>> readerFilters, List<ParquetAdapterFilter<PolyValue>> parentScanFilters, List<ParquetAdapterFilter<PolyValue>> childScanFilters ) {
         super( adapterFilters, readerFilters );
         this.parentFilters = parentFilters == null ? List.of() : List.copyOf( parentFilters );
         this.childFilters = childFilters == null ? List.of() : List.copyOf( childFilters );
@@ -169,7 +170,7 @@ public JoinFiltersContainer( List<ParquetAdapterFilter> parentFilters, List<Parq
      *
      * @return parent-only join-owned filters.
      */
-    public List<ParquetAdapterFilter> parentFilters() {
+    public List<ParquetAdapterFilter<PolyValue>> parentFilters() {
         return parentFilters;
     }
 
@@ -183,7 +184,7 @@ public List<ParquetAdapterFilter> parentFilters() {
      *
      * @return child-only join-owned filters.
      */
-    public List<ParquetAdapterFilter> childFilters() {
+    public List<ParquetAdapterFilter<PolyValue>> childFilters() {
         return childFilters;
     }
 
@@ -198,7 +199,7 @@ public List<ParquetAdapterFilter> childFilters() {
      *
      * @return parent scan filters using physical table indexes.
      */
-    public List<ParquetAdapterFilter> parentScanFilters() {
+    public List<ParquetAdapterFilter<PolyValue>> parentScanFilters() {
         return parentScanFilters;
     }
 
@@ -214,7 +215,7 @@ public List<ParquetAdapterFilter> parentScanFilters() {
      *
      * @return child scan filters using physical table indexes.
      */
-    public List<ParquetAdapterFilter> childScanFilters() {
+    public List<ParquetAdapterFilter<PolyValue>> childScanFilters() {
         return childScanFilters;
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersSplitter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersSplitter.java
index fc89f8264a..b897d5b035 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersSplitter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/JoinFiltersSplitter.java
@@ -19,10 +19,11 @@
 import java.util.ArrayList;
 import java.util.List;
 import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.entity.PolyValue;
 
 public class JoinFiltersSplitter {
 
-    private static FilterKind classifyFilter( ParquetAdapterFilter filter, boolean leftIsParent, int parentFieldCount, int childFieldCount ) {
+    private static FilterKind classifyFilter( ParquetAdapterFilter<PolyValue> filter, boolean leftIsParent, int parentFieldCount, int childFieldCount ) {
         if ( filter.isLogical() ) {
             List<FilterKind> operandSides = filter.operands().stream()
                     .map( operand -> classifyFilter( operand, leftIsParent, parentFieldCount, childFieldCount ) )
@@ -51,7 +52,7 @@ private static FilterKind classifyFilter( ParquetAdapterFilter filter, boolean l
     }
 
 
-    private static boolean canUseAsReaderFilter( ParquetAdapterFilter filter ) {
+    private static boolean canUseAsReaderFilter( ParquetAdapterFilter<PolyValue> filter ) {
         if ( filter.isLogical() ) {
             return !filter.operands().isEmpty() && filter.operands().stream().allMatch( JoinFiltersSplitter::canUseAsReaderFilter );
         }
@@ -59,20 +60,20 @@ private static boolean canUseAsReaderFilter( ParquetAdapterFilter filter ) {
     }
 
 
-    public JoinFiltersContainer split( List<ParquetAdapterFilter> filters, boolean leftIsParent, int parentFieldCount, int childFieldCount ) {
-        List<ParquetAdapterFilter> parentFilters = new ArrayList<>();
-        List<ParquetAdapterFilter> childFilters = new ArrayList<>();
-        List<ParquetAdapterFilter> adapterFilters = new ArrayList<>();
-        List<ParquetAdapterFilter> readerFilters = new ArrayList<>();
+    public JoinFiltersContainer split( List<ParquetAdapterFilter<PolyValue>> filters, boolean leftIsParent, int parentFieldCount, int childFieldCount ) {
+        List<ParquetAdapterFilter<PolyValue>> parentFilters = new ArrayList<>();
+        List<ParquetAdapterFilter<PolyValue>> childFilters = new ArrayList<>();
+        List<ParquetAdapterFilter<PolyValue>> adapterFilters = new ArrayList<>();
+        List<ParquetAdapterFilter<PolyValue>> readerFilters = new ArrayList<>();
 
-        for ( ParquetAdapterFilter filter : filters ) {
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
             splitFilter( filter, leftIsParent, parentFieldCount, childFieldCount, parentFilters, childFilters, adapterFilters, readerFilters );
         }
         return new JoinFiltersContainer( parentFilters, childFilters, adapterFilters, readerFilters );
     }
 
 
-    private void splitFilter( ParquetAdapterFilter filter, boolean leftIsParent, int parentFieldCount, int childFieldCount, List<ParquetAdapterFilter> parentFilters, List<ParquetAdapterFilter> childFilters, List<ParquetAdapterFilter> adapterFilters, List<ParquetAdapterFilter> readerFilters ) {
+    private void splitFilter( ParquetAdapterFilter<PolyValue> filter, boolean leftIsParent, int parentFieldCount, int childFieldCount, List<ParquetAdapterFilter<PolyValue>> parentFilters, List<ParquetAdapterFilter<PolyValue>> childFilters, List<ParquetAdapterFilter<PolyValue>> adapterFilters, List<ParquetAdapterFilter<PolyValue>> readerFilters ) {
         if ( filter.isLogical() && filter.operator() == Kind.AND ) {
             filter.operands().forEach( operand -> splitFilter( operand, leftIsParent, parentFieldCount, childFieldCount, parentFilters, childFilters, adapterFilters, readerFilters ) );
             return;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
index a645b21b68..4d4ad0f82f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetAdapterFilter.java
@@ -28,22 +28,22 @@
  *
  * @param columnIndex - index of filter column
  * @param operator - filter operation
- * @param polyValue - filter value
+ * @param value - filter value
  */
-public record ParquetAdapterFilter( int columnIndex, List<String> pathElements, Kind operator, PolyValue polyValue, Long dynamicParamIndex, List<ParquetAdapterFilter> operands ) {
+public record ParquetAdapterFilter<T>( int columnIndex, List<String> pathElements, Kind operator, T value, Long dynamicParamIndex, List<ParquetAdapterFilter<T>> operands ) {
 
-    public ParquetAdapterFilter( int columnIndex, Kind operator, PolyValue polyValue ) {
-        this( columnIndex, List.of(), operator, polyValue, null, List.of() );
+    public ParquetAdapterFilter( int columnIndex, Kind operator, T value ) {
+        this( columnIndex, List.of(), operator, value, null, List.of() );
     }
 
 
-    public ParquetAdapterFilter( int columnIndex, List<String> pathElements, Kind operator, PolyValue polyValue ) {
-        this( columnIndex, pathElements, operator, polyValue, null, List.of() );
+    public ParquetAdapterFilter( int columnIndex, List<String> pathElements, Kind operator, T value ) {
+        this( columnIndex, pathElements, operator, value, null, List.of() );
     }
 
 
-    public ParquetAdapterFilter( int columnIndex, List<String> pathElements, Kind operator, PolyValue polyValue, Long dynamicParamIndex ) {
-        this( columnIndex, pathElements, operator, polyValue, dynamicParamIndex, List.of() );
+    public ParquetAdapterFilter( int columnIndex, List<String> pathElements, Kind operator, T value, Long dynamicParamIndex ) {
+        this( columnIndex, pathElements, operator, value, dynamicParamIndex, List.of() );
     }
 
 
@@ -54,8 +54,8 @@ public ParquetAdapterFilter( int columnIndex, List<String> pathElements, Kind op
     }
 
 
-    public static ParquetAdapterFilter logical( Kind operator, List<ParquetAdapterFilter> operands ) {
-        return new ParquetAdapterFilter( -1, List.of(), operator, null, null, operands );
+    public static <T> ParquetAdapterFilter<T> logical( Kind operator, List<ParquetAdapterFilter<T>> operands ) {
+        return new ParquetAdapterFilter<>( -1, List.of(), operator, null, null, operands );
     }
 
 
@@ -65,7 +65,7 @@ public Expression toExpression() {
                 Expressions.constant( columnIndex ),
                 EnumUtils.expressionList( pathElements.stream().<Expression>map( Expressions::constant ).toList() ),
                 Expressions.constant( operator, Kind.class ),
-                polyValue == null ? Expressions.constant( null, PolyValue.class ) : polyValue.asExpression(),
+                value == null ? Expressions.constant( null, PolyValue.class ) : ((PolyValue) value).asExpression(),
                 Expressions.constant( dynamicParamIndex, Long.class ),
                 EnumUtils.expressionList( operands.stream().map( ParquetAdapterFilter::toExpression ).toList() ) );
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetFilterEvaluator.java
similarity index 78%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FilterEvaluator.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetFilterEvaluator.java
index 4278221b6f..c89d32af82 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FilterEvaluator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetFilterEvaluator.java
@@ -24,14 +24,14 @@
  * Evaluates Parquet adapter filters against a concrete context.
  * A null result means the evaluator cannot decide, so callers that prune data should keep it.
  */
-public abstract class FilterEvaluator<C> {
+public abstract class ParquetFilterEvaluator<C, V extends Comparable<V>> {
 
-    public boolean matches( C context, ParquetAdapterFilter filter ) {
+    public boolean matches( C context, ParquetAdapterFilter<V> filter ) {
         return !Boolean.FALSE.equals( evaluate( context, filter ) );
     }
 
 
-    public Boolean evaluate( C context, ParquetAdapterFilter filter ) {
+    public Boolean evaluate( C context, ParquetAdapterFilter<V> filter ) {
         if ( filter.isLogical() ) {
             return switch ( filter.operator() ) {
                 case AND -> evaluateAnd( context, filter );
@@ -44,18 +44,18 @@ public Boolean evaluate( C context, ParquetAdapterFilter filter ) {
     }
 
 
-    protected abstract Boolean evaluateLeaf( C context, ParquetAdapterFilter filter );
+    protected abstract Boolean evaluateLeaf( C context, ParquetAdapterFilter<V> filter );
 
 
-    protected Boolean matchesValue( PolyValue actual, Kind operator, PolyValue expected ) {
+    protected Boolean matchesValue( V actual, Kind operator, V expected ) {
         if ( operator == Kind.IS_NULL ) {
-            return actual == null || actual.isNull();
+            return isNull( actual );
         }
         if ( operator == Kind.IS_NOT_NULL ) {
-            return actual != null && !actual.isNull();
+            return !isNull( actual );
         }
 
-        if ( actual == null || actual.isNull() || expected == null || expected.isNull() ) {
+        if ( isNull( actual ) || isNull( expected ) ) {
             return false;
         }
 
@@ -71,9 +71,9 @@ protected Boolean matchesValue( PolyValue actual, Kind operator, PolyValue expec
     }
 
 
-    private Boolean evaluateAnd( C context, ParquetAdapterFilter filter ) {
+    private Boolean evaluateAnd( C context, ParquetAdapterFilter<V> filter ) {
         boolean hasUnknown = false;
-        for ( ParquetAdapterFilter operand : filter.operands() ) {
+        for ( ParquetAdapterFilter<V> operand : filter.operands() ) {
             Boolean result = evaluate( context, operand );
             if ( Boolean.FALSE.equals( result ) ) {
                 return false;
@@ -84,9 +84,9 @@ private Boolean evaluateAnd( C context, ParquetAdapterFilter filter ) {
     }
 
 
-    private Boolean evaluateOr( C context, ParquetAdapterFilter filter ) {
+    private Boolean evaluateOr( C context, ParquetAdapterFilter<V> filter ) {
         boolean hasUnknown = false;
-        for ( ParquetAdapterFilter operand : filter.operands() ) {
+        for ( ParquetAdapterFilter<V> operand : filter.operands() ) {
             Boolean result = evaluate( context, operand );
             if ( Boolean.TRUE.equals( result ) ) {
                 return true;
@@ -102,7 +102,7 @@ private Boolean negate( Boolean value ) {
     }
 
 
-    private int compare( PolyValue actual, PolyValue expected ) {
+    private int compare( V actual, V expected ) {
         try {
             return actual.compareTo( expected );
         } catch ( Exception e ) {
@@ -110,4 +110,9 @@ private int compare( PolyValue actual, PolyValue expected ) {
         }
     }
 
+
+    private boolean isNull( V value ) {
+        return value == null || (value instanceof PolyValue pv && pv.isNull());
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetGroupFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetGroupFilterEvaluator.java
index a5d25410a5..c6c82366ba 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetGroupFilterEvaluator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetGroupFilterEvaluator.java
@@ -28,7 +28,7 @@
  * Filter functionality on Adapter Level
  * works with group (row)
  */
-public class ParquetGroupFilterEvaluator extends FilterEvaluator<Group> {
+public class ParquetGroupFilterEvaluator extends ParquetFilterEvaluator<Group, PolyValue> {
 
     protected final MessageType schema;
     protected final ParquetValueExtractor valueExtractor;
@@ -51,34 +51,34 @@ public MessageType schema() {
 
 
     @Override
-    protected Boolean evaluateLeaf( Group group, ParquetAdapterFilter filter ) {
+    protected Boolean evaluateLeaf( Group group, ParquetAdapterFilter<PolyValue> filter ) {
         if ( !canApplyFilter( group, filter ) ) {
             return null;
         }
         if ( !filterHasValue( group, filter ) ) {
-            return matchesValue( PolyNull.NULL, filter.operator(), filter.polyValue() );
+            return matchesValue( PolyNull.NULL, filter.operator(), filter.value() );
         }
-        return matchesValue( extractValue( group, filter ), filter.operator(), filter.polyValue() );
+        return matchesValue( extractValue( group, filter ), filter.operator(), filter.value() );
     }
 
 
-    protected boolean canApplyFilter( Group group, ParquetAdapterFilter filter ) {
+    protected boolean canApplyFilter( Group group, ParquetAdapterFilter<PolyValue> filter ) {
         if ( filter.columnIndex() < 0 ) {
             return false;
         }
-        if ( filter.polyValue() == null && !isNullCheck( filter.operator() ) ) {
+        if ( filter.value() == null && !isNullCheck( filter.operator() ) ) {
             return false;
         }
         return !filter.pathElements().isEmpty() || filter.columnIndex() < schema.getFieldCount();
     }
 
 
-    protected boolean filterHasValue( Group group, ParquetAdapterFilter filter ) {
+    protected boolean filterHasValue( Group group, ParquetAdapterFilter<PolyValue> filter ) {
         return !filter.pathElements().isEmpty() || group.getFieldRepetitionCount( filter.columnIndex() ) > 0;
     }
 
 
-    protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
+    protected PolyValue extractValue( Group group, ParquetAdapterFilter<PolyValue> filter ) {
         if ( filter.pathElements().isEmpty() ) {
             Type field = schema.getType( filter.columnIndex() );
             return valueExtractor.extractValue( group, filter.columnIndex(), field );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
index 01d68e036c..958b5e2eef 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNativeFilterBuilder.java
@@ -51,7 +51,7 @@ private ParquetNativeFilterBuilder() {
      * @param filters adapter level filters
      * @return FilterCompat.Filter - parquet native
      */
-    public static FilterCompat.Filter build( MessageType schema, List<ParquetAdapterFilter> filters ) {
+    public static FilterCompat.Filter build( MessageType schema, List<ParquetAdapterFilter<PolyValue>> filters ) {
         if ( filters == null || filters.isEmpty() ) {
             return FilterCompat.NOOP;
         }
@@ -70,7 +70,7 @@ public static FilterCompat.Filter build( MessageType schema, List<ParquetAdapter
     }
 
 
-    private static FilterPredicate buildPredicate( MessageType schema, ParquetAdapterFilter filter ) {
+    private static FilterPredicate buildPredicate( MessageType schema, ParquetAdapterFilter<PolyValue> filter ) {
         if ( filter.isLogical() ) {
             return buildLogicalPredicate( schema, filter );
         }
@@ -87,7 +87,7 @@ private static FilterPredicate buildPredicate( MessageType schema, ParquetAdapte
             }
 
             String columnName = schema.getFieldName( index );
-            return buildPredicatePrimitive( filter.operator(), filter.polyValue(), type, columnName );
+            return buildPredicatePrimitive( filter.operator(), filter.value(), type, columnName );
         } else {
             // build native filter to push down for nested fields
             Type type = resolveType( schema, filter.pathElements() );
@@ -100,7 +100,7 @@ private static FilterPredicate buildPredicate( MessageType schema, ParquetAdapte
             }
 
             String columnName = String.join( ".", filter.pathElements() );
-            return buildPredicatePrimitive( filter.operator(), filter.polyValue(), type, columnName );
+            return buildPredicatePrimitive( filter.operator(), filter.value(), type, columnName );
         }
     }
 
@@ -111,7 +111,7 @@ private static FilterPredicate buildPredicate( MessageType schema, ParquetAdapte
      * @param filter ParquetAdapterFilter
      * @return Filter Predicate
      */
-    private static FilterPredicate buildLogicalPredicate( MessageType schema, ParquetAdapterFilter filter ) {
+    private static FilterPredicate buildLogicalPredicate( MessageType schema, ParquetAdapterFilter<PolyValue> filter ) {
         // recursively convert each adapter-level child filter into a native Parquet predicate
         List<FilterPredicate> operands = filter.operands().stream()
                 .map( operand -> buildPredicate( schema, operand ) )
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedFilterEvaluator.java
index 4c2b16ed70..6d7085da06 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedFilterEvaluator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedFilterEvaluator.java
@@ -43,7 +43,7 @@ public ParquetNestedFilterEvaluator( MessageType schema, ParquetValueExtractor v
 
 
     @Override
-    protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
+    protected PolyValue extractValue( Group group, ParquetAdapterFilter<PolyValue> filter ) {
         var pathValueExtractor = (ParquetPathValueExtractor) valueExtractor;
         var virtualGroup = (VirtualGroup) group;
         if ( filter.columnIndex() < 0 || filter.columnIndex() >= columnBindings.size() ) {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedJoinFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedJoinFilterEvaluator.java
index 4c206f7dfb..0a2bc5ecbf 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedJoinFilterEvaluator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedJoinFilterEvaluator.java
@@ -46,7 +46,7 @@ public ParquetNestedJoinFilterEvaluator( MessageType schema, ParquetPathValueExt
 
 
     @Override
-    protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
+    protected PolyValue extractValue( Group group, ParquetAdapterFilter<PolyValue> filter ) {
         ParquetPathValueExtractor pathValueExtractor = (ParquetPathValueExtractor) valueExtractor;
         if ( group instanceof CombinedGroup combinedGroup ) {
             return pathValueExtractor.extractValue(
@@ -72,11 +72,11 @@ protected PolyValue extractValue( Group group, ParquetAdapterFilter filter ) {
      * @return true if the filter is valid and false otherwise.
      */
     @Override
-    protected boolean canApplyFilter( Group group, ParquetAdapterFilter filter ) {
+    protected boolean canApplyFilter( Group group, ParquetAdapterFilter<PolyValue> filter ) {
         if ( filter.columnIndex() < 0 ) {
             return false;
         }
-        if ( filter.polyValue() == null && !isNullCheck( filter.operator() ) ) {
+        if ( filter.value() == null && !isNullCheck( filter.operator() ) ) {
             return false;
         }
         if ( group instanceof CombinedGroup combinedGroup ) {
@@ -94,7 +94,7 @@ protected boolean canApplyFilter( Group group, ParquetAdapterFilter filter ) {
      * @return true if the group has a value at the filter column index and false otherwise.
      */
     @Override
-    protected boolean filterHasValue( Group group, ParquetAdapterFilter filter ) {
+    protected boolean filterHasValue( Group group, ParquetAdapterFilter<PolyValue> filter ) {
         CombinedGroup combinedGroup = (CombinedGroup) group;
         return !combinedGroup.isNullField( filter.columnIndex(), leftIsParent );
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetPrimitiveValueFilterEvaluator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetPrimitiveValueFilterEvaluator.java
new file mode 100644
index 0000000000..33584d4f0a
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetPrimitiveValueFilterEvaluator.java
@@ -0,0 +1,85 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.filter;
+
+import org.apache.parquet.io.api.Binary;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+/**
+ * Evaluates adapter filters against primitive values read from Parquet pages.
+ */
+public class ParquetPrimitiveValueFilterEvaluator extends ParquetFilterEvaluator<Object[], PolyValue> {
+
+    @Override
+    protected Boolean evaluateLeaf( Object[] values, ParquetAdapterFilter<PolyValue> filter ) {
+        if ( filter.columnIndex() < 0 || filter.columnIndex() >= values.length ) {
+            return null;
+        }
+
+        Object actual = values[filter.columnIndex()];
+        PolyValue expected = filter.value();
+        return matchesValue( actual, filter.operator(), expected );
+    }
+
+
+    private Boolean matchesValue( Object actual, Kind operator, PolyValue expected ) {
+        if ( operator == Kind.IS_NULL ) {
+            return actual == null;
+        }
+        if ( operator == Kind.IS_NOT_NULL ) {
+            return actual != null;
+        }
+        if ( actual == null || expected == null || expected.isNull() ) {
+            return false;
+        }
+
+        int comparison = compare( actual, expected );
+        return switch ( operator ) {
+            case EQUALS -> comparison == 0;
+            case NOT_EQUALS -> comparison != 0;
+            case GREATER_THAN -> comparison > 0;
+            case GREATER_THAN_OR_EQUAL -> comparison >= 0;
+            case LESS_THAN -> comparison < 0;
+            case LESS_THAN_OR_EQUAL -> comparison <= 0;
+            default -> true;
+        };
+    }
+
+
+    private int compare( Object actual, PolyValue expected ) {
+        if ( actual instanceof Number number && expected.isNumber() ) {
+            return Double.compare( number.doubleValue(), expected.asNumber().doubleValue() );
+        }
+        if ( actual instanceof Boolean bool ) {
+            boolean expectedValue = expected.isBoolean() ? expected.asBoolean().value : Boolean.parseBoolean( expected.toString() );
+            return Boolean.compare( bool, expectedValue );
+        }
+        if ( actual instanceof Binary binary ) {
+            String expectedValue = expected.isString() ? expected.asString().value : expected.toString();
+            return binary.toStringUsingUTF8().compareTo( expectedValue );
+        }
+        if ( actual instanceof Comparable<?> comparable && actual.getClass().isInstance( expected ) ) {
+            @SuppressWarnings({ "rawtypes", "unchecked" })
+            int result = ((Comparable) comparable).compareTo( expected );
+            return result;
+        }
+        return actual.toString().compareTo( expected.toString() );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetCancellation.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetCancellation.java
new file mode 100644
index 0000000000..d51c7ed64a
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetCancellation.java
@@ -0,0 +1,38 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io;
+
+import java.util.concurrent.atomic.AtomicBoolean;
+
+
+/**
+ * A helper class to reduce access to {@link AtomicBoolean#get()} to gain performance.
+ */
+public final class ParquetCancellation {
+
+    private static final int CHECK_INTERVAL = 4096;
+
+
+    private ParquetCancellation() {
+    }
+
+
+    public static boolean shouldStop( long row, AtomicBoolean cancelFlag ) {
+        return (row & (CHECK_INTERVAL - 1)) == 0 && cancelFlag.get();
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetPrimitivePredicate.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetPrimitivePredicate.java
new file mode 100644
index 0000000000..ad3970d163
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetPrimitivePredicate.java
@@ -0,0 +1,355 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io;
+
+import java.util.ArrayList;
+import java.util.List;
+import org.apache.parquet.column.ColumnDescriptor;
+import org.apache.parquet.column.ColumnReader;
+import org.apache.parquet.io.api.Binary;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.entity.PolyValue;
+
+
+/**
+ * Predicate compiled against a primitive Parquet projection. The idea is to have a predicate that operates directly on parquet values instead of PolyValue.
+ * In addition, predicates read value directly from ColumnReader.
+ */
+public interface ParquetPrimitivePredicate {
+
+    /**
+     * Creates a fast reading filter that uses {@link ColumnReader} directly to read the values. Multiple filters are combined with AND predicate.
+     *
+     * @param projectionSchema a projected schema.
+     * @param filters a list of filters to compile.
+     * @return a compiled predicate the consists of per column type predicate(s).
+     */
+    static ParquetPrimitivePredicate compile( MessageType projectionSchema, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        if ( filters == null || filters.isEmpty() ) {
+            return AlwaysTruePredicate.INSTANCE;
+        }
+
+        ColumnDescriptor[] descriptors = projectionSchema.getColumns().toArray( ColumnDescriptor[]::new );
+        List<ParquetPrimitivePredicate> predicates = new ArrayList<>( filters.size() );
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            ParquetPrimitivePredicate predicate = compile( descriptors, filter );
+            if ( predicate == null ) {
+                return null;
+            }
+            predicates.add( predicate );
+        }
+        return predicates.size() == 1 ? predicates.get( 0 ) : new AndPredicate( predicates );
+    }
+
+
+    /**
+     * Compiles a single filter.
+     *
+     * @param descriptors column descriptors of the projected schema.
+     * @param filter a filter to compile.
+     * @return a compiled predicate.
+     */
+    private static ParquetPrimitivePredicate compile( ColumnDescriptor[] descriptors, ParquetAdapterFilter<PolyValue> filter ) {
+        if ( filter.isLogical() ) {
+            List<ParquetPrimitivePredicate> operands = new ArrayList<>( filter.operands().size() );
+            for ( ParquetAdapterFilter<PolyValue> operand : filter.operands() ) {
+                ParquetPrimitivePredicate predicate = compile( descriptors, operand );
+                if ( predicate == null ) {
+                    return null;
+                }
+                operands.add( predicate );
+            }
+            return filter.operator() == Kind.AND ? new AndPredicate( operands ) : null;
+        }
+
+        int index = filter.columnIndex();
+        if ( index < 0 || index >= descriptors.length ) {
+            return null;
+        }
+
+        ColumnDescriptor descriptor = descriptors[index];
+        Kind operator = filter.operator();
+        if ( operator == Kind.IS_NULL || operator == Kind.IS_NOT_NULL ) {
+            return new NullPredicate( index, descriptor.getMaxDefinitionLevel(), descriptor.getPrimitiveType(), operator == Kind.IS_NOT_NULL );
+        }
+        if ( !isComparison( operator ) ) {
+            return null;
+        }
+        if ( filter.value() == null || filter.value().isNull() ) {
+            return null;
+        }
+
+        Object expected = new ParquetTypeConverter().fromPolyValueToParquetObj( descriptor.getPrimitiveType(), filter.value() );
+        if ( expected == null ) {
+            return null;
+        }
+        return switch ( descriptor.getPrimitiveType().getPrimitiveTypeName() ) {
+            case BOOLEAN -> expected instanceof Boolean value ? new BooleanPredicate( index, descriptor.getMaxDefinitionLevel(), operator, value ) : null;
+            case INT32 -> expected instanceof Integer value ? new IntPredicate( index, descriptor.getMaxDefinitionLevel(), operator, value ) : null;
+            case INT64 -> expected instanceof Long value ? new LongPredicate( index, descriptor.getMaxDefinitionLevel(), operator, value ) : null;
+            case FLOAT -> expected instanceof Float value ? new FloatPredicate( index, descriptor.getMaxDefinitionLevel(), operator, value ) : null;
+            case DOUBLE -> expected instanceof Double value ? new DoublePredicate( index, descriptor.getMaxDefinitionLevel(), operator, value ) : null;
+            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> expected instanceof Binary value ? new BinaryPredicate( index, descriptor.getMaxDefinitionLevel(), operator, descriptor.getPrimitiveType(), value ) : null;
+        };
+    }
+
+
+    /**
+     * Checks if the operator is a comparison operator.
+     *
+     * @param operator an operator to check.
+     * @return true if the provided operator is a comparison operator and false otherwise.
+     */
+    private static boolean isComparison( Kind operator ) {
+        return operator == Kind.EQUALS
+                || operator == Kind.NOT_EQUALS
+                || operator == Kind.GREATER_THAN
+                || operator == Kind.GREATER_THAN_OR_EQUAL
+                || operator == Kind.LESS_THAN
+                || operator == Kind.LESS_THAN_OR_EQUAL;
+    }
+
+
+    /**
+     * Checks if the field is presented in the reader and can be read.
+     *
+     * @param reader a reader.
+     * @param maxDefinitionLevel a definition level.
+     * @return true if the field is presented in the reader and its value can be accessed and false otherwise.
+     */
+    static boolean isPresent( ColumnReader reader, int maxDefinitionLevel ) {
+        return maxDefinitionLevel == 0 || reader.getCurrentDefinitionLevel() == maxDefinitionLevel;
+    }
+
+
+    /**
+     * Compares the result to 0 according to the operator.
+     *
+     * @param result the result to compare.
+     * @param operator the operator.
+     * @return true if comparison is successful and false otherwise.
+     */
+    static boolean compare( int result, Kind operator ) {
+        return switch ( operator ) {
+            case EQUALS -> result == 0;
+            case NOT_EQUALS -> result != 0;
+            case GREATER_THAN -> result > 0;
+            case GREATER_THAN_OR_EQUAL -> result >= 0;
+            case LESS_THAN -> result < 0;
+            case LESS_THAN_OR_EQUAL -> result <= 0;
+            default -> true;
+        };
+    }
+
+
+    /**
+     * Reads a value from the reader according to its type.
+     *
+     * @param reader a reader to read the value from.
+     * @param type a field type.
+     * @return the read value.
+     */
+    static Object readValue( ColumnReader reader, PrimitiveType type ) {
+        return switch ( type.getPrimitiveTypeName() ) {
+            case BOOLEAN -> reader.getBoolean();
+            case INT32 -> reader.getInteger();
+            case INT64 -> reader.getLong();
+            case FLOAT -> reader.getFloat();
+            case DOUBLE -> reader.getDouble();
+            case BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> reader.getBinary().copy();
+        };
+    }
+
+
+    boolean matches( ColumnReader[] readers, boolean[] consumed, Object[] values );
+
+
+    enum AlwaysTruePredicate implements ParquetPrimitivePredicate {
+        INSTANCE;
+
+
+        @Override
+        public boolean matches( ColumnReader[] readers, boolean[] consumed, Object[] values ) {
+            return true;
+        }
+    }
+
+
+    record AndPredicate( List<ParquetPrimitivePredicate> operands ) implements ParquetPrimitivePredicate {
+
+        @Override
+        public boolean matches( ColumnReader[] readers, boolean[] consumed, Object[] values ) {
+            for ( ParquetPrimitivePredicate operand : operands ) {
+                if ( !operand.matches( readers, consumed, values ) ) {
+                    return false;
+                }
+            }
+            return true;
+        }
+
+    }
+
+
+    record NullPredicate( int index, int maxDefinitionLevel, PrimitiveType type, boolean expectedPresent ) implements ParquetPrimitivePredicate {
+
+        @Override
+        public boolean matches( ColumnReader[] readers, boolean[] consumed, Object[] values ) {
+            if ( consumed[index] ) {
+                boolean present = values[index] != null;
+                return present == expectedPresent;
+            }
+            consumed[index] = true;
+            ColumnReader reader = readers[index];
+            boolean present = isPresent( reader, maxDefinitionLevel );
+            values[index] = present ? readValue( reader, type ) : null;
+            reader.consume();
+            return present == expectedPresent;
+        }
+
+    }
+
+
+    record BooleanPredicate( int index, int maxDefinitionLevel, Kind operator, boolean expected ) implements ParquetPrimitivePredicate {
+
+        @Override
+        public boolean matches( ColumnReader[] readers, boolean[] consumed, Object[] values ) {
+            if ( consumed[index] ) {
+                Boolean actual = (Boolean) values[index];
+                return actual != null && compare( Boolean.compare( actual, expected ), operator );
+            }
+            consumed[index] = true;
+            ColumnReader reader = readers[index];
+            boolean present = isPresent( reader, maxDefinitionLevel );
+            boolean actual = present && reader.getBoolean();
+            values[index] = present ? actual : null;
+            boolean result = present && compare( Boolean.compare( actual, expected ), operator );
+            reader.consume();
+            return result;
+        }
+
+    }
+
+
+    record IntPredicate( int index, int maxDefinitionLevel, Kind operator, int expected ) implements ParquetPrimitivePredicate {
+
+        @Override
+        public boolean matches( ColumnReader[] readers, boolean[] consumed, Object[] values ) {
+            if ( consumed[index] ) {
+                Integer actual = (Integer) values[index];
+                return actual != null && compare( Integer.compare( actual, expected ), operator );
+            }
+            consumed[index] = true;
+            ColumnReader reader = readers[index];
+            boolean present = isPresent( reader, maxDefinitionLevel );
+            int actual = present ? reader.getInteger() : 0;
+            values[index] = present ? actual : null;
+            boolean result = present && compare( Integer.compare( actual, expected ), operator );
+            reader.consume();
+            return result;
+        }
+
+    }
+
+
+    record LongPredicate( int index, int maxDefinitionLevel, Kind operator, long expected ) implements ParquetPrimitivePredicate {
+
+        @Override
+        public boolean matches( ColumnReader[] readers, boolean[] consumed, Object[] values ) {
+            if ( consumed[index] ) {
+                Long actual = (Long) values[index];
+                return actual != null && compare( Long.compare( actual, expected ), operator );
+            }
+            consumed[index] = true;
+            ColumnReader reader = readers[index];
+            boolean present = isPresent( reader, maxDefinitionLevel );
+            long actual = present ? reader.getLong() : 0;
+            values[index] = present ? actual : null;
+            boolean result = present && compare( Long.compare( actual, expected ), operator );
+            reader.consume();
+            return result;
+        }
+
+    }
+
+
+    record FloatPredicate( int index, int maxDefinitionLevel, Kind operator, float expected ) implements ParquetPrimitivePredicate {
+
+        @Override
+        public boolean matches( ColumnReader[] readers, boolean[] consumed, Object[] values ) {
+            if ( consumed[index] ) {
+                Float actual = (Float) values[index];
+                return actual != null && compare( Float.compare( actual, expected ), operator );
+            }
+            consumed[index] = true;
+            ColumnReader reader = readers[index];
+            boolean present = isPresent( reader, maxDefinitionLevel );
+            float actual = present ? reader.getFloat() : 0;
+            values[index] = present ? actual : null;
+            boolean result = present && compare( Float.compare( actual, expected ), operator );
+            reader.consume();
+            return result;
+        }
+
+    }
+
+
+    record DoublePredicate( int index, int maxDefinitionLevel, Kind operator, double expected ) implements ParquetPrimitivePredicate {
+
+        @Override
+        public boolean matches( ColumnReader[] readers, boolean[] consumed, Object[] values ) {
+            if ( consumed[index] ) {
+                Double actual = (Double) values[index];
+                return actual != null && compare( Double.compare( actual, expected ), operator );
+            }
+            consumed[index] = true;
+            ColumnReader reader = readers[index];
+            boolean present = isPresent( reader, maxDefinitionLevel );
+            double actual = present ? reader.getDouble() : 0;
+            values[index] = present ? actual : null;
+            boolean result = present && compare( Double.compare( actual, expected ), operator );
+            reader.consume();
+            return result;
+        }
+
+    }
+
+
+    record BinaryPredicate( int index, int maxDefinitionLevel, Kind operator, PrimitiveType type, Binary expected ) implements ParquetPrimitivePredicate {
+
+        @Override
+        public boolean matches( ColumnReader[] readers, boolean[] consumed, Object[] values ) {
+            if ( consumed[index] ) {
+                Binary actual = (Binary) values[index];
+                return actual != null && compare( type.comparator().compare( actual, expected ), operator );
+            }
+            consumed[index] = true;
+            ColumnReader reader = readers[index];
+            boolean present = isPresent( reader, maxDefinitionLevel );
+            Binary actual = present ? reader.getBinary() : null;
+            values[index] = actual == null ? null : actual.copy();
+            boolean result = present && compare( type.comparator().compare( actual, expected ), operator );
+            reader.consume();
+            return result;
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetPrimitiveRowReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetPrimitiveRowReader.java
new file mode 100644
index 0000000000..d0d9eb5329
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetPrimitiveRowReader.java
@@ -0,0 +1,269 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io;
+
+import java.io.IOException;
+import java.net.URI;
+import java.util.Arrays;
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.Path;
+import org.apache.parquet.ParquetReadOptions;
+import org.apache.parquet.column.page.PageReadStore;
+import org.apache.parquet.filter2.compat.FilterCompat;
+import org.apache.parquet.hadoop.ParquetFileReader;
+import org.apache.parquet.hadoop.util.HadoopInputFile;
+import org.apache.parquet.io.ColumnIOFactory;
+import org.apache.parquet.io.MessageColumnIO;
+import org.apache.parquet.io.RecordReader;
+import org.apache.parquet.io.api.Binary;
+import org.apache.parquet.io.api.Converter;
+import org.apache.parquet.io.api.GroupConverter;
+import org.apache.parquet.io.api.PrimitiveConverter;
+import org.apache.parquet.io.api.RecordMaterializer;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetNativeFilterBuilder;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+
+/**
+ * Reads primitive columns directly into PolyValue[].
+ * It supports only primitive non-repeated projections. No nested structures.
+ * It avoids GroupRecordConverter and materializes directly into PolyValue[].
+ * For primitive columns it is much faster than regular ParquetSourceReader.
+ */
+public class ParquetPrimitiveRowReader implements AutoCloseable {
+
+    private final AtomicBoolean cancelFlag;
+    private final ParquetFileReader fileReader;
+    private final boolean useNativeFilter;
+    private final MessageType projectionSchema;
+
+    private RecordReader<PolyValue[]> recordReader;
+    private long rowCountInGroup;
+    private long rowIndexInGroup;
+
+
+    public ParquetPrimitiveRowReader( Source source, AtomicBoolean cancelFlag, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        this.cancelFlag = cancelFlag == null ? new AtomicBoolean( false ) : cancelFlag;
+
+        try {
+            URI uri = source.isFile() ? source.file().toURI() : source.url().toURI();
+            Path path = new Path( uri );
+            Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
+
+            ParquetSchemaReader schemaReader = new ParquetSchemaReader( source );
+            this.projectionSchema = schemaReader.buildProjectionSchema( fields );
+            var recordFilter = ParquetNativeFilterBuilder.build( schemaReader.getSchema(), filters == null ? List.of() : filters );
+            this.useNativeFilter = FilterCompat.isFilteringRequired( recordFilter );
+
+            ParquetReadOptions readOptions = ParquetReadOptions.builder()
+                    .useStatsFilter()
+                    .useDictionaryFilter()
+                    .useColumnIndexFilter()
+                    .useBloomFilter()
+                    .useRecordFilter()
+                    .withRecordFilter( recordFilter )
+                    .build();
+
+            this.fileReader = ParquetFileReader.open( HadoopInputFile.fromPath( path, conf ), readOptions );
+            this.fileReader.setRequestedSchema( projectionSchema );
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Unable to open parquet file: " + source.path(), e );
+        }
+    }
+
+
+    public static boolean supports( MessageType projectionSchema ) {
+        if ( projectionSchema.getFieldCount() == 0 ) {
+            return false;
+        }
+        for ( Type field : projectionSchema.getFields() ) {
+            if ( !field.isPrimitive() || field.isRepetition( Type.Repetition.REPEATED ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    public PolyValue[] next() {
+        try {
+            if ( ParquetCancellation.shouldStop( rowIndexInGroup, cancelFlag ) ) {
+                return null;
+            }
+            if ( !ensureRecordReader() ) {
+                return null;
+            }
+
+            PolyValue[] row = recordReader.read();
+            rowIndexInGroup++;
+            if ( rowIndexInGroup >= rowCountInGroup ) {
+                recordReader = null;
+            }
+            return row;
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Error while reading parquet data", e );
+        }
+    }
+
+
+    @Override
+    public void close() throws IOException {
+        fileReader.close();
+    }
+
+
+    private boolean ensureRecordReader() throws IOException {
+        if ( recordReader != null && rowIndexInGroup < rowCountInGroup ) {
+            return true;
+        }
+
+        PageReadStore pages = useNativeFilter
+                ? fileReader.readNextFilteredRowGroup()
+                : fileReader.readNextRowGroup();
+        if ( pages == null ) {
+            return false;
+        }
+
+        MessageColumnIO columnIO = new ColumnIOFactory().getColumnIO( projectionSchema );
+        this.recordReader = columnIO.getRecordReader( pages, new PrimitiveRowMaterializer( projectionSchema ) );
+        this.rowCountInGroup = pages.getRowCount();
+        this.rowIndexInGroup = 0;
+        return true;
+    }
+
+
+    private static class PrimitiveRowMaterializer extends RecordMaterializer<PolyValue[]> {
+
+        private final PrimitiveRowConverter root;
+        private PolyValue[] currentRow;
+
+
+        private PrimitiveRowMaterializer( MessageType projectionSchema ) {
+            this.root = new PrimitiveRowConverter( projectionSchema );
+        }
+
+
+        @Override
+        public PolyValue[] getCurrentRecord() {
+            return currentRow;
+        }
+
+
+        @Override
+        public GroupConverter getRootConverter() {
+            return root;
+        }
+
+
+        private class PrimitiveRowConverter extends GroupConverter {
+
+            private final Converter[] converters;
+
+
+            private PrimitiveRowConverter( MessageType projectionSchema ) {
+                this.converters = new Converter[projectionSchema.getFieldCount()];
+                for ( int i = 0; i < projectionSchema.getFieldCount(); i++ ) {
+                    converters[i] = new PrimitiveFieldConverter( i, projectionSchema.getType( i ).asPrimitiveType() );
+                }
+            }
+
+
+            @Override
+            public Converter getConverter( int fieldIndex ) {
+                return converters[fieldIndex];
+            }
+
+
+            @Override
+            public void start() {
+                currentRow = new PolyValue[converters.length];
+                Arrays.fill( currentRow, PolyNull.NULL );
+            }
+
+
+            @Override
+            public void end() {
+            }
+
+        }
+
+
+        private class PrimitiveFieldConverter extends PrimitiveConverter {
+
+            private static final ParquetTypeConverter TYPE_CONVERTER = new ParquetTypeConverter();
+
+            private final int index;
+            private final PrimitiveType type;
+
+
+            private PrimitiveFieldConverter( int index, PrimitiveType type ) {
+                this.index = index;
+                this.type = type;
+            }
+
+
+            @Override
+            public void addBoolean( boolean value ) {
+                currentRow[index] = TYPE_CONVERTER.fromObjToPolyValue( type, value );
+            }
+
+
+            @Override
+            public void addInt( int value ) {
+                currentRow[index] = TYPE_CONVERTER.fromObjToPolyValue( type, value );
+            }
+
+
+            @Override
+            public void addLong( long value ) {
+                currentRow[index] = TYPE_CONVERTER.fromObjToPolyValue( type, value );
+            }
+
+
+            @Override
+            public void addFloat( float value ) {
+                currentRow[index] = TYPE_CONVERTER.fromObjToPolyValue( type, value );
+            }
+
+
+            @Override
+            public void addDouble( double value ) {
+                currentRow[index] = TYPE_CONVERTER.fromObjToPolyValue( type, value );
+            }
+
+
+            @Override
+            public void addBinary( Binary value ) {
+                currentRow[index] = TYPE_CONVERTER.fromObjToPolyValue( type, value );
+            }
+
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
index 1522550458..10e1e30967 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java
@@ -38,6 +38,7 @@
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetNativeFilterBuilder;
 import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
 import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.Source;
 
 /**
@@ -67,7 +68,7 @@ public ParquetSourceReader( Source source ) {
     }
 
 
-    public ParquetSourceReader( Source source, AtomicBoolean cancelFlag, int[] fields, List<ParquetAdapterFilter> filters ) {
+    public ParquetSourceReader( Source source, AtomicBoolean cancelFlag, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters ) {
         this.source = source;
         this.cancelFlag = cancelFlag == null ? new AtomicBoolean( false ) : cancelFlag;
 
@@ -102,7 +103,7 @@ public ParquetSourceReader( Source source, AtomicBoolean cancelFlag, int[] field
     public Group next() {
         try {
             for ( ; ; ) {
-                if ( cancelFlag.get() ) {
+                if ( ParquetCancellation.shouldStop( rowIndexInGroup, cancelFlag ) ) {
                     return null;
                 }
                 if ( !ensureRecordReader() ) {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/ParquetGroupedAggregateReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/ParquetGroupedAggregateReader.java
new file mode 100644
index 0000000000..a5a29dbe3a
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/ParquetGroupedAggregateReader.java
@@ -0,0 +1,210 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io.aggregate;
+
+import java.io.IOException;
+import java.net.URI;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.Path;
+import org.apache.parquet.ParquetReadOptions;
+import org.apache.parquet.column.ColumnDescriptor;
+import org.apache.parquet.column.ColumnReadStore;
+import org.apache.parquet.column.impl.ColumnReadStoreImpl;
+import org.apache.parquet.column.page.PageReadStore;
+import org.apache.parquet.filter2.compat.FilterCompat;
+import org.apache.parquet.hadoop.ParquetFileReader;
+import org.apache.parquet.hadoop.util.HadoopInputFile;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetNativeFilterBuilder;
+import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetPrimitivePredicate;
+import org.polypheny.db.adapter.parquet.shared.io.aggregate.page.ParquetCountAggregatePageReader;
+import org.polypheny.db.adapter.parquet.shared.io.aggregate.page.ParquetGroupedAggregatePageReader;
+import org.polypheny.db.adapter.parquet.shared.io.aggregate.page.NoOpColumnConverter;
+import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.Source;
+
+
+/**
+ * This reader performs grouped aggregation from Parquet pages and can handle row filters.
+ */
+public class ParquetGroupedAggregateReader implements AutoCloseable {
+
+    private final AtomicBoolean cancelFlag;
+    private final ParquetFileReader fileReader;
+    private final MessageType projectionSchema;
+    private final String createdBy;
+    private final boolean useNativeFilter;
+    private final List<ParquetAdapterFilter<PolyValue>> aggregateReaderFilters;
+    private final ParquetPrimitivePredicate primitivePredicate;
+    private final int groupFieldCount;
+    private final AggregateCallDescriptor[] aggregateCalls;
+
+
+    public ParquetGroupedAggregateReader( Source source, AtomicBoolean cancelFlag, MessageType projectionSchema, int groupFieldCount, AggregateCallDescriptor[] aggregateCalls, List<ParquetAdapterFilter<PolyValue>> aggregateReaderFilters ) {
+        this.cancelFlag = cancelFlag == null ? new AtomicBoolean( false ) : cancelFlag;
+        this.groupFieldCount = groupFieldCount;
+        this.aggregateCalls = aggregateCalls;
+        this.projectionSchema = projectionSchema;
+        this.aggregateReaderFilters = aggregateReaderFilters == null ? List.of() : List.copyOf( aggregateReaderFilters );
+        this.primitivePredicate = ParquetPrimitivePredicate.compile( projectionSchema, this.aggregateReaderFilters );
+
+        try {
+            URI uri = source.isFile() ? source.file().toURI() : source.url().toURI();
+            Path path = new Path( uri );
+            Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
+            var recordFilter = ParquetNativeFilterBuilder.build( projectionSchema, this.aggregateReaderFilters );
+            this.useNativeFilter = FilterCompat.isFilteringRequired( recordFilter );
+
+            ParquetReadOptions readOptions = ParquetReadOptions.builder()
+                    .useStatsFilter()
+                    .useDictionaryFilter()
+                    .useColumnIndexFilter()
+                    .useBloomFilter()
+                    .useRecordFilter()
+                    .withRecordFilter( recordFilter )
+                    .build();
+
+            this.fileReader = ParquetFileReader.open( HadoopInputFile.fromPath( path, conf ), readOptions );
+            this.fileReader.setRequestedSchema( projectionSchema );
+            this.createdBy = fileReader.getFileMetaData().getCreatedBy();
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Unable to open parquet file: " + source.path(), e );
+        }
+    }
+
+
+    /**
+     * Validates if a reader can support the projected schema. The main criteria is that all fields must be primitive and non-repeated.
+     *
+     * @param projectionSchema a schema to validate.
+     * @return true if the projected schema is supported and false otherwise.
+     */
+    public static boolean supports( MessageType projectionSchema ) {
+        if ( projectionSchema.getFieldCount() == 0 ) {
+            return false;
+        }
+        for ( Type field : projectionSchema.getFields() ) {
+            if ( !field.isPrimitive() || field.isRepetition( Type.Repetition.REPEATED ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Reads all rows from the source file and aggregates them.
+     *
+     * @return an aggregated map.
+     */
+    public Map<GroupKey, AggregateGroupState> readAll() {
+        Map<GroupKey, AggregateGroupState> aggregates = new LinkedHashMap<>();
+        try {
+            PageReadStore pages;
+            while ( !cancelFlag.get() && (pages = nextRowGroup()) != null ) {
+                try {
+                    readPage( pages, aggregates );
+                } finally {
+                    pages.close();
+                }
+            }
+            return aggregates;
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Error while reading parquet group aggregate values", e );
+        }
+    }
+
+
+    @Override
+    public void close() throws IOException {
+        fileReader.close();
+    }
+
+
+    /**
+     * Advances to the next row group if available.
+     *
+     * @return a {@link PageReadStore} representing next row group or null if the end of the file has been reached.
+     * @throws IOException an IO reading error.
+     */
+    private PageReadStore nextRowGroup() throws IOException {
+        return useNativeFilter
+                ? fileReader.readNextFilteredRowGroup()
+                : fileReader.readNextRowGroup();
+    }
+
+
+    /**
+     * Reads a page (row group). Two ways of reading and aggregating are supported by this method:
+     * 1. Only COUNT(*) with no group by fields - the fastest path.
+     * 2. A fallback to a generic aggregate reader with filters support.
+     *
+     * @param pages a page.
+     * @param aggregates a result set.
+     */
+    private void readPage( PageReadStore pages, Map<GroupKey, AggregateGroupState> aggregates ) {
+        ColumnReadStore store = new ColumnReadStoreImpl( pages, new NoOpColumnConverter( projectionSchema ), projectionSchema, createdBy );
+        ColumnDescriptor[] descriptors = projectionSchema.getColumns().toArray( ColumnDescriptor[]::new );
+
+        if ( isNoGroupCountStar() ) {
+            ParquetCountAggregatePageReader pageReader = new ParquetCountAggregatePageReader( pages, store, descriptors, primitivePredicate );
+            pageReader.read( aggregates, aggregateCalls, aggregateReaderFilters, cancelFlag );
+            return;
+        }
+
+        ParquetGroupedAggregatePageReader pageReader = new ParquetGroupedAggregatePageReader( pages, store, descriptors, primitivePredicate );
+        pageReader.read( aggregates, aggregateCalls, aggregateReaderFilters, groupFieldCount, cancelFlag );
+    }
+
+
+    /**
+     * Checks if there is no group by fields and only COUNT(*) aggregation function.
+     *
+     * @return true if the condition is satisfied and false otherwise.
+     */
+    private boolean isNoGroupCountStar() {
+        return groupFieldCount == 0 && supportsCountStarOnly();
+    }
+
+
+    /**
+     * Checks if there is only COUNT(*) aggregation function.
+     *
+     * @return true if only COUNT(*) aggregation function is provided and false otherwise.
+     */
+    private boolean supportsCountStarOnly() {
+        for ( AggregateCallDescriptor aggregateCall : aggregateCalls ) {
+            if ( aggregateCall.kind() != AggregateCallDescriptor.Kind.COUNT || aggregateCall.argumentIndex() != AggregateCallDescriptor.NO_ARGUMENT ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/ParquetNoFilterColumnAggregateReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/ParquetNoFilterColumnAggregateReader.java
new file mode 100644
index 0000000000..32d798ff30
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/ParquetNoFilterColumnAggregateReader.java
@@ -0,0 +1,173 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io.aggregate;
+
+import java.io.IOException;
+import java.net.URI;
+import java.util.Arrays;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.Path;
+import org.apache.parquet.ParquetReadOptions;
+import org.apache.parquet.column.ColumnDescriptor;
+import org.apache.parquet.column.ColumnReadStore;
+import org.apache.parquet.column.impl.ColumnReadStoreImpl;
+import org.apache.parquet.column.page.PageReadStore;
+import org.apache.parquet.hadoop.ParquetFileReader;
+import org.apache.parquet.hadoop.util.HadoopInputFile;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType;
+import org.apache.parquet.schema.Type;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateColumnAccumulator;
+import org.polypheny.db.adapter.parquet.shared.aggregate.ColumnAggregateResult;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.adapter.parquet.shared.io.aggregate.page.NoOpColumnConverter;
+import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.util.Source;
+
+
+/**
+ * This class is the fastest option to aggregate primitive columns with no filters. The columns are being read and aggregated one by one not as a row.
+ */
+public class ParquetNoFilterColumnAggregateReader implements AutoCloseable {
+
+    private final AtomicBoolean cancelFlag;
+    private final ParquetFileReader fileReader;
+    private final MessageType projectionSchema;
+    private final String createdBy;
+
+
+    public ParquetNoFilterColumnAggregateReader( Source source, AtomicBoolean cancelFlag, int[] fields ) {
+        this.cancelFlag = cancelFlag == null ? new AtomicBoolean( false ) : cancelFlag;
+
+        try {
+            URI uri = source.isFile() ? source.file().toURI() : source.url().toURI();
+            Path path = new Path( uri );
+            Configuration conf = HadoopConfigurationFactory.create( this.getClass().getClassLoader() );
+
+            ParquetSchemaReader schemaReader = new ParquetSchemaReader( source );
+            this.projectionSchema = schemaReader.buildProjectionSchema( fields );
+
+            ParquetReadOptions readOptions = ParquetReadOptions.builder()
+                    .useStatsFilter()
+                    .useDictionaryFilter()
+                    .useColumnIndexFilter()
+                    .useBloomFilter()
+                    .build();
+
+            this.fileReader = ParquetFileReader.open( HadoopInputFile.fromPath( path, conf ), readOptions );
+            this.fileReader.setRequestedSchema( projectionSchema );
+            this.createdBy = fileReader.getFileMetaData().getCreatedBy();
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Unable to open parquet file: " + source.path(), e );
+        }
+    }
+
+
+    /**
+     * Checks if the projection schema can be supported by this reader.
+     *
+     * @param projectionSchema a projection schema to validate.
+     * @return {@code true} if the projection schema contains only primitive, numeric and non-repeated columns and {@code false} otherwise.
+     */
+    public static boolean supports( MessageType projectionSchema ) {
+        if ( projectionSchema.getFieldCount() == 0 ) {
+            return false;
+        }
+        for ( Type field : projectionSchema.getFields() ) {
+            if ( !field.isPrimitive() || field.isRepetition( Type.Repetition.REPEATED ) || !isNumeric( field.asPrimitiveType() ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Checks if the column type is a numeric type.
+     *
+     * @param type the column type to check.
+     * @return {@code true} if the column type is numeric type and {@code false} otherwise.
+     */
+    private static boolean isNumeric( PrimitiveType type ) {
+        return switch ( type.getPrimitiveTypeName() ) {
+            case INT32, INT64, FLOAT, DOUBLE -> true;
+            case BOOLEAN, BINARY, FIXED_LEN_BYTE_ARRAY, INT96 -> false;
+        };
+    }
+
+
+    /**
+     * Reads all projection columns one by one and aggregates their values. The supported aggregations are COUNT, SUM, MIN and MAX.
+     *
+     * @return an array of aggregation results per column.
+     */
+    public ColumnAggregateResult[] readAggregateColumns() {
+        ColumnAggregateResult[] results = new ColumnAggregateResult[projectionSchema.getFieldCount()];
+        Arrays.setAll( results, ignored -> ColumnAggregateResult.empty() );
+
+        try {
+            PageReadStore pages;
+
+            while ( !cancelFlag.get() && (pages = fileReader.readNextRowGroup()) != null ) {
+                try {
+                    readPage( pages, results );
+                } finally {
+                    pages.close();
+                }
+            }
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Error while reading parquet aggregate values", e );
+        }
+
+        return results;
+    }
+
+
+    @Override
+    public void close() throws IOException {
+        fileReader.close();
+    }
+
+
+    /**
+     * Reads projected columns from the page and adds their aggregate results.
+     *
+     * @param pages a pages store.
+     * @param results aggregated column results.
+     */
+    private void readPage( PageReadStore pages, ColumnAggregateResult[] results ) {
+        ColumnReadStore store = new ColumnReadStoreImpl( pages, new NoOpColumnConverter( projectionSchema ), projectionSchema, createdBy );
+        ColumnDescriptor[] descriptors = projectionSchema.getColumns().toArray( ColumnDescriptor[]::new );
+        AggregateColumnAccumulator[] accumulators = new AggregateColumnAccumulator[descriptors.length];
+
+        for ( int i = 0; i < descriptors.length; i++ ) {
+            accumulators[i] = AggregateColumnAccumulator.create( store.getColumnReader( descriptors[i] ), descriptors[i], pages.getRowCount(), cancelFlag );
+        }
+
+        for ( AggregateColumnAccumulator accumulator : accumulators ) {
+            accumulator.read();
+        }
+
+        for ( int i = 0; i < accumulators.length; i++ ) {
+            results[i] = results[i].merge( accumulators[i].result() );
+        }
+    }
+
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/NoOpColumnConverter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/NoOpColumnConverter.java
new file mode 100644
index 0000000000..a0d76b5cd7
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/NoOpColumnConverter.java
@@ -0,0 +1,60 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io.aggregate.page;
+
+import org.apache.parquet.io.api.Converter;
+import org.apache.parquet.io.api.GroupConverter;
+import org.apache.parquet.io.api.PrimitiveConverter;
+import org.apache.parquet.schema.MessageType;
+
+
+/**
+ * This is a dummy no operational converter required by some Parquet API's.
+ */
+public class NoOpColumnConverter extends GroupConverter {
+
+    private final Converter[] converters;
+
+
+    public NoOpColumnConverter( MessageType projectionSchema ) {
+        this.converters = new Converter[projectionSchema.getFieldCount()];
+        for ( int i = 0; i < projectionSchema.getFieldCount(); i++ ) {
+            converters[i] = new NoOpPrimitiveConverter();
+        }
+    }
+
+
+    @Override
+    public Converter getConverter( int fieldIndex ) {
+        return converters[fieldIndex];
+    }
+
+
+    @Override
+    public void start() {
+    }
+
+
+    @Override
+    public void end() {
+    }
+
+
+    private static class NoOpPrimitiveConverter extends PrimitiveConverter {
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetCountAggregatePageReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetCountAggregatePageReader.java
new file mode 100644
index 0000000000..d416010647
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetCountAggregatePageReader.java
@@ -0,0 +1,81 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io.aggregate.page;
+
+import java.util.Arrays;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.parquet.column.ColumnDescriptor;
+import org.apache.parquet.column.ColumnReadStore;
+import org.apache.parquet.column.ColumnReader;
+import org.apache.parquet.column.page.PageReadStore;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetPrimitivePredicate;
+import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
+import org.polypheny.db.type.entity.PolyValue;
+
+/**
+ * This is the no-GROUP BY COUNT(*) page reader.
+ */
+public class ParquetCountAggregatePageReader extends ParquetPageReader {
+
+    public ParquetCountAggregatePageReader( PageReadStore pageStore, ColumnReadStore columnStore, ColumnDescriptor[] columns, ParquetPrimitivePredicate predicate ) {
+        super( pageStore, columnStore, columns, predicate );
+    }
+
+
+    public void read( Map<GroupKey, AggregateGroupState> aggregates, AggregateCallDescriptor[] aggregateCalls, List<ParquetAdapterFilter<PolyValue>> filters, AtomicBoolean cancelFlag ) {
+        long count = 0;
+        ColumnReader[] readers = readers();
+
+        if ( predicate != null ) {
+            Object[] values = new Object[readers.length];
+            boolean[] consumed = new boolean[readers.length];
+            for ( long row = 0; row < pageStore.getRowCount(); row++ ) {
+                if ( shouldStop( row, cancelFlag ) ) {
+                    break;
+                }
+                Arrays.fill( consumed, false );
+                if ( predicate.matches( readers, consumed, values ) ) {
+                    count++;
+                }
+                consumeRemaining( readers, consumed );
+            }
+            aggregates.computeIfAbsent( GroupKey.Empty, ignored -> new AggregateGroupState( aggregateCalls ) ).increment( count );
+            return;
+        }
+
+        Object[] values = new Object[readers.length];
+        for ( long row = 0; row < pageStore.getRowCount(); row++ ) {
+            if ( shouldStop( row, cancelFlag ) ) {
+                break;
+            }
+            for ( int i = 0; i < readers.length; i++ ) {
+                values[i] = currentValue( readers[i], columns[i] );
+            }
+            if ( matchesFilters( values, filters ) ) {
+                count++;
+            }
+        }
+
+        aggregates.computeIfAbsent( GroupKey.Empty, ignored -> new AggregateGroupState( aggregateCalls ) ).increment( count );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetGroupedAggregatePageReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetGroupedAggregatePageReader.java
new file mode 100644
index 0000000000..286bb0067b
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetGroupedAggregatePageReader.java
@@ -0,0 +1,93 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io.aggregate.page;
+
+import java.util.Arrays;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.parquet.column.ColumnDescriptor;
+import org.apache.parquet.column.ColumnReadStore;
+import org.apache.parquet.column.ColumnReader;
+import org.apache.parquet.column.page.PageReadStore;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetPrimitivePredicate;
+import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
+import org.polypheny.db.type.entity.PolyValue;
+
+/**
+ * This is a generic grouped page reader that also supports filters.
+ */
+public class ParquetGroupedAggregatePageReader extends ParquetPageReader {
+
+    public ParquetGroupedAggregatePageReader( PageReadStore pageStore, ColumnReadStore columnStore, ColumnDescriptor[] columns, ParquetPrimitivePredicate predicate ) {
+        super( pageStore, columnStore, columns, predicate );
+    }
+
+
+    public void read( Map<GroupKey, AggregateGroupState> aggregates, AggregateCallDescriptor[] aggregateCalls, List<ParquetAdapterFilter<PolyValue>> filters, int groupFieldCount, AtomicBoolean cancelFlag ) {
+        ColumnReader[] readers = readers();
+        Object[] values = new Object[readers.length];
+
+        if ( predicate != null && !filters.isEmpty() ) {
+            boolean[] consumed = new boolean[readers.length];
+            for ( long row = 0; row < pageStore.getRowCount(); row++ ) {
+                if ( shouldStop( row, cancelFlag ) ) {
+                    break;
+                }
+                Arrays.fill( consumed, false );
+                if ( !predicate.matches( readers, consumed, values ) ) {
+                    consumeRemaining( readers, consumed );
+                    continue;
+                }
+                for ( int i = 0; i < readers.length; i++ ) {
+                    if ( !consumed[i] ) {
+                        values[i] = currentValue( readers[i], columns[i] );
+                    }
+                }
+                aggregates.computeIfAbsent( groupKey( values, groupFieldCount ), ignored -> new AggregateGroupState( aggregateCalls ) ).add( values );
+            }
+        } else {
+            for ( long row = 0; row < pageStore.getRowCount(); row++ ) {
+                if ( shouldStop( row, cancelFlag ) ) {
+                    break;
+                }
+                for ( int i = 0; i < readers.length; i++ ) {
+                    values[i] = currentValue( readers[i], columns[i] );
+                }
+                if ( !matchesFilters( values, filters ) ) {
+                    continue;
+                }
+                aggregates.computeIfAbsent( groupKey( values, groupFieldCount ), ignored -> new AggregateGroupState( aggregateCalls ) ).add( values );
+            }
+        }
+    }
+
+
+    private GroupKey groupKey( Object[] values, int groupFieldCount ) {
+        if ( groupFieldCount <= 0 ) {
+            return GroupKey.Empty;
+        }
+        Object[] key = new Object[groupFieldCount];
+        System.arraycopy( values, 0, key, 0, groupFieldCount );
+        return GroupKey.of( key );
+    }
+
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetPageReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetPageReader.java
new file mode 100644
index 0000000000..eb27cf74af
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetPageReader.java
@@ -0,0 +1,96 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io.aggregate.page;
+
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.parquet.column.ColumnDescriptor;
+import org.apache.parquet.column.ColumnReadStore;
+import org.apache.parquet.column.ColumnReader;
+import org.apache.parquet.column.page.PageReadStore;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetPrimitiveValueFilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetCancellation;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetPrimitivePredicate;
+import org.polypheny.db.type.entity.PolyValue;
+
+public abstract class ParquetPageReader {
+
+    protected final PageReadStore pageStore;
+    protected final ColumnReadStore columnStore;
+    protected final ColumnDescriptor[] columns;
+    protected final ParquetPrimitivePredicate predicate;
+
+    private final ParquetPrimitiveValueFilterEvaluator filterEvaluator;
+
+
+    protected ParquetPageReader( PageReadStore pageStore, ColumnReadStore columnStore, ColumnDescriptor[] columns, ParquetPrimitivePredicate predicate ) {
+        this.pageStore = pageStore;
+        this.columnStore = columnStore;
+        this.columns = columns;
+        this.predicate = predicate;
+        this.filterEvaluator = new ParquetPrimitiveValueFilterEvaluator();
+    }
+
+
+    protected ColumnReader[] readers() {
+        if ( columnStore == null ) {
+            return new ColumnReader[0];
+        }
+        ColumnReader[] readers = new ColumnReader[columns.length];
+        for ( int i = 0; i < columns.length; i++ ) {
+            readers[i] = columnStore.getColumnReader( columns[i] );
+        }
+        return readers;
+    }
+
+
+    protected boolean matchesFilters( Object[] values, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            if ( !filterEvaluator.matches( values, filter ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    protected Object currentValue( ColumnReader reader, ColumnDescriptor descriptor ) {
+        try {
+            return descriptor.getMaxDefinitionLevel() == 0 || reader.getCurrentDefinitionLevel() == descriptor.getMaxDefinitionLevel()
+                    ? ParquetPrimitivePredicate.readValue( reader, descriptor.getPrimitiveType() )
+                    : null;
+        } finally {
+            reader.consume();
+        }
+    }
+
+
+    protected void consumeRemaining( ColumnReader[] readers, boolean[] consumed ) {
+        for ( int i = 0; i < readers.length; i++ ) {
+            if ( !consumed[i] ) {
+                readers[i].consume();
+            }
+        }
+    }
+
+
+    protected boolean shouldStop( long row, AtomicBoolean cancelFlag ) {
+        return ParquetCancellation.shouldStop( row, cancelFlag );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetTableStatisticsReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetTableStatisticsReader.java
index e11a4fcd35..f24cebe159 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetTableStatisticsReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetTableStatisticsReader.java
@@ -33,6 +33,7 @@
 import org.polypheny.db.adapter.statistics.ProvidedEntityStatistics;
 import org.polypheny.db.catalog.entity.logical.LogicalColumn;
 import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyNull;
 import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
 
@@ -72,23 +73,23 @@ public Optional<ProvidedEntityStatistics> getEntityStatistics( boolean nestedTab
     public Optional<ProvidedColumnStatistics> getColumnStatistics( LogicalColumn column, int uniqueValueLimit ) {
         ParquetColumnBinding columnBinding = binding.getColumnBinding( column.id );
         if ( columnBinding == null ) {
-            return Optional.of( new ProvidedColumnStatistics( estimateEntityRowCount(), null, null, List.of(), true ) );
+            return Optional.of( new ProvidedColumnStatistics( estimateEntityRowCount(), PolyNull.NULL, PolyNull.NULL, List.of(), true ) );
         }
         if ( columnBinding.role() == ParquetColumnRole.PARTITION ) {
             return Optional.of( partitionColumnStatistics( columnBinding, uniqueValueLimit ) );
         }
         if ( columnBinding.role() != ParquetColumnRole.DATA || columnBinding.sourcePathElements().isEmpty() ) {
-            return Optional.of( new ProvidedColumnStatistics( estimateEntityRowCount(), null, null, List.of(), true ) );
+            return Optional.of( new ProvidedColumnStatistics( estimateEntityRowCount(), PolyNull.NULL, PolyNull.NULL, List.of(), true ) );
         }
 
         Optional<ParquetColumnStatistics> metadataStatistics = aggregateColumnStatistics( columnBinding.sourcePathElements() );
         if ( metadataStatistics.isEmpty() ) {
-            return Optional.of( new ProvidedColumnStatistics( estimateEntityRowCount(), null, null, List.of(), true ) );
+            return Optional.of( new ProvidedColumnStatistics( estimateEntityRowCount(), PolyNull.NULL, PolyNull.NULL, List.of(), true ) );
         }
 
         ParquetColumnStatistics statistics = metadataStatistics.get();
-        PolyValue min = statistics.hasRange() ? typeConverter.fromStringToCompatiblePolyValue( column.type, statistics.type(), statistics.min() ) : null;
-        PolyValue max = statistics.hasRange() ? typeConverter.fromStringToCompatiblePolyValue( column.type, statistics.type(), statistics.max() ) : null;
+        PolyValue min = statistics.hasRange() ? typeConverter.fromStringToCompatiblePolyValue( column.type, statistics.type(), statistics.min() ) : PolyNull.NULL;
+        PolyValue max = statistics.hasRange() ? typeConverter.fromStringToCompatiblePolyValue( column.type, statistics.type(), statistics.max() ) : PolyNull.NULL;
         return Optional.of( new ProvidedColumnStatistics( nonNullCount( statistics ), min, max, List.of(), true ) );
     }
 
@@ -103,7 +104,7 @@ ProvidedColumnStatistics partitionColumnStatistics( ParquetColumnBinding columnB
         }
 
         if ( uniqueValueLimit > 0 && values.size() > uniqueValueLimit ) {
-            return new ProvidedColumnStatistics( estimateEntityRowCount(), null, null, List.of(), true );
+            return new ProvidedColumnStatistics( estimateEntityRowCount(), PolyNull.NULL, PolyNull.NULL, List.of(), true );
         }
 
         List<PolyValue> uniqueValues = values.stream()
@@ -112,7 +113,7 @@ ProvidedColumnStatistics partitionColumnStatistics( ParquetColumnBinding columnB
                 .toList();
         PolyValue min = values.stream().min( Comparator.naturalOrder() ).map( PolyString::of ).orElse( null );
         PolyValue max = values.stream().max( Comparator.naturalOrder() ).map( PolyString::of ).orElse( null );
-        return new ProvidedColumnStatistics( estimateEntityRowCount(), min, max, uniqueValues, false );
+        return new ProvidedColumnStatistics( estimateEntityRowCount(), min == null ? PolyNull.NULL : min, max == null ? PolyNull.NULL : max, uniqueValues, false );
     }
 
 
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java
index 3575f125a1..e6092dd4c3 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java
@@ -33,7 +33,9 @@
 import org.apache.parquet.io.OutputFile;
 import org.apache.parquet.io.PositionOutputStream;
 import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.MessageTypeParser;
 import org.apache.parquet.schema.Types;
+import org.jetbrains.annotations.NotNull;
 import org.junit.jupiter.api.Test;
 import org.junit.jupiter.api.io.TempDir;
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
@@ -165,6 +167,18 @@ void normalizedSchemaUsesDiscoveredMultiFileTable() throws Exception {
     }
 
 
+    @Test
+    void flatDiscoveryBindsTopLevelListColumnToRootPath() throws Exception {
+        writeParquet( tempDir.resolve( "flickr.parquet" ), flickrSchema(), flickrRow() );
+
+        Map<String, DiscoveredTable> tables = ParquetFileDiscovery.discoverTables( tempDir.toUri().toURL(), PREFIX );
+
+        DiscoveredTable table = tables.get( PREFIX + "__flickr" );
+        assertEquals( List.of( "id", "captions", "image" ), columnNames( table.columns() ) );
+        assertEquals( List.of( "captions" ), table.binding().columnPaths().get( "captions" ) );
+    }
+
+
     private static List<String> columnNames( List<ExportedColumn> columns ) {
         return columns.stream().map( ExportedColumn::name ).toList();
     }
@@ -190,6 +204,26 @@ private static MessageType schema( String... columns ) {
     }
 
 
+    private static MessageType flickrSchema() {
+        return MessageTypeParser.parseMessageType( """
+                message test {
+                  optional binary id (STRING);
+                  optional group captions (LIST) {
+                    repeated group list {
+                      optional binary element (STRING);
+                    }
+                  }
+                  optional binary image;
+                }
+                """ );
+    }
+
+
+    private static Object[] flickrRow() {
+        return row( "image-1.jpg", List.of( "a dog in grass", "a puppy outside" ), "image-bytes" );
+    }
+
+
     private static void writeParquet( Path file, MessageType schema, Object[] values ) throws Exception {
         SimpleGroupFactory groupFactory = new SimpleGroupFactory( schema );
         Group group = groupFactory.newGroup();
@@ -197,6 +231,11 @@ private static void writeParquet( Path file, MessageType schema, Object[] values
             Object value = values[i];
             if ( value instanceof String string ) {
                 group.add( i, string );
+            } else if ( value instanceof List<?> list ) {
+                Group captions = group.addGroup( i );
+                for ( Object element : list ) {
+                    captions.addGroup( "list" ).append( "element", (String) element );
+                }
             } else {
                 group.add( i, ((Number) value).longValue() );
             }
@@ -269,7 +308,7 @@ public void write( int b ) throws IOException {
 
 
         @Override
-        public void write( byte[] b, int off, int len ) throws IOException {
+        public void write( byte @NotNull [] b, int off, int len ) throws IOException {
             out.write( b, off, len );
             position += len;
         }
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetGroupedAggregateRelEnumeratorTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetGroupedAggregateRelEnumeratorTest.java
new file mode 100644
index 0000000000..2ac4bc06a0
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetGroupedAggregateRelEnumeratorTest.java
@@ -0,0 +1,233 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.io.IOException;
+import java.io.OutputStream;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.ExampleParquetWriter;
+import org.apache.parquet.io.OutputFile;
+import org.apache.parquet.io.PositionOutputStream;
+import org.apache.parquet.schema.LogicalTypeAnnotation;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+import org.apache.parquet.schema.Types;
+import org.jetbrains.annotations.NotNull;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+import org.polypheny.db.adapter.parquet.relational.execution.aggregate.ParquetGroupedAggregateRelEnumerator;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.catalog.entity.physical.PhysicalColumn;
+import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
+
+
+class ParquetGroupedAggregateRelEnumeratorTest {
+
+    @TempDir
+    Path tempDir;
+
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @Test
+    void supportsDistinctOnlyGroupBy() throws Exception {
+        Path file = tempDir.resolve( "flags.parquet" );
+        MessageType schema = Types.buildMessage()
+                .required( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "flag_a" )
+                .required( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "flag_b" )
+                .named( "test" );
+        writeParquet( file, schema, List.of(
+                row( "Y", "N" ),
+                row( "Y", "N" ),
+                row( "N", "N" ),
+                row( "Y", "Y" ) ) );
+
+        Set<List<String>> values = new HashSet<>();
+        try ( ParquetGroupedAggregateRelEnumerator enumerator = new ParquetGroupedAggregateRelEnumerator(
+                table( file ),
+                new int[]{ 0, 1 },
+                2,
+                new AggregateCallDescriptor[0],
+                List.of(),
+                new AtomicBoolean( false ) ) ) {
+            while ( enumerator.moveNext() ) {
+                PolyValue[] row = enumerator.current();
+                values.add( List.of( row[0].asString().value, row[1].asString().value ) );
+            }
+        }
+
+        assertEquals( Set.of( List.of( "Y", "N" ), List.of( "N", "N" ), List.of( "Y", "Y" ) ), values );
+    }
+
+
+    @Test
+    void scalarCountReturnsZeroWhenStatisticsPruneEveryFile() throws Exception {
+        Path file = tempDir.resolve( "flags.parquet" );
+        MessageType schema = Types.buildMessage()
+                .required( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "flag_a" )
+                .required( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "flag_b" )
+                .named( "test" );
+        writeParquet( file, schema, List.<Object[]>of( row( "Y", "N" ) ) );
+
+        try ( ParquetGroupedAggregateRelEnumerator enumerator = new ParquetGroupedAggregateRelEnumerator(
+                table( file ),
+                new int[]{ 0 },
+                0,
+                new AggregateCallDescriptor[]{ AggregateCallDescriptor.countStar() },
+                List.of( new ParquetAdapterFilter<>( 0, Kind.EQUALS, PolyString.of( "Z" ) ) ),
+                new AtomicBoolean( false ) ) ) {
+            assertTrue( enumerator.moveNext() );
+            assertEquals( 0L, enumerator.current()[0].asNumber().longValue() );
+            assertFalse( enumerator.moveNext() );
+        }
+    }
+
+
+    private static ParquetRelTable table( Path file ) throws Exception {
+        PhysicalColumn flagA = column( 10, "flag_a", 0 );
+        PhysicalColumn flagB = column( 11, "flag_b", 1 );
+        PhysicalTable physicalTable = new PhysicalTable( 1, 1, 1, "flags", List.of( flagA, flagB ), 1, "public", List.of(), 1 );
+        ParquetSourceFile sourceFile = ParquetSourceFile.of( file.toUri().toURL().toString(), Map.of() );
+        ParquetTableBinding binding = new ParquetTableBinding(
+                List.of( sourceFile ),
+                null,
+                List.of(),
+                Map.of(
+                        flagA.id, new ParquetColumnBinding( flagA.id, "flag_a", ParquetColumnRole.DATA, List.of( "flag_a" ) ),
+                        flagB.id, new ParquetColumnBinding( flagB.id, "flag_b", ParquetColumnRole.DATA, List.of( "flag_b" ) ) ) );
+        return new ParquetRelTable( 1, physicalTable, binding, null );
+    }
+
+
+    private static PhysicalColumn column( long id, String name, int position ) {
+        return new PhysicalColumn( id, name, name, 1, 1, 1, position, PolyType.VARCHAR, null, null, null, null, null, false, null, null );
+    }
+
+
+    private static Object[] row( Object... values ) {
+        return values;
+    }
+
+
+    private static void writeParquet( Path file, MessageType schema, List<Object[]> rows ) throws Exception {
+        SimpleGroupFactory groupFactory = new SimpleGroupFactory( schema );
+        try ( ParquetWriter<Group> writer = ExampleParquetWriter.builder( new LocalOutputFile( file ) )
+                .withType( schema )
+                .build() ) {
+            for ( Object[] row : rows ) {
+                Group group = groupFactory.newGroup();
+                group.add( 0, (String) row[0] );
+                group.add( 1, (String) row[1] );
+                writer.write( group );
+            }
+        }
+    }
+
+
+    private record LocalOutputFile( Path path ) implements OutputFile {
+
+        @Override
+        public PositionOutputStream create( long blockSizeHint ) throws IOException {
+            return createOrOverwrite( blockSizeHint );
+        }
+
+
+        @SuppressWarnings("resource")
+        @Override
+        public PositionOutputStream createOrOverwrite( long blockSizeHint ) throws IOException {
+            Files.createDirectories( path.getParent() );
+            OutputStream outputStream = Files.newOutputStream( path );
+            return new PositionOutputStream() {
+                private long position;
+
+
+                @Override
+                public long getPos() {
+                    return position;
+                }
+
+
+                @Override
+                public void write( int b ) throws IOException {
+                    outputStream.write( b );
+                    position++;
+                }
+
+
+                @Override
+                public void write( byte @NotNull [] b, int off, int len ) throws IOException {
+                    outputStream.write( b, off, len );
+                    position += len;
+                }
+
+
+                @Override
+                public void close() throws IOException {
+                    outputStream.close();
+                }
+            };
+        }
+
+
+        @Override
+        public boolean supportsBlockSize() {
+            return false;
+        }
+
+
+        @Override
+        public long defaultBlockSize() {
+            return 0;
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetMetadataAggregateTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetMetadataAggregateTest.java
new file mode 100644
index 0000000000..948b54ea55
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetMetadataAggregateTest.java
@@ -0,0 +1,612 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertInstanceOf;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.io.IOException;
+import java.io.OutputStream;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.Enumerator;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.ExampleParquetWriter;
+import org.apache.parquet.io.OutputFile;
+import org.apache.parquet.io.PositionOutputStream;
+import org.apache.parquet.schema.LogicalTypeAnnotation;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+import org.apache.parquet.schema.Types;
+import org.jetbrains.annotations.NotNull;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.execution.aggregate.ParquetFileGroupedAggregateRelEnumerator;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelDataAggregateExecutor;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelMetadataAggregateExecutor;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelProjectExecutor;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRowRelEnumerator;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnStatistics;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetConstantColumnResolver;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
+import org.polypheny.db.adapter.parquet.shared.aggregate.ColumnAggregateProjection;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.catalog.entity.physical.PhysicalColumn;
+import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.numerical.PolyLong;
+import org.polypheny.db.util.ImmutableBitSet;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
+
+
+class ParquetMetadataAggregateTest {
+
+    @TempDir
+    Path tempDir;
+
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @Test
+    void metadataAggregateAllowsPartitionFiltersAndGroups() throws Exception {
+        ParquetRelTable table = partitionedTable();
+        int[] fields = new int[]{ 0, 1, 2 };
+
+        assertTrue( table.supportsMetadataAggregate(
+                fields,
+                List.of( new ParquetAdapterFilter<>( 1, Kind.EQUALS, null ) ),
+                ImmutableBitSet.of( 1, 2 ),
+                List.of() ) );
+
+        assertFalse( table.supportsMetadataAggregate(
+                fields,
+                List.of( new ParquetAdapterFilter<>( 0, Kind.GREATER_THAN, null ) ),
+                ImmutableBitSet.of(),
+                List.of() ) );
+
+        assertFalse( table.supportsMetadataAggregate(
+                fields,
+                List.of(),
+                ImmutableBitSet.of( 0 ),
+                List.of() ) );
+    }
+
+
+    @Test
+    void projectedScanUsesFolderPartitionValuesWhenPhysicalColumnsCollide() throws Exception {
+        ParquetRelTable table = partitionedTableWithPhysicalPartitionColumns();
+        List<ParquetAdapterFilter<PolyValue>> filters = List.of( ParquetAdapterFilter.logical(
+                Kind.AND,
+                List.of(
+                        new ParquetAdapterFilter<>( 1, Kind.EQUALS, PolyString.of( "2022" ) ),
+                        new ParquetAdapterFilter<>( 2, Kind.EQUALS, PolyString.of( "10" ) ) ) ) );
+
+        List<List<Object>> rows = new java.util.ArrayList<>();
+        try ( Enumerator<PolyValue[]> enumerator = projectExecutor( table ).enumeratorForFirstFile( new int[]{ 0, 1, 2 } ) ) {
+            assertInstanceOf( ParquetRowRelEnumerator.class, enumerator );
+        }
+        try ( Enumerator<PolyValue[]> enumerator = projectExecutor( table ).createEnumerator(
+                parameterContext( Map.of() ),
+                new int[]{ 0, 1, 2 },
+                filters ).enumerator() ) {
+            while ( enumerator.moveNext() ) {
+                PolyValue[] row = enumerator.current();
+                rows.add( List.of( row[0].asNumber().longValue(), row[1].asString().value, row[2].asString().value ) );
+            }
+        }
+
+        assertEquals( List.of( List.of( 1L, "2022", "10" ), List.of( 2L, "2022", "10" ) ), rows );
+    }
+
+
+    @Test
+    void projectedScanReadsMinimalDataColumnWhenOnlyPartitionColumnsAreProjected() throws Exception {
+        ParquetRelTable table = partitionedTable();
+
+        try ( Enumerator<PolyValue[]> enumerator = projectExecutor( table ).enumeratorForFirstFile( new int[]{ 2 } ) ) {
+            assertInstanceOf( ParquetRowRelEnumerator.class, enumerator );
+            assertTrue( enumerator.moveNext() );
+            assertEquals( "01", enumerator.current()[0].asString().value );
+        }
+    }
+
+
+    @Test
+    void metadataAggregateUsesFileConstantPhysicalFilters() throws Exception {
+        ParquetRelTable table = physicalPartitionColumnTable(
+                List.of(
+                        List.of( row( 1L, "2022", "10" ), row( 2L, "2022", "10" ) ),
+                        List.<Object[]>of( row( 3L, "2022", "11" ) ) ) );
+        int[] fields = new int[]{ 0, 1, 2 };
+        List<ParquetAdapterFilter<PolyValue>> filters = List.of( ParquetAdapterFilter.logical(
+                Kind.AND,
+                List.of(
+                        dynamicFilter( 1, 0L ),
+                        dynamicFilter( 2, 1L ) ) ) );
+
+        assertTrue( table.supportsMetadataAggregate( fields, filters, ImmutableBitSet.of(), List.of() ) );
+
+        ParquetRelMetadataAggregateExecutor executor = metadataExecutor( table );
+        DataContext dataContext = parameterContext( Map.of( 0L, PolyString.of( "2022" ), 1L, PolyString.of( "10" ) ) );
+        try ( Enumerator<PolyValue[]> enumerator = executor.createEnumerator(
+                dataContext,
+                fields,
+                filters,
+                new int[0],
+                new String[]{ Kind.COUNT.name() },
+                new int[]{ -1 } ).enumerator() ) {
+            assertTrue( enumerator.moveNext() );
+            assertEquals( 2L, enumerator.current()[0].asNumber().longValue() );
+            assertFalse( enumerator.moveNext() );
+        }
+    }
+
+
+    @Test
+    void metadataAggregateRejectsMixedPhysicalFilterColumns() throws Exception {
+        ParquetRelTable table = physicalPartitionColumnTable(
+                List.of( List.of(
+                        row( 1L, "2022", "10" ),
+                        row( 2L, "2022", "11" ) ) ) );
+        int[] fields = new int[]{ 0, 1, 2 };
+
+        assertFalse( table.supportsMetadataAggregate(
+                fields,
+                List.of( new ParquetAdapterFilter<>( 2, Kind.EQUALS, PolyString.of( "10" ) ) ),
+                ImmutableBitSet.of(),
+                List.of() ) );
+    }
+
+
+    @Test
+    void metadataAggregateGroupsByFileConstantPhysicalColumns() throws Exception {
+        ParquetRelTable table = physicalPartitionColumnTable(
+                List.of(
+                        List.of( row( 1L, "2022", "10" ), row( 2L, "2022", "10" ) ),
+                        List.<Object[]>of( row( 3L, "2022", "11" ) ) ) );
+        int[] fields = new int[]{ 0, 1, 2 };
+
+        assertTrue( table.supportsMetadataAggregate( fields, List.of(), ImmutableBitSet.of( 1, 2 ), List.of() ) );
+
+        Map<List<String>, Long> counts = new LinkedHashMap<>();
+        try ( Enumerator<PolyValue[]> enumerator = metadataExecutor( table ).createEnumerator(
+                parameterContext( Map.of() ),
+                fields,
+                List.of(),
+                new int[]{ 1, 2 },
+                new String[]{ Kind.COUNT.name() },
+                new int[]{ -1 } ).enumerator() ) {
+            while ( enumerator.moveNext() ) {
+                PolyValue[] row = enumerator.current();
+                counts.put( List.of( row[0].asString().value, row[1].asString().value ), row[2].asNumber().longValue() );
+            }
+        }
+
+        assertEquals( Map.of( List.of( "2022", "10" ), 2L, List.of( "2022", "11" ), 1L ), counts );
+    }
+
+
+    @Test
+    void fileGroupedAggregateGroupsPhysicalConstantColumns() throws Exception {
+        ParquetRelTable table = physicalPartitionColumnTable(
+                List.of(
+                        List.of( row( 1L, "2022", "10" ), row( 2L, "2022", "10" ) ),
+                        List.<Object[]>of( row( 3L, "2022", "11" ) ) ) );
+
+        Map<List<String>, Double> sums = new LinkedHashMap<>();
+        try ( ParquetFileGroupedAggregateRelEnumerator enumerator = new ParquetFileGroupedAggregateRelEnumerator(
+                table,
+                new int[]{ 0, 1, 2 },
+                new int[]{ 1, 2 },
+                new String[]{ Kind.SUM.name() },
+                new int[]{ 0 },
+                new ColumnAggregateProjection( new int[]{ 0 }, Map.of( 0, 0 ) ),
+                List.of(),
+                new AtomicBoolean( false ) ) ) {
+            while ( enumerator.moveNext() ) {
+                PolyValue[] row = enumerator.current();
+                sums.put( List.of( row[0].asString().value, row[1].asString().value ), row[2].asNumber().doubleValue() );
+            }
+        }
+
+        assertEquals( Map.of( List.of( "2022", "10" ), 3D, List.of( "2022", "11" ), 3D ), sums );
+    }
+
+
+    @Test
+    void streamingGroupedAggregateSkipsFileConstantPhysicalFilter() throws Exception {
+        ParquetRelTable table = physicalPartitionColumnTable(
+                List.of(
+                        List.of( row( 1L, "2022", "10" ), row( 1L, "2022", "10" ), row( 2L, "2022", "10" ) ),
+                        List.<Object[]>of( row( 3L, "2021", "10" ) ) ) );
+
+        assertEquals(
+                Map.of( 1L, 2L, 2L, 1L ),
+                streamingGroupedCounts( table, List.of( new ParquetAdapterFilter<>( 1, Kind.EQUALS, PolyString.of( "2022" ) ) ) ) );
+    }
+
+
+    @Test
+    void streamingGroupedAggregateRetainsResidualAndFilter() throws Exception {
+        ParquetRelTable table = physicalPartitionColumnTable(
+                List.of(
+                        List.of( row( 1L, "2022", "10" ), row( 1L, "2022", "10" ), row( 2L, "2022", "10" ) ),
+                        List.<Object[]>of( row( 3L, "2021", "10" ) ) ) );
+        ParquetAdapterFilter<PolyValue> filter = ParquetAdapterFilter.logical(
+                Kind.AND,
+                List.of(
+                        new ParquetAdapterFilter<>( 1, Kind.EQUALS, PolyString.of( "2022" ) ),
+                        new ParquetAdapterFilter<>( 0, Kind.GREATER_THAN, PolyLong.of( 1L ) ) ) );
+
+        assertEquals( Map.of( 2L, 1L ), streamingGroupedCounts( table, List.of( filter ) ) );
+    }
+
+
+    @Test
+    void streamingGroupedAggregateRetainsResidualOrFilter() throws Exception {
+        ParquetRelTable table = physicalPartitionColumnTable(
+                List.of(
+                        List.of( row( 1L, "2022", "10" ), row( 2L, "2022", "10" ) ),
+                        List.of( row( 3L, "2021", "10" ), row( 4L, "2021", "10" ) ) ) );
+        ParquetAdapterFilter<PolyValue> filter = ParquetAdapterFilter.logical(
+                Kind.OR,
+                List.of(
+                        new ParquetAdapterFilter<>( 1, Kind.EQUALS, PolyString.of( "2022" ) ),
+                        new ParquetAdapterFilter<>( 0, Kind.EQUALS, PolyLong.of( 3L ) ) ) );
+
+        assertEquals( Map.of( 1L, 1L, 2L, 1L, 3L, 1L ), streamingGroupedCounts( table, List.of( filter ) ) );
+    }
+
+
+    @Test
+    void fileGroupedScalarCountReturnsZeroWhenFiltersRejectEveryFile() throws Exception {
+        ParquetRelTable table = physicalPartitionColumnTable(
+                List.of( List.of( row( 1L, "2022", "10" ), row( 2L, "2022", "10" ) ) ) );
+
+        try ( ParquetFileGroupedAggregateRelEnumerator enumerator = new ParquetFileGroupedAggregateRelEnumerator(
+                table,
+                new int[]{ 0, 1, 2 },
+                new int[0],
+                new String[]{ Kind.COUNT.name() },
+                new int[]{ -1 },
+                new ColumnAggregateProjection( new int[0], Map.of() ),
+                List.of( new ParquetAdapterFilter<>( 1, Kind.EQUALS, PolyString.of( "2099" ) ) ),
+                new AtomicBoolean( false ) ) ) {
+            assertTrue( enumerator.moveNext() );
+            assertEquals( 0L, enumerator.current()[0].asNumber().longValue() );
+            assertFalse( enumerator.moveNext() );
+        }
+    }
+
+
+    @Test
+    void constantColumnResolverRejectsMixedNullAndNonNullValues() {
+        ParquetColumnBinding binding = new ParquetColumnBinding( 10, "month", ParquetColumnRole.DATA, List.of( "month" ) );
+        ParquetSourceFile sourceFile = new ParquetSourceFile(
+                "file:/unused.parquet",
+                Map.of(),
+                Map.of( List.of( "month" ), new ParquetColumnStatistics( PolyType.TEXT, 2, 2, 1L, "10", "10", true ) ) );
+
+        assertTrue( new ParquetConstantColumnResolver().resolve( sourceFile, binding ).isEmpty() );
+    }
+
+
+    private ParquetRelTable partitionedTable() throws Exception {
+        Path file = tempDir.resolve( "part-000.parquet" );
+        writeIdParquet( file, List.of( 1L, 2L ) );
+
+        PhysicalColumn id = column( 10, "id", 0, PolyType.BIGINT );
+        PhysicalColumn year = column( 11, "year", 1, PolyType.VARCHAR );
+        PhysicalColumn month = column( 12, "month", 2, PolyType.VARCHAR );
+        PhysicalTable physicalTable = new PhysicalTable( 1, 1, 1, "orders", List.of( id, year, month ), 1, "public", List.of(), 1 );
+
+        ParquetSourceFile sourceFile = ParquetSourceFile.of( file.toUri().toURL().toString(), Map.of( "year", "2025", "month", "01" ) );
+        ParquetTableBinding binding = new ParquetTableBinding(
+                List.of( sourceFile ),
+                null,
+                List.of(),
+                Map.of(
+                        id.id, new ParquetColumnBinding( id.id, "id", ParquetColumnRole.DATA, List.of( "id" ) ),
+                        year.id, new ParquetColumnBinding( year.id, "year", ParquetColumnRole.PARTITION, List.of() ),
+                        month.id, new ParquetColumnBinding( month.id, "month", ParquetColumnRole.PARTITION, List.of() ) ) );
+        return new ParquetRelTable( 1, physicalTable, binding, null );
+    }
+
+
+    private ParquetRelTable physicalPartitionColumnTable( List<List<Object[]>> fileRows ) throws Exception {
+        PhysicalColumn id = column( 10, "id", 0, PolyType.BIGINT );
+        PhysicalColumn year = column( 11, "year", 1, PolyType.VARCHAR );
+        PhysicalColumn month = column( 12, "month", 2, PolyType.VARCHAR );
+        PhysicalTable physicalTable = new PhysicalTable( 1, 1, 1, "orders", List.of( id, year, month ), 1, "public", List.of(), 1 );
+
+        List<ParquetSourceFile> sourceFiles = new java.util.ArrayList<>();
+        for ( int i = 0; i < fileRows.size(); i++ ) {
+            Path file = tempDir.resolve( "physical-part-" + i + ".parquet" );
+            writePhysicalPartitionParquet( file, fileRows.get( i ) );
+            sourceFiles.add( ParquetSourceFile.of( file.toUri().toURL().toString(), Map.of() ) );
+        }
+        ParquetTableBinding binding = new ParquetTableBinding(
+                sourceFiles,
+                null,
+                List.of(),
+                Map.of(
+                        id.id, new ParquetColumnBinding( id.id, "id", ParquetColumnRole.DATA, List.of( "id" ) ),
+                        year.id, new ParquetColumnBinding( year.id, "year", ParquetColumnRole.DATA, List.of( "year" ) ),
+                        month.id, new ParquetColumnBinding( month.id, "month", ParquetColumnRole.DATA, List.of( "month" ) ) ) );
+        return new ParquetRelTable( 1, physicalTable, binding, null );
+    }
+
+
+    private ParquetRelTable partitionedTableWithPhysicalPartitionColumns() throws Exception {
+        Path file = tempDir.resolve( "partition-collision.parquet" );
+        writePhysicalPartitionParquet( file, List.of( row( 1L, "2099", "99" ), row( 2L, "2099", "99" ) ) );
+
+        PhysicalColumn id = column( 10, "id", 0, PolyType.BIGINT );
+        PhysicalColumn year = column( 11, "year", 1, PolyType.VARCHAR );
+        PhysicalColumn month = column( 12, "month", 2, PolyType.VARCHAR );
+        PhysicalTable physicalTable = new PhysicalTable( 1, 1, 1, "orders", List.of( id, year, month ), 1, "public", List.of(), 1 );
+
+        ParquetSourceFile sourceFile = ParquetSourceFile.of( file.toUri().toURL().toString(), Map.of( "year", "2022", "month", "10" ) );
+        ParquetTableBinding binding = new ParquetTableBinding(
+                List.of( sourceFile ),
+                null,
+                List.of(),
+                Map.of(
+                        id.id, new ParquetColumnBinding( id.id, "id", ParquetColumnRole.DATA, List.of( "id" ) ),
+                        year.id, new ParquetColumnBinding( year.id, "year", ParquetColumnRole.PARTITION, List.of() ),
+                        month.id, new ParquetColumnBinding( month.id, "month", ParquetColumnRole.PARTITION, List.of() ) ) );
+        return new ParquetRelTable( 1, physicalTable, binding, null );
+    }
+
+
+    private static ParquetRelMetadataAggregateExecutor metadataExecutor( ParquetRelTable table ) {
+        ParquetSchemaReader schemaReader = new ParquetSchemaReader( table.getBinding().sourceFiles().stream().map( ParquetSourceFile::asSource ).toList() );
+        return new ParquetRelMetadataAggregateExecutor( table, null, new int[]{ 0, 1, 2 }, schemaReader ) {
+            @Override
+            protected void registerAdapter( DataContext dataContext ) {
+            }
+        };
+    }
+
+
+    private static ParquetRelDataAggregateExecutor aggregateExecutor( ParquetRelTable table ) {
+        ParquetSchemaReader schemaReader = new ParquetSchemaReader( table.getBinding().sourceFiles().stream().map( ParquetSourceFile::asSource ).toList() );
+        return new ParquetRelDataAggregateExecutor( table, null, new int[]{ 0, 1, 2 }, schemaReader ) {
+            @Override
+            protected void registerAdapter( DataContext dataContext ) {
+            }
+        };
+    }
+
+
+    private static TestProjectExecutor projectExecutor( ParquetRelTable table ) {
+        ParquetSchemaReader schemaReader = new ParquetSchemaReader( table.getBinding().sourceFiles().stream().map( ParquetSourceFile::asSource ).toList() );
+        return new TestProjectExecutor( table, schemaReader );
+    }
+
+
+    private static class TestProjectExecutor extends ParquetRelProjectExecutor {
+
+        private TestProjectExecutor( ParquetRelTable table, ParquetSchemaReader schemaReader ) {
+            super( table, null, new int[]{ 0, 1, 2 }, schemaReader );
+        }
+
+
+        private Enumerator<PolyValue[]> enumeratorForFirstFile( int[] fields ) {
+            return enumeratorForFile(
+                    table,
+                    table.getBinding().sourceFiles().get( 0 ),
+                    fields,
+                    fieldIndexes,
+                    schemaReader,
+                    new AtomicBoolean( false ),
+                    FiltersContainer.empty );
+        }
+
+
+        @Override
+        protected void registerAdapter( DataContext dataContext ) {
+        }
+
+    }
+
+
+    private static Map<Long, Long> streamingGroupedCounts( ParquetRelTable table, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        Map<Long, Long> counts = new LinkedHashMap<>();
+        try ( Enumerator<PolyValue[]> enumerator = aggregateExecutor( table ).createEnumerator(
+                parameterContext( Map.of() ),
+                new int[]{ 0, 1, 2 },
+                filters,
+                new int[]{ 0 },
+                new String[]{ Kind.COUNT.name() },
+                new int[]{ -1 } ).enumerator() ) {
+            while ( enumerator.moveNext() ) {
+                PolyValue[] row = enumerator.current();
+                counts.put( row[0].asNumber().longValue(), row[1].asNumber().longValue() );
+            }
+        }
+        return counts;
+    }
+
+
+    private static DataContext parameterContext( Map<Long, PolyValue> values ) {
+        return new DataContext.SlimDataContext() {
+            @Override
+            public List<Map<Long, PolyValue>> getParameterValues() {
+                return List.of( values );
+            }
+        };
+    }
+
+
+    private static ParquetAdapterFilter<PolyValue> dynamicFilter( int columnIndex, long parameterIndex ) {
+        return new ParquetAdapterFilter<>( columnIndex, List.of(), Kind.EQUALS, null, parameterIndex );
+    }
+
+
+    private static PhysicalColumn column( long id, String name, int position, PolyType type ) {
+        return new PhysicalColumn( id, name, name, 1, 1, 1, position, type, null, null, null, null, null, false, null, null );
+    }
+
+
+    private static Object[] row( Object... values ) {
+        return values;
+    }
+
+
+    private static void writeIdParquet( Path file, List<Long> ids ) throws Exception {
+        MessageType schema = Types.buildMessage()
+                .required( PrimitiveTypeName.INT64 ).named( "id" )
+                .named( "test" );
+        SimpleGroupFactory groupFactory = new SimpleGroupFactory( schema );
+        try ( ParquetWriter<Group> writer = ExampleParquetWriter.builder( new LocalOutputFile( file ) )
+                .withType( schema )
+                .build() ) {
+            for ( long id : ids ) {
+                writer.write( groupFactory.newGroup().append( "id", id ) );
+            }
+        }
+    }
+
+
+    private static void writePhysicalPartitionParquet( Path file, List<Object[]> rows ) throws Exception {
+        MessageType schema = Types.buildMessage()
+                .required( PrimitiveTypeName.INT64 ).named( "id" )
+                .required( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "year" )
+                .required( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "month" )
+                .named( "test" );
+        SimpleGroupFactory groupFactory = new SimpleGroupFactory( schema );
+        try ( ParquetWriter<Group> writer = ExampleParquetWriter.builder( new LocalOutputFile( file ) )
+                .withType( schema )
+                .build() ) {
+            for ( Object[] row : rows ) {
+                writer.write( groupFactory.newGroup()
+                        .append( "id", (Long) row[0] )
+                        .append( "year", (String) row[1] )
+                        .append( "month", (String) row[2] ) );
+            }
+        }
+    }
+
+
+    private record LocalOutputFile( Path path ) implements OutputFile {
+
+        @Override
+        public PositionOutputStream create( long blockSizeHint ) throws IOException {
+            return createOrOverwrite( blockSizeHint );
+        }
+
+
+        @Override
+        public PositionOutputStream createOrOverwrite( long blockSizeHint ) throws IOException {
+            return new LocalPositionOutputStream( Files.newOutputStream( path ) );
+        }
+
+
+        @Override
+        public boolean supportsBlockSize() {
+            return false;
+        }
+
+
+        @Override
+        public long defaultBlockSize() {
+            return 0;
+        }
+
+
+        @Override
+        public String getPath() {
+            return path.toString();
+        }
+
+    }
+
+
+    private static class LocalPositionOutputStream extends PositionOutputStream {
+
+        private final OutputStream out;
+        private long position;
+
+
+        private LocalPositionOutputStream( OutputStream out ) {
+            this.out = out;
+        }
+
+
+        @Override
+        public long getPos() {
+            return position;
+        }
+
+
+        @Override
+        public void write( int b ) throws IOException {
+            out.write( b );
+            position++;
+        }
+
+
+        @Override
+        public void write( byte @NotNull [] b, int off, int len ) throws IOException {
+            out.write( b, off, len );
+            position += len;
+        }
+
+
+        @Override
+        public void flush() throws IOException {
+            out.flush();
+        }
+
+
+        @Override
+        public void close() throws IOException {
+            out.close();
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNoFilterColumnAggregateReaderTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNoFilterColumnAggregateReaderTest.java
new file mode 100644
index 0000000000..d57599c2e4
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNoFilterColumnAggregateReaderTest.java
@@ -0,0 +1,165 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.io.IOException;
+import java.io.OutputStream;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.ExampleParquetWriter;
+import org.apache.parquet.io.OutputFile;
+import org.apache.parquet.io.PositionOutputStream;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+import org.apache.parquet.schema.Types;
+import org.jetbrains.annotations.NotNull;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+import org.polypheny.db.adapter.parquet.shared.aggregate.ColumnAggregateResult;
+import org.polypheny.db.adapter.parquet.shared.io.aggregate.ParquetNoFilterColumnAggregateReader;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.util.Sources;
+
+
+class ParquetNoFilterColumnAggregateReaderTest {
+
+    @TempDir
+    Path tempDir;
+
+
+    @Test
+    void readsPrimitiveColumnAggregateResults() throws Exception {
+        Path file = tempDir.resolve( "aggregate.parquet" );
+        MessageType schema = Types.buildMessage()
+                .required( PrimitiveTypeName.INT64 ).named( "id" )
+                .optional( PrimitiveTypeName.DOUBLE ).named( "amount" )
+                .named( "test" );
+        writeParquet( file, schema );
+
+        var source = Sources.of( file.toFile() );
+        var schemaReader = new ParquetSchemaReader( source );
+        assertTrue( ParquetNoFilterColumnAggregateReader.supports( schemaReader.buildProjectionSchema( new int[]{ 0, 1 } ) ) );
+
+        ColumnAggregateResult[] results;
+        try ( ParquetNoFilterColumnAggregateReader reader = new ParquetNoFilterColumnAggregateReader( source, new AtomicBoolean( false ), new int[]{ 0, 1 } ) ) {
+            results = reader.readAggregateColumns();
+        }
+
+        assertEquals( 3D, results[0].sum() );
+        assertEquals( 2, results[0].count() );
+        assertEquals( 1D, results[0].min() );
+        assertEquals( 2D, results[0].max() );
+        assertEquals( 10.5D, results[1].sum() );
+        assertEquals( 1, results[1].count() );
+        assertEquals( 10.5D, results[1].min() );
+        assertEquals( 10.5D, results[1].max() );
+    }
+
+
+    @Test
+    void rejectsNonNumericProjection() {
+        MessageType schema = Types.buildMessage()
+                .required( PrimitiveTypeName.BINARY ).named( "name" )
+                .named( "test" );
+
+        assertFalse( ParquetNoFilterColumnAggregateReader.supports( schema ) );
+    }
+
+
+    private static void writeParquet( Path file, MessageType schema ) throws Exception {
+        SimpleGroupFactory groupFactory = new SimpleGroupFactory( schema );
+        Group first = groupFactory.newGroup()
+                .append( "id", 1L )
+                .append( "amount", 10.5D );
+        Group second = groupFactory.newGroup()
+                .append( "id", 2L );
+
+        try ( ParquetWriter<Group> writer = ExampleParquetWriter.builder( new LocalOutputFile( file ) )
+                .withType( schema )
+                .build() ) {
+            writer.write( first );
+            writer.write( second );
+        }
+    }
+
+
+    private record LocalOutputFile( Path path ) implements OutputFile {
+
+        @Override
+        public PositionOutputStream create( long blockSizeHint ) throws IOException {
+            return createOrOverwrite( blockSizeHint );
+        }
+
+
+        @Override
+        public PositionOutputStream createOrOverwrite( long blockSizeHint ) throws IOException {
+            Files.createDirectories( path.getParent() );
+            @SuppressWarnings("resource") OutputStream outputStream = Files.newOutputStream( path );
+            return new PositionOutputStream() {
+                private long position;
+
+
+                @Override
+                public long getPos() {
+                    return position;
+                }
+
+
+                @Override
+                public void write( int b ) throws IOException {
+                    outputStream.write( b );
+                    position++;
+                }
+
+
+                @Override
+                public void write( byte @NotNull [] b, int off, int len ) throws IOException {
+                    outputStream.write( b, off, len );
+                    position += len;
+                }
+
+
+                @Override
+                public void close() throws IOException {
+                    outputStream.close();
+                }
+            };
+        }
+
+
+        @Override
+        public boolean supportsBlockSize() {
+            return false;
+        }
+
+
+        @Override
+        public long defaultBlockSize() {
+            return 0;
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPrimitiveRowReaderTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPrimitiveRowReaderTest.java
new file mode 100644
index 0000000000..3b6e186e19
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPrimitiveRowReaderTest.java
@@ -0,0 +1,185 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertNull;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.io.IOException;
+import java.io.OutputStream;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.List;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.ExampleParquetWriter;
+import org.apache.parquet.io.OutputFile;
+import org.apache.parquet.io.PositionOutputStream;
+import org.apache.parquet.schema.LogicalTypeAnnotation;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+import org.apache.parquet.schema.Types;
+import org.jetbrains.annotations.NotNull;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetPrimitiveRowReader;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
+import org.polypheny.db.util.Sources;
+
+
+class ParquetPrimitiveRowReaderTest {
+
+    @TempDir
+    Path tempDir;
+
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @Test
+    void readsPrimitiveProjectionWithoutGroupMaterialization() throws Exception {
+        Path file = tempDir.resolve( "flat.parquet" );
+        MessageType schema = Types.buildMessage()
+                .required( PrimitiveTypeName.INT64 ).named( "id" )
+                .optional( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "name" )
+                .required( PrimitiveTypeName.DOUBLE ).named( "amount" )
+                .named( "test" );
+        writeParquet( file, schema );
+
+        var source = Sources.of( file.toFile() );
+        var schemaReader = new ParquetSchemaReader( source );
+        assertTrue( ParquetPrimitiveRowReader.supports( schemaReader.buildProjectionSchema( new int[]{ 0, 1, 2 } ) ) );
+
+        try ( ParquetPrimitiveRowReader reader = new ParquetPrimitiveRowReader( source, new AtomicBoolean( false ), new int[]{ 0, 1, 2 }, List.of() ) ) {
+            PolyValue[] first = reader.next();
+            assertEquals( 1L, first[0].asNumber().longValue() );
+            assertEquals( "Alice", first[1].asString().value );
+            assertEquals( 10.5D, first[2].asNumber().doubleValue() );
+
+            PolyValue[] second = reader.next();
+            assertEquals( 2L, second[0].asNumber().longValue() );
+            assertTrue( second[1].isNull() );
+            assertEquals( 20.25D, second[2].asNumber().doubleValue() );
+
+            assertNull( reader.next() );
+        }
+    }
+
+
+    @Test
+    void rejectsRepeatedProjection() {
+        MessageType schema = Types.buildMessage()
+                .repeated( PrimitiveTypeName.INT64 ).named( "id" )
+                .named( "test" );
+
+        assertFalse( ParquetPrimitiveRowReader.supports( schema ) );
+    }
+
+
+    private static void writeParquet( Path file, MessageType schema ) throws Exception {
+        SimpleGroupFactory groupFactory = new SimpleGroupFactory( schema );
+        Group first = groupFactory.newGroup()
+                .append( "id", 1L )
+                .append( "name", "Alice" )
+                .append( "amount", 10.5D );
+        Group second = groupFactory.newGroup()
+                .append( "id", 2L )
+                .append( "amount", 20.25D );
+
+        try ( ParquetWriter<Group> writer = ExampleParquetWriter.builder( new LocalOutputFile( file ) )
+                .withType( schema )
+                .build() ) {
+            writer.write( first );
+            writer.write( second );
+        }
+    }
+
+
+    private record LocalOutputFile( Path path ) implements OutputFile {
+
+        @Override
+        public PositionOutputStream create( long blockSizeHint ) throws IOException {
+            return createOrOverwrite( blockSizeHint );
+        }
+
+
+        @SuppressWarnings("resource")
+        @Override
+        public PositionOutputStream createOrOverwrite( long blockSizeHint ) throws IOException {
+            Files.createDirectories( path.getParent() );
+            OutputStream outputStream = Files.newOutputStream( path );
+            return new PositionOutputStream() {
+                private long position;
+
+
+                @Override
+                public long getPos() {
+                    return position;
+                }
+
+
+                @Override
+                public void write( int b ) throws IOException {
+                    outputStream.write( b );
+                    position++;
+                }
+
+
+                @Override
+                public void write( byte @NotNull [] b, int off, int len ) throws IOException {
+                    outputStream.write( b, off, len );
+                    position += len;
+                }
+
+
+                @Override
+                public void close() throws IOException {
+                    outputStream.close();
+                }
+            };
+        }
+
+
+        @Override
+        public boolean supportsBlockSize() {
+            return false;
+        }
+
+
+        @Override
+        public long defaultBlockSize() {
+            return 0;
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
index 3cdd7d0cb4..bb59e1c5bb 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRelFilterTranslatorTest.java
@@ -23,8 +23,8 @@
 import org.junit.jupiter.api.BeforeAll;
 import org.junit.jupiter.api.BeforeEach;
 import org.junit.jupiter.api.Test;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelFilterTranslator;
-import org.polypheny.db.adapter.parquet.shared.filter.FilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetRelFilterTranslator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetFilterEvaluator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetNativeFilterBuilder;
 import org.polypheny.db.algebra.constant.Kind;
@@ -87,7 +87,7 @@ void translatesAndOrTree() {
                         call( Kind.EQUALS, ref( 1 ), param( 20 ) ),
                         call( Kind.EQUALS, ref( 1 ), param( 30 ) ) ) );
 
-        ParquetAdapterFilter translated = translator.translate( List.of( PolyType.INTEGER, PolyType.INTEGER ), filter );
+        ParquetAdapterFilter<PolyValue>  translated = translator.translate( List.of( PolyType.INTEGER, PolyType.INTEGER ), filter );
 
         assertNotNull( translated );
         assertEquals( Kind.AND, translated.operator() );
@@ -102,7 +102,7 @@ void translatesAndOrTree() {
     void translatesInAsOrOfEquals() {
         RexNode filter = call( Kind.IN, ref( 0 ), param( 10 ), param( 20 ), param( 30 ) );
 
-        ParquetAdapterFilter translated = translator.translate( List.of( PolyType.INTEGER ), filter );
+        ParquetAdapterFilter<PolyValue>  translated = translator.translate( List.of( PolyType.INTEGER ), filter );
 
         assertNotNull( translated );
         assertEquals( Kind.OR, translated.operator() );
@@ -113,18 +113,18 @@ void translatesInAsOrOfEquals() {
 
     @Test
     void translatesNullChecks() {
-        ParquetAdapterFilter isNull = translator.translate( List.of( PolyType.INTEGER ), call( Kind.IS_NULL, ref( 0 ) ) );
-        ParquetAdapterFilter isNotNull = translator.translate( List.of( PolyType.INTEGER ), call( Kind.IS_NOT_NULL, ref( 0 ) ) );
+        ParquetAdapterFilter<PolyValue> isNull = translator.translate( List.of( PolyType.INTEGER ), call( Kind.IS_NULL, ref( 0 ) ) );
+        ParquetAdapterFilter<PolyValue>  isNotNull = translator.translate( List.of( PolyType.INTEGER ), call( Kind.IS_NOT_NULL, ref( 0 ) ) );
 
         assertNotNull( isNull );
         assertEquals( Kind.IS_NULL, isNull.operator() );
         assertEquals( 0, isNull.columnIndex() );
-        assertNull( isNull.polyValue() );
+        assertNull( isNull.value() );
 
         assertNotNull( isNotNull );
         assertEquals( Kind.IS_NOT_NULL, isNotNull.operator() );
         assertEquals( 0, isNotNull.columnIndex() );
-        assertNull( isNotNull.polyValue() );
+        assertNull( isNotNull.value() );
     }
 
 
@@ -132,10 +132,10 @@ void translatesNullChecks() {
     void evaluatesNullChecks() {
         TestFilterEvaluator evaluator = new TestFilterEvaluator();
 
-        assertEquals( Boolean.TRUE, evaluator.evaluate( PolyNull.NULL, new ParquetAdapterFilter( 0, Kind.IS_NULL, null ) ) );
-        assertEquals( Boolean.FALSE, evaluator.evaluate( PolyString.of( "shipped" ), new ParquetAdapterFilter( 0, Kind.IS_NULL, null ) ) );
-        assertEquals( Boolean.FALSE, evaluator.evaluate( PolyNull.NULL, new ParquetAdapterFilter( 0, Kind.IS_NOT_NULL, null ) ) );
-        assertEquals( Boolean.TRUE, evaluator.evaluate( PolyString.of( "shipped" ), new ParquetAdapterFilter( 0, Kind.IS_NOT_NULL, null ) ) );
+        assertEquals( Boolean.TRUE, evaluator.evaluate( PolyNull.NULL, new ParquetAdapterFilter<>( 0, Kind.IS_NULL, null ) ) );
+        assertEquals( Boolean.FALSE, evaluator.evaluate( PolyString.of( "shipped" ), new ParquetAdapterFilter<>( 0, Kind.IS_NULL, null ) ) );
+        assertEquals( Boolean.FALSE, evaluator.evaluate( PolyNull.NULL, new ParquetAdapterFilter<>( 0, Kind.IS_NOT_NULL, null ) ) );
+        assertEquals( Boolean.TRUE, evaluator.evaluate( PolyString.of( "shipped" ), new ParquetAdapterFilter<>( 0, Kind.IS_NOT_NULL, null ) ) );
     }
 
 
@@ -143,7 +143,7 @@ void evaluatesNullChecks() {
     void reversesComparisonWhenLiteralIsOnTheLeft() {
         RexNode filter = call( Kind.LESS_THAN, param( 10 ), ref( 0 ) );
 
-        ParquetAdapterFilter translated = translator.translate( List.of( PolyType.INTEGER ), filter );
+        ParquetAdapterFilter<PolyValue>  translated = translator.translate( List.of( PolyType.INTEGER ), filter );
 
         assertNotNull( translated );
         assertEquals( Kind.GREATER_THAN, translated.operator() );
@@ -157,7 +157,7 @@ void ignoresUnsupportedInt96TimestampPredicatePushdown() {
                 .named( "ts" )
                 .named( "test_schema" );
 
-        ParquetAdapterFilter filter = new ParquetAdapterFilter( 0, Kind.EQUALS, PolyTimestamp.of( 1_700_000_000_000L ) );
+        ParquetAdapterFilter<PolyValue> filter = new ParquetAdapterFilter<>( 0, Kind.EQUALS, PolyTimestamp.of( 1_700_000_000_000L ) );
 
         assertDoesNotThrow(
                 () -> ParquetNativeFilterBuilder.build( schema, List.of( filter ) ) );
@@ -172,7 +172,7 @@ void buildsNativeNullCheckPredicates() {
                 .named( "test_schema" );
 
         assertDoesNotThrow(
-                () -> ParquetNativeFilterBuilder.build( schema, List.of( new ParquetAdapterFilter( 0, Kind.IS_NOT_NULL, null ) ) ) );
+                () -> ParquetNativeFilterBuilder.build( schema, List.of( new ParquetAdapterFilter<>( 0, Kind.IS_NOT_NULL, null ) ) ) );
     }
 
 
@@ -191,11 +191,11 @@ private RexNode call( Kind kind, RexNode... operands ) {
     }
 
 
-    private static class TestFilterEvaluator extends FilterEvaluator<PolyValue> {
+    private static class TestFilterEvaluator extends ParquetFilterEvaluator<PolyValue, PolyValue> {
 
         @Override
-        protected Boolean evaluateLeaf( PolyValue value, ParquetAdapterFilter filter ) {
-            return matchesValue( value, filter.operator(), filter.polyValue() );
+        protected Boolean evaluateLeaf( PolyValue value, ParquetAdapterFilter<PolyValue> filter ) {
+            return matchesValue( value, filter.operator(), filter.value() );
         }
 
     }
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRowAggregateRelEnumeratorTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRowAggregateRelEnumeratorTest.java
new file mode 100644
index 0000000000..bded741568
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRowAggregateRelEnumeratorTest.java
@@ -0,0 +1,91 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import org.apache.calcite.linq4j.Linq4j;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.polypheny.db.adapter.parquet.relational.execution.aggregate.ParquetRowAggregateRelEnumerator;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.numerical.PolyDouble;
+import org.polypheny.db.type.entity.numerical.PolyLong;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
+
+
+class ParquetRowAggregateRelEnumeratorTest {
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @Test
+    void supportsCountStarWithSumMinAndMax() {
+        PolyValue[][] rows = {
+                { PolyLong.of( 2021 ), PolyLong.of( 10 ), PolyDouble.of( 2D ) },
+                { PolyLong.of( 2021 ), PolyLong.of( 20 ), PolyNull.NULL },
+                { PolyLong.of( 2021 ), PolyLong.of( 30 ), PolyDouble.of( 5D ) },
+                { PolyLong.of( 2022 ), PolyLong.of( 5 ), PolyDouble.of( 4D ) },
+                { PolyLong.of( 2023 ), PolyLong.of( 7 ), PolyNull.NULL }
+        };
+
+        try ( ParquetRowAggregateRelEnumerator enumerator = new ParquetRowAggregateRelEnumerator(
+                Linq4j.asEnumerable( rows ).enumerator(),
+                new int[]{ 0 },
+                new String[]{ Kind.COUNT.name(), Kind.SUM.name(), Kind.MIN.name(), Kind.MAX.name() },
+                new int[]{ -1, 1, 2, 2 } ) ) {
+            assertTrue( enumerator.moveNext() );
+            PolyValue[] first = enumerator.current();
+            assertEquals( 2021L, first[0].asNumber().longValue() );
+            assertEquals( 3L, first[1].asNumber().longValue() );
+            assertEquals( 60D, first[2].asNumber().doubleValue() );
+            assertEquals( 2D, first[3].asNumber().doubleValue() );
+            assertEquals( 5D, first[4].asNumber().doubleValue() );
+
+            assertTrue( enumerator.moveNext() );
+            PolyValue[] second = enumerator.current();
+            assertEquals( 2022L, second[0].asNumber().longValue() );
+            assertEquals( 1L, second[1].asNumber().longValue() );
+            assertEquals( 5D, second[2].asNumber().doubleValue() );
+            assertEquals( 4D, second[3].asNumber().doubleValue() );
+            assertEquals( 4D, second[4].asNumber().doubleValue() );
+
+            assertTrue( enumerator.moveNext() );
+            PolyValue[] third = enumerator.current();
+            assertEquals( 2023L, third[0].asNumber().longValue() );
+            assertEquals( 1L, third[1].asNumber().longValue() );
+            assertEquals( 7D, third[2].asNumber().doubleValue() );
+            assertTrue( third[3].isNull() );
+            assertTrue( third[4].isNull() );
+
+            assertFalse( enumerator.moveNext() );
+        }
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedProjectionTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedProjectionTest.java
new file mode 100644
index 0000000000..a9e3937598
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedProjectionTest.java
@@ -0,0 +1,259 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.execution;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.io.IOException;
+import java.io.OutputStream;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.calcite.linq4j.Enumerator;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.ExampleParquetWriter;
+import org.apache.parquet.io.OutputFile;
+import org.apache.parquet.io.PositionOutputStream;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.MessageTypeParser;
+import org.jetbrains.annotations.NotNull;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSyntheticColumns;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
+import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.catalog.entity.physical.PhysicalColumn;
+import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
+
+
+class ParquetNestedRepeatedProjectionTest {
+
+    @TempDir
+    Path tempDir;
+
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @Test
+    void nestedListTableProjectsParquetRootFieldsFromBindings() throws Exception {
+        Path file = tempDir.resolve( "flickr.parquet" );
+        writeFlickrParquet( file );
+
+        ParquetSourceFile sourceFile = new ParquetSourceFile( file.toUri().toURL().toString(), Map.of(), Map.of() );
+        ParquetRelTable table = captionsListTable( sourceFile );
+        ParquetSchemaReader schemaReader = new ParquetSchemaReader( sourceFile.asSource() );
+
+        try ( Enumerator<PolyValue[]> enumerator = ParquetRelExecutor.enumeratorForFile(
+                table,
+                sourceFile,
+                new int[]{ 0, 1, 2, 3 },
+                new int[]{ 0, 1, 2, 3 },
+                schemaReader,
+                new AtomicBoolean( false ),
+                FiltersContainer.empty ) ) {
+            assertTrue( enumerator.moveNext() );
+            PolyValue[] first = enumerator.current();
+            assertEquals( "0/captions[0]/list[0]", first[0].asString().value );
+            assertEquals( "0/captions[0]", first[1].asString().value );
+            assertEquals( 0L, first[2].asNumber().longValue() );
+            assertEquals( "a dog in grass", first[3].asString().value );
+
+            assertTrue( enumerator.moveNext() );
+            PolyValue[] second = enumerator.current();
+            assertEquals( "0/captions[0]/list[1]", second[0].asString().value );
+            assertEquals( "0/captions[0]", second[1].asString().value );
+            assertEquals( 1L, second[2].asNumber().longValue() );
+            assertEquals( "a puppy outside", second[3].asString().value );
+
+            assertFalse( enumerator.moveNext() );
+        }
+    }
+
+
+    private static ParquetRelTable captionsListTable( ParquetSourceFile sourceFile ) {
+        PhysicalColumn rowId = column( 10, ParquetSyntheticColumns.ROW_ID, 0, PolyType.VARCHAR );
+        PhysicalColumn parentRowId = column( 11, ParquetSyntheticColumns.PARENT_ROW_ID, 1, PolyType.VARCHAR );
+        PhysicalColumn ordinal = column( 12, ParquetSyntheticColumns.ELEM_ORDINAL, 2, PolyType.BIGINT );
+        PhysicalColumn element = column( 13, "element", 3, PolyType.VARCHAR );
+        PhysicalTable physicalTable = new PhysicalTable(
+                1,
+                1,
+                1,
+                "flickr8k__captions__list",
+                List.of( rowId, parentRowId, ordinal, element ),
+                1,
+                "public",
+                List.of(),
+                1 );
+
+        ParquetTableBinding binding = new ParquetTableBinding(
+                List.of( sourceFile ),
+                "flickr8k__captions",
+                List.of( "captions", "list" ),
+                Map.of(
+                        rowId.id, new ParquetColumnBinding( rowId.id, rowId.name, ParquetColumnRole.PRIMARY_KEY, List.of( "captions", "list" ) ),
+                        parentRowId.id, new ParquetColumnBinding( parentRowId.id, parentRowId.name, ParquetColumnRole.PARENT_KEY, List.of( "captions", "list" ) ),
+                        ordinal.id, new ParquetColumnBinding( ordinal.id, ordinal.name, ParquetColumnRole.ORDINAL, List.of( "captions", "list" ) ),
+                        element.id, new ParquetColumnBinding( element.id, element.name, ParquetColumnRole.DATA, List.of( "captions", "list", "element" ) ) ) );
+        return new ParquetRelTable( 1, physicalTable, binding, null );
+    }
+
+
+    private static PhysicalColumn column( long id, String name, int position, PolyType type ) {
+        return new PhysicalColumn( id, name, name, 1, 1, 1, position, type, null, null, null, null, null, false, null, null );
+    }
+
+
+    private static void writeFlickrParquet( Path file ) throws Exception {
+        MessageType schema = MessageTypeParser.parseMessageType( """
+                message test {
+                  optional binary id (STRING);
+                  optional group captions (LIST) {
+                    repeated group list {
+                      optional binary element (STRING);
+                    }
+                  }
+                  optional binary image;
+                }
+                """ );
+
+        SimpleGroupFactory groupFactory = new SimpleGroupFactory( schema );
+        Group row = groupFactory.newGroup().append( "id", "image-1.jpg" );
+        Group captions = row.addGroup( "captions" );
+        captions.addGroup( "list" ).append( "element", "a dog in grass" );
+        captions.addGroup( "list" ).append( "element", "a puppy outside" );
+
+        try ( ParquetWriter<Group> writer = ExampleParquetWriter.builder( new LocalOutputFile( file ) )
+                .withType( schema )
+                .build() ) {
+            writer.write( row );
+        }
+    }
+
+
+    private record LocalOutputFile( Path path ) implements OutputFile {
+
+        @Override
+        public PositionOutputStream create( long blockSizeHint ) {
+            return createOrOverwrite( blockSizeHint );
+        }
+
+
+        @Override
+        public PositionOutputStream createOrOverwrite( long blockSizeHint ) {
+            try {
+                Files.createDirectories( path.getParent() );
+            } catch ( IOException e ) {
+                throw new RuntimeException( e );
+            }
+            try {
+                return new LocalPositionOutputStream( Files.newOutputStream( path ) );
+            } catch ( IOException e ) {
+                throw new RuntimeException( e );
+            }
+        }
+
+
+        @Override
+        public boolean supportsBlockSize() {
+            return false;
+        }
+
+
+        @Override
+        public long defaultBlockSize() {
+            return 0;
+        }
+
+
+        @Override
+        public String getPath() {
+            return path.toString();
+        }
+
+    }
+
+
+    private static class LocalPositionOutputStream extends PositionOutputStream {
+
+        private final OutputStream out;
+        private long position;
+
+
+        private LocalPositionOutputStream( OutputStream out ) {
+            this.out = out;
+        }
+
+
+        @Override
+        public long getPos() {
+            return position;
+        }
+
+
+        @Override
+        public void write( int b ) throws IOException {
+            out.write( b );
+            position++;
+        }
+
+
+        @Override
+        public void write( byte @NotNull [] b, int off, int len ) throws IOException {
+            out.write( b, off, len );
+            position += len;
+        }
+
+
+        @Override
+        public void flush() throws IOException {
+            out.flush();
+        }
+
+
+        @Override
+        public void close() throws IOException {
+            out.close();
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetCountAggregatePageReaderTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetCountAggregatePageReaderTest.java
new file mode 100644
index 0000000000..c0e71e497c
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetCountAggregatePageReaderTest.java
@@ -0,0 +1,179 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io.aggregate.page;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+
+import java.io.IOException;
+import java.io.OutputStream;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.parquet.ParquetReadOptions;
+import org.apache.parquet.column.ColumnDescriptor;
+import org.apache.parquet.column.ColumnReadStore;
+import org.apache.parquet.column.impl.ColumnReadStoreImpl;
+import org.apache.parquet.column.page.PageReadStore;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetFileReader;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.ExampleParquetWriter;
+import org.apache.parquet.hadoop.util.HadoopInputFile;
+import org.apache.parquet.io.OutputFile;
+import org.apache.parquet.io.PositionOutputStream;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+import org.apache.parquet.schema.Types;
+import org.jetbrains.annotations.NotNull;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetPrimitivePredicate;
+import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
+import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.numerical.PolyDouble;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
+
+
+class ParquetCountAggregatePageReaderTest {
+
+    @TempDir
+    Path tempDir;
+
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @Test
+    void evaluatesPredicateForEveryRow() throws Exception {
+        MessageType schema = Types.buildMessage()
+                .required( PrimitiveTypeName.DOUBLE ).named( "trip_miles" )
+                .named( "test" );
+        Path file = writeParquet( schema );
+        List<ParquetAdapterFilter<PolyValue>> filters = List.of(
+                new ParquetAdapterFilter<>( 0, Kind.GREATER_THAN_OR_EQUAL, PolyDouble.of( 5D ) ) );
+
+        Configuration conf = HadoopConfigurationFactory.create( getClass().getClassLoader() );
+        org.apache.hadoop.fs.Path parquetPath = new org.apache.hadoop.fs.Path( file.toUri() );
+        try ( ParquetFileReader fileReader = ParquetFileReader.open( HadoopInputFile.fromPath( parquetPath, conf ), ParquetReadOptions.builder().build() ) ) {
+            fileReader.setRequestedSchema( schema );
+            try ( PageReadStore pages = fileReader.readNextRowGroup() ) {
+                ColumnReadStore store = new ColumnReadStoreImpl( pages, new NoOpColumnConverter( schema ), schema, fileReader.getFileMetaData().getCreatedBy() );
+                ColumnDescriptor[] descriptors = schema.getColumns().toArray( ColumnDescriptor[]::new );
+                AggregateCallDescriptor[] aggregateCalls = new AggregateCallDescriptor[]{ AggregateCallDescriptor.countStar() };
+                Map<GroupKey, AggregateGroupState> aggregates = new LinkedHashMap<>();
+
+                new ParquetCountAggregatePageReader( pages, store, descriptors, ParquetPrimitivePredicate.compile( schema, filters ) )
+                        .read( aggregates, aggregateCalls, filters, new AtomicBoolean( false ) );
+
+                assertEquals( 3, aggregates.get( GroupKey.Empty ).count( 0 ) );
+            }
+        }
+    }
+
+
+    private Path writeParquet( MessageType schema ) throws Exception {
+        Path file = tempDir.resolve( "count.parquet" );
+        SimpleGroupFactory factory = new SimpleGroupFactory( schema );
+        try ( ParquetWriter<Group> writer = ExampleParquetWriter.builder( new LocalOutputFile( file ) )
+                .withType( schema )
+                .build() ) {
+            writer.write( factory.newGroup().append( "trip_miles", 1D ) );
+            writer.write( factory.newGroup().append( "trip_miles", 5D ) );
+            writer.write( factory.newGroup().append( "trip_miles", 7D ) );
+            writer.write( factory.newGroup().append( "trip_miles", 8D ) );
+        }
+        return file;
+    }
+
+
+    private record LocalOutputFile( Path path ) implements OutputFile {
+
+        @Override
+        public PositionOutputStream create( long blockSizeHint ) throws IOException {
+            return createOrOverwrite( blockSizeHint );
+        }
+
+
+        @Override
+        public PositionOutputStream createOrOverwrite( long blockSizeHint ) throws IOException {
+            Files.createDirectories( path.getParent() );
+            @SuppressWarnings("resource") OutputStream outputStream = Files.newOutputStream( path );
+            return new PositionOutputStream() {
+                private long position;
+
+
+                @Override
+                public long getPos() {
+                    return position;
+                }
+
+
+                @Override
+                public void write( int b ) throws IOException {
+                    outputStream.write( b );
+                    position++;
+                }
+
+
+                @Override
+                public void write( byte @NotNull [] b, int off, int len ) throws IOException {
+                    outputStream.write( b, off, len );
+                    position += len;
+                }
+
+
+                @Override
+                public void close() throws IOException {
+                    outputStream.close();
+                }
+            };
+        }
+
+
+        @Override
+        public boolean supportsBlockSize() {
+            return false;
+        }
+
+
+        @Override
+        public long defaultBlockSize() {
+            return 0;
+        }
+
+    }
+
+}

From 7e0239cef0786f2fe78d73f4e36b09e2c37b08fa Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Tue, 9 Jun 2026 10:24:58 +0200
Subject: [PATCH 35/46] Add separate columns for primitive nested filed in flat
 mode

---
 .../docs/simple_nested_column_flat_rel.md     | 142 +++++++++++++++++
 .../parquet/shared/io/ExportedSchema.java     |  36 +++++
 .../shared/io/ParquetFileDiscovery.java       | 106 +++++++++----
 .../shared/io/ParquetSchemaReader.java        | 146 +++++++++++++++---
 .../parquet/ParquetFileDiscoveryTest.java     |  44 +++++-
 .../ParquetNestedRepeatedProjectionTest.java  | 100 ++++++++++++
 6 files changed, 517 insertions(+), 57 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/simple_nested_column_flat_rel.md
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ExportedSchema.java

diff --git a/plugins/parquet-adapter/docs/simple_nested_column_flat_rel.md b/plugins/parquet-adapter/docs/simple_nested_column_flat_rel.md
new file mode 100644
index 0000000000..006d5a7027
--- /dev/null
+++ b/plugins/parquet-adapter/docs/simple_nested_column_flat_rel.md
@@ -0,0 +1,142 @@
+# Add separate columns for primitive nested filed in flat mode
+
+Simple nested field becomes a separate column.
+
+Path-aware discovery was added so flat mode can expose non-repeated 
+nested leaf fields as parent-table columns while repeated/list/map fields stay as nested/document columns.
+
+Example:
+```text
+customer {
+  id
+  address {
+    city
+    country
+  }
+  orders[] ...
+}
+```
+Flat mode now exposes:
+```text
+id
+address_city
+address_country
+orders
+```
+
+### ExportedSchema.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ExportedSchema.java`
+
+Contains:
+- columns - what Polypheny should expose in the catalog
+- columnPaths - adapter-private metadata telling the scanner how to read the real value from the Parquet group. Map: column name - column path
+
+```text
+columns[i] = "shipping_address_city_name"
+columnPaths[i] = {"shipping_address_city_name" -> List.of("Shipping Address", "City Name")}
+```
+
+After flattening, the relational column name is no longer enough to know where the value lives in Parquet.
+
+Before: 
+```text
+Polypheny column name == Parquet top-level field name
+```
+After:
+```text
+address_city -> ["address", "city"]
+address_country -> ["address", "country"]
+orders -> ["orders"]
+```
+
+#### Example of column path:
+
+Parquet Schema:
+
+```text
+customer_id
+address {
+  city
+  country
+}
+orders LIST
+```
+Flat exposed columns:
+
+```text
+customer_id
+address_city
+address_country
+orders
+```
+Column path:
+
+```text
+{
+  "customer_id"     -> ["customer_id"],
+  "address_city"    -> ["address", "city"],
+  "address_country" -> ["address", "country"],
+  "orders"          -> ["orders"]
+}
+```
+
+### ParquetSchemaReader.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java`
+
+Create ExportedSchema instead of ExportedColumns.
+
+#### Changes:
+
+- exportedSchema() - returns ExportedSchema, not only columns
+- collectFlatColumns() - recursively walks non-repeated nested groups. Decides whether a Parquet field becomes a column itself, or whether its children become columns on the parent table.
+- addFlatColumn() - creates the ExportedColumn and stores its path.
+- shouldPreserveAsNestedColumn() - prevents repeated/list/map fields from being flattened
+- uniqueColumnName() - avoids name collisions after flattening.
+
+### ParquetFileDiscovery.java
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java`
+
+ParquetFileDiscovery changed from “discover table names and column lists” to 
+“discover table names, column lists, and the binding metadata required to read those columns correctly.”
+
+Discovery now tracks an ExportedSchema per source file instead of only a list of ExportedColumn.
+For each discovered Parquet file we keep:
+- columns      -  visible Polypheny columns
+- columnPaths  - exported column name -> real Parquet path
+
+#### Changes:
+1. exportedSchema(...) instead of exportedColumns(...)
+2. When it creates a DiscoveredTable, it now stores columnPaths in DiscoveredTableBinding
+3. canConsolidateSchemas(...) also calls columnPathsAreCompatible(tables.values()). 
+  - canConsolidateSchemas(...) decides whether several Parquet files can be exposed as one Polypheny table. 
+  - Before flattened nested columns, checking exported column names and types was mostly enough.
+  - After flattening, this is no longer enough.
+  - Example: 
+    - File1: `a { b int }` Flat exported column: `"a_b" -> "a_b" -> ["a", "b"]`
+    - File2: `a_b int` Flat Exported column: `"a_b" -> ["a_b"]`
+  - So we add validation by path: columnPathsAreCompatible 
+4. Partition collision validation now uses the real source path: `exportedSchema.columnPaths().get(column.name())`
+   - Example of partitioned folder:
+   
+     ```text
+     customers/
+         country=CH/
+             part-1.parquet
+         country=US/
+             part-2.parquet
+     ```
+      
+   - Polypheny adds country as a virtual partition column based on the folder name. But sometimes the Parquet file itself also contains a column with the same exported name: `part-1.parquet:
+     country STRING`. 
+   - That is a collision: `folder partition column: country`, `file data column: country`. 
+   - The existing logic allows this only if the file column always has the same value as the folder partition value.
+   - To validate that, we inspect Parquet column statistics. 
+   - But with flattened nested columns, the exported column name may not be the actual Parquet field path.
+   - When validation sees a collision on exported column name, which is flattened nested column `shipping_address_country`, it cannot look for a top-level Parquet column named `shipping_address_country`. That column does not exist.
+   - It must look at the real Parquet path: `exportedSchema.columnPaths().get(column.name())` which gives: `["Shipping Address", "Country"]`
+   - Then findColumnChunk(...) uses that path to find the real Parquet column statistics.
+
+5. Consolidated/partitioned tables rebuild a combined path map
+   - When Polypheny exposes multiple Parquet files as one logical table, it has to combine their discovered schemas.
+   - ParquetFileDiscovery decides: these files are compatible enough, so expose them as one table
+   - `columnPaths(tables.values(), columns)` collects paths from all per-file ExportedSchema objects, but only keeps paths for columns that are actually present in the final combined table.
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ExportedSchema.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ExportedSchema.java
new file mode 100644
index 0000000000..7ddfe92221
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ExportedSchema.java
@@ -0,0 +1,36 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io;
+
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+
+/***
+ * Contains columns metadata for exposing primitive nested field as separate column in flat mode
+ * @param columns - what Polypheny should expose in the catalog
+ * @param columnPaths - adapter-private metadata telling the scanner how to read the real value from the Parquet group
+ */
+public record ExportedSchema( List<ExportedColumn> columns, Map<String, List<String>> columnPaths ) {
+    public ExportedSchema {
+        columns = List.copyOf( columns );
+        columnPaths = columnPaths.entrySet().stream()
+                .collect( LinkedHashMap::new, ( map, entry ) -> map.put( entry.getKey(), List.copyOf( entry.getValue() ) ), LinkedHashMap::putAll );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
index b68a77a7a6..f448c51054 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java
@@ -23,7 +23,6 @@
 import java.util.Arrays;
 import java.util.Collection;
 import java.util.Collections;
-import java.util.HashMap;
 import java.util.HashSet;
 import java.util.LinkedHashMap;
 import java.util.List;
@@ -80,43 +79,43 @@ public static Map<String, DiscoveredTable> discoverTables( URL baseDir, String t
         }
 
         List<DiscoveredSourceFile> sourceFiles = discoverSourceFiles( baseDir );
-        Map<String, List<ExportedColumn>> tables = new HashMap<>();
+        Map<String, ExportedSchema> tables = new LinkedHashMap<>();
 
         for ( DiscoveredSourceFile sourceFile : sourceFiles ) {
             String tableName = prefixedTableName( tableNamePrefix, ParquetNameNormalizer.computePhysicalTableName( sourceFile.fileName() ) );
-            List<ExportedColumn> columns = schemaReader( sourceFile.url() ).exportedColumns( tableName );
-            if ( !columns.isEmpty() ) {
-                tables.put( sourceFile.key(), columns );
+            ExportedSchema exportedSchema = schemaReader( sourceFile.url() ).exportedSchema( tableName );
+            if ( !exportedSchema.columns().isEmpty() ) {
+                tables.put( sourceFile.key(), exportedSchema );
             }
         }
 
         if ( sourceFiles.stream().anyMatch( sourceFile -> !sourceFile.partitionValues().isEmpty() ) ) {
             String tableName = prefixedTableName( tableNamePrefix, tableNameFromSource( baseDir ) );
-            if ( !canConsolidateSchemas( tables.values() ) ) {
+            if ( !canConsolidateSchemas( tables ) ) {
                 throw new GenericRuntimeException( "Parquet partitioned table '%s' contains files with incompatible schemas.", tableName );
             }
             return Map.of( tableName, discoverPartitionedTable( tableName, tables, sourceFiles ) );
         }
 
-        if ( canExposeAsSingleTable( baseDir, tables.values() ) ) {
+        if ( canExposeAsSingleTable( baseDir, tables ) ) {
             String tableName = prefixedTableName( tableNamePrefix, tableNameFromSource( baseDir ) );
             return Map.of( tableName, discoverConsolidatedTable( tableName, tables, sourceFiles ) );
         }
 
         Map<String, DiscoveredTable> discoveredTables = new LinkedHashMap<>();
 
-        for ( Map.Entry<String, List<ExportedColumn>> entry : tables.entrySet() ) {
-            String tableName = entry.getValue().get( 0 ).physicalTableName();
+        for ( Map.Entry<String, ExportedSchema> entry : tables.entrySet() ) {
+            String tableName = entry.getValue().columns().get( 0 ).physicalTableName();
             discoveredTables.put(
                     tableName,
                     new DiscoveredTable(
                             tableName,
-                            entry.getValue(),
+                            entry.getValue().columns(),
                             new DiscoveredTableBinding(
                                     List.of( toSourceFile( sourceFiles, entry.getKey() ) ),
                                     null,
                                     List.of(),
-                                    columnPaths( entry.getValue() ) ) ) );
+                                    entry.getValue().columnPaths() ) ) );
         }
 
         return discoveredTables;
@@ -259,7 +258,7 @@ private static Map<String, String> parsePartitionFolder( String folderName, Map<
     /**
      * Builds one DiscoveredTable for a partitioned folder layout. Adds data columns plus virtual partition columns.
      */
-    private static DiscoveredTable discoverPartitionedTable( String tableName, Map<String, List<ExportedColumn>> tables, List<DiscoveredSourceFile> sourceFiles ) {
+    private static DiscoveredTable discoverPartitionedTable( String tableName, Map<String, ExportedSchema> tables, List<DiscoveredSourceFile> sourceFiles ) {
         List<String> partitionColumnNames = partitionColumnNames( sourceFiles );
         validatePartitionColumnCollisions( tables, sourceFiles, partitionColumnNames );
         Collection<ExportedColumn> columns = getConsolidatedExportedColumns( tables ).stream()
@@ -276,29 +275,29 @@ private static DiscoveredTable discoverPartitionedTable( String tableName, Map<S
                         sourceFiles.stream().map( ParquetFileDiscovery::toSourceFile ).toList(),
                         null,
                         List.of(),
-                        columnPaths( columns ) ) );
+                        columnPaths( tables.values(), columns ) ) );
     }
 
 
     /**
      * Handles the case where a Parquet file already has a column with the same name as a partition folder key
      */
-    private static void validatePartitionColumnCollisions( Map<String, List<ExportedColumn>> tables, List<DiscoveredSourceFile> sourceFiles, List<String> partitionColumnNames ) {
+    private static void validatePartitionColumnCollisions( Map<String, ExportedSchema> tables, List<DiscoveredSourceFile> sourceFiles, List<String> partitionColumnNames ) {
         for ( DiscoveredSourceFile sourceFile : sourceFiles ) {
-            List<ExportedColumn> columns = tables.get( sourceFile.key() );
-            if ( columns == null ) {
+            ExportedSchema exportedSchema = tables.get( sourceFile.key() );
+            if ( exportedSchema == null ) {
                 continue;
             }
-            for ( ExportedColumn column : columns ) {
+            for ( ExportedColumn column : exportedSchema.columns() ) {
                 if ( partitionColumnNames.contains( column.name() ) ) {
-                    validatePartitionColumnCollision( sourceFile, column );
+                    validatePartitionColumnCollision( sourceFile, column, exportedSchema.columnPaths().get( column.name() ) );
                 }
             }
         }
     }
 
 
-    private static void validatePartitionColumnCollision( DiscoveredSourceFile sourceFile, ExportedColumn column ) {
+    private static void validatePartitionColumnCollision( DiscoveredSourceFile sourceFile, ExportedColumn column, List<String> sourcePath ) {
         String partitionValue = sourceFile.partitionValues().get( column.name() );
         if ( partitionValue == null ) {
             return;
@@ -307,7 +306,7 @@ private static void validatePartitionColumnCollision( DiscoveredSourceFile sourc
         var schemaReader = schemaReader( sourceFile.url() );
         for ( var footer : schemaReader.getFooters() ) {
             for ( BlockMetaData block : footer.getBlocks() ) {
-                ColumnChunkMetaData columnChunk = findColumnChunk( block, column.physicalColumnName() );
+                ColumnChunkMetaData columnChunk = findColumnChunk( block, sourcePath );
                 if ( columnChunk == null ) {
                     continue;
                 }
@@ -323,9 +322,12 @@ private static void validatePartitionColumnCollision( DiscoveredSourceFile sourc
     }
 
 
-    private static ColumnChunkMetaData findColumnChunk( BlockMetaData block, String physicalColumnName ) {
+    private static ColumnChunkMetaData findColumnChunk( BlockMetaData block, List<String> sourcePath ) {
+        if ( sourcePath == null || sourcePath.isEmpty() ) {
+            return null;
+        }
         for ( ColumnChunkMetaData column : block.getColumns() ) {
-            if ( List.of( column.getPath().toArray() ).equals( List.of( physicalColumnName ) ) ) {
+            if ( List.of( column.getPath().toArray() ).equals( sourcePath ) ) {
                 return column;
             }
         }
@@ -357,7 +359,7 @@ private static String statisticValueAsString( Object value ) {
     /**
      * Builds one DiscoveredTable from multiple compatible non-partitioned files.
      */
-    private static DiscoveredTable discoverConsolidatedTable( String tableName, Map<String, List<ExportedColumn>> tables, List<DiscoveredSourceFile> sourceFiles ) {
+    private static DiscoveredTable discoverConsolidatedTable( String tableName, Map<String, ExportedSchema> tables, List<DiscoveredSourceFile> sourceFiles ) {
         Collection<ExportedColumn> columns = getConsolidatedExportedColumns( tables );
         return new DiscoveredTable(
                 tableName,
@@ -366,21 +368,27 @@ private static DiscoveredTable discoverConsolidatedTable( String tableName, Map<
                         sourceFiles.stream().map( ParquetFileDiscovery::toSourceFile ).toList(),
                         null,
                         List.of(),
-                        columnPaths( columns ) ) );
+                        columnPaths( tables.values(), columns ) ) );
     }
 
 
-    private static boolean canExposeAsSingleTable( URL baseDir, Collection<List<ExportedColumn>> columnsSet ) {
-        if ( columnsSet.size() < 2 || Sources.of( baseDir ).file().isFile() ) {
+    private static boolean canExposeAsSingleTable( URL baseDir, Map<String, ExportedSchema> tables ) {
+        if ( tables.size() < 2 || Sources.of( baseDir ).file().isFile() ) {
             return false;
         }
-        return canConsolidateSchemas( columnsSet );
+        return canConsolidateSchemas( tables );
     }
 
     /**
      * Checks whether multiple files are similar enough to be one logical table
      */
-    private static boolean canConsolidateSchemas( Collection<List<ExportedColumn>> columnsSet ) {
+    private static boolean canConsolidateSchemas( Map<String, ExportedSchema> tables ) {
+        return columnTypesAreCompatible( columnLists( tables ) )
+                && columnPathsAreCompatible( tables.values() );
+    }
+
+
+    private static boolean columnTypesAreCompatible( Collection<List<ExportedColumn>> columnsSet ) {
         List<Set<String>> columnSets = new ArrayList<>();
         Map<String, PolyType> consolidatedTypes = new LinkedHashMap<>();
 
@@ -400,6 +408,20 @@ private static boolean canConsolidateSchemas( Collection<List<ExportedColumn>> c
     }
 
 
+    private static boolean columnPathsAreCompatible( Collection<ExportedSchema> schemas ) {
+        Map<String, List<String>> pathsByName = new LinkedHashMap<>();
+        for ( ExportedSchema schema : schemas ) {
+            for ( Map.Entry<String, List<String>> entry : schema.columnPaths().entrySet() ) {
+                List<String> existing = pathsByName.putIfAbsent( entry.getKey(), entry.getValue() );
+                if ( existing != null && !existing.equals( entry.getValue() ) ) {
+                    return false;
+                }
+            }
+        }
+        return true;
+    }
+
+
     private static boolean schemasAreSimilarEnough( List<Set<String>> fileColumnSets ) {
         for ( int left = 0; left < fileColumnSets.size(); left++ ) {
             for ( int right = left + 1; right < fileColumnSets.size(); right++ ) {
@@ -426,10 +448,15 @@ private static double schemaSimilarity( Set<String> left, Set<String> right ) {
     }
 
 
-    private static Collection<ExportedColumn> getConsolidatedExportedColumns( Map<String, List<ExportedColumn>> tables ) {
+    private static Collection<List<ExportedColumn>> columnLists( Map<String, ExportedSchema> tables ) {
+        return tables.values().stream().map( ExportedSchema::columns ).toList();
+    }
+
+
+    private static Collection<ExportedColumn> getConsolidatedExportedColumns( Map<String, ExportedSchema> tables ) {
         Map<String, ExportedColumn> columnsByName = new LinkedHashMap<>();
-        for ( List<ExportedColumn> columns : tables.values() ) {
-            for ( ExportedColumn column : columns ) {
+        for ( ExportedSchema schema : tables.values() ) {
+            for ( ExportedColumn column : schema.columns() ) {
                 columnsByName.putIfAbsent( column.name(), column );
             }
         }
@@ -437,8 +464,21 @@ private static Collection<ExportedColumn> getConsolidatedExportedColumns( Map<St
     }
 
 
-    private static Map<String, List<String>> columnPaths( Collection<ExportedColumn> columns ) {
-        return columns.stream().collect( LinkedHashMap::new, ( map, column ) -> map.put( column.name(), List.of( column.physicalColumnName() ) ), LinkedHashMap::putAll );
+    private static Map<String, List<String>> columnPaths( Collection<ExportedSchema> schemas, Collection<ExportedColumn> columns ) {
+        Map<String, List<String>> pathsByName = new LinkedHashMap<>();
+        for ( ExportedSchema schema : schemas ) {
+            schema.columnPaths().forEach( pathsByName::putIfAbsent );
+        }
+
+        return columns.stream().collect(
+                LinkedHashMap::new,
+                ( map, column ) -> {
+                    List<String> sourcePath = pathsByName.get( column.name() );
+                    if ( sourcePath != null ) {
+                        map.put( column.name(), sourcePath );
+                    }
+                },
+                LinkedHashMap::putAll );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java
index 41d2362b40..8b1019b0a8 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java
@@ -28,6 +28,9 @@
 import org.apache.parquet.hadoop.metadata.BlockMetaData;
 import org.apache.parquet.hadoop.metadata.ParquetMetadata;
 import org.apache.parquet.hadoop.util.HadoopInputFile;
+import org.apache.parquet.schema.LogicalTypeAnnotation;
+import org.apache.parquet.schema.LogicalTypeAnnotation.ListLogicalTypeAnnotation;
+import org.apache.parquet.schema.LogicalTypeAnnotation.MapLogicalTypeAnnotation;
 import org.apache.parquet.schema.MessageType;
 import org.apache.parquet.schema.Type;
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
@@ -115,38 +118,139 @@ public long getEstimatedRowCount() {
     }
 
 
-    public List<ExportedColumn> exportedColumns( String tableName ) {
+    public ExportedSchema exportedSchema( String tableName ) {
         try {
             ParquetTypeConverter typeConverter = new ParquetTypeConverter();
             List<ExportedColumn> columns = new ArrayList<>();
+            Map<String, List<String>> columnPaths = new LinkedHashMap<>();
+            Map<String, Integer> seenColumnNames = new LinkedHashMap<>();
             List<Type> fields = getSchema().getFields();
-            for ( int i = 0; i < fields.size(); i++ ) {
-                Type field = fields.get( i );
-                columns.add(
-                        new ExportedColumn(
-                                ParquetNameNormalizer.normalizeFieldName( field.getName() ),
-                                typeConverter.fromParquetTypeToPolyType( field ),
-                                null,
-                                null,
-                                null,
-                                null,
-                                null,
-                                false,
-                                tableName,
-                                tableName,
-                                field.getName(),
-                                i,
-                                i == 0
-                        )
-                );
+            for ( Type field : fields ) {
+                collectFlatColumns(
+                        tableName,
+                        field,
+                        List.of( field.getName() ),
+                        List.of( normalizedPathElement( field ) ),
+                        columns,
+                        columnPaths,
+                        seenColumnNames,
+                        typeConverter );
             }
-            return columns;
+            return new ExportedSchema( columns, columnPaths );
         } catch ( Exception e ) {
             throw new GenericRuntimeException( e );
         }
     }
 
 
+    /***
+     *  Decides whether a Parquet field becomes a column itself, or whether its children become
+     *  columns on the parent table.
+     */
+    private void collectFlatColumns(
+            String tableName,
+            Type field,
+            List<String> sourcePath,
+            List<String> normalizedPath,
+            List<ExportedColumn> columns,
+            Map<String, List<String>> columnPaths,
+            Map<String, Integer> seenColumnNames,
+            ParquetTypeConverter typeConverter ) {
+        if ( shouldPreserveAsNestedColumn( field ) || field.isPrimitive() ) {
+            addFlatColumn(
+                    tableName,
+                    field,
+                    sourcePath,
+                    normalizedPath,
+                    columns,
+                    columnPaths,
+                    seenColumnNames,
+                    typeConverter );
+            return;
+        }
+
+        for ( Type child : field.asGroupType().getFields() ) {
+            collectFlatColumns(
+                    tableName,
+                    child,
+                    appendPath( sourcePath, child.getName() ),
+                    appendPath( normalizedPath, normalizedPathElement( child ) ),
+                    columns,
+                    columnPaths,
+                    seenColumnNames,
+                    typeConverter );
+        }
+    }
+
+
+    /***
+     * Creates ExportedColumn and stores its path.
+     */
+    private void addFlatColumn(
+            String tableName,
+            Type field,
+            List<String> sourcePath,
+            List<String> normalizedPath,
+            List<ExportedColumn> columns,
+            Map<String, List<String>> columnPaths,
+            Map<String, Integer> seenColumnNames,
+            ParquetTypeConverter typeConverter ) {
+        String columnName = uniqueColumnName( seenColumnNames, String.join( "_", normalizedPath ) );
+        columns.add( new ExportedColumn(
+                columnName,
+                typeConverter.fromParquetTypeToPolyType( field ),
+                null,
+                null,
+                null,
+                null,
+                null,
+                false,
+                tableName,
+                tableName,
+                String.join( ".", sourcePath ),
+                columns.size(),
+                columns.isEmpty() ) );
+        columnPaths.put( columnName, sourcePath );
+    }
+
+
+    /***
+     * Prevents repeated/list/map fields from being flattened
+     */
+    private static boolean shouldPreserveAsNestedColumn( Type field ) {
+        return field.isRepetition( Type.Repetition.REPEATED ) || isLogicalListOrMap( field );
+    }
+
+
+    private static boolean isLogicalListOrMap( Type field ) {
+        LogicalTypeAnnotation logical = field.getLogicalTypeAnnotation();
+        return logical instanceof ListLogicalTypeAnnotation || logical instanceof MapLogicalTypeAnnotation;
+    }
+
+
+    private static String normalizedPathElement( Type field ) {
+        String normalized = ParquetNameNormalizer.normalizeFieldName( field.getName() );
+        return normalized.isBlank() ? "field" : normalized;
+    }
+
+
+    private static String uniqueColumnName( Map<String, Integer> seenColumnNames, String baseName ) {
+        if ( baseName.isBlank() ) {
+            baseName = "field";
+        }
+        int count = seenColumnNames.getOrDefault( baseName, 0 );
+        seenColumnNames.put( baseName, count + 1 );
+        return count == 0 ? baseName : baseName + "_" + (count + 1);
+    }
+
+
+    private static List<String> appendPath( List<String> path, String element ) {
+        List<String> next = new ArrayList<>( path );
+        next.add( element );
+        return next;
+    }
+
+
     public MessageType buildProjectionSchema( int[] fields ) {
         int[] projectedFields = buildProjectedFields( fields, schema.getFieldCount() );
         if ( projectedFields.length == 0 ) {
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java
index e6092dd4c3..cb79abf13a 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java
@@ -17,11 +17,13 @@
 package org.polypheny.db.adapter.parquet;
 
 import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
 import static org.junit.jupiter.api.Assertions.assertThrows;
 import static org.junit.jupiter.api.Assertions.assertTrue;
 
 import java.io.IOException;
 import java.io.OutputStream;
+import java.net.URL;
 import java.nio.file.Files;
 import java.nio.file.Path;
 import java.util.List;
@@ -43,6 +45,7 @@
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSchemaNormalizer;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSyntheticColumns;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetFileDiscovery;
+import org.polypheny.db.type.PolyType;
 
 
 class ParquetFileDiscoveryTest {
@@ -168,17 +171,42 @@ void normalizedSchemaUsesDiscoveredMultiFileTable() throws Exception {
 
 
     @Test
-    void flatDiscoveryBindsTopLevelListColumnToRootPath() throws Exception {
+    void flatDiscoveryFlattensNonRepeatedNestedColumnsAndKeepsRepeatedColumns() throws Exception {
         writeParquet( tempDir.resolve( "flickr.parquet" ), flickrSchema(), flickrRow() );
 
         Map<String, DiscoveredTable> tables = ParquetFileDiscovery.discoverTables( tempDir.toUri().toURL(), PREFIX );
 
         DiscoveredTable table = tables.get( PREFIX + "__flickr" );
-        assertEquals( List.of( "id", "captions", "image" ), columnNames( table.columns() ) );
+        assertEquals( List.of( "id", "metadata_source", "metadata_size_width", "metadata_size_height", "captions", "image" ), columnNames( table.columns() ) );
+        assertFalse( table.columns().get( 0 ).nullable() );
+        assertTrue( table.columns().get( 0 ).primary() );
+        assertEquals( PolyType.DOCUMENT, table.columns().get( 4 ).type() );
+        assertEquals( List.of( "metadata", "source" ), table.binding().columnPaths().get( "metadata_source" ) );
+        assertEquals( List.of( "metadata", "size", "width" ), table.binding().columnPaths().get( "metadata_size_width" ) );
         assertEquals( List.of( "captions" ), table.binding().columnPaths().get( "captions" ) );
     }
 
 
+    @Test
+    void flatDiscoveryKeepsOrdersDbPrimaryKeys() throws Exception {
+        URL ordersDb = ParquetFileDiscoveryTest.class.getClassLoader().getResource( "orders_db" );
+        Map<String, DiscoveredTable> tables = ParquetFileDiscovery.discoverTables( ordersDb, PREFIX );
+
+        assertPrimaryColumn( tables.get( PREFIX + "__customers" ), "customer_id" );
+        assertPrimaryColumn( tables.get( PREFIX + "__products" ), "product_id" );
+        assertPrimaryColumn( tables.get( PREFIX + "__orders" ), "order_id" );
+        assertPrimaryColumn( tables.get( PREFIX + "__order_items" ), "order_item_id" );
+    }
+
+
+    private static void assertPrimaryColumn( DiscoveredTable table, String columnName ) {
+        ExportedColumn column = table.columns().get( 0 );
+        assertEquals( columnName, column.name() );
+        assertFalse( column.nullable() );
+        assertTrue( column.primary() );
+    }
+
+
     private static List<String> columnNames( List<ExportedColumn> columns ) {
         return columns.stream().map( ExportedColumn::name ).toList();
     }
@@ -208,6 +236,13 @@ private static MessageType flickrSchema() {
         return MessageTypeParser.parseMessageType( """
                 message test {
                   optional binary id (STRING);
+                  optional group metadata {
+                    optional binary source (STRING);
+                    optional group size {
+                      optional int64 width;
+                      optional int64 height;
+                    }
+                  }
                   optional group captions (LIST) {
                     repeated group list {
                       optional binary element (STRING);
@@ -220,7 +255,7 @@ optional group captions (LIST) {
 
 
     private static Object[] flickrRow() {
-        return row( "image-1.jpg", List.of( "a dog in grass", "a puppy outside" ), "image-bytes" );
+        return row( "image-1.jpg", null, List.of( "a dog in grass", "a puppy outside" ), "image-bytes" );
     }
 
 
@@ -229,6 +264,9 @@ private static void writeParquet( Path file, MessageType schema, Object[] values
         Group group = groupFactory.newGroup();
         for ( int i = 0; i < values.length; i++ ) {
             Object value = values[i];
+            if ( value == null ) {
+                continue;
+            }
             if ( value instanceof String string ) {
                 group.add( i, string );
             } else if ( value instanceof List<?> list ) {
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedProjectionTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedProjectionTest.java
index a9e3937598..b72907e18c 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedProjectionTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedProjectionTest.java
@@ -51,6 +51,7 @@
 import org.polypheny.db.catalog.entity.physical.PhysicalColumn;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
 import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.PolyphenyHomeDirManager;
 import org.polypheny.db.util.RunMode;
@@ -108,6 +109,37 @@ void nestedListTableProjectsParquetRootFieldsFromBindings() throws Exception {
     }
 
 
+    @Test
+    void flatTableProjectsNonRepeatedNestedFieldsAndRepeatedDocumentsFromBindings() throws Exception {
+        Path file = tempDir.resolve( "flat-nested.parquet" );
+        writeFlatNestedParquet( file );
+
+        ParquetSourceFile sourceFile = new ParquetSourceFile( file.toUri().toURL().toString(), Map.of(), Map.of() );
+        ParquetRelTable table = flatNestedRootTable( sourceFile );
+        ParquetSchemaReader schemaReader = new ParquetSchemaReader( sourceFile.asSource() );
+
+        try ( Enumerator<PolyValue[]> enumerator = ParquetRelExecutor.enumeratorForFile(
+                table,
+                sourceFile,
+                new int[]{ 0, 1, 2, 3, 4 },
+                new int[]{ 0, 1, 2, 3, 4 },
+                schemaReader,
+                new AtomicBoolean( false ),
+                FiltersContainer.empty ) ) {
+            assertTrue( enumerator.moveNext() );
+            PolyValue[] row = enumerator.current();
+            assertEquals( "image-1.jpg", row[0].asString().value );
+            assertEquals( "camera", row[1].asString().value );
+            assertEquals( 640L, row[2].asNumber().longValue() );
+            assertTrue( row[3].isDocument() );
+            assertTrue( row[3].asDocument().containsKey( PolyString.of( "list" ) ) );
+            assertEquals( "image-bytes", row[4].asString().value );
+
+            assertFalse( enumerator.moveNext() );
+        }
+    }
+
+
     private static ParquetRelTable captionsListTable( ParquetSourceFile sourceFile ) {
         PhysicalColumn rowId = column( 10, ParquetSyntheticColumns.ROW_ID, 0, PolyType.VARCHAR );
         PhysicalColumn parentRowId = column( 11, ParquetSyntheticColumns.PARENT_ROW_ID, 1, PolyType.VARCHAR );
@@ -137,6 +169,37 @@ ordinal.id, new ParquetColumnBinding( ordinal.id, ordinal.name, ParquetColumnRol
     }
 
 
+    private static ParquetRelTable flatNestedRootTable( ParquetSourceFile sourceFile ) {
+        PhysicalColumn id = column( 20, "id", 0, PolyType.VARCHAR );
+        PhysicalColumn source = column( 21, "metadata_source", 1, PolyType.VARCHAR );
+        PhysicalColumn width = column( 22, "metadata_size_width", 2, PolyType.BIGINT );
+        PhysicalColumn captions = column( 23, "captions", 3, PolyType.DOCUMENT );
+        PhysicalColumn image = column( 24, "image", 4, PolyType.VARCHAR );
+        PhysicalTable physicalTable = new PhysicalTable(
+                2,
+                1,
+                1,
+                "flickr8k",
+                List.of( id, source, width, captions, image ),
+                1,
+                "public",
+                List.of(),
+                1 );
+
+        ParquetTableBinding binding = new ParquetTableBinding(
+                List.of( sourceFile ),
+                null,
+                List.of(),
+                Map.of(
+                        id.id, new ParquetColumnBinding( id.id, id.name, ParquetColumnRole.DATA, List.of( "id" ) ),
+                        source.id, new ParquetColumnBinding( source.id, source.name, ParquetColumnRole.DATA, List.of( "metadata", "source" ) ),
+                        width.id, new ParquetColumnBinding( width.id, width.name, ParquetColumnRole.DATA, List.of( "metadata", "size", "width" ) ),
+                        captions.id, new ParquetColumnBinding( captions.id, captions.name, ParquetColumnRole.DATA, List.of( "captions" ) ),
+                        image.id, new ParquetColumnBinding( image.id, image.name, ParquetColumnRole.DATA, List.of( "image" ) ) ) );
+        return new ParquetRelTable( 2, physicalTable, binding, null );
+    }
+
+
     private static PhysicalColumn column( long id, String name, int position, PolyType type ) {
         return new PhysicalColumn( id, name, name, 1, 1, 1, position, type, null, null, null, null, null, false, null, null );
     }
@@ -169,6 +232,43 @@ optional group captions (LIST) {
     }
 
 
+    private static void writeFlatNestedParquet( Path file ) throws Exception {
+        MessageType schema = MessageTypeParser.parseMessageType( """
+                message test {
+                  optional binary id (STRING);
+                  optional group metadata {
+                    optional binary source (STRING);
+                    optional group size {
+                      optional int64 width;
+                    }
+                  }
+                  optional group captions (LIST) {
+                    repeated group list {
+                      optional binary element (STRING);
+                    }
+                  }
+                  optional binary image (STRING);
+                }
+                """ );
+
+        SimpleGroupFactory groupFactory = new SimpleGroupFactory( schema );
+        Group row = groupFactory.newGroup()
+                .append( "id", "image-1.jpg" )
+                .append( "image", "image-bytes" );
+        Group metadata = row.addGroup( "metadata" ).append( "source", "camera" );
+        metadata.addGroup( "size" ).append( "width", 640L );
+        Group captions = row.addGroup( "captions" );
+        captions.addGroup( "list" ).append( "element", "a dog in grass" );
+        captions.addGroup( "list" ).append( "element", "a puppy outside" );
+
+        try ( ParquetWriter<Group> writer = ExampleParquetWriter.builder( new LocalOutputFile( file ) )
+                .withType( schema )
+                .build() ) {
+            writer.write( row );
+        }
+    }
+
+
     private record LocalOutputFile( Path path ) implements OutputFile {
 
         @Override

From 0551330b4bd2ed9d548b49ed165d12e0e8cd760b Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Tue, 9 Jun 2026 10:25:53 +0200
Subject: [PATCH 36/46] Add separate columns for primitive nested filed in flat
 mode

---
 .../polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java
index cb79abf13a..3efcbedbf3 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java
@@ -188,7 +188,7 @@ void flatDiscoveryFlattensNonRepeatedNestedColumnsAndKeepsRepeatedColumns() thro
 
 
     @Test
-    void flatDiscoveryKeepsOrdersDbPrimaryKeys() throws Exception {
+    void flatDiscoveryKeepsOrdersDbPrimaryKeys() {
         URL ordersDb = ParquetFileDiscoveryTest.class.getClassLoader().getResource( "orders_db" );
         Map<String, DiscoveredTable> tables = ParquetFileDiscovery.discoverTables( ordersDb, PREFIX );
 

From a8a2f540bba4e39450856757248fc8fe2c45599c Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Tue, 9 Jun 2026 20:21:00 +0200
Subject: [PATCH 37/46] add unit tests

---
 .../db/util/PolyphenyHomeDirManager.java      |   3 +
 .../shared/schema/ParquetTypeConverter.java   |  12 +-
 .../shared/schema/inference/ValueSchema.java  |   2 +-
 .../ParquetAggregateOptimizationTest.java     |  82 +++
 .../parquet/ParquetDocumentPlanningTest.java  | 215 +++++++
 .../ParquetExecutionAndWriterTest.java        | 268 +++++++++
 .../parquet/ParquetFileDiscoveryTest.java     |  21 +
 .../parquet/ParquetFilterPruningTest.java     | 200 +++++++
 .../parquet/ParquetIoUtilitiesTest.java       |  62 ++
 .../ParquetPrimitivePredicateTest.java        | 234 ++++++++
 .../ParquetRowAggregateRelEnumeratorTest.java |  45 ++
 .../parquet/ParquetSchemaUtilitiesTest.java   | 179 ++++++
 .../ParquetSharedDocumentAdapterTest.java     | 305 ++++++++++
 .../ParquetTableStatisticsReaderTest.java     | 419 ++++++++++++++
 .../aggregate/AggregateAccumulatorTest.java   | 261 +++++++++
 .../dag/activities/ActivityRegistryTest.java  |   9 +-
 .../engine/scheduler/GlobalSchedulerTest.java |   1 +
 .../parquet/ParquetWorkflowSupportTest.java   | 536 ++++++++++++++++++
 18 files changed, 2849 insertions(+), 5 deletions(-)
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetAggregateOptimizationTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetExecutionAndWriterTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFilterPruningTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetIoUtilitiesTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPrimitivePredicateTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSchemaUtilitiesTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetTableStatisticsReaderTest.java
 create mode 100644 plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateAccumulatorTest.java
 create mode 100644 plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/parquet/ParquetWorkflowSupportTest.java

diff --git a/config/src/main/java/org/polypheny/db/util/PolyphenyHomeDirManager.java b/config/src/main/java/org/polypheny/db/util/PolyphenyHomeDirManager.java
index 2dceeca45d..221ed1225c 100644
--- a/config/src/main/java/org/polypheny/db/util/PolyphenyHomeDirManager.java
+++ b/config/src/main/java/org/polypheny/db/util/PolyphenyHomeDirManager.java
@@ -103,6 +103,9 @@ private PolyphenyHomeDirManager() {
 
     public static PolyphenyHomeDirManager setModeAndGetInstance( RunMode mode ) {
         if ( PolyphenyHomeDirManager.mode != null ) {
+            if ( PolyphenyHomeDirManager.mode == mode ) {
+                return PolyphenyHomeDirManager.getInstance();
+            }
             throw new RuntimeException( "Could not set the mode." );
         }
         PolyphenyHomeDirManager.mode = mode;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
index 7686f6fcb7..c37ab6b0d1 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetTypeConverter.java
@@ -331,7 +331,7 @@ private Object fromStringToDate( PrimitiveType primitive, String literal ) {
         if ( logical instanceof TimeLogicalTypeAnnotation timeLogical ) {
             long millis = normalizeTimeLiteralToMillis( literal );
             return switch ( timeLogical.getUnit() ) {
-                case MILLIS -> primitive.getPrimitiveTypeName() == PrimitiveTypeName.INT32 ? (int) millis : millis;
+                case MILLIS -> parquetTimeMillisValue( primitive, millis );
                 case MICROS -> millis * 1_000L;
                 case NANOS -> millis * 1_000_000L;
             };
@@ -372,7 +372,7 @@ private Object fromPolyValueToDate( PrimitiveType primitive, PolyValue literal )
                 millis = normalizeTimeLiteralToMillis( literal.toString() );
             }
             return switch ( timeLogical.getUnit() ) {
-                case MILLIS -> primitive.getPrimitiveTypeName() == PrimitiveTypeName.INT32 ? (int) millis : millis;
+                case MILLIS -> parquetTimeMillisValue( primitive, millis );
                 case MICROS -> millis * 1_000L;
                 case NANOS -> millis * 1_000_000L;
             };
@@ -399,6 +399,14 @@ private Object fromPolyValueToDate( PrimitiveType primitive, PolyValue literal )
     }
 
 
+    private Object parquetTimeMillisValue( PrimitiveType primitive, long millis ) {
+        if ( primitive.getPrimitiveTypeName() == PrimitiveTypeName.INT32 ) {
+            return Math.toIntExact( millis );
+        }
+        return millis;
+    }
+
+
     /**
      * Parses an ISO local date string.
      */
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueSchema.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueSchema.java
index 2837477d2c..fe15545b40 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueSchema.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/inference/ValueSchema.java
@@ -99,7 +99,7 @@ public static ValueSchema dateType() {
 
 
     public static ValueSchema timeType() {
-        return new ValueSchema( ValueKind.TIME, PrimitiveType.PrimitiveTypeName.INT64, LogicalTypeAnnotation.timeType( true, LogicalTypeAnnotation.TimeUnit.MILLIS ), null, false, null );
+        return new ValueSchema( ValueKind.TIME, PrimitiveType.PrimitiveTypeName.INT32, LogicalTypeAnnotation.timeType( true, LogicalTypeAnnotation.TimeUnit.MILLIS ), null, false, null );
     }
 
 
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetAggregateOptimizationTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetAggregateOptimizationTest.java
new file mode 100644
index 0000000000..e859ef0861
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetAggregateOptimizationTest.java
@@ -0,0 +1,82 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertSame;
+
+import java.lang.reflect.Proxy;
+import java.util.List;
+import org.junit.jupiter.api.Test;
+import org.polypheny.db.adapter.parquet.relational.optimization.aggregate.AggregateDecomposition;
+import org.polypheny.db.adapter.parquet.relational.optimization.aggregate.PartialAggregate;
+import org.polypheny.db.algebra.AlgCollations;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.core.AggregateCall;
+import org.polypheny.db.algebra.fun.AggFunction;
+import org.polypheny.db.algebra.type.AlgDataTypeFactory;
+import org.polypheny.db.type.PolyType;
+
+class ParquetAggregateOptimizationTest {
+
+    @Test
+    void aggregateDecompositionExposesPartialCallsAndFinalFunctions() {
+        AggFunction countFunction = aggregateFunction( Kind.COUNT, "COUNT" );
+        AggFunction sumFunction = aggregateFunction( Kind.SUM, "SUM0" );
+        AggFunction maxFunction = aggregateFunction( Kind.MAX, "MAX" );
+        AggregateCall count = aggregateCall( countFunction, "c" );
+        AggregateCall max = aggregateCall( maxFunction, "m" );
+        PartialAggregate partialCount = new PartialAggregate( count, sumFunction );
+        PartialAggregate partialMax = new PartialAggregate( max, maxFunction );
+
+        AggregateDecomposition decomposition = new AggregateDecomposition( List.of( partialCount, partialMax ) );
+
+        assertEquals( List.of( count, max ), decomposition.partialCalls() );
+        assertSame( count, partialCount.partialCall() );
+        assertSame( sumFunction, partialCount.finalFunction() );
+        assertSame( maxFunction, partialMax.finalFunction() );
+    }
+
+
+    private static AggregateCall aggregateCall( AggFunction function, String name ) {
+        return AggregateCall.create(
+                function,
+                false,
+                false,
+                List.of( 0 ),
+                -1,
+                AlgCollations.EMPTY,
+                AlgDataTypeFactory.DEFAULT.createPolyType( PolyType.BIGINT ),
+                name );
+    }
+
+
+    private static AggFunction aggregateFunction( Kind kind, String name ) {
+        return (AggFunction) Proxy.newProxyInstance(
+                ParquetAggregateOptimizationTest.class.getClassLoader(),
+                new Class<?>[]{ AggFunction.class },
+                ( proxy, method, args ) -> switch ( method.getName() ) {
+                    case "getKind" -> kind;
+                    case "getName", "toString", "getAllowedSignatures" -> name;
+                    case "isAggregator", "isQuantifierAllowed", "allowsFilter" -> true;
+                    case "hashCode" -> System.identityHashCode( proxy );
+                    case "equals" -> proxy == args[0];
+                    default -> null;
+                } );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java
new file mode 100644
index 0000000000..1b610a0878
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java
@@ -0,0 +1,215 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertNotSame;
+import static org.junit.jupiter.api.Assertions.assertSame;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.lang.reflect.Method;
+import java.nio.file.Path;
+import java.util.List;
+import java.util.Map;
+import sun.misc.Unsafe;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.polypheny.db.adapter.DeployMode;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.annotations.AdapterProperties;
+import org.polypheny.db.adapter.parquet.document.ParquetDocumentSource;
+import org.polypheny.db.adapter.parquet.document.planning.ParquetDocFilter;
+import org.polypheny.db.adapter.parquet.document.planning.ParquetDocFilterRule;
+import org.polypheny.db.adapter.parquet.document.planning.ParquetDocScan;
+import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.core.AlgFactories;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.algebra.type.AlgDataTypeFactory;
+import org.polypheny.db.algebra.type.AlgDataTypeSystem;
+import org.polypheny.db.algebra.type.DocumentType;
+import org.polypheny.db.catalog.entity.physical.PhysicalCollection;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.volcano.VolcanoPlanner;
+import org.polypheny.db.rex.RexBuilder;
+import org.polypheny.db.rex.RexCall;
+import org.polypheny.db.rex.RexIndexRef;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.PolyTypeFactoryImpl;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
+
+class ParquetDocumentPlanningTest {
+
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @Test
+    void documentBuildsTupleTypeFromExportedColumnsAndFallsBackToDocumentId() throws Exception {
+        TestParquetSource.exportedColumns = Map.of(
+                "orders", List.of(
+                        exportedColumn( "name", PolyType.VARCHAR, 0, true ),
+                        exportedColumn( "score", PolyType.INTEGER, 1, false ) ) );
+        ParquetDocument document = new ParquetDocument( collection( "orders" ), List.of( sourceFile() ), testSource() );
+        AlgDataType tupleType = document.getTupleType( AlgDataTypeFactory.DEFAULT );
+
+        assertEquals( List.of( "name", "score" ), tupleType.getFieldNames() );
+        assertTrue( tupleType.getFields().get( 0 ).getType().isNullable() );
+        assertFalse( tupleType.getFields().get( 1 ).getType().isNullable() );
+        assertEquals( List.of( sourceFile() ), document.getSourceFiles() );
+
+        TestParquetSource.exportedColumns = Map.of();
+        ParquetDocument fallback = new ParquetDocument( collection( "missing" ), List.of(), testSource() );
+
+        assertEquals( DocumentType.ofId(), fallback.getTupleType( AlgDataTypeFactory.DEFAULT ) );
+    }
+
+
+    @Test
+    void docScanCopiesFiltersDerivesDocumentRowAndRegistersFilterRule() {
+        ParquetDocument document = new ParquetDocument( collection( "orders" ), List.of( sourceFile() ), null );
+        AlgCluster cluster = cluster();
+        ParquetAdapterFilter<PolyValue> filter = new ParquetAdapterFilter<>( 0, Kind.EQUALS, PolyString.of( "Alice" ) );
+        ParquetDocScan scan = new ParquetDocScan( cluster, document, List.of( filter ) );
+
+        assertEquals( List.of( filter ), scan.getFilters() );
+        assertEquals( List.of( DocumentType.DOCUMENT_ID ), scan.deriveRowType().getFieldNames() );
+        assertTrue( scan.algCompareString().contains( "$filters=" ) );
+
+        AlgNode copied = scan.copy( cluster.traitSetOf( EnumerableConvention.INSTANCE ), List.of() );
+        assertNotSame( scan, copied );
+        assertEquals( scan.getFilters(), ((ParquetDocScan) copied).getFilters() );
+
+        VolcanoPlanner planner = new VolcanoPlanner();
+        scan.register( planner );
+    }
+
+
+    @Test
+    void docFilterCopyPreservesEntityAndDelegatesToScanInput() {
+        ParquetDocument document = new ParquetDocument( collection( "orders" ), List.of( sourceFile() ), null );
+        AlgCluster cluster = cluster();
+        ParquetDocScan scan = new ParquetDocScan( cluster, document, List.of() );
+        RexNode condition = ref( cluster, 0 );
+        ParquetDocFilter filter = new ParquetDocFilter( cluster, cluster.traitSetOf( EnumerableConvention.INSTANCE ), scan, condition, document );
+
+        ParquetDocFilter copied = filter.copy( cluster.traitSetOf( EnumerableConvention.INSTANCE ), scan, condition );
+
+        assertNotSame( filter, copied );
+        assertSame( document, copied.getEntity() );
+        assertSame( scan, copied.getInput() );
+    }
+
+
+    @Test
+    @SuppressWarnings("unchecked")
+    void docFilterRuleSplitsNestedAndConjunctions() throws Exception {
+        AlgDataTypeFactory typeFactory = new PolyTypeFactoryImpl( AlgDataTypeSystem.DEFAULT );
+        AlgDataType boolType = typeFactory.createPolyType( PolyType.BOOLEAN );
+        RexNode left = new RexIndexRef( 0, boolType );
+        RexNode middle = new RexIndexRef( 1, boolType );
+        RexNode right = new RexIndexRef( 2, boolType );
+        RexNode condition = new RexCall(
+                boolType,
+                new org.polypheny.db.nodes.SpecialOperator( Kind.AND.name(), Kind.AND ),
+                List.of( left, new RexCall( boolType, new org.polypheny.db.nodes.SpecialOperator( Kind.AND.name(), Kind.AND ), List.of( middle, right ) ) ) );
+        ParquetDocFilterRule rule = new ParquetDocFilterRule( AlgFactories.LOGICAL_BUILDER, null );
+        Method splitConjunctions = ParquetDocFilterRule.class.getDeclaredMethod( "splitConjunctions", RexNode.class );
+        splitConjunctions.setAccessible( true );
+
+        List<RexNode> split = (List<RexNode>) splitConjunctions.invoke( rule, condition );
+        List<RexNode> single = (List<RexNode>) splitConjunctions.invoke( rule, left );
+
+        assertEquals( List.of( left, middle, right ), split );
+        assertEquals( List.of( left ), single );
+    }
+
+
+    private static AlgCluster cluster() {
+        AlgDataTypeFactory typeFactory = new PolyTypeFactoryImpl( AlgDataTypeSystem.DEFAULT );
+        VolcanoPlanner planner = new VolcanoPlanner();
+        return AlgCluster.createDocument( planner, new RexBuilder( typeFactory ), null );
+    }
+
+
+    @SuppressWarnings("SameParameterValue")
+    private static RexNode ref( AlgCluster cluster, int index ) {
+        return new RexIndexRef( index, cluster.getTypeFactory().createPolyType( PolyType.BOOLEAN ) );
+    }
+
+
+    private static PhysicalCollection collection( String name ) {
+        return new PhysicalCollection( 10L, 20L, 30L, 40L, name, "public", 50L );
+    }
+
+
+    private static ParquetSourceFile sourceFile() {
+        return new ParquetSourceFile( "file:/tmp/orders.parquet", Map.of(), Map.of() );
+    }
+
+
+    private static ExportedColumn exportedColumn( String name, PolyType type, int position, boolean nullable ) {
+        return new ExportedColumn( name, type, null, null, null, null, null, nullable, "public", "orders", name, position, false );
+    }
+
+
+    private static TestParquetSource testSource() throws Exception {
+        java.lang.reflect.Field field = Unsafe.class.getDeclaredField( "theUnsafe" );
+        field.setAccessible( true );
+        return (TestParquetSource) ((Unsafe) field.get( null )).allocateInstance( TestParquetSource.class );
+    }
+
+
+    @AdapterProperties(
+            name = "Test Parquet Source",
+            description = "Test-only source for document planning unit tests.",
+            usedModes = DeployMode.EMBEDDED,
+            defaultMode = DeployMode.EMBEDDED)
+    private static class TestParquetSource extends ParquetDocumentSource {
+
+        private static Map<String, List<ExportedColumn>> exportedColumns = Map.of();
+
+
+        private TestParquetSource( Path directory ) {
+            super( 900L, "test_parquet_doc_source", Map.of( "method", "link", "directory", directory.toString(), "directoryName", directory.toString() ), DeployMode.EMBEDDED );
+        }
+
+
+        @Override
+        public Map<String, List<ExportedColumn>> getExportedColumns() {
+            return exportedColumns;
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetExecutionAndWriterTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetExecutionAndWriterTest.java
new file mode 100644
index 0000000000..ddaa0453c8
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetExecutionAndWriterTest.java
@@ -0,0 +1,268 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertNull;
+import static org.junit.jupiter.api.Assertions.assertSame;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.io.File;
+import java.nio.file.Path;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.io.api.Binary;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+import org.apache.parquet.schema.Types;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+import org.polypheny.db.adapter.parquet.document.execution.ParquetDocValueExtractor;
+import org.polypheny.db.adapter.parquet.relational.execution.FilterableParquetSourceFile;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelEnumerator;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.execution.BufferedIterator;
+import org.polypheny.db.adapter.parquet.shared.execution.CombinedGroup;
+import org.polypheny.db.adapter.parquet.shared.execution.VirtualGroup;
+import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetPrimitiveValueFilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceWriter;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetMessageTypeBuilder;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.SchemaState;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.algebra.type.AlgDataTypeFactory;
+import org.polypheny.db.algebra.type.DocumentType;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyBoolean;
+import org.polypheny.db.type.entity.PolyList;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.document.PolyDocument;
+import org.polypheny.db.type.entity.numerical.PolyInteger;
+import org.polypheny.db.type.entity.numerical.PolyLong;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
+import org.polypheny.db.util.Sources;
+
+class ParquetExecutionAndWriterTest {
+
+    @TempDir
+    Path tempDir;
+
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @Test
+    void bufferedIteratorReplaysSampleRowsBeforeConvertedRemainder() {
+        BufferedIterator<Integer, String> iterator = new BufferedIterator<>( List.of( "sample-1", "sample-2" ), List.of( 3, 4 ).iterator(), value -> "row-" + value );
+
+        assertTrue( iterator.hasNext() );
+        assertEquals( "sample-1", iterator.next() );
+        assertEquals( "sample-2", iterator.next() );
+        assertEquals( "row-3", iterator.next() );
+        assertEquals( "row-4", iterator.next() );
+        assertFalse( iterator.hasNext() );
+    }
+
+
+    @Test
+    void virtualGroupDelegatesToSourceGroupAndKeepsMetadata() {
+        MessageType schema = Types.buildMessage()
+                .required( PrimitiveTypeName.INT64 ).named( "id" )
+                .optional( PrimitiveTypeName.BINARY ).as( org.apache.parquet.schema.LogicalTypeAnnotation.stringType() ).named( "name" )
+                .named( "row" );
+        Group source = new SimpleGroupFactory( schema ).newGroup()
+                .append( "id", 7L )
+                .append( "name", "Alice" );
+        VirtualGroup group = new VirtualGroup( source, "row-7", "parent-1", 3 );
+
+        assertEquals( 7L, group.getLong( 0, 0 ) );
+        assertEquals( "Alice", group.getString( 1, 0 ) );
+        assertEquals( "row-7", group.getMetadata().getRowId() );
+        assertEquals( "parent-1", group.getMetadata().getParentRowId() );
+        assertEquals( 3L, group.getMetadata().getOrdinal() );
+        assertSame( schema, group.getType() );
+    }
+
+
+    @Test
+    void combinedGroupRoutesFieldsByJoinSideAndRejectsIndexedAccess() {
+        VirtualGroup parent = virtualGroup( "parent" );
+        VirtualGroup child = virtualGroup( "child" );
+        ParquetColumnBinding parentColumn = new ParquetColumnBinding( 1L, "parent_id", ParquetColumnRole.DATA, List.of( "parent_id" ) );
+        ParquetColumnBinding childColumn = new ParquetColumnBinding( 2L, "child_id", ParquetColumnRole.DATA, List.of( "child_id" ) );
+        CombinedGroup combined = new CombinedGroup( parent, List.of( parentColumn ), List.of( "orders" ), child, List.of( childColumn ), List.of( "orders", "items" ) );
+
+        assertEquals( 2, combined.fieldCount() );
+        assertSame( parent, combined.groupForField( 0, true ) );
+        assertSame( child, combined.groupForField( 1, true ) );
+        assertSame( child, combined.groupForField( 0, false ) );
+        assertSame( parent, combined.groupForField( 1, false ) );
+        assertEquals( parentColumn, combined.bindingForField( 0, true ) );
+        assertEquals( childColumn, combined.bindingForField( 0, false ) );
+        assertEquals( List.of( "orders" ), combined.tablePathForField( 0, true ) );
+        assertEquals( List.of( "orders", "items" ), combined.tablePathForField( 0, false ) );
+        assertFalse( combined.isNullField( 0, true ) );
+        assertSame( parent.getType(), combined.getType() );
+        assertThrows( UnsupportedOperationException.class, () -> combined.getInteger( 0, 0 ) );
+
+        CombinedGroup unmatchedChild = new CombinedGroup( parent, List.of( parentColumn ), List.of(), null, List.of( childColumn ), List.of() );
+        assertTrue( unmatchedChild.isNullField( 1, true ) );
+    }
+
+
+    @Test
+    void filterableSourceFileDefensivelyCopiesFilters() {
+        ParquetSourceFile sourceFile = new ParquetSourceFile( "file:/tmp/source.parquet", Map.of(), Map.of() );
+        ParquetAdapterFilter<PolyValue> filter = new ParquetAdapterFilter<>( 0, Kind.EQUALS, PolyString.of( "EU" ) );
+        List<ParquetAdapterFilter<PolyValue>> filters = new ArrayList<>( List.of( filter ) );
+
+        FilterableParquetSourceFile filterable = new FilterableParquetSourceFile( sourceFile, filters );
+        filters.clear();
+
+        assertEquals( List.of( filter ), filterable.filters() );
+        assertThrows( UnsupportedOperationException.class, () -> filterable.filters().add( filter ) );
+    }
+
+
+    @Test
+    void primitiveValueFilterEvaluatorHandlesScalarComparisonsAndUnknownIndexes() {
+        ParquetPrimitiveValueFilterEvaluator evaluator = new ParquetPrimitiveValueFilterEvaluator();
+        Object[] values = new Object[]{ 10, true, Binary.fromString( "alpha" ), null };
+
+        assertEquals( Boolean.TRUE, evaluator.evaluate( values, new ParquetAdapterFilter<>( 0, Kind.GREATER_THAN, PolyInteger.of( 7 ) ) ) );
+        assertEquals( Boolean.FALSE, evaluator.evaluate( values, new ParquetAdapterFilter<>( 0, Kind.LESS_THAN, PolyInteger.of( 7 ) ) ) );
+        assertEquals( Boolean.TRUE, evaluator.evaluate( values, new ParquetAdapterFilter<>( 1, Kind.EQUALS, PolyBoolean.of( true ) ) ) );
+        assertEquals( Boolean.TRUE, evaluator.evaluate( values, new ParquetAdapterFilter<>( 2, Kind.EQUALS, PolyString.of( "alpha" ) ) ) );
+        assertEquals( Boolean.TRUE, evaluator.evaluate( values, new ParquetAdapterFilter<>( 3, Kind.IS_NULL, null ) ) );
+        assertEquals( Boolean.FALSE, evaluator.evaluate( values, new ParquetAdapterFilter<>( 3, Kind.IS_NOT_NULL, null ) ) );
+        assertEquals( Boolean.TRUE, evaluator.evaluate( values, new ParquetAdapterFilter<>( 0, Kind.PLUS, PolyInteger.of( 99 ) ) ) );
+        assertNull( evaluator.evaluate( values, new ParquetAdapterFilter<>( 99, Kind.EQUALS, PolyInteger.of( 1 ) ) ) );
+    }
+
+
+    @Test
+    void sourceWriterRoundTripsRelationalRowsThroughRelEnumerator() throws Exception {
+        File file = tempDir.resolve( "writer-rel.parquet" ).toFile();
+        AlgDataType inputType = AlgDataTypeFactory.DEFAULT.builder()
+                .add( "id", null, PolyType.BIGINT )
+                .add( "Name", null, PolyType.VARCHAR )
+                .add( "Score", null, PolyType.INTEGER )
+                .build();
+        SchemaState schemaState = new SchemaState( SchemaState.CONFLICT_FAIL );
+        schemaState.init( inputType, true );
+        schemaState.mergeRelationalRowSchema( List.of( PolyLong.of( 101 ), PolyString.of( "Alice" ), PolyInteger.of( 9 ) ), inputType, true );
+        MessageType schema = new ParquetMessageTypeBuilder( schemaState, "writer_rel" ).build();
+        List<Long> progress = new ArrayList<>();
+
+        try ( ParquetSourceWriter writer = new ParquetSourceWriter( file, schema, ParquetSourceWriter.COMPRESSION_UNCOMPRESSED, schemaState, false ) ) {
+            writer.writeRows( List.of(
+                    List.of( PolyLong.of( 101 ), PolyString.of( "Alice" ), PolyInteger.of( 9 ) ),
+                    List.of( PolyLong.of( 102 ), PolyString.of( "Bob" ), PolyInteger.of( 7 ) ) ).iterator(), progress::add );
+        }
+
+        assertEquals( List.of( 1L, 2L ), progress );
+        try ( ParquetSourceReader reader = new ParquetSourceReader( Sources.of( file ) ) ) {
+            ParquetRelEnumerator enumerator = new ParquetRelEnumerator( reader, FiltersContainer.empty, false );
+            assertTrue( enumerator.moveNext() );
+            PolyValue[] first = enumerator.current();
+            assertEquals( 101L, first[0].asNumber().longValue() );
+            assertEquals( "Alice", first[1].asString().value );
+            assertEquals( 9, first[2].asNumber().intValue() );
+            assertTrue( enumerator.moveNext() );
+            assertEquals( "Bob", enumerator.current()[1].asString().value );
+            assertFalse( enumerator.moveNext() );
+            enumerator.close();
+        }
+    }
+
+
+    @Test
+    void sourceWriterRoundTripsDocumentsAndGeneratesMissingIds() throws Exception {
+        File file = tempDir.resolve( "writer-doc.parquet" ).toFile();
+        PolyDocument document = PolyDocument.ofDocument( Map.of(
+                PolyString.of( DocumentType.DOCUMENT_ID ), PolyString.of( "doc-1" ),
+                PolyString.of( "Name" ), PolyString.of( "Alice" ),
+                PolyString.of( "Tags" ), PolyList.of( List.of( PolyString.of( "red" ), PolyString.of( "blue" ) ) ) ) );
+        SchemaState schemaState = new SchemaState( SchemaState.CONFLICT_FAIL );
+        schemaState.mergeDocumentSchema( document, false );
+        MessageType schema = new ParquetMessageTypeBuilder( schemaState, "writer_doc" ).build();
+
+        try ( ParquetSourceWriter writer = new ParquetSourceWriter( file, schema, ParquetSourceWriter.COMPRESSION_UNCOMPRESSED, schemaState, false ) ) {
+            writer.writeRows( List.of( document ).iterator(), null );
+        }
+
+        try ( ParquetSourceReader reader = new ParquetSourceReader( Sources.of( file ) ) ) {
+            Group group = reader.next();
+            PolyDocument read = new ParquetDocValueExtractor().extractDocument( group, reader.getProjectionSchema(), PolyString.of( "generated-id" ) );
+
+            assertEquals( "Alice", read.get( PolyString.of( "name" ) ).asString().value );
+            assertEquals( "red", read.get( PolyString.of( "tags" ) ).asList().get( 0 ).asString().value );
+            assertEquals( "blue", read.get( PolyString.of( "tags" ) ).asList().get( 1 ).asString().value );
+            assertEquals( "generated-id", read.get( PolyString.of( DocumentType.DOCUMENT_ID ) ).asString().value );
+        }
+    }
+
+
+    @Test
+    void sourceWriterReportsCompressionAndValueConversionFailures() throws Exception {
+        File badCompression = tempDir.resolve( "bad-compression.parquet" ).toFile();
+        AlgDataType inputType = AlgDataTypeFactory.DEFAULT.builder()
+                .add( "amount", null, PolyType.INTEGER )
+                .build();
+        SchemaState schemaState = new SchemaState( SchemaState.CONFLICT_FAIL );
+        schemaState.init( inputType, true );
+        MessageType schema = new ParquetMessageTypeBuilder( schemaState, "writer_errors" ).build();
+
+        //noinspection resource
+        assertThrows( GenericRuntimeException.class, () -> new ParquetSourceWriter( badCompression, schema, "brotli", schemaState, false ) );
+
+        try ( ParquetSourceWriter writer = new ParquetSourceWriter( tempDir.resolve( "bad-value.parquet" ).toFile(), schema, ParquetSourceWriter.COMPRESSION_UNCOMPRESSED, schemaState, false ) ) {
+            assertThrows( GenericRuntimeException.class, () -> writer.writeRows( List.of( List.<PolyValue>of( PolyString.of( "not-a-number" ) ) ).iterator(), null ) );
+        }
+    }
+
+
+    private static VirtualGroup virtualGroup( String id ) {
+        MessageType schema = Types.buildMessage()
+                .required( PrimitiveTypeName.INT64 ).named( "id" )
+                .named( "row" );
+        Group source = new SimpleGroupFactory( schema ).newGroup().append( "id", 1L );
+        return new VirtualGroup( source, id, null, 0 );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java
index 3efcbedbf3..acfb0edbbd 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFileDiscoveryTest.java
@@ -142,6 +142,27 @@ void hidesParquetColumnWhenItMatchesPartitionValue() throws Exception {
     }
 
 
+    @Test
+    void hidesPhysicalColumnWhenNormalizedPartitionNameCollidesAndValuesMatch() throws Exception {
+        Path partition = tempDir.resolve( "Region Name=EU" );
+        Files.createDirectories( partition );
+        MessageType schema = Types.buildMessage()
+                .required( org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName.INT64 ).named( "id" )
+                .required( org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName.BINARY )
+                .as( org.apache.parquet.schema.LogicalTypeAnnotation.stringType() )
+                .named( "region_name" )
+                .named( "test" );
+        writeParquet( partition.resolve( "part-000.parquet" ), schema, row( 1L, "EU" ) );
+
+        Map<String, DiscoveredTable> tables = ParquetFileDiscovery.discoverTables( tempDir.toUri().toURL(), PREFIX );
+
+        DiscoveredTable table = tables.values().iterator().next();
+        assertEquals( List.of( "id", "region_name" ), columnNames( table.columns() ) );
+        assertEquals( Map.of( "id", List.of( "id" ) ), table.binding().columnPaths() );
+        assertEquals( "EU", table.binding().sourceFiles().get( 0 ).partitionValues().get( "region_name" ) );
+    }
+
+
     @Test
     void failsWhenParquetColumnConflictsWithPartitionValue() throws Exception {
         Path partition = tempDir.resolve( "year=2025" );
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFilterPruningTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFilterPruningTest.java
new file mode 100644
index 0000000000..c9ebce1114
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetFilterPruningTest.java
@@ -0,0 +1,200 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertNull;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Objects;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetMultiFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFileFilterReducer;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFilePartitionFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFileStatisticsFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ResidualFilters;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnStatistics;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.numerical.PolyDouble;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
+
+class ParquetFilterPruningTest {
+
+    private static final ParquetColumnBinding REGION = new ParquetColumnBinding( 1L, "region", ParquetColumnRole.PARTITION, List.of() );
+    private static final ParquetColumnBinding AMOUNT = new ParquetColumnBinding( 2L, "amount", ParquetColumnRole.DATA, List.of( "amount" ) );
+    private static final ParquetColumnBinding METADATA = new ParquetColumnBinding( 3L, "metadata", ParquetColumnRole.DATA, List.of( "metadata", "amount" ) );
+
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @Test
+    void partitionEvaluatorMatchesValuesAndTreatsMissingPartitionsAsNull() {
+        ParquetSourceFile sourceFile = sourceFile( Map.of( "region", "EU" ), Map.of() );
+        ParquetSourceFilePartitionFilterEvaluator evaluator = new ParquetSourceFilePartitionFilterEvaluator( ParquetFilterPruningTest::binding );
+
+        assertEquals( Boolean.TRUE, evaluator.evaluate( sourceFile, filter( 1, Kind.EQUALS, PolyString.of( "EU" ) ) ) );
+        assertEquals( Boolean.FALSE, evaluator.evaluate( sourceFile, filter( 1, Kind.EQUALS, PolyString.of( "US" ) ) ) );
+        assertEquals( Boolean.TRUE, evaluator.evaluate( sourceFile( Map.of(), Map.of() ), filter( 1, Kind.IS_NULL, null ) ) );
+        assertNull( evaluator.evaluate( sourceFile, filter( 2, Kind.EQUALS, PolyDouble.of( 12D ) ) ) );
+    }
+
+
+    @Test
+    void statisticsEvaluatorPrunesByRangeAndProvesConstantFiles() {
+        ParquetColumnStatistics range = new ParquetColumnStatistics( PolyType.DOUBLE, 10, 10, 0L, "10.0", "20.0", true );
+        ParquetColumnStatistics constant = new ParquetColumnStatistics( PolyType.DOUBLE, 4, 4, 0L, "12.0", "12.0", true );
+        ParquetColumnStatistics allNull = new ParquetColumnStatistics( PolyType.DOUBLE, 3, 3, 3L, null, null, true );
+        ParquetSourceFile rangeFile = sourceFile( Map.of(), Map.of( List.of( "amount" ), range ) );
+        ParquetSourceFile constantFile = sourceFile( Map.of(), Map.of( List.of( "amount" ), constant ) );
+        ParquetSourceFile nullFile = sourceFile( Map.of(), Map.of( List.of( "amount" ), allNull ) );
+        ParquetSourceFileStatisticsFilterEvaluator evaluator = new ParquetSourceFileStatisticsFilterEvaluator( ParquetFilterPruningTest::binding );
+
+        assertEquals( Boolean.FALSE, evaluator.evaluate( rangeFile, filter( 2, Kind.EQUALS, PolyDouble.of( 30D ) ) ) );
+        assertNull( evaluator.evaluate( rangeFile, filter( 2, Kind.EQUALS, PolyDouble.of( 15D ) ) ) );
+        assertEquals( Boolean.TRUE, evaluator.evaluate( rangeFile, filter( 2, Kind.GREATER_THAN, PolyDouble.of( 5D ) ) ) );
+        assertFalse( evaluator.supportsExactEvaluation( rangeFile, filter( 2, Kind.EQUALS, PolyDouble.of( 15D ) ) ) );
+
+        assertEquals( Boolean.TRUE, evaluator.evaluate( constantFile, filter( 2, Kind.EQUALS, PolyDouble.of( 12D ) ) ) );
+        assertTrue( evaluator.supportsExactEvaluation( constantFile, filter( 2, Kind.EQUALS, PolyDouble.of( 12D ) ) ) );
+        assertEquals( Boolean.TRUE, evaluator.evaluate( nullFile, filter( 2, Kind.IS_NULL, null ) ) );
+        assertEquals( Boolean.FALSE, evaluator.evaluate( nullFile, filter( 2, Kind.IS_NOT_NULL, null ) ) );
+        assertTrue( evaluator.supportsExactEvaluation( nullFile, filter( 2, Kind.IS_NULL, null ) ) );
+
+        assertNull( evaluator.evaluate( rangeFile, filter( 3, Kind.EQUALS, PolyDouble.of( 12D ) ) ) );
+    }
+
+
+    @Test
+    void reducerDropsExactMatchesRejectsExactFailuresAndKeepsResiduals() {
+        ParquetSourceFile sourceFile = sourceFile(
+                Map.of( "region", "EU" ),
+                Map.of( List.of( "amount" ), new ParquetColumnStatistics( PolyType.DOUBLE, 10, 10, 0L, "10.0", "20.0", true ) ) );
+        ParquetMultiFilterEvaluator<ParquetSourceFile> evaluator = new ParquetMultiFilterEvaluator<>(
+                new ParquetSourceFilePartitionFilterEvaluator( ParquetFilterPruningTest::binding ),
+                new ParquetSourceFileStatisticsFilterEvaluator( ParquetFilterPruningTest::binding ) );
+        ParquetAdapterFilter<PolyValue> matchingPartition = filter( 1, Kind.EQUALS, PolyString.of( "EU" ) );
+        ParquetAdapterFilter<PolyValue> rejectingPartition = filter( 1, Kind.EQUALS, PolyString.of( "US" ) );
+        ParquetAdapterFilter<PolyValue> unknownRange = filter( 2, Kind.EQUALS, PolyDouble.of( 15D ) );
+
+        ResidualFilters reduced = ParquetSourceFileFilterReducer.reduce( sourceFile, evaluator, List.of( matchingPartition, unknownRange ) );
+        assertTrue( reduced.matches() );
+        assertEquals( List.of( unknownRange ), reduced.filters() );
+
+        ResidualFilters rejected = ParquetSourceFileFilterReducer.reduce( sourceFile, evaluator, List.of( matchingPartition, rejectingPartition ) );
+        assertFalse( rejected.matches() );
+        assertTrue( rejected.filters().isEmpty() );
+
+        ResidualFilters partialOr = ParquetSourceFileFilterReducer.reduce(
+                sourceFile,
+                evaluator,
+                List.of( ParquetAdapterFilter.logical( Kind.OR, List.of( rejectingPartition, unknownRange ) ) ) );
+        assertTrue( partialOr.matches() );
+        assertEquals( List.of( unknownRange ), partialOr.filters() );
+
+        ResidualFilters residualNot = ParquetSourceFileFilterReducer.reduce(
+                sourceFile,
+                evaluator,
+                List.of( ParquetAdapterFilter.logical( Kind.NOT, List.of( unknownRange ) ) ) );
+        assertTrue( residualNot.matches() );
+        assertEquals( Kind.NOT, residualNot.filters().get( 0 ).operator() );
+        assertEquals( unknownRange, residualNot.filters().get( 0 ).operands().get( 0 ) );
+    }
+
+
+    @Test
+    void filterResolverMapsProjectionIndexesAndResolvesDynamicParameters() {
+        ParquetAdapterFilter<PolyValue> projected = new ParquetAdapterFilter<>( 1, List.of( "old" ), Kind.EQUALS, null, 7L );
+        ParquetAdapterFilter<PolyValue> physical = ParquetFilterResolver.toPhysicalFilter( projected, new int[]{ 2, 4 } );
+        ParquetAdapterFilter<PolyValue> projection = ParquetFilterResolver.toProjectionFilter(
+                new ParquetAdapterFilter<>( 4, List.of( "amount" ), Kind.GREATER_THAN, PolyDouble.of( 10D ) ),
+                index -> index == 4 ? 1 : -1 );
+        DataContext dataContext = new ParameterDataContext( Map.of( 7L, PolyString.of( "EU" ) ) );
+
+        assertEquals( 4, Objects.requireNonNull( physical ).columnIndex() );
+        assertEquals( List.of( "old" ), physical.pathElements() );
+        assertNull( ParquetFilterResolver.toPhysicalFilter( new ParquetAdapterFilter<>( 2, Kind.EQUALS, PolyString.of( "EU" ) ), new int[]{ 2, 4 } ) );
+        assertEquals( 1, Objects.requireNonNull( projection ).columnIndex() );
+        assertTrue( projection.pathElements().isEmpty() );
+
+        List<ParquetAdapterFilter<PolyValue>> resolved = ParquetFilterResolver.resolveFilters( dataContext, List.of( projected ), ignored -> AMOUNT );
+        assertEquals( List.of( "amount" ), resolved.get( 0 ).pathElements() );
+        assertEquals( "EU", resolved.get( 0 ).value().asString().value );
+    }
+
+
+    private static ParquetColumnBinding binding( ParquetAdapterFilter<PolyValue> filter ) {
+        return switch ( filter.columnIndex() ) {
+            case 1 -> REGION;
+            case 2 -> AMOUNT;
+            case 3 -> METADATA;
+            default -> null;
+        };
+    }
+
+
+    private static ParquetAdapterFilter<PolyValue> filter( int columnIndex, Kind kind, PolyValue value ) {
+        return new ParquetAdapterFilter<>( columnIndex, kind, value );
+    }
+
+
+    private static ParquetSourceFile sourceFile( Map<String, String> partitions, Map<List<String>, ParquetColumnStatistics> statistics ) {
+        return new ParquetSourceFile( "file:/tmp/source.parquet", partitions, statistics );
+    }
+
+
+    private static class ParameterDataContext extends DataContext.SlimDataContext {
+
+        private final List<Map<Long, PolyValue>> values;
+
+
+        private ParameterDataContext( Map<Long, PolyValue> values ) {
+            this.values = List.of( new LinkedHashMap<>( values ) );
+        }
+
+
+        @Override
+        public List<Map<Long, PolyValue>> getParameterValues() {
+            return values;
+        }
+
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetIoUtilitiesTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetIoUtilitiesTest.java
new file mode 100644
index 0000000000..9a2af15db8
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetIoUtilitiesTest.java
@@ -0,0 +1,62 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertNull;
+import static org.junit.jupiter.api.Assertions.assertSame;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.net.URL;
+import java.util.concurrent.atomic.AtomicBoolean;
+import org.junit.jupiter.api.Test;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetCancellation;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetUrlResolver;
+
+class ParquetIoUtilitiesTest {
+
+    @Test
+    void cancellationChecksOnlyAtConfiguredRowIntervals() {
+        AtomicBoolean cancelled = new AtomicBoolean( true );
+
+        assertTrue( ParquetCancellation.shouldStop( 0, cancelled ) );
+        assertFalse( ParquetCancellation.shouldStop( 1, cancelled ) );
+        assertFalse( ParquetCancellation.shouldStop( 4095, cancelled ) );
+        assertTrue( ParquetCancellation.shouldStop( 4096, cancelled ) );
+
+        cancelled.set( false );
+        assertFalse( ParquetCancellation.shouldStop( 4096, cancelled ) );
+    }
+
+
+    @Test
+    void urlResolverNormalizesDirectoriesAndKeepsExplicitParquetFiles() throws Exception {
+        URL directory = new URL( "file:/tmp/parquet-data" );
+        URL directoryWithSlash = new URL( "file:/tmp/parquet-data/" );
+        URL sourceFile = new URL( "file:/tmp/parquet-data/orders.parquet" );
+
+        assertNull( ParquetUrlResolver.asDirectoryUrl( null ) );
+        assertEquals( directoryWithSlash, ParquetUrlResolver.asDirectoryUrl( directory ) );
+        assertSame( directoryWithSlash, ParquetUrlResolver.asDirectoryUrl( directoryWithSlash ) );
+        assertEquals( directoryWithSlash, ParquetUrlResolver.asSourceUrl( directory ) );
+        assertSame( sourceFile, ParquetUrlResolver.asSourceUrl( sourceFile ) );
+        assertEquals( new URL( "file:/tmp/parquet-data/part-000.parquet" ), ParquetUrlResolver.resolveFile( directory, "part-000.parquet" ) );
+        assertSame( sourceFile, ParquetUrlResolver.resolveFile( sourceFile, "ignored.parquet" ) );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPrimitivePredicateTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPrimitivePredicateTest.java
new file mode 100644
index 0000000000..c28caa3925
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetPrimitivePredicateTest.java
@@ -0,0 +1,234 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertNull;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.util.List;
+import java.util.Objects;
+import org.apache.parquet.column.ColumnDescriptor;
+import org.apache.parquet.column.ColumnReader;
+import org.apache.parquet.io.api.Binary;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+import org.apache.parquet.schema.Types;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetPrimitivePredicate;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.type.entity.PolyBoolean;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.numerical.PolyInteger;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
+
+class ParquetPrimitivePredicateTest {
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @Test
+    void compileReturnsAlwaysTrueForEmptyFiltersAndRejectsUnsupportedFilters() {
+        MessageType schema = schema();
+
+        assertTrue( Objects.requireNonNull( ParquetPrimitivePredicate.compile( schema, List.of() ) ).matches( new ColumnReader[0], new boolean[0], new Object[0] ) );
+        assertTrue( Objects.requireNonNull( ParquetPrimitivePredicate.compile( schema, null ) ).matches( new ColumnReader[0], new boolean[0], new Object[0] ) );
+        assertNull( ParquetPrimitivePredicate.compile( schema, List.of( new ParquetAdapterFilter<>( 99, Kind.EQUALS, PolyInteger.of( 1 ) ) ) ) );
+        assertNull( ParquetPrimitivePredicate.compile( schema, List.of( new ParquetAdapterFilter<>( 0, Kind.PLUS, PolyInteger.of( 1 ) ) ) ) );
+        assertNull( ParquetPrimitivePredicate.compile( schema, List.of( new ParquetAdapterFilter<>( 0, Kind.EQUALS, null ) ) ) );
+        assertNull( ParquetPrimitivePredicate.compile( schema, List.of( ParquetAdapterFilter.logical( Kind.OR, List.of(
+                new ParquetAdapterFilter<>( 0, Kind.EQUALS, PolyInteger.of( 1 ) ),
+                new ParquetAdapterFilter<>( 0, Kind.EQUALS, PolyInteger.of( 2 ) ) ) ) ) ) );
+    }
+
+
+    @Test
+    void compiledAndPredicateReadsColumnsOnceAndReusesConsumedValues() {
+        MessageType schema = schema();
+        ParquetPrimitivePredicate predicate = ParquetPrimitivePredicate.compile( schema, List.of(
+                new ParquetAdapterFilter<>( 0, Kind.GREATER_THAN, PolyInteger.of( 5 ) ),
+                new ParquetAdapterFilter<>( 1, Kind.EQUALS, PolyString.of( "alpha" ) ),
+                new ParquetAdapterFilter<>( 2, Kind.EQUALS, PolyBoolean.of( true ) ) ) );
+        TestColumnReader amount = TestColumnReader.intReader( 1, 9 );
+        TestColumnReader code = TestColumnReader.binaryReader( 1, "alpha" );
+        TestColumnReader active = TestColumnReader.booleanReader( 1, true );
+        ColumnReader[] readers = { amount, code, active };
+        boolean[] consumed = new boolean[3];
+        Object[] values = new Object[3];
+
+        assertTrue( Objects.requireNonNull( predicate ).matches( readers, consumed, values ) );
+        assertEquals( 1, amount.consumed );
+        assertEquals( 1, code.consumed );
+        assertEquals( 1, active.consumed );
+
+        assertTrue( predicate.matches( readers, consumed, values ) );
+        assertEquals( 1, amount.consumed );
+        assertEquals( 1, code.consumed );
+        assertEquals( 1, active.consumed );
+        assertEquals( 9, values[0] );
+        assertEquals( "alpha", ((Binary) values[1]).toStringUsingUTF8() );
+        assertEquals( true, values[2] );
+    }
+
+
+    @Test
+    void nullPredicatesMatchAbsentAndPresentValues() {
+        MessageType schema = schema();
+        ParquetPrimitivePredicate isNull = ParquetPrimitivePredicate.compile( schema, List.of( new ParquetAdapterFilter<>( 0, Kind.IS_NULL, null ) ) );
+        ParquetPrimitivePredicate isNotNull = ParquetPrimitivePredicate.compile( schema, List.of( new ParquetAdapterFilter<>( 0, Kind.IS_NOT_NULL, null ) ) );
+
+        assertTrue( Objects.requireNonNull( isNull ).matches( new ColumnReader[]{ TestColumnReader.intReader( 0, 0 ) }, new boolean[1], new Object[1] ) );
+        assertTrue( Objects.requireNonNull( isNotNull ).matches( new ColumnReader[]{ TestColumnReader.intReader( 1, 7 ) }, new boolean[1], new Object[1] ) );
+        assertFalse( isNotNull.matches( new ColumnReader[]{ TestColumnReader.intReader( 0, 0 ) }, new boolean[1], new Object[1] ) );
+    }
+
+
+    private static MessageType schema() {
+        return Types.buildMessage()
+                .optional( PrimitiveTypeName.INT32 ).named( "amount" )
+                .optional( PrimitiveTypeName.BINARY ).named( "code" )
+                .optional( PrimitiveTypeName.BOOLEAN ).named( "active" )
+                .named( "predicate_schema" );
+    }
+
+
+    private static class TestColumnReader implements ColumnReader {
+
+        private final PrimitiveTypeName type;
+        private final int definitionLevel;
+        private final Object value;
+        private int consumed;
+
+
+        private TestColumnReader( PrimitiveTypeName type, int definitionLevel, Object value ) {
+            this.type = type;
+            this.definitionLevel = definitionLevel;
+            this.value = value;
+        }
+
+
+        private static TestColumnReader intReader( int definitionLevel, int value ) {
+            return new TestColumnReader( PrimitiveTypeName.INT32, definitionLevel, value );
+        }
+
+
+        private static TestColumnReader binaryReader( int definitionLevel, String value ) {
+            return new TestColumnReader( PrimitiveTypeName.BINARY, definitionLevel, Binary.fromString( value ) );
+        }
+
+
+        private static TestColumnReader booleanReader( int definitionLevel, boolean value ) {
+            return new TestColumnReader( PrimitiveTypeName.BOOLEAN, definitionLevel, value );
+        }
+
+
+        @Override
+        public long getTotalValueCount() {
+            return 1;
+        }
+
+
+        @Override
+        public void consume() {
+            consumed++;
+        }
+
+
+        @Override
+        public int getCurrentRepetitionLevel() {
+            return 0;
+        }
+
+
+        @Override
+        public int getCurrentDefinitionLevel() {
+            return definitionLevel;
+        }
+
+
+        @Override
+        public void writeCurrentValueToConverter() {
+        }
+
+
+        @Override
+        public void skip() {
+        }
+
+
+        @Override
+        public int getCurrentValueDictionaryID() {
+            return 0;
+        }
+
+
+        @Override
+        public int getInteger() {
+            return (Integer) value;
+        }
+
+
+        @Override
+        public boolean getBoolean() {
+            return (Boolean) value;
+        }
+
+
+        @Override
+        public long getLong() {
+            return ((Number) value).longValue();
+        }
+
+
+        @Override
+        public Binary getBinary() {
+            return (Binary) value;
+        }
+
+
+        @Override
+        public float getFloat() {
+            return ((Number) value).floatValue();
+        }
+
+
+        @Override
+        public double getDouble() {
+            return ((Number) value).doubleValue();
+        }
+
+
+        @Override
+        public ColumnDescriptor getDescriptor() {
+            //noinspection deprecation
+            return new ColumnDescriptor( new String[]{ "value" }, type, 0, 1 );
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRowAggregateRelEnumeratorTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRowAggregateRelEnumeratorTest.java
index bded741568..9f5278dd31 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRowAggregateRelEnumeratorTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRowAggregateRelEnumeratorTest.java
@@ -88,4 +88,49 @@ void supportsCountStarWithSumMinAndMax() {
         }
     }
 
+
+    @Test
+    void supportsMultipleGroupingColumnsAndNullGroupKeys() {
+        PolyValue[][] rows = {
+                { PolyLong.of( 2021 ), PolyLong.of( 1 ), PolyDouble.of( 2D ) },
+                { PolyLong.of( 2021 ), PolyLong.of( 1 ), PolyDouble.of( 4D ) },
+                { PolyLong.of( 2021 ), PolyLong.of( 2 ), PolyDouble.of( 8D ) },
+                { PolyNull.NULL, PolyLong.of( 1 ), PolyDouble.of( 16D ) },
+                { PolyNull.NULL, PolyLong.of( 1 ), PolyNull.NULL }
+        };
+
+        try ( ParquetRowAggregateRelEnumerator enumerator = new ParquetRowAggregateRelEnumerator(
+                Linq4j.asEnumerable( rows ).enumerator(),
+                new int[]{ 0, 1 },
+                new String[]{ Kind.COUNT.name(), Kind.SUM.name(), Kind.MIN.name(), Kind.MAX.name() },
+                new int[]{ -1, 2, 2, 2 } ) ) {
+            assertTrue( enumerator.moveNext() );
+            PolyValue[] first = enumerator.current();
+            assertEquals( 2021L, first[0].asNumber().longValue() );
+            assertEquals( 1L, first[1].asNumber().longValue() );
+            assertEquals( 2L, first[2].asNumber().longValue() );
+            assertEquals( 6D, first[3].asNumber().doubleValue() );
+            assertEquals( 2D, first[4].asNumber().doubleValue() );
+            assertEquals( 4D, first[5].asNumber().doubleValue() );
+
+            assertTrue( enumerator.moveNext() );
+            PolyValue[] second = enumerator.current();
+            assertEquals( 2021L, second[0].asNumber().longValue() );
+            assertEquals( 2L, second[1].asNumber().longValue() );
+            assertEquals( 1L, second[2].asNumber().longValue() );
+            assertEquals( 8D, second[3].asNumber().doubleValue() );
+
+            assertTrue( enumerator.moveNext() );
+            PolyValue[] third = enumerator.current();
+            assertTrue( third[0].isNull() );
+            assertEquals( 1L, third[1].asNumber().longValue() );
+            assertEquals( 2L, third[2].asNumber().longValue() );
+            assertEquals( 16D, third[3].asNumber().doubleValue() );
+            assertEquals( 16D, third[4].asNumber().doubleValue() );
+            assertEquals( 16D, third[5].asNumber().doubleValue() );
+
+            assertFalse( enumerator.moveNext() );
+        }
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSchemaUtilitiesTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSchemaUtilitiesTest.java
new file mode 100644
index 0000000000..a62d7fe59c
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSchemaUtilitiesTest.java
@@ -0,0 +1,179 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertNotNull;
+import static org.junit.jupiter.api.Assertions.assertNull;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+
+import java.util.ArrayList;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+import org.apache.parquet.schema.Type;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetBindingSerializer;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnStatistics;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetMessageTypeBuilder;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.FieldSchema;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.SchemaState;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.ValueKind;
+import org.polypheny.db.adapter.parquet.shared.schema.inference.ValueSchema;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.algebra.type.AlgDataTypeFactory;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.numerical.PolyLong;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
+
+class ParquetSchemaUtilitiesTest {
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @Test
+    void bindingSerializerRoundTripsSourceFilesPartitionsColumnsAndStatistics() {
+        Map<List<String>, ParquetColumnStatistics> statistics = new LinkedHashMap<>();
+        statistics.put( List.of( "Amount Total" ), new ParquetColumnStatistics( PolyType.DOUBLE, 3, 3, 1L, "10.5", "42.0", true ) );
+        statistics.put( List.of( "Nested", "Count" ), new ParquetColumnStatistics( PolyType.INTEGER, 3, 2, null, "1", "8", false ) );
+
+        ParquetSourceFile sourceFile = new ParquetSourceFile(
+                "file:/tmp/orders|2026.parquet",
+                Map.of( "region/name", "EMEA=North", "batch", "2026&06" ),
+                statistics );
+        Map<Long, ParquetColumnBinding> columns = new LinkedHashMap<>();
+        columns.put( 7L, new ParquetColumnBinding( 7L, "Amount Total", ParquetColumnRole.DATA, List.of( "Amount Total" ) ) );
+        columns.put( 8L, new ParquetColumnBinding( 8L, "region/name", ParquetColumnRole.PARTITION, List.of() ) );
+        ParquetTableBinding binding = new ParquetTableBinding( List.of( sourceFile ), "Parent Table", List.of( "orders", "items" ), columns );
+
+        Map<Long, ParquetTableBinding> bindings = new LinkedHashMap<>();
+        bindings.put( 99L, binding );
+
+        Map<Long, ParquetTableBinding> restored = ParquetBindingSerializer.deserialize( ParquetBindingSerializer.serialize( bindings ) );
+
+        assertEquals( binding, restored.get( 99L ) );
+    }
+
+
+    @Test
+    void nameNormalizerNormalizesAndUniquifiesNestedFields() {
+        ValueSchema nested = ValueSchema.groupType();
+        nested.nested().add( new FieldSchema( "Line #", "", -1, false, ValueSchema.stringType() ) );
+        nested.nested().add( new FieldSchema( "Line !", "", -1, false, ValueSchema.int32Type() ) );
+        List<FieldSchema> fields = new ArrayList<>( List.of(
+                new FieldSchema( "Customer Name", "", 0, false, ValueSchema.stringType() ),
+                new FieldSchema( "customer-name", "", 1, false, ValueSchema.stringType() ),
+                new FieldSchema( "Items", "", 2, false, nested ) ) );
+
+        ParquetNameNormalizer.uniquifyParquetFieldNames( fields );
+
+        assertEquals( "ordersdb", ParquetNameNormalizer.computePhysicalTableName( "Orders DB.parquet" ) );
+        assertEquals( "customer_name", fields.get( 0 ).getParquetName() );
+        assertEquals( "customer_name_2", fields.get( 1 ).getParquetName() );
+        assertEquals( "items", fields.get( 2 ).getParquetName() );
+        assertEquals( "line_", nested.nested().get( 0 ).getParquetName() );
+        assertEquals( "line__2", nested.nested().get( 1 ).getParquetName() );
+    }
+
+
+    @Test
+    void typeConverterConvertsTypedStringsAndComparesNumerically() {
+        ParquetTypeConverter converter = new ParquetTypeConverter();
+
+        assertEquals( 7, converter.fromStringToPolyValue( PolyType.INTEGER, "7" ).asNumber().intValue() );
+        assertEquals( "hello", converter.fromStringToPolyValue( PolyType.TEXT, "hello" ).asString().value );
+        assertNotNull( converter.fromStringToCompatiblePolyValue( PolyType.BIGINT, PolyType.INTEGER, "42" ) );
+        assertNull( converter.fromStringToCompatiblePolyValue( PolyType.BOOLEAN, PolyType.INTEGER, "42" ) );
+        assertEquals( 1, converter.compareStringValues( PolyType.INTEGER, "10", "2" ) );
+    }
+
+
+    @Test
+    void schemaStateSkipsPrimaryKeyAndStringifiesConflictingSampleValues() {
+        AlgDataType inputType = AlgDataTypeFactory.DEFAULT.builder()
+                .add( "_key", null, PolyType.BIGINT )
+                .add( "Mixed Value", null, PolyType.INTEGER )
+                .build();
+        SchemaState schemaState = new SchemaState( SchemaState.CONFLICT_STRINGIFY );
+        schemaState.init( inputType, false );
+
+        assertEquals( 1, schemaState.getFields().size() );
+        assertEquals( 1, schemaState.getFields().get( 0 ).getSourceIndex() );
+        assertEquals( "mixed_value", schemaState.getFields().get( 0 ).getParquetName() );
+
+        schemaState.mergeRelationalRowSchema( List.of( PolyLong.of( 1 ), PolyString.of( "not-an-int" ) ), inputType, false );
+
+        assertEquals( ValueKind.STRING, schemaState.getFields().get( 0 ).getValueSchema().kind() );
+    }
+
+
+    @Test
+    void valueSchemaMergesNestedFieldsAndWidensNumericTypes() {
+        assertEquals( ValueKind.DOUBLE, ValueSchema.int32Type().mergeValueSchemas( ValueSchema.doubleType(), SchemaState.CONFLICT_FAIL ).kind() );
+
+        ValueSchema left = ValueSchema.groupType();
+        left.mergeNested( "count", ValueSchema.int32Type(), SchemaState.CONFLICT_FAIL );
+        ValueSchema right = ValueSchema.groupType();
+        right.mergeNested( "count", ValueSchema.int64Type(), SchemaState.CONFLICT_FAIL );
+        right.mergeNested( "label", ValueSchema.stringType(), SchemaState.CONFLICT_FAIL );
+
+        ValueSchema merged = left.mergeValueSchemas( right, SchemaState.CONFLICT_FAIL );
+
+        assertEquals( ValueKind.GROUP, merged.kind() );
+        assertEquals( 2, merged.nested().size() );
+        assertEquals( ValueKind.INT64, merged.nested().get( 0 ).getValueSchema().kind() );
+        assertEquals( "label", merged.nested().get( 1 ).getParquetName() );
+    }
+
+
+    @Test
+    void messageTypeBuilderBuildsRepeatedAndNestedFields() {
+        SchemaState schemaState = new SchemaState( SchemaState.CONFLICT_FAIL );
+        ValueSchema details = ValueSchema.groupType();
+        details.nested().add( new FieldSchema( "Name", "name", -1, false, ValueSchema.stringType() ) );
+        schemaState.addField( new FieldSchema( "Tags", "tags", -1, true, ValueSchema.stringType() ) );
+        schemaState.addField( new FieldSchema( "Details", "details", -1, false, details ) );
+
+        MessageType messageType = new ParquetMessageTypeBuilder( schemaState, "test_schema" ).build();
+
+        assertEquals( Type.Repetition.REPEATED, messageType.getType( "tags" ).getRepetition() );
+        assertFalse( messageType.getType( "details" ).isPrimitive() );
+        assertEquals( PrimitiveTypeName.BINARY, messageType.getType( "details" ).asGroupType().getType( "name" ).asPrimitiveType().getPrimitiveTypeName() );
+        assertThrows( GenericRuntimeException.class, () -> new ParquetMessageTypeBuilder( new SchemaState( SchemaState.CONFLICT_FAIL ), "empty" ).build() );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java
new file mode 100644
index 0000000000..4c2e73138b
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java
@@ -0,0 +1,305 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertNotNull;
+import static org.junit.jupiter.api.Assertions.assertNull;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.io.IOException;
+import java.io.OutputStream;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.List;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.ExampleParquetWriter;
+import org.apache.parquet.io.OutputFile;
+import org.apache.parquet.io.PositionOutputStream;
+import org.apache.parquet.schema.LogicalTypeAnnotation;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+import org.apache.parquet.schema.Types;
+import org.jetbrains.annotations.NotNull;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.parquet.document.execution.ParquetDocEnumerator;
+import org.polypheny.db.adapter.parquet.document.execution.ParquetDocFilterTranslator;
+import org.polypheny.db.adapter.parquet.document.execution.ParquetDocValueExtractor;
+import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.algebra.type.AlgDataTypeFactory;
+import org.polypheny.db.algebra.type.AlgDataTypeSystem;
+import org.polypheny.db.nodes.SpecialOperator;
+import org.polypheny.db.rex.RexCall;
+import org.polypheny.db.rex.RexDynamicParam;
+import org.polypheny.db.rex.RexNameRef;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.PolyTypeFactoryImpl;
+import org.polypheny.db.type.entity.PolyList;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.document.PolyDocument;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
+import org.polypheny.db.util.Sources;
+
+class ParquetSharedDocumentAdapterTest {
+
+    @TempDir
+    Path tempDir;
+
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @Test
+    void extractDocumentNormalizesNestedValuesAndGeneratesMissingId() {
+        MessageType schema = documentSchema();
+        SimpleGroupFactory factory = new SimpleGroupFactory( schema );
+        Group row = factory.newGroup()
+                .append( "Full Name", "Alice" )
+                .append( "Tags", "vip" )
+                .append( "Tags", "north" );
+        row.addGroup( "Meta Data" ).append( "Source Name", "crm" );
+
+        PolyDocument document = new ParquetDocValueExtractor().extractDocument( row, schema, PolyString.of( "generated-id" ) );
+
+        assertEquals( "Alice", document.get( PolyString.of( "full_name" ) ).asString().value );
+        assertEquals( "crm", document.get( PolyString.of( "meta_data" ) ).asDocument().get( PolyString.of( "source_name" ) ).asString().value );
+        PolyList<PolyValue> tags = document.get( PolyString.of( "tags" ) ).asList();
+        assertEquals( 2, tags.size() );
+        assertEquals( "vip", tags.get( 0 ).asString().value );
+        assertEquals( "generated-id", document.get( PolyString.of( "_id" ) ).asString().value );
+    }
+
+
+    @Test
+    void extractDocumentPreservesSourceIdAndGeneratesIdForAllNullRows() {
+        MessageType schema = Types.buildMessage()
+                .optional( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "_id" )
+                .optional( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "Full Name" )
+                .named( "doc_schema" );
+        SimpleGroupFactory factory = new SimpleGroupFactory( schema );
+        ParquetDocValueExtractor extractor = new ParquetDocValueExtractor();
+
+        PolyDocument withId = extractor.extractDocument(
+                factory.newGroup()
+                        .append( "_id", "source-id" )
+                        .append( "Full Name", "Alice" ),
+                schema,
+                PolyString.of( "generated-id" ) );
+        PolyDocument allNull = extractor.extractDocument( factory.newGroup(), schema, PolyString.of( "generated-empty-id" ) );
+
+        assertEquals( "source-id", withId.get( PolyString.of( "_id" ) ).asString().value );
+        assertEquals( "Alice", withId.get( PolyString.of( "full_name" ) ).asString().value );
+        assertEquals( 1, allNull.size() );
+        assertEquals( "generated-empty-id", allNull.get( PolyString.of( "_id" ) ).asString().value );
+    }
+
+
+    @Test
+    void extractDocumentConvertsRepeatedNestedGroupsToDocumentLists() {
+        MessageType schema = Types.buildMessage()
+                .repeatedGroup()
+                .optional( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "City" )
+                .optional( PrimitiveTypeName.INT64 ).named( "Zip" )
+                .named( "Addresses" )
+                .named( "doc_schema" );
+        SimpleGroupFactory factory = new SimpleGroupFactory( schema );
+        Group row = factory.newGroup();
+        row.addGroup( "Addresses" )
+                .append( "City", "Berlin" )
+                .append( "Zip", 10115L );
+        row.addGroup( "Addresses" )
+                .append( "City", "Zurich" )
+                .append( "Zip", 8001L );
+
+        PolyDocument document = new ParquetDocValueExtractor().extractDocument( row, schema, PolyString.of( "generated-id" ) );
+
+        PolyList<PolyValue> addresses = document.get( PolyString.of( "addresses" ) ).asList();
+        assertEquals( 2, addresses.size() );
+        assertEquals( "Berlin", addresses.get( 0 ).asDocument().get( PolyString.of( "city" ) ).asString().value );
+        assertEquals( 10115L, addresses.get( 0 ).asDocument().get( PolyString.of( "zip" ) ).asNumber().longValue() );
+        assertEquals( "Zurich", addresses.get( 1 ).asDocument().get( PolyString.of( "city" ) ).asString().value );
+    }
+
+
+    @Test
+    void docFilterTranslatorTranslatesTopLevelNameAndRejectsNestedPaths() {
+        AlgDataTypeFactory typeFactory = new PolyTypeFactoryImpl( AlgDataTypeSystem.DEFAULT );
+        AlgDataType stringType = typeFactory.createPolyType( PolyType.VARCHAR );
+        AlgDataType boolType = typeFactory.createPolyType( PolyType.BOOLEAN );
+        ParquetDocFilterTranslator translator = new ParquetDocFilterTranslator();
+        List<ExportedColumn> columns = List.of( new ExportedColumn(
+                "status",
+                PolyType.TEXT,
+                null,
+                null,
+                null,
+                null,
+                null,
+                true,
+                "documents",
+                "documents",
+                "status",
+                3,
+                false ) );
+
+        ParquetAdapterFilter<?> translated = translator.translate(
+                columns,
+                call( boolType, Kind.EQUALS, new RexNameRef( "Status", null, stringType ), new RexDynamicParam( stringType, 4 ) ) );
+        RexNode nested = call( boolType, Kind.EQUALS, new RexNameRef( List.of( "address", "city" ), null, stringType ), new RexDynamicParam( stringType, 5 ) );
+
+        assertNotNull( translated );
+        assertEquals( 3, translated.columnIndex() );
+        assertEquals( Kind.EQUALS, translated.operator() );
+        assertEquals( 4L, translated.dynamicParamIndex() );
+        assertNull( translator.translate( columns, nested ) );
+    }
+
+
+    @Test
+    void docEnumeratorAppliesFiltersAndGeneratesIds() throws Exception {
+        Path file = tempDir.resolve( "documents.parquet" );
+        writeDocumentParquet( file );
+        ParquetAdapterFilter<PolyValue> filter = new ParquetAdapterFilter<>( 0, Kind.EQUALS, PolyString.of( "Alice" ) );
+
+        try ( ParquetDocEnumerator enumerator = new ParquetDocEnumerator(
+                new ParquetSourceReader( Sources.of( file.toFile() ) ),
+                FiltersContainer.shared( List.of( filter ) ) ) ) {
+            assertTrue( enumerator.moveNext() );
+            PolyDocument document = enumerator.current()[0].asDocument();
+            assertEquals( "Alice", document.get( PolyString.of( "full_name" ) ).asString().value );
+            assertTrue( document.get( PolyString.of( "_id" ) ).asString().value.endsWith( "documents.parquet#0" ) );
+            assertFalse( enumerator.moveNext() );
+        }
+    }
+
+
+    private static RexNode call( AlgDataType boolType, Kind kind, RexNode... operands ) {
+        return new RexCall( boolType, new SpecialOperator( kind.name(), kind ), operands );
+    }
+
+
+    private static MessageType documentSchema() {
+        return Types.buildMessage()
+                .optional( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "Full Name" )
+                .optionalGroup()
+                .optional( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "Source Name" )
+                .named( "Meta Data" )
+                .repeated( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "Tags" )
+                .named( "doc_schema" );
+    }
+
+
+    private static void writeDocumentParquet( Path file ) throws Exception {
+        MessageType schema = documentSchema();
+        SimpleGroupFactory factory = new SimpleGroupFactory( schema );
+        Group first = factory.newGroup()
+                .append( "Full Name", "Alice" )
+                .append( "Tags", "vip" );
+        first.addGroup( "Meta Data" ).append( "Source Name", "crm" );
+        Group second = factory.newGroup()
+                .append( "Full Name", "Bob" )
+                .append( "Tags", "south" );
+        second.addGroup( "Meta Data" ).append( "Source Name", "erp" );
+
+        try ( ParquetWriter<Group> writer = ExampleParquetWriter.builder( new LocalOutputFile( file ) )
+                .withType( schema )
+                .build() ) {
+            writer.write( first );
+            writer.write( second );
+        }
+    }
+
+
+    private record LocalOutputFile( Path path ) implements OutputFile {
+
+        @Override
+        public PositionOutputStream create( long blockSizeHint ) throws IOException {
+            return createOrOverwrite( blockSizeHint );
+        }
+
+
+        @SuppressWarnings("resource")
+        @Override
+        public PositionOutputStream createOrOverwrite( long blockSizeHint ) throws IOException {
+            Files.createDirectories( path.getParent() );
+            OutputStream outputStream = Files.newOutputStream( path );
+            return new PositionOutputStream() {
+                private long position;
+
+
+                @Override
+                public long getPos() {
+                    return position;
+                }
+
+
+                @Override
+                public void write( int b ) throws IOException {
+                    outputStream.write( b );
+                    position++;
+                }
+
+
+                @Override
+                public void write( byte @NotNull [] b, int off, int len ) throws IOException {
+                    outputStream.write( b, off, len );
+                    position += len;
+                }
+
+
+                @Override
+                public void close() throws IOException {
+                    outputStream.close();
+                }
+            };
+        }
+
+
+        @Override
+        public boolean supportsBlockSize() {
+            return false;
+        }
+
+
+        @Override
+        public long defaultBlockSize() {
+            return 0;
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetTableStatisticsReaderTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetTableStatisticsReaderTest.java
new file mode 100644
index 0000000000..102290710a
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetTableStatisticsReaderTest.java
@@ -0,0 +1,419 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertNull;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.io.IOException;
+import java.io.OutputStream;
+import java.net.URL;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.ArrayList;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Objects;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.ExampleParquetWriter;
+import org.apache.parquet.io.OutputFile;
+import org.apache.parquet.io.PositionOutputStream;
+import org.apache.parquet.schema.LogicalTypeAnnotation;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+import org.apache.parquet.schema.Types;
+import org.jetbrains.annotations.NotNull;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnStatistics;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetNamespace;
+import org.polypheny.db.adapter.parquet.shared.statistics.ParquetColumnStatisticsReader;
+import org.polypheny.db.adapter.parquet.shared.statistics.ParquetTableStatisticsReader;
+import org.polypheny.db.adapter.statistics.ProvidedColumnStatistics;
+import org.polypheny.db.adapter.statistics.ProvidedEntityStatistics;
+import org.polypheny.db.catalog.entity.logical.LogicalColumn;
+import org.polypheny.db.catalog.entity.physical.PhysicalColumn;
+import org.polypheny.db.catalog.entity.physical.PhysicalTable;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
+import org.polypheny.db.util.Sources;
+
+class ParquetTableStatisticsReaderTest {
+
+    private static final long TABLE_ID = 200L;
+    private static final long NAMESPACE_ID = 1L;
+    private static final long ALLOCATION_ID = 300L;
+    private static final long ADAPTER_ID = 400L;
+
+    @TempDir
+    Path tempDir;
+
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @Test
+    void providedColumnStatisticsDefensivelyCopiesUniqueValues() {
+        List<PolyValue> uniqueValues = new ArrayList<>();
+        uniqueValues.add( PolyString.of( "EMEA" ) );
+
+        ProvidedColumnStatistics statistics = new ProvidedColumnStatistics( 1L, PolyString.of( "EMEA" ), PolyString.of( "EMEA" ), uniqueValues, false );
+        uniqueValues.add( PolyString.of( "APAC" ) );
+
+        assertEquals( 1, statistics.uniqueValues().size() );
+        assertThrows( UnsupportedOperationException.class, () -> statistics.uniqueValues().add( PolyString.of( "NA" ) ) );
+        assertTrue( new ProvidedColumnStatistics( 1L, null, null, null, true ).uniqueValues().isEmpty() );
+    }
+
+
+    @Test
+    void readerAggregatesDataAndPartitionColumnStatistics() throws Exception {
+        Path file = writeStatsParquet();
+        ParquetTableStatisticsReader reader = new ParquetTableStatisticsReader( new ParquetSchemaReader( Sources.of( file.toFile() ) ), bindingFor( file ) );
+
+        assertEquals( 5L, reader.getEntityStatistics( false ).orElseThrow().rowCount() );
+
+        ProvidedColumnStatistics amount = reader.getColumnStatistics( logicalColumn( 10L, "amount", TABLE_ID, PolyType.DOUBLE, 0 ), 10 ).orElseThrow();
+        assertEquals( 4L, amount.count() );
+        assertEquals( 5.0D, Objects.requireNonNull( amount.min() ).asNumber().doubleValue() );
+        assertEquals( 60.0D, Objects.requireNonNull( amount.max() ).asNumber().doubleValue() );
+
+        ProvidedColumnStatistics region = reader.getColumnStatistics( logicalColumn( 11L, "region", TABLE_ID, PolyType.VARCHAR, 1 ), 10 ).orElseThrow();
+        assertEquals( 5L, region.count() );
+        assertEquals( "APAC", Objects.requireNonNull( region.min() ).asString().value );
+        assertEquals( "EMEA", Objects.requireNonNull( region.max() ).asString().value );
+        assertEquals( List.of( "EMEA", "APAC" ), region.uniqueValues().stream().map( value -> value.asString().value ).toList() );
+
+        ProvidedColumnStatistics limited = reader.getColumnStatistics( logicalColumn( 11L, "region", TABLE_ID, PolyType.VARCHAR, 1 ), 1 ).orElseThrow();
+        assertTrue( limited.uniqueValues().isEmpty() );
+        assertTrue( limited.full() );
+    }
+
+
+    @Test
+    void columnStatisticsReaderConvertsTemporalAndBinaryFooterStatistics() throws Exception {
+        Path file = writeTemporalStatsParquet();
+
+        Map<List<String>, ParquetColumnStatistics> statistics = ParquetColumnStatisticsReader.readAll( new ParquetSchemaReader( Sources.of( file.toFile() ) ) );
+
+        ParquetColumnStatistics createdDate = statistics.get( List.of( "created_date" ) );
+        assertEquals( PolyType.DATE, createdDate.type() );
+        assertEquals( "19000", createdDate.min() );
+        assertEquals( "19001", createdDate.max() );
+
+        ParquetColumnStatistics createdTime = statistics.get( List.of( "created_time" ) );
+        assertEquals( PolyType.TIME, createdTime.type() );
+        assertEquals( "1000", createdTime.min() );
+        assertEquals( "2500", createdTime.max() );
+
+        ParquetColumnStatistics createdAt = statistics.get( List.of( "created_at" ) );
+        assertEquals( PolyType.TIMESTAMP, createdAt.type() );
+        assertEquals( "1609459200000", createdAt.min() );
+        assertEquals( "1609462800000", createdAt.max() );
+
+        ParquetColumnStatistics payload = statistics.get( List.of( "payload" ) );
+        assertEquals( PolyType.VARBINARY, payload.type() );
+        assertEquals( "alpha", payload.min() );
+        assertEquals( "zulu", payload.max() );
+    }
+
+
+    @Test
+    void readerFallsBackForMissingColumnsAndUnreliableRanges() throws Exception {
+        Path file = writeStatsParquet();
+        Map<List<String>, ParquetColumnStatistics> firstStats = new LinkedHashMap<>();
+        firstStats.put( List.of( "amount" ), new ParquetColumnStatistics( PolyType.DOUBLE, 2, 2, 0L, "1.0", "2.0", true ) );
+        Map<List<String>, ParquetColumnStatistics> secondStats = new LinkedHashMap<>();
+        secondStats.put( List.of( "amount" ), new ParquetColumnStatistics( PolyType.VARCHAR, 3, 3, 0L, "a", "z", true ) );
+        List<ParquetSourceFile> sourceFiles = List.of(
+                new ParquetSourceFile( file.toUri().toURL().toExternalForm(), Map.of(), firstStats ),
+                new ParquetSourceFile( file.toUri().toURL().toExternalForm(), Map.of(), secondStats ) );
+        Map<Long, ParquetColumnBinding> columns = new LinkedHashMap<>();
+        columns.put( 10L, new ParquetColumnBinding( 10L, "amount", ParquetColumnRole.DATA, List.of( "amount" ) ) );
+        columns.put( 12L, new ParquetColumnBinding( 12L, "missing", ParquetColumnRole.DATA, List.of( "missing" ) ) );
+        ParquetTableStatisticsReader reader = new ParquetTableStatisticsReader(
+                new ParquetSchemaReader( Sources.of( file.toFile() ) ),
+                new ParquetTableBinding( sourceFiles, null, List.of(), columns ) );
+
+        ProvidedColumnStatistics unreliableAmount = reader.getColumnStatistics( logicalColumn( 10L, "amount", TABLE_ID, PolyType.DOUBLE, 0 ), 10 ).orElseThrow();
+        assertEquals( 5L, unreliableAmount.count() );
+        assertTrue( Objects.requireNonNull( unreliableAmount.min() ).isNull() );
+        assertTrue( Objects.requireNonNull( unreliableAmount.max() ).isNull() );
+
+        ProvidedColumnStatistics missing = reader.getColumnStatistics( logicalColumn( 12L, "missing", TABLE_ID, PolyType.DOUBLE, 1 ), 10 ).orElseThrow();
+        assertEquals( 5L, missing.count() );
+        assertTrue( Objects.requireNonNull( missing.min() ).isNull() );
+        assertTrue( Objects.requireNonNull( missing.max() ).isNull() );
+        assertTrue( missing.full() );
+    }
+
+
+    @Test
+    void nestedEntityStatisticsUsesLargestNestedValueCount() throws Exception {
+        Path file = writeStatsParquet();
+        Map<List<String>, ParquetColumnStatistics> stats = new LinkedHashMap<>();
+        stats.put( List.of( "items", "sku" ), new ParquetColumnStatistics( PolyType.VARCHAR, 2, 5, 0L, "a", "z", true ) );
+        stats.put( List.of( "items", "quantity" ), new ParquetColumnStatistics( PolyType.INTEGER, 2, 7, 0L, "1", "9", true ) );
+        ParquetSourceFile sourceFile = new ParquetSourceFile( file.toUri().toURL().toExternalForm(), Map.of(), stats );
+        Map<Long, ParquetColumnBinding> columns = new LinkedHashMap<>();
+        columns.put( 20L, new ParquetColumnBinding( 20L, "sku", ParquetColumnRole.DATA, List.of( "items", "sku" ) ) );
+        columns.put( 21L, new ParquetColumnBinding( 21L, "quantity", ParquetColumnRole.DATA, List.of( "items", "quantity" ) ) );
+        ParquetTableStatisticsReader reader = new ParquetTableStatisticsReader(
+                new ParquetSchemaReader( Sources.of( file.toFile() ) ),
+                new ParquetTableBinding( List.of( sourceFile ), "orders", List.of( "items" ), columns ) );
+
+        assertEquals( 7L, reader.getEntityStatistics( true ).orElseThrow().rowCount() );
+        assertEquals( 2L, reader.getEntityStatistics( false ).orElseThrow().rowCount() );
+    }
+
+
+    @Test
+    void providedEntityStatisticsAllowsUnknownRowCount() {
+        ProvidedEntityStatistics statistics = new ProvidedEntityStatistics( null );
+
+        assertNull( statistics.rowCount() );
+    }
+
+
+    @Test
+    void parquetRelTableProvidesStatisticsOnlyForMatchingLogicalEntity() throws Exception {
+        Path file = writeStatsParquet();
+        LogicalColumn amount = logicalColumn( 10L, "amount", TABLE_ID, PolyType.DOUBLE, 0 );
+        PhysicalColumn amountPhysical = new PhysicalColumn(
+                amount.id,
+                "amount",
+                amount.name,
+                ALLOCATION_ID,
+                TABLE_ID,
+                ADAPTER_ID,
+                0,
+                amount.type,
+                null,
+                null,
+                null,
+                null,
+                null,
+                true,
+                null,
+                null );
+        PhysicalTable physicalTable = new PhysicalTable(
+                500L,
+                ALLOCATION_ID,
+                TABLE_ID,
+                "orders",
+                List.of( amountPhysical ),
+                NAMESPACE_ID,
+                "public",
+                List.of( amount.id ),
+                ADAPTER_ID );
+        ParquetRelTable relTable = new ParquetRelTable( 600L, physicalTable, bindingFor( file ), null );
+
+        assertEquals( 5L, relTable.getEntityStatistics( TABLE_ID ).orElseThrow().rowCount() );
+        assertTrue( relTable.getEntityStatistics( TABLE_ID + 1 ).isEmpty() );
+        assertEquals( 4L, relTable.getColumnStatistics( amount, 10 ).orElseThrow().count() );
+        assertTrue( relTable.getColumnStatistics( logicalColumn( amount.id, amount.name, TABLE_ID + 1, amount.type, 0 ), 10 ).isEmpty() );
+    }
+
+
+    @Test
+    void namespaceCreatesRootBindingAndEntityWrappers() throws Exception {
+        Path source = writeStatsParquet();
+        Path ordersFile = tempDir.resolve( "orders.parquet" );
+        Files.copy( source, ordersFile );
+        LogicalColumn amount = logicalColumn( 10L, "amount", TABLE_ID, PolyType.DOUBLE, 0 );
+        PhysicalColumn amountPhysical = new PhysicalColumn(
+                amount.id,
+                "amount",
+                amount.name,
+                ALLOCATION_ID,
+                TABLE_ID,
+                ADAPTER_ID,
+                0,
+                amount.type,
+                null,
+                null,
+                null,
+                null,
+                null,
+                true,
+                null,
+                null );
+        PhysicalTable physicalTable = new PhysicalTable(
+                500L,
+                ALLOCATION_ID,
+                TABLE_ID,
+                "orders",
+                List.of( amountPhysical ),
+                NAMESPACE_ID,
+                "public",
+                List.of( amount.id ),
+                ADAPTER_ID );
+        ParquetNamespace namespace = new ParquetNamespace( NAMESPACE_ID, ADAPTER_ID, tempDir.toUri().toURL() );
+
+        ParquetTableBinding binding = namespace.createRootBinding( physicalTable );
+        ParquetRelTable relTable = namespace.createParquetTable( 600L, physicalTable, binding, null );
+
+        assertEquals( 1, binding.sourceFiles().size() );
+        assertEquals( List.of( "amount" ), binding.columnsByColumnId().get( amount.id ).sourcePathElements() );
+        assertEquals( 1L, relTable.getEntityStatistics( TABLE_ID ).orElseThrow().rowCount() );
+    }
+
+
+    private ParquetTableBinding bindingFor( Path file ) throws Exception {
+        URL fileUrl = file.toUri().toURL();
+        Map<List<String>, ParquetColumnStatistics> firstStats = new LinkedHashMap<>();
+        firstStats.put( List.of( "amount" ), new ParquetColumnStatistics( PolyType.DOUBLE, 2, 2, 0L, "10.5", "42.0", true ) );
+        Map<List<String>, ParquetColumnStatistics> secondStats = new LinkedHashMap<>();
+        secondStats.put( List.of( "amount" ), new ParquetColumnStatistics( PolyType.DOUBLE, 3, 3, 1L, "5.0", "60.0", true ) );
+        List<ParquetSourceFile> sourceFiles = List.of(
+                new ParquetSourceFile( fileUrl.toExternalForm(), Map.of( "region", "EMEA" ), firstStats ),
+                new ParquetSourceFile( fileUrl.toExternalForm(), Map.of( "region", "APAC" ), secondStats ) );
+        Map<Long, ParquetColumnBinding> columns = new LinkedHashMap<>();
+        columns.put( 10L, new ParquetColumnBinding( 10L, "amount", ParquetColumnRole.DATA, List.of( "amount" ) ) );
+        columns.put( 11L, new ParquetColumnBinding( 11L, "region", ParquetColumnRole.PARTITION, List.of() ) );
+        return new ParquetTableBinding( sourceFiles, null, List.of(), columns );
+    }
+
+
+    private static LogicalColumn logicalColumn( long id, String name, long tableId, PolyType type, int position ) {
+        return new LogicalColumn( id, name, tableId, NAMESPACE_ID, position, type, null, null, null, null, null, true, null, null );
+    }
+
+
+    private Path writeStatsParquet() throws Exception {
+        Path file = tempDir.resolve( "stats.parquet" );
+        MessageType schema = Types.buildMessage()
+                .optional( PrimitiveTypeName.DOUBLE ).named( "amount" )
+                .named( "stats_schema" );
+        SimpleGroupFactory factory = new SimpleGroupFactory( schema );
+        try ( ParquetWriter<Group> writer = ExampleParquetWriter.builder( new LocalOutputFile( file ) )
+                .withType( schema )
+                .build() ) {
+            writer.write( factory.newGroup().append( "amount", 1.0D ) );
+        }
+        return file;
+    }
+
+
+    private Path writeTemporalStatsParquet() throws Exception {
+        Path file = tempDir.resolve( "temporal-stats.parquet" );
+        MessageType schema = Types.buildMessage()
+                .optional( PrimitiveTypeName.INT32 ).as( LogicalTypeAnnotation.dateType() ).named( "created_date" )
+                .optional( PrimitiveTypeName.INT32 ).as( LogicalTypeAnnotation.timeType( true, LogicalTypeAnnotation.TimeUnit.MILLIS ) ).named( "created_time" )
+                .optional( PrimitiveTypeName.INT64 ).as( LogicalTypeAnnotation.timestampType( true, LogicalTypeAnnotation.TimeUnit.MILLIS ) ).named( "created_at" )
+                .optional( PrimitiveTypeName.BINARY ).named( "payload" )
+                .named( "temporal_stats_schema" );
+        SimpleGroupFactory factory = new SimpleGroupFactory( schema );
+        try ( ParquetWriter<Group> writer = ExampleParquetWriter.builder( new LocalOutputFile( file ) )
+                .withType( schema )
+                .build() ) {
+            writer.write( factory.newGroup()
+                    .append( "created_date", 19000 )
+                    .append( "created_time", 1000 )
+                    .append( "created_at", 1609459200000L )
+                    .append( "payload", "alpha" ) );
+            writer.write( factory.newGroup()
+                    .append( "created_date", 19001 )
+                    .append( "created_time", 2500 )
+                    .append( "created_at", 1609462800000L )
+                    .append( "payload", "zulu" ) );
+        }
+        return file;
+    }
+
+
+    private record LocalOutputFile( Path path ) implements OutputFile {
+
+        @Override
+        public PositionOutputStream create( long blockSizeHint ) throws IOException {
+            return createOrOverwrite( blockSizeHint );
+        }
+
+
+        @SuppressWarnings("resource")
+        @Override
+        public PositionOutputStream createOrOverwrite( long blockSizeHint ) throws IOException {
+            Files.createDirectories( path.getParent() );
+            OutputStream outputStream = Files.newOutputStream( path );
+            return new PositionOutputStream() {
+                private long position;
+
+
+                @Override
+                public long getPos() {
+                    return position;
+                }
+
+
+                @Override
+                public void write( int b ) throws IOException {
+                    outputStream.write( b );
+                    position++;
+                }
+
+
+                @Override
+                public void write( byte @NotNull [] b, int off, int len ) throws IOException {
+                    outputStream.write( b, off, len );
+                    position += len;
+                }
+
+
+                @Override
+                public void close() throws IOException {
+                    outputStream.close();
+                }
+            };
+        }
+
+
+        @Override
+        public boolean supportsBlockSize() {
+            return false;
+        }
+
+
+        @Override
+        public long defaultBlockSize() {
+            return 0;
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateAccumulatorTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateAccumulatorTest.java
new file mode 100644
index 0000000000..a2e4aee5e8
--- /dev/null
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateAccumulatorTest.java
@@ -0,0 +1,261 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.aggregate;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import org.apache.parquet.column.ColumnDescriptor;
+import org.apache.parquet.column.ColumnReader;
+import org.apache.parquet.io.api.Binary;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+import org.junit.jupiter.api.Test;
+import org.polypheny.db.type.entity.PolyNull;
+
+class AggregateAccumulatorTest {
+
+    @Test
+    void aggregateInputColumnAddsPresentNumericValuesAndSkipsNulls() {
+        AggregateCallDescriptor[] calls = calls();
+        AggregateInputColumn column = new AggregateInputColumn( 0, new int[]{ 0, 1 }, new int[]{ 2 }, new int[]{ 3 }, new int[]{ 4 }, 1, PrimitiveTypeName.INT64 );
+        AggregateGroupState values = new AggregateGroupState( calls );
+        TestColumnReader reader = TestColumnReader.longReader( 1, 7L );
+
+        column.add( values, new ColumnReader[]{ reader } );
+
+        assertEquals( 1L, values.result( 0 ).asNumber().longValue() );
+        assertEquals( 1L, values.result( 1 ).asNumber().longValue() );
+        assertEquals( 7D, values.result( 2 ).asNumber().doubleValue() );
+        assertEquals( 7D, values.result( 3 ).asNumber().doubleValue() );
+        assertEquals( 7D, values.result( 4 ).asNumber().doubleValue() );
+        assertEquals( 1, reader.consumed );
+
+        AggregateGroupState nullValues = new AggregateGroupState( calls );
+        TestColumnReader nullReader = TestColumnReader.longReader( 0, 99L );
+        column.add( nullValues, new ColumnReader[]{ nullReader } );
+
+        assertEquals( 0L, nullValues.result( 0 ).asNumber().longValue() );
+        assertTrue( nullValues.result( 2 ).isNull() );
+        assertEquals( 1, nullReader.consumed );
+    }
+
+
+    @Test
+    void aggregateInputColumnSharesConsumedValuesAcrossCalls() {
+        AggregateCallDescriptor[] calls = calls();
+        AggregateInputColumn column = new AggregateInputColumn( 0, new int[]{ 0 }, new int[]{ 2 }, new int[0], new int[0], 1, PrimitiveTypeName.DOUBLE );
+        AggregateGroupState values = new AggregateGroupState( calls );
+        TestColumnReader reader = TestColumnReader.doubleReader( 1, 42D );
+        boolean[] consumed = new boolean[1];
+        boolean[] present = new boolean[1];
+        double[] numericValues = new double[1];
+
+        column.add( values, new ColumnReader[]{ reader }, consumed, present, numericValues );
+        column.add( values, new ColumnReader[]{ reader }, consumed, present, numericValues );
+
+        assertEquals( 1, reader.consumed );
+        assertEquals( 2L, values.result( 0 ).asNumber().longValue() );
+        assertEquals( 84D, values.result( 2 ).asNumber().doubleValue() );
+    }
+
+
+    @Test
+    void aggregateRowAccumulatorBuildsCountStarAndColumnAggregates() {
+        AggregateCallDescriptor[] calls = new AggregateCallDescriptor[]{
+                AggregateCallDescriptor.countStar(),
+                new AggregateCallDescriptor( AggregateCallDescriptor.Kind.COUNT, 0 ),
+                new AggregateCallDescriptor( AggregateCallDescriptor.Kind.SUM, 0 ),
+                new AggregateCallDescriptor( AggregateCallDescriptor.Kind.MIN, 0 ),
+                new AggregateCallDescriptor( AggregateCallDescriptor.Kind.MAX, 0 )
+        };
+        @SuppressWarnings("deprecation") ColumnDescriptor[] descriptors = new ColumnDescriptor[]{
+                new ColumnDescriptor( new String[]{ "amount" }, PrimitiveTypeName.INT32, 0, 1 )
+        };
+        AggregateRowAccumulator accumulator = AggregateRowAccumulator.build( calls, descriptors );
+        AggregateGroupState values = new AggregateGroupState( calls );
+
+        accumulator.add( values, new ColumnReader[]{ TestColumnReader.intReader( 1, 5 ) } );
+
+        assertEquals( 1L, values.result( 0 ).asNumber().longValue() );
+        assertEquals( 1L, values.result( 1 ).asNumber().longValue() );
+        assertEquals( 5D, values.result( 2 ).asNumber().doubleValue() );
+        assertEquals( 5D, values.result( 3 ).asNumber().doubleValue() );
+        assertEquals( 5D, values.result( 4 ).asNumber().doubleValue() );
+    }
+
+
+    @Test
+    void aggregateInputColumnRejectsUnsupportedPrimitiveTypes() {
+        AggregateInputColumn column = new AggregateInputColumn( 0, new int[0], new int[]{ 0 }, new int[0], new int[0], 1, PrimitiveTypeName.BOOLEAN );
+
+        assertThrows( IllegalArgumentException.class, () -> column.add(
+                new AggregateGroupState( new AggregateCallDescriptor[]{ new AggregateCallDescriptor( AggregateCallDescriptor.Kind.SUM, 0 ) } ),
+                new ColumnReader[]{ TestColumnReader.booleanReader( true ) } ) );
+    }
+
+
+    @Test
+    void aggregateGroupStateHandlesObjectRowsAndMergesPartialStates() {
+        AggregateCallDescriptor[] calls = calls();
+        AggregateGroupState left = new AggregateGroupState( calls );
+        AggregateGroupState right = new AggregateGroupState( calls );
+
+        left.add( new Object[]{ 3 } );
+        left.add( new Object[]{ PolyNull.NULL } );
+        right.add( new Object[]{ 9 } );
+        left.merge( right );
+
+        assertEquals( 3L, left.result( 0 ).asNumber().longValue() );
+        assertEquals( 2L, left.result( 1 ).asNumber().longValue() );
+        assertEquals( 12D, left.result( 2 ).asNumber().doubleValue() );
+        assertEquals( 3D, left.result( 3 ).asNumber().doubleValue() );
+        assertEquals( 9D, left.result( 4 ).asNumber().doubleValue() );
+    }
+
+
+    private static AggregateCallDescriptor[] calls() {
+        return new AggregateCallDescriptor[]{
+                AggregateCallDescriptor.countStar(),
+                new AggregateCallDescriptor( AggregateCallDescriptor.Kind.COUNT, 0 ),
+                new AggregateCallDescriptor( AggregateCallDescriptor.Kind.SUM, 0 ),
+                new AggregateCallDescriptor( AggregateCallDescriptor.Kind.MIN, 0 ),
+                new AggregateCallDescriptor( AggregateCallDescriptor.Kind.MAX, 0 )
+        };
+    }
+
+
+    private static class TestColumnReader implements ColumnReader {
+
+        private final PrimitiveTypeName type;
+        private final int definitionLevel;
+        private final Object value;
+        private int consumed;
+
+
+        private TestColumnReader( PrimitiveTypeName type, int definitionLevel, Object value ) {
+            this.type = type;
+            this.definitionLevel = definitionLevel;
+            this.value = value;
+        }
+
+
+        private static TestColumnReader intReader( int definitionLevel, int value ) {
+            return new TestColumnReader( PrimitiveTypeName.INT32, definitionLevel, value );
+        }
+
+
+        private static TestColumnReader longReader( int definitionLevel, long value ) {
+            return new TestColumnReader( PrimitiveTypeName.INT64, definitionLevel, value );
+        }
+
+
+        private static TestColumnReader doubleReader( int definitionLevel, double value ) {
+            return new TestColumnReader( PrimitiveTypeName.DOUBLE, definitionLevel, value );
+        }
+
+
+        private static TestColumnReader booleanReader( boolean value ) {
+            return new TestColumnReader( PrimitiveTypeName.BOOLEAN, 1, value );
+        }
+
+
+        @Override
+        public long getTotalValueCount() {
+            return 1;
+        }
+
+
+        @Override
+        public void consume() {
+            consumed++;
+        }
+
+
+        @Override
+        public int getCurrentRepetitionLevel() {
+            return 0;
+        }
+
+
+        @Override
+        public int getCurrentDefinitionLevel() {
+            return definitionLevel;
+        }
+
+
+        @Override
+        public void writeCurrentValueToConverter() {
+        }
+
+
+        @Override
+        public void skip() {
+        }
+
+
+        @Override
+        public int getCurrentValueDictionaryID() {
+            return 0;
+        }
+
+
+        @Override
+        public int getInteger() {
+            return (Integer) value;
+        }
+
+
+        @Override
+        public boolean getBoolean() {
+            return (Boolean) value;
+        }
+
+
+        @Override
+        public long getLong() {
+            return (Long) value;
+        }
+
+
+        @Override
+        public Binary getBinary() {
+            return Binary.fromString( String.valueOf( value ) );
+        }
+
+
+        @Override
+        public float getFloat() {
+            return ((Number) value).floatValue();
+        }
+
+
+        @Override
+        public double getDouble() {
+            return ((Number) value).doubleValue();
+        }
+
+
+        @Override
+        public ColumnDescriptor getDescriptor() {
+            //noinspection deprecation
+            return new ColumnDescriptor( new String[]{ "value" }, type, 0, 1 );
+        }
+
+    }
+
+}
diff --git a/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/dag/activities/ActivityRegistryTest.java b/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/dag/activities/ActivityRegistryTest.java
index 9434c92c88..b3ea252423 100644
--- a/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/dag/activities/ActivityRegistryTest.java
+++ b/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/dag/activities/ActivityRegistryTest.java
@@ -26,7 +26,6 @@
 import com.fasterxml.jackson.databind.ObjectMapper;
 import com.fasterxml.jackson.databind.node.IntNode;
 import com.fasterxml.jackson.databind.node.ObjectNode;
-import java.sql.SQLException;
 import java.util.HashMap;
 import java.util.HashSet;
 import java.util.Map;
@@ -48,7 +47,7 @@
 class ActivityRegistryTest {
 
     @BeforeAll
-    public static void start() throws SQLException {
+    public static void start() {
         //noinspection ResultOfMethodCallIgnored
         TestHelper.getInstance(); // required for access to the plugin classloader, itself a requirement for detecting all activities
     }
@@ -78,6 +77,12 @@ public void extractParquetActivityIsRegistered() {
     }
 
 
+    @Test
+    public void loadParquetActivityIsRegistered() {
+        assertTrue( ActivityRegistry.getRegistry().containsKey( "loadParquet" ) );
+    }
+
+
     @Test
     public void checkActivityPortsTest() {
         for ( ActivityDef activity : ActivityRegistry.getRegistry().values() ) {
diff --git a/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/engine/scheduler/GlobalSchedulerTest.java b/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/engine/scheduler/GlobalSchedulerTest.java
index 081768ec66..bc5d975bec 100644
--- a/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/engine/scheduler/GlobalSchedulerTest.java
+++ b/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/engine/scheduler/GlobalSchedulerTest.java
@@ -306,6 +306,7 @@ void concurrentActivityExecutionTest() throws Exception {
         for ( int i = 0; i <= nBranches; i++ ) { // also checks initial activity
             assertTrue( sm.hasCheckpoint( ids.get( i ), 0 ) );
         }
+        scheduler.awaitResultProcessor( 5000 );
     }
 
 
diff --git a/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/parquet/ParquetWorkflowSupportTest.java b/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/parquet/ParquetWorkflowSupportTest.java
new file mode 100644
index 0000000000..26b3e5065f
--- /dev/null
+++ b/plugins/workflow-engine/src/test/java/org/polypheny/db/workflow/parquet/ParquetWorkflowSupportTest.java
@@ -0,0 +1,536 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.workflow.parquet;
+
+import static org.junit.jupiter.api.Assertions.assertArrayEquals;
+import static org.junit.jupiter.api.Assertions.assertDoesNotThrow;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertNotEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.io.File;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.Iterator;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Optional;
+import org.jetbrains.annotations.NotNull;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.algebra.type.AlgDataTypeFactory;
+import org.polypheny.db.algebra.type.DocumentType;
+import org.polypheny.db.algebra.type.GraphType;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.transaction.Transaction;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyBinary;
+import org.polypheny.db.type.entity.PolyString;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.document.PolyDocument;
+import org.polypheny.db.type.entity.numerical.PolyInteger;
+import org.polypheny.db.type.entity.numerical.PolyLong;
+import org.polypheny.db.type.entity.temporal.PolyDate;
+import org.polypheny.db.type.entity.temporal.PolyTime;
+import org.polypheny.db.type.entity.temporal.PolyTimestamp;
+import org.polypheny.db.util.PolyphenyHomeDirManager;
+import org.polypheny.db.util.RunMode;
+import org.polypheny.db.util.Sources;
+import org.polypheny.db.util.temporal.DateTimeUtils;
+import org.polypheny.db.workflow.dag.activities.Activity;
+import org.polypheny.db.workflow.dag.activities.ActivityException.InvalidInputException;
+import org.polypheny.db.workflow.dag.activities.TypePreview.RelType;
+import org.polypheny.db.workflow.dag.activities.impl.extract.ParquetExtractActivity;
+import org.polypheny.db.workflow.dag.activities.impl.load.ParquetLoadActivity;
+import org.polypheny.db.workflow.dag.settings.BoolValue;
+import org.polypheny.db.workflow.dag.settings.FileValue;
+import org.polypheny.db.workflow.dag.settings.FileValue.SourceType;
+import org.polypheny.db.workflow.dag.settings.IntValue;
+import org.polypheny.db.workflow.dag.settings.SettingDef.SettingValue;
+import org.polypheny.db.workflow.dag.settings.SettingDef.Settings;
+import org.polypheny.db.workflow.dag.settings.SettingDef.SettingsPreview;
+import org.polypheny.db.workflow.dag.settings.StringValue;
+import org.polypheny.db.workflow.dag.variables.ReadableVariableStore;
+import org.polypheny.db.workflow.engine.execution.context.PipeExecutionContext;
+import org.polypheny.db.workflow.engine.execution.pipe.InputPipe;
+import org.polypheny.db.workflow.engine.execution.pipe.OutputPipe;
+
+class ParquetWorkflowSupportTest {
+
+    @TempDir
+    Path tempDir;
+
+
+    @BeforeAll
+    static void initHomeDir() {
+        try {
+            PolyphenyHomeDirManager.setModeAndGetInstance( RunMode.TEST );
+        } catch ( Exception e ) {
+            // Already initialized by another test.
+        }
+    }
+
+
+    @Test
+    void prepareTargetFileFailsOrDeletesExistingFile() throws Exception {
+        Path target = tempDir.resolve( "target.parquet" );
+        Files.writeString( target, "old" );
+
+        assertThrows( GenericRuntimeException.class, () -> ParquetWorkflowLoadSupport.prepareTargetFile( target.toFile(), ParquetWorkflowLoadSupport.MODE_FAIL ) );
+        assertTrue( Files.exists( target ) );
+
+        ParquetWorkflowLoadSupport.prepareTargetFile( target.toFile(), ParquetWorkflowLoadSupport.MODE_DROP );
+        assertFalse( Files.exists( target ) );
+
+        Files.writeString( target, "old" );
+        assertThrows( IllegalArgumentException.class, () -> ParquetWorkflowLoadSupport.prepareTargetFile( target.toFile(), "unknown" ) );
+    }
+
+
+    @Test
+    void relationalWriteAndExtractRoundTripHonorsPrimaryKeyFileNameAndMaxCount() throws Exception {
+        File target = tempDir.resolve( "relational.parquet" ).toFile();
+        AlgDataType inputType = AlgDataTypeFactory.DEFAULT.builder()
+                .add( Activity.PK_COL, null, PolyType.BIGINT )
+                .add( "Customer Name", null, PolyType.VARCHAR )
+                .add( "Score", null, PolyType.INTEGER )
+                .build();
+        TestPipeExecutionContext ctx = new TestPipeExecutionContext( List.of( 2L ) );
+
+        ParquetWorkflowLoadSupport.writeRelational(
+                new TestInputPipe( inputType, List.of(
+                        List.of( PolyLong.of( 101 ), PolyString.of( "Alice" ), PolyInteger.of( 7 ) ),
+                        List.of( PolyLong.of( 102 ), PolyString.of( "Bob" ), PolyInteger.of( 9 ) ) ) ),
+                target,
+                ParquetWorkflowLoadSupport.COMPRESSION_UNCOMPRESSED,
+                2,
+                ParquetWorkflowLoadSupport.CONFLICT_FAIL,
+                false,
+                2,
+                ctx );
+
+        AlgDataType outputType = ParquetWorkflowExtractSupport.getOutputType( Sources.of( target ), ParquetWorkflowExtractSupport.OUTPUT_RELATIONAL, true );
+        CollectingOutputPipe output = new CollectingOutputPipe( outputType );
+        ParquetWorkflowExtractSupport.writeRows( output, Sources.of( target ), true, 1 );
+
+        assertEquals( List.of( Activity.PK_COL, "customer_name", "score", "fileName" ), outputType.getFieldNames() );
+        assertEquals( List.of( 0.5D, 1.0D, 1.0D ), ctx.progress );
+        assertEquals( 1, output.rows.size() );
+        List<PolyValue> row = output.rows.get( 0 );
+        assertEquals( 0L, row.get( 0 ).asNumber().longValue() );
+        assertEquals( "Alice", row.get( 1 ).asString().value );
+        assertEquals( 7, row.get( 2 ).asNumber().intValue() );
+        assertEquals( target.getName(), row.get( 3 ).asString().value );
+    }
+
+
+    @Test
+    void documentWriteAndExtractRoundTripHonorsKeepIdNameFieldAndMaxCount() throws Exception {
+        File target = tempDir.resolve( "documents.parquet" ).toFile();
+        PolyDocument first = doc( Map.of(
+                Activity.docId, PolyString.of( "doc-1" ),
+                PolyString.of( "Name" ), PolyString.of( "Alice" ),
+                PolyString.of( "Details" ), doc( Map.of( PolyString.of( "city" ), PolyString.of( "Berlin" ) ) ) ) );
+        PolyDocument second = doc( Map.of(
+                Activity.docId, PolyString.of( "doc-2" ),
+                PolyString.of( "Name" ), PolyString.of( "Bob" ) ) );
+
+        ParquetWorkflowLoadSupport.writeDocuments(
+                new TestInputPipe( DocumentType.ofId(), List.of( List.of( first ), List.of( second ) ) ),
+                target,
+                ParquetWorkflowLoadSupport.COMPRESSION_UNCOMPRESSED,
+                2,
+                ParquetWorkflowLoadSupport.CONFLICT_FAIL,
+                false,
+                2,
+                new TestPipeExecutionContext( List.of( 2L ) ) );
+
+        CollectingOutputPipe output = new CollectingOutputPipe( DocumentType.ofId() );
+        ParquetWorkflowExtractSupport.writeDocuments( output, Sources.of( target ), true, 1 );
+
+        assertEquals( 1, output.rows.size() );
+        PolyDocument document = output.rows.get( 0 ).get( 0 ).asDocument();
+        assertEquals( "Alice", document.get( PolyString.of( "name" ) ).asString().value );
+        assertEquals( "Berlin", document.get( PolyString.of( "details" ) ).asDocument().get( PolyString.of( "city" ) ).asString().value );
+        assertNotEquals( "doc-1", document.get( Activity.docId ).asString().value );
+        assertTrue( document.get( Activity.docId ).asString().value.endsWith( target.getName() + "#0" ) );
+        assertEquals( target.getName(), document.get( PolyString.of( "fileName" ) ).asString().value );
+    }
+
+
+    @Test
+    void loadActivityPipeWritesRelationalWithKeptPrimaryKeyAndGzip() throws Exception {
+        File target = tempDir.resolve( "activity-relational.parquet" ).toFile();
+        AlgDataType inputType = AlgDataTypeFactory.DEFAULT.builder()
+                .add( Activity.PK_COL, null, PolyType.BIGINT )
+                .add( "Name", null, PolyType.VARCHAR )
+                .build();
+        ParquetLoadActivity activity = new ParquetLoadActivity();
+
+        activity.pipe(
+                List.of( new TestInputPipe( inputType, List.of( List.of( PolyLong.of( 7 ), PolyString.of( "Alice" ) ) ) ) ),
+                null,
+                loadSettings( target, ParquetWorkflowLoadSupport.COMPRESSION_GZIP, ParquetWorkflowLoadSupport.CONFLICT_FAIL, true, true ),
+                new TestPipeExecutionContext( List.of( 1L ) ) );
+
+        assertTrue( target.isFile() );
+        AlgDataType outputType = ParquetWorkflowExtractSupport.getOutputType( Sources.of( target ), ParquetWorkflowExtractSupport.OUTPUT_RELATIONAL, false );
+        CollectingOutputPipe output = new CollectingOutputPipe( outputType );
+        ParquetWorkflowExtractSupport.writeRows( output, Sources.of( target ), false, -1 );
+
+        assertEquals( 3, outputType.getFieldCount() );
+        assertTrue( outputType.getFieldNames().contains( "name" ) );
+        assertEquals( 1, output.rows.size() );
+        assertEquals( 0L, output.rows.get( 0 ).get( 0 ).asNumber().longValue() );
+        assertEquals( 7L, output.rows.get( 0 ).get( 1 ).asNumber().longValue() );
+        assertEquals( "Alice", output.rows.get( 0 ).get( 2 ).asString().value );
+    }
+
+
+    @Test
+    void relationalWriterRoundTripsTemporalAndBinaryValues() throws Exception {
+        File target = tempDir.resolve( "temporal-binary.parquet" ).toFile();
+        AlgDataType inputType = AlgDataTypeFactory.DEFAULT.builder()
+                .add( Activity.PK_COL, null, PolyType.BIGINT )
+                .add( "Created Date", null, PolyType.DATE )
+                .add( "Created Time", null, PolyType.TIME )
+                .add( "Updated At", null, PolyType.TIMESTAMP )
+                .add( "Payload", null, PolyType.VARBINARY )
+                .build();
+
+        ParquetWorkflowLoadSupport.writeRelational(
+                new TestInputPipe( inputType, List.of( List.of(
+                        PolyLong.of( 1 ),
+                        PolyDate.of( 19000L * DateTimeUtils.MILLIS_PER_DAY ),
+                        PolyTime.of( 12345 ),
+                        PolyTimestamp.of( 1609459200123L ),
+                        PolyBinary.of( new byte[]{ 1, 2, 3 } ) ) ) ),
+                target,
+                ParquetWorkflowLoadSupport.COMPRESSION_UNCOMPRESSED,
+                1,
+                ParquetWorkflowLoadSupport.CONFLICT_FAIL,
+                false,
+                1,
+                new TestPipeExecutionContext( List.of( 1L ) ) );
+
+        AlgDataType outputType = ParquetWorkflowExtractSupport.getOutputType( Sources.of( target ), ParquetWorkflowExtractSupport.OUTPUT_RELATIONAL, false );
+        CollectingOutputPipe output = new CollectingOutputPipe( outputType );
+        ParquetWorkflowExtractSupport.writeRows( output, Sources.of( target ), false, -1 );
+        List<PolyValue> row = output.rows.get( 0 );
+
+        assertEquals( List.of( Activity.PK_COL, "created_date", "created_time", "updated_at", "payload" ), outputType.getFieldNames() );
+        assertEquals( 19000L, row.get( 1 ).asDate().getDaysSinceEpoch() );
+        assertEquals( 12345, row.get( 2 ).asTime().ofDay );
+        assertEquals( 1609459200123L, row.get( 3 ).asTimestamp().millisSinceEpoch );
+        assertArrayEquals( new byte[]{ 1, 2, 3 }, row.get( 4 ).asBinary().value );
+    }
+
+
+    @Test
+    void loadActivityPipeRejectsGraphInputAtRuntime() {
+        ParquetLoadActivity activity = new ParquetLoadActivity();
+        File target = tempDir.resolve( "graph.parquet" ).toFile();
+
+        assertThrows( InvalidInputException.class, () -> activity.pipe(
+                List.of( new TestInputPipe( GraphType.of(), List.of() ) ),
+                null,
+                loadSettings( target, ParquetWorkflowLoadSupport.COMPRESSION_UNCOMPRESSED, ParquetWorkflowLoadSupport.CONFLICT_FAIL, true, true ),
+                new TestPipeExecutionContext( List.of() ) ) );
+    }
+
+
+    @Test
+    void loadActivityPipeFailsWhenSampleConflictsWithDeclaredRelationalSchema() {
+        ParquetLoadActivity activity = new ParquetLoadActivity();
+        File target = tempDir.resolve( "conflict.parquet" ).toFile();
+        AlgDataType inputType = AlgDataTypeFactory.DEFAULT.builder()
+                .add( Activity.PK_COL, null, PolyType.BIGINT )
+                .add( "Amount", null, PolyType.INTEGER )
+                .build();
+
+        assertThrows( GenericRuntimeException.class, () -> activity.pipe(
+                List.of( new TestInputPipe( inputType, List.of( List.of( PolyLong.of( 1 ), PolyString.of( "not-an-int" ) ) ) ) ),
+                null,
+                loadSettings( target, ParquetWorkflowLoadSupport.COMPRESSION_UNCOMPRESSED, ParquetWorkflowLoadSupport.CONFLICT_FAIL, true, false ),
+                new TestPipeExecutionContext( List.of( 1L ) ) ) );
+    }
+
+
+    @Test
+    void extractActivityLocksOutputTypeAndPipesMultipleSources() throws Exception {
+        Path sourceDir = tempDir.resolve( "sources" );
+        Files.createDirectories( sourceDir );
+        AlgDataType inputType = AlgDataTypeFactory.DEFAULT.builder()
+                .add( Activity.PK_COL, null, PolyType.BIGINT )
+                .add( "Name", null, PolyType.VARCHAR )
+                .build();
+        ParquetWorkflowLoadSupport.writeRelational(
+                new TestInputPipe( inputType, List.of( List.of( PolyLong.of( 1 ), PolyString.of( "Alice" ) ) ) ),
+                sourceDir.resolve( "first.parquet" ).toFile(),
+                ParquetWorkflowLoadSupport.COMPRESSION_UNCOMPRESSED,
+                1,
+                ParquetWorkflowLoadSupport.CONFLICT_FAIL,
+                false,
+                1,
+                new TestPipeExecutionContext( List.of( 1L ) ) );
+        ParquetWorkflowLoadSupport.writeRelational(
+                new TestInputPipe( inputType, List.of( List.of( PolyLong.of( 2 ), PolyString.of( "Bob" ) ) ) ),
+                sourceDir.resolve( "second.parquet" ).toFile(),
+                ParquetWorkflowLoadSupport.COMPRESSION_UNCOMPRESSED,
+                1,
+                ParquetWorkflowLoadSupport.CONFLICT_FAIL,
+                false,
+                1,
+                new TestPipeExecutionContext( List.of( 1L ) ) );
+        ParquetExtractActivity activity = new ParquetExtractActivity();
+        Settings settings = extractSettings( sourceDir, ParquetWorkflowExtractSupport.OUTPUT_RELATIONAL, true, -1 );
+
+        AlgDataType outputType = activity.lockOutputType( List.of(), settings );
+        CollectingOutputPipe output = new CollectingOutputPipe( outputType );
+        activity.pipe( List.of(), output, settings, new TestPipeExecutionContext( List.of() ) );
+
+        assertEquals( List.of( Activity.PK_COL, "name", "fileName" ), outputType.getFieldNames() );
+        assertEquals( 2L, activity.estimateTupleCount( List.of(), settings, List.of(), () -> null ) );
+        assertEquals( 2, output.rows.size() );
+        Map<String, String> namesByFile = new LinkedHashMap<>();
+        for ( List<PolyValue> row : output.rows ) {
+            namesByFile.put( row.get( 2 ).asString().value, row.get( 1 ).asString().value );
+        }
+        assertEquals( Map.of( "first.parquet", "Alice", "second.parquet", "Bob" ), namesByFile );
+    }
+
+
+    @Test
+    void documentWriteKeepsOnlyIdWhenRequestedAndRejectsEmptyDroppedIdSchema() throws Exception {
+        File target = tempDir.resolve( "only-id.parquet" ).toFile();
+        PolyDocument onlyId = doc( Map.of( Activity.docId, PolyString.of( "doc-1" ) ) );
+
+        ParquetWorkflowLoadSupport.writeDocuments(
+                new TestInputPipe( DocumentType.ofId(), List.of( List.of( onlyId ) ) ),
+                target,
+                ParquetWorkflowLoadSupport.COMPRESSION_UNCOMPRESSED,
+                1,
+                ParquetWorkflowLoadSupport.CONFLICT_FAIL,
+                true,
+                1,
+                new TestPipeExecutionContext( List.of( 1L ) ) );
+
+        CollectingOutputPipe output = new CollectingOutputPipe( DocumentType.ofId() );
+        ParquetWorkflowExtractSupport.writeDocuments( output, Sources.of( target ), false, -1 );
+        assertEquals( "doc-1", output.rows.get( 0 ).get( 0 ).asDocument().get( Activity.docId ).asString().value );
+
+        assertThrows( GenericRuntimeException.class, () -> ParquetWorkflowLoadSupport.writeDocuments(
+                new TestInputPipe( DocumentType.ofId(), List.of( List.of( onlyId ) ) ),
+                tempDir.resolve( "empty-doc.parquet" ).toFile(),
+                ParquetWorkflowLoadSupport.COMPRESSION_UNCOMPRESSED,
+                1,
+                ParquetWorkflowLoadSupport.CONFLICT_FAIL,
+                false,
+                1,
+                new TestPipeExecutionContext( List.of( 1L ) ) ) );
+    }
+
+
+    @Test
+    void loadActivityPreviewRejectsEmptyRelationalExportAndBuildsDynamicName() {
+        ParquetLoadActivity activity = new ParquetLoadActivity();
+        AlgDataType onlyPk = AlgDataTypeFactory.DEFAULT.builder()
+                .add( Activity.PK_COL, null, PolyType.BIGINT )
+                .build();
+        SettingsPreview keepPkFalse = new SettingsPreview( Map.of( "keepPk", Optional.of( new BoolValue( false ) ) ) );
+        SettingsPreview keepPkTrue = new SettingsPreview( Map.of( "keepPk", Optional.of( new BoolValue( true ) ) ) );
+        Path longTarget = tempDir.resolve( "abcdefghijklmnopqrstuvwxyz0123456789-extra.parquet" );
+        SettingsPreview fileSettings = new SettingsPreview( Map.of( "file", Optional.of( new FileValue( longTarget.toString(), SourceType.ABS_FILE, false ) ) ) );
+
+        assertThrows( InvalidInputException.class, () -> activity.previewOutTypes( List.of( RelType.of( onlyPk ) ), keepPkFalse ) );
+        assertDoesNotThrow( () -> activity.previewOutTypes( List.of( RelType.of( onlyPk ) ), keepPkTrue ) );
+        assertTrue( activity.getDynamicName( List.of(), fileSettings ).startsWith( "Load to abcdefghijklmnopqrstuvwxyz" ) );
+    }
+
+
+    @Test
+    void extractSupportBuildsDynamicNamesAndDocumentOutputType() throws Exception {
+        File target = tempDir.resolve( "single.parquet" ).toFile();
+        AlgDataType inputType = AlgDataTypeFactory.DEFAULT.builder()
+                .add( Activity.PK_COL, null, PolyType.BIGINT )
+                .add( "Name", null, PolyType.VARCHAR )
+                .build();
+        ParquetWorkflowLoadSupport.writeRelational(
+                new TestInputPipe( inputType, List.of( List.of( PolyLong.of( 1 ), PolyString.of( "Alice" ) ) ) ),
+                target,
+                ParquetWorkflowLoadSupport.COMPRESSION_UNCOMPRESSED,
+                1,
+                ParquetWorkflowLoadSupport.CONFLICT_FAIL,
+                false,
+                1,
+                new TestPipeExecutionContext( List.of( 1L ) ) );
+
+        assertEquals( DocumentType.ofId(), ParquetWorkflowExtractSupport.getOutputType( Sources.of( target ), ParquetWorkflowExtractSupport.OUTPUT_DOCUMENT, false ) );
+        assertEquals( "Extract Parquet Table: single.parquet", ParquetWorkflowExtractSupport.getDynamicName( ParquetWorkflowExtractSupport.OUTPUT_RELATIONAL, List.of( Sources.of( target ) ) ) );
+        assertEquals( "Extract Parquet Documents", ParquetWorkflowExtractSupport.getDynamicName( ParquetWorkflowExtractSupport.OUTPUT_DOCUMENT, List.of( Sources.of( target ), Sources.of( target ) ) ) );
+        assertEquals( 1L, ParquetWorkflowExtractSupport.estimateTupleCount( List.of( Sources.of( target ) ) ) );
+    }
+
+
+    private static PolyDocument doc( Map<PolyString, PolyValue> values ) {
+        return PolyDocument.ofDocument( values );
+    }
+
+
+    private static Settings loadSettings( File file, String compression, String conflictMode, boolean keepId, boolean keepPk ) {
+        Map<String, SettingValue> values = new LinkedHashMap<>();
+        values.put( "file", new FileValue( file.getAbsolutePath(), SourceType.ABS_FILE, false ) );
+        values.put( "mode", StringValue.of( ParquetWorkflowLoadSupport.MODE_FAIL ) );
+        values.put( "compression", StringValue.of( compression ) );
+        values.put( "schemaSampleSize", new IntValue( 1 ) );
+        values.put( "conflictMode", StringValue.of( conflictMode ) );
+        values.put( "keepId", new BoolValue( keepId ) );
+        values.put( "keepPk", new BoolValue( keepPk ) );
+        return new Settings( values );
+    }
+
+
+    private static Settings extractSettings( Path path, String outputModel, boolean nameField, int maxCount ) {
+        Map<String, SettingValue> values = new LinkedHashMap<>();
+        values.put( "file", new FileValue( path.toString(), SourceType.ABS_FILE, true ) );
+        values.put( "outputModel", StringValue.of( outputModel ) );
+        values.put( "nameField", new BoolValue( nameField ) );
+        values.put( "maxCount", new IntValue( maxCount ) );
+        return new Settings( values );
+    }
+
+
+    private record TestInputPipe( AlgDataType type, List<List<PolyValue>> rows ) implements InputPipe {
+
+        @Override
+        public AlgDataType getType() {
+            return type;
+        }
+
+
+        @Override
+        public @NotNull Iterator<List<PolyValue>> iterator() {
+            return rows.iterator();
+        }
+
+
+        @Override
+        public void finishIteration() {
+        }
+
+    }
+
+
+    private static class CollectingOutputPipe implements OutputPipe {
+
+        private final AlgDataType type;
+        private final List<List<PolyValue>> rows = new java.util.ArrayList<>();
+
+
+        private CollectingOutputPipe( AlgDataType type ) {
+            this.type = type;
+        }
+
+
+        @Override
+        public boolean put( List<PolyValue> value ) {
+            rows.add( value );
+            return true;
+        }
+
+
+        @Override
+        public AlgDataType getType() {
+            return type;
+        }
+
+
+        @Override
+        public double getEstimatedProgress() {
+            return -1;
+        }
+
+
+        @Override
+        public void close() {
+        }
+
+    }
+
+
+    private static class TestPipeExecutionContext implements PipeExecutionContext {
+
+        private final List<Long> estimatedInCounts;
+        private final List<Double> progress = new java.util.ArrayList<>();
+
+
+        private TestPipeExecutionContext( List<Long> estimatedInCounts ) {
+            this.estimatedInCounts = estimatedInCounts;
+        }
+
+
+        @Override
+        public void checkPipeInterrupted() {
+        }
+
+
+        @Override
+        public void updateProgress( double value ) {
+            progress.add( value );
+        }
+
+
+        @Override
+        public Transaction getTransaction() {
+            return null;
+        }
+
+
+        @Override
+        public ReadableVariableStore getVariableStore() {
+            return null;
+        }
+
+
+        @Override
+        public void logInfo( String message ) {
+        }
+
+
+        @Override
+        public void logWarning( String message ) {
+        }
+
+
+        @Override
+        public void logError( String message ) {
+        }
+
+
+        @Override
+        public List<Long> getEstimatedInCounts() {
+            return estimatedInCounts;
+        }
+
+    }
+
+}

From 81d5f441ce10efda6f60cf55064e5ee36d5ebe50 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Fri, 12 Jun 2026 09:51:41 +0200
Subject: [PATCH 38/46] fix filter support in document adapter - MQL queries

---
 .../docs/doc_adapter_support_filters.md       |  78 +++++++++++
 .../db/adapter/parquet/ParquetPlugin.java     |  15 +++
 .../execution/ParquetDocFilterTranslator.java |  37 +++++-
 .../document/planning/ParquetDocCalcRule.java |  83 ++++++++++++
 .../document/planning/ParquetDocFilter.java   |  67 ----------
 .../planning/ParquetDocFilterRule.java        | 104 ---------------
 .../document/planning/ParquetDocScan.java     |  36 +++++-
 .../planning/ParquetRelAggregate.java         |   1 +
 .../relational/planning/ParquetRelJoin.java   |   1 +
 .../relational/planning/ParquetRelScan.java   |   1 +
 .../planning/ParquetPolyAlgDisplay.java       | 122 ++++++++++++++++++
 .../parquet/ParquetDocumentPlanningTest.java  | 115 +++++------------
 .../ParquetSharedDocumentAdapterTest.java     |  28 ++++
 13 files changed, 422 insertions(+), 266 deletions(-)
 create mode 100644 plugins/parquet-adapter/docs/doc_adapter_support_filters.md
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocCalcRule.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilter.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/planning/ParquetPolyAlgDisplay.java

diff --git a/plugins/parquet-adapter/docs/doc_adapter_support_filters.md b/plugins/parquet-adapter/docs/doc_adapter_support_filters.md
new file mode 100644
index 0000000000..76a3dd8767
--- /dev/null
+++ b/plugins/parquet-adapter/docs/doc_adapter_support_filters.md
@@ -0,0 +1,78 @@
+# Document Adapter Filter Pushdown Changeset
+
+Document Parquet scans now support filter pushdown by translating supported document predicates into `ParquetAdapterFilter` instances stored directly on `ParquetDocScan`.
+For MQL document queries, Polypheny lowers field access in filters to `MQL_QUERY_VALUE(document, ARRAY['field'])`; the translator now recognizes this top-level field access form and can push supported comparison predicates with literals or dynamic parameters into the Parquet scan.
+The implementation moves document filtering from a custom filter node/rule to an `EnumerableCalc`-based planner rule, while keeping the Calc as a residual correctness check.
+
+## Changed Files
+
+## ParquetPlugin.java
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java` 
+Registers `ParquetDocScan` as `P_DOC_SCAN` in PolyAlg with fields and filters display parameters.
+
+
+## ParquetDocFilterTranslator.java
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java`
+Extends document filter translation to support lowered `MQL_QUERY_VALUE` top-level field access in addition to simple name references.
+
+
+## ParquetDocCalcRule.java
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocCalcRule.java`
+Adds a planner rule that pushes supported `EnumerableCalc` conditions into `ParquetDocScan` filters.
+
+
+## ParquetDocFilter.java
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilter.java`
+Removes the obsolete Parquet-specific document filter node.
+
+
+## ParquetDocFilterRule.java
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java`
+Removes the obsolete logical filter rewrite rule replaced by calc-based pushdown.
+
+
+## ParquetDocScan.java
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java`
+Stores typed pushed filters, combines additional filters, exposes them in PolyAlg, adjusts filtered scan cost, and registers the new calc rule.
+
+
+## ParquetRelAggregate.java
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java`
+Switches PolyAlg display support to the shared Parquet display helper.
+
+
+## ParquetRelJoin.java
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java`
+Switches PolyAlg display support to the shared Parquet display helper.
+
+
+## ParquetRelScan.java
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java`
+Switches PolyAlg display support to the shared Parquet display helper.
+
+
+## ParquetPolyAlgDisplay.java
+
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/planning/ParquetPolyAlgDisplay.java`
+Adds a shared helper for formatting Parquet fields and filters in PolyAlg output.
+
+
+## ParquetDocumentPlanningTest.java
+
+`plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java`
+Updates document planning tests for scan-carried filters and removes tests for the deleted filter node/rule.
+
+
+## ParquetSharedDocumentAdapterTest.java
+
+`plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java`
+Adds translator coverage for comparison filters and lowered MQL field access.
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
index 987acb225b..a853d7abe1 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
@@ -19,6 +19,7 @@
 import com.google.common.collect.ImmutableList;
 import org.polypheny.db.adapter.AdapterManager;
 import org.polypheny.db.adapter.parquet.document.ParquetDocumentSource;
+import org.polypheny.db.adapter.parquet.document.planning.ParquetDocScan;
 import org.polypheny.db.adapter.parquet.relational.ParquetRelationalSource;
 import org.polypheny.db.adapter.parquet.relational.planning.EnumerableParquet;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetEnumerableUnion;
@@ -30,6 +31,7 @@
 import org.polypheny.db.algebra.enumerable.EnumerableConvention;
 import org.polypheny.db.algebra.enumerable.EnumerableJoin;
 import org.polypheny.db.algebra.enumerable.EnumerableUnion;
+import org.polypheny.db.algebra.logical.document.LogicalDocumentScan;
 import org.polypheny.db.algebra.logical.relational.LogicalRelScan;
 import org.polypheny.db.algebra.polyalg.PolyAlgDeclaration;
 import org.polypheny.db.algebra.polyalg.PolyAlgDeclaration.OperatorTag;
@@ -76,11 +78,13 @@ private void registerPolyAlg() {
                 .model( DataModel.RELATIONAL )
                 .opName( "PE_CALC" ).convention( EnumerableConvention.INSTANCE ).numInputs( 1 ).opTags( physTags )
                 .build() );
+
         PolyAlgRegistry.register( ParquetEnumerableUnion.class, PolyAlgDeclaration.builder()
                 .model( DataModel.RELATIONAL )
                 .opName( "PE_UNION" ).convention( EnumerableConvention.INSTANCE ).numInputs( -1 ).opTags( physTags )
                 .params( PolyAlgRegistry.getParams( EnumerableUnion.class ) )
                 .build() );
+
         PolyAlgRegistry.register( ParquetRelScan.class, PolyAlgDeclaration.builder()
                 .model( DataModel.RELATIONAL )
                 .opName( "P_SCAN" ).convention( ParquetConvention.INSTANCE ).numInputs( 0 ).opTags( physTags )
@@ -88,6 +92,7 @@ private void registerPolyAlg() {
                 .param( Parameter.builder().name( "fields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .build() );
+
         PolyAlgRegistry.register( ParquetRelMetadataScan.class, PolyAlgDeclaration.builder()
                 .model( DataModel.RELATIONAL )
                 .opName( "P_METADATA_SCAN" ).convention( ParquetConvention.INSTANCE ).numInputs( 0 ).opTags( physTags )
@@ -95,6 +100,7 @@ private void registerPolyAlg() {
                 .param( Parameter.builder().name( "fields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .build() );
+
         PolyAlgRegistry.register( ParquetRelJoin.class, PolyAlgDeclaration.builder()
                 .model( DataModel.RELATIONAL )
                 .opName( "P_JOIN" ).convention( ParquetConvention.INSTANCE ).numInputs( 2 ).opTags( physTags )
@@ -106,6 +112,7 @@ private void registerPolyAlg() {
                 .param( Parameter.builder().name( "rightFilters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .param( Parameter.builder().name( "joinFilters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .build() );
+
         PolyAlgRegistry.register( ParquetRelAggregate.class, PolyAlgDeclaration.builder()
                 .model( DataModel.RELATIONAL )
                 .opName( "P_AGGREGATE" ).convention( ParquetConvention.INSTANCE ).numInputs( 1 ).opTags( physTags )
@@ -116,6 +123,14 @@ private void registerPolyAlg() {
                 .param( Parameter.builder().name( "aggregates" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .build() );
+
+        PolyAlgRegistry.register( ParquetDocScan.class, PolyAlgDeclaration.builder()
+                .model( DataModel.DOCUMENT )
+                .opName( "P_DOC_SCAN" ).convention( EnumerableConvention.INSTANCE ).numInputs( 0 ).opTags( physTags )
+                .params( PolyAlgRegistry.getParams( LogicalDocumentScan.class ) )
+                .param( Parameter.builder().name( "fields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .build() );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
index 712b3b9951..43d2ff1f7c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
@@ -21,8 +21,12 @@
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractFilterTranslator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.rex.RexCall;
+import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNameRef;
 import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.type.entity.PolyValue;
 
 /**
  * Translates supported document filter expressions into Parquet filter instances.
@@ -36,7 +40,7 @@ public class ParquetDocFilterTranslator extends AbstractFilterTranslator {
      * @param filter - RexNode
      * @return ParquetFilter
      */
-    public ParquetAdapterFilter translate( List<ExportedColumn> columns, RexNode filter ) {
+    public ParquetAdapterFilter<PolyValue> translate( List<ExportedColumn> columns, RexNode filter ) {
         ParsedFilter parsed = parse( filter );
         if ( parsed == null ) {
             return null;
@@ -45,15 +49,11 @@ public ParquetAdapterFilter translate( List<ExportedColumn> columns, RexNode fil
         RexNode left = parsed.left();
         RexNode right = parsed.right();
 
-        if ( !(left instanceof RexNameRef nameRef) ) {
+        String fieldName = fieldName( left );
+        if ( fieldName == null ) {
             return null;
         }
 
-        if ( nameRef.names.size() != 1 ) {
-            return null;
-        }
-
-        String fieldName = nameRef.names.get( 0 );
         if ( !isValueOperand( right ) ) {
             return null;
         }
@@ -66,4 +66,27 @@ public ParquetAdapterFilter translate( List<ExportedColumn> columns, RexNode fil
         return null;
     }
 
+
+    private String fieldName( RexNode node ) {
+        if ( node instanceof RexNameRef nameRef ) {
+            return nameRef.names.size() == 1 ? nameRef.names.get( 0 ) : null;
+        }
+
+        if ( !(node instanceof RexCall call) || call.getKind() != Kind.MQL_QUERY_VALUE || call.getOperands().size() != 2 ) {
+            return null;
+        }
+
+        RexNode path = call.getOperands().get( 1 );
+        if ( !(path instanceof RexCall pathCall) || pathCall.getKind() != Kind.ARRAY_VALUE_CONSTRUCTOR || pathCall.getOperands().size() != 1 ) {
+            return null;
+        }
+
+        RexNode element = pathCall.getOperands().get( 0 );
+        if ( !(element instanceof RexLiteral literal) || literal.getValue() == null || !literal.getValue().isString() ) {
+            return null;
+        }
+
+        return literal.getValue().asString().value;
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocCalcRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocCalcRule.java
new file mode 100644
index 0000000000..7be6cddd31
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocCalcRule.java
@@ -0,0 +1,83 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.document.planning;
+
+import java.util.ArrayList;
+import java.util.HashSet;
+import java.util.List;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.parquet.document.execution.ParquetDocFilterTranslator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.enumerable.EnumerableCalc;
+import org.polypheny.db.plan.AlgOptRule;
+import org.polypheny.db.plan.AlgOptRuleCall;
+import org.polypheny.db.plan.AlgOptUtil;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.rex.RexProgram;
+import org.polypheny.db.tools.AlgBuilderFactory;
+import org.polypheny.db.type.entity.PolyValue;
+
+/**
+ * Pushes supported document Calc filters into a Parquet document scan.
+ */
+public class ParquetDocCalcRule extends AlgOptRule {
+
+    private final ParquetDocFilterTranslator translator = new ParquetDocFilterTranslator();
+
+
+    public ParquetDocCalcRule( AlgBuilderFactory algBuilderFactory ) {
+        super( operand( EnumerableCalc.class, operand( ParquetDocScan.class, none() ) ), algBuilderFactory, ParquetDocCalcRule.class.getSimpleName() );
+    }
+
+
+    @Override
+    public void onMatch( AlgOptRuleCall call ) {
+        EnumerableCalc calc = call.alg( 0 );
+        ParquetDocScan scan = call.alg( 1 );
+        RexProgram program = calc.getProgram();
+        if ( program.getCondition() == null ) {
+            return;
+        }
+
+        List<ExportedColumn> columns = scan.getEntity().getParquetSource().getExportedColumns().get( scan.getEntity().name );
+        if ( columns == null ) {
+            return;
+        }
+
+        List<RexNode> predicates = new ArrayList<>();
+        AlgOptUtil.decomposeConjunction( program.expandLocalRef( program.getCondition() ), predicates );
+        if ( predicates.isEmpty() ) {
+            return;
+        }
+
+        List<ParquetAdapterFilter<PolyValue>> filters = new ArrayList<>();
+        for ( RexNode predicate : predicates ) {
+            ParquetAdapterFilter<PolyValue> filter = translator.translate( columns, predicate );
+            if ( filter == null ) {
+                return;
+            }
+            filters.add( filter );
+        }
+
+        if ( new HashSet<>( scan.getFilters() ).containsAll( filters ) ) {
+            return;
+        }
+
+        call.transformTo( EnumerableCalc.create( scan.withFilters( filters ), program ) );
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilter.java
deleted file mode 100644
index 119493ae91..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilter.java
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.document.planning;
-
-import lombok.Getter;
-import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
-import org.polypheny.db.algebra.AlgNode;
-import org.polypheny.db.algebra.core.Filter;
-import org.polypheny.db.algebra.enumerable.EnumerableAlg;
-import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
-import org.polypheny.db.algebra.enumerable.EnumerableConvention;
-import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
-import org.polypheny.db.plan.AlgCluster;
-import org.polypheny.db.plan.AlgOptCost;
-import org.polypheny.db.plan.AlgPlanner;
-import org.polypheny.db.plan.AlgTraitSet;
-import org.polypheny.db.rex.RexNode;
-
-/**
- * Enumerable filter node used when a document filter can stay
- * inside the Parquet-specific execution path.
- */
-@Getter
-public class ParquetDocFilter extends Filter implements EnumerableAlg {
-
-    private final ParquetDocument entity;
-
-
-    public ParquetDocFilter( AlgCluster cluster, AlgTraitSet traitSet, AlgNode input, RexNode condition, ParquetDocument entity ) {
-        super( cluster, traitSet.replace( EnumerableConvention.INSTANCE ), input, condition );
-        this.entity = entity;
-    }
-
-
-    @Override
-    public ParquetDocFilter copy( AlgTraitSet traitSet, AlgNode input, RexNode condition ) {
-        return new ParquetDocFilter( getCluster(), traitSet, input, condition, entity );
-    }
-
-
-    @Override
-    public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
-        return super.computeSelfCost( planner, mq ).multiplyBy( ((double) entity.getParquetSource().getExportedColumns().size() + 2D) / ((double) entity.getTupleType().getFieldCount() + 2D) );
-    }
-
-
-    @Override
-    public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
-        var scan = (ParquetDocScan) input;
-        return scan.implement( implementor, pref );
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java
deleted file mode 100644
index 830f4c4d7c..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java
+++ /dev/null
@@ -1,104 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.document.planning;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
-import org.polypheny.db.adapter.parquet.document.execution.ParquetDocFilterTranslator;
-import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.algebra.constant.Kind;
-import org.polypheny.db.algebra.enumerable.EnumerableConvention;
-import org.polypheny.db.algebra.logical.relational.LogicalRelFilter;
-import org.polypheny.db.plan.AlgOptRule;
-import org.polypheny.db.plan.AlgOptRuleCall;
-import org.polypheny.db.rex.RexCall;
-import org.polypheny.db.rex.RexNode;
-import org.polypheny.db.tools.AlgBuilderFactory;
-
-/**
- * Planner rule that recognizes supported document filters
- * and rewrites them into `ParquetDocFilter` plus `ParquetDocScan`
- */
-public class ParquetDocFilterRule extends AlgOptRule {
-
-    private final ParquetDocFilterTranslator translator = new ParquetDocFilterTranslator();
-    private final ParquetDocument document;
-
-
-    public ParquetDocFilterRule( AlgBuilderFactory algBuilderFactory, ParquetDocument document ) {
-        super( operand( LogicalRelFilter.class, any() ), algBuilderFactory, ParquetDocFilterRule.class.getSimpleName() );
-        this.document = document;
-    }
-
-
-    /**
-     * On match read the matched node as LogicalRelFilter filter:
-     * - split received filter information into predicates
-     * - translate them into ParquetFilters
-     * - produce a ParquetDocScan with those filters
-     * - wrap it in ParquetDocFilter
-     * @param call Rule call - passed in by query optimizer when this rule matches, re-written
-     */
-    @Override
-    public void onMatch( AlgOptRuleCall call ) {
-        LogicalRelFilter filter = call.alg( 0 );
-
-        // column list is needed because filter translation
-        // is done against actual exported field names and physical positions
-        List<ExportedColumn> columns = document.getParquetSource().getExportedColumns().get( document.name );
-        // separate predicates - the rule validates each predicate individually
-        List<RexNode> predicates = splitConjunctions( filter.getCondition() );
-        if ( columns == null || predicates.isEmpty() ) {
-            return;
-        }
-
-        List<ParquetAdapterFilter> parquetAdapterFilters = new ArrayList<>();
-        for ( RexNode predicate : predicates ) {
-            ParquetAdapterFilter parquetAdapterFilter = translator.translate( columns, predicate );
-            if ( parquetAdapterFilter == null ) {
-                return;
-            }
-            parquetAdapterFilters.add( parquetAdapterFilter );
-        }
-
-        // replaces the generic logical filter with a new ParquetDocScan
-        // carrying the translated parquet filter wrapped in a ParquetDocFilter
-        call.transformTo(
-                new ParquetDocFilter(
-                        filter.getCluster(),
-                        filter.getTraitSet().replace( EnumerableConvention.INSTANCE ),
-                        new ParquetDocScan( filter.getCluster(), document, parquetAdapterFilters ),
-                        filter.getCondition(),
-                        document ) );
-    }
-
-
-    private List<RexNode> splitConjunctions( RexNode node ) {
-        if ( node.getKind() != Kind.AND ) {
-            return List.of( node );
-        }
-        List<RexNode> predicates = new ArrayList<>();
-        RexCall call = (RexCall) node;
-        for ( RexNode operand : call.getOperands() ) {
-            predicates.addAll( splitConjunctions( operand ) );
-        }
-        return predicates;
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
index a0d9d3d074..4ee18ddf8e 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
@@ -16,13 +16,17 @@
 
 package org.polypheny.db.adapter.parquet.document.planning;
 
+import java.util.ArrayList;
 import java.util.List;
+import java.util.stream.IntStream;
 import lombok.Getter;
 import org.apache.calcite.linq4j.tree.Blocks;
 import org.apache.calcite.linq4j.tree.Expression;
 import org.apache.calcite.linq4j.tree.Expressions;
 import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.planning.ParquetPolyAlgDisplay;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.AlgWriter;
 import org.polypheny.db.algebra.core.AlgFactories;
@@ -34,6 +38,9 @@
 import org.polypheny.db.algebra.enumerable.PhysType;
 import org.polypheny.db.algebra.enumerable.PhysTypeImpl;
 import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
+import org.polypheny.db.algebra.polyalg.arguments.ListArg;
+import org.polypheny.db.algebra.polyalg.arguments.PolyAlgArgs;
+import org.polypheny.db.algebra.polyalg.arguments.StringArg;
 import org.polypheny.db.algebra.type.AlgDataType;
 import org.polypheny.db.algebra.type.AlgDataTypeField;
 import org.polypheny.db.algebra.type.DocumentType;
@@ -41,6 +48,7 @@
 import org.polypheny.db.plan.AlgOptCost;
 import org.polypheny.db.plan.AlgPlanner;
 import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.type.entity.PolyValue;
 
 /**
  * The planner node that sets up document reading.
@@ -50,10 +58,10 @@
 @Getter
 public class ParquetDocScan extends DocumentScan<ParquetDocument> implements EnumerableAlg {
 
-    private final List<ParquetAdapterFilter> filters;
+    private final List<ParquetAdapterFilter<PolyValue>> filters;
 
 
-    public ParquetDocScan( AlgCluster cluster, ParquetDocument entity, List<ParquetAdapterFilter> filters ) {
+    public ParquetDocScan( AlgCluster cluster, ParquetDocument entity, List<ParquetAdapterFilter<PolyValue>> filters ) {
         super( cluster, cluster.traitSetOf( EnumerableConvention.INSTANCE ), entity );
         this.filters = List.copyOf( filters );
     }
@@ -71,6 +79,13 @@ public AlgWriter explainTerms( AlgWriter pw ) {
     }
 
 
+    public ParquetDocScan withFilters( List<ParquetAdapterFilter<PolyValue>> filters ) {
+        List<ParquetAdapterFilter<PolyValue>> combinedFilters = new ArrayList<>( this.filters );
+        combinedFilters.addAll( filters );
+        return new ParquetDocScan( getCluster(), entity, combinedFilters );
+    }
+
+
     @Override
     public AlgDataType deriveRowType() {
         final List<AlgDataTypeField> fieldList = DocumentType.ofId().getFields();
@@ -84,9 +99,22 @@ public String algCompareString() {
     }
 
 
+    @Override
+    public PolyAlgArgs bindArguments() {
+        int[] fields = IntStream.range( 0, entity.getTupleType().getFields().size() ).toArray();
+        List<String> fieldNames = ParquetPolyAlgDisplay.fieldNames( entity, fields );
+        List<String> tableFieldNames = ParquetPolyAlgDisplay.fieldNames( entity );
+        return super.bindArguments()
+                .put( "fields", new ListArg<>( fieldNames, StringArg::new ) )
+                .put( "filters", new ListArg<>( ParquetPolyAlgDisplay.filters( filters, tableFieldNames ), StringArg::new ) );
+    }
+
+
     @Override
     public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
-        return super.computeSelfCost( planner, mq ).multiplyBy( ((double) entity.getParquetSource().getExportedColumns().size() + 2D) / ((double) entity.getTupleType().getFieldCount() + 2D) );
+        double fieldRatio = ((double) entity.getParquetSource().getExportedColumns().size() + 2D) / ((double) entity.getTupleType().getFieldCount() + 2D);
+        double filterRatio = filters.isEmpty() ? 1D : 0.5D;
+        return super.computeSelfCost( planner, mq ).multiplyBy( ParquetConvention.COST_MULTIPLIER * fieldRatio * filterRatio );
     }
 
 
@@ -108,7 +136,7 @@ public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
 
     @Override
     public void register( AlgPlanner planner ) {
-        planner.addRuleDuringRuntime( new ParquetDocFilterRule( AlgFactories.LOGICAL_BUILDER, entity ) );
+        planner.addRuleDuringRuntime( new ParquetDocCalcRule( AlgFactories.LOGICAL_BUILDER ) );
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java
index 43571b6668..14e70c7814 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java
@@ -23,6 +23,7 @@
 import org.apache.calcite.linq4j.tree.Expressions;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.planning.ParquetPolyAlgDisplay;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.AlgWriter;
 import org.polypheny.db.algebra.SingleAlg;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
index c0a98d58bd..88aa9575fa 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
@@ -26,6 +26,7 @@
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.planning.ParquetPolyAlgDisplay;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.AlgWriter;
 import org.polypheny.db.algebra.core.CorrelationId;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
index 60ae99246e..cee8f5bf0f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
@@ -25,6 +25,7 @@
 import org.apache.calcite.linq4j.tree.Primitive;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.planning.ParquetPolyAlgDisplay;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.AlgWriter;
 import org.polypheny.db.algebra.core.relational.RelScan;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/planning/ParquetPolyAlgDisplay.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/planning/ParquetPolyAlgDisplay.java
new file mode 100644
index 0000000000..af8e024316
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/planning/ParquetPolyAlgDisplay.java
@@ -0,0 +1,122 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.planning;
+
+import java.util.Arrays;
+import java.util.List;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.type.AlgDataTypeField;
+import org.polypheny.db.catalog.entity.physical.PhysicalEntity;
+import org.polypheny.db.type.entity.PolyValue;
+
+public final class ParquetPolyAlgDisplay {
+
+    private ParquetPolyAlgDisplay() {
+    }
+
+
+    public static List<String> fieldNames( PhysicalEntity entity, int[] fields ) {
+        List<AlgDataTypeField> tableFields = entity.getTupleType().getFields();
+        return Arrays.stream( fields )
+                .mapToObj( field -> fieldName( tableFields, field ) )
+                .toList();
+    }
+
+
+    public static List<String> fieldNames( PhysicalEntity entity ) {
+        List<AlgDataTypeField> tableFields = entity.getTupleType().getFields();
+        return java.util.stream.IntStream.range( 0, tableFields.size() )
+                .mapToObj( index -> fieldName( tableFields, index ) )
+                .toList();
+    }
+
+
+    public static List<String> prefixedFieldNames( PhysicalEntity entity, String prefix ) {
+        return fieldNames( entity ).stream()
+                .map( fieldName -> prefix + "." + fieldName )
+                .toList();
+    }
+
+
+    public static List<String> filters( List<ParquetAdapterFilter<PolyValue>> filters, List<String> fieldNames ) {
+        return filters.stream()
+                .map( filter -> filter( filter, fieldNames ) )
+                .toList();
+    }
+
+
+    public static String filter( ParquetAdapterFilter<PolyValue> filter, List<String> fieldNames ) {
+        if ( filter.isLogical() ) {
+            return filter.operator().name() + "("
+                    + String.join( ", ", filter.operands().stream()
+                    .map( operand -> filter( operand, fieldNames ) )
+                    .toList() )
+                    + ")";
+        }
+
+        String field = fieldRef( fieldNames, filter.columnIndex() );
+        if ( filter.operator() == Kind.IS_NULL || filter.operator() == Kind.IS_NOT_NULL ) {
+            return field + " " + operator( filter );
+        }
+
+        String value;
+        if ( filter.dynamicParamIndex() != null ) {
+            value = "?" + filter.dynamicParamIndex();
+        } else if ( filter.value() == null ) {
+            value = "null";
+        } else {
+            value = filter.value().toString();
+        }
+
+        return field + " " + operator( filter ) + " " + value;
+    }
+
+
+    public static List<String> joinedFieldNames( ParquetRelTable leftTable, int[] leftFields, ParquetRelTable rightTable, int[] rightFields ) {
+        List<String> leftNames = fieldNames( leftTable, leftFields ).stream()
+                .map( name -> "left." + name )
+                .toList();
+        List<String> rightNames = fieldNames( rightTable, rightFields ).stream()
+                .map( name -> "right." + name )
+                .toList();
+        return java.util.stream.Stream.concat( leftNames.stream(), rightNames.stream() ).toList();
+    }
+
+
+    private static String fieldRef( List<String> fieldNames, int index ) {
+        if ( index >= 0 && index < fieldNames.size() ) {
+            return fieldNames.get( index );
+        }
+        return "#" + index;
+    }
+
+
+    private static String operator( ParquetAdapterFilter<PolyValue> filter ) {
+        return filter.operator().sql == null ? filter.operator().name() : filter.operator().sql;
+    }
+
+
+    private static String fieldName( List<AlgDataTypeField> fields, int index ) {
+        if ( index >= 0 && index < fields.size() ) {
+            return fields.get( index ).getName() + " (#" + index + ")";
+        }
+        return "#" + index;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java
index 1b610a0878..0efe402ff2 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java
@@ -19,29 +19,23 @@
 import static org.junit.jupiter.api.Assertions.assertEquals;
 import static org.junit.jupiter.api.Assertions.assertFalse;
 import static org.junit.jupiter.api.Assertions.assertNotSame;
-import static org.junit.jupiter.api.Assertions.assertSame;
 import static org.junit.jupiter.api.Assertions.assertTrue;
 
-import java.lang.reflect.Method;
 import java.nio.file.Path;
 import java.util.List;
 import java.util.Map;
-import sun.misc.Unsafe;
 import org.junit.jupiter.api.BeforeAll;
 import org.junit.jupiter.api.Test;
 import org.polypheny.db.adapter.DeployMode;
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
 import org.polypheny.db.adapter.annotations.AdapterProperties;
 import org.polypheny.db.adapter.parquet.document.ParquetDocumentSource;
-import org.polypheny.db.adapter.parquet.document.planning.ParquetDocFilter;
-import org.polypheny.db.adapter.parquet.document.planning.ParquetDocFilterRule;
 import org.polypheny.db.adapter.parquet.document.planning.ParquetDocScan;
 import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.constant.Kind;
-import org.polypheny.db.algebra.core.AlgFactories;
 import org.polypheny.db.algebra.enumerable.EnumerableConvention;
 import org.polypheny.db.algebra.type.AlgDataType;
 import org.polypheny.db.algebra.type.AlgDataTypeFactory;
@@ -51,15 +45,14 @@
 import org.polypheny.db.plan.AlgCluster;
 import org.polypheny.db.plan.volcano.VolcanoPlanner;
 import org.polypheny.db.rex.RexBuilder;
-import org.polypheny.db.rex.RexCall;
-import org.polypheny.db.rex.RexIndexRef;
-import org.polypheny.db.rex.RexNode;
 import org.polypheny.db.type.PolyType;
 import org.polypheny.db.type.PolyTypeFactoryImpl;
 import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.PolyphenyHomeDirManager;
 import org.polypheny.db.util.RunMode;
+import sun.misc.Unsafe;
+
 
 class ParquetDocumentPlanningTest {
 
@@ -74,6 +67,35 @@ static void initHomeDir() {
     }
 
 
+    private static AlgCluster cluster() {
+        AlgDataTypeFactory typeFactory = new PolyTypeFactoryImpl( AlgDataTypeSystem.DEFAULT );
+        VolcanoPlanner planner = new VolcanoPlanner();
+        return AlgCluster.createDocument( planner, new RexBuilder( typeFactory ), null );
+    }
+
+
+    private static PhysicalCollection collection( String name ) {
+        return new PhysicalCollection( 10L, 20L, 30L, 40L, name, "public", 50L );
+    }
+
+
+    private static ParquetSourceFile sourceFile() {
+        return new ParquetSourceFile( "file:/tmp/orders.parquet", Map.of(), Map.of() );
+    }
+
+
+    private static ExportedColumn exportedColumn( String name, PolyType type, int position, boolean nullable ) {
+        return new ExportedColumn( name, type, null, null, null, null, null, nullable, "public", "orders", name, position, false );
+    }
+
+
+    private static TestParquetSource testSource() throws Exception {
+        java.lang.reflect.Field field = Unsafe.class.getDeclaredField( "theUnsafe" );
+        field.setAccessible( true );
+        return (TestParquetSource) ((Unsafe) field.get( null )).allocateInstance( TestParquetSource.class );
+    }
+
+
     @Test
     void documentBuildsTupleTypeFromExportedColumnsAndFallsBackToDocumentId() throws Exception {
         TestParquetSource.exportedColumns = Map.of(
@@ -115,81 +137,6 @@ void docScanCopiesFiltersDerivesDocumentRowAndRegistersFilterRule() {
     }
 
 
-    @Test
-    void docFilterCopyPreservesEntityAndDelegatesToScanInput() {
-        ParquetDocument document = new ParquetDocument( collection( "orders" ), List.of( sourceFile() ), null );
-        AlgCluster cluster = cluster();
-        ParquetDocScan scan = new ParquetDocScan( cluster, document, List.of() );
-        RexNode condition = ref( cluster, 0 );
-        ParquetDocFilter filter = new ParquetDocFilter( cluster, cluster.traitSetOf( EnumerableConvention.INSTANCE ), scan, condition, document );
-
-        ParquetDocFilter copied = filter.copy( cluster.traitSetOf( EnumerableConvention.INSTANCE ), scan, condition );
-
-        assertNotSame( filter, copied );
-        assertSame( document, copied.getEntity() );
-        assertSame( scan, copied.getInput() );
-    }
-
-
-    @Test
-    @SuppressWarnings("unchecked")
-    void docFilterRuleSplitsNestedAndConjunctions() throws Exception {
-        AlgDataTypeFactory typeFactory = new PolyTypeFactoryImpl( AlgDataTypeSystem.DEFAULT );
-        AlgDataType boolType = typeFactory.createPolyType( PolyType.BOOLEAN );
-        RexNode left = new RexIndexRef( 0, boolType );
-        RexNode middle = new RexIndexRef( 1, boolType );
-        RexNode right = new RexIndexRef( 2, boolType );
-        RexNode condition = new RexCall(
-                boolType,
-                new org.polypheny.db.nodes.SpecialOperator( Kind.AND.name(), Kind.AND ),
-                List.of( left, new RexCall( boolType, new org.polypheny.db.nodes.SpecialOperator( Kind.AND.name(), Kind.AND ), List.of( middle, right ) ) ) );
-        ParquetDocFilterRule rule = new ParquetDocFilterRule( AlgFactories.LOGICAL_BUILDER, null );
-        Method splitConjunctions = ParquetDocFilterRule.class.getDeclaredMethod( "splitConjunctions", RexNode.class );
-        splitConjunctions.setAccessible( true );
-
-        List<RexNode> split = (List<RexNode>) splitConjunctions.invoke( rule, condition );
-        List<RexNode> single = (List<RexNode>) splitConjunctions.invoke( rule, left );
-
-        assertEquals( List.of( left, middle, right ), split );
-        assertEquals( List.of( left ), single );
-    }
-
-
-    private static AlgCluster cluster() {
-        AlgDataTypeFactory typeFactory = new PolyTypeFactoryImpl( AlgDataTypeSystem.DEFAULT );
-        VolcanoPlanner planner = new VolcanoPlanner();
-        return AlgCluster.createDocument( planner, new RexBuilder( typeFactory ), null );
-    }
-
-
-    @SuppressWarnings("SameParameterValue")
-    private static RexNode ref( AlgCluster cluster, int index ) {
-        return new RexIndexRef( index, cluster.getTypeFactory().createPolyType( PolyType.BOOLEAN ) );
-    }
-
-
-    private static PhysicalCollection collection( String name ) {
-        return new PhysicalCollection( 10L, 20L, 30L, 40L, name, "public", 50L );
-    }
-
-
-    private static ParquetSourceFile sourceFile() {
-        return new ParquetSourceFile( "file:/tmp/orders.parquet", Map.of(), Map.of() );
-    }
-
-
-    private static ExportedColumn exportedColumn( String name, PolyType type, int position, boolean nullable ) {
-        return new ExportedColumn( name, type, null, null, null, null, null, nullable, "public", "orders", name, position, false );
-    }
-
-
-    private static TestParquetSource testSource() throws Exception {
-        java.lang.reflect.Field field = Unsafe.class.getDeclaredField( "theUnsafe" );
-        field.setAccessible( true );
-        return (TestParquetSource) ((Unsafe) field.get( null )).allocateInstance( TestParquetSource.class );
-    }
-
-
     @AdapterProperties(
             name = "Test Parquet Source",
             description = "Test-only source for document planning unit tests.",
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java
index 4c2e73138b..4a1afcaa28 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java
@@ -52,9 +52,12 @@
 import org.polypheny.db.algebra.type.AlgDataType;
 import org.polypheny.db.algebra.type.AlgDataTypeFactory;
 import org.polypheny.db.algebra.type.AlgDataTypeSystem;
+import org.polypheny.db.algebra.type.DocumentType;
 import org.polypheny.db.nodes.SpecialOperator;
+import org.polypheny.db.rex.RexBuilder;
 import org.polypheny.db.rex.RexCall;
 import org.polypheny.db.rex.RexDynamicParam;
+import org.polypheny.db.rex.RexIndexRef;
 import org.polypheny.db.rex.RexNameRef;
 import org.polypheny.db.rex.RexNode;
 import org.polypheny.db.type.PolyType;
@@ -179,12 +182,37 @@ void docFilterTranslatorTranslatesTopLevelNameAndRejectsNestedPaths() {
         ParquetAdapterFilter<?> translated = translator.translate(
                 columns,
                 call( boolType, Kind.EQUALS, new RexNameRef( "Status", null, stringType ), new RexDynamicParam( stringType, 4 ) ) );
+        ParquetAdapterFilter<?> mqlGt = translator.translate(
+                columns,
+                call( boolType, Kind.GREATER_THAN, new RexNameRef( "status", null, stringType ), new RexDynamicParam( stringType, 6 ) ) );
         RexNode nested = call( boolType, Kind.EQUALS, new RexNameRef( List.of( "address", "city" ), null, stringType ), new RexDynamicParam( stringType, 5 ) );
+        RexBuilder rexBuilder = new RexBuilder( typeFactory );
+        RexCall path = new RexCall(
+                typeFactory.createArrayType( typeFactory.createPolyType( PolyType.CHAR, 6 ), 1 ),
+                new SpecialOperator( Kind.ARRAY_VALUE_CONSTRUCTOR.name(), Kind.ARRAY_VALUE_CONSTRUCTOR ),
+                List.of( rexBuilder.makeLiteral( "status" ) ) );
+        RexNode loweredMqlFilter = call(
+                boolType,
+                Kind.GREATER_THAN,
+                new RexCall(
+                        stringType,
+                        new SpecialOperator( Kind.MQL_QUERY_VALUE.name(), Kind.MQL_QUERY_VALUE ),
+                        List.of( RexIndexRef.of( 0, DocumentType.ofDoc() ), path ) ),
+                new RexDynamicParam( stringType, 7 ) );
 
         assertNotNull( translated );
         assertEquals( 3, translated.columnIndex() );
         assertEquals( Kind.EQUALS, translated.operator() );
         assertEquals( 4L, translated.dynamicParamIndex() );
+        assertNotNull( mqlGt );
+        assertEquals( 3, mqlGt.columnIndex() );
+        assertEquals( Kind.GREATER_THAN, mqlGt.operator() );
+        assertEquals( 6L, mqlGt.dynamicParamIndex() );
+        ParquetAdapterFilter<?> loweredMql = translator.translate( columns, loweredMqlFilter );
+        assertNotNull( loweredMql );
+        assertEquals( 3, loweredMql.columnIndex() );
+        assertEquals( Kind.GREATER_THAN, loweredMql.operator() );
+        assertEquals( 7L, loweredMql.dynamicParamIndex() );
         assertNull( translator.translate( columns, nested ) );
     }
 

From 5ba6e0cab88e65462d2382fe24ee58f01c6c3f04 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Sun, 14 Jun 2026 12:37:20 +0200
Subject: [PATCH 39/46] benchmarks specification, queries, scripts, results

---
 plugins/parquet-adapter/benchmarks/README.md  |   92 ++
 .../benchmarks/benchmark-plan.md              |  299 ++++-
 .../benchmarks/benchmark-query-set.md         |  152 ---
 .../benchmark_results/benchmark_results.md    |   18 -
 .../benchmarks/datasets/nested_customer.md    |   16 +
 .../parquet-adapter/benchmarks/query-list.md  | 1144 -----------------
 .../access_model_comparison_mql.mql           |   46 +
 ...ss_model_comparison_query_specification.md |    9 +
 .../access_model_comparison_rf.sql            |   35 +
 .../access_model_comparison_sql.sql           |   35 +
 .../query_lists/aggregation_queries.md        |  130 --
 .../arrregation/aggregation_polypheny.sql     |   72 ++
 .../arrregation/aggregation_polypheny_mql.sql |  107 ++
 .../aggregation_query_specification.md        |   24 +
 .../arrregation/aggregation_sql.sql           |   72 ++
 .../query_lists/data_configuration.md         |   89 ++
 .../nested_data/nested_data_duckdb.sql        |  117 ++
 .../nested_data/nested_data_mql.mql           |   91 ++
 .../nested_data_polypheny_normalized.sql      |   76 ++
 .../nested_data_query_specification.md        |   13 +
 .../nested_data/nested_data_spark.sql         |   69 +
 .../query_lists/nested_flickr8k_queries.md    |  104 --
 .../partitioning_query_specification.md       |   29 +
 .../partitioning_repartitioned_sql.sql        |   27 +
 ..._repartitioned_unpartitioned_polypheny.sql |   57 +
 .../partitioning_unpartitioned_sql.sql        |   27 +
 ...s_model_comparison_duckdb_tlcp_results.csv |   31 +
 ..._comparison_polypheny_mql_tlcp_results.csv |   31 +
 ...l_comparison_polypheny_rf_tlcp_results.csv |   31 +
 ...ss_model_comparison_spark_tlcp_results.csv |   31 +
 .../access_model_comparison_tlcp_results.md   |  151 +++
 .../access_model_comparison_tlcp_summary.md   |   58 +
 ...s_model_comparison_duckdb_tlcp_results.csv |   31 +
 ..._comparison_polypheny_mql_tlcp_results.csv |   31 +
 ...l_comparison_polypheny_rf_tlcp_results.csv |   31 +
 ...ss_model_comparison_spark_tlcp_results.csv |   31 +
 ..._model_comparison_tlcp_results_analysis.md |   35 +
 .../access_model_comparison_tlcp_summary.md   |   58 +
 .../aggregation_duckdb_results.csv            |   61 +
 .../aggregation_polypheny_results.csv         |   61 +
 .../aggregation_results_analysis.md           |   37 +
 .../aggregation/aggregation_spark_results.csv |   61 +
 .../aggregation/aggregation_summary.md        |   78 ++
 .../nested_data_duckdb_results.csv            |   18 +
 .../nested_data_polypheny_mql_results.csv     |    1 +
 ...sted_data_polypheny_normalized_results.csv |   31 +
 .../nested_data/nested_data_spark_results.csv |   31 +
 ...titioning_duckdb_repartitioned_results.csv |   31 +
 ...titioning_duckdb_unpartitioned_results.csv |   31 +
 .../partitioning_polypheny_results.csv        |   61 +
 .../partitioning_results_analysis.md          |   40 +
 ...rtitioning_spark_repartitioned_results.csv |   31 +
 ...rtitioning_spark_unpartitioned_results.csv |   31 +
 .../partitioning/partitioning_summary.md      |   79 ++
 .../plots/access_model_comparison_plot.pdf    |  Bin 0 -> 5109 bytes
 .../plots/access_model_comparison_plot.svg    |   69 +
 .../access_model_comparison_tlcp_plot.svg     |   68 +
 .../results/plots/partitioning_plot.svg       |   79 ++
 ...run_access_model_comparison_bm_pipeline.md |   82 ++
 .../run_aggregation_bm_pipeline.md            |  108 ++
 .../run_nested_data_bm_pipeline.md            |  103 ++
 .../run_partitioning_bm_pipeline.md           |  122 ++
 .../benchmarks/running_benchmarks.md          |   35 -
 .../TLC_preprocessing_spec.md                 |   58 +
 .../TlcUnpartitionedMaterializer.java         |  284 ++++
 .../materialize_tlc_repartitioned.ps1         |  113 ++
 .../materialize_tlc_unpartitioned.ps1         |   57 +
 .../ds_preprocessing/prepare_tlc_benchmark.py |  329 +++++
 .../implementation/DuckDbJdbcBenchmark.java   |  310 +++++
 .../PolyphenyJdbcBenchmark.java               |  331 +++++
 .../implementation/PolyphenyMqlBenchmark.java |  566 ++++++++
 .../implementation/SparkSqlBenchmark.py       |  286 +++++
 .../benchmark_result_plot_generator.py        |  215 ++++
 .../nested/run_duckdb_nested_benchmark.ps1    |   61 +
 .../run_polypheny_mql_nested_benchmark.ps1    |   57 +
 .../nested/run_polypheny_nested_benchmark.ps1 |   60 +
 .../nested/run_spark_nested_benchmark.ps1     |   86 ++
 .../scripts/runners/run_duckdb_benchmark.ps1  |   80 ++
 .../runners/run_polypheny_benchmark.ps1       |   78 ++
 .../runners/run_polypheny_mql_benchmark.ps1   |   86 ++
 .../scripts/runners/run_spark_benchmark.ps1   |  209 +++
 .../scripts/summarize_benchmark_results.py    |  298 +++++
 ...parquet_workflow_load_class_diagram.drawio |  109 --
 .../docs/unit_tests/test_coverage.md          |  212 +++
 84 files changed, 7032 insertions(+), 1702 deletions(-)
 create mode 100644 plugins/parquet-adapter/benchmarks/README.md
 delete mode 100644 plugins/parquet-adapter/benchmarks/benchmark-query-set.md
 delete mode 100644 plugins/parquet-adapter/benchmarks/benchmark_results/benchmark_results.md
 delete mode 100644 plugins/parquet-adapter/benchmarks/query-list.md
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_mql.mql
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_query_specification.md
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_rf.sql
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_sql.sql
 delete mode 100644 plugins/parquet-adapter/benchmarks/query_lists/aggregation_queries.md
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny.sql
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny_mql.sql
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_specification.md
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_sql.sql
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/data_configuration.md
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_duckdb.sql
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_mql.mql
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_polypheny_normalized.sql
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_query_specification.md
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_spark.sql
 delete mode 100644 plugins/parquet-adapter/benchmarks/query_lists/nested_flickr8k_queries.md
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_sql.sql
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_unpartitioned_polypheny.sql
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_unpartitioned_sql.sql
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_duckdb_tlcp_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_mql_tlcp_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_rf_tlcp_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_spark_tlcp_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_results.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_summary.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_duckdb_tlcp_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_mql_tlcp_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rf_tlcp_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_spark_tlcp_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_results_analysis.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_results_analysis.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_duckdb_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_mql_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_spark_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_repartitioned_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_unpartitioned_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_polypheny_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_results_analysis.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_repartitioned_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_unpartitioned_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_plot.pdf
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_plot.svg
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot.svg
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/partitioning_plot.svg
 create mode 100644 plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md
 create mode 100644 plugins/parquet-adapter/benchmarks/run_pipeline/run_aggregation_bm_pipeline.md
 create mode 100644 plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md
 create mode 100644 plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md
 delete mode 100644 plugins/parquet-adapter/benchmarks/running_benchmarks.md
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/TLC_preprocessing_spec.md
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/TlcUnpartitionedMaterializer.java
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/materialize_tlc_repartitioned.ps1
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/materialize_tlc_unpartitioned.ps1
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/prepare_tlc_benchmark.py
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/implementation/DuckDbJdbcBenchmark.java
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyJdbcBenchmark.java
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyMqlBenchmark.java
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/implementation/SparkSqlBenchmark.py
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_duckdb_nested_benchmark.ps1
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_polypheny_mql_nested_benchmark.ps1
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_polypheny_nested_benchmark.ps1
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_spark_nested_benchmark.ps1
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/runners/run_duckdb_benchmark.ps1
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_benchmark.ps1
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_mql_benchmark.ps1
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/runners/run_spark_benchmark.ps1
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/summarize_benchmark_results.py
 delete mode 100644 plugins/parquet-adapter/docs/diagrams/parquet_workflow_load_class_diagram.drawio
 create mode 100644 plugins/parquet-adapter/docs/unit_tests/test_coverage.md

diff --git a/plugins/parquet-adapter/benchmarks/README.md b/plugins/parquet-adapter/benchmarks/README.md
new file mode 100644
index 0000000000..eba4b7438d
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/README.md
@@ -0,0 +1,92 @@
+# Parquet Adapter Benchmarks
+
+`plugins/parquet-adapter/benchmarks` folder contains benchmark definitions, execution pipelines, runner code,
+and result artifacts for the Polypheny Parquet adapter comparison.
+
+## Main Files
+
+| Path                          | Purpose                                                                                       |
+|-------------------------------|-----------------------------------------------------------------------------------------------|
+| `benchmark-plan.md`           | Benchmark suite overview: goals, datasets, query files, result locations, and pipeline links. |
+| `compatitive-technologies.md` | Technical comparison of Polypheny, DuckDB, and Apache Spark for Parquet workloads.            |
+| `README.md`                   | Folder structure and file map.                                                                |
+
+## Folder Structure
+
+| Folder          | Contents                                                                                               |
+|-----------------|--------------------------------------------------------------------------------------------------------|
+| `datasets/`     | Dataset descriptions.                                                                                  |
+| `query_lists/`  | Runnable SQL/MQL query files and query specifications.                                                 |
+| `run_pipeline/` | Step-by-step benchmark execution commands. Use these files to run each full suite pipeline.            |
+| `scripts/`      | Benchmark clients, PowerShell runners, preprocessing scripts, summary generation, and plot generation. |
+| `results/`      | Raw CSV results, generated summaries, short analysis notes, and plots.                                 |
+
+## Query Lists
+
+| Path                                   | Meaning                                                                                                                                            |
+|----------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------|
+| `query_lists/data_configuration.md`    | Local data paths and Polypheny adapter names used by the query files.                                                                              |
+| `query_lists/access_model_comparison/` | Access-model comparison benchmark queries for Polypheny relational, Polypheny MQL, DuckDB, and Spark.                                              |
+| `query_lists/nested_data/`             | Nested Customer benchmark queries. Engine-specific files are used because nested access syntax differs.                                            |
+| `query_lists/arrregation/`             | Aggregation benchmark query files for Polypheny relational, Polypheny MQL, DuckDB, and Spark. The directory name is kept as-is for existing paths. | 
+| `query_lists/partitioning/`            | Partitioning benchmark queries for repartitioned and unpartitioned TLC layouts.                                                                    |
+
+File naming:
+
+- `*_polypheny*.sql`: Polypheny relational SQL with explicit adapter-prefixed table names.
+- `*_sql.sql`: SQL shared by DuckDB and Spark where both expose the same logical temp views.
+- `*.mql`: Polypheny document adapter queries.
+- `*_query_specification.md`: logical query intent and query groups.
+
+## Run Pipelines
+
+Run suite pipelines from:
+
+```text
+run_pipeline/
+```
+
+| Pipeline file                                | Suite                   |
+|----------------------------------------------|-------------------------|
+| `run_access_model_comparison_bm_pipeline.md` | Access model comparison |
+| `run_nested_data_bm_pipeline.md`             | Nested data             |
+| `run_aggregation_bm_pipeline.md`             | Aggregation             |
+| `run_partitioning_bm_pipeline.md`            | Partitioning            |
+
+Each pipeline file contains the commands to build/start Polypheny where needed,
+run the benchmark clients, and generate the suite summary.
+
+## Scripts
+
+| Path                                     | Purpose                                                                      |
+|------------------------------------------|------------------------------------------------------------------------------|
+| `scripts/runners/`                       | PowerShell entry points for Polypheny SQL, Polypheny MQL, DuckDB, and Spark. |
+| `scripts/runners/nested/`                | Nested-data-specific runner wrappers.                                        |
+| `scripts/implementation/`                | Java/Python benchmark clients called by the runners.                         |
+| `scripts/ds_preprocessing/`              | Dataset materialization and preprocessing utilities.                         |
+| `scripts/summarize_benchmark_results.py` | Creates markdown summaries from benchmark CSV files.                         |
+| `scripts/plot_generation/`               | Plot generation scripts, including the generic benchmark CSV plot generator.  |
+
+## Results
+
+Raw benchmark outputs are stored as CSV files under:
+
+```text
+results/
+```
+
+Result conventions:
+
+- `*_results.csv`: raw benchmark output from a runner.
+- `*_summary.md`: generated comparison summary from `scripts/summarize_benchmark_results.py`.
+- `*_results_analysis.md`: short manual interpretation of a generated summary.
+- `results/plots/`: generated plot artifacts such as SVG and PDF files.
+
+Current result folders:
+
+| Path                               | Contents                                      |
+|------------------------------------|-----------------------------------------------|
+| `results/access_model_comparison/` | Access-model raw CSVs, summary, and analysis. |
+| `results/aggregation/`             | Aggregation raw CSVs, summary, and analysis.  |
+| `results/nested_data/`             | Nested-data raw CSVs.                         |
+| `results/plots/`                   | Generated benchmark plots.                    |
diff --git a/plugins/parquet-adapter/benchmarks/benchmark-plan.md b/plugins/parquet-adapter/benchmarks/benchmark-plan.md
index e5ade85120..dbd7fe5f2f 100644
--- a/plugins/parquet-adapter/benchmarks/benchmark-plan.md
+++ b/plugins/parquet-adapter/benchmarks/benchmark-plan.md
@@ -1,22 +1,301 @@
 # Benchmark Plan
 
-## Relational Schema
+## Goals
 
-We use a set of benchmark queries (query-list.md) to evaluate different functionalities of the Parquet adapter. The workload primarily consists of aggregation queries.
+The benchmarks evaluate the Parquet adapter from following perspectives:
 
-### 1. Flat Partitioned Dataset
+- correctness of the implemented access paths;
+- runtime performance for representative analytical workloads;
+- comparison of Polypheny access models;
+- comparison with DuckDB and Apache Spark;
+- impact of Parquet-specific optimizations.
 
-The benchmark uses four years of the TLC dataset partitioned by year and month. The year and month values are not stored within the Parquet files and are instead derived from the partition structure.
 
-All benchmark queries are executed on Polypheny, Apache Spark, and DuckDB to establish a baseline. After performance optimizations are implemented, the same queries are executed again to measure their impact.
+## Common Execution Setup
 
-### 2. Flat Unpartitioned Dataset
+Unless a benchmark suite states otherwise, each query is executed with:
 
-The same TLC dataset is used without partitioning. In this configuration, the year and month values are stored as columns within the Parquet files.
+- one warmup run;
+- five measured runs;
+- elapsed wall-clock time recorded in milliseconds;
+- result row count and column count recorded for correctness checks;
+- failures and timeouts recorded instead of runtime values.
 
-All benchmark queries are executed on Polypheny, Apache Spark, and DuckDB to establish a baseline. After performance optimizations are implemented, the same queries are executed again to measure their impact.
+Warmup runs are excluded from reported results. The benchmark clients consume
+query results according to the result boundary described in the report
+methodology. Polypheny and DuckDB use JDBC clients that drain the complete result
+set. Spark is executed in local mode inside Docker and consumes rows in the
+Spark runner.
 
-### 3. Partitioned vs. Unpartitioned Dataset
+Raw benchmark outputs are stored as CSV files. Summary tables are generated with:
 
-The performance of all benchmark queries is compared on Polypheny to evaluate the impact of partitioning.
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py
+```
 
+## Datasets
+
+| Dataset                | Local input                   | Main purpose                                           |
+|------------------------|-------------------------------|--------------------------------------------------------|
+| NYC TLC Trip Records   | `C:\PolyData\tlc_partitioned` | Flat analytical data and Hive-style partitioning       |
+| Nested Customer        | `C:\PolyData\nested_customer` | Deep nested structures and generated normalized tables |
+
+The TLC snapshot contains data from January 2020 through January 2023. This
+period is used because newer TLC files introduce schema changes that make a
+single consolidated benchmark table less stable. 
+
+## Compared Access Paths and Systems
+
+The benchmark plan includes the following Polypheny access paths:
+
+- Polypheny relational adapter in flat schema mode;
+- Polypheny relational adapter in normalized schema mode;
+- Polypheny document adapter queried through MQL.
+
+DuckDB and Apache Spark are used as external reference systems for equivalent
+Parquet workloads where the same logical operation can be expressed.
+
+## Suite 1: Access Model Comparison
+
+Status: implemented and executed for the TLC `green_tripdata` table.
+
+### Purpose
+
+This suite compares basic access patterns across Polypheny access models and
+external Parquet engines. It focuses on flat data access rather than nested
+schema generation or partition pruning.
+
+### Compared Systems and Access Paths
+
+- Polypheny relational adapter in flat schema mode;
+- Polypheny document adapter queried through MQL;
+- DuckDB;
+- Apache Spark.
+
+Normalized relational mode is not included in this suite because the selected
+`green_tripdata` table is flat and does not produce meaningful generated child
+tables.
+
+### Dataset
+
+Dataset description:
+`plugins/parquet-adapter/benchmarks/datasets/tlc_dataset_desc.md`
+
+Input table: `green_tripdata` from the partitioned TLC dataset.
+
+Polypheny table and collection names used by the current query files:
+
+- relational table: `tlcp__green_tripdata`;
+- document collection: `tlcpd_document.tlcpd__green_tripdata`;
+- DuckDB and Spark view: `green_tripdata`.
+
+### Query Files
+
+| System                 | Query file                                                                                               |
+|------------------------|----------------------------------------------------------------------------------------------------------|
+| Polypheny relational   | `plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_rf.sql`  |
+| Polypheny document MQL | `plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_mql.mql` |
+| DuckDB and Spark       | `plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_sql.sql` |
+
+### Query Groups
+
+Specification stored in:
+```text
+plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_query_specification.md
+```
+
+### Result Files
+
+Raw and summarized results are stored in:
+
+```text[..](..)
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/
+```
+
+The current summary file is:
+
+```text
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
+```
+
+### Run Pipeline
+
+Runnable commands for this suite are documented in:
+
+```text
+plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md
+```
+
+
+## Suite 2: Nested Data
+
+Status: planned.
+
+### Purpose
+
+This suite evaluates nested Parquet data, repeated fields, large binary fields,
+and generated normalized relational tables.
+
+### Dataset
+
+- Nested Customer for deeply nested customer, order, and line-item structures.
+  
+Dataset description:
+`plugins/parquet-adapter/benchmarks/datasets/nested_customer.md`
+
+### Compared Systems and Access Paths
+
+- Polypheny relational adapter in normalized mode;
+- Polypheny document adapter queried through MQL;
+- DuckDB;
+- Apache Spark.
+
+### Query Files
+
+| System                                  | Query file                                                                                        |
+|-----------------------------------------|---------------------------------------------------------------------------------------------------|
+| Polypheny relational normalized adapter | `plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_polypheny_normalized.sql` |
+| Polypheny document MQL                  | `plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_mql.mql`                  |
+| DuckDB                                  | `plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_duckdb.sql`               |
+| Apache Spark                            | `plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_spark.sql`                |
+
+
+### Query Groups
+
+Specification stored in:
+```text
+plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_query_specification.md
+```
+
+### Result Files
+
+Raw and summarized results are stored in:
+
+```text
+plugins/parquet-adapter/benchmarks/results/nested_data/
+```
+
+Summary file:
+
+```text
+plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_summary.md
+```
+
+### Run Pipeline
+
+Runnable commands for this suite are documented in:
+
+```text
+plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md
+```
+
+## Suite 3: Aggregation and Optimization
+
+Status: planned.
+
+### Purpose
+
+This suite evaluates analytical aggregations and the effect of optimization work
+introduced during development.
+
+### Dataset
+
+TLC tables from `C:\PolyData\tlc_partitioned`. The aggregation suite runs on
+`yellow_tripdata` for Q01-Q05 and `fhvhv_tripdata` for Q06-Q10.
+
+### Query Files
+
+| System                 | Query file                                                                                 |
+|------------------------|--------------------------------------------------------------------------------------------|
+| Polypheny relational   | `plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny.sql`     |
+| Polypheny document MQL | `plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny_mql.sql` |
+| DuckDB and Spark       | `plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_sql.sql`           |
+
+Human-readable query source:
+
+```text
+plugins/parquet-adapter/benchmarks/query_lists/aggregation_queries.md
+```
+
+### Query Groups
+
+Specification stored in:
+
+```text
+plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_specification.md
+```
+
+### Compared Systems and Access Paths
+
+- Polypheny relational before and after optimization, where both versions are available;
+- Polypheny document adapter queried through MQL;
+- DuckDB;
+- Apache Spark.
+
+### Query Themes
+
+- full-table counts;
+- counts over one partition month;
+- selective filtered counts;
+- grouped monthly aggregations;
+- grouped aggregations over large TLC tables.
+
+### Run Pipeline
+
+Runnable commands for this suite are documented in:
+
+```text
+plugins/parquet-adapter/benchmarks/run_pipeline/run_aggregation_bm_pipeline.md
+```
+
+## Suite 4: Partitioning
+
+Status: planned.
+
+### Purpose
+
+This suite evaluates the effect of Hive-style partitioning and partition pruning
+on Parquet workloads.
+
+### Dataset
+
+Partitioned and derived unpartitioned or repartitioned variants of the TLC
+dataset.
+
+### Compared Systems and Access Paths
+
+- Polypheny over partitioned TLC data;
+- Polypheny over equivalent unpartitioned TLC data;
+- DuckDB and Spark where equivalent partitioned and unpartitioned layouts are
+  available.
+
+### Query Files
+
+| System/layout                   | Query file                                                                                                          |
+|---------------------------------|---------------------------------------------------------------------------------------------------------------------|
+| Polypheny relational            | `plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_unpartitioned_polypheny.sql` |
+| DuckDB and Spark, repartitioned | `plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_sql.sql`                    |
+| DuckDB and Spark, unpartitioned | `plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_unpartitioned_sql.sql`                    |
+
+### Query Groups
+
+Specification stored in:
+
+```text
+plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md
+```
+
+### Query Themes
+
+- filters on partition columns such as `year` and `month`;
+- filters on physical Parquet columns;
+- combinations of partition filters and data filters;
+- aggregations grouped by partition columns.
+
+### Run Pipeline
+
+Runnable commands for this suite are documented in:
+
+```text
+plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md
+```
diff --git a/plugins/parquet-adapter/benchmarks/benchmark-query-set.md b/plugins/parquet-adapter/benchmarks/benchmark-query-set.md
deleted file mode 100644
index 5db68cb067..0000000000
--- a/plugins/parquet-adapter/benchmarks/benchmark-query-set.md
+++ /dev/null
@@ -1,152 +0,0 @@
-# Parquet Benchmark Query Set
-
-This file selects 10 queries from the original Q01-Q20 benchmark set for the
-current Parquet adapter feature scope. The set covers the supported aggregate
-functions: `COUNT`, `SUM`, `MIN`, and `MAX`.
-
-The selected queries avoid functionality that is no longer part of the adapter
-benchmark target:
-
-- no average aggregate
-- no `WITH` / CTE queries, because these are not supported by the Polypheny JDBC benchmark path
-- no aggregate grouping by calculated expressions
-- no aggregate arguments produced by a Calc expression
-
-SQL uses the DuckDB table names from the existing benchmark files. The Polypheny
-benchmark client maps these names to adapter tables such as
-`tlc__yellow_tripdata`, `tlc__green_tripdata`, `tlc__fhvhv_tripdata`, and
-`tlc__fhv_tripdata`.
-
-The selected original queries are Q01, Q02, Q04, Q05, Q06, Q09, Q10, Q12, Q13,
-and Q15. The Q06 and Q13 versions below keep the original monthly aggregate
-shape but replace average distance metrics with minimum and maximum distance
-metrics.
-
-## S01 - Original Q01: Full Yellow Taxi Row Count
-
-**Description:** Find how many yellow taxi trips are in the dataset.
-
-```sql
-SELECT count(*) AS row_count
-FROM yellow_tripdata;
-```
-
-## S02 - Original Q02: Yellow Taxi Count For One Partition Month
-
-**Description:** Find how many yellow taxi trips were in October 2022.
-
-```sql
-SELECT count(*) AS row_count
-FROM yellow_tripdata
-WHERE "year" = '2022'
-  AND "month" = '10';
-```
-
-## S03 - Original Q04: Yellow Taxi Trips On One Day
-
-**Description:** Find how many yellow taxi trips started on October 15, 2022.
-
-```sql
-SELECT count(*) AS row_count
-FROM yellow_tripdata
-WHERE tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
-  AND tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00';
-```
-
-## S04 - Original Q05: Long And Expensive Yellow Taxi Trips
-
-**Description:** Find how many yellow taxi trips were at least 10 miles long and
-cost at least 40 dollars.
-
-```sql
-SELECT count(*) AS row_count
-FROM yellow_tripdata
-WHERE trip_distance >= 10.0
-  AND total_amount >= 40.0;
-```
-
-## S05 - Original Q06 Adjusted: Monthly Yellow Taxi Amount And Distance Summary
-
-**Description:** Find, for each year and month, how many yellow taxi trips there
-were, the total amount charged, and the shortest and longest trip distances.
-
-```sql
-SELECT
-  "year",
-  "month",
-  count(*) AS trips,
-  sum(total_amount) AS gross_amount,
-  min(trip_distance) AS min_distance,
-  max(trip_distance) AS max_distance
-FROM yellow_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
-```
-
-## S06 - Original Q09: Full High-Volume FHV Row Count
-
-**Description:** Find how many high-volume FHV trips are in the dataset.
-
-```sql
-SELECT count(*) AS row_count
-FROM fhvhv_tripdata;
-```
-
-## S07 - Original Q10: High-Volume FHV Count For One Partition Month
-
-**Description:** Find how many high-volume FHV trips were in October 2022.
-
-```sql
-SELECT count(*) AS row_count
-FROM fhvhv_tripdata
-WHERE "year" = '2022'
-  AND "month" = '10';
-```
-
-## S08 - Original Q12: Long And Expensive High-Volume FHV Trips
-
-**Description:** Find how many high-volume FHV trips were at least 10 miles long
-and had a base passenger fare of at least 40 dollars.
-
-```sql
-SELECT count(*) AS row_count
-FROM fhvhv_tripdata
-WHERE trip_miles >= 10.0
-  AND base_passenger_fare >= 40.0;
-```
-
-## S09 - Original Q13 Adjusted: Monthly High-Volume FHV Fare And Distance Summary
-
-**Description:** Find, for each year and month, how many high-volume FHV trips
-there were, the total passenger fare, the total driver pay, and the shortest and
-longest trip distances.
-
-```sql
-SELECT
-  "year",
-  "month",
-  count(*) AS trips,
-  sum(base_passenger_fare) AS passenger_fare,
-  sum(driver_pay) AS driver_pay,
-  min(trip_miles) AS min_miles,
-  max(trip_miles) AS max_miles
-FROM fhvhv_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
-```
-
-## S10 - Original Q15: High-Volume FHV Shared-Ride Flag Distribution
-
-**Description:** Find how many 2022 high-volume FHV trips fall into each
-combination of shared-request and shared-match flags.
-
-```sql
-SELECT
-  shared_request_flag,
-  shared_match_flag,
-  count(*) AS trips
-FROM fhvhv_tripdata
-WHERE "year" = '2022'
-GROUP BY shared_request_flag, shared_match_flag
-ORDER BY trips DESC;
-```
diff --git a/plugins/parquet-adapter/benchmarks/benchmark_results/benchmark_results.md b/plugins/parquet-adapter/benchmarks/benchmark_results/benchmark_results.md
deleted file mode 100644
index 6913c0bd21..0000000000
--- a/plugins/parquet-adapter/benchmarks/benchmark_results/benchmark_results.md
+++ /dev/null
@@ -1,18 +0,0 @@
-# Benchmark Results
-
-## Aggregation Results 
-
-Time displayed in milliseconds (ms)
-
-| Query | Description                                       | Polypheny before optimization | Polypheny after optimization | DuckDB | Spark | 
-|-------|---------------------------------------------------|------------------------------:|-----------------------------:|-------:|------:|
-| Q01   | Full Yellow Taxi Row Count                        |                         86707 |                              |        |       |                   
-| Q02   | Partition-pruned count, one yellow taxi "month"   |                          3833 |                              |        |       |                   
-| Q03   | Yellow Taxi Count For One Partition Month         |                         24707 |                              |        |       |                    
-| Q04   | Long And Expensive Yellow Taxi Trips              |                         95229 |                              |        |       |                   
-| Q05   | Monthly Yellow Taxi Amount And Distance Summary   |                        159617 |                              |        |       |                   
-| Q06   | Full High-Volume FHV Row Count                    |                        787924 |                              |        |       |                    
-| Q07   | High-Volume FHV Count For One Partition Month     |                         28321 |                              |        |       |                    
-| Q08   | Long And Expensive High-Volume FHV Trips          |                        759776 |                              |        |       |                    
-| Q09   | Monthly High-Volume FHV Fare And Distance Summary |                       1224409 |                              |        |       |                    
-| Q10   | High-Volume FHV Shared-Ride Flag Distribution     |                        376649 |                              |        |       |                    
diff --git a/plugins/parquet-adapter/benchmarks/datasets/nested_customer.md b/plugins/parquet-adapter/benchmarks/datasets/nested_customer.md
index 84e3800b4b..d3a0224171 100644
--- a/plugins/parquet-adapter/benchmarks/datasets/nested_customer.md
+++ b/plugins/parquet-adapter/benchmarks/datasets/nested_customer.md
@@ -127,6 +127,22 @@ c_orders: array<struct<
 >>
 ```
 
+```text
+Customer
+  Order #123
+    Lineitem 1: part A, quantity 2, price 50
+    Lineitem 2: part B, quantity 1, price 20
+    Lineitem 3: part C, quantity 5, price 10
+```
+
+```text
+customer
+  c_orders
+    order
+      o_lineitems
+        lineitem
+```
+
 ## Benchmark Use
 
 Recommended benchmark role:
diff --git a/plugins/parquet-adapter/benchmarks/query-list.md b/plugins/parquet-adapter/benchmarks/query-list.md
deleted file mode 100644
index 2e3a53ef85..0000000000
--- a/plugins/parquet-adapter/benchmarks/query-list.md
+++ /dev/null
@@ -1,1144 +0,0 @@
-# SQL Query List
-
-This section lists every benchmark query. SQL uses the DuckDB table names; the Polypheny client maps them to the `tlc__*` adapter tables.
-
-## Q01 - Full scan count, yellow taxi
-
-```sql
-SELECT count(*) AS row_count
-FROM yellow_tripdata;
-```
-
-### Checks:
-- full-table row counting.
-- the fastest possible aggregate path.
-
-### Base Functionality:
-- count all rows in the yellow taxi table.
-
-### Benchmark-Phase Optimization:
-- calculate the total from Parquet metadata without reading trip rows.
-
-
-## Q02 - Partition-pruned count, one yellow taxi "month"
-
-```sql
-SELECT count(*) AS row_count
-FROM yellow_tripdata
-WHERE "year" = '2022'
-  AND "month" = '10';
-```
-### Checks:
-- partition pruning for a selected month.
-- the fast aggregate path after partition pruning.
-
-### Base Functionality:
-- discard files outside October 2022.
-
-### Benchmark-Phase Optimization:
-- calculate the total from metadata of the matching files without reading trip rows.
-
-## Q03 - Projection-heavy scan, yellow taxi
-
-```sql
-SELECT
-  PULocationID,
-  DOLocationID,
-  trip_distance,
-  total_amount
-FROM yellow_tripdata
-WHERE "year" = '2022'
-  AND "month" = '10'
-LIMIT 100000;
-```
-
-### Checks:
-- partition pruning for a selected month.
-- efficient reading and transfer of 100,000 result rows.
-- projection of selected columns from a wider table.
-
-### Base Functionality:
-- discard files outside October 2022.
-- stop scanning after 100,000 rows have been produced.
-
-### Benchmark-Phase Optimization:
-- materialize flat projected values more efficiently using the lightweight row reader.
-
-## Q04 - Timestamp row-group filter, yellow taxi
-
-```sql
-SELECT count(*) AS row_count
-FROM yellow_tripdata
-WHERE tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
-  AND tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00';
-```
-
-### Checks:
-- timestamp predicate pushdown.
-- row-group pruning using pickup-time statistics.
-- filtered row counting.
-
-### Base Functionality:
-- push the pickup-time range filter into the Parquet reader.
-- skip row groups that cannot contain trips from October 15, 2022.
-
-### Benchmark-Phase Optimization:
-- count matching rows inside the adapter without materializing complete taxi rows.
-- read only the timestamp column needed to evaluate the filter.
-
-
-## Q05 - Numeric row-group filter, yellow taxi
-
-```sql
-SELECT count(*) AS row_count
-FROM yellow_tripdata
-WHERE trip_distance >= 10.0
-  AND total_amount >= 40.0;
-```
-
-### Checks:
-- numeric predicate pushdown.
-- row-group pruning using distance and fare statistics.
-- filtered row counting with multiple conditions.
-
-### Base Functionality:
-- push both numeric filters into the Parquet reader.
-- skip row groups that cannot contain matching trips.
-
-### Benchmark-Phase Optimization:
-- count matching rows inside the adapter without materializing complete taxi rows.
-- read only the two numeric columns needed to evaluate the filters.
-
-## Q06 - Monthly yellow taxi aggregates
-
-```sql
-SELECT
-  "year",
-  "month",
-  count(*) AS trips,
-  sum(total_amount) AS gross_amount,
-  avg(trip_distance) AS avg_distance
-FROM yellow_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
-```
-
-### Checks:
-- grouping by partition columns.
-- calculation of `COUNT`, `SUM`, and `AVG`.
-- ordering of monthly aggregate results.
-
-### Base Functionality:
-- expose `year` and `month` folder values as table columns.
-- group results by month and order them chronologically.
-
-### Benchmark-Phase Optimization:
-- determine the monthly group from folder partition values instead of reading grouping values from each row.
-- calculate counts from metadata and aggregate only the required numeric columns without materializing complete taxi rows.
-
-## Q07 - Top pickup zones by yellow taxi trip count, low-cardinality grouping
-
-```sql
-SELECT
-  PULocationID,
-  count(*) AS trips,
-  avg(total_amount) AS avg_total_amount
-FROM yellow_tripdata
-WHERE "year" = '2022'
-GROUP BY PULocationID
-ORDER BY trips DESC
-LIMIT 20;
-```
-
-### Checks:
-- partition pruning for a selected year.
-- grouping by pickup location.
-- calculation of `COUNT` and `AVG`.
-- ordering groups and returning the top 20 results.
-
-### Base Functionality:
-- discard files outside 2022.
-- order pickup locations by trip count and return the 20 busiest locations.
-
-### Benchmark-Phase Optimization:
-- calculate grouped aggregates inside the Parquet adapter.
-- read only the pickup-location and total-amount columns instead of complete taxi rows.
-
-
-## Q08 - Top pickup/dropoff pairs for yellow taxi
-
-```sql
-SELECT
-  PULocationID,
-  DOLocationID,
-  count(*) AS trips,
-  avg(trip_distance) AS avg_distance,
-  avg(total_amount) AS avg_total_amount
-FROM yellow_tripdata
-WHERE "year" = '2022'
-GROUP BY PULocationID, DOLocationID
-ORDER BY trips DESC
-LIMIT 20;
-```
-
-### Checks:
-- partition pruning for a selected year.
-- grouping by two location columns.
-- calculation of `COUNT` and `AVG`.
-- ordering groups and returning the top 20 results. 
-- compared with Q07, this creates more groups and tests the additional cost of tracking pickup(start)/dropoff(dest) combinations.
-
-### Base Functionality:
-- discard files outside 2022.
-- order pickup/dropoff pairs by trip count and return the 20 busiest pairs.
-
-### Benchmark-Phase Optimization:
-- calculate grouped aggregates inside the Parquet adapter.
-- read only the pickup-location, dropoff-location, distance, and total-amount columns instead of complete taxi rows.
-- use an efficient representation for the two-column grouping key.
-
-
-## Q09 - Full scan count, high-volume FHV
-
-```sql
-SELECT count(*) AS row_count
-FROM fhvhv_tripdata;
-```
-
-### Checks:
-- full-table row counting on the larger high-volume FHV dataset.
-- the fastest possible aggregate path at a larger scale.
-
-### Base Functionality:
-- count all rows in the high-volume FHV table.
-
-### Benchmark-Phase Optimization:
-- calculate the total from Parquet metadata without reading trip rows.
-
-## Q10 - Partition-pruned count, one high-volume FHV "month"
-
-```sql
-SELECT count(*) AS row_count
-FROM fhvhv_tripdata
-WHERE "year" = '2022'
-  AND "month" = '10';
-```
-
-### Checks:
-- partition pruning for a selected month on the larger **high-volume** FHV dataset.
-- the fast aggregate path after partition pruning.
-- This is the large-table counterpart of Q02
-
-### Base Functionality:
-- discard files outside October 2022.
-
-### Benchmark-Phase Optimization:
-- calculate the total from metadata of the matching files without reading trip rows.
-
-
-## Q11 - Timestamp row-group filter, high-volume FHV
-
-```sql
-SELECT count(*) AS row_count
-FROM fhvhv_tripdata
-WHERE pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
-  AND pickup_datetime < TIMESTAMP '2022-10-16 00:00:00';
-```
-
-### Checks:
-- timestamp predicate pushdown on the larger high-volume FHV dataset.
-- row-group pruning using pickup-time statistics.
-- filtered row counting.
-
-### Base Functionality:
-- push the pickup-time range filter into the Parquet reader.
-- skip row groups that cannot contain trips from October 15, 2022.
-
-### Benchmark-Phase Optimization:
-- count matching rows inside the adapter without materializing complete trip rows.
-- read only the timestamp column needed to evaluate the filter.
-
-
-## Q12 - High-volume FHV fare and distance filter
-
-```sql
-SELECT count(*) AS row_count
-FROM fhvhv_tripdata
-WHERE trip_miles >= 10.0
-  AND base_passenger_fare >= 40.0;
-```
-
-### Checks:
-- numeric predicate pushdown on the larger high-volume FHV dataset.
-- row-group pruning using distance and fare statistics.
-- filtered row counting with multiple conditions.
-
-### Base Functionality:
-- push both numeric filters into the Parquet reader.
-- skip row groups that cannot contain matching trips.
-
-### Benchmark-Phase Optimization:
-- count matching rows inside the adapter without materializing complete trip rows.
-- read only the distance and fare columns needed to evaluate the filters.
-
-
-## Q13 - Monthly high-volume FHV aggregates
-
-```sql
-SELECT
-  "year",
-  "month",
-  count(*) AS trips,
-  sum(base_passenger_fare) AS passenger_fare,
-  sum(driver_pay) AS driver_pay,
-  avg(trip_miles) AS avg_miles
-FROM fhvhv_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
-```
-
-### Checks:
-- grouping by partition columns on the larger high-volume FHV dataset.
-- calculation of `COUNT`, multiple `SUM` values, and `AVG`.
-- ordering of monthly aggregate results.
-
-### Base Functionality:
-- expose `year` and `month` folder values as table columns.
-- group results by month and order them chronologically.
-
-### Benchmark-Phase Optimization:
-- calculate monthly aggregates inside the Parquet adapter.
-- determine the monthly group from folder partition values instead of reading grouping values from each row.
-- calculate counts from metadata and aggregate only the required fare, driver-pay, and distance columns without materializing complete trip rows.
-
-## Q14 - Top high-volume FHV pickup/dropoff pairs
-
-```sql
-SELECT
-  PULocationID,
-  DOLocationID,
-  count(*) AS trips,
-  avg(trip_miles) AS avg_miles,
-  avg(base_passenger_fare) AS avg_fare
-FROM fhvhv_tripdata
-WHERE "year" = '2022'
-GROUP BY PULocationID, DOLocationID
-ORDER BY trips DESC
-LIMIT 20;
-```
-
-### Checks:
-- partition pruning for a selected year.
-- grouping by starting-location and destination-location pairs.
-- calculation of `COUNT` and `AVG`.
-- ordering groups and returning the top 20 results on the larger high-volume FHV dataset.
-- This is the larger-table counterpart of Q08.
-
-### Base Functionality:
-- discard files outside 2022.
-- order location pairs by trip count and return the 20 busiest pairs.
-
-### Benchmark-Phase Optimization:
-- calculate grouped aggregates inside the Parquet adapter.
-- read only the starting-location, destination-location, distance, and fare columns instead of complete trip rows.
-- use an efficient representation for the two-column grouping key.
-
-## Q15 - Low-cardinality shared-ride flag distribution, high-volume FHV
-
-```sql
-SELECT
-  shared_request_flag,
-  shared_match_flag,
-  count(*) AS trips
-FROM fhvhv_tripdata
-WHERE "year" = '2022'
-GROUP BY shared_request_flag, shared_match_flag
-ORDER BY trips DESC;
-```
-
-### Checks:
-- partition pruning for a selected year.
-- grouping by two low-cardinality text flags. (column has only a small number of distinct values)
-- counting trips for each shared-ride flag combination.
-- ordering combinations by trip count.
-
-### Base Functionality:
-- discard files outside 2022.
-- order shared-ride flag combinations from most common to least common.
-
-### Benchmark-Phase Optimization:
-- calculate grouped counts inside the Parquet adapter.
-- read only the two shared-ride flag columns instead of complete trip rows.
-
-## Q16 - Green taxi monthly aggregates
-
-```sql
-SELECT
-  "year",
-  "month",
-  count(*) AS trips,
-  sum(total_amount) AS gross_amount,
-  avg(trip_distance) AS avg_distance
-FROM green_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
-```
-
-### Checks:
-- grouping by partition columns on the smaller green taxi dataset.
-- calculation of `COUNT`, `SUM`, and `AVG`.
-- ordering of monthly aggregate results.
-
-### Base Functionality:
-- expose `year` and `month` folder values as table columns.
-- group results by month and order them chronologically.
-
-### Benchmark-Phase Optimization:
-- calculate monthly aggregates inside the Parquet adapter.
-- determine the monthly group from folder partition values instead of reading grouping values from each row.
-- calculate counts from metadata and aggregate only the required amount and distance columns without materializing complete trip rows.
-
-Smaller-table counterpart of Q06
-
-## Q17 - FHV monthly trip counts
-
-```sql
-SELECT
-  "year",
-  "month",
-  count(*) AS trips
-FROM fhv_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
-```
-
-### Checks:
-- grouping by partition columns on the classic FHV dataset.
-- monthly `COUNT(*)` calculation.
-- ordering of monthly results.
-
-### Base Functionality:
-- expose `year` and `month` folder values as table columns.
-- group results by month and order them chronologically.
-
-### Benchmark-Phase Optimization:
-- determine the monthly group from folder partition values.
-- calculate each monthly count from Parquet metadata without reading trip rows.
-
-Simpler than Q06, Q13, and Q16 because it calculates only row counts
-
-## Q18 - Yellow vs green monthly taxi totals
-
-```sql
-WITH yellow_monthly AS (
-  SELECT "year", "month", count(*) AS yellow_trips, sum(total_amount) AS yellow_amount
-  FROM yellow_tripdata
-  GROUP BY "year", "month"
-),
-green_monthly AS (
-  SELECT "year", "month", count(*) AS green_trips, sum(total_amount) AS green_amount
-  FROM green_tripdata
-  GROUP BY "year", "month"
-)
-SELECT
-  y."year",
-  y."month",
-  y.yellow_trips,
-  g.green_trips,
-  y.yellow_amount,
-  g.green_amount
-FROM yellow_monthly y
-JOIN green_monthly g
-  ON y."year" = g."year"
- AND y."month" = g."month"
-ORDER BY y."year", y."month";
-```
-
-### Checks:
-- monthly aggregation on two taxi datasets.
-- calculation of `COUNT` and `SUM`.
-- joining monthly aggregate results by `year` and `month`.
-- ordering the joined results chronologically.
-
-### Base Functionality:
-- join matching yellow and green taxi months.
-- order the combined monthly results.
-
-### Benchmark-Phase Optimization:
-- calculate aggregates separately inside the Parquet adapter before joining the datasets.
-- determine monthly groups from folder partition values.
-- calculate counts from metadata and aggregate only the required amount columns.
-- join the small monthly result sets instead of joining individual trip rows. 
-
-The key idea is early data reduction: millions of trip rows become a few dozen monthly rows before the join occurs.
-
-## Q19 - Taxi vs high-volume FHV monthly counts
-
-```sql
-WITH yellow_monthly AS (
-  SELECT "year", "month", count(*) AS yellow_trips
-  FROM yellow_tripdata
-  GROUP BY "year", "month"
-),
-fhvhv_monthly AS (
-  SELECT "year", "month", count(*) AS fhvhv_trips
-  FROM fhvhv_tripdata
-  GROUP BY "year", "month"
-)
-SELECT
-  y."year",
-  y."month",
-  y.yellow_trips,
-  f.fhvhv_trips
-FROM yellow_monthly y
-JOIN fhvhv_monthly f
-  ON y."year" = f."year"
- AND y."month" = f."month"
-ORDER BY y."year", y."month";
-```
-
-### Checks:
-- monthly trip counting on two datasets with very different sizes.
-- grouping by partition columns.
-- joining monthly count results by `year` and `month`.
-- ordering the joined results chronologically.
-
-### Base Functionality:
-- join matching yellow taxi and high-volume FHV months.
-- order the combined monthly results.
-
-### Benchmark-Phase Optimization:
-- determine monthly groups from folder partition values.
-- calculate both monthly counts from Parquet metadata without reading trip rows.
-- join the small monthly result sets instead of joining individual trip rows.
-
-This is a metadata-only counterpart of Q18. It verifies that early aggregation remains effective even when one input is the much larger high-volume FHV table.
-
-## Q20 - Wide high-volume FHV scan with partition pruning
-
-```sql
-SELECT
-  hvfhs_license_num,
-  dispatching_base_num,
-  originating_base_num,
-  request_datetime,
-  on_scene_datetime,
-  pickup_datetime,
-  dropoff_datetime,
-  PULocationID,
-  DOLocationID,
-  trip_miles,
-  trip_time,
-  base_passenger_fare,
-  tolls,
-  sales_tax,
-  congestion_surcharge,
-  airport_fee,
-  tips,
-  driver_pay,
-  shared_request_flag,
-  shared_match_flag,
-  access_a_ride_flag,
-  wav_request_flag,
-  wav_match_flag
-FROM fhvhv_tripdata
-WHERE "year" = '2022'
-  AND "month" = '10'
-LIMIT 100000;
-```
-
-### Checks:
-- partition pruning for a selected month.
-- projection of 23 columns from the wider high-volume FHV table.
-- efficient reading and transfer of 100,000 wide result rows.
-
-### Base Functionality:
-- discard files outside October 2022.
-- push the projection into the Parquet adapter.
-- read only the requested columns.
-- stop scanning after 100,000 rows have been produced.
-
-### Benchmark-Phase Optimization:
-- materialize flat projected values more efficiently using the lightweight row reader.
-
-This is the larger and wider counterpart of Q03. It measures how the optimized row reader behaves when each returned row contains substantially more data.
-
-## Q21 - Yellow taxi projected scan without partition pruning
-
-```sql
-SELECT
-  PULocationID,
-  DOLocationID,
-  trip_distance,
-  total_amount
-FROM yellow_tripdata
-LIMIT 100000;
-```
-
-### Checks:
-- projection of selected columns without partition pruning.
-- efficient reading and transfer of 100,000 result rows.
-- projected scan performance when files cannot be discarded first.
-
-### Base Functionality:
-- push the projection into the Parquet adapter.
-- read only the four requested columns.
-- stop scanning after 100,000 rows have been produced.
-
-### Benchmark-Phase Optimization:
-- materialize flat projected values more efficiently using the lightweight row reader.
-
-This is the no-partition-filter counterpart of Q03. It isolates projected row reading from partition pruning.
-
-
-## Q22 - High-volume FHV wide projected scan without partition pruning
-
-```sql
-SELECT
-  hvfhs_license_num,
-  dispatching_base_num,
-  pickup_datetime,
-  dropoff_datetime,
-  PULocationID,
-  DOLocationID,
-  trip_miles,
-  trip_time,
-  base_passenger_fare,
-  tolls,
-  sales_tax,
-  tips,
-  driver_pay,
-  shared_request_flag,
-  shared_match_flag
-FROM fhvhv_tripdata
-LIMIT 100000;
-```
-
-### Checks:
-- projection of 15 columns without partition pruning.
-- efficient reading and transfer of 100,000 wide result rows.
-- projected scan performance on the larger high-volume FHV dataset when files cannot be discarded first.
-
-### Base Functionality:
-- push the projection into the Parquet adapter.
-- read only the requested columns.
-- stop scanning after 100,000 rows have been produced.
-
-### Benchmark-Phase Optimization:
-- materialize flat projected values more efficiently using the lightweight row reader.
-
-This is the no-partition-filter counterpart of Q20.
-
-## Q23 - Yellow taxi timestamp and numeric conjunctive filter
-
-```sql
-SELECT count(*) AS matching_trips
-FROM yellow_tripdata
-WHERE tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
-  AND tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00'
-  AND trip_distance >= 2.0
-  AND total_amount >= 15.0;
-```
-
-### Checks:
-- filtering with multiple conditions combined using `AND`.
-- timestamp and numeric predicate pushdown.
-- row-group pruning using pickup-time, distance, and amount statistics.
-- filtered row counting.
-
-### Base Functionality:
-- push the timestamp, distance, and amount filters into the Parquet reader.
-- skip files and row groups that cannot contain matching trips.
-
-### Benchmark-Phase Optimization:
-- count matching rows inside the adapter without materializing complete taxi rows.
-- read only the timestamp, distance, and amount columns needed to evaluate the filters.
-
-This extends Q04 and Q05 by combining timestamp and numeric filtering in one query.
-
-## Q24 - Yellow taxi numeric OR filter
-
-```sql
-SELECT count(*) AS matching_trips
-FROM yellow_tripdata
-WHERE trip_distance >= 20.0
-   OR total_amount >= 100.0;
-```
-
-### Checks:
-- filtering with numeric conditions combined using `OR`.
-- predicate pushdown for OR filter.
-- filtered row counting for a more difficult filter shape.
-
-### Base Functionality:
-- push the distance and amount filter into the Parquet reader.
-- retain a trip when either numeric condition matches.
-- skip files and row groups only when neither condition can match.
-
-### Benchmark-Phase Optimization:
-- count matching rows inside the adapter without materializing complete taxi rows.
-- read only the distance and amount columns needed to evaluate the filter.
-
-OR is more difficult to optimize than AND: data can be discarded only when every alternative is impossible.
-
-## Q25 - Yellow taxi IN-list and numeric filter
-
-```sql
-SELECT count(*) AS matching_trips
-FROM yellow_tripdata
-WHERE PULocationID IN (132, 138, 161, 236, 237)
-  AND trip_distance >= 1.0
-  AND total_amount >= 5.0;
-```
-
-### Checks:
-- filtering by a list of pickup zones using `IN`.
-- combining an `IN` list with numeric conditions using `AND`.
-- filtered row counting.
-
-### Base Functionality:
-- retain trips starting in one of the five selected pickup zones.
-- require matching trips to also satisfy the distance and amount conditions.
-
-### Benchmark-Phase Optimization:
-- convert the `IN` list into equivalent equality alternatives for predicate pushdown.
-- count matching rows inside the adapter without materializing complete taxi rows.
-- read only the pickup-zone, distance, and amount columns needed to evaluate the filters.
-
-
-Conceptually: `PULocationID IN (132, 138, 161, 236, 237)` means `PULocationID = 132 OR PULocationID = 138 OR ...`
-
-## Q26 - High-volume FHV combination of timestamp and text filtering
-
-```sql
-SELECT count(*) AS matching_trips
-FROM fhvhv_tripdata
-WHERE pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
-  AND pickup_datetime < TIMESTAMP '2022-10-16 00:00:00'
-  AND shared_request_flag = 'Y';
-```
-
-### Checks:
-- filtering with timestamp conditions and a text flag condition combined using `AND`.
-- predicate pushdown on the larger high-volume FHV dataset.
-- filtered row counting.
-
-### Base Functionality:
-- retain trips from October 15, 2022 where a shared ride was requested.
-- skip files and row groups that cannot contain matching trips.
-
-### Benchmark-Phase Optimization:
-- count matching rows inside the adapter without materializing complete trip rows.
-- read only the pickup-time and shared-request flag columns needed to evaluate the filters.
-
-The shared_request_flag = 'Y' condition keeps trips for which the passenger requested a shared ride.
-
-## Q27 - High-volume FHV numeric conjunctive filter
-
-```sql
-SELECT count(*) AS matching_trips
-FROM fhvhv_tripdata
-WHERE trip_miles >= 5.0
-  AND base_passenger_fare >= 25.0
-  AND driver_pay >= 10.0;
-```
-
-### Checks:
-- filtering with three numeric conditions combined using `AND`.
-- predicate pushdown across multiple physical columns.
-- filtered row counting on the larger high-volume FHV dataset.
-
-### Base Functionality:
-- retain trips only when distance, passenger fare, and driver pay satisfy their thresholds.
-- skip files and row groups that cannot contain matching trips.
-
-### Benchmark-Phase Optimization:
-- count matching rows inside the adapter without materializing complete trip rows.
-- read only the distance, passenger-fare, and driver-pay columns needed to evaluate the filters.
-
-This extends Q12 by adding a third numeric condition.
-
-## Q28 - High-volume FHV text flag OR filter
-
-```sql
-SELECT count(*) AS matching_trips
-FROM fhvhv_tripdata
-WHERE shared_request_flag = 'Y'
-   OR shared_match_flag = 'Y'
-   OR wav_request_flag = 'Y';
-```
-
-### Checks:
-- filtering with three text flag conditions combined using `OR`.
-- predicate pushdown for a more difficult text-filter shape.
-- filtered row counting on the larger high-volume FHV dataset.
-
-### Base Functionality:
-- retain a trip when any selected flag is set to `Y`.
-- skip files and row groups only when none of the flag conditions can match.
-
-### Benchmark-Phase Optimization:
-- count matching rows inside the adapter without materializing complete trip rows.
-- read only the three flag columns needed to evaluate the filter.
-
-This is intentionally difficult: OR reduces pruning opportunities, and text flags usually match many rows.
-
-## Q29 - Yellow taxi scalar aggregate over physical columns
-
-```sql
-SELECT
-  count(*) AS trips,
-  sum(total_amount) AS gross_amount,
-  avg(total_amount) AS avg_total_amount,
-  min(trip_distance) AS min_distance,
-  max(trip_distance) AS max_distance
-FROM yellow_tripdata;
-```
-
-### Checks:
-- calculation of multiple aggregates without grouping.
-- support for `COUNT`, `SUM`, `AVG`, `MIN`, and `MAX`.
-- aggregation over physical Parquet columns.
-
-### Base Functionality:
-- return one summary row for the full yellow taxi table.
-
-### Benchmark-Phase Optimization:
-- calculate aggregates inside the Parquet adapter.
-- obtain the row count from metadata.
-- aggregate only the amount and distance columns instead of materializing complete taxi rows.
-  
-Scalar aggregate means that the query has no GROUP BY and returns a single summary row.
-
-## Q30 - High-volume FHV scalar aggregate over physical columns
-
-```sql
-SELECT
-  count(*) AS trips,
-  sum(base_passenger_fare) AS passenger_fare,
-  sum(driver_pay) AS driver_pay,
-  avg(trip_miles) AS avg_miles,
-  max(trip_time) AS max_trip_time
-FROM fhvhv_tripdata;
-```
-
-### Checks:
-- calculation of multiple aggregates without grouping on the larger high-volume FHV dataset.
-- support for `COUNT`, multiple `SUM` values, `AVG`, and `MAX`.
-- aggregation over physical Parquet columns.
-
-### Base Functionality:
-- return one summary row for the full high-volume FHV table.
-
-### Benchmark-Phase Optimization:
-- calculate aggregates inside the Parquet adapter.
-- obtain the row count from metadata.
-- aggregate only the fare, driver-pay, distance, and trip-time columns instead of materializing complete trip rows.
-
-This is the larger and wider counterpart of Q29. A scalar aggregate summarizes an entire selected dataset into one result row.
-
-## Q31 - Yellow taxi grouped aggregate by low-cardinality pickup location without partition pruning
-
-```sql
-SELECT
-  PULocationID,
-  count(*) AS trips,
-  sum(total_amount) AS gross_amount,
-  avg(trip_distance) AS avg_distance
-FROM yellow_tripdata
-GROUP BY PULocationID
-ORDER BY trips DESC
-LIMIT 50;
-```
-
-### Checks:
-- grouping by pickup location without partition pruning.
-- calculation of `COUNT`, `SUM`, and `AVG`.
-- ordering groups and returning the top 50 results.
-
-### Base Functionality:
-- order pickup locations by trip count and return the 50 busiest locations.
-
-### Benchmark-Phase Optimization:
-- calculate grouped aggregates inside the Parquet adapter.
-- read only the pickup-location, amount, and distance columns instead of complete taxi rows.
-
-This is the no-partition-filter counterpart of Q07.
-
-## Q32 - Yellow taxi grouped aggregate with `HAVING`
-
-```sql
-SELECT
-  DOLocationID,
-  count(*) AS trips,
-  avg(total_amount) AS avg_total_amount
-FROM yellow_tripdata
-GROUP BY DOLocationID
-HAVING count(*) >= 100000
-ORDER BY avg_total_amount DESC
-LIMIT 50;
-```
-
-### Checks:
-- grouping by destination location without partition pruning.
-- calculation of `COUNT` and `AVG`.
-- filtering aggregate groups using `HAVING`.
-- ordering groups and returning the top 50 results.
-
-### Base Functionality:
-- retain only destination locations with at least 100,000 trips.
-- order the remaining locations by average amount and return the top 50 results.
-
-### Benchmark-Phase Optimization:
-- calculate grouped aggregates inside the Parquet adapter before applying `HAVING`.
-- read only the destination-location and amount columns instead of complete taxi rows.
-
-The `HAVING` condition is applied to the grouped result rather than to individual trip rows.
-
-## Q33 - Yellow taxi two-key grouped aggregate without partition pruning
-
-```sql
-SELECT
-  PULocationID,
-  DOLocationID,
-  count(*) AS trips,
-  avg(total_amount) AS avg_total_amount
-FROM yellow_tripdata
-GROUP BY PULocationID, DOLocationID
-ORDER BY trips DESC
-LIMIT 50;
-```
-
-### Checks:
-- grouping by starting-location and destination-location pairs without partition pruning.
-- calculation of `COUNT` and `AVG`.
-- ordering groups and returning the top 50 results.
-
-### Base Functionality:
-- order location pairs by trip count and return the 50 busiest pairs.
-
-### Benchmark-Phase Optimization:
-- calculate grouped aggregates inside the Parquet adapter.
-- read only the starting-location, destination-location, and amount columns instead of complete taxi rows.
-- use an efficient representation for the two-column grouping key.
-
-This is the no-partition-filter counterpart of Q08.
-
-## Q34 - Low-cardinality shared-ride flag aggregates without partition pruning, high-volume FHV
-
-```sql
-SELECT
-  shared_request_flag,
-  shared_match_flag,
-  count(*) AS trips,
-  avg(trip_miles) AS avg_miles
-FROM fhvhv_tripdata
-GROUP BY shared_request_flag, shared_match_flag
-ORDER BY trips DESC;
-```
-
-### Checks:
-- grouping by two low-cardinality text flags without partition pruning.
-- calculation of `COUNT` and `AVG`.
-- ordering shared-ride flag combinations by trip count.
-
-### Base Functionality:
-- order shared-ride flag combinations from most common to least common.
-
-### Benchmark-Phase Optimization:
-- calculate grouped aggregates inside the Parquet adapter.
-- read only the two shared-ride flag columns and the distance column instead of complete trip rows.
-
-This is the no-partition-filter counterpart of Q15.
-
-## Q35 - High-volume FHV grouped aggregate by license and base
-
-```sql
-SELECT
-  hvfhs_license_num,
-  dispatching_base_num,
-  count(*) AS trips,
-  avg(base_passenger_fare) AS avg_fare,
-  avg(driver_pay) AS avg_driver_pay
-FROM fhvhv_tripdata
-GROUP BY hvfhs_license_num, dispatching_base_num
-ORDER BY trips DESC
-LIMIT 50;
-```
-
-### Checks:
-- grouping by license and dispatching-base identifiers.
-- calculation of `COUNT` and multiple `AVG` values.
-- ordering groups and returning the top 50 results.
-
-### Base Functionality:
-- order license and dispatching-base combinations by trip count.
-- return the 50 busiest combinations.
-
-### Benchmark-Phase Optimization:
-- calculate grouped aggregates inside the Parquet adapter.
-- read only the license, dispatching-base, fare, and driver-pay columns instead of complete trip rows.
-
-## Q36 - High-volume FHV two-key zone grouped aggregate without partition pruning
-
-```sql
-SELECT
-  PULocationID,
-  DOLocationID,
-  count(*) AS trips,
-  avg(trip_miles) AS avg_miles,
-  avg(base_passenger_fare) AS avg_fare
-FROM fhvhv_tripdata
-GROUP BY PULocationID, DOLocationID
-ORDER BY trips DESC
-LIMIT 50;
-```
-
-### Checks:
-- grouping by starting-location and destination-location pairs over the full high-volume FHV table.
-- calculation of `COUNT` and multiple `AVG` values.
-- ordering groups and returning the top 50 results.
-
-### Base Functionality:
-- order location pairs by trip count and return the 50 busiest pairs.
-
-### Benchmark-Phase Optimization:
-- calculate grouped aggregates inside the Parquet adapter.
-- read only the starting-location, destination-location, distance, and fare columns instead of complete trip rows.
-- use an efficient representation for the two-column grouping key.
-
-This is the full-table counterpart of Q14.
-
-## Q37 - Green taxi scalar aggregate over physical columns
-
-```sql
-SELECT
-  count(*) AS trips,
-  sum(total_amount) AS gross_amount,
-  avg(total_amount) AS avg_total_amount,
-  avg(trip_distance) AS avg_distance
-FROM green_tripdata;
-```
-
-### Checks:
-- calculation of multiple aggregates without grouping on the smaller green taxi dataset.
-- support for `COUNT`, `SUM`, and multiple `AVG` values.
-- aggregation over physical Parquet columns.
-
-### Base Functionality:
-- return one summary row for the full green taxi table.
-
-### Benchmark-Phase Optimization:
-- calculate aggregates inside the Parquet adapter.
-- obtain the row count from metadata.
-- aggregate only the amount and distance columns instead of materializing complete taxi rows.
-
-This is the smaller-table counterpart of Q29.
-
-## Q38 - Yellow and high-volume FHV pickup-location aggregate join
-
-```sql
-WITH yellow_pickups AS (
-  SELECT PULocationID, count(*) AS yellow_trips, sum(total_amount) AS yellow_amount
-  FROM yellow_tripdata
-  GROUP BY PULocationID
-),
-fhvhv_pickups AS (
-  SELECT PULocationID, count(*) AS fhvhv_trips, sum(base_passenger_fare) AS fhvhv_fare
-  FROM fhvhv_tripdata
-  GROUP BY PULocationID
-)
-SELECT
-  y.PULocationID,
-  y.yellow_trips,
-  f.fhvhv_trips,
-  y.yellow_amount,
-  f.fhvhv_fare
-FROM yellow_pickups y
-JOIN fhvhv_pickups f
-  ON y.PULocationID = f.PULocationID
-ORDER BY f.fhvhv_trips DESC
-LIMIT 50;
-```
-
-### Checks:
-- grouping by pickup location on two datasets without partition pruning.
-- calculation of `COUNT` and `SUM`.
-- joining pickup-location aggregate results.
-- ordering groups and returning the top 50 results.
-
-### Base Functionality:
-- join matching pickup locations from the yellow taxi and high-volume FHV datasets.
-- order locations by high-volume FHV trip count and return the top 50 results.
-
-### Benchmark-Phase Optimization:
-- calculate grouped aggregates separately inside the Parquet adapter before joining the datasets.
-- read only the pickup-location and amount columns needed by each dataset.
-- join the small pickup-location result sets instead of joining individual trip rows.
-
-This is the no-partition grouped-aggregate counterpart of Q18 and Q19.
-
-## Q39 - Distinct low-cardinality flag combinations, high-volume FHV
-
-```sql
-SELECT DISTINCT
-  shared_request_flag,
-  shared_match_flag,
-  access_a_ride_flag,
-  wav_request_flag,
-  wav_match_flag
-FROM fhvhv_tripdata
-ORDER BY shared_request_flag, shared_match_flag, access_a_ride_flag, wav_request_flag, wav_match_flag;
-```
-
-### Checks:
-- selecting distinct combinations of several low-cardinality text flags.
-- grouping behavior without aggregate functions.
-- ordering the distinct combinations.
-
-### Base Functionality:
-- remove duplicate flag combinations.
-- order the distinct combinations by flag value.
-
-### Benchmark-Phase Optimization:
-- calculate distinct combinations as grouped results inside the Parquet adapter.
-- read only the five flag columns instead of complete trip rows.
-
-## Q40 - Yellow taxi distance bucket aggregate
-
-```sql
-SELECT
-  CASE
-    WHEN trip_distance < 1.0 THEN '00_short'
-    WHEN trip_distance < 5.0 THEN '01_medium'
-    WHEN trip_distance < 20.0 THEN '02_long'
-    ELSE '03_very_long'
-  END AS distance_bucket,
-  count(*) AS trips,
-  avg(total_amount) AS avg_total_amount
-FROM yellow_tripdata
-GROUP BY
-  CASE
-    WHEN trip_distance < 1.0 THEN '00_short'
-    WHEN trip_distance < 5.0 THEN '01_medium'
-    WHEN trip_distance < 20.0 THEN '02_long'
-    ELSE '03_very_long'
-  END
-ORDER BY distance_bucket;
-```
-
-### Checks:
-- grouping by a distance category derived from a `CASE` expression.
-- calculation of `COUNT` and `AVG`.
-- ordering the derived distance categories.
-
-### Base Functionality:
-- assign each trip to a distance category.
-- order the category summaries.
-
-### Benchmark-Phase Optimization:
-- evaluate the derived grouping expression and calculate aggregates inside the Parquet adapter.
-- read only the distance and amount columns instead of complete taxi rows.
-- return one summary row per distance category instead of materializing complete taxi rows outside the adapter.
-
-This checks the generic calculated-aggregate path rather than grouping by direct physical columns.
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_mql.mql b/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_mql.mql
new file mode 100644
index 0000000000..173f3d40db
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_mql.mql
@@ -0,0 +1,46 @@
+-- Q01: Full document access
+tlcpd_document.tlcpd__green_tripdata.find({});
+
+-- Q02: Projection
+tlcpd_document.tlcpd__green_tripdata.find(
+  {},
+  {
+    "lpep_pickup_datetime": 1,
+    "lpep_dropoff_datetime": 1,
+    "pulocationid": 1,
+    "dolocationid": 1,
+    "total_amount": 1
+  }
+);
+
+-- Q03: Filtered count
+tlcpd_document.tlcpd__green_tripdata.aggregate([
+  {
+    "$match": {
+      "trip_distance": { "$gte": 10.0 },
+      "total_amount": { "$gte": 40.0 }
+    }
+  },
+  { "$count": "row_count" }
+]);
+
+-- Q04: Filtered full document access
+tlcpd_document.tlcpd__green_tripdata.find({
+  "trip_distance": { "$gte": 10.0 },
+  "total_amount": { "$gte": 40.0 }
+});
+
+-- Q05: Filtered projection
+tlcpd_document.tlcpd__green_tripdata.find(
+  {
+    "trip_distance": { "$gte": 10.0 },
+    "total_amount": { "$gte": 40.0 }
+  },
+  {
+    "lpep_pickup_datetime": 1,
+    "pulocationid": 1,
+    "dolocationid": 1,
+    "trip_distance": 1,
+    "total_amount": 1
+  }
+);
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_query_specification.md b/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_query_specification.md
new file mode 100644
index 0000000000..fbe92ba377
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_query_specification.md
@@ -0,0 +1,9 @@
+# Access Model Comparison Query Specification
+
+| Query | Operation           | Predicate                                          | Returned data                                      | Purpose                                                    |
+|-------|---------------------|----------------------------------------------------|----------------------------------------------------|------------------------------------------------------------|
+| Q01   | Full scan           | None                                               | All fields                                         | Measures full dataset read performance                     |
+| Q02   | Projection          | None                                               | Selected fields                                    | Measures projection behavior                               |
+| Q03   | Filtered count      | `trip_distance >= 10.0` and `total_amount >= 40.0` | Aggregate count                                    | Measures filter evaluation without returning matching rows |
+| Q04   | Filtered scan       | `trip_distance >= 10.0` and `total_amount >= 40.0` | All matching records or documents                  | Measures filtering with full result consumption            |
+| Q05   | Filtered projection | `trip_distance >= 10.0` and `total_amount >= 40.0` | Selected fields from matching records or documents | Measures combined filtering and projection                 |
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_rf.sql b/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_rf.sql
new file mode 100644
index 0000000000..a78a234b1b
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_rf.sql
@@ -0,0 +1,35 @@
+-- Q01: Full record access
+SELECT *
+FROM tlcp__green_tripdata;
+
+-- Q02: Projection
+SELECT
+  lpep_pickup_datetime,
+  lpep_dropoff_datetime,
+  pulocationid,
+  dolocationid,
+  total_amount
+FROM tlcp__green_tripdata;
+
+-- Q03: Filtered count
+SELECT count(*) AS row_count
+FROM tlcp__green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+
+-- Q04: Filtered full record access
+SELECT *
+FROM tlcp__green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+
+-- Q05: Filtered projection
+SELECT
+  lpep_pickup_datetime,
+  pulocationid,
+  dolocationid,
+  trip_distance,
+  total_amount
+FROM tlcp__green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_sql.sql b/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_sql.sql
new file mode 100644
index 0000000000..fdeb8c25ae
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_sql.sql
@@ -0,0 +1,35 @@
+-- Q01: Full record access
+SELECT *
+FROM green_tripdata;
+
+-- Q02: Projection
+SELECT
+  lpep_pickup_datetime,
+  lpep_dropoff_datetime,
+  pulocationid,
+  dolocationid,
+  total_amount
+FROM green_tripdata;
+
+-- Q03: Filtered count
+SELECT count(*) AS row_count
+FROM green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+
+-- Q04: Filtered full record access
+SELECT *
+FROM green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+
+-- Q05: Filtered projection
+SELECT
+  lpep_pickup_datetime,
+  pulocationid,
+  dolocationid,
+  trip_distance,
+  total_amount
+FROM green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/aggregation_queries.md b/plugins/parquet-adapter/benchmarks/query_lists/aggregation_queries.md
deleted file mode 100644
index e21ed9ea27..0000000000
--- a/plugins/parquet-adapter/benchmarks/query_lists/aggregation_queries.md
+++ /dev/null
@@ -1,130 +0,0 @@
-# Aggregation Queries
-
-## Q01: Full Yellow Taxi Row Count
-
-Find how many yellow taxi trips are in the dataset.
-
-```sql
-SELECT count(*) AS row_count
-FROM tlc__yellow_tripdata;
-```
-
-## Q02: Yellow Taxi Count For One Partition Month
-
-Find how many yellow taxi trips were in October 2022.
-
-```sql
-SELECT count(*) AS row_count
-FROM yellow_tripdata
-WHERE "year" = '2022'
-  AND "month" = '10';
-```
-
-## Q03: Yellow Taxi Trips On One Day
-
-Find how many yellow taxi trips started on October 15, 2022.
-
-```sql
-SELECT count(*) AS row_count
-FROM yellow_tripdata
-WHERE tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
-  AND tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00';
-```
-
-## Q04: Long And Expensive Yellow Taxi Trips
-
-Find how many yellow taxi trips were at least 10 miles long and
-cost at least 40 dollars.
-
-```sql
-SELECT count(*) AS row_count
-FROM yellow_tripdata
-WHERE trip_distance >= 10.0
-  AND total_amount >= 40.0;
-```
-
-## Q05: Monthly Yellow Taxi Amount And Distance Summary
-
-Find, for each year and month, how many yellow taxi trips there
-were, the total amount charged, and the shortest and longest trip distances.
-
-```sql
-SELECT
-  "year",
-  "month",
-  count(*) AS trips,
-  sum(total_amount) AS gross_amount,
-  min(trip_distance) AS min_distance,
-  max(trip_distance) AS max_distance
-FROM yellow_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
-```
-
-## Q06: Full High-Volume FHV Row Count
-
-**Description:** Find how many high-volume FHV trips are in the dataset.
-
-```sql
-SELECT count(*) AS row_count
-FROM fhvhv_tripdata;
-```
-
-## Q07: High-Volume FHV Count For One Partition Month
-
-Find how many high-volume FHV trips were in October 2022.
-
-```sql
-SELECT count(*) AS row_count
-FROM fhvhv_tripdata
-WHERE "year" = '2022'
-  AND "month" = '10';
-```
-
-## Q08: Long And Expensive High-Volume FHV Trips
-
-Find how many high-volume FHV trips were at least 10 miles long
-and had a base passenger fare of at least 40 dollars.
-
-```sql
-SELECT count(*) AS row_count
-FROM fhvhv_tripdata
-WHERE trip_miles >= 10.0
-  AND base_passenger_fare >= 40.0;
-```
-
-## Q09: Monthly High-Volume FHV Fare And Distance Summary
-
-Find, for each year and month, how many high-volume FHV trips
-there were, the total passenger fare, the total driver pay, and the shortest and
-longest trip distances.
-
-```sql
-SELECT
-  "year",
-  "month",
-  count(*) AS trips,
-  sum(base_passenger_fare) AS passenger_fare,
-  sum(driver_pay) AS driver_pay,
-  min(trip_miles) AS min_miles,
-  max(trip_miles) AS max_miles
-FROM fhvhv_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
-```
-
-## Q10: High-Volume FHV Shared-Ride Flag Distribution
-
-Find how many 2022 high-volume FHV trips fall into each
-combination of shared-request and shared-match flags.
-
-```sql
-SELECT
-  shared_request_flag,
-  shared_match_flag,
-  count(*) AS trips
-FROM fhvhv_tripdata
-WHERE "year" = '2022'
-GROUP BY shared_request_flag, shared_match_flag
-ORDER BY trips DESC;
-```
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny.sql b/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny.sql
new file mode 100644
index 0000000000..000275a5fb
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny.sql
@@ -0,0 +1,72 @@
+-- Q01: Full Yellow Taxi Row Count
+SELECT count(*) AS row_count
+FROM tlcp__yellow_tripdata;
+
+-- Q02: Yellow Taxi Count For One Partition Month
+SELECT count(*) AS row_count
+FROM tlcp__yellow_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+
+-- Q03: Yellow Taxi Trips On One Day
+SELECT count(*) AS row_count
+FROM tlcp__yellow_tripdata
+WHERE tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
+  AND tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00';
+
+-- Q04: Long And Expensive Yellow Taxi Trips
+SELECT count(*) AS row_count
+FROM tlcp__yellow_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+
+-- Q05: Monthly Yellow Taxi Amount And Distance Summary
+SELECT
+  "year",
+  "month",
+  count(*) AS trips,
+  sum(total_amount) AS gross_amount,
+  min(trip_distance) AS min_distance,
+  max(trip_distance) AS max_distance
+FROM tlcp__yellow_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+
+-- Q06: Full High-Volume FHV Row Count
+SELECT count(*) AS row_count
+FROM tlcp__fhvhv_tripdata;
+
+-- Q07: High-Volume FHV Count For One Partition Month
+SELECT count(*) AS row_count
+FROM tlcp__fhvhv_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+
+-- Q08: Long And Expensive High-Volume FHV Trips
+SELECT count(*) AS row_count
+FROM tlcp__fhvhv_tripdata
+WHERE trip_miles >= 10.0
+  AND base_passenger_fare >= 40.0;
+
+-- Q09: Monthly High-Volume FHV Fare And Distance Summary
+SELECT
+  "year",
+  "month",
+  count(*) AS trips,
+  sum(base_passenger_fare) AS passenger_fare,
+  sum(driver_pay) AS driver_pay,
+  min(trip_miles) AS min_miles,
+  max(trip_miles) AS max_miles
+FROM tlcp__fhvhv_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+
+-- Q10: High-Volume FHV Shared-Ride Flag Distribution
+SELECT
+  shared_request_flag,
+  shared_match_flag,
+  count(*) AS trips
+FROM tlcp__fhvhv_tripdata
+WHERE "year" = '2022'
+GROUP BY shared_request_flag, shared_match_flag
+ORDER BY trips DESC;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny_mql.sql b/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny_mql.sql
new file mode 100644
index 0000000000..62568bf272
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny_mql.sql
@@ -0,0 +1,107 @@
+-- Q01: Full Yellow Taxi Row Count
+tlcpd_document.tlcpd__yellow_tripdata.aggregate([
+  { "$count": "row_count" }
+]);
+
+-- Q02: Yellow Taxi Count For One Partition Month
+tlcpd_document.tlcpd__yellow_tripdata.aggregate([
+  {
+    "$match": {
+      "year": "2022",
+      "month": "10"
+    }
+  },
+  { "$count": "row_count" }
+]);
+
+-- Q03: Yellow Taxi Trips On One Day
+tlcpd_document.tlcpd__yellow_tripdata.aggregate([
+  {
+    "$match": {
+      "tpep_pickup_datetime": {
+        "$gte": "2022-10-15 00:00:00",
+        "$lt": "2022-10-16 00:00:00"
+      }
+    }
+  },
+  { "$count": "row_count" }
+]);
+
+-- Q04: Long And Expensive Yellow Taxi Trips
+tlcpd_document.tlcpd__yellow_tripdata.aggregate([
+  {
+    "$match": {
+      "trip_distance": { "$gte": 10.0 },
+      "total_amount": { "$gte": 40.0 }
+    }
+  },
+  { "$count": "row_count" }
+]);
+
+-- Q05: Monthly Yellow Taxi Amount And Distance Summary
+tlcpd_document.tlcpd__yellow_tripdata.aggregate([
+  {
+    "$group": {
+      "_id": "$year",
+      "gross_amount": { "$sum": "$total_amount" },
+      "min_distance": { "$min": "$trip_distance" },
+      "max_distance": { "$max": "$trip_distance" }
+    }
+  },
+  { "$sort": { "_id": 1 } }
+]);
+
+-- Q06: Full High-Volume FHV Row Count
+tlcpd_document.tlcpd__fhvhv_tripdata.aggregate([
+  { "$count": "row_count" }
+]);
+
+-- Q07: High-Volume FHV Count For One Partition Month
+tlcpd_document.tlcpd__fhvhv_tripdata.aggregate([
+  {
+    "$match": {
+      "year": "2022",
+      "month": "10"
+    }
+  },
+  { "$count": "row_count" }
+]);
+
+-- Q08: Long And Expensive High-Volume FHV Trips
+tlcpd_document.tlcpd__fhvhv_tripdata.aggregate([
+  {
+    "$match": {
+      "trip_miles": { "$gte": 10.0 },
+      "base_passenger_fare": { "$gte": 40.0 }
+    }
+  },
+  { "$count": "row_count" }
+]);
+
+-- Q09: Monthly High-Volume FHV Fare And Distance Summary
+tlcpd_document.tlcpd__fhvhv_tripdata.aggregate([
+  {
+    "$group": {
+      "_id": "$year",
+      "passenger_fare": { "$sum": "$base_passenger_fare" },
+      "driver_pay": { "$sum": "$driver_pay" },
+      "min_miles": { "$min": "$trip_miles" },
+      "max_miles": { "$max": "$trip_miles" }
+    }
+  },
+  { "$sort": { "_id": 1 } }
+]);
+
+-- Q10: High-Volume FHV Shared-Ride Flag Distribution
+tlcpd_document.tlcpd__fhvhv_tripdata.aggregate([
+  {
+    "$match": {
+      "year": "2022"
+    }
+  },
+  {
+    "$group": {
+      "_id": "$shared_request_flag"
+    }
+  }
+]);
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_specification.md b/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_specification.md
new file mode 100644
index 0000000000..24c6136e11
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_specification.md
@@ -0,0 +1,24 @@
+# Aggregation Query Specification
+
+| Query | Operation                                         | Predicate                                                                                                              | Returned data                                                                   | Purpose                                             |
+|-------|---------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------|-----------------------------------------------------|
+| Q01   | Full Yellow Taxi row count                        | None                                                                                                                   | Aggregate count                                                                 | Measures metadata-like count over `yellow_tripdata` |
+| Q02   | Yellow Taxi one-month partition count             | `year = '2022'` and `month = '10'`                                                                                     | Aggregate count                                                                 | Measures count over one partition month             |
+| Q03   | Yellow Taxi one-day timestamp count               | `tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'` and `tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00'` | Aggregate count                                                                 | Measures count with a physical timestamp predicate  |
+| Q04   | Yellow Taxi filtered count                        | `trip_distance >= 10.0` and `total_amount >= 40.0`                                                                     | Aggregate count                                                                 | Measures selective filtering over data columns      |
+| Q05   | Yellow Taxi monthly amount and distance summary   | None                                                                                                                   | Monthly count, total amount, minimum distance, maximum distance                 | Measures grouped aggregation over `yellow_tripdata` |
+| Q06   | Full High-Volume FHV row count                    | None                                                                                                                   | Aggregate count                                                                 | Measures metadata-like count over `fhvhv_tripdata`  |
+| Q07   | High-Volume FHV one-month partition count         | `year = '2022'` and `month = '10'`                                                                                     | Aggregate count                                                                 | Measures count over one partition month             |
+| Q08   | High-Volume FHV filtered count                    | `trip_miles >= 10.0` and `base_passenger_fare >= 40.0`                                                                 | Aggregate count                                                                 | Measures selective filtering over data columns      |
+| Q09   | High-Volume FHV monthly fare and distance summary | None                                                                                                                   | Monthly count, passenger fare sum, driver pay sum, minimum miles, maximum miles | Measures grouped aggregation over `fhvhv_tripdata`  |
+| Q10   | High-Volume FHV shared-ride flag distribution     | `year = '2022'`                                                                                                        | Count by shared-request and shared-match flags                                  | Measures grouped aggregation over categorical flags |
+
+Q01-Q05 run on `yellow_tripdata`. Q06-Q10 run on `fhvhv_tripdata`.
+
+Runnable query files:
+
+```text
+plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny.sql
+plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny_mql.sql
+plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_sql.sql
+```
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_sql.sql b/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_sql.sql
new file mode 100644
index 0000000000..cb8aca32ca
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_sql.sql
@@ -0,0 +1,72 @@
+-- Q01: Full Yellow Taxi Row Count
+SELECT count(*) AS row_count
+FROM yellow_tripdata;
+
+-- Q02: Yellow Taxi Count For One Partition Month
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+
+-- Q03: Yellow Taxi Trips On One Day
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
+  AND tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00';
+
+-- Q04: Long And Expensive Yellow Taxi Trips
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+
+-- Q05: Monthly Yellow Taxi Amount And Distance Summary
+SELECT
+  "year",
+  "month",
+  count(*) AS trips,
+  sum(total_amount) AS gross_amount,
+  min(trip_distance) AS min_distance,
+  max(trip_distance) AS max_distance
+FROM yellow_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+
+-- Q06: Full High-Volume FHV Row Count
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata;
+
+-- Q07: High-Volume FHV Count For One Partition Month
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+
+-- Q08: Long And Expensive High-Volume FHV Trips
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata
+WHERE trip_miles >= 10.0
+  AND base_passenger_fare >= 40.0;
+
+-- Q09: Monthly High-Volume FHV Fare And Distance Summary
+SELECT
+  "year",
+  "month",
+  count(*) AS trips,
+  sum(base_passenger_fare) AS passenger_fare,
+  sum(driver_pay) AS driver_pay,
+  min(trip_miles) AS min_miles,
+  max(trip_miles) AS max_miles
+FROM fhvhv_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+
+-- Q10: High-Volume FHV Shared-Ride Flag Distribution
+SELECT
+  shared_request_flag,
+  shared_match_flag,
+  count(*) AS trips
+FROM fhvhv_tripdata
+WHERE "year" = '2022'
+GROUP BY shared_request_flag, shared_match_flag
+ORDER BY trips DESC;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/data_configuration.md b/plugins/parquet-adapter/benchmarks/query_lists/data_configuration.md
new file mode 100644
index 0000000000..b71f2e99ec
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/data_configuration.md
@@ -0,0 +1,89 @@
+# Polypheny Parquet Adapter Data Configuration for Benchmarks
+
+## Access Model Comparison
+
+```text
+location on disk:
+C:\PolyData\tlc_partitioned
+
+url:
+file:///C:/PolyData/tlc_partitioned
+
+relational adapter name:
+tlcp
+
+flat mode
+
+document adapter name:
+tlcpd
+
+```
+
+## Nested Data
+
+```text
+location on disk:
+C:\PolyData\nested_customer\nestedcustomer.parquet
+
+url:
+file:///C:/PolyData/nested_customer/nestedcustomer.parquet
+
+relational adapter name:
+ncp
+
+adapter normalized mode
+
+document adapter name:
+ncpd
+```
+
+## Aggregation
+
+same relational adapter as Access Model Comparison
+
+```text
+location on disk:
+C:\PolyData\tlc_partitioned
+
+url:
+file:///C:/PolyData/tlc_partitioned
+
+relational adapter name:
+tlcp
+
+
+document adapter name:
+tlcpd
+
+```
+
+
+## Partitioning
+
+### Repartitioned data, flat
+
+```text
+location on disk:
+C:\PolyData\tlc_repartitioned
+
+url:
+file:///C:/PolyData/tlc_repartitioned
+
+relational adapter name:
+tlcr
+
+```
+
+### Unpartitioned data, flat
+
+```text
+location on disk:
+C:\PolyData\tlc_unpartitioned
+
+url:
+file:///C:/PolyData/tlc_unpartitioned
+
+relational adapter name:
+tlcu
+
+```
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_duckdb.sql b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_duckdb.sql
new file mode 100644
index 0000000000..806173a3e8
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_duckdb.sql
@@ -0,0 +1,117 @@
+-- Q01: Filtered full customer rows.
+SELECT *
+FROM nested_customer
+WHERE c_mktsegment = 'BUILDING'
+  AND c_nationkey BETWEEN 5 AND 15
+LIMIT 100000;
+
+-- Q02: Filtered explicit customer projection.
+SELECT
+  c_custkey,
+  c_name,
+  c_address,
+  c_nationkey,
+  c_phone,
+  c_acctbal,
+  c_mktsegment,
+  c_comment
+FROM nested_customer
+WHERE c_mktsegment = 'BUILDING'
+  AND c_acctbal >= 0
+LIMIT 100000;
+
+-- Q03: Filtered order projection.
+SELECT
+  o.o_orderkey,
+  o.o_orderstatus,
+  o.o_totalprice,
+  o.o_orderdate,
+  o.o_orderpriority,
+  o.o_clerk
+FROM (
+  SELECT list_transform(
+    c_orders,
+    o -> struct_pack(
+      o_orderkey := o.o_orderkey,
+      o_orderstatus := o.o_orderstatus,
+      o_totalprice := o.o_totalprice,
+      o_orderdate := o.o_orderdate,
+      o_orderpriority := o.o_orderpriority,
+      o_clerk := o.o_clerk
+    )
+  ) AS c_orders
+  FROM nested_customer
+) c,
+UNNEST(c.c_orders) AS orders(o)
+WHERE o.o_orderstatus = 'F'
+  AND o.o_totalprice >= 100000
+LIMIT 100000;
+
+-- Q04: Filtered lineitem projection.
+SELECT
+  l.l_partkey,
+  l.l_suppkey,
+  l.l_linenumber,
+  l.l_quantity,
+  l.l_extendedprice,
+  l.l_discount,
+  l.l_returnflag,
+  l.l_shipmode
+FROM nested_customer c,
+UNNEST(c.c_orders) AS orders(o),
+UNNEST(
+  list_transform(
+    o.o_lineitems,
+    l -> struct_pack(
+      l_partkey := l.l_partkey,
+      l_suppkey := l.l_suppkey,
+      l_linenumber := l.l_linenumber,
+      l_quantity := l.l_quantity,
+      l_extendedprice := l.l_extendedprice,
+      l_discount := l.l_discount,
+      l_returnflag := l.l_returnflag,
+      l_shipmode := l.l_shipmode
+    )
+  )
+) AS lineitems(l)
+WHERE l.l_returnflag = 'R'
+  AND l.l_shipmode = 'MAIL'
+LIMIT 100000;
+
+-- Q05: Unfiltered two-level nested lineitem projection.
+SELECT
+  o.o_orderkey,
+  o.o_orderstatus,
+  l.l_partkey,
+  l.l_suppkey,
+  l.l_linenumber,
+  l.l_quantity,
+  l.l_extendedprice,
+  l.l_discount,
+  l.l_returnflag,
+  l.l_shipmode
+FROM nested_customer c,
+UNNEST(
+  list_transform(
+    c.c_orders,
+    o -> struct_pack(
+      o_orderkey := o.o_orderkey,
+      o_orderstatus := o.o_orderstatus,
+      o_lineitems := list_transform(
+        o.o_lineitems,
+        l -> struct_pack(
+          l_partkey := l.l_partkey,
+          l_suppkey := l.l_suppkey,
+          l_linenumber := l.l_linenumber,
+          l_quantity := l.l_quantity,
+          l_extendedprice := l.l_extendedprice,
+          l_discount := l.l_discount,
+          l_returnflag := l.l_returnflag,
+          l_shipmode := l.l_shipmode
+        )
+      )
+    )
+  )
+) AS orders(o),
+UNNEST(o.o_lineitems) AS lineitems(l)
+LIMIT 100000;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_mql.mql b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_mql.mql
new file mode 100644
index 0000000000..44b2d9bfda
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_mql.mql
@@ -0,0 +1,91 @@
+-- Q01: Filtered full customer documents.
+ncpd_document.ncpd__nestedcustomer.find({
+  "c_mktsegment": "BUILDING",
+  "c_nationkey": { "$gte": 5, "$lte": 15 }
+}).limit(100000);
+
+-- Q02: Filtered explicit customer projection.
+ncpd_document.ncpd__nestedcustomer.find(
+  {
+    "c_mktsegment": "BUILDING",
+    "c_acctbal": { "$gte": 0 }
+  },
+  {
+    "c_custkey": 1,
+    "c_name": 1,
+    "c_address": 1,
+    "c_nationkey": 1,
+    "c_phone": 1,
+    "c_acctbal": 1,
+    "c_mktsegment": 1,
+    "c_comment": 1
+  }
+).limit(100000);
+
+-- Q03: Filtered order projection.
+ncpd_document.ncpd__nestedcustomer.aggregate([
+  { "$unwind": "$c_orders" },
+  {
+    "$match": {
+      "c_orders.o_orderstatus": "F",
+      "c_orders.o_totalprice": { "$gte": 100000 }
+    }
+  },
+  {
+    "$project": {
+      "o_orderkey": "$c_orders.o_orderkey",
+      "o_orderstatus": "$c_orders.o_orderstatus",
+      "o_totalprice": "$c_orders.o_totalprice",
+      "o_orderdate": "$c_orders.o_orderdate",
+      "o_orderpriority": "$c_orders.o_orderpriority",
+      "o_clerk": "$c_orders.o_clerk"
+    }
+  },
+  { "$limit": 100000 }
+]);
+
+-- Q04: Filtered lineitem projection.
+ncpd_document.ncpd__nestedcustomer.aggregate([
+  { "$unwind": "$c_orders" },
+  { "$unwind": "$c_orders.o_lineitems" },
+  {
+    "$match": {
+      "c_orders.o_lineitems.l_returnflag": "R",
+      "c_orders.o_lineitems.l_shipmode": "MAIL"
+    }
+  },
+  {
+    "$project": {
+      "l_partkey": "$c_orders.o_lineitems.l_partkey",
+      "l_suppkey": "$c_orders.o_lineitems.l_suppkey",
+      "l_linenumber": "$c_orders.o_lineitems.l_linenumber",
+      "l_quantity": "$c_orders.o_lineitems.l_quantity",
+      "l_extendedprice": "$c_orders.o_lineitems.l_extendedprice",
+      "l_discount": "$c_orders.o_lineitems.l_discount",
+      "l_returnflag": "$c_orders.o_lineitems.l_returnflag",
+      "l_shipmode": "$c_orders.o_lineitems.l_shipmode"
+    }
+  },
+  { "$limit": 100000 }
+]);
+
+-- Q05: Unfiltered two-level nested lineitem projection.
+ncpd_document.ncpd__nestedcustomer.aggregate([
+  { "$unwind": "$c_orders" },
+  { "$unwind": "$c_orders.o_lineitems" },
+  {
+    "$project": {
+      "o_orderkey": "$c_orders.o_orderkey",
+      "o_orderstatus": "$c_orders.o_orderstatus",
+      "l_partkey": "$c_orders.o_lineitems.l_partkey",
+      "l_suppkey": "$c_orders.o_lineitems.l_suppkey",
+      "l_linenumber": "$c_orders.o_lineitems.l_linenumber",
+      "l_quantity": "$c_orders.o_lineitems.l_quantity",
+      "l_extendedprice": "$c_orders.o_lineitems.l_extendedprice",
+      "l_discount": "$c_orders.o_lineitems.l_discount",
+      "l_returnflag": "$c_orders.o_lineitems.l_returnflag",
+      "l_shipmode": "$c_orders.o_lineitems.l_shipmode"
+    }
+  },
+  { "$limit": 100000 }
+]);
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_polypheny_normalized.sql b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_polypheny_normalized.sql
new file mode 100644
index 0000000000..fa052ced87
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_polypheny_normalized.sql
@@ -0,0 +1,76 @@
+-- Q01: Filtered full customer rows.
+SELECT *
+FROM ncp__nestedcustomer
+WHERE c_mktsegment = 'BUILDING'
+  AND c_nationkey BETWEEN 5 AND 15
+LIMIT 100000;
+
+-- Q02: Filtered explicit customer projection.
+SELECT
+  c_custkey,
+  c_name,
+  c_address,
+  c_nationkey,
+  c_phone,
+  c_acctbal,
+  c_mktsegment,
+  c_comment
+FROM ncp__nestedcustomer
+WHERE c_mktsegment = 'BUILDING'
+  AND c_acctbal >= 0
+LIMIT 100000;
+
+-- Q03: One-join filtered order element projection.
+SELECT
+  col.__polypheny_row_id AS orders_list_row_id,
+  col.__polypheny_parent_row_id AS orders_group_row_id,
+  o.o_orderkey,
+  o.o_orderstatus,
+  o.o_totalprice,
+  o.o_orderdate,
+  o.o_orderpriority,
+  o.o_clerk
+FROM ncp__nestedcustomer__c_orders__list col
+JOIN ncp__nestedcustomer__c_orders__list__element o
+  ON o.__polypheny_parent_row_id = col.__polypheny_row_id
+WHERE o.o_orderstatus = 'F'
+  AND o.o_totalprice >= 100000
+LIMIT 100000;
+
+-- Q04: One-join filtered lineitem element projection.
+SELECT
+  olil.__polypheny_row_id AS lineitems_list_row_id,
+  olil.__polypheny_parent_row_id AS lineitems_group_row_id,
+  l.l_partkey,
+  l.l_suppkey,
+  l.l_linenumber,
+  l.l_quantity,
+  l.l_extendedprice,
+  l.l_discount,
+  l.l_returnflag,
+  l.l_shipmode
+FROM ncp__nestedcustomer__c_orders__list__element__o_lineitems__list olil
+JOIN ncp__nestedcustomer__c_orders__list__element__o_lineitems__list__element l
+  ON l.__polypheny_parent_row_id = olil.__polypheny_row_id
+WHERE l.l_returnflag = 'R'
+  AND l.l_shipmode = 'MAIL'
+LIMIT 100000;
+
+-- Q05: Unfiltered two-join lineitem projection.
+SELECT
+  oli.__polypheny_row_id AS lineitems_group_row_id,
+  olil.__polypheny_row_id AS lineitems_list_row_id,
+  l.l_partkey,
+  l.l_suppkey,
+  l.l_linenumber,
+  l.l_quantity,
+  l.l_extendedprice,
+  l.l_discount,
+  l.l_returnflag,
+  l.l_shipmode
+FROM ncp__nestedcustomer__c_orders__list__element__o_lineitems oli
+JOIN ncp__nestedcustomer__c_orders__list__element__o_lineitems__list olil
+  ON olil.__polypheny_parent_row_id = oli.__polypheny_row_id
+JOIN ncp__nestedcustomer__c_orders__list__element__o_lineitems__list__element l
+  ON l.__polypheny_parent_row_id = olil.__polypheny_row_id
+LIMIT 100000;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_query_specification.md b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_query_specification.md
new file mode 100644
index 0000000000..f998491028
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_query_specification.md
@@ -0,0 +1,13 @@
+# Nested Data Query Specification
+
+| Query | Operation                                       | Predicate                                                      | Returned data                      | Purpose                                                                |
+|-------|-------------------------------------------------|----------------------------------------------------------------|------------------------------------|------------------------------------------------------------------------|
+| Q01   | Filtered full customer scan                     | `c_mktsegment = 'BUILDING'` and `c_nationkey BETWEEN 5 AND 15` | Full customer rows or documents    | Measures filtered root-level access with full customer materialization |
+| Q02   | Filtered customer projection                    | `c_mktsegment = 'BUILDING'` and `c_acctbal >= 0`               | Explicit customer scalar fields    | Measures filtered projection over root-level customer fields           |
+| Q03   | Filtered nested order projection                | `o_orderstatus = 'F'` and `o_totalprice >= 100000`             | Selected order fields              | Measures one-level nested access to order elements                     |
+| Q04   | Filtered deeply nested lineitem projection      | `l_returnflag = 'R'` and `l_shipmode = 'MAIL'`                 | Selected lineitem fields           | Measures deeper nested access to lineitem elements                     |
+| Q05   | Unfiltered two-level nested lineitem projection | None                                                           | Selected order and lineitem fields | Measures multi-step nested traversal without filter interaction        |
+
+All queries cap the returned result set at `100000` rows or documents. The
+Polypheny relational query list intentionally keeps Q03 and Q04 to one
+parent-child join, while Q05 is the explicit two-join nested traversal probe.
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_spark.sql b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_spark.sql
new file mode 100644
index 0000000000..4b4782281a
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_spark.sql
@@ -0,0 +1,69 @@
+-- Q01: Filtered full customer rows.
+SELECT *
+FROM nested_customer
+WHERE c_mktsegment = 'BUILDING'
+  AND c_nationkey BETWEEN 5 AND 15
+LIMIT 100000;
+
+-- Q02: Filtered explicit customer projection.
+SELECT
+  c_custkey,
+  c_name,
+  c_address,
+  c_nationkey,
+  c_phone,
+  c_acctbal,
+  c_mktsegment,
+  c_comment
+FROM nested_customer
+WHERE c_mktsegment = 'BUILDING'
+  AND c_acctbal >= 0
+LIMIT 100000;
+
+-- Q03: Filtered order projection.
+SELECT
+  o.o_orderkey,
+  o.o_orderstatus,
+  o.o_totalprice,
+  o.o_orderdate,
+  o.o_orderpriority,
+  o.o_clerk
+FROM nested_customer c
+LATERAL VIEW explode(c_orders) orders_view AS o
+WHERE o.o_orderstatus = 'F'
+  AND o.o_totalprice >= 100000
+LIMIT 100000;
+
+-- Q04: Filtered lineitem projection.
+SELECT
+  l.l_partkey,
+  l.l_suppkey,
+  l.l_linenumber,
+  l.l_quantity,
+  l.l_extendedprice,
+  l.l_discount,
+  l.l_returnflag,
+  l.l_shipmode
+FROM nested_customer c
+LATERAL VIEW explode(c_orders) orders_view AS o
+LATERAL VIEW explode(o.o_lineitems) lineitems_view AS l
+WHERE l.l_returnflag = 'R'
+  AND l.l_shipmode = 'MAIL'
+LIMIT 100000;
+
+-- Q05: Unfiltered two-level nested lineitem projection.
+SELECT
+  o.o_orderkey,
+  o.o_orderstatus,
+  l.l_partkey,
+  l.l_suppkey,
+  l.l_linenumber,
+  l.l_quantity,
+  l.l_extendedprice,
+  l.l_discount,
+  l.l_returnflag,
+  l.l_shipmode
+FROM nested_customer c
+LATERAL VIEW explode(c_orders) orders_view AS o
+LATERAL VIEW explode(o.o_lineitems) lineitems_view AS l
+LIMIT 100000;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/nested_flickr8k_queries.md b/plugins/parquet-adapter/benchmarks/query_lists/nested_flickr8k_queries.md
deleted file mode 100644
index 4b3db27f51..0000000000
--- a/plugins/parquet-adapter/benchmarks/query_lists/nested_flickr8k_queries.md
+++ /dev/null
@@ -1,104 +0,0 @@
-# Nested Data Queries
-
-## Q01: Full Flickr8k Row Count
-
-Find how many Flickr8k images are in the dataset.
-
-```sql
-SELECT count(*) AS row_count
-FROM public.ff__flickr8k;
-```
-
-## Q02: Image Id Projection
-
-Find all image identifiers without reading captions or image bytes.
-
-```sql
-SELECT id
-FROM public.ff__flickr8k;
-```
-
-## Q03: Ordered Image Id Sample
-
-Find a deterministic sample of image identifiers without reading nested
-captions or image bytes.
-
-```sql
-SELECT id
-FROM public.ff__flickr8k
-ORDER BY id
-LIMIT 100;
-```
-
-## Q04: Repeated Caption List Projection
-
-Find the repeated caption list for every image without reading image bytes.
-
-```sql
-SELECT captions
-FROM public.ff__flickr8k;
-```
-
-## Q05: Image Ids With Caption Lists
-
-Find image identifiers together with their repeated caption lists, while
-excluding image bytes.
-
-```sql
-SELECT id, captions
-FROM public.ff__flickr8k;
-```
-
-## Q06: Ordered Image Ids With Caption Lists Sample
-
-Find a deterministic sample of image identifiers and repeated caption lists,
-while excluding image bytes.
-
-```sql
-SELECT id, captions
-FROM public.ff__flickr8k
-ORDER BY id
-LIMIT 100;
-```
-
-## Q07: Binary Image Projection
-
-Find image identifiers together with the binary JPEG bytes, while excluding
-caption lists.
-
-```sql
-SELECT id, image
-FROM public.ff__flickr8k;
-```
-
-## Q08: Binary Image Only Projection
-
-Find only the binary JPEG bytes to measure the cost of reading the large image
-column without metadata or captions.
-
-```sql
-SELECT id, "image"
-FROM public.ff__flickr8k;
-```
-
-## Q09: Full Flickr8k Row Projection
-
-Find image identifiers, repeated caption lists, and binary JPEG bytes together.
-
-```sql
-SELECT id, captions, "image"
-FROM public.ff__flickr8k;
-```
-
-## Q10: Filtered Caption Projection Without Image Bytes
-
-Find image identifiers and repeated caption lists for one filename range,
-while excluding image bytes.
-
-```sql
-SELECT id, captions
-FROM public.ff__flickr8k
-WHERE id >= '1000000000'
-  AND id < '2000000000'
-ORDER BY id;
-```
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md
new file mode 100644
index 0000000000..6e7724ce94
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md
@@ -0,0 +1,29 @@
+# Partitioning Query Specification
+
+| Query | Layout        | Operation                         | Predicate                                                            | Returned data   | Purpose                                                                    |
+|-------|---------------|-----------------------------------|----------------------------------------------------------------------|-----------------|----------------------------------------------------------------------------|
+| Q01   | Repartitioned | Full table baseline               | None                                                                 | Aggregate count | Measures baseline count over repartitioned `yellow_tripdata`               |
+| Q02   | Repartitioned | Partition by year                 | `year = '2022'`                                                      | Aggregate count | Measures year-level partition pruning                                      |
+| Q03   | Repartitioned | Partition by month                | `year = '2022'` and `month = '10'`                                   | Aggregate count | Measures month-level partition pruning                                     |
+| Q04   | Repartitioned | Filtered count, full scan         | `trip_distance >= 10.0` and `total_amount >= 40.0`                   | Aggregate count | Measures data-column filtering without partition restriction               |
+| Q05   | Repartitioned | Filtered count, partition by year | `year = '2022'`, `trip_distance >= 10.0`, and `total_amount >= 40.0` | Aggregate count | Measures combined partition pruning and data-column filtering              |
+| Q06   | Unpartitioned | Full table baseline               | None                                                                 | Aggregate count | Measures baseline count over unpartitioned `yellow_tripdata`               |
+| Q07   | Unpartitioned | Partition-column filter by year   | `year = '2022'`                                                      | Aggregate count | Measures filtering on physical partition columns in unpartitioned data     |
+| Q08   | Unpartitioned | Partition-column filter by month  | `year = '2022'` and `month = '10'`                                   | Aggregate count | Measures filtering on physical year/month columns in unpartitioned data    |
+| Q09   | Unpartitioned | Filtered count, full scan         | `trip_distance >= 10.0` and `total_amount >= 40.0`                   | Aggregate count | Measures data-column filtering on unpartitioned data                       |
+| Q10   | Unpartitioned | Filtered count with year filter   | `year = '2022'`, `trip_distance >= 10.0`, and `total_amount >= 40.0` | Aggregate count | Measures combined physical year-column filtering and data-column filtering |
+
+Q01-Q03 and Q06-Q08 run on `yellow_tripdata`. Q04-Q05 and Q09-Q10 run on
+`green_tripdata`.
+
+Polypheny uses one combined query file because it can reference both adapters in
+one SQL file. DuckDB and Spark use separate query files because their runners
+bind one `DataDir` per execution.
+
+Runnable query files:
+
+```text
+plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_unpartitioned_polypheny.sql
+plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_sql.sql
+plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_unpartitioned_sql.sql
+```
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_sql.sql b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_sql.sql
new file mode 100644
index 0000000000..684bfa397c
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_sql.sql
@@ -0,0 +1,27 @@
+-- Q01: Repartitioned full table baseline
+SELECT count(*) AS row_count
+FROM yellow_tripdata;
+
+-- Q02: Repartitioned partition by year
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE "year" = '2022';
+
+-- Q03: Repartitioned partition by month
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+
+-- Q04: Repartitioned filtered count, full scan
+SELECT count(*) AS row_count
+FROM green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+
+-- Q05: Repartitioned filtered count, partition by year
+SELECT count(*) AS row_count
+FROM green_tripdata
+WHERE  "year" = '2022'
+  AND trip_distance >= 10.0
+  AND total_amount >= 40.0;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_unpartitioned_polypheny.sql b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_unpartitioned_polypheny.sql
new file mode 100644
index 0000000000..f39275d1c8
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_unpartitioned_polypheny.sql
@@ -0,0 +1,57 @@
+-- Q01: Repartitioned full table baseline
+SELECT count(*) AS row_count
+FROM tlcr__yellow_tripdata;
+
+-- Q02: Repartitioned partition by year
+SELECT count(*) AS row_count
+FROM tlcr__yellow_tripdata
+WHERE "year" = '2022';
+
+-- Q03: Repartitioned partition by month
+SELECT count(*) AS row_count
+FROM tlcr__yellow_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+
+-- Q04: Repartitioned filtered count, full scan
+SELECT count(*) AS row_count
+FROM tlcr__green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+
+-- Q05: Repartitioned filtered count, partition by year
+SELECT count(*) AS row_count
+FROM tlcr__green_tripdata
+WHERE  "year" = '2022'
+  AND trip_distance >= 10.0
+  AND total_amount >= 40.0;
+
+-------------Unpartitioned
+
+-- Q06: Unpartitioned full table baseline
+SELECT count(*) AS row_count
+FROM tlcu__yellow_tripdata;
+
+-- Q07: Unpartitioned partition by year
+SELECT count(*) AS row_count
+FROM tlcu__yellow_tripdata
+WHERE "year" = '2022';
+
+-- Q08: Unpartitioned partition by month
+SELECT count(*) AS row_count
+FROM tlcu__yellow_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+
+-- Q09: Unpartitioned filtered count, full scan
+SELECT count(*) AS row_count
+FROM tlcu__green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+
+-- Q10: Unpartitioned filtered count, partition by year
+SELECT count(*) AS row_count
+FROM tlcu__green_tripdata
+WHERE  "year" = '2022'
+  AND trip_distance >= 10.0
+  AND total_amount >= 40.0;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_unpartitioned_sql.sql b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_unpartitioned_sql.sql
new file mode 100644
index 0000000000..ef763d5223
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_unpartitioned_sql.sql
@@ -0,0 +1,27 @@
+-- Q06: Unpartitioned full table baseline
+SELECT count(*) AS row_count
+FROM yellow_tripdata;
+
+-- Q07: Unpartitioned partition by year
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE "year" = '2022';
+
+-- Q08: Unpartitioned partition by month
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+
+-- Q09: Unpartitioned filtered count, full scan
+SELECT count(*) AS row_count
+FROM green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+
+-- Q10: Unpartitioned filtered count, partition by year
+SELECT count(*) AS row_count
+FROM green_tripdata
+WHERE  "year" = '2022'
+  AND trip_distance >= 10.0
+  AND total_amount >= 40.0;
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_duckdb_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_duckdb_tlcp_results.csv
new file mode 100644
index 0000000000..79160dc352
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_duckdb_tlcp_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-11T20:06:31.581772500Z","Q01","Full record access","warmup",1,2568,3711544,22,true,""
+"2026-06-11T20:06:34.366902100Z","Q01","Full record access","measured",1,2786,3711544,22,true,""
+"2026-06-11T20:06:36.924498100Z","Q01","Full record access","measured",2,2558,3711544,22,true,""
+"2026-06-11T20:06:39.735769300Z","Q01","Full record access","measured",3,2807,3711544,22,true,""
+"2026-06-11T20:06:42.398329600Z","Q01","Full record access","measured",4,2662,3711544,22,true,""
+"2026-06-11T20:06:45.678604900Z","Q01","Full record access","measured",5,3269,3711544,22,true,""
+"2026-06-11T20:06:45.842128700Z","Q02","Projection","warmup",1,161,3711544,5,true,""
+"2026-06-11T20:06:46.009288800Z","Q02","Projection","measured",1,168,3711544,5,true,""
+"2026-06-11T20:06:46.183710500Z","Q02","Projection","measured",2,173,3711544,5,true,""
+"2026-06-11T20:06:46.369324Z","Q02","Projection","measured",3,183,3711544,5,true,""
+"2026-06-11T20:06:46.550356800Z","Q02","Projection","measured",4,184,3711544,5,true,""
+"2026-06-11T20:06:46.732551300Z","Q02","Projection","measured",5,179,3711544,5,true,""
+"2026-06-11T20:06:46.768651200Z","Q03","Filtered count","warmup",1,32,1,1,true,""
+"2026-06-11T20:06:46.799113100Z","Q03","Filtered count","measured",1,31,1,1,true,""
+"2026-06-11T20:06:46.836965200Z","Q03","Filtered count","measured",2,35,1,1,true,""
+"2026-06-11T20:06:46.883365600Z","Q03","Filtered count","measured",3,46,1,1,true,""
+"2026-06-11T20:06:46.927137900Z","Q03","Filtered count","measured",4,41,1,1,true,""
+"2026-06-11T20:06:46.970439800Z","Q03","Filtered count","measured",5,42,1,1,true,""
+"2026-06-11T20:06:46.983052700Z","Q04","Filtered full record access","warmup",1,11,0,22,true,""
+"2026-06-11T20:06:46.998657800Z","Q04","Filtered full record access","measured",1,13,0,22,true,""
+"2026-06-11T20:06:47.018166900Z","Q04","Filtered full record access","measured",2,17,0,22,true,""
+"2026-06-11T20:06:47.030161300Z","Q04","Filtered full record access","measured",3,12,0,22,true,""
+"2026-06-11T20:06:47.045945500Z","Q04","Filtered full record access","measured",4,13,0,22,true,""
+"2026-06-11T20:06:47.063416700Z","Q04","Filtered full record access","measured",5,18,0,22,true,""
+"2026-06-11T20:06:47.076852700Z","Q05","Filtered projection","warmup",1,13,0,5,true,""
+"2026-06-11T20:06:47.092357100Z","Q05","Filtered projection","measured",1,13,0,5,true,""
+"2026-06-11T20:06:47.107606100Z","Q05","Filtered projection","measured",2,12,0,5,true,""
+"2026-06-11T20:06:47.122789200Z","Q05","Filtered projection","measured",3,16,0,5,true,""
+"2026-06-11T20:06:47.138406400Z","Q05","Filtered projection","measured",4,12,0,5,true,""
+"2026-06-11T20:06:47.148626400Z","Q05","Filtered projection","measured",5,12,0,5,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_mql_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_mql_tlcp_results.csv
new file mode 100644
index 0000000000..c3996828ae
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_mql_tlcp_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-11T18:57:18.950866100Z","Q01","Full document access","warmup",1,146487,3711544,20,true,""
+"2026-06-11T18:59:40.878060600Z","Q01","Full document access","measured",1,141914,3711544,20,true,""
+"2026-06-11T19:02:01.558648500Z","Q01","Full document access","measured",2,140681,3711544,20,true,""
+"2026-06-11T19:04:22.409971200Z","Q01","Full document access","measured",3,140847,3711544,20,true,""
+"2026-06-11T19:06:43.085124100Z","Q01","Full document access","measured",4,140674,3711544,20,true,""
+"2026-06-11T19:09:02.860315100Z","Q01","Full document access","measured",5,139777,3711544,20,true,""
+"2026-06-11T19:10:49.613562100Z","Q02","Projection","warmup",1,106748,3711544,6,true,""
+"2026-06-11T19:12:37.236250Z","Q02","Projection","measured",1,107622,3711544,6,true,""
+"2026-06-11T19:14:24.269443500Z","Q02","Projection","measured",2,107031,3711544,6,true,""
+"2026-06-11T19:16:11.689652200Z","Q02","Projection","measured",3,107420,3711544,6,true,""
+"2026-06-11T19:17:58.630301400Z","Q02","Projection","measured",4,106939,3711544,6,true,""
+"2026-06-11T19:19:50.212246Z","Q02","Projection","measured",5,111583,3711544,6,true,""
+"2026-06-11T19:21:11.230251400Z","Q03","Filtered count","warmup",1,81015,1,1,true,""
+"2026-06-11T19:22:32.692593700Z","Q03","Filtered count","measured",1,81461,1,1,true,""
+"2026-06-11T19:23:54.995095900Z","Q03","Filtered count","measured",2,82302,1,1,true,""
+"2026-06-11T19:25:16.529502700Z","Q03","Filtered count","measured",3,81532,1,1,true,""
+"2026-06-11T19:26:37.617071Z","Q03","Filtered count","measured",4,81087,1,1,true,""
+"2026-06-11T19:27:57.890478900Z","Q03","Filtered count","measured",5,80273,1,1,true,""
+"2026-06-11T19:29:24.111617200Z","Q04","Filtered full document access","warmup",1,86220,283006,20,true,""
+"2026-06-11T19:30:49.465861100Z","Q04","Filtered full document access","measured",1,85352,283006,20,true,""
+"2026-06-11T19:32:15.176393900Z","Q04","Filtered full document access","measured",2,85709,283006,20,true,""
+"2026-06-11T19:33:40.304629900Z","Q04","Filtered full document access","measured",3,85128,283006,20,true,""
+"2026-06-11T19:35:05.504422200Z","Q04","Filtered full document access","measured",4,85199,283006,20,true,""
+"2026-06-11T19:36:30.339635500Z","Q04","Filtered full document access","measured",5,84831,283006,20,true,""
+"2026-06-11T19:37:53.751224200Z","Q05","Filtered projection","warmup",1,83409,283006,6,true,""
+"2026-06-11T19:39:17.115780200Z","Q05","Filtered projection","measured",1,83364,283006,6,true,""
+"2026-06-11T19:40:40.715102800Z","Q05","Filtered projection","measured",2,83598,283006,6,true,""
+"2026-06-11T19:42:03.812402600Z","Q05","Filtered projection","measured",3,83096,283006,6,true,""
+"2026-06-11T19:43:27.234388Z","Q05","Filtered projection","measured",4,83422,283006,6,true,""
+"2026-06-11T19:44:50.397084400Z","Q05","Filtered projection","measured",5,83162,283006,6,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_rf_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_rf_tlcp_results.csv
new file mode 100644
index 0000000000..176802a828
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_rf_tlcp_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-11T20:02:04.322905500Z","Q01","Full record access","warmup",1,27460,3711544,22,true,""
+"2026-06-11T20:02:30.702631600Z","Q01","Full record access","measured",1,26375,3711544,22,true,""
+"2026-06-11T20:02:56.508888700Z","Q01","Full record access","measured",2,25808,3711544,22,true,""
+"2026-06-11T20:03:22.371052800Z","Q01","Full record access","measured",3,25859,3711544,22,true,""
+"2026-06-11T20:03:48.611189800Z","Q01","Full record access","measured",4,26238,3711544,22,true,""
+"2026-06-11T20:04:15.053012Z","Q01","Full record access","measured",5,26432,3711544,22,true,""
+"2026-06-11T20:04:23.672906800Z","Q02","Projection","warmup",1,8618,3711544,5,true,""
+"2026-06-11T20:04:32.827854800Z","Q02","Projection","measured",1,9154,3711544,5,true,""
+"2026-06-11T20:04:41.665483800Z","Q02","Projection","measured",2,8836,3711544,5,true,""
+"2026-06-11T20:04:50.278581600Z","Q02","Projection","measured",3,8612,3711544,5,true,""
+"2026-06-11T20:04:58.725750700Z","Q02","Projection","measured",4,8446,3711544,5,true,""
+"2026-06-11T20:05:07.676704800Z","Q02","Projection","measured",5,8953,3711544,5,true,""
+"2026-06-11T20:05:08.635764Z","Q03","Filtered count","warmup",1,955,1,1,true,""
+"2026-06-11T20:05:09.194810Z","Q03","Filtered count","measured",1,560,1,1,true,""
+"2026-06-11T20:05:09.719193900Z","Q03","Filtered count","measured",2,523,1,1,true,""
+"2026-06-11T20:05:10.229289800Z","Q03","Filtered count","measured",3,508,1,1,true,""
+"2026-06-11T20:05:10.740282Z","Q03","Filtered count","measured",4,509,1,1,true,""
+"2026-06-11T20:05:11.371187100Z","Q03","Filtered count","measured",5,631,1,1,true,""
+"2026-06-11T20:05:11.451489900Z","Q04","Filtered full record access","warmup",1,77,0,22,true,""
+"2026-06-11T20:05:11.463724200Z","Q04","Filtered full record access","measured",1,11,0,22,true,""
+"2026-06-11T20:05:11.476779800Z","Q04","Filtered full record access","measured",2,12,0,22,true,""
+"2026-06-11T20:05:11.488827900Z","Q04","Filtered full record access","measured",3,12,0,22,true,""
+"2026-06-11T20:05:11.502028800Z","Q04","Filtered full record access","measured",4,12,0,22,true,""
+"2026-06-11T20:05:11.512541700Z","Q04","Filtered full record access","measured",5,11,0,22,true,""
+"2026-06-11T20:05:11.590520800Z","Q05","Filtered projection","warmup",1,74,0,5,true,""
+"2026-06-11T20:05:11.599519500Z","Q05","Filtered projection","measured",1,8,0,5,true,""
+"2026-06-11T20:05:11.612525500Z","Q05","Filtered projection","measured",2,12,0,5,true,""
+"2026-06-11T20:05:11.622031100Z","Q05","Filtered projection","measured",3,8,0,5,true,""
+"2026-06-11T20:05:11.633037Z","Q05","Filtered projection","measured",4,10,0,5,true,""
+"2026-06-11T20:05:11.644040600Z","Q05","Filtered projection","measured",5,10,0,5,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_spark_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_spark_tlcp_results.csv
new file mode 100644
index 0000000000..241abb87c2
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_spark_tlcp_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+2026-06-11T20:08:11.202281Z,Q01,Full record access,warmup,1,27845,3711544,22,true,
+2026-06-11T20:08:31.149931Z,Q01,Full record access,measured,1,19947,3711544,22,true,
+2026-06-11T20:08:52.997061Z,Q01,Full record access,measured,2,19449,3711544,22,true,
+2026-06-11T20:09:12.591150Z,Q01,Full record access,measured,3,19593,3711544,22,true,
+2026-06-11T20:09:35.059883Z,Q01,Full record access,measured,4,20172,3711544,22,true,
+2026-06-11T20:09:56.709063Z,Q01,Full record access,measured,5,20031,3711544,22,true,
+2026-06-11T20:10:10.017439Z,Q02,Projection,warmup,1,13307,3711544,5,true,
+2026-06-11T20:10:25.359033Z,Q02,Projection,measured,1,13229,3711544,5,true,
+2026-06-11T20:10:38.260598Z,Q02,Projection,measured,2,12901,3711544,5,true,
+2026-06-11T20:10:53.632397Z,Q02,Projection,measured,3,13294,3711544,5,true,
+2026-06-11T20:11:05.793956Z,Q02,Projection,measured,4,12161,3711544,5,true,
+2026-06-11T20:11:18.581505Z,Q02,Projection,measured,5,12787,3711544,5,true,
+2026-06-11T20:11:20.488438Z,Q03,Filtered count,warmup,1,1905,1,1,true,
+2026-06-11T20:11:23.866016Z,Q03,Filtered count,measured,1,1264,1,1,true,
+2026-06-11T20:11:24.992046Z,Q03,Filtered count,measured,2,1125,1,1,true,
+2026-06-11T20:11:25.983035Z,Q03,Filtered count,measured,3,990,1,1,true,
+2026-06-11T20:11:26.877336Z,Q03,Filtered count,measured,4,893,1,1,true,
+2026-06-11T20:11:27.903714Z,Q03,Filtered count,measured,5,1025,1,1,true,
+2026-06-11T20:11:28.029976Z,Q04,Filtered full record access,warmup,1,124,0,22,true,
+2026-06-11T20:11:28.121348Z,Q04,Filtered full record access,measured,1,90,0,22,true,
+2026-06-11T20:11:28.216447Z,Q04,Filtered full record access,measured,2,94,0,22,true,
+2026-06-11T20:11:28.298063Z,Q04,Filtered full record access,measured,3,81,0,22,true,
+2026-06-11T20:11:28.392095Z,Q04,Filtered full record access,measured,4,93,0,22,true,
+2026-06-11T20:11:28.481201Z,Q04,Filtered full record access,measured,5,88,0,22,true,
+2026-06-11T20:11:28.599850Z,Q05,Filtered projection,warmup,1,117,0,5,true,
+2026-06-11T20:11:28.697370Z,Q05,Filtered projection,measured,1,96,0,5,true,
+2026-06-11T20:11:28.811912Z,Q05,Filtered projection,measured,2,113,0,5,true,
+2026-06-11T20:11:28.926642Z,Q05,Filtered projection,measured,3,114,0,5,true,
+2026-06-11T20:11:29.012868Z,Q05,Filtered projection,measured,4,85,0,5,true,
+2026-06-11T20:11:29.096270Z,Q05,Filtered projection,measured,5,82,0,5,true,
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_results.md b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_results.md
new file mode 100644
index 0000000000..762d8ad853
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_results.md
@@ -0,0 +1,151 @@
+# Access Model Comparison TLCP Results
+
+This file contains the raw benchmark result rows from the TLCP access model comparison CSV files.
+
+## Polypheny Relational
+
+Source: `access_model_comparison_polypheny_rf_tlcp_results.csv`
+
+| timestamp                      | query_id | description                 | phase    | run | elapsed_ms | rows    | columns | success | error |
+|--------------------------------|----------|-----------------------------|----------|-----|------------|---------|---------|---------|-------|
+| 2026-06-11T20:02:04.322905500Z | Q01      | Full record access          | warmup   | 1   | 27460      | 3711544 | 22      | true    |       |
+| 2026-06-11T20:02:30.702631600Z | Q01      | Full record access          | measured | 1   | 26375      | 3711544 | 22      | true    |       |
+| 2026-06-11T20:02:56.508888700Z | Q01      | Full record access          | measured | 2   | 25808      | 3711544 | 22      | true    |       |
+| 2026-06-11T20:03:22.371052800Z | Q01      | Full record access          | measured | 3   | 25859      | 3711544 | 22      | true    |       |
+| 2026-06-11T20:03:48.611189800Z | Q01      | Full record access          | measured | 4   | 26238      | 3711544 | 22      | true    |       |
+| 2026-06-11T20:04:15.053012Z    | Q01      | Full record access          | measured | 5   | 26432      | 3711544 | 22      | true    |       |
+| 2026-06-11T20:04:23.672906800Z | Q02      | Projection                  | warmup   | 1   | 8618       | 3711544 | 5       | true    |       |
+| 2026-06-11T20:04:32.827854800Z | Q02      | Projection                  | measured | 1   | 9154       | 3711544 | 5       | true    |       |
+| 2026-06-11T20:04:41.665483800Z | Q02      | Projection                  | measured | 2   | 8836       | 3711544 | 5       | true    |       |
+| 2026-06-11T20:04:50.278581600Z | Q02      | Projection                  | measured | 3   | 8612       | 3711544 | 5       | true    |       |
+| 2026-06-11T20:04:58.725750700Z | Q02      | Projection                  | measured | 4   | 8446       | 3711544 | 5       | true    |       |
+| 2026-06-11T20:05:07.676704800Z | Q02      | Projection                  | measured | 5   | 8953       | 3711544 | 5       | true    |       |
+| 2026-06-11T20:05:08.635764Z    | Q03      | Filtered count              | warmup   | 1   | 955        | 1       | 1       | true    |       |
+| 2026-06-11T20:05:09.194810Z    | Q03      | Filtered count              | measured | 1   | 560        | 1       | 1       | true    |       |
+| 2026-06-11T20:05:09.719193900Z | Q03      | Filtered count              | measured | 2   | 523        | 1       | 1       | true    |       |
+| 2026-06-11T20:05:10.229289800Z | Q03      | Filtered count              | measured | 3   | 508        | 1       | 1       | true    |       |
+| 2026-06-11T20:05:10.740282Z    | Q03      | Filtered count              | measured | 4   | 509        | 1       | 1       | true    |       |
+| 2026-06-11T20:05:11.371187100Z | Q03      | Filtered count              | measured | 5   | 631        | 1       | 1       | true    |       |
+| 2026-06-11T20:05:11.451489900Z | Q04      | Filtered full record access | warmup   | 1   | 77         | 0       | 22      | true    |       |
+| 2026-06-11T20:05:11.463724200Z | Q04      | Filtered full record access | measured | 1   | 11         | 0       | 22      | true    |       |
+| 2026-06-11T20:05:11.476779800Z | Q04      | Filtered full record access | measured | 2   | 12         | 0       | 22      | true    |       |
+| 2026-06-11T20:05:11.488827900Z | Q04      | Filtered full record access | measured | 3   | 12         | 0       | 22      | true    |       |
+| 2026-06-11T20:05:11.502028800Z | Q04      | Filtered full record access | measured | 4   | 12         | 0       | 22      | true    |       |
+| 2026-06-11T20:05:11.512541700Z | Q04      | Filtered full record access | measured | 5   | 11         | 0       | 22      | true    |       |
+| 2026-06-11T20:05:11.590520800Z | Q05      | Filtered projection         | warmup   | 1   | 74         | 0       | 5       | true    |       |
+| 2026-06-11T20:05:11.599519500Z | Q05      | Filtered projection         | measured | 1   | 8          | 0       | 5       | true    |       |
+| 2026-06-11T20:05:11.612525500Z | Q05      | Filtered projection         | measured | 2   | 12         | 0       | 5       | true    |       |
+| 2026-06-11T20:05:11.622031100Z | Q05      | Filtered projection         | measured | 3   | 8          | 0       | 5       | true    |       |
+| 2026-06-11T20:05:11.633037Z    | Q05      | Filtered projection         | measured | 4   | 10         | 0       | 5       | true    |       |
+| 2026-06-11T20:05:11.644040600Z | Q05      | Filtered projection         | measured | 5   | 10         | 0       | 5       | true    |       |
+
+## Polypheny Document MQL
+
+Source: `access_model_comparison_polypheny_mql_tlcp_results.csv`
+
+| timestamp                      | query_id | description                   | phase    | run | elapsed_ms | rows    | columns | success | error |
+|--------------------------------|----------|-------------------------------|----------|-----|------------|---------|---------|---------|-------|
+| 2026-06-11T18:57:18.950866100Z | Q01      | Full document access          | warmup   | 1   | 146487     | 3711544 | 20      | true    |       |
+| 2026-06-11T18:59:40.878060600Z | Q01      | Full document access          | measured | 1   | 141914     | 3711544 | 20      | true    |       |
+| 2026-06-11T19:02:01.558648500Z | Q01      | Full document access          | measured | 2   | 140681     | 3711544 | 20      | true    |       |
+| 2026-06-11T19:04:22.409971200Z | Q01      | Full document access          | measured | 3   | 140847     | 3711544 | 20      | true    |       |
+| 2026-06-11T19:06:43.085124100Z | Q01      | Full document access          | measured | 4   | 140674     | 3711544 | 20      | true    |       |
+| 2026-06-11T19:09:02.860315100Z | Q01      | Full document access          | measured | 5   | 139777     | 3711544 | 20      | true    |       |
+| 2026-06-11T19:10:49.613562100Z | Q02      | Projection                    | warmup   | 1   | 106748     | 3711544 | 6       | true    |       |
+| 2026-06-11T19:12:37.236250Z    | Q02      | Projection                    | measured | 1   | 107622     | 3711544 | 6       | true    |       |
+| 2026-06-11T19:14:24.269443500Z | Q02      | Projection                    | measured | 2   | 107031     | 3711544 | 6       | true    |       |
+| 2026-06-11T19:16:11.689652200Z | Q02      | Projection                    | measured | 3   | 107420     | 3711544 | 6       | true    |       |
+| 2026-06-11T19:17:58.630301400Z | Q02      | Projection                    | measured | 4   | 106939     | 3711544 | 6       | true    |       |
+| 2026-06-11T19:19:50.212246Z    | Q02      | Projection                    | measured | 5   | 111583     | 3711544 | 6       | true    |       |
+| 2026-06-11T19:21:11.230251400Z | Q03      | Filtered count                | warmup   | 1   | 81015      | 1       | 1       | true    |       |
+| 2026-06-11T19:22:32.692593700Z | Q03      | Filtered count                | measured | 1   | 81461      | 1       | 1       | true    |       |
+| 2026-06-11T19:23:54.995095900Z | Q03      | Filtered count                | measured | 2   | 82302      | 1       | 1       | true    |       |
+| 2026-06-11T19:25:16.529502700Z | Q03      | Filtered count                | measured | 3   | 81532      | 1       | 1       | true    |       |
+| 2026-06-11T19:26:37.617071Z    | Q03      | Filtered count                | measured | 4   | 81087      | 1       | 1       | true    |       |
+| 2026-06-11T19:27:57.890478900Z | Q03      | Filtered count                | measured | 5   | 80273      | 1       | 1       | true    |       |
+| 2026-06-11T19:29:24.111617200Z | Q04      | Filtered full document access | warmup   | 1   | 86220      | 283006  | 20      | true    |       |
+| 2026-06-11T19:30:49.465861100Z | Q04      | Filtered full document access | measured | 1   | 85352      | 283006  | 20      | true    |       |
+| 2026-06-11T19:32:15.176393900Z | Q04      | Filtered full document access | measured | 2   | 85709      | 283006  | 20      | true    |       |
+| 2026-06-11T19:33:40.304629900Z | Q04      | Filtered full document access | measured | 3   | 85128      | 283006  | 20      | true    |       |
+| 2026-06-11T19:35:05.504422200Z | Q04      | Filtered full document access | measured | 4   | 85199      | 283006  | 20      | true    |       |
+| 2026-06-11T19:36:30.339635500Z | Q04      | Filtered full document access | measured | 5   | 84831      | 283006  | 20      | true    |       |
+| 2026-06-11T19:37:53.751224200Z | Q05      | Filtered projection           | warmup   | 1   | 83409      | 283006  | 6       | true    |       |
+| 2026-06-11T19:39:17.115780200Z | Q05      | Filtered projection           | measured | 1   | 83364      | 283006  | 6       | true    |       |
+| 2026-06-11T19:40:40.715102800Z | Q05      | Filtered projection           | measured | 2   | 83598      | 283006  | 6       | true    |       |
+| 2026-06-11T19:42:03.812402600Z | Q05      | Filtered projection           | measured | 3   | 83096      | 283006  | 6       | true    |       |
+| 2026-06-11T19:43:27.234388Z    | Q05      | Filtered projection           | measured | 4   | 83422      | 283006  | 6       | true    |       |
+| 2026-06-11T19:44:50.397084400Z | Q05      | Filtered projection           | measured | 5   | 83162      | 283006  | 6       | true    |       |
+
+## DuckDB
+
+Source: `access_model_comparison_duckdb_tlcp_results.csv`
+
+| timestamp                      | query_id | description                 | phase    | run | elapsed_ms | rows    | columns | success | error |
+|--------------------------------|----------|-----------------------------|----------|-----|------------|---------|---------|---------|-------|
+| 2026-06-11T20:06:31.581772500Z | Q01      | Full record access          | warmup   | 1   | 2568       | 3711544 | 22      | true    |       |
+| 2026-06-11T20:06:34.366902100Z | Q01      | Full record access          | measured | 1   | 2786       | 3711544 | 22      | true    |       |
+| 2026-06-11T20:06:36.924498100Z | Q01      | Full record access          | measured | 2   | 2558       | 3711544 | 22      | true    |       |
+| 2026-06-11T20:06:39.735769300Z | Q01      | Full record access          | measured | 3   | 2807       | 3711544 | 22      | true    |       |
+| 2026-06-11T20:06:42.398329600Z | Q01      | Full record access          | measured | 4   | 2662       | 3711544 | 22      | true    |       |
+| 2026-06-11T20:06:45.678604900Z | Q01      | Full record access          | measured | 5   | 3269       | 3711544 | 22      | true    |       |
+| 2026-06-11T20:06:45.842128700Z | Q02      | Projection                  | warmup   | 1   | 161        | 3711544 | 5       | true    |       |
+| 2026-06-11T20:06:46.009288800Z | Q02      | Projection                  | measured | 1   | 168        | 3711544 | 5       | true    |       |
+| 2026-06-11T20:06:46.183710500Z | Q02      | Projection                  | measured | 2   | 173        | 3711544 | 5       | true    |       |
+| 2026-06-11T20:06:46.369324Z    | Q02      | Projection                  | measured | 3   | 183        | 3711544 | 5       | true    |       |
+| 2026-06-11T20:06:46.550356800Z | Q02      | Projection                  | measured | 4   | 184        | 3711544 | 5       | true    |       |
+| 2026-06-11T20:06:46.732551300Z | Q02      | Projection                  | measured | 5   | 179        | 3711544 | 5       | true    |       |
+| 2026-06-11T20:06:46.768651200Z | Q03      | Filtered count              | warmup   | 1   | 32         | 1       | 1       | true    |       |
+| 2026-06-11T20:06:46.799113100Z | Q03      | Filtered count              | measured | 1   | 31         | 1       | 1       | true    |       |
+| 2026-06-11T20:06:46.836965200Z | Q03      | Filtered count              | measured | 2   | 35         | 1       | 1       | true    |       |
+| 2026-06-11T20:06:46.883365600Z | Q03      | Filtered count              | measured | 3   | 46         | 1       | 1       | true    |       |
+| 2026-06-11T20:06:46.927137900Z | Q03      | Filtered count              | measured | 4   | 41         | 1       | 1       | true    |       |
+| 2026-06-11T20:06:46.970439800Z | Q03      | Filtered count              | measured | 5   | 42         | 1       | 1       | true    |       |
+| 2026-06-11T20:06:46.983052700Z | Q04      | Filtered full record access | warmup   | 1   | 11         | 0       | 22      | true    |       |
+| 2026-06-11T20:06:46.998657800Z | Q04      | Filtered full record access | measured | 1   | 13         | 0       | 22      | true    |       |
+| 2026-06-11T20:06:47.018166900Z | Q04      | Filtered full record access | measured | 2   | 17         | 0       | 22      | true    |       |
+| 2026-06-11T20:06:47.030161300Z | Q04      | Filtered full record access | measured | 3   | 12         | 0       | 22      | true    |       |
+| 2026-06-11T20:06:47.045945500Z | Q04      | Filtered full record access | measured | 4   | 13         | 0       | 22      | true    |       |
+| 2026-06-11T20:06:47.063416700Z | Q04      | Filtered full record access | measured | 5   | 18         | 0       | 22      | true    |       |
+| 2026-06-11T20:06:47.076852700Z | Q05      | Filtered projection         | warmup   | 1   | 13         | 0       | 5       | true    |       |
+| 2026-06-11T20:06:47.092357100Z | Q05      | Filtered projection         | measured | 1   | 13         | 0       | 5       | true    |       |
+| 2026-06-11T20:06:47.107606100Z | Q05      | Filtered projection         | measured | 2   | 12         | 0       | 5       | true    |       |
+| 2026-06-11T20:06:47.122789200Z | Q05      | Filtered projection         | measured | 3   | 16         | 0       | 5       | true    |       |
+| 2026-06-11T20:06:47.138406400Z | Q05      | Filtered projection         | measured | 4   | 12         | 0       | 5       | true    |       |
+| 2026-06-11T20:06:47.148626400Z | Q05      | Filtered projection         | measured | 5   | 12         | 0       | 5       | true    |       |
+
+## Apache Spark
+
+Source: `access_model_comparison_spark_tlcp_results.csv`
+
+| timestamp                   | query_id | description                 | phase    | run | elapsed_ms | rows    | columns | success | error |
+|-----------------------------|----------|-----------------------------|----------|-----|------------|---------|---------|---------|-------|
+| 2026-06-11T20:08:11.202281Z | Q01      | Full record access          | warmup   | 1   | 27845      | 3711544 | 22      | true    |       |
+| 2026-06-11T20:08:31.149931Z | Q01      | Full record access          | measured | 1   | 19947      | 3711544 | 22      | true    |       |
+| 2026-06-11T20:08:52.997061Z | Q01      | Full record access          | measured | 2   | 19449      | 3711544 | 22      | true    |       |
+| 2026-06-11T20:09:12.591150Z | Q01      | Full record access          | measured | 3   | 19593      | 3711544 | 22      | true    |       |
+| 2026-06-11T20:09:35.059883Z | Q01      | Full record access          | measured | 4   | 20172      | 3711544 | 22      | true    |       |
+| 2026-06-11T20:09:56.709063Z | Q01      | Full record access          | measured | 5   | 20031      | 3711544 | 22      | true    |       |
+| 2026-06-11T20:10:10.017439Z | Q02      | Projection                  | warmup   | 1   | 13307      | 3711544 | 5       | true    |       |
+| 2026-06-11T20:10:25.359033Z | Q02      | Projection                  | measured | 1   | 13229      | 3711544 | 5       | true    |       |
+| 2026-06-11T20:10:38.260598Z | Q02      | Projection                  | measured | 2   | 12901      | 3711544 | 5       | true    |       |
+| 2026-06-11T20:10:53.632397Z | Q02      | Projection                  | measured | 3   | 13294      | 3711544 | 5       | true    |       |
+| 2026-06-11T20:11:05.793956Z | Q02      | Projection                  | measured | 4   | 12161      | 3711544 | 5       | true    |       |
+| 2026-06-11T20:11:18.581505Z | Q02      | Projection                  | measured | 5   | 12787      | 3711544 | 5       | true    |       |
+| 2026-06-11T20:11:20.488438Z | Q03      | Filtered count              | warmup   | 1   | 1905       | 1       | 1       | true    |       |
+| 2026-06-11T20:11:23.866016Z | Q03      | Filtered count              | measured | 1   | 1264       | 1       | 1       | true    |       |
+| 2026-06-11T20:11:24.992046Z | Q03      | Filtered count              | measured | 2   | 1125       | 1       | 1       | true    |       |
+| 2026-06-11T20:11:25.983035Z | Q03      | Filtered count              | measured | 3   | 990        | 1       | 1       | true    |       |
+| 2026-06-11T20:11:26.877336Z | Q03      | Filtered count              | measured | 4   | 893        | 1       | 1       | true    |       |
+| 2026-06-11T20:11:27.903714Z | Q03      | Filtered count              | measured | 5   | 1025       | 1       | 1       | true    |       |
+| 2026-06-11T20:11:28.029976Z | Q04      | Filtered full record access | warmup   | 1   | 124        | 0       | 22      | true    |       |
+| 2026-06-11T20:11:28.121348Z | Q04      | Filtered full record access | measured | 1   | 90         | 0       | 22      | true    |       |
+| 2026-06-11T20:11:28.216447Z | Q04      | Filtered full record access | measured | 2   | 94         | 0       | 22      | true    |       |
+| 2026-06-11T20:11:28.298063Z | Q04      | Filtered full record access | measured | 3   | 81         | 0       | 22      | true    |       |
+| 2026-06-11T20:11:28.392095Z | Q04      | Filtered full record access | measured | 4   | 93         | 0       | 22      | true    |       |
+| 2026-06-11T20:11:28.481201Z | Q04      | Filtered full record access | measured | 5   | 88         | 0       | 22      | true    |       |
+| 2026-06-11T20:11:28.599850Z | Q05      | Filtered projection         | warmup   | 1   | 117        | 0       | 5       | true    |       |
+| 2026-06-11T20:11:28.697370Z | Q05      | Filtered projection         | measured | 1   | 96         | 0       | 5       | true    |       |
+| 2026-06-11T20:11:28.811912Z | Q05      | Filtered projection         | measured | 2   | 113        | 0       | 5       | true    |       |
+| 2026-06-11T20:11:28.926642Z | Q05      | Filtered projection         | measured | 3   | 114        | 0       | 5       | true    |       |
+| 2026-06-11T20:11:29.012868Z | Q05      | Filtered projection         | measured | 4   | 85         | 0       | 5       | true    |       |
+| 2026-06-11T20:11:29.096270Z | Q05      | Filtered projection         | measured | 5   | 82         | 0       | 5       | true    |       |
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_summary.md b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_summary.md
new file mode 100644
index 0000000000..a25f686176
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_summary.md
@@ -0,0 +1,58 @@
+# Access Model Comparison TLCP Summary
+
+Phase summarized: `measured`.
+Warmup rows are excluded. Mean and median values use successful runs only.
+
+## Source Files
+
+| System                 | CSV                                                                                                 |
+|------------------------|-----------------------------------------------------------------------------------------------------|
+| Polypheny Relational   | `plugins\parquet-adapter\benchmarks\results\access_model_comparison_polypheny_rf_tlcp_results.csv`  |
+| Polypheny Document MQL | `plugins\parquet-adapter\benchmarks\results\access_model_comparison_polypheny_mql_tlcp_results.csv` |
+| DuckDB                 | `plugins\parquet-adapter\benchmarks\results\access_model_comparison_duckdb_tlcp_results.csv`        |
+| Apache Spark           | `plugins\parquet-adapter\benchmarks\results\access_model_comparison_spark_tlcp_results.csv`         |
+
+## Mean Elapsed Time (ms)
+
+| Query | Description                 | Polypheny Relational | Polypheny Document MQL | DuckDB  | Apache Spark | Row counts |
+|-------|-----------------------------|----------------------|------------------------|---------|--------------|------------|
+| Q01   | Full record access          | 26,142.4             | 140,778.6              | 2,816.4 | 19,838.4     | 3,711,544  |
+| Q02   | Projection                  | 8,800.2              | 108,119.0              | 177.4   | 12,874.4     | 3,711,544  |
+| Q03   | Filtered count              | 546.2                | 81,331.0               | 39.0    | 1,059.4      | 1          |
+| Q04   | Filtered full record access | 11.6                 | 85,243.8               | 14.6    | 89.2         | differs    |
+| Q05   | Filtered projection         | 9.6                  | 83,328.4               | 13.0    | 98.0         | differs    |
+
+## Result Row Counts 
+
+| Query | Description                 | Polypheny Relational | Polypheny Document MQL | DuckDB    | Apache Spark |
+|-------|-----------------------------|----------------------|------------------------|-----------|--------------|
+| Q01   | Full record access          | 3,711,544            | 3,711,544              | 3,711,544 | 3,711,544    |
+| Q02   | Projection                  | 3,711,544            | 3,711,544              | 3,711,544 | 3,711,544    |
+| Q03   | Filtered count              | 1                    | 1                      | 1         | 1            |
+| Q04   | Filtered full record access | 0                    | 283,006                | 0         | 0            |
+| Q05   | Filtered projection         | 0                    | 283,006                | 0         | 0            |
+
+## Detailed Summary (ms)
+
+| System                 | Query | Description                   | Runs | Mean      | Median    | Min       | Max       | Rows      | Columns | Status |
+|------------------------|-------|-------------------------------|------|-----------|-----------|-----------|-----------|-----------|---------|--------|
+| Polypheny Relational   | Q01   | Full record access            | 5/5  | 26,142.4  | 26,238.0  | 25,808.0  | 26,432.0  | 3,711,544 | 22      | ok     |
+| Polypheny Relational   | Q02   | Projection                    | 5/5  | 8,800.2   | 8,836.0   | 8,446.0   | 9,154.0   | 3,711,544 | 5       | ok     |
+| Polypheny Relational   | Q03   | Filtered count                | 5/5  | 546.2     | 523.0     | 508.0     | 631.0     | 1         | 1       | ok     |
+| Polypheny Relational   | Q04   | Filtered full record access   | 5/5  | 11.6      | 12.0      | 11.0      | 12.0      | 0         | 22      | ok     |
+| Polypheny Relational   | Q05   | Filtered projection           | 5/5  | 9.6       | 10.0      | 8.0       | 12.0      | 0         | 5       | ok     |
+| Polypheny Document MQL | Q01   | Full document access          | 5/5  | 140,778.6 | 140,681.0 | 139,777.0 | 141,914.0 | 3,711,544 | 20      | ok     |
+| Polypheny Document MQL | Q02   | Projection                    | 5/5  | 108,119.0 | 107,420.0 | 106,939.0 | 111,583.0 | 3,711,544 | 6       | ok     |
+| Polypheny Document MQL | Q03   | Filtered count                | 5/5  | 81,331.0  | 81,461.0  | 80,273.0  | 82,302.0  | 1         | 1       | ok     |
+| Polypheny Document MQL | Q04   | Filtered full document access | 5/5  | 85,243.8  | 85,199.0  | 84,831.0  | 85,709.0  | 283,006   | 20      | ok     |
+| Polypheny Document MQL | Q05   | Filtered projection           | 5/5  | 83,328.4  | 83,364.0  | 83,096.0  | 83,598.0  | 283,006   | 6       | ok     |
+| DuckDB                 | Q01   | Full record access            | 5/5  | 2,816.4   | 2,786.0   | 2,558.0   | 3,269.0   | 3,711,544 | 22      | ok     |
+| DuckDB                 | Q02   | Projection                    | 5/5  | 177.4     | 179.0     | 168.0     | 184.0     | 3,711,544 | 5       | ok     |
+| DuckDB                 | Q03   | Filtered count                | 5/5  | 39.0      | 41.0      | 31.0      | 46.0      | 1         | 1       | ok     |
+| DuckDB                 | Q04   | Filtered full record access   | 5/5  | 14.6      | 13.0      | 12.0      | 18.0      | 0         | 22      | ok     |
+| DuckDB                 | Q05   | Filtered projection           | 5/5  | 13.0      | 12.0      | 12.0      | 16.0      | 0         | 5       | ok     |
+| Apache Spark           | Q01   | Full record access            | 5/5  | 19,838.4  | 19,947.0  | 19,449.0  | 20,172.0  | 3,711,544 | 22      | ok     |
+| Apache Spark           | Q02   | Projection                    | 5/5  | 12,874.4  | 12,901.0  | 12,161.0  | 13,294.0  | 3,711,544 | 5       | ok     |
+| Apache Spark           | Q03   | Filtered count                | 5/5  | 1,059.4   | 1,025.0   | 893.0     | 1,264.0   | 1         | 1       | ok     |
+| Apache Spark           | Q04   | Filtered full record access   | 5/5  | 89.2      | 90.0      | 81.0      | 94.0      | 0         | 22      | ok     |
+| Apache Spark           | Q05   | Filtered projection           | 5/5  | 98.0      | 96.0      | 82.0      | 114.0     | 0         | 5       | ok     |
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_duckdb_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_duckdb_tlcp_results.csv
new file mode 100644
index 0000000000..4c65de1a62
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_duckdb_tlcp_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-12T09:46:50.648077Z","Q01","Full record access","warmup",1,8517,3711544,22,true,""
+"2026-06-12T09:47:01.193663400Z","Q01","Full record access","measured",1,10540,3711544,22,true,""
+"2026-06-12T09:47:11.738263700Z","Q01","Full record access","measured",2,10545,3711544,22,true,""
+"2026-06-12T09:47:21.648860300Z","Q01","Full record access","measured",3,9907,3711544,22,true,""
+"2026-06-12T09:47:30.567001400Z","Q01","Full record access","measured",4,8916,3711544,22,true,""
+"2026-06-12T09:47:43.442874600Z","Q01","Full record access","measured",5,12826,3711544,22,true,""
+"2026-06-12T09:47:44.057229700Z","Q02","Projection","warmup",1,569,3711544,5,true,""
+"2026-06-12T09:47:44.670079900Z","Q02","Projection","measured",1,606,3711544,5,true,""
+"2026-06-12T09:47:45.269937800Z","Q02","Projection","measured",2,597,3711544,5,true,""
+"2026-06-12T09:47:45.940713600Z","Q02","Projection","measured",3,670,3711544,5,true,""
+"2026-06-12T09:47:46.610987500Z","Q02","Projection","measured",4,666,3711544,5,true,""
+"2026-06-12T09:47:47.254055500Z","Q02","Projection","measured",5,639,3711544,5,true,""
+"2026-06-12T09:47:47.365132800Z","Q03","Filtered count","warmup",1,99,1,1,true,""
+"2026-06-12T09:47:47.456690200Z","Q03","Filtered count","measured",1,90,1,1,true,""
+"2026-06-12T09:47:47.604777600Z","Q03","Filtered count","measured",2,141,1,1,true,""
+"2026-06-12T09:47:47.719009700Z","Q03","Filtered count","measured",3,113,1,1,true,""
+"2026-06-12T09:47:47.822657600Z","Q03","Filtered count","measured",4,101,1,1,true,""
+"2026-06-12T09:47:47.918083300Z","Q03","Filtered count","measured",5,94,1,1,true,""
+"2026-06-12T09:47:49.207444600Z","Q04","Filtered full record access","warmup",1,1286,283006,22,true,""
+"2026-06-12T09:47:50.490971900Z","Q04","Filtered full record access","measured",1,1252,283006,22,true,""
+"2026-06-12T09:47:51.956861Z","Q04","Filtered full record access","measured",2,1451,283006,22,true,""
+"2026-06-12T09:47:53.357309100Z","Q04","Filtered full record access","measured",3,1386,283006,22,true,""
+"2026-06-12T09:47:54.849118800Z","Q04","Filtered full record access","measured",4,1488,283006,22,true,""
+"2026-06-12T09:47:56.446559700Z","Q04","Filtered full record access","measured",5,1592,283006,22,true,""
+"2026-06-12T09:47:56.773658700Z","Q05","Filtered projection","warmup",1,323,283006,5,true,""
+"2026-06-12T09:47:57.018475200Z","Q05","Filtered projection","measured",1,239,283006,5,true,""
+"2026-06-12T09:47:57.237897800Z","Q05","Filtered projection","measured",2,217,283006,5,true,""
+"2026-06-12T09:47:57.537328300Z","Q05","Filtered projection","measured",3,296,283006,5,true,""
+"2026-06-12T09:47:57.775825200Z","Q05","Filtered projection","measured",4,236,283006,5,true,""
+"2026-06-12T09:47:58.058269200Z","Q05","Filtered projection","measured",5,280,283006,5,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_mql_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_mql_tlcp_results.csv
new file mode 100644
index 0000000000..6527d81beb
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_mql_tlcp_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-12T09:50:36.833283500Z","Q01","Full document access","warmup",1,290589,3711544,20,true,""
+"2026-06-12T09:54:35.760834700Z","Q01","Full document access","measured",1,238912,3711544,20,true,""
+"2026-06-12T09:56:45.003602300Z","Q01","Full document access","measured",2,129238,3711544,20,true,""
+"2026-06-12T09:58:54.228624900Z","Q01","Full document access","measured",3,129224,3711544,20,true,""
+"2026-06-12T10:01:03.330599200Z","Q01","Full document access","measured",4,129101,3711544,20,true,""
+"2026-06-12T10:03:12.055590200Z","Q01","Full document access","measured",5,128715,3711544,20,true,""
+"2026-06-12T10:04:50.818928Z","Q02","Projection","warmup",1,98763,3711544,6,true,""
+"2026-06-12T10:06:28.991072300Z","Q02","Projection","measured",1,98171,3711544,6,true,""
+"2026-06-12T10:08:07.107897600Z","Q02","Projection","measured",2,98115,3711544,6,true,""
+"2026-06-12T10:09:44.582687400Z","Q02","Projection","measured",3,97476,3711544,6,true,""
+"2026-06-12T10:11:22.319387Z","Q02","Projection","measured",4,97734,3711544,6,true,""
+"2026-06-12T10:13:00.087204500Z","Q02","Projection","measured",5,97767,3711544,6,true,""
+"2026-06-12T10:14:14.609274600Z","Q03","Filtered count","warmup",1,74522,1,1,true,""
+"2026-06-12T10:15:29.870523500Z","Q03","Filtered count","measured",1,75260,1,1,true,""
+"2026-06-12T10:16:44.354726300Z","Q03","Filtered count","measured",2,74482,1,1,true,""
+"2026-06-12T10:17:58.690538600Z","Q03","Filtered count","measured",3,74334,1,1,true,""
+"2026-06-12T10:19:13.083381700Z","Q03","Filtered count","measured",4,74392,1,1,true,""
+"2026-06-12T10:20:28.965763600Z","Q03","Filtered count","measured",5,75880,1,1,true,""
+"2026-06-12T10:21:47.827442800Z","Q04","Filtered full document access","warmup",1,78862,283006,20,true,""
+"2026-06-12T10:23:06.365838400Z","Q04","Filtered full document access","measured",1,78538,283006,20,true,""
+"2026-06-12T10:24:25.064815800Z","Q04","Filtered full document access","measured",2,78697,283006,20,true,""
+"2026-06-12T10:25:44.770667100Z","Q04","Filtered full document access","measured",3,79703,283006,20,true,""
+"2026-06-12T10:27:04.131447300Z","Q04","Filtered full document access","measured",4,79361,283006,20,true,""
+"2026-06-12T10:28:22.893644600Z","Q04","Filtered full document access","measured",5,78759,283006,20,true,""
+"2026-06-12T10:29:40.789254700Z","Q05","Filtered projection","warmup",1,77895,283006,6,true,""
+"2026-06-12T10:30:57.257155900Z","Q05","Filtered projection","measured",1,76467,283006,6,true,""
+"2026-06-12T10:32:14.530637400Z","Q05","Filtered projection","measured",2,77273,283006,6,true,""
+"2026-06-12T10:33:31.285965200Z","Q05","Filtered projection","measured",3,76754,283006,6,true,""
+"2026-06-12T10:34:47.726504900Z","Q05","Filtered projection","measured",4,76439,283006,6,true,""
+"2026-06-12T10:36:04.162332700Z","Q05","Filtered projection","measured",5,76437,283006,6,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rf_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rf_tlcp_results.csv
new file mode 100644
index 0000000000..1344da857c
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rf_tlcp_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-12T09:45:54.362874400Z","Q01","Full record access","warmup",1,40791,3711544,22,true,""
+"2026-06-12T09:46:34.530473300Z","Q01","Full record access","measured",1,40161,3711544,22,true,""
+"2026-06-12T09:47:37.440857300Z","Q01","Full record access","measured",2,62904,3711544,22,true,""
+"2026-06-12T09:48:54.548951100Z","Q01","Full record access","measured",3,77106,3711544,22,true,""
+"2026-06-12T09:49:58.954841800Z","Q01","Full record access","measured",4,64403,3711544,22,true,""
+"2026-06-12T09:50:57.972229400Z","Q01","Full record access","measured",5,59007,3711544,22,true,""
+"2026-06-12T09:51:20.414646Z","Q02","Projection","warmup",1,22439,3711544,5,true,""
+"2026-06-12T09:51:41.635903200Z","Q02","Projection","measured",1,21219,3711544,5,true,""
+"2026-06-12T09:52:02.873664800Z","Q02","Projection","measured",2,21236,3711544,5,true,""
+"2026-06-12T09:52:23.746682Z","Q02","Projection","measured",3,20870,3711544,5,true,""
+"2026-06-12T09:52:43.365204500Z","Q02","Projection","measured",4,19616,3711544,5,true,""
+"2026-06-12T09:53:10.157633100Z","Q02","Projection","measured",5,26790,3711544,5,true,""
+"2026-06-12T09:53:11.888295400Z","Q03","Filtered count","warmup",1,1724,1,1,true,""
+"2026-06-12T09:53:12.219333100Z","Q03","Filtered count","measured",1,329,1,1,true,""
+"2026-06-12T09:53:12.603726Z","Q03","Filtered count","measured",2,383,1,1,true,""
+"2026-06-12T09:53:12.964772900Z","Q03","Filtered count","measured",3,359,1,1,true,""
+"2026-06-12T09:53:13.304016900Z","Q03","Filtered count","measured",4,338,1,1,true,""
+"2026-06-12T09:53:13.687276900Z","Q03","Filtered count","measured",5,381,1,1,true,""
+"2026-06-12T09:53:29.042161500Z","Q04","Filtered full record access","warmup",1,15353,283006,22,true,""
+"2026-06-12T09:53:39.072189100Z","Q04","Filtered full record access","measured",1,10030,283006,22,true,""
+"2026-06-12T09:53:46.817912Z","Q04","Filtered full record access","measured",2,7742,283006,22,true,""
+"2026-06-12T09:53:53.031570100Z","Q04","Filtered full record access","measured",3,6212,283006,22,true,""
+"2026-06-12T09:53:59.616799600Z","Q04","Filtered full record access","measured",4,6585,283006,22,true,""
+"2026-06-12T09:54:06.204763800Z","Q04","Filtered full record access","measured",5,6586,283006,22,true,""
+"2026-06-12T09:54:08.465553700Z","Q05","Filtered projection","warmup",1,2260,283006,5,true,""
+"2026-06-12T09:54:10.725788900Z","Q05","Filtered projection","measured",1,2259,283006,5,true,""
+"2026-06-12T09:54:13.092607100Z","Q05","Filtered projection","measured",2,2368,283006,5,true,""
+"2026-06-12T09:54:15.472430900Z","Q05","Filtered projection","measured",3,2377,283006,5,true,""
+"2026-06-12T09:54:17.689137Z","Q05","Filtered projection","measured",4,2217,283006,5,true,""
+"2026-06-12T09:54:19.890105800Z","Q05","Filtered projection","measured",5,2199,283006,5,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_spark_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_spark_tlcp_results.csv
new file mode 100644
index 0000000000..455c0b9cd1
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_spark_tlcp_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+2026-06-12T09:48:19.913258Z,Q01,Full record access,warmup,1,43259,3711544,22,true,
+2026-06-12T09:48:51.169823Z,Q01,Full record access,measured,1,27921,3711544,22,true,
+2026-06-12T09:49:16.732182Z,Q01,Full record access,measured,2,25561,3711544,22,true,
+2026-06-12T09:49:44.410944Z,Q01,Full record access,measured,3,24345,3711544,22,true,
+2026-06-12T09:50:13.120790Z,Q01,Full record access,measured,4,25364,3711544,22,true,
+2026-06-12T09:50:41.221098Z,Q01,Full record access,measured,5,24764,3711544,22,true,
+2026-06-12T09:51:01.314001Z,Q02,Projection,warmup,1,16752,3711544,5,true,
+2026-06-12T09:51:18.305782Z,Q02,Projection,measured,1,16991,3711544,5,true,
+2026-06-12T09:51:39.398316Z,Q02,Projection,measured,2,17755,3711544,5,true,
+2026-06-12T09:51:55.868125Z,Q02,Projection,measured,3,16468,3711544,5,true,
+2026-06-12T09:52:14.968439Z,Q02,Projection,measured,4,15765,3711544,5,true,
+2026-06-12T09:52:31.499685Z,Q02,Projection,measured,5,16530,3711544,5,true,
+2026-06-12T09:52:34.301926Z,Q03,Filtered count,warmup,1,2799,1,1,true,
+2026-06-12T09:52:36.028543Z,Q03,Filtered count,measured,1,1725,1,1,true,
+2026-06-12T09:52:40.922759Z,Q03,Filtered count,measured,2,1556,1,1,true,
+2026-06-12T09:52:42.398228Z,Q03,Filtered count,measured,3,1474,1,1,true,
+2026-06-12T09:52:43.814541Z,Q03,Filtered count,measured,4,1415,1,1,true,
+2026-06-12T09:52:45.179240Z,Q03,Filtered count,measured,5,1363,1,1,true,
+2026-06-12T09:52:48.861576Z,Q04,Filtered full record access,warmup,1,3680,283006,22,true,
+2026-06-12T09:52:52.032020Z,Q04,Filtered full record access,measured,1,3169,283006,22,true,
+2026-06-12T09:52:55.733280Z,Q04,Filtered full record access,measured,2,3700,283006,22,true,
+2026-06-12T09:52:58.926331Z,Q04,Filtered full record access,measured,3,3192,283006,22,true,
+2026-06-12T09:53:01.968822Z,Q04,Filtered full record access,measured,4,3041,283006,22,true,
+2026-06-12T09:53:05.092640Z,Q04,Filtered full record access,measured,5,3122,283006,22,true,
+2026-06-12T09:53:07.331107Z,Q05,Filtered projection,warmup,1,2236,283006,5,true,
+2026-06-12T09:53:09.402292Z,Q05,Filtered projection,measured,1,2069,283006,5,true,
+2026-06-12T09:53:15.233590Z,Q05,Filtered projection,measured,2,2495,283006,5,true,
+2026-06-12T09:53:17.848310Z,Q05,Filtered projection,measured,3,2612,283006,5,true,
+2026-06-12T09:53:21.189570Z,Q05,Filtered projection,measured,4,3339,283006,5,true,
+2026-06-12T09:53:23.983169Z,Q05,Filtered projection,measured,5,2792,283006,5,true,
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_results_analysis.md b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_results_analysis.md
new file mode 100644
index 0000000000..aa21283cb4
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_results_analysis.md
@@ -0,0 +1,35 @@
+# Access Model Comparison TLCP Analysis
+
+Source summary:
+
+```text
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
+```
+
+The access model comparison run completed successfully for all systems. Every
+query has `5/5` successful measured runs, and the result row counts match across
+Polypheny relational, Polypheny document MQL, DuckDB, and Apache Spark.
+
+## Main Findings
+
+DuckDB is fastest for all five access-pattern queries.
+
+Polypheny relational is much faster than Polypheny document MQL on this flat TLC
+table. This is expected for a relational dataset because the document path adds
+document materialization overhead without a nested-data advantage.
+
+Projection has a clear effect. In Polypheny relational, the full scan Q01 takes
+60716.2 ms, while the projection Q02 takes 21946.2 ms. The same pattern appears
+for filtered access: Q04 takes 7431.0 ms, while filtered projection Q05 takes
+2284.0 ms.
+
+## Interpretation
+
+Polypheny relational behaves best when the query can avoid full row
+materialization. It is especially competitive with Spark on filtered count and
+filtered projection queries, but DuckDB remains the strongest baseline.
+
+Polypheny document MQL is not a good fit for this flat-table access-model
+workload. Its runtimes stay high even for filtered count and projection queries,
+which suggests limited benefit from projection and filter pushdown through the
+document benchmark path.
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
new file mode 100644
index 0000000000..740207df44
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
@@ -0,0 +1,58 @@
+# Access Model Comparison TLCP Summary
+
+Phase summarized: `measured`.
+Warmup rows are excluded. Mean and median values use successful runs only.
+
+## Source Files
+
+| System | CSV |
+| --- | --- |
+| Polypheny Relational | `plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rf_tlcp_results.csv` |
+| Polypheny Document MQL | `plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_mql_tlcp_results.csv` |
+| DuckDB | `plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_duckdb_tlcp_results.csv` |
+| Apache Spark | `plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_spark_tlcp_results.csv` |
+
+## Mean Elapsed Time (ms)
+
+| Query | Description                 | Polypheny Relational | Polypheny Document MQL | DuckDB   | Apache Spark | Row counts |
+|-------|-----------------------------|----------------------|------------------------|----------|--------------|------------|
+| Q01   | Full record access          | 60,716.2             | 151,038.0              | 10,546.8 | 25,591.0     | 3,711,544  |
+| Q02   | Projection                  | 21,946.2             | 97,852.6               | 635.6    | 16,701.8     | 3,711,544  |
+| Q03   | Filtered count              | 358.0                | 74,869.6               | 107.8    | 1,506.6      | 1          |
+| Q04   | Filtered full record access | 7,431.0              | 79,011.6               | 1,433.8  | 3,244.8      | 283,006    |
+| Q05   | Filtered projection         | 2,284.0              | 76,674.0               | 253.6    | 2,661.4      | 283,006    |
+
+## Result Row Counts
+
+| Query | Description                 | Polypheny Relational | Polypheny Document MQL | DuckDB    | Apache Spark |
+|-------|-----------------------------|----------------------|------------------------|-----------|--------------|
+| Q01   | Full record access          | 3,711,544            | 3,711,544              | 3,711,544 | 3,711,544    |
+| Q02   | Projection                  | 3,711,544            | 3,711,544              | 3,711,544 | 3,711,544    |
+| Q03   | Filtered count              | 1                    | 1                      | 1         | 1            |
+| Q04   | Filtered full record access | 283,006              | 283,006                | 283,006   | 283,006      |
+| Q05   | Filtered projection         | 283,006              | 283,006                | 283,006   | 283,006      |
+
+## Detailed Summary (ms)
+
+| System                 | Query | Description                   | Runs | Mean      | Median    | Min       | Max       | Rows      | Columns | Status |
+|------------------------|-------|-------------------------------|------|-----------|-----------|-----------|-----------|-----------|---------|--------|
+| Polypheny Relational   | Q01   | Full record access            | 5/5  | 60,716.2  | 62,904.0  | 40,161.0  | 77,106.0  | 3,711,544 | 22      | ok     |
+| Polypheny Relational   | Q02   | Projection                    | 5/5  | 21,946.2  | 21,219.0  | 19,616.0  | 26,790.0  | 3,711,544 | 5       | ok     |
+| Polypheny Relational   | Q03   | Filtered count                | 5/5  | 358.0     | 359.0     | 329.0     | 383.0     | 1         | 1       | ok     |
+| Polypheny Relational   | Q04   | Filtered full record access   | 5/5  | 7,431.0   | 6,586.0   | 6,212.0   | 10,030.0  | 283,006   | 22      | ok     |
+| Polypheny Relational   | Q05   | Filtered projection           | 5/5  | 2,284.0   | 2,259.0   | 2,199.0   | 2,377.0   | 283,006   | 5       | ok     |
+| Polypheny Document MQL | Q01   | Full document access          | 5/5  | 151,038.0 | 129,224.0 | 128,715.0 | 238,912.0 | 3,711,544 | 20      | ok     |
+| Polypheny Document MQL | Q02   | Projection                    | 5/5  | 97,852.6  | 97,767.0  | 97,476.0  | 98,171.0  | 3,711,544 | 6       | ok     |
+| Polypheny Document MQL | Q03   | Filtered count                | 5/5  | 74,869.6  | 74,482.0  | 74,334.0  | 75,880.0  | 1         | 1       | ok     |
+| Polypheny Document MQL | Q04   | Filtered full document access | 5/5  | 79,011.6  | 78,759.0  | 78,538.0  | 79,703.0  | 283,006   | 20      | ok     |
+| Polypheny Document MQL | Q05   | Filtered projection           | 5/5  | 76,674.0  | 76,467.0  | 76,437.0  | 77,273.0  | 283,006   | 6       | ok     |
+| DuckDB                 | Q01   | Full record access            | 5/5  | 10,546.8  | 10,540.0  | 8,916.0   | 12,826.0  | 3,711,544 | 22      | ok     |
+| DuckDB                 | Q02   | Projection                    | 5/5  | 635.6     | 639.0     | 597.0     | 670.0     | 3,711,544 | 5       | ok     |
+| DuckDB                 | Q03   | Filtered count                | 5/5  | 107.8     | 101.0     | 90.0      | 141.0     | 1         | 1       | ok     |
+| DuckDB                 | Q04   | Filtered full record access   | 5/5  | 1,433.8   | 1,451.0   | 1,252.0   | 1,592.0   | 283,006   | 22      | ok     |
+| DuckDB                 | Q05   | Filtered projection           | 5/5  | 253.6     | 239.0     | 217.0     | 296.0     | 283,006   | 5       | ok     |
+| Apache Spark           | Q01   | Full record access            | 5/5  | 25,591.0  | 25,364.0  | 24,345.0  | 27,921.0  | 3,711,544 | 22      | ok     |
+| Apache Spark           | Q02   | Projection                    | 5/5  | 16,701.8  | 16,530.0  | 15,765.0  | 17,755.0  | 3,711,544 | 5       | ok     |
+| Apache Spark           | Q03   | Filtered count                | 5/5  | 1,506.6   | 1,474.0   | 1,363.0   | 1,725.0   | 1         | 1       | ok     |
+| Apache Spark           | Q04   | Filtered full record access   | 5/5  | 3,244.8   | 3,169.0   | 3,041.0   | 3,700.0   | 283,006   | 22      | ok     |
+| Apache Spark           | Q05   | Filtered projection           | 5/5  | 2,661.4   | 2,612.0   | 2,069.0   | 3,339.0   | 283,006   | 5       | ok     |
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_results.csv b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_results.csv
new file mode 100644
index 0000000000..2c14e65769
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_results.csv
@@ -0,0 +1,61 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-13T16:18:39.812840Z","Q01","Full Yellow Taxi Row Count","warmup",1,32,1,1,true,""
+"2026-06-13T16:18:39.844002700Z","Q01","Full Yellow Taxi Row Count","measured",1,29,1,1,true,""
+"2026-06-13T16:18:39.878139800Z","Q01","Full Yellow Taxi Row Count","measured",2,33,1,1,true,""
+"2026-06-13T16:18:39.915438100Z","Q01","Full Yellow Taxi Row Count","measured",3,36,1,1,true,""
+"2026-06-13T16:18:39.954922300Z","Q01","Full Yellow Taxi Row Count","measured",4,37,1,1,true,""
+"2026-06-13T16:18:39.990441500Z","Q01","Full Yellow Taxi Row Count","measured",5,34,1,1,true,""
+"2026-06-13T16:18:40.012618400Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,12,1,1,true,""
+"2026-06-13T16:18:40.026616100Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,13,1,1,true,""
+"2026-06-13T16:18:40.041616700Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,13,1,1,true,""
+"2026-06-13T16:18:40.058627Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,16,1,1,true,""
+"2026-06-13T16:18:40.073150700Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,13,1,1,true,""
+"2026-06-13T16:18:40.088150200Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,14,1,1,true,""
+"2026-06-13T16:18:40.363886500Z","Q03","Yellow Taxi Trips On One Day","warmup",1,277,1,1,true,""
+"2026-06-13T16:18:40.710424200Z","Q03","Yellow Taxi Trips On One Day","measured",1,344,1,1,true,""
+"2026-06-13T16:18:41.042412200Z","Q03","Yellow Taxi Trips On One Day","measured",2,329,1,1,true,""
+"2026-06-13T16:18:41.375731Z","Q03","Yellow Taxi Trips On One Day","measured",3,333,1,1,true,""
+"2026-06-13T16:18:41.718607100Z","Q03","Yellow Taxi Trips On One Day","measured",4,345,1,1,true,""
+"2026-06-13T16:18:42.080891600Z","Q03","Yellow Taxi Trips On One Day","measured",5,356,1,1,true,""
+"2026-06-13T16:18:43.026594500Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,943,1,1,true,""
+"2026-06-13T16:18:43.844050400Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,815,1,1,true,""
+"2026-06-13T16:18:44.629106600Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,783,1,1,true,""
+"2026-06-13T16:18:45.473488300Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,843,1,1,true,""
+"2026-06-13T16:18:46.283838700Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,816,1,1,true,""
+"2026-06-13T16:18:47.002618600Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,709,1,1,true,""
+"2026-06-13T16:18:48.077630400Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","warmup",1,1073,37,6,true,""
+"2026-06-13T16:18:49.103492700Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",1,1024,37,6,true,""
+"2026-06-13T16:18:50.207139200Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",2,1102,37,6,true,""
+"2026-06-13T16:18:51.297515500Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",3,1097,37,6,true,""
+"2026-06-13T16:18:52.312841200Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",4,1007,37,6,true,""
+"2026-06-13T16:18:53.449097400Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",5,1132,37,6,true,""
+"2026-06-13T16:18:53.546584Z","Q06","Full High-Volume FHV Row Count","warmup",1,95,1,1,true,""
+"2026-06-13T16:18:53.649126400Z","Q06","Full High-Volume FHV Row Count","measured",1,100,1,1,true,""
+"2026-06-13T16:18:53.745853100Z","Q06","Full High-Volume FHV Row Count","measured",2,96,1,1,true,""
+"2026-06-13T16:18:53.845552700Z","Q06","Full High-Volume FHV Row Count","measured",3,97,1,1,true,""
+"2026-06-13T16:18:53.948296100Z","Q06","Full High-Volume FHV Row Count","measured",4,101,1,1,true,""
+"2026-06-13T16:18:54.039826Z","Q06","Full High-Volume FHV Row Count","measured",5,90,1,1,true,""
+"2026-06-13T16:18:54.067834900Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,26,1,1,true,""
+"2026-06-13T16:18:54.092361400Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,23,1,1,true,""
+"2026-06-13T16:18:54.123006100Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,30,1,1,true,""
+"2026-06-13T16:18:54.154161500Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,29,1,1,true,""
+"2026-06-13T16:18:54.180673400Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,25,1,1,true,""
+"2026-06-13T16:18:54.207137100Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,25,1,1,true,""
+"2026-06-13T16:18:59.591629900Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,5383,1,1,true,""
+"2026-06-13T16:19:04.682341Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,5089,1,1,true,""
+"2026-06-13T16:19:10.019509400Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,5335,1,1,true,""
+"2026-06-13T16:19:15.619102400Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,5598,1,1,true,""
+"2026-06-13T16:19:23.891127200Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,8269,1,1,true,""
+"2026-06-13T16:19:31.241675600Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,7348,1,1,true,""
+"2026-06-13T16:19:49.576679Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","warmup",1,18332,37,7,true,""
+"2026-06-13T16:20:01.796796700Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",1,12223,37,7,true,""
+"2026-06-13T16:20:11.503071400Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",2,9701,37,7,true,""
+"2026-06-13T16:20:21.066618600Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",3,9562,37,7,true,""
+"2026-06-13T16:20:29.996161100Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",4,8930,37,7,true,""
+"2026-06-13T16:20:38.031790900Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",5,8037,37,7,true,""
+"2026-06-13T16:20:39.501626800Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","warmup",1,1464,4,3,true,""
+"2026-06-13T16:20:41.099732Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",1,1604,4,3,true,""
+"2026-06-13T16:20:42.734104100Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",2,1625,4,3,true,""
+"2026-06-13T16:20:44.125904500Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",3,1391,4,3,true,""
+"2026-06-13T16:20:45.702649700Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",4,1575,4,3,true,""
+"2026-06-13T16:20:47.305453500Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",5,1602,4,3,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv
new file mode 100644
index 0000000000..19d39ed41d
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv
@@ -0,0 +1,61 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-13T16:26:06.585658100Z","Q01","Full Yellow Taxi Row Count","warmup",1,584,1,1,true,""
+"2026-06-13T16:26:06.608468900Z","Q01","Full Yellow Taxi Row Count","measured",1,18,1,1,true,""
+"2026-06-13T16:26:06.617475300Z","Q01","Full Yellow Taxi Row Count","measured",2,9,1,1,true,""
+"2026-06-13T16:26:06.628187400Z","Q01","Full Yellow Taxi Row Count","measured",3,9,1,1,true,""
+"2026-06-13T16:26:06.638183400Z","Q01","Full Yellow Taxi Row Count","measured",4,10,1,1,true,""
+"2026-06-13T16:26:06.649188900Z","Q01","Full Yellow Taxi Row Count","measured",5,10,1,1,true,""
+"2026-06-13T16:26:06.854975800Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,205,1,1,true,""
+"2026-06-13T16:26:06.863974800Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,8,1,1,true,""
+"2026-06-13T16:26:06.873975700Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,9,1,1,true,""
+"2026-06-13T16:26:06.884975100Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,9,1,1,true,""
+"2026-06-13T16:26:06.895982100Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,11,1,1,true,""
+"2026-06-13T16:26:06.908495800Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,11,1,1,true,""
+"2026-06-13T16:26:09.322150700Z","Q03","Yellow Taxi Trips On One Day","warmup",1,2412,1,1,true,""
+"2026-06-13T16:26:10.285954300Z","Q03","Yellow Taxi Trips On One Day","measured",1,963,1,1,true,""
+"2026-06-13T16:26:11.249436900Z","Q03","Yellow Taxi Trips On One Day","measured",2,962,1,1,true,""
+"2026-06-13T16:26:12.249714200Z","Q03","Yellow Taxi Trips On One Day","measured",3,1004,1,1,true,""
+"2026-06-13T16:26:13.519290500Z","Q03","Yellow Taxi Trips On One Day","measured",4,1263,1,1,true,""
+"2026-06-13T16:26:14.812354800Z","Q03","Yellow Taxi Trips On One Day","measured",5,1295,1,1,true,""
+"2026-06-13T16:26:17.728352Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,2911,1,1,true,""
+"2026-06-13T16:26:20.549590Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,2820,1,1,true,""
+"2026-06-13T16:26:23.064638300Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,2513,1,1,true,""
+"2026-06-13T16:26:25.362808600Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,2297,1,1,true,""
+"2026-06-13T16:26:28.815385100Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,3450,1,1,true,""
+"2026-06-13T16:26:32.565408500Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,3748,1,1,true,""
+"2026-06-13T16:26:35.584987200Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","warmup",1,3017,37,6,true,""
+"2026-06-13T16:26:37.952868100Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",1,2366,37,6,true,""
+"2026-06-13T16:26:40.488911Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",2,2534,37,6,true,""
+"2026-06-13T16:26:42.987704700Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",3,2497,37,6,true,""
+"2026-06-13T16:26:45.357084700Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",4,2365,37,6,true,""
+"2026-06-13T16:26:47.858459700Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",5,2499,37,6,true,""
+"2026-06-13T16:26:48.002164Z","Q06","Full High-Volume FHV Row Count","warmup",1,141,1,1,true,""
+"2026-06-13T16:26:48.020884700Z","Q06","Full High-Volume FHV Row Count","measured",1,17,1,1,true,""
+"2026-06-13T16:26:48.038950900Z","Q06","Full High-Volume FHV Row Count","measured",2,15,1,1,true,""
+"2026-06-13T16:26:48.056527200Z","Q06","Full High-Volume FHV Row Count","measured",3,15,1,1,true,""
+"2026-06-13T16:26:48.075595900Z","Q06","Full High-Volume FHV Row Count","measured",4,17,1,1,true,""
+"2026-06-13T16:26:48.094382Z","Q06","Full High-Volume FHV Row Count","measured",5,16,1,1,true,""
+"2026-06-13T16:26:48.348679300Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,252,1,1,true,""
+"2026-06-13T16:26:48.366529300Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,16,1,1,true,""
+"2026-06-13T16:26:48.391129800Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,21,1,1,true,""
+"2026-06-13T16:26:48.411675500Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,18,1,1,true,""
+"2026-06-13T16:26:48.430610Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,18,1,1,true,""
+"2026-06-13T16:26:48.454762200Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,22,1,1,true,""
+"2026-06-13T16:27:07.210831600Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,18757,1,1,true,""
+"2026-06-13T16:27:22.809335Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,15594,1,1,true,""
+"2026-06-13T16:27:40.880589200Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,18070,1,1,true,""
+"2026-06-13T16:28:01.492578500Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,20611,1,1,true,""
+"2026-06-13T16:28:23.241874700Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,21747,1,1,true,""
+"2026-06-13T16:28:44.985877Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,21742,1,1,true,""
+"2026-06-13T16:29:11.666136Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","warmup",1,26678,37,7,true,""
+"2026-06-13T16:29:33.414716400Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",1,21746,37,7,true,""
+"2026-06-13T16:30:03.958506600Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",2,30554,37,7,true,""
+"2026-06-13T16:30:32.533876100Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",3,28563,37,7,true,""
+"2026-06-13T16:30:55.183413600Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",4,22647,37,7,true,""
+"2026-06-13T16:31:16.082232200Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",5,20897,37,7,true,""
+"2026-06-13T16:31:24.109868600Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","warmup",1,8026,4,3,true,""
+"2026-06-13T16:31:32.663960Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",1,8552,4,3,true,""
+"2026-06-13T16:31:41.281998800Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",2,8616,4,3,true,""
+"2026-06-13T16:31:49.097291200Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",3,7814,4,3,true,""
+"2026-06-13T16:31:56.551277700Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",4,7451,4,3,true,""
+"2026-06-13T16:32:04.932238200Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",5,8380,4,3,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_results_analysis.md b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_results_analysis.md
new file mode 100644
index 0000000000..94515ca358
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_results_analysis.md
@@ -0,0 +1,37 @@
+# Aggregation Benchmark Analysis
+
+Source summary:
+
+```text
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
+```
+
+The aggregation run completed successfully for all three systems. Every query
+has `5/5` successful measured runs, and the result row counts match across
+Polypheny, DuckDB, and Apache Spark.
+
+## Main Findings
+
+Polypheny is fastest for the simple count queries that can be answered with very
+little data materialization
+
+
+## Interpretation
+
+The strongest Polypheny result is metadata-like counting. Q01, Q02, Q06, and Q07
+are all faster in Polypheny than in DuckDB and much faster than Spark. This
+suggests that simple count and partition-constrained count queries are currently
+a good case for the Polypheny Parquet path.
+
+The main performance gap is on row-level filtering and aggregation over data
+columns. DuckDB is about 2.3x to 5.2x faster than Polypheny on Q03-Q05 and
+Q08-Q10. The gap is larger on `fhvhv_tripdata` than on `yellow_tripdata`, which
+is expected because the high-volume FHV table is larger and the queries touch
+more data.
+
+Spark is not competitive on the small/simple count queries because fixed job
+overhead dominates. It becomes closer on the larger `fhvhv_tripdata` workloads:
+Spark is faster than Polypheny on Q08 and Q10, but DuckDB remains fastest for
+both.
+
+
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_results.csv b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_results.csv
new file mode 100644
index 0000000000..accc5ba3b6
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_results.csv
@@ -0,0 +1,61 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+2026-06-13T16:27:27.097966Z,Q01,Full Yellow Taxi Row Count,warmup,1,6934,1,1,true,
+2026-06-13T16:27:33.047541Z,Q01,Full Yellow Taxi Row Count,measured,1,5948,1,1,true,
+2026-06-13T16:27:38.204716Z,Q01,Full Yellow Taxi Row Count,measured,2,5150,1,1,true,
+2026-06-13T16:27:39.829272Z,Q01,Full Yellow Taxi Row Count,measured,3,1623,1,1,true,
+2026-06-13T16:27:40.997995Z,Q01,Full Yellow Taxi Row Count,measured,4,1167,1,1,true,
+2026-06-13T16:27:44.819868Z,Q01,Full Yellow Taxi Row Count,measured,5,3820,1,1,true,
+2026-06-13T16:27:45.473107Z,Q02,Yellow Taxi Count For One Partition Month,warmup,1,650,1,1,true,
+2026-06-13T16:27:46.403049Z,Q02,Yellow Taxi Count For One Partition Month,measured,1,928,1,1,true,
+2026-06-13T16:27:47.612824Z,Q02,Yellow Taxi Count For One Partition Month,measured,2,1202,1,1,true,
+2026-06-13T16:27:49.478912Z,Q02,Yellow Taxi Count For One Partition Month,measured,3,1845,1,1,true,
+2026-06-13T16:27:50.003638Z,Q02,Yellow Taxi Count For One Partition Month,measured,4,521,1,1,true,
+2026-06-13T16:27:51.656940Z,Q02,Yellow Taxi Count For One Partition Month,measured,5,1644,1,1,true,
+2026-06-13T16:28:09.599118Z,Q03,Yellow Taxi Trips On One Day,warmup,1,16432,1,1,true,
+2026-06-13T16:28:24.111656Z,Q03,Yellow Taxi Trips On One Day,measured,1,14493,1,1,true,
+2026-06-13T16:28:40.255167Z,Q03,Yellow Taxi Trips On One Day,measured,2,14638,1,1,true,
+2026-06-13T16:28:51.115402Z,Q03,Yellow Taxi Trips On One Day,measured,3,10858,1,1,true,
+2026-06-13T16:29:08.169250Z,Q03,Yellow Taxi Trips On One Day,measured,4,14919,1,1,true,
+2026-06-13T16:29:14.911980Z,Q03,Yellow Taxi Trips On One Day,measured,5,6741,1,1,true,
+2026-06-13T16:29:27.690275Z,Q04,Long And Expensive Yellow Taxi Trips,warmup,1,12775,1,1,true,
+2026-06-13T16:29:33.822154Z,Q04,Long And Expensive Yellow Taxi Trips,measured,1,4584,1,1,true,
+2026-06-13T16:29:39.689255Z,Q04,Long And Expensive Yellow Taxi Trips,measured,2,5865,1,1,true,
+2026-06-13T16:29:59.335622Z,Q04,Long And Expensive Yellow Taxi Trips,measured,3,19634,1,1,true,
+2026-06-13T16:30:05.971641Z,Q04,Long And Expensive Yellow Taxi Trips,measured,4,5323,1,1,true,
+2026-06-13T16:30:18.476296Z,Q04,Long And Expensive Yellow Taxi Trips,measured,5,12495,1,1,true,
+2026-06-13T16:30:37.026744Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,warmup,1,16944,37,6,true,
+2026-06-13T16:30:51.328729Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,1,14301,37,6,true,
+2026-06-13T16:30:58.388773Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,2,7059,37,6,true,
+2026-06-13T16:31:13.154915Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,3,13174,37,6,true,
+2026-06-13T16:31:23.338272Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,4,10182,37,6,true,
+2026-06-13T16:31:43.923948Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,5,19037,37,6,true,
+2026-06-13T16:31:55.661309Z,Q06,Full High-Volume FHV Row Count,warmup,1,11728,1,1,true,
+2026-06-13T16:32:04.718399Z,Q06,Full High-Volume FHV Row Count,measured,1,9053,1,1,true,
+2026-06-13T16:32:08.338039Z,Q06,Full High-Volume FHV Row Count,measured,2,2000,1,1,true,
+2026-06-13T16:32:10.296066Z,Q06,Full High-Volume FHV Row Count,measured,3,1957,1,1,true,
+2026-06-13T16:32:12.162010Z,Q06,Full High-Volume FHV Row Count,measured,4,1865,1,1,true,
+2026-06-13T16:32:14.216618Z,Q06,Full High-Volume FHV Row Count,measured,5,2053,1,1,true,
+2026-06-13T16:32:14.491550Z,Q07,High-Volume FHV Count For One Partition Month,warmup,1,272,1,1,true,
+2026-06-13T16:32:14.790299Z,Q07,High-Volume FHV Count For One Partition Month,measured,1,297,1,1,true,
+2026-06-13T16:32:15.075488Z,Q07,High-Volume FHV Count For One Partition Month,measured,2,284,1,1,true,
+2026-06-13T16:32:15.401778Z,Q07,High-Volume FHV Count For One Partition Month,measured,3,325,1,1,true,
+2026-06-13T16:32:15.665155Z,Q07,High-Volume FHV Count For One Partition Month,measured,4,262,1,1,true,
+2026-06-13T16:32:15.925830Z,Q07,High-Volume FHV Count For One Partition Month,measured,5,259,1,1,true,
+2026-06-13T16:32:28.784124Z,Q08,Long And Expensive High-Volume FHV Trips,warmup,1,12856,1,1,true,
+2026-06-13T16:32:43.262753Z,Q08,Long And Expensive High-Volume FHV Trips,measured,1,12908,1,1,true,
+2026-06-13T16:32:54.654463Z,Q08,Long And Expensive High-Volume FHV Trips,measured,2,11390,1,1,true,
+2026-06-13T16:33:06.909118Z,Q08,Long And Expensive High-Volume FHV Trips,measured,3,12253,1,1,true,
+2026-06-13T16:33:20.344540Z,Q08,Long And Expensive High-Volume FHV Trips,measured,4,11661,1,1,true,
+2026-06-13T16:33:32.517470Z,Q08,Long And Expensive High-Volume FHV Trips,measured,5,12172,1,1,true,
+2026-06-13T16:34:05.124084Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,warmup,1,31077,37,7,true,
+2026-06-13T16:34:34.390226Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,1,27588,37,7,true,
+2026-06-13T16:35:04.261228Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,2,28279,37,7,true,
+2026-06-13T16:35:34.053722Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,3,28142,37,7,true,
+2026-06-13T16:36:03.300281Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,4,27662,37,7,true,
+2026-06-13T16:36:32.996928Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,5,27946,37,7,true,
+2026-06-13T16:36:40.298599Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,warmup,1,7300,4,3,true,
+2026-06-13T16:36:47.835222Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,1,7536,4,3,true,
+2026-06-13T16:36:56.675794Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,2,7230,4,3,true,
+2026-06-13T16:37:04.739867Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,3,8063,4,3,true,
+2026-06-13T16:37:11.998048Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,4,7257,4,3,true,
+2026-06-13T16:37:19.144591Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,5,7145,4,3,true,
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
new file mode 100644
index 0000000000..2f90d50c07
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
@@ -0,0 +1,78 @@
+# Aggregation Summary
+
+Phase summarized: `measured`.
+Warmup rows are excluded. Mean and median values use successful runs only.
+
+## Source Files
+
+| System               | CSV                                                                                        |
+|----------------------|--------------------------------------------------------------------------------------------|
+| Polypheny Relational | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_results.csv` |
+| DuckDB               | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_results.csv`    |
+| Apache Spark         | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_results.csv`     |
+
+## Mean Elapsed Time (ms)
+
+| Query | Description                                       | Polypheny Relational | DuckDB  | Apache Spark | Row counts |
+|-------|---------------------------------------------------|----------------------|---------|--------------|------------|
+| Q01   | Full Yellow Taxi Row Count                        | 11.2                 | 33.8    | 3,541.6      | 1          |
+| Q02   | Yellow Taxi Count For One Partition Month         | 9.6                  | 13.8    | 1,228.0      | 1          |
+| Q03   | Yellow Taxi Trips On One Day                      | 1,097.4              | 341.4   | 12,329.8     | 1          |
+| Q04   | Long And Expensive Yellow Taxi Trips              | 2,965.6              | 793.2   | 9,580.2      | 1          |
+| Q05   | Monthly Yellow Taxi Amount And Distance Summary   | 2,452.2              | 1,072.4 | 12,750.6     | 37         |
+| Q06   | Full High-Volume FHV Row Count                    | 16.0                 | 96.8    | 3,385.6      | 1          |
+| Q07   | High-Volume FHV Count For One Partition Month     | 19.0                 | 26.4    | 285.4        | 1          |
+| Q08   | Long And Expensive High-Volume FHV Trips          | 19,552.8             | 6,327.8 | 12,076.8     | 1          |
+| Q09   | Monthly High-Volume FHV Fare And Distance Summary | 24,881.4             | 9,690.6 | 27,923.4     | 37         |
+| Q10   | High-Volume FHV Shared-Ride Flag Distribution     | 8,162.6              | 1,559.4 | 7,446.2      | 4          |
+
+## Result Row Counts
+
+| Query | Description                                       | Polypheny Relational | DuckDB | Apache Spark |
+|-------|---------------------------------------------------|----------------------|--------|--------------|
+| Q01   | Full Yellow Taxi Row Count                        | 1                    | 1      | 1            |
+| Q02   | Yellow Taxi Count For One Partition Month         | 1                    | 1      | 1            |
+| Q03   | Yellow Taxi Trips On One Day                      | 1                    | 1      | 1            |
+| Q04   | Long And Expensive Yellow Taxi Trips              | 1                    | 1      | 1            |
+| Q05   | Monthly Yellow Taxi Amount And Distance Summary   | 37                   | 37     | 37           |
+| Q06   | Full High-Volume FHV Row Count                    | 1                    | 1      | 1            |
+| Q07   | High-Volume FHV Count For One Partition Month     | 1                    | 1      | 1            |
+| Q08   | Long And Expensive High-Volume FHV Trips          | 1                    | 1      | 1            |
+| Q09   | Monthly High-Volume FHV Fare And Distance Summary | 37                   | 37     | 37           |
+| Q10   | High-Volume FHV Shared-Ride Flag Distribution     | 4                    | 4      | 4            |
+
+## Detailed Summary (ms)
+
+| System               | Query | Description                                       | Runs | Mean     | Median   | Min      | Max      | Rows | Columns | Status |
+|----------------------|-------|---------------------------------------------------|------|----------|----------|----------|----------|------|---------|--------|
+| Polypheny Relational | Q01   | Full Yellow Taxi Row Count                        | 5/5  | 11.2     | 10.0     | 9.0      | 18.0     | 1    | 1       | ok     |
+| Polypheny Relational | Q02   | Yellow Taxi Count For One Partition Month         | 5/5  | 9.6      | 9.0      | 8.0      | 11.0     | 1    | 1       | ok     |
+| Polypheny Relational | Q03   | Yellow Taxi Trips On One Day                      | 5/5  | 1,097.4  | 1,004.0  | 962.0    | 1,295.0  | 1    | 1       | ok     |
+| Polypheny Relational | Q04   | Long And Expensive Yellow Taxi Trips              | 5/5  | 2,965.6  | 2,820.0  | 2,297.0  | 3,748.0  | 1    | 1       | ok     |
+| Polypheny Relational | Q05   | Monthly Yellow Taxi Amount And Distance Summary   | 5/5  | 2,452.2  | 2,497.0  | 2,365.0  | 2,534.0  | 37   | 6       | ok     |
+| Polypheny Relational | Q06   | Full High-Volume FHV Row Count                    | 5/5  | 16.0     | 16.0     | 15.0     | 17.0     | 1    | 1       | ok     |
+| Polypheny Relational | Q07   | High-Volume FHV Count For One Partition Month     | 5/5  | 19.0     | 18.0     | 16.0     | 22.0     | 1    | 1       | ok     |
+| Polypheny Relational | Q08   | Long And Expensive High-Volume FHV Trips          | 5/5  | 19,552.8 | 20,611.0 | 15,594.0 | 21,747.0 | 1    | 1       | ok     |
+| Polypheny Relational | Q09   | Monthly High-Volume FHV Fare And Distance Summary | 5/5  | 24,881.4 | 22,647.0 | 20,897.0 | 30,554.0 | 37   | 7       | ok     |
+| Polypheny Relational | Q10   | High-Volume FHV Shared-Ride Flag Distribution     | 5/5  | 8,162.6  | 8,380.0  | 7,451.0  | 8,616.0  | 4    | 3       | ok     |
+| DuckDB               | Q01   | Full Yellow Taxi Row Count                        | 5/5  | 33.8     | 34.0     | 29.0     | 37.0     | 1    | 1       | ok     |
+| DuckDB               | Q02   | Yellow Taxi Count For One Partition Month         | 5/5  | 13.8     | 13.0     | 13.0     | 16.0     | 1    | 1       | ok     |
+| DuckDB               | Q03   | Yellow Taxi Trips On One Day                      | 5/5  | 341.4    | 344.0    | 329.0    | 356.0    | 1    | 1       | ok     |
+| DuckDB               | Q04   | Long And Expensive Yellow Taxi Trips              | 5/5  | 793.2    | 815.0    | 709.0    | 843.0    | 1    | 1       | ok     |
+| DuckDB               | Q05   | Monthly Yellow Taxi Amount And Distance Summary   | 5/5  | 1,072.4  | 1,097.0  | 1,007.0  | 1,132.0  | 37   | 6       | ok     |
+| DuckDB               | Q06   | Full High-Volume FHV Row Count                    | 5/5  | 96.8     | 97.0     | 90.0     | 101.0    | 1    | 1       | ok     |
+| DuckDB               | Q07   | High-Volume FHV Count For One Partition Month     | 5/5  | 26.4     | 25.0     | 23.0     | 30.0     | 1    | 1       | ok     |
+| DuckDB               | Q08   | Long And Expensive High-Volume FHV Trips          | 5/5  | 6,327.8  | 5,598.0  | 5,089.0  | 8,269.0  | 1    | 1       | ok     |
+| DuckDB               | Q09   | Monthly High-Volume FHV Fare And Distance Summary | 5/5  | 9,690.6  | 9,562.0  | 8,037.0  | 12,223.0 | 37   | 7       | ok     |
+| DuckDB               | Q10   | High-Volume FHV Shared-Ride Flag Distribution     | 5/5  | 1,559.4  | 1,602.0  | 1,391.0  | 1,625.0  | 4    | 3       | ok     |
+| Apache Spark         | Q01   | Full Yellow Taxi Row Count                        | 5/5  | 3,541.6  | 3,820.0  | 1,167.0  | 5,948.0  | 1    | 1       | ok     |
+| Apache Spark         | Q02   | Yellow Taxi Count For One Partition Month         | 5/5  | 1,228.0  | 1,202.0  | 521.0    | 1,845.0  | 1    | 1       | ok     |
+| Apache Spark         | Q03   | Yellow Taxi Trips On One Day                      | 5/5  | 12,329.8 | 14,493.0 | 6,741.0  | 14,919.0 | 1    | 1       | ok     |
+| Apache Spark         | Q04   | Long And Expensive Yellow Taxi Trips              | 5/5  | 9,580.2  | 5,865.0  | 4,584.0  | 19,634.0 | 1    | 1       | ok     |
+| Apache Spark         | Q05   | Monthly Yellow Taxi Amount And Distance Summary   | 5/5  | 12,750.6 | 13,174.0 | 7,059.0  | 19,037.0 | 37   | 6       | ok     |
+| Apache Spark         | Q06   | Full High-Volume FHV Row Count                    | 5/5  | 3,385.6  | 2,000.0  | 1,865.0  | 9,053.0  | 1    | 1       | ok     |
+| Apache Spark         | Q07   | High-Volume FHV Count For One Partition Month     | 5/5  | 285.4    | 284.0    | 259.0    | 325.0    | 1    | 1       | ok     |
+| Apache Spark         | Q08   | Long And Expensive High-Volume FHV Trips          | 5/5  | 12,076.8 | 12,172.0 | 11,390.0 | 12,908.0 | 1    | 1       | ok     |
+| Apache Spark         | Q09   | Monthly High-Volume FHV Fare And Distance Summary | 5/5  | 27,923.4 | 27,946.0 | 27,588.0 | 28,279.0 | 37   | 7       | ok     |
+| Apache Spark         | Q10   | High-Volume FHV Shared-Ride Flag Distribution     | 5/5  | 7,446.2  | 7,257.0  | 7,145.0  | 8,063.0  | 4    | 3       | ok     |
+
diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_duckdb_results.csv b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_duckdb_results.csv
new file mode 100644
index 0000000000..1f356e79a9
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_duckdb_results.csv
@@ -0,0 +1,18 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-13T14:42:21.456562600Z","Q01","Filtered full customer rows.","warmup",1,104215,100000,9,true,""
+"2026-06-13T14:44:07.803427300Z","Q01","Filtered full customer rows.","measured",1,106342,100000,9,true,""
+"2026-06-13T14:45:50.530715Z","Q01","Filtered full customer rows.","measured",2,102726,100000,9,true,""
+"2026-06-13T14:47:22.212279100Z","Q01","Filtered full customer rows.","measured",3,91689,100000,9,true,""
+"2026-06-13T14:49:07.150430300Z","Q01","Filtered full customer rows.","measured",4,104938,100000,9,true,""
+"2026-06-13T14:51:37.594162400Z","Q01","Filtered full customer rows.","measured",5,150445,100000,9,true,""
+"2026-06-13T14:51:37.958279800Z","Q02","Filtered explicit customer projection.","warmup",1,349,100000,8,true,""
+"2026-06-13T14:51:38.275145200Z","Q02","Filtered explicit customer projection.","measured",1,328,100000,8,true,""
+"2026-06-13T14:51:38.656120Z","Q02","Filtered explicit customer projection.","measured",2,368,100000,8,true,""
+"2026-06-13T14:51:39.069117100Z","Q02","Filtered explicit customer projection.","measured",3,425,100000,8,true,""
+"2026-06-13T14:51:39.434538700Z","Q02","Filtered explicit customer projection.","measured",4,357,100000,8,true,""
+"2026-06-13T14:51:39.815308200Z","Q02","Filtered explicit customer projection.","measured",5,386,100000,8,true,""
+"2026-06-13T14:55:13.388359700Z","Q03","Filtered order projection.","warmup",1,213562,100000,6,true,""
+"2026-06-13T15:02:26.950192400Z","Q03","Filtered order projection.","measured",1,433558,100000,6,true,""
+"2026-06-13T15:17:07.908231900Z","Q03","Filtered order projection.","measured",2,880957,100000,6,true,""
+"2026-06-13T15:36:12.177990900Z","Q03","Filtered order projection.","measured",3,1144268,100000,6,true,""
+"2026-06-13T16:03:22.285892400Z","Q03","Filtered order projection.","measured",4,1630107,100000,6,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_mql_results.csv b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_mql_results.csv
new file mode 100644
index 0000000000..7c8e1c5cd5
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_mql_results.csv
@@ -0,0 +1 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv
new file mode 100644
index 0000000000..395c874a5d
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-13T14:29:14.755292300Z","Q01","Filtered full customer rows.","warmup",1,3513,100000,9,true,""
+"2026-06-13T14:29:17.094407400Z","Q01","Filtered full customer rows.","measured",1,2335,100000,9,true,""
+"2026-06-13T14:29:18.833510700Z","Q01","Filtered full customer rows.","measured",2,1738,100000,9,true,""
+"2026-06-13T14:29:20.219792500Z","Q01","Filtered full customer rows.","measured",3,1385,100000,9,true,""
+"2026-06-13T14:29:22.104736Z","Q01","Filtered full customer rows.","measured",4,1884,100000,9,true,""
+"2026-06-13T14:29:23.645477900Z","Q01","Filtered full customer rows.","measured",5,1539,100000,9,true,""
+"2026-06-13T14:29:24.674253700Z","Q02","Filtered explicit customer projection.","warmup",1,1028,100000,8,true,""
+"2026-06-13T14:29:25.674541900Z","Q02","Filtered explicit customer projection.","measured",1,1000,100000,8,true,""
+"2026-06-13T14:29:26.650035700Z","Q02","Filtered explicit customer projection.","measured",2,974,100000,8,true,""
+"2026-06-13T14:29:27.593525500Z","Q02","Filtered explicit customer projection.","measured",3,942,100000,8,true,""
+"2026-06-13T14:29:28.443429Z","Q02","Filtered explicit customer projection.","measured",4,849,100000,8,true,""
+"2026-06-13T14:29:29.239153800Z","Q02","Filtered explicit customer projection.","measured",5,795,100000,8,true,""
+"2026-06-13T14:29:32.612414400Z","Q03","One-join filtered order element projection.","warmup",1,3372,100000,8,true,""
+"2026-06-13T14:29:34.909719900Z","Q03","One-join filtered order element projection.","measured",1,2297,100000,8,true,""
+"2026-06-13T14:29:36.811671Z","Q03","One-join filtered order element projection.","measured",2,1901,100000,8,true,""
+"2026-06-13T14:29:38.910326900Z","Q03","One-join filtered order element projection.","measured",3,2098,100000,8,true,""
+"2026-06-13T14:29:40.845728100Z","Q03","One-join filtered order element projection.","measured",4,1934,100000,8,true,""
+"2026-06-13T14:29:42.843197900Z","Q03","One-join filtered order element projection.","measured",5,1996,100000,8,true,""
+"2026-06-13T14:29:51.302852100Z","Q04","One-join filtered lineitem element projection.","warmup",1,8458,100000,10,true,""
+"2026-06-13T14:29:59.444764300Z","Q04","One-join filtered lineitem element projection.","measured",1,8143,100000,10,true,""
+"2026-06-13T14:30:07.651110200Z","Q04","One-join filtered lineitem element projection.","measured",2,8203,100000,10,true,""
+"2026-06-13T14:30:16.226858900Z","Q04","One-join filtered lineitem element projection.","measured",3,8575,100000,10,true,""
+"2026-06-13T14:30:24.650699700Z","Q04","One-join filtered lineitem element projection.","measured",4,8422,100000,10,true,""
+"2026-06-13T14:30:32.953027800Z","Q04","One-join filtered lineitem element projection.","measured",5,8301,100000,10,true,""
+"2026-06-13T14:30:35.217931400Z","Q05","One-join filtered order-to-lineitems group projection.","warmup",1,2262,100000,6,true,""
+"2026-06-13T14:30:37.099857300Z","Q05","One-join filtered order-to-lineitems group projection.","measured",1,1883,100000,6,true,""
+"2026-06-13T14:30:39.143149300Z","Q05","One-join filtered order-to-lineitems group projection.","measured",2,2041,100000,6,true,""
+"2026-06-13T14:30:40.800154900Z","Q05","One-join filtered order-to-lineitems group projection.","measured",3,1659,100000,6,true,""
+"2026-06-13T14:30:42.889555300Z","Q05","One-join filtered order-to-lineitems group projection.","measured",4,2089,100000,6,true,""
+"2026-06-13T14:30:44.682764500Z","Q05","One-join filtered order-to-lineitems group projection.","measured",5,1788,100000,6,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_spark_results.csv b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_spark_results.csv
new file mode 100644
index 0000000000..0501e8d405
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_spark_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+2026-06-13T10:30:41.656029Z,Q01,Count root customer rows after filtering top-level customer fields.,warmup,1,7106,1,1,true,
+2026-06-13T10:30:42.977580Z,Q01,Count root customer rows after filtering top-level customer fields.,measured,1,1320,1,1,true,
+2026-06-13T10:30:44.230603Z,Q01,Count root customer rows after filtering top-level customer fields.,measured,2,1251,1,1,true,
+2026-06-13T10:30:45.507038Z,Q01,Count root customer rows after filtering top-level customer fields.,measured,3,1273,1,1,true,
+2026-06-13T10:30:46.688411Z,Q01,Count root customer rows after filtering top-level customer fields.,measured,4,1179,1,1,true,
+2026-06-13T10:30:48.144617Z,Q01,Count root customer rows after filtering top-level customer fields.,measured,5,1454,1,1,true,
+2026-06-13T10:31:31.276410Z,Q02,Count nested orders after filtering customer and order fields.,warmup,1,42947,1,1,true,
+2026-06-13T10:32:13.935904Z,Q02,Count nested orders after filtering customer and order fields.,measured,1,42537,1,1,true,
+2026-06-13T10:32:47.929137Z,Q02,Count nested orders after filtering customer and order fields.,measured,2,33898,1,1,true,
+2026-06-13T10:33:17.289671Z,Q02,Count nested orders after filtering customer and order fields.,measured,3,29267,1,1,true,
+2026-06-13T10:33:55.135017Z,Q02,Count nested orders after filtering customer and order fields.,measured,4,37746,1,1,true,
+2026-06-13T10:34:32.230284Z,Q02,Count nested orders after filtering customer and order fields.,measured,5,36899,1,1,true,
+2026-06-13T10:35:03.102713Z,Q03,Count deeply nested lineitems after filtering order and lineitem fields.,warmup,1,30748,1,1,true,
+2026-06-13T10:35:36.812003Z,Q03,Count deeply nested lineitems after filtering order and lineitem fields.,measured,1,33604,1,1,true,
+2026-06-13T10:36:06.535773Z,Q03,Count deeply nested lineitems after filtering order and lineitem fields.,measured,2,29609,1,1,true,
+2026-06-13T10:36:37.694455Z,Q03,Count deeply nested lineitems after filtering order and lineitem fields.,measured,3,31070,1,1,true,
+2026-06-13T10:37:07.109566Z,Q03,Count deeply nested lineitems after filtering order and lineitem fields.,measured,4,29315,1,1,true,
+2026-06-13T10:37:40.923054Z,Q03,Count deeply nested lineitems after filtering order and lineitem fields.,measured,5,33689,1,1,true,
+2026-06-13T10:38:19.478809Z,Q04,Sum order prices by nested order status.,warmup,1,38438,3,2,true,
+2026-06-13T10:38:53.103006Z,Q04,Sum order prices by nested order status.,measured,1,33534,3,2,true,
+2026-06-13T10:39:26.127713Z,Q04,Sum order prices by nested order status.,measured,2,32922,3,2,true,
+2026-06-13T10:39:56.184541Z,Q04,Sum order prices by nested order status.,measured,3,29954,3,2,true,
+2026-06-13T10:40:27.645915Z,Q04,Sum order prices by nested order status.,measured,4,31334,3,2,true,
+2026-06-13T10:40:59.041749Z,Q04,Sum order prices by nested order status.,measured,5,31293,3,2,true,
+2026-06-13T10:41:25.807310Z,Q05,Sum line numbers by deeply nested lineitem return flag.,warmup,1,26662,3,2,true,
+2026-06-13T10:41:54.139902Z,Q05,Sum line numbers by deeply nested lineitem return flag.,measured,1,28225,3,2,true,
+2026-06-13T10:42:19.491004Z,Q05,Sum line numbers by deeply nested lineitem return flag.,measured,2,25244,3,2,true,
+2026-06-13T10:42:47.015660Z,Q05,Sum line numbers by deeply nested lineitem return flag.,measured,3,27421,3,2,true,
+2026-06-13T10:43:12.909295Z,Q05,Sum line numbers by deeply nested lineitem return flag.,measured,4,25790,3,2,true,
+2026-06-13T10:43:40.419789Z,Q05,Sum line numbers by deeply nested lineitem return flag.,measured,5,27397,3,2,true,
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_repartitioned_results.csv b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_repartitioned_results.csv
new file mode 100644
index 0000000000..67e4ebea14
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_repartitioned_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-14T08:22:06.970170Z","Q01","Repartitioned full table baseline","warmup",1,73,1,1,true,""
+"2026-06-14T08:22:07.040521500Z","Q01","Repartitioned full table baseline","measured",1,68,1,1,true,""
+"2026-06-14T08:22:07.135844700Z","Q01","Repartitioned full table baseline","measured",2,93,1,1,true,""
+"2026-06-14T08:22:07.217900100Z","Q01","Repartitioned full table baseline","measured",3,81,1,1,true,""
+"2026-06-14T08:22:07.299493400Z","Q01","Repartitioned full table baseline","measured",4,79,1,1,true,""
+"2026-06-14T08:22:07.396249200Z","Q01","Repartitioned full table baseline","measured",5,95,1,1,true,""
+"2026-06-14T08:22:07.455089600Z","Q02","Repartitioned partition by year","warmup",1,56,1,1,true,""
+"2026-06-14T08:22:07.530148500Z","Q02","Repartitioned partition by year","measured",1,71,1,1,true,""
+"2026-06-14T08:22:07.583963100Z","Q02","Repartitioned partition by year","measured",2,52,1,1,true,""
+"2026-06-14T08:22:07.641150500Z","Q02","Repartitioned partition by year","measured",3,55,1,1,true,""
+"2026-06-14T08:22:07.712686500Z","Q02","Repartitioned partition by year","measured",4,70,1,1,true,""
+"2026-06-14T08:22:07.783916800Z","Q02","Repartitioned partition by year","measured",5,69,1,1,true,""
+"2026-06-14T08:22:07.829839200Z","Q03","Repartitioned partition by month","warmup",1,43,1,1,true,""
+"2026-06-14T08:22:07.863879300Z","Q03","Repartitioned partition by month","measured",1,33,1,1,true,""
+"2026-06-14T08:22:07.896069300Z","Q03","Repartitioned partition by month","measured",2,30,1,1,true,""
+"2026-06-14T08:22:07.926855400Z","Q03","Repartitioned partition by month","measured",3,29,1,1,true,""
+"2026-06-14T08:22:07.957042200Z","Q03","Repartitioned partition by month","measured",4,30,1,1,true,""
+"2026-06-14T08:22:07.986544Z","Q03","Repartitioned partition by month","measured",5,27,1,1,true,""
+"2026-06-14T08:22:08.029952600Z","Q04","Repartitioned filtered count, full scan","warmup",1,41,1,1,true,""
+"2026-06-14T08:22:08.075321400Z","Q04","Repartitioned filtered count, full scan","measured",1,44,1,1,true,""
+"2026-06-14T08:22:08.114334Z","Q04","Repartitioned filtered count, full scan","measured",2,37,1,1,true,""
+"2026-06-14T08:22:08.174515900Z","Q04","Repartitioned filtered count, full scan","measured",3,58,1,1,true,""
+"2026-06-14T08:22:08.209018900Z","Q04","Repartitioned filtered count, full scan","measured",4,33,1,1,true,""
+"2026-06-14T08:22:08.247720900Z","Q04","Repartitioned filtered count, full scan","measured",5,37,1,1,true,""
+"2026-06-14T08:22:08.271176700Z","Q05","Repartitioned filtered count, partition by year","warmup",1,27,1,1,true,""
+"2026-06-14T08:22:08.300928300Z","Q05","Repartitioned filtered count, partition by year","measured",1,27,1,1,true,""
+"2026-06-14T08:22:08.331976100Z","Q05","Repartitioned filtered count, partition by year","measured",2,26,1,1,true,""
+"2026-06-14T08:22:08.357783200Z","Q05","Repartitioned filtered count, partition by year","measured",3,25,1,1,true,""
+"2026-06-14T08:22:08.372358Z","Q05","Repartitioned filtered count, partition by year","measured",4,19,1,1,true,""
+"2026-06-14T08:22:08.395158800Z","Q05","Repartitioned filtered count, partition by year","measured",5,16,1,1,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_unpartitioned_results.csv b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_unpartitioned_results.csv
new file mode 100644
index 0000000000..c357d09790
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_unpartitioned_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-14T08:23:06.112269700Z","Q06","Unpartitioned full table baseline","warmup",1,60,1,1,true,""
+"2026-06-14T08:23:06.174293800Z","Q06","Unpartitioned full table baseline","measured",1,59,1,1,true,""
+"2026-06-14T08:23:06.234429100Z","Q06","Unpartitioned full table baseline","measured",2,59,1,1,true,""
+"2026-06-14T08:23:06.291205300Z","Q06","Unpartitioned full table baseline","measured",3,56,1,1,true,""
+"2026-06-14T08:23:06.350725600Z","Q06","Unpartitioned full table baseline","measured",4,58,1,1,true,""
+"2026-06-14T08:23:06.410112100Z","Q06","Unpartitioned full table baseline","measured",5,58,1,1,true,""
+"2026-06-14T08:23:06.489755400Z","Q07","Unpartitioned partition by year","warmup",1,78,1,1,true,""
+"2026-06-14T08:23:06.572798200Z","Q07","Unpartitioned partition by year","measured",1,81,1,1,true,""
+"2026-06-14T08:23:06.659082700Z","Q07","Unpartitioned partition by year","measured",2,84,1,1,true,""
+"2026-06-14T08:23:06.751608900Z","Q07","Unpartitioned partition by year","measured",3,91,1,1,true,""
+"2026-06-14T08:23:06.837184900Z","Q07","Unpartitioned partition by year","measured",4,84,1,1,true,""
+"2026-06-14T08:23:06.927667200Z","Q07","Unpartitioned partition by year","measured",5,89,1,1,true,""
+"2026-06-14T08:23:07.008618300Z","Q08","Unpartitioned partition by month","warmup",1,78,1,1,true,""
+"2026-06-14T08:23:07.088438600Z","Q08","Unpartitioned partition by month","measured",1,79,1,1,true,""
+"2026-06-14T08:23:07.165860100Z","Q08","Unpartitioned partition by month","measured",2,75,1,1,true,""
+"2026-06-14T08:23:07.238305800Z","Q08","Unpartitioned partition by month","measured",3,72,1,1,true,""
+"2026-06-14T08:23:07.312396900Z","Q08","Unpartitioned partition by month","measured",4,72,1,1,true,""
+"2026-06-14T08:23:07.388516500Z","Q08","Unpartitioned partition by month","measured",5,74,1,1,true,""
+"2026-06-14T08:23:07.415603300Z","Q09","Unpartitioned filtered count, full scan","warmup",1,25,1,1,true,""
+"2026-06-14T08:23:07.438613200Z","Q09","Unpartitioned filtered count, full scan","measured",1,22,1,1,true,""
+"2026-06-14T08:23:07.461674500Z","Q09","Unpartitioned filtered count, full scan","measured",2,21,1,1,true,""
+"2026-06-14T08:23:07.483671700Z","Q09","Unpartitioned filtered count, full scan","measured",3,21,1,1,true,""
+"2026-06-14T08:23:07.506671200Z","Q09","Unpartitioned filtered count, full scan","measured",4,22,1,1,true,""
+"2026-06-14T08:23:07.528679100Z","Q09","Unpartitioned filtered count, full scan","measured",5,20,1,1,true,""
+"2026-06-14T08:23:07.547203100Z","Q10","Unpartitioned filtered count, partition by year","warmup",1,17,1,1,true,""
+"2026-06-14T08:23:07.567214100Z","Q10","Unpartitioned filtered count, partition by year","measured",1,19,1,1,true,""
+"2026-06-14T08:23:07.587313Z","Q10","Unpartitioned filtered count, partition by year","measured",2,18,1,1,true,""
+"2026-06-14T08:23:07.606943800Z","Q10","Unpartitioned filtered count, partition by year","measured",3,18,1,1,true,""
+"2026-06-14T08:23:07.625303200Z","Q10","Unpartitioned filtered count, partition by year","measured",4,17,1,1,true,""
+"2026-06-14T08:23:07.644226300Z","Q10","Unpartitioned filtered count, partition by year","measured",5,16,1,1,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_polypheny_results.csv b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_polypheny_results.csv
new file mode 100644
index 0000000000..758df0b405
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_polypheny_results.csv
@@ -0,0 +1,61 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-14T08:21:51.087884500Z","Q01","Repartitioned full table baseline","warmup",1,1133,1,1,true,""
+"2026-06-14T08:21:51.117551500Z","Q01","Repartitioned full table baseline","measured",1,24,1,1,true,""
+"2026-06-14T08:21:51.137868400Z","Q01","Repartitioned full table baseline","measured",2,19,1,1,true,""
+"2026-06-14T08:21:51.158880400Z","Q01","Repartitioned full table baseline","measured",3,20,1,1,true,""
+"2026-06-14T08:21:51.175875700Z","Q01","Repartitioned full table baseline","measured",4,15,1,1,true,""
+"2026-06-14T08:21:51.202877Z","Q01","Repartitioned full table baseline","measured",5,17,1,1,true,""
+"2026-06-14T08:21:51.478782900Z","Q02","Repartitioned partition by year","warmup",1,273,1,1,true,""
+"2026-06-14T08:21:51.495680900Z","Q02","Repartitioned partition by year","measured",1,15,1,1,true,""
+"2026-06-14T08:21:51.515489700Z","Q02","Repartitioned partition by year","measured",2,18,1,1,true,""
+"2026-06-14T08:21:51.533822600Z","Q02","Repartitioned partition by year","measured",3,17,1,1,true,""
+"2026-06-14T08:21:51.552355300Z","Q02","Repartitioned partition by year","measured",4,17,1,1,true,""
+"2026-06-14T08:21:51.571553300Z","Q02","Repartitioned partition by year","measured",5,17,1,1,true,""
+"2026-06-14T08:21:51.866632100Z","Q03","Repartitioned partition by month","warmup",1,292,1,1,true,""
+"2026-06-14T08:21:51.889162200Z","Q03","Repartitioned partition by month","measured",1,21,1,1,true,""
+"2026-06-14T08:21:51.911982500Z","Q03","Repartitioned partition by month","measured",2,21,1,1,true,""
+"2026-06-14T08:21:51.931861200Z","Q03","Repartitioned partition by month","measured",3,19,1,1,true,""
+"2026-06-14T08:21:51.955056700Z","Q03","Repartitioned partition by month","measured",4,20,1,1,true,""
+"2026-06-14T08:21:51.973058100Z","Q03","Repartitioned partition by month","measured",5,17,1,1,true,""
+"2026-06-14T08:21:54.381224300Z","Q04","Repartitioned filtered count, full scan","warmup",1,2406,1,1,true,""
+"2026-06-14T08:21:54.678133300Z","Q04","Repartitioned filtered count, full scan","measured",1,308,1,1,true,""
+"2026-06-14T08:21:54.958043Z","Q04","Repartitioned filtered count, full scan","measured",2,266,1,1,true,""
+"2026-06-14T08:21:55.244932900Z","Q04","Repartitioned filtered count, full scan","measured",3,285,1,1,true,""
+"2026-06-14T08:21:55.503415700Z","Q04","Repartitioned filtered count, full scan","measured",4,256,1,1,true,""
+"2026-06-14T08:21:55.791166100Z","Q04","Repartitioned filtered count, full scan","measured",5,284,1,1,true,""
+"2026-06-14T08:21:56.174647600Z","Q05","Repartitioned filtered count, partition by year","warmup",1,381,1,1,true,""
+"2026-06-14T08:21:56.261252Z","Q05","Repartitioned filtered count, partition by year","measured",1,85,1,1,true,""
+"2026-06-14T08:21:56.355601800Z","Q05","Repartitioned filtered count, partition by year","measured",2,92,1,1,true,""
+"2026-06-14T08:21:56.439768200Z","Q05","Repartitioned filtered count, partition by year","measured",3,83,1,1,true,""
+"2026-06-14T08:21:56.528297700Z","Q05","Repartitioned filtered count, partition by year","measured",4,86,1,1,true,""
+"2026-06-14T08:21:56.607746600Z","Q05","Repartitioned filtered count, partition by year","measured",5,77,1,1,true,""
+"2026-06-14T08:21:56.666926900Z","Q06","Unpartitioned full table baseline","warmup",1,57,1,1,true,""
+"2026-06-14T08:21:56.681926700Z","Q06","Unpartitioned full table baseline","measured",1,14,1,1,true,""
+"2026-06-14T08:21:56.696928900Z","Q06","Unpartitioned full table baseline","measured",2,13,1,1,true,""
+"2026-06-14T08:21:56.710938300Z","Q06","Unpartitioned full table baseline","measured",3,13,1,1,true,""
+"2026-06-14T08:21:56.725938900Z","Q06","Unpartitioned full table baseline","measured",4,13,1,1,true,""
+"2026-06-14T08:21:56.741209200Z","Q06","Unpartitioned full table baseline","measured",5,13,1,1,true,""
+"2026-06-14T08:21:56.817224800Z","Q07","Unpartitioned partition by year","warmup",1,78,1,1,true,""
+"2026-06-14T08:21:56.835191500Z","Q07","Unpartitioned partition by year","measured",1,14,1,1,true,""
+"2026-06-14T08:21:56.849236100Z","Q07","Unpartitioned partition by year","measured",2,14,1,1,true,""
+"2026-06-14T08:21:56.865149800Z","Q07","Unpartitioned partition by year","measured",3,14,1,1,true,""
+"2026-06-14T08:21:56.881047500Z","Q07","Unpartitioned partition by year","measured",4,13,1,1,true,""
+"2026-06-14T08:21:56.897100900Z","Q07","Unpartitioned partition by year","measured",5,14,1,1,true,""
+"2026-06-14T08:21:56.977498100Z","Q08","Unpartitioned partition by month","warmup",1,87,1,1,true,""
+"2026-06-14T08:21:57.002151400Z","Q08","Unpartitioned partition by month","measured",1,15,1,1,true,""
+"2026-06-14T08:21:57.015868300Z","Q08","Unpartitioned partition by month","measured",2,16,1,1,true,""
+"2026-06-14T08:21:57.035757600Z","Q08","Unpartitioned partition by month","measured",3,17,1,1,true,""
+"2026-06-14T08:21:57.051851900Z","Q08","Unpartitioned partition by month","measured",4,15,1,1,true,""
+"2026-06-14T08:21:57.072529300Z","Q08","Unpartitioned partition by month","measured",5,16,1,1,true,""
+"2026-06-14T08:21:57.382211400Z","Q09","Unpartitioned filtered count, full scan","warmup",1,307,1,1,true,""
+"2026-06-14T08:21:57.679738600Z","Q09","Unpartitioned filtered count, full scan","measured",1,291,1,1,true,""
+"2026-06-14T08:21:57.914067600Z","Q09","Unpartitioned filtered count, full scan","measured",2,228,1,1,true,""
+"2026-06-14T08:21:58.179423700Z","Q09","Unpartitioned filtered count, full scan","measured",3,262,1,1,true,""
+"2026-06-14T08:21:58.428230400Z","Q09","Unpartitioned filtered count, full scan","measured",4,245,1,1,true,""
+"2026-06-14T08:21:58.704018300Z","Q09","Unpartitioned filtered count, full scan","measured",5,274,1,1,true,""
+"2026-06-14T08:21:58.933195600Z","Q10","Unpartitioned filtered count, partition by year","warmup",1,223,1,1,true,""
+"2026-06-14T08:21:59.032699600Z","Q10","Unpartitioned filtered count, partition by year","measured",1,98,1,1,true,""
+"2026-06-14T08:21:59.123420800Z","Q10","Unpartitioned filtered count, partition by year","measured",2,90,1,1,true,""
+"2026-06-14T08:21:59.208227800Z","Q10","Unpartitioned filtered count, partition by year","measured",3,83,1,1,true,""
+"2026-06-14T08:21:59.280231100Z","Q10","Unpartitioned filtered count, partition by year","measured",4,71,1,1,true,""
+"2026-06-14T08:21:59.342796400Z","Q10","Unpartitioned filtered count, partition by year","measured",5,62,1,1,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_results_analysis.md b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_results_analysis.md
new file mode 100644
index 0000000000..91b8f6c2f2
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_results_analysis.md
@@ -0,0 +1,40 @@
+# Partitioning Benchmark Analysis
+
+Source summary:
+
+```text
+plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
+```
+
+The partitioning run completed successfully for all systems. Every query has
+`5/5` successful measured runs, and all queries returned one result row.
+
+## Main Findings
+
+DuckDB and Spark benefit clearly from the repartitioned layout on partition
+predicates. DuckDB improves from 85.8 ms to 63.4 ms for the year filter and from
+74.4 ms to 29.8 ms for the month filter. Spark improves from 1978.8 ms to
+705.4 ms for the year filter and from 1054.2 ms to 426.4 ms for the month
+filter.
+
+Polypheny is fastest for simple count queries in both layouts. The repartitioned
+count queries Q01-Q03 run around 16.8-19.6 ms, and the unpartitioned count
+queries Q06-Q08 run around 13.2-15.8 ms.
+
+DuckDB is fastest for the filtered count queries over `green_tripdata`. This is
+true for both repartitioned and unpartitioned layouts.
+
+## Interpretation
+
+The partitioning effect is most visible for DuckDB and Spark because their
+runtime changes substantially when `year` and `month` can be used as partition
+columns.
+
+For Polypheny, simple count queries appear metadata-like and are already very
+cheap, so partition pruning is not strongly visible there. The stronger signal
+is the filtered count pair: repartitioned Q04 takes 279.8 ms, while
+partition-constrained Q05 takes 84.6 ms.
+
+Full-table counts are slightly faster on the unpartitioned layout for all
+systems, which is expected when no partition pruning is possible and the
+partitioned directory layout adds overhead.
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_repartitioned_results.csv b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_repartitioned_results.csv
new file mode 100644
index 0000000000..bbe8df8728
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_repartitioned_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+2026-06-14T08:23:44.772895Z,Q01,Repartitioned full table baseline,warmup,1,5950,1,1,true,
+2026-06-14T08:23:46.622161Z,Q01,Repartitioned full table baseline,measured,1,1848,1,1,true,
+2026-06-14T08:23:48.453592Z,Q01,Repartitioned full table baseline,measured,2,1830,1,1,true,
+2026-06-14T08:23:50.120659Z,Q01,Repartitioned full table baseline,measured,3,1666,1,1,true,
+2026-06-14T08:23:51.350358Z,Q01,Repartitioned full table baseline,measured,4,1219,1,1,true,
+2026-06-14T08:23:52.639312Z,Q01,Repartitioned full table baseline,measured,5,1287,1,1,true,
+2026-06-14T08:23:53.714761Z,Q02,Repartitioned partition by year,warmup,1,1018,1,1,true,
+2026-06-14T08:23:54.508928Z,Q02,Repartitioned partition by year,measured,1,792,1,1,true,
+2026-06-14T08:23:55.265645Z,Q02,Repartitioned partition by year,measured,2,755,1,1,true,
+2026-06-14T08:23:55.998682Z,Q02,Repartitioned partition by year,measured,3,731,1,1,true,
+2026-06-14T08:23:56.617615Z,Q02,Repartitioned partition by year,measured,4,618,1,1,true,
+2026-06-14T08:23:57.250702Z,Q02,Repartitioned partition by year,measured,5,631,1,1,true,
+2026-06-14T08:23:57.875402Z,Q03,Repartitioned partition by month,warmup,1,622,1,1,true,
+2026-06-14T08:23:58.406162Z,Q03,Repartitioned partition by month,measured,1,529,1,1,true,
+2026-06-14T08:23:58.816122Z,Q03,Repartitioned partition by month,measured,2,408,1,1,true,
+2026-06-14T08:23:59.217964Z,Q03,Repartitioned partition by month,measured,3,400,1,1,true,
+2026-06-14T08:23:59.621444Z,Q03,Repartitioned partition by month,measured,4,402,1,1,true,
+2026-06-14T08:24:00.015666Z,Q03,Repartitioned partition by month,measured,5,393,1,1,true,
+2026-06-14T08:24:02.806021Z,Q04,"Repartitioned filtered count, full scan",warmup,1,2786,1,1,true,
+2026-06-14T08:24:04.713989Z,Q04,"Repartitioned filtered count, full scan",measured,1,1907,1,1,true,
+2026-06-14T08:24:06.553719Z,Q04,"Repartitioned filtered count, full scan",measured,2,1838,1,1,true,
+2026-06-14T08:24:08.468095Z,Q04,"Repartitioned filtered count, full scan",measured,3,1913,1,1,true,
+2026-06-14T08:24:10.011627Z,Q04,"Repartitioned filtered count, full scan",measured,4,1542,1,1,true,
+2026-06-14T08:24:11.594315Z,Q04,"Repartitioned filtered count, full scan",measured,5,1581,1,1,true,
+2026-06-14T08:24:12.301062Z,Q05,"Repartitioned filtered count, partition by year",warmup,1,704,1,1,true,
+2026-06-14T08:24:13.312396Z,Q05,"Repartitioned filtered count, partition by year",measured,1,1010,1,1,true,
+2026-06-14T08:24:14.695225Z,Q05,"Repartitioned filtered count, partition by year",measured,2,1381,1,1,true,
+2026-06-14T08:24:15.412474Z,Q05,"Repartitioned filtered count, partition by year",measured,3,716,1,1,true,
+2026-06-14T08:24:15.970896Z,Q05,"Repartitioned filtered count, partition by year",measured,4,557,1,1,true,
+2026-06-14T08:24:16.696270Z,Q05,"Repartitioned filtered count, partition by year",measured,5,723,1,1,true,
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_unpartitioned_results.csv b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_unpartitioned_results.csv
new file mode 100644
index 0000000000..5ac397498e
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_unpartitioned_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+2026-06-14T08:24:16.197698Z,Q06,Unpartitioned full table baseline,warmup,1,5148,1,1,true,
+2026-06-14T08:24:17.916656Z,Q06,Unpartitioned full table baseline,measured,1,1717,1,1,true,
+2026-06-14T08:24:19.799721Z,Q06,Unpartitioned full table baseline,measured,2,1878,1,1,true,
+2026-06-14T08:24:20.839271Z,Q06,Unpartitioned full table baseline,measured,3,1037,1,1,true,
+2026-06-14T08:24:22.046981Z,Q06,Unpartitioned full table baseline,measured,4,1197,1,1,true,
+2026-06-14T08:24:23.095196Z,Q06,Unpartitioned full table baseline,measured,5,1047,1,1,true,
+2026-06-14T08:24:26.347347Z,Q07,Unpartitioned partition by year,warmup,1,3195,1,1,true,
+2026-06-14T08:24:28.247837Z,Q07,Unpartitioned partition by year,measured,1,1898,1,1,true,
+2026-06-14T08:24:30.428185Z,Q07,Unpartitioned partition by year,measured,2,2179,1,1,true,
+2026-06-14T08:24:32.652966Z,Q07,Unpartitioned partition by year,measured,3,2224,1,1,true,
+2026-06-14T08:24:34.377067Z,Q07,Unpartitioned partition by year,measured,4,1723,1,1,true,
+2026-06-14T08:24:36.248432Z,Q07,Unpartitioned partition by year,measured,5,1870,1,1,true,
+2026-06-14T08:24:37.549694Z,Q08,Unpartitioned partition by month,warmup,1,1298,1,1,true,
+2026-06-14T08:24:38.750059Z,Q08,Unpartitioned partition by month,measured,1,1199,1,1,true,
+2026-06-14T08:24:39.853595Z,Q08,Unpartitioned partition by month,measured,2,1102,1,1,true,
+2026-06-14T08:24:40.853512Z,Q08,Unpartitioned partition by month,measured,3,999,1,1,true,
+2026-06-14T08:24:41.805078Z,Q08,Unpartitioned partition by month,measured,4,950,1,1,true,
+2026-06-14T08:24:42.826969Z,Q08,Unpartitioned partition by month,measured,5,1021,1,1,true,
+2026-06-14T08:24:44.352110Z,Q09,"Unpartitioned filtered count, full scan",warmup,1,1522,1,1,true,
+2026-06-14T08:24:45.506055Z,Q09,"Unpartitioned filtered count, full scan",measured,1,1152,1,1,true,
+2026-06-14T08:24:46.322848Z,Q09,"Unpartitioned filtered count, full scan",measured,2,815,1,1,true,
+2026-06-14T08:24:47.041329Z,Q09,"Unpartitioned filtered count, full scan",measured,3,717,1,1,true,
+2026-06-14T08:24:47.880424Z,Q09,"Unpartitioned filtered count, full scan",measured,4,837,1,1,true,
+2026-06-14T08:24:48.889131Z,Q09,"Unpartitioned filtered count, full scan",measured,5,1007,1,1,true,
+2026-06-14T08:24:49.863963Z,Q10,"Unpartitioned filtered count, partition by year",warmup,1,971,1,1,true,
+2026-06-14T08:24:50.720219Z,Q10,"Unpartitioned filtered count, partition by year",measured,1,855,1,1,true,
+2026-06-14T08:24:51.501215Z,Q10,"Unpartitioned filtered count, partition by year",measured,2,780,1,1,true,
+2026-06-14T08:24:52.174468Z,Q10,"Unpartitioned filtered count, partition by year",measured,3,672,1,1,true,
+2026-06-14T08:24:52.793530Z,Q10,"Unpartitioned filtered count, partition by year",measured,4,618,1,1,true,
+2026-06-14T08:24:53.447557Z,Q10,"Unpartitioned filtered count, partition by year",measured,5,615,1,1,true,
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
new file mode 100644
index 0000000000..3ef49f4d1a
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
@@ -0,0 +1,79 @@
+# Partitioning Summary
+
+Phase summarized: `measured`.
+Warmup rows are excluded. Mean and median values use successful runs only.
+
+## Source Files
+
+| System                     | CSV                                                                                                     |
+|----------------------------|---------------------------------------------------------------------------------------------------------|
+| Polypheny Relational       | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_polypheny_results.csv`            |
+| DuckDB Repartitioned       | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_repartitioned_results.csv` |
+| DuckDB Unpartitioned       | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_unpartitioned_results.csv` |
+| Apache Spark Repartitioned | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_repartitioned_results.csv`  |
+| Apache Spark Unpartitioned | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_unpartitioned_results.csv`  |
+
+## Mean Elapsed Time (ms)
+
+| Query | Description                                     | Polypheny Relational | DuckDB Repartitioned | DuckDB Unpartitioned | Apache Spark Repartitioned | Apache Spark Unpartitioned | Row counts |
+|-------|-------------------------------------------------|----------------------|----------------------|----------------------|----------------------------|----------------------------|------------|
+| Q01   | Repartitioned full table baseline               | 19.0                 | 83.2                 |                      | 1,570.0                    |                            | 1          |
+| Q02   | Repartitioned partition by year                 | 16.8                 | 63.4                 |                      | 705.4                      |                            | 1          |
+| Q03   | Repartitioned partition by month                | 19.6                 | 29.8                 |                      | 426.4                      |                            | 1          |
+| Q04   | Repartitioned filtered count, full scan         | 279.8                | 41.8                 |                      | 1,756.2                    |                            | 1          |
+| Q05   | Repartitioned filtered count, partition by year | 84.6                 | 22.6                 |                      | 877.4                      |                            | 1          |
+| Q06   | Unpartitioned full table baseline               | 13.2                 |                      | 58.0                 |                            | 1,375.2                    | 1          |
+| Q07   | Unpartitioned partition by year                 | 13.8                 |                      | 85.8                 |                            | 1,978.8                    | 1          |
+| Q08   | Unpartitioned partition by month                | 15.8                 |                      | 74.4                 |                            | 1,054.2                    | 1          |
+| Q09   | Unpartitioned filtered count, full scan         | 260.0                |                      | 21.2                 |                            | 905.6                      | 1          |
+| Q10   | Unpartitioned filtered count, partition by year | 80.8                 |                      | 17.6                 |                            | 708.0                      | 1          |
+
+## Result Row Counts
+
+| Query | Description                                     | Polypheny Relational | DuckDB Repartitioned | DuckDB Unpartitioned | Apache Spark Repartitioned | Apache Spark Unpartitioned |
+|-------|-------------------------------------------------|----------------------|----------------------|----------------------|----------------------------|----------------------------|
+| Q01   | Repartitioned full table baseline               | 1                    | 1                    |                      | 1                          |                            |
+| Q02   | Repartitioned partition by year                 | 1                    | 1                    |                      | 1                          |                            |
+| Q03   | Repartitioned partition by month                | 1                    | 1                    |                      | 1                          |                            |
+| Q04   | Repartitioned filtered count, full scan         | 1                    | 1                    |                      | 1                          |                            |
+| Q05   | Repartitioned filtered count, partition by year | 1                    | 1                    |                      | 1                          |                            |
+| Q06   | Unpartitioned full table baseline               | 1                    |                      | 1                    |                            | 1                          |
+| Q07   | Unpartitioned partition by year                 | 1                    |                      | 1                    |                            | 1                          |
+| Q08   | Unpartitioned partition by month                | 1                    |                      | 1                    |                            | 1                          |
+| Q09   | Unpartitioned filtered count, full scan         | 1                    |                      | 1                    |                            | 1                          |
+| Q10   | Unpartitioned filtered count, partition by year | 1                    |                      | 1                    |                            | 1                          |
+
+## Detailed Summary (ms)
+
+| System                     | Query | Description                                     | Runs | Mean    | Median  | Min     | Max     | Rows | Columns | Status |
+|----------------------------|-------|-------------------------------------------------|------|---------|---------|---------|---------|------|---------|--------|
+| Polypheny Relational       | Q01   | Repartitioned full table baseline               | 5/5  | 19.0    | 19.0    | 15.0    | 24.0    | 1    | 1       | ok     |
+| Polypheny Relational       | Q02   | Repartitioned partition by year                 | 5/5  | 16.8    | 17.0    | 15.0    | 18.0    | 1    | 1       | ok     |
+| Polypheny Relational       | Q03   | Repartitioned partition by month                | 5/5  | 19.6    | 20.0    | 17.0    | 21.0    | 1    | 1       | ok     |
+| Polypheny Relational       | Q04   | Repartitioned filtered count, full scan         | 5/5  | 279.8   | 284.0   | 256.0   | 308.0   | 1    | 1       | ok     |
+| Polypheny Relational       | Q05   | Repartitioned filtered count, partition by year | 5/5  | 84.6    | 85.0    | 77.0    | 92.0    | 1    | 1       | ok     |
+| Polypheny Relational       | Q06   | Unpartitioned full table baseline               | 5/5  | 13.2    | 13.0    | 13.0    | 14.0    | 1    | 1       | ok     |
+| Polypheny Relational       | Q07   | Unpartitioned partition by year                 | 5/5  | 13.8    | 14.0    | 13.0    | 14.0    | 1    | 1       | ok     |
+| Polypheny Relational       | Q08   | Unpartitioned partition by month                | 5/5  | 15.8    | 16.0    | 15.0    | 17.0    | 1    | 1       | ok     |
+| Polypheny Relational       | Q09   | Unpartitioned filtered count, full scan         | 5/5  | 260.0   | 262.0   | 228.0   | 291.0   | 1    | 1       | ok     |
+| Polypheny Relational       | Q10   | Unpartitioned filtered count, partition by year | 5/5  | 80.8    | 83.0    | 62.0    | 98.0    | 1    | 1       | ok     |
+| DuckDB Repartitioned       | Q01   | Repartitioned full table baseline               | 5/5  | 83.2    | 81.0    | 68.0    | 95.0    | 1    | 1       | ok     |
+| DuckDB Repartitioned       | Q02   | Repartitioned partition by year                 | 5/5  | 63.4    | 69.0    | 52.0    | 71.0    | 1    | 1       | ok     |
+| DuckDB Repartitioned       | Q03   | Repartitioned partition by month                | 5/5  | 29.8    | 30.0    | 27.0    | 33.0    | 1    | 1       | ok     |
+| DuckDB Repartitioned       | Q04   | Repartitioned filtered count, full scan         | 5/5  | 41.8    | 37.0    | 33.0    | 58.0    | 1    | 1       | ok     |
+| DuckDB Repartitioned       | Q05   | Repartitioned filtered count, partition by year | 5/5  | 22.6    | 25.0    | 16.0    | 27.0    | 1    | 1       | ok     |
+| DuckDB Unpartitioned       | Q06   | Unpartitioned full table baseline               | 5/5  | 58.0    | 58.0    | 56.0    | 59.0    | 1    | 1       | ok     |
+| DuckDB Unpartitioned       | Q07   | Unpartitioned partition by year                 | 5/5  | 85.8    | 84.0    | 81.0    | 91.0    | 1    | 1       | ok     |
+| DuckDB Unpartitioned       | Q08   | Unpartitioned partition by month                | 5/5  | 74.4    | 74.0    | 72.0    | 79.0    | 1    | 1       | ok     |
+| DuckDB Unpartitioned       | Q09   | Unpartitioned filtered count, full scan         | 5/5  | 21.2    | 21.0    | 20.0    | 22.0    | 1    | 1       | ok     |
+| DuckDB Unpartitioned       | Q10   | Unpartitioned filtered count, partition by year | 5/5  | 17.6    | 18.0    | 16.0    | 19.0    | 1    | 1       | ok     |
+| Apache Spark Repartitioned | Q01   | Repartitioned full table baseline               | 5/5  | 1,570.0 | 1,666.0 | 1,219.0 | 1,848.0 | 1    | 1       | ok     |
+| Apache Spark Repartitioned | Q02   | Repartitioned partition by year                 | 5/5  | 705.4   | 731.0   | 618.0   | 792.0   | 1    | 1       | ok     |
+| Apache Spark Repartitioned | Q03   | Repartitioned partition by month                | 5/5  | 426.4   | 402.0   | 393.0   | 529.0   | 1    | 1       | ok     |
+| Apache Spark Repartitioned | Q04   | Repartitioned filtered count, full scan         | 5/5  | 1,756.2 | 1,838.0 | 1,542.0 | 1,913.0 | 1    | 1       | ok     |
+| Apache Spark Repartitioned | Q05   | Repartitioned filtered count, partition by year | 5/5  | 877.4   | 723.0   | 557.0   | 1,381.0 | 1    | 1       | ok     |
+| Apache Spark Unpartitioned | Q06   | Unpartitioned full table baseline               | 5/5  | 1,375.2 | 1,197.0 | 1,037.0 | 1,878.0 | 1    | 1       | ok     |
+| Apache Spark Unpartitioned | Q07   | Unpartitioned partition by year                 | 5/5  | 1,978.8 | 1,898.0 | 1,723.0 | 2,224.0 | 1    | 1       | ok     |
+| Apache Spark Unpartitioned | Q08   | Unpartitioned partition by month                | 5/5  | 1,054.2 | 1,021.0 | 950.0   | 1,199.0 | 1    | 1       | ok     |
+| Apache Spark Unpartitioned | Q09   | Unpartitioned filtered count, full scan         | 5/5  | 905.6   | 837.0   | 717.0   | 1,152.0 | 1    | 1       | ok     |
+| Apache Spark Unpartitioned | Q10   | Unpartitioned filtered count, partition by year | 5/5  | 708.0   | 672.0   | 615.0   | 855.0   | 1    | 1       | ok     |
diff --git a/plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_plot.pdf b/plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_plot.pdf
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diff --git a/plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_plot.svg b/plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_plot.svg
new file mode 100644
index 0000000000..17b39c4ae2
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_plot.svg
@@ -0,0 +1,69 @@
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+<rect x="541.70" y="452.76" width="9.60" height="127.24" fill="#4f9d55"><title>DuckDB Unpartitioned Q06: 0.058 s</title></rect>
+<rect x="566.90" y="223.61" width="9.60" height="356.39" fill="#7b61b9"><title>Apache Spark Unpartitioned Q06: 1.375 s</title></rect>
+<text x="546.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q06</text>
+<rect x="599.50" y="556.69" width="9.60" height="23.31" fill="#2f6fbb"><title>Polypheny Relational Q07: 0.014 s</title></rect>
+<rect x="624.70" y="424.42" width="9.60" height="155.58" fill="#4f9d55"><title>DuckDB Unpartitioned Q07: 0.086 s</title></rect>
+<rect x="649.90" y="197.27" width="9.60" height="382.73" fill="#7b61b9"><title>Apache Spark Unpartitioned Q07: 1.979 s</title></rect>
+<text x="629.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q07</text>
+<rect x="682.50" y="546.89" width="9.60" height="33.11" fill="#2f6fbb"><title>Polypheny Relational Q08: 0.016 s</title></rect>
+<rect x="707.70" y="434.74" width="9.60" height="145.26" fill="#4f9d55"><title>DuckDB Unpartitioned Q08: 0.074 s</title></rect>
+<rect x="732.90" y="242.85" width="9.60" height="337.15" fill="#7b61b9"><title>Apache Spark Unpartitioned Q08: 1.054 s</title></rect>
+<text x="712.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q08</text>
+<rect x="765.50" y="344.17" width="9.60" height="235.83" fill="#2f6fbb"><title>Polypheny Relational Q09: 0.260 s</title></rect>
+<rect x="790.70" y="525.61" width="9.60" height="54.39" fill="#4f9d55"><title>DuckDB Unpartitioned Q09: 0.021 s</title></rect>
+<rect x="815.90" y="253.84" width="9.60" height="326.16" fill="#7b61b9"><title>Apache Spark Unpartitioned Q09: 0.906 s</title></rect>
+<text x="795.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q09</text>
+<rect x="848.50" y="428.76" width="9.60" height="151.24" fill="#2f6fbb"><title>Polypheny Relational Q10: 0.081 s</title></rect>
+<rect x="873.70" y="539.08" width="9.60" height="40.92" fill="#4f9d55"><title>DuckDB Unpartitioned Q10: 0.018 s</title></rect>
+<rect x="898.90" y="271.66" width="9.60" height="308.34" fill="#7b61b9"><title>Apache Spark Unpartitioned Q10: 0.708 s</title></rect>
+<text x="878.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q10</text>
+<text x="955" y="77" font-family="Helvetica, Arial, sans-serif" font-size="13" font-weight="700">System</text>
+<rect x="955" y="83" width="16" height="16" fill="#2f6fbb"/>
+<text x="979" y="96" font-family="Helvetica, Arial, sans-serif" font-size="12">Polypheny Relational</text>
+<rect x="955" y="111" width="16" height="16" fill="#c94c4c"/>
+<text x="979" y="124" font-family="Helvetica, Arial, sans-serif" font-size="12">DuckDB Repartitioned</text>
+<rect x="955" y="139" width="16" height="16" fill="#4f9d55"/>
+<text x="979" y="152" font-family="Helvetica, Arial, sans-serif" font-size="12">DuckDB Unpartitioned</text>
+<rect x="955" y="167" width="16" height="16" fill="#f0a22e"/>
+<text x="979" y="180" font-family="Helvetica, Arial, sans-serif" font-size="12">Apache Spark Repartitioned</text>
+<rect x="955" y="195" width="16" height="16" fill="#7b61b9"/>
+<text x="979" y="208" font-family="Helvetica, Arial, sans-serif" font-size="12">Apache Spark Unpartitioned</text>
+</svg>
\ No newline at end of file
diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md
new file mode 100644
index 0000000000..b9ca3af32c
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md
@@ -0,0 +1,82 @@
+# Benchmark pipeline
+
+## Run benchmark:
+
+cd C:\github\Polypheny-DB
+
+build jar:
+.\gradlew.bat :dbms:shadowJar --no-daemon
+
+run jar:
+java -Xms8g -Xmx16g "-Dfile.encoding=UTF-8" -jar .\dbms\build\libs\dbms-0.10.1-SNAPSHOT.jar
+
+PR
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\run_polypheny_benchmark.ps1 `
+-Queries plugins\parquet-adapter\benchmarks\query_lists\access_model_comparison\access_model_comparison_rf.sql `
+  -Output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rf_tlcp_results.csv `
+-Warmups 1 `
+  -Runs 5 `
+-NoTableNameMapping
+```
+
+PD
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\run_polypheny_mql_benchmark.ps1 `
+-Namespace tlcpd_document `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\access_model_comparison\access_model_comparison_mql.mql `
+-Output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_mql_tlcp_results.csv `
+  -Warmups 1 `
+-Runs 5
+```
+
+DuckDB
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\run_duckdb_benchmark.ps1 `
+-DataDir C:\PolyData\tlc_partitioned `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\access_model_comparison\access_model_comparison_sql.sql `
+-Output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_duckdb_tlcp_results.csv `
+  -Warmups 1 `
+-Runs 5
+```
+
+Spark
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\run_spark_benchmark.ps1 `
+-DataDir C:\PolyData\tlc_partitioned `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\access_model_comparison\access_model_comparison_sql.sql `
+-Output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_spark_tlcp_results.csv `
+  -Warmups 1 `
+-Runs 5
+```
+
+## Create summary
+
+
+```powershell
+
+python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py `
+  --title "Access Model Comparison TLCP Summary" `
+--output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_tlcp_summary.md `
+  "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rf_tlcp_results.csv" `
+"Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_mql_tlcp_results.csv" `
+  "DuckDB=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_duckdb_tlcp_results.csv" `
+"Apache Spark=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_spark_tlcp_results.csv"
+
+```
+
+## Create plot
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_result_plot_generator.py `
+  --title "Access Model Comparison TLCP" `
+  --name access_model_comparison_tlcp_plot `
+  "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rf_tlcp_results.csv" `
+  "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_mql_tlcp_results.csv" `
+  "DuckDB=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_duckdb_tlcp_results.csv" `
+  "Apache Spark=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_spark_tlcp_results.csv"
+```
diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_aggregation_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_aggregation_bm_pipeline.md
new file mode 100644
index 0000000000..818a3ab691
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_aggregation_bm_pipeline.md
@@ -0,0 +1,108 @@
+# Aggregation benchmark pipeline
+
+## Dataset
+
+The pipeline uses the partitioned TLC dataset:
+
+```text
+C:\PolyData\tlc_partitioned
+```
+
+The current aggregation query files run Q01-Q05 on `yellow_tripdata` and
+Q06-Q10 on `fhvhv_tripdata`.
+
+## Run benchmark
+
+```powershell
+cd C:\github\Polypheny-DB
+```
+
+Build jar:
+
+```powershell
+.\gradlew.bat :dbms:shadowJar --no-daemon
+```
+
+Run jar:
+
+```powershell
+java -Xms8g -Xmx16g "-Dfile.encoding=UTF-8" -jar .\dbms\build\libs\dbms-0.10.1-SNAPSHOT.jar
+```
+
+The Polypheny relational run assumes a Parquet relational adapter named `tlcp`,
+which exposes `tlcp__yellow_tripdata` and `tlcp__fhvhv_tripdata`.
+
+The Polypheny document run assumes a Parquet document adapter named `tlcpd`,
+exposed through namespace `tlcpd_document`.
+
+PR
+
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\run_polypheny_benchmark.ps1 `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\arrregation\aggregation_polypheny.sql `
+  -Output plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_results.csv `
+  -Warmups 1 `
+  -Runs 5 `
+  -NoTableNameMapping
+```
+
+PD
+
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\run_polypheny_mql_benchmark.ps1 `
+  -Namespace tlcpd_document `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\arrregation\aggregation_polypheny_mql.sql `
+  -Output plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_mql_results.csv `
+  -Warmups 1 `
+  -Runs 5
+```
+
+DuckDB
+
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\run_duckdb_benchmark.ps1 `
+  -DataDir C:\PolyData\tlc_partitioned `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\arrregation\aggregation_sql.sql `
+  -Output plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_results.csv `
+  -Warmups 1 `
+  -Runs 5
+```
+
+Spark
+
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\run_spark_benchmark.ps1 `
+  -DataDir C:\PolyData\tlc_partitioned `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\arrregation\aggregation_sql.sql `
+  -Output plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_results.csv `
+  -Warmups 1 `
+  -Runs 5
+```
+
+## Create summary
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py `
+  --title "Aggregation Summary" `
+  --output plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_summary.md `
+  "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_results.csv" `
+  "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_mql_results.csv" `
+  "DuckDB=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_results.csv" `
+  "Apache Spark=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_results.csv"
+```
+
+## Create plot
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_result_plot_generator.py `
+  --title "Aggregation" `
+  --name aggregation_plot `
+  "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_results.csv" `
+  "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_mql_results.csv" `
+  "DuckDB=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_results.csv" `
+  "Apache Spark=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_results.csv"
+```
diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md
new file mode 100644
index 0000000000..9d2fba639f
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md
@@ -0,0 +1,103 @@
+# Nested data benchmark pipeline
+
+## Dataset
+
+The pipeline uses the Nested Customer Parquet file:
+
+```text
+C:\PolyData\nested_customer\nestedcustomer.parquet
+```
+
+## Run benchmark
+
+```powershell
+cd C:\github\Polypheny-DB
+```
+
+Build jar:
+
+```powershell
+.\gradlew.bat :dbms:shadowJar --no-daemon
+```
+
+Run jar:
+
+```powershell
+java -Xms8g -Xmx16g "-Dfile.encoding=UTF-8" -jar .\dbms\build\libs\dbms-0.10.1-SNAPSHOT.jar
+```
+
+Polypheny relational normalized mode assumes a Parquet relational adapter named
+`ncp`, as defined in `benchmarks\query_lists\data_configuration.md`. The document run assumes a Parquet document adapter named
+`ncpd`, exposed through namespace `ncpd_document`. If the nested document adapter is
+too heavy to load, skip the PD command and omit it from the summary command.
+
+PR normalized
+
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\nested\run_polypheny_nested_benchmark.ps1 `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\nested_data\nested_data_polypheny_normalized.sql `
+  -Output plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_normalized_results.csv `
+  -Warmups 1 `
+  -Runs 5
+```
+
+PD
+
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\nested\run_polypheny_mql_nested_benchmark.ps1 `
+  -Namespace ncpd_document `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\nested_data\nested_data_mql.mql `
+  -Output plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_mql_results.csv `
+  -Warmups 1 `
+  -Runs 5
+```
+
+DuckDB
+
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\nested\run_duckdb_nested_benchmark.ps1 `
+  -DataFile C:\PolyData\nested_customer\nestedcustomer.parquet `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\nested_data\nested_data_duckdb.sql `
+  -Output plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_duckdb_results.csv `
+  -Warmups 1 `
+  -Runs 5
+```
+
+Spark
+
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\nested\run_spark_nested_benchmark.ps1 `
+  -DataFile C:\PolyData\nested_customer\nestedcustomer.parquet `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\nested_data\nested_data_spark.sql `
+  -Output plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_spark_results.csv `
+  -Warmups 1 `
+  -Runs 5
+```
+
+## Create summary
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py `
+  --title "Nested Data Summary" `
+  --output plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_summary.md `
+  "Polypheny Relational Normalized=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_normalized_results.csv" `
+  "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_mql_results.csv" `
+  "DuckDB=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_duckdb_results.csv" `
+  "Apache Spark=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_spark_results.csv"
+```
+
+## Create plot
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_result_plot_generator.py `
+  --title "Nested Data" `
+  --name nested_data_plot `
+  "Polypheny Relational Normalized=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_normalized_results.csv" `
+  "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_mql_results.csv" `
+  "DuckDB=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_duckdb_results.csv" `
+  "Apache Spark=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_spark_results.csv"
+```
diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md
new file mode 100644
index 0000000000..bc7e6c4cd4
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md
@@ -0,0 +1,122 @@
+# Partitioning benchmark pipeline
+
+## Dataset
+
+The pipeline compares two TLC layouts:
+
+```text
+C:\PolyData\tlc_repartitioned
+C:\PolyData\tlc_unpartitioned
+```
+
+The Polypheny run assumes two Parquet relational adapters:
+
+- `tlcr` for repartitioned data;
+- `tlcu` for unpartitioned data.
+
+DuckDB and Spark bind one data directory per run, so they are executed once for
+the repartitioned layout and once for the unpartitioned layout.
+
+## Run benchmark
+
+```powershell
+cd C:\github\Polypheny-DB
+```
+
+Build jar:
+
+```powershell
+.\gradlew.bat :dbms:shadowJar --no-daemon
+```
+
+Run jar:
+
+```powershell
+java -Xms8g -Xmx16g "-Dfile.encoding=UTF-8" -jar .\dbms\build\libs\dbms-0.10.1-SNAPSHOT.jar
+```
+
+PR
+
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\run_polypheny_benchmark.ps1 `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\partitioning\partitioning_repartitioned_unpartitioned_polypheny.sql `
+  -Output plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_polypheny_results.csv `
+  -Warmups 1 `
+  -Runs 5 `
+  -NoTableNameMapping
+```
+
+DuckDB repartitioned
+
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\run_duckdb_benchmark.ps1 `
+  -DataDir C:\PolyData\tlc_repartitioned `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\partitioning\partitioning_repartitioned_sql.sql `
+  -Output plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_repartitioned_results.csv `
+  -Warmups 1 `
+  -Runs 5
+```
+
+DuckDB unpartitioned
+
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\run_duckdb_benchmark.ps1 `
+  -DataDir C:\PolyData\tlc_unpartitioned `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\partitioning\partitioning_unpartitioned_sql.sql `
+  -Output plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_unpartitioned_results.csv `
+  -Warmups 1 `
+  -Runs 5
+```
+
+Spark repartitioned
+
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\run_spark_benchmark.ps1 `
+  -DataDir C:\PolyData\tlc_repartitioned `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\partitioning\partitioning_repartitioned_sql.sql `
+  -Output plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_repartitioned_results.csv `
+  -Warmups 1 `
+  -Runs 5
+```
+
+Spark unpartitioned
+
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\run_spark_benchmark.ps1 `
+  -DataDir C:\PolyData\tlc_unpartitioned `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\partitioning\partitioning_unpartitioned_sql.sql `
+  -Output plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_unpartitioned_results.csv `
+  -Warmups 1 `
+  -Runs 5
+```
+
+## Create summary
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py `
+  --title "Partitioning Summary" `
+  --output plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_summary.md `
+  "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_polypheny_results.csv" `
+  "DuckDB Repartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_repartitioned_results.csv" `
+  "DuckDB Unpartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_unpartitioned_results.csv" `
+  "Apache Spark Repartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_repartitioned_results.csv" `
+  "Apache Spark Unpartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_unpartitioned_results.csv"
+```
+
+## Create plot
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_result_plot_generator.py `
+  --title "Partitioning" `
+  --name partitioning_plot `
+  "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_polypheny_results.csv" `
+  "DuckDB Repartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_repartitioned_results.csv" `
+  "DuckDB Unpartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_unpartitioned_results.csv" `
+  "Apache Spark Repartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_repartitioned_results.csv" `
+  "Apache Spark Unpartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_unpartitioned_results.csv"
+```
diff --git a/plugins/parquet-adapter/benchmarks/running_benchmarks.md b/plugins/parquet-adapter/benchmarks/running_benchmarks.md
deleted file mode 100644
index 1e6790ab59..0000000000
--- a/plugins/parquet-adapter/benchmarks/running_benchmarks.md
+++ /dev/null
@@ -1,35 +0,0 @@
-## DuckDB
-
-DuckDB runs as an embedded database inside the Java benchmark process.
-
-At a high level:
-
-- The PowerShell runner compiles the Java benchmark client.
-- The client loads DuckDB’s JDBC driver.
-- DuckDB creates views over the Parquet folders.
-- The client executes each query and drains the full result set.
-- Timings are written to a CSV file.
-
-## Apache Spark
-Spark runs inside a Docker container, so no local Spark installation is required.
-
-At a high level:
-
-- The PowerShell runner starts a Spark Docker image.
-- The repository and Parquet dataset are mounted into the container.
-- A Python script creates Spark temporary views over the Parquet folders.
-- Spark executes the same SQL queries.
-- Results are fully materialized and timings are written to CSV.
-
-## Polypheny
-Polypheny runs as a separate database server, outside the benchmark client.
-
-At a high level:
-
-- Start Polypheny normally.
-- Add the Parquet adapter and point it to relevant data
-- Run the PowerShell benchmark wrapper
-- The wrapper launches the Java benchmark client, which connects to Polypheny through the Prism JDBC interface
-- The client maps query table names, executes each query, drains the full result set, and records elapsed time.
-
-Unlike Spark, the benchmark wrapper does not start Polypheny automatically. The DBMS must already be running with the Parquet adapter configured.
diff --git a/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/TLC_preprocessing_spec.md b/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/TLC_preprocessing_spec.md
new file mode 100644
index 0000000000..6a7fc8a926
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/TLC_preprocessing_spec.md
@@ -0,0 +1,58 @@
+
+
+# NYC TLC nested benchmark dataset
+
+`prepare_tlc_benchmark.py` converts the official NYC TLC monthly Parquet files
+into a Hive-style partitioned dataset with nested Parquet fields.
+
+Default input:
+
+```text
+C:\tmp\tlc\original
+```
+
+Default output:
+
+```text
+E:\tmp\tlc\benchmark_nested
+```
+
+The generated layout is:
+
+```text
+benchmark_nested/
+  yellow_trips_nested/year=2021/month=01/part-yellow-2021-01.parquet
+  green_trips_nested/year=2021/month=01/part-green-2021-01.parquet
+  fhv_trips_nested/year=2021/month=01/part-fhv-2021-01.parquet
+  fhvhv_trips_nested/year=2021/month=01/part-fhvhv-2021-01.parquet
+```
+
+Each table contains nested `struct` columns. Taxi and HVFHV tables also contain a
+repeated `fare.components` list of structs; FHV contains a repeated `events`
+list of structs.
+
+Run the full conversion:
+
+```powershell
+& "C:\Users\igorc\.cache\codex-runtimes\codex-primary-runtime\dependencies\python\python.exe" `
+  plugins\parquet-adapter\benchmarks\scripts\ds_preprocessing\prepare_tlc_benchmark.py `
+  --output-dir E:\tmp\tlc\benchmark_nested
+```
+
+Run a small sample:
+
+```powershell
+& "C:\Users\igorc\.cache\codex-runtimes\codex-primary-runtime\dependencies\python\python.exe" `
+  plugins\parquet-adapter\benchmarks\scripts\ds_preprocessing\prepare_tlc_benchmark.py `
+  --output-dir E:\tmp\tlc\benchmark_nested_sample `
+  --years 2021 `
+  --limit-per-file 1000 `
+  --overwrite
+```
+
+The script requires DuckDB in the local dependency folder:
+
+```powershell
+& "C:\Users\igorc\.cache\codex-runtimes\codex-primary-runtime\dependencies\python\python.exe" `
+  -m pip install --target C:\tmp\tlc\.pydeps duckdb
+```
diff --git a/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/TlcUnpartitionedMaterializer.java b/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/TlcUnpartitionedMaterializer.java
new file mode 100644
index 0000000000..b11816b408
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/TlcUnpartitionedMaterializer.java
@@ -0,0 +1,284 @@
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.nio.file.StandardCopyOption;
+import java.sql.Connection;
+import java.sql.DriverManager;
+import java.sql.ResultSet;
+import java.sql.SQLException;
+import java.sql.Statement;
+import java.time.Duration;
+import java.time.Instant;
+import java.util.ArrayList;
+import java.util.Comparator;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Locale;
+import java.util.Map;
+import java.util.regex.Matcher;
+import java.util.regex.Pattern;
+import java.util.stream.Stream;
+
+
+public class TlcUnpartitionedMaterializer {
+
+    private static final Pattern YEAR_FOLDER = Pattern.compile( "year=(\\d{4})" );
+    private static final Pattern MONTH_FOLDER = Pattern.compile( "month=(\\d{2})" );
+
+    private record Config(
+            Path inputDir,
+            Path outputDir,
+            int threads,
+            String memoryLimit,
+            String compression,
+            boolean overwrite,
+            boolean dryRun,
+            boolean verifyOnly ) {
+    }
+
+
+    private record SourceFile( Path source, Path output, String table, String year, String month ) {
+    }
+
+
+    public static void main( String[] args ) throws Exception {
+        Config config = parseArgs( args );
+        if ( !Files.isDirectory( config.inputDir() ) ) {
+            throw new IllegalArgumentException( "Input directory does not exist: " + config.inputDir() );
+        }
+
+        List<SourceFile> files = discoverFiles( config );
+        if ( files.isEmpty() ) {
+            throw new IllegalArgumentException( "No partitioned Parquet files found in " + config.inputDir() );
+        }
+
+        Class.forName( "org.duckdb.DuckDBDriver" );
+        try ( Connection connection = DriverManager.getConnection( "jdbc:duckdb:" );
+                Statement statement = connection.createStatement() ) {
+            statement.execute( "PRAGMA threads=" + config.threads() );
+            statement.execute( "SET memory_limit=" + sqlString( config.memoryLimit() ) );
+
+            System.out.printf( "Input:       %s%n", config.inputDir() );
+            System.out.printf( "Output:      %s%n", config.outputDir() );
+            System.out.printf( "Files:       %d%n", files.size() );
+            System.out.printf( "Compression: %s%n", config.compression() );
+            System.out.printf( "Mode:        %s%n%n", config.verifyOnly() ? "verify" : config.dryRun() ? "dry-run" : "materialize" );
+
+            int skipped = 0;
+            int processed = 0;
+            Instant allStarted = Instant.now();
+            for ( int index = 0; index < files.size(); index++ ) {
+                SourceFile file = files.get( index );
+                Instant started = Instant.now();
+                if ( config.verifyOnly() ) {
+                    verifyFile( statement, file );
+                    System.out.printf( "[%d/%d] verified %s (%s)%n", index + 1, files.size(), file.output(), elapsed( started ) );
+                    processed++;
+                    continue;
+                }
+
+                if ( Files.exists( file.output() ) && !config.overwrite() ) {
+                    System.out.printf( "[%d/%d] skip existing %s%n", index + 1, files.size(), file.output() );
+                    skipped++;
+                    continue;
+                }
+
+                System.out.printf(
+                        "[%d/%d] %s year=%s month=%s -> %s%n",
+                        index + 1,
+                        files.size(),
+                        file.table(),
+                        file.year(),
+                        file.month(),
+                        file.output() );
+                if ( config.dryRun() ) {
+                    processed++;
+                    continue;
+                }
+
+                Files.createDirectories( file.output().getParent() );
+                Path temporary = Path.of( file.output() + ".tmp" );
+                if ( Files.exists( temporary ) && !config.overwrite() ) {
+                    throw new IllegalStateException( "Temporary output already exists. Remove it or rerun with -Overwrite: " + temporary );
+                }
+                if ( config.overwrite() ) {
+                    Files.deleteIfExists( temporary );
+                    Files.deleteIfExists( file.output() );
+                }
+
+                materializeFile( statement, file, temporary, config.compression() );
+                verifyFile( statement, new SourceFile( file.source(), temporary, file.table(), file.year(), file.month() ) );
+                Files.move( temporary, file.output(), StandardCopyOption.ATOMIC_MOVE );
+                System.out.printf( "        completed in %s%n", elapsed( started ) );
+                processed++;
+            }
+
+            System.out.printf(
+                    "%nDone. Processed=%d skipped=%d total=%d elapsed=%s%n",
+                    processed,
+                    skipped,
+                    files.size(),
+                    elapsed( allStarted ) );
+        }
+    }
+
+
+    private static List<SourceFile> discoverFiles( Config config ) throws Exception {
+        List<SourceFile> files = new ArrayList<>();
+        Map<Path, Path> outputOwners = new LinkedHashMap<>();
+        try ( Stream<Path> stream = Files.walk( config.inputDir() ) ) {
+            for ( Path source : stream.filter( Files::isRegularFile ).filter( TlcUnpartitionedMaterializer::isParquet ).sorted().toList() ) {
+                Path relative = config.inputDir().relativize( source );
+                if ( relative.getNameCount() != 4 ) {
+                    throw new IllegalArgumentException( "Expected <table>/year=YYYY/month=MM/<file>.parquet but found: " + relative );
+                }
+
+                String table = relative.getName( 0 ).toString();
+                String year = partitionValue( YEAR_FOLDER, relative.getName( 1 ).toString(), relative );
+                String month = partitionValue( MONTH_FOLDER, relative.getName( 2 ).toString(), relative );
+                Path output = config.outputDir().resolve( table ).resolve( relative.getFileName().toString() );
+                Path previous = outputOwners.putIfAbsent( output, source );
+                if ( previous != null ) {
+                    throw new IllegalArgumentException( "Output file collision for " + previous + " and " + source + ": " + output );
+                }
+                files.add( new SourceFile( source, output, table, year, month ) );
+            }
+        }
+        files.sort( Comparator.comparing( SourceFile::source ) );
+        return files;
+    }
+
+
+    private static void materializeFile( Statement statement, SourceFile file, Path temporary, String compression ) throws SQLException {
+        String sql = """
+                COPY (
+                  SELECT
+                    *,
+                    CAST(%s AS VARCHAR) AS "year",
+                    CAST(%s AS VARCHAR) AS "month"
+                  FROM read_parquet(%s, hive_partitioning = false)
+                ) TO %s (FORMAT PARQUET, COMPRESSION %s)
+                """.formatted(
+                sqlString( file.year() ),
+                sqlString( file.month() ),
+                sqlString( file.source() ),
+                sqlString( temporary ),
+                compression );
+        statement.execute( sql );
+    }
+
+
+    private static void verifyFile( Statement statement, SourceFile file ) throws SQLException {
+        if ( !Files.isRegularFile( file.output() ) ) {
+            throw new IllegalStateException( "Output file does not exist: " + file.output() );
+        }
+
+        long sourceRows = parquetRows( statement, file.source() );
+        long outputRows = parquetRows( statement, file.output() );
+        if ( sourceRows != outputRows ) {
+            throw new IllegalStateException( "Row-count mismatch for " + file.output() + ": source=" + sourceRows + " output=" + outputRows );
+        }
+
+        String sql = """
+                SELECT
+                  count(DISTINCT row_group_id) AS row_groups,
+                  count(*) FILTER (WHERE path_in_schema = 'year') AS year_stats,
+                  count(*) FILTER (WHERE path_in_schema = 'month') AS month_stats,
+                  count(*) FILTER (
+                    WHERE path_in_schema = 'year'
+                      AND (stats_min <> %s OR stats_max <> %s OR stats_null_count <> 0)
+                  ) AS bad_year,
+                  count(*) FILTER (
+                    WHERE path_in_schema = 'month'
+                      AND (stats_min <> %s OR stats_max <> %s OR stats_null_count <> 0)
+                  ) AS bad_month
+                FROM parquet_metadata(%s)
+                """.formatted(
+                sqlString( file.year() ),
+                sqlString( file.year() ),
+                sqlString( file.month() ),
+                sqlString( file.month() ),
+                sqlString( file.output() ) );
+        try ( ResultSet result = statement.executeQuery( sql ) ) {
+            result.next();
+            long rowGroups = result.getLong( "row_groups" );
+            if ( rowGroups == 0
+                    || result.getLong( "year_stats" ) != rowGroups
+                    || result.getLong( "month_stats" ) != rowGroups
+                    || result.getLong( "bad_year" ) != 0
+                    || result.getLong( "bad_month" ) != 0 ) {
+                throw new IllegalStateException( "Physical partition-column verification failed for " + file.output() );
+            }
+        }
+    }
+
+
+    private static long parquetRows( Statement statement, Path file ) throws SQLException {
+        try ( ResultSet result = statement.executeQuery( "SELECT sum(num_rows) FROM parquet_file_metadata(" + sqlString( file ) + ")" ) ) {
+            result.next();
+            return result.getLong( 1 );
+        }
+    }
+
+
+    private static String partitionValue( Pattern pattern, String folder, Path relative ) {
+        Matcher matcher = pattern.matcher( folder );
+        if ( !matcher.matches() ) {
+            throw new IllegalArgumentException( "Unexpected partition folder in " + relative + ": " + folder );
+        }
+        return matcher.group( 1 );
+    }
+
+
+    private static boolean isParquet( Path path ) {
+        return path.getFileName().toString().toLowerCase( Locale.ROOT ).endsWith( ".parquet" );
+    }
+
+
+    private static String elapsed( Instant started ) {
+        Duration duration = Duration.between( started, Instant.now() );
+        long seconds = duration.toSeconds();
+        return "%dm %02ds".formatted( seconds / 60, seconds % 60 );
+    }
+
+
+    private static String sqlString( Object value ) {
+        return "'" + value.toString().replace( "\\", "/" ).replace( "'", "''" ) + "'";
+    }
+
+
+    private static Config parseArgs( String[] args ) {
+        Map<String, String> values = new LinkedHashMap<>();
+        boolean overwrite = false;
+        boolean dryRun = false;
+        boolean verifyOnly = false;
+
+        for ( int i = 0; i < args.length; i++ ) {
+            switch ( args[i] ) {
+                case "--overwrite" -> overwrite = true;
+                case "--dry-run" -> dryRun = true;
+                case "--verify-only" -> verifyOnly = true;
+                default -> {
+                    if ( !args[i].startsWith( "--" ) || i + 1 >= args.length ) {
+                        throw new IllegalArgumentException( "Unexpected argument: " + args[i] );
+                    }
+                    values.put( args[i].substring( 2 ), args[++i] );
+                }
+            }
+        }
+
+        String compression = values.getOrDefault( "compression", "SNAPPY" ).toUpperCase( Locale.ROOT );
+        if ( !List.of( "SNAPPY", "ZSTD", "GZIP", "UNCOMPRESSED" ).contains( compression ) ) {
+            throw new IllegalArgumentException( "Unsupported compression: " + compression );
+        }
+        return new Config(
+                Path.of( values.getOrDefault( "input-dir", "C:\\PolyData\\tlc_partitioned" ) ),
+                Path.of( values.getOrDefault( "output-dir", "C:\\PolyData\\tlc_unpartitioned" ) ),
+                Integer.parseInt( values.getOrDefault( "threads", "8" ) ),
+                values.getOrDefault( "memory-limit", "16GB" ),
+                compression,
+                overwrite,
+                dryRun,
+                verifyOnly );
+    }
+
+}
diff --git a/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/materialize_tlc_repartitioned.ps1 b/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/materialize_tlc_repartitioned.ps1
new file mode 100644
index 0000000000..cdaf53e1f0
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/materialize_tlc_repartitioned.ps1
@@ -0,0 +1,113 @@
+param(
+    [string]$InputDir = "C:\PolyData\tlc_unpartitioned",
+    [string]$OutputDir = "C:\PolyData\tlc_repartitioned",
+    [switch]$Copy,
+    [switch]$Overwrite,
+    [switch]$DryRun,
+    [switch]$VerifyOnly
+)
+
+$ErrorActionPreference = "Stop"
+
+$inputRoot = (Resolve-Path -LiteralPath $InputDir).Path
+$outputRoot = [System.IO.Path]::GetFullPath($OutputDir)
+$filePattern = '^(?<table>.+_tripdata)_(?<year>\d{4})-(?<month>\d{2})\.parquet$'
+
+if ($inputRoot -eq $outputRoot) {
+    throw "Input and output directories must be different."
+}
+if ($outputRoot.StartsWith("$inputRoot\", [System.StringComparison]::OrdinalIgnoreCase)) {
+    throw "Output directory must not be nested under the input directory."
+}
+
+$sourceFiles = @(
+    Get-ChildItem -LiteralPath $inputRoot -Recurse -File -Filter "*.parquet" |
+        Sort-Object FullName
+)
+if ($sourceFiles.Count -eq 0) {
+    throw "No parquet files found in $inputRoot."
+}
+
+$planned = foreach ($sourceFile in $sourceFiles) {
+    $relative = $sourceFile.FullName.Substring($inputRoot.Length).TrimStart([char[]]"\/")
+    $parts = $relative -split '[\\/]'
+    if ($parts.Count -ne 2) {
+        throw "Expected <table>\<file>.parquet but found: $relative"
+    }
+
+    $match = [regex]::Match($sourceFile.Name, $filePattern)
+    if (-not $match.Success) {
+        throw "Expected <table>_YYYY-MM.parquet but found: $relative"
+    }
+    $table = $match.Groups["table"].Value
+    if ($parts[0] -ne $table) {
+        throw "File name table '$table' does not match parent folder '$($parts[0])': $relative"
+    }
+
+    $year = $match.Groups["year"].Value
+    $month = $match.Groups["month"].Value
+    $outputFile = Join-Path $outputRoot "$table\year=$year\month=$month\$($sourceFile.Name)"
+    [pscustomobject]@{
+        Source = $sourceFile.FullName
+        Output = $outputFile
+        Table = $table
+        Year = $year
+        Month = $month
+        Length = $sourceFile.Length
+    }
+}
+
+Write-Host "Input:  $inputRoot"
+Write-Host "Output: $outputRoot"
+Write-Host "Files:  $($planned.Count)"
+Write-Host "Mode:   $(if ($VerifyOnly) { "verify" } elseif ($DryRun) { "dry-run" } elseif ($Copy) { "copy" } else { "hard-link" })"
+Write-Host ""
+
+$processed = 0
+$skipped = 0
+foreach ($file in $planned) {
+    if ($VerifyOnly) {
+        if (-not (Test-Path -LiteralPath $file.Output -PathType Leaf)) {
+            throw "Missing output file: $($file.Output)"
+        }
+        $outputFile = Get-Item -LiteralPath $file.Output
+        if ($outputFile.Length -ne $file.Length) {
+            throw "File length mismatch: $($file.Output)"
+        }
+        Write-Host "verified $($file.Output)"
+        $processed++
+        continue
+    }
+
+    if (Test-Path -LiteralPath $file.Output -PathType Leaf) {
+        $outputFile = Get-Item -LiteralPath $file.Output
+        if (-not $Overwrite -and $outputFile.Length -eq $file.Length) {
+            Write-Host "skip existing $($file.Output)"
+            $skipped++
+            continue
+        }
+        if (-not $Overwrite) {
+            throw "Output file already exists with a different length. Remove it or use -Overwrite: $($file.Output)"
+        }
+    }
+
+    Write-Host "$($file.Table) year=$($file.Year) month=$($file.Month) -> $($file.Output)"
+    if ($DryRun) {
+        $processed++
+        continue
+    }
+
+    New-Item -ItemType Directory -Force -Path (Split-Path -Parent $file.Output) | Out-Null
+    if ($Overwrite) {
+        Remove-Item -LiteralPath $file.Output -Force -ErrorAction SilentlyContinue
+    }
+    if ($Copy) {
+        Copy-Item -LiteralPath $file.Source -Destination $file.Output
+    } else {
+        New-Item -ItemType HardLink -Path $file.Output -Target $file.Source | Out-Null
+    }
+    $processed++
+}
+
+Write-Host ""
+Write-Host "Done. Processed=$processed skipped=$skipped total=$($planned.Count)"
diff --git a/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/materialize_tlc_unpartitioned.ps1 b/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/materialize_tlc_unpartitioned.ps1
new file mode 100644
index 0000000000..066ea06841
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/materialize_tlc_unpartitioned.ps1
@@ -0,0 +1,57 @@
+param(
+    [string]$InputDir = "C:\PolyData\tlc_partitioned",
+    [string]$OutputDir = "C:\PolyData\tlc_unpartitioned",
+    [int]$Threads = 8,
+    [string]$MemoryLimit = "16GB",
+    [string]$Compression = "SNAPPY",
+    [string]$DuckDbVersion = "1.5.2.1",
+    [switch]$Overwrite,
+    [switch]$DryRun,
+    [switch]$VerifyOnly
+)
+
+$ErrorActionPreference = "Stop"
+
+$root = Resolve-Path "$PSScriptRoot\..\..\..\..\.."
+$source = Join-Path $PSScriptRoot "TlcUnpartitionedMaterializer.java"
+$classDir = Join-Path $root "build\tlc-unpartitioned-materializer"
+New-Item -ItemType Directory -Force -Path $classDir | Out-Null
+
+if ($DuckDbVersion -eq "latest") {
+    $metadata = [xml](Invoke-WebRequest -Uri "https://repo1.maven.org/maven2/org/duckdb/duckdb_jdbc/maven-metadata.xml" -UseBasicParsing).Content
+    $DuckDbVersion = $metadata.metadata.versioning.release
+}
+
+$dependencyRoot = Join-Path $root "build\benchmark-deps\duckdb_jdbc\$DuckDbVersion"
+$driver = Join-Path $dependencyRoot "duckdb_jdbc-$DuckDbVersion.jar"
+
+if (-not (Test-Path $driver)) {
+    New-Item -ItemType Directory -Force -Path $dependencyRoot | Out-Null
+    $downloadUrl = "https://repo1.maven.org/maven2/org/duckdb/duckdb_jdbc/$DuckDbVersion/duckdb_jdbc-$DuckDbVersion.jar"
+    Write-Host "Downloading DuckDB JDBC $DuckDbVersion from $downloadUrl"
+    Invoke-WebRequest -Uri $downloadUrl -OutFile $driver
+}
+
+javac -cp $driver -d $classDir $source
+
+$argsList = @(
+    "--input-dir", $InputDir,
+    "--output-dir", $OutputDir,
+    "--threads", "$Threads",
+    "--memory-limit", $MemoryLimit,
+    "--compression", $Compression
+)
+
+if ($Overwrite) {
+    $argsList += "--overwrite"
+}
+
+if ($DryRun) {
+    $argsList += "--dry-run"
+}
+
+if ($VerifyOnly) {
+    $argsList += "--verify-only"
+}
+
+java -cp "$classDir;$driver" TlcUnpartitionedMaterializer @argsList
diff --git a/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/prepare_tlc_benchmark.py b/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/prepare_tlc_benchmark.py
new file mode 100644
index 0000000000..d7b7fdf1cb
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/ds_preprocessing/prepare_tlc_benchmark.py
@@ -0,0 +1,329 @@
+#!/usr/bin/env python3
+"""Prepare NYC TLC Parquet files for Parquet adapter benchmarks.
+
+The script reads the official flat TLC monthly Parquet files and writes a
+Hive-style partitioned dataset with nested structs and a small repeated field.
+Raw input files are never modified.
+"""
+
+from __future__ import annotations
+
+import argparse
+import re
+import sys
+from pathlib import Path
+
+
+DEFAULT_INPUT_DIR = Path(r"C:\tmp\tlc\original")
+DEFAULT_OUTPUT_DIR = Path(r"E:\tmp\tlc\benchmark_nested")
+DEFAULT_DEPS_DIR = Path(r"C:\tmp\tlc\.pydeps")
+
+FILE_RE = re.compile(r"(?P<trip_type>.+)_tripdata_(?P<year>\d{4})-(?P<month>\d{2})\.parquet$")
+
+
+def load_duckdb(deps_dir: Path):
+    if deps_dir.exists():
+        sys.path.insert(0, str(deps_dir))
+    try:
+        import duckdb  # type: ignore
+    except ModuleNotFoundError as exc:
+        raise SystemExit(
+            "duckdb is required. Install it with:\n"
+            f'  "{sys.executable}" -m pip install --target "{deps_dir}" duckdb'
+        ) from exc
+    return duckdb
+
+
+def sql_string(value: str | Path) -> str:
+    return "'" + str(value).replace("\\", "/").replace("'", "''") + "'"
+
+
+def sql_ident(name: str) -> str:
+    return '"' + name.replace('"', '""') + '"'
+
+
+def find_col(columns: set[str], *candidates: str) -> str | None:
+    by_lower = {col.lower(): col for col in columns}
+    for candidate in candidates:
+        found = by_lower.get(candidate.lower())
+        if found is not None:
+            return found
+    return None
+
+
+def col(columns: set[str], name: str, fallback: str = "NULL") -> str:
+    found = find_col(columns, name)
+    return sql_ident(found) if found else fallback
+
+
+def typed_col(columns: set[str], name: str, sql_type: str) -> str:
+    found = find_col(columns, name)
+    if found is None:
+        return f"NULL::{sql_type}"
+    return f"try_cast({sql_ident(found)} AS {sql_type})"
+
+
+def typed_col_any(columns: set[str], names: tuple[str, ...], sql_type: str) -> str:
+    for name in names:
+        found = find_col(columns, name)
+        if found is not None:
+            return f"try_cast({sql_ident(found)} AS {sql_type})"
+    return f"NULL::{sql_type}"
+
+
+def duration_expr(pickup: str, dropoff: str) -> str:
+    return f"date_diff('second', {pickup}, {dropoff})"
+
+
+def trip_id_expr(year: str, month: str) -> str:
+    return f"({int(year)}::BIGINT * 10000000000 + {int(month)}::BIGINT * 100000000 + row_number() OVER ())"
+
+
+def taxi_select(trip_type: str, year: str, month: str, source_file: Path, columns: set[str], limit: int | None) -> str:
+    pickup_ts = typed_col(columns, "tpep_pickup_datetime" if trip_type == "yellow" else "lpep_pickup_datetime", "TIMESTAMP")
+    dropoff_ts = typed_col(columns, "tpep_dropoff_datetime" if trip_type == "yellow" else "lpep_dropoff_datetime", "TIMESTAMP")
+    airport_fee = typed_col_any(columns, ("airport_fee", "Airport_fee"), "DOUBLE")
+
+    limit_sql = f"\n  LIMIT {limit}" if limit else ""
+    return f"""
+SELECT
+  {trip_id_expr(year, month)} AS trip_id,
+  struct_pack(
+    trip_type := {sql_string(trip_type)},
+    source_file := {sql_string(source_file.name)}
+  ) AS source,
+  struct_pack(
+    vendor_id := {typed_col(columns, "VendorID", "BIGINT")},
+    rate_code_id := {typed_col(columns, "RatecodeID", "DOUBLE")},
+    store_and_fwd_flag := {typed_col(columns, "store_and_fwd_flag", "VARCHAR")}
+  ) AS vendor,
+  struct_pack(
+    ts := {pickup_ts},
+    location_id := {typed_col(columns, "PULocationID", "BIGINT")}
+  ) AS pickup,
+  struct_pack(
+    ts := {dropoff_ts},
+    location_id := {typed_col(columns, "DOLocationID", "BIGINT")}
+  ) AS dropoff,
+  struct_pack(
+    count := {typed_col(columns, "passenger_count", "DOUBLE")}
+  ) AS passenger,
+  struct_pack(
+    distance := {typed_col(columns, "trip_distance", "DOUBLE")},
+    duration_seconds := {duration_expr(pickup_ts, dropoff_ts)}
+  ) AS trip,
+  struct_pack(
+    payment_type := {typed_col(columns, "payment_type", "DOUBLE")},
+    trip_type := {typed_col(columns, "trip_type", "DOUBLE")}
+  ) AS payment,
+  struct_pack(
+    base := {typed_col(columns, "fare_amount", "DOUBLE")},
+    extra := {typed_col(columns, "extra", "DOUBLE")},
+    mta_tax := {typed_col(columns, "mta_tax", "DOUBLE")},
+    tip := {typed_col(columns, "tip_amount", "DOUBLE")},
+    tolls := {typed_col(columns, "tolls_amount", "DOUBLE")},
+    improvement := {typed_col(columns, "improvement_surcharge", "DOUBLE")},
+    congestion := {typed_col(columns, "congestion_surcharge", "DOUBLE")},
+    airport := {airport_fee},
+    total := {typed_col(columns, "total_amount", "DOUBLE")},
+    components := [
+      struct_pack(name := 'tip', amount := {typed_col(columns, "tip_amount", "DOUBLE")})
+    ]
+  ) AS fare
+FROM read_parquet({sql_string(source_file)}){limit_sql}
+"""
+
+
+def hvfhv_select(year: str, month: str, source_file: Path, columns: set[str], limit: int | None) -> str:
+    pickup_ts = typed_col(columns, "pickup_datetime", "TIMESTAMP")
+    dropoff_ts = typed_col(columns, "dropoff_datetime", "TIMESTAMP")
+    limit_sql = f"\n  LIMIT {limit}" if limit else ""
+    return f"""
+SELECT
+  {trip_id_expr(year, month)} AS trip_id,
+  struct_pack(
+    trip_type := 'fhvhv',
+    source_file := {sql_string(source_file.name)}
+  ) AS source,
+  struct_pack(
+    hvfhs_license_num := {typed_col(columns, "hvfhs_license_num", "VARCHAR")},
+    dispatching_base_num := {typed_col(columns, "dispatching_base_num", "VARCHAR")},
+    originating_base_num := {typed_col(columns, "originating_base_num", "VARCHAR")}
+  ) AS operator,
+  struct_pack(
+    request_ts := {typed_col(columns, "request_datetime", "TIMESTAMP")},
+    on_scene_ts := {typed_col(columns, "on_scene_datetime", "TIMESTAMP")},
+    pickup_ts := {pickup_ts},
+    dropoff_ts := {dropoff_ts}
+  ) AS lifecycle,
+  struct_pack(
+    ts := {pickup_ts},
+    location_id := {typed_col(columns, "PULocationID", "BIGINT")}
+  ) AS pickup,
+  struct_pack(
+    ts := {dropoff_ts},
+    location_id := {typed_col(columns, "DOLocationID", "BIGINT")}
+  ) AS dropoff,
+  struct_pack(
+    distance := {typed_col(columns, "trip_miles", "DOUBLE")},
+    duration_seconds := {typed_col(columns, "trip_time", "BIGINT")}
+  ) AS trip,
+  struct_pack(
+    shared_request_flag := {typed_col(columns, "shared_request_flag", "VARCHAR")},
+    shared_match_flag := {typed_col(columns, "shared_match_flag", "VARCHAR")},
+    access_a_ride_flag := {typed_col(columns, "access_a_ride_flag", "VARCHAR")},
+    wav_request_flag := {typed_col(columns, "wav_request_flag", "VARCHAR")},
+    wav_match_flag := {typed_col(columns, "wav_match_flag", "VARCHAR")}
+  ) AS flags,
+  struct_pack(
+    base := {typed_col(columns, "base_passenger_fare", "DOUBLE")},
+    tolls := {typed_col(columns, "tolls", "DOUBLE")},
+    bcf := {typed_col(columns, "bcf", "DOUBLE")},
+    sales_tax := {typed_col(columns, "sales_tax", "DOUBLE")},
+    congestion := {typed_col(columns, "congestion_surcharge", "DOUBLE")},
+    airport := {typed_col(columns, "airport_fee", "DOUBLE")},
+    tip := {typed_col(columns, "tips", "DOUBLE")},
+    driver_pay := {typed_col(columns, "driver_pay", "DOUBLE")},
+    components := [
+      struct_pack(name := 'tip', amount := {typed_col(columns, "tips", "DOUBLE")})
+    ]
+  ) AS fare
+FROM read_parquet({sql_string(source_file)}){limit_sql}
+"""
+
+
+def fhv_select(year: str, month: str, source_file: Path, columns: set[str], limit: int | None) -> str:
+    pickup_ts = typed_col(columns, "pickup_datetime", "TIMESTAMP")
+    dropoff_ts = typed_col(columns, "dropOff_datetime", "TIMESTAMP")
+    limit_sql = f"\n  LIMIT {limit}" if limit else ""
+    return f"""
+SELECT
+  {trip_id_expr(year, month)} AS trip_id,
+  struct_pack(
+    trip_type := 'fhv',
+    source_file := {sql_string(source_file.name)}
+  ) AS source,
+  struct_pack(
+    dispatching_base_num := {typed_col(columns, "dispatching_base_num", "VARCHAR")},
+    affiliated_base_number := {typed_col(columns, "Affiliated_base_number", "VARCHAR")}
+  ) AS operator,
+  struct_pack(
+    ts := {pickup_ts},
+    location_id := {typed_col(columns, "PUlocationID", "BIGINT")}
+  ) AS pickup,
+  struct_pack(
+    ts := {dropoff_ts},
+    location_id := {typed_col(columns, "DOlocationID", "BIGINT")}
+  ) AS dropoff,
+  struct_pack(
+    duration_seconds := {duration_expr(pickup_ts, dropoff_ts)},
+    sr_flag := {typed_col(columns, "SR_Flag", "VARCHAR")}
+  ) AS trip,
+  [
+    struct_pack(name := 'pickup', ts := {pickup_ts}),
+    struct_pack(name := 'dropoff', ts := {dropoff_ts})
+  ] AS events
+FROM read_parquet({sql_string(source_file)}){limit_sql}
+"""
+
+
+def build_select(trip_type: str, year: str, month: str, source_file: Path, columns: set[str], limit: int | None) -> str:
+    if trip_type in {"yellow", "green"}:
+        return taxi_select(trip_type, year, month, source_file, columns, limit)
+    if trip_type == "fhvhv":
+        return hvfhv_select(year, month, source_file, columns, limit)
+    if trip_type == "fhv":
+        return fhv_select(year, month, source_file, columns, limit)
+    raise ValueError(f"Unsupported TLC trip type: {trip_type}")
+
+
+def discover_files(input_dir: Path, trip_types: set[str], years: set[str]) -> list[tuple[Path, str, str, str]]:
+    files: list[tuple[Path, str, str, str]] = []
+    for path in sorted(input_dir.glob("*.parquet")):
+        match = FILE_RE.fullmatch(path.name)
+        if not match:
+            continue
+        trip_type = match.group("trip_type")
+        year = match.group("year")
+        month = match.group("month")
+        if trip_types and trip_type not in trip_types:
+            continue
+        if years and year not in years:
+            continue
+        files.append((path, trip_type, year, month))
+    return files
+
+
+def parquet_columns(duckdb, source_file: Path) -> set[str]:
+    con = duckdb.connect()
+    try:
+        rows = con.execute(f"DESCRIBE SELECT * FROM read_parquet({sql_string(source_file)})").fetchall()
+    finally:
+        con.close()
+    return {row[0] for row in rows}
+
+
+def write_file(duckdb, select_sql: str, output_file: Path, compression: str, threads: int) -> None:
+    output_file.parent.mkdir(parents=True, exist_ok=True)
+    con = duckdb.connect()
+    try:
+        con.execute(f"PRAGMA threads={threads}")
+        con.execute(
+            f"COPY ({select_sql}) TO {sql_string(output_file)} "
+            f"(FORMAT PARQUET, COMPRESSION {compression})"
+        )
+    finally:
+        con.close()
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--input-dir", type=Path, default=DEFAULT_INPUT_DIR)
+    parser.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT_DIR)
+    parser.add_argument("--deps-dir", type=Path, default=DEFAULT_DEPS_DIR)
+    parser.add_argument("--trip-types", nargs="*", default=["yellow", "green", "fhv", "fhvhv"])
+    parser.add_argument("--years", nargs="*", default=["2021", "2022", "2023"])
+    parser.add_argument("--compression", default="SNAPPY", choices=["SNAPPY", "ZSTD", "GZIP", "UNCOMPRESSED"])
+    parser.add_argument("--threads", type=int, default=4)
+    parser.add_argument("--limit-per-file", type=int, default=None)
+    parser.add_argument("--overwrite", action="store_true")
+    parser.add_argument("--dry-run", action="store_true")
+    args = parser.parse_args()
+
+    if not args.input_dir.exists():
+        raise SystemExit(f"Input directory does not exist: {args.input_dir}")
+
+    duckdb = load_duckdb(args.deps_dir)
+    trip_types = set(args.trip_types or [])
+    years = set(args.years or [])
+    files = discover_files(args.input_dir, trip_types, years)
+    if not files:
+        raise SystemExit("No matching TLC Parquet files found.")
+
+    print(f"Input:  {args.input_dir}")
+    print(f"Output: {args.output_dir}")
+    print(f"Files:  {len(files)}")
+
+    for index, (source_file, trip_type, year, month) in enumerate(files, start=1):
+        table_dir = f"{trip_type}_trips_nested"
+        output_file = args.output_dir / table_dir / f"year={year}" / f"month={month}" / f"part-{trip_type}-{year}-{month}.parquet"
+
+        if output_file.exists() and not args.overwrite:
+            print(f"[{index}/{len(files)}] skip existing {output_file}")
+            continue
+        if output_file.exists() and args.overwrite:
+            output_file.unlink()
+
+        columns = parquet_columns(duckdb, source_file)
+        select_sql = build_select(trip_type, year, month, source_file, columns, args.limit_per_file)
+
+        print(f"[{index}/{len(files)}] {source_file.name} -> {output_file}")
+        if args.dry_run:
+            continue
+        write_file(duckdb, select_sql, output_file, args.compression, args.threads)
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/plugins/parquet-adapter/benchmarks/scripts/implementation/DuckDbJdbcBenchmark.java b/plugins/parquet-adapter/benchmarks/scripts/implementation/DuckDbJdbcBenchmark.java
new file mode 100644
index 0000000000..9f4caae84e
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/implementation/DuckDbJdbcBenchmark.java
@@ -0,0 +1,310 @@
+import java.io.BufferedWriter;
+import java.io.IOException;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.sql.Connection;
+import java.sql.DriverManager;
+import java.sql.ResultSet;
+import java.sql.ResultSetMetaData;
+import java.sql.SQLException;
+import java.sql.Statement;
+import java.time.Instant;
+import java.util.ArrayList;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Locale;
+import java.util.Map;
+import java.util.Set;
+import java.util.stream.Collectors;
+
+public class DuckDbJdbcBenchmark {
+
+    private record Query( String id, String description, String sql ) {
+    }
+
+
+    private record Config(
+            String url,
+            Path dataDir,
+            Path queries,
+            Path output,
+            int warmups,
+            int runs,
+            boolean printRows,
+            int fetchSize,
+            int queryTimeoutSeconds,
+            int threads,
+            String memoryLimit,
+            Path nestedCustomerFile,
+            String sql,
+            Path sqlFile,
+            Set<String> only ) {
+    }
+
+
+    public static void main( String[] args ) throws Exception {
+        Config config = parseArgs( args );
+        String adHocSql = adHocSql( config );
+        List<Query> queries = adHocSql.isBlank()
+                ? loadQueries( config.queries() )
+                : List.of( new Query( "SQL", "Ad hoc SQL", adHocSql ) );
+        if ( !config.only().isEmpty() ) {
+            queries = queries.stream()
+                    .filter( query -> config.only().contains( query.id().toUpperCase( Locale.ROOT ) ) )
+                    .toList();
+        }
+        if ( queries.isEmpty() ) {
+            throw new IllegalArgumentException( "No queries found in " + config.queries() );
+        }
+
+        Class.forName( "org.duckdb.DuckDBDriver" );
+
+        if ( config.output().getParent() != null ) {
+            Files.createDirectories( config.output().getParent() );
+        }
+
+        try ( Connection connection = DriverManager.getConnection( config.url() );
+                BufferedWriter writer = Files.newBufferedWriter( config.output(), StandardCharsets.UTF_8 ) ) {
+            setupDuckDb( connection, config );
+            writer.write( "timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error" );
+            writer.newLine();
+            writer.flush();
+
+            System.out.printf( "Connected to %s%n", config.url() );
+            System.out.printf( "Data directory %s%n", config.dataDir() );
+            if ( adHocSql.isBlank() ) {
+                System.out.printf( "Loaded %d queries from %s%n", queries.size(), config.queries() );
+            } else {
+                System.out.printf( "Loaded ad hoc SQL query%n" );
+            }
+            System.out.printf( "Writing CSV to %s%n", config.output() );
+
+            for ( Query query : queries ) {
+                System.out.printf( "%n%s %s%n", query.id(), query.description() );
+                for ( int i = 1; i <= config.warmups(); i++ ) {
+                    executeAndRecord( connection, writer, query, "warmup", i, config );
+                }
+                for ( int i = 1; i <= config.runs(); i++ ) {
+                    executeAndRecord( connection, writer, query, "measured", i, config );
+                }
+            }
+        }
+    }
+
+
+    private static void setupDuckDb( Connection connection, Config config ) throws SQLException {
+        try ( Statement statement = connection.createStatement() ) {
+            statement.execute( "SET memory_limit = '" + config.memoryLimit().replace( "'", "''" ) + "'" );
+            statement.execute( "SET threads = " + config.threads() );
+            if ( config.nestedCustomerFile() != null ) {
+                String file = duckDbPath( config.nestedCustomerFile() );
+                statement.execute( "CREATE OR REPLACE VIEW nested_customer AS SELECT * FROM read_parquet('" + file + "')" );
+                return;
+            }
+            String dataDir = duckDbPath( config.dataDir() );
+            statement.execute( "CREATE OR REPLACE VIEW yellow_tripdata AS SELECT * FROM read_parquet('" + dataDir + "/yellow_tripdata/**/*.parquet', hive_partitioning = true)" );
+            statement.execute( "CREATE OR REPLACE VIEW green_tripdata AS SELECT * FROM read_parquet('" + dataDir + "/green_tripdata/**/*.parquet', hive_partitioning = true)" );
+            statement.execute( "CREATE OR REPLACE VIEW fhv_tripdata AS SELECT * FROM read_parquet('" + dataDir + "/fhv_tripdata/**/*.parquet', hive_partitioning = true)" );
+            statement.execute( "CREATE OR REPLACE VIEW fhvhv_tripdata AS SELECT * FROM read_parquet('" + dataDir + "/fhvhv_tripdata/**/*.parquet', hive_partitioning = true)" );
+        }
+    }
+
+
+    private static String duckDbPath( Path path ) {
+        return path.toAbsolutePath().normalize().toString().replace( "\\", "/" ).replace( "'", "''" );
+    }
+
+
+    private static String adHocSql( Config config ) throws IOException {
+        if ( config.sqlFile() != null ) {
+            return Files.readString( config.sqlFile(), StandardCharsets.UTF_8 );
+        }
+        return config.sql();
+    }
+
+
+    private static void executeAndRecord( Connection connection, BufferedWriter writer, Query query, String phase, int run, Config config ) throws IOException {
+        long start = System.nanoTime();
+        long rows = 0;
+        int columns = 0;
+        boolean success = false;
+        String error = "";
+
+        try ( Statement statement = connection.createStatement() ) {
+            if ( config.fetchSize() > 0 ) {
+                statement.setFetchSize( config.fetchSize() );
+            }
+            if ( config.queryTimeoutSeconds() > 0 ) {
+                statement.setQueryTimeout( config.queryTimeoutSeconds() );
+            }
+
+            boolean hasResultSet = statement.execute( query.sql() );
+            if ( hasResultSet ) {
+                try ( ResultSet resultSet = statement.getResultSet() ) {
+                    ResultSetMetaData metaData = resultSet.getMetaData();
+                    columns = metaData.getColumnCount();
+                    while ( resultSet.next() ) {
+                        rows++;
+                        if ( config.printRows() ) {
+                            printRow( resultSet, metaData );
+                        }
+                    }
+                }
+            } else {
+                rows = statement.getUpdateCount();
+            }
+            success = true;
+        } catch ( SQLException e ) {
+            error = e.getClass().getSimpleName() + ": " + e.getMessage();
+            System.out.printf( "  %s %d failed: %s%n", phase, run, error );
+        }
+
+        long elapsedMs = Math.round( (System.nanoTime() - start) / 1_000_000.0 );
+        if ( success ) {
+            System.out.printf( "  %s %d: %d ms, rows=%d%n", phase, run, elapsedMs, rows );
+        }
+        writeCsvRow( writer, query, phase, run, elapsedMs, rows, columns, success, error );
+    }
+
+
+    private static void printRow( ResultSet resultSet, ResultSetMetaData metaData ) throws SQLException {
+        List<String> values = new ArrayList<>();
+        for ( int i = 1; i <= metaData.getColumnCount(); i++ ) {
+            Object value = resultSet.getObject( i );
+            values.add( value == null ? "NULL" : value.toString() );
+        }
+        System.out.println( String.join( "\t", values ) );
+    }
+
+
+    private static void writeCsvRow( BufferedWriter writer, Query query, String phase, int run, long elapsedMs, long rows, int columns, boolean success, String error ) throws IOException {
+        writer.write( String.join( ",",
+                csv( Instant.now().toString() ),
+                csv( query.id() ),
+                csv( query.description() ),
+                csv( phase ),
+                Integer.toString( run ),
+                Long.toString( elapsedMs ),
+                Long.toString( rows ),
+                Integer.toString( columns ),
+                Boolean.toString( success ),
+                csv( error ) ) );
+        writer.newLine();
+        writer.flush();
+    }
+
+
+    private static String csv( String value ) {
+        String escaped = value == null ? "" : value.replace( "\"", "\"\"" );
+        return "\"" + escaped + "\"";
+    }
+
+
+    private static List<Query> loadQueries( Path path ) throws IOException {
+        List<String> lines = Files.readAllLines( path, StandardCharsets.UTF_8 );
+        List<Query> queries = new ArrayList<>();
+        String currentId = null;
+        String currentDescription = null;
+        StringBuilder sql = new StringBuilder();
+
+        for ( String line : lines ) {
+            String trimmed = line.trim();
+            if ( trimmed.startsWith( "-- Q" ) && trimmed.contains( ":" ) ) {
+                addQuery( queries, currentId, currentDescription, sql );
+                int colon = trimmed.indexOf( ':' );
+                currentId = trimmed.substring( 3, colon ).trim().toUpperCase( Locale.ROOT );
+                currentDescription = trimmed.substring( colon + 1 ).trim();
+                sql.setLength( 0 );
+                continue;
+            }
+            if ( trimmed.startsWith( "--" ) || trimmed.isEmpty() ) {
+                continue;
+            }
+            sql.append( line ).append( System.lineSeparator() );
+            if ( trimmed.endsWith( ";" ) ) {
+                addQuery( queries, currentId, currentDescription, sql );
+                currentId = null;
+                currentDescription = null;
+                sql.setLength( 0 );
+            }
+        }
+        addQuery( queries, currentId, currentDescription, sql );
+        return queries;
+    }
+
+
+    private static void addQuery( List<Query> queries, String id, String description, StringBuilder sql ) {
+        String statement = sql.toString().trim();
+        if ( statement.isEmpty() ) {
+            return;
+        }
+        if ( statement.endsWith( ";" ) ) {
+            statement = statement.substring( 0, statement.length() - 1 );
+        }
+        queries.add( new Query( id == null ? "SQL" : id, description == null ? "" : description, statement ) );
+    }
+
+
+    private static Config parseArgs( String[] args ) {
+        Map<String, String> values = new LinkedHashMap<>();
+        Set<String> flags = Set.of( "print-rows" );
+        for ( int i = 0; i < args.length; i++ ) {
+            String arg = args[i];
+            if ( !arg.startsWith( "--" ) ) {
+                throw new IllegalArgumentException( "Unexpected argument: " + arg );
+            }
+            String key = arg.substring( 2 );
+            if ( flags.contains( key ) ) {
+                values.put( key, "true" );
+                continue;
+            }
+            if ( key.contains( "=" ) ) {
+                String[] split = key.split( "=", 2 );
+                values.put( split[0], split[1] );
+                continue;
+            }
+            if ( i + 1 >= args.length ) {
+                throw new IllegalArgumentException( "Missing value for " + arg );
+            }
+            values.put( key, args[++i] );
+        }
+
+        Path sqlFile = values.containsKey( "sql-file" ) && !values.get( "sql-file" ).isBlank()
+                ? Path.of( values.get( "sql-file" ) )
+                : null;
+        Path nestedCustomerFile = values.containsKey( "nested-customer-file" ) && !values.get( "nested-customer-file" ).isBlank()
+                ? Path.of( values.get( "nested-customer-file" ) )
+                : null;
+        String only = values.getOrDefault( "only", "" );
+        return new Config(
+                values.getOrDefault( "url", "jdbc:duckdb:" ),
+                Path.of( values.getOrDefault( "data-dir", "C:\\tmp\\tlc\\flat_partitioned" ) ),
+                Path.of( values.getOrDefault( "queries", "plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_sql.sql" ) ),
+                Path.of( values.getOrDefault( "output", "plugins/parquet-adapter/benchmarks/results/access_model_comparison/duckdb_results.csv" ) ),
+                parseInt( values, "warmups", 1 ),
+                parseInt( values, "runs", 5 ),
+                parseBoolean( values, "print-rows", false ),
+                parseInt( values, "fetch-size", 1000 ),
+                parseInt( values, "query-timeout", 0 ),
+                parseInt( values, "threads", 8 ),
+                values.getOrDefault( "memory-limit", "16GB" ),
+                nestedCustomerFile,
+                values.getOrDefault( "sql", "" ),
+                sqlFile,
+                only.isBlank()
+                        ? Set.of()
+                        : Set.of( only.split( "," ) ).stream().map( s -> s.trim().toUpperCase( Locale.ROOT ) ).collect( Collectors.toSet() ) );
+    }
+
+
+    private static int parseInt( Map<String, String> values, String key, int defaultValue ) {
+        return values.containsKey( key ) ? Integer.parseInt( values.get( key ) ) : defaultValue;
+    }
+
+
+    private static boolean parseBoolean( Map<String, String> values, String key, boolean defaultValue ) {
+        return values.containsKey( key ) ? Boolean.parseBoolean( values.get( key ) ) : defaultValue;
+    }
+
+}
diff --git a/plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyJdbcBenchmark.java b/plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyJdbcBenchmark.java
new file mode 100644
index 0000000000..b5afd47f5a
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyJdbcBenchmark.java
@@ -0,0 +1,331 @@
+import java.io.BufferedWriter;
+import java.io.IOException;
+import java.math.BigDecimal;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.sql.Connection;
+import java.sql.DriverManager;
+import java.sql.ResultSet;
+import java.sql.ResultSetMetaData;
+import java.sql.SQLException;
+import java.sql.Statement;
+import java.time.Instant;
+import java.util.ArrayList;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Locale;
+import java.util.Map;
+import java.util.Set;
+import java.util.stream.Collectors;
+
+public class PolyphenyJdbcBenchmark {
+
+    private static final String DEFAULT_URL = "jdbc:polypheny://pa:@localhost:20590/public";
+
+    private record Query(String id, String description, String sql) {
+    }
+
+    private record Config(
+            String url,
+            Path queries,
+            Path output,
+            int warmups,
+            int runs,
+            boolean polyphenyTableNames,
+            String polyphenyTablePrefix,
+            boolean printRows,
+            int fetchSize,
+            int queryTimeoutSeconds,
+            String sql,
+            Path sqlFile,
+            Set<String> only ) {
+    }
+
+
+    public static void main(String[] args) throws Exception {
+        Config config = parseArgs(args);
+        String adHocSql = adHocSql(config);
+        List<Query> queries = adHocSql.isBlank()
+                ? loadQueries(config.queries(), config.polyphenyTableNames(), config.polyphenyTablePrefix())
+                : List.of(new Query("SQL", "Ad hoc SQL", prepareSql(adHocSql, config.polyphenyTableNames(), config.polyphenyTablePrefix())));
+        if (!config.only().isEmpty()) {
+            queries = queries.stream()
+                    .filter(query -> config.only().contains(query.id().toUpperCase(Locale.ROOT)))
+                    .toList();
+        }
+        if (queries.isEmpty()) {
+            throw new IllegalArgumentException("No queries found in " + config.queries());
+        }
+
+        Class.forName("org.polypheny.jdbc.PolyphenyDriver");
+
+        if (config.output().getParent() != null) {
+            Files.createDirectories(config.output().getParent());
+        }
+
+        try (Connection connection = DriverManager.getConnection(config.url());
+                BufferedWriter writer = Files.newBufferedWriter(config.output(), StandardCharsets.UTF_8)) {
+            connection.setReadOnly(true);
+            writer.write("timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error");
+            writer.newLine();
+            writer.flush();
+
+            System.out.printf("Connected to %s%n", config.url());
+            if (adHocSql.isBlank()) {
+                System.out.printf("Loaded %d queries from %s%n", queries.size(), config.queries());
+            } else {
+                System.out.printf("Loaded ad hoc SQL query%n");
+            }
+            System.out.printf("Writing CSV to %s%n", config.output());
+
+            for (Query query : queries) {
+                System.out.printf("%n%s %s%n", query.id(), query.description());
+                for (int i = 1; i <= config.warmups(); i++) {
+                    executeAndRecord(connection, writer, query, "warmup", i, config);
+                }
+                for (int i = 1; i <= config.runs(); i++) {
+                    executeAndRecord(connection, writer, query, "measured", i, config);
+                }
+            }
+        }
+    }
+
+
+    private static String adHocSql(Config config) throws IOException {
+        if (config.sqlFile() != null) {
+            return Files.readString(config.sqlFile(), StandardCharsets.UTF_8);
+        }
+        return config.sql();
+    }
+
+
+    private static void executeAndRecord(Connection connection, BufferedWriter writer, Query query, String phase, int run, Config config) throws IOException {
+        long start = System.nanoTime();
+        long rows = 0;
+        int columns = 0;
+        boolean success = false;
+        String error = "";
+
+        try (Statement statement = connection.createStatement()) {
+            if (config.fetchSize() > 0) {
+                statement.setFetchSize(config.fetchSize());
+            }
+            if (config.queryTimeoutSeconds() > 0) {
+                statement.setQueryTimeout(config.queryTimeoutSeconds());
+            }
+
+            boolean hasResultSet = statement.execute(query.sql());
+            if (hasResultSet) {
+                try (ResultSet resultSet = statement.getResultSet()) {
+                    ResultSetMetaData metaData = resultSet.getMetaData();
+                    columns = metaData.getColumnCount();
+                    while (resultSet.next()) {
+                        rows++;
+                        if (config.printRows()) {
+                            printRow(resultSet, metaData);
+                        }
+                    }
+                }
+            } else {
+                rows = statement.getUpdateCount();
+            }
+            success = true;
+        } catch (SQLException e) {
+            error = e.getClass().getSimpleName() + ": " + e.getMessage();
+            System.out.printf("  %s %d failed: %s%n", phase, run, error);
+        }
+
+        long elapsedMs = Math.round((System.nanoTime() - start) / 1_000_000.0);
+        if (success) {
+            System.out.printf("  %s %d: %d ms, rows=%d%n", phase, run, elapsedMs, rows);
+        }
+        writeCsvRow(writer, query, phase, run, elapsedMs, rows, columns, success, error);
+    }
+
+
+    private static void printRow(ResultSet resultSet, ResultSetMetaData metaData) throws SQLException {
+        List<String> values = new ArrayList<>();
+        for (int i = 1; i <= metaData.getColumnCount(); i++) {
+            Object value = resultSet.getObject(i);
+            values.add(value == null ? "NULL" : value.toString());
+        }
+        System.out.println(String.join("\t", values));
+    }
+
+
+    private static void writeCsvRow(BufferedWriter writer, Query query, String phase, int run, long elapsedMs, long rows, int columns, boolean success, String error) throws IOException {
+        writer.write(String.join(",",
+                csv(Instant.now().toString()),
+                csv(query.id()),
+                csv(query.description()),
+                csv(phase),
+                Integer.toString(run),
+                Long.toString(elapsedMs),
+                Long.toString(rows),
+                Integer.toString(columns),
+                Boolean.toString(success),
+                csv(error)));
+        writer.newLine();
+        writer.flush();
+    }
+
+
+    private static String csv(String value) {
+        String escaped = value.replace("\"", "\"\"");
+        return "\"" + escaped + "\"";
+    }
+
+
+    private static List<Query> loadQueries(Path path, boolean polyphenyTableNames, String polyphenyTablePrefix) throws IOException {
+        List<String> lines = Files.readAllLines(path, StandardCharsets.UTF_8);
+        List<Query> queries = new ArrayList<>();
+        String currentId = null;
+        String currentDescription = null;
+        StringBuilder sql = new StringBuilder();
+
+        for (String line : lines) {
+            String trimmed = line.trim();
+            if (trimmed.startsWith("-- Q") && trimmed.contains(":")) {
+                addQuery(queries, currentId, currentDescription, sql, polyphenyTableNames, polyphenyTablePrefix);
+                int colon = trimmed.indexOf(':');
+                currentId = trimmed.substring(3, colon).trim();
+                currentDescription = trimmed.substring(colon + 1).trim();
+                sql.setLength(0);
+                continue;
+            }
+            if (currentId == null || trimmed.startsWith("--")) {
+                continue;
+            }
+            sql.append(line).append('\n');
+            if (trimmed.endsWith(";")) {
+                addQuery(queries, currentId, currentDescription, sql, polyphenyTableNames, polyphenyTablePrefix);
+                currentId = null;
+                currentDescription = null;
+                sql.setLength(0);
+            }
+        }
+        addQuery(queries, currentId, currentDescription, sql, polyphenyTableNames, polyphenyTablePrefix);
+        return queries;
+    }
+
+
+    private static void addQuery(List<Query> queries, String id, String description, StringBuilder sql, boolean polyphenyTableNames, String polyphenyTablePrefix) {
+        if (id == null) {
+            return;
+        }
+        String statement = sql.toString().trim();
+        if (statement.isEmpty()) {
+            return;
+        }
+        queries.add(new Query(id, description == null ? "" : description, prepareSql(statement, polyphenyTableNames, polyphenyTablePrefix)));
+    }
+
+
+    private static String prepareSql(String sql, boolean polyphenyTableNames, String polyphenyTablePrefix) {
+        String statement = sql.trim();
+        if (statement.endsWith(";")) {
+            statement = statement.substring(0, statement.length() - 1);
+        }
+        if (polyphenyTableNames) {
+            statement = mapToPolyphenyTableNames(statement, polyphenyTablePrefix);
+        }
+        return statement;
+    }
+
+
+    private static String mapToPolyphenyTableNames(String sql, String tablePrefix) {
+        return sql
+                .replaceAll("(?i)\\byellow_tripdata\\b", tablePrefix + "yellow_tripdata")
+                .replaceAll("(?i)\\bgreen_tripdata\\b", tablePrefix + "green_tripdata")
+                .replaceAll("(?i)\\bfhv_tripdata\\b", tablePrefix + "fhv_tripdata")
+                .replaceAll("(?i)\\bfhvhv_tripdata\\b", tablePrefix + "fhvhv_tripdata");
+    }
+
+
+    private static Config parseArgs(String[] args) {
+        Map<String, String> values = new LinkedHashMap<>();
+        for (int i = 0; i < args.length; i++) {
+            String arg = args[i];
+            if (!arg.startsWith("--")) {
+                throw new IllegalArgumentException("Unexpected argument: " + arg);
+            }
+            String key;
+            String value;
+            int equals = arg.indexOf('=');
+            if (equals >= 0) {
+                key = arg.substring(2, equals);
+                value = arg.substring(equals + 1);
+            } else {
+                key = arg.substring(2);
+                if (isBooleanFlag(key)) {
+                    value = "true";
+                } else {
+                    if (i + 1 >= args.length) {
+                        throw new IllegalArgumentException("Missing value for " + arg);
+                    }
+                    value = args[++i];
+                }
+            }
+            values.put(key.toLowerCase(Locale.ROOT), value);
+        }
+
+        Path defaultQueries = Path.of("plugins", "parquet-adapter", "benchmarks", "query_lists", "access_model_comparison", "access_model_comparison_rf.sql");
+        Path defaultOutput = Path.of("plugins", "parquet-adapter", "benchmarks", "results", "access_model_comparison", "polypheny_results.csv");
+        Path sqlFile = values.containsKey("sql-file") ? Path.of(values.get("sql-file")) : null;
+
+        return new Config(
+                values.getOrDefault("url", DEFAULT_URL),
+                Path.of(values.getOrDefault("queries", defaultQueries.toString())),
+                Path.of(values.getOrDefault("output", defaultOutput.toString())),
+                parseInt(values, "warmups", 1),
+                parseInt(values, "runs", 5),
+                parseBoolean(values, "polypheny-table-names", true),
+                parseTablePrefix(values),
+                parseBoolean(values, "print-rows", false),
+                parseInt(values, "fetch-size", 1000),
+                parseInt(values, "query-timeout", 0),
+                values.getOrDefault("sql", ""),
+                sqlFile,
+                parseOnly(values.get("only")));
+    }
+
+
+    private static String parseTablePrefix(Map<String, String> values) {
+        String prefix = values.getOrDefault("polypheny-table-prefix", "tlc__");
+        if (!prefix.matches("[A-Za-z_][A-Za-z0-9_]*")) {
+            throw new IllegalArgumentException("Invalid Polypheny table prefix: " + prefix);
+        }
+        return prefix;
+    }
+
+
+    private static boolean isBooleanFlag(String key) {
+        return key.equalsIgnoreCase("polypheny-table-names")
+                || key.equalsIgnoreCase("print-rows");
+    }
+
+
+    private static int parseInt(Map<String, String> values, String key, int defaultValue) {
+        String value = values.get(key);
+        return value == null ? defaultValue : Integer.parseInt(value);
+    }
+
+
+    private static boolean parseBoolean(Map<String, String> values, String key, boolean defaultValue) {
+        String value = values.get(key);
+        return value == null ? defaultValue : Boolean.parseBoolean(value);
+    }
+
+
+    private static Set<String> parseOnly(String value) {
+        if (value == null || value.isBlank()) {
+            return Set.of();
+        }
+        return List.of(value.split(",")).stream()
+                .map(String::trim)
+                .filter(part -> !part.isEmpty())
+                .map(part -> part.toUpperCase(Locale.ROOT))
+                .collect(Collectors.toSet());
+    }
+}
diff --git a/plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyMqlBenchmark.java b/plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyMqlBenchmark.java
new file mode 100644
index 0000000000..2a90559eda
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyMqlBenchmark.java
@@ -0,0 +1,566 @@
+import java.io.BufferedWriter;
+import java.io.Closeable;
+import java.io.DataInputStream;
+import java.io.EOFException;
+import java.io.IOException;
+import java.io.OutputStream;
+import java.net.InetSocketAddress;
+import java.net.Socket;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.time.Instant;
+import java.util.ArrayList;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Locale;
+import java.util.Map;
+import java.util.Set;
+import java.util.stream.Collectors;
+import org.polypheny.jdbc.dependency.prism.CloseStatementRequest;
+import org.polypheny.jdbc.dependency.prism.ConnectionProperties;
+import org.polypheny.jdbc.dependency.prism.ConnectionRequest;
+import org.polypheny.jdbc.dependency.prism.DisconnectRequest;
+import org.polypheny.jdbc.dependency.prism.DocumentFrame;
+import org.polypheny.jdbc.dependency.prism.ExecuteUnparameterizedStatementRequest;
+import org.polypheny.jdbc.dependency.prism.FetchRequest;
+import org.polypheny.jdbc.dependency.prism.Frame;
+import org.polypheny.jdbc.dependency.prism.ProtoDocument;
+import org.polypheny.jdbc.dependency.prism.RelationalFrame;
+import org.polypheny.jdbc.dependency.prism.Request;
+import org.polypheny.jdbc.dependency.prism.Response;
+import org.polypheny.jdbc.dependency.prism.StatementResponse;
+import org.polypheny.jdbc.dependency.prism.StatementResult;
+
+public class PolyphenyMqlBenchmark {
+
+    private static final String PLAIN_TRANSPORT_VERSION = "plain-v1@polypheny.com";
+
+    private record Query(String id, String description, String mql) {
+    }
+
+    private record Config(
+            String host,
+            int port,
+            String username,
+            String password,
+            String namespace,
+            String language,
+            int apiMajor,
+            int apiMinor,
+            Path queries,
+            Path output,
+            int warmups,
+            int runs,
+            int fetchSize,
+            boolean printRows,
+            String mql,
+            Path mqlFile,
+            Set<String> only ) {
+    }
+
+    private record RunResult(long rows, int columns) {
+    }
+
+    private record FrameStats(long rows, int columns) {
+    }
+
+
+    public static void main(String[] args) throws Exception {
+        Config config = parseArgs(args);
+        String adHocMql = adHocMql(config);
+        List<Query> queries = adHocMql.isBlank()
+                ? loadQueries(config.queries())
+                : List.of(new Query("MQL", "Ad hoc MQL", prepareMql(adHocMql)));
+        if (!config.only().isEmpty()) {
+            queries = queries.stream()
+                    .filter(query -> config.only().contains(query.id().toUpperCase(Locale.ROOT)))
+                    .toList();
+        }
+        if (queries.isEmpty()) {
+            throw new IllegalArgumentException("No queries found in " + config.queries());
+        }
+
+        if (config.output().getParent() != null) {
+            Files.createDirectories(config.output().getParent());
+        }
+
+        try (PrismClient client = PrismClient.connect(config);
+                BufferedWriter writer = Files.newBufferedWriter(config.output(), StandardCharsets.UTF_8)) {
+            writer.write("timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error");
+            writer.newLine();
+            writer.flush();
+
+            System.out.printf("Connected to prism://%s:%d as %s%n", config.host(), config.port(), config.username());
+            System.out.printf("Using language=%s namespace=%s fetchSize=%d%n", config.language(), config.namespace(), config.fetchSize());
+            if (adHocMql.isBlank()) {
+                System.out.printf("Loaded %d queries from %s%n", queries.size(), config.queries());
+            } else {
+                System.out.printf("Loaded ad hoc MQL query%n");
+            }
+            System.out.printf("Writing CSV to %s%n", config.output());
+
+            for (Query query : queries) {
+                System.out.printf("%n%s %s%n", query.id(), query.description());
+                for (int i = 1; i <= config.warmups(); i++) {
+                    executeAndRecord(client, writer, query, "warmup", i, config);
+                }
+                for (int i = 1; i <= config.runs(); i++) {
+                    executeAndRecord(client, writer, query, "measured", i, config);
+                }
+            }
+        }
+    }
+
+
+    private static String adHocMql(Config config) throws IOException {
+        if (config.mqlFile() != null) {
+            return Files.readString(config.mqlFile(), StandardCharsets.UTF_8);
+        }
+        return config.mql();
+    }
+
+
+    private static void executeAndRecord(PrismClient client, BufferedWriter writer, Query query, String phase, int run, Config config) throws IOException {
+        long start = System.nanoTime();
+        long rows = 0;
+        int columns = 0;
+        boolean success = false;
+        String error = "";
+
+        try {
+            RunResult result = client.executeAndDrain(query.mql(), config);
+            rows = result.rows();
+            columns = result.columns();
+            success = true;
+        } catch (Exception e) {
+            error = e.getClass().getSimpleName() + ": " + e.getMessage();
+            System.out.printf("  %s %d failed: %s%n", phase, run, error);
+        }
+
+        long elapsedMs = Math.round((System.nanoTime() - start) / 1_000_000.0);
+        if (success) {
+            System.out.printf("  %s %d: %d ms, rows=%d%n", phase, run, elapsedMs, rows);
+        }
+        writeCsvRow(writer, query, phase, run, elapsedMs, rows, columns, success, error);
+    }
+
+
+    private static void writeCsvRow(BufferedWriter writer, Query query, String phase, int run, long elapsedMs, long rows, int columns, boolean success, String error) throws IOException {
+        writer.write(String.join(",",
+                csv(Instant.now().toString()),
+                csv(query.id()),
+                csv(query.description()),
+                csv(phase),
+                Integer.toString(run),
+                Long.toString(elapsedMs),
+                Long.toString(rows),
+                Integer.toString(columns),
+                Boolean.toString(success),
+                csv(error)));
+        writer.newLine();
+        writer.flush();
+    }
+
+
+    private static String csv(String value) {
+        String escaped = value.replace("\"", "\"\"");
+        return "\"" + escaped + "\"";
+    }
+
+
+    private static List<Query> loadQueries(Path path) throws IOException {
+        List<String> lines = Files.readAllLines(path, StandardCharsets.UTF_8);
+        List<Query> queries = new ArrayList<>();
+        String currentId = null;
+        String currentDescription = null;
+        StringBuilder mql = new StringBuilder();
+
+        for (String line : lines) {
+            String trimmed = line.trim();
+            if (trimmed.startsWith("-- Q") && trimmed.contains(":")) {
+                addQuery(queries, currentId, currentDescription, mql);
+                int colon = trimmed.indexOf(':');
+                currentId = trimmed.substring(3, colon).trim();
+                currentDescription = trimmed.substring(colon + 1).trim();
+                mql.setLength(0);
+                continue;
+            }
+            if (currentId == null || trimmed.startsWith("--")) {
+                continue;
+            }
+            mql.append(line).append('\n');
+            if (trimmed.endsWith(";")) {
+                addQuery(queries, currentId, currentDescription, mql);
+                currentId = null;
+                currentDescription = null;
+                mql.setLength(0);
+            }
+        }
+        addQuery(queries, currentId, currentDescription, mql);
+        return queries;
+    }
+
+
+    private static void addQuery(List<Query> queries, String id, String description, StringBuilder mql) {
+        if (id == null) {
+            return;
+        }
+        String statement = mql.toString().trim();
+        if (statement.isEmpty()) {
+            return;
+        }
+        queries.add(new Query(id, description == null ? "" : description, prepareMql(statement)));
+    }
+
+
+    private static String prepareMql(String mql) {
+        String statement = mql.trim();
+        if (statement.endsWith(";")) {
+            statement = statement.substring(0, statement.length() - 1);
+        }
+        return statement;
+    }
+
+
+    private static Config parseArgs(String[] args) {
+        Map<String, String> values = new LinkedHashMap<>();
+        for (int i = 0; i < args.length; i++) {
+            String arg = args[i];
+            if (!arg.startsWith("--")) {
+                throw new IllegalArgumentException("Unexpected argument: " + arg);
+            }
+            String key;
+            String value;
+            int equals = arg.indexOf('=');
+            if (equals >= 0) {
+                key = arg.substring(2, equals);
+                value = arg.substring(equals + 1);
+            } else {
+                key = arg.substring(2);
+                if (isBooleanFlag(key)) {
+                    value = "true";
+                } else {
+                    if (i + 1 >= args.length) {
+                        throw new IllegalArgumentException("Missing value for " + arg);
+                    }
+                    value = args[++i];
+                }
+            }
+            values.put(key.toLowerCase(Locale.ROOT), value);
+        }
+
+        Path defaultQueries = Path.of(
+                "plugins",
+                "parquet-adapter",
+                "benchmarks",
+                "query_lists",
+                "access_model_comparison",
+                "access_model_comparison_mql.mql");
+        Path defaultOutput = Path.of("plugins", "parquet-adapter", "benchmarks", "results", "access_model_comparison", "polypheny_mql_results.csv");
+        Path mqlFile = values.containsKey("mql-file") ? Path.of(values.get("mql-file")) : null;
+
+        return new Config(
+                values.getOrDefault("host", "localhost"),
+                parseInt(values, "port", 20590),
+                values.getOrDefault("username", "pa"),
+                values.getOrDefault("password", ""),
+                values.getOrDefault("namespace", "pd_document"),
+                values.getOrDefault("language", "mongo"),
+                parseInt(values, "api-major", 1),
+                parseInt(values, "api-minor", 9),
+                Path.of(values.getOrDefault("queries", defaultQueries.toString())),
+                Path.of(values.getOrDefault("output", defaultOutput.toString())),
+                parseInt(values, "warmups", 1),
+                parseInt(values, "runs", 5),
+                parseInt(values, "fetch-size", 1000),
+                parseBoolean(values, "print-rows", false),
+                values.getOrDefault("mql", ""),
+                mqlFile,
+                parseOnly(values.get("only")));
+    }
+
+
+    private static boolean isBooleanFlag(String key) {
+        return key.equalsIgnoreCase("print-rows");
+    }
+
+
+    private static int parseInt(Map<String, String> values, String key, int defaultValue) {
+        String value = values.get(key);
+        return value == null ? defaultValue : Integer.parseInt(value);
+    }
+
+
+    private static boolean parseBoolean(Map<String, String> values, String key, boolean defaultValue) {
+        String value = values.get(key);
+        return value == null ? defaultValue : Boolean.parseBoolean(value);
+    }
+
+
+    private static Set<String> parseOnly(String value) {
+        if (value == null || value.isBlank()) {
+            return Set.of();
+        }
+        return List.of(value.split(",")).stream()
+                .map(String::trim)
+                .filter(part -> !part.isEmpty())
+                .map(part -> part.toUpperCase(Locale.ROOT))
+                .collect(Collectors.toSet());
+    }
+
+
+    private static final class PrismClient implements Closeable {
+
+        private final Socket socket;
+        private final DataInputStream input;
+        private final OutputStream output;
+        private long nextRequestId = 1;
+
+
+        private PrismClient(Socket socket) throws IOException {
+            this.socket = socket;
+            this.input = new DataInputStream(socket.getInputStream());
+            this.output = socket.getOutputStream();
+        }
+
+
+        static PrismClient connect(Config config) throws IOException {
+            Socket socket = new Socket();
+            socket.setTcpNoDelay(true);
+            socket.connect(new InetSocketAddress(config.host(), config.port()));
+            PrismClient client = new PrismClient(socket);
+            client.exchangeVersion();
+            client.authenticate(config);
+            return client;
+        }
+
+
+        RunResult executeAndDrain(String mql, Config config) throws IOException {
+            int statementId = -1;
+            try {
+                long requestId = nextRequestId++;
+                ExecuteUnparameterizedStatementRequest executeRequest = ExecuteUnparameterizedStatementRequest.newBuilder()
+                        .setLanguageName(config.language())
+                        .setStatement(mql)
+                        .setFetchSize(config.fetchSize())
+                        .setNamespaceName(config.namespace())
+                        .build();
+                send(Request.newBuilder()
+                        .setId(requestId)
+                        .setExecuteUnparameterizedStatementRequest(executeRequest)
+                        .build());
+
+                StatementResult result = null;
+                while (true) {
+                    Response response = receive(requestId);
+                    if (!response.hasStatementResponse()) {
+                        throw new IOException("Expected statement_response, got " + response.getTypeCase());
+                    }
+                    StatementResponse statementResponse = response.getStatementResponse();
+                    statementId = statementResponse.getStatementId();
+                    if (statementResponse.hasResult()) {
+                        result = statementResponse.getResult();
+                    }
+                    if (response.getLast()) {
+                        break;
+                    }
+                }
+
+                if (result == null) {
+                    return new RunResult(0, 0);
+                }
+                if (!result.hasFrame()) {
+                    return new RunResult(result.getScalar(), 0);
+                }
+                return drainFrames(statementId, result.getFrame(), config);
+            } finally {
+                if (statementId > 0) {
+                    closeStatement(statementId);
+                }
+            }
+        }
+
+
+        private RunResult drainFrames(int statementId, Frame firstFrame, Config config) throws IOException {
+            long rows = 0;
+            int columns = 0;
+            Frame frame = firstFrame;
+            while (true) {
+                FrameStats stats = countFrame(frame, config.printRows());
+                rows += stats.rows();
+                columns = Math.max(columns, stats.columns());
+                if (frame.getIsLast()) {
+                    return new RunResult(rows, columns);
+                }
+                frame = fetchFrame(statementId, config.fetchSize());
+            }
+        }
+
+
+        private FrameStats countFrame(Frame frame, boolean printRows) {
+            if (frame.hasDocumentFrame()) {
+                DocumentFrame documentFrame = frame.getDocumentFrame();
+                int columns = 0;
+                for (ProtoDocument document : documentFrame.getDocumentsList()) {
+                    columns = Math.max(columns, document.getEntriesCount());
+                    if (printRows) {
+                        System.out.println(document);
+                    }
+                }
+                return new FrameStats(documentFrame.getDocumentsCount(), columns);
+            }
+            if (frame.hasRelationalFrame()) {
+                RelationalFrame relationalFrame = frame.getRelationalFrame();
+                if (printRows) {
+                    relationalFrame.getRowsList().forEach(System.out::println);
+                }
+                return new FrameStats(relationalFrame.getRowsCount(), relationalFrame.getColumnMetaCount());
+            }
+            return new FrameStats(0, 0);
+        }
+
+
+        private Frame fetchFrame(int statementId, int fetchSize) throws IOException {
+            long requestId = nextRequestId++;
+            FetchRequest fetchRequest = FetchRequest.newBuilder()
+                    .setStatementId(statementId)
+                    .setFetchSize(fetchSize)
+                    .build();
+            send(Request.newBuilder()
+                    .setId(requestId)
+                    .setFetchRequest(fetchRequest)
+                    .build());
+
+            Response response = receive(requestId);
+            if (!response.hasFrame()) {
+                throw new IOException("Expected frame, got " + response.getTypeCase());
+            }
+            return response.getFrame();
+        }
+
+
+        private void closeStatement(int statementId) throws IOException {
+            long requestId = nextRequestId++;
+            CloseStatementRequest closeRequest = CloseStatementRequest.newBuilder()
+                    .setStatementId(statementId)
+                    .build();
+            send(Request.newBuilder()
+                    .setId(requestId)
+                    .setCloseStatementRequest(closeRequest)
+                    .build());
+            receive(requestId);
+        }
+
+
+        private void authenticate(Config config) throws IOException {
+            long requestId = nextRequestId++;
+            ConnectionProperties properties = ConnectionProperties.newBuilder()
+                    .setIsAutoCommit(true)
+                    .setNamespaceName(config.namespace())
+                    .build();
+            ConnectionRequest connectionRequest = ConnectionRequest.newBuilder()
+                    .setMajorApiVersion(config.apiMajor())
+                    .setMinorApiVersion(config.apiMinor())
+                    .setUsername(config.username())
+                    .setPassword(config.password())
+                    .setConnectionProperties(properties)
+                    .build();
+            send(Request.newBuilder()
+                    .setId(requestId)
+                    .setConnectionRequest(connectionRequest)
+                    .build());
+
+            Response response = receive(requestId);
+            if (!response.hasConnectionResponse()) {
+                throw new IOException("Expected connection_response, got " + response.getTypeCase());
+            }
+            if (!response.getConnectionResponse().getIsCompatible()) {
+                throw new IOException("Incompatible Prism API. Server reports "
+                        + response.getConnectionResponse().getMajorApiVersion()
+                        + "."
+                        + response.getConnectionResponse().getMinorApiVersion());
+            }
+        }
+
+
+        private void exchangeVersion() throws IOException {
+            byte[] version = PLAIN_TRANSPORT_VERSION.getBytes(StandardCharsets.US_ASCII);
+            int length = version.length + 1;
+            if (length > 255) {
+                throw new IOException("Plain transport version string is too long");
+            }
+            output.write(length);
+            output.write(version);
+            output.write('\n');
+            output.flush();
+
+            byte[] response = readExact(1 + length);
+            if ((response[0] & 0xff) != length) {
+                throw new IOException("Invalid Prism plain transport version response length");
+            }
+            String responseVersion = new String(response, 1, length - 1, StandardCharsets.US_ASCII);
+            if (!PLAIN_TRANSPORT_VERSION.equals(responseVersion) || response[response.length - 1] != '\n') {
+                throw new IOException("Invalid Prism plain transport version response");
+            }
+        }
+
+
+        private void send(Request request) throws IOException {
+            byte[] message = request.toByteArray();
+            ByteBuffer length = ByteBuffer.allocate(8).order(ByteOrder.LITTLE_ENDIAN);
+            length.putLong(message.length);
+            output.write(length.array());
+            output.write(message);
+            output.flush();
+        }
+
+
+        private Response receive(long requestId) throws IOException {
+            byte[] lengthBytes = readExact(8);
+            long length = ByteBuffer.wrap(lengthBytes).order(ByteOrder.LITTLE_ENDIAN).getLong();
+            if (length <= 0 || length > Integer.MAX_VALUE) {
+                throw new IOException("Invalid Prism message length: " + length);
+            }
+            Response response = Response.parseFrom(readExact((int) length));
+            if (response.getId() != requestId) {
+                throw new IOException("Expected response id " + requestId + ", got " + response.getId());
+            }
+            if (response.hasErrorResponse()) {
+                throw new IOException(response.getErrorResponse().getMessage());
+            }
+            return response;
+        }
+
+
+        private byte[] readExact(int length) throws IOException {
+            byte[] bytes = new byte[length];
+            try {
+                input.readFully(bytes);
+            } catch (EOFException e) {
+                throw new EOFException("Prism connection closed while reading " + length + " bytes");
+            }
+            return bytes;
+        }
+
+
+        @Override
+        public void close() throws IOException {
+            if (!socket.isClosed()) {
+                try {
+                    long requestId = nextRequestId++;
+                    send(Request.newBuilder()
+                            .setId(requestId)
+                            .setDisconnectRequest(DisconnectRequest.newBuilder().build())
+                            .build());
+                    receive(requestId);
+                } catch (IOException ignored) {
+                    // Closing the socket below is enough if the server already ended the session.
+                }
+            }
+            socket.close();
+        }
+    }
+}
diff --git a/plugins/parquet-adapter/benchmarks/scripts/implementation/SparkSqlBenchmark.py b/plugins/parquet-adapter/benchmarks/scripts/implementation/SparkSqlBenchmark.py
new file mode 100644
index 0000000000..c18d4f79a2
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/implementation/SparkSqlBenchmark.py
@@ -0,0 +1,286 @@
+#!/usr/bin/env python3
+
+import argparse
+import csv
+import re
+import sys
+import time
+from datetime import datetime, timezone
+from pathlib import Path
+
+from pyspark.sql import SparkSession
+
+
+TABLES = (
+    "yellow_tripdata",
+    "green_tripdata",
+    "fhv_tripdata",
+    "fhvhv_tripdata",
+)
+
+
+class Query:
+
+    def __init__(self, query_id, description, sql):
+        self.query_id = query_id
+        self.description = description
+        self.sql = sql
+
+
+def main():
+    args = parse_args()
+    ad_hoc_sql = read_ad_hoc_sql(args)
+    queries = (
+        [Query("SQL", "Ad hoc SQL", ad_hoc_sql)]
+        if ad_hoc_sql.strip()
+        else load_queries(Path(args.queries))
+    )
+
+    if args.only:
+        allowed = {part.strip().upper() for part in args.only.split(",") if part.strip()}
+        queries = [query for query in queries if query.query_id.upper() in allowed]
+
+    if not queries:
+        raise ValueError(f"No queries found in {args.queries}")
+
+    output = Path(args.output)
+    if output.parent:
+        output.parent.mkdir(parents=True, exist_ok=True)
+
+    spark = create_spark(args)
+    try:
+        setup_views(spark, args.data_dir)
+        run_queries(spark, queries, args, output)
+    finally:
+        spark.stop()
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Run the Parquet adapter benchmark query set with Apache Spark SQL."
+    )
+    parser.add_argument("--data-dir", default="/data")
+    parser.add_argument("--nested-customer-file", default="")
+    parser.add_argument(
+        "--queries",
+        default="/repo/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_sql.sql",
+    )
+    parser.add_argument(
+        "--output",
+        default="/repo/plugins/parquet-adapter/benchmarks/results/access_model_comparison/spark_results.csv",
+    )
+    parser.add_argument("--warmups", type=int, default=1)
+    parser.add_argument("--runs", type=int, default=5)
+    parser.add_argument("--only", default="")
+    parser.add_argument("--sql", default="")
+    parser.add_argument("--sql-file", default="")
+    parser.add_argument("--app-name", default="ParquetAdapterSparkBenchmark")
+    parser.add_argument("--shuffle-partitions", type=int, default=8)
+    parser.add_argument("--console-prefix", default="")
+    parser.add_argument(
+        "--drain-mode",
+        choices=("executor", "driver"),
+        default="executor",
+        help=(
+            "executor drains result rows in Spark tasks and returns row counts only; "
+            "driver streams all rows back through PySpark's local iterator."
+        ),
+    )
+    parser.add_argument("--print-rows", action="store_true")
+    return parser.parse_args()
+
+
+def create_spark(args):
+    return (
+        SparkSession.builder.appName(args.app_name)
+        .config("spark.sql.shuffle.partitions", str(args.shuffle_partitions))
+        .config("spark.sql.session.timeZone", "UTC")
+        .config("spark.sql.caseSensitive", "false")
+        .config("spark.sql.sources.partitionColumnTypeInference.enabled", "false")
+        .config("spark.parquetBenchmark.consolePrefix", args.console_prefix)
+        .config("spark.parquetBenchmark.nestedCustomerFile", args.nested_customer_file)
+        .getOrCreate()
+    )
+
+
+def setup_views(spark, data_dir):
+    data_dir = data_dir.rstrip("/\\")
+    spark.sparkContext.setLogLevel("ERROR")
+    log(spark, f"Data directory {data_dir}")
+    nested_customer_file = spark.conf.get("spark.parquetBenchmark.nestedCustomerFile", "")
+    if nested_customer_file:
+        if not path_exists(nested_customer_file):
+            raise FileNotFoundError(f"Missing nested customer Parquet file: {nested_customer_file}")
+        dataframe = spark.read.parquet(nested_customer_file)
+        dataframe.createOrReplaceTempView("nested_customer")
+        log(spark, f"Created Spark temp view nested_customer from {nested_customer_file}")
+        return
+    for table in TABLES:
+        table_path = join_path(data_dir, table)
+        if not path_exists(table_path):
+            raise FileNotFoundError(f"Missing Spark benchmark table path: {table_path}")
+        dataframe = spark.read.option("basePath", table_path).parquet(table_path)
+        dataframe.createOrReplaceTempView(table)
+        log(spark, f"Created Spark temp view {table} from {table_path}")
+
+
+def run_queries(spark, queries, args, output):
+    with output.open("w", encoding="utf-8", newline="") as handle:
+        writer = csv.writer(handle)
+        writer.writerow(
+            [
+                "timestamp",
+                "query_id",
+                "description",
+                "phase",
+                "run",
+                "elapsed_ms",
+                "rows",
+                "columns",
+                "success",
+                "error",
+            ]
+        )
+        handle.flush()
+
+        log(spark, f"Loaded {len(queries)} queries")
+        log(spark, f"Writing CSV to {output}")
+
+        for query in queries:
+            log(spark)
+            log(spark, f"{query.query_id} {query.description}")
+            for run in range(1, args.warmups + 1):
+                execute_and_record(spark, writer, handle, query, "warmup", run, args)
+            for run in range(1, args.runs + 1):
+                execute_and_record(spark, writer, handle, query, "measured", run, args)
+
+
+def execute_and_record(spark, writer, handle, query, phase, run, args):
+    start = time.perf_counter()
+    rows = 0
+    columns = 0
+    success = False
+    error = ""
+
+    try:
+        result = spark.sql(prepare_sql_for_spark(query.sql))
+        columns = len(result.columns)
+        if args.drain_mode == "driver" or args.print_rows:
+            for row in result.toLocalIterator():
+                rows += 1
+                if args.print_rows:
+                    log(spark, "\t".join(format_cell(value) for value in row))
+        else:
+            rows = sum(result.rdd.mapPartitions(count_partition_rows).collect())
+        success = True
+    except Exception as exc:
+        error = f"{exc.__class__.__name__}: {exc}".replace("\r", " ").replace("\n", " ")
+        log(spark, f"  {phase} {run} failed: {error}")
+
+    elapsed_ms = round((time.perf_counter() - start) * 1000)
+    if success:
+        log(spark, f"  {phase} {run}: {elapsed_ms} ms, rows={rows}")
+
+    writer.writerow(
+        [
+            timestamp_utc(),
+            query.query_id,
+            query.description,
+            phase,
+            run,
+            elapsed_ms,
+            rows,
+            columns,
+            str(success).lower(),
+            error,
+        ]
+    )
+    handle.flush()
+
+
+def load_queries(path):
+    lines = path.read_text(encoding="utf-8").splitlines()
+    queries = []
+    current_id = None
+    current_description = None
+    sql_lines = []
+
+    for line in lines:
+        trimmed = line.strip()
+        if trimmed.startswith("-- Q") and ":" in trimmed:
+            add_query(queries, current_id, current_description, sql_lines)
+            colon = trimmed.index(":")
+            current_id = trimmed[3:colon].strip().upper()
+            current_description = trimmed[colon + 1 :].strip()
+            sql_lines = []
+            continue
+        if trimmed.startswith("--") or not trimmed:
+            continue
+        sql_lines.append(line)
+        if trimmed.endswith(";"):
+            add_query(queries, current_id, current_description, sql_lines)
+            current_id = None
+            current_description = None
+            sql_lines = []
+
+    add_query(queries, current_id, current_description, sql_lines)
+    return queries
+
+
+def add_query(queries, query_id, description, sql_lines):
+    statement = "\n".join(sql_lines).strip()
+    if not statement:
+        return
+    if statement.endswith(";"):
+        statement = statement[:-1]
+    queries.append(Query(query_id or "SQL", description or "", statement))
+
+
+def read_ad_hoc_sql(args):
+    if args.sql_file:
+        return Path(args.sql_file).read_text(encoding="utf-8")
+    return args.sql
+
+
+def prepare_sql_for_spark(sql):
+    return re.sub(r'"([A-Za-z_][A-Za-z0-9_]*)"', r"`\1`", sql).rstrip(";")
+
+
+def count_partition_rows(iterator):
+    count = 0
+    for _ in iterator:
+        count += 1
+    return [count]
+
+
+def log(spark, message=""):
+    prefix = spark.conf.get("spark.parquetBenchmark.consolePrefix", "")
+    print(f"{prefix}{message}", flush=True)
+
+
+def join_path(parent, child):
+    if parent.startswith("/") or parent.startswith("file:"):
+        return f"{parent.rstrip('/')}/{child}"
+    return str(Path(parent) / child)
+
+
+def path_exists(path):
+    if path.startswith("file:"):
+        return Path(path[5:]).exists()
+    return Path(path).exists()
+
+
+def timestamp_utc():
+    return datetime.now(timezone.utc).isoformat().replace("+00:00", "Z")
+
+
+def format_cell(value):
+    return "NULL" if value is None else str(value)
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except Exception as exc:
+        print(f"Spark benchmark failed: {exc}", file=sys.stderr)
+        raise
diff --git a/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py b/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py
new file mode 100644
index 0000000000..10e09f3b37
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py
@@ -0,0 +1,215 @@
+#!/usr/bin/env python3
+
+import argparse
+import csv
+import html
+import math
+import statistics
+from dataclasses import dataclass
+from pathlib import Path
+
+
+COLORS = (
+    "#2f6fbb",
+    "#c94c4c",
+    "#4f9d55",
+    "#f0a22e",
+    "#7b61b9",
+    "#4aa3a1",
+)
+
+
+@dataclass
+class Series:
+    label: str
+    color: str
+    values: dict[str, float]
+
+
+def main():
+    args = parse_args()
+    inputs = [parse_input(value) for value in args.csv]
+    series = [
+        Series(label, COLORS[index % len(COLORS)], read_means(path, args.phase))
+        for index, (label, path) in enumerate(inputs)
+    ]
+    query_ids = sorted({query_id for item in series for query_id in item.values}, key=natural_key)
+    if not query_ids:
+        raise ValueError("No successful benchmark rows found.")
+
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    output = output_dir / f"{args.name}.svg"
+    output.write_text(render_svg(series, query_ids, args.title), encoding="utf-8")
+    print(f"Wrote {output}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Generate an SVG runtime plot from benchmark result CSV files."
+    )
+    parser.add_argument(
+        "csv",
+        nargs="+",
+        help="CSV input. Use LABEL=path.csv to control the legend label.",
+    )
+    parser.add_argument("--title", required=True, help="Plot title.")
+    parser.add_argument("--name", required=True, help="Output file name without extension.")
+    parser.add_argument(
+        "--output-dir",
+        default="plugins/parquet-adapter/benchmarks/results/plots",
+        help="Directory where the SVG plot is written.",
+    )
+    parser.add_argument(
+        "--phase",
+        default="measured",
+        help="Benchmark phase to plot. Default: measured.",
+    )
+    return parser.parse_args()
+
+
+def parse_input(value):
+    if "=" in value:
+        label, path = value.split("=", 1)
+        return label.strip(), Path(path.strip())
+    path = Path(value)
+    return path.stem, path
+
+
+def read_means(path, phase):
+    elapsed_by_query = {}
+    with path.open(newline="", encoding="utf-8-sig") as handle:
+        for row in csv.DictReader(handle):
+            if row.get("phase") != phase:
+                continue
+            if row.get("success", "").strip().lower() != "true":
+                continue
+            query_id = row.get("query_id", "").strip()
+            elapsed_ms = row.get("elapsed_ms", "").strip()
+            if not query_id or not elapsed_ms:
+                continue
+            elapsed_by_query.setdefault(query_id, []).append(float(elapsed_ms) / 1000.0)
+    return {
+        query_id: statistics.mean(values)
+        for query_id, values in elapsed_by_query.items()
+        if values
+    }
+
+
+def render_svg(series, query_ids, title):
+    width = 1180
+    height = 700
+    left = 90
+    right = 260
+    top = 80
+    bottom = 120
+    plot_width = width - left - right
+    plot_height = height - top - bottom
+    lower, upper = plot_bounds(series)
+    ticks = nice_log_ticks(lower, upper)
+
+    lines = [
+        '<?xml version="1.0" encoding="UTF-8"?>',
+        f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}">',
+        '<rect width="100%" height="100%" fill="white"/>',
+        f'<text x="{width / 2}" y="34" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="24" font-weight="700">{escape(title)}</text>',
+        f'<text x="{left}" y="62" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Mean execution time (s, log scale)</text>',
+    ]
+
+    for tick in ticks:
+        y = y_for(tick, lower, upper, top, plot_height)
+        lines.append(f'<line x1="{left}" y1="{y:.2f}" x2="{left + plot_width}" y2="{y:.2f}" stroke="#e2e2e2" stroke-width="1"/>')
+        lines.append(f'<text x="{left - 12}" y="{y + 4:.2f}" text-anchor="end" font-family="Helvetica, Arial, sans-serif" font-size="11" fill="#333">{format_tick(tick)}</text>')
+
+    lines.append(f'<line x1="{left}" y1="{top}" x2="{left}" y2="{top + plot_height}" stroke="#333" stroke-width="1.2"/>')
+    lines.append(f'<line x1="{left}" y1="{top + plot_height}" x2="{left + plot_width}" y2="{top + plot_height}" stroke="#333" stroke-width="1.2"/>')
+
+    group_width = plot_width / len(query_ids)
+    bar_gap = 3
+    bar_width = min(24, (group_width - 20) / len(series) - bar_gap)
+
+    for query_index, query_id in enumerate(query_ids):
+        group_x = left + query_index * group_width
+        total_bar_width = len(series) * bar_width + (len(series) - 1) * bar_gap
+        start_x = group_x + (group_width - total_bar_width) / 2
+        for series_index, item in enumerate(series):
+            value = item.values.get(query_id)
+            if value is None:
+                continue
+            x = start_x + series_index * (bar_width + bar_gap)
+            y = y_for(value, lower, upper, top, plot_height)
+            h = top + plot_height - y
+            lines.append(
+                f'<rect x="{x:.2f}" y="{y:.2f}" width="{bar_width:.2f}" height="{h:.2f}" fill="{item.color}">'
+                f'<title>{escape(item.label)} {escape(query_id)}: {value:.3f} s</title></rect>'
+            )
+        center = group_x + group_width / 2
+        lines.append(f'<text x="{center:.2f}" y="{top + plot_height + 30}" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">{escape(query_id)}</text>')
+
+    legend_x = left + plot_width + 35
+    legend_y = top + 15
+    lines.append(f'<text x="{legend_x}" y="{legend_y - 18}" font-family="Helvetica, Arial, sans-serif" font-size="13" font-weight="700">System</text>')
+    for index, item in enumerate(series):
+        y = legend_y + index * 28
+        lines.append(f'<rect x="{legend_x}" y="{y - 12}" width="16" height="16" fill="{item.color}"/>')
+        lines.append(f'<text x="{legend_x + 24}" y="{y + 1}" font-family="Helvetica, Arial, sans-serif" font-size="12">{escape(item.label)}</text>')
+
+    lines.append("</svg>")
+    return "\n".join(lines)
+
+
+def plot_bounds(series):
+    values = [value for item in series for value in item.values.values() if value > 0]
+    if not values:
+        raise ValueError("Log-scale plot requires positive elapsed values.")
+    lower = 10 ** math.floor(math.log10(min(values)))
+    upper = 10 ** math.ceil(math.log10(max(values)))
+    return lower, upper
+
+
+def nice_log_ticks(lower, upper):
+    ticks = []
+    lower_exp = math.floor(math.log10(lower))
+    upper_exp = math.ceil(math.log10(upper))
+    for exp in range(lower_exp, upper_exp + 1):
+        for multiplier in (1, 2, 5):
+            value = multiplier * (10 ** exp)
+            if lower <= value <= upper:
+                ticks.append(value)
+    return ticks
+
+
+def y_for(value, lower, upper, top, height):
+    lower_log = math.log10(lower)
+    upper_log = math.log10(upper)
+    value_log = math.log10(value)
+    ratio = (value_log - lower_log) / (upper_log - lower_log)
+    return top + height - ratio * height
+
+
+def format_tick(value):
+    if value >= 1:
+        return f"{value:g}"
+    return f"{value:.3f}".rstrip("0").rstrip(".")
+
+
+def natural_key(value):
+    parts = []
+    current = ""
+    for char in value:
+        if char.isdigit() == (current[-1:].isdigit() if current else char.isdigit()):
+            current += char
+        else:
+            parts.append(current)
+            current = char
+    if current:
+        parts.append(current)
+    return [int(part) if part.isdigit() else part for part in parts]
+
+
+def escape(value):
+    return html.escape(str(value), quote=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_duckdb_nested_benchmark.ps1 b/plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_duckdb_nested_benchmark.ps1
new file mode 100644
index 0000000000..7e98078af9
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_duckdb_nested_benchmark.ps1
@@ -0,0 +1,61 @@
+param(
+    [string]$DataFile = "C:\PolyData\nested_customer\nestedcustomer.parquet",
+    [string]$Url = "jdbc:duckdb:",
+    [string]$Queries = (Join-Path $PSScriptRoot "..\..\..\query_lists\nested_data\nested_data_duckdb.sql"),
+    [string]$Output = (Join-Path $PSScriptRoot "duckdb_nested_results.csv"),
+    [int]$Warmups = 1,
+    [int]$Runs = 5,
+    [int]$FetchSize = 1000,
+    [int]$QueryTimeout = 0,
+    [int]$Threads = 8,
+    [string]$MemoryLimit = "16GB",
+    [string]$Only = "",
+    [string]$Sql = "",
+    [string]$SqlFile = "",
+    [string]$DuckDbVersion = "1.5.2.1",
+    [switch]$PrintRows
+)
+
+$ErrorActionPreference = "Stop"
+
+if (-not (Test-Path $DataFile)) {
+    throw "Nested customer Parquet file does not exist: $DataFile"
+}
+
+$root = Resolve-Path "$PSScriptRoot\..\..\..\..\..\.."
+$runner = Join-Path $PSScriptRoot "..\run_duckdb_benchmark.ps1"
+$dataFileFull = [System.IO.Path]::GetFullPath((Resolve-Path $DataFile).Path)
+$dataDir = Split-Path $dataFileFull -Parent
+
+$runnerParams = @{
+    Url = $Url
+    DataDir = $dataDir
+    NestedCustomerFile = $dataFileFull
+    Queries = $Queries
+    Output = $Output
+    Warmups = $Warmups
+    Runs = $Runs
+    FetchSize = $FetchSize
+    QueryTimeout = $QueryTimeout
+    Threads = $Threads
+    MemoryLimit = $MemoryLimit
+    DuckDbVersion = $DuckDbVersion
+}
+
+if ($Only) {
+    $runnerParams.Only = $Only
+}
+
+if ($Sql) {
+    $runnerParams.Sql = $Sql
+}
+
+if ($SqlFile) {
+    $runnerParams.SqlFile = $SqlFile
+}
+
+if ($PrintRows) {
+    $runnerParams.PrintRows = $true
+}
+
+& $runner @runnerParams
diff --git a/plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_polypheny_mql_nested_benchmark.ps1 b/plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_polypheny_mql_nested_benchmark.ps1
new file mode 100644
index 0000000000..35ce669c86
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_polypheny_mql_nested_benchmark.ps1
@@ -0,0 +1,57 @@
+param(
+    [string]$Server = "localhost",
+    [int]$Port = 20590,
+    [string]$Username = "pa",
+    [string]$Password = "",
+    [string]$Namespace = "ncpd_document",
+    [string]$Language = "mongo",
+    [string]$Queries = (Join-Path $PSScriptRoot "..\..\..\query_lists\nested_data\nested_data_mql.mql"),
+    [string]$Output = (Join-Path $PSScriptRoot "polypheny_mql_nested_results.csv"),
+    [int]$Warmups = 1,
+    [int]$Runs = 5,
+    [int]$FetchSize = 1000,
+    [int]$ApiMajor = 1,
+    [int]$ApiMinor = 9,
+    [string]$Only = "",
+    [string]$Mql = "",
+    [string]$MqlFile = "",
+    [switch]$PrintRows
+)
+
+$ErrorActionPreference = "Stop"
+
+$runner = Join-Path $PSScriptRoot "..\run_polypheny_mql_benchmark.ps1"
+
+$runnerParams = @{
+    Server = $Server
+    Port = $Port
+    Username = $Username
+    Password = $Password
+    Namespace = $Namespace
+    Language = $Language
+    Queries = $Queries
+    Output = $Output
+    Warmups = $Warmups
+    Runs = $Runs
+    FetchSize = $FetchSize
+    ApiMajor = $ApiMajor
+    ApiMinor = $ApiMinor
+}
+
+if ($Only) {
+    $runnerParams.Only = $Only
+}
+
+if ($Mql) {
+    $runnerParams.Mql = $Mql
+}
+
+if ($MqlFile) {
+    $runnerParams.MqlFile = $MqlFile
+}
+
+if ($PrintRows) {
+    $runnerParams.PrintRows = $true
+}
+
+& $runner @runnerParams
diff --git a/plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_polypheny_nested_benchmark.ps1 b/plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_polypheny_nested_benchmark.ps1
new file mode 100644
index 0000000000..9109b60c15
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_polypheny_nested_benchmark.ps1
@@ -0,0 +1,60 @@
+param(
+    [string]$Url = "jdbc:polypheny://pa:@localhost:20590/public",
+    [string]$Queries = (Join-Path $PSScriptRoot "..\..\..\query_lists\nested_data\nested_data_polypheny_normalized.sql"),
+    [string]$Output = (Join-Path $PSScriptRoot "polypheny_nested_results.csv"),
+    [string]$AdapterName = "ncp",
+    [int]$Warmups = 1,
+    [int]$Runs = 5,
+    [int]$FetchSize = 1000,
+    [int]$QueryTimeout = 0,
+    [string]$Only = "",
+    [string]$Sql = "",
+    [string]$SqlFile = "",
+    [switch]$PrintRows
+)
+
+$ErrorActionPreference = "Stop"
+
+$root = Resolve-Path "$PSScriptRoot\..\..\..\..\..\.."
+$runner = Join-Path $PSScriptRoot "..\run_polypheny_benchmark.ps1"
+$effectiveQueries = $Queries
+$temporaryQueries = $null
+
+if ($AdapterName -ne "ncp" -and -not $Sql -and -not $SqlFile) {
+    $tempDir = Join-Path $root "build\nested-customer-benchmark"
+    New-Item -ItemType Directory -Force -Path $tempDir | Out-Null
+    $temporaryQueries = Join-Path $tempDir "nested_data_polypheny_normalized_$AdapterName.sql"
+    $content = Get-Content -Raw -Path $Queries
+    $content = $content -replace "\bncp__", ($AdapterName + "__")
+    Set-Content -Path $temporaryQueries -Value $content -Encoding UTF8
+    $effectiveQueries = $temporaryQueries
+}
+
+$runnerParams = @{
+    Url = $Url
+    Queries = $effectiveQueries
+    Output = $Output
+    Warmups = $Warmups
+    Runs = $Runs
+    FetchSize = $FetchSize
+    QueryTimeout = $QueryTimeout
+    NoTableNameMapping = $true
+}
+
+if ($Only) {
+    $runnerParams.Only = $Only
+}
+
+if ($Sql) {
+    $runnerParams.Sql = $Sql
+}
+
+if ($SqlFile) {
+    $runnerParams.SqlFile = $SqlFile
+}
+
+if ($PrintRows) {
+    $runnerParams.PrintRows = $true
+}
+
+& $runner @runnerParams
diff --git a/plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_spark_nested_benchmark.ps1 b/plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_spark_nested_benchmark.ps1
new file mode 100644
index 0000000000..b43ec94c45
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/runners/nested/run_spark_nested_benchmark.ps1
@@ -0,0 +1,86 @@
+param(
+    [string]$DataFile = "C:\PolyData\nested_customer\nestedcustomer.parquet",
+    [string]$Queries = (Join-Path $PSScriptRoot "..\..\..\query_lists\nested_data\nested_data_spark.sql"),
+    [string]$Output = (Join-Path $PSScriptRoot "spark_nested_results.csv"),
+    [int]$Warmups = 1,
+    [int]$Runs = 5,
+    [int]$Threads = 8,
+    [int]$ShufflePartitions = 8,
+    [string]$DriverMemory = "16g",
+    [ValidateSet("executor", "driver")]
+    [string]$DrainMode = "executor",
+    [string]$Only = "",
+    [string]$Sql = "",
+    [string]$SqlFile = "",
+    [string]$Image = "apache/spark:3.5.3-java17-python3",
+    [string[]]$SparkConf = @(),
+    [switch]$PullImage,
+    [switch]$DryRun,
+    [switch]$VerboseSparkLogs,
+    [switch]$PrintRows
+)
+
+$ErrorActionPreference = "Stop"
+
+if (-not (Test-Path $DataFile)) {
+    throw "Nested customer Parquet file does not exist: $DataFile"
+}
+
+$root = Resolve-Path "$PSScriptRoot\..\..\..\..\..\.."
+$runner = Join-Path $PSScriptRoot "..\run_spark_benchmark.ps1"
+$dataFileFull = [System.IO.Path]::GetFullPath((Resolve-Path $DataFile).Path)
+$dataDir = Split-Path $dataFileFull -Parent
+$dataFileName = Split-Path $dataFileFull -Leaf
+
+$runnerParams = @{
+    DataDir = $dataDir
+    NestedCustomerFile = $dataFileName
+    Queries = $Queries
+    Output = $Output
+    Warmups = $Warmups
+    Runs = $Runs
+    Threads = $Threads
+    ShufflePartitions = $ShufflePartitions
+    DriverMemory = $DriverMemory
+    DrainMode = $DrainMode
+    Image = $Image
+}
+
+foreach ($conf in $SparkConf) {
+    if (-not [string]::IsNullOrWhiteSpace($conf)) {
+        if (-not $runnerParams.ContainsKey("SparkConf")) {
+            $runnerParams.SparkConf = @()
+        }
+        $runnerParams.SparkConf += $conf
+    }
+}
+
+if ($Only) {
+    $runnerParams.Only = $Only
+}
+
+if ($Sql) {
+    $runnerParams.Sql = $Sql
+}
+
+if ($SqlFile) {
+    $runnerParams.SqlFile = $SqlFile
+}
+
+if ($PullImage) {
+    $runnerParams.PullImage = $true
+}
+
+if ($DryRun) {
+    $runnerParams.DryRun = $true
+}
+
+if ($VerboseSparkLogs) {
+    $runnerParams.VerboseSparkLogs = $true
+}
+
+if ($PrintRows) {
+    $runnerParams.PrintRows = $true
+}
+
+& $runner @runnerParams
diff --git a/plugins/parquet-adapter/benchmarks/scripts/runners/run_duckdb_benchmark.ps1 b/plugins/parquet-adapter/benchmarks/scripts/runners/run_duckdb_benchmark.ps1
new file mode 100644
index 0000000000..8ff4b2f948
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/runners/run_duckdb_benchmark.ps1
@@ -0,0 +1,80 @@
+param(
+    [string]$Url = "jdbc:duckdb:",
+    [string]$DataDir = "C:\PolyData\tlc_partitioned",
+    [string]$Queries = (Join-Path $PSScriptRoot "..\..\query_lists\access_model_comparison\access_model_comparison_sql.sql"),
+    [string]$Output = (Join-Path $PSScriptRoot "..\..\results\access_model_comparison\duckdb_results.csv"),
+    [int]$Warmups = 1,
+    [int]$Runs = 5,
+    [int]$FetchSize = 1000,
+    [int]$QueryTimeout = 0,
+    [int]$Threads = 8,
+    [string]$MemoryLimit = "16GB",
+    [string]$NestedCustomerFile = "",
+    [string]$Only = "",
+    [string]$Sql = "",
+    [string]$SqlFile = "",
+    [string]$DuckDbVersion = "1.5.2.1",
+    [switch]$PrintRows
+)
+
+$ErrorActionPreference = "Stop"
+
+$root = Resolve-Path "$PSScriptRoot\..\..\..\..\.."
+$source = Join-Path $root "plugins\parquet-adapter\benchmarks\scripts\implementation\DuckDbJdbcBenchmark.java"
+$classDir = Join-Path $root "build\duckdb-benchmark-client"
+New-Item -ItemType Directory -Force -Path $classDir | Out-Null
+
+if ($DuckDbVersion -eq "latest") {
+    $metadata = [xml](Invoke-WebRequest -Uri "https://repo1.maven.org/maven2/org/duckdb/duckdb_jdbc/maven-metadata.xml" -UseBasicParsing).Content
+    $DuckDbVersion = $metadata.metadata.versioning.release
+}
+
+$dependencyRoot = Join-Path $root "build\benchmark-deps\duckdb_jdbc\$DuckDbVersion"
+$driver = Join-Path $dependencyRoot "duckdb_jdbc-$DuckDbVersion.jar"
+
+if (-not (Test-Path $driver)) {
+    New-Item -ItemType Directory -Force -Path $dependencyRoot | Out-Null
+    $downloadUrl = "https://repo1.maven.org/maven2/org/duckdb/duckdb_jdbc/$DuckDbVersion/duckdb_jdbc-$DuckDbVersion.jar"
+    Write-Host "Downloading DuckDB JDBC $DuckDbVersion from $downloadUrl"
+    Invoke-WebRequest -Uri $downloadUrl -OutFile $driver
+}
+
+javac -cp $driver -d $classDir $source
+if ($LASTEXITCODE -ne 0) {
+    exit $LASTEXITCODE
+}
+
+$argsList = @(
+    "--url", $Url,
+    "--data-dir", $DataDir,
+    "--queries", $Queries,
+    "--output", $Output,
+    "--warmups", "$Warmups",
+    "--runs", "$Runs",
+    "--fetch-size", "$FetchSize",
+    "--query-timeout", "$QueryTimeout",
+    "--threads", "$Threads",
+    "--memory-limit", $MemoryLimit
+)
+
+if ($NestedCustomerFile) {
+    $argsList += @("--nested-customer-file", $NestedCustomerFile)
+}
+
+if ($Only) {
+    $argsList += @("--only", $Only)
+}
+
+if ($Sql) {
+    $argsList += @("--sql", $Sql)
+}
+
+if ($SqlFile) {
+    $argsList += @("--sql-file", $SqlFile)
+}
+
+if ($PrintRows) {
+    $argsList += "--print-rows"
+}
+
+java -cp "$classDir;$driver" DuckDbJdbcBenchmark @argsList
diff --git a/plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_benchmark.ps1 b/plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_benchmark.ps1
new file mode 100644
index 0000000000..113adf9904
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_benchmark.ps1
@@ -0,0 +1,78 @@
+param(
+    [string]$Url = "jdbc:polypheny://pa:@localhost:20590/public",
+    [string]$Queries = (Join-Path $PSScriptRoot "..\..\query_lists\access_model_comparison\access_model_comparison_rf.sql"),
+    [string]$Output = (Join-Path $PSScriptRoot "..\..\results\access_model_comparison\polypheny_results.csv"),
+    [int]$Warmups = 1,
+    [int]$Runs = 5,
+    [int]$FetchSize = 1000,
+    [int]$QueryTimeout = 0,
+    [string]$TablePrefix = "tlc__",
+    [string]$Only = "",
+    [string]$Sql = "",
+    [string]$SqlFile = "",
+    [switch]$PrintRows,
+    [switch]$NoTableNameMapping
+)
+
+$ErrorActionPreference = "Stop"
+
+$root = Resolve-Path "$PSScriptRoot\..\..\..\..\.."
+$source = Join-Path $root "plugins\parquet-adapter\benchmarks\scripts\implementation\PolyphenyJdbcBenchmark.java"
+$classDir = Join-Path $root "build\parquet-benchmark-client"
+New-Item -ItemType Directory -Force -Path $classDir | Out-Null
+
+$workspaceDriver = Join-Path $root "build\benchmark-deps\polypheny_jdbc_driver\2.2\polypheny-jdbc-driver-2.2.jar"
+$driver = $null
+
+if (Test-Path $workspaceDriver) {
+    $driver = Get-Item $workspaceDriver
+} else {
+    $gradleDriver = Get-ChildItem -Recurse -File "$env:USERPROFILE\.gradle\caches\modules-2\files-2.1\org.polypheny\polypheny-jdbc-driver\2.2" -ErrorAction SilentlyContinue |
+        Where-Object { $_.Name -eq "polypheny-jdbc-driver-2.2.jar" } |
+        Select-Object -First 1
+
+    if ($gradleDriver) {
+        New-Item -ItemType Directory -Force -Path (Split-Path $workspaceDriver) | Out-Null
+        Copy-Item -LiteralPath $gradleDriver.FullName -Destination $workspaceDriver -Force
+        $driver = Get-Item $workspaceDriver
+    }
+}
+
+if (-not $driver) {
+    throw "Could not find polypheny-jdbc-driver-2.2.jar in the Gradle cache or build\benchmark-deps."
+}
+
+javac -cp $driver.FullName -d $classDir $source
+if ($LASTEXITCODE -ne 0) {
+    exit $LASTEXITCODE
+}
+
+$argsList = @(
+    "--url", $Url,
+    "--queries", $Queries,
+    "--output", $Output,
+    "--warmups", "$Warmups",
+    "--runs", "$Runs",
+    "--fetch-size", "$FetchSize",
+    "--query-timeout", "$QueryTimeout",
+    "--polypheny-table-prefix", $TablePrefix,
+    "--polypheny-table-names=$(-not $NoTableNameMapping)"
+)
+
+if ($Only) {
+    $argsList += @("--only", $Only)
+}
+
+if ($Sql) {
+    $argsList += @("--sql", $Sql)
+}
+
+if ($SqlFile) {
+    $argsList += @("--sql-file", $SqlFile)
+}
+
+if ($PrintRows) {
+    $argsList += "--print-rows"
+}
+
+java -cp "$classDir;$($driver.FullName)" PolyphenyJdbcBenchmark @argsList
diff --git a/plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_mql_benchmark.ps1 b/plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_mql_benchmark.ps1
new file mode 100644
index 0000000000..7b5d303a54
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_mql_benchmark.ps1
@@ -0,0 +1,86 @@
+param(
+    [string]$Server = "localhost",
+    [int]$Port = 20590,
+    [string]$Username = "pa",
+    [string]$Password = "",
+    [string]$Namespace = "pd_document",
+    [string]$Language = "mongo",
+    [string]$Queries = (Join-Path $PSScriptRoot "..\..\query_lists\access_model_comparison\access_model_comparison_mql.mql"),
+    [string]$Output = (Join-Path $PSScriptRoot "..\..\results\access_model_comparison\polypheny_mql_results.csv"),
+    [int]$Warmups = 1,
+    [int]$Runs = 5,
+    [int]$FetchSize = 1000,
+    [int]$ApiMajor = 1,
+    [int]$ApiMinor = 9,
+    [string]$Only = "",
+    [string]$Mql = "",
+    [string]$MqlFile = "",
+    [switch]$PrintRows
+)
+
+$ErrorActionPreference = "Stop"
+
+$root = Resolve-Path "$PSScriptRoot\..\..\..\..\.."
+$source = Join-Path $root "plugins\parquet-adapter\benchmarks\scripts\implementation\PolyphenyMqlBenchmark.java"
+$classDir = Join-Path $root "build\parquet-mql-benchmark-client"
+New-Item -ItemType Directory -Force -Path $classDir | Out-Null
+
+$workspaceDriver = Join-Path $root "build\benchmark-deps\polypheny_jdbc_driver\2.2\polypheny-jdbc-driver-2.2.jar"
+$driver = $null
+
+if (Test-Path $workspaceDriver) {
+    $driver = Get-Item $workspaceDriver
+} else {
+    $gradleDriver = Get-ChildItem -Recurse -File "$env:USERPROFILE\.gradle\caches\modules-2\files-2.1\org.polypheny\polypheny-jdbc-driver\2.2" -ErrorAction SilentlyContinue |
+        Where-Object { $_.Name -eq "polypheny-jdbc-driver-2.2.jar" } |
+        Select-Object -First 1
+
+    if ($gradleDriver) {
+        New-Item -ItemType Directory -Force -Path (Split-Path $workspaceDriver) | Out-Null
+        Copy-Item -LiteralPath $gradleDriver.FullName -Destination $workspaceDriver -Force
+        $driver = Get-Item $workspaceDriver
+    }
+}
+
+if (-not $driver) {
+    throw "Could not find polypheny-jdbc-driver-2.2.jar in the Gradle cache or build\benchmark-deps."
+}
+
+javac -cp $driver.FullName -d $classDir $source
+if ($LASTEXITCODE -ne 0) {
+    exit $LASTEXITCODE
+}
+
+$argsList = @(
+    "--host", $Server,
+    "--port", "$Port",
+    "--username", $Username,
+    "--password=$Password",
+    "--namespace", $Namespace,
+    "--language", $Language,
+    "--api-major", "$ApiMajor",
+    "--api-minor", "$ApiMinor",
+    "--queries", $Queries,
+    "--output", $Output,
+    "--warmups", "$Warmups",
+    "--runs", "$Runs",
+    "--fetch-size", "$FetchSize"
+)
+
+if ($Only) {
+    $argsList += @("--only", $Only)
+}
+
+if ($Mql) {
+    $argsList += @("--mql", $Mql)
+}
+
+if ($MqlFile) {
+    $argsList += @("--mql-file", $MqlFile)
+}
+
+if ($PrintRows) {
+    $argsList += "--print-rows"
+}
+
+java -cp "$classDir;$($driver.FullName)" PolyphenyMqlBenchmark @argsList
diff --git a/plugins/parquet-adapter/benchmarks/scripts/runners/run_spark_benchmark.ps1 b/plugins/parquet-adapter/benchmarks/scripts/runners/run_spark_benchmark.ps1
new file mode 100644
index 0000000000..f5d843ae38
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/runners/run_spark_benchmark.ps1
@@ -0,0 +1,209 @@
+param(
+    [string]$DataDir = "C:\PolyData\tlc_partitioned",
+    [string]$NestedCustomerFile = "",
+    [string]$Queries = (Join-Path $PSScriptRoot "..\..\query_lists\access_model_comparison\access_model_comparison_sql.sql"),
+    [string]$Output = (Join-Path $PSScriptRoot "..\..\results\access_model_comparison\spark_results.csv"),
+    [int]$Warmups = 1,
+    [int]$Runs = 5,
+    [int]$Threads = 8,
+    [int]$ShufflePartitions = 8,
+    [string]$DriverMemory = "16g",
+    [ValidateSet("executor", "driver")]
+    [string]$DrainMode = "executor",
+    [string]$Only = "",
+    [string]$Sql = "",
+    [string]$SqlFile = "",
+    [string]$Image = "apache/spark:3.5.3-java17-python3",
+    [string[]]$SparkConf = @(),
+    [switch]$PullImage,
+    [switch]$DryRun,
+    [switch]$VerboseSparkLogs,
+    [switch]$PrintRows
+)
+
+$ErrorActionPreference = "Stop"
+
+$root = Resolve-Path "$PSScriptRoot\..\..\..\..\.."
+$rootFull = [System.IO.Path]::GetFullPath($root.Path).TrimEnd('\', '/')
+$source = Join-Path $rootFull "plugins\parquet-adapter\benchmarks\scripts\implementation\SparkSqlBenchmark.py"
+
+if (-not (Test-Path $source)) {
+    throw "Could not find SparkSqlBenchmark.py at $source"
+}
+
+function Resolve-UnderRepo {
+    param(
+        [string]$PathValue,
+        [switch]$MustExist
+    )
+
+    if ([string]::IsNullOrWhiteSpace($PathValue)) {
+        return $null
+    }
+
+    $candidate = if ([System.IO.Path]::IsPathRooted($PathValue)) {
+        $PathValue
+    } else {
+        Join-Path $rootFull $PathValue
+    }
+    $full = [System.IO.Path]::GetFullPath($candidate)
+
+    if ($MustExist -and -not (Test-Path $full)) {
+        throw "Path does not exist: $full"
+    }
+    if (-not $full.StartsWith($rootFull, [System.StringComparison]::OrdinalIgnoreCase)) {
+        throw "Path must be inside the repository because only the repo is mounted into the Spark container: $full"
+    }
+    return $full
+}
+
+function Convert-ToContainerRepoPath {
+    param([string]$HostPath)
+
+    $relative = $HostPath.Substring($rootFull.Length).TrimStart('\', '/')
+    if ([string]::IsNullOrWhiteSpace($relative)) {
+        return "/repo"
+    }
+    return "/repo/" + ($relative -replace '\\', '/')
+}
+
+$queriesHost = Resolve-UnderRepo $Queries -MustExist
+$outputHost = Resolve-UnderRepo $Output
+$outputParent = Split-Path $outputHost -Parent
+if ($outputParent) {
+    New-Item -ItemType Directory -Force -Path $outputParent | Out-Null
+}
+
+$sqlFileContainer = ""
+if ($SqlFile) {
+    $sqlFileHost = Resolve-UnderRepo $SqlFile -MustExist
+    $sqlFileContainer = Convert-ToContainerRepoPath $sqlFileHost
+}
+
+if (-not (Test-Path $DataDir)) {
+    throw "Data directory does not exist: $DataDir"
+}
+$dataDirFull = [System.IO.Path]::GetFullPath((Resolve-Path $DataDir).Path).TrimEnd('\', '/')
+
+$nestedCustomerContainer = ""
+if ($NestedCustomerFile) {
+    $nestedCustomerHost = if ([System.IO.Path]::IsPathRooted($NestedCustomerFile)) {
+        [System.IO.Path]::GetFullPath($NestedCustomerFile)
+    } else {
+        [System.IO.Path]::GetFullPath((Join-Path $dataDirFull $NestedCustomerFile))
+    }
+    if (-not (Test-Path $nestedCustomerHost)) {
+        throw "Nested customer Parquet file does not exist: $nestedCustomerHost"
+    }
+    if (-not $nestedCustomerHost.StartsWith($dataDirFull, [System.StringComparison]::OrdinalIgnoreCase)) {
+        throw "Nested customer file must be inside DataDir because only DataDir is mounted into the Spark container: $nestedCustomerHost"
+    }
+    $nestedRelative = $nestedCustomerHost.Substring($dataDirFull.Length).TrimStart('\', '/')
+    $nestedCustomerContainer = "/data/" + ($nestedRelative -replace '\\', '/')
+}
+
+if ($PullImage) {
+    docker pull $Image
+    if ($LASTEXITCODE -ne 0) {
+        exit $LASTEXITCODE
+    }
+}
+
+$sparkSubmitArgs = @(
+    "--master", "local[$Threads]",
+    "--driver-memory", $DriverMemory,
+    "--conf", "spark.sql.shuffle.partitions=$ShufflePartitions",
+    "--conf", "spark.sql.session.timeZone=UTC",
+    "--conf", "spark.sql.sources.partitionColumnTypeInference.enabled=false"
+)
+
+foreach ($conf in $SparkConf) {
+    if (-not [string]::IsNullOrWhiteSpace($conf)) {
+        $sparkSubmitArgs += @("--conf", $conf)
+    }
+}
+
+$sourceContainer = Convert-ToContainerRepoPath $source
+$queriesContainer = Convert-ToContainerRepoPath $queriesHost
+$outputContainer = Convert-ToContainerRepoPath $outputHost
+$consolePrefix = "__SPARK_BENCHMARK__ "
+
+$benchmarkArgs = @(
+    $sourceContainer,
+    "--data-dir", "/data",
+    "--queries", $queriesContainer,
+    "--output", $outputContainer,
+    "--warmups", "$Warmups",
+    "--runs", "$Runs",
+    "--shuffle-partitions", "$ShufflePartitions",
+    "--drain-mode", $DrainMode,
+    "--console-prefix", $consolePrefix
+)
+
+if ($nestedCustomerContainer) {
+    $benchmarkArgs += @("--nested-customer-file", $nestedCustomerContainer)
+}
+
+if ($Only) {
+    $benchmarkArgs += @("--only", $Only)
+}
+
+if ($Sql) {
+    $benchmarkArgs += @("--sql", $Sql)
+}
+
+if ($sqlFileContainer) {
+    $benchmarkArgs += @("--sql-file", $sqlFileContainer)
+}
+
+if ($PrintRows) {
+    $benchmarkArgs += "--print-rows"
+}
+
+$dockerArgs = @(
+    "run",
+    "--rm",
+    "-v", "${rootFull}:/repo",
+    "-v", "${dataDirFull}:/data:ro",
+    "-w", "/repo",
+    "-e", "SPARK_DRIVER_BIND_ADDRESS=127.0.0.1",
+    $Image,
+    "driver"
+) + $sparkSubmitArgs + $benchmarkArgs
+
+if ($DryRun) {
+    $printable = $dockerArgs | ForEach-Object {
+        if ($_ -match '\s') {
+            '"' + ($_ -replace '"', '\"') + '"'
+        } else {
+            $_
+        }
+    }
+    Write-Host ("docker " + ($printable -join " "))
+    exit 0
+}
+
+if ($VerboseSparkLogs) {
+    docker @dockerArgs
+    if ($LASTEXITCODE -ne 0) {
+        exit $LASTEXITCODE
+    }
+} else {
+    $previousErrorActionPreference = $ErrorActionPreference
+    $ErrorActionPreference = "Continue"
+    try {
+        docker @dockerArgs 2>&1 | ForEach-Object {
+            $line = $_.ToString()
+            if ($line.StartsWith($consolePrefix, [System.StringComparison]::Ordinal)) {
+                Write-Host $line.Substring($consolePrefix.Length)
+            }
+        }
+        $exitCode = $LASTEXITCODE
+    } finally {
+        $ErrorActionPreference = $previousErrorActionPreference
+    }
+    if ($exitCode -ne 0) {
+        Write-Error "Spark benchmark failed with exit code $exitCode. Re-run with -VerboseSparkLogs to see the full Spark/Docker log."
+        exit $exitCode
+    }
+}
diff --git a/plugins/parquet-adapter/benchmarks/scripts/summarize_benchmark_results.py b/plugins/parquet-adapter/benchmarks/scripts/summarize_benchmark_results.py
new file mode 100644
index 0000000000..a58997965e
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/summarize_benchmark_results.py
@@ -0,0 +1,298 @@
+#!/usr/bin/env python3
+
+import argparse
+import csv
+import re
+import statistics
+from dataclasses import dataclass
+from pathlib import Path
+
+
+@dataclass
+class Summary:
+    system: str
+    query_id: str
+    description: str
+    total_runs: int
+    successful_runs: int
+    elapsed_values: list[int]
+    row_values: list[int]
+    column_values: list[int]
+    errors: list[str]
+
+
+def main():
+    args = parse_args()
+    inputs = [parse_input(value) for value in args.csv]
+    summaries = []
+
+    for system, path in inputs:
+        summaries.extend(read_summaries(system, path, args.phase))
+
+    output = Path(args.output)
+    output.parent.mkdir(parents=True, exist_ok=True)
+    output.write_text(render_markdown(summaries, inputs, args), encoding="utf-8")
+    print(f"Wrote {output}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Create a markdown comparison table from benchmark result CSV files."
+    )
+    parser.add_argument(
+        "csv",
+        nargs="+",
+        help="CSV input. Use LABEL=path.csv to control the system column name; otherwise the label is inferred.",
+    )
+    parser.add_argument(
+        "-o",
+        "--output",
+        required=True,
+        help="Output markdown file.",
+    )
+    parser.add_argument(
+        "--phase",
+        default="measured",
+        help="Benchmark phase to summarize. Default: measured.",
+    )
+    parser.add_argument(
+        "--unit",
+        choices=("ms", "s"),
+        default="ms",
+        help="Time unit used in comparison tables. Default: ms.",
+    )
+    parser.add_argument(
+        "--title",
+        default="Benchmark Result Summary",
+        help="Markdown title.",
+    )
+    return parser.parse_args()
+
+
+def parse_input(value):
+    if "=" in value:
+        label, path = value.split("=", 1)
+        return label.strip(), Path(path.strip())
+    path = Path(value)
+    return infer_label(path), path
+
+
+def infer_label(path):
+    name = path.stem
+    name = re.sub(r"^access_model_comparison_", "", name)
+    name = re.sub(r"_results$", "", name)
+    name = re.sub(r"_tlcp$", "", name)
+    name = name.replace("_", " ")
+    return name.title()
+
+
+def read_summaries(system, path, phase):
+    groups = {}
+    with path.open(newline="", encoding="utf-8-sig") as handle:
+        for row in csv.DictReader(handle):
+            if row.get("phase") != phase:
+                continue
+            query_id = row.get("query_id", "")
+            groups.setdefault(query_id, []).append(row)
+
+    summaries = []
+    for query_id, rows in groups.items():
+        successful = [row for row in rows if is_success(row.get("success", ""))]
+        summaries.append(
+            Summary(
+                system=system,
+                query_id=query_id,
+                description=first_non_empty(row.get("description", "") for row in rows),
+                total_runs=len(rows),
+                successful_runs=len(successful),
+                elapsed_values=[parse_int(row.get("elapsed_ms", "")) for row in successful if is_int(row.get("elapsed_ms", ""))],
+                row_values=sorted({parse_int(row.get("rows", "")) for row in successful if is_int(row.get("rows", ""))}),
+                column_values=sorted({parse_int(row.get("columns", "")) for row in successful if is_int(row.get("columns", ""))}),
+                errors=[row.get("error", "") for row in rows if row.get("error", "")],
+            )
+        )
+    return summaries
+
+
+def render_markdown(summaries, inputs, args):
+    systems = [label for label, _ in inputs]
+    by_system_query = {(summary.system, summary.query_id): summary for summary in summaries}
+    query_ids = sorted({summary.query_id for summary in summaries}, key=natural_key)
+    description_by_query = description_map(summaries)
+    unit_label = "ms" if args.unit == "ms" else "s"
+
+    lines = [
+        f"# {args.title}",
+        "",
+        f"Phase summarized: `{args.phase}`.",
+        "Warmup rows are excluded. Mean and median values use successful runs only.",
+        "",
+        "## Source Files",
+        "",
+        "| System | CSV |",
+        "| --- | --- |",
+    ]
+    for system, path in inputs:
+        lines.append(f"| {escape_md(system)} | `{escape_md(str(path))}` |")
+
+    lines.extend(
+        [
+            "",
+            f"## Mean Elapsed Time ({unit_label})",
+            "",
+            "| Query | Description | " + " | ".join(escape_md(system) for system in systems) + " | Row counts |",
+            "| --- | --- | " + " | ".join("---" for _ in systems) + " | --- |",
+        ]
+    )
+    for query_id in query_ids:
+        row = [
+            escape_md(query_id),
+            escape_md(description_by_query.get(query_id, "")),
+        ]
+        for system in systems:
+            row.append(format_mean(by_system_query.get((system, query_id)), args.unit))
+        row.append(format_row_consistency([by_system_query.get((system, query_id)) for system in systems]))
+        lines.append("| " + " | ".join(row) + " |")
+
+    lines.extend(
+        [
+            "",
+            "## Result Row Counts",
+            "",
+            "| Query | Description | " + " | ".join(escape_md(system) for system in systems) + " |",
+            "| --- | --- | " + " | ".join("---" for _ in systems) + " |",
+        ]
+    )
+    for query_id in query_ids:
+        row = [
+            escape_md(query_id),
+            escape_md(description_by_query.get(query_id, "")),
+        ]
+        for system in systems:
+            row.append(format_values(by_system_query.get((system, query_id)), "rows"))
+        lines.append("| " + " | ".join(row) + " |")
+
+    lines.extend(
+        [
+            "",
+            f"## Detailed Summary ({unit_label})",
+            "",
+            "| System | Query | Description | Runs | Mean | Median | Min | Max | Rows | Columns | Status |",
+            "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |",
+        ]
+    )
+    for system in systems:
+        for query_id in query_ids:
+            summary = by_system_query.get((system, query_id))
+            if summary is None:
+                continue
+            lines.append(
+                "| "
+                + " | ".join(
+                    [
+                        escape_md(system),
+                        escape_md(query_id),
+                        escape_md(summary.description),
+                        f"{summary.successful_runs}/{summary.total_runs}",
+                        format_stat(summary, statistics.mean, args.unit),
+                        format_stat(summary, statistics.median, args.unit),
+                        format_stat(summary, min, args.unit),
+                        format_stat(summary, max, args.unit),
+                        format_values(summary, "rows"),
+                        format_values(summary, "columns"),
+                        escape_md(status(summary)),
+                    ]
+                )
+                + " |"
+            )
+
+    lines.append("")
+    return "\n".join(lines)
+
+
+def description_map(summaries):
+    result = {}
+    for summary in sorted(summaries, key=lambda item: natural_key(item.query_id)):
+        result.setdefault(summary.query_id, summary.description)
+    return result
+
+
+def format_mean(summary, unit):
+    return format_stat(summary, statistics.mean, unit)
+
+
+def format_stat(summary, fn, unit):
+    if summary is None or not summary.elapsed_values:
+        return ""
+    value = fn(summary.elapsed_values)
+    if unit == "s":
+        return f"{value / 1000.0:,.3f}"
+    return f"{value:,.1f}"
+
+
+def format_row_consistency(summaries):
+    values = []
+    for summary in summaries:
+        if summary is None:
+            continue
+        if len(summary.row_values) == 1:
+            values.append(summary.row_values[0])
+        else:
+            return "differs"
+    return format_int(values[0]) if values and all(value == values[0] for value in values) else "differs"
+
+
+def format_values(summary, attr):
+    if summary is None:
+        return ""
+    values = summary.row_values if attr == "rows" else summary.column_values
+    if not values:
+        return ""
+    return ", ".join(format_int(value) for value in values)
+
+
+def status(summary):
+    if summary.successful_runs == summary.total_runs and not summary.errors:
+        return "ok"
+    if summary.errors:
+        return f"{summary.successful_runs}/{summary.total_runs} ok; " + "; ".join(summary.errors)
+    return f"{summary.successful_runs}/{summary.total_runs} ok"
+
+
+def first_non_empty(values):
+    for value in values:
+        if value:
+            return value
+    return ""
+
+
+def is_success(value):
+    return str(value).strip().lower() == "true"
+
+
+def is_int(value):
+    try:
+        int(value)
+        return True
+    except (TypeError, ValueError):
+        return False
+
+
+def parse_int(value):
+    return int(value)
+
+
+def format_int(value):
+    return f"{value:,}"
+
+
+def natural_key(value):
+    return [int(part) if part.isdigit() else part for part in re.split(r"(\d+)", value)]
+
+
+def escape_md(value):
+    return str(value).replace("|", "\\|").replace("\n", " ")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/plugins/parquet-adapter/docs/diagrams/parquet_workflow_load_class_diagram.drawio b/plugins/parquet-adapter/docs/diagrams/parquet_workflow_load_class_diagram.drawio
deleted file mode 100644
index 5217ead954..0000000000
--- a/plugins/parquet-adapter/docs/diagrams/parquet_workflow_load_class_diagram.drawio
+++ /dev/null
@@ -1,109 +0,0 @@
-<mxfile host="Electron" agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/29.6.1 Chrome/142.0.7444.265 Electron/39.8.0 Safari/537.36" version="29.6.1">
-  <diagram id="parquet-workflow-load" name="Parquet Workflow Load">
-    <mxGraphModel dx="1558" dy="912" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="1600" pageHeight="1000" math="0" shadow="0">
-      <root>
-        <mxCell id="0" />
-        <mxCell id="1" parent="0" />
-        <mxCell id="2" parent="1" style="rounded=0;whiteSpace=wrap;html=1;fillColor=#dae8fc;strokeColor=#6c8ebf;fontSize=14;" value="&lt;b&gt;ParquetLoadActivity&lt;/b&gt;&lt;br&gt;workflow node&lt;br&gt;validates input&lt;br&gt;dispatches export" vertex="1">
-          <mxGeometry height="100" width="220" x="70" y="629" as="geometry" />
-        </mxCell>
-        <mxCell id="3" parent="1" style="rounded=0;whiteSpace=wrap;html=1;fillColor=#d5e8d4;strokeColor=#82b366;fontSize=14;" value="&lt;b&gt;ParquetWorkflowLoadSupport&lt;/b&gt;&lt;br&gt;workflow helper&lt;br&gt;prepares target file,&amp;nbsp;infers schema,&amp;nbsp;&lt;div&gt;calls writing functionality&lt;/div&gt;" vertex="1">
-          <mxGeometry height="100" width="260" x="360" y="629" as="geometry" />
-        </mxCell>
-        <mxCell id="4" parent="1" style="rounded=0;whiteSpace=wrap;html=1;fillColor=#fff2cc;strokeColor=#d6b656;fontSize=14;" value="&lt;b&gt;SchemaState&lt;/b&gt;&lt;br&gt;accumulates inferred&lt;br&gt;field definitions" vertex="1">
-          <mxGeometry height="90" width="190" x="737" y="435" as="geometry" />
-        </mxCell>
-        <mxCell id="6Bm5j8vTThmp4rjQpBF9-22" edge="1" parent="1" source="5" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" target="6">
-          <mxGeometry relative="1" as="geometry" />
-        </mxCell>
-        <mxCell id="6Bm5j8vTThmp4rjQpBF9-23" connectable="0" parent="6Bm5j8vTThmp4rjQpBF9-22" style="edgeLabel;html=1;align=center;verticalAlign=middle;resizable=0;points=[];" value="contains" vertex="1">
-          <mxGeometry relative="1" x="-0.2013" y="1" as="geometry">
-            <mxPoint y="-1" as="offset" />
-          </mxGeometry>
-        </mxCell>
-        <mxCell id="5" parent="1" style="rounded=0;whiteSpace=wrap;html=1;fillColor=#fff2cc;strokeColor=#d6b656;fontSize=14;" value="&lt;b&gt;FieldSchema&lt;/b&gt;&lt;br&gt;single field info" vertex="1">
-          <mxGeometry height="90" width="190" x="1039" y="434" as="geometry" />
-        </mxCell>
-        <mxCell id="6" parent="1" style="rounded=0;whiteSpace=wrap;html=1;fillColor=#fff2cc;strokeColor=#d6b656;fontSize=14;" value="&lt;b&gt;ValueSchema&lt;/b&gt;&lt;br&gt;single value info:&lt;div&gt;primitive / group / repeated&lt;br&gt;&lt;/div&gt;" vertex="1">
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diff --git a/plugins/parquet-adapter/docs/unit_tests/test_coverage.md b/plugins/parquet-adapter/docs/unit_tests/test_coverage.md
new file mode 100644
index 0000000000..dbcb9fa085
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+++ b/plugins/parquet-adapter/docs/unit_tests/test_coverage.md
@@ -0,0 +1,212 @@
+# Test Coverage
+
+This document tracks the existing Parquet-related tests. The main inventory is grouped by feature area.
+
+## Discovery And Schema Normalization
+
+| Test                                                                   | File                            | What It Checks                                                                                           |
+|------------------------------------------------------------------------|---------------------------------|----------------------------------------------------------------------------------------------------------|
+| `groupsSchemaEvolvedFilesIntoOneTable`                                 | `ParquetFileDiscoveryTest.java` | Groups compatible schema-evolved Parquet files into one discovered table and keeps the union of columns. |
+| `keepsUnrelatedFilesAsSeparateTables`                                  | `ParquetFileDiscoveryTest.java` | Keeps Parquet files with unrelated schemas as separate discovered tables.                                |
+| `discoversPartitionFoldersAsOneTable`                                  | `ParquetFileDiscoveryTest.java` | Discovers Hive-style partition folders as one table and exposes folder values as columns.                |
+| `discoversPartitionedTableFoldersUnderRoot`                            | `ParquetFileDiscoveryTest.java` | Discovers multiple table folders below a dataset root, including a partitioned table folder.             |
+| `hidesParquetColumnWhenItMatchesPartitionValue`                        | `ParquetFileDiscoveryTest.java` | Hides a physical Parquet column when it duplicates a matching folder partition value.                    |
+| `hidesPhysicalColumnWhenNormalizedPartitionNameCollidesAndValuesMatch`  | `ParquetFileDiscoveryTest.java` | Hides a physical column when a normalized partition folder key collides with it and values match.        |
+| `failsWhenParquetColumnConflictsWithPartitionValue`                    | `ParquetFileDiscoveryTest.java` | Rejects a file when a physical Parquet column conflicts with the folder partition value.                 |
+| `normalizedSchemaUsesDiscoveredMultiFileTable`                         | `ParquetFileDiscoveryTest.java` | Normalizes a multi-file partitioned table and adds the synthetic row-id column.                          |
+| `flatDiscoveryFlattensNonRepeatedNestedColumnsAndKeepsRepeatedColumns` | `ParquetFileDiscoveryTest.java` | Flattens non-repeated nested fields in flat mode and keeps repeated fields as document columns.          |
+| `flatDiscoveryKeepsOrdersDbPrimaryKeys`                                | `ParquetFileDiscoveryTest.java` | Verifies primary-key detection for the bundled `orders_db` flat tables.                                  |
+
+## Filter Translation And Containers
+
+| Test                                                | File                                  | What It Checks                                                                                          |
+|-----------------------------------------------------|---------------------------------------|---------------------------------------------------------------------------------------------------------|
+| `removesOnlyAdapterFilterPaths`                     | `FiltersContainerTest.java`           | Removes path elements only from adapter filters while keeping native filter paths intact.               |
+| `keepsReaderParentFilterForAdapterValidation`       | `JoinFiltersSplitterTest.java`        | Keeps a parent-side filter available both for native Parquet reading and parent-row adapter validation. |
+| `keepsCrossSideOrAsJoinedRowFilter`                 | `JoinFiltersSplitterTest.java`        | Keeps an `OR` spanning parent and child sides as a joined-row adapter filter.                           |
+| `translatesAndOrTree`                               | `ParquetRelFilterTranslatorTest.java` | Translates nested `AND`/`OR` Rex filters into Parquet adapter filter trees.                             |
+| `translatesInAsOrOfEquals`                          | `ParquetRelFilterTranslatorTest.java` | Rewrites `IN` filters as an `OR` of equality filters.                                                   |
+| `translatesNullChecks`                              | `ParquetRelFilterTranslatorTest.java` | Translates `IS NULL` and `IS NOT NULL` predicates.                                                      |
+| `evaluatesNullChecks`                               | `ParquetRelFilterTranslatorTest.java` | Evaluates Parquet null-check filter semantics against null and non-null values.                         |
+| `reversesComparisonWhenLiteralIsOnTheLeft`          | `ParquetRelFilterTranslatorTest.java` | Reverses comparison direction when the literal or dynamic parameter appears on the left side.           |
+| `ignoresUnsupportedInt96TimestampPredicatePushdown` | `ParquetRelFilterTranslatorTest.java` | Avoids failing native predicate creation for unsupported INT96 timestamp pushdown.                      |
+| `buildsNativeNullCheckPredicates`                   | `ParquetRelFilterTranslatorTest.java` | Builds native Parquet predicates for supported null checks.                                             |
+| `docFilterTranslatorTranslatesTopLevelNameAndRejectsNestedPaths` | `ParquetSharedDocumentAdapterTest.java` | Translates top-level document field filters and avoids unsupported nested-path pushdown.                |
+
+## File-Level Filter Pruning
+
+| Test                                                         | File                            | What It Checks                                                                                                  |
+|--------------------------------------------------------------|---------------------------------|-----------------------------------------------------------------------------------------------------------------|
+| `partitionEvaluatorMatchesValuesAndTreatsMissingPartitionsAsNull` | `ParquetFilterPruningTest.java` | Evaluates partition-value filters, mismatches, missing partition values, and non-partition fallback.            |
+| `statisticsEvaluatorPrunesByRangeAndProvesConstantFiles`     | `ParquetFilterPruningTest.java` | Uses footer min/max/null statistics to reject files, prove constant-file matches, and keep unknown cases.       |
+| `reducerDropsExactMatchesRejectsExactFailuresAndKeepsResiduals` | `ParquetFilterPruningTest.java` | Removes exact file-level matches, rejects impossible files, and preserves residual filters for `OR` and `NOT`. |
+| `filterResolverMapsProjectionIndexesAndResolvesDynamicParameters` | `ParquetFilterPruningTest.java` | Rewrites projected/physical filter indexes and resolves dynamic parameters with source path bindings.          |
+| `primitiveValueFilterEvaluatorHandlesScalarComparisonsAndUnknownIndexes` | `ParquetExecutionAndWriterTest.java` | Evaluates primitive row filter comparisons for numeric, boolean, binary, null, default, and unknown-index cases. |
+
+## Primitive Reading And Projection
+
+| Test                                                                       | File                                       | What It Checks                                                                                             |
+|----------------------------------------------------------------------------|--------------------------------------------|------------------------------------------------------------------------------------------------------------|
+| `readsPrimitiveProjectionWithoutGroupMaterialization`                      | `ParquetPrimitiveRowReaderTest.java`       | Reads a flat primitive projection directly, including nullable values, without full group materialization. |
+| `rejectsRepeatedProjection`                                                | `ParquetPrimitiveRowReaderTest.java`       | Rejects repeated fields for the primitive-row fast path.                                                   |
+| `nestedListTableProjectsParquetRootFieldsFromBindings`                     | `ParquetNestedRepeatedProjectionTest.java` | Projects repeated nested list rows using table bindings and generated row/parent/ordinal fields.           |
+| `flatTableProjectsNonRepeatedNestedFieldsAndRepeatedDocumentsFromBindings` | `ParquetNestedRepeatedProjectionTest.java` | Projects flattened nested scalar fields and repeated document fields from binding paths.                   |
+
+## Document Adapter Behavior
+
+| Test                                                    | File                              | What It Checks                                                                                  |
+|---------------------------------------------------------|-----------------------------------|-------------------------------------------------------------------------------------------------|
+| `extractDocumentNormalizesNestedValuesAndGeneratesMissingId` | `ParquetSharedDocumentAdapterTest.java` | Converts Parquet rows into documents, normalizes field names, preserves nested values, and adds `_id`. |
+| `extractDocumentPreservesSourceIdAndGeneratesIdForAllNullRows` | `ParquetSharedDocumentAdapterTest.java` | Preserves source `_id` values and generates `_id` for rows where all optional fields are absent. |
+| `extractDocumentConvertsRepeatedNestedGroupsToDocumentLists` | `ParquetSharedDocumentAdapterTest.java` | Converts repeated nested Parquet groups into `PolyList` values containing nested documents. |
+| `docEnumeratorAppliesFiltersAndGeneratesIds`            | `ParquetSharedDocumentAdapterTest.java` | Enumerates document rows through real Parquet IO, applies filters, and creates generated row ids. |
+
+## Document Planning And Schema
+
+| Test                                                    | File                               | What It Checks                                                                                   |
+|---------------------------------------------------------|------------------------------------|--------------------------------------------------------------------------------------------------|
+| `documentBuildsTupleTypeFromExportedColumnsAndFallsBackToDocumentId` | `ParquetDocumentPlanningTest.java` | Builds document tuple types from exported columns and falls back to the `_id` document type.      |
+| `docScanCopiesFiltersDerivesDocumentRowAndRegistersFilterRule` | `ParquetDocumentPlanningTest.java` | Copies document scan filters, derives the document row shape, includes filters in compare strings, and registers the filter rule. |
+| `docFilterCopyPreservesEntityAndDelegatesToScanInput`  | `ParquetDocumentPlanningTest.java` | Copies Parquet document filter nodes while preserving the entity and input scan.                  |
+| `docFilterRuleSplitsNestedAndConjunctions`             | `ParquetDocumentPlanningTest.java` | Splits nested `AND` Rex conditions into individual document filter predicates.                    |
+
+## Schema And Binding Utilities
+
+| Test                                                       | File                               | What It Checks                                                                                           |
+|------------------------------------------------------------|------------------------------------|----------------------------------------------------------------------------------------------------------|
+| `bindingSerializerRoundTripsSourceFilesPartitionsColumnsAndStatistics` | `ParquetSchemaUtilitiesTest.java` | Round-trips table bindings with encoded paths, partitions, column roles, and metadata statistics.       |
+| `nameNormalizerNormalizesAndUniquifiesNestedFields`        | `ParquetSchemaUtilitiesTest.java`  | Normalizes physical table/field names and resolves duplicate top-level and nested Parquet names.        |
+| `typeConverterConvertsTypedStringsAndComparesNumerically`  | `ParquetSchemaUtilitiesTest.java`  | Converts string statistics into typed PolyValues and compares numeric strings by numeric value.         |
+| `schemaStateSkipsPrimaryKeyAndStringifiesConflictingSampleValues` | `ParquetSchemaUtilitiesTest.java` | Initializes relational schema inference, skips workflow PK export, and stringifies incompatible samples. |
+| `valueSchemaMergesNestedFieldsAndWidensNumericTypes`       | `ParquetSchemaUtilitiesTest.java`  | Merges nested schemas and widens numeric value schemas during inference.                                |
+| `messageTypeBuilderBuildsRepeatedAndNestedFields`          | `ParquetSchemaUtilitiesTest.java`  | Builds repeated and nested Parquet fields and rejects empty Parquet schemas.                            |
+| `namespaceCreatesRootBindingAndEntityWrappers`             | `ParquetTableStatisticsReaderTest.java` | Creates namespace root table bindings from a physical table and wraps them in a `ParquetRelTable`.      |
+
+## IO Utilities
+
+| Test                                                    | File                         | What It Checks                                                                                 |
+|---------------------------------------------------------|------------------------------|------------------------------------------------------------------------------------------------|
+| `cancellationChecksOnlyAtConfiguredRowIntervals`        | `ParquetIoUtilitiesTest.java` | Checks cancellation only at the configured row interval and respects the cancellation flag.     |
+| `urlResolverNormalizesDirectoriesAndKeepsExplicitParquetFiles` | `ParquetIoUtilitiesTest.java` | Normalizes directory URLs, preserves explicit `.parquet` source URLs, and resolves child files. |
+| `bufferedIteratorReplaysSampleRowsBeforeConvertedRemainder` | `ParquetExecutionAndWriterTest.java` | Replays buffered schema-sample rows before converting the remaining iterator values. |
+| `virtualGroupDelegatesToSourceGroupAndKeepsMetadata` | `ParquetExecutionAndWriterTest.java` | Delegates Parquet group access through `VirtualGroup` and preserves synthetic row metadata. |
+| `combinedGroupRoutesFieldsByJoinSideAndRejectsIndexedAccess` | `ParquetExecutionAndWriterTest.java` | Routes joined parent/child fields by join side, detects null join rows, and rejects direct indexed access. |
+| `filterableSourceFileDefensivelyCopiesFilters` | `ParquetExecutionAndWriterTest.java` | Defensively copies filter lists attached to source files. |
+| `sourceWriterRoundTripsRelationalRowsThroughRelEnumerator` | `ParquetExecutionAndWriterTest.java` | Writes relational rows with `ParquetSourceWriter` and reads them back through `ParquetRelEnumerator`. |
+| `sourceWriterRoundTripsDocumentsAndGeneratesMissingIds` | `ParquetExecutionAndWriterTest.java` | Writes document rows, skips source `_id` when requested, reads values back, and generates missing ids. |
+| `sourceWriterReportsCompressionAndValueConversionFailures` | `ParquetExecutionAndWriterTest.java` | Reports unknown compression settings and incompatible value/schema writes as runtime errors. |
+| `compileReturnsAlwaysTrueForEmptyFiltersAndRejectsUnsupportedFilters` | `ParquetPrimitivePredicateTest.java` | Compiles empty primitive predicates and rejects invalid indexes, unsupported operators, null comparisons, and unsupported logical `OR`. |
+| `compiledAndPredicateReadsColumnsOnceAndReusesConsumedValues` | `ParquetPrimitivePredicateTest.java` | Compiles primitive `AND` predicates, reads int/binary/boolean values once, and reuses consumed cached values. |
+| `nullPredicatesMatchAbsentAndPresentValues` | `ParquetPrimitivePredicateTest.java` | Evaluates primitive `IS NULL` and `IS NOT NULL` predicates for absent and present optional values. |
+
+## Aggregates
+
+| Test                                                 | File                                            | What It Checks                                                                                     |
+|------------------------------------------------------|-------------------------------------------------|----------------------------------------------------------------------------------------------------|
+| `readsPrimitiveColumnAggregateResults`               | `ParquetNoFilterColumnAggregateReaderTest.java` | Reads SUM, COUNT, MIN, and MAX for primitive numeric columns without row filters.                  |
+| `rejectsNonNumericProjection`                        | `ParquetNoFilterColumnAggregateReaderTest.java` | Rejects non-numeric projections for the no-filter column aggregate reader.                         |
+| `supportsDistinctOnlyGroupBy`                        | `ParquetGroupedAggregateRelEnumeratorTest.java` | Supports distinct-only grouped output without aggregate calls.                                     |
+| `scalarCountReturnsZeroWhenStatisticsPruneEveryFile` | `ParquetGroupedAggregateRelEnumeratorTest.java` | Returns scalar `COUNT(*) = 0` when statistics/file pruning rejects all files.                      |
+| `supportsCountStarWithSumMinAndMax`                  | `ParquetRowAggregateRelEnumeratorTest.java`     | Computes grouped row aggregates with `COUNT(*)`, `SUM`, `MIN`, and `MAX`, including null handling. |
+| `supportsMultipleGroupingColumnsAndNullGroupKeys`     | `ParquetRowAggregateRelEnumeratorTest.java`     | Aggregates with multiple grouping columns and preserves groups whose key contains null values.     |
+| `evaluatesPredicateForEveryRow`                      | `ParquetCountAggregatePageReaderTest.java`      | Applies a primitive predicate to every row while counting through the page reader.                 |
+| `aggregateInputColumnAddsPresentNumericValuesAndSkipsNulls` | `AggregateAccumulatorTest.java` | Adds present numeric column values to count/sum/min/max slots and skips optional null values. |
+| `aggregateInputColumnSharesConsumedValuesAcrossCalls` | `AggregateAccumulatorTest.java` | Reuses consumed numeric column values across multiple aggregate calls without rereading the column. |
+| `aggregateRowAccumulatorBuildsCountStarAndColumnAggregates` | `AggregateAccumulatorTest.java` | Builds row accumulators from aggregate descriptors and column descriptors. |
+| `aggregateInputColumnRejectsUnsupportedPrimitiveTypes` | `AggregateAccumulatorTest.java` | Rejects unsupported primitive types for numeric aggregate input columns. |
+| `aggregateGroupStateHandlesObjectRowsAndMergesPartialStates` | `AggregateAccumulatorTest.java` | Aggregates object rows, ignores null values, and merges partial aggregate states. |
+| `aggregateDecompositionExposesPartialCallsAndFinalFunctions` | `ParquetAggregateOptimizationTest.java` | Exposes partial aggregate calls and their final aggregation functions for aggregate decomposition. |
+
+## Metadata And File-Constant Aggregates
+
+| Test                                                                      | File                                | What It Checks                                                                                           |
+|---------------------------------------------------------------------------|-------------------------------------|----------------------------------------------------------------------------------------------------------|
+| `metadataAggregateAllowsPartitionFiltersAndGroups`                        | `ParquetMetadataAggregateTest.java` | Accepts metadata aggregates with partition filters/groups and rejects unsupported data-column cases.     |
+| `projectedScanUsesFolderPartitionValuesWhenPhysicalColumnsCollide`        | `ParquetMetadataAggregateTest.java` | Uses folder partition values when physical Parquet columns with the same names contain different values. |
+| `projectedScanReadsMinimalDataColumnWhenOnlyPartitionColumnsAreProjected` | `ParquetMetadataAggregateTest.java` | Reads a minimal backing data column when projecting only partition columns.                              |
+| `metadataAggregateUsesFileConstantPhysicalFilters`                        | `ParquetMetadataAggregateTest.java` | Executes metadata aggregate counts using dynamic filters on file-constant physical columns.              |
+| `metadataAggregateRejectsMixedPhysicalFilterColumns`                      | `ParquetMetadataAggregateTest.java` | Rejects metadata aggregation when a physical column is not constant across the relevant file.            |
+| `metadataAggregateGroupsByFileConstantPhysicalColumns`                    | `ParquetMetadataAggregateTest.java` | Groups metadata aggregate output by file-constant physical columns.                                      |
+| `fileGroupedAggregateGroupsPhysicalConstantColumns`                       | `ParquetMetadataAggregateTest.java` | Groups file-level aggregate results by physical columns that are constant per file.                      |
+| `streamingGroupedAggregateSkipsFileConstantPhysicalFilter`                | `ParquetMetadataAggregateTest.java` | Drops file-constant filters from residual row evaluation after file pruning.                             |
+| `streamingGroupedAggregateRetainsResidualAndFilter`                       | `ParquetMetadataAggregateTest.java` | Keeps residual row filters when an `AND` also contains file-constant predicates.                         |
+| `streamingGroupedAggregateRetainsResidualOrFilter`                        | `ParquetMetadataAggregateTest.java` | Keeps residual row filters for `OR` predicates that cannot be fully decided at file level.               |
+| `fileGroupedScalarCountReturnsZeroWhenFiltersRejectEveryFile`             | `ParquetMetadataAggregateTest.java` | Returns scalar `COUNT(*) = 0` when file-level grouped aggregate filters reject every file.               |
+| `constantColumnResolverRejectsMixedNullAndNonNullValues`                  | `ParquetMetadataAggregateTest.java` | Rejects file-constant resolution when statistics show mixed null and non-null values.                    |
+
+## Statistics Provider
+
+| Test                                                   | File                                  | What It Checks                                                                                         |
+|--------------------------------------------------------|---------------------------------------|--------------------------------------------------------------------------------------------------------|
+| `providedColumnStatisticsDefensivelyCopiesUniqueValues` | `ParquetTableStatisticsReaderTest.java` | Defensively copies adapter-provided unique values and handles null unique-value input.                 |
+| `readerAggregatesDataAndPartitionColumnStatistics`     | `ParquetTableStatisticsReaderTest.java` | Aggregates metadata counts/ranges for data columns and derives bounded partition-column unique values. |
+| `columnStatisticsReaderConvertsTemporalAndBinaryFooterStatistics` | `ParquetTableStatisticsReaderTest.java` | Converts footer statistics for date, time, timestamp, and binary Parquet columns.                       |
+| `readerFallsBackForMissingColumnsAndUnreliableRanges`  | `ParquetTableStatisticsReaderTest.java` | Returns full/null-range statistics for missing columns and type-mismatched or unreliable ranges.       |
+| `nestedEntityStatisticsUsesLargestNestedValueCount`    | `ParquetTableStatisticsReaderTest.java` | Estimates nested-table row count from the largest nested data-column value count.                       |
+| `providedEntityStatisticsAllowsUnknownRowCount`        | `ParquetTableStatisticsReaderTest.java` | Allows adapter-provided entity statistics to represent unknown row counts.                              |
+| `parquetRelTableProvidesStatisticsOnlyForMatchingLogicalEntity` | `ParquetTableStatisticsReaderTest.java` | Exposes entity/column statistics through `ParquetRelTable` only for matching logical table ids.        |
+
+## Nested Join Planning
+
+| Test                                            | File                                 | What It Checks                                                                                |
+|-------------------------------------------------|--------------------------------------|-----------------------------------------------------------------------------------------------|
+| `plansFullJoinWithProjectedFilteredParentInput` | `ParquetNestedJoinPlanningTest.java` | Executes a full parent-child join where the parent input is projected and filtered.           |
+| `appliesLimitAfterFullJoinFilter`               | `ParquetNestedJoinPlanningTest.java` | Applies `LIMIT` correctly after a filtered full parent-child join.                            |
+| `plansFullJoinWithLimitAsParquetJoin`           | `ParquetNestedJoinPlanningTest.java` | Confirms a full parent-child join with limit plans as `ParquetRelJoin`, not `EnumerableJoin`. |
+| `plansLeftJoinWithLimitAsParquetJoin`           | `ParquetNestedJoinPlanningTest.java` | Confirms a left parent-child join with limit plans as `ParquetRelJoin`, not `EnumerableJoin`. |
+
+## Plugin Integration
+
+| Test                                    | File                     | What It Checks                                                                                         |
+|-----------------------------------------|--------------------------|--------------------------------------------------------------------------------------------------------|
+| `importsAllTablesAndReadsRows`          | `ParquetPluginTest.java` | Creates a Parquet source from bundled `orders_db` resources and verifies imported tables contain rows. |
+| `parquetSourceIsReadOnly`               | `ParquetPluginTest.java` | Rejects `DELETE` statements against a Parquet source table.                                            |
+| `readsExpectedRowsFromCustomers`        | `ParquetPluginTest.java` | Reads known customer rows and validates values, including timestamp conversion.                        |
+| `filtersRowsWithWhereClause`            | `ParquetPluginTest.java` | Executes a text equality filter with ordering and limit.                                               |
+| `projectsOnlyRequestedColumns`          | `ParquetPluginTest.java` | Reads only requested columns from a projected query.                                                   |
+| `supportsGreaterThanFilter`             | `ParquetPluginTest.java` | Executes a greater-than filter against the imported customer table.                                    |
+| `supportsAllComparisonFilterOperations` | `ParquetPluginTest.java` | Executes `=`, `!=`, `>`, `>=`, `<`, and `<=` filters through JDBC.                                     |
+| `rejectsUpdateOnParquetSource`          | `ParquetPluginTest.java` | Rejects `UPDATE` statements against a Parquet source table.                                            |
+
+## Workflow Registry
+
+| Test                                 | File                        | What It Checks                                                         |
+|--------------------------------------|-----------------------------|------------------------------------------------------------------------|
+| `extractParquetActivityIsRegistered` | `ActivityRegistryTest.java` | Verifies the workflow registry contains the `extractParquet` activity. |
+| `loadParquetActivityIsRegistered`    | `ActivityRegistryTest.java` | Verifies the workflow registry contains the `loadParquet` activity.    |
+
+## Workflow Parquet Support
+
+| Test                                                                    | File                              | What It Checks                                                                                                        |
+|-------------------------------------------------------------------------|-----------------------------------|-----------------------------------------------------------------------------------------------------------------------|
+| `prepareTargetFileFailsOrDeletesExistingFile`                           | `ParquetWorkflowSupportTest.java` | Covers fail, overwrite, and invalid target-file handling before Parquet export.                                       |
+| `relationalWriteAndExtractRoundTripHonorsPrimaryKeyFileNameAndMaxCount` | `ParquetWorkflowSupportTest.java` | Writes relational workflow rows, skips the workflow PK, extracts rows with file-name column, and enforces `maxCount`. |
+| `documentWriteAndExtractRoundTripHonorsKeepIdNameFieldAndMaxCount`      | `ParquetWorkflowSupportTest.java` | Writes documents without `_id`, extracts generated ids and file-name fields, and enforces `maxCount`.                 |
+| `loadActivityPipeWritesRelationalWithKeptPrimaryKeyAndGzip`             | `ParquetWorkflowSupportTest.java` | Executes `ParquetLoadActivity.pipe` for relational input with `keepPk=true` and gzip compression.                    |
+| `relationalWriterRoundTripsTemporalAndBinaryValues`                     | `ParquetWorkflowSupportTest.java` | Round-trips DATE, TIME, TIMESTAMP, and VARBINARY values through the workflow Parquet writer and extractor.           |
+| `loadActivityPipeRejectsGraphInputAtRuntime`                            | `ParquetWorkflowSupportTest.java` | Verifies runtime graph rejection in `ParquetLoadActivity.pipe`.                                                       |
+| `loadActivityPipeFailsWhenSampleConflictsWithDeclaredRelationalSchema`  | `ParquetWorkflowSupportTest.java` | Verifies `CONFLICT_FAIL` rejects sampled values incompatible with the declared relational schema.                    |
+| `extractActivityLocksOutputTypeAndPipesMultipleSources`                 | `ParquetWorkflowSupportTest.java` | Locks output type and extracts multiple selected Parquet sources through `ParquetExtractActivity.pipe`.              |
+| `documentWriteKeepsOnlyIdWhenRequestedAndRejectsEmptyDroppedIdSchema`   | `ParquetWorkflowSupportTest.java` | Writes documents containing only `_id` when kept and rejects empty schemas when `_id` is dropped.                    |
+| `loadActivityPreviewRejectsEmptyRelationalExportAndBuildsDynamicName`   | `ParquetWorkflowSupportTest.java` | Validates `ParquetLoadActivity` relational preview behavior and dynamic names.                                        |
+| `extractSupportBuildsDynamicNamesAndDocumentOutputType`                 | `ParquetWorkflowSupportTest.java` | Validates extract output types, dynamic names, and tuple-count estimation.                                            |
+
+## To Do
+
+### Coverage Check Summary
+
+The current suite covers the main relational read path, flat and partitioned discovery, primitive projection, primitive and group filter evaluation, file-level pruning, metadata aggregates, row and column aggregate accumulation, aggregate-decomposition data holders, nested join planning, plugin-level relational integration, selected document conversion/enumeration behavior, document tuple-type and planner-node basics, namespace wrapper creation, source writer/enumerator round trips, statistics provider basics, schema utilities, IO utilities, and workflow helper/activity round trips.
+
+The remaining gaps are mostly full integration paths, planner rewrite matrix cases, catalog lifecycle behavior, and hard-to-simulate IO failure paths. Focused unit coverage has been added for the previously listed workflow dispatch, document conversion, document planning helpers, footer statistics, writer/enumerator handling, partition collision, file pruning, aggregate, and IO utility branches.
+
+### Known Remaining Gaps
+
+| Area                                | Representative Classes                                                                                                                                                       | Still Missing                                                                                                                                                                                                                                                       |
+|-------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Document source integration         | `ParquetDocumentSource`, `ParquetDocument`                                                                                                                                   | Creating/restoring document collections through the adapter catalog, imported document collections queried through Polypheny, multi-file document collection scans, document read-only behavior, and `scanFiltered` dynamic-parameter resolution. |
+| Document planner rewrite            | `ParquetDocScan`, `ParquetDocFilter`, `ParquetDocFilterRule`                                                                                                                 | Planner-level checks that supported document filters become `ParquetDocFilter`/filtered `ParquetDocScan`, unsupported predicates stay outside the adapter path, translated `AND` predicates are pushed together through an optimizer rule call, and document scan/filter cost scaling remains stable. |
+| Relational source/catalog lifecycle | `ParquetRelationalSource`, `AbstractParquetSource`, `ParquetNamespace`, `ParquetBindingSerializer`                                                                           | Adapter creation from `upload`/`link`/`url` settings, source URL resolution failures, serialized binding restore into physical tables/collections, table restore/drop behavior, namespace updates, and catalog metadata consistency after restore. |
+| Partition integration               | `ParquetFileDiscovery`, `ParquetSchemaNormalizer`, `ParquetTableBinding`, `ParquetRelTable`                                                                                  | Missing partition columns across files, mixed partition values and data columns beyond collision checks, multi-level folder behavior in Polypheny partition metadata, restore of partitioned tables, and partition values projected through nested join paths. |
+| Planner and optimization matrix     | `ParquetRules`, `PatternMatcher`, `PatternMatchers`, `ParquetRelAggregate`, `ParquetRelMetadataScan`, `ParquetEnumerableUnion` | Matrix coverage from `join_test_list.md` and `partition_test_list.md`, aggregate decomposition/fallback decisions inside `PatternMatchers`, metadata-scan vs data-scan planning, union planning, unsupported join/filter/aggregate fallback to Enumerable, and plan-display stability. |
+| Aggregate stress cases              | `ParquetRowAggregator`, `AggregateColumnAccumulator`, `ParquetGroupedAggregatePageReader`                                                                                    | Decimal/float precision stress, all-null groups, multiple grouping columns combined with row filters, unsupported aggregate rejection beyond current numeric checks, and overflow/large-count behavior. |
+| IO failure paths                    | `ParquetSourceReader`, `ParquetPrimitiveRowReader`, `HadoopConfigurationFactory`                                                                                              | Cancellation during active reads, invalid URL/path handling that requires filesystem/Hadoop failures, projection schema errors, mixed-schema multi-file read failures outside discovery, Hadoop configuration failures, and reader close/error propagation. |

From 94fa6d46dcaa046713e9bf9d4b1fcd596e59005d Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Mon, 15 Jun 2026 21:20:17 +0200
Subject: [PATCH 40/46] aggregation optimizations for document, benchmarks

---
 plugins/parquet-adapter/benchmarks/README.md  |  17 +-
 .../benchmarks/benchmark-plan.md              |  35 +-
 ...ss_model_comparison_query_specification.md | 102 +++
 .../access_model_comparison_rn.sql            |  35 +
 .../aggregation_query_specification.md        | 189 ++++++
 .../query_lists/data_configuration.md         | 112 +++-
 .../nested_data_query_specification.md        | 137 ++++
 .../partitioning_query_specification.md       | 175 +++++
 ...s_model_comparison_duckdb_tlcp_results.csv |  60 +-
 ..._comparison_polypheny_mql_tlcp_results.csv |  60 +-
 ...l_comparison_polypheny_rf_tlcp_results.csv |  60 +-
 ..._comparison_polypheny_rn_tlcpn_results.csv |  31 +
 ...ss_model_comparison_spark_tlcp_results.csv |  60 +-
 ..._model_comparison_tlcp_results_analysis.md |  34 +-
 .../access_model_comparison_tlcp_summary.md   |  90 +--
 .../aggregation_duckdb_results.csv            | 120 ++--
 .../aggregation_polypheny_mql_results.csv     |  61 ++
 .../aggregation_polypheny_results.csv         | 120 ++--
 .../aggregation/aggregation_spark_results.csv | 120 ++--
 .../aggregation/aggregation_summary.md        | 132 ++--
 ...sted_data_polypheny_normalized_results.csv |  54 +-
 .../plots/access_model_comparison_plot.pdf    | Bin 5109 -> 0 bytes
 .../plots/access_model_comparison_plot.svg    |  69 --
 .../access_model_comparison_tlcp_plot.pdf     | Bin 0 -> 5553 bytes
 .../access_model_comparison_tlcp_plot.png     | Bin 0 -> 25861 bytes
 .../access_model_comparison_tlcp_plot.svg     | 109 +--
 .../results/plots/aggregation_plot.pdf        | Bin 0 -> 6126 bytes
 .../results/plots/aggregation_plot.png        | Bin 0 -> 22099 bytes
 .../results/plots/aggregation_plot.svg        |  93 +++
 ...run_access_model_comparison_bm_pipeline.md |  56 +-
 .../benchmark_result_plot_generator.py        | 359 ++++++++--
 plugins/parquet-adapter/docs/TODO.md          |   8 -
 .../parquet-adapter/docs/aggregation_flow.md  |  16 +-
 .../aggregation/aggragation_flow.drawio.png   | Bin 103902 -> 93752 bytes
 .../docs/rel_execution_flows.md               | 251 +++++++
 .../parquet-adapter/docs/todo_partitions.md   |  24 -
 .../db/adapter/parquet/ParquetPlugin.java     |  35 +-
 .../document/ParquetDocumentSource.java       |  10 +-
 .../ParquetDocAggregateExecutor.java          | 207 ++++++
 .../execution/ParquetDocFilterTranslator.java |  44 ++
 .../planning/ParquetDocAggregate.java         | 264 ++++++++
 .../document/planning/ParquetDocCalcRule.java |  83 ---
 .../planning/ParquetDocConvention.java        |  69 ++
 .../planning/ParquetDocMetadataScan.java      |  58 ++
 .../planning/ParquetDocPatternMatchers.java   | 320 +++++++++
 .../document/planning/ParquetDocRules.java    |  85 +++
 .../document/planning/ParquetDocScan.java     |  33 +-
 .../document/schema/ParquetDocument.java      |  90 ++-
 .../execution/ParquetEnumeratorsFactory.java  |   3 +-
 .../ParquetRelDataAggregateExecutor.java      | 537 +--------------
 .../execution/ParquetRelExecutor.java         |  54 +-
 .../ParquetRelMetadataAggregateExecutor.java  | 371 +----------
 .../ParquetRelNestedJoinExecutor.java         |   3 +-
 .../aggregate/AggregateRowProjector.java      |   2 +-
 .../DirectAggregateRowProjector.java          |   4 +-
 .../aggregate/ParquetRowAggregator.java       |   4 +-
 .../optimization/PatternMatchers.java         | 571 ----------------
 .../planning/EnumerableParquet.java           |   2 +-
 .../planning/ParquetRelAggregate.java         |  23 +-
 ...vention.java => ParquetRelConvention.java} |  12 +-
 .../relational/planning/ParquetRelJoin.java   |   4 +-
 .../planning/ParquetRelPatternMatchers.java   | 304 +++++++++
 .../ParquetRelRules.java}                     |  36 +-
 .../relational/planning/ParquetRelScan.java   |   6 +-
 .../relational/schema/ParquetRelTable.java    |   4 +-
 .../parquet/shared/AbstractParquetSource.java |   2 +-
 .../execution/AbstractFilterTranslator.java   |   1 +
 .../aggregate/ParquetAggregateSource.java     |  52 ++
 .../ParquetDataAggregateExecutor.java         | 618 ++++++++++++++++++
 ...arquetFileGroupedAggregateEnumerator.java} |  43 +-
 .../ParquetGroupedAggregateEnumerator.java}   |  89 ++-
 .../ParquetMetadataAggregateExecutor.java     | 421 ++++++++++++
 .../ParquetRowAggregateEnumerator.java}       |  21 +-
 .../parquet/shared/io/OutputLocalFile.java    | 118 ++++
 .../shared/io/ParquetSourceWriter.java        |  23 +-
 .../ParquetAggregatePatternMatchers.java      | 225 +++++++
 .../optimization/ParquetAlgOptRule.java       |   6 +-
 .../ParquetOptimizationSettings.java          |  53 ++
 .../optimization/PatternMatcher.java          |   5 +-
 .../aggregate/AggregateDecomposition.java     |   7 +-
 .../aggregate/ParquetAggregateSupport.java    |  41 ++
 .../aggregate/PartialAggregate.java           |   2 +-
 .../planning/ParquetEnumerableUnion.java      |   7 +-
 .../shared/schema/ParquetNamespace.java       |   5 +-
 .../AggregateAccumulatorTest.java             |   6 +-
 .../ParquetAggregateOptimizationTest.java     |  51 +-
 .../ParquetCountAggregatePageReaderTest.java  |   4 +-
 .../parquet/ParquetDocumentPlanningTest.java  | 265 +++++++-
 .../ParquetExecutionAndWriterTest.java        |   9 +-
 ...quetGroupedAggregateRelEnumeratorTest.java |  47 +-
 .../parquet/ParquetMetadataAggregateTest.java |  46 +-
 .../ParquetNestedRepeatedProjectionTest.java  |   3 +-
 .../ParquetRowAggregateRelEnumeratorTest.java |   6 +-
 .../ParquetSharedDocumentAdapterTest.java     |  39 ++
 94 files changed, 5752 insertions(+), 2490 deletions(-)
 create mode 100644 plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_rn.sql
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rn_tlcpn_results.csv
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_results.csv
 delete mode 100644 plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_plot.pdf
 delete mode 100644 plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_plot.svg
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot.pdf
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot.png
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/aggregation_plot.pdf
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/aggregation_plot.png
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/aggregation_plot.svg
 delete mode 100644 plugins/parquet-adapter/docs/TODO.md
 create mode 100644 plugins/parquet-adapter/docs/rel_execution_flows.md
 delete mode 100644 plugins/parquet-adapter/docs/todo_partitions.md
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocAggregateExecutor.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocAggregate.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocCalcRule.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocConvention.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocMetadataScan.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocPatternMatchers.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocRules.java
 delete mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/PatternMatchers.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/{ParquetConvention.java => ParquetRelConvention.java} (82%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelPatternMatchers.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/{optimization/ParquetRules.java => planning/ParquetRelRules.java} (55%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetAggregateSource.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetDataAggregateExecutor.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{relational/execution/aggregate/ParquetFileGroupedAggregateRelEnumerator.java => shared/execution/aggregate/ParquetFileGroupedAggregateEnumerator.java} (78%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{relational/execution/aggregate/ParquetGroupedAggregateRelEnumerator.java => shared/execution/aggregate/ParquetGroupedAggregateEnumerator.java} (80%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetMetadataAggregateExecutor.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{relational/execution/aggregate/ParquetRowAggregateRelEnumerator.java => shared/execution/aggregate/ParquetRowAggregateEnumerator.java} (79%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/OutputLocalFile.java
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/ParquetAggregatePatternMatchers.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{relational => shared}/optimization/ParquetAlgOptRule.java (89%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/ParquetOptimizationSettings.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{relational => shared}/optimization/PatternMatcher.java (94%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{relational => shared}/optimization/aggregate/AggregateDecomposition.java (97%)
 create mode 100644 plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/aggregate/ParquetAggregateSupport.java
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{relational => shared}/optimization/aggregate/PartialAggregate.java (93%)
 rename plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/{relational => shared}/planning/ParquetEnumerableUnion.java (87%)
 rename plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/{shared/aggregate => }/AggregateAccumulatorTest.java (96%)
 rename plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/{shared/io/aggregate/page => }/ParquetCountAggregatePageReaderTest.java (96%)
 rename plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/{relational/execution => }/ParquetNestedRepeatedProjectionTest.java (99%)

diff --git a/plugins/parquet-adapter/benchmarks/README.md b/plugins/parquet-adapter/benchmarks/README.md
index eba4b7438d..5d299e1b13 100644
--- a/plugins/parquet-adapter/benchmarks/README.md
+++ b/plugins/parquet-adapter/benchmarks/README.md
@@ -26,17 +26,19 @@ and result artifacts for the Polypheny Parquet adapter comparison.
 | Path                                   | Meaning                                                                                                                                            |
 |----------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------|
 | `query_lists/data_configuration.md`    | Local data paths and Polypheny adapter names used by the query files.                                                                              |
-| `query_lists/access_model_comparison/` | Access-model comparison benchmark queries for Polypheny relational, Polypheny MQL, DuckDB, and Spark.                                              |
+| `query_lists/access_model_comparison/` | Access-model comparison benchmark queries for Polypheny relational flat, Polypheny relational normalized, Polypheny MQL, DuckDB, and Spark.        |
 | `query_lists/nested_data/`             | Nested Customer benchmark queries. Engine-specific files are used because nested access syntax differs.                                            |
 | `query_lists/arrregation/`             | Aggregation benchmark query files for Polypheny relational, Polypheny MQL, DuckDB, and Spark. The directory name is kept as-is for existing paths. | 
 | `query_lists/partitioning/`            | Partitioning benchmark queries for repartitioned and unpartitioned TLC layouts.                                                                    |
 
 File naming:
 
-- `*_polypheny*.sql`: Polypheny relational SQL with explicit adapter-prefixed table names.
+- `*_rf.sql`: Polypheny relational flat SQL with explicit adapter-prefixed table names.
+- `*_rn.sql`: Polypheny relational normalized SQL with explicit adapter-prefixed table names.
+- `*_polypheny*.sql`: other Polypheny relational SQL with explicit adapter-prefixed table names.
 - `*_sql.sql`: SQL shared by DuckDB and Spark where both expose the same logical temp views.
 - `*.mql`: Polypheny document adapter queries.
-- `*_query_specification.md`: logical query intent and query groups.
+- `*_query_specification.md`: logical query intent, query groups, and textual query descriptions.
 
 ## Run Pipelines
 
@@ -54,7 +56,7 @@ run_pipeline/
 | `run_partitioning_bm_pipeline.md`            | Partitioning            |
 
 Each pipeline file contains the commands to build/start Polypheny where needed,
-run the benchmark clients, and generate the suite summary.
+run the benchmark clients, and generate the suite summary and plot.
 
 ## Scripts
 
@@ -65,7 +67,10 @@ run the benchmark clients, and generate the suite summary.
 | `scripts/implementation/`                | Java/Python benchmark clients called by the runners.                         |
 | `scripts/ds_preprocessing/`              | Dataset materialization and preprocessing utilities.                         |
 | `scripts/summarize_benchmark_results.py` | Creates markdown summaries from benchmark CSV files.                         |
-| `scripts/plot_generation/`               | Plot generation scripts, including the generic benchmark CSV plot generator.  |
+| `scripts/plot_generation/`               | Plot generation scripts, including the generic SVG/PDF/PNG benchmark CSV plot generator. |
+
+PNG plot output requires Pillow. SVG and PDF output use only the Python
+standard library.
 
 ## Results
 
@@ -80,7 +85,7 @@ Result conventions:
 - `*_results.csv`: raw benchmark output from a runner.
 - `*_summary.md`: generated comparison summary from `scripts/summarize_benchmark_results.py`.
 - `*_results_analysis.md`: short manual interpretation of a generated summary.
-- `results/plots/`: generated plot artifacts such as SVG and PDF files.
+- `results/plots/`: generated plot artifacts such as SVG, PDF, and PNG files.
 
 Current result folders:
 
diff --git a/plugins/parquet-adapter/benchmarks/benchmark-plan.md b/plugins/parquet-adapter/benchmarks/benchmark-plan.md
index dbd7fe5f2f..afa1fa72f8 100644
--- a/plugins/parquet-adapter/benchmarks/benchmark-plan.md
+++ b/plugins/parquet-adapter/benchmarks/benchmark-plan.md
@@ -57,7 +57,7 @@ Parquet workloads where the same logical operation can be expressed.
 
 ## Suite 1: Access Model Comparison
 
-Status: implemented and executed for the TLC `green_tripdata` table.
+Status: implemented for the TLC `green_tripdata` table.
 
 ### Purpose
 
@@ -68,14 +68,11 @@ schema generation or partition pruning.
 ### Compared Systems and Access Paths
 
 - Polypheny relational adapter in flat schema mode;
+- Polypheny relational adapter in normalized schema mode;
 - Polypheny document adapter queried through MQL;
 - DuckDB;
 - Apache Spark.
 
-Normalized relational mode is not included in this suite because the selected
-`green_tripdata` table is flat and does not produce meaningful generated child
-tables.
-
 ### Dataset
 
 Dataset description:
@@ -85,17 +82,19 @@ Input table: `green_tripdata` from the partitioned TLC dataset.
 
 Polypheny table and collection names used by the current query files:
 
-- relational table: `tlcp__green_tripdata`;
+- relational flat table: `tlcp__green_tripdata`;
+- relational normalized table: `tlcpn__green_tripdata`;
 - document collection: `tlcpd_document.tlcpd__green_tripdata`;
 - DuckDB and Spark view: `green_tripdata`.
 
 ### Query Files
 
-| System                 | Query file                                                                                               |
-|------------------------|----------------------------------------------------------------------------------------------------------|
-| Polypheny relational   | `plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_rf.sql`  |
-| Polypheny document MQL | `plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_mql.mql` |
-| DuckDB and Spark       | `plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_sql.sql` |
+| System                           | Query file                                                                                               |
+|----------------------------------|----------------------------------------------------------------------------------------------------------|
+| Polypheny relational flat        | `plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_rf.sql`  |
+| Polypheny relational normalized  | `plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_rn.sql`  |
+| Polypheny document MQL           | `plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_mql.mql` |
+| DuckDB and Spark                 | `plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_sql.sql` |
 
 ### Query Groups
 
@@ -108,10 +107,20 @@ plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_mo
 
 Raw and summarized results are stored in:
 
-```text[..](..)
+```text
 plugins/parquet-adapter/benchmarks/results/access_model_comparison/
 ```
 
+Expected raw result files:
+
+```text
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rf_tlcp_results.csv
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rn_tlcpn_results.csv
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_mql_tlcp_results.csv
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_duckdb_tlcp_results.csv
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_spark_tlcp_results.csv
+```
+
 The current summary file is:
 
 ```text
@@ -227,7 +236,7 @@ plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_spe
 
 ### Compared Systems and Access Paths
 
-- Polypheny relational before and after optimization, where both versions are available;
+- Polypheny relational flat before and after optimization, where both versions are available;
 - Polypheny document adapter queried through MQL;
 - DuckDB;
 - Apache Spark.
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_query_specification.md b/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_query_specification.md
index fbe92ba377..3c2f16b775 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_query_specification.md
+++ b/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_query_specification.md
@@ -1,5 +1,10 @@
 # Access Model Comparison Query Specification
 
+The same logical query set is executed across Polypheny relational flat,
+Polypheny relational normalized, Polypheny document MQL, DuckDB, and Apache
+Spark. The normalized relational variant intentionally uses only the root flat
+table fields, with no generated child-table joins.
+
 | Query | Operation           | Predicate                                          | Returned data                                      | Purpose                                                    |
 |-------|---------------------|----------------------------------------------------|----------------------------------------------------|------------------------------------------------------------|
 | Q01   | Full scan           | None                                               | All fields                                         | Measures full dataset read performance                     |
@@ -7,3 +12,100 @@
 | Q03   | Filtered count      | `trip_distance >= 10.0` and `total_amount >= 40.0` | Aggregate count                                    | Measures filter evaluation without returning matching rows |
 | Q04   | Filtered scan       | `trip_distance >= 10.0` and `total_amount >= 40.0` | All matching records or documents                  | Measures filtering with full result consumption            |
 | Q05   | Filtered projection | `trip_distance >= 10.0` and `total_amount >= 40.0` | Selected fields from matching records or documents | Measures combined filtering and projection                 |
+
+
+## Q1 - Full scan
+
+Text:
+
+```text
+Find all green taxi trip records and return every available field.
+```
+
+SQL:
+
+```text
+SELECT *
+FROM green_tripdata;
+```
+
+## Q2 - Projection
+
+Text:
+
+```text
+Find all green taxi trip records and return only pickup time, drop-off time,
+pickup location, drop-off location, and total amount.
+```
+
+SQL:
+
+```text
+SELECT
+  lpep_pickup_datetime,
+  lpep_dropoff_datetime,
+  pulocationid,
+  dolocationid,
+  total_amount
+FROM green_tripdata;
+```
+
+## Q3 - Filtered count
+
+Text:
+
+```text
+Count green taxi trip records where trip distance is at least 10.0 and total
+amount is at least 40.0.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+```
+
+## Q4 - Filtered full scan
+
+Text:
+
+```text
+Find green taxi trip records where trip distance is at least 10.0 and total
+amount is at least 40.0, and return every available field.
+```
+
+SQL:
+
+```text
+SELECT *
+FROM green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+```
+
+## Q5 - Filtered projection
+
+Text:
+
+```text
+Find green taxi trip records where trip distance is at least 10.0 and total
+amount is at least 40.0, and return only pickup time, pickup location, drop-off
+location, trip distance, and total amount.
+```
+
+SQL:
+
+```text
+SELECT
+  lpep_pickup_datetime,
+  pulocationid,
+  dolocationid,
+  trip_distance,
+  total_amount
+FROM green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+```
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_rn.sql b/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_rn.sql
new file mode 100644
index 0000000000..69757a99c6
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_rn.sql
@@ -0,0 +1,35 @@
+-- Q01: Full record access
+SELECT *
+FROM tlcpn__green_tripdata;
+
+-- Q02: Projection
+SELECT
+  lpep_pickup_datetime,
+  lpep_dropoff_datetime,
+  pulocationid,
+  dolocationid,
+  total_amount
+FROM tlcpn__green_tripdata;
+
+-- Q03: Filtered count
+SELECT count(*) AS row_count
+FROM tlcpn__green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+
+-- Q04: Filtered full record access
+SELECT *
+FROM tlcpn__green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+
+-- Q05: Filtered projection
+SELECT
+  lpep_pickup_datetime,
+  pulocationid,
+  dolocationid,
+  trip_distance,
+  total_amount
+FROM tlcpn__green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_specification.md b/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_specification.md
index 24c6136e11..b7e47b98a0 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_specification.md
+++ b/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_specification.md
@@ -22,3 +22,192 @@ plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny
 plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny_mql.sql
 plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_sql.sql
 ```
+
+## Q1 - Full Yellow Taxi row count
+
+Text:
+
+```text
+Count all Yellow Taxi trip records.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM yellow_tripdata;
+```
+
+## Q2 - Yellow Taxi one-month partition count
+
+Text:
+
+```text
+Count Yellow Taxi trip records in partition year 2022 and partition month 10.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+```
+
+## Q3 - Yellow Taxi one-day timestamp count
+
+Text:
+
+```text
+Count Yellow Taxi trip records with pickup timestamps on October 15, 2022.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
+  AND tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00';
+```
+
+## Q4 - Yellow Taxi filtered count
+
+Text:
+
+```text
+Count Yellow Taxi trip records where trip distance is at least 10.0 and total
+amount is at least 40.0.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM yellow_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+```
+
+## Q5 - Yellow Taxi monthly amount and distance summary
+
+Text:
+
+```text
+For each Yellow Taxi partition year and month, calculate trip count, total gross
+amount, minimum trip distance, and maximum trip distance.
+```
+
+SQL:
+
+```text
+SELECT
+  "year",
+  "month",
+  count(*) AS trips,
+  sum(total_amount) AS gross_amount,
+  min(trip_distance) AS min_distance,
+  max(trip_distance) AS max_distance
+FROM yellow_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+```
+
+## Q6 - Full High-Volume FHV row count
+
+Text:
+
+```text
+Count all High-Volume FHV trip records.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata;
+```
+
+## Q7 - High-Volume FHV one-month partition count
+
+Text:
+
+```text
+Count High-Volume FHV trip records in partition year 2022 and partition month
+10.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+```
+
+## Q8 - High-Volume FHV filtered count
+
+Text:
+
+```text
+Count High-Volume FHV trip records where trip miles are at least 10.0 and base
+passenger fare is at least 40.0.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM fhvhv_tripdata
+WHERE trip_miles >= 10.0
+  AND base_passenger_fare >= 40.0;
+```
+
+## Q9 - High-Volume FHV monthly fare and distance summary
+
+Text:
+
+```text
+For each High-Volume FHV partition year and month, calculate trip count, total
+passenger fare, total driver pay, minimum trip miles, and maximum trip miles.
+```
+
+SQL:
+
+```text
+SELECT
+  "year",
+  "month",
+  count(*) AS trips,
+  sum(base_passenger_fare) AS passenger_fare,
+  sum(driver_pay) AS driver_pay,
+  min(trip_miles) AS min_miles,
+  max(trip_miles) AS max_miles
+FROM fhvhv_tripdata
+GROUP BY "year", "month"
+ORDER BY "year", "month";
+```
+
+## Q10 - High-Volume FHV shared-ride flag distribution
+
+Text:
+
+```text
+For High-Volume FHV trip records in partition year 2022, count trips by shared
+request flag and shared match flag.
+```
+
+SQL:
+
+```text
+SELECT
+  shared_request_flag,
+  shared_match_flag,
+  count(*) AS trips
+FROM fhvhv_tripdata
+WHERE "year" = '2022'
+GROUP BY shared_request_flag, shared_match_flag
+ORDER BY trips DESC;
+```
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/data_configuration.md b/plugins/parquet-adapter/benchmarks/query_lists/data_configuration.md
index b71f2e99ec..bb301181c6 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/data_configuration.md
+++ b/plugins/parquet-adapter/benchmarks/query_lists/data_configuration.md
@@ -3,87 +3,139 @@
 ## Access Model Comparison
 
 ```text
-location on disk:
+Data location on disk:
 C:\PolyData\tlc_partitioned
+```
 
-url:
-file:///C:/PolyData/tlc_partitioned
+#### Relational Flat adapter:
 
-relational adapter name:
+```text
+name:
 tlcp
 
 flat mode
 
-document adapter name:
+url:
+file:///C:/PolyData/tlc_partitioned
+
+```
+
+#### Relational Normalized Adapter:
+
+```text
+name:
+tlcpn
+
+normalized mode
+
+url:
+file:///C:/PolyData/tlc_partitioned
+
+```
+
+#### Document Adapter:
+
+```text
+
+name:
 tlcpd
 
+url:
+file:///C:/PolyData/tlc_partitioned
+
 ```
 
 ## Nested Data
 
 ```text
-location on disk:
+Data location on disk:
 C:\PolyData\nested_customer\nestedcustomer.parquet
+```
 
-url:
-file:///C:/PolyData/nested_customer/nestedcustomer.parquet
+#### Relational Normalized Adapter
 
-relational adapter name:
+```text
+name:
 ncp
 
-adapter normalized mode
+normalized mode
+
+url:
+file:///C:/PolyData/nested_customer/nestedcustomer.parquet
+```
+#### Document Adapter
 
-document adapter name:
+```text
+name:
 ncpd
+
+url:
+file:///C:/PolyData/nested_customer/nestedcustomer.parquet
 ```
 
 ## Aggregation
-
-same relational adapter as Access Model Comparison
+Same adapters as Access Model Comparison
 
 ```text
-location on disk:
+Data location on disk:
 C:\PolyData\tlc_partitioned
+```
 
-url:
-file:///C:/PolyData/tlc_partitioned
+#### Relational Adapter
 
-relational adapter name:
+```text
+name:
 tlcp
 
+flat mode
 
-document adapter name:
+url:
+file:///C:/PolyData/tlc_partitioned
+```
+
+#### Document Adapter
+
+```text
+name:
 tlcpd
 
+url:
+file:///C:/PolyData/tlc_partitioned
+
 ```
 
 
 ## Partitioning
 
-### Repartitioned data, flat
-
 ```text
-location on disk:
+Data location on disk:
+
 C:\PolyData\tlc_repartitioned
+C:\PolyData\tlc_unpartitioned
+```
 
-url:
-file:///C:/PolyData/tlc_repartitioned
+### Relational Adapter, Repartitioned data, flat
 
-relational adapter name:
+```text
+name:
 tlcr
 
+flat
+
+url:
+file:///C:/PolyData/tlc_repartitioned
+
 ```
 
-### Unpartitioned data, flat
+### Relational Adapter, Unpartitioned data, flat
 
 ```text
-location on disk:
-C:\PolyData\tlc_unpartitioned
+name:
+tlcu
+
+flat
 
 url:
 file:///C:/PolyData/tlc_unpartitioned
 
-relational adapter name:
-tlcu
-
 ```
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_query_specification.md b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_query_specification.md
index f998491028..92af24584f 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_query_specification.md
+++ b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_query_specification.md
@@ -11,3 +11,140 @@
 All queries cap the returned result set at `100000` rows or documents. The
 Polypheny relational query list intentionally keeps Q03 and Q04 to one
 parent-child join, while Q05 is the explicit two-join nested traversal probe.
+
+## Q1 - Filtered full customer scan
+
+Text:
+
+```text
+Find customer records from the BUILDING market segment where the nation key is
+between 5 and 15, and return every available customer field.
+```
+
+SQL:
+
+```text
+SELECT *
+FROM ncp__nestedcustomer
+WHERE c_mktsegment = 'BUILDING'
+  AND c_nationkey BETWEEN 5 AND 15
+LIMIT 100000;
+```
+
+## Q2 - Filtered customer projection
+
+Text:
+
+```text
+Find customer records from the BUILDING market segment with a non-negative
+account balance, and return only the explicit customer scalar fields.
+```
+
+SQL:
+
+```text
+SELECT
+  c_custkey,
+  c_name,
+  c_address,
+  c_nationkey,
+  c_phone,
+  c_acctbal,
+  c_mktsegment,
+  c_comment
+FROM ncp__nestedcustomer
+WHERE c_mktsegment = 'BUILDING'
+  AND c_acctbal >= 0
+LIMIT 100000;
+```
+
+## Q3 - Filtered nested order projection
+
+Text:
+
+```text
+Find nested order elements with final order status and total price at least
+100000, and return selected order fields.
+```
+
+SQL:
+
+```text
+SELECT
+  col.__polypheny_row_id AS orders_list_row_id,
+  col.__polypheny_parent_row_id AS orders_group_row_id,
+  o.o_orderkey,
+  o.o_orderstatus,
+  o.o_totalprice,
+  o.o_orderdate,
+  o.o_orderpriority,
+  o.o_clerk
+FROM ncp__nestedcustomer__c_orders__list col
+JOIN ncp__nestedcustomer__c_orders__list__element o
+  ON o.__polypheny_parent_row_id = col.__polypheny_row_id
+WHERE o.o_orderstatus = 'F'
+  AND o.o_totalprice >= 100000
+LIMIT 100000;
+```
+
+## Q4 - Filtered deeply nested lineitem projection
+
+Text:
+
+```text
+Find nested lineitem elements with return flag R and ship mode MAIL, and return
+selected lineitem fields.
+```
+
+SQL:
+
+```text
+SELECT
+  olil.__polypheny_row_id AS lineitems_list_row_id,
+  olil.__polypheny_parent_row_id AS lineitems_group_row_id,
+  l.l_partkey,
+  l.l_suppkey,
+  l.l_linenumber,
+  l.l_quantity,
+  l.l_extendedprice,
+  l.l_discount,
+  l.l_returnflag,
+  l.l_shipmode
+FROM ncp__nestedcustomer__c_orders__list__element__o_lineitems__list olil
+JOIN ncp__nestedcustomer__c_orders__list__element__o_lineitems__list__element l
+  ON l.__polypheny_parent_row_id = olil.__polypheny_row_id
+WHERE l.l_returnflag = 'R'
+  AND l.l_shipmode = 'MAIL'
+LIMIT 100000;
+```
+
+## Q5 - Unfiltered two-level nested lineitem projection
+
+Text:
+
+```text
+Traverse from nested order lineitem groups to lineitem list elements without a
+filter, and return selected lineitem fields.
+```
+
+SQL:
+
+```text
+SELECT
+  oli.__polypheny_row_id AS lineitems_group_row_id,
+  olil.__polypheny_row_id AS lineitems_list_row_id,
+  l.l_partkey,
+  l.l_suppkey,
+  l.l_linenumber,
+  l.l_quantity,
+  l.l_extendedprice,
+  l.l_discount,
+  l.l_returnflag,
+  l.l_shipmode
+FROM ncp__nestedcustomer__c_orders__list__element__o_lineitems oli
+JOIN ncp__nestedcustomer__c_orders__list__element__o_lineitems__list olil
+  ON olil.__polypheny_parent_row_id = oli.__polypheny_row_id
+JOIN ncp__nestedcustomer__c_orders__list__element__o_lineitems__list__element l
+  ON l.__polypheny_parent_row_id = olil.__polypheny_row_id
+LIMIT 100000;
+```
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md
index 6e7724ce94..8da739d14f 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md
+++ b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md
@@ -27,3 +27,178 @@ plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_reparti
 plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_sql.sql
 plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_unpartitioned_sql.sql
 ```
+
+## Q1 - Repartitioned full table baseline
+
+Text:
+
+```text
+Count all Yellow Taxi trip records in the repartitioned dataset.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM tlcr__yellow_tripdata;
+```
+
+## Q2 - Repartitioned partition by year
+
+Text:
+
+```text
+Count Yellow Taxi trip records in the repartitioned dataset for partition year
+2022.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM tlcr__yellow_tripdata
+WHERE "year" = '2022';
+```
+
+## Q3 - Repartitioned partition by month
+
+Text:
+
+```text
+Count Yellow Taxi trip records in the repartitioned dataset for partition year
+2022 and partition month 10.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM tlcr__yellow_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+```
+
+## Q4 - Repartitioned filtered count, full scan
+
+Text:
+
+```text
+Count Green Taxi trip records in the repartitioned dataset where trip distance
+is at least 10.0 and total amount is at least 40.0.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM tlcr__green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+```
+
+## Q5 - Repartitioned filtered count, partition by year
+
+Text:
+
+```text
+Count Green Taxi trip records in the repartitioned dataset for partition year
+2022 where trip distance is at least 10.0 and total amount is at least 40.0.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM tlcr__green_tripdata
+WHERE "year" = '2022'
+  AND trip_distance >= 10.0
+  AND total_amount >= 40.0;
+```
+
+## Q6 - Unpartitioned full table baseline
+
+Text:
+
+```text
+Count all Yellow Taxi trip records in the unpartitioned dataset.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM tlcu__yellow_tripdata;
+```
+
+## Q7 - Unpartitioned partition-column filter by year
+
+Text:
+
+```text
+Count Yellow Taxi trip records in the unpartitioned dataset where the physical
+year column is 2022.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM tlcu__yellow_tripdata
+WHERE "year" = '2022';
+```
+
+## Q8 - Unpartitioned partition-column filter by month
+
+Text:
+
+```text
+Count Yellow Taxi trip records in the unpartitioned dataset where the physical
+year column is 2022 and the physical month column is 10.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM tlcu__yellow_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
+```
+
+## Q9 - Unpartitioned filtered count, full scan
+
+Text:
+
+```text
+Count Green Taxi trip records in the unpartitioned dataset where trip distance
+is at least 10.0 and total amount is at least 40.0.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM tlcu__green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
+```
+
+## Q10 - Unpartitioned filtered count with year filter
+
+Text:
+
+```text
+Count Green Taxi trip records in the unpartitioned dataset where the physical
+year column is 2022, trip distance is at least 10.0, and total amount is at
+least 40.0.
+```
+
+SQL:
+
+```text
+SELECT count(*) AS row_count
+FROM tlcu__green_tripdata
+WHERE "year" = '2022'
+  AND trip_distance >= 10.0
+  AND total_amount >= 40.0;
+```
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_duckdb_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_duckdb_tlcp_results.csv
index 4c65de1a62..75719245b7 100644
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_duckdb_tlcp_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_duckdb_tlcp_results.csv
@@ -1,31 +1,31 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-12T09:46:50.648077Z","Q01","Full record access","warmup",1,8517,3711544,22,true,""
-"2026-06-12T09:47:01.193663400Z","Q01","Full record access","measured",1,10540,3711544,22,true,""
-"2026-06-12T09:47:11.738263700Z","Q01","Full record access","measured",2,10545,3711544,22,true,""
-"2026-06-12T09:47:21.648860300Z","Q01","Full record access","measured",3,9907,3711544,22,true,""
-"2026-06-12T09:47:30.567001400Z","Q01","Full record access","measured",4,8916,3711544,22,true,""
-"2026-06-12T09:47:43.442874600Z","Q01","Full record access","measured",5,12826,3711544,22,true,""
-"2026-06-12T09:47:44.057229700Z","Q02","Projection","warmup",1,569,3711544,5,true,""
-"2026-06-12T09:47:44.670079900Z","Q02","Projection","measured",1,606,3711544,5,true,""
-"2026-06-12T09:47:45.269937800Z","Q02","Projection","measured",2,597,3711544,5,true,""
-"2026-06-12T09:47:45.940713600Z","Q02","Projection","measured",3,670,3711544,5,true,""
-"2026-06-12T09:47:46.610987500Z","Q02","Projection","measured",4,666,3711544,5,true,""
-"2026-06-12T09:47:47.254055500Z","Q02","Projection","measured",5,639,3711544,5,true,""
-"2026-06-12T09:47:47.365132800Z","Q03","Filtered count","warmup",1,99,1,1,true,""
-"2026-06-12T09:47:47.456690200Z","Q03","Filtered count","measured",1,90,1,1,true,""
-"2026-06-12T09:47:47.604777600Z","Q03","Filtered count","measured",2,141,1,1,true,""
-"2026-06-12T09:47:47.719009700Z","Q03","Filtered count","measured",3,113,1,1,true,""
-"2026-06-12T09:47:47.822657600Z","Q03","Filtered count","measured",4,101,1,1,true,""
-"2026-06-12T09:47:47.918083300Z","Q03","Filtered count","measured",5,94,1,1,true,""
-"2026-06-12T09:47:49.207444600Z","Q04","Filtered full record access","warmup",1,1286,283006,22,true,""
-"2026-06-12T09:47:50.490971900Z","Q04","Filtered full record access","measured",1,1252,283006,22,true,""
-"2026-06-12T09:47:51.956861Z","Q04","Filtered full record access","measured",2,1451,283006,22,true,""
-"2026-06-12T09:47:53.357309100Z","Q04","Filtered full record access","measured",3,1386,283006,22,true,""
-"2026-06-12T09:47:54.849118800Z","Q04","Filtered full record access","measured",4,1488,283006,22,true,""
-"2026-06-12T09:47:56.446559700Z","Q04","Filtered full record access","measured",5,1592,283006,22,true,""
-"2026-06-12T09:47:56.773658700Z","Q05","Filtered projection","warmup",1,323,283006,5,true,""
-"2026-06-12T09:47:57.018475200Z","Q05","Filtered projection","measured",1,239,283006,5,true,""
-"2026-06-12T09:47:57.237897800Z","Q05","Filtered projection","measured",2,217,283006,5,true,""
-"2026-06-12T09:47:57.537328300Z","Q05","Filtered projection","measured",3,296,283006,5,true,""
-"2026-06-12T09:47:57.775825200Z","Q05","Filtered projection","measured",4,236,283006,5,true,""
-"2026-06-12T09:47:58.058269200Z","Q05","Filtered projection","measured",5,280,283006,5,true,""
+"2026-06-14T16:27:50.041020500Z","Q01","Full record access","warmup",1,3113,3711544,22,true,""
+"2026-06-14T16:27:53.231149400Z","Q01","Full record access","measured",1,3200,3711544,22,true,""
+"2026-06-14T16:27:57.100720300Z","Q01","Full record access","measured",2,3858,3711544,22,true,""
+"2026-06-14T16:28:00.962230300Z","Q01","Full record access","measured",3,3859,3711544,22,true,""
+"2026-06-14T16:28:04.589305600Z","Q01","Full record access","measured",4,3625,3711544,22,true,""
+"2026-06-14T16:28:08.411903800Z","Q01","Full record access","measured",5,3820,3711544,22,true,""
+"2026-06-14T16:28:08.611464Z","Q02","Projection","warmup",1,198,3711544,5,true,""
+"2026-06-14T16:28:08.826196500Z","Q02","Projection","measured",1,213,3711544,5,true,""
+"2026-06-14T16:28:09.027522600Z","Q02","Projection","measured",2,201,3711544,5,true,""
+"2026-06-14T16:28:09.252846100Z","Q02","Projection","measured",3,223,3711544,5,true,""
+"2026-06-14T16:28:09.488607900Z","Q02","Projection","measured",4,235,3711544,5,true,""
+"2026-06-14T16:28:09.711438300Z","Q02","Projection","measured",5,222,3711544,5,true,""
+"2026-06-14T16:28:09.751100Z","Q03","Filtered count","warmup",1,37,1,1,true,""
+"2026-06-14T16:28:09.785217200Z","Q03","Filtered count","measured",1,33,1,1,true,""
+"2026-06-14T16:28:09.821217600Z","Q03","Filtered count","measured",2,34,1,1,true,""
+"2026-06-14T16:28:09.861749500Z","Q03","Filtered count","measured",3,39,1,1,true,""
+"2026-06-14T16:28:09.899930700Z","Q03","Filtered count","measured",4,36,1,1,true,""
+"2026-06-14T16:28:09.936337700Z","Q03","Filtered count","measured",5,35,1,1,true,""
+"2026-06-14T16:28:10.428752700Z","Q04","Filtered full record access","warmup",1,491,283006,22,true,""
+"2026-06-14T16:28:10.859842200Z","Q04","Filtered full record access","measured",1,429,283006,22,true,""
+"2026-06-14T16:28:11.395Z","Q04","Filtered full record access","measured",2,538,283006,22,true,""
+"2026-06-14T16:28:11.908048400Z","Q04","Filtered full record access","measured",3,506,283006,22,true,""
+"2026-06-14T16:28:12.379206800Z","Q04","Filtered full record access","measured",4,470,283006,22,true,""
+"2026-06-14T16:28:12.838228500Z","Q04","Filtered full record access","measured",5,457,283006,22,true,""
+"2026-06-14T16:28:12.957753500Z","Q05","Filtered projection","warmup",1,117,283006,5,true,""
+"2026-06-14T16:28:13.074274Z","Q05","Filtered projection","measured",1,115,283006,5,true,""
+"2026-06-14T16:28:13.185959800Z","Q05","Filtered projection","measured",2,116,283006,5,true,""
+"2026-06-14T16:28:13.306733200Z","Q05","Filtered projection","measured",3,114,283006,5,true,""
+"2026-06-14T16:28:13.420558300Z","Q05","Filtered projection","measured",4,121,283006,5,true,""
+"2026-06-14T16:28:13.542542900Z","Q05","Filtered projection","measured",5,110,283006,5,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_mql_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_mql_tlcp_results.csv
index 6527d81beb..caa5f931ff 100644
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_mql_tlcp_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_mql_tlcp_results.csv
@@ -1,31 +1,31 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-12T09:50:36.833283500Z","Q01","Full document access","warmup",1,290589,3711544,20,true,""
-"2026-06-12T09:54:35.760834700Z","Q01","Full document access","measured",1,238912,3711544,20,true,""
-"2026-06-12T09:56:45.003602300Z","Q01","Full document access","measured",2,129238,3711544,20,true,""
-"2026-06-12T09:58:54.228624900Z","Q01","Full document access","measured",3,129224,3711544,20,true,""
-"2026-06-12T10:01:03.330599200Z","Q01","Full document access","measured",4,129101,3711544,20,true,""
-"2026-06-12T10:03:12.055590200Z","Q01","Full document access","measured",5,128715,3711544,20,true,""
-"2026-06-12T10:04:50.818928Z","Q02","Projection","warmup",1,98763,3711544,6,true,""
-"2026-06-12T10:06:28.991072300Z","Q02","Projection","measured",1,98171,3711544,6,true,""
-"2026-06-12T10:08:07.107897600Z","Q02","Projection","measured",2,98115,3711544,6,true,""
-"2026-06-12T10:09:44.582687400Z","Q02","Projection","measured",3,97476,3711544,6,true,""
-"2026-06-12T10:11:22.319387Z","Q02","Projection","measured",4,97734,3711544,6,true,""
-"2026-06-12T10:13:00.087204500Z","Q02","Projection","measured",5,97767,3711544,6,true,""
-"2026-06-12T10:14:14.609274600Z","Q03","Filtered count","warmup",1,74522,1,1,true,""
-"2026-06-12T10:15:29.870523500Z","Q03","Filtered count","measured",1,75260,1,1,true,""
-"2026-06-12T10:16:44.354726300Z","Q03","Filtered count","measured",2,74482,1,1,true,""
-"2026-06-12T10:17:58.690538600Z","Q03","Filtered count","measured",3,74334,1,1,true,""
-"2026-06-12T10:19:13.083381700Z","Q03","Filtered count","measured",4,74392,1,1,true,""
-"2026-06-12T10:20:28.965763600Z","Q03","Filtered count","measured",5,75880,1,1,true,""
-"2026-06-12T10:21:47.827442800Z","Q04","Filtered full document access","warmup",1,78862,283006,20,true,""
-"2026-06-12T10:23:06.365838400Z","Q04","Filtered full document access","measured",1,78538,283006,20,true,""
-"2026-06-12T10:24:25.064815800Z","Q04","Filtered full document access","measured",2,78697,283006,20,true,""
-"2026-06-12T10:25:44.770667100Z","Q04","Filtered full document access","measured",3,79703,283006,20,true,""
-"2026-06-12T10:27:04.131447300Z","Q04","Filtered full document access","measured",4,79361,283006,20,true,""
-"2026-06-12T10:28:22.893644600Z","Q04","Filtered full document access","measured",5,78759,283006,20,true,""
-"2026-06-12T10:29:40.789254700Z","Q05","Filtered projection","warmup",1,77895,283006,6,true,""
-"2026-06-12T10:30:57.257155900Z","Q05","Filtered projection","measured",1,76467,283006,6,true,""
-"2026-06-12T10:32:14.530637400Z","Q05","Filtered projection","measured",2,77273,283006,6,true,""
-"2026-06-12T10:33:31.285965200Z","Q05","Filtered projection","measured",3,76754,283006,6,true,""
-"2026-06-12T10:34:47.726504900Z","Q05","Filtered projection","measured",4,76439,283006,6,true,""
-"2026-06-12T10:36:04.162332700Z","Q05","Filtered projection","measured",5,76437,283006,6,true,""
+"2026-06-14T15:44:29.455571Z","Q01","Full document access","warmup",1,166518,3711544,20,true,""
+"2026-06-14T15:47:05.842198500Z","Q01","Full document access","measured",1,156366,3711544,20,true,""
+"2026-06-14T15:49:24.237618Z","Q01","Full document access","measured",2,138395,3711544,20,true,""
+"2026-06-14T15:51:44.372184500Z","Q01","Full document access","measured",3,140144,3711544,20,true,""
+"2026-06-14T15:54:03.191489900Z","Q01","Full document access","measured",4,138809,3711544,20,true,""
+"2026-06-14T15:56:21.678686800Z","Q01","Full document access","measured",5,138490,3711544,20,true,""
+"2026-06-14T15:58:00.868545700Z","Q02","Projection","warmup",1,99185,3711544,6,true,""
+"2026-06-14T15:59:40.348341200Z","Q02","Projection","measured",1,99480,3711544,6,true,""
+"2026-06-14T16:01:19.620141700Z","Q02","Projection","measured",2,99273,3711544,6,true,""
+"2026-06-14T16:02:58.349980700Z","Q02","Projection","measured",3,98725,3711544,6,true,""
+"2026-06-14T16:04:54.707042300Z","Q02","Projection","measured",4,116357,3711544,6,true,""
+"2026-06-14T16:06:54.345503600Z","Q02","Projection","measured",5,119637,3711544,6,true,""
+"2026-06-14T16:06:55.123892500Z","Q03","Filtered count","warmup",1,777,1,1,true,""
+"2026-06-14T16:06:55.587678300Z","Q03","Filtered count","measured",1,462,1,1,true,""
+"2026-06-14T16:06:56.007963700Z","Q03","Filtered count","measured",2,420,1,1,true,""
+"2026-06-14T16:06:56.406266200Z","Q03","Filtered count","measured",3,396,1,1,true,""
+"2026-06-14T16:06:56.831640200Z","Q03","Filtered count","measured",4,425,1,1,true,""
+"2026-06-14T16:06:57.245307700Z","Q03","Filtered count","measured",5,421,1,1,true,""
+"2026-06-14T16:07:13.369376800Z","Q04","Filtered full document access","warmup",1,16114,283006,20,true,""
+"2026-06-14T16:07:27.500732200Z","Q04","Filtered full document access","measured",1,14130,283006,20,true,""
+"2026-06-14T16:07:42.563591300Z","Q04","Filtered full document access","measured",2,15062,283006,20,true,""
+"2026-06-14T16:07:57.572047200Z","Q04","Filtered full document access","measured",3,15007,283006,20,true,""
+"2026-06-14T16:08:12.123056200Z","Q04","Filtered full document access","measured",4,14549,283006,20,true,""
+"2026-06-14T16:08:27.139753300Z","Q04","Filtered full document access","measured",5,15015,283006,20,true,""
+"2026-06-14T16:08:39.773332400Z","Q05","Filtered projection","warmup",1,12636,283006,6,true,""
+"2026-06-14T16:08:52.896157200Z","Q05","Filtered projection","measured",1,13119,283006,6,true,""
+"2026-06-14T16:09:05.485797800Z","Q05","Filtered projection","measured",2,12592,283006,6,true,""
+"2026-06-14T16:09:17.986830800Z","Q05","Filtered projection","measured",3,12495,283006,6,true,""
+"2026-06-14T16:09:30.759425200Z","Q05","Filtered projection","measured",4,12772,283006,6,true,""
+"2026-06-14T16:09:43.183545500Z","Q05","Filtered projection","measured",5,12438,283006,6,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rf_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rf_tlcp_results.csv
index 1344da857c..70f17987e0 100644
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rf_tlcp_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rf_tlcp_results.csv
@@ -1,31 +1,31 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-12T09:45:54.362874400Z","Q01","Full record access","warmup",1,40791,3711544,22,true,""
-"2026-06-12T09:46:34.530473300Z","Q01","Full record access","measured",1,40161,3711544,22,true,""
-"2026-06-12T09:47:37.440857300Z","Q01","Full record access","measured",2,62904,3711544,22,true,""
-"2026-06-12T09:48:54.548951100Z","Q01","Full record access","measured",3,77106,3711544,22,true,""
-"2026-06-12T09:49:58.954841800Z","Q01","Full record access","measured",4,64403,3711544,22,true,""
-"2026-06-12T09:50:57.972229400Z","Q01","Full record access","measured",5,59007,3711544,22,true,""
-"2026-06-12T09:51:20.414646Z","Q02","Projection","warmup",1,22439,3711544,5,true,""
-"2026-06-12T09:51:41.635903200Z","Q02","Projection","measured",1,21219,3711544,5,true,""
-"2026-06-12T09:52:02.873664800Z","Q02","Projection","measured",2,21236,3711544,5,true,""
-"2026-06-12T09:52:23.746682Z","Q02","Projection","measured",3,20870,3711544,5,true,""
-"2026-06-12T09:52:43.365204500Z","Q02","Projection","measured",4,19616,3711544,5,true,""
-"2026-06-12T09:53:10.157633100Z","Q02","Projection","measured",5,26790,3711544,5,true,""
-"2026-06-12T09:53:11.888295400Z","Q03","Filtered count","warmup",1,1724,1,1,true,""
-"2026-06-12T09:53:12.219333100Z","Q03","Filtered count","measured",1,329,1,1,true,""
-"2026-06-12T09:53:12.603726Z","Q03","Filtered count","measured",2,383,1,1,true,""
-"2026-06-12T09:53:12.964772900Z","Q03","Filtered count","measured",3,359,1,1,true,""
-"2026-06-12T09:53:13.304016900Z","Q03","Filtered count","measured",4,338,1,1,true,""
-"2026-06-12T09:53:13.687276900Z","Q03","Filtered count","measured",5,381,1,1,true,""
-"2026-06-12T09:53:29.042161500Z","Q04","Filtered full record access","warmup",1,15353,283006,22,true,""
-"2026-06-12T09:53:39.072189100Z","Q04","Filtered full record access","measured",1,10030,283006,22,true,""
-"2026-06-12T09:53:46.817912Z","Q04","Filtered full record access","measured",2,7742,283006,22,true,""
-"2026-06-12T09:53:53.031570100Z","Q04","Filtered full record access","measured",3,6212,283006,22,true,""
-"2026-06-12T09:53:59.616799600Z","Q04","Filtered full record access","measured",4,6585,283006,22,true,""
-"2026-06-12T09:54:06.204763800Z","Q04","Filtered full record access","measured",5,6586,283006,22,true,""
-"2026-06-12T09:54:08.465553700Z","Q05","Filtered projection","warmup",1,2260,283006,5,true,""
-"2026-06-12T09:54:10.725788900Z","Q05","Filtered projection","measured",1,2259,283006,5,true,""
-"2026-06-12T09:54:13.092607100Z","Q05","Filtered projection","measured",2,2368,283006,5,true,""
-"2026-06-12T09:54:15.472430900Z","Q05","Filtered projection","measured",3,2377,283006,5,true,""
-"2026-06-12T09:54:17.689137Z","Q05","Filtered projection","measured",4,2217,283006,5,true,""
-"2026-06-12T09:54:19.890105800Z","Q05","Filtered projection","measured",5,2199,283006,5,true,""
+"2026-06-14T16:23:10.715132100Z","Q01","Full record access","warmup",1,31119,3711544,22,true,""
+"2026-06-14T16:23:40.280725500Z","Q01","Full record access","measured",1,29562,3711544,22,true,""
+"2026-06-14T16:24:08.936536600Z","Q01","Full record access","measured",2,28654,3711544,22,true,""
+"2026-06-14T16:24:38.597744300Z","Q01","Full record access","measured",3,29660,3711544,22,true,""
+"2026-06-14T16:25:06.863735100Z","Q01","Full record access","measured",4,28270,3711544,22,true,""
+"2026-06-14T16:25:39.432254400Z","Q01","Full record access","measured",5,32563,3711544,22,true,""
+"2026-06-14T16:25:49.206873500Z","Q02","Projection","warmup",1,9774,3711544,5,true,""
+"2026-06-14T16:25:58.929835200Z","Q02","Projection","measured",1,9721,3711544,5,true,""
+"2026-06-14T16:26:09.007085600Z","Q02","Projection","measured",2,10076,3711544,5,true,""
+"2026-06-14T16:26:18.215564800Z","Q02","Projection","measured",3,9208,3711544,5,true,""
+"2026-06-14T16:26:28.190616Z","Q02","Projection","measured",4,9972,3711544,5,true,""
+"2026-06-14T16:26:37.468649Z","Q02","Projection","measured",5,9276,3711544,5,true,""
+"2026-06-14T16:26:37.777891400Z","Q03","Filtered count","warmup",1,308,1,1,true,""
+"2026-06-14T16:26:37.932876900Z","Q03","Filtered count","measured",1,153,1,1,true,""
+"2026-06-14T16:26:38.081380Z","Q03","Filtered count","measured",2,147,1,1,true,""
+"2026-06-14T16:26:38.232068400Z","Q03","Filtered count","measured",3,149,1,1,true,""
+"2026-06-14T16:26:38.390678400Z","Q03","Filtered count","measured",4,158,1,1,true,""
+"2026-06-14T16:26:38.565415Z","Q03","Filtered count","measured",5,180,1,1,true,""
+"2026-06-14T16:26:45.207154800Z","Q04","Filtered full record access","warmup",1,6634,283006,22,true,""
+"2026-06-14T16:26:51.534018400Z","Q04","Filtered full record access","measured",1,6328,283006,22,true,""
+"2026-06-14T16:26:57.503060700Z","Q04","Filtered full record access","measured",2,5964,283006,22,true,""
+"2026-06-14T16:27:03.586319100Z","Q04","Filtered full record access","measured",3,6083,283006,22,true,""
+"2026-06-14T16:27:09.457301800Z","Q04","Filtered full record access","measured",4,5872,283006,22,true,""
+"2026-06-14T16:27:15.578598700Z","Q04","Filtered full record access","measured",5,6120,283006,22,true,""
+"2026-06-14T16:27:17.888088500Z","Q05","Filtered projection","warmup",1,2307,283006,5,true,""
+"2026-06-14T16:27:20.156461500Z","Q05","Filtered projection","measured",1,2266,283006,5,true,""
+"2026-06-14T16:27:22.861423800Z","Q05","Filtered projection","measured",2,2702,283006,5,true,""
+"2026-06-14T16:27:25.313359600Z","Q05","Filtered projection","measured",3,2462,283006,5,true,""
+"2026-06-14T16:27:27.842086800Z","Q05","Filtered projection","measured",4,2517,283006,5,true,""
+"2026-06-14T16:27:30.540422100Z","Q05","Filtered projection","measured",5,2697,283006,5,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rn_tlcpn_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rn_tlcpn_results.csv
new file mode 100644
index 0000000000..0a7ab5bfa7
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rn_tlcpn_results.csv
@@ -0,0 +1,31 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-14T16:16:37.341692Z","Q01","Full record access","warmup",1,38942,3711544,23,true,""
+"2026-06-14T16:17:13.778784800Z","Q01","Full record access","measured",1,36433,3711544,23,true,""
+"2026-06-14T16:17:49.661472300Z","Q01","Full record access","measured",2,35881,3711544,23,true,""
+"2026-06-14T16:18:25.524578600Z","Q01","Full record access","measured",3,35872,3711544,23,true,""
+"2026-06-14T16:19:01.327697400Z","Q01","Full record access","measured",4,35800,3711544,23,true,""
+"2026-06-14T16:19:37.587345Z","Q01","Full record access","measured",5,36262,3711544,23,true,""
+"2026-06-14T16:19:47.343335400Z","Q02","Projection","warmup",1,9748,3711544,5,true,""
+"2026-06-14T16:19:56.715404600Z","Q02","Projection","measured",1,9367,3711544,5,true,""
+"2026-06-14T16:20:09.070808600Z","Q02","Projection","measured",2,12355,3711544,5,true,""
+"2026-06-14T16:20:19.844304400Z","Q02","Projection","measured",3,10775,3711544,5,true,""
+"2026-06-14T16:20:30.055608600Z","Q02","Projection","measured",4,10207,3711544,5,true,""
+"2026-06-14T16:20:40.132668800Z","Q02","Projection","measured",5,10075,3711544,5,true,""
+"2026-06-14T16:20:40.760886200Z","Q03","Filtered count","warmup",1,634,1,1,true,""
+"2026-06-14T16:20:40.943007500Z","Q03","Filtered count","measured",1,174,1,1,true,""
+"2026-06-14T16:20:41.112775400Z","Q03","Filtered count","measured",2,167,1,1,true,""
+"2026-06-14T16:20:41.298228600Z","Q03","Filtered count","measured",3,185,1,1,true,""
+"2026-06-14T16:20:41.473638500Z","Q03","Filtered count","measured",4,175,1,1,true,""
+"2026-06-14T16:20:41.627465900Z","Q03","Filtered count","measured",5,153,1,1,true,""
+"2026-06-14T16:20:49.294897700Z","Q04","Filtered full record access","warmup",1,7667,283006,23,true,""
+"2026-06-14T16:20:57.070962200Z","Q04","Filtered full record access","measured",1,7772,283006,23,true,""
+"2026-06-14T16:21:05.133703500Z","Q04","Filtered full record access","measured",2,8067,283006,23,true,""
+"2026-06-14T16:21:12.604239400Z","Q04","Filtered full record access","measured",3,7463,283006,23,true,""
+"2026-06-14T16:21:20.105170400Z","Q04","Filtered full record access","measured",4,7509,283006,23,true,""
+"2026-06-14T16:21:27.275032100Z","Q04","Filtered full record access","measured",5,7161,283006,23,true,""
+"2026-06-14T16:21:29.514826600Z","Q05","Filtered projection","warmup",1,2238,283006,5,true,""
+"2026-06-14T16:21:31.841192200Z","Q05","Filtered projection","measured",1,2325,283006,5,true,""
+"2026-06-14T16:21:33.653167300Z","Q05","Filtered projection","measured",2,1811,283006,5,true,""
+"2026-06-14T16:21:36.088383800Z","Q05","Filtered projection","measured",3,2435,283006,5,true,""
+"2026-06-14T16:21:38.631180500Z","Q05","Filtered projection","measured",4,2543,283006,5,true,""
+"2026-06-14T16:21:41.034781600Z","Q05","Filtered projection","measured",5,2401,283006,5,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_spark_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_spark_tlcp_results.csv
index 455c0b9cd1..83530c4887 100644
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_spark_tlcp_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_spark_tlcp_results.csv
@@ -1,31 +1,31 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-2026-06-12T09:48:19.913258Z,Q01,Full record access,warmup,1,43259,3711544,22,true,
-2026-06-12T09:48:51.169823Z,Q01,Full record access,measured,1,27921,3711544,22,true,
-2026-06-12T09:49:16.732182Z,Q01,Full record access,measured,2,25561,3711544,22,true,
-2026-06-12T09:49:44.410944Z,Q01,Full record access,measured,3,24345,3711544,22,true,
-2026-06-12T09:50:13.120790Z,Q01,Full record access,measured,4,25364,3711544,22,true,
-2026-06-12T09:50:41.221098Z,Q01,Full record access,measured,5,24764,3711544,22,true,
-2026-06-12T09:51:01.314001Z,Q02,Projection,warmup,1,16752,3711544,5,true,
-2026-06-12T09:51:18.305782Z,Q02,Projection,measured,1,16991,3711544,5,true,
-2026-06-12T09:51:39.398316Z,Q02,Projection,measured,2,17755,3711544,5,true,
-2026-06-12T09:51:55.868125Z,Q02,Projection,measured,3,16468,3711544,5,true,
-2026-06-12T09:52:14.968439Z,Q02,Projection,measured,4,15765,3711544,5,true,
-2026-06-12T09:52:31.499685Z,Q02,Projection,measured,5,16530,3711544,5,true,
-2026-06-12T09:52:34.301926Z,Q03,Filtered count,warmup,1,2799,1,1,true,
-2026-06-12T09:52:36.028543Z,Q03,Filtered count,measured,1,1725,1,1,true,
-2026-06-12T09:52:40.922759Z,Q03,Filtered count,measured,2,1556,1,1,true,
-2026-06-12T09:52:42.398228Z,Q03,Filtered count,measured,3,1474,1,1,true,
-2026-06-12T09:52:43.814541Z,Q03,Filtered count,measured,4,1415,1,1,true,
-2026-06-12T09:52:45.179240Z,Q03,Filtered count,measured,5,1363,1,1,true,
-2026-06-12T09:52:48.861576Z,Q04,Filtered full record access,warmup,1,3680,283006,22,true,
-2026-06-12T09:52:52.032020Z,Q04,Filtered full record access,measured,1,3169,283006,22,true,
-2026-06-12T09:52:55.733280Z,Q04,Filtered full record access,measured,2,3700,283006,22,true,
-2026-06-12T09:52:58.926331Z,Q04,Filtered full record access,measured,3,3192,283006,22,true,
-2026-06-12T09:53:01.968822Z,Q04,Filtered full record access,measured,4,3041,283006,22,true,
-2026-06-12T09:53:05.092640Z,Q04,Filtered full record access,measured,5,3122,283006,22,true,
-2026-06-12T09:53:07.331107Z,Q05,Filtered projection,warmup,1,2236,283006,5,true,
-2026-06-12T09:53:09.402292Z,Q05,Filtered projection,measured,1,2069,283006,5,true,
-2026-06-12T09:53:15.233590Z,Q05,Filtered projection,measured,2,2495,283006,5,true,
-2026-06-12T09:53:17.848310Z,Q05,Filtered projection,measured,3,2612,283006,5,true,
-2026-06-12T09:53:21.189570Z,Q05,Filtered projection,measured,4,3339,283006,5,true,
-2026-06-12T09:53:23.983169Z,Q05,Filtered projection,measured,5,2792,283006,5,true,
+2026-06-14T16:29:06.720884Z,Q01,Full record access,warmup,1,27284,3711544,22,true,
+2026-06-14T16:29:30.109969Z,Q01,Full record access,measured,1,21448,3711544,22,true,
+2026-06-14T16:29:52.233193Z,Q01,Full record access,measured,2,22122,3711544,22,true,
+2026-06-14T16:30:16.034637Z,Q01,Full record access,measured,3,21838,3711544,22,true,
+2026-06-14T16:30:39.900848Z,Q01,Full record access,measured,4,21880,3711544,22,true,
+2026-06-14T16:31:03.288551Z,Q01,Full record access,measured,5,21403,3711544,22,true,
+2026-06-14T16:31:17.236679Z,Q02,Projection,warmup,1,13946,3711544,5,true,
+2026-06-14T16:31:30.923592Z,Q02,Projection,measured,1,13686,3711544,5,true,
+2026-06-14T16:31:46.522533Z,Q02,Projection,measured,2,13656,3711544,5,true,
+2026-06-14T16:31:59.861165Z,Q02,Projection,measured,3,13338,3711544,5,true,
+2026-06-14T16:32:15.910406Z,Q02,Projection,measured,4,14034,3711544,5,true,
+2026-06-14T16:32:29.535322Z,Q02,Projection,measured,5,13624,3711544,5,true,
+2026-06-14T16:32:31.748950Z,Q03,Filtered count,warmup,1,2211,1,1,true,
+2026-06-14T16:32:33.096012Z,Q03,Filtered count,measured,1,1346,1,1,true,
+2026-06-14T16:32:34.381052Z,Q03,Filtered count,measured,2,1284,1,1,true,
+2026-06-14T16:32:35.585960Z,Q03,Filtered count,measured,3,1204,1,1,true,
+2026-06-14T16:32:38.561445Z,Q03,Filtered count,measured,4,1001,1,1,true,
+2026-06-14T16:32:39.639383Z,Q03,Filtered count,measured,5,1077,1,1,true,
+2026-06-14T16:32:42.948919Z,Q04,Filtered full record access,warmup,1,3307,283006,22,true,
+2026-06-14T16:32:45.595065Z,Q04,Filtered full record access,measured,1,2645,283006,22,true,
+2026-06-14T16:32:47.823459Z,Q04,Filtered full record access,measured,2,2227,283006,22,true,
+2026-06-14T16:32:50.588787Z,Q04,Filtered full record access,measured,3,2764,283006,22,true,
+2026-06-14T16:32:53.654263Z,Q04,Filtered full record access,measured,4,3063,283006,22,true,
+2026-06-14T16:32:56.289451Z,Q04,Filtered full record access,measured,5,2634,283006,22,true,
+2026-06-14T16:32:58.322968Z,Q05,Filtered projection,warmup,1,2031,283006,5,true,
+2026-06-14T16:33:00.517794Z,Q05,Filtered projection,measured,1,2193,283006,5,true,
+2026-06-14T16:33:02.145589Z,Q05,Filtered projection,measured,2,1626,283006,5,true,
+2026-06-14T16:33:04.167749Z,Q05,Filtered projection,measured,3,2021,283006,5,true,
+2026-06-14T16:33:06.228424Z,Q05,Filtered projection,measured,4,2060,283006,5,true,
+2026-06-14T16:33:09.996918Z,Q05,Filtered projection,measured,5,1807,283006,5,true,
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_results_analysis.md b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_results_analysis.md
index aa21283cb4..9a4e78d695 100644
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_results_analysis.md
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_results_analysis.md
@@ -7,29 +7,31 @@ plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_
 ```
 
 The access model comparison run completed successfully for all systems. Every
-query has `5/5` successful measured runs, and the result row counts match across
-Polypheny relational, Polypheny document MQL, DuckDB, and Apache Spark.
+query has `5/5` successful measured runs, and result row counts now match across
+Polypheny relational flat, Polypheny relational normalized, Polypheny document
+MQL, DuckDB, and Apache Spark.
 
 ## Main Findings
 
 DuckDB is fastest for all five access-pattern queries.
 
-Polypheny relational is much faster than Polypheny document MQL on this flat TLC
-table. This is expected for a relational dataset because the document path adds
-document materialization overhead without a nested-data advantage.
+Polypheny relational flat and normalized mode are close for root-table access.
+Normalized mode is somewhat slower for full-row reads and filtered full-row
+reads, but it does not show a major performance regression for these flat
+queries. Filtered projection is slightly faster in normalized mode.
 
-Projection has a clear effect. In Polypheny relational, the full scan Q01 takes
-60716.2 ms, while the projection Q02 takes 21946.2 ms. The same pattern appears
-for filtered access: Q04 takes 7431.0 ms, while filtered projection Q05 takes
-2284.0 ms.
+The MQL filter issue is resolved. Q04 and Q05 now return `283006` rows, matching
+the SQL systems. MQL remains slower than Polypheny relational for document
+materialization, but filtered MQL queries are now much closer to the relational
+runtime than in the previous bad run.
 
 ## Interpretation
 
-Polypheny relational behaves best when the query can avoid full row
-materialization. It is especially competitive with Spark on filtered count and
-filtered projection queries, but DuckDB remains the strongest baseline.
+Projection has a strong effect across engines. In Polypheny relational flat,
+Q01 takes `29741.8 ms`, while Q02 takes `9650.6 ms`; Q04 takes `6073.4 ms`,
+while Q05 takes `2528.8 ms`.
 
-Polypheny document MQL is not a good fit for this flat-table access-model
-workload. Its runtimes stay high even for filtered count and projection queries,
-which suggests limited benefit from projection and filter pushdown through the
-document benchmark path.
+For this flat TLC workload, normalized relational mode does not appear to damage
+performance significantly when queries access only root fields. The document
+path is still more expensive for large result materialization, but filter
+pushdown now behaves consistently with the other systems.
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
index 740207df44..31ddf64693 100644
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
@@ -5,54 +5,60 @@ Warmup rows are excluded. Mean and median values use successful runs only.
 
 ## Source Files
 
-| System | CSV |
-| --- | --- |
-| Polypheny Relational | `plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rf_tlcp_results.csv` |
-| Polypheny Document MQL | `plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_mql_tlcp_results.csv` |
-| DuckDB | `plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_duckdb_tlcp_results.csv` |
-| Apache Spark | `plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_spark_tlcp_results.csv` |
+| System                          | CSV                                                                                                                         |
+|---------------------------------|-----------------------------------------------------------------------------------------------------------------------------|
+| Polypheny Relational Flat       | `plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rf_tlcp_results.csv`  |
+| Polypheny Relational Normalized | `plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rn_tlcpn_results.csv` |
+| Polypheny Document MQL          | `plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_mql_tlcp_results.csv` |
+| DuckDB                          | `plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_duckdb_tlcp_results.csv`        |
+| Apache Spark                    | `plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_spark_tlcp_results.csv`         |
 
 ## Mean Elapsed Time (ms)
 
-| Query | Description                 | Polypheny Relational | Polypheny Document MQL | DuckDB   | Apache Spark | Row counts |
-|-------|-----------------------------|----------------------|------------------------|----------|--------------|------------|
-| Q01   | Full record access          | 60,716.2             | 151,038.0              | 10,546.8 | 25,591.0     | 3,711,544  |
-| Q02   | Projection                  | 21,946.2             | 97,852.6               | 635.6    | 16,701.8     | 3,711,544  |
-| Q03   | Filtered count              | 358.0                | 74,869.6               | 107.8    | 1,506.6      | 1          |
-| Q04   | Filtered full record access | 7,431.0              | 79,011.6               | 1,433.8  | 3,244.8      | 283,006    |
-| Q05   | Filtered projection         | 2,284.0              | 76,674.0               | 253.6    | 2,661.4      | 283,006    |
+| Query | Description                 | Polypheny Relational Flat | Polypheny Relational Normalized | Polypheny Document MQL | DuckDB  | Apache Spark | Row counts |
+|-------|-----------------------------|---------------------------|---------------------------------|------------------------|---------|--------------|------------|
+| Q01   | Full record access          | 29,741.8                  | 36,049.6                        | 142,440.8              | 3,672.4 | 21,738.2     | 3,711,544  |
+| Q02   | Projection                  | 9,650.6                   | 10,555.8                        | 106,694.4              | 218.8   | 13,667.6     | 3,711,544  |
+| Q03   | Filtered count              | 157.4                     | 170.8                           | 424.8                  | 35.4    | 1,182.4      | 1          |
+| Q04   | Filtered full record access | 6,073.4                   | 7,594.4                         | 14,752.6               | 480.0   | 2,666.6      | 283,006    |
+| Q05   | Filtered projection         | 2,528.8                   | 2,303.0                         | 12,683.2               | 115.2   | 1,941.4      | 283,006    |
 
 ## Result Row Counts
 
-| Query | Description                 | Polypheny Relational | Polypheny Document MQL | DuckDB    | Apache Spark |
-|-------|-----------------------------|----------------------|------------------------|-----------|--------------|
-| Q01   | Full record access          | 3,711,544            | 3,711,544              | 3,711,544 | 3,711,544    |
-| Q02   | Projection                  | 3,711,544            | 3,711,544              | 3,711,544 | 3,711,544    |
-| Q03   | Filtered count              | 1                    | 1                      | 1         | 1            |
-| Q04   | Filtered full record access | 283,006              | 283,006                | 283,006   | 283,006      |
-| Q05   | Filtered projection         | 283,006              | 283,006                | 283,006   | 283,006      |
+| Query | Description                 | Polypheny Relational Flat | Polypheny Relational Normalized | Polypheny Document MQL | DuckDB    | Apache Spark |
+|-------|-----------------------------|---------------------------|---------------------------------|------------------------|-----------|--------------|
+| Q01   | Full record access          | 3,711,544                 | 3,711,544                       | 3,711,544              | 3,711,544 | 3,711,544    |
+| Q02   | Projection                  | 3,711,544                 | 3,711,544                       | 3,711,544              | 3,711,544 | 3,711,544    |
+| Q03   | Filtered count              | 1                         | 1                               | 1                      | 1         | 1            |
+| Q04   | Filtered full record access | 283,006                   | 283,006                         | 283,006                | 283,006   | 283,006      |
+| Q05   | Filtered projection         | 283,006                   | 283,006                         | 283,006                | 283,006   | 283,006      |
 
 ## Detailed Summary (ms)
 
-| System                 | Query | Description                   | Runs | Mean      | Median    | Min       | Max       | Rows      | Columns | Status |
-|------------------------|-------|-------------------------------|------|-----------|-----------|-----------|-----------|-----------|---------|--------|
-| Polypheny Relational   | Q01   | Full record access            | 5/5  | 60,716.2  | 62,904.0  | 40,161.0  | 77,106.0  | 3,711,544 | 22      | ok     |
-| Polypheny Relational   | Q02   | Projection                    | 5/5  | 21,946.2  | 21,219.0  | 19,616.0  | 26,790.0  | 3,711,544 | 5       | ok     |
-| Polypheny Relational   | Q03   | Filtered count                | 5/5  | 358.0     | 359.0     | 329.0     | 383.0     | 1         | 1       | ok     |
-| Polypheny Relational   | Q04   | Filtered full record access   | 5/5  | 7,431.0   | 6,586.0   | 6,212.0   | 10,030.0  | 283,006   | 22      | ok     |
-| Polypheny Relational   | Q05   | Filtered projection           | 5/5  | 2,284.0   | 2,259.0   | 2,199.0   | 2,377.0   | 283,006   | 5       | ok     |
-| Polypheny Document MQL | Q01   | Full document access          | 5/5  | 151,038.0 | 129,224.0 | 128,715.0 | 238,912.0 | 3,711,544 | 20      | ok     |
-| Polypheny Document MQL | Q02   | Projection                    | 5/5  | 97,852.6  | 97,767.0  | 97,476.0  | 98,171.0  | 3,711,544 | 6       | ok     |
-| Polypheny Document MQL | Q03   | Filtered count                | 5/5  | 74,869.6  | 74,482.0  | 74,334.0  | 75,880.0  | 1         | 1       | ok     |
-| Polypheny Document MQL | Q04   | Filtered full document access | 5/5  | 79,011.6  | 78,759.0  | 78,538.0  | 79,703.0  | 283,006   | 20      | ok     |
-| Polypheny Document MQL | Q05   | Filtered projection           | 5/5  | 76,674.0  | 76,467.0  | 76,437.0  | 77,273.0  | 283,006   | 6       | ok     |
-| DuckDB                 | Q01   | Full record access            | 5/5  | 10,546.8  | 10,540.0  | 8,916.0   | 12,826.0  | 3,711,544 | 22      | ok     |
-| DuckDB                 | Q02   | Projection                    | 5/5  | 635.6     | 639.0     | 597.0     | 670.0     | 3,711,544 | 5       | ok     |
-| DuckDB                 | Q03   | Filtered count                | 5/5  | 107.8     | 101.0     | 90.0      | 141.0     | 1         | 1       | ok     |
-| DuckDB                 | Q04   | Filtered full record access   | 5/5  | 1,433.8   | 1,451.0   | 1,252.0   | 1,592.0   | 283,006   | 22      | ok     |
-| DuckDB                 | Q05   | Filtered projection           | 5/5  | 253.6     | 239.0     | 217.0     | 296.0     | 283,006   | 5       | ok     |
-| Apache Spark           | Q01   | Full record access            | 5/5  | 25,591.0  | 25,364.0  | 24,345.0  | 27,921.0  | 3,711,544 | 22      | ok     |
-| Apache Spark           | Q02   | Projection                    | 5/5  | 16,701.8  | 16,530.0  | 15,765.0  | 17,755.0  | 3,711,544 | 5       | ok     |
-| Apache Spark           | Q03   | Filtered count                | 5/5  | 1,506.6   | 1,474.0   | 1,363.0   | 1,725.0   | 1         | 1       | ok     |
-| Apache Spark           | Q04   | Filtered full record access   | 5/5  | 3,244.8   | 3,169.0   | 3,041.0   | 3,700.0   | 283,006   | 22      | ok     |
-| Apache Spark           | Q05   | Filtered projection           | 5/5  | 2,661.4   | 2,612.0   | 2,069.0   | 3,339.0   | 283,006   | 5       | ok     |
+| System                          | Query | Description                   | Runs | Mean      | Median    | Min       | Max       | Rows      | Columns | Status |
+|---------------------------------|-------|-------------------------------|------|-----------|-----------|-----------|-----------|-----------|---------|--------|
+| Polypheny Relational Flat       | Q01   | Full record access            | 5/5  | 29,741.8  | 29,562.0  | 28,270.0  | 32,563.0  | 3,711,544 | 22      | ok     |
+| Polypheny Relational Flat       | Q02   | Projection                    | 5/5  | 9,650.6   | 9,721.0   | 9,208.0   | 10,076.0  | 3,711,544 | 5       | ok     |
+| Polypheny Relational Flat       | Q03   | Filtered count                | 5/5  | 157.4     | 153.0     | 147.0     | 180.0     | 1         | 1       | ok     |
+| Polypheny Relational Flat       | Q04   | Filtered full record access   | 5/5  | 6,073.4   | 6,083.0   | 5,872.0   | 6,328.0   | 283,006   | 22      | ok     |
+| Polypheny Relational Flat       | Q05   | Filtered projection           | 5/5  | 2,528.8   | 2,517.0   | 2,266.0   | 2,702.0   | 283,006   | 5       | ok     |
+| Polypheny Relational Normalized | Q01   | Full record access            | 5/5  | 36,049.6  | 35,881.0  | 35,800.0  | 36,433.0  | 3,711,544 | 23      | ok     |
+| Polypheny Relational Normalized | Q02   | Projection                    | 5/5  | 10,555.8  | 10,207.0  | 9,367.0   | 12,355.0  | 3,711,544 | 5       | ok     |
+| Polypheny Relational Normalized | Q03   | Filtered count                | 5/5  | 170.8     | 174.0     | 153.0     | 185.0     | 1         | 1       | ok     |
+| Polypheny Relational Normalized | Q04   | Filtered full record access   | 5/5  | 7,594.4   | 7,509.0   | 7,161.0   | 8,067.0   | 283,006   | 23      | ok     |
+| Polypheny Relational Normalized | Q05   | Filtered projection           | 5/5  | 2,303.0   | 2,401.0   | 1,811.0   | 2,543.0   | 283,006   | 5       | ok     |
+| Polypheny Document MQL          | Q01   | Full document access          | 5/5  | 142,440.8 | 138,809.0 | 138,395.0 | 156,366.0 | 3,711,544 | 20      | ok     |
+| Polypheny Document MQL          | Q02   | Projection                    | 5/5  | 106,694.4 | 99,480.0  | 98,725.0  | 119,637.0 | 3,711,544 | 6       | ok     |
+| Polypheny Document MQL          | Q03   | Filtered count                | 5/5  | 424.8     | 421.0     | 396.0     | 462.0     | 1         | 1       | ok     |
+| Polypheny Document MQL          | Q04   | Filtered full document access | 5/5  | 14,752.6  | 15,007.0  | 14,130.0  | 15,062.0  | 283,006   | 20      | ok     |
+| Polypheny Document MQL          | Q05   | Filtered projection           | 5/5  | 12,683.2  | 12,592.0  | 12,438.0  | 13,119.0  | 283,006   | 6       | ok     |
+| DuckDB                          | Q01   | Full record access            | 5/5  | 3,672.4   | 3,820.0   | 3,200.0   | 3,859.0   | 3,711,544 | 22      | ok     |
+| DuckDB                          | Q02   | Projection                    | 5/5  | 218.8     | 222.0     | 201.0     | 235.0     | 3,711,544 | 5       | ok     |
+| DuckDB                          | Q03   | Filtered count                | 5/5  | 35.4      | 35.0      | 33.0      | 39.0      | 1         | 1       | ok     |
+| DuckDB                          | Q04   | Filtered full record access   | 5/5  | 480.0     | 470.0     | 429.0     | 538.0     | 283,006   | 22      | ok     |
+| DuckDB                          | Q05   | Filtered projection           | 5/5  | 115.2     | 115.0     | 110.0     | 121.0     | 283,006   | 5       | ok     |
+| Apache Spark                    | Q01   | Full record access            | 5/5  | 21,738.2  | 21,838.0  | 21,403.0  | 22,122.0  | 3,711,544 | 22      | ok     |
+| Apache Spark                    | Q02   | Projection                    | 5/5  | 13,667.6  | 13,656.0  | 13,338.0  | 14,034.0  | 3,711,544 | 5       | ok     |
+| Apache Spark                    | Q03   | Filtered count                | 5/5  | 1,182.4   | 1,204.0   | 1,001.0   | 1,346.0   | 1         | 1       | ok     |
+| Apache Spark                    | Q04   | Filtered full record access   | 5/5  | 2,666.6   | 2,645.0   | 2,227.0   | 3,063.0   | 283,006   | 22      | ok     |
+| Apache Spark                    | Q05   | Filtered projection           | 5/5  | 1,941.4   | 2,021.0   | 1,626.0   | 2,193.0   | 283,006   | 5       | ok     |
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_results.csv b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_results.csv
index 2c14e65769..0e3616cc8a 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_results.csv
@@ -1,61 +1,61 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-13T16:18:39.812840Z","Q01","Full Yellow Taxi Row Count","warmup",1,32,1,1,true,""
-"2026-06-13T16:18:39.844002700Z","Q01","Full Yellow Taxi Row Count","measured",1,29,1,1,true,""
-"2026-06-13T16:18:39.878139800Z","Q01","Full Yellow Taxi Row Count","measured",2,33,1,1,true,""
-"2026-06-13T16:18:39.915438100Z","Q01","Full Yellow Taxi Row Count","measured",3,36,1,1,true,""
-"2026-06-13T16:18:39.954922300Z","Q01","Full Yellow Taxi Row Count","measured",4,37,1,1,true,""
-"2026-06-13T16:18:39.990441500Z","Q01","Full Yellow Taxi Row Count","measured",5,34,1,1,true,""
-"2026-06-13T16:18:40.012618400Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,12,1,1,true,""
-"2026-06-13T16:18:40.026616100Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,13,1,1,true,""
-"2026-06-13T16:18:40.041616700Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,13,1,1,true,""
-"2026-06-13T16:18:40.058627Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,16,1,1,true,""
-"2026-06-13T16:18:40.073150700Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,13,1,1,true,""
-"2026-06-13T16:18:40.088150200Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,14,1,1,true,""
-"2026-06-13T16:18:40.363886500Z","Q03","Yellow Taxi Trips On One Day","warmup",1,277,1,1,true,""
-"2026-06-13T16:18:40.710424200Z","Q03","Yellow Taxi Trips On One Day","measured",1,344,1,1,true,""
-"2026-06-13T16:18:41.042412200Z","Q03","Yellow Taxi Trips On One Day","measured",2,329,1,1,true,""
-"2026-06-13T16:18:41.375731Z","Q03","Yellow Taxi Trips On One Day","measured",3,333,1,1,true,""
-"2026-06-13T16:18:41.718607100Z","Q03","Yellow Taxi Trips On One Day","measured",4,345,1,1,true,""
-"2026-06-13T16:18:42.080891600Z","Q03","Yellow Taxi Trips On One Day","measured",5,356,1,1,true,""
-"2026-06-13T16:18:43.026594500Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,943,1,1,true,""
-"2026-06-13T16:18:43.844050400Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,815,1,1,true,""
-"2026-06-13T16:18:44.629106600Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,783,1,1,true,""
-"2026-06-13T16:18:45.473488300Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,843,1,1,true,""
-"2026-06-13T16:18:46.283838700Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,816,1,1,true,""
-"2026-06-13T16:18:47.002618600Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,709,1,1,true,""
-"2026-06-13T16:18:48.077630400Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","warmup",1,1073,37,6,true,""
-"2026-06-13T16:18:49.103492700Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",1,1024,37,6,true,""
-"2026-06-13T16:18:50.207139200Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",2,1102,37,6,true,""
-"2026-06-13T16:18:51.297515500Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",3,1097,37,6,true,""
-"2026-06-13T16:18:52.312841200Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",4,1007,37,6,true,""
-"2026-06-13T16:18:53.449097400Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",5,1132,37,6,true,""
-"2026-06-13T16:18:53.546584Z","Q06","Full High-Volume FHV Row Count","warmup",1,95,1,1,true,""
-"2026-06-13T16:18:53.649126400Z","Q06","Full High-Volume FHV Row Count","measured",1,100,1,1,true,""
-"2026-06-13T16:18:53.745853100Z","Q06","Full High-Volume FHV Row Count","measured",2,96,1,1,true,""
-"2026-06-13T16:18:53.845552700Z","Q06","Full High-Volume FHV Row Count","measured",3,97,1,1,true,""
-"2026-06-13T16:18:53.948296100Z","Q06","Full High-Volume FHV Row Count","measured",4,101,1,1,true,""
-"2026-06-13T16:18:54.039826Z","Q06","Full High-Volume FHV Row Count","measured",5,90,1,1,true,""
-"2026-06-13T16:18:54.067834900Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,26,1,1,true,""
-"2026-06-13T16:18:54.092361400Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,23,1,1,true,""
-"2026-06-13T16:18:54.123006100Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,30,1,1,true,""
-"2026-06-13T16:18:54.154161500Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,29,1,1,true,""
-"2026-06-13T16:18:54.180673400Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,25,1,1,true,""
-"2026-06-13T16:18:54.207137100Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,25,1,1,true,""
-"2026-06-13T16:18:59.591629900Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,5383,1,1,true,""
-"2026-06-13T16:19:04.682341Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,5089,1,1,true,""
-"2026-06-13T16:19:10.019509400Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,5335,1,1,true,""
-"2026-06-13T16:19:15.619102400Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,5598,1,1,true,""
-"2026-06-13T16:19:23.891127200Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,8269,1,1,true,""
-"2026-06-13T16:19:31.241675600Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,7348,1,1,true,""
-"2026-06-13T16:19:49.576679Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","warmup",1,18332,37,7,true,""
-"2026-06-13T16:20:01.796796700Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",1,12223,37,7,true,""
-"2026-06-13T16:20:11.503071400Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",2,9701,37,7,true,""
-"2026-06-13T16:20:21.066618600Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",3,9562,37,7,true,""
-"2026-06-13T16:20:29.996161100Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",4,8930,37,7,true,""
-"2026-06-13T16:20:38.031790900Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",5,8037,37,7,true,""
-"2026-06-13T16:20:39.501626800Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","warmup",1,1464,4,3,true,""
-"2026-06-13T16:20:41.099732Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",1,1604,4,3,true,""
-"2026-06-13T16:20:42.734104100Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",2,1625,4,3,true,""
-"2026-06-13T16:20:44.125904500Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",3,1391,4,3,true,""
-"2026-06-13T16:20:45.702649700Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",4,1575,4,3,true,""
-"2026-06-13T16:20:47.305453500Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",5,1602,4,3,true,""
+"2026-06-14T18:43:37.639272400Z","Q01","Full Yellow Taxi Row Count","warmup",1,28,1,1,true,""
+"2026-06-14T18:43:37.670383800Z","Q01","Full Yellow Taxi Row Count","measured",1,27,1,1,true,""
+"2026-06-14T18:43:37.700889100Z","Q01","Full Yellow Taxi Row Count","measured",2,29,1,1,true,""
+"2026-06-14T18:43:37.729014700Z","Q01","Full Yellow Taxi Row Count","measured",3,29,1,1,true,""
+"2026-06-14T18:43:37.762333200Z","Q01","Full Yellow Taxi Row Count","measured",4,30,1,1,true,""
+"2026-06-14T18:43:37.791863100Z","Q01","Full Yellow Taxi Row Count","measured",5,28,1,1,true,""
+"2026-06-14T18:43:37.807876500Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,15,1,1,true,""
+"2026-06-14T18:43:37.818875100Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,10,1,1,true,""
+"2026-06-14T18:43:37.833873500Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,14,1,1,true,""
+"2026-06-14T18:43:37.847874500Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,13,1,1,true,""
+"2026-06-14T18:43:37.858872800Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,9,1,1,true,""
+"2026-06-14T18:43:37.874872600Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,14,1,1,true,""
+"2026-06-14T18:43:38.034929700Z","Q03","Yellow Taxi Trips On One Day","warmup",1,159,1,1,true,""
+"2026-06-14T18:43:38.222989400Z","Q03","Yellow Taxi Trips On One Day","measured",1,187,1,1,true,""
+"2026-06-14T18:43:38.434603400Z","Q03","Yellow Taxi Trips On One Day","measured",2,211,1,1,true,""
+"2026-06-14T18:43:38.668860600Z","Q03","Yellow Taxi Trips On One Day","measured",3,232,1,1,true,""
+"2026-06-14T18:43:38.918927900Z","Q03","Yellow Taxi Trips On One Day","measured",4,248,1,1,true,""
+"2026-06-14T18:43:39.207028400Z","Q03","Yellow Taxi Trips On One Day","measured",5,285,1,1,true,""
+"2026-06-14T18:43:39.930224Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,723,1,1,true,""
+"2026-06-14T18:43:40.664837900Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,731,1,1,true,""
+"2026-06-14T18:43:41.387462100Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,721,1,1,true,""
+"2026-06-14T18:43:42.070013200Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,680,1,1,true,""
+"2026-06-14T18:43:42.720995800Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,649,1,1,true,""
+"2026-06-14T18:43:43.395078700Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,672,1,1,true,""
+"2026-06-14T18:43:44.576659500Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","warmup",1,1179,37,6,true,""
+"2026-06-14T18:43:45.787046400Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",1,1208,37,6,true,""
+"2026-06-14T18:43:46.907023700Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",2,1118,37,6,true,""
+"2026-06-14T18:43:48.091605200Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",3,1182,37,6,true,""
+"2026-06-14T18:43:49.354577200Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",4,1262,37,6,true,""
+"2026-06-14T18:43:50.435423900Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",5,1078,37,6,true,""
+"2026-06-14T18:43:50.527229700Z","Q06","Full High-Volume FHV Row Count","warmup",1,91,1,1,true,""
+"2026-06-14T18:43:50.614349800Z","Q06","Full High-Volume FHV Row Count","measured",1,87,1,1,true,""
+"2026-06-14T18:43:50.710301300Z","Q06","Full High-Volume FHV Row Count","measured",2,94,1,1,true,""
+"2026-06-14T18:43:50.805533Z","Q06","Full High-Volume FHV Row Count","measured",3,89,1,1,true,""
+"2026-06-14T18:43:50.900552700Z","Q06","Full High-Volume FHV Row Count","measured",4,94,1,1,true,""
+"2026-06-14T18:43:51.000088100Z","Q06","Full High-Volume FHV Row Count","measured",5,98,1,1,true,""
+"2026-06-14T18:43:51.022616100Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,21,1,1,true,""
+"2026-06-14T18:43:51.052617500Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,28,1,1,true,""
+"2026-06-14T18:43:51.082620600Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,28,1,1,true,""
+"2026-06-14T18:43:51.111163200Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,27,1,1,true,""
+"2026-06-14T18:43:51.134160700Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,22,1,1,true,""
+"2026-06-14T18:43:51.162169700Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,27,1,1,true,""
+"2026-06-14T18:43:56.167603200Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,5003,1,1,true,""
+"2026-06-14T18:44:01.305197800Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,5134,1,1,true,""
+"2026-06-14T18:44:06.351612800Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,5045,1,1,true,""
+"2026-06-14T18:44:11.321718600Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,4969,1,1,true,""
+"2026-06-14T18:44:16.379083Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,5055,1,1,true,""
+"2026-06-14T18:44:21.127130900Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,4747,1,1,true,""
+"2026-06-14T18:44:32.333756800Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","warmup",1,11207,37,7,true,""
+"2026-06-14T18:44:41.783208100Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",1,9444,37,7,true,""
+"2026-06-14T18:44:50.829426700Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",2,9045,37,7,true,""
+"2026-06-14T18:44:59.944287300Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",3,9118,37,7,true,""
+"2026-06-14T18:45:08.973503700Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",4,9026,37,7,true,""
+"2026-06-14T18:45:18.427029500Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",5,9451,37,7,true,""
+"2026-06-14T18:45:19.691872100Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","warmup",1,1263,4,3,true,""
+"2026-06-14T18:45:21.013469100Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",1,1322,4,3,true,""
+"2026-06-14T18:45:22.133541500Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",2,1117,4,3,true,""
+"2026-06-14T18:45:23.218791200Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",3,1084,4,3,true,""
+"2026-06-14T18:45:24.681179500Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",4,1461,4,3,true,""
+"2026-06-14T18:45:25.962680400Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",5,1279,4,3,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_results.csv b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_results.csv
new file mode 100644
index 0000000000..ed35759f2f
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_results.csv
@@ -0,0 +1,61 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-14T18:39:06.727807300Z","Q01","Full Yellow Taxi Row Count","warmup",1,317,1,1,true,""
+"2026-06-14T18:39:06.864668900Z","Q01","Full Yellow Taxi Row Count","measured",1,130,1,1,true,""
+"2026-06-14T18:39:06.981579900Z","Q01","Full Yellow Taxi Row Count","measured",2,116,1,1,true,""
+"2026-06-14T18:39:07.105175200Z","Q01","Full Yellow Taxi Row Count","measured",3,125,1,1,true,""
+"2026-06-14T18:39:07.214003700Z","Q01","Full Yellow Taxi Row Count","measured",4,108,1,1,true,""
+"2026-06-14T18:39:07.355728700Z","Q01","Full Yellow Taxi Row Count","measured",5,130,1,1,true,""
+"2026-06-14T18:39:07.521759900Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,163,1,1,true,""
+"2026-06-14T18:39:07.677739700Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,155,1,1,true,""
+"2026-06-14T18:39:07.816653Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,141,1,1,true,""
+"2026-06-14T18:39:08.011048600Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,192,1,1,true,""
+"2026-06-14T18:39:08.168791300Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,158,1,1,true,""
+"2026-06-14T18:39:08.338398800Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,166,1,1,true,""
+"2026-06-14T18:39:09.010490Z","Q03","Yellow Taxi Trips On One Day","warmup",1,663,1,1,true,""
+"2026-06-14T18:39:09.627315800Z","Q03","Yellow Taxi Trips On One Day","measured",1,616,1,1,true,""
+"2026-06-14T18:39:10.225926200Z","Q03","Yellow Taxi Trips On One Day","measured",2,599,1,1,true,""
+"2026-06-14T18:39:10.832457800Z","Q03","Yellow Taxi Trips On One Day","measured",3,609,1,1,true,""
+"2026-06-14T18:39:11.448210700Z","Q03","Yellow Taxi Trips On One Day","measured",4,611,1,1,true,""
+"2026-06-14T18:39:12.074721300Z","Q03","Yellow Taxi Trips On One Day","measured",5,626,1,1,true,""
+"2026-06-14T18:39:13.306381300Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,1229,1,1,true,""
+"2026-06-14T18:39:14.576455100Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,1270,1,1,true,""
+"2026-06-14T18:39:15.738683Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,1162,1,1,true,""
+"2026-06-14T18:39:18.234628700Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,2493,1,1,true,""
+"2026-06-14T18:39:21.626709300Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,3390,1,1,true,""
+"2026-06-14T18:39:24.235796600Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,2609,1,1,true,""
+"2026-06-14T18:39:26.404533600Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","warmup",1,2165,4,4,true,""
+"2026-06-14T18:39:28.807867600Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",1,2400,4,4,true,""
+"2026-06-14T18:39:31.329162900Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",2,2522,4,4,true,""
+"2026-06-14T18:39:33.826662100Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",3,2493,4,4,true,""
+"2026-06-14T18:39:36.214475Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",4,2386,4,4,true,""
+"2026-06-14T18:39:38.547850300Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",5,2331,4,4,true,""
+"2026-06-14T18:39:39.008803800Z","Q06","Full High-Volume FHV Row Count","warmup",1,458,1,1,true,""
+"2026-06-14T18:39:39.428348700Z","Q06","Full High-Volume FHV Row Count","measured",1,418,1,1,true,""
+"2026-06-14T18:39:39.794032100Z","Q06","Full High-Volume FHV Row Count","measured",2,364,1,1,true,""
+"2026-06-14T18:39:40.141464700Z","Q06","Full High-Volume FHV Row Count","measured",3,346,1,1,true,""
+"2026-06-14T18:39:40.406417600Z","Q06","Full High-Volume FHV Row Count","measured",4,263,1,1,true,""
+"2026-06-14T18:39:40.629583300Z","Q06","Full High-Volume FHV Row Count","measured",5,224,1,1,true,""
+"2026-06-14T18:39:40.889667800Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,257,1,1,true,""
+"2026-06-14T18:39:41.103907100Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,212,1,1,true,""
+"2026-06-14T18:39:41.387645400Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,283,1,1,true,""
+"2026-06-14T18:39:41.661220Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,273,1,1,true,""
+"2026-06-14T18:39:41.918291Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,257,1,1,true,""
+"2026-06-14T18:39:42.135223700Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,217,1,1,true,""
+"2026-06-14T18:39:55.275981Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,13138,1,1,true,""
+"2026-06-14T18:40:08.347333900Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,13069,1,1,true,""
+"2026-06-14T18:40:25.452405900Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,17103,1,1,true,""
+"2026-06-14T18:40:38.634345200Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,13155,1,1,true,""
+"2026-06-14T18:41:00.171441300Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,21529,1,1,true,""
+"2026-06-14T18:41:14.843351500Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,14670,1,1,true,""
+"2026-06-14T18:41:29.687714600Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","warmup",1,14843,4,5,true,""
+"2026-06-14T18:41:44.362076700Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",1,14673,4,5,true,""
+"2026-06-14T18:41:59.738012700Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",2,15375,4,5,true,""
+"2026-06-14T18:42:14.459089100Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",3,14719,4,5,true,""
+"2026-06-14T18:42:29.115529600Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",4,14655,4,5,true,""
+"2026-06-14T18:42:44.017663200Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",5,14900,4,5,true,""
+"2026-06-14T18:42:48.962237300Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","warmup",1,4943,2,1,true,""
+"2026-06-14T18:42:54.229056900Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",1,5265,2,1,true,""
+"2026-06-14T18:42:59.924825700Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",2,5694,2,1,true,""
+"2026-06-14T18:43:04.706655200Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",3,4780,2,1,true,""
+"2026-06-14T18:43:09.454408700Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",4,4747,2,1,true,""
+"2026-06-14T18:43:14.508725800Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",5,5052,2,1,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv
index 19d39ed41d..a7ad52244f 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv
@@ -1,61 +1,61 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-13T16:26:06.585658100Z","Q01","Full Yellow Taxi Row Count","warmup",1,584,1,1,true,""
-"2026-06-13T16:26:06.608468900Z","Q01","Full Yellow Taxi Row Count","measured",1,18,1,1,true,""
-"2026-06-13T16:26:06.617475300Z","Q01","Full Yellow Taxi Row Count","measured",2,9,1,1,true,""
-"2026-06-13T16:26:06.628187400Z","Q01","Full Yellow Taxi Row Count","measured",3,9,1,1,true,""
-"2026-06-13T16:26:06.638183400Z","Q01","Full Yellow Taxi Row Count","measured",4,10,1,1,true,""
-"2026-06-13T16:26:06.649188900Z","Q01","Full Yellow Taxi Row Count","measured",5,10,1,1,true,""
-"2026-06-13T16:26:06.854975800Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,205,1,1,true,""
-"2026-06-13T16:26:06.863974800Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,8,1,1,true,""
-"2026-06-13T16:26:06.873975700Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,9,1,1,true,""
-"2026-06-13T16:26:06.884975100Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,9,1,1,true,""
-"2026-06-13T16:26:06.895982100Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,11,1,1,true,""
-"2026-06-13T16:26:06.908495800Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,11,1,1,true,""
-"2026-06-13T16:26:09.322150700Z","Q03","Yellow Taxi Trips On One Day","warmup",1,2412,1,1,true,""
-"2026-06-13T16:26:10.285954300Z","Q03","Yellow Taxi Trips On One Day","measured",1,963,1,1,true,""
-"2026-06-13T16:26:11.249436900Z","Q03","Yellow Taxi Trips On One Day","measured",2,962,1,1,true,""
-"2026-06-13T16:26:12.249714200Z","Q03","Yellow Taxi Trips On One Day","measured",3,1004,1,1,true,""
-"2026-06-13T16:26:13.519290500Z","Q03","Yellow Taxi Trips On One Day","measured",4,1263,1,1,true,""
-"2026-06-13T16:26:14.812354800Z","Q03","Yellow Taxi Trips On One Day","measured",5,1295,1,1,true,""
-"2026-06-13T16:26:17.728352Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,2911,1,1,true,""
-"2026-06-13T16:26:20.549590Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,2820,1,1,true,""
-"2026-06-13T16:26:23.064638300Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,2513,1,1,true,""
-"2026-06-13T16:26:25.362808600Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,2297,1,1,true,""
-"2026-06-13T16:26:28.815385100Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,3450,1,1,true,""
-"2026-06-13T16:26:32.565408500Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,3748,1,1,true,""
-"2026-06-13T16:26:35.584987200Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","warmup",1,3017,37,6,true,""
-"2026-06-13T16:26:37.952868100Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",1,2366,37,6,true,""
-"2026-06-13T16:26:40.488911Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",2,2534,37,6,true,""
-"2026-06-13T16:26:42.987704700Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",3,2497,37,6,true,""
-"2026-06-13T16:26:45.357084700Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",4,2365,37,6,true,""
-"2026-06-13T16:26:47.858459700Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",5,2499,37,6,true,""
-"2026-06-13T16:26:48.002164Z","Q06","Full High-Volume FHV Row Count","warmup",1,141,1,1,true,""
-"2026-06-13T16:26:48.020884700Z","Q06","Full High-Volume FHV Row Count","measured",1,17,1,1,true,""
-"2026-06-13T16:26:48.038950900Z","Q06","Full High-Volume FHV Row Count","measured",2,15,1,1,true,""
-"2026-06-13T16:26:48.056527200Z","Q06","Full High-Volume FHV Row Count","measured",3,15,1,1,true,""
-"2026-06-13T16:26:48.075595900Z","Q06","Full High-Volume FHV Row Count","measured",4,17,1,1,true,""
-"2026-06-13T16:26:48.094382Z","Q06","Full High-Volume FHV Row Count","measured",5,16,1,1,true,""
-"2026-06-13T16:26:48.348679300Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,252,1,1,true,""
-"2026-06-13T16:26:48.366529300Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,16,1,1,true,""
-"2026-06-13T16:26:48.391129800Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,21,1,1,true,""
-"2026-06-13T16:26:48.411675500Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,18,1,1,true,""
-"2026-06-13T16:26:48.430610Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,18,1,1,true,""
-"2026-06-13T16:26:48.454762200Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,22,1,1,true,""
-"2026-06-13T16:27:07.210831600Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,18757,1,1,true,""
-"2026-06-13T16:27:22.809335Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,15594,1,1,true,""
-"2026-06-13T16:27:40.880589200Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,18070,1,1,true,""
-"2026-06-13T16:28:01.492578500Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,20611,1,1,true,""
-"2026-06-13T16:28:23.241874700Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,21747,1,1,true,""
-"2026-06-13T16:28:44.985877Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,21742,1,1,true,""
-"2026-06-13T16:29:11.666136Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","warmup",1,26678,37,7,true,""
-"2026-06-13T16:29:33.414716400Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",1,21746,37,7,true,""
-"2026-06-13T16:30:03.958506600Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",2,30554,37,7,true,""
-"2026-06-13T16:30:32.533876100Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",3,28563,37,7,true,""
-"2026-06-13T16:30:55.183413600Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",4,22647,37,7,true,""
-"2026-06-13T16:31:16.082232200Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",5,20897,37,7,true,""
-"2026-06-13T16:31:24.109868600Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","warmup",1,8026,4,3,true,""
-"2026-06-13T16:31:32.663960Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",1,8552,4,3,true,""
-"2026-06-13T16:31:41.281998800Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",2,8616,4,3,true,""
-"2026-06-13T16:31:49.097291200Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",3,7814,4,3,true,""
-"2026-06-13T16:31:56.551277700Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",4,7451,4,3,true,""
-"2026-06-13T16:32:04.932238200Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",5,8380,4,3,true,""
+"2026-06-14T18:34:06.309325200Z","Q01","Full Yellow Taxi Row Count","warmup",1,638,1,1,true,""
+"2026-06-14T18:34:06.329871500Z","Q01","Full Yellow Taxi Row Count","measured",1,17,1,1,true,""
+"2026-06-14T18:34:06.339868800Z","Q01","Full Yellow Taxi Row Count","measured",2,10,1,1,true,""
+"2026-06-14T18:34:06.347866900Z","Q01","Full Yellow Taxi Row Count","measured",3,7,1,1,true,""
+"2026-06-14T18:34:06.358871Z","Q01","Full Yellow Taxi Row Count","measured",4,10,1,1,true,""
+"2026-06-14T18:34:06.366869400Z","Q01","Full Yellow Taxi Row Count","measured",5,7,1,1,true,""
+"2026-06-14T18:34:06.571950900Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,203,1,1,true,""
+"2026-06-14T18:34:06.583950800Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,11,1,1,true,""
+"2026-06-14T18:34:06.595953400Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,12,1,1,true,""
+"2026-06-14T18:34:06.609953Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,13,1,1,true,""
+"2026-06-14T18:34:06.619960400Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,9,1,1,true,""
+"2026-06-14T18:34:06.633493800Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,13,1,1,true,""
+"2026-06-14T18:34:08.736407100Z","Q03","Yellow Taxi Trips On One Day","warmup",1,2105,1,1,true,""
+"2026-06-14T18:34:09.711177400Z","Q03","Yellow Taxi Trips On One Day","measured",1,970,1,1,true,""
+"2026-06-14T18:34:10.828544900Z","Q03","Yellow Taxi Trips On One Day","measured",2,1116,1,1,true,""
+"2026-06-14T18:34:11.910741800Z","Q03","Yellow Taxi Trips On One Day","measured",3,1081,1,1,true,""
+"2026-06-14T18:34:13.091403700Z","Q03","Yellow Taxi Trips On One Day","measured",4,1179,1,1,true,""
+"2026-06-14T18:34:14.207271700Z","Q03","Yellow Taxi Trips On One Day","measured",5,1116,1,1,true,""
+"2026-06-14T18:34:17.687087200Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,3477,1,1,true,""
+"2026-06-14T18:34:19.685634Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,2001,1,1,true,""
+"2026-06-14T18:34:21.647462200Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,1956,1,1,true,""
+"2026-06-14T18:34:23.560154Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,1911,1,1,true,""
+"2026-06-14T18:34:25.540854200Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,1978,1,1,true,""
+"2026-06-14T18:34:27.397676500Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,1861,1,1,true,""
+"2026-06-14T18:34:29.312283900Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","warmup",1,1909,37,6,true,""
+"2026-06-14T18:34:31.087535300Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",1,1778,37,6,true,""
+"2026-06-14T18:34:33.116183400Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",2,2022,37,6,true,""
+"2026-06-14T18:34:34.751787Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",3,1633,37,6,true,""
+"2026-06-14T18:34:36.425439800Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",4,1675,37,6,true,""
+"2026-06-14T18:34:37.876620700Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",5,1447,37,6,true,""
+"2026-06-14T18:34:37.967139400Z","Q06","Full High-Volume FHV Row Count","warmup",1,89,1,1,true,""
+"2026-06-14T18:34:37.979141500Z","Q06","Full High-Volume FHV Row Count","measured",1,10,1,1,true,""
+"2026-06-14T18:34:37.993141200Z","Q06","Full High-Volume FHV Row Count","measured",2,12,1,1,true,""
+"2026-06-14T18:34:38.005141100Z","Q06","Full High-Volume FHV Row Count","measured",3,11,1,1,true,""
+"2026-06-14T18:34:38.015141200Z","Q06","Full High-Volume FHV Row Count","measured",4,8,1,1,true,""
+"2026-06-14T18:34:38.029139100Z","Q06","Full High-Volume FHV Row Count","measured",5,13,1,1,true,""
+"2026-06-14T18:34:38.164139600Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,133,1,1,true,""
+"2026-06-14T18:34:38.179139Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,13,1,1,true,""
+"2026-06-14T18:34:38.194138800Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,13,1,1,true,""
+"2026-06-14T18:34:38.204139100Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,10,1,1,true,""
+"2026-06-14T18:34:38.218138Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,12,1,1,true,""
+"2026-06-14T18:34:38.232253300Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,13,1,1,true,""
+"2026-06-14T18:34:53.386664700Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,15154,1,1,true,""
+"2026-06-14T18:35:04.916488200Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,11529,1,1,true,""
+"2026-06-14T18:35:16.454210600Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,11536,1,1,true,""
+"2026-06-14T18:35:27.902533700Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,11448,1,1,true,""
+"2026-06-14T18:35:39.144143300Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,11244,1,1,true,""
+"2026-06-14T18:35:50.698519Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,11552,1,1,true,""
+"2026-06-14T18:36:04.368622900Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","warmup",1,13669,37,7,true,""
+"2026-06-14T18:36:17.179441700Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",1,12810,37,7,true,""
+"2026-06-14T18:36:30.004603100Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",2,12826,37,7,true,""
+"2026-06-14T18:36:43.337583700Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",3,13331,37,7,true,""
+"2026-06-14T18:36:56.552930100Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",4,13213,37,7,true,""
+"2026-06-14T18:37:10.605938500Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",5,14053,37,7,true,""
+"2026-06-14T18:37:16.030560400Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","warmup",1,5424,4,3,true,""
+"2026-06-14T18:37:21.799342Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",1,5768,4,3,true,""
+"2026-06-14T18:37:27.066088500Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",2,5266,4,3,true,""
+"2026-06-14T18:37:32.610836300Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",3,5543,4,3,true,""
+"2026-06-14T18:37:38.692573500Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",4,6081,4,3,true,""
+"2026-06-14T18:37:45.199175200Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",5,6506,4,3,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_results.csv b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_results.csv
index accc5ba3b6..fc66975710 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_results.csv
@@ -1,61 +1,61 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-2026-06-13T16:27:27.097966Z,Q01,Full Yellow Taxi Row Count,warmup,1,6934,1,1,true,
-2026-06-13T16:27:33.047541Z,Q01,Full Yellow Taxi Row Count,measured,1,5948,1,1,true,
-2026-06-13T16:27:38.204716Z,Q01,Full Yellow Taxi Row Count,measured,2,5150,1,1,true,
-2026-06-13T16:27:39.829272Z,Q01,Full Yellow Taxi Row Count,measured,3,1623,1,1,true,
-2026-06-13T16:27:40.997995Z,Q01,Full Yellow Taxi Row Count,measured,4,1167,1,1,true,
-2026-06-13T16:27:44.819868Z,Q01,Full Yellow Taxi Row Count,measured,5,3820,1,1,true,
-2026-06-13T16:27:45.473107Z,Q02,Yellow Taxi Count For One Partition Month,warmup,1,650,1,1,true,
-2026-06-13T16:27:46.403049Z,Q02,Yellow Taxi Count For One Partition Month,measured,1,928,1,1,true,
-2026-06-13T16:27:47.612824Z,Q02,Yellow Taxi Count For One Partition Month,measured,2,1202,1,1,true,
-2026-06-13T16:27:49.478912Z,Q02,Yellow Taxi Count For One Partition Month,measured,3,1845,1,1,true,
-2026-06-13T16:27:50.003638Z,Q02,Yellow Taxi Count For One Partition Month,measured,4,521,1,1,true,
-2026-06-13T16:27:51.656940Z,Q02,Yellow Taxi Count For One Partition Month,measured,5,1644,1,1,true,
-2026-06-13T16:28:09.599118Z,Q03,Yellow Taxi Trips On One Day,warmup,1,16432,1,1,true,
-2026-06-13T16:28:24.111656Z,Q03,Yellow Taxi Trips On One Day,measured,1,14493,1,1,true,
-2026-06-13T16:28:40.255167Z,Q03,Yellow Taxi Trips On One Day,measured,2,14638,1,1,true,
-2026-06-13T16:28:51.115402Z,Q03,Yellow Taxi Trips On One Day,measured,3,10858,1,1,true,
-2026-06-13T16:29:08.169250Z,Q03,Yellow Taxi Trips On One Day,measured,4,14919,1,1,true,
-2026-06-13T16:29:14.911980Z,Q03,Yellow Taxi Trips On One Day,measured,5,6741,1,1,true,
-2026-06-13T16:29:27.690275Z,Q04,Long And Expensive Yellow Taxi Trips,warmup,1,12775,1,1,true,
-2026-06-13T16:29:33.822154Z,Q04,Long And Expensive Yellow Taxi Trips,measured,1,4584,1,1,true,
-2026-06-13T16:29:39.689255Z,Q04,Long And Expensive Yellow Taxi Trips,measured,2,5865,1,1,true,
-2026-06-13T16:29:59.335622Z,Q04,Long And Expensive Yellow Taxi Trips,measured,3,19634,1,1,true,
-2026-06-13T16:30:05.971641Z,Q04,Long And Expensive Yellow Taxi Trips,measured,4,5323,1,1,true,
-2026-06-13T16:30:18.476296Z,Q04,Long And Expensive Yellow Taxi Trips,measured,5,12495,1,1,true,
-2026-06-13T16:30:37.026744Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,warmup,1,16944,37,6,true,
-2026-06-13T16:30:51.328729Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,1,14301,37,6,true,
-2026-06-13T16:30:58.388773Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,2,7059,37,6,true,
-2026-06-13T16:31:13.154915Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,3,13174,37,6,true,
-2026-06-13T16:31:23.338272Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,4,10182,37,6,true,
-2026-06-13T16:31:43.923948Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,5,19037,37,6,true,
-2026-06-13T16:31:55.661309Z,Q06,Full High-Volume FHV Row Count,warmup,1,11728,1,1,true,
-2026-06-13T16:32:04.718399Z,Q06,Full High-Volume FHV Row Count,measured,1,9053,1,1,true,
-2026-06-13T16:32:08.338039Z,Q06,Full High-Volume FHV Row Count,measured,2,2000,1,1,true,
-2026-06-13T16:32:10.296066Z,Q06,Full High-Volume FHV Row Count,measured,3,1957,1,1,true,
-2026-06-13T16:32:12.162010Z,Q06,Full High-Volume FHV Row Count,measured,4,1865,1,1,true,
-2026-06-13T16:32:14.216618Z,Q06,Full High-Volume FHV Row Count,measured,5,2053,1,1,true,
-2026-06-13T16:32:14.491550Z,Q07,High-Volume FHV Count For One Partition Month,warmup,1,272,1,1,true,
-2026-06-13T16:32:14.790299Z,Q07,High-Volume FHV Count For One Partition Month,measured,1,297,1,1,true,
-2026-06-13T16:32:15.075488Z,Q07,High-Volume FHV Count For One Partition Month,measured,2,284,1,1,true,
-2026-06-13T16:32:15.401778Z,Q07,High-Volume FHV Count For One Partition Month,measured,3,325,1,1,true,
-2026-06-13T16:32:15.665155Z,Q07,High-Volume FHV Count For One Partition Month,measured,4,262,1,1,true,
-2026-06-13T16:32:15.925830Z,Q07,High-Volume FHV Count For One Partition Month,measured,5,259,1,1,true,
-2026-06-13T16:32:28.784124Z,Q08,Long And Expensive High-Volume FHV Trips,warmup,1,12856,1,1,true,
-2026-06-13T16:32:43.262753Z,Q08,Long And Expensive High-Volume FHV Trips,measured,1,12908,1,1,true,
-2026-06-13T16:32:54.654463Z,Q08,Long And Expensive High-Volume FHV Trips,measured,2,11390,1,1,true,
-2026-06-13T16:33:06.909118Z,Q08,Long And Expensive High-Volume FHV Trips,measured,3,12253,1,1,true,
-2026-06-13T16:33:20.344540Z,Q08,Long And Expensive High-Volume FHV Trips,measured,4,11661,1,1,true,
-2026-06-13T16:33:32.517470Z,Q08,Long And Expensive High-Volume FHV Trips,measured,5,12172,1,1,true,
-2026-06-13T16:34:05.124084Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,warmup,1,31077,37,7,true,
-2026-06-13T16:34:34.390226Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,1,27588,37,7,true,
-2026-06-13T16:35:04.261228Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,2,28279,37,7,true,
-2026-06-13T16:35:34.053722Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,3,28142,37,7,true,
-2026-06-13T16:36:03.300281Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,4,27662,37,7,true,
-2026-06-13T16:36:32.996928Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,5,27946,37,7,true,
-2026-06-13T16:36:40.298599Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,warmup,1,7300,4,3,true,
-2026-06-13T16:36:47.835222Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,1,7536,4,3,true,
-2026-06-13T16:36:56.675794Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,2,7230,4,3,true,
-2026-06-13T16:37:04.739867Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,3,8063,4,3,true,
-2026-06-13T16:37:11.998048Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,4,7257,4,3,true,
-2026-06-13T16:37:19.144591Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,5,7145,4,3,true,
+2026-06-14T18:46:57.626163Z,Q01,Full Yellow Taxi Row Count,warmup,1,3914,1,1,true,
+2026-06-14T18:46:59.175829Z,Q01,Full Yellow Taxi Row Count,measured,1,1548,1,1,true,
+2026-06-14T18:47:00.772894Z,Q01,Full Yellow Taxi Row Count,measured,2,1595,1,1,true,
+2026-06-14T18:47:02.064235Z,Q01,Full Yellow Taxi Row Count,measured,3,1290,1,1,true,
+2026-06-14T18:47:03.213326Z,Q01,Full Yellow Taxi Row Count,measured,4,1148,1,1,true,
+2026-06-14T18:47:04.673745Z,Q01,Full Yellow Taxi Row Count,measured,5,1460,1,1,true,
+2026-06-14T18:47:05.196475Z,Q02,Yellow Taxi Count For One Partition Month,warmup,1,521,1,1,true,
+2026-06-14T18:47:05.572867Z,Q02,Yellow Taxi Count For One Partition Month,measured,1,375,1,1,true,
+2026-06-14T18:47:05.950536Z,Q02,Yellow Taxi Count For One Partition Month,measured,2,376,1,1,true,
+2026-06-14T18:47:06.322005Z,Q02,Yellow Taxi Count For One Partition Month,measured,3,370,1,1,true,
+2026-06-14T18:47:06.672694Z,Q02,Yellow Taxi Count For One Partition Month,measured,4,349,1,1,true,
+2026-06-14T18:47:07.050763Z,Q02,Yellow Taxi Count For One Partition Month,measured,5,377,1,1,true,
+2026-06-14T18:47:14.845373Z,Q03,Yellow Taxi Trips On One Day,warmup,1,7792,1,1,true,
+2026-06-14T18:47:19.837260Z,Q03,Yellow Taxi Trips On One Day,measured,1,4990,1,1,true,
+2026-06-14T18:47:23.154042Z,Q03,Yellow Taxi Trips On One Day,measured,2,5105,1,1,true,
+2026-06-14T18:47:28.062939Z,Q03,Yellow Taxi Trips On One Day,measured,3,4908,1,1,true,
+2026-06-14T18:47:33.924855Z,Q03,Yellow Taxi Trips On One Day,measured,4,5861,1,1,true,
+2026-06-14T18:47:38.813486Z,Q03,Yellow Taxi Trips On One Day,measured,5,4887,1,1,true,
+2026-06-14T18:47:43.337841Z,Q04,Long And Expensive Yellow Taxi Trips,warmup,1,4522,1,1,true,
+2026-06-14T18:47:46.687755Z,Q04,Long And Expensive Yellow Taxi Trips,measured,1,3348,1,1,true,
+2026-06-14T18:47:54.577920Z,Q04,Long And Expensive Yellow Taxi Trips,measured,2,3313,1,1,true,
+2026-06-14T18:47:57.645728Z,Q04,Long And Expensive Yellow Taxi Trips,measured,3,3067,1,1,true,
+2026-06-14T18:48:00.655573Z,Q04,Long And Expensive Yellow Taxi Trips,measured,4,3009,1,1,true,
+2026-06-14T18:48:03.873606Z,Q04,Long And Expensive Yellow Taxi Trips,measured,5,3217,1,1,true,
+2026-06-14T18:48:09.789603Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,warmup,1,5914,37,6,true,
+2026-06-14T18:48:15.129028Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,1,5338,37,6,true,
+2026-06-14T18:48:20.151033Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,2,5021,37,6,true,
+2026-06-14T18:48:27.498245Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,3,4831,37,6,true,
+2026-06-14T18:48:32.322695Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,4,4823,37,6,true,
+2026-06-14T18:48:37.060983Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,5,4737,37,6,true,
+2026-06-14T18:48:39.026187Z,Q06,Full High-Volume FHV Row Count,warmup,1,1962,1,1,true,
+2026-06-14T18:48:40.830284Z,Q06,Full High-Volume FHV Row Count,measured,1,1803,1,1,true,
+2026-06-14T18:48:42.508572Z,Q06,Full High-Volume FHV Row Count,measured,2,1677,1,1,true,
+2026-06-14T18:48:44.316583Z,Q06,Full High-Volume FHV Row Count,measured,3,1807,1,1,true,
+2026-06-14T18:48:46.104285Z,Q06,Full High-Volume FHV Row Count,measured,4,1787,1,1,true,
+2026-06-14T18:48:47.557076Z,Q06,Full High-Volume FHV Row Count,measured,5,1452,1,1,true,
+2026-06-14T18:48:47.759762Z,Q07,High-Volume FHV Count For One Partition Month,warmup,1,201,1,1,true,
+2026-06-14T18:48:48.017302Z,Q07,High-Volume FHV Count For One Partition Month,measured,1,256,1,1,true,
+2026-06-14T18:48:48.455477Z,Q07,High-Volume FHV Count For One Partition Month,measured,2,437,1,1,true,
+2026-06-14T18:48:48.819004Z,Q07,High-Volume FHV Count For One Partition Month,measured,3,362,1,1,true,
+2026-06-14T18:48:49.150754Z,Q07,High-Volume FHV Count For One Partition Month,measured,4,330,1,1,true,
+2026-06-14T18:48:49.449777Z,Q07,High-Volume FHV Count For One Partition Month,measured,5,293,1,1,true,
+2026-06-14T18:49:07.258136Z,Q08,Long And Expensive High-Volume FHV Trips,warmup,1,15078,1,1,true,
+2026-06-14T18:49:20.494132Z,Q08,Long And Expensive High-Volume FHV Trips,measured,1,13234,1,1,true,
+2026-06-14T18:49:33.539853Z,Q08,Long And Expensive High-Volume FHV Trips,measured,2,12972,1,1,true,
+2026-06-14T18:49:50.883874Z,Q08,Long And Expensive High-Volume FHV Trips,measured,3,14109,1,1,true,
+2026-06-14T18:50:04.841410Z,Q08,Long And Expensive High-Volume FHV Trips,measured,4,13922,1,1,true,
+2026-06-14T18:50:16.950307Z,Q08,Long And Expensive High-Volume FHV Trips,measured,5,12108,1,1,true,
+2026-06-14T18:50:53.742328Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,warmup,1,34062,37,7,true,
+2026-06-14T18:51:26.644801Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,1,30380,37,7,true,
+2026-06-14T18:51:59.878114Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,2,30646,37,7,true,
+2026-06-14T18:52:33.600018Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,3,31253,37,7,true,
+2026-06-14T18:53:08.272774Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,4,30810,37,7,true,
+2026-06-14T18:53:41.197506Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,5,31569,37,7,true,
+2026-06-14T18:53:51.893848Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,warmup,1,7961,4,3,true,
+2026-06-14T18:53:59.674404Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,1,7779,4,3,true,
+2026-06-14T18:54:06.913554Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,2,7238,4,3,true,
+2026-06-14T18:54:14.760827Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,3,7846,4,3,true,
+2026-06-14T18:54:26.033292Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,4,8694,4,3,true,
+2026-06-14T18:54:33.595054Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,5,7561,4,3,true,
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
index 2f90d50c07..fba1206a04 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
@@ -5,74 +5,84 @@ Warmup rows are excluded. Mean and median values use successful runs only.
 
 ## Source Files
 
-| System               | CSV                                                                                        |
-|----------------------|--------------------------------------------------------------------------------------------|
+| System | CSV |
+| --- | --- |
 | Polypheny Relational | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_results.csv` |
-| DuckDB               | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_results.csv`    |
-| Apache Spark         | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_results.csv`     |
+| Polypheny Document MQL | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_mql_results.csv` |
+| DuckDB | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_results.csv` |
+| Apache Spark | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_results.csv` |
 
 ## Mean Elapsed Time (ms)
 
-| Query | Description                                       | Polypheny Relational | DuckDB  | Apache Spark | Row counts |
-|-------|---------------------------------------------------|----------------------|---------|--------------|------------|
-| Q01   | Full Yellow Taxi Row Count                        | 11.2                 | 33.8    | 3,541.6      | 1          |
-| Q02   | Yellow Taxi Count For One Partition Month         | 9.6                  | 13.8    | 1,228.0      | 1          |
-| Q03   | Yellow Taxi Trips On One Day                      | 1,097.4              | 341.4   | 12,329.8     | 1          |
-| Q04   | Long And Expensive Yellow Taxi Trips              | 2,965.6              | 793.2   | 9,580.2      | 1          |
-| Q05   | Monthly Yellow Taxi Amount And Distance Summary   | 2,452.2              | 1,072.4 | 12,750.6     | 37         |
-| Q06   | Full High-Volume FHV Row Count                    | 16.0                 | 96.8    | 3,385.6      | 1          |
-| Q07   | High-Volume FHV Count For One Partition Month     | 19.0                 | 26.4    | 285.4        | 1          |
-| Q08   | Long And Expensive High-Volume FHV Trips          | 19,552.8             | 6,327.8 | 12,076.8     | 1          |
-| Q09   | Monthly High-Volume FHV Fare And Distance Summary | 24,881.4             | 9,690.6 | 27,923.4     | 37         |
-| Q10   | High-Volume FHV Shared-Ride Flag Distribution     | 8,162.6              | 1,559.4 | 7,446.2      | 4          |
+| Query | Description | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark | Row counts |
+| --- | --- | --- | --- | --- | --- | --- |
+| Q01 | Full Yellow Taxi Row Count | 10.2 | 121.8 | 28.6 | 1,408.2 | 1 |
+| Q02 | Yellow Taxi Count For One Partition Month | 11.6 | 162.4 | 12.0 | 369.4 | 1 |
+| Q03 | Yellow Taxi Trips On One Day | 1,092.4 | 612.2 | 232.6 | 5,150.2 | 1 |
+| Q04 | Long And Expensive Yellow Taxi Trips | 1,941.4 | 2,184.8 | 690.6 | 3,190.8 | 1 |
+| Q05 | Monthly Yellow Taxi Amount And Distance Summary | 1,711.0 | 2,426.4 | 1,169.6 | 4,950.0 | differs |
+| Q06 | Full High-Volume FHV Row Count | 10.8 | 323.0 | 92.4 | 1,705.2 | 1 |
+| Q07 | High-Volume FHV Count For One Partition Month | 12.2 | 248.4 | 26.4 | 335.6 | 1 |
+| Q08 | Long And Expensive High-Volume FHV Trips | 11,461.8 | 15,905.2 | 4,990.0 | 13,269.0 | 1 |
+| Q09 | Monthly High-Volume FHV Fare And Distance Summary | 13,246.6 | 14,864.4 | 9,216.8 | 30,931.6 | differs |
+| Q10 | High-Volume FHV Shared-Ride Flag Distribution | 5,832.8 | 5,107.6 | 1,252.6 | 7,823.6 | differs |
 
 ## Result Row Counts
 
-| Query | Description                                       | Polypheny Relational | DuckDB | Apache Spark |
-|-------|---------------------------------------------------|----------------------|--------|--------------|
-| Q01   | Full Yellow Taxi Row Count                        | 1                    | 1      | 1            |
-| Q02   | Yellow Taxi Count For One Partition Month         | 1                    | 1      | 1            |
-| Q03   | Yellow Taxi Trips On One Day                      | 1                    | 1      | 1            |
-| Q04   | Long And Expensive Yellow Taxi Trips              | 1                    | 1      | 1            |
-| Q05   | Monthly Yellow Taxi Amount And Distance Summary   | 37                   | 37     | 37           |
-| Q06   | Full High-Volume FHV Row Count                    | 1                    | 1      | 1            |
-| Q07   | High-Volume FHV Count For One Partition Month     | 1                    | 1      | 1            |
-| Q08   | Long And Expensive High-Volume FHV Trips          | 1                    | 1      | 1            |
-| Q09   | Monthly High-Volume FHV Fare And Distance Summary | 37                   | 37     | 37           |
-| Q10   | High-Volume FHV Shared-Ride Flag Distribution     | 4                    | 4      | 4            |
+| Query | Description | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark |
+| --- | --- | --- | --- | --- | --- |
+| Q01 | Full Yellow Taxi Row Count | 1 | 1 | 1 | 1 |
+| Q02 | Yellow Taxi Count For One Partition Month | 1 | 1 | 1 | 1 |
+| Q03 | Yellow Taxi Trips On One Day | 1 | 1 | 1 | 1 |
+| Q04 | Long And Expensive Yellow Taxi Trips | 1 | 1 | 1 | 1 |
+| Q05 | Monthly Yellow Taxi Amount And Distance Summary | 37 | 4 | 37 | 37 |
+| Q06 | Full High-Volume FHV Row Count | 1 | 1 | 1 | 1 |
+| Q07 | High-Volume FHV Count For One Partition Month | 1 | 1 | 1 | 1 |
+| Q08 | Long And Expensive High-Volume FHV Trips | 1 | 1 | 1 | 1 |
+| Q09 | Monthly High-Volume FHV Fare And Distance Summary | 37 | 4 | 37 | 37 |
+| Q10 | High-Volume FHV Shared-Ride Flag Distribution | 4 | 2 | 4 | 4 |
 
 ## Detailed Summary (ms)
 
-| System               | Query | Description                                       | Runs | Mean     | Median   | Min      | Max      | Rows | Columns | Status |
-|----------------------|-------|---------------------------------------------------|------|----------|----------|----------|----------|------|---------|--------|
-| Polypheny Relational | Q01   | Full Yellow Taxi Row Count                        | 5/5  | 11.2     | 10.0     | 9.0      | 18.0     | 1    | 1       | ok     |
-| Polypheny Relational | Q02   | Yellow Taxi Count For One Partition Month         | 5/5  | 9.6      | 9.0      | 8.0      | 11.0     | 1    | 1       | ok     |
-| Polypheny Relational | Q03   | Yellow Taxi Trips On One Day                      | 5/5  | 1,097.4  | 1,004.0  | 962.0    | 1,295.0  | 1    | 1       | ok     |
-| Polypheny Relational | Q04   | Long And Expensive Yellow Taxi Trips              | 5/5  | 2,965.6  | 2,820.0  | 2,297.0  | 3,748.0  | 1    | 1       | ok     |
-| Polypheny Relational | Q05   | Monthly Yellow Taxi Amount And Distance Summary   | 5/5  | 2,452.2  | 2,497.0  | 2,365.0  | 2,534.0  | 37   | 6       | ok     |
-| Polypheny Relational | Q06   | Full High-Volume FHV Row Count                    | 5/5  | 16.0     | 16.0     | 15.0     | 17.0     | 1    | 1       | ok     |
-| Polypheny Relational | Q07   | High-Volume FHV Count For One Partition Month     | 5/5  | 19.0     | 18.0     | 16.0     | 22.0     | 1    | 1       | ok     |
-| Polypheny Relational | Q08   | Long And Expensive High-Volume FHV Trips          | 5/5  | 19,552.8 | 20,611.0 | 15,594.0 | 21,747.0 | 1    | 1       | ok     |
-| Polypheny Relational | Q09   | Monthly High-Volume FHV Fare And Distance Summary | 5/5  | 24,881.4 | 22,647.0 | 20,897.0 | 30,554.0 | 37   | 7       | ok     |
-| Polypheny Relational | Q10   | High-Volume FHV Shared-Ride Flag Distribution     | 5/5  | 8,162.6  | 8,380.0  | 7,451.0  | 8,616.0  | 4    | 3       | ok     |
-| DuckDB               | Q01   | Full Yellow Taxi Row Count                        | 5/5  | 33.8     | 34.0     | 29.0     | 37.0     | 1    | 1       | ok     |
-| DuckDB               | Q02   | Yellow Taxi Count For One Partition Month         | 5/5  | 13.8     | 13.0     | 13.0     | 16.0     | 1    | 1       | ok     |
-| DuckDB               | Q03   | Yellow Taxi Trips On One Day                      | 5/5  | 341.4    | 344.0    | 329.0    | 356.0    | 1    | 1       | ok     |
-| DuckDB               | Q04   | Long And Expensive Yellow Taxi Trips              | 5/5  | 793.2    | 815.0    | 709.0    | 843.0    | 1    | 1       | ok     |
-| DuckDB               | Q05   | Monthly Yellow Taxi Amount And Distance Summary   | 5/5  | 1,072.4  | 1,097.0  | 1,007.0  | 1,132.0  | 37   | 6       | ok     |
-| DuckDB               | Q06   | Full High-Volume FHV Row Count                    | 5/5  | 96.8     | 97.0     | 90.0     | 101.0    | 1    | 1       | ok     |
-| DuckDB               | Q07   | High-Volume FHV Count For One Partition Month     | 5/5  | 26.4     | 25.0     | 23.0     | 30.0     | 1    | 1       | ok     |
-| DuckDB               | Q08   | Long And Expensive High-Volume FHV Trips          | 5/5  | 6,327.8  | 5,598.0  | 5,089.0  | 8,269.0  | 1    | 1       | ok     |
-| DuckDB               | Q09   | Monthly High-Volume FHV Fare And Distance Summary | 5/5  | 9,690.6  | 9,562.0  | 8,037.0  | 12,223.0 | 37   | 7       | ok     |
-| DuckDB               | Q10   | High-Volume FHV Shared-Ride Flag Distribution     | 5/5  | 1,559.4  | 1,602.0  | 1,391.0  | 1,625.0  | 4    | 3       | ok     |
-| Apache Spark         | Q01   | Full Yellow Taxi Row Count                        | 5/5  | 3,541.6  | 3,820.0  | 1,167.0  | 5,948.0  | 1    | 1       | ok     |
-| Apache Spark         | Q02   | Yellow Taxi Count For One Partition Month         | 5/5  | 1,228.0  | 1,202.0  | 521.0    | 1,845.0  | 1    | 1       | ok     |
-| Apache Spark         | Q03   | Yellow Taxi Trips On One Day                      | 5/5  | 12,329.8 | 14,493.0 | 6,741.0  | 14,919.0 | 1    | 1       | ok     |
-| Apache Spark         | Q04   | Long And Expensive Yellow Taxi Trips              | 5/5  | 9,580.2  | 5,865.0  | 4,584.0  | 19,634.0 | 1    | 1       | ok     |
-| Apache Spark         | Q05   | Monthly Yellow Taxi Amount And Distance Summary   | 5/5  | 12,750.6 | 13,174.0 | 7,059.0  | 19,037.0 | 37   | 6       | ok     |
-| Apache Spark         | Q06   | Full High-Volume FHV Row Count                    | 5/5  | 3,385.6  | 2,000.0  | 1,865.0  | 9,053.0  | 1    | 1       | ok     |
-| Apache Spark         | Q07   | High-Volume FHV Count For One Partition Month     | 5/5  | 285.4    | 284.0    | 259.0    | 325.0    | 1    | 1       | ok     |
-| Apache Spark         | Q08   | Long And Expensive High-Volume FHV Trips          | 5/5  | 12,076.8 | 12,172.0 | 11,390.0 | 12,908.0 | 1    | 1       | ok     |
-| Apache Spark         | Q09   | Monthly High-Volume FHV Fare And Distance Summary | 5/5  | 27,923.4 | 27,946.0 | 27,588.0 | 28,279.0 | 37   | 7       | ok     |
-| Apache Spark         | Q10   | High-Volume FHV Shared-Ride Flag Distribution     | 5/5  | 7,446.2  | 7,257.0  | 7,145.0  | 8,063.0  | 4    | 3       | ok     |
-
+| System | Query | Description | Runs | Mean | Median | Min | Max | Rows | Columns | Status |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| Polypheny Relational | Q01 | Full Yellow Taxi Row Count | 5/5 | 10.2 | 10.0 | 7.0 | 17.0 | 1 | 1 | ok |
+| Polypheny Relational | Q02 | Yellow Taxi Count For One Partition Month | 5/5 | 11.6 | 12.0 | 9.0 | 13.0 | 1 | 1 | ok |
+| Polypheny Relational | Q03 | Yellow Taxi Trips On One Day | 5/5 | 1,092.4 | 1,116.0 | 970.0 | 1,179.0 | 1 | 1 | ok |
+| Polypheny Relational | Q04 | Long And Expensive Yellow Taxi Trips | 5/5 | 1,941.4 | 1,956.0 | 1,861.0 | 2,001.0 | 1 | 1 | ok |
+| Polypheny Relational | Q05 | Monthly Yellow Taxi Amount And Distance Summary | 5/5 | 1,711.0 | 1,675.0 | 1,447.0 | 2,022.0 | 37 | 6 | ok |
+| Polypheny Relational | Q06 | Full High-Volume FHV Row Count | 5/5 | 10.8 | 11.0 | 8.0 | 13.0 | 1 | 1 | ok |
+| Polypheny Relational | Q07 | High-Volume FHV Count For One Partition Month | 5/5 | 12.2 | 13.0 | 10.0 | 13.0 | 1 | 1 | ok |
+| Polypheny Relational | Q08 | Long And Expensive High-Volume FHV Trips | 5/5 | 11,461.8 | 11,529.0 | 11,244.0 | 11,552.0 | 1 | 1 | ok |
+| Polypheny Relational | Q09 | Monthly High-Volume FHV Fare And Distance Summary | 5/5 | 13,246.6 | 13,213.0 | 12,810.0 | 14,053.0 | 37 | 7 | ok |
+| Polypheny Relational | Q10 | High-Volume FHV Shared-Ride Flag Distribution | 5/5 | 5,832.8 | 5,768.0 | 5,266.0 | 6,506.0 | 4 | 3 | ok |
+| Polypheny Document MQL | Q01 | Full Yellow Taxi Row Count | 5/5 | 121.8 | 125.0 | 108.0 | 130.0 | 1 | 1 | ok |
+| Polypheny Document MQL | Q02 | Yellow Taxi Count For One Partition Month | 5/5 | 162.4 | 158.0 | 141.0 | 192.0 | 1 | 1 | ok |
+| Polypheny Document MQL | Q03 | Yellow Taxi Trips On One Day | 5/5 | 612.2 | 611.0 | 599.0 | 626.0 | 1 | 1 | ok |
+| Polypheny Document MQL | Q04 | Long And Expensive Yellow Taxi Trips | 5/5 | 2,184.8 | 2,493.0 | 1,162.0 | 3,390.0 | 1 | 1 | ok |
+| Polypheny Document MQL | Q05 | Monthly Yellow Taxi Amount And Distance Summary | 5/5 | 2,426.4 | 2,400.0 | 2,331.0 | 2,522.0 | 4 | 4 | ok |
+| Polypheny Document MQL | Q06 | Full High-Volume FHV Row Count | 5/5 | 323.0 | 346.0 | 224.0 | 418.0 | 1 | 1 | ok |
+| Polypheny Document MQL | Q07 | High-Volume FHV Count For One Partition Month | 5/5 | 248.4 | 257.0 | 212.0 | 283.0 | 1 | 1 | ok |
+| Polypheny Document MQL | Q08 | Long And Expensive High-Volume FHV Trips | 5/5 | 15,905.2 | 14,670.0 | 13,069.0 | 21,529.0 | 1 | 1 | ok |
+| Polypheny Document MQL | Q09 | Monthly High-Volume FHV Fare And Distance Summary | 5/5 | 14,864.4 | 14,719.0 | 14,655.0 | 15,375.0 | 4 | 5 | ok |
+| Polypheny Document MQL | Q10 | High-Volume FHV Shared-Ride Flag Distribution | 5/5 | 5,107.6 | 5,052.0 | 4,747.0 | 5,694.0 | 2 | 1 | ok |
+| DuckDB | Q01 | Full Yellow Taxi Row Count | 5/5 | 28.6 | 29.0 | 27.0 | 30.0 | 1 | 1 | ok |
+| DuckDB | Q02 | Yellow Taxi Count For One Partition Month | 5/5 | 12.0 | 13.0 | 9.0 | 14.0 | 1 | 1 | ok |
+| DuckDB | Q03 | Yellow Taxi Trips On One Day | 5/5 | 232.6 | 232.0 | 187.0 | 285.0 | 1 | 1 | ok |
+| DuckDB | Q04 | Long And Expensive Yellow Taxi Trips | 5/5 | 690.6 | 680.0 | 649.0 | 731.0 | 1 | 1 | ok |
+| DuckDB | Q05 | Monthly Yellow Taxi Amount And Distance Summary | 5/5 | 1,169.6 | 1,182.0 | 1,078.0 | 1,262.0 | 37 | 6 | ok |
+| DuckDB | Q06 | Full High-Volume FHV Row Count | 5/5 | 92.4 | 94.0 | 87.0 | 98.0 | 1 | 1 | ok |
+| DuckDB | Q07 | High-Volume FHV Count For One Partition Month | 5/5 | 26.4 | 27.0 | 22.0 | 28.0 | 1 | 1 | ok |
+| DuckDB | Q08 | Long And Expensive High-Volume FHV Trips | 5/5 | 4,990.0 | 5,045.0 | 4,747.0 | 5,134.0 | 1 | 1 | ok |
+| DuckDB | Q09 | Monthly High-Volume FHV Fare And Distance Summary | 5/5 | 9,216.8 | 9,118.0 | 9,026.0 | 9,451.0 | 37 | 7 | ok |
+| DuckDB | Q10 | High-Volume FHV Shared-Ride Flag Distribution | 5/5 | 1,252.6 | 1,279.0 | 1,084.0 | 1,461.0 | 4 | 3 | ok |
+| Apache Spark | Q01 | Full Yellow Taxi Row Count | 5/5 | 1,408.2 | 1,460.0 | 1,148.0 | 1,595.0 | 1 | 1 | ok |
+| Apache Spark | Q02 | Yellow Taxi Count For One Partition Month | 5/5 | 369.4 | 375.0 | 349.0 | 377.0 | 1 | 1 | ok |
+| Apache Spark | Q03 | Yellow Taxi Trips On One Day | 5/5 | 5,150.2 | 4,990.0 | 4,887.0 | 5,861.0 | 1 | 1 | ok |
+| Apache Spark | Q04 | Long And Expensive Yellow Taxi Trips | 5/5 | 3,190.8 | 3,217.0 | 3,009.0 | 3,348.0 | 1 | 1 | ok |
+| Apache Spark | Q05 | Monthly Yellow Taxi Amount And Distance Summary | 5/5 | 4,950.0 | 4,831.0 | 4,737.0 | 5,338.0 | 37 | 6 | ok |
+| Apache Spark | Q06 | Full High-Volume FHV Row Count | 5/5 | 1,705.2 | 1,787.0 | 1,452.0 | 1,807.0 | 1 | 1 | ok |
+| Apache Spark | Q07 | High-Volume FHV Count For One Partition Month | 5/5 | 335.6 | 330.0 | 256.0 | 437.0 | 1 | 1 | ok |
+| Apache Spark | Q08 | Long And Expensive High-Volume FHV Trips | 5/5 | 13,269.0 | 13,234.0 | 12,108.0 | 14,109.0 | 1 | 1 | ok |
+| Apache Spark | Q09 | Monthly High-Volume FHV Fare And Distance Summary | 5/5 | 30,931.6 | 30,810.0 | 30,380.0 | 31,569.0 | 37 | 7 | ok |
+| Apache Spark | Q10 | High-Volume FHV Shared-Ride Flag Distribution | 5/5 | 7,823.6 | 7,779.0 | 7,238.0 | 8,694.0 | 4 | 3 | ok |
diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv
index 395c874a5d..7b26f6369d 100644
--- a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv
@@ -1,31 +1,25 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-13T14:29:14.755292300Z","Q01","Filtered full customer rows.","warmup",1,3513,100000,9,true,""
-"2026-06-13T14:29:17.094407400Z","Q01","Filtered full customer rows.","measured",1,2335,100000,9,true,""
-"2026-06-13T14:29:18.833510700Z","Q01","Filtered full customer rows.","measured",2,1738,100000,9,true,""
-"2026-06-13T14:29:20.219792500Z","Q01","Filtered full customer rows.","measured",3,1385,100000,9,true,""
-"2026-06-13T14:29:22.104736Z","Q01","Filtered full customer rows.","measured",4,1884,100000,9,true,""
-"2026-06-13T14:29:23.645477900Z","Q01","Filtered full customer rows.","measured",5,1539,100000,9,true,""
-"2026-06-13T14:29:24.674253700Z","Q02","Filtered explicit customer projection.","warmup",1,1028,100000,8,true,""
-"2026-06-13T14:29:25.674541900Z","Q02","Filtered explicit customer projection.","measured",1,1000,100000,8,true,""
-"2026-06-13T14:29:26.650035700Z","Q02","Filtered explicit customer projection.","measured",2,974,100000,8,true,""
-"2026-06-13T14:29:27.593525500Z","Q02","Filtered explicit customer projection.","measured",3,942,100000,8,true,""
-"2026-06-13T14:29:28.443429Z","Q02","Filtered explicit customer projection.","measured",4,849,100000,8,true,""
-"2026-06-13T14:29:29.239153800Z","Q02","Filtered explicit customer projection.","measured",5,795,100000,8,true,""
-"2026-06-13T14:29:32.612414400Z","Q03","One-join filtered order element projection.","warmup",1,3372,100000,8,true,""
-"2026-06-13T14:29:34.909719900Z","Q03","One-join filtered order element projection.","measured",1,2297,100000,8,true,""
-"2026-06-13T14:29:36.811671Z","Q03","One-join filtered order element projection.","measured",2,1901,100000,8,true,""
-"2026-06-13T14:29:38.910326900Z","Q03","One-join filtered order element projection.","measured",3,2098,100000,8,true,""
-"2026-06-13T14:29:40.845728100Z","Q03","One-join filtered order element projection.","measured",4,1934,100000,8,true,""
-"2026-06-13T14:29:42.843197900Z","Q03","One-join filtered order element projection.","measured",5,1996,100000,8,true,""
-"2026-06-13T14:29:51.302852100Z","Q04","One-join filtered lineitem element projection.","warmup",1,8458,100000,10,true,""
-"2026-06-13T14:29:59.444764300Z","Q04","One-join filtered lineitem element projection.","measured",1,8143,100000,10,true,""
-"2026-06-13T14:30:07.651110200Z","Q04","One-join filtered lineitem element projection.","measured",2,8203,100000,10,true,""
-"2026-06-13T14:30:16.226858900Z","Q04","One-join filtered lineitem element projection.","measured",3,8575,100000,10,true,""
-"2026-06-13T14:30:24.650699700Z","Q04","One-join filtered lineitem element projection.","measured",4,8422,100000,10,true,""
-"2026-06-13T14:30:32.953027800Z","Q04","One-join filtered lineitem element projection.","measured",5,8301,100000,10,true,""
-"2026-06-13T14:30:35.217931400Z","Q05","One-join filtered order-to-lineitems group projection.","warmup",1,2262,100000,6,true,""
-"2026-06-13T14:30:37.099857300Z","Q05","One-join filtered order-to-lineitems group projection.","measured",1,1883,100000,6,true,""
-"2026-06-13T14:30:39.143149300Z","Q05","One-join filtered order-to-lineitems group projection.","measured",2,2041,100000,6,true,""
-"2026-06-13T14:30:40.800154900Z","Q05","One-join filtered order-to-lineitems group projection.","measured",3,1659,100000,6,true,""
-"2026-06-13T14:30:42.889555300Z","Q05","One-join filtered order-to-lineitems group projection.","measured",4,2089,100000,6,true,""
-"2026-06-13T14:30:44.682764500Z","Q05","One-join filtered order-to-lineitems group projection.","measured",5,1788,100000,6,true,""
+"2026-06-15T06:35:50.302775Z","Q01","Filtered full customer rows.","warmup",1,3060,100000,9,true,""
+"2026-06-15T06:35:52.955918800Z","Q01","Filtered full customer rows.","measured",1,2645,100000,9,true,""
+"2026-06-15T06:35:54.809585500Z","Q01","Filtered full customer rows.","measured",2,1852,100000,9,true,""
+"2026-06-15T06:35:56.112984200Z","Q01","Filtered full customer rows.","measured",3,1306,100000,9,true,""
+"2026-06-15T06:35:57.360767100Z","Q01","Filtered full customer rows.","measured",4,1248,100000,9,true,""
+"2026-06-15T06:35:58.585865700Z","Q01","Filtered full customer rows.","measured",5,1221,100000,9,true,""
+"2026-06-15T06:35:59.305961900Z","Q02","Filtered explicit customer projection.","warmup",1,719,100000,8,true,""
+"2026-06-15T06:36:00.034878500Z","Q02","Filtered explicit customer projection.","measured",1,727,100000,8,true,""
+"2026-06-15T06:36:00.761478500Z","Q02","Filtered explicit customer projection.","measured",2,728,100000,8,true,""
+"2026-06-15T06:36:01.532056400Z","Q02","Filtered explicit customer projection.","measured",3,768,100000,8,true,""
+"2026-06-15T06:36:02.218668Z","Q02","Filtered explicit customer projection.","measured",4,685,100000,8,true,""
+"2026-06-15T06:36:02.842936Z","Q02","Filtered explicit customer projection.","measured",5,624,100000,8,true,""
+"2026-06-15T06:36:04.823740800Z","Q03","One-join filtered order element projection.","warmup",1,1980,100000,8,true,""
+"2026-06-15T06:36:06.609642500Z","Q03","One-join filtered order element projection.","measured",1,1785,100000,8,true,""
+"2026-06-15T06:36:08.196334800Z","Q03","One-join filtered order element projection.","measured",2,1588,100000,8,true,""
+"2026-06-15T06:36:09.691568200Z","Q03","One-join filtered order element projection.","measured",3,1492,100000,8,true,""
+"2026-06-15T06:36:11.520673800Z","Q03","One-join filtered order element projection.","measured",4,1829,100000,8,true,""
+"2026-06-15T06:36:13.427036900Z","Q03","One-join filtered order element projection.","measured",5,1904,100000,8,true,""
+"2026-06-15T06:36:20.911396300Z","Q04","One-join filtered lineitem element projection.","warmup",1,7487,100000,10,true,""
+"2026-06-15T06:36:28.062280500Z","Q04","One-join filtered lineitem element projection.","measured",1,7145,100000,10,true,""
+"2026-06-15T06:36:35.748171400Z","Q04","One-join filtered lineitem element projection.","measured",2,7684,100000,10,true,""
+"2026-06-15T06:36:43.277546800Z","Q04","One-join filtered lineitem element projection.","measured",3,7528,100000,10,true,""
+"2026-06-15T06:36:50.452826900Z","Q04","One-join filtered lineitem element projection.","measured",4,7178,100000,10,true,""
+"2026-06-15T06:37:00.668515400Z","Q04","One-join filtered lineitem element projection.","measured",5,10213,100000,10,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_plot.pdf b/plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_plot.pdf
deleted file mode 100644
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+<rect x="465.00" y="321.50" width="12.75" height="258.50" fill="#4f9d55"><title>DuckDB Q05: 1.170 s</title></rect>
+<rect x="480.75" y="243.17" width="12.75" height="336.83" fill="#f0a22e"><title>Apache Spark Q05: 4.950 s</title></rect>
+<rect x="516.50" y="575.82" width="12.75" height="4.18" fill="#2f6fbb"><title>Polypheny Relational Q06: 0.011 s</title></rect>
+<rect x="532.25" y="391.35" width="12.75" height="188.65" fill="#c94c4c"><title>Polypheny Document MQL Q06: 0.323 s</title></rect>
+<rect x="548.00" y="459.29" width="12.75" height="120.71" fill="#4f9d55"><title>DuckDB Q06: 0.092 s</title></rect>
+<rect x="563.75" y="301.03" width="12.75" height="278.97" fill="#f0a22e"><title>Apache Spark Q06: 1.705 s</title></rect>
+<rect x="599.50" y="569.21" width="12.75" height="10.79" fill="#2f6fbb"><title>Polypheny Relational Q07: 0.012 s</title></rect>
+<rect x="615.25" y="405.61" width="12.75" height="174.39" fill="#c94c4c"><title>Polypheny Document MQL Q07: 0.248 s</title></rect>
+<rect x="631.00" y="527.30" width="12.75" height="52.70" fill="#4f9d55"><title>DuckDB Q07: 0.026 s</title></rect>
+<rect x="646.75" y="389.27" width="12.75" height="190.73" fill="#f0a22e"><title>Apache Spark Q07: 0.336 s</title></rect>
+<rect x="682.50" y="197.59" width="12.75" height="382.41" fill="#2f6fbb"><title>Polypheny Relational Q08: 11.462 s</title></rect>
+<rect x="698.25" y="179.81" width="12.75" height="400.19" fill="#c94c4c"><title>Polypheny Document MQL Q08: 15.905 s</title></rect>
+<rect x="714.00" y="242.74" width="12.75" height="337.26" fill="#4f9d55"><title>DuckDB Q08: 4.990 s</title></rect>
+<rect x="729.75" y="189.65" width="12.75" height="390.35" fill="#f0a22e"><title>Apache Spark Q08: 13.269 s</title></rect>
+<rect x="765.50" y="189.74" width="12.75" height="390.26" fill="#2f6fbb"><title>Polypheny Relational Q09: 13.247 s</title></rect>
+<rect x="781.25" y="183.48" width="12.75" height="396.52" fill="#c94c4c"><title>Polypheny Document MQL Q09: 14.864 s</title></rect>
+<rect x="797.00" y="209.43" width="12.75" height="370.57" fill="#4f9d55"><title>DuckDB Q09: 9.217 s</title></rect>
+<rect x="812.75" y="143.70" width="12.75" height="436.30" fill="#f0a22e"><title>Apache Spark Q09: 30.932 s</title></rect>
+<rect x="848.50" y="234.27" width="12.75" height="345.73" fill="#2f6fbb"><title>Polypheny Relational Q10: 5.833 s</title></rect>
+<rect x="864.25" y="241.47" width="12.75" height="338.53" fill="#c94c4c"><title>Polypheny Document MQL Q10: 5.108 s</title></rect>
+<rect x="880.00" y="317.77" width="12.75" height="262.23" fill="#4f9d55"><title>DuckDB Q10: 1.253 s</title></rect>
+<rect x="895.75" y="218.32" width="12.75" height="361.68" fill="#f0a22e"><title>Apache Spark Q10: 7.824 s</title></rect>
+<text x="131.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q01</text>
+<text x="214.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q02</text>
+<text x="297.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q03</text>
+<text x="380.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q04</text>
+<text x="463.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q05</text>
+<text x="546.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q06</text>
+<text x="629.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q07</text>
+<text x="712.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q08</text>
+<text x="795.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q09</text>
+<text x="878.50" y="610" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q10</text>
+<text x="955" y="77" font-family="Helvetica, Arial, sans-serif" font-size="13" font-weight="700">System</text>
+<rect x="955" y="83" width="16" height="16" fill="#2f6fbb"/>
+<text x="979" y="96" font-family="Helvetica, Arial, sans-serif" font-size="12">Polypheny Relational</text>
+<rect x="955" y="111" width="16" height="16" fill="#c94c4c"/>
+<text x="979" y="124" font-family="Helvetica, Arial, sans-serif" font-size="12">Polypheny Document MQL</text>
+<rect x="955" y="139" width="16" height="16" fill="#4f9d55"/>
+<text x="979" y="152" font-family="Helvetica, Arial, sans-serif" font-size="12">DuckDB</text>
+<rect x="955" y="167" width="16" height="16" fill="#f0a22e"/>
+<text x="979" y="180" font-family="Helvetica, Arial, sans-serif" font-size="12">Apache Spark</text>
+</svg>
\ No newline at end of file
diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md
index b9ca3af32c..c015b69115 100644
--- a/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md
@@ -10,48 +10,64 @@ build jar:
 run jar:
 java -Xms8g -Xmx16g "-Dfile.encoding=UTF-8" -jar .\dbms\build\libs\dbms-0.10.1-SNAPSHOT.jar
 
-PR
+PR flat
+
 ```powershell
 powershell -ExecutionPolicy Bypass `
   -File plugins\parquet-adapter\benchmarks\scripts\runners\run_polypheny_benchmark.ps1 `
--Queries plugins\parquet-adapter\benchmarks\query_lists\access_model_comparison\access_model_comparison_rf.sql `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\access_model_comparison\access_model_comparison_rf.sql `
   -Output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rf_tlcp_results.csv `
--Warmups 1 `
+  -Warmups 1 `
   -Runs 5 `
--NoTableNameMapping
+  -NoTableNameMapping
+```
+
+PR normalized
+
+```powershell
+powershell -ExecutionPolicy Bypass `
+  -File plugins\parquet-adapter\benchmarks\scripts\runners\run_polypheny_benchmark.ps1 `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\access_model_comparison\access_model_comparison_rn.sql `
+  -Output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rn_tlcpn_results.csv `
+  -Warmups 1 `
+  -Runs 5 `
+  -NoTableNameMapping
 ```
 
 PD
+
 ```powershell
 powershell -ExecutionPolicy Bypass `
   -File plugins\parquet-adapter\benchmarks\scripts\runners\run_polypheny_mql_benchmark.ps1 `
--Namespace tlcpd_document `
+  -Namespace tlcpd_document `
   -Queries plugins\parquet-adapter\benchmarks\query_lists\access_model_comparison\access_model_comparison_mql.mql `
--Output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_mql_tlcp_results.csv `
+  -Output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_mql_tlcp_results.csv `
   -Warmups 1 `
--Runs 5
+  -Runs 5
 ```
 
 DuckDB
+
 ```powershell
 powershell -ExecutionPolicy Bypass `
   -File plugins\parquet-adapter\benchmarks\scripts\runners\run_duckdb_benchmark.ps1 `
--DataDir C:\PolyData\tlc_partitioned `
+  -DataDir C:\PolyData\tlc_partitioned `
   -Queries plugins\parquet-adapter\benchmarks\query_lists\access_model_comparison\access_model_comparison_sql.sql `
--Output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_duckdb_tlcp_results.csv `
+  -Output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_duckdb_tlcp_results.csv `
   -Warmups 1 `
--Runs 5
+  -Runs 5
 ```
 
 Spark
+
 ```powershell
 powershell -ExecutionPolicy Bypass `
   -File plugins\parquet-adapter\benchmarks\scripts\runners\run_spark_benchmark.ps1 `
--DataDir C:\PolyData\tlc_partitioned `
+  -DataDir C:\PolyData\tlc_partitioned `
   -Queries plugins\parquet-adapter\benchmarks\query_lists\access_model_comparison\access_model_comparison_sql.sql `
--Output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_spark_tlcp_results.csv `
+  -Output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_spark_tlcp_results.csv `
   -Warmups 1 `
--Runs 5
+  -Runs 5
 ```
 
 ## Create summary
@@ -61,11 +77,12 @@ powershell -ExecutionPolicy Bypass `
 
 python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py `
   --title "Access Model Comparison TLCP Summary" `
---output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_tlcp_summary.md `
-  "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rf_tlcp_results.csv" `
-"Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_mql_tlcp_results.csv" `
+  --output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_tlcp_summary.md `
+  "Polypheny Relational Flat=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rf_tlcp_results.csv" `
+  "Polypheny Relational Normalized=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rn_tlcpn_results.csv" `
+  "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_mql_tlcp_results.csv" `
   "DuckDB=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_duckdb_tlcp_results.csv" `
-"Apache Spark=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_spark_tlcp_results.csv"
+  "Apache Spark=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_spark_tlcp_results.csv"
 
 ```
 
@@ -73,9 +90,10 @@ python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py
 
 ```powershell
 python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_result_plot_generator.py `
-  --title "Access Model Comparison TLCP" `
+  --title "Access Model Comparison" `
   --name access_model_comparison_tlcp_plot `
-  "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rf_tlcp_results.csv" `
+  "Polypheny Relational Flat=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rf_tlcp_results.csv" `
+  "Polypheny Relational Normalized=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rn_tlcpn_results.csv" `
   "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_mql_tlcp_results.csv" `
   "DuckDB=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_duckdb_tlcp_results.csv" `
   "Apache Spark=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_spark_tlcp_results.csv"
diff --git a/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py b/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py
index 10e09f3b37..f21a0840d9 100644
--- a/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py
+++ b/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py
@@ -26,6 +26,24 @@ class Series:
     values: dict[str, float]
 
 
+@dataclass
+class PlotGeometry:
+    width: int
+    height: int
+    left: int
+    right: int
+    top: int
+    bottom: int
+    plot_width: int
+    plot_height: int
+    lower: float
+    upper: float
+    ticks: list[float]
+    group_width: float
+    bar_gap: int
+    bar_width: float
+
+
 def main():
     args = parse_args()
     inputs = [parse_input(value) for value in args.csv]
@@ -39,14 +57,22 @@ def main():
 
     output_dir = Path(args.output_dir)
     output_dir.mkdir(parents=True, exist_ok=True)
-    output = output_dir / f"{args.name}.svg"
-    output.write_text(render_svg(series, query_ids, args.title), encoding="utf-8")
-    print(f"Wrote {output}")
+    for file_format in parse_formats(args.formats):
+        output = output_dir / f"{args.name}.{file_format}"
+        if file_format == "svg":
+            output.write_text(render_svg(series, query_ids, args.title), encoding="utf-8")
+        elif file_format == "png":
+            write_png(output, series, query_ids, args.title)
+        elif file_format == "pdf":
+            write_pdf(output, series, query_ids, args.title)
+        else:
+            raise ValueError(f"Unsupported plot format: {file_format}")
+        print(f"Wrote {output}")
 
 
 def parse_args():
     parser = argparse.ArgumentParser(
-        description="Generate an SVG runtime plot from benchmark result CSV files."
+        description="Generate runtime plots from benchmark result CSV files."
     )
     parser.add_argument(
         "csv",
@@ -58,7 +84,12 @@ def parse_args():
     parser.add_argument(
         "--output-dir",
         default="plugins/parquet-adapter/benchmarks/results/plots",
-        help="Directory where the SVG plot is written.",
+        help="Directory where plot files are written.",
+    )
+    parser.add_argument(
+        "--formats",
+        default="svg,pdf,png",
+        help="Comma-separated output formats to write. Default: svg,pdf,png.",
     )
     parser.add_argument(
         "--phase",
@@ -68,6 +99,21 @@ def parse_args():
     return parser.parse_args()
 
 
+def parse_formats(value):
+    valid = {"svg", "pdf", "png"}
+    formats = []
+    for part in value.split(","):
+        file_format = part.strip().lower()
+        if not file_format:
+            continue
+        if file_format not in valid:
+            raise ValueError(f"Unsupported plot format: {file_format}")
+        formats.append(file_format)
+    if not formats:
+        raise ValueError("At least one plot format must be selected.")
+    return formats
+
+
 def parse_input(value):
     if "=" in value:
         label, path = value.split("=", 1)
@@ -97,6 +143,234 @@ def read_means(path, phase):
 
 
 def render_svg(series, query_ids, title):
+    geometry = make_geometry(series, query_ids)
+
+    lines = [
+        '<?xml version="1.0" encoding="UTF-8"?>',
+        f'<svg xmlns="http://www.w3.org/2000/svg" width="{geometry.width}" height="{geometry.height}" viewBox="0 0 {geometry.width} {geometry.height}">',
+        '<rect width="100%" height="100%" fill="white"/>',
+        f'<text x="{geometry.width / 2}" y="34" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="24" font-weight="700">{escape(title)}</text>',
+        f'<text x="{geometry.left}" y="62" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Mean execution time (s, log scale)</text>',
+    ]
+
+    for tick in geometry.ticks:
+        y = y_for(tick, geometry)
+        lines.append(f'<line x1="{geometry.left}" y1="{y:.2f}" x2="{geometry.left + geometry.plot_width}" y2="{y:.2f}" stroke="#e2e2e2" stroke-width="1"/>')
+        lines.append(f'<text x="{geometry.left - 12}" y="{y + 4:.2f}" text-anchor="end" font-family="Helvetica, Arial, sans-serif" font-size="11" fill="#333">{format_tick(tick)}</text>')
+
+    lines.append(f'<line x1="{geometry.left}" y1="{geometry.top}" x2="{geometry.left}" y2="{geometry.top + geometry.plot_height}" stroke="#333" stroke-width="1.2"/>')
+    lines.append(f'<line x1="{geometry.left}" y1="{geometry.top + geometry.plot_height}" x2="{geometry.left + geometry.plot_width}" y2="{geometry.top + geometry.plot_height}" stroke="#333" stroke-width="1.2"/>')
+
+    for bar in bars(series, query_ids, geometry):
+        x, y, width, height, item, query_id, value = bar
+        lines.append(
+            f'<rect x="{x:.2f}" y="{y:.2f}" width="{width:.2f}" height="{height:.2f}" fill="{item.color}">'
+            f'<title>{escape(item.label)} {escape(query_id)}: {value:.3f} s</title></rect>'
+        )
+
+    for query_index, query_id in enumerate(query_ids):
+        center = geometry.left + query_index * geometry.group_width + geometry.group_width / 2
+        lines.append(f'<text x="{center:.2f}" y="{geometry.top + geometry.plot_height + 30}" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">{escape(query_id)}</text>')
+
+    legend_x = geometry.left + geometry.plot_width + 35
+    legend_y = geometry.top + 15
+    lines.append(f'<text x="{legend_x}" y="{legend_y - 18}" font-family="Helvetica, Arial, sans-serif" font-size="13" font-weight="700">System</text>')
+    for index, item in enumerate(series):
+        y = legend_y + index * 28
+        lines.append(f'<rect x="{legend_x}" y="{y - 12}" width="16" height="16" fill="{item.color}"/>')
+        lines.append(f'<text x="{legend_x + 24}" y="{y + 1}" font-family="Helvetica, Arial, sans-serif" font-size="12">{escape(item.label)}</text>')
+
+    lines.append("</svg>")
+    return "\n".join(lines)
+
+
+def write_png(output, series, query_ids, title):
+    try:
+        from PIL import Image, ImageDraw, ImageFont
+    except ImportError as error:
+        raise RuntimeError("PNG output requires Pillow.") from error
+
+    geometry = make_geometry(series, query_ids)
+    image = Image.new("RGB", (geometry.width, geometry.height), "white")
+    draw = ImageDraw.Draw(image)
+    fonts = {
+        "title": load_font(ImageFont, 24, bold=True),
+        "subtitle": load_font(ImageFont, 13),
+        "tick": load_font(ImageFont, 11),
+        "axis": load_font(ImageFont, 12, bold=True),
+        "legend": load_font(ImageFont, 12),
+        "legend_title": load_font(ImageFont, 13, bold=True),
+    }
+
+    draw.text((geometry.width / 2, 34), title, anchor="mm", font=fonts["title"], fill="#000000")
+    draw.text((geometry.left, 62), "Mean execution time (s, log scale)", anchor="la", font=fonts["subtitle"], fill="#333333")
+
+    for tick in geometry.ticks:
+        y = y_for(tick, geometry)
+        draw.line((geometry.left, y, geometry.left + geometry.plot_width, y), fill="#e2e2e2", width=1)
+        draw.text((geometry.left - 12, y), format_tick(tick), anchor="rm", font=fonts["tick"], fill="#333333")
+
+    draw.line((geometry.left, geometry.top, geometry.left, geometry.top + geometry.plot_height), fill="#333333", width=1)
+    draw.line((geometry.left, geometry.top + geometry.plot_height, geometry.left + geometry.plot_width, geometry.top + geometry.plot_height), fill="#333333", width=1)
+
+    for x, y, width, height, item, _query_id, _value in bars(series, query_ids, geometry):
+        draw.rectangle((x, y, x + width, y + height), fill=item.color)
+
+    for query_index, query_id in enumerate(query_ids):
+        center = geometry.left + query_index * geometry.group_width + geometry.group_width / 2
+        draw.text((center, geometry.top + geometry.plot_height + 30), query_id, anchor="mm", font=fonts["axis"], fill="#000000")
+
+    legend_x = geometry.left + geometry.plot_width + 35
+    legend_y = geometry.top + 15
+    draw.text((legend_x, legend_y - 18), "System", anchor="la", font=fonts["legend_title"], fill="#000000")
+    for index, item in enumerate(series):
+        y = legend_y + index * 28
+        draw.rectangle((legend_x, y - 12, legend_x + 16, y + 4), fill=item.color)
+        draw.text((legend_x + 24, y), item.label, anchor="lm", font=fonts["legend"], fill="#000000")
+
+    image.save(output)
+
+
+def write_pdf(output, series, query_ids, title):
+    geometry = make_geometry(series, query_ids)
+    commands = []
+
+    def py(y):
+        return geometry.height - y
+
+    def set_fill(color):
+        r, g, b = color_parts(color)
+        commands.append(f"{r:.3f} {g:.3f} {b:.3f} rg")
+
+    def set_stroke(color):
+        r, g, b = color_parts(color)
+        commands.append(f"{r:.3f} {g:.3f} {b:.3f} RG")
+
+    def rect(x, y, width, height, color):
+        set_fill(color)
+        commands.append(f"{x:.2f} {py(y + height):.2f} {width:.2f} {height:.2f} re f")
+
+    def line(x1, y1, x2, y2, color, width=1):
+        set_stroke(color)
+        commands.append(f"{width:.2f} w")
+        commands.append(f"{x1:.2f} {py(y1):.2f} m {x2:.2f} {py(y2):.2f} l S")
+
+    def text(x, y, value, size, color="#000000", bold=False, anchor="left"):
+        set_fill(color)
+        font = "/F2" if bold else "/F1"
+        adjusted_x = aligned_text_x(x, value, size, bold, anchor)
+        commands.append(
+            f"BT {font} {size} Tf 1 0 0 1 {adjusted_x:.2f} {py(y):.2f} Tm ({pdf_escape(value)}) Tj ET"
+        )
+
+    rect(0, 0, geometry.width, geometry.height, "#ffffff")
+    text(geometry.width / 2, 34, title, 24, bold=True, anchor="center")
+    text(geometry.left, 62, "Mean execution time (s, log scale)", 13, color="#333333")
+
+    for tick in geometry.ticks:
+        y = y_for(tick, geometry)
+        line(geometry.left, y, geometry.left + geometry.plot_width, y, "#e2e2e2")
+        text(geometry.left - 12, y + 4, format_tick(tick), 11, color="#333333", anchor="right")
+
+    line(geometry.left, geometry.top, geometry.left, geometry.top + geometry.plot_height, "#333333", 1.2)
+    line(
+        geometry.left,
+        geometry.top + geometry.plot_height,
+        geometry.left + geometry.plot_width,
+        geometry.top + geometry.plot_height,
+        "#333333",
+        1.2,
+    )
+
+    for x, y, width, height, item, _query_id, _value in bars(series, query_ids, geometry):
+        rect(x, y, width, height, item.color)
+
+    for query_index, query_id in enumerate(query_ids):
+        center = geometry.left + query_index * geometry.group_width + geometry.group_width / 2
+        text(center, geometry.top + geometry.plot_height + 30, query_id, 12, bold=True, anchor="center")
+
+    legend_x = geometry.left + geometry.plot_width + 35
+    legend_y = geometry.top + 15
+    text(legend_x, legend_y - 18, "System", 13, bold=True)
+    for index, item in enumerate(series):
+        y = legend_y + index * 28
+        rect(legend_x, y - 12, 16, 16, item.color)
+        text(legend_x + 24, y + 1, item.label, 12)
+
+    write_simple_pdf(output, geometry.width, geometry.height, "\n".join(commands))
+
+
+def color_parts(color):
+    value = color.lstrip("#")
+    return tuple(int(value[index:index + 2], 16) / 255.0 for index in (0, 2, 4))
+
+
+def aligned_text_x(x, value, size, bold, anchor):
+    if anchor == "left":
+        return x
+    width = approximate_text_width(value, size, bold)
+    if anchor == "center":
+        return x - width / 2
+    if anchor == "right":
+        return x - width
+    raise ValueError(f"Unsupported text anchor: {anchor}")
+
+
+def approximate_text_width(value, size, bold):
+    factor = 0.58 if bold else 0.52
+    return len(str(value)) * size * factor
+
+
+def pdf_escape(value):
+    return str(value).replace("\\", "\\\\").replace("(", "\\(").replace(")", "\\)")
+
+
+def write_simple_pdf(output, width, height, content):
+    content_bytes = content.encode("latin-1", errors="replace")
+    objects = [
+        b"<< /Type /Catalog /Pages 2 0 R >>",
+        b"<< /Type /Pages /Kids [3 0 R] /Count 1 >>",
+        (
+            f"<< /Type /Page /Parent 2 0 R /MediaBox [0 0 {width} {height}] "
+            f"/Resources << /Font << /F1 4 0 R /F2 5 0 R >> >> /Contents 6 0 R >>"
+        ).encode("ascii"),
+        b"<< /Type /Font /Subtype /Type1 /BaseFont /Helvetica >>",
+        b"<< /Type /Font /Subtype /Type1 /BaseFont /Helvetica-Bold >>",
+        b"<< /Length " + str(len(content_bytes)).encode("ascii") + b" >>\nstream\n" + content_bytes + b"\nendstream",
+    ]
+
+    pdf = b"%PDF-1.4\n"
+    offsets = [0]
+    for index, obj in enumerate(objects, start=1):
+        offsets.append(len(pdf))
+        pdf += f"{index} 0 obj\n".encode("ascii") + obj + b"\nendobj\n"
+
+    xref_start = len(pdf)
+    pdf += f"xref\n0 {len(objects) + 1}\n".encode("ascii")
+    pdf += b"0000000000 65535 f \n"
+    for offset in offsets[1:]:
+        pdf += f"{offset:010d} 00000 n \n".encode("ascii")
+    pdf += (
+        f"trailer\n<< /Size {len(objects) + 1} /Root 1 0 R >>\n"
+        f"startxref\n{xref_start}\n%%EOF\n"
+    ).encode("ascii")
+
+    output.write_bytes(pdf)
+
+
+def load_font(image_font, size, bold=False):
+    candidates = (
+        ("arialbd.ttf", "DejaVuSans-Bold.ttf") if bold else ("arial.ttf", "DejaVuSans.ttf")
+    )
+    for candidate in candidates:
+        try:
+            return image_font.truetype(candidate, size)
+        except OSError:
+            continue
+    return image_font.load_default()
+
+
+def make_geometry(series, query_ids):
     width = 1180
     height = 700
     left = 90
@@ -107,55 +381,40 @@ def render_svg(series, query_ids, title):
     plot_height = height - top - bottom
     lower, upper = plot_bounds(series)
     ticks = nice_log_ticks(lower, upper)
-
-    lines = [
-        '<?xml version="1.0" encoding="UTF-8"?>',
-        f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}">',
-        '<rect width="100%" height="100%" fill="white"/>',
-        f'<text x="{width / 2}" y="34" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="24" font-weight="700">{escape(title)}</text>',
-        f'<text x="{left}" y="62" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Mean execution time (s, log scale)</text>',
-    ]
-
-    for tick in ticks:
-        y = y_for(tick, lower, upper, top, plot_height)
-        lines.append(f'<line x1="{left}" y1="{y:.2f}" x2="{left + plot_width}" y2="{y:.2f}" stroke="#e2e2e2" stroke-width="1"/>')
-        lines.append(f'<text x="{left - 12}" y="{y + 4:.2f}" text-anchor="end" font-family="Helvetica, Arial, sans-serif" font-size="11" fill="#333">{format_tick(tick)}</text>')
-
-    lines.append(f'<line x1="{left}" y1="{top}" x2="{left}" y2="{top + plot_height}" stroke="#333" stroke-width="1.2"/>')
-    lines.append(f'<line x1="{left}" y1="{top + plot_height}" x2="{left + plot_width}" y2="{top + plot_height}" stroke="#333" stroke-width="1.2"/>')
-
     group_width = plot_width / len(query_ids)
     bar_gap = 3
     bar_width = min(24, (group_width - 20) / len(series) - bar_gap)
+    return PlotGeometry(
+        width,
+        height,
+        left,
+        right,
+        top,
+        bottom,
+        plot_width,
+        plot_height,
+        lower,
+        upper,
+        ticks,
+        group_width,
+        bar_gap,
+        max(1.0, bar_width),
+    )
+
 
+def bars(series, query_ids, geometry):
     for query_index, query_id in enumerate(query_ids):
-        group_x = left + query_index * group_width
-        total_bar_width = len(series) * bar_width + (len(series) - 1) * bar_gap
-        start_x = group_x + (group_width - total_bar_width) / 2
+        group_x = geometry.left + query_index * geometry.group_width
+        total_bar_width = len(series) * geometry.bar_width + (len(series) - 1) * geometry.bar_gap
+        start_x = group_x + (geometry.group_width - total_bar_width) / 2
         for series_index, item in enumerate(series):
             value = item.values.get(query_id)
             if value is None:
                 continue
-            x = start_x + series_index * (bar_width + bar_gap)
-            y = y_for(value, lower, upper, top, plot_height)
-            h = top + plot_height - y
-            lines.append(
-                f'<rect x="{x:.2f}" y="{y:.2f}" width="{bar_width:.2f}" height="{h:.2f}" fill="{item.color}">'
-                f'<title>{escape(item.label)} {escape(query_id)}: {value:.3f} s</title></rect>'
-            )
-        center = group_x + group_width / 2
-        lines.append(f'<text x="{center:.2f}" y="{top + plot_height + 30}" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">{escape(query_id)}</text>')
-
-    legend_x = left + plot_width + 35
-    legend_y = top + 15
-    lines.append(f'<text x="{legend_x}" y="{legend_y - 18}" font-family="Helvetica, Arial, sans-serif" font-size="13" font-weight="700">System</text>')
-    for index, item in enumerate(series):
-        y = legend_y + index * 28
-        lines.append(f'<rect x="{legend_x}" y="{y - 12}" width="16" height="16" fill="{item.color}"/>')
-        lines.append(f'<text x="{legend_x + 24}" y="{y + 1}" font-family="Helvetica, Arial, sans-serif" font-size="12">{escape(item.label)}</text>')
-
-    lines.append("</svg>")
-    return "\n".join(lines)
+            x = start_x + series_index * (geometry.bar_width + geometry.bar_gap)
+            y = y_for(value, geometry)
+            height = geometry.top + geometry.plot_height - y
+            yield x, y, geometry.bar_width, height, item, query_id, value
 
 
 def plot_bounds(series):
@@ -164,6 +423,8 @@ def plot_bounds(series):
         raise ValueError("Log-scale plot requires positive elapsed values.")
     lower = 10 ** math.floor(math.log10(min(values)))
     upper = 10 ** math.ceil(math.log10(max(values)))
+    if lower == upper:
+        upper = lower * 10
     return lower, upper
 
 
@@ -179,12 +440,12 @@ def nice_log_ticks(lower, upper):
     return ticks
 
 
-def y_for(value, lower, upper, top, height):
-    lower_log = math.log10(lower)
-    upper_log = math.log10(upper)
+def y_for(value, geometry):
+    lower_log = math.log10(geometry.lower)
+    upper_log = math.log10(geometry.upper)
     value_log = math.log10(value)
     ratio = (value_log - lower_log) / (upper_log - lower_log)
-    return top + height - ratio * height
+    return geometry.top + geometry.plot_height - ratio * geometry.plot_height
 
 
 def format_tick(value):
diff --git a/plugins/parquet-adapter/docs/TODO.md b/plugins/parquet-adapter/docs/TODO.md
deleted file mode 100644
index ab2893a786..0000000000
--- a/plugins/parquet-adapter/docs/TODO.md
+++ /dev/null
@@ -1,8 +0,0 @@
-select * from prf__fhv_tripdata where "year" > '2025';
-- partitions does not work
-
-
-settings - not URL
-
-
-delete adapter does not work
diff --git a/plugins/parquet-adapter/docs/aggregation_flow.md b/plugins/parquet-adapter/docs/aggregation_flow.md
index bd6b848e2e..41869c6a16 100644
--- a/plugins/parquet-adapter/docs/aggregation_flow.md
+++ b/plugins/parquet-adapter/docs/aggregation_flow.md
@@ -1,19 +1,19 @@
 # Aggregation Flow
 
-## ParquetRelMetadataAggregateExecutor
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelMetadataAggregateExecutor.java`
+## ParquetMetadataAggregateExecutor
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetMetadataAggregateExecutor.java`
 Performs the following:
 1. filter by file level: partition values / column statistics
 2. find group key for each file
 3. update COUNT / MIN / MAX using file metadata
 4. return result as Enumerable
 
-## ParquetRelDataAggregateExecutor
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelDataAggregateExecutor.java'`
-ParquetRelAggregateExecutor creates following Enumerators:
-1. ParquetFileGroupedAggregateRelEnumerator
+## ParquetDataAggregateExecutor
+`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetDataAggregateExecutor.java'`
+ParquetAggregateExecutor creates following Enumerators:
+1. ParquetFileGroupedAggregateEnumerator
    - aggregation can be done per whole file, because each file belongs to exactly one group.
-2. ParquetGroupedAggregateRelEnumerator 
+2. ParquetGroupedAggregateEnumerator 
    - grouped COUNT(*). For queries like:
       ```text
        SELECT category, COUNT(*)
@@ -34,7 +34,7 @@ ParquetRelAggregateExecutor creates following Enumerators:
       Conditions:
       - filters are supported 
       - aggregate functions by column
-3. ParquetRowAggregateRelEnumerator - default
+3. ParquetRowAggregateEnumerator - default
 
 ## Flow Diagram 
 
diff --git a/plugins/parquet-adapter/docs/images/aggregation/aggragation_flow.drawio.png b/plugins/parquet-adapter/docs/images/aggregation/aggragation_flow.drawio.png
index e278515d151418144f23bdcf823cd9a762b16a0a..245fe9ef8f9c048b854b371774bc852de467b714 100644
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diff --git a/plugins/parquet-adapter/docs/rel_execution_flows.md b/plugins/parquet-adapter/docs/rel_execution_flows.md
new file mode 100644
index 0000000000..aeb799c695
--- /dev/null
+++ b/plugins/parquet-adapter/docs/rel_execution_flows.md
@@ -0,0 +1,251 @@
+# Parquet Relational And Document Execution Flows
+
+These diagrams describe the current Parquet runtime paths after aggregate
+execution was moved into shared infrastructure and document aggregation pushdown
+was added. They focus on runtime entry points reached from generated planner
+nodes, not on full planner rule matching.
+
+Relational execution starts at `ParquetRelTable`. Document execution starts at
+`ParquetDocument`. Both models use the shared aggregate executors through the
+`ParquetAggregateSource` abstraction.
+
+## Aggregate Entry Points
+
+```mermaid
+flowchart TD
+    relPlan["ParquetRelAggregate.implement(...)"]
+    docPlan["ParquetDocAggregate.implement(...)"]
+
+    relDataEntry["ParquetRelTable.dataAggregate(...)"]
+    relMetadataEntry["ParquetRelTable.metadataAggregate(...)"]
+    docDataEntry["ParquetDocument.dataAggregate(...)"]
+    docMetadataEntry["ParquetDocument.metadataAggregate(...)"]
+
+    relDataWrapper["ParquetRelDataAggregateExecutor"]
+    relMetadataWrapper["ParquetRelMetadataAggregateExecutor"]
+    docWrapper["ParquetDocAggregateExecutor"]
+
+    relSource["relational ParquetAggregateSource"]
+    docSource["DocumentAggregateSource"]
+
+    dataExecutor["ParquetDataAggregateExecutor"]
+    metadataExecutor["ParquetMetadataAggregateExecutor"]
+
+    relPlan -->|"mode = DATA"| relDataEntry
+    relPlan -->|"mode = METADATA"| relMetadataEntry
+    docPlan -->|"mode = DATA"| docDataEntry
+    docPlan -->|"mode = METADATA"| docMetadataEntry
+
+    relDataEntry --> relDataWrapper --> relSource --> dataExecutor
+    relMetadataEntry --> relMetadataWrapper --> relSource --> metadataExecutor
+    docDataEntry --> docWrapper --> docSource --> dataExecutor
+    docMetadataEntry --> docWrapper --> docSource --> metadataExecutor
+```
+
+## Shared Data Aggregate Flow
+
+```mermaid
+flowchart TD
+    relEntry["ParquetRelDataAggregateExecutor.createEnumerator(...)"]
+    docEntry["ParquetDocAggregateExecutor.createDataEnumerator(...)"]
+    shared["ParquetDataAggregateExecutor.createEnumerator(...)"]
+
+    resolveFilters["ParquetFilterResolver.resolveFilters(...)"]
+    strategy{"enumerator strategy"}
+
+    fileProjection["tryBuildAggregateProjection(...)"]
+    fileEnumerator["ParquetFileGroupedAggregateEnumerator"]
+    fileFilter["partition/statistics file filters"]
+    fileGroup["fileGroupKey(...) from ParquetConstantColumnResolver"]
+    countStarRows["COUNT(*) from source row count"]
+    columnReader["ParquetNoFilterColumnAggregateReader"]
+    columnAccumulator["AggregateColumnAccumulator"]
+    fileRows["build aggregate result rows"]
+
+    groupedCountProjection["tryBuildGroupedCountProjection(...)"]
+    groupedProjection["tryBuildGroupedAggregateProjection(...)"]
+    groupedEnumerator["ParquetGroupedAggregateEnumerator"]
+    groupedReader["ParquetGroupedAggregateReader"]
+    pageStrategy{"page strategy"}
+    countPage["ParquetCountAggregatePageReader"]
+    groupedPage["ParquetGroupedAggregatePageReader"]
+
+    fallbackRows["relational fallback rows from ParquetEnumeratorsFactory"]
+    rowAggregate["ParquetRowAggregateEnumerator"]
+    rowEngine["ParquetRowAggregator"]
+    directProjector["DirectAggregateRowProjector"]
+
+    unsupported["GenericRuntimeException"]
+
+    relEntry -->|"with row fallback"| shared
+    docEntry -->|"without row fallback"| shared
+    shared --> resolveFilters --> strategy
+
+    strategy -->|"file-constant groups and exact file filters"| fileProjection --> fileEnumerator
+    fileEnumerator --> fileFilter --> fileGroup
+    fileGroup --> countStarRows --> fileRows
+    fileGroup -->|"aggregate columns projected"| columnReader --> columnAccumulator --> fileRows
+
+    strategy -->|"COUNT(*) grouped by flat data columns"| groupedCountProjection --> groupedEnumerator
+    strategy -->|"flat grouped aggregates with supported filters"| groupedProjection --> groupedEnumerator
+    groupedEnumerator --> groupedReader --> pageStrategy
+    pageStrategy -->|"COUNT(*) only and no group fields"| countPage
+    pageStrategy -->|"default grouped path"| groupedPage
+
+    strategy -->|"no aggregate reader strategy and relational fallback exists"| fallbackRows
+    fallbackRows --> rowAggregate --> rowEngine --> directProjector
+    strategy -->|"no aggregate reader strategy and no fallback"| unsupported
+```
+
+## Shared Metadata Aggregate Flow
+
+```mermaid
+flowchart TD
+    relEntry["ParquetRelMetadataAggregateExecutor.createEnumerator(...)"]
+    docEntry["ParquetDocAggregateExecutor.createMetadataEnumerator(...)"]
+    executor["ParquetMetadataAggregateExecutor.createEnumerator(...)"]
+
+    resolveFilters["ParquetFilterResolver.resolveFilters(...)"]
+    evaluator["ParquetMultiFilterEvaluator"]
+    partitionEval["ParquetSourceFilePartitionFilterEvaluator"]
+    statsEval["ParquetSourceFileStatisticsFilterEvaluator"]
+
+    loop["iterate ParquetAggregateSource.sourceFiles()"]
+    matches{"matchesExactly(...)"}
+    skip["skip source file"]
+    groupKey["fileGroupKey(...) from ParquetConstantColumnResolver"]
+    accumulator["MetadataAggregateAccumulator[] per group"]
+    aggregateKind{"aggregate kind"}
+    countStar["COUNT(*) uses sourceRowCount(...)"]
+    countColumn["COUNT(column) uses rowCount - nullCount"]
+    minMax["MIN/MAX uses ParquetColumnStatistics ranges"]
+    emptyGroup["if no groups and no matches, create empty aggregate row"]
+    rows["build PolyValue[] result rows"]
+    enumerable["Linq4j.asEnumerable(rows)"]
+
+    supportGate["supportsMetadataAggregate(...)"]
+    supportChecks["file-constant groups, metadata-decidable filters, COUNT/MIN/MAX support"]
+
+    relEntry --> executor
+    docEntry --> executor
+    executor --> resolveFilters --> evaluator
+    evaluator --> partitionEval
+    evaluator --> statsEval
+    evaluator --> loop --> matches
+    matches -->|"false"| skip
+    matches -->|"true"| groupKey --> accumulator --> aggregateKind
+    aggregateKind -->|"COUNT(*)"| countStar --> rows
+    aggregateKind -->|"COUNT(column)"| countColumn --> rows
+    aggregateKind -->|"MIN/MAX"| minMax --> rows
+    loop --> emptyGroup --> rows --> enumerable
+
+    supportGate -.-> supportChecks -.-> executor
+```
+
+## Relational Projection And Row Scan Flow
+
+```mermaid
+flowchart TD
+    entry["ParquetRelTable.project(...)"]
+    factory["ParquetRelExecutorsFactory.getExecutor(ParquetRelProjectExecutor.class)"]
+    executor["ParquetRelProjectExecutor.createEnumerator(...)"]
+    resolveFilters["ParquetFilterResolver.resolveFilters(...)"]
+    enumeratorsFactory["ParquetEnumeratorsFactory.create(...)"]
+    multiFile["ParquetMultiFileEnumerator"]
+    fileEvaluator["partition/statistics source-file evaluator"]
+    fileMatches{"source file matches?"}
+    skip["skip source file"]
+    enumForFile["ParquetRelExecutor.enumeratorForFile(...)"]
+    shape{"table and projection shape"}
+
+    primitiveReader["ParquetPrimitiveRowReader"]
+    readerFilters["readerFilters(...)"]
+    outputProjection["primitiveRowProjection(...)"]
+    primitiveEnumerator["ParquetRowRelEnumerator"]
+
+    sourceReaderNested["ParquetSourceReader"]
+    nestedRepeated["ParquetNestedRepeatedRelEnumerator"]
+
+    sourceReaderBinding["ParquetSourceReader"]
+    nestedNonRepeated["ParquetNestedNonRepeatedRelEnumerator"]
+
+    sourceReaderGeneric["ParquetSourceReader"]
+    relationalEnumerator["ParquetRelEnumerator"]
+
+    entry --> factory --> executor --> resolveFilters --> enumeratorsFactory --> multiFile
+    multiFile --> fileEvaluator --> fileMatches
+    fileMatches -->|"false"| skip
+    fileMatches -->|"true"| enumForFile --> shape
+
+    shape -->|"flat primitive projection"| primitiveReader --> readerFilters --> outputProjection --> primitiveEnumerator
+    shape -->|"nested table"| sourceReaderNested --> nestedRepeated
+    shape -->|"binding scan needed"| sourceReaderBinding --> nestedNonRepeated
+    shape -->|"generic flat row path"| sourceReaderGeneric --> relationalEnumerator
+```
+
+## Document Scan Flow
+
+```mermaid
+flowchart TD
+    plan["ParquetDocScan.implement(...)"]
+    scan["ParquetDocument.scanFiltered(...)"]
+    register["register involved Parquet adapter"]
+    resolveDynamic["resolve dynamic filter parameters"]
+    filters["FiltersContainer.shared(...)"]
+    multiFile["ParquetMultiFileEnumerator"]
+    sourceReader["ParquetSourceReader"]
+    docEnumerator["ParquetDocEnumerator"]
+    extractor["ParquetDocValueExtractor"]
+    row["PolyValue[] containing one PolyDocument"]
+
+    plan --> scan --> register --> resolveDynamic --> filters --> multiFile
+    multiFile -->|"for each source file"| sourceReader
+    sourceReader --> docEnumerator --> extractor --> row
+```
+
+## Document Aggregate Planning To Runtime
+
+```mermaid
+flowchart TD
+    scan["ParquetDocScan"]
+    filterRule["attachDocumentFiltersToScanUnderCalc"]
+    aggregateRules{"document aggregate rule"}
+    aggregateOnScan["documentAggregateOnScan"]
+    aggregateOnProject["documentAggregateOnProjectScan"]
+    aggregateOnCalc["documentAggregateOnCalcScan"]
+    fieldMapping["map top-level document fields to exported Parquet columns"]
+    aggregateNode["ParquetDocAggregate"]
+    mode{"aggregate mode"}
+    metadataScan["ParquetDocMetadataScan"]
+    metadataRuntime["ParquetDocument.metadataAggregate(...)"]
+    dataRuntime["ParquetDocument.dataAggregate(...)"]
+    sharedMetadata["ParquetMetadataAggregateExecutor"]
+    sharedData["ParquetDataAggregateExecutor"]
+
+    scan --> filterRule --> scan
+    scan --> aggregateRules
+    aggregateRules --> aggregateOnScan
+    aggregateRules --> aggregateOnProject
+    aggregateRules --> aggregateOnCalc
+    aggregateOnScan --> fieldMapping
+    aggregateOnProject --> fieldMapping
+    aggregateOnCalc --> fieldMapping
+    fieldMapping --> aggregateNode --> mode
+    mode -->|"metadata supported"| metadataScan --> metadataRuntime --> sharedMetadata
+    mode -->|"data aggregate supported"| dataRuntime --> sharedData
+```
+
+## Notes
+
+- Aggregate planner rules are registered only when
+  `ParquetOptimizationSettings.isOptimizeAggregation()` is enabled.
+- Metadata aggregation is exact-only. It requires file-constant group fields,
+  metadata-decidable filters, and supported `COUNT`, `MIN`, or `MAX` aggregate
+  calls.
+- Data aggregation supports the shared fast paths for file-level aggregates,
+  grouped counts, and grouped flat-column aggregates. The relational path also
+  provides a row fallback; the document path relies on the shared aggregate
+  readers.
+- Document aggregate pushdown only maps projected top-level document fields that
+  correspond to exported Parquet columns. More complex document expressions stay
+  outside the adapter aggregate path.
diff --git a/plugins/parquet-adapter/docs/todo_partitions.md b/plugins/parquet-adapter/docs/todo_partitions.md
deleted file mode 100644
index a3b781938e..0000000000
--- a/plugins/parquet-adapter/docs/todo_partitions.md
+++ /dev/null
@@ -1,24 +0,0 @@
-# Partitioning Issues
-
-## Partitioned Join Flow
-
-Adapter-level joins currently depend on both sides having the same backing source-file set. Polypheny catalog partitioning splits a root table into several physical allocations, while generated child tables can still describe the full file set. Because of that, a valid parent-child join over partitioned data may not be selected by the Parquet join rule, or it may need a more precise source compatibility check.
-
-Partition columns also need explicit handling in the nested join runtime. Root scans materialize folder-derived partition values through `ParquetNestedNonRepeatedRelEnumerator`, but `ParquetPathValueExtractor` still returns `NULL` for `PARTITION`. If a partition column is projected or filtered through the nested join path, the join result can lose that value.
-
-## Restore Flow
-
-`ParquetRelationalSource.restoreTable` restores only the first physical entity from the provided entity list. A partitioned table can have multiple physical entities, so restore should be checked to make sure every partition allocation is re-registered.
-
-## Polypheny Partition Model
-
-Only the first discovered partition column is mapped to Polypheny catalog partitioning. This is a current Polypheny-side limitation and is not planned to be extended now. Multi-level folders such as `year=2025/month=01` can still be exposed as adapter partition columns, but only one column should drive the catalog partition layout.
-
-## Partition Names
-
-Generated partition names are normalized from `column_value`. Different raw values can normalize to the same name, for example `a-b`, `a_b`, and case-only variants. The creation flow should eventually add collision handling.
-
-## Schema Consolidation
-
-Schema consolidation was made stricter by requiring at least `0.8` column-name similarity between files. This reduces accidental grouping of unrelated files. If schema evolution support grows, the threshold should probably become a named constant or adapter setting and should be covered by tests for missing-column null materialization.
-
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
index a853d7abe1..f61965bf73 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java
@@ -19,11 +19,14 @@
 import com.google.common.collect.ImmutableList;
 import org.polypheny.db.adapter.AdapterManager;
 import org.polypheny.db.adapter.parquet.document.ParquetDocumentSource;
+import org.polypheny.db.adapter.parquet.document.planning.ParquetDocAggregate;
+import org.polypheny.db.adapter.parquet.document.planning.ParquetDocMetadataScan;
 import org.polypheny.db.adapter.parquet.document.planning.ParquetDocScan;
+import org.polypheny.db.adapter.parquet.document.planning.ParquetDocConvention;
 import org.polypheny.db.adapter.parquet.relational.ParquetRelationalSource;
 import org.polypheny.db.adapter.parquet.relational.planning.EnumerableParquet;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetEnumerableUnion;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
+import org.polypheny.db.adapter.parquet.shared.planning.ParquetEnumerableUnion;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelConvention;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelAggregate;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelJoin;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelMetadataScan;
@@ -87,7 +90,7 @@ private void registerPolyAlg() {
 
         PolyAlgRegistry.register( ParquetRelScan.class, PolyAlgDeclaration.builder()
                 .model( DataModel.RELATIONAL )
-                .opName( "P_SCAN" ).convention( ParquetConvention.INSTANCE ).numInputs( 0 ).opTags( physTags )
+                .opName( "P_SCAN" ).convention( ParquetRelConvention.INSTANCE ).numInputs( 0 ).opTags( physTags )
                 .params( PolyAlgRegistry.getParams( LogicalRelScan.class ) )
                 .param( Parameter.builder().name( "fields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
@@ -95,7 +98,7 @@ private void registerPolyAlg() {
 
         PolyAlgRegistry.register( ParquetRelMetadataScan.class, PolyAlgDeclaration.builder()
                 .model( DataModel.RELATIONAL )
-                .opName( "P_METADATA_SCAN" ).convention( ParquetConvention.INSTANCE ).numInputs( 0 ).opTags( physTags )
+                .opName( "P_METADATA_SCAN" ).convention( ParquetRelConvention.INSTANCE ).numInputs( 0 ).opTags( physTags )
                 .params( PolyAlgRegistry.getParams( LogicalRelScan.class ) )
                 .param( Parameter.builder().name( "fields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
@@ -103,7 +106,7 @@ private void registerPolyAlg() {
 
         PolyAlgRegistry.register( ParquetRelJoin.class, PolyAlgDeclaration.builder()
                 .model( DataModel.RELATIONAL )
-                .opName( "P_JOIN" ).convention( ParquetConvention.INSTANCE ).numInputs( 2 ).opTags( physTags )
+                .opName( "P_JOIN" ).convention( ParquetRelConvention.INSTANCE ).numInputs( 2 ).opTags( physTags )
                 .params( PolyAlgRegistry.getParams( EnumerableJoin.class ) )
                 .param( Parameter.builder().name( "leftIsParent" ).type( ParamType.BOOLEAN ).defaultValue( BooleanArg.FALSE ).build() )
                 .param( Parameter.builder().name( "leftFields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
@@ -115,7 +118,7 @@ private void registerPolyAlg() {
 
         PolyAlgRegistry.register( ParquetRelAggregate.class, PolyAlgDeclaration.builder()
                 .model( DataModel.RELATIONAL )
-                .opName( "P_AGGREGATE" ).convention( ParquetConvention.INSTANCE ).numInputs( 1 ).opTags( physTags )
+                .opName( "P_AGGREGATE" ).convention( ParquetRelConvention.INSTANCE ).numInputs( 1 ).opTags( physTags )
                 .param( Parameter.builder().name( "mode" ).type( ParamType.STRING ).build() )
                 .param( Parameter.builder().name( "fields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .param( Parameter.builder().name( "groups" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
@@ -126,11 +129,29 @@ private void registerPolyAlg() {
 
         PolyAlgRegistry.register( ParquetDocScan.class, PolyAlgDeclaration.builder()
                 .model( DataModel.DOCUMENT )
-                .opName( "P_DOC_SCAN" ).convention( EnumerableConvention.INSTANCE ).numInputs( 0 ).opTags( physTags )
+                .opName( "P_DOC_SCAN" ).convention( ParquetDocConvention.INSTANCE ).numInputs( 0 ).opTags( physTags )
                 .params( PolyAlgRegistry.getParams( LogicalDocumentScan.class ) )
                 .param( Parameter.builder().name( "fields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
                 .build() );
+
+        PolyAlgRegistry.register( ParquetDocMetadataScan.class, PolyAlgDeclaration.builder()
+                .model( DataModel.DOCUMENT )
+                .opName( "P_DOC_METADATA_SCAN" ).convention( ParquetDocConvention.INSTANCE ).numInputs( 0 ).opTags( physTags )
+                .params( PolyAlgRegistry.getParams( LogicalDocumentScan.class ) )
+                .param( Parameter.builder().name( "fields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .build() );
+
+        PolyAlgRegistry.register( ParquetDocAggregate.class, PolyAlgDeclaration.builder()
+                .model( DataModel.DOCUMENT )
+                .opName( "P_DOC_AGGREGATE" ).convention( ParquetDocConvention.INSTANCE ).numInputs( 1 ).opTags( physTags )
+                .param( Parameter.builder().name( "mode" ).type( ParamType.STRING ).build() )
+                .param( Parameter.builder().name( "fields" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "groups" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "aggregates" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .param( Parameter.builder().name( "filters" ).multiValued( 1 ).type( ParamType.STRING ).defaultValue( ListArg.EMPTY ).build() )
+                .build() );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java
index 3e6b28ee5f..5ef5778ccf 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/ParquetDocumentSource.java
@@ -29,7 +29,6 @@
 import org.polypheny.db.adapter.annotations.AdapterSettingList;
 import org.polypheny.db.adapter.annotations.AdapterSettingString;
 import org.polypheny.db.adapter.parquet.relational.schema.DiscoveredTableBinding;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.catalog.entity.allocation.AllocationCollection;
 import org.polypheny.db.catalog.entity.allocation.AllocationGraph;
@@ -98,7 +97,7 @@ public List<PhysicalEntity> createCollection( Context context, LogicalCollection
         // handle multi-files
         var physical = currentNamespace.createParquetCollection(
                 collection,
-                getCollectionSourceFiles( logical.getName() ),
+                getCollectionBinding( logical.getName() ),
                 this );
         adapterCatalog.replacePhysical( physical );
         return List.of( physical );
@@ -112,7 +111,7 @@ public void restoreCollection( AllocationCollection alloc, List<PhysicalEntity>
         // handle multi-files
         var physical = currentNamespace.createParquetCollection(
                 collection.unwrapOrThrow( PhysicalCollection.class ),
-                getCollectionSourceFiles( collection.name ),
+                getCollectionBinding( collection.name ),
                 this );
         adapterCatalog.addPhysical( alloc, physical );
     }
@@ -168,11 +167,10 @@ public void renameLogicalColumn( long id, String newColumnName ) {
     }
 
 
-    private List<ParquetSourceFile> getCollectionSourceFiles( String collectionName ) {
+    private DiscoveredTableBinding getCollectionBinding( String collectionName ) {
         // handle multi-files
         return getTableBinding( collectionName )
-                .map( DiscoveredTableBinding::sourceFiles )
-                .filter( sourceFiles -> !sourceFiles.isEmpty() )
+                .filter( binding -> !binding.sourceFiles().isEmpty() )
                 .orElseThrow( () -> new GenericRuntimeException( "Missing discovered Parquet file binding for collection: %s", collectionName ) );
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocAggregateExecutor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocAggregateExecutor.java
new file mode 100644
index 0000000000..413e5ac114
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocAggregateExecutor.java
@@ -0,0 +1,207 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.document.execution;
+
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import org.apache.calcite.linq4j.Enumerable;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.execution.aggregate.ParquetAggregateSource;
+import org.polypheny.db.adapter.parquet.shared.execution.aggregate.ParquetDataAggregateExecutor;
+import org.polypheny.db.adapter.parquet.shared.execution.aggregate.ParquetMetadataAggregateExecutor;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.algebra.core.AggregateCall;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.ImmutableBitSet;
+
+
+/**
+ * Document entry point for data aggregate execution.
+ */
+public class ParquetDocAggregateExecutor {
+
+    private final ParquetAggregateSource aggregateSource;
+
+
+    public ParquetDocAggregateExecutor( List<ParquetSourceFile> sourceFiles, List<ExportedColumn> columns, Map<String, List<String>> columnPaths ) {
+        this.aggregateSource = new DocumentAggregateSource( sourceFiles, columns, columnPaths );
+    }
+
+
+    /**
+     * Creates a data enumerator that should go though the parquet file.
+     *
+     * @param dataContext a Polypheny data context.
+     * @param fields an array of field indexes.
+     * @param filters a list of filters to be applied.
+     * @param groupFields an array of group field indexes.
+     * @param aggregateKinds an array of aggregate functions.
+     * @param aggregateArgs an array of aggregate functions arguments indexes.
+     * @return an enumerator
+     */
+    public Enumerable<PolyValue[]> createDataEnumerator( DataContext dataContext, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        return new ParquetDataAggregateExecutor( aggregateSource, null )
+                .createEnumerator( dataContext, fields, filters, groupFields, aggregateKinds, aggregateArgs );
+    }
+
+
+    /**
+     * Creates a metadata enumerator that reads only parquet file statistics, no data is being read.
+     *
+     * @param dataContext a Polypheny data context.
+     * @param fields an array of field indexes.
+     * @param filters a list of filters to be applied.
+     * @param groupFields an array of group field indexes.
+     * @param aggregateKinds an array of aggregate functions.
+     * @param aggregateArgs an array of aggregate functions arguments indexes.
+     * @return an enumerator.
+     */
+    public Enumerable<PolyValue[]> createMetadataEnumerator( DataContext dataContext, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        return new ParquetMetadataAggregateExecutor( aggregateSource )
+                .createEnumerator( dataContext, fields, filters, groupFields, aggregateKinds, aggregateArgs );
+    }
+
+
+    /**
+     * Checks whether all provided aggregate functions are supported by data aggregates. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
+     *
+     * @param fields an array of field indexes.
+     * @param groupSet a set of group by fields.
+     * @param aggregateCalls a list of aggregation functions to validate.
+     * @return {@code true} if all provided aggregation functions can be applied and {@code false} otherwise.
+     */
+    public boolean supportsDataAggregate( int[] fields, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
+        return new ParquetDataAggregateExecutor( aggregateSource, null )
+                .supportsDataAggregate( fields, groupSet, aggregateCalls );
+    }
+
+
+    /**
+     * Checks whether all provided aggregate functions are supported by metadata aggregates. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
+     *
+     * @param fields an array of field indexes.
+     * @param filters a list of filters.
+     * @param groupSet a set of group by fields.
+     * @param aggregateCalls a list of aggregation functions to validate.
+     * @return {@code true} if all provided aggregation functions can be applied and {@code false} otherwise.
+     */
+    public boolean supportsMetadataAggregate( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
+        return new ParquetMetadataAggregateExecutor( aggregateSource )
+                .supportsMetadataAggregate( fields, filters, groupSet, aggregateCalls );
+    }
+
+
+    /**
+     * Checks whether all provided aggregate functions are supported. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
+     *
+     * @param fields an array of field indexes.
+     * @param filters a list of filters.
+     * @param groupFields an array of group field indexes.
+     * @param aggregateKinds an array of aggregate functions.
+     * @param aggregateArgs an array of aggregate functions argument indexes.
+     * @return {@code true} if all provided aggregation functions can be applied and {@code false} otherwise.
+     */
+    public boolean supportsAggregate( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        return new ParquetDataAggregateExecutor( aggregateSource, null )
+                .supportsDataAggregate( fields, filters, groupFields, aggregateKinds, aggregateArgs );
+    }
+
+
+    /**
+     * An implementation of document aggregate source on top of a Parquet file.
+     */
+    private static class DocumentAggregateSource implements ParquetAggregateSource {
+
+        private final List<ParquetSourceFile> sourceFiles;
+        private final List<ExportedColumn> columns;
+        private final Map<Integer, ParquetColumnBinding> bindingsByField;
+        private final ParquetSchemaReader schemaReader;
+
+
+        private DocumentAggregateSource( List<ParquetSourceFile> sourceFiles, List<ExportedColumn> columns, Map<String, List<String>> columnPaths ) {
+            this.sourceFiles = List.copyOf( sourceFiles );
+            this.columns = List.copyOf( columns );
+            this.bindingsByField = bindingsByField( this.sourceFiles, this.columns, columnPaths );
+            this.schemaReader = new ParquetSchemaReader( this.sourceFiles.stream().map( ParquetSourceFile::asSource ).toList() );
+        }
+
+
+        private static Map<Integer, ParquetColumnBinding> bindingsByField( List<ParquetSourceFile> sourceFiles, List<ExportedColumn> columns, Map<String, List<String>> columnPaths ) {
+            Map<Integer, ParquetColumnBinding> bindings = new LinkedHashMap<>();
+            for ( ExportedColumn column : columns ) {
+                List<String> sourcePath = columnPaths.get( column.name() );
+                ParquetColumnRole role = sourcePath == null && isPartitionColumn( sourceFiles, column.name() )
+                        ? ParquetColumnRole.PARTITION
+                        : ParquetColumnRole.DATA;
+                bindings.put(
+                        column.physicalPosition(),
+                        new ParquetColumnBinding( column.physicalPosition(), column.name(), role, sourcePath == null ? List.of() : sourcePath ) );
+            }
+            return bindings;
+        }
+
+
+        private static boolean isPartitionColumn( List<ParquetSourceFile> sourceFiles, String columnName ) {
+            return sourceFiles.stream().anyMatch( sourceFile -> sourceFile.partitionValues().containsKey( columnName ) );
+        }
+
+
+        @Override
+        public List<ParquetSourceFile> sourceFiles() {
+            return sourceFiles;
+        }
+
+
+        @Override
+        public ParquetSchemaReader schemaReader() {
+            return schemaReader;
+        }
+
+
+        @Override
+        public int fieldCount() {
+            return columns.size();
+        }
+
+
+        @Override
+        public PolyType fieldType( int field ) {
+            ExportedColumn column = column( field );
+            return column == null ? null : column.type();
+        }
+
+
+        @Override
+        public ParquetColumnBinding binding( int field ) {
+            return bindingsByField.get( field );
+        }
+
+
+        private ExportedColumn column( int field ) {
+            return columns.stream().filter( column -> column.physicalPosition() == field ).findFirst().orElse( null );
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
index 43d2ff1f7c..af46dfd9ad 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java
@@ -18,6 +18,7 @@
 
 
 import java.util.List;
+import java.util.Objects;
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractFilterTranslator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
@@ -41,6 +42,17 @@ public class ParquetDocFilterTranslator extends AbstractFilterTranslator {
      * @return ParquetFilter
      */
     public ParquetAdapterFilter<PolyValue> translate( List<ExportedColumn> columns, RexNode filter ) {
+        filter = unwrapCast( filter );
+        if ( filter instanceof RexCall call ) {
+            if ( filter.isA( Kind.AND ) || filter.isA( Kind.OR ) ) {
+                return translateLogical( columns, filter.getKind(), call.getOperands() );
+            }
+            if ( filter.isA( Kind.NOT ) && call.getOperands().size() == 1 ) {
+                ParquetAdapterFilter<PolyValue> operand = translate( columns, call.getOperands().get( 0 ) );
+                return operand == null ? null : ParquetAdapterFilter.logical( Kind.NOT, List.of( operand ) );
+            }
+        }
+
         ParsedFilter parsed = parse( filter );
         if ( parsed == null ) {
             return null;
@@ -67,6 +79,22 @@ public ParquetAdapterFilter<PolyValue> translate( List<ExportedColumn> columns,
     }
 
 
+    private ParquetAdapterFilter<PolyValue> translateLogical( List<ExportedColumn> columns, Kind operator, List<RexNode> operands ) {
+        if ( operands.isEmpty() ) {
+            return null;
+        }
+
+        List<ParquetAdapterFilter<PolyValue>> translated = operands.stream()
+                .map( operand -> translate( columns, operand ) )
+                .toList();
+        if ( translated.stream().anyMatch( Objects::isNull ) ) {
+            return null;
+        }
+
+        return ParquetAdapterFilter.logical( operator, translated );
+    }
+
+
     private String fieldName( RexNode node ) {
         if ( node instanceof RexNameRef nameRef ) {
             return nameRef.names.size() == 1 ? nameRef.names.get( 0 ) : null;
@@ -77,6 +105,10 @@ private String fieldName( RexNode node ) {
         }
 
         RexNode path = call.getOperands().get( 1 );
+        String foldedPath = foldedSingleFieldPath( path );
+        if ( foldedPath != null ) {
+            return foldedPath;
+        }
         if ( !(path instanceof RexCall pathCall) || pathCall.getKind() != Kind.ARRAY_VALUE_CONSTRUCTOR || pathCall.getOperands().size() != 1 ) {
             return null;
         }
@@ -89,4 +121,16 @@ private String fieldName( RexNode node ) {
         return literal.getValue().asString().value;
     }
 
+
+    private String foldedSingleFieldPath( RexNode path ) {
+        if ( !(path instanceof RexLiteral literal) || literal.getValue() == null || !literal.getValue().isList() ) {
+            return null;
+        }
+        List<? extends PolyValue> elements = literal.getValue().asList();
+        if ( elements.size() != 1 || !elements.get( 0 ).isString() ) {
+            return null;
+        }
+        return elements.get( 0 ).asString().value;
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocAggregate.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocAggregate.java
new file mode 100644
index 0000000000..c70b35cf22
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocAggregate.java
@@ -0,0 +1,264 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.document.planning;
+
+import java.util.Arrays;
+import java.util.List;
+import org.apache.calcite.linq4j.tree.Expression;
+import org.apache.calcite.linq4j.tree.Expressions;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.optimization.aggregate.ParquetAggregateSupport;
+import org.polypheny.db.adapter.parquet.shared.planning.ParquetPolyAlgDisplay;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.AlgWriter;
+import org.polypheny.db.algebra.SingleAlg;
+import org.polypheny.db.algebra.core.Aggregate;
+import org.polypheny.db.algebra.core.AggregateCall;
+import org.polypheny.db.algebra.enumerable.EnumUtils;
+import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
+import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
+import org.polypheny.db.algebra.polyalg.arguments.ListArg;
+import org.polypheny.db.algebra.polyalg.arguments.PolyAlgArgs;
+import org.polypheny.db.algebra.polyalg.arguments.StringArg;
+import org.polypheny.db.algebra.type.AlgDataType;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgOptCost;
+import org.polypheny.db.plan.AlgPlanner;
+import org.polypheny.db.plan.AlgTraitSet;
+
+/**
+ * Parquet-convention aggregate for document scans after field extraction has been mapped to primitive Parquet columns.
+ */
+public class ParquetDocAggregate extends SingleAlg implements org.polypheny.db.adapter.parquet.relational.planning.ParquetAlg {
+
+    private static final int NO_ARGUMENT = -1;
+
+    private final ParquetDocScan scan;
+    private final int[] fields;
+    private final AggregateMode mode;
+    private final int[] groupFields;
+    private final String[] aggregateKinds;
+    private final int[] aggregateArgs;
+    private final AlgDataType rowType;
+
+
+    private ParquetDocAggregate( AlgCluster cluster, AlgTraitSet traitSet, AlgNode input, ParquetDocScan scan, int[] fields, AggregateMode mode, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs, AlgDataType rowType ) {
+        super( cluster, traitSet, input );
+        this.scan = scan;
+        this.fields = fields.clone();
+        this.mode = mode;
+        this.groupFields = groupFields.clone();
+        this.aggregateKinds = aggregateKinds.clone();
+        this.aggregateArgs = aggregateArgs.clone();
+        this.rowType = rowType;
+    }
+
+
+    public static ParquetDocAggregate create( ParquetDocScan scan, Aggregate aggregate, int[] fields ) {
+        if ( aggregate.indicator || !Aggregate.isSimple( aggregate ) ) {
+            return null;
+        }
+        int[] groupFields = aggregate.getGroupSet().asList().stream().mapToInt( Integer::intValue ).toArray();
+        List<AggregateCall> aggregateCalls = normalizeDocumentRootCounts( fields, aggregate.getAggCallList() );
+        AggregateMode mode = aggregateMode( scan, fields, aggregateCalls, aggregate.getGroupSet() );
+        if ( mode == null ) {
+            return null;
+        }
+        return new ParquetDocAggregate(
+                aggregate.getCluster(),
+                aggregate.getCluster().traitSetOf( ParquetDocConvention.INSTANCE ),
+                mode == AggregateMode.METADATA ? new ParquetDocMetadataScan( scan ) : scan,
+                scan,
+                fields,
+                mode,
+                groupFields,
+                aggregateKinds( aggregateCalls ),
+                aggregateArgs( aggregateCalls ),
+                aggregate.getTupleType() );
+    }
+
+
+    private static AggregateMode aggregateMode( ParquetDocScan scan, int[] fields, List<AggregateCall> aggregateCalls, org.polypheny.db.util.ImmutableBitSet groupSet ) {
+        boolean metadataCandidate = true;
+        for ( AggregateCall aggregateCall : aggregateCalls ) {
+            if ( !ParquetAggregateSupport.supportsMetadataAggregateCall( aggregateCall ) ) {
+                metadataCandidate = false;
+                break;
+            }
+        }
+        if ( metadataCandidate && scan.getEntity().supportsMetadataAggregate( fields, scan.getFilters(), groupSet, aggregateCalls ) ) {
+            return AggregateMode.METADATA;
+        }
+        if ( scan.getEntity().supportsDataAggregate( fields, groupSet, aggregateCalls ) ) {
+            return AggregateMode.DATA;
+        }
+        return null;
+    }
+
+
+    private static String[] aggregateKinds( List<AggregateCall> aggregateCalls ) {
+        return aggregateCalls.stream()
+                .map( aggregateCall -> aggregateCall.getAggregation().getKind().name() )
+                .toArray( String[]::new );
+    }
+
+
+    private static int[] aggregateArgs( List<AggregateCall> aggregateCalls ) {
+        return aggregateCalls.stream()
+                .mapToInt( aggregateCall -> aggregateCall.getArgList().isEmpty() ? NO_ARGUMENT : aggregateCall.getArgList().get( 0 ) )
+                .toArray();
+    }
+
+
+    private static List<AggregateCall> normalizeDocumentRootCounts( int[] fields, List<AggregateCall> aggregateCalls ) {
+        if ( fields.length != 0 ) {
+            return aggregateCalls;
+        }
+        return aggregateCalls.stream()
+                .map( aggregateCall -> isDocumentRootCount( aggregateCall )
+                        ? aggregateCall.copy( List.of(), aggregateCall.filterArg, aggregateCall.getCollation() )
+                        : aggregateCall )
+                .toList();
+    }
+
+
+    private static boolean isDocumentRootCount( AggregateCall aggregateCall ) {
+        if ( aggregateCall.getAggregation().getKind() != org.polypheny.db.algebra.constant.Kind.COUNT ) {
+            return false;
+        }
+        return aggregateCall.getArgList().isEmpty()
+                || (aggregateCall.getArgList().size() == 1 && aggregateCall.getArgList().get( 0 ) == 0);
+    }
+
+
+    @Override
+    public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
+        return new ParquetDocAggregate( getCluster(), traitSet, inputs.get( 0 ), scan, fields, mode, groupFields, aggregateKinds, aggregateArgs, rowType );
+    }
+
+
+    @Override
+    protected AlgDataType deriveRowType() {
+        return rowType;
+    }
+
+
+    @Override
+    public AlgWriter explainTerms( AlgWriter pw ) {
+        return super.explainTerms( pw )
+                .item( "mode", modeName() )
+                .item( "fields", fieldNames() )
+                .item( "groups", groupNames() )
+                .item( "aggregates", aggregateNames() )
+                .item( "filters", filterNames() );
+    }
+
+
+    @Override
+    public String algCompareString() {
+        return getClass().getSimpleName()
+                + "$input=" + input.algCompareString()
+                + "$mode=" + modeName()
+                + "$fields=" + fieldNames()
+                + "$groups=" + groupNames()
+                + "$aggregates=" + aggregateNames()
+                + "$filters=" + filterNames();
+    }
+
+
+    @Override
+    public PolyAlgArgs bindArguments() {
+        return super.bindArguments()
+                .put( "mode", new StringArg( modeName() ) )
+                .put( "fields", new ListArg<>( fieldNames(), StringArg::new ) )
+                .put( "groups", new ListArg<>( groupNames(), StringArg::new ) )
+                .put( "aggregates", new ListArg<>( aggregateNames(), StringArg::new ) )
+                .put( "filters", new ListArg<>( filterNames(), StringArg::new ) );
+    }
+
+
+    @Override
+    public void register( AlgPlanner planner ) {
+        ParquetDocConvention.INSTANCE.register( planner );
+    }
+
+
+    @Override
+    public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
+        return super.computeSelfCost( planner, mq ).multiplyBy( ParquetDocConvention.INSTANCE.getCostMultiplier() * 0.01D );
+    }
+
+
+    @Override
+    public Expression implement( EnumerableAlgImplementor implementor ) {
+        Expression runtimeFilters = EnumUtils.expressionList( scan.getFilters().stream().map( ParquetAdapterFilter::toExpression ).toList() );
+        return Expressions.call(
+                scan.getEntity().asExpression(),
+                mode == AggregateMode.METADATA ? "metadataAggregate" : "dataAggregate",
+                implementor.getRootExpression(),
+                Expressions.constant( fields ),
+                runtimeFilters,
+                Expressions.constant( groupFields ),
+                Expressions.constant( aggregateKinds ),
+                Expressions.constant( aggregateArgs ) );
+    }
+
+
+    private List<String> fieldNames() {
+        return ParquetPolyAlgDisplay.fieldNames( scan.getEntity(), fields );
+    }
+
+
+    private String modeName() {
+        return switch ( mode ) {
+            case METADATA -> "metadataAggregate";
+            case DATA -> "dataAggregate";
+        };
+    }
+
+
+    private List<String> groupNames() {
+        List<String> fieldNames = fieldNames();
+        return Arrays.stream( groupFields )
+                .mapToObj( index -> index >= 0 && index < fieldNames.size() ? fieldNames.get( index ) : "#" + index )
+                .toList();
+    }
+
+
+    private List<String> aggregateNames() {
+        List<String> fieldNames = fieldNames();
+        java.util.ArrayList<String> values = new java.util.ArrayList<>( aggregateKinds.length );
+        for ( int i = 0; i < aggregateKinds.length; i++ ) {
+            int aggregateArg = aggregateArgs[i];
+            String argument = aggregateArg == NO_ARGUMENT ? "*" : aggregateArg >= 0 && aggregateArg < fieldNames.size() ? fieldNames.get( aggregateArg ) : "#" + aggregateArg;
+            values.add( aggregateKinds[i] + "(" + argument + ")" );
+        }
+        return values;
+    }
+
+
+    private List<String> filterNames() {
+        return ParquetPolyAlgDisplay.filters( scan.getFilters(), ParquetPolyAlgDisplay.fieldNames( scan.getEntity() ) );
+    }
+
+
+    private enum AggregateMode {
+        METADATA,
+        DATA
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocCalcRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocCalcRule.java
deleted file mode 100644
index 7be6cddd31..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocCalcRule.java
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.document.planning;
-
-import java.util.ArrayList;
-import java.util.HashSet;
-import java.util.List;
-import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
-import org.polypheny.db.adapter.parquet.document.execution.ParquetDocFilterTranslator;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.algebra.enumerable.EnumerableCalc;
-import org.polypheny.db.plan.AlgOptRule;
-import org.polypheny.db.plan.AlgOptRuleCall;
-import org.polypheny.db.plan.AlgOptUtil;
-import org.polypheny.db.rex.RexNode;
-import org.polypheny.db.rex.RexProgram;
-import org.polypheny.db.tools.AlgBuilderFactory;
-import org.polypheny.db.type.entity.PolyValue;
-
-/**
- * Pushes supported document Calc filters into a Parquet document scan.
- */
-public class ParquetDocCalcRule extends AlgOptRule {
-
-    private final ParquetDocFilterTranslator translator = new ParquetDocFilterTranslator();
-
-
-    public ParquetDocCalcRule( AlgBuilderFactory algBuilderFactory ) {
-        super( operand( EnumerableCalc.class, operand( ParquetDocScan.class, none() ) ), algBuilderFactory, ParquetDocCalcRule.class.getSimpleName() );
-    }
-
-
-    @Override
-    public void onMatch( AlgOptRuleCall call ) {
-        EnumerableCalc calc = call.alg( 0 );
-        ParquetDocScan scan = call.alg( 1 );
-        RexProgram program = calc.getProgram();
-        if ( program.getCondition() == null ) {
-            return;
-        }
-
-        List<ExportedColumn> columns = scan.getEntity().getParquetSource().getExportedColumns().get( scan.getEntity().name );
-        if ( columns == null ) {
-            return;
-        }
-
-        List<RexNode> predicates = new ArrayList<>();
-        AlgOptUtil.decomposeConjunction( program.expandLocalRef( program.getCondition() ), predicates );
-        if ( predicates.isEmpty() ) {
-            return;
-        }
-
-        List<ParquetAdapterFilter<PolyValue>> filters = new ArrayList<>();
-        for ( RexNode predicate : predicates ) {
-            ParquetAdapterFilter<PolyValue> filter = translator.translate( columns, predicate );
-            if ( filter == null ) {
-                return;
-            }
-            filters.add( filter );
-        }
-
-        if ( new HashSet<>( scan.getFilters() ).containsAll( filters ) ) {
-            return;
-        }
-
-        call.transformTo( EnumerableCalc.create( scan.withFilters( filters ), program ) );
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocConvention.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocConvention.java
new file mode 100644
index 0000000000..518cba6fe9
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocConvention.java
@@ -0,0 +1,69 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.document.planning;
+
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetAlg;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelConvention;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.plan.AlgOptRule;
+import org.polypheny.db.plan.AlgPlanner;
+import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.plan.Convention;
+import org.polypheny.db.plan.ConventionTraitDef;
+
+/**
+ * Calling convention for document operators executed directly by the Parquet adapter.
+ */
+public class ParquetDocConvention extends Convention.Impl {
+
+    public static final ParquetDocConvention INSTANCE = new ParquetDocConvention();
+
+
+    private ParquetDocConvention() {
+        super( "PARQUET_DOCUMENT", ParquetAlg.class );
+    }
+
+
+    /**
+     * Register Rules
+     * @param planner Planner
+     */
+    @Override
+    public void register( AlgPlanner planner ) {
+        for ( AlgOptRule rule : ParquetDocRules.rules( this ) ) {
+            planner.addRuleDuringRuntime( rule );
+        }
+    }
+
+
+    @Override
+    public boolean canConvertConvention( Convention toConvention ) {
+        return toConvention == EnumerableConvention.INSTANCE;
+    }
+
+
+    @Override
+    public boolean useAbstractConvertersForConversion( AlgTraitSet fromTraits, AlgTraitSet toTraits ) {
+        return toTraits.getTrait( ConventionTraitDef.INSTANCE ) == EnumerableConvention.INSTANCE;
+    }
+
+
+    public double getCostMultiplier() {
+        return ParquetRelConvention.COST_MULTIPLIER;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocMetadataScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocMetadataScan.java
new file mode 100644
index 0000000000..6c1695b4dc
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocMetadataScan.java
@@ -0,0 +1,58 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.document.planning;
+
+import java.util.List;
+import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
+import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgOptCost;
+import org.polypheny.db.plan.AlgPlanner;
+import org.polypheny.db.plan.AlgTraitSet;
+import org.polypheny.db.type.entity.PolyValue;
+
+/**
+ * Structural metadata-access input for document metadata aggregates.
+ * Runtime execution is owned by the aggregate node, so no row scan cost is added.
+ */
+public class ParquetDocMetadataScan extends ParquetDocScan {
+
+    public ParquetDocMetadataScan( ParquetDocScan scan ) {
+        this( scan.getCluster(), scan.getEntity(), scan.getFilters() );
+    }
+
+
+    private ParquetDocMetadataScan( AlgCluster cluster, ParquetDocument entity, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        super( cluster, entity, filters );
+    }
+
+
+    @Override
+    public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
+        assert inputs.isEmpty();
+        return new ParquetDocMetadataScan( getCluster(), entity, getFilters() );
+    }
+
+
+    @Override
+    public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
+        return planner.getCostFactory().makeZeroCost();
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocPatternMatchers.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocPatternMatchers.java
new file mode 100644
index 0000000000..73d8fc21ef
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocPatternMatchers.java
@@ -0,0 +1,320 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.document.planning;
+
+import static org.polypheny.db.plan.AlgOptRule.none;
+import static org.polypheny.db.plan.AlgOptRule.operand;
+
+import java.util.ArrayList;
+import java.util.HashSet;
+import java.util.List;
+import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.parquet.document.execution.ParquetDocFilterTranslator;
+import org.polypheny.db.adapter.parquet.relational.planning.EnumerableParquet;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.optimization.PatternMatcher;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.core.Aggregate;
+import org.polypheny.db.algebra.enumerable.EnumerableAggregate;
+import org.polypheny.db.algebra.enumerable.EnumerableCalc;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.enumerable.EnumerableProject;
+import org.polypheny.db.plan.AlgOptRuleCall;
+import org.polypheny.db.plan.AlgOptUtil;
+import org.polypheny.db.rex.RexCall;
+import org.polypheny.db.rex.RexIndexRef;
+import org.polypheny.db.rex.RexLiteral;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.rex.RexProgram;
+import org.polypheny.db.schema.trait.ModelTrait;
+import org.polypheny.db.tools.AlgBuilderFactory;
+import org.polypheny.db.type.entity.PolyValue;
+
+public final class ParquetDocPatternMatchers {
+
+    private static final ParquetDocFilterTranslator translator = new ParquetDocFilterTranslator();
+
+
+    private ParquetDocPatternMatchers() {
+    }
+
+
+    private static EnumerableParquet toEnumerable( AlgNode source, AlgNode input ) {
+        return new EnumerableParquet(
+                source.getCluster(),
+                input.getTraitSet().replace( EnumerableConvention.INSTANCE ).replace( ModelTrait.DOCUMENT ),
+                input );
+    }
+
+
+    /**
+     * Pushes supported document Calc filters into a Parquet document scan.
+     */
+    public static PatternMatcher attachFiltersToScanUnderCalc(ParquetDocConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableCalc.class,
+                        operand( EnumerableParquet.class, operand( ParquetDocScan.class, none() ) )
+                ),
+                "attachDocumentFiltersToScanUnderCalc",
+                call -> {
+                    EnumerableCalc calc = call.alg( 0 );
+                    ParquetDocScan scan = call.alg( 2 );
+                    ParquetDocScan updatedScan = applyFilters( calc, scan );
+                    if ( updatedScan == null ) {
+                        return;
+                    }
+                    if ( calc.getProgram().projectsOnlyIdentity() ) {
+                        call.transformTo( toEnumerable( calc, updatedScan ) );
+                    } else {
+                        call.transformTo( EnumerableCalc.create( toEnumerable( calc, updatedScan ), calc.getProgram() ) );
+                    }
+                }
+        );
+    }
+
+
+    /**
+     * Replaces supported field-independent aggregates directly over a Parquet document scan.
+     */
+    public static PatternMatcher aggregateOnScan(ParquetDocConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableAggregate.class,
+                        operand( EnumerableParquet.class, operand( ParquetDocScan.class, none() ) )
+                ),
+                "documentAggregateOnScan",
+                call -> {
+                    EnumerableAggregate aggregate = call.alg( 0 );
+                    ParquetDocScan scan = call.alg( 2 );
+                    if ( aggregateNeedsProjectedFields( aggregate ) ) {
+                        return;
+                    }
+                    transformAggregate( call, aggregate, scan, new int[0] );
+                }
+        );
+    }
+
+
+    /**
+     * Replaces supported aggregates over document field projections with a Parquet document aggregate.
+     */
+    public static PatternMatcher aggregateOnProjectScan(ParquetDocConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableAggregate.class,
+                        operand(
+                                EnumerableProject.class,
+                                operand( EnumerableParquet.class, operand( ParquetDocScan.class, none() ) )
+                        )
+                ),
+                "documentAggregateOnProjectScan",
+                call -> {
+                    EnumerableAggregate aggregate = call.alg( 0 );
+                    EnumerableProject project = call.alg( 1 );
+                    ParquetDocScan scan = call.alg( 3 );
+                    int[] fields = documentFields( project.getProjects(), scan );
+                    if ( fields == null && (aggregateNeedsProjectedFields( aggregate ) || !isDocumentRootProjection( project.getProjects() )) ) {
+                        return;
+                    }
+                    if ( fields == null ) {
+                        fields = new int[0];
+                    }
+                    transformAggregate( call, aggregate, scan, fields );
+                }
+        );
+    }
+
+
+    /**
+     * Replaces supported aggregates over project-only document Calcs with a Parquet document aggregate.
+     */
+    public static PatternMatcher aggregateOnCalcScan(ParquetDocConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableAggregate.class,
+                        operand(
+                                EnumerableCalc.class,
+                                operand( EnumerableParquet.class, operand( ParquetDocScan.class, none() ) )
+                        )
+                ),
+                "documentAggregateOnCalcScan",
+                call -> {
+                    EnumerableAggregate aggregate = call.alg( 0 );
+                    EnumerableCalc calc = call.alg( 1 );
+                    ParquetDocScan scan = call.alg( 3 );
+                    ParquetDocScan aggregateScan = scan;
+                    if ( calc.getProgram().getCondition() != null ) {
+                        aggregateScan = applyFilters( calc, scan );
+                    }
+                    if ( aggregateScan == null ) {
+                        return;
+                    }
+                    List<RexNode> projects = calc.getProgram().getProjectList().stream()
+                            .map( calc.getProgram()::expandLocalRef )
+                            .toList();
+                    int[] fields = documentFields( projects, aggregateScan );
+                    if ( fields == null && (aggregateNeedsProjectedFields( aggregate ) || !isDocumentRootProjection( projects )) ) {
+                        return;
+                    }
+                    if ( fields == null ) {
+                        fields = new int[0];
+                    }
+                    transformAggregate( call, aggregate, aggregateScan, fields );
+                }
+        );
+    }
+
+
+    static ParquetDocScan applyFilters( EnumerableCalc calc, ParquetDocScan scan ) {
+        RexProgram program = calc.getProgram();
+        if ( program.getCondition() == null ) {
+            return null;
+        }
+
+        List<ExportedColumn> columns = scan.getEntity().getParquetSource().getExportedColumns().get( scan.getEntity().name );
+        if ( columns == null ) {
+            return null;
+        }
+
+        List<RexNode> predicates = new ArrayList<>();
+        AlgOptUtil.decomposeConjunction( program.expandLocalRef( program.getCondition() ), predicates );
+        if ( predicates.isEmpty() ) {
+            return null;
+        }
+
+        List<ParquetAdapterFilter<PolyValue>> filters = new ArrayList<>();
+        for ( RexNode predicate : predicates ) {
+            ParquetAdapterFilter<PolyValue> filter = translator.translate( columns, predicate );
+            if ( filter == null ) {
+                return null;
+            }
+            filters.add( filter );
+        }
+
+        if ( new HashSet<>( scan.getFilters() ).containsAll( filters ) ) {
+            return null;
+        }
+
+        return scan.withFilters( filters );
+    }
+
+
+    private static void transformAggregate( AlgOptRuleCall call, Aggregate aggregate, ParquetDocScan scan, int[] fields ) {
+        ParquetDocAggregate parquetAggregate = ParquetDocAggregate.create( scan, aggregate, fields );
+        if ( parquetAggregate != null ) {
+            call.transformTo( toEnumerable( aggregate, parquetAggregate ) );
+        }
+    }
+
+
+    private static boolean aggregateNeedsProjectedFields( Aggregate aggregate ) {
+        if ( !aggregate.getGroupSet().isEmpty() ) {
+            return true;
+        }
+        return aggregate.getAggCallList().stream().anyMatch( aggregateCall -> !isDocumentRootCount( aggregateCall ) );
+    }
+
+
+    private static boolean isDocumentRootCount( org.polypheny.db.algebra.core.AggregateCall aggregateCall ) {
+        if ( aggregateCall.getAggregation().getKind() != Kind.COUNT ) {
+            return false;
+        }
+        return aggregateCall.getArgList().isEmpty()
+                || (aggregateCall.getArgList().size() == 1 && aggregateCall.getArgList().get( 0 ) == 0);
+    }
+
+
+    private static boolean isDocumentRootProjection( List<? extends RexNode> projects ) {
+        return projects.size() == 1
+                && projects.get( 0 ) instanceof RexIndexRef indexRef
+                && indexRef.getIndex() == 0;
+    }
+
+
+    private static int[] documentFields( List<? extends RexNode> projects, ParquetDocScan scan ) {
+        List<ExportedColumn> columns = scan.getEntity().getParquetSource().getExportedColumns().get( scan.getEntity().name );
+        if ( columns == null ) {
+            return null;
+        }
+        int[] fields = new int[projects.size()];
+        for ( int i = 0; i < projects.size(); i++ ) {
+            String fieldName = documentFieldName( projects.get( i ) );
+            if ( fieldName == null ) {
+                return null;
+            }
+            ExportedColumn column = columns.stream()
+                    .filter( candidate -> candidate.name().equalsIgnoreCase( fieldName ) )
+                    .findFirst()
+                    .orElse( null );
+            if ( column == null ) {
+                return null;
+            }
+            fields[i] = column.physicalPosition();
+        }
+        return fields;
+    }
+
+
+    private static String documentFieldName( RexNode node ) {
+        while ( node.isA( Kind.CAST ) ) {
+            node = ((RexCall) node).getOperands().get( 0 );
+        }
+        if ( !(node instanceof RexCall call) || call.getKind() != Kind.MQL_QUERY_VALUE || call.getOperands().size() != 2 ) {
+            return null;
+        }
+        if ( !(call.getOperands().get( 0 ) instanceof RexIndexRef indexRef) || indexRef.getIndex() != 0 ) {
+            return null;
+        }
+
+        RexNode path = call.getOperands().get( 1 );
+        String foldedPath = foldedSingleFieldPath( path );
+        if ( foldedPath != null ) {
+            return foldedPath;
+        }
+        if ( !(path instanceof RexCall pathCall) || pathCall.getKind() != Kind.ARRAY_VALUE_CONSTRUCTOR || pathCall.getOperands().size() != 1 ) {
+            return null;
+        }
+        RexNode element = pathCall.getOperands().get( 0 );
+        if ( !(element instanceof RexLiteral literal) || literal.getValue() == null || !literal.getValue().isString() ) {
+            return null;
+        }
+        return literal.getValue().asString().value;
+    }
+
+
+    private static String foldedSingleFieldPath( RexNode path ) {
+        if ( !(path instanceof RexLiteral literal) || literal.getValue() == null || !literal.getValue().isList() ) {
+            return null;
+        }
+        List<? extends PolyValue> elements = literal.getValue().asList();
+        if ( elements.size() != 1 || !elements.get( 0 ).isString() ) {
+            return null;
+        }
+        return elements.get( 0 ).asString().value;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocRules.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocRules.java
new file mode 100644
index 0000000000..5029f29aee
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocRules.java
@@ -0,0 +1,85 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.document.planning;
+
+import java.util.ArrayList;
+import java.util.List;
+import org.polypheny.db.adapter.parquet.relational.planning.EnumerableParquet;
+import org.polypheny.db.adapter.parquet.shared.optimization.ParquetAggregatePatternMatchers;
+import org.polypheny.db.adapter.parquet.shared.optimization.ParquetAlgOptRule;
+import org.polypheny.db.adapter.parquet.shared.optimization.ParquetOptimizationSettings;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.convert.ConverterRule;
+import org.polypheny.db.algebra.core.AlgFactories;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.plan.AlgOptRule;
+import org.polypheny.db.schema.trait.ModelTrait;
+import org.polypheny.db.tools.AlgBuilderFactory;
+
+/**
+ * Rules that build the Parquet adapter document convention path.
+ */
+public final class ParquetDocRules {
+
+    private ParquetDocRules() {
+    }
+
+
+    public static List<AlgOptRule> rules( ParquetDocConvention out ) {
+        return rules( out, AlgFactories.LOGICAL_BUILDER );
+    }
+
+
+    public static List<AlgOptRule> rules(ParquetDocConvention out, AlgBuilderFactory factory ) {
+        List<AlgOptRule> rules = new ArrayList<>();
+        rules.add( new ParquetAlgOptRule( ParquetDocPatternMatchers.attachFiltersToScanUnderCalc( out, factory ) ) );
+        rules.add( new EnumerableParquetDocumentRule( out, factory ) );
+        if ( ParquetOptimizationSettings.isOptimizeAggregation() ) {
+            rules.add( new ParquetAlgOptRule( ParquetDocPatternMatchers.aggregateOnScan( out, factory ) ) );
+            rules.add( new ParquetAlgOptRule( ParquetDocPatternMatchers.aggregateOnProjectScan( out, factory ) ) );
+            rules.add( new ParquetAlgOptRule( ParquetDocPatternMatchers.aggregateOnCalcScan( out, factory ) ) );
+            rules.add( new ParquetAlgOptRule( ParquetAggregatePatternMatchers.partialAggregateOnUnion( out, factory ) ) );
+            rules.add( new ParquetAlgOptRule( ParquetAggregatePatternMatchers.partialAggregateOnCalcUnion( out, factory ) ) );
+        }
+        return rules;
+    }
+
+
+    /**
+     * Rule that converts a plan from Parquet document convention into Enumerable convention.
+     */
+    public static class EnumerableParquetDocumentRule extends ConverterRule {
+
+        public EnumerableParquetDocumentRule(ParquetDocConvention in, AlgBuilderFactory algBuilderFactory ) {
+            super(
+                    AlgNode.class,
+                    alg -> true,
+                    in,
+                    EnumerableConvention.INSTANCE,
+                    algBuilderFactory,
+                    "EnumerableParquetDocumentRule" );
+        }
+
+
+        @Override
+        public AlgNode convert( AlgNode alg ) {
+            return new EnumerableParquet( alg.getCluster(), alg.getTraitSet().replace( getOutTrait() ).replace( ModelTrait.DOCUMENT ), alg );
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
index 4ee18ddf8e..b6601aeb25 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java
@@ -20,23 +20,18 @@
 import java.util.List;
 import java.util.stream.IntStream;
 import lombok.Getter;
-import org.apache.calcite.linq4j.tree.Blocks;
 import org.apache.calcite.linq4j.tree.Expression;
 import org.apache.calcite.linq4j.tree.Expressions;
 import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetAlg;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelConvention;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.planning.ParquetPolyAlgDisplay;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.AlgWriter;
-import org.polypheny.db.algebra.core.AlgFactories;
 import org.polypheny.db.algebra.core.document.DocumentScan;
 import org.polypheny.db.algebra.enumerable.EnumUtils;
-import org.polypheny.db.algebra.enumerable.EnumerableAlg;
 import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
-import org.polypheny.db.algebra.enumerable.EnumerableConvention;
-import org.polypheny.db.algebra.enumerable.PhysType;
-import org.polypheny.db.algebra.enumerable.PhysTypeImpl;
 import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
 import org.polypheny.db.algebra.polyalg.arguments.ListArg;
 import org.polypheny.db.algebra.polyalg.arguments.PolyAlgArgs;
@@ -56,13 +51,13 @@
  * the entity’s scanFiltered() method
  */
 @Getter
-public class ParquetDocScan extends DocumentScan<ParquetDocument> implements EnumerableAlg {
+public class ParquetDocScan extends DocumentScan<ParquetDocument> implements ParquetAlg {
 
     private final List<ParquetAdapterFilter<PolyValue>> filters;
 
 
     public ParquetDocScan( AlgCluster cluster, ParquetDocument entity, List<ParquetAdapterFilter<PolyValue>> filters ) {
-        super( cluster, cluster.traitSetOf( EnumerableConvention.INSTANCE ), entity );
+        super( cluster, cluster.traitSetOf( ParquetDocConvention.INSTANCE ), entity );
         this.filters = List.copyOf( filters );
     }
 
@@ -114,29 +109,25 @@ public PolyAlgArgs bindArguments() {
     public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
         double fieldRatio = ((double) entity.getParquetSource().getExportedColumns().size() + 2D) / ((double) entity.getTupleType().getFieldCount() + 2D);
         double filterRatio = filters.isEmpty() ? 1D : 0.5D;
-        return super.computeSelfCost( planner, mq ).multiplyBy( ParquetConvention.COST_MULTIPLIER * fieldRatio * filterRatio );
+        return super.computeSelfCost( planner, mq ).multiplyBy( ParquetRelConvention.COST_MULTIPLIER * fieldRatio * filterRatio );
     }
 
 
     @Override
-    public Result implement( EnumerableAlgImplementor implementor, Prefer pref ) {
-        PhysType physType = PhysTypeImpl.of( implementor.getTypeFactory(), getTupleType(), pref.preferArray() );
+    public Expression implement( EnumerableAlgImplementor implementor ) {
         Expression runtimeFilters = EnumUtils.expressionList( filters.stream().map( ParquetAdapterFilter::toExpression ).toList() );
         // create runtime code that will call scanFiltered() on the ParquetDocument entity
-        return implementor.result(
-                physType,
-                Blocks.toBlock(
-                        Expressions.call(
-                                entity.asExpression(),
-                                "scanFiltered",
-                                implementor.getRootExpression(),
-                                runtimeFilters ) ) );
+        return Expressions.call(
+                entity.asExpression(),
+                "scanFiltered",
+                implementor.getRootExpression(),
+                runtimeFilters );
     }
 
 
     @Override
     public void register( AlgPlanner planner ) {
-        planner.addRuleDuringRuntime( new ParquetDocCalcRule( AlgFactories.LOGICAL_BUILDER ) );
+        ParquetDocConvention.INSTANCE.register( planner );
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
index 7ea561468b..36fd38d041 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
@@ -18,6 +18,7 @@
 
 import java.util.ArrayList;
 import java.util.List;
+import java.util.Map;
 import java.util.concurrent.atomic.AtomicBoolean;
 import lombok.Getter;
 import org.apache.calcite.linq4j.AbstractEnumerable;
@@ -27,15 +28,18 @@
 import org.apache.calcite.linq4j.tree.Expressions;
 import org.polypheny.db.adapter.DataContext;
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
+import org.polypheny.db.adapter.parquet.document.execution.ParquetDocAggregateExecutor;
 import org.polypheny.db.adapter.parquet.document.execution.ParquetDocEnumerator;
 import org.polypheny.db.adapter.parquet.document.planning.ParquetDocScan;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetMultiFileEnumerator;
+import org.polypheny.db.adapter.parquet.relational.schema.DiscoveredTableBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.core.AggregateCall;
 import org.polypheny.db.algebra.type.AlgDataType;
 import org.polypheny.db.algebra.type.AlgDataTypeFactory;
 import org.polypheny.db.algebra.type.AlgDataTypeField;
@@ -47,6 +51,7 @@
 import org.polypheny.db.schema.types.ScannableEntity;
 import org.polypheny.db.schema.types.TranslatableEntity;
 import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.ImmutableBitSet;
 
 /**
  * Physical collection wrapper for the document model.
@@ -56,10 +61,11 @@
 public class ParquetDocument extends PhysicalCollection implements ScannableEntity, TranslatableEntity {
 
     private final List<ParquetSourceFile> sourceFiles;
+    private final Map<String, List<String>> columnPaths;
     private final AbstractParquetSource parquetSource;
 
 
-    public ParquetDocument( PhysicalCollection collection, List<ParquetSourceFile> sourceFiles, AbstractParquetSource parquetSource ) {
+    public ParquetDocument( PhysicalCollection collection, DiscoveredTableBinding binding, AbstractParquetSource parquetSource ) {
         super(
                 collection.id,
                 collection.allocationId,
@@ -68,7 +74,8 @@ public ParquetDocument( PhysicalCollection collection, List<ParquetSourceFile> s
                 collection.name,
                 collection.namespaceName,
                 collection.adapterId );
-        this.sourceFiles = List.copyOf( sourceFiles ); // add multi-file handling
+        this.sourceFiles = List.copyOf( binding.sourceFiles() ); // add multi-file handling
+        this.columnPaths = Map.copyOf( binding.columnPaths() );
         this.parquetSource = parquetSource;
     }
 
@@ -130,6 +137,73 @@ public Enumerator<PolyValue[]> enumerator() {
     }
 
 
+    /**
+     * Creates a data enumerator that should go though the parquet file.
+     *
+     * @param dataContext a Polypheny data context.
+     * @param fields an array of field indexes.
+     * @param filters a list of filters to be applied.
+     * @param groupFields an array of group field indexes.
+     * @param aggregateKinds an array of aggregate functions.
+     * @param aggregateArgs an array of aggregate functions arguments indexes.
+     * @return an enumerator
+     */
+    @SuppressWarnings("unused")
+    public Enumerable<PolyValue[]> dataAggregate( DataContext dataContext, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
+        return new ParquetDocAggregateExecutor( sourceFiles, exportedColumns(), columnPaths )
+                .createDataEnumerator( dataContext, fields, filters, groupFields, aggregateKinds, aggregateArgs );
+    }
+
+
+    /**
+     * Creates a metadata enumerator that reads only parquet file statistics, no data is being read.
+     *
+     * @param dataContext a Polypheny data context.
+     * @param fields an array of field indexes.
+     * @param filters a list of filters to be applied.
+     * @param groupFields an array of group field indexes.
+     * @param aggregateKinds an array of aggregate functions.
+     * @param aggregateArgs an array of aggregate functions arguments indexes.
+     * @return an enumerator.
+     */
+    @SuppressWarnings("unused")
+    public Enumerable<PolyValue[]> metadataAggregate( DataContext dataContext, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
+        return new ParquetDocAggregateExecutor( sourceFiles, exportedColumns(), columnPaths )
+                .createMetadataEnumerator( dataContext, fields, filters, groupFields, aggregateKinds, aggregateArgs );
+    }
+
+
+    /**
+     * Checks whether all provided aggregate functions are supported by data aggregates. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
+     *
+     * @param fields an array of field indexes.
+     * @param groupSet a set of group by fields.
+     * @param aggregateCalls a list of aggregation functions to validate.
+     * @return {@code true} if all provided aggregation functions can be applied and {@code false} otherwise.
+     */
+    public boolean supportsDataAggregate( int[] fields, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
+        return new ParquetDocAggregateExecutor( sourceFiles, exportedColumns(), columnPaths )
+                .supportsDataAggregate( fields, groupSet, aggregateCalls );
+    }
+
+
+    /**
+     * Checks whether all provided aggregate functions are supported by metadata aggregates. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
+     *
+     * @param fields an array of field indexes.
+     * @param filters a list of filters.
+     * @param groupSet a set of group by fields.
+     * @param aggregateCalls a list of aggregation functions to validate.
+     * @return {@code true} if all provided aggregation functions can be applied and {@code false} otherwise.
+     */
+    public boolean supportsMetadataAggregate( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
+        return new ParquetDocAggregateExecutor( sourceFiles, exportedColumns(), columnPaths )
+                .supportsMetadataAggregate( fields, filters, groupSet, aggregateCalls );
+    }
+
+
     @Override
     public AlgNode toAlg( AlgCluster cluster, AlgTraitSet traitSet ) {
         return new ParquetDocScan( cluster, this, List.of() );
@@ -149,7 +223,7 @@ public AlgDataType getTupleType( AlgDataTypeFactory typeFactory ) {
 
 
     private AlgDataType buildDocumentType( AlgDataTypeFactory typeFactory ) {
-        List<ExportedColumn> columns = parquetSource.getExportedColumns().get( name );
+        List<ExportedColumn> columns = exportedColumns();
         if ( columns == null || columns.isEmpty() ) {
             return DocumentType.ofId();
         }
@@ -166,6 +240,11 @@ private AlgDataType buildDocumentType( AlgDataTypeFactory typeFactory ) {
     }
 
 
+    private List<ExportedColumn> exportedColumns() {
+        return parquetSource.getExportedColumns().getOrDefault( name, List.of() );
+    }
+
+
     /**
      * add dynamic parameters to parquet filter if needed
      *
@@ -174,6 +253,11 @@ private AlgDataType buildDocumentType( AlgDataTypeFactory typeFactory ) {
      * @return ParquetFilter
      */
     private ParquetAdapterFilter<PolyValue> resolveFilter( DataContext dataContext, ParquetAdapterFilter<PolyValue> filter ) {
+        if ( filter.isLogical() ) {
+            return ParquetAdapterFilter.logical( filter.operator(), filter.operands().stream()
+                    .map( operand -> resolveFilter( dataContext, operand ) )
+                    .toList() );
+        }
         if ( filter.dynamicParamIndex() == null ) {
             return filter;
         }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetEnumeratorsFactory.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetEnumeratorsFactory.java
index 401c1b7aaa..a75167d01b 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetEnumeratorsFactory.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetEnumeratorsFactory.java
@@ -23,6 +23,7 @@
 import org.polypheny.db.adapter.parquet.relational.filter.ParquetMultiFilterEvaluator;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.execution.aggregate.ParquetDataAggregateExecutor;
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
@@ -48,7 +49,7 @@ public ParquetEnumeratorsFactory( ParquetRelTable table, int[] fields, int[] all
         this.allFields = Arrays.copyOf( allFields, allFields.length );
         this.schemaReader = schemaReader;
         this.cancelFlag = cancelFlag;
-        this.sourceFileEvaluator = ParquetRelExecutor.createParquetSourceFileEvaluatorsChain( f -> ParquetRelExecutor.selectPhysicalBinding( table, f.columnIndex() ) );
+        this.sourceFileEvaluator = ParquetDataAggregateExecutor.createParquetSourceFileEvaluatorsChain( f -> ParquetRelExecutor.selectPhysicalBinding( table, f.columnIndex() ) );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelDataAggregateExecutor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelDataAggregateExecutor.java
index b4dcd5b01d..b9832b92ca 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelDataAggregateExecutor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelDataAggregateExecutor.java
@@ -16,555 +16,86 @@
 
 package org.polypheny.db.adapter.parquet.relational.execution;
 
-import java.util.LinkedHashMap;
 import java.util.List;
-import java.util.Map;
-import java.util.concurrent.atomic.AtomicBoolean;
-import org.apache.calcite.linq4j.AbstractEnumerable;
 import org.apache.calcite.linq4j.Enumerable;
-import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.DataContext;
-import org.polypheny.db.adapter.parquet.relational.execution.aggregate.ParquetFileGroupedAggregateRelEnumerator;
-import org.polypheny.db.adapter.parquet.relational.execution.aggregate.ParquetGroupedAggregateRelEnumerator;
-import org.polypheny.db.adapter.parquet.relational.execution.aggregate.ParquetRowAggregateRelEnumerator;
-import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetConstantColumnResolver;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
-import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFileStatisticsFilterEvaluator;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
-import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
-import org.polypheny.db.adapter.parquet.shared.aggregate.ColumnAggregateProjection;
+import org.polypheny.db.adapter.parquet.shared.execution.aggregate.ParquetAggregateSource;
+import org.polypheny.db.adapter.parquet.shared.execution.aggregate.ParquetDataAggregateExecutor;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.adapter.parquet.shared.io.aggregate.ParquetGroupedAggregateReader;
-import org.polypheny.db.adapter.parquet.shared.io.aggregate.ParquetNoFilterColumnAggregateReader;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
-import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.algebra.core.AggregateCall;
 import org.polypheny.db.type.PolyType;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.ImmutableBitSet;
 
+
 /**
- * An entry point for all aggregated enumerators that cannot be calculated based on metadata. The executor decides what enumerator should be executed for fastest results.
- * Supported enumerators are:
- * 1. Enumerator for COUNT(*) / COUNT(col) / SUM(col) / MIN(col) / MAX(col) with numeric aggregation columns and no row level filters.
- * 2. Enumerator for COUNT(*) / COUNT(col) / SUM(col) / MIN(col) / MAX(col) with aggregation columns defined as partitions and no row level filters.
- * 3. A fallback enumerator that supports the same aggregation functions for numeric columns but this time with row level filters.
+ * Relational entry point for data aggregate execution.
  */
 public class ParquetRelDataAggregateExecutor extends ParquetRelExecutor {
 
-    private final ParquetConstantColumnResolver constantColumnResolver;
-    private final ParquetSourceFileStatisticsFilterEvaluator statisticsFilterEvaluator;
-
-
-    public ParquetRelDataAggregateExecutor(ParquetRelTable table, AbstractParquetSource parquetSource, int[] fieldIndexes, ParquetSchemaReader schemaReader ) {
+    public ParquetRelDataAggregateExecutor( ParquetRelTable table, AbstractParquetSource parquetSource, int[] fieldIndexes, ParquetSchemaReader schemaReader ) {
         super( table, parquetSource, fieldIndexes, schemaReader );
-        this.constantColumnResolver = new ParquetConstantColumnResolver();
-        this.statisticsFilterEvaluator = new ParquetSourceFileStatisticsFilterEvaluator( f -> selectPhysicalBinding( table, f.columnIndex() ) );
     }
 
 
-    /**
-     * Creates an enumerator for aggregated rows.
-     *
-     * @param dataContext a data context.
-     * @param fields an array of field indexes.
-     * @param filters a list of filters to be applied.
-     * @param groupFields a list of group by field indexes.
-     * @param aggregateKinds an array of aggregation function kinds.
-     * @param aggregateArgs an array of aggregation function arguments. There should be one argument per aggregation function.
-     * @return an enumerator.
-     */
     public Enumerable<PolyValue[]> createEnumerator( DataContext dataContext, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
         registerAdapter( dataContext );
-
-        List<ParquetAdapterFilter<PolyValue>> resolvedFilters = ParquetFilterResolver.resolveFilters( dataContext, filters, f -> selectPhysicalBinding( table, f.columnIndex() ) );
-        AtomicBoolean cancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
-
-        return new AbstractEnumerable<>() {
-            @Override
-            public Enumerator<PolyValue[]> enumerator() {
-                ColumnAggregateProjection aggregateProjection = tryBuildAggregateProjection( fields, filters, groupFields, aggregateKinds, aggregateArgs );
-                if ( aggregateProjection != null ) {
-                    return new ParquetFileGroupedAggregateRelEnumerator(
-                            table,
-                            fields,
-                            groupFields,
-                            aggregateKinds,
-                            aggregateArgs,
-                            aggregateProjection,
-                            resolvedFilters,
-                            cancelFlag
-                    );
-                }
-
-                int[] groupedCountProjection = tryBuildGroupedCountProjection( fields, filters, groupFields, aggregateKinds, aggregateArgs );
-                if ( groupedCountProjection != null ) {
-                    return new ParquetGroupedAggregateRelEnumerator(
-                            table,
-                            groupedCountProjection,
-                            groupedCountProjection.length,
-                            new AggregateCallDescriptor[]{ AggregateCallDescriptor.countStar() },
-                            resolvedFilters,
-                            cancelFlag
-                    );
-                }
-
-                GroupAggregateProjection groupedAggregateProjection = tryBuildGroupedAggregateProjection( fields, filters, groupFields, aggregateKinds, aggregateArgs );
-                if ( groupedAggregateProjection != null ) {
-                    return new ParquetGroupedAggregateRelEnumerator(
-                            table,
-                            groupedAggregateProjection.fields(),
-                            groupedAggregateProjection.groupFieldCount(),
-                            groupedAggregateProjection.aggregateCalls(),
-                            resolvedFilters,
-                            cancelFlag
-                    );
-                }
-
-                Enumerator<PolyValue[]> rows = new ParquetEnumeratorsFactory( table, fields, fieldIndexes, schemaReader, cancelFlag ).create( resolvedFilters );
-                return new ParquetRowAggregateRelEnumerator( rows, groupFields, aggregateKinds, aggregateArgs );
-            }
-        };
-    }
-
-
-    /**
-     * Converts a relative field index into a parquet physical index and adds it into projection.
-     *
-     * @param field a relative field index.
-     * @param parquetFieldProjectionIndexes a projection.
-     * @return parquet physical index if found and null otherwise.
-     */
-    private Integer addDataProjectionField( int field, Map<Integer, Integer> parquetFieldProjectionIndexes ) {
-        ParquetColumnBinding binding = selectPhysicalBinding( table, field );
-        if ( binding == null || binding.role() != ParquetColumnRole.DATA || binding.sourcePathElements().size() != 1 ) {
-            return null;
-        }
-        int parquetField = parquetFieldIndex( binding.sourcePathElements().get( 0 ) );
-        if ( parquetField < 0 ) {
-            return null;
-        }
-        return parquetFieldProjectionIndexes.computeIfAbsent( parquetField, ignored -> parquetFieldProjectionIndexes.size() );
-    }
-
-
-    /**
-     * Adds filter indexes to a projection. This is required in order for a filter column to be available during the reading operation.
-     *
-     * @param filters a list of filters.
-     * @param parquetFieldProjectionIndexes a projection.
-     * @return true if the index was added and false otherwise.
-     */
-    private boolean addFilterProjectionFields( List<ParquetAdapterFilter<PolyValue>> filters, Map<Integer, Integer> parquetFieldProjectionIndexes ) {
-        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
-            if ( !addFilterProjectionField( filter, parquetFieldProjectionIndexes, true ) ) {
-                return false;
-            }
-        }
-        return true;
-    }
-
-
-    /**
-     * Adds one filter to the reader projection unless file metadata can evaluate it exactly.
-     *
-     * @param filter a filter.
-     * @param parquetFieldProjectionIndexes a projection.
-     * @param canSkipExact whether an exact file-level filter can be omitted in this logical context.
-     * @return true if all required fields were added and false otherwise.
-     */
-    private boolean addFilterProjectionField( ParquetAdapterFilter<PolyValue> filter, Map<Integer, Integer> parquetFieldProjectionIndexes, boolean canSkipExact ) {
-        if ( canSkipExact && supportsExactFileFilter( filter ) ) {
-            return true;
-        }
-        if ( filter.isLogical() ) {
-            boolean childCanSkipExact = canSkipExact && filter.operator() == Kind.AND;
-            for ( ParquetAdapterFilter<PolyValue> operand : filter.operands() ) {
-                if ( !addFilterProjectionField( operand, parquetFieldProjectionIndexes, childCanSkipExact ) ) {
-                    return false;
-                }
-            }
-            return true;
-        }
-        if ( isPartitionColumn( table, filter.columnIndex() ) ) {
-            return true;
-        }
-        return addDataProjectionField( filter.columnIndex(), parquetFieldProjectionIndexes ) != null;
-    }
-
-
-    /**
-     * Validates if all filters are supported.
-     *
-     * @param filters a list of filters to validate.
-     * @return true if all filters are supported and false otherwise.
-     */
-    private boolean supportsAggregateReaderFilters( List<ParquetAdapterFilter<PolyValue>> filters ) {
-        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
-            if ( !supportsAggregateReaderFilter( filter, false ) ) {
-                return false;
-            }
-        }
-        return true;
-    }
-
-
-    /**
-     * Validates if the filter is supported by this executor.
-     *
-     * @param filter a filter to validate.
-     * @param insideNonAnd indicates whether a filter is not inside the logical AND filter. This is mostly relevant for partition filters.
-     * Only AND logical operator is supported between the partition filters.
-     * @return true if the filter is supported and false otherwise.
-     */
-    private boolean supportsAggregateReaderFilter( ParquetAdapterFilter<PolyValue> filter, boolean insideNonAnd ) {
-        if ( filter.isLogical() ) {
-            boolean childInsideNonAnd = insideNonAnd || filter.operator() != Kind.AND;
-            for ( ParquetAdapterFilter<PolyValue> operand : filter.operands() ) {
-                if ( !supportsAggregateReaderFilter( operand, childInsideNonAnd ) ) {
-                    return false;
-                }
-            }
-            return true;
-        }
-        if ( isPartitionColumn( table, filter.columnIndex() ) ) {
-            return !insideNonAnd;
-        }
-        return isFlatDataColumn( filter.columnIndex() );
-    }
-
-
-    /**
-     * Validates if the field is a data field.
-     *
-     * @param field a field index.
-     * @return true if the field is a data field and false otherwise.
-     */
-    private boolean isFlatDataColumn( int field ) {
-        ParquetColumnBinding binding = selectPhysicalBinding( table, field );
-        return binding != null && binding.role() == ParquetColumnRole.DATA && binding.sourcePathElements().size() == 1;
+        return new ParquetDataAggregateExecutor(
+                aggregateSource(),
+                resolvedFilters -> new ParquetEnumeratorsFactory( table, fields, fieldIndexes, schemaReader, DataContext.Variable.CANCEL_FLAG.get( dataContext ) ).create( resolvedFilters )
+        )
+        .createEnumerator( dataContext, fields, filters, groupFields, aggregateKinds, aggregateArgs );
     }
 
 
     /**
-     * Validates if the aggregated function type is supported by this executor.
-     *
-     * @param field a field index.
-     * @param aggregateKind an aggregated function type.
-     * @return true if supported and false otherwise.
-     */
-    private boolean supportsGroupedAggregateCall( int field, String aggregateKind ) {
-        if ( Kind.COUNT.name().equals( aggregateKind ) ) {
-            return true;
-        }
-        if ( !isNumericAggregateKind( aggregateKind ) ) {
-            return false;
-        }
-        return PolyType.NUMERIC_TYPES.contains( table.columns.get( field ).type );
-    }
-
-
-    /**
-     * Attempts to build a grouped aggregate projection. The main criteria is:
-     * 1. Filters must be applied only on data columns or partition columns. If a filter column is a partition column only AND logical filter can be applied on top of it.
-     * 2. aggregation function is COUNT(*), COUNT(col), SUM(col), MIN(col) or MAX(col).
-     * 3. aggregation column must be numeric non-repeated.
+     * Checks whether all provided aggregate functions are supported by data aggregates. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
      *
      * @param fields an array of field indexes.
-     * @param filters a list of filters.
-     * @param groupFields an array of group by field indexes.
-     * @param aggregateKinds an array of aggregation functions types.
-     * @param aggregateArgs an array of aggregation functions arguments. One argument per aggregation function and should be aligned with aggregateKinds.
-     * @return a grouped aggregate projection if successful or null otherwise.
-     */
-    private GroupAggregateProjection tryBuildGroupedAggregateProjection( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
-        if ( aggregateKinds.length != aggregateArgs.length ) {
-            return null;
-        }
-        if ( !supportsAggregateReaderFilters( filters ) ) {
-            return null;
-        }
-
-        Map<Integer, Integer> parquetFieldProjectionIndexes = new LinkedHashMap<>();
-        for ( int groupField : groupFields ) {
-            if ( groupField < 0 || groupField >= fields.length ) {
-                return null;
-            }
-            Integer projectionIndex = addDataProjectionField( fields[groupField], parquetFieldProjectionIndexes );
-            if ( projectionIndex == null ) {
-                return null;
-            }
-        }
-
-        AggregateCallDescriptor[] aggregateCalls = new AggregateCallDescriptor[aggregateKinds.length];
-        for ( int i = 0; i < aggregateKinds.length; i++ ) {
-            int aggregateArg = aggregateArgs[i];
-            if ( aggregateArg == AggregateCallDescriptor.NO_ARGUMENT ) {
-                if ( !Kind.COUNT.name().equals( aggregateKinds[i] ) ) {
-                    return null;
-                }
-                aggregateCalls[i] = AggregateCallDescriptor.of( aggregateKinds[i], AggregateCallDescriptor.NO_ARGUMENT );
-                continue;
-            }
-            if ( aggregateArg < 0 || aggregateArg >= fields.length ) {
-                return null;
-            }
-            if ( !supportsGroupedAggregateCall( fields[aggregateArg], aggregateKinds[i] ) ) {
-                return null;
-            }
-            Integer projectionIndex = addDataProjectionField( fields[aggregateArg], parquetFieldProjectionIndexes );
-            if ( projectionIndex == null ) {
-                return null;
-            }
-            aggregateCalls[i] = AggregateCallDescriptor.of( aggregateKinds[i], projectionIndex );
-        }
-
-        if ( !addFilterProjectionFields( filters, parquetFieldProjectionIndexes ) ) {
-            return null;
-        }
-        int[] projectedFields = parquetFieldProjectionIndexes.keySet().stream().mapToInt( Integer::intValue ).toArray();
-        if ( projectedFields.length == 0 || !ParquetGroupedAggregateReader.supports( schemaReader.buildProjectionSchema( projectedFields ) ) ) {
-            return null;
-        }
-        return new GroupAggregateProjection( projectedFields, groupFields.length, aggregateCalls );
-    }
-
-
-    /**
-     * Attempts to build grouped count projection. The main criteria is:
-     * 1. fields must be numeric
-     * 2. aggregation function is COUNT(*)
-     * 3. filters are empty or only partition filters.
-     * 4. aggregation column is a simple not nested data column (not a partition, not a key).
-     *
-     * @param fields an array of field indexes.
-     * @param filters a list of filters.
-     * @param groupFields an array of group by field indexes.
-     * @param aggregateKinds an array of aggregation functions types.
-     * @param aggregateArgs an array of aggregation functions arguments. One argument per aggregation function and should be aligned with aggregateKinds.
-     * @return a grouped count projection if successful or null otherwise.
+     * @param groupSet a set of group by fields.
+     * @param aggregateCalls a list of aggregation functions to validate.
+     * @return {@code true} if all provided aggregation functions can be applied and {@code false} otherwise.
      */
-    private int[] tryBuildGroupedCountProjection( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
-        if ( aggregateKinds.length == 0 || aggregateKinds.length != aggregateArgs.length ) {
-            return null;
-        }
-        for ( int i = 0; i < aggregateKinds.length; i++ ) {
-            if ( !Kind.COUNT.name().equals( aggregateKinds[i] ) || aggregateArgs[i] >= 0 ) {
-                return null;
-            }
-        }
-        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
-            if ( !isPartitionFilter( table, filter ) ) {
-                return null;
-            }
-        }
-
-        int[] projectedFields = new int[groupFields.length];
-        for ( int i = 0; i < groupFields.length; i++ ) {
-            int groupField = groupFields[i];
-            if ( groupField < 0 || groupField >= fields.length ) {
-                return null;
-            }
-            ParquetColumnBinding binding = selectPhysicalBinding( table, fields[groupField] );
-            if ( binding == null || binding.role() != ParquetColumnRole.DATA || binding.sourcePathElements().size() != 1 ) {
-                return null;
-            }
-            int parquetField = parquetFieldIndex( binding.sourcePathElements().get( 0 ) );
-            if ( parquetField < 0 ) {
-                return null;
-            }
-            projectedFields[i] = parquetField;
-        }
-        if ( !ParquetGroupedAggregateReader.supports( schemaReader.buildProjectionSchema( projectedFields ) ) ) {
-            return null;
-        }
-        return projectedFields;
+    public boolean supportsDataAggregate( int[] fields, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
+        return new ParquetDataAggregateExecutor( aggregateSource(), null ).supportsDataAggregate( fields, groupSet, aggregateCalls );
     }
 
 
-    /**
-     * This method attempts to build aggregate projection. In order for the aggregate projection to be built the following conditions should apply:
-     * 1. All group by fields are file-constant fields.
-     * 2. All filters can be evaluated exactly for a complete source file.
-     * 3. Aggregated columns are data columns with numeric type and does not belong to a nested table.
-     *
-     * @param fields an array of field indexes.
-     * @param filters a list of filters.
-     * @param groupFields an array of group by field indexes.
-     * @param aggregateKinds an array of aggregation functions types.
-     * @param aggregateArgs an array of aggregated column indexes.
-     * @return {@link ColumnAggregateProjection} if fast aggregation can be applied and {@code null} otherwise.
-     */
-    private ColumnAggregateProjection tryBuildAggregateProjection( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
-        if ( aggregateKinds.length == 0 || aggregateKinds.length != aggregateArgs.length ) {
-            return null;
-        }
-        for ( int groupField : groupFields ) {
-            if ( groupField < 0 || groupField >= fields.length || !isFileConstantColumn( fields[groupField] ) ) {
-                return null;
-            }
-        }
-        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
-            if ( !supportsExactFileFilter( filter ) ) {
-                return null;
-            }
-        }
-
-        Map<Integer, Integer> parquetFieldProjectionIndexes = new LinkedHashMap<>();
-        Map<Integer, Integer> aggregateProjectionIndexes = new LinkedHashMap<>();
-        for ( int i = 0; i < aggregateArgs.length; i++ ) {
-            int aggregateArg = aggregateArgs[i];
-            if ( aggregateArg < 0 ) {
-                if ( !Kind.COUNT.name().equals( aggregateKinds[i] ) ) {
-                    return null;
-                }
-                continue;
-            }
-            if ( aggregateArg >= fields.length ) {
-                return null;
-            }
-            if ( !supportsGroupedAggregateCall( fields[aggregateArg], aggregateKinds[i] ) ) {
-                return null;
-            }
-            ParquetColumnBinding binding = selectPhysicalBinding( table, fields[aggregateArg] );
-            if ( binding == null || binding.role() != ParquetColumnRole.DATA || binding.sourcePathElements().size() != 1 ) {
-                return null;
-            }
-            int parquetField = parquetFieldIndex( binding.sourcePathElements().get( 0 ) );
-            if ( parquetField < 0 ) {
-                return null;
+    private ParquetAggregateSource aggregateSource() {
+        return new ParquetAggregateSource() {
+            @Override
+            public List<org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile> sourceFiles() {
+                return table.getBinding().sourceFiles();
             }
-            int projectionIndex = parquetFieldProjectionIndexes.computeIfAbsent( parquetField, ignored -> parquetFieldProjectionIndexes.size() );
-            aggregateProjectionIndexes.put( aggregateArg, projectionIndex );
-        }
-        int[] projectedFields = parquetFieldProjectionIndexes.keySet().stream().mapToInt( Integer::intValue ).toArray();
-        if ( projectedFields.length > 0 && !ParquetNoFilterColumnAggregateReader.supports( schemaReader.buildProjectionSchema( projectedFields ) ) ) {
-            return null;
-        }
-        return new ColumnAggregateProjection( projectedFields, aggregateProjectionIndexes );
-    }
 
 
-    /**
-     * Checks whether one field has a constant value in every source file.
-     *
-     * @param field a physical table field.
-     * @return true when each file has one value for this field.
-     */
-    private boolean isFileConstantColumn( int field ) {
-        ParquetColumnBinding binding = selectPhysicalBinding( table, field );
-        return table.getBinding().sourceFiles().stream().allMatch( sourceFile -> constantColumnResolver.resolve( sourceFile, binding ).isPresent() );
-    }
-
-
-    /**
-     * Checks whether a filter can be evaluated exactly for each complete source file.
-     *
-     * @param filter a filter.
-     * @return true when the fast file aggregate path can apply the filter.
-     */
-    private boolean supportsExactFileFilter( ParquetAdapterFilter<PolyValue> filter ) {
-        if ( filter.isLogical() ) {
-            if ( filter.operator() == Kind.NOT ) {
-                return filter.operands().size() == 1 && supportsExactFileFilter( filter.operands().get( 0 ) );
-            }
-            if ( filter.operator() != Kind.AND && filter.operator() != Kind.OR ) {
-                return false;
+            @Override
+            public ParquetSchemaReader schemaReader() {
+                return schemaReader;
             }
-            return !filter.operands().isEmpty() && filter.operands().stream().allMatch( this::supportsExactFileFilter );
-        }
-        if ( isPartitionColumn( table, filter.columnIndex() ) ) {
-            return true;
-        }
-        return table.getBinding().sourceFiles().stream().allMatch( sourceFile -> statisticsFilterEvaluator.supportsExactEvaluation( sourceFile, filter ) );
-    }
 
 
-    /**
-     * Gets a physical parquet field index.
-     *
-     * @param fieldName a field name for a lookup.
-     * @return a physical parquet field index if found or -1 otherwise.
-     */
-    private int parquetFieldIndex( String fieldName ) {
-        for ( int i = 0; i < schemaReader.getSchema().getFieldCount(); i++ ) {
-            if ( schemaReader.getSchema().getFieldName( i ).equals( fieldName ) ) {
-                return i;
+            @Override
+            public int fieldCount() {
+                return table.columns.size();
             }
-        }
-        return -1;
-    }
 
 
-    /**
-     * Checks whether all provided aggregate functions are supported. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
-     *
-     * @param fields an array of field indexes.
-     * @param groupSet a set of group by fields.
-     * @param aggregateCalls a list of aggregation functions to validate.
-     * @return {@code true} if all provided aggregation functions can be applied and {@code false} otherwise.
-     */
-    public boolean supportsDataAggregate( int[] fields, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
-        for ( int groupField : groupSet.asList() ) {
-            if ( groupField < 0 || groupField >= fields.length ) {
-                return false;
-            }
-        }
-        for ( AggregateCall aggregateCall : aggregateCalls ) {
-            if ( !supportsDataAggregateCall( fields, aggregateCall ) ) {
-                return false;
+            @Override
+            public PolyType fieldType( int field ) {
+                return table.columns.get( field ).type;
             }
-        }
-        return true;
-    }
 
 
-    /**
-     * Checks whether specified aggregation function is supported. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
-     *
-     * @param fields an array of field indexes.
-     * @param aggregateCall an aggregation function to validate.
-     * @return {@code true} if the aggregation function is supported and {@code false} otherwise.
-     */
-    private boolean supportsDataAggregateCall( int[] fields, AggregateCall aggregateCall ) {
-        if ( aggregateCall.isDistinct() || aggregateCall.isApproximate() || aggregateCall.filterArg >= 0 ) {
-            return false;
-        }
-        Kind kind = aggregateCall.getAggregation().getKind();
-        if ( kind == Kind.COUNT ) {
-            if ( aggregateCall.getArgList().isEmpty() ) {
-                return true;
-            }
-            if ( aggregateCall.getArgList().size() != 1 ) {
-                return false;
+            @Override
+            public ParquetColumnBinding binding( int field ) {
+                return ParquetRelExecutor.selectPhysicalBinding( table, field );
             }
-            int inputIndex = aggregateCall.getArgList().get( 0 );
-            return inputIndex >= 0 && inputIndex < fields.length;
-        }
-        if ( !isNumericAggregateKind( kind.name() ) ) {
-            return false;
-        }
-        if ( aggregateCall.getArgList().size() != 1 ) {
-            return false;
-        }
-        int inputIndex = aggregateCall.getArgList().get( 0 );
-        return inputIndex >= 0 && inputIndex < fields.length && PolyType.NUMERIC_TYPES.contains( table.columns.get( fields[inputIndex] ).type );
-    }
-
-
-    private boolean isNumericAggregateKind( String aggregateKind ) {
-        return Kind.SUM.name().equals( aggregateKind )
-                || Kind.MIN.name().equals( aggregateKind )
-                || Kind.MAX.name().equals( aggregateKind );
-    }
-
-
-    private record GroupAggregateProjection( int[] fields, int groupFieldCount, AggregateCallDescriptor[] aggregateCalls ) {
-
+        };
     }
 
-
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelExecutor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelExecutor.java
index 8dadb19ec6..b6c9a936a3 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelExecutor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelExecutor.java
@@ -21,12 +21,8 @@
 import java.util.List;
 import java.util.Objects;
 import java.util.concurrent.atomic.AtomicBoolean;
-import java.util.function.Function;
 import org.apache.calcite.linq4j.Enumerator;
 import org.polypheny.db.adapter.DataContext;
-import org.polypheny.db.adapter.parquet.relational.filter.ParquetMultiFilterEvaluator;
-import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFilePartitionFilterEvaluator;
-import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFileStatisticsFilterEvaluator;
 import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
@@ -80,7 +76,7 @@ public ParquetRelExecutor( ParquetRelTable table, AbstractParquetSource parquetS
      * @param filtersContainer a container for multiple lists of filters.
      * @return one of the selected enumerators.
      */
-    protected static Enumerator<PolyValue[]> enumeratorForFile( ParquetRelTable table, ParquetSourceFile sourceFile, int[] fields, int[] allFields, ParquetSchemaReader schemaReader, AtomicBoolean cancelFlag, FiltersContainer filtersContainer ) {
+    public static Enumerator<PolyValue[]> enumeratorForFile( ParquetRelTable table, ParquetSourceFile sourceFile, int[] fields, int[] allFields, ParquetSchemaReader schemaReader, AtomicBoolean cancelFlag, FiltersContainer filtersContainer ) {
         boolean nestedTable = isNestedTable( table );
         boolean requiresRowValidation = !filtersContainer.adapterFilters().isEmpty();
         boolean bindingScan = nestedTable || needsBindingScan( table, schemaReader, fields );
@@ -327,23 +323,6 @@ private static int parquetFieldIndex( ParquetSchemaReader schemaReader, String f
     }
 
 
-    /**
-     * Creates a chain of source file filter evaluators. Currently, supports two filter evaluators:
-     * 1. partition value based filter evaluator
-     * 2. source file statistics based filter evaluator.
-     * Those filter evaluators can skip files if they do not contain filtered data.
-     *
-     * @param selector a column binding selector.
-     * @return an evaluators chain.
-     */
-    public static ParquetMultiFilterEvaluator<ParquetSourceFile> createParquetSourceFileEvaluatorsChain( Function<ParquetAdapterFilter<PolyValue>, ParquetColumnBinding> selector ) {
-        return new ParquetMultiFilterEvaluator<>( List.of(
-                new ParquetSourceFilePartitionFilterEvaluator( selector ),
-                new ParquetSourceFileStatisticsFilterEvaluator( selector ) )
-        );
-    }
-
-
     /**
      * Selects a column binding according to a physical column index.
      *
@@ -359,37 +338,6 @@ public static ParquetColumnBinding selectPhysicalBinding( ParquetRelTable table,
     }
 
 
-    /**
-     * Checks if the specified filter is a partition filter.
-     *
-     * @param table a table containing the binding information.
-     * @param filter a filter to check.
-     * @return {@code true} if the provided filter is a partition filter and {@code false} otherwise.
-     */
-    protected static boolean isPartitionFilter( ParquetRelTable table, ParquetAdapterFilter<PolyValue> filter ) {
-        if ( filter.isLogical() ) {
-            return filter.operands().stream().allMatch( operand -> isPartitionFilter( table, operand ) );
-        }
-        return isPartitionColumn( table, filter.columnIndex() );
-    }
-
-
-    /**
-     * Checks if the specified filter is a partition filter.
-     *
-     * @param table a table containing the binding information.
-     * @param columnIndex a column index of the filter.
-     * @return {@code true} if the provided filter is a partition filter and {@code false} otherwise.
-     */
-    protected static boolean isPartitionColumn( ParquetRelTable table, int columnIndex ) {
-        if ( columnIndex < 0 || columnIndex >= table.columns.size() ) {
-            return false;
-        }
-        ParquetColumnBinding columnBinding = table.getBinding().getColumnBinding( table.columns.get( columnIndex ).id );
-        return columnBinding != null && columnBinding.role() == ParquetColumnRole.PARTITION;
-    }
-
-
     /**
      * Gets a list of column bindings for the provided projected fields.
      *
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelMetadataAggregateExecutor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelMetadataAggregateExecutor.java
index 6cd553fb49..b7c8066c42 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelMetadataAggregateExecutor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelMetadataAggregateExecutor.java
@@ -16,393 +16,84 @@
 
 package org.polypheny.db.adapter.parquet.relational.execution;
 
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.LinkedHashMap;
 import java.util.List;
-import java.util.Map;
-import java.util.Optional;
 import org.apache.calcite.linq4j.Enumerable;
-import org.apache.calcite.linq4j.Linq4j;
 import org.polypheny.db.adapter.DataContext;
-import org.polypheny.db.adapter.parquet.relational.filter.ParquetMultiFilterEvaluator;
-import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFilePartitionFilterEvaluator;
-import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFileStatisticsFilterEvaluator;
-import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnStatistics;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetConstantColumnResolver;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.execution.aggregate.ParquetAggregateSource;
+import org.polypheny.db.adapter.parquet.shared.execution.aggregate.ParquetMetadataAggregateExecutor;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
-import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
-import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.algebra.core.AggregateCall;
-import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
-import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.PolyType;
 import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.type.entity.numerical.PolyLong;
 import org.polypheny.db.util.ImmutableBitSet;
 
 /**
- * Executes aggregations that can be done on metadata only.
+ * Relational entry point for metadata aggregate execution.
  */
 public class ParquetRelMetadataAggregateExecutor extends ParquetRelExecutor {
 
-    private final ParquetTypeConverter typeConverter;
-    private final ParquetSourceFileStatisticsFilterEvaluator statisticsFilterEvaluator;
-    private final ParquetConstantColumnResolver constantColumnResolver;
-
-
     public ParquetRelMetadataAggregateExecutor( ParquetRelTable table, AbstractParquetSource parquetSource, int[] fieldIndexes, ParquetSchemaReader schemaReader ) {
         super( table, parquetSource, fieldIndexes, schemaReader );
-        this.typeConverter = new ParquetTypeConverter();
-        this.statisticsFilterEvaluator = new ParquetSourceFileStatisticsFilterEvaluator( f -> selectPhysicalBinding( table, f.columnIndex() ) );
-        this.constantColumnResolver = new ParquetConstantColumnResolver();
-    }
-
-
-    /**
-     * Calculates rows count based on file statistics. In case statistics are not presented fallback to a schema reader for actual calculation.
-     *
-     * @param sourceFile a source file.
-     * @return a row count.
-     */
-    private static long sourceRowCount( ParquetSourceFile sourceFile ) {
-        Optional<Long> rowCount = sourceFile.columnStatistics().values().stream()
-                .map( ParquetColumnStatistics::rowCount )
-                .findFirst();
-        return rowCount.orElseGet( () -> new ParquetSchemaReader( sourceFile.asSource() ).getEstimatedRowCount() );
     }
 
 
-    /**
-     * Creates an enumerable over parquet files metadata statistics.
-     *
-     * @param dataContext a data context
-     * @param fields an array of physical field indexes of columns used in a group by statement.
-     * @param filters a list of filters to be applied
-     * @param groupFields an array of group by field indexes in fields.
-     * @param aggregateKinds the names of the aggregation function.
-     * @param aggregateArgs input field indexes used by aggregation functions.
-     * @return an enumerable.
-     */
     public Enumerable<PolyValue[]> createEnumerator( DataContext dataContext, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
         registerAdapter( dataContext );
-
-        List<ParquetAdapterFilter<PolyValue>> resolvedFilters = ParquetFilterResolver.resolveFilters( dataContext, filters, f -> selectPhysicalBinding( table, f.columnIndex() ) );
-        ParquetMultiFilterEvaluator<ParquetSourceFile> multiFilterEvaluator = new ParquetMultiFilterEvaluator<>(
-                new ParquetSourceFilePartitionFilterEvaluator( f -> selectPhysicalBinding( table, f.columnIndex() ) ),
-                statisticsFilterEvaluator
-        );
-
-        Map<List<PolyValue>, MetadataAggregateAccumulator[]> aggregates = new LinkedHashMap<>();
-        for ( ParquetSourceFile sourceFile : table.getBinding().sourceFiles() ) {
-            if ( !matchesExactly( multiFilterEvaluator, sourceFile, resolvedFilters ) ) {
-                continue;
-            }
-            List<PolyValue> groupKey = fileGroupKey( sourceFile, fields, groupFields );
-            MetadataAggregateAccumulator[] groupAggregates = aggregates.computeIfAbsent( groupKey, key -> createAccumulators( aggregateKinds ) );
-            for ( int i = 0; i < groupAggregates.length; i++ ) {
-                groupAggregates[i].add( sourceFile, fields, aggregateArgs[i] );
-            }
-        }
-
-        if ( groupFields.length == 0 && aggregates.isEmpty() ) {
-            aggregates.put( List.of(), createAccumulators( aggregateKinds ) );
-        }
-
-        List<PolyValue[]> rows = new ArrayList<>( aggregates.size() );
-        for ( Map.Entry<List<PolyValue>, MetadataAggregateAccumulator[]> entry : aggregates.entrySet() ) {
-            PolyValue[] row = new PolyValue[entry.getKey().size() + aggregateKinds.length];
-            for ( int i = 0; i < entry.getKey().size(); i++ ) {
-                row[i] = entry.getKey().get( i );
-            }
-            for ( int i = 0; i < aggregateKinds.length; i++ ) {
-                row[entry.getKey().size() + i] = entry.getValue()[i].result();
-            }
-            rows.add( row );
-        }
-        return Linq4j.asEnumerable( rows );
+        return new ParquetMetadataAggregateExecutor( aggregateSource() )
+                .createEnumerator( dataContext, fields, filters, groupFields, aggregateKinds, aggregateArgs );
     }
 
 
     /**
-     * Checks whether filters, groups and aggregate functions can be executed on metadata only.
+     * Checks whether all provided aggregate functions are supported by metadata aggregates. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
      *
-     * @param fields an array of physical field indexes of columns used in a group by statement.
-     * @param filters a list of filters to be validated.
-     * @param groupSet a group by set.
-     * @param aggregateCalls a list of aggregate calls (aggregate functions)
-     * @return {@code true} filters, groups and aggregate functions can be executed on metadata only and {@code false} otherwise.
+     * @param fields an array of field indexes.
+     * @param groupSet a set of group by fields.
+     * @param aggregateCalls a list of aggregation functions to validate.
+     * @return {@code true} if all provided aggregation functions can be applied and {@code false} otherwise.
      */
     public boolean supportsMetadataAggregate( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
-        for ( int groupField : groupSet.asList() ) {
-            if ( groupField < 0 || groupField >= fields.length || !isFileConstantColumn( fields[groupField] ) ) {
-                return false;
-            }
-        }
-        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
-            if ( !supportsMetadataFilter( statisticsFilterEvaluator, filter ) ) {
-                return false;
-            }
-        }
-        for ( AggregateCall aggregateCall : aggregateCalls ) {
-            if ( !supportsMetadataAggregateCall( fields, aggregateCall ) ) {
-                return false;
-            }
-        }
-        return true;
-    }
-
-
-    /**
-     * Checks whether a filter can be evaluated exactly from partition values or
-     * file-level statistics. Statistics filters are limited to file-constant
-     * physical columns so retained files can contribute their full metadata
-     * row count without reading data pages.
-     *
-     * @param statisticsFilterEvaluator evaluates physical-column file statistics.
-     * @param filter a filter to validate.
-     * @return {@code true} if the filter can be evaluated exactly for every source file.
-     */
-    private boolean supportsMetadataFilter( ParquetSourceFileStatisticsFilterEvaluator statisticsFilterEvaluator, ParquetAdapterFilter<PolyValue> filter ) {
-        if ( filter.isLogical() ) {
-            if ( filter.operator() == Kind.NOT ) {
-                return filter.operands().size() == 1 && isPartitionFilter( table, filter );
-            }
-            if ( filter.operator() != Kind.AND && filter.operator() != Kind.OR ) {
-                return false;
-            }
-            return !filter.operands().isEmpty() && filter.operands().stream().allMatch( operand -> supportsMetadataFilter( statisticsFilterEvaluator, operand ) );
-        }
-        if ( isPartitionColumn( table, filter.columnIndex() ) ) {
-            return true;
-        }
-        return table.getBinding().sourceFiles().stream().allMatch( sourceFile -> statisticsFilterEvaluator.supportsExactEvaluation( sourceFile, filter ) );
-    }
-
-
-    /**
-     * Applies filters that were proven metadata-decidable during planning.
-     *
-     * @param evaluator combines partition-value and file-statistics evaluators.
-     * @param sourceFile a source file.
-     * @param filters filters to apply.
-     * @return {@code true} if the complete file matches the filters.
-     */
-    private boolean matchesExactly( ParquetMultiFilterEvaluator<ParquetSourceFile> evaluator, ParquetSourceFile sourceFile, List<ParquetAdapterFilter<PolyValue>> filters ) {
-        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
-            Boolean result = evaluator.evaluate( sourceFile, filter );
-            if ( result == null ) {
-                throw new GenericRuntimeException( "Metadata aggregate filter could not be evaluated exactly for " + sourceFile.fileUrl() );
-            }
-            if ( !result ) {
-                return false;
-            }
-        }
-        return true;
-    }
-
-
-    /**
-     * Creates a joined key from file-constant group by fields.
-     *
-     * @param sourceFile a source file.
-     * @param fields an array of physical field indexes of columns used in a group by statement.
-     * @param groupFields an array of group by field indexes in fields.
-     * @return a list of {@link PolyValue}s representing a key.
-     */
-    private List<PolyValue> fileGroupKey( ParquetSourceFile sourceFile, int[] fields, int[] groupFields ) {
-        List<PolyValue> key = new ArrayList<>( groupFields.length );
-        for ( int groupField : groupFields ) {
-            int physicalField = fields[groupField];
-            ParquetColumnBinding columnBinding = selectPhysicalBinding( table, physicalField );
-            key.add( constantColumnResolver.resolve( sourceFile, columnBinding )
-                    .orElseThrow( () -> new GenericRuntimeException( "Metadata aggregate group column is not constant for " + sourceFile.fileUrl() ) ) );
-        }
-        return List.copyOf( key );
-    }
-
-
-    /**
-     * Checks whether one field has a constant value in every source file.
-     *
-     * @param field a physical table field.
-     * @return true when each file has one value for this field.
-     */
-    private boolean isFileConstantColumn( int field ) {
-        ParquetColumnBinding binding = selectPhysicalBinding( table, field );
-        return table.getBinding().sourceFiles().stream().allMatch( sourceFile -> constantColumnResolver.resolve( sourceFile, binding ).isPresent() );
+        return new ParquetMetadataAggregateExecutor( aggregateSource() )
+                .supportsMetadataAggregate( fields, filters, groupSet, aggregateCalls );
     }
 
 
-    /**
-     * Checks if a metadata aggregate call can be calculated from source file statistics.
-     *
-     * @param fields an array of physical field indexes of columns used in a group by statement.
-     * @param aggregateCall an aggregate call to validate.
-     * @return {@code true} if the aggregate call can be calculated from metadata and {@code false} otherwise.
-     */
-    private boolean supportsMetadataAggregateCall( int[] fields, AggregateCall aggregateCall ) {
-        if ( aggregateCall.getAggregation().getKind() == Kind.COUNT ) {
-            if ( aggregateCall.getArgList().isEmpty() ) {
-                return true;
+    private ParquetAggregateSource aggregateSource() {
+        return new ParquetAggregateSource() {
+            @Override
+            public List<ParquetSourceFile> sourceFiles() {
+                return table.getBinding().sourceFiles();
             }
-            return aggregateCall.getArgList().size() == 1 && supportsCountColumn( fields, aggregateCall.getArgList().get( 0 ) );
-        }
-        if ( aggregateCall.getAggregation().getKind() != Kind.MIN && aggregateCall.getAggregation().getKind() != Kind.MAX ) {
-            return false;
-        }
-        if ( aggregateCall.getArgList().size() != 1 ) {
-            return false;
-        }
 
-        ParquetColumnBinding columnBinding = dataColumnBinding( fields, aggregateCall.getArgList().get( 0 ) );
-        if ( columnBinding == null ) {
-            return false;
-        }
 
-        for ( ParquetSourceFile sourceFile : table.getBinding().sourceFiles() ) {
-            ParquetColumnStatistics statistics = sourceFile.columnStatistics().get( columnBinding.sourcePathElements() );
-            if ( statistics == null || (!statistics.hasRange() && !statistics.hasOnlyNulls()) ) {
-                return false;
+            @Override
+            public ParquetSchemaReader schemaReader() {
+                return schemaReader;
             }
-        }
-        return true;
-    }
-
 
-    /**
-     * Checks if a COUNT on a column is supported. Column aggregation is supported only if it has statistics and nulls count.
-     *
-     * @param fields an array of field indexes.
-     * @param inputIndex a column index.
-     * @return {@code true} if COUNT can be applied on the specified column and {@code false} otherwise.
-     */
-    private boolean supportsCountColumn( int[] fields, int inputIndex ) {
-        ParquetColumnBinding columnBinding = dataColumnBinding( fields, inputIndex );
-        if ( columnBinding == null ) {
-            return false;
-        }
 
-        for ( ParquetSourceFile sourceFile : table.getBinding().sourceFiles() ) {
-            ParquetColumnStatistics statistics = sourceFile.columnStatistics().get( columnBinding.sourcePathElements() );
-            if ( statistics == null || statistics.nullCount() == null ) {
-                return false;
+            @Override
+            public int fieldCount() {
+                return table.columns.size();
             }
-        }
-        return true;
-    }
-
-
-    /**
-     * Gets a binding for the specified column.
-     *
-     * @param fields an array of field indexes.
-     * @param inputIndex a column index.
-     * @return {@link ParquetColumnBinding}.
-     */
-    private ParquetColumnBinding dataColumnBinding( int[] fields, int inputIndex ) {
-        if ( inputIndex < 0 || inputIndex >= fields.length ) {
-            return null;
-        }
-
-        ParquetColumnBinding columnBinding = selectPhysicalBinding( table, fields[inputIndex] );
-        if ( columnBinding == null || columnBinding.role() != ParquetColumnRole.DATA || columnBinding.sourcePathElements().isEmpty() ) {
-            return null;
-        }
-        return columnBinding;
-    }
 
 
-    /**
-     * Creates an accumulator for each aggregation function.
-     *
-     * @param aggregateKinds the names of aggregation function.
-     * @return an array of aggregate accumulators.
-     */
-    private MetadataAggregateAccumulator[] createAccumulators( String[] aggregateKinds ) {
-        return Arrays.stream( aggregateKinds )
-                .map( MetadataAggregateAccumulator::new )
-                .toArray( MetadataAggregateAccumulator[]::new );
-    }
-
-
-    /**
-     * Holds partial metadata aggregate values for one result group.
-     */
-    private class MetadataAggregateAccumulator {
-
-        private final String aggregateKind;
-        private long count;
-        private ParquetColumnStatistics selectedStatistics;
-        private String selectedValue;
-
-
-        private MetadataAggregateAccumulator( String aggregateKind ) {
-            this.aggregateKind = aggregateKind;
-        }
-
-
-        private void add( ParquetSourceFile sourceFile, int[] fields, int aggregateArg ) {
-            if ( Kind.COUNT.name().equals( aggregateKind ) ) {
-                if ( aggregateArg < 0 ) {
-                    count += sourceRowCount( sourceFile );
-                    return;
-                }
-
-                ParquetColumnStatistics statistics = statistics( sourceFile, fields, aggregateArg );
-                if ( statistics != null && statistics.nullCount() != null ) {
-                    count += Math.max( 0L, statistics.rowCount() - statistics.nullCount() );
-                }
-                return;
+            @Override
+            public PolyType fieldType( int field ) {
+                return table.columns.get( field ).type;
             }
 
-            ParquetColumnStatistics statistics = statistics( sourceFile, fields, aggregateArg );
-            if ( statistics == null || !statistics.hasRange() ) {
-                return;
-            }
-            String candidate = Kind.MIN.name().equals( aggregateKind ) ? statistics.min() : statistics.max();
-            if ( selectedValue == null || compare( statistics, candidate, selectedValue ) ) {
-                selectedStatistics = statistics;
-                selectedValue = candidate;
-            }
-        }
-
 
-        private PolyValue result() {
-            if ( Kind.COUNT.name().equals( aggregateKind ) ) {
-                return PolyLong.of( count );
+            @Override
+            public ParquetColumnBinding binding( int field ) {
+                return ParquetRelExecutor.selectPhysicalBinding( table, field );
             }
-            if ( selectedValue == null || selectedStatistics == null ) {
-                return PolyNull.NULL;
-            }
-            PolyValue value = typeConverter.fromStringToPolyValue( selectedStatistics.type(), selectedValue );
-            return value == null ? PolyNull.NULL : value;
-        }
-
-
-        private boolean compare( ParquetColumnStatistics candidateStatistics, String candidate, String current ) {
-            int comparison = typeConverter.compareStringValues( candidateStatistics.type(), candidate, current );
-            return Kind.MIN.name().equals( aggregateKind ) ? comparison < 0 : comparison > 0;
-        }
-
-
-        private ParquetColumnStatistics statistics( ParquetSourceFile sourceFile, int[] fields, int aggregateArg ) {
-            if ( aggregateArg < 0 || aggregateArg >= fields.length ) {
-                return null;
-            }
-            ParquetColumnBinding columnBinding = selectPhysicalBinding( table, fields[aggregateArg] );
-            if ( columnBinding == null ) {
-                return null;
-            }
-            return sourceFile.columnStatistics().get( columnBinding.sourcePathElements() );
-        }
-
+        };
     }
 
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelNestedJoinExecutor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelNestedJoinExecutor.java
index 15f3e72532..58fe070bda 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelNestedJoinExecutor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelNestedJoinExecutor.java
@@ -30,6 +30,7 @@
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
+import org.polypheny.db.adapter.parquet.shared.execution.aggregate.ParquetDataAggregateExecutor;
 import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.JoinFiltersSplitter;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
@@ -168,7 +169,7 @@ public Enumerator<PolyValue[]> enumerator() {
                 return new ParquetMultiFileEnumerator(
                         parent.table().getBinding().sourceFiles(),
                         sourceFile -> nestedJoinEnumeratorForFile( parent.table(), sourceFile, child.table(), parent.fields(), child.fields(), cancelFlag, filterContainer, leftIsParent, emitUnmatchedParents ),
-                        createParquetSourceFileEvaluatorsChain( f -> parent.selectPhysicalBinding( f.columnIndex() ) ),
+                        ParquetDataAggregateExecutor.createParquetSourceFileEvaluatorsChain( f -> parent.selectPhysicalBinding( f.columnIndex() ) ),
                         parentFileFilters );
             }
         };
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/AggregateRowProjector.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/AggregateRowProjector.java
index b4732e67ef..bcaec9e498 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/AggregateRowProjector.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/AggregateRowProjector.java
@@ -24,7 +24,7 @@
 /**
  * Maps an input row into the grouping and aggregate values consumed by the generic row aggregate engine.
  */
-interface AggregateRowProjector {
+public interface AggregateRowProjector {
 
     /**
      * Evaluates condition expression if there is any.
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/DirectAggregateRowProjector.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/DirectAggregateRowProjector.java
index 914d9432ee..968dae89b3 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/DirectAggregateRowProjector.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/DirectAggregateRowProjector.java
@@ -25,12 +25,12 @@
 /**
  * Projects aggregate input directly from row fields.
  */
-final class DirectAggregateRowProjector implements AggregateRowProjector {
+public final class DirectAggregateRowProjector implements AggregateRowProjector {
 
     private final int[] groupFields;
 
 
-    DirectAggregateRowProjector( int[] groupFields ) {
+    public DirectAggregateRowProjector( int[] groupFields ) {
         this.groupFields = Arrays.copyOf( groupFields, groupFields.length );
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregator.java
index be45ccedb7..8229935a1f 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregator.java
@@ -28,7 +28,7 @@
 /**
  * Generic row-by-row aggregate fallback shared by direct and Calc projections.
  */
-final class ParquetRowAggregator {
+public final class ParquetRowAggregator {
 
     private ParquetRowAggregator() {
     }
@@ -42,7 +42,7 @@ private ParquetRowAggregator() {
      * @param projector a row projector that customizes values reading.
      * @return a grouped aggregations.
      */
-    static Map<GroupKey, AggregateGroupState> aggregate( Enumerator<PolyValue[]> rows, AggregateCallDescriptor[] aggregateCalls, AggregateRowProjector projector ) {
+    public static Map<GroupKey, AggregateGroupState> aggregate( Enumerator<PolyValue[]> rows, AggregateCallDescriptor[] aggregateCalls, AggregateRowProjector projector ) {
         Map<GroupKey, AggregateGroupState> aggregates = new LinkedHashMap<>();
         try ( rows ) {
             while ( rows.moveNext() ) {
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/PatternMatchers.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/PatternMatchers.java
deleted file mode 100644
index 155f3d0020..0000000000
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/PatternMatchers.java
+++ /dev/null
@@ -1,571 +0,0 @@
-/*
- * Copyright 2019-2026 The Polypheny Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.polypheny.db.adapter.parquet.relational.optimization;
-
-import static org.polypheny.db.plan.AlgOptRule.any;
-import static org.polypheny.db.plan.AlgOptRule.none;
-import static org.polypheny.db.plan.AlgOptRule.operand;
-
-import com.google.common.collect.ImmutableList;
-import java.util.ArrayList;
-import java.util.List;
-import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
-import org.polypheny.db.adapter.parquet.relational.filter.ParquetRelFilterTranslator;
-import org.polypheny.db.adapter.parquet.relational.optimization.aggregate.AggregateDecomposition;
-import org.polypheny.db.adapter.parquet.relational.optimization.aggregate.PartialAggregate;
-import org.polypheny.db.adapter.parquet.relational.planning.EnumerableParquet;
-import org.polypheny.db.adapter.parquet.relational.planning.JoinDirection;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetEnumerableUnion;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelAggregate;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelJoin;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
-import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.algebra.AlgNode;
-import org.polypheny.db.algebra.InvalidAlgException;
-import org.polypheny.db.algebra.core.AggregateCall;
-import org.polypheny.db.algebra.enumerable.EnumerableAggregate;
-import org.polypheny.db.algebra.enumerable.EnumerableCalc;
-import org.polypheny.db.algebra.enumerable.EnumerableConvention;
-import org.polypheny.db.algebra.enumerable.EnumerableJoin;
-import org.polypheny.db.algebra.enumerable.EnumerableUnion;
-import org.polypheny.db.algebra.fun.AggFunction;
-import org.polypheny.db.algebra.operators.OperatorName;
-import org.polypheny.db.languages.OperatorRegistry;
-import org.polypheny.db.plan.AlgOptRuleCall;
-import org.polypheny.db.plan.AlgOptUtil;
-import org.polypheny.db.rex.RexIndexRef;
-import org.polypheny.db.rex.RexNode;
-import org.polypheny.db.rex.RexProgram;
-import org.polypheny.db.schema.trait.ModelTrait;
-import org.polypheny.db.tools.AlgBuilderFactory;
-import org.polypheny.db.type.PolyType;
-import org.polypheny.db.type.entity.PolyValue;
-import org.polypheny.db.util.ImmutableBitSet;
-
-public final class PatternMatchers {
-
-    private final static ParquetRelFilterTranslator translator = new ParquetRelFilterTranslator();
-
-
-    private static void transformTo( AlgOptRuleCall call, EnumerableJoin join, ParquetRelScan left, ParquetRelScan right ) {
-        JoinDirection direction = ParquetRelJoin.supportedDirection( join, left, right );
-        if ( direction == null ) {
-            return;
-        }
-        ParquetRelJoin parquetJoin = ParquetRelJoin.create( left, right, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent() );
-        call.transformTo( toEnumerable( join, parquetJoin ) );
-    }
-
-
-    private static EnumerableParquet toEnumerable( AlgNode source, AlgNode input ) {
-        return new EnumerableParquet(
-                source.getCluster(),
-                input.getTraitSet().replace( EnumerableConvention.INSTANCE ).replace( ModelTrait.RELATIONAL ),
-                input );
-    }
-
-
-    /**
-     * Converts E_AGGREGATE node into P_AGGREGATE node.
-     *
-     * @param aggregate an aggregate node to convert.
-     * @param scan a P_SCAN to add to P_AGGREGATE node as an input.
-     * @return a new P_AGGREGATE node.
-     */
-    private static AlgNode parquetAggregate( EnumerableAggregate aggregate, ParquetRelScan scan ) {
-        ParquetRelAggregate parquetAggregate = ParquetRelAggregate.create( scan, aggregate );
-        return parquetAggregate == null ? null : toEnumerable( aggregate, parquetAggregate );
-    }
-
-
-    /**
-     * Validates if the provided E_AGGREGATE node contains only aggregation functions supported by metadata pushdown.
-     *
-     * @param aggregate an aggregate to check.
-     * @return {@code true} if the E_AGGREGATE node contains only supported metadata aggregation functions and {@code false} otherwise.
-     */
-    private static boolean supportsMetadataAggregate( EnumerableAggregate aggregate ) {
-        if ( !isSimpleAggregate( aggregate ) ) {
-            return false;
-        }
-        for ( AggregateCall aggregateCall : aggregate.getAggCallList() ) {
-            if ( !ParquetRelAggregate.supportsMetadataAggregateCall( aggregateCall ) ) {
-                return false;
-            }
-        }
-        return true;
-    }
-
-
-    /**
-     * Selects the aggregate function that combines partial metadata aggregate results.
-     *
-     * @param aggregateCall the original aggregate call.
-     * @return the aggregate function for the top-level aggregate.
-     */
-    private static AggFunction topLevelAggregateFunction( AggregateCall aggregateCall ) {
-        return switch ( aggregateCall.getAggregation().getKind() ) {
-            case COUNT -> OperatorRegistry.getAgg( OperatorName.SUM0 );
-            case MIN -> OperatorRegistry.getAgg( OperatorName.MIN );
-            case MAX -> OperatorRegistry.getAgg( OperatorName.MAX );
-            default -> aggregateCall.getAggregation();
-        };
-    }
-
-
-    /**
-     * Convert an input into an {@link EnumerableAggregate}.
-     *
-     * @param aggregate a base aggregate to use for additional properties.
-     * @param input an input to be converted.
-     * @return a new {@link EnumerableAggregate} with the provided input.
-     * @throws InvalidAlgException an exception to be thrown in case of an error.
-     */
-    private static EnumerableAggregate toAggregate( EnumerableAggregate aggregate, AlgNode input, AggregateDecomposition decomposition ) throws InvalidAlgException {
-        return new EnumerableAggregate(
-                aggregate.getCluster(),
-                aggregate.getTraitSet(),
-                input,
-                false,
-                aggregate.getGroupSet(),
-                aggregate.getGroupSets(),
-                decomposition.partialCalls() );
-    }
-
-
-    /**
-     * Converts input node into a top level {@link EnumerableAggregate} that combines all underlying partial aggregates.
-     *
-     * @param aggregate an initial aggregate used for additional properties.
-     * @param input an input to wrap.
-     * @return a new {@link EnumerableAggregate}.
-     * @throws InvalidAlgException an exception to be thrown in case of an error.
-     */
-    private static AlgNode toTopLevelAggregate( EnumerableAggregate aggregate, AlgNode input, AggregateDecomposition decomposition ) throws InvalidAlgException {
-        ImmutableBitSet finalGroupSet = ImmutableBitSet.range( aggregate.getGroupSet().cardinality() );
-        return new EnumerableAggregate(
-                aggregate.getCluster(),
-                aggregate.getTraitSet(),
-                input,
-                false,
-                finalGroupSet,
-                List.of( finalGroupSet ),
-                decomposition.buildFinalCalls( aggregate, input ) );
-    }
-
-
-    /**
-     * Convert each input into an {@link EnumerableAggregate}.
-     * The method is called to replace any input under the E_UNION node into an E_AGGREGATE in order for the {@link PatternMatchers::metadataAggregateOnScan()} to fire.
-     *
-     * @param aggregate an initial aggregate to be used for additional properties.
-     * @param inputs a list of inputs to convert.
-     * @return a list of new aggregates.
-     * @throws InvalidAlgException an exception to be thrown in case of an error.
-     */
-    private static List<AlgNode> toAggregates( EnumerableAggregate aggregate, List<AlgNode> inputs, AggregateDecomposition decomposition ) throws InvalidAlgException {
-        ImmutableList.Builder<AlgNode> builder = ImmutableList.builder();
-        for ( AlgNode input : inputs ) {
-            builder.add( toAggregate( aggregate, input, decomposition ) );
-        }
-        return builder.build();
-    }
-
-
-    /**
-     * Convert each input into the same calc followed by an {@link EnumerableAggregate}.
-     * This lets partial aggregate pushdown work when the aggregate input is a projection/filter over a UNION ALL.
-     *
-     * @param aggregate an initial aggregate to be used for additional properties.
-     * @param calc the calc between the aggregate and union.
-     * @param inputs a list of union inputs to convert.
-     * @return a list of new aggregate-on-calc inputs.
-     * @throws InvalidAlgException an exception to be thrown in case of an error.
-     */
-    private static List<AlgNode> toCalcAggregates( EnumerableAggregate aggregate, EnumerableCalc calc, List<AlgNode> inputs, AggregateDecomposition decomposition ) throws InvalidAlgException {
-        ImmutableList.Builder<AlgNode> builder = ImmutableList.builder();
-        for ( AlgNode input : inputs ) {
-            EnumerableCalc inputCalc = EnumerableCalc.create( input, calc.getProgram() );
-            builder.add( toAggregate( aggregate, inputCalc, decomposition ) );
-        }
-        return builder.build();
-    }
-
-
-    /**
-     * Checks if the provided aggregate represents a simple aggregate supported by the Parquet adapter.
-     *
-     * @param aggregate an aggregate to check.
-     * @return true if the provide aggregate is a simple aggregate and false otherwise.
-     */
-    private static boolean isSimpleAggregate( EnumerableAggregate aggregate ) {
-        return !aggregate.indicator && EnumerableAggregate.isSimple( aggregate );
-    }
-
-
-    /**
-     * Creates a decomposition of an aggregate call over UNION ALL optimization.
-     * The {@link AggregateDecomposition} describes what each union child should compute, and how the top aggregate should combine those partial results.
-     * <pre>
-     *     For example:
-     *     - COUNT: child computes COUNT, final combines with SUM0
-     *     - SUM, MIN, MAX: child computes the same aggregate, final combines with the same aggregate
-     *     - metadata-supported aggregates: COUNT, MIN, MAX, with COUNT combined as SUM0
-     * </pre>
-     *
-     * @param aggregate an original aggregate call to decompose.
-     * @return {@link AggregateDecomposition}.
-     */
-    private static AggregateDecomposition aggregateDecomposition( EnumerableAggregate aggregate ) {
-        if ( !isSimpleAggregate( aggregate ) ) {
-            return null;
-        }
-
-        List<PartialAggregate> partialAggregates = new ArrayList<>();
-        if ( supportsMetadataAggregate( aggregate ) ) {
-            for ( AggregateCall aggregateCall : aggregate.getAggCallList() ) {
-                partialAggregates.add( new PartialAggregate( aggregateCall, topLevelAggregateFunction( aggregateCall ) ) );
-            }
-            return new AggregateDecomposition( partialAggregates );
-        }
-
-        for ( AggregateCall aggregateCall : aggregate.getAggCallList() ) {
-            if ( aggregateCall.isDistinct() || aggregateCall.isApproximate() || aggregateCall.filterArg >= 0 ) {
-                return null;
-            }
-            switch ( aggregateCall.getAggregation().getKind() ) {
-                case COUNT -> {
-                    if ( aggregateCall.getArgList().size() > 1 ) {
-                        return null;
-                    }
-                    partialAggregates.add( new PartialAggregate( aggregateCall, OperatorRegistry.getAgg( OperatorName.SUM0 ) ) );
-                }
-                case SUM, MIN, MAX -> {
-                    if ( aggregateCall.getArgList().size() != 1 ) {
-                        return null;
-                    }
-                    partialAggregates.add( new PartialAggregate( aggregateCall, aggregateCall.getAggregation() ) );
-                }
-                default -> {
-                    return null;
-                }
-            }
-        }
-        return new AggregateDecomposition( partialAggregates );
-    }
-
-
-    /**
-     * Translates a {@link RexNode} condition into {@link ParquetAdapterFilter}.
-     *
-     * @param input an input node.
-     * @param condition a condition.
-     * @return a filter.
-     */
-    private static ParquetAdapterFilter<PolyValue> translate( AlgNode input, RexNode condition ) {
-        if ( input == null ) {
-            return null;
-        }
-        List<PolyType> fieldTypes = input.getTupleType().getFields().stream()
-                .map( field -> field.getType().getPolyType() )
-                .toList();
-        return translator.translate( fieldTypes, condition );
-    }
-
-
-    /**
-     * Converts {@link RexNode}'s to field indexes.
-     *
-     * @param rexNodes the nodes to convert.
-     * @return an array of projected filed indexes.
-     */
-    private static int[] getProjectFields( List<RexNode> rexNodes ) {
-        final int[] fields = new int[rexNodes.size()];
-        for ( int i = 0; i < rexNodes.size(); i++ ) {
-            final RexNode exp = rexNodes.get( i );
-            if ( exp instanceof RexIndexRef ) {
-                fields[i] = ((RexIndexRef) exp).getIndex();
-            } else {
-                return null;
-            }
-        }
-        return fields;
-    }
-
-
-    /**
-     * Gets a list of projected fields, if any, and filters, if any, and pushes them into the provided scan.
-     *
-     * @param calc the E_CALC node to retrieve the projected fields and filters from.
-     * @param scan the P_SCAN node to push the fields and filters into.
-     * @return a new P_SCAN node with added fields and filters.
-     */
-    private static ParquetRelScan applyCalc( EnumerableCalc calc, ParquetRelScan scan ) {
-        RexProgram program = calc.getProgram();
-        List<RexNode> projects = program.getProjectList().stream()
-                .map( program::expandLocalRef )
-                .toList();
-
-        int[] fields = getProjectFields( projects );
-        if ( fields == null ) {
-            return null;
-        }
-
-        int[] currentFields = scan.getFields();
-        int[] projectedFields = new int[fields.length];
-        for ( int i = 0; i < fields.length; i++ ) {
-            if ( fields[i] < 0 || fields[i] >= currentFields.length ) {
-                return null;
-            }
-            projectedFields[i] = currentFields[fields[i]];
-        }
-
-        ParquetAdapterFilter<PolyValue> adapterFilter = null;
-
-        if ( program.getCondition() != null ) {
-            RexNode condition = program.expandLocalRef( program.getCondition() );
-            adapterFilter = translate( scan, condition );
-            if ( adapterFilter == null ) {
-                return null;
-            }
-            adapterFilter = ParquetFilterResolver.toPhysicalFilter( adapterFilter, currentFields );
-            if ( adapterFilter == null ) {
-                return null;
-            }
-        }
-
-        ParquetRelScan updatedScan = scan.withFields( projectedFields );
-        return adapterFilter == null
-                ? updatedScan
-                : updatedScan.withFilters( List.of( adapterFilter ) );
-    }
-
-
-    /**
-     * Rule that converts a generic enumerable join into a Parquet adapter-level join.
-     */
-    public static PatternMatcher joinWithScanOnLeftAndScanOnRight( ParquetConvention out, AlgBuilderFactory factory ) {
-        return new PatternMatcher(
-                out,
-                factory,
-                operand(
-                        EnumerableJoin.class,
-                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) ),
-                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
-                ),
-                "joinWithScanOnLeftAndScanOnRight",
-                call -> {
-                    EnumerableJoin join = call.alg( 0 );
-                    ParquetRelScan left = call.alg( 2 );
-                    ParquetRelScan right = call.alg( 4 );
-                    transformTo( call, join, left, right );
-                }
-        );
-    }
-
-
-    /**
-     * Rule that pushes a filter from a Calc above a ParquetRelJoin into the join node.
-     */
-    public static PatternMatcher attachFilterToJoinUnderCalc( ParquetConvention out, AlgBuilderFactory factory ) {
-        return new PatternMatcher(
-                out,
-                factory,
-                operand(
-                        EnumerableCalc.class,
-                        operand( EnumerableParquet.class, operand( ParquetRelJoin.class, any() ) )
-                ),
-                "attachFilterToJoinUnderCalc",
-                call -> {
-                    EnumerableCalc calc = call.alg( 0 );
-                    ParquetRelJoin join = call.alg( 2 );
-                    if ( !AlgOptUtil.areRowTypesEqual( calc.getInput().getTupleType(), join.getTupleType(), false ) ) {
-                        return;
-                    }
-                    RexProgram program = calc.getProgram();
-                    if ( program.getCondition() == null ) {
-                        return;
-                    }
-                    RexNode condition = program.expandLocalRef( program.getCondition() );
-                    ParquetAdapterFilter<PolyValue> adapterFilter = translate( join, condition );
-                    if ( adapterFilter == null ) {
-                        return;
-                    }
-                    call.transformTo( toEnumerable( calc, join.withFilters( List.of( adapterFilter ) ) ) );
-                }
-        );
-    }
-
-
-    /**
-     * Rule that pushes a simple EnumerableCalc projection/filter into a ParquetRelScan
-     */
-    public static PatternMatcher attachFieldsAndFiltersToScanUnderCalc( ParquetConvention out, AlgBuilderFactory factory ) {
-        return new PatternMatcher(
-                out,
-                factory,
-                operand(
-                        EnumerableCalc.class,
-                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
-                ),
-                "attachFieldsAndFiltersToScanUnderCalc",
-                call -> {
-                    EnumerableCalc calc = call.alg( 0 );
-                    ParquetRelScan scan = applyCalc( calc, call.alg( 2 ) );
-                    if ( scan == null ) {
-                        return;
-                    }
-                    call.transformTo( toEnumerable( calc, scan ) );
-                }
-        );
-    }
-
-
-    /**
-     * Rule that replaces a supported EnumerableAggregate on top of a Parquet scan with a metadata or streaming aggregate if supported.
-     * The supported aggregations are:
-     * 1. COUNT(*), COUNT(col), SUM(col), MIN(col), MAX(col)
-     * 2. only numeric columns are supported
-     * 3. for metadata aggregate only partition filters are supported
-     */
-    public static PatternMatcher aggregateOnScan( ParquetConvention out, AlgBuilderFactory factory ) {
-        return new PatternMatcher(
-                out,
-                factory,
-                operand(
-                        EnumerableAggregate.class,
-                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
-                ),
-                "aggregateOnScan",
-                call -> {
-                    EnumerableAggregate aggregate = call.alg( 0 );
-                    ParquetRelScan scan = call.alg( 2 );
-                    AlgNode replacement = parquetAggregate( aggregate, scan );
-                    if ( replacement != null ) {
-                        call.transformTo( replacement );
-                    }
-                }
-        );
-    }
-
-
-    /**
-     * Rule that replaces a supported EnumerableAggregate on top of a simple calc and Parquet scan with a metadata or streaming aggregate if supported.
-     */
-    public static PatternMatcher aggregateOnCalcScan( ParquetConvention out, AlgBuilderFactory factory ) {
-        return new PatternMatcher(
-                out,
-                factory,
-                operand(
-                        EnumerableAggregate.class,
-                        operand(
-                                EnumerableCalc.class,
-                                operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
-                        )
-                ),
-                "aggregateOnCalcScan",
-                call -> {
-                    EnumerableAggregate aggregate = call.alg( 0 );
-                    EnumerableCalc calc = call.alg( 1 );
-                    ParquetRelScan scan = applyCalc( calc, call.alg( 3 ) );
-                    if ( scan == null ) {
-                        return;
-                    }
-                    AlgNode replacement = parquetAggregate( aggregate, scan );
-                    if ( replacement != null ) {
-                        call.transformTo( replacement );
-                    }
-                }
-        );
-    }
-
-
-    /**
-     * Rule that splits supported aggregates through a UNION ALL. Each child aggregate can then be optimized independently.
-     */
-    public static PatternMatcher partialAggregateOnUnion( ParquetConvention out, AlgBuilderFactory factory ) {
-        return new PatternMatcher(
-                out,
-                factory,
-                operand(
-                        EnumerableAggregate.class,
-                        operand( EnumerableUnion.class, any() )
-                ),
-                "partialAggregateOnUnion",
-                call -> {
-                    EnumerableAggregate aggregate = call.alg( 0 );
-                    EnumerableUnion union = call.alg( 1 );
-                    if ( !union.all ) {
-                        return;
-                    }
-
-                    AggregateDecomposition decomposition = aggregateDecomposition( aggregate );
-                    if ( decomposition == null ) {
-                        return;
-                    }
-
-                    try {
-                        List<AlgNode> inputs = toAggregates( aggregate, union.getInputs(), decomposition );
-                        ParquetEnumerableUnion partialUnion = new ParquetEnumerableUnion( union.getCluster(), union.getTraitSet(), inputs );
-                        call.transformTo( toTopLevelAggregate( aggregate, partialUnion, decomposition ) );
-                    } catch ( InvalidAlgException ignored ) {
-                    }
-                }
-        );
-    }
-
-
-    /**
-     * Rule that splits supported aggregates through a Calc over UNION ALL. Each child receives the same Calc and aggregate, allowing child-specific scan rules to optimize independently.
-     */
-    public static PatternMatcher partialAggregateOnCalcUnion( ParquetConvention out, AlgBuilderFactory factory ) {
-        return new PatternMatcher(
-                out,
-                factory,
-                operand(
-                        EnumerableAggregate.class,
-                        operand(
-                                EnumerableCalc.class,
-                                operand( EnumerableUnion.class, any() )
-                        )
-                ),
-                "partialAggregateOnCalcUnion",
-                call -> {
-                    EnumerableAggregate aggregate = call.alg( 0 );
-                    EnumerableCalc calc = call.alg( 1 );
-                    EnumerableUnion union = call.alg( 2 );
-                    if ( !union.all ) {
-                        return;
-                    }
-
-                    AggregateDecomposition decomposition = aggregateDecomposition( aggregate );
-                    if ( decomposition == null ) {
-                        return;
-                    }
-
-                    try {
-                        List<AlgNode> inputs = toCalcAggregates( aggregate, calc, union.getInputs(), decomposition );
-                        ParquetEnumerableUnion partialUnion = new ParquetEnumerableUnion( union.getCluster(), union.getTraitSet(), inputs );
-                        call.transformTo( toTopLevelAggregate( aggregate, partialUnion, decomposition ) );
-                    } catch ( InvalidAlgException ignored ) {
-                    }
-                }
-        );
-    }
-
-}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/EnumerableParquet.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/EnumerableParquet.java
index 5d93dd3f11..fefd180de4 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/EnumerableParquet.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/EnumerableParquet.java
@@ -50,7 +50,7 @@ public AlgNode copy( AlgTraitSet traitSet, List<AlgNode> inputs ) {
 
     @Override
     public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
-        return super.computeSelfCost( planner, mq ).multiplyBy( ParquetConvention.COST_MULTIPLIER );
+        return super.computeSelfCost( planner, mq ).multiplyBy( ParquetRelConvention.COST_MULTIPLIER );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java
index 14e70c7814..1567245e8b 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java
@@ -23,16 +23,15 @@
 import org.apache.calcite.linq4j.tree.Expressions;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.optimization.aggregate.ParquetAggregateSupport;
 import org.polypheny.db.adapter.parquet.shared.planning.ParquetPolyAlgDisplay;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.AlgWriter;
 import org.polypheny.db.algebra.SingleAlg;
-import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.algebra.core.Aggregate;
 import org.polypheny.db.algebra.core.AggregateCall;
 import org.polypheny.db.algebra.enumerable.EnumUtils;
 import org.polypheny.db.algebra.enumerable.EnumerableAlgImplementor;
-import org.polypheny.db.algebra.fun.AggFunction;
 import org.polypheny.db.algebra.metadata.AlgMetadataQuery;
 import org.polypheny.db.algebra.polyalg.arguments.ListArg;
 import org.polypheny.db.algebra.polyalg.arguments.PolyAlgArgs;
@@ -74,7 +73,7 @@ public static ParquetRelAggregate create( ParquetRelScan scan, Aggregate aggrega
         }
         return new ParquetRelAggregate(
                 aggregate.getCluster(),
-                aggregate.getCluster().traitSetOf( ParquetConvention.INSTANCE ).replace( ModelTrait.RELATIONAL ),
+                aggregate.getCluster().traitSetOf( ParquetRelConvention.INSTANCE ).replace( ModelTrait.RELATIONAL ),
                 mode == AggregateMode.METADATA ? new ParquetRelMetadataScan( scan ) : scan,
                 scan.getEntity(),
                 scan.getFields(),
@@ -99,7 +98,7 @@ private ParquetRelAggregate(
             String[] aggregateKinds,
             int[] aggregateArgs,
             AlgDataType rowType ) {
-        super( cluster, traitSet.replace( ParquetConvention.INSTANCE ).replace( ModelTrait.RELATIONAL ), input );
+        super( cluster, traitSet.replace( ParquetRelConvention.INSTANCE ).replace( ModelTrait.RELATIONAL ), input );
         this.table = table;
         this.fields = fields.clone();
         this.filters = List.copyOf( filters );
@@ -133,17 +132,7 @@ public static boolean supports( ParquetRelScan scan, Aggregate aggregate ) {
      * @return {@code true} if the aggregate call is supported and {@code false} otherwise.
      */
     public static boolean supportsMetadataAggregateCall( AggregateCall aggregateCall ) {
-        if ( aggregateCall.isDistinct() || aggregateCall.isApproximate() || aggregateCall.filterArg >= 0 ) {
-            return false;
-        }
-        AggFunction aggregation = aggregateCall.getAggregation();
-        if ( aggregation.getKind() == Kind.COUNT ) {
-            return aggregateCall.getArgList().size() <= 1;
-        }
-        if ( aggregation.getKind() == Kind.MIN || aggregation.getKind() == Kind.MAX ) {
-            return aggregateCall.getArgList().size() == 1;
-        }
-        return false;
+        return ParquetAggregateSupport.supportsMetadataAggregateCall( aggregateCall );
     }
 
 
@@ -221,13 +210,13 @@ public PolyAlgArgs bindArguments() {
 
     @Override
     public void register( AlgPlanner planner ) {
-        ParquetConvention.INSTANCE.register( planner );
+        ParquetRelConvention.INSTANCE.register( planner );
     }
 
 
     @Override
     public AlgOptCost computeSelfCost( AlgPlanner planner, AlgMetadataQuery mq ) {
-        return super.computeSelfCost( planner, mq ).multiplyBy( ParquetConvention.COST_MULTIPLIER * 0.01D );
+        return super.computeSelfCost( planner, mq ).multiplyBy( ParquetRelConvention.COST_MULTIPLIER * 0.01D );
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelConvention.java
similarity index 82%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelConvention.java
index 66160c95d3..7ab520cdf6 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetConvention.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelConvention.java
@@ -17,7 +17,6 @@
 package org.polypheny.db.adapter.parquet.relational.planning;
 
 import org.polypheny.db.algebra.enumerable.EnumerableConvention;
-import org.polypheny.db.adapter.parquet.relational.optimization.ParquetRules;
 import org.polypheny.db.plan.AlgOptRule;
 import org.polypheny.db.plan.AlgPlanner;
 import org.polypheny.db.plan.AlgTraitSet;
@@ -27,24 +26,25 @@
 /**
  * Calling convention for relational operators executed directly by the Parquet adapter.
  */
-public class ParquetConvention extends Convention.Impl {
+public class ParquetRelConvention extends Convention.Impl {
 
-    public static final ParquetConvention INSTANCE = new ParquetConvention();
+    public static final ParquetRelConvention INSTANCE = new ParquetRelConvention();
     public static final double COST_MULTIPLIER = 0.05d;
 
 
-    private ParquetConvention() {
-        super( "PARQUET", ParquetAlg.class );
+    private ParquetRelConvention() {
+        super( "PARQUET_RELATIONAL", ParquetAlg.class );
     }
 
 
     /**
      * Register Rules
+     *
      * @param planner Planner
      */
     @Override
     public void register( AlgPlanner planner ) {
-        for ( AlgOptRule rule : ParquetRules.rules( this ) ) {
+        for ( AlgOptRule rule : ParquetRelRules.rules( this ) ) {
             planner.addRuleDuringRuntime( rule );
         }
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
index 88aa9575fa..187cc39704 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java
@@ -81,7 +81,7 @@ public ParquetRelJoin(
 
     public static ParquetRelJoin create(ParquetRelScan left, ParquetRelScan right, RexNode condition, Set<CorrelationId> variablesSet, JoinAlgType joinType, boolean leftIsParent) {
         AlgCluster cluster = left.getCluster();
-        AlgTraitSet traitSet = cluster.traitSetOf(ParquetConvention.INSTANCE).replace(ModelTrait.RELATIONAL);
+        AlgTraitSet traitSet = cluster.traitSetOf(ParquetRelConvention.INSTANCE).replace(ModelTrait.RELATIONAL);
         return new ParquetRelJoin(
                 cluster,
                 traitSet,
@@ -196,7 +196,7 @@ public PolyAlgArgs bindArguments() {
     @Override
     public AlgOptCost computeSelfCost(AlgPlanner planner, AlgMetadataQuery mq) {
         Optional<Double> count = mq.getTupleCount(this);
-        return planner.getCostFactory().makeCost(count.orElse(estimateTupleCount(mq)), 0, 0).multiplyBy(ParquetConvention.COST_MULTIPLIER);
+        return planner.getCostFactory().makeCost(count.orElse(estimateTupleCount(mq)), 0, 0).multiplyBy(ParquetRelConvention.COST_MULTIPLIER);
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelPatternMatchers.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelPatternMatchers.java
new file mode 100644
index 0000000000..5ab2d6e2ee
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelPatternMatchers.java
@@ -0,0 +1,304 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.relational.planning;
+
+import static org.polypheny.db.plan.AlgOptRule.any;
+import static org.polypheny.db.plan.AlgOptRule.none;
+import static org.polypheny.db.plan.AlgOptRule.operand;
+
+import java.util.List;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetRelFilterTranslator;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.optimization.PatternMatcher;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.enumerable.EnumerableAggregate;
+import org.polypheny.db.algebra.enumerable.EnumerableCalc;
+import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.enumerable.EnumerableJoin;
+import org.polypheny.db.plan.AlgOptRuleCall;
+import org.polypheny.db.plan.AlgOptUtil;
+import org.polypheny.db.rex.RexIndexRef;
+import org.polypheny.db.rex.RexNode;
+import org.polypheny.db.rex.RexProgram;
+import org.polypheny.db.schema.trait.ModelTrait;
+import org.polypheny.db.tools.AlgBuilderFactory;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyValue;
+
+public final class ParquetRelPatternMatchers {
+
+    private final static ParquetRelFilterTranslator translator = new ParquetRelFilterTranslator();
+
+
+    private static void transformTo( AlgOptRuleCall call, EnumerableJoin join, ParquetRelScan left, ParquetRelScan right ) {
+        JoinDirection direction = ParquetRelJoin.supportedDirection( join, left, right );
+        if ( direction == null ) {
+            return;
+        }
+        ParquetRelJoin parquetJoin = ParquetRelJoin.create( left, right, join.getCondition(), join.getVariablesSet(), join.getJoinType(), direction.leftIsParent() );
+        call.transformTo( toEnumerable( join, parquetJoin ) );
+    }
+
+
+    private static EnumerableParquet toEnumerable( AlgNode source, AlgNode input ) {
+        return new EnumerableParquet(
+                source.getCluster(),
+                input.getTraitSet().replace( EnumerableConvention.INSTANCE ).replace( ModelTrait.RELATIONAL ),
+                input );
+    }
+
+
+    /**
+     * Converts E_AGGREGATE node into P_AGGREGATE node.
+     *
+     * @param aggregate an aggregate node to convert.
+     * @param scan a P_SCAN to add to P_AGGREGATE node as an input.
+     * @return a new P_AGGREGATE node.
+     */
+    private static AlgNode parquetAggregate( EnumerableAggregate aggregate, ParquetRelScan scan ) {
+        ParquetRelAggregate parquetAggregate = ParquetRelAggregate.create( scan, aggregate );
+        return parquetAggregate == null ? null : toEnumerable( aggregate, parquetAggregate );
+    }
+
+
+    /**
+     * Translates a {@link RexNode} condition into {@link ParquetAdapterFilter}.
+     *
+     * @param input an input node.
+     * @param condition a condition.
+     * @return a filter.
+     */
+    private static ParquetAdapterFilter<PolyValue> translate( AlgNode input, RexNode condition ) {
+        if ( input == null ) {
+            return null;
+        }
+        List<PolyType> fieldTypes = input.getTupleType().getFields().stream()
+                .map( field -> field.getType().getPolyType() )
+                .toList();
+        return translator.translate( fieldTypes, condition );
+    }
+
+
+    /**
+     * Converts {@link RexNode}'s to field indexes.
+     *
+     * @param rexNodes the nodes to convert.
+     * @return an array of projected filed indexes.
+     */
+    private static int[] getProjectFields( List<RexNode> rexNodes ) {
+        final int[] fields = new int[rexNodes.size()];
+        for ( int i = 0; i < rexNodes.size(); i++ ) {
+            final RexNode exp = rexNodes.get( i );
+            if ( exp instanceof RexIndexRef ) {
+                fields[i] = ((RexIndexRef) exp).getIndex();
+            } else {
+                return null;
+            }
+        }
+        return fields;
+    }
+
+
+    /**
+     * Gets a list of projected fields, if any, and filters, if any, and pushes them into the provided scan.
+     *
+     * @param calc the E_CALC node to retrieve the projected fields and filters from.
+     * @param scan the P_SCAN node to push the fields and filters into.
+     * @return a new P_SCAN node with added fields and filters.
+     */
+    private static ParquetRelScan applyCalc( EnumerableCalc calc, ParquetRelScan scan ) {
+        RexProgram program = calc.getProgram();
+        List<RexNode> projects = program.getProjectList().stream()
+                .map( program::expandLocalRef )
+                .toList();
+
+        int[] fields = getProjectFields( projects );
+        if ( fields == null ) {
+            return null;
+        }
+
+        int[] currentFields = scan.getFields();
+        int[] projectedFields = new int[fields.length];
+        for ( int i = 0; i < fields.length; i++ ) {
+            if ( fields[i] < 0 || fields[i] >= currentFields.length ) {
+                return null;
+            }
+            projectedFields[i] = currentFields[fields[i]];
+        }
+
+        ParquetAdapterFilter<PolyValue> adapterFilter = null;
+
+        if ( program.getCondition() != null ) {
+            RexNode condition = program.expandLocalRef( program.getCondition() );
+            adapterFilter = translate( scan, condition );
+            if ( adapterFilter == null ) {
+                return null;
+            }
+            adapterFilter = ParquetFilterResolver.toPhysicalFilter( adapterFilter, currentFields );
+            if ( adapterFilter == null ) {
+                return null;
+            }
+        }
+
+        ParquetRelScan updatedScan = scan.withFields( projectedFields );
+        return adapterFilter == null
+                ? updatedScan
+                : updatedScan.withFilters( List.of( adapterFilter ) );
+    }
+
+
+    /**
+     * Rule that converts a generic enumerable join into a Parquet adapter-level join.
+     */
+    public static PatternMatcher joinWithScanOnLeftAndScanOnRight(ParquetRelConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableJoin.class,
+                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) ),
+                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
+                ),
+                "joinWithScanOnLeftAndScanOnRight",
+                call -> {
+                    EnumerableJoin join = call.alg( 0 );
+                    ParquetRelScan left = call.alg( 2 );
+                    ParquetRelScan right = call.alg( 4 );
+                    transformTo( call, join, left, right );
+                }
+        );
+    }
+
+
+    /**
+     * Rule that pushes a filter from a Calc above a ParquetRelJoin into the join node.
+     */
+    public static PatternMatcher attachFilterToJoinUnderCalc(ParquetRelConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableCalc.class,
+                        operand( EnumerableParquet.class, operand( ParquetRelJoin.class, any() ) )
+                ),
+                "attachFilterToJoinUnderCalc",
+                call -> {
+                    EnumerableCalc calc = call.alg( 0 );
+                    ParquetRelJoin join = call.alg( 2 );
+                    if ( !AlgOptUtil.areRowTypesEqual( calc.getInput().getTupleType(), join.getTupleType(), false ) ) {
+                        return;
+                    }
+                    RexProgram program = calc.getProgram();
+                    if ( program.getCondition() == null ) {
+                        return;
+                    }
+                    RexNode condition = program.expandLocalRef( program.getCondition() );
+                    ParquetAdapterFilter<PolyValue> adapterFilter = translate( join, condition );
+                    if ( adapterFilter == null ) {
+                        return;
+                    }
+                    call.transformTo( toEnumerable( calc, join.withFilters( List.of( adapterFilter ) ) ) );
+                }
+        );
+    }
+
+
+    /**
+     * Rule that pushes a simple EnumerableCalc projection/filter into a ParquetRelScan
+     */
+    public static PatternMatcher attachFieldsAndFiltersToScanUnderCalc(ParquetRelConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableCalc.class,
+                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
+                ),
+                "attachFieldsAndFiltersToScanUnderCalc",
+                call -> {
+                    EnumerableCalc calc = call.alg( 0 );
+                    ParquetRelScan scan = applyCalc( calc, call.alg( 2 ) );
+                    if ( scan == null ) {
+                        return;
+                    }
+                    call.transformTo( toEnumerable( calc, scan ) );
+                }
+        );
+    }
+
+
+    /**
+     * Rule that replaces a supported EnumerableAggregate on top of a Parquet scan with a metadata or streaming aggregate if supported.
+     * The supported aggregations are:
+     * 1. COUNT(*), COUNT(col), SUM(col), MIN(col), MAX(col)
+     * 2. only numeric columns are supported
+     * 3. for metadata aggregate only partition filters are supported
+     */
+    public static PatternMatcher aggregateOnScan(ParquetRelConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableAggregate.class,
+                        operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
+                ),
+                "aggregateOnScan",
+                call -> {
+                    EnumerableAggregate aggregate = call.alg( 0 );
+                    ParquetRelScan scan = call.alg( 2 );
+                    AlgNode replacement = parquetAggregate( aggregate, scan );
+                    if ( replacement != null ) {
+                        call.transformTo( replacement );
+                    }
+                }
+        );
+    }
+
+
+    /**
+     * Rule that replaces a supported EnumerableAggregate on top of a simple calc and Parquet scan with a metadata or streaming aggregate if supported.
+     */
+    public static PatternMatcher aggregateOnCalcScan(ParquetRelConvention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableAggregate.class,
+                        operand(
+                                EnumerableCalc.class,
+                                operand( EnumerableParquet.class, operand( ParquetRelScan.class, none() ) )
+                        )
+                ),
+                "aggregateOnCalcScan",
+                call -> {
+                    EnumerableAggregate aggregate = call.alg( 0 );
+                    EnumerableCalc calc = call.alg( 1 );
+                    ParquetRelScan scan = applyCalc( calc, call.alg( 3 ) );
+                    if ( scan == null ) {
+                        return;
+                    }
+                    AlgNode replacement = parquetAggregate( aggregate, scan );
+                    if ( replacement != null ) {
+                        call.transformTo( replacement );
+                    }
+                }
+        );
+    }
+
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/ParquetRules.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelRules.java
similarity index 55%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/ParquetRules.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelRules.java
index 840e23e556..b0472fa2fa 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/ParquetRules.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelRules.java
@@ -14,16 +14,17 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.optimization;
+package org.polypheny.db.adapter.parquet.relational.planning;
 
 import java.util.ArrayList;
 import java.util.List;
+import org.polypheny.db.adapter.parquet.shared.optimization.ParquetAggregatePatternMatchers;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.convert.ConverterRule;
 import org.polypheny.db.algebra.core.AlgFactories;
 import org.polypheny.db.algebra.enumerable.EnumerableConvention;
-import org.polypheny.db.adapter.parquet.relational.planning.EnumerableParquet;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
+import org.polypheny.db.adapter.parquet.shared.optimization.ParquetAlgOptRule;
+import org.polypheny.db.adapter.parquet.shared.optimization.ParquetOptimizationSettings;
 import org.polypheny.db.plan.AlgOptRule;
 import org.polypheny.db.schema.trait.ModelTrait;
 import org.polypheny.db.tools.AlgBuilderFactory;
@@ -31,31 +32,28 @@
 /**
  * Rules that build the Parquet adapter convention path.
  */
-public final class ParquetRules {
+public final class ParquetRelRules {
 
-    private static final boolean isOptimizeAggregation = true;
-
-
-    private ParquetRules() {
+    private ParquetRelRules() {
     }
 
 
-    public static List<AlgOptRule> rules( ParquetConvention out ) {
+    public static List<AlgOptRule> rules( ParquetRelConvention out ) {
         return rules( out, AlgFactories.LOGICAL_BUILDER );
     }
 
 
-    public static List<AlgOptRule> rules( ParquetConvention out, AlgBuilderFactory factory ) {
+    public static List<AlgOptRule> rules(ParquetRelConvention out, AlgBuilderFactory factory ) {
         List<AlgOptRule> rules = new ArrayList<>();
-        rules.add( new ParquetAlgOptRule( PatternMatchers.joinWithScanOnLeftAndScanOnRight( out, factory ) ) );
-        rules.add( new ParquetAlgOptRule( PatternMatchers.attachFilterToJoinUnderCalc( out, factory ) ) );
-        rules.add( new ParquetAlgOptRule( PatternMatchers.attachFieldsAndFiltersToScanUnderCalc( out, factory ) ) );
+        rules.add( new ParquetAlgOptRule( ParquetRelPatternMatchers.attachFilterToJoinUnderCalc( out, factory ) ) );
+        rules.add( new ParquetAlgOptRule( ParquetRelPatternMatchers.attachFieldsAndFiltersToScanUnderCalc( out, factory ) ) );
         rules.add( new EnumerableParquetRule( out, factory ) );
-        if ( isOptimizeAggregation ) {
-            rules.add( new ParquetAlgOptRule( PatternMatchers.aggregateOnScan( out, factory ) ) );
-            rules.add( new ParquetAlgOptRule( PatternMatchers.aggregateOnCalcScan( out, factory ) ) );
-            rules.add( new ParquetAlgOptRule( PatternMatchers.partialAggregateOnUnion( out, factory ) ) );
-            rules.add( new ParquetAlgOptRule( PatternMatchers.partialAggregateOnCalcUnion( out, factory ) ) );
+        if ( ParquetOptimizationSettings.isOptimizeAggregation() ) {
+            rules.add( new ParquetAlgOptRule( ParquetRelPatternMatchers.joinWithScanOnLeftAndScanOnRight( out, factory ) ) );
+            rules.add( new ParquetAlgOptRule( ParquetRelPatternMatchers.aggregateOnScan( out, factory ) ) );
+            rules.add( new ParquetAlgOptRule( ParquetRelPatternMatchers.aggregateOnCalcScan( out, factory ) ) );
+            rules.add( new ParquetAlgOptRule( ParquetAggregatePatternMatchers.partialAggregateOnUnion( out, factory ) ) );
+            rules.add( new ParquetAlgOptRule( ParquetAggregatePatternMatchers.partialAggregateOnCalcUnion( out, factory ) ) );
         }
         return rules;
     }
@@ -66,7 +64,7 @@ public static List<AlgOptRule> rules( ParquetConvention out, AlgBuilderFactory f
      */
     public static class EnumerableParquetRule extends ConverterRule {
 
-        public EnumerableParquetRule( ParquetConvention in, AlgBuilderFactory algBuilderFactory ) {
+        public EnumerableParquetRule(ParquetRelConvention in, AlgBuilderFactory algBuilderFactory ) {
             super(
                     AlgNode.class,
                     alg -> true,
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
index cee8f5bf0f..dee905b6cb 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java
@@ -61,7 +61,7 @@ public ParquetRelScan(AlgCluster cluster, ParquetRelTable table, int[] fields) {
 
 
     public ParquetRelScan(AlgCluster cluster, ParquetRelTable table, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters) {
-        super(cluster, cluster.traitSetOf(ParquetConvention.INSTANCE).replace(ModelTrait.RELATIONAL), table);
+        super(cluster, cluster.traitSetOf(ParquetRelConvention.INSTANCE).replace(ModelTrait.RELATIONAL), table);
         this.fields = fields;
         this.filters = List.copyOf(filters);
     }
@@ -109,7 +109,7 @@ public AlgDataType deriveRowType() {
 
     @Override
     public void register(AlgPlanner planner) {
-        ParquetConvention.INSTANCE.register(planner);
+        ParquetRelConvention.INSTANCE.register(planner);
     }
 
 
@@ -117,7 +117,7 @@ public void register(AlgPlanner planner) {
     public AlgOptCost computeSelfCost(AlgPlanner planner, AlgMetadataQuery mq) {
         double fieldRatio = ((double) fields.length + 2D) / ((double) entity.getTupleType().getFieldCount() + 2D);
         double filterRatio = filters.isEmpty() ? 1D : 0.5D;
-        return super.computeSelfCost(planner, mq).multiplyBy(ParquetConvention.COST_MULTIPLIER * fieldRatio * filterRatio);
+        return super.computeSelfCost(planner, mq).multiplyBy(ParquetRelConvention.COST_MULTIPLIER * fieldRatio * filterRatio);
     }
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
index 9a3a267675..18cfa8152c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java
@@ -27,7 +27,7 @@
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelNestedJoinExecutor;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelProjectExecutor;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelDataAggregateExecutor;
-import org.polypheny.db.adapter.parquet.relational.planning.ParquetConvention;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelConvention;
 import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelScan;
 import org.polypheny.db.adapter.parquet.relational.planning.PhysicalScan;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
@@ -127,7 +127,7 @@ public Optional<ProvidedColumnStatistics> getColumnStatistics( LogicalColumn col
      */
     @Override
     public AlgNode toAlg( AlgCluster cluster, AlgTraitSet traitSet ) {
-        ParquetConvention.INSTANCE.register( cluster.getPlanner() );
+        ParquetRelConvention.INSTANCE.register( cluster.getPlanner() );
         return new ParquetRelScan( cluster, this, fieldIndexes );
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
index acebf35e7a..6ddaedfb43 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java
@@ -136,7 +136,7 @@ public Map<String, List<ExportedColumn>> getExportedColumns() {
     }
 
 
-    protected Optional<DiscoveredTableBinding> getTableBinding( String tableName ) {
+    public Optional<DiscoveredTableBinding> getTableBinding( String tableName ) {
         if ( discoveredTables == null ) {
             discoveredTables = ParquetFileDiscovery.discoverTables( parquetDir, getUniqueName() );
         }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
index c00d9a42a1..f0668fc0df 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java
@@ -38,6 +38,7 @@ public abstract class AbstractFilterTranslator {
      * @return ParsedFilter
      */
     protected ParsedFilter parse( RexNode filter ) {
+        filter = unwrapCast( filter );
         if ( !(filter instanceof RexCall call) || !isSupportedOperator( filter.getKind() ) || call.getOperands().size() != 2 ) {
             return null;
         }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetAggregateSource.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetAggregateSource.java
new file mode 100644
index 0000000000..78a73130d3
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetAggregateSource.java
@@ -0,0 +1,52 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.execution.aggregate;
+
+import java.util.List;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.type.PolyType;
+
+
+/**
+ * Model-independent view required by Parquet aggregate optimizations.
+ */
+public interface ParquetAggregateSource {
+
+    List<ParquetSourceFile> sourceFiles();
+
+    ParquetSchemaReader schemaReader();
+
+    int fieldCount();
+
+    PolyType fieldType( int field );
+
+    ParquetColumnBinding binding( int field );
+
+
+    default int parquetFieldIndex( String fieldName ) {
+        for ( int i = 0; i < schemaReader().getSchema().getFieldCount(); i++ ) {
+            if ( schemaReader().getSchema().getFieldName( i ).equals( fieldName ) ) {
+                return i;
+            }
+        }
+        return -1;
+    }
+
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetDataAggregateExecutor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetDataAggregateExecutor.java
new file mode 100644
index 0000000000..11bc743729
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetDataAggregateExecutor.java
@@ -0,0 +1,618 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.execution.aggregate;
+
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.atomic.AtomicBoolean;
+import java.util.function.Function;
+import org.apache.calcite.linq4j.AbstractEnumerable;
+import org.apache.calcite.linq4j.Enumerable;
+import org.apache.calcite.linq4j.Enumerator;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetMultiFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFilePartitionFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFileStatisticsFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetConstantColumnResolver;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
+import org.polypheny.db.adapter.parquet.shared.aggregate.ColumnAggregateProjection;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.aggregate.ParquetGroupedAggregateReader;
+import org.polypheny.db.adapter.parquet.shared.io.aggregate.ParquetNoFilterColumnAggregateReader;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.core.AggregateCall;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.PolyType;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.ImmutableBitSet;
+
+
+/**
+ * An entry point for all aggregated enumerators that cannot be calculated based on metadata. The executor decides what enumerator should be executed for fastest results.
+ * Supported enumerators are:
+ * 1. Enumerator for COUNT(*) / COUNT(col) / SUM(col) / MIN(col) / MAX(col) with numeric aggregation columns and no row level filters.
+ * 2. Enumerator for COUNT(*) / COUNT(col) / SUM(col) / MIN(col) / MAX(col) with aggregation columns defined as partitions and no row level filters.
+ * 3. A fallback enumerator that supports the same aggregation functions for numeric columns but this time with row level filters.
+ */
+public class ParquetDataAggregateExecutor {
+
+    private final ParquetAggregateSource source;
+    private final ParquetConstantColumnResolver constantColumnResolver;
+    private final ParquetSourceFileStatisticsFilterEvaluator statisticsFilterEvaluator;
+    private final Function<List<ParquetAdapterFilter<PolyValue>>, Enumerator<PolyValue[]>> fallbackRows;
+
+
+    public ParquetDataAggregateExecutor( ParquetAggregateSource source, Function<List<ParquetAdapterFilter<PolyValue>>, Enumerator<PolyValue[]>> fallbackRows ) {
+        this.source = source;
+        this.constantColumnResolver = new ParquetConstantColumnResolver();
+        this.statisticsFilterEvaluator = new ParquetSourceFileStatisticsFilterEvaluator( filter -> source.binding( filter.columnIndex() ) );
+        this.fallbackRows = fallbackRows;
+    }
+
+
+    /**
+     * Creates a chain of source file filter evaluators. Currently, supports two filter evaluators:
+     * 1. partition value based filter evaluator
+     * 2. source file statistics based filter evaluator.
+     * Those filter evaluators can skip files if they do not contain filtered data.
+     *
+     * @param selector a column binding selector.
+     * @return an evaluators chain.
+     */
+    public static ParquetMultiFilterEvaluator<ParquetSourceFile> createParquetSourceFileEvaluatorsChain( Function<ParquetAdapterFilter<PolyValue>, ParquetColumnBinding> selector ) {
+        return new ParquetMultiFilterEvaluator<>( List.of(
+                new ParquetSourceFilePartitionFilterEvaluator( selector ),
+                new ParquetSourceFileStatisticsFilterEvaluator( selector ) )
+        );
+    }
+
+
+    /**
+     * Creates an enumerator for aggregated rows.
+     *
+     * @param dataContext a data context.
+     * @param fields an array of field indexes.
+     * @param filters a list of filters to be applied.
+     * @param groupFields a list of group by field indexes.
+     * @param aggregateKinds an array of aggregation function kinds.
+     * @param aggregateArgs an array of aggregation function arguments. There should be one argument per aggregation function.
+     * @return an enumerator.
+     */
+    public Enumerable<PolyValue[]> createEnumerator( DataContext dataContext, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        List<ParquetAdapterFilter<PolyValue>> resolvedFilters = ParquetFilterResolver.resolveFilters( dataContext, filters, filter -> source.binding( filter.columnIndex() ) );
+        AtomicBoolean contextCancelFlag = DataContext.Variable.CANCEL_FLAG.get( dataContext );
+        AtomicBoolean cancelFlag = contextCancelFlag == null ? new AtomicBoolean( false ) : contextCancelFlag;
+
+        return new AbstractEnumerable<>() {
+            @Override
+            public Enumerator<PolyValue[]> enumerator() {
+                ColumnAggregateProjection aggregateProjection = tryBuildAggregateProjection( fields, filters, groupFields, aggregateKinds, aggregateArgs );
+                if ( aggregateProjection != null ) {
+                    return new ParquetFileGroupedAggregateEnumerator(
+                            source,
+                            fields,
+                            groupFields,
+                            aggregateKinds,
+                            aggregateArgs,
+                            aggregateProjection,
+                            resolvedFilters,
+                            cancelFlag );
+                }
+
+                int[] groupedCountProjection = tryBuildGroupedCountProjection( fields, filters, groupFields, aggregateKinds, aggregateArgs );
+                if ( groupedCountProjection != null ) {
+                    return new ParquetGroupedAggregateEnumerator(
+                            source,
+                            groupedCountProjection,
+                            groupedCountProjection.length,
+                            new AggregateCallDescriptor[]{ AggregateCallDescriptor.countStar() },
+                            resolvedFilters,
+                            cancelFlag );
+                }
+
+                GroupAggregateProjection groupedAggregateProjection = tryBuildGroupedAggregateProjection( fields, filters, groupFields, aggregateKinds, aggregateArgs );
+                if ( groupedAggregateProjection != null ) {
+                    return new ParquetGroupedAggregateEnumerator(
+                            source,
+                            groupedAggregateProjection.fields(),
+                            groupedAggregateProjection.groupFieldCount(),
+                            groupedAggregateProjection.aggregateCalls(),
+                            resolvedFilters,
+                            cancelFlag );
+                }
+
+                if ( fallbackRows == null ) {
+                    throw new GenericRuntimeException( "Unsupported Parquet aggregate projection." );
+                }
+                return new ParquetRowAggregateEnumerator( fallbackRows.apply( resolvedFilters ), groupFields, aggregateKinds, aggregateArgs );
+            }
+        };
+    }
+
+
+    /**
+     * Converts a relative field index into a parquet physical index and adds it into projection.
+     *
+     * @param field a relative field index.
+     * @param parquetFieldProjectionIndexes a projection.
+     * @return parquet physical index if found and null otherwise.
+     */
+    private Integer addDataProjectionField( int field, Map<Integer, Integer> parquetFieldProjectionIndexes ) {
+        ParquetColumnBinding binding = source.binding( field );
+        if ( binding == null || binding.role() != ParquetColumnRole.DATA || binding.sourcePathElements().size() != 1 ) {
+            return null;
+        }
+        int parquetField = source.parquetFieldIndex( binding.sourcePathElements().get( 0 ) );
+        if ( parquetField < 0 ) {
+            return null;
+        }
+        return parquetFieldProjectionIndexes.computeIfAbsent( parquetField, ignored -> parquetFieldProjectionIndexes.size() );
+    }
+
+
+    /**
+     * Adds filter indexes to a projection. This is required in order for a filter column to be available during the reading operation.
+     *
+     * @param filters a list of filters.
+     * @param parquetFieldProjectionIndexes a projection.
+     * @return true if the index was added and false otherwise.
+     */
+    private boolean addFilterProjectionFields( List<ParquetAdapterFilter<PolyValue>> filters, Map<Integer, Integer> parquetFieldProjectionIndexes ) {
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            if ( !addFilterProjectionField( filter, parquetFieldProjectionIndexes, true ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Adds one filter to the reader projection unless file metadata can evaluate it exactly.
+     *
+     * @param filter a filter.
+     * @param parquetFieldProjectionIndexes a projection.
+     * @param canSkipExact whether an exact file-level filter can be omitted in this logical context.
+     * @return true if all required fields were added and false otherwise.
+     */
+    private boolean addFilterProjectionField( ParquetAdapterFilter<PolyValue> filter, Map<Integer, Integer> parquetFieldProjectionIndexes, boolean canSkipExact ) {
+        if ( canSkipExact && supportsExactFileFilter( filter ) ) {
+            return true;
+        }
+        if ( filter.isLogical() ) {
+            boolean childCanSkipExact = canSkipExact && filter.operator() == Kind.AND;
+            for ( ParquetAdapterFilter<PolyValue> operand : filter.operands() ) {
+                if ( !addFilterProjectionField( operand, parquetFieldProjectionIndexes, childCanSkipExact ) ) {
+                    return false;
+                }
+            }
+            return true;
+        }
+        if ( isPartitionColumn( filter.columnIndex() ) ) {
+            return true;
+        }
+        return addDataProjectionField( filter.columnIndex(), parquetFieldProjectionIndexes ) != null;
+    }
+
+
+    /**
+     * Validates if all filters are supported.
+     *
+     * @param filters a list of filters to validate.
+     * @return true if all filters are supported and false otherwise.
+     */
+    private boolean supportsAggregateReaderFilters( List<ParquetAdapterFilter<PolyValue>> filters ) {
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            if ( !supportsAggregateReaderFilter( filter, false ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Validates if the filter is supported by this executor.
+     *
+     * @param filter a filter to validate.
+     * @param insideNonAnd indicates whether a filter is not inside the logical AND filter. This is mostly relevant for partition filters.
+     * Only AND logical operator is supported between the partition filters.
+     * @return true if the filter is supported and false otherwise.
+     */
+    private boolean supportsAggregateReaderFilter( ParquetAdapterFilter<PolyValue> filter, boolean insideNonAnd ) {
+        if ( filter.isLogical() ) {
+            boolean childInsideNonAnd = insideNonAnd || filter.operator() != Kind.AND;
+            for ( ParquetAdapterFilter<PolyValue> operand : filter.operands() ) {
+                if ( !supportsAggregateReaderFilter( operand, childInsideNonAnd ) ) {
+                    return false;
+                }
+            }
+            return true;
+        }
+        if ( isPartitionColumn( filter.columnIndex() ) ) {
+            return !insideNonAnd;
+        }
+        return isFlatDataColumn( filter.columnIndex() );
+    }
+
+
+    /**
+     * Validates if the field is a data field.
+     *
+     * @param field a field index.
+     * @return true if the field is a data field and false otherwise.
+     */
+    private boolean isFlatDataColumn( int field ) {
+        ParquetColumnBinding binding = source.binding( field );
+        return binding != null && binding.role() == ParquetColumnRole.DATA && binding.sourcePathElements().size() == 1;
+    }
+
+
+    /**
+     * Validates if the aggregated function type is supported by this executor.
+     *
+     * @param field a field index.
+     * @param aggregateKind an aggregated function type.
+     * @return true if supported and false otherwise.
+     */
+    private boolean supportsGroupedAggregateCall( int field, String aggregateKind ) {
+        if ( Kind.COUNT.name().equals( aggregateKind ) ) {
+            return true;
+        }
+        if ( !isNumericAggregateKind( aggregateKind ) ) {
+            return false;
+        }
+        return PolyType.NUMERIC_TYPES.contains( source.fieldType( field ) );
+    }
+
+
+    /**
+     * Attempts to build a grouped aggregate projection. The main criteria is:
+     * 1. Filters must be applied only on data columns or partition columns. If a filter column is a partition column only AND logical filter can be applied on top of it.
+     * 2. aggregation function is COUNT(*), COUNT(col), SUM(col), MIN(col) or MAX(col).
+     * 3. aggregation column must be numeric non-repeated.
+     *
+     * @param fields an array of field indexes.
+     * @param filters a list of filters.
+     * @param groupFields an array of group by field indexes.
+     * @param aggregateKinds an array of aggregation functions types.
+     * @param aggregateArgs an array of aggregation functions arguments. One argument per aggregation function and should be aligned with aggregateKinds.
+     * @return a grouped aggregate projection if successful or null otherwise.
+     */
+    private GroupAggregateProjection tryBuildGroupedAggregateProjection( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        if ( aggregateKinds.length != aggregateArgs.length ) {
+            return null;
+        }
+        if ( !supportsAggregateReaderFilters( filters ) ) {
+            return null;
+        }
+
+        Map<Integer, Integer> parquetFieldProjectionIndexes = new LinkedHashMap<>();
+        for ( int groupField : groupFields ) {
+            if ( groupField < 0 || groupField >= fields.length ) {
+                return null;
+            }
+            Integer projectionIndex = addDataProjectionField( fields[groupField], parquetFieldProjectionIndexes );
+            if ( projectionIndex == null ) {
+                return null;
+            }
+        }
+
+        AggregateCallDescriptor[] aggregateCalls = new AggregateCallDescriptor[aggregateKinds.length];
+        for ( int i = 0; i < aggregateKinds.length; i++ ) {
+            int aggregateArg = aggregateArgs[i];
+            if ( aggregateArg == AggregateCallDescriptor.NO_ARGUMENT ) {
+                if ( !Kind.COUNT.name().equals( aggregateKinds[i] ) ) {
+                    return null;
+                }
+                aggregateCalls[i] = AggregateCallDescriptor.of( aggregateKinds[i], AggregateCallDescriptor.NO_ARGUMENT );
+                continue;
+            }
+            if ( aggregateArg < 0 || aggregateArg >= fields.length ) {
+                return null;
+            }
+            if ( !supportsGroupedAggregateCall( fields[aggregateArg], aggregateKinds[i] ) ) {
+                return null;
+            }
+            Integer projectionIndex = addDataProjectionField( fields[aggregateArg], parquetFieldProjectionIndexes );
+            if ( projectionIndex == null ) {
+                return null;
+            }
+            aggregateCalls[i] = AggregateCallDescriptor.of( aggregateKinds[i], projectionIndex );
+        }
+
+        if ( !addFilterProjectionFields( filters, parquetFieldProjectionIndexes ) ) {
+            return null;
+        }
+        int[] projectedFields = parquetFieldProjectionIndexes.keySet().stream().mapToInt( Integer::intValue ).toArray();
+        if ( projectedFields.length == 0 || !ParquetGroupedAggregateReader.supports( source.schemaReader().buildProjectionSchema( projectedFields ) ) ) {
+            return null;
+        }
+        return new GroupAggregateProjection( projectedFields, groupFields.length, aggregateCalls );
+    }
+
+
+    /**
+     * Attempts to build grouped count projection. The main criteria is:
+     * 1. fields must be numeric
+     * 2. aggregation function is COUNT(*)
+     * 3. filters are empty or only partition filters.
+     * 4. aggregation column is a simple not nested data column (not a partition, not a key).
+     *
+     * @param fields an array of field indexes.
+     * @param filters a list of filters.
+     * @param groupFields an array of group by field indexes.
+     * @param aggregateKinds an array of aggregation functions types.
+     * @param aggregateArgs an array of aggregation functions arguments. One argument per aggregation function and should be aligned with aggregateKinds.
+     * @return a grouped count projection if successful or null otherwise.
+     */
+    private int[] tryBuildGroupedCountProjection( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        if ( aggregateKinds.length == 0 || aggregateKinds.length != aggregateArgs.length ) {
+            return null;
+        }
+        for ( int i = 0; i < aggregateKinds.length; i++ ) {
+            if ( !Kind.COUNT.name().equals( aggregateKinds[i] ) || aggregateArgs[i] >= 0 ) {
+                return null;
+            }
+        }
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            if ( !isPartitionFilter( filter ) ) {
+                return null;
+            }
+        }
+
+        int[] projectedFields = new int[groupFields.length];
+        for ( int i = 0; i < groupFields.length; i++ ) {
+            int groupField = groupFields[i];
+            if ( groupField < 0 || groupField >= fields.length ) {
+                return null;
+            }
+            ParquetColumnBinding binding = source.binding( fields[groupField] );
+            if ( binding == null || binding.role() != ParquetColumnRole.DATA || binding.sourcePathElements().size() != 1 ) {
+                return null;
+            }
+            int parquetField = source.parquetFieldIndex( binding.sourcePathElements().get( 0 ) );
+            if ( parquetField < 0 ) {
+                return null;
+            }
+            projectedFields[i] = parquetField;
+        }
+        if ( !ParquetGroupedAggregateReader.supports( source.schemaReader().buildProjectionSchema( projectedFields ) ) ) {
+            return null;
+        }
+        return projectedFields;
+    }
+
+
+    /**
+     * This method attempts to build aggregate projection. In order for the aggregate projection to be built the following conditions should apply:
+     * 1. All group by fields are file-constant fields.
+     * 2. All filters can be evaluated exactly for a complete source file.
+     * 3. Aggregated columns are data columns with numeric type and does not belong to a nested table.
+     *
+     * @param fields an array of field indexes.
+     * @param filters a list of filters.
+     * @param groupFields an array of group by field indexes.
+     * @param aggregateKinds an array of aggregation functions types.
+     * @param aggregateArgs an array of aggregated column indexes.
+     * @return {@link ColumnAggregateProjection} if fast aggregation can be applied and {@code null} otherwise.
+     */
+    private ColumnAggregateProjection tryBuildAggregateProjection( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        if ( aggregateKinds.length == 0 || aggregateKinds.length != aggregateArgs.length ) {
+            return null;
+        }
+        for ( int groupField : groupFields ) {
+            if ( groupField < 0 || groupField >= fields.length || !isFileConstantColumn( fields[groupField] ) ) {
+                return null;
+            }
+        }
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            if ( !supportsExactFileFilter( filter ) ) {
+                return null;
+            }
+        }
+
+        Map<Integer, Integer> parquetFieldProjectionIndexes = new LinkedHashMap<>();
+        Map<Integer, Integer> aggregateProjectionIndexes = new LinkedHashMap<>();
+        for ( int i = 0; i < aggregateArgs.length; i++ ) {
+            int aggregateArg = aggregateArgs[i];
+            if ( aggregateArg < 0 ) {
+                if ( !Kind.COUNT.name().equals( aggregateKinds[i] ) ) {
+                    return null;
+                }
+                continue;
+            }
+            if ( aggregateArg >= fields.length ) {
+                return null;
+            }
+            if ( !supportsGroupedAggregateCall( fields[aggregateArg], aggregateKinds[i] ) ) {
+                return null;
+            }
+            ParquetColumnBinding binding = source.binding( fields[aggregateArg] );
+            if ( binding == null || binding.role() != ParquetColumnRole.DATA || binding.sourcePathElements().size() != 1 ) {
+                return null;
+            }
+            int parquetField = source.parquetFieldIndex( binding.sourcePathElements().get( 0 ) );
+            if ( parquetField < 0 ) {
+                return null;
+            }
+            int projectionIndex = parquetFieldProjectionIndexes.computeIfAbsent( parquetField, ignored -> parquetFieldProjectionIndexes.size() );
+            aggregateProjectionIndexes.put( aggregateArg, projectionIndex );
+        }
+        int[] projectedFields = parquetFieldProjectionIndexes.keySet().stream().mapToInt( Integer::intValue ).toArray();
+        if ( projectedFields.length > 0 && !ParquetNoFilterColumnAggregateReader.supports( source.schemaReader().buildProjectionSchema( projectedFields ) ) ) {
+            return null;
+        }
+        return new ColumnAggregateProjection( projectedFields, aggregateProjectionIndexes );
+    }
+
+
+    /**
+     * Checks whether one field has a constant value in every source file.
+     *
+     * @param field a physical table field.
+     * @return true when each file has one value for this field.
+     */
+    private boolean isFileConstantColumn( int field ) {
+        ParquetColumnBinding binding = source.binding( field );
+        return source.sourceFiles().stream().allMatch( sourceFile -> constantColumnResolver.resolve( sourceFile, binding ).isPresent() );
+    }
+
+
+    /**
+     * Checks whether a filter can be evaluated exactly for each complete source file.
+     *
+     * @param filter a filter.
+     * @return true when the fast file aggregate path can apply the filter.
+     */
+    private boolean supportsExactFileFilter( ParquetAdapterFilter<PolyValue> filter ) {
+        if ( filter.isLogical() ) {
+            if ( filter.operator() == Kind.NOT ) {
+                return filter.operands().size() == 1 && supportsExactFileFilter( filter.operands().get( 0 ) );
+            }
+            if ( filter.operator() != Kind.AND && filter.operator() != Kind.OR ) {
+                return false;
+            }
+            return !filter.operands().isEmpty() && filter.operands().stream().allMatch( this::supportsExactFileFilter );
+        }
+        if ( isPartitionColumn( filter.columnIndex() ) ) {
+            return true;
+        }
+        return source.sourceFiles().stream().allMatch( sourceFile -> statisticsFilterEvaluator.supportsExactEvaluation( sourceFile, filter ) );
+    }
+
+
+    /**
+     * Checks whether all provided aggregate functions are supported. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
+     *
+     * @param fields an array of field indexes.
+     * @param groupSet a set of group by fields.
+     * @param aggregateCalls a list of aggregation functions to validate.
+     * @return {@code true} if all provided aggregation functions can be applied and {@code false} otherwise.
+     */
+    public boolean supportsDataAggregate( int[] fields, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
+        for ( int groupField : groupSet.asList() ) {
+            if ( groupField < 0 || groupField >= fields.length ) {
+                return false;
+            }
+        }
+        for ( AggregateCall aggregateCall : aggregateCalls ) {
+            if ( !supportsDataAggregateCall( fields, aggregateCall ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Checks whether all provided aggregate functions are supported. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
+     *
+     * @param fields an array of field indexes.
+     * @param filters a list of filters.
+     * @param groupFields an array of group field indexes.
+     * @param aggregateKinds an array of aggregate functions.
+     * @param aggregateArgs an array of aggregate functions argument indexes.
+     * @return {@code true} if all provided aggregation functions can be applied and {@code false} otherwise.
+     */
+    public boolean supportsDataAggregate( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        return tryBuildAggregateProjection( fields, filters, groupFields, aggregateKinds, aggregateArgs ) != null
+                || tryBuildGroupedCountProjection( fields, filters, groupFields, aggregateKinds, aggregateArgs ) != null
+                || tryBuildGroupedAggregateProjection( fields, filters, groupFields, aggregateKinds, aggregateArgs ) != null
+                || fallbackRows != null;
+    }
+
+
+    /**
+     * Checks whether specified aggregation function is supported. Currently only COUNT, SUM, MIN and MAX for the numeric types are supported.
+     *
+     * @param fields an array of field indexes.
+     * @param aggregateCall an aggregation function to validate.
+     * @return {@code true} if the aggregation function is supported and {@code false} otherwise.
+     */
+    private boolean supportsDataAggregateCall( int[] fields, AggregateCall aggregateCall ) {
+        if ( aggregateCall.isDistinct() || aggregateCall.isApproximate() || aggregateCall.filterArg >= 0 ) {
+            return false;
+        }
+        Kind kind = aggregateCall.getAggregation().getKind();
+        if ( kind == Kind.COUNT ) {
+            if ( aggregateCall.getArgList().isEmpty() ) {
+                return true;
+            }
+            if ( aggregateCall.getArgList().size() != 1 ) {
+                return false;
+            }
+            int inputIndex = aggregateCall.getArgList().get( 0 );
+            return inputIndex >= 0 && inputIndex < fields.length;
+        }
+        if ( !isNumericAggregateKind( kind.name() ) ) {
+            return false;
+        }
+        if ( aggregateCall.getArgList().size() != 1 ) {
+            return false;
+        }
+        int inputIndex = aggregateCall.getArgList().get( 0 );
+        return inputIndex >= 0 && inputIndex < fields.length && PolyType.NUMERIC_TYPES.contains( source.fieldType( fields[inputIndex] ) );
+    }
+
+
+    /**
+     * Checks if the specified filter is a partition filter.
+     *
+     * @param filter a filter to check.
+     * @return {@code true} if the provided filter is a partition filter and {@code false} otherwise.
+     */
+    private boolean isPartitionFilter( ParquetAdapterFilter<PolyValue> filter ) {
+        if ( filter.isLogical() ) {
+            return filter.operands().stream().allMatch( this::isPartitionFilter );
+        }
+        return isPartitionColumn( filter.columnIndex() );
+    }
+
+
+    /**
+     * Checks if the specified filter is a partition filter.
+     *
+     * @param columnIndex a column index of the filter.
+     * @return {@code true} if the provided filter is a partition filter and {@code false} otherwise.
+     */
+    private boolean isPartitionColumn( int columnIndex ) {
+        if ( columnIndex < 0 || columnIndex >= source.fieldCount() ) {
+            return false;
+        }
+        ParquetColumnBinding columnBinding = source.binding( columnIndex );
+        return columnBinding != null && columnBinding.role() == ParquetColumnRole.PARTITION;
+    }
+
+
+    private boolean isNumericAggregateKind( String aggregateKind ) {
+        return Kind.SUM.name().equals( aggregateKind )
+                || Kind.MIN.name().equals( aggregateKind )
+                || Kind.MAX.name().equals( aggregateKind );
+    }
+
+
+    private record GroupAggregateProjection( int[] fields, int groupFieldCount, AggregateCallDescriptor[] aggregateCalls ) {
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetFileGroupedAggregateRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetFileGroupedAggregateEnumerator.java
similarity index 78%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetFileGroupedAggregateRelEnumerator.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetFileGroupedAggregateEnumerator.java
index ae6f942757..c66fa5a53c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetFileGroupedAggregateRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetFileGroupedAggregateEnumerator.java
@@ -14,9 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.execution.aggregate;
-
-import static org.polypheny.db.adapter.parquet.relational.execution.ParquetRelExecutor.selectPhysicalBinding;
+package org.polypheny.db.adapter.parquet.shared.execution.aggregate;
 
 import java.util.LinkedHashMap;
 import java.util.List;
@@ -24,34 +22,33 @@
 import java.util.concurrent.atomic.AtomicBoolean;
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.calcite.linq4j.Linq4j;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelExecutor;
 import org.polypheny.db.adapter.parquet.relational.filter.ParquetMultiFilterEvaluator;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetConstantColumnResolver;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
 import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
 import org.polypheny.db.adapter.parquet.shared.aggregate.ColumnAggregateProjection;
 import org.polypheny.db.adapter.parquet.shared.aggregate.ColumnAggregateResult;
+import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractAggregateEnumerator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.io.aggregate.ParquetNoFilterColumnAggregateReader;
-import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
 import org.polypheny.db.type.entity.PolyValue;
 
+
 /**
- * This is the file-aware aggregate path. It groups from file-constant values and uses the no-filter column aggregate reader for data columns.
+ * File-aware aggregate path. It groups from file-constant values and uses column aggregate readers for data columns.
  */
-public class ParquetFileGroupedAggregateRelEnumerator extends AbstractAggregateEnumerator {
+public class ParquetFileGroupedAggregateEnumerator extends AbstractAggregateEnumerator {
 
-    private static final ParquetConstantColumnResolver constantColumnResolver = new ParquetConstantColumnResolver();
+    private static final ParquetConstantColumnResolver CONSTANT_COLUMN_RESOLVER = new ParquetConstantColumnResolver();
 
 
     /**
      * Creates a new instance of fast aggregate enumerator.
      *
-     * @param table a source table.
+     * @param source an aggregate source.
      * @param fields an array of field indexes.
      * @param groupFields an array of group by field indexes.
      * @param aggregateKinds an array of aggregate function kinds: COUNT, SUM, MIN, MAX.
@@ -60,15 +57,15 @@ public class ParquetFileGroupedAggregateRelEnumerator extends AbstractAggregateE
      * @param filters a list of filters.
      * @param cancelFlag a cancellation flag.
      */
-    public ParquetFileGroupedAggregateRelEnumerator( ParquetRelTable table, int[] fields, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs, ColumnAggregateProjection projection, List<ParquetAdapterFilter<PolyValue>> filters, AtomicBoolean cancelFlag ) {
-        super( () -> buildEnumerator( table, fields, groupFields, aggregateKinds, aggregateArgs, projection, filters, cancelFlag ) );
+    public ParquetFileGroupedAggregateEnumerator( ParquetAggregateSource source, int[] fields, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs, ColumnAggregateProjection projection, List<ParquetAdapterFilter<PolyValue>> filters, AtomicBoolean cancelFlag ) {
+        super( () -> buildEnumerator( source, fields, groupFields, aggregateKinds, aggregateArgs, projection, filters, cancelFlag ) );
     }
 
 
     /**
      * Reads data from a single source file.
      *
-     * @param table a parquet table
+     * @param source an aggregate source.
      * @param sourceFile a source file
      * @param fields projected field indexes
      * @param groupFields group by fields indexes
@@ -76,9 +73,9 @@ public ParquetFileGroupedAggregateRelEnumerator( ParquetRelTable table, int[] fi
      * @param cancelFlag a cancel operation flag
      * @return aggregated data
      */
-    private static Map<GroupKey, AggregateGroupState> readAll( ParquetRelTable table, ParquetSourceFile sourceFile, int[] fields, int[] groupFields, AggregateCallDescriptor[] aggregateCalls, ColumnAggregateProjection projection, AtomicBoolean cancelFlag ) {
+    private static Map<GroupKey, AggregateGroupState> readAll( ParquetAggregateSource source, ParquetSourceFile sourceFile, int[] fields, int[] groupFields, AggregateCallDescriptor[] aggregateCalls, ColumnAggregateProjection projection, AtomicBoolean cancelFlag ) {
         Map<GroupKey, AggregateGroupState> aggregates = new LinkedHashMap<>();
-        GroupKey groupKey = fileGroupKey( table, sourceFile, fields, groupFields );
+        GroupKey groupKey = fileGroupKey( source, sourceFile, fields, groupFields );
         AggregateGroupState groupAggregates = aggregates.computeIfAbsent( groupKey, key -> new AggregateGroupState( aggregateCalls ) );
         addCountStarRows( sourceFile, aggregateCalls, groupAggregates );
 
@@ -153,17 +150,17 @@ private static void addCountStarRows( ParquetSourceFile sourceFile, AggregateCal
     /**
      * Creates a group key based on file-constant values.
      *
-     * @param table a source table.
+     * @param source an aggregate source.
      * @param sourceFile a source file.
      * @param fields an array of field indexes.
      * @param groupFields an array of group by field indexes.
      * @return returns a list of {@link PolyValue}s representing a key.
      */
-    private static GroupKey fileGroupKey( ParquetRelTable table, ParquetSourceFile sourceFile, int[] fields, int[] groupFields ) {
+    private static GroupKey fileGroupKey( ParquetAggregateSource source, ParquetSourceFile sourceFile, int[] fields, int[] groupFields ) {
         Object[] key = new Object[groupFields.length];
         for ( int i = 0; i < groupFields.length; i++ ) {
-            ParquetColumnBinding binding = selectPhysicalBinding( table, fields[groupFields[i]] );
-            key[i] = constantColumnResolver
+            ParquetColumnBinding binding = source.binding( fields[groupFields[i]] );
+            key[i] = CONSTANT_COLUMN_RESOLVER
                     .resolve( sourceFile, binding )
                     .orElseThrow( () -> new IllegalStateException( "Group column is not constant for " + sourceFile.fileUrl() ) );
         }
@@ -193,11 +190,11 @@ private static boolean matchesExactly( ParquetMultiFilterEvaluator<ParquetSource
     }
 
 
-    private static Enumerator<PolyValue[]> buildEnumerator( ParquetRelTable table, int[] fields, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs, ColumnAggregateProjection projection, List<ParquetAdapterFilter<PolyValue>> filters, AtomicBoolean cancelFlag ) {
+    private static Enumerator<PolyValue[]> buildEnumerator( ParquetAggregateSource source, int[] fields, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs, ColumnAggregateProjection projection, List<ParquetAdapterFilter<PolyValue>> filters, AtomicBoolean cancelFlag ) {
         AggregateCallDescriptor[] aggregateCalls = aggregateCalls( aggregateKinds, aggregateArgs );
-        ParquetMultiFilterEvaluator<ParquetSourceFile> fileFilterEvaluator = ParquetRelExecutor.createParquetSourceFileEvaluatorsChain(f -> selectPhysicalBinding( table, f.columnIndex() ) );
+        ParquetMultiFilterEvaluator<ParquetSourceFile> fileFilterEvaluator = ParquetDataAggregateExecutor.createParquetSourceFileEvaluatorsChain( filter -> source.binding( filter.columnIndex() ) );
 
-        List<ParquetSourceFile> sourceFiles = table.getBinding().sourceFiles().stream()
+        List<ParquetSourceFile> sourceFiles = source.sourceFiles().stream()
                 .filter( sourceFile -> matchesExactly( fileFilterEvaluator, sourceFile, filters ) )
                 .toList();
 
@@ -206,7 +203,7 @@ private static Enumerator<PolyValue[]> buildEnumerator( ParquetRelTable table, i
             return Linq4j.asEnumerable( buildRows( groupFields.length, aggregates, aggregateCalls ) ).enumerator();
         }
 
-        Map<GroupKey, AggregateGroupState> aggregates = readAll( sourceFiles, ( f ) -> readAll( table, f, fields, groupFields, aggregateCalls, projection, cancelFlag ), aggregateCalls, cancelFlag );
+        Map<GroupKey, AggregateGroupState> aggregates = readAll( sourceFiles, f -> readAll( source, f, fields, groupFields, aggregateCalls, projection, cancelFlag ), aggregateCalls, cancelFlag );
         return Linq4j.asEnumerable( buildRows( groupFields.length, aggregates, aggregateCalls ) ).enumerator();
     }
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetGroupedAggregateRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetGroupedAggregateEnumerator.java
similarity index 80%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetGroupedAggregateRelEnumerator.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetGroupedAggregateEnumerator.java
index 722c472650..001afd331c 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetGroupedAggregateRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetGroupedAggregateEnumerator.java
@@ -14,9 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.execution.aggregate;
-
-import static org.polypheny.db.adapter.parquet.relational.execution.ParquetRelExecutor.selectPhysicalBinding;
+package org.polypheny.db.adapter.parquet.shared.execution.aggregate;
 
 import java.util.ArrayList;
 import java.util.LinkedHashMap;
@@ -27,29 +25,28 @@
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.calcite.linq4j.Linq4j;
 import org.apache.parquet.schema.MessageType;
-import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelExecutor;
-import org.polypheny.db.adapter.parquet.relational.filter.ResidualFilters;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
 import org.polypheny.db.adapter.parquet.relational.filter.ParquetMultiFilterEvaluator;
 import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFileFilterReducer;
-import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
-import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
+import org.polypheny.db.adapter.parquet.relational.filter.ResidualFilters;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
 import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
+import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
 import org.polypheny.db.adapter.parquet.shared.execution.AbstractAggregateEnumerator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
-import org.polypheny.db.adapter.parquet.shared.io.aggregate.ParquetGroupedAggregateReader;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
-import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
+import org.polypheny.db.adapter.parquet.shared.io.aggregate.ParquetGroupedAggregateReader;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
 import org.polypheny.db.type.entity.PolyValue;
 import org.polypheny.db.util.Source;
 
 
 /**
- * SQL GROUP BY over real data columns (not partition columns).
+ * GROUP BY over real Parquet data columns.
  */
-public class ParquetGroupedAggregateRelEnumerator extends AbstractAggregateEnumerator {
+public class ParquetGroupedAggregateEnumerator extends AbstractAggregateEnumerator {
 
     private static final ParquetTypeConverter TYPE_CONVERTER = new ParquetTypeConverter();
 
@@ -57,15 +54,15 @@ public class ParquetGroupedAggregateRelEnumerator extends AbstractAggregateEnume
     /**
      * Creates a new instance of an enumerator.
      *
-     * @param table a relational table reference.
+     * @param source an aggregate source.
      * @param projectedFields an array of projected field indexes.
      * @param groupFieldCount a number of groups in Group By statement.
      * @param aggregateCalls an array of aggregate function descriptors.
      * @param filters a list of filters to be applied.
      * @param cancelFlag a flag indicating that the operation has been canceled.
      */
-    public ParquetGroupedAggregateRelEnumerator( ParquetRelTable table, int[] projectedFields, int groupFieldCount, AggregateCallDescriptor[] aggregateCalls, List<ParquetAdapterFilter<PolyValue>> filters, AtomicBoolean cancelFlag ) {
-        super( () -> buildEnumerator( table, projectedFields, groupFieldCount, aggregateCalls, filters, cancelFlag ) );
+    public ParquetGroupedAggregateEnumerator( ParquetAggregateSource source, int[] projectedFields, int groupFieldCount, AggregateCallDescriptor[] aggregateCalls, List<ParquetAdapterFilter<PolyValue>> filters, AtomicBoolean cancelFlag ) {
+        super( () -> buildEnumerator( source, projectedFields, groupFieldCount, aggregateCalls, filters, cancelFlag ) );
     }
 
 
@@ -142,16 +139,15 @@ private static PolyValue[] buildRow( GroupKey key, AggregateGroupState values, M
     /**
      * Converts filter indexes from relative to projection to physical parquet indexes.
      *
-     * @param table a relation table reference.
-     * @param schemaReader a schema reader.
+     * @param source an aggregate source.
      * @param projectedFields a projected field indexes.
      * @param filters filters to convert.
      * @return a converted list of filters with physical parquet indexes.
      */
-    private static List<ParquetAdapterFilter<PolyValue>> readerFilters( ParquetRelTable table, ParquetSchemaReader schemaReader, int[] projectedFields, List<ParquetAdapterFilter<PolyValue>> filters ) {
+    private static List<ParquetAdapterFilter<PolyValue>> readerFilters( ParquetAggregateSource source, int[] projectedFields, List<ParquetAdapterFilter<PolyValue>> filters ) {
         List<ParquetAdapterFilter<PolyValue>> mapped = new ArrayList<>( filters.size() );
         for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
-            ParquetAdapterFilter<PolyValue> projectionFilter = ParquetFilterResolver.toProjectionFilter( filter, field -> readerIndex( table, schemaReader, projectedFields, field ) );
+            ParquetAdapterFilter<PolyValue> projectionFilter = ParquetFilterResolver.toProjectionFilter( filter, field -> readerIndex( source, projectedFields, field ) );
             if ( projectionFilter == null ) {
                 throw new IllegalArgumentException( "Unable to map grouped aggregate filter to its parquet projection." );
             }
@@ -164,14 +160,17 @@ private static List<ParquetAdapterFilter<PolyValue>> readerFilters( ParquetRelTa
     /**
      * Converts a projected index into a physical parquet file index.
      *
-     * @param table a relational table reference.
-     * @param schemaReader a schema reader.
+     * @param source an aggregate source.
      * @param projectedFields projected field indexes.
      * @param field a projected field index to convert.
      * @return a physical parquet index.
      */
-    private static int readerIndex( ParquetRelTable table, ParquetSchemaReader schemaReader, int[] projectedFields, int field ) {
-        int parquetField = ParquetRelExecutor.parquetFieldIndex( table, schemaReader, field );
+    private static int readerIndex( ParquetAggregateSource source, int[] projectedFields, int field ) {
+        ParquetColumnBinding binding = source.binding( field );
+        if ( binding == null || binding.sourcePathElements().size() != 1 ) {
+            return -1;
+        }
+        int parquetField = source.parquetFieldIndex( binding.sourcePathElements().get( 0 ) );
         for ( int i = 0; i < projectedFields.length; i++ ) {
             if ( projectedFields[i] == parquetField ) {
                 return i;
@@ -181,34 +180,18 @@ private static int readerIndex( ParquetRelTable table, ParquetSchemaReader schem
     }
 
 
-    /**
-     * A source file with projection.
-     *
-     * @param file a source file.
-     * @param projection a projection.
-     * @param readerFilters native filters for a reader.
-     */
-    private record ProjectedSourceFile( ParquetSourceFile file, MessageType projection, List<ParquetAdapterFilter<PolyValue>> readerFilters ) {
-
-        public Source source() {
-            return file.asSource();
-        }
-
-    }
-
-
     /**
      * Enriches {@link ParquetSourceFile} with projection and filters if available.
      * The method also evaluates file level filters and can discard the file.
      *
-     * @param table a relational table.
+     * @param source an aggregate source.
      * @param projectedFields an array of projected indexes.
      * @param sourceFile a source file to enrich and evaluate.
      * @param evaluator a filter evaluator.
      * @param filters a list of filters.
      * @return {@link Optional<ProjectedSourceFile>} if source file is valid and {@link Optional#empty()} otherwise
      */
-    private static Optional<ProjectedSourceFile> projectedSourceFile( ParquetRelTable table, int[] projectedFields, ParquetSourceFile sourceFile, ParquetMultiFilterEvaluator<ParquetSourceFile> evaluator, List<ParquetAdapterFilter<PolyValue>> filters ) {
+    private static Optional<ProjectedSourceFile> projectedSourceFile( ParquetAggregateSource source, int[] projectedFields, ParquetSourceFile sourceFile, ParquetMultiFilterEvaluator<ParquetSourceFile> evaluator, List<ParquetAdapterFilter<PolyValue>> filters ) {
         ResidualFilters residualFilters = ParquetSourceFileFilterReducer.reduce( sourceFile, evaluator, filters );
         if ( !residualFilters.matches() ) {
             return Optional.empty();
@@ -217,15 +200,15 @@ private static Optional<ProjectedSourceFile> projectedSourceFile( ParquetRelTabl
         return Optional.of( new ProjectedSourceFile(
                 sourceFile,
                 schemaReader.buildProjectionSchema( projectedFields ),
-                readerFilters( table, schemaReader, projectedFields, residualFilters.filters() ) ) );
+                readerFilters( source, projectedFields, residualFilters.filters() ) ) );
     }
 
 
-    private static Enumerator<PolyValue[]> buildEnumerator( ParquetRelTable table, int[] projectedFields, int groupFieldCount, AggregateCallDescriptor[] aggregateCalls, List<ParquetAdapterFilter<PolyValue>> filters, AtomicBoolean cancelFlag ) {
-        ParquetMultiFilterEvaluator<ParquetSourceFile> fileFilterEvaluator = ParquetRelExecutor.createParquetSourceFileEvaluatorsChain( f -> selectPhysicalBinding( table, f.columnIndex() ) );
+    private static Enumerator<PolyValue[]> buildEnumerator( ParquetAggregateSource source, int[] projectedFields, int groupFieldCount, AggregateCallDescriptor[] aggregateCalls, List<ParquetAdapterFilter<PolyValue>> filters, AtomicBoolean cancelFlag ) {
+        ParquetMultiFilterEvaluator<ParquetSourceFile> fileFilterEvaluator = ParquetDataAggregateExecutor.createParquetSourceFileEvaluatorsChain( filter -> source.binding( filter.columnIndex() ) );
 
-        List<ProjectedSourceFile> sourceFiles = table.getBinding().sourceFiles().stream()
-                .map( sourceFile -> projectedSourceFile( table, projectedFields, sourceFile, fileFilterEvaluator, filters ) )
+        List<ProjectedSourceFile> sourceFiles = source.sourceFiles().stream()
+                .map( sourceFile -> projectedSourceFile( source, projectedFields, sourceFile, fileFilterEvaluator, filters ) )
                 .flatMap( Optional::stream )
                 .toList();
 
@@ -236,4 +219,20 @@ private static Enumerator<PolyValue[]> buildEnumerator( ParquetRelTable table, i
         return Linq4j.asEnumerable( buildRows( sourceFiles, groupFieldCount, aggregateCalls, aggregates ) ).enumerator();
     }
 
+
+    /**
+     * A source file with projection.
+     *
+     * @param file a source file.
+     * @param projection a projection.
+     * @param readerFilters native filters for a reader.
+     */
+    private record ProjectedSourceFile( ParquetSourceFile file, MessageType projection, List<ParquetAdapterFilter<PolyValue>> readerFilters ) {
+
+        public Source source() {
+            return file.asSource();
+        }
+
+    }
+
 }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetMetadataAggregateExecutor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetMetadataAggregateExecutor.java
new file mode 100644
index 0000000000..34397753d4
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetMetadataAggregateExecutor.java
@@ -0,0 +1,421 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.execution.aggregate;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Optional;
+import org.apache.calcite.linq4j.Enumerable;
+import org.apache.calcite.linq4j.Linq4j;
+import org.polypheny.db.adapter.DataContext;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetFilterResolver;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetMultiFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFilePartitionFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.filter.ParquetSourceFileStatisticsFilterEvaluator;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnStatistics;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetConstantColumnResolver;
+import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
+import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
+import org.polypheny.db.adapter.parquet.shared.schema.ParquetTypeConverter;
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.core.AggregateCall;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
+import org.polypheny.db.type.entity.PolyNull;
+import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.type.entity.numerical.PolyLong;
+import org.polypheny.db.util.ImmutableBitSet;
+
+/**
+ * Executes aggregations that can be done on Parquet metadata only.
+ */
+public class ParquetMetadataAggregateExecutor {
+
+    private final ParquetAggregateSource source;
+    private final ParquetTypeConverter typeConverter;
+    private final ParquetSourceFileStatisticsFilterEvaluator statisticsFilterEvaluator;
+    private final ParquetConstantColumnResolver constantColumnResolver;
+
+
+    public ParquetMetadataAggregateExecutor( ParquetAggregateSource source ) {
+        this.source = source;
+        this.typeConverter = new ParquetTypeConverter();
+        this.statisticsFilterEvaluator = new ParquetSourceFileStatisticsFilterEvaluator( filter -> source.binding( filter.columnIndex() ) );
+        this.constantColumnResolver = new ParquetConstantColumnResolver();
+    }
+
+
+    /**
+     * Calculates rows count based on file statistics. In case statistics are not presented fallback to a schema reader for actual calculation.
+     *
+     * @param sourceFile a source file.
+     * @return a row count.
+     */
+    private static long sourceRowCount( ParquetSourceFile sourceFile ) {
+        Optional<Long> rowCount = sourceFile.columnStatistics().values().stream()
+                .map( ParquetColumnStatistics::rowCount )
+                .findFirst();
+        return rowCount.orElseGet( () -> new ParquetSchemaReader( sourceFile.asSource() ).getEstimatedRowCount() );
+    }
+
+
+    /**
+     * Creates an enumerable over parquet files metadata statistics.
+     *
+     * @param dataContext a data context
+     * @param fields an array of physical field indexes of columns used in a group by statement.
+     * @param filters a list of filters to be applied
+     * @param groupFields an array of group by field indexes in fields.
+     * @param aggregateKinds the names of the aggregation function.
+     * @param aggregateArgs input field indexes used by aggregation functions.
+     * @return an enumerable.
+     */
+    public Enumerable<PolyValue[]> createEnumerator( DataContext dataContext, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        List<ParquetAdapterFilter<PolyValue>> resolvedFilters = ParquetFilterResolver.resolveFilters( dataContext, filters, filter -> source.binding( filter.columnIndex() ) );
+        ParquetMultiFilterEvaluator<ParquetSourceFile> multiFilterEvaluator = new ParquetMultiFilterEvaluator<>(
+                new ParquetSourceFilePartitionFilterEvaluator( filter -> source.binding( filter.columnIndex() ) ),
+                statisticsFilterEvaluator
+        );
+
+        Map<List<PolyValue>, MetadataAggregateAccumulator[]> aggregates = new LinkedHashMap<>();
+        for ( ParquetSourceFile sourceFile : source.sourceFiles() ) {
+            if ( !matchesExactly( multiFilterEvaluator, sourceFile, resolvedFilters ) ) {
+                continue;
+            }
+            List<PolyValue> groupKey = fileGroupKey( sourceFile, fields, groupFields );
+            MetadataAggregateAccumulator[] groupAggregates = aggregates.computeIfAbsent( groupKey, key -> createAccumulators( aggregateKinds ) );
+            for ( int i = 0; i < groupAggregates.length; i++ ) {
+                groupAggregates[i].add( sourceFile, fields, aggregateArgs[i] );
+            }
+        }
+
+        if ( groupFields.length == 0 && aggregates.isEmpty() ) {
+            aggregates.put( List.of(), createAccumulators( aggregateKinds ) );
+        }
+
+        List<PolyValue[]> rows = new ArrayList<>( aggregates.size() );
+        for ( Map.Entry<List<PolyValue>, MetadataAggregateAccumulator[]> entry : aggregates.entrySet() ) {
+            PolyValue[] row = new PolyValue[entry.getKey().size() + aggregateKinds.length];
+            for ( int i = 0; i < entry.getKey().size(); i++ ) {
+                row[i] = entry.getKey().get( i );
+            }
+            for ( int i = 0; i < aggregateKinds.length; i++ ) {
+                row[entry.getKey().size() + i] = entry.getValue()[i].result();
+            }
+            rows.add( row );
+        }
+        return Linq4j.asEnumerable( rows );
+    }
+
+
+    /**
+     * Checks whether filters, groups and aggregate functions can be executed on metadata only.
+     *
+     * @param fields an array of physical field indexes of columns used in a group by statement.
+     * @param filters a list of filters to be validated.
+     * @param groupSet a group by set.
+     * @param aggregateCalls a list of aggregate calls (aggregate functions)
+     * @return {@code true} filters, groups and aggregate functions can be executed on metadata only and {@code false} otherwise.
+     */
+    public boolean supportsMetadataAggregate( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
+        for ( int groupField : groupSet.asList() ) {
+            if ( groupField < 0 || groupField >= fields.length || !isFileConstantColumn( fields[groupField] ) ) {
+                return false;
+            }
+        }
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            if ( !supportsMetadataFilter( filter ) ) {
+                return false;
+            }
+        }
+        for ( AggregateCall aggregateCall : aggregateCalls ) {
+            if ( !supportsMetadataAggregateCall( fields, aggregateCall ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Checks whether a filter can be evaluated exactly from partition values or
+     * file-level statistics. Statistics filters are limited to file-constant
+     * physical columns so retained files can contribute their full metadata
+     * row count without reading data pages.
+     *
+     * @param filter a filter to validate.
+     * @return {@code true} if the filter can be evaluated exactly for every source file.
+     */
+    private boolean supportsMetadataFilter( ParquetAdapterFilter<PolyValue> filter ) {
+        if ( filter.isLogical() ) {
+            if ( filter.operator() == Kind.NOT ) {
+                return filter.operands().size() == 1 && isPartitionFilter( filter );
+            }
+            if ( filter.operator() != Kind.AND && filter.operator() != Kind.OR ) {
+                return false;
+            }
+            return !filter.operands().isEmpty() && filter.operands().stream().allMatch( this::supportsMetadataFilter );
+        }
+        if ( isPartitionColumn( filter.columnIndex() ) ) {
+            return true;
+        }
+        return source.sourceFiles().stream().allMatch( sourceFile -> statisticsFilterEvaluator.supportsExactEvaluation( sourceFile, filter ) );
+    }
+
+
+    /**
+     * Applies filters that were proven metadata-decidable during planning.
+     *
+     * @param evaluator combines partition-value and file-statistics evaluators.
+     * @param sourceFile a source file.
+     * @param filters filters to apply.
+     * @return {@code true} if the complete file matches the filters.
+     */
+    private boolean matchesExactly( ParquetMultiFilterEvaluator<ParquetSourceFile> evaluator, ParquetSourceFile sourceFile, List<ParquetAdapterFilter<PolyValue>> filters ) {
+        for ( ParquetAdapterFilter<PolyValue> filter : filters ) {
+            Boolean result = evaluator.evaluate( sourceFile, filter );
+            if ( result == null ) {
+                throw new GenericRuntimeException( "Metadata aggregate filter could not be evaluated exactly for " + sourceFile.fileUrl() );
+            }
+            if ( !result ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Creates a joined key from file-constant group by fields.
+     *
+     * @param sourceFile a source file.
+     * @param fields an array of physical field indexes of columns used in a group by statement.
+     * @param groupFields an array of group by field indexes in fields.
+     * @return a list of {@link PolyValue}s representing a key.
+     */
+    private List<PolyValue> fileGroupKey( ParquetSourceFile sourceFile, int[] fields, int[] groupFields ) {
+        List<PolyValue> key = new ArrayList<>( groupFields.length );
+        for ( int groupField : groupFields ) {
+            int physicalField = fields[groupField];
+            ParquetColumnBinding columnBinding = source.binding( physicalField );
+            key.add( constantColumnResolver.resolve( sourceFile, columnBinding )
+                    .orElseThrow( () -> new GenericRuntimeException( "Metadata aggregate group column is not constant for " + sourceFile.fileUrl() ) ) );
+        }
+        return List.copyOf( key );
+    }
+
+
+    /**
+     * Checks whether one field has a constant value in every source file.
+     *
+     * @param field a physical table field.
+     * @return true when each file has one value for this field.
+     */
+    private boolean isFileConstantColumn( int field ) {
+        ParquetColumnBinding binding = source.binding( field );
+        return source.sourceFiles().stream().allMatch( sourceFile -> constantColumnResolver.resolve( sourceFile, binding ).isPresent() );
+    }
+
+
+    /**
+     * Checks if a metadata aggregate call can be calculated from source file statistics.
+     *
+     * @param fields an array of physical field indexes of columns used in a group by statement.
+     * @param aggregateCall an aggregate call to validate.
+     * @return {@code true} if the aggregate call can be calculated from metadata and {@code false} otherwise.
+     */
+    private boolean supportsMetadataAggregateCall( int[] fields, AggregateCall aggregateCall ) {
+        if ( aggregateCall.getAggregation().getKind() == Kind.COUNT ) {
+            if ( aggregateCall.getArgList().isEmpty() ) {
+                return true;
+            }
+            return aggregateCall.getArgList().size() == 1 && supportsCountColumn( fields, aggregateCall.getArgList().get( 0 ) );
+        }
+        if ( aggregateCall.getAggregation().getKind() != Kind.MIN && aggregateCall.getAggregation().getKind() != Kind.MAX ) {
+            return false;
+        }
+        if ( aggregateCall.getArgList().size() != 1 ) {
+            return false;
+        }
+
+        ParquetColumnBinding columnBinding = dataColumnBinding( fields, aggregateCall.getArgList().get( 0 ) );
+        if ( columnBinding == null ) {
+            return false;
+        }
+
+        for ( ParquetSourceFile sourceFile : source.sourceFiles() ) {
+            ParquetColumnStatistics statistics = sourceFile.columnStatistics().get( columnBinding.sourcePathElements() );
+            if ( statistics == null || (!statistics.hasRange() && !statistics.hasOnlyNulls()) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Checks if a COUNT on a column is supported. Column aggregation is supported only if it has statistics and nulls count.
+     *
+     * @param fields an array of field indexes.
+     * @param inputIndex a column index.
+     * @return {@code true} if COUNT can be applied on the specified column and {@code false} otherwise.
+     */
+    private boolean supportsCountColumn( int[] fields, int inputIndex ) {
+        ParquetColumnBinding columnBinding = dataColumnBinding( fields, inputIndex );
+        if ( columnBinding == null ) {
+            return false;
+        }
+
+        for ( ParquetSourceFile sourceFile : source.sourceFiles() ) {
+            ParquetColumnStatistics statistics = sourceFile.columnStatistics().get( columnBinding.sourcePathElements() );
+            if ( statistics == null || statistics.nullCount() == null ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    /**
+     * Gets a binding for the specified column.
+     *
+     * @param fields an array of field indexes.
+     * @param inputIndex a column index.
+     * @return {@link ParquetColumnBinding}.
+     */
+    private ParquetColumnBinding dataColumnBinding( int[] fields, int inputIndex ) {
+        if ( inputIndex < 0 || inputIndex >= fields.length ) {
+            return null;
+        }
+
+        ParquetColumnBinding columnBinding = source.binding( fields[inputIndex] );
+        if ( columnBinding == null || columnBinding.role() != ParquetColumnRole.DATA || columnBinding.sourcePathElements().isEmpty() ) {
+            return null;
+        }
+        return columnBinding;
+    }
+
+
+    /**
+     * Creates an accumulator for each aggregation function.
+     *
+     * @param aggregateKinds the names of aggregation function.
+     * @return an array of aggregate accumulators.
+     */
+    private MetadataAggregateAccumulator[] createAccumulators( String[] aggregateKinds ) {
+        return Arrays.stream( aggregateKinds )
+                .map( MetadataAggregateAccumulator::new )
+                .toArray( MetadataAggregateAccumulator[]::new );
+    }
+
+
+    private boolean isPartitionFilter( ParquetAdapterFilter<PolyValue> filter ) {
+        if ( filter.isLogical() ) {
+            return filter.operands().stream().allMatch( this::isPartitionFilter );
+        }
+        return isPartitionColumn( filter.columnIndex() );
+    }
+
+
+    private boolean isPartitionColumn( int columnIndex ) {
+        if ( columnIndex < 0 || columnIndex >= source.fieldCount() ) {
+            return false;
+        }
+        ParquetColumnBinding columnBinding = source.binding( columnIndex );
+        return columnBinding != null && columnBinding.role() == ParquetColumnRole.PARTITION;
+    }
+
+
+    /**
+     * Holds partial metadata aggregate values for one result group.
+     */
+    private class MetadataAggregateAccumulator {
+
+        private final String aggregateKind;
+        private long count;
+        private ParquetColumnStatistics selectedStatistics;
+        private String selectedValue;
+
+
+        private MetadataAggregateAccumulator( String aggregateKind ) {
+            this.aggregateKind = aggregateKind;
+        }
+
+
+        private void add( ParquetSourceFile sourceFile, int[] fields, int aggregateArg ) {
+            if ( Kind.COUNT.name().equals( aggregateKind ) ) {
+                if ( aggregateArg < 0 ) {
+                    count += sourceRowCount( sourceFile );
+                    return;
+                }
+
+                ParquetColumnStatistics statistics = statistics( sourceFile, fields, aggregateArg );
+                if ( statistics != null && statistics.nullCount() != null ) {
+                    count += Math.max( 0L, statistics.rowCount() - statistics.nullCount() );
+                }
+                return;
+            }
+
+            ParquetColumnStatistics statistics = statistics( sourceFile, fields, aggregateArg );
+            if ( statistics == null || !statistics.hasRange() ) {
+                return;
+            }
+            String candidate = Kind.MIN.name().equals( aggregateKind ) ? statistics.min() : statistics.max();
+            if ( selectedValue == null || compare( statistics, candidate, selectedValue ) ) {
+                selectedStatistics = statistics;
+                selectedValue = candidate;
+            }
+        }
+
+
+        private PolyValue result() {
+            if ( Kind.COUNT.name().equals( aggregateKind ) ) {
+                return PolyLong.of( count );
+            }
+            if ( selectedValue == null || selectedStatistics == null ) {
+                return PolyNull.NULL;
+            }
+            PolyValue value = typeConverter.fromStringToPolyValue( selectedStatistics.type(), selectedValue );
+            return value == null ? PolyNull.NULL : value;
+        }
+
+
+        private boolean compare( ParquetColumnStatistics candidateStatistics, String candidate, String current ) {
+            int comparison = typeConverter.compareStringValues( candidateStatistics.type(), candidate, current );
+            return Kind.MIN.name().equals( aggregateKind ) ? comparison < 0 : comparison > 0;
+        }
+
+
+        private ParquetColumnStatistics statistics( ParquetSourceFile sourceFile, int[] fields, int aggregateArg ) {
+            if ( aggregateArg < 0 || aggregateArg >= fields.length ) {
+                return null;
+            }
+            ParquetColumnBinding columnBinding = source.binding( fields[aggregateArg] );
+            if ( columnBinding == null ) {
+                return null;
+            }
+            return sourceFile.columnStatistics().get( columnBinding.sourcePathElements() );
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregateRelEnumerator.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetRowAggregateEnumerator.java
similarity index 79%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregateRelEnumerator.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetRowAggregateEnumerator.java
index 983e8f75dc..5eedde4a69 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/aggregate/ParquetRowAggregateRelEnumerator.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetRowAggregateEnumerator.java
@@ -14,21 +14,29 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.execution.aggregate;
+package org.polypheny.db.adapter.parquet.shared.execution.aggregate;
 
 import java.util.Map;
 import org.apache.calcite.linq4j.Enumerator;
 import org.apache.calcite.linq4j.Linq4j;
+import org.polypheny.db.adapter.parquet.relational.execution.aggregate.DirectAggregateRowProjector;
+import org.polypheny.db.adapter.parquet.relational.execution.aggregate.ParquetRowAggregator;
 import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
 import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
-import org.polypheny.db.adapter.parquet.shared.execution.AbstractAggregateEnumerator;
 import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
+import org.polypheny.db.adapter.parquet.shared.execution.AbstractAggregateEnumerator;
 import org.polypheny.db.type.entity.PolyValue;
 
+
 /**
- * A generic aggregate enumerator that goes row by row (using PolyValue[] for a row) and aggregates the columns.
+ * Generic row-by-row aggregate enumerator for {@link PolyValue[]} rows.
  */
-public class ParquetRowAggregateRelEnumerator extends AbstractAggregateEnumerator {
+public class ParquetRowAggregateEnumerator extends AbstractAggregateEnumerator {
+
+    public ParquetRowAggregateEnumerator( Enumerator<PolyValue[]> rows, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
+        super( () -> buildEnumerator( rows, groupFields, aggregateKinds, aggregateArgs ) );
+    }
+
 
     /**
      * Creates a new instance of the enumerator.
@@ -38,11 +46,6 @@ public class ParquetRowAggregateRelEnumerator extends AbstractAggregateEnumerato
      * @param aggregateKinds an array of aggregation function kinds.
      * @param aggregateArgs an array of aggregation function arguments. There should be one argument per aggregation function.
      */
-    public ParquetRowAggregateRelEnumerator( Enumerator<PolyValue[]> rows, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
-        super( () -> buildEnumerator( rows, groupFields, aggregateKinds, aggregateArgs ) );
-    }
-
-
     private static Enumerator<PolyValue[]> buildEnumerator( Enumerator<PolyValue[]> rows, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
         AggregateCallDescriptor[] aggregateCalls = aggregateCalls( aggregateKinds, aggregateArgs );
         Map<GroupKey, AggregateGroupState> aggregates = ParquetRowAggregator.aggregate( rows, aggregateCalls, new DirectAggregateRowProjector( groupFields ) );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/OutputLocalFile.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/OutputLocalFile.java
new file mode 100644
index 0000000000..0dfe16ec67
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/OutputLocalFile.java
@@ -0,0 +1,118 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.io;
+
+import java.io.File;
+import java.io.IOException;
+import java.io.OutputStream;
+import java.nio.file.Files;
+import java.nio.file.StandardOpenOption;
+import org.apache.parquet.io.OutputFile;
+import org.apache.parquet.io.PositionOutputStream;
+import org.jetbrains.annotations.NotNull;
+
+
+/**
+ * A local file wrapper of Parquet {@link OutputFile} to avoid HADOOP_HOME validation access by hadoop infra.
+ */
+public class OutputLocalFile implements OutputFile {
+
+    private static final long DEFAULT_BLOCK_SIZE = 64L * 1024L * 1024L;
+
+    private final File file;
+
+
+    public OutputLocalFile( File file ) {
+        this.file = file;
+    }
+
+
+    @Override
+    public PositionOutputStream create( long blockSizeHint ) throws IOException {
+        return new LocalPositionOutputStream( Files.newOutputStream( file.toPath(), StandardOpenOption.CREATE_NEW, StandardOpenOption.WRITE ) );
+    }
+
+
+    @Override
+    public PositionOutputStream createOrOverwrite( long blockSizeHint ) throws IOException {
+        return new LocalPositionOutputStream( Files.newOutputStream( file.toPath(), StandardOpenOption.CREATE, StandardOpenOption.TRUNCATE_EXISTING, StandardOpenOption.WRITE ) );
+    }
+
+
+    @Override
+    public boolean supportsBlockSize() {
+        return false;
+    }
+
+
+    @Override
+    public long defaultBlockSize() {
+        return DEFAULT_BLOCK_SIZE;
+    }
+
+
+    @Override
+    public String getPath() {
+        return file.getAbsolutePath();
+    }
+
+
+    private static class LocalPositionOutputStream extends PositionOutputStream {
+
+        private final OutputStream outputStream;
+        private long position;
+
+
+        private LocalPositionOutputStream( OutputStream outputStream ) {
+            this.outputStream = outputStream;
+        }
+
+
+        @Override
+        public long getPos() {
+            return position;
+        }
+
+
+        @Override
+        public void write( int b ) throws IOException {
+            outputStream.write( b );
+            position++;
+        }
+
+
+        @Override
+        public void write( byte @NotNull [] b, int off, int len ) throws IOException {
+            outputStream.write( b, off, len );
+            position += len;
+        }
+
+
+        @Override
+        public void flush() throws IOException {
+            outputStream.flush();
+        }
+
+
+        @Override
+        public void close() throws IOException {
+            outputStream.close();
+        }
+
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceWriter.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceWriter.java
index db37658cd5..20069701c6 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceWriter.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceWriter.java
@@ -26,6 +26,7 @@
 import org.apache.parquet.hadoop.ParquetWriter;
 import org.apache.parquet.hadoop.example.ExampleParquetWriter;
 import org.apache.parquet.hadoop.metadata.CompressionCodecName;
+import org.apache.parquet.io.OutputFile;
 import org.apache.parquet.io.api.Binary;
 import org.apache.parquet.schema.MessageType;
 import org.apache.parquet.schema.PrimitiveType;
@@ -62,11 +63,31 @@ public class ParquetSourceWriter implements AutoCloseable {
      * @param compression - compression type setting
      */
     public ParquetSourceWriter( File file, MessageType schema, String compression, SchemaState schemaState, boolean keepId ) {
+        this( file, new Path( file.toURI() ), schema, compression, schemaState, keepId );
+    }
+
+
+    public ParquetSourceWriter( OutputFile outputFile, MessageType schema, String compression, SchemaState schemaState, boolean keepId ) {
+        try {
+            this.schemaState = schemaState;
+            this.keepId = keepId;
+            this.groupFactory = new SimpleGroupFactory( schema );
+            this.writer = ExampleParquetWriter.builder( outputFile )
+                    .withType( schema )
+                    .withCompressionCodec( getCompressionCodec( compression ) )
+                    .build();
+        } catch ( Exception e ) {
+            throw new GenericRuntimeException( "Unable to open parquet output for writing: " + outputFile.getPath(), e );
+        }
+    }
+
+
+    private ParquetSourceWriter( File file, Path path, MessageType schema, String compression, SchemaState schemaState, boolean keepId ) {
         try {
             this.schemaState = schemaState;
             this.keepId = keepId;
             this.groupFactory = new SimpleGroupFactory( schema );
-            this.writer = ExampleParquetWriter.builder( new Path( file.toURI() ) )
+            this.writer = ExampleParquetWriter.builder( path )
                     .withType( schema )
                     .withCompressionCodec( getCompressionCodec( compression ) )
                     .build();
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/ParquetAggregatePatternMatchers.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/ParquetAggregatePatternMatchers.java
new file mode 100644
index 0000000000..6667f1a982
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/ParquetAggregatePatternMatchers.java
@@ -0,0 +1,225 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.optimization;
+
+import static org.polypheny.db.plan.AlgOptRule.any;
+import static org.polypheny.db.plan.AlgOptRule.operand;
+
+import com.google.common.collect.ImmutableList;
+import java.util.ArrayList;
+import java.util.List;
+import org.polypheny.db.adapter.parquet.shared.optimization.aggregate.AggregateDecomposition;
+import org.polypheny.db.adapter.parquet.shared.optimization.aggregate.ParquetAggregateSupport;
+import org.polypheny.db.adapter.parquet.shared.optimization.aggregate.PartialAggregate;
+import org.polypheny.db.adapter.parquet.shared.planning.ParquetEnumerableUnion;
+import org.polypheny.db.algebra.AlgNode;
+import org.polypheny.db.algebra.InvalidAlgException;
+import org.polypheny.db.algebra.core.AggregateCall;
+import org.polypheny.db.algebra.enumerable.EnumerableAggregate;
+import org.polypheny.db.algebra.enumerable.EnumerableCalc;
+import org.polypheny.db.algebra.enumerable.EnumerableUnion;
+import org.polypheny.db.algebra.fun.AggFunction;
+import org.polypheny.db.algebra.operators.OperatorName;
+import org.polypheny.db.languages.OperatorRegistry;
+import org.polypheny.db.plan.Convention;
+import org.polypheny.db.tools.AlgBuilderFactory;
+import org.polypheny.db.util.ImmutableBitSet;
+
+public final class ParquetAggregatePatternMatchers {
+
+    private ParquetAggregatePatternMatchers() {
+    }
+
+
+    public static PatternMatcher partialAggregateOnUnion( Convention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableAggregate.class,
+                        operand( EnumerableUnion.class, any() )
+                ),
+                "partialAggregateOnUnion",
+                call -> {
+                    EnumerableAggregate aggregate = call.alg( 0 );
+                    EnumerableUnion union = call.alg( 1 );
+                    if ( !union.all ) {
+                        return;
+                    }
+
+                    AggregateDecomposition decomposition = aggregateDecomposition( aggregate );
+                    if ( decomposition == null ) {
+                        return;
+                    }
+
+                    try {
+                        List<AlgNode> inputs = toAggregates( aggregate, union.getInputs(), decomposition );
+                        ParquetEnumerableUnion partialUnion = new ParquetEnumerableUnion( union.getCluster(), union.getTraitSet(), inputs );
+                        call.transformTo( toTopLevelAggregate( aggregate, partialUnion, decomposition ) );
+                    } catch ( InvalidAlgException ignored ) {
+                    }
+                }
+        );
+    }
+
+
+    public static PatternMatcher partialAggregateOnCalcUnion( Convention out, AlgBuilderFactory factory ) {
+        return new PatternMatcher(
+                out,
+                factory,
+                operand(
+                        EnumerableAggregate.class,
+                        operand(
+                                EnumerableCalc.class,
+                                operand( EnumerableUnion.class, any() )
+                        )
+                ),
+                "partialAggregateOnCalcUnion",
+                call -> {
+                    EnumerableAggregate aggregate = call.alg( 0 );
+                    EnumerableCalc calc = call.alg( 1 );
+                    EnumerableUnion union = call.alg( 2 );
+                    if ( !union.all ) {
+                        return;
+                    }
+
+                    AggregateDecomposition decomposition = aggregateDecomposition( aggregate );
+                    if ( decomposition == null ) {
+                        return;
+                    }
+
+                    try {
+                        List<AlgNode> inputs = toCalcAggregates( aggregate, calc, union.getInputs(), decomposition );
+                        ParquetEnumerableUnion partialUnion = new ParquetEnumerableUnion( union.getCluster(), union.getTraitSet(), inputs );
+                        call.transformTo( toTopLevelAggregate( aggregate, partialUnion, decomposition ) );
+                    } catch ( InvalidAlgException ignored ) {
+                    }
+                }
+        );
+    }
+
+
+    private static EnumerableAggregate toAggregate( EnumerableAggregate aggregate, AlgNode input, AggregateDecomposition decomposition ) throws InvalidAlgException {
+        return new EnumerableAggregate(
+                aggregate.getCluster(),
+                aggregate.getTraitSet(),
+                input,
+                false,
+                aggregate.getGroupSet(),
+                aggregate.getGroupSets(),
+                decomposition.partialCalls() );
+    }
+
+
+    private static AlgNode toTopLevelAggregate( EnumerableAggregate aggregate, AlgNode input, AggregateDecomposition decomposition ) throws InvalidAlgException {
+        ImmutableBitSet finalGroupSet = ImmutableBitSet.range( aggregate.getGroupSet().cardinality() );
+        return new EnumerableAggregate(
+                aggregate.getCluster(),
+                aggregate.getTraitSet(),
+                input,
+                false,
+                finalGroupSet,
+                List.of( finalGroupSet ),
+                decomposition.buildFinalCalls( aggregate, input ) );
+    }
+
+
+    private static List<AlgNode> toAggregates( EnumerableAggregate aggregate, List<AlgNode> inputs, AggregateDecomposition decomposition ) throws InvalidAlgException {
+        ImmutableList.Builder<AlgNode> builder = ImmutableList.builder();
+        for ( AlgNode input : inputs ) {
+            builder.add( toAggregate( aggregate, input, decomposition ) );
+        }
+        return builder.build();
+    }
+
+
+    private static List<AlgNode> toCalcAggregates( EnumerableAggregate aggregate, EnumerableCalc calc, List<AlgNode> inputs, AggregateDecomposition decomposition ) throws InvalidAlgException {
+        ImmutableList.Builder<AlgNode> builder = ImmutableList.builder();
+        for ( AlgNode input : inputs ) {
+            builder.add( toAggregate( aggregate, EnumerableCalc.create( input, calc.getProgram() ), decomposition ) );
+        }
+        return builder.build();
+    }
+
+
+    private static boolean isSimpleAggregate( EnumerableAggregate aggregate ) {
+        return !aggregate.indicator && EnumerableAggregate.isSimple( aggregate );
+    }
+
+
+    private static AggregateDecomposition aggregateDecomposition( EnumerableAggregate aggregate ) {
+        if ( !isSimpleAggregate( aggregate ) ) {
+            return null;
+        }
+
+        List<PartialAggregate> partialAggregates = new ArrayList<>();
+        if ( supportsMetadataAggregate( aggregate ) ) {
+            for ( AggregateCall aggregateCall : aggregate.getAggCallList() ) {
+                partialAggregates.add( new PartialAggregate( aggregateCall, topLevelAggregateFunction( aggregateCall ) ) );
+            }
+            return new AggregateDecomposition( partialAggregates );
+        }
+
+        for ( AggregateCall aggregateCall : aggregate.getAggCallList() ) {
+            if ( aggregateCall.isDistinct() || aggregateCall.isApproximate() || aggregateCall.filterArg >= 0 ) {
+                return null;
+            }
+            switch ( aggregateCall.getAggregation().getKind() ) {
+                case COUNT -> {
+                    if ( aggregateCall.getArgList().size() > 1 ) {
+                        return null;
+                    }
+                    partialAggregates.add( new PartialAggregate( aggregateCall, OperatorRegistry.getAgg( OperatorName.SUM0 ) ) );
+                }
+                case SUM, MIN, MAX -> {
+                    if ( aggregateCall.getArgList().size() != 1 ) {
+                        return null;
+                    }
+                    partialAggregates.add( new PartialAggregate( aggregateCall, aggregateCall.getAggregation() ) );
+                }
+                default -> {
+                    return null;
+                }
+            }
+        }
+        return new AggregateDecomposition( partialAggregates );
+    }
+
+
+    private static boolean supportsMetadataAggregate( EnumerableAggregate aggregate ) {
+        if ( !isSimpleAggregate( aggregate ) ) {
+            return false;
+        }
+        for ( AggregateCall aggregateCall : aggregate.getAggCallList() ) {
+            if ( !ParquetAggregateSupport.supportsMetadataAggregateCall( aggregateCall ) ) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+
+    private static AggFunction topLevelAggregateFunction( AggregateCall aggregateCall ) {
+        return switch ( aggregateCall.getAggregation().getKind() ) {
+            case COUNT -> OperatorRegistry.getAgg( OperatorName.SUM0 );
+            case MIN -> OperatorRegistry.getAgg( OperatorName.MIN );
+            case MAX -> OperatorRegistry.getAgg( OperatorName.MAX );
+            default -> aggregateCall.getAggregation();
+        };
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/ParquetAlgOptRule.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/ParquetAlgOptRule.java
similarity index 89%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/ParquetAlgOptRule.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/ParquetAlgOptRule.java
index dd93aa984f..beb3e287a1 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/ParquetAlgOptRule.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/ParquetAlgOptRule.java
@@ -14,13 +14,13 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.optimization;
+package org.polypheny.db.adapter.parquet.shared.optimization;
 
 import org.polypheny.db.plan.AlgOptRule;
 import org.polypheny.db.plan.AlgOptRuleCall;
 
 /**
- * Adapter between Polypheny's rule engine and PatternMatcher abstraction
+ * Adapter between Polypheny's rule engine and PatternMatcher abstraction.
  * It does not contain Parquet planning logic itself. It only delegates.
  */
 public class ParquetAlgOptRule extends AlgOptRule {
@@ -28,7 +28,7 @@ public class ParquetAlgOptRule extends AlgOptRule {
     private final PatternMatcher matcher;
 
 
-    public ParquetAlgOptRule(PatternMatcher matcher ) {
+    public ParquetAlgOptRule( PatternMatcher matcher ) {
         super( matcher.operand(), matcher.factory(), matcher.description() );
         this.matcher = matcher;
     }
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/ParquetOptimizationSettings.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/ParquetOptimizationSettings.java
new file mode 100644
index 0000000000..782fca44f0
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/ParquetOptimizationSettings.java
@@ -0,0 +1,53 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.optimization;
+
+/**
+ * Shared switches for Parquet planner optimizations.
+ */
+public final class ParquetOptimizationSettings {
+
+    public static final String OPTIMIZE_AGGREGATION_PROPERTY = "polypheny.parquet.optimizeAggregation";
+    public static final String OPTIMIZE_AGGREGATION_ENV = "POLYPHENY_PARQUET_OPTIMIZE_AGGREGATION";
+
+
+    private ParquetOptimizationSettings() {
+    }
+
+
+    public static boolean isOptimizeAggregation() {
+        String property = System.getProperty( OPTIMIZE_AGGREGATION_PROPERTY );
+        if ( property != null ) {
+            return parseBoolean( property, true );
+        }
+        return parseBoolean( System.getenv( OPTIMIZE_AGGREGATION_ENV ), true );
+    }
+
+
+    @SuppressWarnings("SameParameterValue")
+    private static boolean parseBoolean( String value, boolean fallback ) {
+        if ( value == null || value.isBlank() ) {
+            return fallback;
+        }
+        return switch ( value.trim().toLowerCase() ) {
+            case "true", "1", "yes", "y", "on" -> true;
+            case "false", "0", "no", "n", "off" -> false;
+            default -> fallback;
+        };
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/PatternMatcher.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/PatternMatcher.java
similarity index 94%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/PatternMatcher.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/PatternMatcher.java
index e0cb721dbe..a61ac3a539 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/PatternMatcher.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/PatternMatcher.java
@@ -14,13 +14,13 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.optimization;
+package org.polypheny.db.adapter.parquet.shared.optimization;
 
+import java.util.function.Consumer;
 import org.polypheny.db.plan.AlgOptRuleCall;
 import org.polypheny.db.plan.AlgOptRuleOperand;
 import org.polypheny.db.plan.Convention;
 import org.polypheny.db.tools.AlgBuilderFactory;
-import java.util.function.Consumer;
 
 public record PatternMatcher( Convention out, AlgBuilderFactory factory, AlgOptRuleOperand operand, String description, Consumer<AlgOptRuleCall> consumer ) {
 
@@ -29,4 +29,3 @@ public void onMatch( AlgOptRuleCall call ) {
     }
 
 }
-
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/aggregate/AggregateDecomposition.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/aggregate/AggregateDecomposition.java
similarity index 97%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/aggregate/AggregateDecomposition.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/aggregate/AggregateDecomposition.java
index f247245d79..b9bb2daa46 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/aggregate/AggregateDecomposition.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/aggregate/AggregateDecomposition.java
@@ -14,16 +14,15 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.optimization.aggregate;
+package org.polypheny.db.adapter.parquet.shared.optimization.aggregate;
 
+import java.util.List;
+import java.util.stream.IntStream;
 import org.polypheny.db.algebra.AlgCollations;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.core.AggregateCall;
 import org.polypheny.db.algebra.enumerable.EnumerableAggregate;
 
-import java.util.List;
-import java.util.stream.IntStream;
-
 /**
  * Used for the partial aggregate over UNION ALL optimization.
  * The class describes what each union child should compute, and how the top aggregate should combine those partial results.
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/aggregate/ParquetAggregateSupport.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/aggregate/ParquetAggregateSupport.java
new file mode 100644
index 0000000000..8478c10ed6
--- /dev/null
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/aggregate/ParquetAggregateSupport.java
@@ -0,0 +1,41 @@
+/*
+ * Copyright 2019-2026 The Polypheny Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.polypheny.db.adapter.parquet.shared.optimization.aggregate;
+
+import org.polypheny.db.algebra.constant.Kind;
+import org.polypheny.db.algebra.core.AggregateCall;
+
+public final class ParquetAggregateSupport {
+
+    private ParquetAggregateSupport() {
+    }
+
+
+    public static boolean supportsMetadataAggregateCall( AggregateCall aggregateCall ) {
+        if ( aggregateCall.isDistinct() || aggregateCall.isApproximate() || aggregateCall.filterArg >= 0 ) {
+            return false;
+        }
+        if ( aggregateCall.getAggregation().getKind() == Kind.COUNT ) {
+            return aggregateCall.getArgList().size() <= 1;
+        }
+        if ( aggregateCall.getAggregation().getKind() == Kind.MIN || aggregateCall.getAggregation().getKind() == Kind.MAX ) {
+            return aggregateCall.getArgList().size() == 1;
+        }
+        return false;
+    }
+
+}
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/aggregate/PartialAggregate.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/aggregate/PartialAggregate.java
similarity index 93%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/aggregate/PartialAggregate.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/aggregate/PartialAggregate.java
index f6c7b1a4bb..147bd44190 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/optimization/aggregate/PartialAggregate.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/optimization/aggregate/PartialAggregate.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.optimization.aggregate;
+package org.polypheny.db.adapter.parquet.shared.optimization.aggregate;
 
 import org.polypheny.db.algebra.core.AggregateCall;
 import org.polypheny.db.algebra.fun.AggFunction;
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableUnion.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/planning/ParquetEnumerableUnion.java
similarity index 87%
rename from plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableUnion.java
rename to plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/planning/ParquetEnumerableUnion.java
index d8a2a36584..4047dd93f6 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableUnion.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/planning/ParquetEnumerableUnion.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.planning;
+package org.polypheny.db.adapter.parquet.shared.planning;
 
 import java.util.List;
 import org.polypheny.db.algebra.AlgNode;
@@ -25,10 +25,9 @@
 import org.polypheny.db.plan.AlgCluster;
 import org.polypheny.db.plan.AlgTraitSet;
 
-
 /**
- * Enumerable UNION ALL used for Parquet metadata aggregate partial results.
- * Note: its main purpose is to be a marker for a planner to avoid recursive planner rule execution.
+ * Enumerable UNION ALL used for Parquet partial aggregate results.
+ * Its main purpose is to mark the rewritten UNION and avoid recursive rule execution.
  */
 public class ParquetEnumerableUnion extends Union implements EnumerableAlg {
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
index 892e8f4513..ca73386fb2 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java
@@ -4,6 +4,7 @@
 import java.util.List;
 import org.jetbrains.annotations.Nullable;
 import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
+import org.polypheny.db.adapter.parquet.relational.schema.DiscoveredTableBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
 import org.polypheny.db.adapter.parquet.shared.AbstractParquetSource;
@@ -49,8 +50,8 @@ public ParquetRelTable createParquetTable( long id, PhysicalTable table, Parquet
 
 
 
-    public ParquetDocument createParquetCollection( PhysicalCollection collection, List<ParquetSourceFile> sourceFiles, AbstractParquetSource sourceAdapter ) {
-        return new ParquetDocument( collection, sourceFiles, sourceAdapter );     // handle multi-files
+    public ParquetDocument createParquetCollection( PhysicalCollection collection, DiscoveredTableBinding binding, AbstractParquetSource sourceAdapter ) {
+        return new ParquetDocument( collection, binding, sourceAdapter );     // handle multi-files
     }
 
 
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateAccumulatorTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/AggregateAccumulatorTest.java
similarity index 96%
rename from plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateAccumulatorTest.java
rename to plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/AggregateAccumulatorTest.java
index a2e4aee5e8..fd0213a927 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/shared/aggregate/AggregateAccumulatorTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/AggregateAccumulatorTest.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.shared.aggregate;
+package org.polypheny.db.adapter.parquet;
 
 import static org.junit.jupiter.api.Assertions.assertEquals;
 import static org.junit.jupiter.api.Assertions.assertThrows;
@@ -25,6 +25,10 @@
 import org.apache.parquet.io.api.Binary;
 import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
 import org.junit.jupiter.api.Test;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateGroupState;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateInputColumn;
+import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateRowAccumulator;
 import org.polypheny.db.type.entity.PolyNull;
 
 class AggregateAccumulatorTest {
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetAggregateOptimizationTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetAggregateOptimizationTest.java
index e859ef0861..0d701b024a 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetAggregateOptimizationTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetAggregateOptimizationTest.java
@@ -17,13 +17,20 @@
 package org.polypheny.db.adapter.parquet;
 
 import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
 import static org.junit.jupiter.api.Assertions.assertSame;
+import static org.junit.jupiter.api.Assertions.assertTrue;
 
 import java.lang.reflect.Proxy;
 import java.util.List;
 import org.junit.jupiter.api.Test;
-import org.polypheny.db.adapter.parquet.relational.optimization.aggregate.AggregateDecomposition;
-import org.polypheny.db.adapter.parquet.relational.optimization.aggregate.PartialAggregate;
+import org.polypheny.db.adapter.parquet.document.planning.ParquetDocRules;
+import org.polypheny.db.adapter.parquet.document.planning.ParquetDocConvention;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelRules;
+import org.polypheny.db.adapter.parquet.relational.planning.ParquetRelConvention;
+import org.polypheny.db.adapter.parquet.shared.optimization.ParquetOptimizationSettings;
+import org.polypheny.db.adapter.parquet.shared.optimization.aggregate.AggregateDecomposition;
+import org.polypheny.db.adapter.parquet.shared.optimization.aggregate.PartialAggregate;
 import org.polypheny.db.algebra.AlgCollations;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.algebra.core.AggregateCall;
@@ -52,6 +59,46 @@ void aggregateDecompositionExposesPartialCallsAndFinalFunctions() {
     }
 
 
+    @Test
+    void aggregateOptimizationRulesCanBeDisabledWithSystemProperty() {
+        String previous = System.getProperty( ParquetOptimizationSettings.OPTIMIZE_AGGREGATION_PROPERTY );
+        try {
+            System.setProperty( ParquetOptimizationSettings.OPTIMIZE_AGGREGATION_PROPERTY, "false" );
+
+            List<String> relationalRules = ParquetRelRules.rules( ParquetRelConvention.INSTANCE ).stream()
+                    .map( Object::toString )
+                    .toList();
+            List<String> documentRules = ParquetDocRules.rules( ParquetDocConvention.INSTANCE ).stream()
+                    .map( Object::toString )
+                    .toList();
+
+            assertFalse( containsRule( relationalRules, "aggregateOnScan" ) );
+            assertFalse( containsRule( relationalRules, "aggregateOnCalcScan" ) );
+            assertFalse( containsRule( relationalRules, "partialAggregateOnUnion" ) );
+            assertFalse( containsRule( relationalRules, "partialAggregateOnCalcUnion" ) );
+            assertFalse( containsRule( documentRules, "documentAggregateOnScan" ) );
+            assertFalse( containsRule( documentRules, "documentAggregateOnProjectScan" ) );
+            assertFalse( containsRule( documentRules, "documentAggregateOnCalcScan" ) );
+            assertFalse( containsRule( documentRules, "partialAggregateOnUnion" ) );
+            assertFalse( containsRule( documentRules, "partialAggregateOnCalcUnion" ) );
+
+            assertTrue( containsRule( relationalRules, "EnumerableParquetRule" ) );
+            assertTrue( containsRule( documentRules, "EnumerableParquetDocumentRule" ) );
+        } finally {
+            if ( previous == null ) {
+                System.clearProperty( ParquetOptimizationSettings.OPTIMIZE_AGGREGATION_PROPERTY );
+            } else {
+                System.setProperty( ParquetOptimizationSettings.OPTIMIZE_AGGREGATION_PROPERTY, previous );
+            }
+        }
+    }
+
+
+    private static boolean containsRule( List<String> rules, String name ) {
+        return rules.stream().anyMatch( rule -> rule.contains( name ) );
+    }
+
+
     private static AggregateCall aggregateCall( AggFunction function, String name ) {
         return AggregateCall.create(
                 function,
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetCountAggregatePageReaderTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetCountAggregatePageReaderTest.java
similarity index 96%
rename from plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetCountAggregatePageReaderTest.java
rename to plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetCountAggregatePageReaderTest.java
index c0e71e497c..44d539fd09 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetCountAggregatePageReaderTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetCountAggregatePageReaderTest.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.shared.io.aggregate.page;
+package org.polypheny.db.adapter.parquet;
 
 import static org.junit.jupiter.api.Assertions.assertEquals;
 
@@ -52,6 +52,8 @@
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetPrimitivePredicate;
 import org.polypheny.db.adapter.parquet.shared.aggregate.GroupKey;
+import org.polypheny.db.adapter.parquet.shared.io.aggregate.page.NoOpColumnConverter;
+import org.polypheny.db.adapter.parquet.shared.io.aggregate.page.ParquetCountAggregatePageReader;
 import org.polypheny.db.adapter.parquet.shared.util.HadoopConfigurationFactory;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.type.entity.PolyValue;
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java
index 0efe402ff2..693b7bac77 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java
@@ -18,37 +18,66 @@
 
 import static org.junit.jupiter.api.Assertions.assertEquals;
 import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertInstanceOf;
 import static org.junit.jupiter.api.Assertions.assertNotSame;
 import static org.junit.jupiter.api.Assertions.assertTrue;
 
+import java.io.IOException;
+import java.io.OutputStream;
+import java.lang.reflect.Proxy;
+import java.nio.file.Files;
 import java.nio.file.Path;
+import java.util.LinkedHashMap;
 import java.util.List;
 import java.util.Map;
+import org.apache.calcite.linq4j.Enumerator;
+import org.apache.parquet.example.data.Group;
+import org.apache.parquet.example.data.simple.SimpleGroupFactory;
+import org.apache.parquet.hadoop.ParquetWriter;
+import org.apache.parquet.hadoop.example.ExampleParquetWriter;
+import org.apache.parquet.io.OutputFile;
+import org.apache.parquet.io.PositionOutputStream;
+import org.apache.parquet.schema.LogicalTypeAnnotation;
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
+import org.apache.parquet.schema.Types;
+import org.jetbrains.annotations.NotNull;
 import org.junit.jupiter.api.BeforeAll;
 import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+import org.polypheny.db.adapter.DataContext;
 import org.polypheny.db.adapter.DeployMode;
 import org.polypheny.db.adapter.RelationalDataSource.ExportedColumn;
 import org.polypheny.db.adapter.annotations.AdapterProperties;
 import org.polypheny.db.adapter.parquet.document.ParquetDocumentSource;
+import org.polypheny.db.adapter.parquet.document.execution.ParquetDocAggregateExecutor;
+import org.polypheny.db.adapter.parquet.document.planning.ParquetDocMetadataScan;
+import org.polypheny.db.adapter.parquet.document.planning.ParquetDocRules;
 import org.polypheny.db.adapter.parquet.document.planning.ParquetDocScan;
+import org.polypheny.db.adapter.parquet.document.planning.ParquetDocConvention;
 import org.polypheny.db.adapter.parquet.document.schema.ParquetDocument;
+import org.polypheny.db.adapter.parquet.relational.schema.DiscoveredTableBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.algebra.AlgCollations;
 import org.polypheny.db.algebra.AlgNode;
 import org.polypheny.db.algebra.constant.Kind;
-import org.polypheny.db.algebra.enumerable.EnumerableConvention;
+import org.polypheny.db.algebra.core.AggregateCall;
+import org.polypheny.db.algebra.fun.AggFunction;
+import org.polypheny.db.algebra.operators.OperatorName;
 import org.polypheny.db.algebra.type.AlgDataType;
 import org.polypheny.db.algebra.type.AlgDataTypeFactory;
 import org.polypheny.db.algebra.type.AlgDataTypeSystem;
 import org.polypheny.db.algebra.type.DocumentType;
 import org.polypheny.db.catalog.entity.physical.PhysicalCollection;
 import org.polypheny.db.plan.AlgCluster;
+import org.polypheny.db.plan.AlgOptRule;
 import org.polypheny.db.plan.volcano.VolcanoPlanner;
 import org.polypheny.db.rex.RexBuilder;
 import org.polypheny.db.type.PolyType;
 import org.polypheny.db.type.PolyTypeFactoryImpl;
 import org.polypheny.db.type.entity.PolyString;
 import org.polypheny.db.type.entity.PolyValue;
+import org.polypheny.db.util.ImmutableBitSet;
 import org.polypheny.db.util.PolyphenyHomeDirManager;
 import org.polypheny.db.util.RunMode;
 import sun.misc.Unsafe;
@@ -56,6 +85,9 @@
 
 class ParquetDocumentPlanningTest {
 
+    @TempDir
+    Path tempDir;
+
 
     @BeforeAll
     static void initHomeDir() {
@@ -84,6 +116,11 @@ private static ParquetSourceFile sourceFile() {
     }
 
 
+    private static DiscoveredTableBinding binding( List<ParquetSourceFile> sourceFiles, Map<String, List<String>> columnPaths ) {
+        return new DiscoveredTableBinding( sourceFiles, null, List.of(), columnPaths );
+    }
+
+
     private static ExportedColumn exportedColumn( String name, PolyType type, int position, boolean nullable ) {
         return new ExportedColumn( name, type, null, null, null, null, null, nullable, "public", "orders", name, position, false );
     }
@@ -102,7 +139,7 @@ void documentBuildsTupleTypeFromExportedColumnsAndFallsBackToDocumentId() throws
                 "orders", List.of(
                         exportedColumn( "name", PolyType.VARCHAR, 0, true ),
                         exportedColumn( "score", PolyType.INTEGER, 1, false ) ) );
-        ParquetDocument document = new ParquetDocument( collection( "orders" ), List.of( sourceFile() ), testSource() );
+        ParquetDocument document = new ParquetDocument( collection( "orders" ), binding( List.of( sourceFile() ), Map.of( "name", List.of( "name" ), "score", List.of( "score" ) ) ), testSource() );
         AlgDataType tupleType = document.getTupleType( AlgDataTypeFactory.DEFAULT );
 
         assertEquals( List.of( "name", "score" ), tupleType.getFieldNames() );
@@ -111,7 +148,7 @@ void documentBuildsTupleTypeFromExportedColumnsAndFallsBackToDocumentId() throws
         assertEquals( List.of( sourceFile() ), document.getSourceFiles() );
 
         TestParquetSource.exportedColumns = Map.of();
-        ParquetDocument fallback = new ParquetDocument( collection( "missing" ), List.of(), testSource() );
+        ParquetDocument fallback = new ParquetDocument( collection( "missing" ), binding( List.of(), Map.of() ), testSource() );
 
         assertEquals( DocumentType.ofId(), fallback.getTupleType( AlgDataTypeFactory.DEFAULT ) );
     }
@@ -119,7 +156,7 @@ void documentBuildsTupleTypeFromExportedColumnsAndFallsBackToDocumentId() throws
 
     @Test
     void docScanCopiesFiltersDerivesDocumentRowAndRegistersFilterRule() {
-        ParquetDocument document = new ParquetDocument( collection( "orders" ), List.of( sourceFile() ), null );
+        ParquetDocument document = new ParquetDocument( collection( "orders" ), binding( List.of( sourceFile() ), Map.of() ), null );
         AlgCluster cluster = cluster();
         ParquetAdapterFilter<PolyValue> filter = new ParquetAdapterFilter<>( 0, Kind.EQUALS, PolyString.of( "Alice" ) );
         ParquetDocScan scan = new ParquetDocScan( cluster, document, List.of( filter ) );
@@ -127,8 +164,9 @@ void docScanCopiesFiltersDerivesDocumentRowAndRegistersFilterRule() {
         assertEquals( List.of( filter ), scan.getFilters() );
         assertEquals( List.of( DocumentType.DOCUMENT_ID ), scan.deriveRowType().getFieldNames() );
         assertTrue( scan.algCompareString().contains( "$filters=" ) );
+        assertTrue( scan.getTraitSet().containsIfApplicable( ParquetDocConvention.INSTANCE ) );
 
-        AlgNode copied = scan.copy( cluster.traitSetOf( EnumerableConvention.INSTANCE ), List.of() );
+        AlgNode copied = scan.copy( cluster.traitSetOf( ParquetDocConvention.INSTANCE ), List.of() );
         assertNotSame( scan, copied );
         assertEquals( scan.getFilters(), ((ParquetDocScan) copied).getFilters() );
 
@@ -137,6 +175,223 @@ void docScanCopiesFiltersDerivesDocumentRowAndRegistersFilterRule() {
     }
 
 
+    @Test
+    void documentRulesIncludePartialAggregateRewrites() {
+        List<String> ruleNames = ParquetDocRules.rules( ParquetDocConvention.INSTANCE ).stream()
+                .map( AlgOptRule::toString )
+                .toList();
+
+        assertTrue( ruleNames.stream().anyMatch( name -> name.contains( "documentAggregateOnScan" ) ) );
+        assertTrue( ruleNames.stream().anyMatch( name -> name.contains( "partialAggregateOnUnion" ) ) );
+        assertTrue( ruleNames.stream().anyMatch( name -> name.contains( "partialAggregateOnCalcUnion" ) ) );
+    }
+
+
+    @Test
+    void documentAggregateExecutorGroupsPrimitiveColumns() throws Exception {
+        Path file = tempDir.resolve( "orders.parquet" );
+        writeOrdersParquet( file );
+        List<ExportedColumn> columns = List.of(
+                exportedColumn( "customer", PolyType.VARCHAR, 0, false ),
+                exportedColumn( "amount", PolyType.DOUBLE, 1, false ) );
+        ParquetDocAggregateExecutor executor = new ParquetDocAggregateExecutor(
+                List.of( ParquetSourceFile.of( file.toUri().toURL().toString(), Map.of() ) ),
+                columns,
+                Map.of( "customer", List.of( "customer" ), "amount", List.of( "amount" ) ) );
+
+        Map<String, List<Number>> rows = new LinkedHashMap<>();
+        try ( Enumerator<PolyValue[]> enumerator = executor.createDataEnumerator(
+                new DataContext.SlimDataContext(),
+                new int[]{ 0, 1 },
+                List.of(),
+                new int[]{ 0 },
+                new String[]{ Kind.COUNT.name(), Kind.SUM.name() },
+                new int[]{ -1, 1 } ).enumerator() ) {
+            while ( enumerator.moveNext() ) {
+                PolyValue[] row = enumerator.current();
+                rows.put( row[0].asString().value, List.of( row[1].asNumber().longValue(), row[2].asNumber().doubleValue() ) );
+            }
+        }
+
+        assertEquals( Map.of(
+                "Alice", List.of( 2L, 17.5D ),
+                "Bob", List.of( 1L, 3.0D ) ), rows );
+        assertTrue( executor.supportsAggregate(
+                new int[]{ 0, 1 },
+                List.of(),
+                new int[]{ 0 },
+                new String[]{ Kind.COUNT.name(), Kind.SUM.name() },
+                new int[]{ -1, 1 } ) );
+    }
+
+
+    @Test
+    void documentAggregateExecutorUsesSharedMetadataPath() throws Exception {
+        Path file = tempDir.resolve( "metadata-orders.parquet" );
+        writeOrdersParquet( file );
+        List<ExportedColumn> columns = List.of(
+                exportedColumn( "customer", PolyType.VARCHAR, 0, false ),
+                exportedColumn( "amount", PolyType.DOUBLE, 1, false ) );
+        ParquetDocAggregateExecutor executor = new ParquetDocAggregateExecutor(
+                List.of( ParquetSourceFile.of( file.toUri().toURL().toString(), Map.of() ) ),
+                columns,
+                Map.of( "customer", List.of( "customer" ), "amount", List.of( "amount" ) ) );
+
+        AggregateCall count = AggregateCall.create(
+                aggregateFunction( Kind.COUNT, "COUNT" ),
+                false,
+                false,
+                List.of(),
+                -1,
+                AlgCollations.EMPTY,
+                AlgDataTypeFactory.DEFAULT.createPolyType( PolyType.BIGINT ),
+                "count" );
+
+        assertTrue( executor.supportsMetadataAggregate( new int[]{ 0, 1 }, List.of(), ImmutableBitSet.of(), List.of( count ) ) );
+        try ( Enumerator<PolyValue[]> enumerator = executor.createMetadataEnumerator(
+                new DataContext.SlimDataContext(),
+                new int[]{ 0, 1 },
+                List.of(),
+                new int[0],
+                new String[]{ Kind.COUNT.name() },
+                new int[]{ -1 } ).enumerator() ) {
+            assertTrue( enumerator.moveNext() );
+            assertEquals( 3L, enumerator.current()[0].asNumber().longValue() );
+            assertFalse( enumerator.moveNext() );
+        }
+    }
+
+
+    @Test
+    void documentMetadataScanCopiesAndHasZeroCost() throws Exception {
+        TestParquetSource.exportedColumns = Map.of(
+                "orders", List.of(
+                        exportedColumn( "customer", PolyType.VARCHAR, 0, false ),
+                        exportedColumn( "amount", PolyType.DOUBLE, 1, false ) ) );
+        ParquetDocument document = new ParquetDocument(
+                collection( "orders" ),
+                binding( List.of( sourceFile() ), Map.of( "customer", List.of( "customer" ), "amount", List.of( "amount" ) ) ),
+                testSource() );
+        AlgCluster cluster = cluster();
+        ParquetDocScan scan = new ParquetDocScan( cluster, document, List.of() );
+        ParquetDocMetadataScan metadataScan = new ParquetDocMetadataScan( scan );
+
+        assertInstanceOf( ParquetDocMetadataScan.class, metadataScan.copy( metadataScan.getTraitSet(), List.of() ) );
+        assertEquals( 0D, metadataScan.computeSelfCost( new VolcanoPlanner(), null ).getRows() );
+    }
+
+
+    private static void writeOrdersParquet( Path file ) throws Exception {
+        org.apache.parquet.schema.MessageType schema = Types.buildMessage()
+                .required( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "customer" )
+                .required( PrimitiveTypeName.DOUBLE ).named( "amount" )
+                .named( "orders" );
+        SimpleGroupFactory groupFactory = new SimpleGroupFactory( schema );
+        try ( ParquetWriter<Group> writer = ExampleParquetWriter.builder( new LocalOutputFile( file ) )
+                .withType( schema )
+                .build() ) {
+            writer.write( groupFactory.newGroup().append( "customer", "Alice" ).append( "amount", 10.0D ) );
+            writer.write( groupFactory.newGroup().append( "customer", "Bob" ).append( "amount", 3.0D ) );
+            writer.write( groupFactory.newGroup().append( "customer", "Alice" ).append( "amount", 7.5D ) );
+        }
+    }
+
+
+    private static AggFunction aggregateFunction( Kind kind, String name ) {
+        return (AggFunction) Proxy.newProxyInstance(
+                ParquetDocumentPlanningTest.class.getClassLoader(),
+                new Class<?>[]{ AggFunction.class },
+                ( proxy, method, args ) -> switch ( method.getName() ) {
+                    case "getKind" -> kind;
+                    case "getName", "toString", "getAllowedSignatures" -> name;
+                    case "getOperatorName" -> OperatorName.COUNT;
+                    case "isAggregator", "isQuantifierAllowed", "allowsFilter" -> true;
+                    case "hashCode" -> System.identityHashCode( proxy );
+                    case "equals" -> proxy == args[0];
+                    default -> null;
+                } );
+    }
+
+
+    private record LocalOutputFile( Path path ) implements OutputFile {
+
+        @Override
+        public PositionOutputStream create( long blockSizeHint ) throws IOException {
+            return createOrOverwrite( blockSizeHint );
+        }
+
+
+        @Override
+        public PositionOutputStream createOrOverwrite( long blockSizeHint ) throws IOException {
+            return new LocalPositionOutputStream( Files.newOutputStream( path ) );
+        }
+
+
+        @Override
+        public boolean supportsBlockSize() {
+            return false;
+        }
+
+
+        @Override
+        public long defaultBlockSize() {
+            return 0;
+        }
+
+
+        @Override
+        public String getPath() {
+            return path.toString();
+        }
+
+    }
+
+
+    private static class LocalPositionOutputStream extends PositionOutputStream {
+
+        private final OutputStream out;
+        private long position;
+
+
+        private LocalPositionOutputStream( OutputStream out ) {
+            this.out = out;
+        }
+
+
+        @Override
+        public long getPos() {
+            return position;
+        }
+
+
+        @Override
+        public void write( int b ) throws IOException {
+            out.write( b );
+            position++;
+        }
+
+
+        @Override
+        public void write( byte @NotNull [] b, int off, int len ) throws IOException {
+            out.write( b, off, len );
+            position += len;
+        }
+
+
+        @Override
+        public void flush() throws IOException {
+            out.flush();
+        }
+
+
+        @Override
+        public void close() throws IOException {
+            out.close();
+        }
+
+    }
+
+
     @AdapterProperties(
             name = "Test Parquet Source",
             description = "Test-only source for document planning unit tests.",
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetExecutionAndWriterTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetExecutionAndWriterTest.java
index ddaa0453c8..4f7246139d 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetExecutionAndWriterTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetExecutionAndWriterTest.java
@@ -49,6 +49,7 @@
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetPrimitiveValueFilterEvaluator;
+import org.polypheny.db.adapter.parquet.shared.io.OutputLocalFile;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceReader;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSourceWriter;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetMessageTypeBuilder;
@@ -189,7 +190,7 @@ void sourceWriterRoundTripsRelationalRowsThroughRelEnumerator() throws Exception
         MessageType schema = new ParquetMessageTypeBuilder( schemaState, "writer_rel" ).build();
         List<Long> progress = new ArrayList<>();
 
-        try ( ParquetSourceWriter writer = new ParquetSourceWriter( file, schema, ParquetSourceWriter.COMPRESSION_UNCOMPRESSED, schemaState, false ) ) {
+        try ( ParquetSourceWriter writer = new ParquetSourceWriter( new OutputLocalFile( file ), schema, ParquetSourceWriter.COMPRESSION_UNCOMPRESSED, schemaState, false ) ) {
             writer.writeRows( List.of(
                     List.of( PolyLong.of( 101 ), PolyString.of( "Alice" ), PolyInteger.of( 9 ) ),
                     List.of( PolyLong.of( 102 ), PolyString.of( "Bob" ), PolyInteger.of( 7 ) ) ).iterator(), progress::add );
@@ -222,7 +223,7 @@ void sourceWriterRoundTripsDocumentsAndGeneratesMissingIds() throws Exception {
         schemaState.mergeDocumentSchema( document, false );
         MessageType schema = new ParquetMessageTypeBuilder( schemaState, "writer_doc" ).build();
 
-        try ( ParquetSourceWriter writer = new ParquetSourceWriter( file, schema, ParquetSourceWriter.COMPRESSION_UNCOMPRESSED, schemaState, false ) ) {
+        try ( ParquetSourceWriter writer = new ParquetSourceWriter( new OutputLocalFile( file ), schema, ParquetSourceWriter.COMPRESSION_UNCOMPRESSED, schemaState, false ) ) {
             writer.writeRows( List.of( document ).iterator(), null );
         }
 
@@ -249,9 +250,9 @@ void sourceWriterReportsCompressionAndValueConversionFailures() throws Exception
         MessageType schema = new ParquetMessageTypeBuilder( schemaState, "writer_errors" ).build();
 
         //noinspection resource
-        assertThrows( GenericRuntimeException.class, () -> new ParquetSourceWriter( badCompression, schema, "brotli", schemaState, false ) );
+        assertThrows( GenericRuntimeException.class, () -> new ParquetSourceWriter( new OutputLocalFile( badCompression ), schema, "brotli", schemaState, false ) );
 
-        try ( ParquetSourceWriter writer = new ParquetSourceWriter( tempDir.resolve( "bad-value.parquet" ).toFile(), schema, ParquetSourceWriter.COMPRESSION_UNCOMPRESSED, schemaState, false ) ) {
+        try ( ParquetSourceWriter writer = new ParquetSourceWriter( new OutputLocalFile( tempDir.resolve( "bad-value.parquet" ).toFile() ), schema, ParquetSourceWriter.COMPRESSION_UNCOMPRESSED, schemaState, false ) ) {
             assertThrows( GenericRuntimeException.class, () -> writer.writeRows( List.of( List.<PolyValue>of( PolyString.of( "not-a-number" ) ) ).iterator(), null ) );
         }
     }
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetGroupedAggregateRelEnumeratorTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetGroupedAggregateRelEnumeratorTest.java
index 2ac4bc06a0..e668145101 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetGroupedAggregateRelEnumeratorTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetGroupedAggregateRelEnumeratorTest.java
@@ -43,14 +43,16 @@
 import org.junit.jupiter.api.BeforeAll;
 import org.junit.jupiter.api.Test;
 import org.junit.jupiter.api.io.TempDir;
-import org.polypheny.db.adapter.parquet.relational.execution.aggregate.ParquetGroupedAggregateRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
 import org.polypheny.db.adapter.parquet.shared.aggregate.AggregateCallDescriptor;
+import org.polypheny.db.adapter.parquet.shared.execution.aggregate.ParquetAggregateSource;
+import org.polypheny.db.adapter.parquet.shared.execution.aggregate.ParquetGroupedAggregateEnumerator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
+import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.catalog.entity.physical.PhysicalColumn;
 import org.polypheny.db.catalog.entity.physical.PhysicalTable;
@@ -91,8 +93,8 @@ void supportsDistinctOnlyGroupBy() throws Exception {
                 row( "Y", "Y" ) ) );
 
         Set<List<String>> values = new HashSet<>();
-        try ( ParquetGroupedAggregateRelEnumerator enumerator = new ParquetGroupedAggregateRelEnumerator(
-                table( file ),
+        try ( ParquetGroupedAggregateEnumerator enumerator = new ParquetGroupedAggregateEnumerator(
+                aggregateSource( table( file ) ),
                 new int[]{ 0, 1 },
                 2,
                 new AggregateCallDescriptor[0],
@@ -117,8 +119,8 @@ void scalarCountReturnsZeroWhenStatisticsPruneEveryFile() throws Exception {
                 .named( "test" );
         writeParquet( file, schema, List.<Object[]>of( row( "Y", "N" ) ) );
 
-        try ( ParquetGroupedAggregateRelEnumerator enumerator = new ParquetGroupedAggregateRelEnumerator(
-                table( file ),
+        try ( ParquetGroupedAggregateEnumerator enumerator = new ParquetGroupedAggregateEnumerator(
+                aggregateSource( table( file ) ),
                 new int[]{ 0 },
                 0,
                 new AggregateCallDescriptor[]{ AggregateCallDescriptor.countStar() },
@@ -147,6 +149,41 @@ flagA.id, new ParquetColumnBinding( flagA.id, "flag_a", ParquetColumnRole.DATA,
     }
 
 
+    private static ParquetAggregateSource aggregateSource( ParquetRelTable table ) {
+        ParquetSchemaReader schemaReader = new ParquetSchemaReader( table.getBinding().sourceFiles().stream().map( ParquetSourceFile::asSource ).toList() );
+        return new ParquetAggregateSource() {
+            @Override
+            public List<ParquetSourceFile> sourceFiles() {
+                return table.getBinding().sourceFiles();
+            }
+
+
+            @Override
+            public ParquetSchemaReader schemaReader() {
+                return schemaReader;
+            }
+
+
+            @Override
+            public int fieldCount() {
+                return table.columns.size();
+            }
+
+
+            @Override
+            public PolyType fieldType( int field ) {
+                return table.columns.get( field ).type;
+            }
+
+
+            @Override
+            public ParquetColumnBinding binding( int field ) {
+                return table.getBinding().getColumnBinding( table.columns.get( field ).id );
+            }
+        };
+    }
+
+
     private static PhysicalColumn column( long id, String name, int position ) {
         return new PhysicalColumn( id, name, name, 1, 1, 1, position, PolyType.VARCHAR, null, null, null, null, null, false, null, null );
     }
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetMetadataAggregateTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetMetadataAggregateTest.java
index 948b54ea55..6722243a84 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetMetadataAggregateTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetMetadataAggregateTest.java
@@ -45,7 +45,6 @@
 import org.junit.jupiter.api.Test;
 import org.junit.jupiter.api.io.TempDir;
 import org.polypheny.db.adapter.DataContext;
-import org.polypheny.db.adapter.parquet.relational.execution.aggregate.ParquetFileGroupedAggregateRelEnumerator;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelDataAggregateExecutor;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelMetadataAggregateExecutor;
 import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelProjectExecutor;
@@ -58,6 +57,8 @@
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetSourceFile;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetTableBinding;
 import org.polypheny.db.adapter.parquet.shared.aggregate.ColumnAggregateProjection;
+import org.polypheny.db.adapter.parquet.shared.execution.aggregate.ParquetAggregateSource;
+import org.polypheny.db.adapter.parquet.shared.execution.aggregate.ParquetFileGroupedAggregateEnumerator;
 import org.polypheny.db.adapter.parquet.shared.filter.ParquetAdapterFilter;
 import org.polypheny.db.adapter.parquet.shared.filter.FiltersContainer;
 import org.polypheny.db.adapter.parquet.shared.io.ParquetSchemaReader;
@@ -236,8 +237,8 @@ void fileGroupedAggregateGroupsPhysicalConstantColumns() throws Exception {
                         List.<Object[]>of( row( 3L, "2022", "11" ) ) ) );
 
         Map<List<String>, Double> sums = new LinkedHashMap<>();
-        try ( ParquetFileGroupedAggregateRelEnumerator enumerator = new ParquetFileGroupedAggregateRelEnumerator(
-                table,
+        try ( ParquetFileGroupedAggregateEnumerator enumerator = new ParquetFileGroupedAggregateEnumerator(
+                aggregateSource( table ),
                 new int[]{ 0, 1, 2 },
                 new int[]{ 1, 2 },
                 new String[]{ Kind.SUM.name() },
@@ -305,8 +306,8 @@ void fileGroupedScalarCountReturnsZeroWhenFiltersRejectEveryFile() throws Except
         ParquetRelTable table = physicalPartitionColumnTable(
                 List.of( List.of( row( 1L, "2022", "10" ), row( 2L, "2022", "10" ) ) ) );
 
-        try ( ParquetFileGroupedAggregateRelEnumerator enumerator = new ParquetFileGroupedAggregateRelEnumerator(
-                table,
+        try ( ParquetFileGroupedAggregateEnumerator enumerator = new ParquetFileGroupedAggregateEnumerator(
+                aggregateSource( table ),
                 new int[]{ 0, 1, 2 },
                 new int[0],
                 new String[]{ Kind.COUNT.name() },
@@ -421,6 +422,41 @@ protected void registerAdapter( DataContext dataContext ) {
     }
 
 
+    private static ParquetAggregateSource aggregateSource( ParquetRelTable table ) {
+        ParquetSchemaReader schemaReader = new ParquetSchemaReader( table.getBinding().sourceFiles().stream().map( ParquetSourceFile::asSource ).toList() );
+        return new ParquetAggregateSource() {
+            @Override
+            public List<ParquetSourceFile> sourceFiles() {
+                return table.getBinding().sourceFiles();
+            }
+
+
+            @Override
+            public ParquetSchemaReader schemaReader() {
+                return schemaReader;
+            }
+
+
+            @Override
+            public int fieldCount() {
+                return table.columns.size();
+            }
+
+
+            @Override
+            public PolyType fieldType( int field ) {
+                return table.columns.get( field ).type;
+            }
+
+
+            @Override
+            public ParquetColumnBinding binding( int field ) {
+                return table.getBinding().getColumnBinding( table.columns.get( field ).id );
+            }
+        };
+    }
+
+
     private static TestProjectExecutor projectExecutor( ParquetRelTable table ) {
         ParquetSchemaReader schemaReader = new ParquetSchemaReader( table.getBinding().sourceFiles().stream().map( ParquetSourceFile::asSource ).toList() );
         return new TestProjectExecutor( table, schemaReader );
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedProjectionTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNestedRepeatedProjectionTest.java
similarity index 99%
rename from plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedProjectionTest.java
rename to plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNestedRepeatedProjectionTest.java
index b72907e18c..c735feb646 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedProjectionTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetNestedRepeatedProjectionTest.java
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-package org.polypheny.db.adapter.parquet.relational.execution;
+package org.polypheny.db.adapter.parquet;
 
 import static org.junit.jupiter.api.Assertions.assertEquals;
 import static org.junit.jupiter.api.Assertions.assertFalse;
@@ -40,6 +40,7 @@
 import org.junit.jupiter.api.BeforeAll;
 import org.junit.jupiter.api.Test;
 import org.junit.jupiter.api.io.TempDir;
+import org.polypheny.db.adapter.parquet.relational.execution.ParquetRelExecutor;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnBinding;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetColumnRole;
 import org.polypheny.db.adapter.parquet.relational.schema.ParquetRelTable;
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRowAggregateRelEnumeratorTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRowAggregateRelEnumeratorTest.java
index 9f5278dd31..c2d29b8b38 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRowAggregateRelEnumeratorTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetRowAggregateRelEnumeratorTest.java
@@ -23,7 +23,7 @@
 import org.apache.calcite.linq4j.Linq4j;
 import org.junit.jupiter.api.BeforeAll;
 import org.junit.jupiter.api.Test;
-import org.polypheny.db.adapter.parquet.relational.execution.aggregate.ParquetRowAggregateRelEnumerator;
+import org.polypheny.db.adapter.parquet.shared.execution.aggregate.ParquetRowAggregateEnumerator;
 import org.polypheny.db.algebra.constant.Kind;
 import org.polypheny.db.type.entity.PolyNull;
 import org.polypheny.db.type.entity.PolyValue;
@@ -55,7 +55,7 @@ void supportsCountStarWithSumMinAndMax() {
                 { PolyLong.of( 2023 ), PolyLong.of( 7 ), PolyNull.NULL }
         };
 
-        try ( ParquetRowAggregateRelEnumerator enumerator = new ParquetRowAggregateRelEnumerator(
+        try ( ParquetRowAggregateEnumerator enumerator = new ParquetRowAggregateEnumerator(
                 Linq4j.asEnumerable( rows ).enumerator(),
                 new int[]{ 0 },
                 new String[]{ Kind.COUNT.name(), Kind.SUM.name(), Kind.MIN.name(), Kind.MAX.name() },
@@ -99,7 +99,7 @@ void supportsMultipleGroupingColumnsAndNullGroupKeys() {
                 { PolyNull.NULL, PolyLong.of( 1 ), PolyNull.NULL }
         };
 
-        try ( ParquetRowAggregateRelEnumerator enumerator = new ParquetRowAggregateRelEnumerator(
+        try ( ParquetRowAggregateEnumerator enumerator = new ParquetRowAggregateEnumerator(
                 Linq4j.asEnumerable( rows ).enumerator(),
                 new int[]{ 0, 1 },
                 new String[]{ Kind.COUNT.name(), Kind.SUM.name(), Kind.MIN.name(), Kind.MAX.name() },
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java
index 4a1afcaa28..c3f6412d9b 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java
@@ -58,6 +58,7 @@
 import org.polypheny.db.rex.RexCall;
 import org.polypheny.db.rex.RexDynamicParam;
 import org.polypheny.db.rex.RexIndexRef;
+import org.polypheny.db.rex.RexLiteral;
 import org.polypheny.db.rex.RexNameRef;
 import org.polypheny.db.rex.RexNode;
 import org.polypheny.db.type.PolyType;
@@ -191,6 +192,10 @@ void docFilterTranslatorTranslatesTopLevelNameAndRejectsNestedPaths() {
                 typeFactory.createArrayType( typeFactory.createPolyType( PolyType.CHAR, 6 ), 1 ),
                 new SpecialOperator( Kind.ARRAY_VALUE_CONSTRUCTOR.name(), Kind.ARRAY_VALUE_CONSTRUCTOR ),
                 List.of( rexBuilder.makeLiteral( "status" ) ) );
+        RexLiteral foldedPath = new RexLiteral(
+                PolyList.of( PolyString.of( "status" ) ),
+                typeFactory.createArrayType( typeFactory.createPolyType( PolyType.CHAR, 255 ), -1 ),
+                PolyType.ARRAY );
         RexNode loweredMqlFilter = call(
                 boolType,
                 Kind.GREATER_THAN,
@@ -199,6 +204,29 @@ void docFilterTranslatorTranslatesTopLevelNameAndRejectsNestedPaths() {
                         new SpecialOperator( Kind.MQL_QUERY_VALUE.name(), Kind.MQL_QUERY_VALUE ),
                         List.of( RexIndexRef.of( 0, DocumentType.ofDoc() ), path ) ),
                 new RexDynamicParam( stringType, 7 ) );
+        RexNode castedConjunction = call(
+                boolType,
+                Kind.CAST,
+                call(
+                        boolType,
+                        Kind.AND,
+                        loweredMqlFilter,
+                        call(
+                                boolType,
+                                Kind.LESS_THAN,
+                                new RexCall(
+                                        stringType,
+                                        new SpecialOperator( Kind.MQL_QUERY_VALUE.name(), Kind.MQL_QUERY_VALUE ),
+                        List.of( RexIndexRef.of( 0, DocumentType.ofDoc() ), path ) ),
+                                new RexDynamicParam( stringType, 8 ) ) ) );
+        RexNode foldedMqlFilter = call(
+                boolType,
+                Kind.LESS_THAN,
+                new RexCall(
+                        stringType,
+                        new SpecialOperator( Kind.MQL_QUERY_VALUE.name(), Kind.MQL_QUERY_VALUE ),
+                        List.of( RexIndexRef.of( 0, DocumentType.ofDoc() ), foldedPath ) ),
+                new RexDynamicParam( stringType, 9 ) );
 
         assertNotNull( translated );
         assertEquals( 3, translated.columnIndex() );
@@ -213,6 +241,17 @@ void docFilterTranslatorTranslatesTopLevelNameAndRejectsNestedPaths() {
         assertEquals( 3, loweredMql.columnIndex() );
         assertEquals( Kind.GREATER_THAN, loweredMql.operator() );
         assertEquals( 7L, loweredMql.dynamicParamIndex() );
+        ParquetAdapterFilter<?> logical = translator.translate( columns, castedConjunction );
+        assertNotNull( logical );
+        assertEquals( Kind.AND, logical.operator() );
+        assertEquals( 2, logical.operands().size() );
+        assertEquals( Kind.GREATER_THAN, logical.operands().get( 0 ).operator() );
+        assertEquals( Kind.LESS_THAN, logical.operands().get( 1 ).operator() );
+        ParquetAdapterFilter<?> foldedMql = translator.translate( columns, foldedMqlFilter );
+        assertNotNull( foldedMql );
+        assertEquals( 3, foldedMql.columnIndex() );
+        assertEquals( Kind.LESS_THAN, foldedMql.operator() );
+        assertEquals( 9L, foldedMql.dynamicParamIndex() );
         assertNull( translator.translate( columns, nested ) );
     }
 

From ff8b35e6c89a28268a4b7af9fe1ff91ed29de830 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Sun, 28 Jun 2026 12:52:01 +0200
Subject: [PATCH 41/46] benchmarks, bug fixes

---
 plugins/parquet-adapter/benchmarks/README.md  |  17 +-
 .../benchmarks/benchmark-plan.md              | 152 ++++--
 ...ss_model_comparison_query_specification.md |   8 +-
 .../aggregation_polypheny.sql                 |  19 +-
 .../aggregation_polypheny_mql.sql             |   6 +-
 .../aggregation_query_specification.md        |  69 +--
 .../aggregation_sql.sql                       |  19 +-
 .../nested_data/nested_data_duckdb.sql        |  39 +-
 .../nested_data/nested_data_mql.mql           |  19 +-
 .../nested_data_polypheny_normalized.sql      |  18 +-
 .../nested_data_query_specification.md        |  43 +-
 .../nested_data/nested_data_spark.sql         |  14 +-
 .../partitioning_query_specification.md       | 121 ++---
 .../partitioning_repartitioned_sql.sql        |  10 +-
 ..._repartitioned_unpartitioned_polypheny.sql |  20 +-
 .../partitioning_unpartitioned_sql.sql        |  10 +-
 ...s_model_comparison_tlcp_result_analysis.md |  46 ++
 ..._model_comparison_tlcp_results_analysis.md |  37 --
 .../access_model_comparison_tlcp_summary.md   |  78 ++--
 .../aggregation_correctness_summary.md        | 158 +++++++
 .../aggregation_duckdb_results.csv            | 120 ++---
 .../aggregation_duckdb_values.jsonl           |  10 +
 .../aggregation_polypheny_mql_results.csv     | 120 ++---
 .../aggregation_polypheny_mql_values.jsonl    |  10 +
 .../aggregation_polypheny_results.csv         | 120 ++---
 .../aggregation_polypheny_results.csv.old     |  61 +++
 .../aggregation_polypheny_values.jsonl        |  10 +
 .../aggregation_polypheny_values.jsonl.old    |  10 +
 .../aggregation_result_analysis.md            |  64 +++
 .../aggregation_results_analysis.md           |  37 --
 .../aggregation/aggregation_spark_results.csv | 120 ++---
 .../aggregation_spark_values.jsonl            |  10 +
 .../aggregation/aggregation_summary.md        | 144 +++---
 .../nested_data_duckdb_results.csv            |  47 +-
 .../nested_data_polypheny_mql_results.csv     |  30 ++
 ...sted_data_polypheny_normalized_results.csv |  54 ++-
 .../nested_data/nested_data_spark_results.csv |  60 +--
 .../nested_data/nested_data_summary.md        |  58 +++
 ...titioning_duckdb_repartitioned_results.csv |  60 +--
 ...titioning_duckdb_unpartitioned_results.csv |  60 +--
 .../partitioning_polypheny_results.csv        | 120 ++---
 .../partitioning_results_analysis.md          |  80 +++-
 ...rtitioning_spark_repartitioned_results.csv |  60 +--
 ...rtitioning_spark_unpartitioned_results.csv |  60 +--
 .../partitioning/partitioning_summary.md      | 128 ++---
 .../access_model_comparison_tlcp_plot.pdf     | Bin 5553 -> 5967 bytes
 .../access_model_comparison_tlcp_plot.png     | Bin 25861 -> 28577 bytes
 .../access_model_comparison_tlcp_plot.svg     | 153 +++---
 .../results/plots/aggregation_plot.pdf        | Bin 6126 -> 8367 bytes
 .../results/plots/aggregation_plot.png        | Bin 22099 -> 34568 bytes
 .../results/plots/aggregation_plot.svg        | 205 ++++----
 .../results/plots/nested_data_plot.pdf        | Bin 0 -> 5394 bytes
 .../results/plots/nested_data_plot.png        | Bin 0 -> 25585 bytes
 .../results/plots/nested_data_plot.svg        |  76 +++
 .../results/plots/partitioning_plot.pdf       | Bin 0 -> 6237 bytes
 .../results/plots/partitioning_plot.png       | Bin 0 -> 27090 bytes
 .../results/plots/partitioning_plot.svg       | 157 ++++---
 ...run_access_model_comparison_bm_pipeline.md |   4 +
 .../run_aggregation_bm_pipeline.md            |  31 +-
 .../run_nested_data_bm_pipeline.md            |   4 +
 .../run_partitioning_bm_pipeline.md           |   7 +-
 .../scripts/compare_aggregation_results.py    | 386 +++++++++++++++
 .../implementation/DuckDbJdbcBenchmark.java   | 160 +++++++
 .../PolyphenyJdbcBenchmark.java               | 159 +++++++
 .../implementation/PolyphenyMqlBenchmark.java | 290 ++++++++++++
 .../implementation/SparkSqlBenchmark.py       |  42 ++
 .../benchmark_result_plot_generator.py        | 438 ++++++++++++++++--
 .../scripts/runners/run_duckdb_benchmark.ps1  |   5 +
 .../runners/run_polypheny_benchmark.ps1       |   5 +
 .../runners/run_polypheny_mql_benchmark.ps1   |   5 +
 .../scripts/runners/run_spark_benchmark.ps1   |  15 +
 .../scripts/summarize_benchmark_results.py    |  24 +-
 .../AbstractParquetValueExtractor.java        |  42 ++
 .../io/aggregate/page/ParquetPageReader.java  |   7 +-
 .../ParquetCountAggregatePageReaderTest.java  |  45 ++
 .../ParquetSharedDocumentAdapterTest.java     |  35 ++
 76 files changed, 3541 insertions(+), 1310 deletions(-)
 rename plugins/parquet-adapter/benchmarks/query_lists/{arrregation => aggregation}/aggregation_polypheny.sql (81%)
 rename plugins/parquet-adapter/benchmarks/query_lists/{arrregation => aggregation}/aggregation_polypheny_mql.sql (92%)
 rename plugins/parquet-adapter/benchmarks/query_lists/{arrregation => aggregation}/aggregation_query_specification.md (65%)
 rename plugins/parquet-adapter/benchmarks/query_lists/{arrregation => aggregation}/aggregation_sql.sql (80%)
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_result_analysis.md
 delete mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_results_analysis.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_correctness_summary.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_values.jsonl
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_values.jsonl
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv.old
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_values.jsonl
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_values.jsonl.old
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_result_analysis.md
 delete mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_results_analysis.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_values.jsonl
 create mode 100644 plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_summary.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/nested_data_plot.pdf
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/nested_data_plot.png
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/nested_data_plot.svg
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/partitioning_plot.pdf
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/partitioning_plot.png
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/compare_aggregation_results.py

diff --git a/plugins/parquet-adapter/benchmarks/README.md b/plugins/parquet-adapter/benchmarks/README.md
index 5d299e1b13..b22436beaf 100644
--- a/plugins/parquet-adapter/benchmarks/README.md
+++ b/plugins/parquet-adapter/benchmarks/README.md
@@ -28,7 +28,7 @@ and result artifacts for the Polypheny Parquet adapter comparison.
 | `query_lists/data_configuration.md`    | Local data paths and Polypheny adapter names used by the query files.                                                                              |
 | `query_lists/access_model_comparison/` | Access-model comparison benchmark queries for Polypheny relational flat, Polypheny relational normalized, Polypheny MQL, DuckDB, and Spark.        |
 | `query_lists/nested_data/`             | Nested Customer benchmark queries. Engine-specific files are used because nested access syntax differs.                                            |
-| `query_lists/arrregation/`             | Aggregation benchmark query files for Polypheny relational, Polypheny MQL, DuckDB, and Spark. The directory name is kept as-is for existing paths. | 
+| `query_lists/aggregation/`             | Aggregation benchmark query files for Polypheny relational, Polypheny MQL, DuckDB, and Spark.                                                       |
 | `query_lists/partitioning/`            | Partitioning benchmark queries for repartitioned and unpartitioned TLC layouts.                                                                    |
 
 File naming:
@@ -67,11 +67,25 @@ run the benchmark clients, and generate the suite summary and plot.
 | `scripts/implementation/`                | Java/Python benchmark clients called by the runners.                         |
 | `scripts/ds_preprocessing/`              | Dataset materialization and preprocessing utilities.                         |
 | `scripts/summarize_benchmark_results.py` | Creates markdown summaries from benchmark CSV files.                         |
+| `scripts/compare_aggregation_results.py` | Compares captured aggregation result values across systems.                  |
 | `scripts/plot_generation/`               | Plot generation scripts, including the generic SVG/PDF/PNG benchmark CSV plot generator. |
 
 PNG plot output requires Pillow. SVG and PDF output use only the Python
 standard library.
 
+### Plot Color Convention
+
+Benchmark plots use a fixed color convention so that the same system or access
+path has the same color across all result figures.
+
+| System or access path | Color |
+|-----------------------|-------|
+| Polypheny Relational Flat | Green (`#4f9d55`) |
+| Polypheny Relational Normalized | Red (`#c94c4c`) |
+| Polypheny Document | Purple (`#7b61b9`) |
+| DuckDB | Orange (`#f0a22e`) |
+| Apache Spark | Blue (`#2f6fbb`) |
+
 ## Results
 
 Raw benchmark outputs are stored as CSV files under:
@@ -83,6 +97,7 @@ results/
 Result conventions:
 
 - `*_results.csv`: raw benchmark output from a runner.
+- `*_values.jsonl`: captured query result values used for correctness checks.
 - `*_summary.md`: generated comparison summary from `scripts/summarize_benchmark_results.py`.
 - `*_results_analysis.md`: short manual interpretation of a generated summary.
 - `results/plots/`: generated plot artifacts such as SVG, PDF, and PNG files.
diff --git a/plugins/parquet-adapter/benchmarks/benchmark-plan.md b/plugins/parquet-adapter/benchmarks/benchmark-plan.md
index afa1fa72f8..4dc4b289a4 100644
--- a/plugins/parquet-adapter/benchmarks/benchmark-plan.md
+++ b/plugins/parquet-adapter/benchmarks/benchmark-plan.md
@@ -2,7 +2,7 @@
 
 ## Goals
 
-The benchmarks evaluate the Parquet adapter from following perspectives:
+The benchmarks evaluate the Parquet adapter from the following perspectives:
 
 - correctness of the implemented access paths;
 - runtime performance for representative analytical workloads;
@@ -21,11 +21,12 @@ Unless a benchmark suite states otherwise, each query is executed with:
 - result row count and column count recorded for correctness checks;
 - failures and timeouts recorded instead of runtime values.
 
-Warmup runs are excluded from reported results. The benchmark clients consume
-query results according to the result boundary described in the report
-methodology. Polypheny and DuckDB use JDBC clients that drain the complete result
-set. Spark is executed in local mode inside Docker and consumes rows in the
-Spark runner.
+Warmup runs are excluded from reported results. Summary tables report mean,
+median, sample standard deviation, minimum, and maximum elapsed time over
+successful measured runs only. The benchmark clients consume query results
+according to the result boundary described in the report methodology. Polypheny
+and DuckDB use JDBC clients that drain the complete result set. Spark is
+executed in local mode inside Docker and consumes rows in the Spark runner.
 
 Raw benchmark outputs are stored as CSV files. Summary tables are generated with:
 
@@ -35,10 +36,12 @@ python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py
 
 ## Datasets
 
-| Dataset                | Local input                   | Main purpose                                           |
-|------------------------|-------------------------------|--------------------------------------------------------|
-| NYC TLC Trip Records   | `C:\PolyData\tlc_partitioned` | Flat analytical data and Hive-style partitioning       |
-| Nested Customer        | `C:\PolyData\nested_customer` | Deep nested structures and generated normalized tables |
+| Dataset                     | Local input                                      | Main purpose                                           |
+|-----------------------------|--------------------------------------------------|--------------------------------------------------------|
+| NYC TLC partitioned         | `C:\PolyData\tlc_partitioned`                    | Access model and aggregation workloads                 |
+| NYC TLC repartitioned       | `C:\PolyData\tlc_repartitioned`                  | Hive-style partitioning and partition-pruning checks   |
+| NYC TLC unpartitioned       | `C:\PolyData\tlc_unpartitioned`                  | Physical `year`/`month` column comparison              |
+| Nested Customer             | `C:\PolyData\nested_customer\nestedcustomer.parquet` | Deep nested structures and generated normalized tables |
 
 The TLC snapshot contains data from January 2020 through January 2023. This
 period is used because newer TLC files introduce schema changes that make a
@@ -57,7 +60,9 @@ Parquet workloads where the same logical operation can be expressed.
 
 ## Suite 1: Access Model Comparison
 
-Status: implemented for the TLC `green_tripdata` table.
+Status: executed for the TLC `green_tripdata` table. All five evaluated
+systems completed `5/5` measured runs for Q01-Q05, and result row counts matched
+across systems.
 
 ### Purpose
 
@@ -111,7 +116,7 @@ Raw and summarized results are stored in:
 plugins/parquet-adapter/benchmarks/results/access_model_comparison/
 ```
 
-Expected raw result files:
+Executed raw result files:
 
 ```text
 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rf_tlcp_results.csv
@@ -121,10 +126,11 @@ plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_
 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_spark_tlcp_results.csv
 ```
 
-The current summary file is:
+The current summary and analysis files are:
 
 ```text
 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_results_analysis.md
 ```
 
 ### Run Pipeline
@@ -138,12 +144,15 @@ plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_p
 
 ## Suite 2: Nested Data
 
-Status: planned.
+Status: executed. Polypheny relational normalized, DuckDB, and Apache Spark
+completed `5/5` measured runs for Q01-Q05. Polypheny document MQL completed
+Q01-Q02, while Q03-Q05 failed in all measured runs because the nested MQL path
+hit runtime index/conversion errors recorded in the summary.
 
 ### Purpose
 
-This suite evaluates nested Parquet data, repeated fields, large binary fields,
-and generated normalized relational tables.
+This suite evaluates nested Parquet data, repeated fields, and generated
+normalized relational tables.
 
 ### Dataset
 
@@ -152,6 +161,8 @@ and generated normalized relational tables.
 Dataset description:
 `plugins/parquet-adapter/benchmarks/datasets/nested_customer.md`
 
+Input file: `C:\PolyData\nested_customer\nestedcustomer.parquet`.
+
 ### Compared Systems and Access Paths
 
 - Polypheny relational adapter in normalized mode;
@@ -184,9 +195,13 @@ Raw and summarized results are stored in:
 plugins/parquet-adapter/benchmarks/results/nested_data/
 ```
 
-Summary file:
+Executed raw result and summary files:
 
 ```text
+plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv
+plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_mql_results.csv
+plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_duckdb_results.csv
+plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_spark_results.csv
 plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_summary.md
 ```
 
@@ -200,12 +215,15 @@ plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md
 
 ## Suite 3: Aggregation and Optimization
 
-Status: planned.
+Status: executed. Polypheny relational, Polypheny document MQL, DuckDB, and
+Apache Spark completed `5/5` measured runs for Q01-Q10. Result row counts match
+across systems in the timing summary; the correctness comparison records
+remaining MQL grouped-result column-shape differences for Q05, Q09, and Q10.
 
 ### Purpose
 
-This suite evaluates analytical aggregations and the effect of optimization work
-introduced during development.
+This suite evaluates analytical aggregations and the effect of Parquet-specific
+optimization work introduced during development.
 
 ### Dataset
 
@@ -216,14 +234,14 @@ TLC tables from `C:\PolyData\tlc_partitioned`. The aggregation suite runs on
 
 | System                 | Query file                                                                                 |
 |------------------------|--------------------------------------------------------------------------------------------|
-| Polypheny relational   | `plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny.sql`     |
-| Polypheny document MQL | `plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny_mql.sql` |
-| DuckDB and Spark       | `plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_sql.sql`           |
+| Polypheny relational   | `plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_polypheny.sql`     |
+| Polypheny document MQL | `plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_polypheny_mql.sql` |
+| DuckDB and Spark       | `plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_sql.sql`           |
 
 Human-readable query source:
 
 ```text
-plugins/parquet-adapter/benchmarks/query_lists/aggregation_queries.md
+plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_query_specification.md
 ```
 
 ### Query Groups
@@ -231,12 +249,12 @@ plugins/parquet-adapter/benchmarks/query_lists/aggregation_queries.md
 Specification stored in:
 
 ```text
-plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_specification.md
+plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_query_specification.md
 ```
 
 ### Compared Systems and Access Paths
 
-- Polypheny relational flat before and after optimization, where both versions are available;
+- Polypheny relational adapter;
 - Polypheny document adapter queried through MQL;
 - DuckDB;
 - Apache Spark.
@@ -246,9 +264,43 @@ plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_spe
 - full-table counts;
 - counts over one partition month;
 - selective filtered counts;
-- grouped monthly aggregations;
+- grouped yearly aggregations;
 - grouped aggregations over large TLC tables.
 
+### Result Files
+
+Raw and summarized results are stored in:
+
+```text
+plugins/parquet-adapter/benchmarks/results/aggregation/
+```
+
+Executed raw result files:
+
+```text
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_results.csv
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_results.csv
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_results.csv
+```
+
+Captured result values used for correctness checks:
+
+```text
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_values.jsonl
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_values.jsonl
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_values.jsonl
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_values.jsonl
+```
+
+The current summary, correctness summary, and analysis files are:
+
+```text
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_correctness_summary.md
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_results_analysis.md
+```
+
 ### Run Pipeline
 
 Runnable commands for this suite are documented in:
@@ -259,7 +311,9 @@ plugins/parquet-adapter/benchmarks/run_pipeline/run_aggregation_bm_pipeline.md
 
 ## Suite 4: Partitioning
 
-Status: planned.
+Status: executed. Polypheny relational, DuckDB repartitioned, DuckDB
+unpartitioned, Apache Spark repartitioned, and Apache Spark unpartitioned all
+completed `5/5` measured runs for their applicable query/layout combinations.
 
 ### Purpose
 
@@ -269,7 +323,12 @@ on Parquet workloads.
 ### Dataset
 
 Partitioned and derived unpartitioned or repartitioned variants of the TLC
-dataset.
+dataset:
+
+```text
+C:\PolyData\tlc_repartitioned
+C:\PolyData\tlc_unpartitioned
+```
 
 ### Compared Systems and Access Paths
 
@@ -294,12 +353,45 @@ Specification stored in:
 plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md
 ```
 
+The query identifiers use paired layout suffixes:
+
+- `Q1_P` / `Q1_NP`: full count baseline;
+- `Q2_P` / `Q2_NP`: year predicate;
+- `Q3_P` / `Q3_NP`: year and month predicate;
+- `Q4_P` / `Q4_NP`: data-column filtered count;
+- `Q5_P` / `Q5_NP`: year plus data-column filtered count.
+
+### Result Files
+
+Raw and summarized results are stored in:
+
+```text
+plugins/parquet-adapter/benchmarks/results/partitioning/
+```
+
+Executed raw result files:
+
+```text
+plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_polypheny_results.csv
+plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_repartitioned_results.csv
+plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_unpartitioned_results.csv
+plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_repartitioned_results.csv
+plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_unpartitioned_results.csv
+```
+
+The current summary and analysis files are:
+
+```text
+plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
+plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_results_analysis.md
+```
+
 ### Query Themes
 
 - filters on partition columns such as `year` and `month`;
 - filters on physical Parquet columns;
 - combinations of partition filters and data filters;
-- aggregations grouped by partition columns.
+- count aggregation over equivalent partitioned and unpartitioned layouts.
 
 ### Run Pipeline
 
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_query_specification.md b/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_query_specification.md
index 3c2f16b775..87645d6221 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_query_specification.md
+++ b/plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_query_specification.md
@@ -7,14 +7,14 @@ table fields, with no generated child-table joins.
 
 | Query | Operation           | Predicate                                          | Returned data                                      | Purpose                                                    |
 |-------|---------------------|----------------------------------------------------|----------------------------------------------------|------------------------------------------------------------|
-| Q01   | Full scan           | None                                               | All fields                                         | Measures full dataset read performance                     |
+| Q01   | Full record or document access | None                                      | All fields                                         | Measures full dataset read performance                     |
 | Q02   | Projection          | None                                               | Selected fields                                    | Measures projection behavior                               |
 | Q03   | Filtered count      | `trip_distance >= 10.0` and `total_amount >= 40.0` | Aggregate count                                    | Measures filter evaluation without returning matching rows |
-| Q04   | Filtered scan       | `trip_distance >= 10.0` and `total_amount >= 40.0` | All matching records or documents                  | Measures filtering with full result consumption            |
+| Q04   | Filtered full record or document access | `trip_distance >= 10.0` and `total_amount >= 40.0` | All matching records or documents                  | Measures filtering with full result consumption            |
 | Q05   | Filtered projection | `trip_distance >= 10.0` and `total_amount >= 40.0` | Selected fields from matching records or documents | Measures combined filtering and projection                 |
 
 
-## Q1 - Full scan
+## Q1 - Full record or document access
 
 Text:
 
@@ -68,7 +68,7 @@ WHERE trip_distance >= 10.0
   AND total_amount >= 40.0;
 ```
 
-## Q4 - Filtered full scan
+## Q4 - Filtered full record or document access
 
 Text:
 
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny.sql b/plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_polypheny.sql
similarity index 81%
rename from plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny.sql
rename to plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_polypheny.sql
index 000275a5fb..13a755c567 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny.sql
+++ b/plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_polypheny.sql
@@ -20,17 +20,16 @@ FROM tlcp__yellow_tripdata
 WHERE trip_distance >= 10.0
   AND total_amount >= 40.0;
 
--- Q05: Monthly Yellow Taxi Amount And Distance Summary
+-- Q05: Yearly Yellow Taxi Amount And Distance Summary
 SELECT
   "year",
-  "month",
   count(*) AS trips,
   sum(total_amount) AS gross_amount,
   min(trip_distance) AS min_distance,
   max(trip_distance) AS max_distance
 FROM tlcp__yellow_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
+GROUP BY "year"
+ORDER BY "year";
 
 -- Q06: Full High-Volume FHV Row Count
 SELECT count(*) AS row_count
@@ -48,25 +47,23 @@ FROM tlcp__fhvhv_tripdata
 WHERE trip_miles >= 10.0
   AND base_passenger_fare >= 40.0;
 
--- Q09: Monthly High-Volume FHV Fare And Distance Summary
+-- Q09: Yearly High-Volume FHV Fare And Distance Summary
 SELECT
   "year",
-  "month",
   count(*) AS trips,
   sum(base_passenger_fare) AS passenger_fare,
   sum(driver_pay) AS driver_pay,
   min(trip_miles) AS min_miles,
   max(trip_miles) AS max_miles
 FROM tlcp__fhvhv_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
+GROUP BY "year"
+ORDER BY "year";
 
--- Q10: High-Volume FHV Shared-Ride Flag Distribution
+-- Q10: High-Volume FHV Shared-Request Flag Distribution
 SELECT
   shared_request_flag,
-  shared_match_flag,
   count(*) AS trips
 FROM tlcp__fhvhv_tripdata
 WHERE "year" = '2022'
-GROUP BY shared_request_flag, shared_match_flag
+GROUP BY shared_request_flag
 ORDER BY trips DESC;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny_mql.sql b/plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_polypheny_mql.sql
similarity index 92%
rename from plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny_mql.sql
rename to plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_polypheny_mql.sql
index 62568bf272..0caccc8c17 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny_mql.sql
+++ b/plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_polypheny_mql.sql
@@ -38,7 +38,7 @@ tlcpd_document.tlcpd__yellow_tripdata.aggregate([
   { "$count": "row_count" }
 ]);
 
--- Q05: Monthly Yellow Taxi Amount And Distance Summary
+-- Q05: Yearly Yellow Taxi Amount And Distance Summary
 tlcpd_document.tlcpd__yellow_tripdata.aggregate([
   {
     "$group": {
@@ -78,7 +78,7 @@ tlcpd_document.tlcpd__fhvhv_tripdata.aggregate([
   { "$count": "row_count" }
 ]);
 
--- Q09: Monthly High-Volume FHV Fare And Distance Summary
+-- Q09: Yearly High-Volume FHV Fare And Distance Summary
 tlcpd_document.tlcpd__fhvhv_tripdata.aggregate([
   {
     "$group": {
@@ -92,7 +92,7 @@ tlcpd_document.tlcpd__fhvhv_tripdata.aggregate([
   { "$sort": { "_id": 1 } }
 ]);
 
--- Q10: High-Volume FHV Shared-Ride Flag Distribution
+-- Q10: High-Volume FHV Shared-Request Flag Distribution
 tlcpd_document.tlcpd__fhvhv_tripdata.aggregate([
   {
     "$match": {
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_specification.md b/plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_query_specification.md
similarity index 65%
rename from plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_specification.md
rename to plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_query_specification.md
index b7e47b98a0..59e3b3bddc 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_query_specification.md
+++ b/plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_query_specification.md
@@ -3,26 +3,31 @@
 | Query | Operation                                         | Predicate                                                                                                              | Returned data                                                                   | Purpose                                             |
 |-------|---------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------|-----------------------------------------------------|
 | Q01   | Full Yellow Taxi row count                        | None                                                                                                                   | Aggregate count                                                                 | Measures metadata-like count over `yellow_tripdata` |
-| Q02   | Yellow Taxi one-month partition count             | `year = '2022'` and `month = '10'`                                                                                     | Aggregate count                                                                 | Measures count over one partition month             |
-| Q03   | Yellow Taxi one-day timestamp count               | `tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'` and `tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00'` | Aggregate count                                                                 | Measures count with a physical timestamp predicate  |
-| Q04   | Yellow Taxi filtered count                        | `trip_distance >= 10.0` and `total_amount >= 40.0`                                                                     | Aggregate count                                                                 | Measures selective filtering over data columns      |
-| Q05   | Yellow Taxi monthly amount and distance summary   | None                                                                                                                   | Monthly count, total amount, minimum distance, maximum distance                 | Measures grouped aggregation over `yellow_tripdata` |
+| Q02   | Yellow Taxi count for one partition month         | `year = '2022'` and `month = '10'`                                                                                     | Aggregate count                                                                 | Measures count over one partition month             |
+| Q03   | Yellow Taxi trips on one day                      | `tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'` and `tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00'` | Aggregate count                                                                 | Measures count with a physical timestamp predicate  |
+| Q04   | Long and expensive Yellow Taxi trips              | `trip_distance >= 10.0` and `total_amount >= 40.0`                                                                     | Aggregate count                                                                 | Measures selective filtering over data columns      |
+| Q05   | Yellow Taxi yearly amount and distance summary    | None                                                                                                                   | Yearly total amount, minimum distance, and maximum distance; SQL-based variants also return trip count | Measures grouped aggregation over `yellow_tripdata` |
 | Q06   | Full High-Volume FHV row count                    | None                                                                                                                   | Aggregate count                                                                 | Measures metadata-like count over `fhvhv_tripdata`  |
-| Q07   | High-Volume FHV one-month partition count         | `year = '2022'` and `month = '10'`                                                                                     | Aggregate count                                                                 | Measures count over one partition month             |
-| Q08   | High-Volume FHV filtered count                    | `trip_miles >= 10.0` and `base_passenger_fare >= 40.0`                                                                 | Aggregate count                                                                 | Measures selective filtering over data columns      |
-| Q09   | High-Volume FHV monthly fare and distance summary | None                                                                                                                   | Monthly count, passenger fare sum, driver pay sum, minimum miles, maximum miles | Measures grouped aggregation over `fhvhv_tripdata`  |
-| Q10   | High-Volume FHV shared-ride flag distribution     | `year = '2022'`                                                                                                        | Count by shared-request and shared-match flags                                  | Measures grouped aggregation over categorical flags |
+| Q07   | High-Volume FHV count for one partition month     | `year = '2022'` and `month = '10'`                                                                                     | Aggregate count                                                                 | Measures count over one partition month             |
+| Q08   | Long and expensive High-Volume FHV trips          | `trip_miles >= 10.0` and `base_passenger_fare >= 40.0`                                                                 | Aggregate count                                                                 | Measures selective filtering over data columns      |
+| Q09   | High-Volume FHV yearly fare and distance summary  | None                                                                                                                   | Yearly passenger fare sum, driver pay sum, minimum miles, and maximum miles; SQL-based variants also return trip count | Measures grouped aggregation over `fhvhv_tripdata`  |
+| Q10   | High-Volume FHV shared-request flag distribution  | `year = '2022'`                                                                                                        | Shared-request flag groups; SQL-based variants also return trip count by flag   | Measures grouped aggregation over a categorical flag |
 
 Q01-Q05 run on `yellow_tripdata`. Q06-Q10 run on `fhvhv_tripdata`.
 
 Runnable query files:
 
 ```text
-plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny.sql
-plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_polypheny_mql.sql
-plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_sql.sql
+plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_polypheny.sql
+plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_polypheny_mql.sql
+plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_sql.sql
 ```
 
+The SQL-based variants and the Polypheny MQL variant use the same filters and
+grouping keys. For Q05, Q09, and Q10, the MQL variant returns fewer aggregate
+columns than the SQL-based variants: it omits the trip-count column in Q05 and
+Q09, and returns only the grouped shared-request flag values in Q10.
+
 ## Q1 - Full Yellow Taxi row count
 
 Text:
@@ -38,7 +43,7 @@ SELECT count(*) AS row_count
 FROM yellow_tripdata;
 ```
 
-## Q2 - Yellow Taxi one-month partition count
+## Q2 - Yellow Taxi count for one partition month
 
 Text:
 
@@ -55,7 +60,7 @@ WHERE "year" = '2022'
   AND "month" = '10';
 ```
 
-## Q3 - Yellow Taxi one-day timestamp count
+## Q3 - Yellow Taxi trips on one day
 
 Text:
 
@@ -72,7 +77,7 @@ WHERE tpep_pickup_datetime >= TIMESTAMP '2022-10-15 00:00:00'
   AND tpep_pickup_datetime < TIMESTAMP '2022-10-16 00:00:00';
 ```
 
-## Q4 - Yellow Taxi filtered count
+## Q4 - Long and expensive Yellow Taxi trips
 
 Text:
 
@@ -90,13 +95,13 @@ WHERE trip_distance >= 10.0
   AND total_amount >= 40.0;
 ```
 
-## Q5 - Yellow Taxi monthly amount and distance summary
+## Q5 - Yellow Taxi yearly amount and distance summary
 
 Text:
 
 ```text
-For each Yellow Taxi partition year and month, calculate trip count, total gross
-amount, minimum trip distance, and maximum trip distance.
+For each Yellow Taxi partition year, calculate total gross amount, minimum trip
+distance, and maximum trip distance. SQL-based variants also return trip count.
 ```
 
 SQL:
@@ -104,14 +109,13 @@ SQL:
 ```text
 SELECT
   "year",
-  "month",
   count(*) AS trips,
   sum(total_amount) AS gross_amount,
   min(trip_distance) AS min_distance,
   max(trip_distance) AS max_distance
 FROM yellow_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
+GROUP BY "year"
+ORDER BY "year";
 ```
 
 ## Q6 - Full High-Volume FHV row count
@@ -129,7 +133,7 @@ SELECT count(*) AS row_count
 FROM fhvhv_tripdata;
 ```
 
-## Q7 - High-Volume FHV one-month partition count
+## Q7 - High-Volume FHV count for one partition month
 
 Text:
 
@@ -147,7 +151,7 @@ WHERE "year" = '2022'
   AND "month" = '10';
 ```
 
-## Q8 - High-Volume FHV filtered count
+## Q8 - Long and expensive High-Volume FHV trips
 
 Text:
 
@@ -165,13 +169,14 @@ WHERE trip_miles >= 10.0
   AND base_passenger_fare >= 40.0;
 ```
 
-## Q9 - High-Volume FHV monthly fare and distance summary
+## Q9 - High-Volume FHV yearly fare and distance summary
 
 Text:
 
 ```text
-For each High-Volume FHV partition year and month, calculate trip count, total
-passenger fare, total driver pay, minimum trip miles, and maximum trip miles.
+For each High-Volume FHV partition year, calculate total passenger fare, total
+driver pay, minimum trip miles, and maximum trip miles. SQL-based variants also
+return trip count.
 ```
 
 SQL:
@@ -179,24 +184,23 @@ SQL:
 ```text
 SELECT
   "year",
-  "month",
   count(*) AS trips,
   sum(base_passenger_fare) AS passenger_fare,
   sum(driver_pay) AS driver_pay,
   min(trip_miles) AS min_miles,
   max(trip_miles) AS max_miles
 FROM fhvhv_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
+GROUP BY "year"
+ORDER BY "year";
 ```
 
-## Q10 - High-Volume FHV shared-ride flag distribution
+## Q10 - High-Volume FHV shared-request flag distribution
 
 Text:
 
 ```text
-For High-Volume FHV trip records in partition year 2022, count trips by shared
-request flag and shared match flag.
+For High-Volume FHV trip records in partition year 2022, group records by shared
+request flag. SQL-based variants also return the trip count per flag.
 ```
 
 SQL:
@@ -204,10 +208,9 @@ SQL:
 ```text
 SELECT
   shared_request_flag,
-  shared_match_flag,
   count(*) AS trips
 FROM fhvhv_tripdata
 WHERE "year" = '2022'
-GROUP BY shared_request_flag, shared_match_flag
+GROUP BY shared_request_flag
 ORDER BY trips DESC;
 ```
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_sql.sql b/plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_sql.sql
similarity index 80%
rename from plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_sql.sql
rename to plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_sql.sql
index cb8aca32ca..f228b4da0a 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/arrregation/aggregation_sql.sql
+++ b/plugins/parquet-adapter/benchmarks/query_lists/aggregation/aggregation_sql.sql
@@ -20,17 +20,16 @@ FROM yellow_tripdata
 WHERE trip_distance >= 10.0
   AND total_amount >= 40.0;
 
--- Q05: Monthly Yellow Taxi Amount And Distance Summary
+-- Q05: Yearly Yellow Taxi Amount And Distance Summary
 SELECT
   "year",
-  "month",
   count(*) AS trips,
   sum(total_amount) AS gross_amount,
   min(trip_distance) AS min_distance,
   max(trip_distance) AS max_distance
 FROM yellow_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
+GROUP BY "year"
+ORDER BY "year";
 
 -- Q06: Full High-Volume FHV Row Count
 SELECT count(*) AS row_count
@@ -48,25 +47,23 @@ FROM fhvhv_tripdata
 WHERE trip_miles >= 10.0
   AND base_passenger_fare >= 40.0;
 
--- Q09: Monthly High-Volume FHV Fare And Distance Summary
+-- Q09: Yearly High-Volume FHV Fare And Distance Summary
 SELECT
   "year",
-  "month",
   count(*) AS trips,
   sum(base_passenger_fare) AS passenger_fare,
   sum(driver_pay) AS driver_pay,
   min(trip_miles) AS min_miles,
   max(trip_miles) AS max_miles
 FROM fhvhv_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
+GROUP BY "year"
+ORDER BY "year";
 
--- Q10: High-Volume FHV Shared-Ride Flag Distribution
+-- Q10: High-Volume FHV Shared-Request Flag Distribution
 SELECT
   shared_request_flag,
-  shared_match_flag,
   count(*) AS trips
 FROM fhvhv_tripdata
 WHERE "year" = '2022'
-GROUP BY shared_request_flag, shared_match_flag
+GROUP BY shared_request_flag
 ORDER BY trips DESC;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_duckdb.sql b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_duckdb.sql
index 806173a3e8..e9b3528254 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_duckdb.sql
+++ b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_duckdb.sql
@@ -78,40 +78,19 @@ WHERE l.l_returnflag = 'R'
   AND l.l_shipmode = 'MAIL'
 LIMIT 100000;
 
--- Q05: Unfiltered two-level nested lineitem projection.
+-- Q05: Nested lineitem MAX aggregation grouped by return flag.
 SELECT
-  o.o_orderkey,
-  o.o_orderstatus,
-  l.l_partkey,
-  l.l_suppkey,
-  l.l_linenumber,
-  l.l_quantity,
-  l.l_extendedprice,
-  l.l_discount,
   l.l_returnflag,
-  l.l_shipmode
+  MAX(l.l_extendedprice) AS max_extendedprice
 FROM nested_customer c,
+UNNEST(c.c_orders) AS orders(o),
 UNNEST(
   list_transform(
-    c.c_orders,
-    o -> struct_pack(
-      o_orderkey := o.o_orderkey,
-      o_orderstatus := o.o_orderstatus,
-      o_lineitems := list_transform(
-        o.o_lineitems,
-        l -> struct_pack(
-          l_partkey := l.l_partkey,
-          l_suppkey := l.l_suppkey,
-          l_linenumber := l.l_linenumber,
-          l_quantity := l.l_quantity,
-          l_extendedprice := l.l_extendedprice,
-          l_discount := l.l_discount,
-          l_returnflag := l.l_returnflag,
-          l_shipmode := l.l_shipmode
-        )
-      )
+    o.o_lineitems,
+    l -> struct_pack(
+      l_extendedprice := l.l_extendedprice,
+      l_returnflag := l.l_returnflag
     )
   )
-) AS orders(o),
-UNNEST(o.o_lineitems) AS lineitems(l)
-LIMIT 100000;
+) AS lineitems(l)
+GROUP BY l.l_returnflag;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_mql.mql b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_mql.mql
index 44b2d9bfda..1899c41e6a 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_mql.mql
+++ b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_mql.mql
@@ -69,23 +69,14 @@ ncpd_document.ncpd__nestedcustomer.aggregate([
   { "$limit": 100000 }
 ]);
 
--- Q05: Unfiltered two-level nested lineitem projection.
+-- Q05: Nested lineitem MAX aggregation grouped by return flag.
 ncpd_document.ncpd__nestedcustomer.aggregate([
   { "$unwind": "$c_orders" },
   { "$unwind": "$c_orders.o_lineitems" },
   {
-    "$project": {
-      "o_orderkey": "$c_orders.o_orderkey",
-      "o_orderstatus": "$c_orders.o_orderstatus",
-      "l_partkey": "$c_orders.o_lineitems.l_partkey",
-      "l_suppkey": "$c_orders.o_lineitems.l_suppkey",
-      "l_linenumber": "$c_orders.o_lineitems.l_linenumber",
-      "l_quantity": "$c_orders.o_lineitems.l_quantity",
-      "l_extendedprice": "$c_orders.o_lineitems.l_extendedprice",
-      "l_discount": "$c_orders.o_lineitems.l_discount",
-      "l_returnflag": "$c_orders.o_lineitems.l_returnflag",
-      "l_shipmode": "$c_orders.o_lineitems.l_shipmode"
+    "$group": {
+      "_id": "$c_orders.o_lineitems.l_returnflag",
+      "max_extendedprice": { "$max": "$c_orders.o_lineitems.l_extendedprice" }
     }
-  },
-  { "$limit": 100000 }
+  }
 ]);
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_polypheny_normalized.sql b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_polypheny_normalized.sql
index fa052ced87..e1842dd9d1 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_polypheny_normalized.sql
+++ b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_polypheny_normalized.sql
@@ -56,21 +56,11 @@ WHERE l.l_returnflag = 'R'
   AND l.l_shipmode = 'MAIL'
 LIMIT 100000;
 
--- Q05: Unfiltered two-join lineitem projection.
+-- Q05: Nested lineitem MAX aggregation grouped by return flag.
 SELECT
-  oli.__polypheny_row_id AS lineitems_group_row_id,
-  olil.__polypheny_row_id AS lineitems_list_row_id,
-  l.l_partkey,
-  l.l_suppkey,
-  l.l_linenumber,
-  l.l_quantity,
-  l.l_extendedprice,
-  l.l_discount,
   l.l_returnflag,
-  l.l_shipmode
-FROM ncp__nestedcustomer__c_orders__list__element__o_lineitems oli
-JOIN ncp__nestedcustomer__c_orders__list__element__o_lineitems__list olil
-  ON olil.__polypheny_parent_row_id = oli.__polypheny_row_id
+  MAX(l.l_extendedprice) AS max_extendedprice
+FROM ncp__nestedcustomer__c_orders__list__element__o_lineitems__list olil
 JOIN ncp__nestedcustomer__c_orders__list__element__o_lineitems__list__element l
   ON l.__polypheny_parent_row_id = olil.__polypheny_row_id
-LIMIT 100000;
+GROUP BY l.l_returnflag;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_query_specification.md b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_query_specification.md
index 92af24584f..1859dad52c 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_query_specification.md
+++ b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_query_specification.md
@@ -2,17 +2,18 @@
 
 | Query | Operation                                       | Predicate                                                      | Returned data                      | Purpose                                                                |
 |-------|-------------------------------------------------|----------------------------------------------------------------|------------------------------------|------------------------------------------------------------------------|
-| Q01   | Filtered full customer scan                     | `c_mktsegment = 'BUILDING'` and `c_nationkey BETWEEN 5 AND 15` | Full customer rows or documents    | Measures filtered root-level access with full customer materialization |
+| Q01   | Filtered full customer access                   | `c_mktsegment = 'BUILDING'` and `c_nationkey BETWEEN 5 AND 15` | Full customer rows or documents    | Measures filtered root-level access with full customer materialization |
 | Q02   | Filtered customer projection                    | `c_mktsegment = 'BUILDING'` and `c_acctbal >= 0`               | Explicit customer scalar fields    | Measures filtered projection over root-level customer fields           |
-| Q03   | Filtered nested order projection                | `o_orderstatus = 'F'` and `o_totalprice >= 100000`             | Selected order fields              | Measures one-level nested access to order elements                     |
-| Q04   | Filtered deeply nested lineitem projection      | `l_returnflag = 'R'` and `l_shipmode = 'MAIL'`                 | Selected lineitem fields           | Measures deeper nested access to lineitem elements                     |
-| Q05   | Unfiltered two-level nested lineitem projection | None                                                           | Selected order and lineitem fields | Measures multi-step nested traversal without filter interaction        |
+| Q03   | Filtered order projection                       | `o_orderstatus = 'F'` and `o_totalprice >= 100000`             | Selected order fields              | Measures one-level nested access to order elements                     |
+| Q04   | Filtered lineitem projection                    | `l_returnflag = 'R'` and `l_shipmode = 'MAIL'`                 | Selected lineitem fields           | Measures deeper nested access to lineitem elements                     |
+| Q05   | Nested lineitem MAX aggregation grouped by return flag | None                                                     | Return flag and maximum extended price | Measures aggregation over deeply nested repeated lineitem values       |
 
-All queries cap the returned result set at `100000` rows or documents. The
-Polypheny relational query list intentionally keeps Q03 and Q04 to one
-parent-child join, while Q05 is the explicit two-join nested traversal probe.
+Q01-Q04 cap the returned result set at `100000` rows or documents. Q05 returns
+one aggregate row per lineitem return flag. The Polypheny relational query list
+keeps Q03 and Q04 to one parent-child join, while Q05 uses the same deeply
+nested lineitem relationship for an aggregate query.
 
-## Q1 - Filtered full customer scan
+## Q1 - Filtered full customer access
 
 Text:
 
@@ -58,7 +59,7 @@ WHERE c_mktsegment = 'BUILDING'
 LIMIT 100000;
 ```
 
-## Q3 - Filtered nested order projection
+## Q3 - Filtered order projection
 
 Text:
 
@@ -87,7 +88,7 @@ WHERE o.o_orderstatus = 'F'
 LIMIT 100000;
 ```
 
-## Q4 - Filtered deeply nested lineitem projection
+## Q4 - Filtered lineitem projection
 
 Text:
 
@@ -118,33 +119,23 @@ WHERE l.l_returnflag = 'R'
 LIMIT 100000;
 ```
 
-## Q5 - Unfiltered two-level nested lineitem projection
+## Q5 - Nested lineitem MAX aggregation grouped by return flag
 
 Text:
 
 ```text
-Traverse from nested order lineitem groups to lineitem list elements without a
-filter, and return selected lineitem fields.
+Traverse nested lineitem elements, group them by return flag, and return the
+maximum extended price for each group.
 ```
 
 SQL:
 
 ```text
 SELECT
-  oli.__polypheny_row_id AS lineitems_group_row_id,
-  olil.__polypheny_row_id AS lineitems_list_row_id,
-  l.l_partkey,
-  l.l_suppkey,
-  l.l_linenumber,
-  l.l_quantity,
-  l.l_extendedprice,
-  l.l_discount,
   l.l_returnflag,
-  l.l_shipmode
-FROM ncp__nestedcustomer__c_orders__list__element__o_lineitems oli
-JOIN ncp__nestedcustomer__c_orders__list__element__o_lineitems__list olil
-  ON olil.__polypheny_parent_row_id = oli.__polypheny_row_id
+  MAX(l.l_extendedprice) AS max_extendedprice
+FROM ncp__nestedcustomer__c_orders__list__element__o_lineitems__list olil
 JOIN ncp__nestedcustomer__c_orders__list__element__o_lineitems__list__element l
   ON l.__polypheny_parent_row_id = olil.__polypheny_row_id
-LIMIT 100000;
+GROUP BY l.l_returnflag;
 ```
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_spark.sql b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_spark.sql
index 4b4782281a..5ac41feb9c 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_spark.sql
+++ b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_spark.sql
@@ -51,19 +51,11 @@ WHERE l.l_returnflag = 'R'
   AND l.l_shipmode = 'MAIL'
 LIMIT 100000;
 
--- Q05: Unfiltered two-level nested lineitem projection.
+-- Q05: Nested lineitem MAX aggregation grouped by return flag.
 SELECT
-  o.o_orderkey,
-  o.o_orderstatus,
-  l.l_partkey,
-  l.l_suppkey,
-  l.l_linenumber,
-  l.l_quantity,
-  l.l_extendedprice,
-  l.l_discount,
   l.l_returnflag,
-  l.l_shipmode
+  MAX(l.l_extendedprice) AS max_extendedprice
 FROM nested_customer c
 LATERAL VIEW explode(c_orders) orders_view AS o
 LATERAL VIEW explode(o.o_lineitems) lineitems_view AS l
-LIMIT 100000;
+GROUP BY l.l_returnflag;
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md
index 8da739d14f..8e7524082d 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md
+++ b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md
@@ -1,20 +1,27 @@
 # Partitioning Query Specification
 
-| Query | Layout        | Operation                         | Predicate                                                            | Returned data   | Purpose                                                                    |
-|-------|---------------|-----------------------------------|----------------------------------------------------------------------|-----------------|----------------------------------------------------------------------------|
-| Q01   | Repartitioned | Full table baseline               | None                                                                 | Aggregate count | Measures baseline count over repartitioned `yellow_tripdata`               |
-| Q02   | Repartitioned | Partition by year                 | `year = '2022'`                                                      | Aggregate count | Measures year-level partition pruning                                      |
-| Q03   | Repartitioned | Partition by month                | `year = '2022'` and `month = '10'`                                   | Aggregate count | Measures month-level partition pruning                                     |
-| Q04   | Repartitioned | Filtered count, full scan         | `trip_distance >= 10.0` and `total_amount >= 40.0`                   | Aggregate count | Measures data-column filtering without partition restriction               |
-| Q05   | Repartitioned | Filtered count, partition by year | `year = '2022'`, `trip_distance >= 10.0`, and `total_amount >= 40.0` | Aggregate count | Measures combined partition pruning and data-column filtering              |
-| Q06   | Unpartitioned | Full table baseline               | None                                                                 | Aggregate count | Measures baseline count over unpartitioned `yellow_tripdata`               |
-| Q07   | Unpartitioned | Partition-column filter by year   | `year = '2022'`                                                      | Aggregate count | Measures filtering on physical partition columns in unpartitioned data     |
-| Q08   | Unpartitioned | Partition-column filter by month  | `year = '2022'` and `month = '10'`                                   | Aggregate count | Measures filtering on physical year/month columns in unpartitioned data    |
-| Q09   | Unpartitioned | Filtered count, full scan         | `trip_distance >= 10.0` and `total_amount >= 40.0`                   | Aggregate count | Measures data-column filtering on unpartitioned data                       |
-| Q10   | Unpartitioned | Filtered count with year filter   | `year = '2022'`, `trip_distance >= 10.0`, and `total_amount >= 40.0` | Aggregate count | Measures combined physical year-column filtering and data-column filtering |
-
-Q01-Q03 and Q06-Q08 run on `yellow_tripdata`. Q04-Q05 and Q09-Q10 run on
-`green_tripdata`.
+The partitioning benchmark uses five logical count queries. Each query is run
+once on the repartitioned layout and once on the unpartitioned layout. The query
+identifier suffix indicates the layout:
+
+- `P`: repartitioned, Hive-style partitioned layout
+- `NP`: unpartitioned layout with `year` and `month` stored as physical columns
+
+| Query | Layout        | Operation                                      | Predicate                                                            | Returned data   | Purpose                                                               |
+|-------|---------------|------------------------------------------------|----------------------------------------------------------------------|-----------------|-----------------------------------------------------------------------|
+| Q1_P | Repartitioned | Full table baseline                            | None                                                                 | Aggregate count | Measures baseline count over repartitioned `yellow_tripdata`          |
+| Q1_NP | Unpartitioned | Full table baseline                            | None                                                                 | Aggregate count | Measures baseline count over unpartitioned `yellow_tripdata`          |
+| Q2_P | Repartitioned | Filter by year                                 | `year = '2022'`                                                      | Aggregate count | Measures year-level partition pruning                                 |
+| Q2_NP | Unpartitioned | Physical year-column filter                    | `year = '2022'`                                                      | Aggregate count | Measures filtering on the physical year column                        |
+| Q3_P | Repartitioned | Filter by year and month                       | `year = '2022'` and `month = '10'`                                   | Aggregate count | Measures pruning with both partition levels                           |
+| Q3_NP | Unpartitioned | Physical year/month-column filter              | `year = '2022'` and `month = '10'`                                   | Aggregate count | Measures filtering on physical year and month columns                 |
+| Q4_P | Repartitioned | Filtered count, full scan                      | `trip_distance >= 10.0` and `total_amount >= 40.0`                   | Aggregate count | Measures data-column filtering without partition restriction          |
+| Q4_NP | Unpartitioned | Filtered count, full scan                      | `trip_distance >= 10.0` and `total_amount >= 40.0`                   | Aggregate count | Measures data-column filtering on unpartitioned data                  |
+| Q5_P | Repartitioned | Filtered count with year partition             | `year = '2022'`, `trip_distance >= 10.0`, and `total_amount >= 40.0` | Aggregate count | Measures combined partition pruning and data-column filtering         |
+| Q5_NP | Unpartitioned | Filtered count with physical year column       | `year = '2022'`, `trip_distance >= 10.0`, and `total_amount >= 40.0` | Aggregate count | Measures combined physical-column filtering and data-column filtering |
+
+Q1_P, Q1_NP, Q2_P, Q2_NP, Q3_P, and Q3_NP run on `yellow_tripdata`.
+Q4_P, Q4_NP, Q5_P, and Q5_NP run on `green_tripdata`.
 
 Polypheny uses one combined query file because it can reference both adapters in
 one SQL file. DuckDB and Spark use separate query files because their runners
@@ -28,7 +35,7 @@ plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_reparti
 plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_unpartitioned_sql.sql
 ```
 
-## Q1 - Repartitioned full table baseline
+## Q1_P - Repartitioned full table baseline
 
 Text:
 
@@ -43,30 +50,28 @@ SELECT count(*) AS row_count
 FROM tlcr__yellow_tripdata;
 ```
 
-## Q2 - Repartitioned partition by year
+## Q1_NP - Unpartitioned full table baseline
 
 Text:
 
 ```text
-Count Yellow Taxi trip records in the repartitioned dataset for partition year
-2022.
+Count all Yellow Taxi trip records in the unpartitioned dataset.
 ```
 
 SQL:
 
 ```text
 SELECT count(*) AS row_count
-FROM tlcr__yellow_tripdata
-WHERE "year" = '2022';
+FROM tlcu__yellow_tripdata;
 ```
 
-## Q3 - Repartitioned partition by month
+## Q2_P - Repartitioned filter by year
 
 Text:
 
 ```text
 Count Yellow Taxi trip records in the repartitioned dataset for partition year
-2022 and partition month 10.
+2022.
 ```
 
 SQL:
@@ -74,116 +79,118 @@ SQL:
 ```text
 SELECT count(*) AS row_count
 FROM tlcr__yellow_tripdata
-WHERE "year" = '2022'
-  AND "month" = '10';
+WHERE "year" = '2022';
 ```
 
-## Q4 - Repartitioned filtered count, full scan
+## Q2_NP - Unpartitioned physical year-column filter
 
 Text:
 
 ```text
-Count Green Taxi trip records in the repartitioned dataset where trip distance
-is at least 10.0 and total amount is at least 40.0.
+Count Yellow Taxi trip records in the unpartitioned dataset where the physical
+year column is 2022.
 ```
 
 SQL:
 
 ```text
 SELECT count(*) AS row_count
-FROM tlcr__green_tripdata
-WHERE trip_distance >= 10.0
-  AND total_amount >= 40.0;
+FROM tlcu__yellow_tripdata
+WHERE "year" = '2022';
 ```
 
-## Q5 - Repartitioned filtered count, partition by year
+## Q3_P - Repartitioned filter by year and month
 
 Text:
 
 ```text
-Count Green Taxi trip records in the repartitioned dataset for partition year
-2022 where trip distance is at least 10.0 and total amount is at least 40.0.
+Count Yellow Taxi trip records in the repartitioned dataset for partition year
+2022 and partition month 10.
 ```
 
 SQL:
 
 ```text
 SELECT count(*) AS row_count
-FROM tlcr__green_tripdata
+FROM tlcr__yellow_tripdata
 WHERE "year" = '2022'
-  AND trip_distance >= 10.0
-  AND total_amount >= 40.0;
+  AND "month" = '10';
 ```
 
-## Q6 - Unpartitioned full table baseline
+## Q3_NP - Unpartitioned physical year/month-column filter
 
 Text:
 
 ```text
-Count all Yellow Taxi trip records in the unpartitioned dataset.
+Count Yellow Taxi trip records in the unpartitioned dataset where the physical
+year column is 2022 and the physical month column is 10.
 ```
 
 SQL:
 
 ```text
 SELECT count(*) AS row_count
-FROM tlcu__yellow_tripdata;
+FROM tlcu__yellow_tripdata
+WHERE "year" = '2022'
+  AND "month" = '10';
 ```
 
-## Q7 - Unpartitioned partition-column filter by year
+## Q4_P - Repartitioned filtered count, full scan
 
 Text:
 
 ```text
-Count Yellow Taxi trip records in the unpartitioned dataset where the physical
-year column is 2022.
+Count Green Taxi trip records in the repartitioned dataset where trip distance
+is at least 10.0 and total amount is at least 40.0.
 ```
 
 SQL:
 
 ```text
 SELECT count(*) AS row_count
-FROM tlcu__yellow_tripdata
-WHERE "year" = '2022';
+FROM tlcr__green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
 ```
 
-## Q8 - Unpartitioned partition-column filter by month
+## Q4_NP - Unpartitioned filtered count, full scan
 
 Text:
 
 ```text
-Count Yellow Taxi trip records in the unpartitioned dataset where the physical
-year column is 2022 and the physical month column is 10.
+Count Green Taxi trip records in the unpartitioned dataset where trip distance
+is at least 10.0 and total amount is at least 40.0.
 ```
 
 SQL:
 
 ```text
 SELECT count(*) AS row_count
-FROM tlcu__yellow_tripdata
-WHERE "year" = '2022'
-  AND "month" = '10';
+FROM tlcu__green_tripdata
+WHERE trip_distance >= 10.0
+  AND total_amount >= 40.0;
 ```
 
-## Q9 - Unpartitioned filtered count, full scan
+## Q5_P - Repartitioned filtered count with year partition
 
 Text:
 
 ```text
-Count Green Taxi trip records in the unpartitioned dataset where trip distance
-is at least 10.0 and total amount is at least 40.0.
+Count Green Taxi trip records in the repartitioned dataset for partition year
+2022 where trip distance is at least 10.0 and total amount is at least 40.0.
 ```
 
 SQL:
 
 ```text
 SELECT count(*) AS row_count
-FROM tlcu__green_tripdata
-WHERE trip_distance >= 10.0
+FROM tlcr__green_tripdata
+WHERE "year" = '2022'
+  AND trip_distance >= 10.0
   AND total_amount >= 40.0;
 ```
 
-## Q10 - Unpartitioned filtered count with year filter
+## Q5_NP - Unpartitioned filtered count with physical year column
 
 Text:
 
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_sql.sql b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_sql.sql
index 684bfa397c..e4a99c3c20 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_sql.sql
+++ b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_sql.sql
@@ -1,25 +1,25 @@
--- Q01: Repartitioned full table baseline
+-- Q1_P: Repartitioned full table baseline
 SELECT count(*) AS row_count
 FROM yellow_tripdata;
 
--- Q02: Repartitioned partition by year
+-- Q2_P: Repartitioned filter by year
 SELECT count(*) AS row_count
 FROM yellow_tripdata
 WHERE "year" = '2022';
 
--- Q03: Repartitioned partition by month
+-- Q3_P: Repartitioned filter by year and month
 SELECT count(*) AS row_count
 FROM yellow_tripdata
 WHERE "year" = '2022'
   AND "month" = '10';
 
--- Q04: Repartitioned filtered count, full scan
+-- Q4_P: Repartitioned filtered count, full scan
 SELECT count(*) AS row_count
 FROM green_tripdata
 WHERE trip_distance >= 10.0
   AND total_amount >= 40.0;
 
--- Q05: Repartitioned filtered count, partition by year
+-- Q5_P: Repartitioned filtered count with year partition
 SELECT count(*) AS row_count
 FROM green_tripdata
 WHERE  "year" = '2022'
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_unpartitioned_polypheny.sql b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_unpartitioned_polypheny.sql
index f39275d1c8..952f530e63 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_unpartitioned_polypheny.sql
+++ b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_repartitioned_unpartitioned_polypheny.sql
@@ -1,25 +1,25 @@
--- Q01: Repartitioned full table baseline
+-- Q1_P: Repartitioned full table baseline
 SELECT count(*) AS row_count
 FROM tlcr__yellow_tripdata;
 
--- Q02: Repartitioned partition by year
+-- Q2_P: Repartitioned filter by year
 SELECT count(*) AS row_count
 FROM tlcr__yellow_tripdata
 WHERE "year" = '2022';
 
--- Q03: Repartitioned partition by month
+-- Q3_P: Repartitioned filter by year and month
 SELECT count(*) AS row_count
 FROM tlcr__yellow_tripdata
 WHERE "year" = '2022'
   AND "month" = '10';
 
--- Q04: Repartitioned filtered count, full scan
+-- Q4_P: Repartitioned filtered count, full scan
 SELECT count(*) AS row_count
 FROM tlcr__green_tripdata
 WHERE trip_distance >= 10.0
   AND total_amount >= 40.0;
 
--- Q05: Repartitioned filtered count, partition by year
+-- Q5_P: Repartitioned filtered count with year partition
 SELECT count(*) AS row_count
 FROM tlcr__green_tripdata
 WHERE  "year" = '2022'
@@ -28,28 +28,28 @@ WHERE  "year" = '2022'
 
 -------------Unpartitioned
 
--- Q06: Unpartitioned full table baseline
+-- Q1_NP: Unpartitioned full table baseline
 SELECT count(*) AS row_count
 FROM tlcu__yellow_tripdata;
 
--- Q07: Unpartitioned partition by year
+-- Q2_NP: Unpartitioned physical year-column filter
 SELECT count(*) AS row_count
 FROM tlcu__yellow_tripdata
 WHERE "year" = '2022';
 
--- Q08: Unpartitioned partition by month
+-- Q3_NP: Unpartitioned physical year/month-column filter
 SELECT count(*) AS row_count
 FROM tlcu__yellow_tripdata
 WHERE "year" = '2022'
   AND "month" = '10';
 
--- Q09: Unpartitioned filtered count, full scan
+-- Q4_NP: Unpartitioned filtered count, full scan
 SELECT count(*) AS row_count
 FROM tlcu__green_tripdata
 WHERE trip_distance >= 10.0
   AND total_amount >= 40.0;
 
--- Q10: Unpartitioned filtered count, partition by year
+-- Q5_NP: Unpartitioned filtered count with physical year column
 SELECT count(*) AS row_count
 FROM tlcu__green_tripdata
 WHERE  "year" = '2022'
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_unpartitioned_sql.sql b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_unpartitioned_sql.sql
index ef763d5223..a817e93679 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_unpartitioned_sql.sql
+++ b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_unpartitioned_sql.sql
@@ -1,25 +1,25 @@
--- Q06: Unpartitioned full table baseline
+-- Q1_NP: Unpartitioned full table baseline
 SELECT count(*) AS row_count
 FROM yellow_tripdata;
 
--- Q07: Unpartitioned partition by year
+-- Q2_NP: Unpartitioned physical year-column filter
 SELECT count(*) AS row_count
 FROM yellow_tripdata
 WHERE "year" = '2022';
 
--- Q08: Unpartitioned partition by month
+-- Q3_NP: Unpartitioned physical year/month-column filter
 SELECT count(*) AS row_count
 FROM yellow_tripdata
 WHERE "year" = '2022'
   AND "month" = '10';
 
--- Q09: Unpartitioned filtered count, full scan
+-- Q4_NP: Unpartitioned filtered count, full scan
 SELECT count(*) AS row_count
 FROM green_tripdata
 WHERE trip_distance >= 10.0
   AND total_amount >= 40.0;
 
--- Q10: Unpartitioned filtered count, partition by year
+-- Q5_NP: Unpartitioned filtered count with physical year column
 SELECT count(*) AS row_count
 FROM green_tripdata
 WHERE  "year" = '2022'
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_result_analysis.md b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_result_analysis.md
new file mode 100644
index 0000000000..0d301e6594
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_result_analysis.md
@@ -0,0 +1,46 @@
+# Access Model Comparison - Benchmark Analysis
+
+## Results Plot
+![access_model_comparison_tlcp_plot.png](../plots/access_model_comparison_tlcp_plot.png)
+
+## Interpretation
+
+### General
+- The access model comparison run completed successfully for all systems. 
+- Every query has `5/5` successful measured runs
+- Result row counts match across Polypheny relational flat, Polypheny relational normalized, Polypheny document
+MQL, DuckDB, and Apache Spark.
+
+
+### Main Findings
+
+- DuckDB is fastest for all five access-pattern queries.
+- Polypheny relational Flat and normalized mode are very close, which is logical because only root tables accessed.
+
+- Normalized mode is slightly slower for full-record access because it adds and materializes a synthetic row identifier for every record. This produces one additional result column and requires a binding-aware row-extraction path. When the synthetic identifier is not returned, flat and normalized performance is broadly comparable.
+
+- MQL (Polypheny Document) is slower than Polypheny relational because of document materialization.
+
+- Projection consistently improves performance across all tested systems. In both the unfiltered case (Q02 compared with Q01) and the filtered case (Q05 compared with Q04), queries that return only selected fields execute faster than queries that return full records. This indicates that reducing the number of retrieved columns lowers the amount of data that must be read, transferred, and materialized, which has a measurable positive effect for relational, document, DuckDB, and Spark-based access paths.
+
+
+## Related Documents
+
+Full summary:
+
+```text
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
+
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rf_tlcp_results.csv
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rn_tlcpn_results.csv
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_mql_tlcp_results.csv
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_duckdb_tlcp_results.csv
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_spark_tlcp_results.csv
+```
+
+Plot:
+```text
+plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot.pdf
+plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot.png
+plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot.svg
+```
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_results_analysis.md b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_results_analysis.md
deleted file mode 100644
index 9a4e78d695..0000000000
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_results_analysis.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Access Model Comparison TLCP Analysis
-
-Source summary:
-
-```text
-plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
-```
-
-The access model comparison run completed successfully for all systems. Every
-query has `5/5` successful measured runs, and result row counts now match across
-Polypheny relational flat, Polypheny relational normalized, Polypheny document
-MQL, DuckDB, and Apache Spark.
-
-## Main Findings
-
-DuckDB is fastest for all five access-pattern queries.
-
-Polypheny relational flat and normalized mode are close for root-table access.
-Normalized mode is somewhat slower for full-row reads and filtered full-row
-reads, but it does not show a major performance regression for these flat
-queries. Filtered projection is slightly faster in normalized mode.
-
-The MQL filter issue is resolved. Q04 and Q05 now return `283006` rows, matching
-the SQL systems. MQL remains slower than Polypheny relational for document
-materialization, but filtered MQL queries are now much closer to the relational
-runtime than in the previous bad run.
-
-## Interpretation
-
-Projection has a strong effect across engines. In Polypheny relational flat,
-Q01 takes `29741.8 ms`, while Q02 takes `9650.6 ms`; Q04 takes `6073.4 ms`,
-while Q05 takes `2528.8 ms`.
-
-For this flat TLC workload, normalized relational mode does not appear to damage
-performance significantly when queries access only root fields. The document
-path is still more expensive for large result materialization, but filter
-pushdown now behaves consistently with the other systems.
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
index 31ddf64693..87c6df7e79 100644
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
@@ -1,7 +1,7 @@
 # Access Model Comparison TLCP Summary
 
 Phase summarized: `measured`.
-Warmup rows are excluded. Mean and median values use successful runs only.
+Warmup rows are excluded. Mean, median, and standard deviation values use successful runs only.
 
 ## Source Files
 
@@ -35,30 +35,52 @@ Warmup rows are excluded. Mean and median values use successful runs only.
 
 ## Detailed Summary (ms)
 
-| System                          | Query | Description                   | Runs | Mean      | Median    | Min       | Max       | Rows      | Columns | Status |
-|---------------------------------|-------|-------------------------------|------|-----------|-----------|-----------|-----------|-----------|---------|--------|
-| Polypheny Relational Flat       | Q01   | Full record access            | 5/5  | 29,741.8  | 29,562.0  | 28,270.0  | 32,563.0  | 3,711,544 | 22      | ok     |
-| Polypheny Relational Flat       | Q02   | Projection                    | 5/5  | 9,650.6   | 9,721.0   | 9,208.0   | 10,076.0  | 3,711,544 | 5       | ok     |
-| Polypheny Relational Flat       | Q03   | Filtered count                | 5/5  | 157.4     | 153.0     | 147.0     | 180.0     | 1         | 1       | ok     |
-| Polypheny Relational Flat       | Q04   | Filtered full record access   | 5/5  | 6,073.4   | 6,083.0   | 5,872.0   | 6,328.0   | 283,006   | 22      | ok     |
-| Polypheny Relational Flat       | Q05   | Filtered projection           | 5/5  | 2,528.8   | 2,517.0   | 2,266.0   | 2,702.0   | 283,006   | 5       | ok     |
-| Polypheny Relational Normalized | Q01   | Full record access            | 5/5  | 36,049.6  | 35,881.0  | 35,800.0  | 36,433.0  | 3,711,544 | 23      | ok     |
-| Polypheny Relational Normalized | Q02   | Projection                    | 5/5  | 10,555.8  | 10,207.0  | 9,367.0   | 12,355.0  | 3,711,544 | 5       | ok     |
-| Polypheny Relational Normalized | Q03   | Filtered count                | 5/5  | 170.8     | 174.0     | 153.0     | 185.0     | 1         | 1       | ok     |
-| Polypheny Relational Normalized | Q04   | Filtered full record access   | 5/5  | 7,594.4   | 7,509.0   | 7,161.0   | 8,067.0   | 283,006   | 23      | ok     |
-| Polypheny Relational Normalized | Q05   | Filtered projection           | 5/5  | 2,303.0   | 2,401.0   | 1,811.0   | 2,543.0   | 283,006   | 5       | ok     |
-| Polypheny Document MQL          | Q01   | Full document access          | 5/5  | 142,440.8 | 138,809.0 | 138,395.0 | 156,366.0 | 3,711,544 | 20      | ok     |
-| Polypheny Document MQL          | Q02   | Projection                    | 5/5  | 106,694.4 | 99,480.0  | 98,725.0  | 119,637.0 | 3,711,544 | 6       | ok     |
-| Polypheny Document MQL          | Q03   | Filtered count                | 5/5  | 424.8     | 421.0     | 396.0     | 462.0     | 1         | 1       | ok     |
-| Polypheny Document MQL          | Q04   | Filtered full document access | 5/5  | 14,752.6  | 15,007.0  | 14,130.0  | 15,062.0  | 283,006   | 20      | ok     |
-| Polypheny Document MQL          | Q05   | Filtered projection           | 5/5  | 12,683.2  | 12,592.0  | 12,438.0  | 13,119.0  | 283,006   | 6       | ok     |
-| DuckDB                          | Q01   | Full record access            | 5/5  | 3,672.4   | 3,820.0   | 3,200.0   | 3,859.0   | 3,711,544 | 22      | ok     |
-| DuckDB                          | Q02   | Projection                    | 5/5  | 218.8     | 222.0     | 201.0     | 235.0     | 3,711,544 | 5       | ok     |
-| DuckDB                          | Q03   | Filtered count                | 5/5  | 35.4      | 35.0      | 33.0      | 39.0      | 1         | 1       | ok     |
-| DuckDB                          | Q04   | Filtered full record access   | 5/5  | 480.0     | 470.0     | 429.0     | 538.0     | 283,006   | 22      | ok     |
-| DuckDB                          | Q05   | Filtered projection           | 5/5  | 115.2     | 115.0     | 110.0     | 121.0     | 283,006   | 5       | ok     |
-| Apache Spark                    | Q01   | Full record access            | 5/5  | 21,738.2  | 21,838.0  | 21,403.0  | 22,122.0  | 3,711,544 | 22      | ok     |
-| Apache Spark                    | Q02   | Projection                    | 5/5  | 13,667.6  | 13,656.0  | 13,338.0  | 14,034.0  | 3,711,544 | 5       | ok     |
-| Apache Spark                    | Q03   | Filtered count                | 5/5  | 1,182.4   | 1,204.0   | 1,001.0   | 1,346.0   | 1         | 1       | ok     |
-| Apache Spark                    | Q04   | Filtered full record access   | 5/5  | 2,666.6   | 2,645.0   | 2,227.0   | 3,063.0   | 283,006   | 22      | ok     |
-| Apache Spark                    | Q05   | Filtered projection           | 5/5  | 1,941.4   | 2,021.0   | 1,626.0   | 2,193.0   | 283,006   | 5       | ok     |
+### Q01 - Full Record Access
+
+| System                 | Runs | Mean      | Median    | Std Dev | Min      | Max      | Rows      | Columns | Status |
+|------------------------|------|-----------|-----------|---------|----------|----------|-----------|---------|--------|
+| Polypheny Flat         | 5/5  | 29,741.8  | 29,562.0  | 1,684.3 | 28,270.0 | 32,563.0 | 3,711,544 | 22      | ok     |
+| Polypheny Normalized   | 5/5  | 36,049.6  | 35,881.0  | 280.3   | 35,800.0 | 36,433.0 | 3,711,544 | 23      | ok     |
+| Polypheny Document MQL | 5/5  | 142,440.8 | 138,809.0 | 7,815.9 | 138,395.0 | 156,366.0 | 3,711,544 | 20      | ok     |
+| DuckDB                 | 5/5  | 3,672.4   | 3,820.0   | 281.3   | 3,200.0  | 3,859.0  | 3,711,544 | 22      | ok     |
+| Apache Spark           | 5/5  | 21,738.2  | 21,838.0  | 305.8   | 21,403.0 | 22,122.0 | 3,711,544 | 22      | ok     |
+
+### Q02 - Projection
+
+| System                 | Runs | Mean      | Median   | Std Dev | Min      | Max      | Rows      | Columns | Status |
+|------------------------|------|-----------|----------|---------|----------|----------|-----------|---------|--------|
+| Polypheny Flat         | 5/5  | 9,650.6   | 9,721.0  | 395.4   | 9,208.0  | 10,076.0 | 3,711,544 | 5       | ok     |
+| Polypheny Normalized   | 5/5  | 10,555.8  | 10,207.0 | 1,123.7 | 9,367.0  | 12,355.0 | 3,711,544 | 5       | ok     |
+| Polypheny Document MQL | 5/5  | 106,694.4 | 99,480.0 | 10,386.4 | 98,725.0 | 119,637.0 | 3,711,544 | 6       | ok     |
+| DuckDB                 | 5/5  | 218.8     | 222.0    | 12.7    | 201.0    | 235.0    | 3,711,544 | 5       | ok     |
+| Apache Spark           | 5/5  | 13,667.6  | 13,656.0 | 247.6   | 13,338.0 | 14,034.0 | 3,711,544 | 5       | ok     |
+
+### Q03 - Filtered Count
+
+| System                 | Runs | Mean    | Median  | Std Dev | Min     | Max     | Rows | Columns | Status |
+|------------------------|------|---------|---------|---------|---------|---------|------|---------|--------|
+| Polypheny Flat         | 5/5  | 157.4   | 153.0   | 13.3    | 147.0   | 180.0   | 1    | 1       | ok     |
+| Polypheny Normalized   | 5/5  | 170.8   | 174.0   | 11.8    | 153.0   | 185.0   | 1    | 1       | ok     |
+| Polypheny Document MQL | 5/5  | 424.8   | 421.0   | 23.7    | 396.0   | 462.0   | 1    | 1       | ok     |
+| DuckDB                 | 5/5  | 35.4    | 35.0    | 2.3     | 33.0    | 39.0    | 1    | 1       | ok     |
+| Apache Spark           | 5/5  | 1,182.4 | 1,204.0 | 142.8   | 1,001.0 | 1,346.0 | 1    | 1       | ok     |
+
+### Q04 - Filtered Full Record Access
+
+| System                 | Runs | Mean     | Median   | Std Dev | Min      | Max      | Rows    | Columns | Status |
+|------------------------|------|----------|----------|---------|----------|----------|---------|---------|--------|
+| Polypheny Flat         | 5/5  | 6,073.4  | 6,083.0  | 172.9   | 5,872.0  | 6,328.0  | 283,006 | 22      | ok     |
+| Polypheny Normalized   | 5/5  | 7,594.4  | 7,509.0  | 341.8   | 7,161.0  | 8,067.0  | 283,006 | 23      | ok     |
+| Polypheny Document MQL | 5/5  | 14,752.6 | 15,007.0 | 405.7   | 14,130.0 | 15,062.0 | 283,006 | 20      | ok     |
+| DuckDB                 | 5/5  | 480.0    | 470.0    | 42.6    | 429.0    | 538.0    | 283,006 | 22      | ok     |
+| Apache Spark           | 5/5  | 2,666.6  | 2,645.0  | 300.6   | 2,227.0  | 3,063.0  | 283,006 | 22      | ok     |
+
+### Q05 - Filtered Projection
+
+| System                 | Runs | Mean     | Median   | Std Dev | Min      | Max      | Rows    | Columns | Status |
+|------------------------|------|----------|----------|---------|----------|----------|---------|---------|--------|
+| Polypheny Flat         | 5/5  | 2,528.8  | 2,517.0  | 181.6   | 2,266.0  | 2,702.0  | 283,006 | 5       | ok     |
+| Polypheny Normalized   | 5/5  | 2,303.0  | 2,401.0  | 286.0   | 1,811.0  | 2,543.0  | 283,006 | 5       | ok     |
+| Polypheny Document MQL | 5/5  | 12,683.2 | 12,592.0 | 274.6   | 12,438.0 | 13,119.0 | 283,006 | 6       | ok     |
+| DuckDB                 | 5/5  | 115.2    | 115.0    | 4.0     | 110.0    | 121.0    | 283,006 | 5       | ok     |
+| Apache Spark           | 5/5  | 1,941.4  | 2,021.0  | 224.3   | 1,626.0  | 2,193.0  | 283,006 | 5       | ok     |
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_correctness_summary.md b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_correctness_summary.md
new file mode 100644
index 0000000000..8bf234d568
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_correctness_summary.md
@@ -0,0 +1,158 @@
+# Aggregation Correctness Summary
+
+Reference system: `DuckDB`.
+Numeric values are compared with absolute tolerance `0.000001` and relative tolerance `0.000001`.
+
+## Source Files
+
+| System | Values file |
+| --- | --- |
+| Polypheny Relational | `plugins\\parquet-adapter\\benchmarks\\results\\aggregation\\aggregation_polypheny_values.jsonl` |
+| Polypheny Document MQL | `plugins\\parquet-adapter\\benchmarks\\results\\aggregation\\aggregation_polypheny_mql_values.jsonl` |
+| DuckDB | `plugins\\parquet-adapter\\benchmarks\\results\\aggregation\\aggregation_duckdb_values.jsonl` |
+| Apache Spark | `plugins\\parquet-adapter\\benchmarks\\results\\aggregation\\aggregation_spark_values.jsonl` |
+
+## Comparison
+
+| Query | Polypheny Relational | Polypheny Document MQL | Apache Spark |
+| --- | --- | --- | --- |
+| Q01 | ok | ok | ok |
+| Q02 | ok | ok | ok |
+| Q03 | ok | ok | ok |
+| Q04 | ok | ok | ok |
+| Q05 | ok | differs: columns differ | ok |
+| Q06 | ok | ok | ok |
+| Q07 | ok | ok | ok |
+| Q08 | ok | ok | ok |
+| Q09 | ok | differs: columns differ | ok |
+| Q10 | ok | differs: columns differ | ok |
+
+## Compared Values
+
+The following tables align captured result values by query.
+Each table includes the reference system so that mismatches can be inspected directly.
+
+### Q01
+
+| Result | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark |
+| --- | --- | --- | --- | --- |
+| row_count | 98276264 | 98276264 | 98276264 | 98276264 |
+
+### Q02
+
+| Result | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark |
+| --- | --- | --- | --- | --- |
+| row_count | 3675411 | 3675411 | 3675411 | 3675411 |
+
+### Q03
+
+| Result | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark |
+| --- | --- | --- | --- | --- |
+| row_count | 127002 | 127002 | 127002 | 127002 |
+
+### Q04
+
+| Result | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark |
+| --- | --- | --- | --- | --- |
+| row_count | 6438540 | 6438540 | 6438540 | 6438540 |
+
+### Q05
+
+| Result | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark |
+| --- | --- | --- | --- | --- |
+| year=2020 / trips | 24649092 |  | 24649092 | 24649092 |
+| year=2020 / gross_amount | 454079334.16108584 |  | 4.540793341610861E8 | 454079334.14322543 |
+| year=2020 / min_distance | -30.62 |  | -30.62 | -30.62 |
+| year=2020 / max_distance | 350914.89 |  | 350914.89 | 350914.89 |
+| year=2021 / trips | 30904308 |  | 30904308 | 30904308 |
+| year=2021 / gross_amount | 608700670.9132242 |  | 6.087006709132242E8 | 608700670.9059402 |
+| year=2021 / min_distance | 0.0 |  | 0.0 | 0.0 |
+| year=2021 / max_distance | 351613.36 |  | 351613.36 | 351613.36 |
+| year=2022 / trips | 39656098 |  | 39656098 | 39656098 |
+| year=2022 / gross_amount | 859397945.173023 |  | 8.593979451730231E8 | 859397945.1733704 |
+| year=2022 / min_distance | 0.0 |  | 0.0 | 0.0 |
+| year=2022 / max_distance | 389678.46 |  | 389678.46 | 389678.46 |
+| year=2023 / trips | 3066766 |  | 3066766 | 3066766 |
+| year=2023 / gross_amount | 82865192.2197824 |  | 8.28651922197824E7 | 82865192.2197824 |
+| year=2023 / min_distance | 0.0 |  | 0.0 | 0.0 |
+| year=2023 / max_distance | 258928.15 |  | 258928.15 | 258928.15 |
+| _id=2020 / max_distance |  | 350914.89 |  |  |
+| _id=2020 / min_distance |  | -30.62 |  |  |
+| _id=2020 / gross_amount |  | 454079334.16108584 |  |  |
+| _id=2021 / max_distance |  | 351613.36 |  |  |
+| _id=2021 / min_distance |  | 0.0 |  |  |
+| _id=2021 / gross_amount |  | 608700670.9132242 |  |  |
+| _id=2022 / max_distance |  | 389678.46 |  |  |
+| _id=2022 / min_distance |  | 0.0 |  |  |
+| _id=2022 / gross_amount |  | 859397945.173023 |  |  |
+| _id=2023 / max_distance |  | 258928.15 |  |  |
+| _id=2023 / min_distance |  | 0.0 |  |  |
+| _id=2023 / gross_amount |  | 82865192.2197824 |  |  |
+
+### Q06
+
+| Result | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark |
+| --- | --- | --- | --- | --- |
+| row_count | 548801637 | 548801637 | 548801637 | 548801637 |
+
+### Q07
+
+| Result | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark |
+| --- | --- | --- | --- | --- |
+| row_count | 19306090 | 19306090 | 19306090 | 19306090 |
+
+### Q08
+
+| Result | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark |
+| --- | --- | --- | --- | --- |
+| row_count | 42659531 | 42659531 | 42659531 | 42659531 |
+
+### Q09
+
+| Result | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark |
+| --- | --- | --- | --- | --- |
+| year=2020 / trips | 143309871 |  | 143309871 | 143309871 |
+| year=2020 / passenger_fare | 2527548383.5690694 |  | 2.527548383569069E9 | 2527548383.5680666 |
+| year=2020 / driver_pay | 2036707828.5292335 |  | 2.0367078285292332E9 | 2036707828.5286038 |
+| year=2020 / min_miles | 0.0 |  | 0.0 | 0.0 |
+| year=2020 / max_miles | 1310.51 |  | 1310.51 | 1310.51 |
+| year=2021 / trips | 174596652 |  | 174596652 | 174596652 |
+| year=2021 / passenger_fare | 3839172144.575017 |  | 3.839172144575017E9 | 3839172144.575018 |
+| year=2021 / driver_pay | 3085990462.630042 |  | 3.085990462630042E9 | 3085990462.6300426 |
+| year=2021 / min_miles | 0.0 |  | 0.0 | 0.0 |
+| year=2021 / max_miles | 738.95 |  | 738.95 | 738.95 |
+| year=2022 / trips | 212416083 |  | 212416083 | 212416083 |
+| year=2022 / passenger_fare | 5029617856.472827 |  | 5.029617856472826E9 | 5029617856.472827 |
+| year=2022 / driver_pay | 3966957717.7361007 |  | 3.9669577177361016E9 | 3966957717.736101 |
+| year=2022 / min_miles | 0.0 |  | 0.0 | 0.0 |
+| year=2022 / max_miles | 634.32 |  | 634.32 | 634.32 |
+| year=2023 / trips | 18479031 |  | 18479031 | 18479031 |
+| year=2023 / passenger_fare | 398327570.2230424 |  | 3.983275702230424E8 | 398327570.2230424 |
+| year=2023 / driver_pay | 310164377.32070553 |  | 3.1016437732070553E8 | 310164377.32070553 |
+| year=2023 / min_miles | 0.0 |  | 0.0 | 0.0 |
+| year=2023 / max_miles | 407.563 |  | 407.563 | 407.563 |
+| _id=2020 / driver_pay |  | 2036707828.5292335 |  |  |
+| _id=2020 / passenger_fare |  | 2527548383.5690694 |  |  |
+| _id=2020 / max_miles |  | 1310.51 |  |  |
+| _id=2020 / min_miles |  | 0.0 |  |  |
+| _id=2021 / driver_pay |  | 3085990462.630042 |  |  |
+| _id=2021 / passenger_fare |  | 3839172144.575017 |  |  |
+| _id=2021 / max_miles |  | 738.95 |  |  |
+| _id=2021 / min_miles |  | 0.0 |  |  |
+| _id=2022 / driver_pay |  | 3966957717.7361007 |  |  |
+| _id=2022 / passenger_fare |  | 5029617856.472827 |  |  |
+| _id=2022 / max_miles |  | 634.32 |  |  |
+| _id=2022 / min_miles |  | 0.0 |  |  |
+| _id=2023 / driver_pay |  | 310164377.32070553 |  |  |
+| _id=2023 / passenger_fare |  | 398327570.2230424 |  |  |
+| _id=2023 / max_miles |  | 407.563 |  |  |
+| _id=2023 / min_miles |  | 0.0 |  |  |
+
+### Q10
+
+| Result | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark |
+| --- | --- | --- | --- | --- |
+| shared_request_flag=N / trips | 210564721 |  | 210564721 | 210564721 |
+| shared_request_flag=Y / trips | 1851362 |  | 1851362 | 1851362 |
+| _id |  | N |  |  |
+| _id #2 |  | Y |  |  |
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_results.csv b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_results.csv
index 0e3616cc8a..5987f20fe0 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_results.csv
@@ -1,61 +1,61 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-14T18:43:37.639272400Z","Q01","Full Yellow Taxi Row Count","warmup",1,28,1,1,true,""
-"2026-06-14T18:43:37.670383800Z","Q01","Full Yellow Taxi Row Count","measured",1,27,1,1,true,""
-"2026-06-14T18:43:37.700889100Z","Q01","Full Yellow Taxi Row Count","measured",2,29,1,1,true,""
-"2026-06-14T18:43:37.729014700Z","Q01","Full Yellow Taxi Row Count","measured",3,29,1,1,true,""
-"2026-06-14T18:43:37.762333200Z","Q01","Full Yellow Taxi Row Count","measured",4,30,1,1,true,""
-"2026-06-14T18:43:37.791863100Z","Q01","Full Yellow Taxi Row Count","measured",5,28,1,1,true,""
-"2026-06-14T18:43:37.807876500Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,15,1,1,true,""
-"2026-06-14T18:43:37.818875100Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,10,1,1,true,""
-"2026-06-14T18:43:37.833873500Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,14,1,1,true,""
-"2026-06-14T18:43:37.847874500Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,13,1,1,true,""
-"2026-06-14T18:43:37.858872800Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,9,1,1,true,""
-"2026-06-14T18:43:37.874872600Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,14,1,1,true,""
-"2026-06-14T18:43:38.034929700Z","Q03","Yellow Taxi Trips On One Day","warmup",1,159,1,1,true,""
-"2026-06-14T18:43:38.222989400Z","Q03","Yellow Taxi Trips On One Day","measured",1,187,1,1,true,""
-"2026-06-14T18:43:38.434603400Z","Q03","Yellow Taxi Trips On One Day","measured",2,211,1,1,true,""
-"2026-06-14T18:43:38.668860600Z","Q03","Yellow Taxi Trips On One Day","measured",3,232,1,1,true,""
-"2026-06-14T18:43:38.918927900Z","Q03","Yellow Taxi Trips On One Day","measured",4,248,1,1,true,""
-"2026-06-14T18:43:39.207028400Z","Q03","Yellow Taxi Trips On One Day","measured",5,285,1,1,true,""
-"2026-06-14T18:43:39.930224Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,723,1,1,true,""
-"2026-06-14T18:43:40.664837900Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,731,1,1,true,""
-"2026-06-14T18:43:41.387462100Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,721,1,1,true,""
-"2026-06-14T18:43:42.070013200Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,680,1,1,true,""
-"2026-06-14T18:43:42.720995800Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,649,1,1,true,""
-"2026-06-14T18:43:43.395078700Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,672,1,1,true,""
-"2026-06-14T18:43:44.576659500Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","warmup",1,1179,37,6,true,""
-"2026-06-14T18:43:45.787046400Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",1,1208,37,6,true,""
-"2026-06-14T18:43:46.907023700Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",2,1118,37,6,true,""
-"2026-06-14T18:43:48.091605200Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",3,1182,37,6,true,""
-"2026-06-14T18:43:49.354577200Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",4,1262,37,6,true,""
-"2026-06-14T18:43:50.435423900Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",5,1078,37,6,true,""
-"2026-06-14T18:43:50.527229700Z","Q06","Full High-Volume FHV Row Count","warmup",1,91,1,1,true,""
-"2026-06-14T18:43:50.614349800Z","Q06","Full High-Volume FHV Row Count","measured",1,87,1,1,true,""
-"2026-06-14T18:43:50.710301300Z","Q06","Full High-Volume FHV Row Count","measured",2,94,1,1,true,""
-"2026-06-14T18:43:50.805533Z","Q06","Full High-Volume FHV Row Count","measured",3,89,1,1,true,""
-"2026-06-14T18:43:50.900552700Z","Q06","Full High-Volume FHV Row Count","measured",4,94,1,1,true,""
-"2026-06-14T18:43:51.000088100Z","Q06","Full High-Volume FHV Row Count","measured",5,98,1,1,true,""
-"2026-06-14T18:43:51.022616100Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,21,1,1,true,""
-"2026-06-14T18:43:51.052617500Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,28,1,1,true,""
-"2026-06-14T18:43:51.082620600Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,28,1,1,true,""
-"2026-06-14T18:43:51.111163200Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,27,1,1,true,""
-"2026-06-14T18:43:51.134160700Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,22,1,1,true,""
-"2026-06-14T18:43:51.162169700Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,27,1,1,true,""
-"2026-06-14T18:43:56.167603200Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,5003,1,1,true,""
-"2026-06-14T18:44:01.305197800Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,5134,1,1,true,""
-"2026-06-14T18:44:06.351612800Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,5045,1,1,true,""
-"2026-06-14T18:44:11.321718600Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,4969,1,1,true,""
-"2026-06-14T18:44:16.379083Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,5055,1,1,true,""
-"2026-06-14T18:44:21.127130900Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,4747,1,1,true,""
-"2026-06-14T18:44:32.333756800Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","warmup",1,11207,37,7,true,""
-"2026-06-14T18:44:41.783208100Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",1,9444,37,7,true,""
-"2026-06-14T18:44:50.829426700Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",2,9045,37,7,true,""
-"2026-06-14T18:44:59.944287300Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",3,9118,37,7,true,""
-"2026-06-14T18:45:08.973503700Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",4,9026,37,7,true,""
-"2026-06-14T18:45:18.427029500Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",5,9451,37,7,true,""
-"2026-06-14T18:45:19.691872100Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","warmup",1,1263,4,3,true,""
-"2026-06-14T18:45:21.013469100Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",1,1322,4,3,true,""
-"2026-06-14T18:45:22.133541500Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",2,1117,4,3,true,""
-"2026-06-14T18:45:23.218791200Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",3,1084,4,3,true,""
-"2026-06-14T18:45:24.681179500Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",4,1461,4,3,true,""
-"2026-06-14T18:45:25.962680400Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",5,1279,4,3,true,""
+"2026-06-21T10:36:27.507902200Z","Q01","Full Yellow Taxi Row Count","warmup",1,25,1,1,true,""
+"2026-06-21T10:36:27.529910500Z","Q01","Full Yellow Taxi Row Count","measured",1,21,1,1,true,""
+"2026-06-21T10:36:27.554420200Z","Q01","Full Yellow Taxi Row Count","measured",2,23,1,1,true,""
+"2026-06-21T10:36:27.580423800Z","Q01","Full Yellow Taxi Row Count","measured",3,25,1,1,true,""
+"2026-06-21T10:36:27.602420800Z","Q01","Full Yellow Taxi Row Count","measured",4,21,1,1,true,""
+"2026-06-21T10:36:27.626425700Z","Q01","Full Yellow Taxi Row Count","measured",5,23,1,1,true,""
+"2026-06-21T10:36:27.637428700Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,10,1,1,true,""
+"2026-06-21T10:36:27.646934600Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,8,1,1,true,""
+"2026-06-21T10:36:27.656936600Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,10,1,1,true,""
+"2026-06-21T10:36:27.666933300Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,9,1,1,true,""
+"2026-06-21T10:36:27.676932600Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,9,1,1,true,""
+"2026-06-21T10:36:27.686934800Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,10,1,1,true,""
+"2026-06-21T10:36:27.859851Z","Q03","Yellow Taxi Trips On One Day","warmup",1,171,1,1,true,""
+"2026-06-21T10:36:28.022505800Z","Q03","Yellow Taxi Trips On One Day","measured",1,162,1,1,true,""
+"2026-06-21T10:36:28.203539700Z","Q03","Yellow Taxi Trips On One Day","measured",2,180,1,1,true,""
+"2026-06-21T10:36:28.372812Z","Q03","Yellow Taxi Trips On One Day","measured",3,169,1,1,true,""
+"2026-06-21T10:36:28.555933500Z","Q03","Yellow Taxi Trips On One Day","measured",4,182,1,1,true,""
+"2026-06-21T10:36:28.758668800Z","Q03","Yellow Taxi Trips On One Day","measured",5,202,1,1,true,""
+"2026-06-21T10:36:29.311048400Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,551,1,1,true,""
+"2026-06-21T10:36:29.969203600Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,655,1,1,true,""
+"2026-06-21T10:36:30.702066500Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,730,1,1,true,""
+"2026-06-21T10:36:31.515648600Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,812,1,1,true,""
+"2026-06-21T10:36:32.354913700Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,837,1,1,true,""
+"2026-06-21T10:36:33.150111800Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,793,1,1,true,""
+"2026-06-21T10:36:34.243851800Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","warmup",1,1091,4,5,true,""
+"2026-06-21T10:36:35.485663600Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",1,1240,4,5,true,""
+"2026-06-21T10:36:36.811807100Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",2,1324,4,5,true,""
+"2026-06-21T10:36:37.963293600Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",3,1150,4,5,true,""
+"2026-06-21T10:36:39.047081100Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",4,1081,4,5,true,""
+"2026-06-21T10:36:40.215448900Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",5,1170,4,5,true,""
+"2026-06-21T10:36:40.351639800Z","Q06","Full High-Volume FHV Row Count","warmup",1,134,1,1,true,""
+"2026-06-21T10:36:40.499166Z","Q06","Full High-Volume FHV Row Count","measured",1,142,1,1,true,""
+"2026-06-21T10:36:40.648492500Z","Q06","Full High-Volume FHV Row Count","measured",2,148,1,1,true,""
+"2026-06-21T10:36:40.794694Z","Q06","Full High-Volume FHV Row Count","measured",3,146,1,1,true,""
+"2026-06-21T10:36:40.940123900Z","Q06","Full High-Volume FHV Row Count","measured",4,141,1,1,true,""
+"2026-06-21T10:36:41.093287Z","Q06","Full High-Volume FHV Row Count","measured",5,150,1,1,true,""
+"2026-06-21T10:36:41.131619500Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,38,1,1,true,""
+"2026-06-21T10:36:41.170618Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,36,1,1,true,""
+"2026-06-21T10:36:41.211931100Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,38,1,1,true,""
+"2026-06-21T10:36:41.242919600Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,29,1,1,true,""
+"2026-06-21T10:36:41.273369500Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,30,1,1,true,""
+"2026-06-21T10:36:41.308924400Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,37,1,1,true,""
+"2026-06-21T10:36:46.482415400Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,5169,1,1,true,""
+"2026-06-21T10:36:50.944464900Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,4462,1,1,true,""
+"2026-06-21T10:36:55.458952700Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,4512,1,1,true,""
+"2026-06-21T10:36:59.675270100Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,4216,1,1,true,""
+"2026-06-21T10:37:04.376576500Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,4697,1,1,true,""
+"2026-06-21T10:37:09.415221600Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,5036,1,1,true,""
+"2026-06-21T10:37:18.040371100Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","warmup",1,8627,4,6,true,""
+"2026-06-21T10:37:27.146856Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",1,9102,4,6,true,""
+"2026-06-21T10:37:35.490519700Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",2,8342,4,6,true,""
+"2026-06-21T10:37:44.046410800Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",3,8556,4,6,true,""
+"2026-06-21T10:37:53.191422300Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",4,9145,4,6,true,""
+"2026-06-21T10:38:02.269049700Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",5,9076,4,6,true,""
+"2026-06-21T10:38:02.689623500Z","Q10","High-Volume FHV Shared-Request Flag Distribution","warmup",1,415,2,2,true,""
+"2026-06-21T10:38:03.147214900Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",1,456,2,2,true,""
+"2026-06-21T10:38:03.626350500Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",2,480,2,2,true,""
+"2026-06-21T10:38:04.087397600Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",3,460,2,2,true,""
+"2026-06-21T10:38:04.488123300Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",4,397,2,2,true,""
+"2026-06-21T10:38:04.871635300Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",5,384,2,2,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_values.jsonl b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_values.jsonl
new file mode 100644
index 0000000000..22bb6f5a09
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_values.jsonl
@@ -0,0 +1,10 @@
+{"timestamp":"2026-06-21T10:38:04.912299Z","query_id":"Q01","description":"Full Yellow Taxi Row Count","success":true,"error":"","columns":["row_count"],"rows":[["98276264"]]}
+{"timestamp":"2026-06-21T10:38:04.927523600Z","query_id":"Q02","description":"Yellow Taxi Count For One Partition Month","success":true,"error":"","columns":["row_count"],"rows":[["3675411"]]}
+{"timestamp":"2026-06-21T10:38:05.175418200Z","query_id":"Q03","description":"Yellow Taxi Trips On One Day","success":true,"error":"","columns":["row_count"],"rows":[["127002"]]}
+{"timestamp":"2026-06-21T10:38:05.775286600Z","query_id":"Q04","description":"Long And Expensive Yellow Taxi Trips","success":true,"error":"","columns":["row_count"],"rows":[["6438540"]]}
+{"timestamp":"2026-06-21T10:38:06.763925500Z","query_id":"Q05","description":"Yearly Yellow Taxi Amount And Distance Summary","success":true,"error":"","columns":["year","trips","gross_amount","min_distance","max_distance"],"rows":[["2020","24649092","4.540793341610861E8","-30.62","350914.89"],["2021","30904308","6.087006709132242E8","0.0","351613.36"],["2022","39656098","8.593979451730231E8","0.0","389678.46"],["2023","3066766","8.28651922197824E7","0.0","258928.15"]]}
+{"timestamp":"2026-06-21T10:38:06.865894900Z","query_id":"Q06","description":"Full High-Volume FHV Row Count","success":true,"error":"","columns":["row_count"],"rows":[["548801637"]]}
+{"timestamp":"2026-06-21T10:38:06.891884100Z","query_id":"Q07","description":"High-Volume FHV Count For One Partition Month","success":true,"error":"","columns":["row_count"],"rows":[["19306090"]]}
+{"timestamp":"2026-06-21T10:38:11.358100100Z","query_id":"Q08","description":"Long And Expensive High-Volume FHV Trips","success":true,"error":"","columns":["row_count"],"rows":[["42659531"]]}
+{"timestamp":"2026-06-21T10:38:23.123379800Z","query_id":"Q09","description":"Yearly High-Volume FHV Fare And Distance Summary","success":true,"error":"","columns":["year","trips","passenger_fare","driver_pay","min_miles","max_miles"],"rows":[["2020","143309871","2.527548383569069E9","2.0367078285292332E9","0.0","1310.51"],["2021","174596652","3.839172144575017E9","3.085990462630042E9","0.0","738.95"],["2022","212416083","5.029617856472826E9","3.9669577177361016E9","0.0","634.32"],["2023","18479031","3.983275702230424E8","3.1016437732070553E8","0.0","407.563"]]}
+{"timestamp":"2026-06-21T10:38:23.659513100Z","query_id":"Q10","description":"High-Volume FHV Shared-Request Flag Distribution","success":true,"error":"","columns":["shared_request_flag","trips"],"rows":[["N","210564721"],["Y","1851362"]]}
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_results.csv b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_results.csv
index ed35759f2f..df63d5edae 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_results.csv
@@ -1,61 +1,61 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-14T18:39:06.727807300Z","Q01","Full Yellow Taxi Row Count","warmup",1,317,1,1,true,""
-"2026-06-14T18:39:06.864668900Z","Q01","Full Yellow Taxi Row Count","measured",1,130,1,1,true,""
-"2026-06-14T18:39:06.981579900Z","Q01","Full Yellow Taxi Row Count","measured",2,116,1,1,true,""
-"2026-06-14T18:39:07.105175200Z","Q01","Full Yellow Taxi Row Count","measured",3,125,1,1,true,""
-"2026-06-14T18:39:07.214003700Z","Q01","Full Yellow Taxi Row Count","measured",4,108,1,1,true,""
-"2026-06-14T18:39:07.355728700Z","Q01","Full Yellow Taxi Row Count","measured",5,130,1,1,true,""
-"2026-06-14T18:39:07.521759900Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,163,1,1,true,""
-"2026-06-14T18:39:07.677739700Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,155,1,1,true,""
-"2026-06-14T18:39:07.816653Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,141,1,1,true,""
-"2026-06-14T18:39:08.011048600Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,192,1,1,true,""
-"2026-06-14T18:39:08.168791300Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,158,1,1,true,""
-"2026-06-14T18:39:08.338398800Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,166,1,1,true,""
-"2026-06-14T18:39:09.010490Z","Q03","Yellow Taxi Trips On One Day","warmup",1,663,1,1,true,""
-"2026-06-14T18:39:09.627315800Z","Q03","Yellow Taxi Trips On One Day","measured",1,616,1,1,true,""
-"2026-06-14T18:39:10.225926200Z","Q03","Yellow Taxi Trips On One Day","measured",2,599,1,1,true,""
-"2026-06-14T18:39:10.832457800Z","Q03","Yellow Taxi Trips On One Day","measured",3,609,1,1,true,""
-"2026-06-14T18:39:11.448210700Z","Q03","Yellow Taxi Trips On One Day","measured",4,611,1,1,true,""
-"2026-06-14T18:39:12.074721300Z","Q03","Yellow Taxi Trips On One Day","measured",5,626,1,1,true,""
-"2026-06-14T18:39:13.306381300Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,1229,1,1,true,""
-"2026-06-14T18:39:14.576455100Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,1270,1,1,true,""
-"2026-06-14T18:39:15.738683Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,1162,1,1,true,""
-"2026-06-14T18:39:18.234628700Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,2493,1,1,true,""
-"2026-06-14T18:39:21.626709300Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,3390,1,1,true,""
-"2026-06-14T18:39:24.235796600Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,2609,1,1,true,""
-"2026-06-14T18:39:26.404533600Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","warmup",1,2165,4,4,true,""
-"2026-06-14T18:39:28.807867600Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",1,2400,4,4,true,""
-"2026-06-14T18:39:31.329162900Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",2,2522,4,4,true,""
-"2026-06-14T18:39:33.826662100Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",3,2493,4,4,true,""
-"2026-06-14T18:39:36.214475Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",4,2386,4,4,true,""
-"2026-06-14T18:39:38.547850300Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",5,2331,4,4,true,""
-"2026-06-14T18:39:39.008803800Z","Q06","Full High-Volume FHV Row Count","warmup",1,458,1,1,true,""
-"2026-06-14T18:39:39.428348700Z","Q06","Full High-Volume FHV Row Count","measured",1,418,1,1,true,""
-"2026-06-14T18:39:39.794032100Z","Q06","Full High-Volume FHV Row Count","measured",2,364,1,1,true,""
-"2026-06-14T18:39:40.141464700Z","Q06","Full High-Volume FHV Row Count","measured",3,346,1,1,true,""
-"2026-06-14T18:39:40.406417600Z","Q06","Full High-Volume FHV Row Count","measured",4,263,1,1,true,""
-"2026-06-14T18:39:40.629583300Z","Q06","Full High-Volume FHV Row Count","measured",5,224,1,1,true,""
-"2026-06-14T18:39:40.889667800Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,257,1,1,true,""
-"2026-06-14T18:39:41.103907100Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,212,1,1,true,""
-"2026-06-14T18:39:41.387645400Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,283,1,1,true,""
-"2026-06-14T18:39:41.661220Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,273,1,1,true,""
-"2026-06-14T18:39:41.918291Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,257,1,1,true,""
-"2026-06-14T18:39:42.135223700Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,217,1,1,true,""
-"2026-06-14T18:39:55.275981Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,13138,1,1,true,""
-"2026-06-14T18:40:08.347333900Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,13069,1,1,true,""
-"2026-06-14T18:40:25.452405900Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,17103,1,1,true,""
-"2026-06-14T18:40:38.634345200Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,13155,1,1,true,""
-"2026-06-14T18:41:00.171441300Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,21529,1,1,true,""
-"2026-06-14T18:41:14.843351500Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,14670,1,1,true,""
-"2026-06-14T18:41:29.687714600Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","warmup",1,14843,4,5,true,""
-"2026-06-14T18:41:44.362076700Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",1,14673,4,5,true,""
-"2026-06-14T18:41:59.738012700Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",2,15375,4,5,true,""
-"2026-06-14T18:42:14.459089100Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",3,14719,4,5,true,""
-"2026-06-14T18:42:29.115529600Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",4,14655,4,5,true,""
-"2026-06-14T18:42:44.017663200Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",5,14900,4,5,true,""
-"2026-06-14T18:42:48.962237300Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","warmup",1,4943,2,1,true,""
-"2026-06-14T18:42:54.229056900Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",1,5265,2,1,true,""
-"2026-06-14T18:42:59.924825700Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",2,5694,2,1,true,""
-"2026-06-14T18:43:04.706655200Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",3,4780,2,1,true,""
-"2026-06-14T18:43:09.454408700Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",4,4747,2,1,true,""
-"2026-06-14T18:43:14.508725800Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",5,5052,2,1,true,""
+"2026-06-21T10:27:08.705109600Z","Q01","Full Yellow Taxi Row Count","warmup",1,388,1,1,true,""
+"2026-06-21T10:27:08.920209600Z","Q01","Full Yellow Taxi Row Count","measured",1,202,1,1,true,""
+"2026-06-21T10:27:09.094492300Z","Q01","Full Yellow Taxi Row Count","measured",2,174,1,1,true,""
+"2026-06-21T10:27:09.278847900Z","Q01","Full Yellow Taxi Row Count","measured",3,183,1,1,true,""
+"2026-06-21T10:27:09.447981400Z","Q01","Full Yellow Taxi Row Count","measured",4,168,1,1,true,""
+"2026-06-21T10:27:09.620104600Z","Q01","Full Yellow Taxi Row Count","measured",5,171,1,1,true,""
+"2026-06-21T10:27:09.838046Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,217,1,1,true,""
+"2026-06-21T10:27:10.057274800Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,218,1,1,true,""
+"2026-06-21T10:27:10.246639Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,188,1,1,true,""
+"2026-06-21T10:27:10.457196200Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,211,1,1,true,""
+"2026-06-21T10:27:10.677319600Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,219,1,1,true,""
+"2026-06-21T10:27:10.887690700Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,209,1,1,true,""
+"2026-06-21T10:27:11.633505100Z","Q03","Yellow Taxi Trips On One Day","warmup",1,744,1,1,true,""
+"2026-06-21T10:27:12.522231Z","Q03","Yellow Taxi Trips On One Day","measured",1,888,1,1,true,""
+"2026-06-21T10:27:13.358164500Z","Q03","Yellow Taxi Trips On One Day","measured",2,834,1,1,true,""
+"2026-06-21T10:27:14.310113400Z","Q03","Yellow Taxi Trips On One Day","measured",3,951,1,1,true,""
+"2026-06-21T10:27:15.394896500Z","Q03","Yellow Taxi Trips On One Day","measured",4,1083,1,1,true,""
+"2026-06-21T10:27:16.410605500Z","Q03","Yellow Taxi Trips On One Day","measured",5,1014,1,1,true,""
+"2026-06-21T10:27:19.041087100Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,2629,1,1,true,""
+"2026-06-21T10:27:22.208869400Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,3165,1,1,true,""
+"2026-06-21T10:27:25.799706900Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,3588,1,1,true,""
+"2026-06-21T10:27:28.808291900Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,3006,1,1,true,""
+"2026-06-21T10:27:32.222477900Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,3412,1,1,true,""
+"2026-06-21T10:27:35.168557300Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,2945,1,1,true,""
+"2026-06-21T10:27:37.493881400Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","warmup",1,2323,4,4,true,""
+"2026-06-21T10:27:39.375624600Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",1,1879,4,4,true,""
+"2026-06-21T10:27:41.758962700Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",2,2382,4,4,true,""
+"2026-06-21T10:27:43.880701200Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",3,2119,4,4,true,""
+"2026-06-21T10:27:45.780722600Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",4,1900,4,4,true,""
+"2026-06-21T10:27:47.540780400Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",5,1758,4,4,true,""
+"2026-06-21T10:27:47.766526200Z","Q06","Full High-Volume FHV Row Count","warmup",1,227,1,1,true,""
+"2026-06-21T10:27:47.976538700Z","Q06","Full High-Volume FHV Row Count","measured",1,204,1,1,true,""
+"2026-06-21T10:27:48.162732600Z","Q06","Full High-Volume FHV Row Count","measured",2,187,1,1,true,""
+"2026-06-21T10:27:48.336901200Z","Q06","Full High-Volume FHV Row Count","measured",3,173,1,1,true,""
+"2026-06-21T10:27:48.500854100Z","Q06","Full High-Volume FHV Row Count","measured",4,162,1,1,true,""
+"2026-06-21T10:27:48.663371300Z","Q06","Full High-Volume FHV Row Count","measured",5,162,1,1,true,""
+"2026-06-21T10:27:48.859406800Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,194,1,1,true,""
+"2026-06-21T10:27:49.060236800Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,200,1,1,true,""
+"2026-06-21T10:27:49.258164200Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,196,1,1,true,""
+"2026-06-21T10:27:49.458728500Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,199,1,1,true,""
+"2026-06-21T10:27:49.646791100Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,191,1,1,true,""
+"2026-06-21T10:27:49.894462200Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,241,1,1,true,""
+"2026-06-21T10:28:05.125630400Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,15228,1,1,true,""
+"2026-06-21T10:28:19.066893800Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,13940,1,1,true,""
+"2026-06-21T10:28:32.664362100Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,13599,1,1,true,""
+"2026-06-21T10:28:46.303936Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,13636,1,1,true,""
+"2026-06-21T10:28:59.732948800Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,13426,1,1,true,""
+"2026-06-21T10:29:13.404434800Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,13670,1,1,true,""
+"2026-06-21T10:29:27.036915200Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","warmup",1,13631,4,5,true,""
+"2026-06-21T10:29:40.401370800Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",1,13367,4,5,true,""
+"2026-06-21T10:29:53.893779400Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",2,13488,4,5,true,""
+"2026-06-21T10:30:07.675697500Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",3,13783,4,5,true,""
+"2026-06-21T10:30:20.840255200Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",4,13161,4,5,true,""
+"2026-06-21T10:30:34.180043200Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",5,13338,4,5,true,""
+"2026-06-21T10:30:38.733403500Z","Q10","High-Volume FHV Shared-Request Flag Distribution","warmup",1,4552,2,1,true,""
+"2026-06-21T10:30:43.135521300Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",1,4405,2,1,true,""
+"2026-06-21T10:30:47.640880300Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",2,4499,2,1,true,""
+"2026-06-21T10:30:52.054600900Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",3,4413,2,1,true,""
+"2026-06-21T10:30:56.412188500Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",4,4356,2,1,true,""
+"2026-06-21T10:31:00.345306700Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",5,3932,2,1,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_values.jsonl b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_values.jsonl
new file mode 100644
index 0000000000..879e060bf6
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_values.jsonl
@@ -0,0 +1,10 @@
+{"timestamp":"2026-06-21T10:31:00.603741600Z","query_id":"Q01","description":"Full Yellow Taxi Row Count","success":true,"error":"","columns":["row_count"],"rows":[["98276264"]]}
+{"timestamp":"2026-06-21T10:31:00.876668700Z","query_id":"Q02","description":"Yellow Taxi Count For One Partition Month","success":true,"error":"","columns":["row_count"],"rows":[["3675411"]]}
+{"timestamp":"2026-06-21T10:31:01.865009700Z","query_id":"Q03","description":"Yellow Taxi Trips On One Day","success":true,"error":"","columns":["row_count"],"rows":[["127002"]]}
+{"timestamp":"2026-06-21T10:31:04.176449300Z","query_id":"Q04","description":"Long And Expensive Yellow Taxi Trips","success":true,"error":"","columns":["row_count"],"rows":[["6438540"]]}
+{"timestamp":"2026-06-21T10:31:05.920949800Z","query_id":"Q05","description":"Yearly Yellow Taxi Amount And Distance Summary","success":true,"error":"","columns":["max_distance","min_distance","_id","gross_amount"],"rows":[["350914.89","-30.62","2020","454079334.16108584"],["351613.36","0.0","2021","608700670.9132242"],["389678.46","0.0","2022","859397945.173023"],["258928.15","0.0","2023","82865192.2197824"]]}
+{"timestamp":"2026-06-21T10:31:06.146784400Z","query_id":"Q06","description":"Full High-Volume FHV Row Count","success":true,"error":"","columns":["row_count"],"rows":[["548801637"]]}
+{"timestamp":"2026-06-21T10:31:06.403439900Z","query_id":"Q07","description":"High-Volume FHV Count For One Partition Month","success":true,"error":"","columns":["row_count"],"rows":[["19306090"]]}
+{"timestamp":"2026-06-21T10:31:20.134493100Z","query_id":"Q08","description":"Long And Expensive High-Volume FHV Trips","success":true,"error":"","columns":["row_count"],"rows":[["42659531"]]}
+{"timestamp":"2026-06-21T10:31:33.677407800Z","query_id":"Q09","description":"Yearly High-Volume FHV Fare And Distance Summary","success":true,"error":"","columns":["driver_pay","passenger_fare","_id","max_miles","min_miles"],"rows":[["2036707828.5292335","2527548383.5690694","2020","1310.51","0.0"],["3085990462.630042","3839172144.575017","2021","738.95","0.0"],["3966957717.7361007","5029617856.472827","2022","634.32","0.0"],["310164377.32070553","398327570.2230424","2023","407.563","0.0"]]}
+{"timestamp":"2026-06-21T10:31:37.565885500Z","query_id":"Q10","description":"High-Volume FHV Shared-Request Flag Distribution","success":true,"error":"","columns":["_id"],"rows":[["N"],["Y"]]}
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv
index a7ad52244f..488b14a8b2 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv
@@ -1,61 +1,61 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-14T18:34:06.309325200Z","Q01","Full Yellow Taxi Row Count","warmup",1,638,1,1,true,""
-"2026-06-14T18:34:06.329871500Z","Q01","Full Yellow Taxi Row Count","measured",1,17,1,1,true,""
-"2026-06-14T18:34:06.339868800Z","Q01","Full Yellow Taxi Row Count","measured",2,10,1,1,true,""
-"2026-06-14T18:34:06.347866900Z","Q01","Full Yellow Taxi Row Count","measured",3,7,1,1,true,""
-"2026-06-14T18:34:06.358871Z","Q01","Full Yellow Taxi Row Count","measured",4,10,1,1,true,""
-"2026-06-14T18:34:06.366869400Z","Q01","Full Yellow Taxi Row Count","measured",5,7,1,1,true,""
-"2026-06-14T18:34:06.571950900Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,203,1,1,true,""
-"2026-06-14T18:34:06.583950800Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,11,1,1,true,""
-"2026-06-14T18:34:06.595953400Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,12,1,1,true,""
-"2026-06-14T18:34:06.609953Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,13,1,1,true,""
-"2026-06-14T18:34:06.619960400Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,9,1,1,true,""
-"2026-06-14T18:34:06.633493800Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,13,1,1,true,""
-"2026-06-14T18:34:08.736407100Z","Q03","Yellow Taxi Trips On One Day","warmup",1,2105,1,1,true,""
-"2026-06-14T18:34:09.711177400Z","Q03","Yellow Taxi Trips On One Day","measured",1,970,1,1,true,""
-"2026-06-14T18:34:10.828544900Z","Q03","Yellow Taxi Trips On One Day","measured",2,1116,1,1,true,""
-"2026-06-14T18:34:11.910741800Z","Q03","Yellow Taxi Trips On One Day","measured",3,1081,1,1,true,""
-"2026-06-14T18:34:13.091403700Z","Q03","Yellow Taxi Trips On One Day","measured",4,1179,1,1,true,""
-"2026-06-14T18:34:14.207271700Z","Q03","Yellow Taxi Trips On One Day","measured",5,1116,1,1,true,""
-"2026-06-14T18:34:17.687087200Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,3477,1,1,true,""
-"2026-06-14T18:34:19.685634Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,2001,1,1,true,""
-"2026-06-14T18:34:21.647462200Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,1956,1,1,true,""
-"2026-06-14T18:34:23.560154Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,1911,1,1,true,""
-"2026-06-14T18:34:25.540854200Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,1978,1,1,true,""
-"2026-06-14T18:34:27.397676500Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,1861,1,1,true,""
-"2026-06-14T18:34:29.312283900Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","warmup",1,1909,37,6,true,""
-"2026-06-14T18:34:31.087535300Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",1,1778,37,6,true,""
-"2026-06-14T18:34:33.116183400Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",2,2022,37,6,true,""
-"2026-06-14T18:34:34.751787Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",3,1633,37,6,true,""
-"2026-06-14T18:34:36.425439800Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",4,1675,37,6,true,""
-"2026-06-14T18:34:37.876620700Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",5,1447,37,6,true,""
-"2026-06-14T18:34:37.967139400Z","Q06","Full High-Volume FHV Row Count","warmup",1,89,1,1,true,""
-"2026-06-14T18:34:37.979141500Z","Q06","Full High-Volume FHV Row Count","measured",1,10,1,1,true,""
-"2026-06-14T18:34:37.993141200Z","Q06","Full High-Volume FHV Row Count","measured",2,12,1,1,true,""
-"2026-06-14T18:34:38.005141100Z","Q06","Full High-Volume FHV Row Count","measured",3,11,1,1,true,""
-"2026-06-14T18:34:38.015141200Z","Q06","Full High-Volume FHV Row Count","measured",4,8,1,1,true,""
-"2026-06-14T18:34:38.029139100Z","Q06","Full High-Volume FHV Row Count","measured",5,13,1,1,true,""
-"2026-06-14T18:34:38.164139600Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,133,1,1,true,""
-"2026-06-14T18:34:38.179139Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,13,1,1,true,""
-"2026-06-14T18:34:38.194138800Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,13,1,1,true,""
-"2026-06-14T18:34:38.204139100Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,10,1,1,true,""
-"2026-06-14T18:34:38.218138Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,12,1,1,true,""
-"2026-06-14T18:34:38.232253300Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,13,1,1,true,""
-"2026-06-14T18:34:53.386664700Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,15154,1,1,true,""
-"2026-06-14T18:35:04.916488200Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,11529,1,1,true,""
-"2026-06-14T18:35:16.454210600Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,11536,1,1,true,""
-"2026-06-14T18:35:27.902533700Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,11448,1,1,true,""
-"2026-06-14T18:35:39.144143300Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,11244,1,1,true,""
-"2026-06-14T18:35:50.698519Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,11552,1,1,true,""
-"2026-06-14T18:36:04.368622900Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","warmup",1,13669,37,7,true,""
-"2026-06-14T18:36:17.179441700Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",1,12810,37,7,true,""
-"2026-06-14T18:36:30.004603100Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",2,12826,37,7,true,""
-"2026-06-14T18:36:43.337583700Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",3,13331,37,7,true,""
-"2026-06-14T18:36:56.552930100Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",4,13213,37,7,true,""
-"2026-06-14T18:37:10.605938500Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",5,14053,37,7,true,""
-"2026-06-14T18:37:16.030560400Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","warmup",1,5424,4,3,true,""
-"2026-06-14T18:37:21.799342Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",1,5768,4,3,true,""
-"2026-06-14T18:37:27.066088500Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",2,5266,4,3,true,""
-"2026-06-14T18:37:32.610836300Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",3,5543,4,3,true,""
-"2026-06-14T18:37:38.692573500Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",4,6081,4,3,true,""
-"2026-06-14T18:37:45.199175200Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",5,6506,4,3,true,""
+"2026-06-21T10:21:08.612263900Z","Q01","Full Yellow Taxi Row Count","warmup",1,624,1,1,true,""
+"2026-06-21T10:21:08.633321800Z","Q01","Full Yellow Taxi Row Count","measured",1,17,1,1,true,""
+"2026-06-21T10:21:08.645323800Z","Q01","Full Yellow Taxi Row Count","measured",2,12,1,1,true,""
+"2026-06-21T10:21:08.658321700Z","Q01","Full Yellow Taxi Row Count","measured",3,12,1,1,true,""
+"2026-06-21T10:21:08.671321900Z","Q01","Full Yellow Taxi Row Count","measured",4,12,1,1,true,""
+"2026-06-21T10:21:08.681332800Z","Q01","Full Yellow Taxi Row Count","measured",5,8,1,1,true,""
+"2026-06-21T10:21:08.898946200Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,216,1,1,true,""
+"2026-06-21T10:21:08.909958800Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,10,1,1,true,""
+"2026-06-21T10:21:08.922958700Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,12,1,1,true,""
+"2026-06-21T10:21:08.931953700Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,8,1,1,true,""
+"2026-06-21T10:21:08.943956900Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,11,1,1,true,""
+"2026-06-21T10:21:08.955955800Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,11,1,1,true,""
+"2026-06-21T10:21:10.567325700Z","Q03","Yellow Taxi Trips On One Day","warmup",1,1610,1,1,true,""
+"2026-06-21T10:21:11.595318500Z","Q03","Yellow Taxi Trips On One Day","measured",1,1026,1,1,true,""
+"2026-06-21T10:21:13.210264500Z","Q03","Yellow Taxi Trips On One Day","measured",2,1614,1,1,true,""
+"2026-06-21T10:21:14.798358300Z","Q03","Yellow Taxi Trips On One Day","measured",3,1586,1,1,true,""
+"2026-06-21T10:21:16.031608400Z","Q03","Yellow Taxi Trips On One Day","measured",4,1234,1,1,true,""
+"2026-06-21T10:21:17.097965300Z","Q03","Yellow Taxi Trips On One Day","measured",5,1064,1,1,true,""
+"2026-06-21T10:21:20.666111800Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,3571,1,1,true,""
+"2026-06-21T10:21:23.484278900Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,2812,1,1,true,""
+"2026-06-21T10:21:25.688324600Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,2202,1,1,true,""
+"2026-06-21T10:21:28.406429100Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,2720,1,1,true,""
+"2026-06-21T10:21:30.857978500Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,2446,1,1,true,""
+"2026-06-21T10:21:33.018900200Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,2164,1,1,true,""
+"2026-06-21T10:21:35.230834400Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","warmup",1,2205,4,5,true,""
+"2026-06-21T10:21:37.172994700Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",1,1941,4,5,true,""
+"2026-06-21T10:21:39.069892800Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",2,1895,4,5,true,""
+"2026-06-21T10:21:40.773371900Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",3,1702,4,5,true,""
+"2026-06-21T10:21:42.377490500Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",4,1603,4,5,true,""
+"2026-06-21T10:21:44.444270500Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",5,2065,4,5,true,""
+"2026-06-21T10:21:44.537798400Z","Q06","Full High-Volume FHV Row Count","warmup",1,92,1,1,true,""
+"2026-06-21T10:21:44.554802600Z","Q06","Full High-Volume FHV Row Count","measured",1,15,1,1,true,""
+"2026-06-21T10:21:44.565802200Z","Q06","Full High-Volume FHV Row Count","measured",2,10,1,1,true,""
+"2026-06-21T10:21:44.579800800Z","Q06","Full High-Volume FHV Row Count","measured",3,12,1,1,true,""
+"2026-06-21T10:21:44.594801800Z","Q06","Full High-Volume FHV Row Count","measured",4,14,1,1,true,""
+"2026-06-21T10:21:44.606825Z","Q06","Full High-Volume FHV Row Count","measured",5,11,1,1,true,""
+"2026-06-21T10:21:44.736884600Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,129,1,1,true,""
+"2026-06-21T10:21:44.753883700Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,15,1,1,true,""
+"2026-06-21T10:21:44.768886100Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,15,1,1,true,""
+"2026-06-21T10:21:44.781885400Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,12,1,1,true,""
+"2026-06-21T10:21:44.798898500Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,15,1,1,true,""
+"2026-06-21T10:21:44.814432300Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,14,1,1,true,""
+"2026-06-21T10:22:03.505430200Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,18690,1,1,true,""
+"2026-06-21T10:22:20.138726800Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,16633,1,1,true,""
+"2026-06-21T10:22:35.952063200Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,15812,1,1,true,""
+"2026-06-21T10:22:52.315994100Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,16362,1,1,true,""
+"2026-06-21T10:23:07.855979500Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,15539,1,1,true,""
+"2026-06-21T10:23:24.379514300Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,16525,1,1,true,""
+"2026-06-21T10:23:39.791342Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","warmup",1,15408,4,6,true,""
+"2026-06-21T10:23:55.180741300Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",1,15390,4,6,true,""
+"2026-06-21T10:24:10.393069400Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",2,15210,4,6,true,""
+"2026-06-21T10:24:27.467185600Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",3,17074,4,6,true,""
+"2026-06-21T10:24:42.468969400Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",4,14999,4,6,true,""
+"2026-06-21T10:24:57.746039100Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",5,15278,4,6,true,""
+"2026-06-21T10:25:02.678621Z","Q10","High-Volume FHV Shared-Request Flag Distribution","warmup",1,4929,2,2,true,""
+"2026-06-21T10:25:07.736440300Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",1,5057,2,2,true,""
+"2026-06-21T10:25:14.280574500Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",2,6541,2,2,true,""
+"2026-06-21T10:25:19.804605200Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",3,5525,2,2,true,""
+"2026-06-21T10:25:24.664604500Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",4,4858,2,2,true,""
+"2026-06-21T10:25:29.978175300Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",5,5310,2,2,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv.old b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv.old
new file mode 100644
index 0000000000..e0e796c122
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv.old
@@ -0,0 +1,61 @@
+timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-19T11:51:54.833675900Z","Q01","Full Yellow Taxi Row Count","warmup",1,1082,1,1,true,""
+"2026-06-19T11:51:54.873240400Z","Q01","Full Yellow Taxi Row Count","measured",1,29,1,1,true,""
+"2026-06-19T11:51:54.891060300Z","Q01","Full Yellow Taxi Row Count","measured",2,16,1,1,true,""
+"2026-06-19T11:51:54.908237600Z","Q01","Full Yellow Taxi Row Count","measured",3,15,1,1,true,""
+"2026-06-19T11:51:54.927507Z","Q01","Full Yellow Taxi Row Count","measured",4,18,1,1,true,""
+"2026-06-19T11:51:54.943173Z","Q01","Full Yellow Taxi Row Count","measured",5,18,1,1,true,""
+"2026-06-19T11:51:55.386453600Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,437,1,1,true,""
+"2026-06-19T11:51:55.407273400Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,19,1,1,true,""
+"2026-06-19T11:51:55.428284500Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,19,1,1,true,""
+"2026-06-19T11:51:55.448942500Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,19,1,1,true,""
+"2026-06-19T11:51:55.463580400Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,13,1,1,true,""
+"2026-06-19T11:51:55.485583800Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,20,1,1,true,""
+"2026-06-19T11:52:02.558846700Z","Q03","Yellow Taxi Trips On One Day","warmup",1,7072,1,1,true,""
+"2026-06-19T11:52:03.985797Z","Q03","Yellow Taxi Trips On One Day","measured",1,1424,1,1,true,""
+"2026-06-19T11:52:05.595292200Z","Q03","Yellow Taxi Trips On One Day","measured",2,1609,1,1,true,""
+"2026-06-19T11:52:07.526335700Z","Q03","Yellow Taxi Trips On One Day","measured",3,1928,1,1,true,""
+"2026-06-19T11:52:09.290226Z","Q03","Yellow Taxi Trips On One Day","measured",4,1765,1,1,true,""
+"2026-06-19T11:52:10.937756Z","Q03","Yellow Taxi Trips On One Day","measured",5,1642,1,1,true,""
+"2026-06-19T11:52:15.312364500Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,4372,1,1,true,""
+"2026-06-19T11:52:18.466980800Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,3153,1,1,true,""
+"2026-06-19T11:52:21.658756Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,3192,1,1,true,""
+"2026-06-19T11:52:25.041391600Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,3380,1,1,true,""
+"2026-06-19T11:52:28.131937300Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,3086,1,1,true,""
+"2026-06-19T11:52:30.999832200Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,2867,1,1,true,""
+"2026-06-19T11:52:33.905787700Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","warmup",1,2904,37,6,true,""
+"2026-06-19T11:52:37.384198900Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",1,3475,37,6,true,""
+"2026-06-19T11:52:40.571239400Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",2,3186,37,6,true,""
+"2026-06-19T11:52:43.459403500Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",3,2886,37,6,true,""
+"2026-06-19T11:52:46.317799500Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",4,2858,37,6,true,""
+"2026-06-19T11:52:48.634601900Z","Q05","Monthly Yellow Taxi Amount And Distance Summary","measured",5,2315,37,6,true,""
+"2026-06-19T11:52:48.770024200Z","Q06","Full High-Volume FHV Row Count","warmup",1,133,1,1,true,""
+"2026-06-19T11:52:48.783419200Z","Q06","Full High-Volume FHV Row Count","measured",1,15,1,1,true,""
+"2026-06-19T11:52:48.801236300Z","Q06","Full High-Volume FHV Row Count","measured",2,13,1,1,true,""
+"2026-06-19T11:52:48.818170300Z","Q06","Full High-Volume FHV Row Count","measured",3,12,1,1,true,""
+"2026-06-19T11:52:48.836675Z","Q06","Full High-Volume FHV Row Count","measured",4,17,1,1,true,""
+"2026-06-19T11:52:48.849255900Z","Q06","Full High-Volume FHV Row Count","measured",5,14,1,1,true,""
+"2026-06-19T11:52:49.031112500Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,179,1,1,true,""
+"2026-06-19T11:52:49.048309700Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,15,1,1,true,""
+"2026-06-19T11:52:49.066442Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,16,1,1,true,""
+"2026-06-19T11:52:49.082116900Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,15,1,1,true,""
+"2026-06-19T11:52:49.101205300Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,17,1,1,true,""
+"2026-06-19T11:52:49.116854100Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,17,1,1,true,""
+"2026-06-19T11:53:09.240164400Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,20121,1,1,true,""
+"2026-06-19T11:53:25.018110700Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,15776,1,1,true,""
+"2026-06-19T11:53:41.160419800Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,16140,1,1,true,""
+"2026-06-19T11:53:56.663916Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,15506,1,1,true,""
+"2026-06-19T11:54:12.302342700Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,15632,1,1,true,""
+"2026-06-19T11:54:29.905713900Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,17607,1,1,true,""
+"2026-06-19T11:54:51.563620300Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","warmup",1,21655,37,7,true,""
+"2026-06-19T11:55:12.190034400Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",1,20624,37,7,true,""
+"2026-06-19T11:55:38.304053400Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",2,26112,37,7,true,""
+"2026-06-19T11:56:04.204491100Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",3,25899,37,7,true,""
+"2026-06-19T11:56:39.445109900Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",4,35240,37,7,true,""
+"2026-06-19T11:57:08.798981900Z","Q09","Monthly High-Volume FHV Fare And Distance Summary","measured",5,29351,37,7,true,""
+"2026-06-19T11:57:17.884927400Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","warmup",1,9087,4,3,true,""
+"2026-06-19T11:57:28.150320300Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",1,10261,4,3,true,""
+"2026-06-19T11:57:42.401404400Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",2,14253,4,3,true,""
+"2026-06-19T11:57:56.150552700Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",3,13743,4,3,true,""
+"2026-06-19T11:58:09.619435500Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",4,13469,4,3,true,""
+"2026-06-19T11:58:21.972269900Z","Q10","High-Volume FHV Shared-Ride Flag Distribution","measured",5,12349,4,3,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_values.jsonl b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_values.jsonl
new file mode 100644
index 0000000000..8f7d6a1874
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_values.jsonl
@@ -0,0 +1,10 @@
+{"timestamp":"2026-06-21T10:25:30.001596700Z","query_id":"Q01","description":"Full Yellow Taxi Row Count","success":true,"error":"","columns":["row_count"],"rows":[["98276264"]]}
+{"timestamp":"2026-06-21T10:25:30.016598Z","query_id":"Q02","description":"Yellow Taxi Count For One Partition Month","success":true,"error":"","columns":["row_count"],"rows":[["3675411"]]}
+{"timestamp":"2026-06-21T10:25:31.598062600Z","query_id":"Q03","description":"Yellow Taxi Trips On One Day","success":true,"error":"","columns":["row_count"],"rows":[["127002"]]}
+{"timestamp":"2026-06-21T10:25:34.154289800Z","query_id":"Q04","description":"Long And Expensive Yellow Taxi Trips","success":true,"error":"","columns":["row_count"],"rows":[["6438540"]]}
+{"timestamp":"2026-06-21T10:25:36.501536200Z","query_id":"Q05","description":"Yearly Yellow Taxi Amount And Distance Summary","success":true,"error":"","columns":["year","trips","gross_amount","min_distance","max_distance"],"rows":[["2020","24649092","454079334.16108584","-30.62","350914.89"],["2021","30904308","608700670.9132242","0.0","351613.36"],["2022","39656098","859397945.173023","0.0","389678.46"],["2023","3066766","82865192.2197824","0.0","258928.15"]]}
+{"timestamp":"2026-06-21T10:25:36.509534400Z","query_id":"Q06","description":"Full High-Volume FHV Row Count","success":true,"error":"","columns":["row_count"],"rows":[["548801637"]]}
+{"timestamp":"2026-06-21T10:25:36.517535400Z","query_id":"Q07","description":"High-Volume FHV Count For One Partition Month","success":true,"error":"","columns":["row_count"],"rows":[["19306090"]]}
+{"timestamp":"2026-06-21T10:25:54.024255300Z","query_id":"Q08","description":"Long And Expensive High-Volume FHV Trips","success":true,"error":"","columns":["row_count"],"rows":[["42659531"]]}
+{"timestamp":"2026-06-21T10:26:12.309848200Z","query_id":"Q09","description":"Yearly High-Volume FHV Fare And Distance Summary","success":true,"error":"","columns":["year","trips","passenger_fare","driver_pay","min_miles","max_miles"],"rows":[["2020","143309871","2527548383.5690694","2036707828.5292335","0.0","1310.51"],["2021","174596652","3839172144.575017","3085990462.630042","0.0","738.95"],["2022","212416083","5029617856.472827","3966957717.7361007","0.0","634.32"],["2023","18479031","398327570.2230424","310164377.32070553","0.0","407.563"]]}
+{"timestamp":"2026-06-21T10:26:16.796949600Z","query_id":"Q10","description":"High-Volume FHV Shared-Request Flag Distribution","success":true,"error":"","columns":["shared_request_flag","trips"],"rows":[["N","210564721"],["Y","1851362"]]}
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_values.jsonl.old b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_values.jsonl.old
new file mode 100644
index 0000000000..a660162970
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_values.jsonl.old
@@ -0,0 +1,10 @@
+{"timestamp":"2026-06-19T11:58:21.993777600Z","query_id":"Q01","description":"Full Yellow Taxi Row Count","success":true,"error":"","columns":["row_count"],"rows":[["98276264"]]}
+{"timestamp":"2026-06-19T11:58:22.010467Z","query_id":"Q02","description":"Yellow Taxi Count For One Partition Month","success":true,"error":"","columns":["row_count"],"rows":[["3675411"]]}
+{"timestamp":"2026-06-19T11:58:23.964558300Z","query_id":"Q03","description":"Yellow Taxi Trips On One Day","success":true,"error":"","columns":["row_count"],"rows":[["127002"]]}
+{"timestamp":"2026-06-19T11:58:28.826757100Z","query_id":"Q04","description":"Long And Expensive Yellow Taxi Trips","success":true,"error":"","columns":["row_count"],"rows":[["674318"]]}
+{"timestamp":"2026-06-19T11:58:33.979968500Z","query_id":"Q05","description":"Monthly Yellow Taxi Amount And Distance Summary","success":true,"error":"","columns":["year","month","trips","gross_amount","min_distance","max_distance"],"rows":[["2020","01","6405008","119537617.35004498","-30.62","210240.07"],["2020","02","6299367","117112545.71004479","0.0","57051.09"],["2020","03","3007687","56670732.91001961","0.0","269803.73"],["2020","04","238073","3907246.9200038556","0.0","126501.77"],["2020","05","348415","6854829.1900025355","0.0","297004.51"],["2020","06","549797","10319016.730008483","0.0","220386.23"],["2020","07","800412","14912844.090012174","0.0","256069.13"],["2020","08","1007286","18705597.360131156","0.0","138953.37"],["2020","09","1341017","23630505.770236924","0.0","239679.02"],["2020","10","1681132","30364865.890017144","0.0","178867.75"],["2020","11","1509000","26336882.98028816","0.0","349692.3"],["2020","12","1461898","25726649.26027608","0.0","350914.89"],["2021","01","1369769","23935896.090235535","0.0","263163.28"],["2021","02","1371709","24152554.68023674","0.0","221188.25"],["2021","03","1925152","34497561.26042992","0.0","191290.1"],["2021","04","2171187","40297643.900283895","0.0","280567.84"],["2021","05","2507109","47355964.949803784","0.0","332541.19"],["2021","06","2834264","55020387.72929749","0.0","294037.47"],["2021","07","2821746","56088554.10926468","0.0","305397.57"],["2021","08","2788757","56330383.809282534","0.0","307938.58"],["2021","09","2963793","61853398.77899661","0.0","317182.45"],["2021","10","3463504","70230890.50847894","0.0","345124.27"],["2021","11","3472949","71801509.8583849","0.0","351613.36"],["2021","12","3214369","67135925.23852916","0.0","335093.7"],["2022","01","2463931","47232005.22967679","0.0","306159.28"],["2022","02","2979431","58092213.639126375","0.0","348798.53"],["2022","03","3627882","74711295.99821456","0.0","286259.84"],["2022","04","3599920","76282899.59811652","0.0","329408.43"],["2022","05","3588295","79223815.68807013","0.0","357192.65"],["2022","06","3558124","78700064.08807346","0.0","307007.11"],["2022","07","3174394","68044219.12863928","0.0","234062.32"],["2022","08","3152677","67952543.90862983","0.0","330578.66"],["2022","09","3183767","72137018.77857432","0.0","303133.41"],["2022","10","3675411","81768856.5279092","0.0","389678.46"],["2022","11","3252717","71787266.38854252","0.0","305756.36"],["2022","12","3399549","83465746.19944999","0.0","222730.37"],["2023","01","3066766","82865192.2197824","0.0","258928.15"]]}
+{"timestamp":"2026-06-19T11:58:34.008031800Z","query_id":"Q06","description":"Full High-Volume FHV Row Count","success":true,"error":"","columns":["row_count"],"rows":[["548801637"]]}
+{"timestamp":"2026-06-19T11:58:34.032931100Z","query_id":"Q07","description":"High-Volume FHV Count For One Partition Month","success":true,"error":"","columns":["row_count"],"rows":[["19306090"]]}
+{"timestamp":"2026-06-19T11:59:01.994219600Z","query_id":"Q08","description":"Long And Expensive High-Volume FHV Trips","success":true,"error":"","columns":["row_count"],"rows":[["7034458"]]}
+{"timestamp":"2026-06-19T11:59:42.844799900Z","query_id":"Q09","description":"Monthly High-Volume FHV Fare And Distance Summary","success":true,"error":"","columns":["year","month","trips","passenger_fare","driver_pay","min_miles","max_miles"],"rows":[["2020","01","20569368","350030803.27185345","269747497.15095705","0.0","339.55"],["2020","02","21725100","359880714.2439911","295271582.5538237","0.0","468.28"],["2020","03","13392928","219104663.84973925","183258703.76322785","0.0","1310.51"],["2020","04","4312909","70655784.87001048","54690188.79998169","0.0","565.06"],["2020","05","6089999","106910116.8387132","81977679.98009613","0.0","597.66"],["2020","06","7555193","147744198.6386629","117401547.52993228","0.0","351.49"],["2020","07","9958454","193380742.9088356","158515082.60975698","0.0","347.02"],["2020","08","11096852","204181971.51960284","167156407.79964975","0.0","411.98"],["2020","09","12106669","218691820.55028528","180690210.61047208","0.0","616.4"],["2020","10","13268411","240767464.21999794","193655356.6899604","0.0","657.61"],["2020","11","11596865","210731656.34868482","167093551.35066825","0.0","425.32"],["2020","12","11637123","205468446.30869246","167250019.69070733","0.0","400.36"],["2021","01","11908468","204741577.3497488","165794425.21074623","0.0","738.95"],["2021","02","11613942","222032130.07917023","183049379.7301403","0.0","527.11"],["2021","03","14227393","279934266.6407024","228817503.7696113","0.0","401.76"],["2021","04","14111371","300720501.4512585","236443477.16972822","0.0","425.1"],["2021","05","14719171","333298581.1342021","267981354.8695524","0.0","412.13"],["2021","06","14961892","355852144.8730783","285182358.8699747","0.0","370.34"],["2021","07","15027174","352106222.67049867","275961652.53956","0.0","568.64"],["2021","08","14499696","336482144.24455714","264262885.38936767","0.0","505.31"],["2021","09","14886055","357695890.5453669","288495610.54028094","0.0","475.58"],["2021","10","16545356","374931667.52390206","311849920.53078336","0.0","392.85"],["2021","11","16041639","363157474.5754467","294275662.44034064","0.0","400.9"],["2021","12","16054495","358219543.4870856","283876231.569957","0.0","385.99"],["2022","01","14751591","289885397.8684815","230691743.73934838","0.0","440.71"],["2022","02","16019283","338298927.5854084","263230377.58946705","0.0","496.71"],["2022","03","18453548","414400409.0190753","333581458.7318609","0.0","628.7"],["2022","04","17752561","427197071.9760024","337768243.0503949","0.0","616.31"],["2022","05","18157335","464628122.5067997","366211933.18152875","0.0","471.73"],["2022","06","17780075","445885433.1875207","354223882.82153225","0.0","625.96"],["2022","07","17464619","411105395.10540646","324691375.40139186","0.0","614.202"],["2022","08","17185687","408796881.63677907","316347669.01094645","0.0","616.81"],["2022","09","17793551","451381618.7857157","360688621.7020854","0.0","503.34"],["2022","10","19306090","467708048.0162383","376905021.0725299","0.0","634.32"],["2022","11","18085896","430011752.2461695","334986034.14183545","0.0","630.36"],["2022","12","19665847","480318798.5392303","367631357.29318","0.0","373.48"],["2023","01","18479031","398327570.2230424","310164377.32070553","0.0","407.563"]]}
+{"timestamp":"2026-06-19T11:59:54.625946200Z","query_id":"Q10","description":"High-Volume FHV Shared-Ride Flag Distribution","success":true,"error":"","columns":["shared_request_flag","shared_match_flag","trips"],"rows":[["N","N","210516748"],["Y","N","1399327"],["Y","Y","452035"],["N","Y","47973"]]}
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_result_analysis.md b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_result_analysis.md
new file mode 100644
index 0000000000..ce1f0987ea
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_result_analysis.md
@@ -0,0 +1,64 @@
+# Aggregation - Benchmark Analysis
+
+## Results Plot
+![aggregation_plot.png](../plots/aggregation_plot.png)
+
+## Interpretation
+
+### General
+The aggregation run completed successfully for all evaluated systems. 
+All 10 queries completed successfully, with 5/5 successful measured runs per query for every system. 
+The result row counts match across Polypheny relational, Polypheny document MQL, DuckDB, and Apache Spark.
+
+### Correctness
+Correctness checks confirm consistent aggregation results across systems: all row counts match, while Polypheny relational, DuckDB, and Apache Spark also produce matching grouping keys and COUNT, SUM, MIN, and MAX results within the configured numeric tolerance. Polypheny document MQL produces the same available aggregate values and groups, but Q05, Q09, and Q10 have intentionally different result schemas because the MQL variants omit some count fields and represent grouping keys through _id. These are output-shape differences rather than conflicting aggregation results.
+
+### Main Findings
+
+- Polypheny relational is strongest overall on metadata-oriented count queries: Q01, Q02, Q06, and Q07. It completes these metadata-optimized queries in approximately 10-14 ms, leading Q01, Q06, and Q07 and closely matching DuckDB on Q02. 
+- The document adapter also benefits from the metadata optimized count path, although document-processing overhead results in higher runtimes.
+
+DuckDB is fastest for most data-reading aggregation queries. It has the best
+runtime for Q03, Q04, Q05, Q08, Q09, and Q10. The gap is especially visible for
+Q10, where DuckDB evaluates the shared-request grouping much faster than the
+other systems.
+
+The Polypheny document path is competitive with, and sometimes faster than, the
+relational path for large data-reading aggregation queries. This is visible for
+Q03, Q08, Q09, and Q10. These results show that pushing aggregation into the
+Parquet-backed execution path also benefits document workloads when the
+aggregate can be expressed over primitive Parquet fields.
+
+Spark has substantial overhead on small metadata-oriented count queries. It
+becomes more comparable on the larger high-volume FHV filtered count Q08, but
+it remains slower than DuckDB for every aggregation query in this run.
+
+## Interpretation
+
+The query adjustment makes the grouped aggregation comparison much cleaner:
+Q05, Q09, and Q10 now use the same grouping level across the SQL systems and
+the document adapter. Remaining MQL correctness warnings should therefore be
+interpreted as output-shape differences, not as evidence that a different
+number of groups was produced.
+
+The benchmark confirms that the Parquet adapter's optimized count path is very
+effective, while DuckDB remains the strongest reference point for general
+single-node analytical aggregation over Parquet. The document aggregation
+results are useful because they show that document queries can still benefit
+from relational-style Parquet optimizations when the planner can map document
+fields back to Parquet-backed primitive columns.
+
+
+## Related Documents
+
+Source summary:
+
+```text
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
+```
+
+Source correctness comparison:
+
+```text
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_correctness_summary.md
+```
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_results_analysis.md b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_results_analysis.md
deleted file mode 100644
index 94515ca358..0000000000
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_results_analysis.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Aggregation Benchmark Analysis
-
-Source summary:
-
-```text
-plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
-```
-
-The aggregation run completed successfully for all three systems. Every query
-has `5/5` successful measured runs, and the result row counts match across
-Polypheny, DuckDB, and Apache Spark.
-
-## Main Findings
-
-Polypheny is fastest for the simple count queries that can be answered with very
-little data materialization
-
-
-## Interpretation
-
-The strongest Polypheny result is metadata-like counting. Q01, Q02, Q06, and Q07
-are all faster in Polypheny than in DuckDB and much faster than Spark. This
-suggests that simple count and partition-constrained count queries are currently
-a good case for the Polypheny Parquet path.
-
-The main performance gap is on row-level filtering and aggregation over data
-columns. DuckDB is about 2.3x to 5.2x faster than Polypheny on Q03-Q05 and
-Q08-Q10. The gap is larger on `fhvhv_tripdata` than on `yellow_tripdata`, which
-is expected because the high-volume FHV table is larger and the queries touch
-more data.
-
-Spark is not competitive on the small/simple count queries because fixed job
-overhead dominates. It becomes closer on the larger `fhvhv_tripdata` workloads:
-Spark is faster than Polypheny on Q08 and Q10, but DuckDB remains fastest for
-both.
-
-
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_results.csv b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_results.csv
index fc66975710..971605ba11 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_results.csv
@@ -1,61 +1,61 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-2026-06-14T18:46:57.626163Z,Q01,Full Yellow Taxi Row Count,warmup,1,3914,1,1,true,
-2026-06-14T18:46:59.175829Z,Q01,Full Yellow Taxi Row Count,measured,1,1548,1,1,true,
-2026-06-14T18:47:00.772894Z,Q01,Full Yellow Taxi Row Count,measured,2,1595,1,1,true,
-2026-06-14T18:47:02.064235Z,Q01,Full Yellow Taxi Row Count,measured,3,1290,1,1,true,
-2026-06-14T18:47:03.213326Z,Q01,Full Yellow Taxi Row Count,measured,4,1148,1,1,true,
-2026-06-14T18:47:04.673745Z,Q01,Full Yellow Taxi Row Count,measured,5,1460,1,1,true,
-2026-06-14T18:47:05.196475Z,Q02,Yellow Taxi Count For One Partition Month,warmup,1,521,1,1,true,
-2026-06-14T18:47:05.572867Z,Q02,Yellow Taxi Count For One Partition Month,measured,1,375,1,1,true,
-2026-06-14T18:47:05.950536Z,Q02,Yellow Taxi Count For One Partition Month,measured,2,376,1,1,true,
-2026-06-14T18:47:06.322005Z,Q02,Yellow Taxi Count For One Partition Month,measured,3,370,1,1,true,
-2026-06-14T18:47:06.672694Z,Q02,Yellow Taxi Count For One Partition Month,measured,4,349,1,1,true,
-2026-06-14T18:47:07.050763Z,Q02,Yellow Taxi Count For One Partition Month,measured,5,377,1,1,true,
-2026-06-14T18:47:14.845373Z,Q03,Yellow Taxi Trips On One Day,warmup,1,7792,1,1,true,
-2026-06-14T18:47:19.837260Z,Q03,Yellow Taxi Trips On One Day,measured,1,4990,1,1,true,
-2026-06-14T18:47:23.154042Z,Q03,Yellow Taxi Trips On One Day,measured,2,5105,1,1,true,
-2026-06-14T18:47:28.062939Z,Q03,Yellow Taxi Trips On One Day,measured,3,4908,1,1,true,
-2026-06-14T18:47:33.924855Z,Q03,Yellow Taxi Trips On One Day,measured,4,5861,1,1,true,
-2026-06-14T18:47:38.813486Z,Q03,Yellow Taxi Trips On One Day,measured,5,4887,1,1,true,
-2026-06-14T18:47:43.337841Z,Q04,Long And Expensive Yellow Taxi Trips,warmup,1,4522,1,1,true,
-2026-06-14T18:47:46.687755Z,Q04,Long And Expensive Yellow Taxi Trips,measured,1,3348,1,1,true,
-2026-06-14T18:47:54.577920Z,Q04,Long And Expensive Yellow Taxi Trips,measured,2,3313,1,1,true,
-2026-06-14T18:47:57.645728Z,Q04,Long And Expensive Yellow Taxi Trips,measured,3,3067,1,1,true,
-2026-06-14T18:48:00.655573Z,Q04,Long And Expensive Yellow Taxi Trips,measured,4,3009,1,1,true,
-2026-06-14T18:48:03.873606Z,Q04,Long And Expensive Yellow Taxi Trips,measured,5,3217,1,1,true,
-2026-06-14T18:48:09.789603Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,warmup,1,5914,37,6,true,
-2026-06-14T18:48:15.129028Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,1,5338,37,6,true,
-2026-06-14T18:48:20.151033Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,2,5021,37,6,true,
-2026-06-14T18:48:27.498245Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,3,4831,37,6,true,
-2026-06-14T18:48:32.322695Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,4,4823,37,6,true,
-2026-06-14T18:48:37.060983Z,Q05,Monthly Yellow Taxi Amount And Distance Summary,measured,5,4737,37,6,true,
-2026-06-14T18:48:39.026187Z,Q06,Full High-Volume FHV Row Count,warmup,1,1962,1,1,true,
-2026-06-14T18:48:40.830284Z,Q06,Full High-Volume FHV Row Count,measured,1,1803,1,1,true,
-2026-06-14T18:48:42.508572Z,Q06,Full High-Volume FHV Row Count,measured,2,1677,1,1,true,
-2026-06-14T18:48:44.316583Z,Q06,Full High-Volume FHV Row Count,measured,3,1807,1,1,true,
-2026-06-14T18:48:46.104285Z,Q06,Full High-Volume FHV Row Count,measured,4,1787,1,1,true,
-2026-06-14T18:48:47.557076Z,Q06,Full High-Volume FHV Row Count,measured,5,1452,1,1,true,
-2026-06-14T18:48:47.759762Z,Q07,High-Volume FHV Count For One Partition Month,warmup,1,201,1,1,true,
-2026-06-14T18:48:48.017302Z,Q07,High-Volume FHV Count For One Partition Month,measured,1,256,1,1,true,
-2026-06-14T18:48:48.455477Z,Q07,High-Volume FHV Count For One Partition Month,measured,2,437,1,1,true,
-2026-06-14T18:48:48.819004Z,Q07,High-Volume FHV Count For One Partition Month,measured,3,362,1,1,true,
-2026-06-14T18:48:49.150754Z,Q07,High-Volume FHV Count For One Partition Month,measured,4,330,1,1,true,
-2026-06-14T18:48:49.449777Z,Q07,High-Volume FHV Count For One Partition Month,measured,5,293,1,1,true,
-2026-06-14T18:49:07.258136Z,Q08,Long And Expensive High-Volume FHV Trips,warmup,1,15078,1,1,true,
-2026-06-14T18:49:20.494132Z,Q08,Long And Expensive High-Volume FHV Trips,measured,1,13234,1,1,true,
-2026-06-14T18:49:33.539853Z,Q08,Long And Expensive High-Volume FHV Trips,measured,2,12972,1,1,true,
-2026-06-14T18:49:50.883874Z,Q08,Long And Expensive High-Volume FHV Trips,measured,3,14109,1,1,true,
-2026-06-14T18:50:04.841410Z,Q08,Long And Expensive High-Volume FHV Trips,measured,4,13922,1,1,true,
-2026-06-14T18:50:16.950307Z,Q08,Long And Expensive High-Volume FHV Trips,measured,5,12108,1,1,true,
-2026-06-14T18:50:53.742328Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,warmup,1,34062,37,7,true,
-2026-06-14T18:51:26.644801Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,1,30380,37,7,true,
-2026-06-14T18:51:59.878114Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,2,30646,37,7,true,
-2026-06-14T18:52:33.600018Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,3,31253,37,7,true,
-2026-06-14T18:53:08.272774Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,4,30810,37,7,true,
-2026-06-14T18:53:41.197506Z,Q09,Monthly High-Volume FHV Fare And Distance Summary,measured,5,31569,37,7,true,
-2026-06-14T18:53:51.893848Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,warmup,1,7961,4,3,true,
-2026-06-14T18:53:59.674404Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,1,7779,4,3,true,
-2026-06-14T18:54:06.913554Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,2,7238,4,3,true,
-2026-06-14T18:54:14.760827Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,3,7846,4,3,true,
-2026-06-14T18:54:26.033292Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,4,8694,4,3,true,
-2026-06-14T18:54:33.595054Z,Q10,High-Volume FHV Shared-Ride Flag Distribution,measured,5,7561,4,3,true,
+2026-06-21T10:39:08.580707Z,Q01,Full Yellow Taxi Row Count,warmup,1,4110,1,1,true,
+2026-06-21T10:39:09.743551Z,Q01,Full Yellow Taxi Row Count,measured,1,1162,1,1,true,
+2026-06-21T10:39:11.196037Z,Q01,Full Yellow Taxi Row Count,measured,2,1451,1,1,true,
+2026-06-21T10:39:12.745612Z,Q01,Full Yellow Taxi Row Count,measured,3,1548,1,1,true,
+2026-06-21T10:39:14.045853Z,Q01,Full Yellow Taxi Row Count,measured,4,1299,1,1,true,
+2026-06-21T10:39:15.663827Z,Q01,Full Yellow Taxi Row Count,measured,5,1616,1,1,true,
+2026-06-21T10:39:16.227073Z,Q02,Yellow Taxi Count For One Partition Month,warmup,1,560,1,1,true,
+2026-06-21T10:39:16.639453Z,Q02,Yellow Taxi Count For One Partition Month,measured,1,411,1,1,true,
+2026-06-21T10:39:17.001040Z,Q02,Yellow Taxi Count For One Partition Month,measured,2,361,1,1,true,
+2026-06-21T10:39:17.342078Z,Q02,Yellow Taxi Count For One Partition Month,measured,3,340,1,1,true,
+2026-06-21T10:39:17.743320Z,Q02,Yellow Taxi Count For One Partition Month,measured,4,400,1,1,true,
+2026-06-21T10:39:18.128192Z,Q02,Yellow Taxi Count For One Partition Month,measured,5,384,1,1,true,
+2026-06-21T10:39:27.650901Z,Q03,Yellow Taxi Trips On One Day,warmup,1,8791,1,1,true,
+2026-06-21T10:39:33.848730Z,Q03,Yellow Taxi Trips On One Day,measured,1,6196,1,1,true,
+2026-06-21T10:39:38.811511Z,Q03,Yellow Taxi Trips On One Day,measured,2,4752,1,1,true,
+2026-06-21T10:39:44.087987Z,Q03,Yellow Taxi Trips On One Day,measured,3,5275,1,1,true,
+2026-06-21T10:39:49.563373Z,Q03,Yellow Taxi Trips On One Day,measured,4,5474,1,1,true,
+2026-06-21T10:39:55.299933Z,Q03,Yellow Taxi Trips On One Day,measured,5,5735,1,1,true,
+2026-06-21T10:39:59.876247Z,Q04,Long And Expensive Yellow Taxi Trips,warmup,1,4574,1,1,true,
+2026-06-21T10:40:03.785347Z,Q04,Long And Expensive Yellow Taxi Trips,measured,1,3908,1,1,true,
+2026-06-21T10:40:08.350116Z,Q04,Long And Expensive Yellow Taxi Trips,measured,2,3930,1,1,true,
+2026-06-21T10:40:12.154383Z,Q04,Long And Expensive Yellow Taxi Trips,measured,3,3803,1,1,true,
+2026-06-21T10:40:16.189430Z,Q04,Long And Expensive Yellow Taxi Trips,measured,4,4034,1,1,true,
+2026-06-21T10:40:19.645098Z,Q04,Long And Expensive Yellow Taxi Trips,measured,5,3454,1,1,true,
+2026-06-21T10:40:29.212152Z,Q05,Yearly Yellow Taxi Amount And Distance Summary,warmup,1,9564,4,5,true,
+2026-06-21T10:40:34.935667Z,Q05,Yearly Yellow Taxi Amount And Distance Summary,measured,1,5721,4,5,true,
+2026-06-21T10:40:41.113074Z,Q05,Yearly Yellow Taxi Amount And Distance Summary,measured,2,5561,4,5,true,
+2026-06-21T10:40:46.820407Z,Q05,Yearly Yellow Taxi Amount And Distance Summary,measured,3,5706,4,5,true,
+2026-06-21T10:40:51.871842Z,Q05,Yearly Yellow Taxi Amount And Distance Summary,measured,4,5050,4,5,true,
+2026-06-21T10:40:57.071650Z,Q05,Yearly Yellow Taxi Amount And Distance Summary,measured,5,5198,4,5,true,
+2026-06-21T10:40:59.399814Z,Q06,Full High-Volume FHV Row Count,warmup,1,2324,1,1,true,
+2026-06-21T10:41:01.529299Z,Q06,Full High-Volume FHV Row Count,measured,1,2128,1,1,true,
+2026-06-21T10:41:03.585716Z,Q06,Full High-Volume FHV Row Count,measured,2,2055,1,1,true,
+2026-06-21T10:41:05.470450Z,Q06,Full High-Volume FHV Row Count,measured,3,1883,1,1,true,
+2026-06-21T10:41:07.810645Z,Q06,Full High-Volume FHV Row Count,measured,4,1822,1,1,true,
+2026-06-21T10:41:10.060862Z,Q06,Full High-Volume FHV Row Count,measured,5,2249,1,1,true,
+2026-06-21T10:41:10.374383Z,Q07,High-Volume FHV Count For One Partition Month,warmup,1,311,1,1,true,
+2026-06-21T10:41:10.659710Z,Q07,High-Volume FHV Count For One Partition Month,measured,1,284,1,1,true,
+2026-06-21T10:41:10.929656Z,Q07,High-Volume FHV Count For One Partition Month,measured,2,269,1,1,true,
+2026-06-21T10:41:11.197188Z,Q07,High-Volume FHV Count For One Partition Month,measured,3,266,1,1,true,
+2026-06-21T10:41:11.482727Z,Q07,High-Volume FHV Count For One Partition Month,measured,4,284,1,1,true,
+2026-06-21T10:41:11.758888Z,Q07,High-Volume FHV Count For One Partition Month,measured,5,275,1,1,true,
+2026-06-21T10:41:26.020770Z,Q08,Long And Expensive High-Volume FHV Trips,warmup,1,14260,1,1,true,
+2026-06-21T10:41:41.164172Z,Q08,Long And Expensive High-Volume FHV Trips,measured,1,14638,1,1,true,
+2026-06-21T10:41:55.727370Z,Q08,Long And Expensive High-Volume FHV Trips,measured,2,14562,1,1,true,
+2026-06-21T10:42:10.529191Z,Q08,Long And Expensive High-Volume FHV Trips,measured,3,14289,1,1,true,
+2026-06-21T10:42:24.454225Z,Q08,Long And Expensive High-Volume FHV Trips,measured,4,13924,1,1,true,
+2026-06-21T10:42:38.811570Z,Q08,Long And Expensive High-Volume FHV Trips,measured,5,13841,1,1,true,
+2026-06-21T10:43:16.624294Z,Q09,Yearly High-Volume FHV Fare And Distance Summary,warmup,1,37293,4,6,true,
+2026-06-21T10:43:51.814146Z,Q09,Yearly High-Volume FHV Fare And Distance Summary,measured,1,34671,4,6,true,
+2026-06-21T10:44:25.956072Z,Q09,Yearly High-Volume FHV Fare And Distance Summary,measured,2,33625,4,6,true,
+2026-06-21T10:45:01.664856Z,Q09,Yearly High-Volume FHV Fare And Distance Summary,measured,3,35189,4,6,true,
+2026-06-21T10:45:35.669290Z,Q09,Yearly High-Volume FHV Fare And Distance Summary,measured,4,33487,4,6,true,
+2026-06-21T10:46:10.841580Z,Q09,Yearly High-Volume FHV Fare And Distance Summary,measured,5,34651,4,6,true,
+2026-06-21T10:46:16.602726Z,Q10,High-Volume FHV Shared-Request Flag Distribution,warmup,1,5240,2,2,true,
+2026-06-21T10:46:21.549559Z,Q10,High-Volume FHV Shared-Request Flag Distribution,measured,1,4945,2,2,true,
+2026-06-21T10:46:27.061621Z,Q10,High-Volume FHV Shared-Request Flag Distribution,measured,2,5510,2,2,true,
+2026-06-21T10:46:32.319650Z,Q10,High-Volume FHV Shared-Request Flag Distribution,measured,3,5256,2,2,true,
+2026-06-21T10:46:37.936388Z,Q10,High-Volume FHV Shared-Request Flag Distribution,measured,4,5615,2,2,true,
+2026-06-21T10:46:44.164591Z,Q10,High-Volume FHV Shared-Request Flag Distribution,measured,5,5713,2,2,true,
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_values.jsonl b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_values.jsonl
new file mode 100644
index 0000000000..90224208dc
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_values.jsonl
@@ -0,0 +1,10 @@
+{"timestamp":"2026-06-21T10:46:44.184479Z","query_id":"Q01","description":"Full Yellow Taxi Row Count","success":true,"error":"","columns":["row_count"],"rows":[["98276264"]]}
+{"timestamp":"2026-06-21T10:46:45.224299Z","query_id":"Q02","description":"Yellow Taxi Count For One Partition Month","success":true,"error":"","columns":["row_count"],"rows":[["3675411"]]}
+{"timestamp":"2026-06-21T10:46:45.374916Z","query_id":"Q03","description":"Yellow Taxi Trips On One Day","success":true,"error":"","columns":["row_count"],"rows":[["127002"]]}
+{"timestamp":"2026-06-21T10:46:50.045151Z","query_id":"Q04","description":"Long And Expensive Yellow Taxi Trips","success":true,"error":"","columns":["row_count"],"rows":[["6438540"]]}
+{"timestamp":"2026-06-21T10:46:53.641328Z","query_id":"Q05","description":"Yearly Yellow Taxi Amount And Distance Summary","success":true,"error":"","columns":["year","trips","gross_amount","min_distance","max_distance"],"rows":[["2020","24649092","454079334.14322543","-30.62","350914.89"],["2021","30904308","608700670.9059402","0.0","351613.36"],["2022","39656098","859397945.1733704","0.0","389678.46"],["2023","3066766","82865192.2197824","0.0","258928.15"]]}
+{"timestamp":"2026-06-21T10:46:58.872478Z","query_id":"Q06","description":"Full High-Volume FHV Row Count","success":true,"error":"","columns":["row_count"],"rows":[["548801637"]]}
+{"timestamp":"2026-06-21T10:47:00.823550Z","query_id":"Q07","description":"High-Volume FHV Count For One Partition Month","success":true,"error":"","columns":["row_count"],"rows":[["19306090"]]}
+{"timestamp":"2026-06-21T10:47:00.978003Z","query_id":"Q08","description":"Long And Expensive High-Volume FHV Trips","success":true,"error":"","columns":["row_count"],"rows":[["42659531"]]}
+{"timestamp":"2026-06-21T10:47:15.526443Z","query_id":"Q09","description":"Yearly High-Volume FHV Fare And Distance Summary","success":true,"error":"","columns":["year","trips","passenger_fare","driver_pay","min_miles","max_miles"],"rows":[["2020","143309871","2527548383.5680666","2036707828.5286038","0.0","1310.51"],["2021","174596652","3839172144.575018","3085990462.6300426","0.0","738.95"],["2022","212416083","5029617856.472827","3966957717.736101","0.0","634.32"],["2023","18479031","398327570.2230424","310164377.32070553","0.0","407.563"]]}
+{"timestamp":"2026-06-21T10:47:50.199117Z","query_id":"Q10","description":"High-Volume FHV Shared-Request Flag Distribution","success":true,"error":"","columns":["shared_request_flag","trips"],"rows":[["N","210564721"],["Y","1851362"]]}
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
index fba1206a04..08a18ec21b 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
@@ -1,88 +1,88 @@
 # Aggregation Summary
 
 Phase summarized: `measured`.
-Warmup rows are excluded. Mean and median values use successful runs only.
+Warmup rows are excluded. Mean, median, and standard deviation values use successful runs only.
 
 ## Source Files
 
-| System | CSV |
-| --- | --- |
-| Polypheny Relational | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_results.csv` |
+| System                 | CSV                                                                                            |
+|------------------------|------------------------------------------------------------------------------------------------|
+| Polypheny Relational   | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_results.csv`     |
 | Polypheny Document MQL | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_mql_results.csv` |
-| DuckDB | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_results.csv` |
-| Apache Spark | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_results.csv` |
+| DuckDB                 | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_results.csv`        |
+| Apache Spark           | `plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_results.csv`         |
 
 ## Mean Elapsed Time (ms)
 
-| Query | Description | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark | Row counts |
-| --- | --- | --- | --- | --- | --- | --- |
-| Q01 | Full Yellow Taxi Row Count | 10.2 | 121.8 | 28.6 | 1,408.2 | 1 |
-| Q02 | Yellow Taxi Count For One Partition Month | 11.6 | 162.4 | 12.0 | 369.4 | 1 |
-| Q03 | Yellow Taxi Trips On One Day | 1,092.4 | 612.2 | 232.6 | 5,150.2 | 1 |
-| Q04 | Long And Expensive Yellow Taxi Trips | 1,941.4 | 2,184.8 | 690.6 | 3,190.8 | 1 |
-| Q05 | Monthly Yellow Taxi Amount And Distance Summary | 1,711.0 | 2,426.4 | 1,169.6 | 4,950.0 | differs |
-| Q06 | Full High-Volume FHV Row Count | 10.8 | 323.0 | 92.4 | 1,705.2 | 1 |
-| Q07 | High-Volume FHV Count For One Partition Month | 12.2 | 248.4 | 26.4 | 335.6 | 1 |
-| Q08 | Long And Expensive High-Volume FHV Trips | 11,461.8 | 15,905.2 | 4,990.0 | 13,269.0 | 1 |
-| Q09 | Monthly High-Volume FHV Fare And Distance Summary | 13,246.6 | 14,864.4 | 9,216.8 | 30,931.6 | differs |
-| Q10 | High-Volume FHV Shared-Ride Flag Distribution | 5,832.8 | 5,107.6 | 1,252.6 | 7,823.6 | differs |
+| Query | Description                                      | Polypheny Relational | Polypheny Document MQL | DuckDB  | Apache Spark | Row counts |
+|-------|--------------------------------------------------|----------------------|------------------------|---------|--------------|------------|
+| Q01   | Full Yellow Taxi Row Count                       | 12.2                 | 179.6                  | 22.6    | 1,415.2      | 1          |
+| Q02   | Yellow Taxi Count For One Partition Month        | 10.4                 | 209.0                  | 9.2     | 379.2        | 1          |
+| Q03   | Yellow Taxi Trips On One Day                     | 1,304.8              | 954.0                  | 179.0   | 5,486.4      | 1          |
+| Q04   | Long And Expensive Yellow Taxi Trips             | 2,468.8              | 3,223.2                | 765.4   | 3,825.8      | 1          |
+| Q05   | Yearly Yellow Taxi Amount And Distance Summary   | 1,841.2              | 2,007.6                | 1,193.0 | 5,447.2      | 4          |
+| Q06   | Full High-Volume FHV Row Count                   | 12.4                 | 177.6                  | 145.4   | 2,027.4      | 1          |
+| Q07   | High-Volume FHV Count For One Partition Month    | 14.2                 | 205.4                  | 34.0    | 275.6        | 1          |
+| Q08   | Long And Expensive High-Volume FHV Trips         | 16,174.2             | 13,654.2               | 4,584.6 | 14,250.8     | 1          |
+| Q09   | Yearly High-Volume FHV Fare And Distance Summary | 15,590.2             | 13,427.4               | 8,844.2 | 34,324.6     | 4          |
+| Q10   | High-Volume FHV Shared-Request Flag Distribution | 5,458.2              | 4,321.0                | 435.4   | 5,407.8      | 2          |
 
 ## Result Row Counts
 
-| Query | Description | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark |
-| --- | --- | --- | --- | --- | --- |
-| Q01 | Full Yellow Taxi Row Count | 1 | 1 | 1 | 1 |
-| Q02 | Yellow Taxi Count For One Partition Month | 1 | 1 | 1 | 1 |
-| Q03 | Yellow Taxi Trips On One Day | 1 | 1 | 1 | 1 |
-| Q04 | Long And Expensive Yellow Taxi Trips | 1 | 1 | 1 | 1 |
-| Q05 | Monthly Yellow Taxi Amount And Distance Summary | 37 | 4 | 37 | 37 |
-| Q06 | Full High-Volume FHV Row Count | 1 | 1 | 1 | 1 |
-| Q07 | High-Volume FHV Count For One Partition Month | 1 | 1 | 1 | 1 |
-| Q08 | Long And Expensive High-Volume FHV Trips | 1 | 1 | 1 | 1 |
-| Q09 | Monthly High-Volume FHV Fare And Distance Summary | 37 | 4 | 37 | 37 |
-| Q10 | High-Volume FHV Shared-Ride Flag Distribution | 4 | 2 | 4 | 4 |
+| Query | Description                                      | Polypheny Relational | Polypheny Document MQL | DuckDB | Apache Spark |
+|-------|--------------------------------------------------|----------------------|------------------------|--------|--------------|
+| Q01   | Full Yellow Taxi Row Count                       | 1                    | 1                      | 1      | 1            |
+| Q02   | Yellow Taxi Count For One Partition Month        | 1                    | 1                      | 1      | 1            |
+| Q03   | Yellow Taxi Trips On One Day                     | 1                    | 1                      | 1      | 1            |
+| Q04   | Long And Expensive Yellow Taxi Trips             | 1                    | 1                      | 1      | 1            |
+| Q05   | Yearly Yellow Taxi Amount And Distance Summary   | 4                    | 4                      | 4      | 4            |
+| Q06   | Full High-Volume FHV Row Count                   | 1                    | 1                      | 1      | 1            |
+| Q07   | High-Volume FHV Count For One Partition Month    | 1                    | 1                      | 1      | 1            |
+| Q08   | Long And Expensive High-Volume FHV Trips         | 1                    | 1                      | 1      | 1            |
+| Q09   | Yearly High-Volume FHV Fare And Distance Summary | 4                    | 4                      | 4      | 4            |
+| Q10   | High-Volume FHV Shared-Request Flag Distribution | 2                    | 2                      | 2      | 2            |
 
 ## Detailed Summary (ms)
 
-| System | Query | Description | Runs | Mean | Median | Min | Max | Rows | Columns | Status |
-| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-| Polypheny Relational | Q01 | Full Yellow Taxi Row Count | 5/5 | 10.2 | 10.0 | 7.0 | 17.0 | 1 | 1 | ok |
-| Polypheny Relational | Q02 | Yellow Taxi Count For One Partition Month | 5/5 | 11.6 | 12.0 | 9.0 | 13.0 | 1 | 1 | ok |
-| Polypheny Relational | Q03 | Yellow Taxi Trips On One Day | 5/5 | 1,092.4 | 1,116.0 | 970.0 | 1,179.0 | 1 | 1 | ok |
-| Polypheny Relational | Q04 | Long And Expensive Yellow Taxi Trips | 5/5 | 1,941.4 | 1,956.0 | 1,861.0 | 2,001.0 | 1 | 1 | ok |
-| Polypheny Relational | Q05 | Monthly Yellow Taxi Amount And Distance Summary | 5/5 | 1,711.0 | 1,675.0 | 1,447.0 | 2,022.0 | 37 | 6 | ok |
-| Polypheny Relational | Q06 | Full High-Volume FHV Row Count | 5/5 | 10.8 | 11.0 | 8.0 | 13.0 | 1 | 1 | ok |
-| Polypheny Relational | Q07 | High-Volume FHV Count For One Partition Month | 5/5 | 12.2 | 13.0 | 10.0 | 13.0 | 1 | 1 | ok |
-| Polypheny Relational | Q08 | Long And Expensive High-Volume FHV Trips | 5/5 | 11,461.8 | 11,529.0 | 11,244.0 | 11,552.0 | 1 | 1 | ok |
-| Polypheny Relational | Q09 | Monthly High-Volume FHV Fare And Distance Summary | 5/5 | 13,246.6 | 13,213.0 | 12,810.0 | 14,053.0 | 37 | 7 | ok |
-| Polypheny Relational | Q10 | High-Volume FHV Shared-Ride Flag Distribution | 5/5 | 5,832.8 | 5,768.0 | 5,266.0 | 6,506.0 | 4 | 3 | ok |
-| Polypheny Document MQL | Q01 | Full Yellow Taxi Row Count | 5/5 | 121.8 | 125.0 | 108.0 | 130.0 | 1 | 1 | ok |
-| Polypheny Document MQL | Q02 | Yellow Taxi Count For One Partition Month | 5/5 | 162.4 | 158.0 | 141.0 | 192.0 | 1 | 1 | ok |
-| Polypheny Document MQL | Q03 | Yellow Taxi Trips On One Day | 5/5 | 612.2 | 611.0 | 599.0 | 626.0 | 1 | 1 | ok |
-| Polypheny Document MQL | Q04 | Long And Expensive Yellow Taxi Trips | 5/5 | 2,184.8 | 2,493.0 | 1,162.0 | 3,390.0 | 1 | 1 | ok |
-| Polypheny Document MQL | Q05 | Monthly Yellow Taxi Amount And Distance Summary | 5/5 | 2,426.4 | 2,400.0 | 2,331.0 | 2,522.0 | 4 | 4 | ok |
-| Polypheny Document MQL | Q06 | Full High-Volume FHV Row Count | 5/5 | 323.0 | 346.0 | 224.0 | 418.0 | 1 | 1 | ok |
-| Polypheny Document MQL | Q07 | High-Volume FHV Count For One Partition Month | 5/5 | 248.4 | 257.0 | 212.0 | 283.0 | 1 | 1 | ok |
-| Polypheny Document MQL | Q08 | Long And Expensive High-Volume FHV Trips | 5/5 | 15,905.2 | 14,670.0 | 13,069.0 | 21,529.0 | 1 | 1 | ok |
-| Polypheny Document MQL | Q09 | Monthly High-Volume FHV Fare And Distance Summary | 5/5 | 14,864.4 | 14,719.0 | 14,655.0 | 15,375.0 | 4 | 5 | ok |
-| Polypheny Document MQL | Q10 | High-Volume FHV Shared-Ride Flag Distribution | 5/5 | 5,107.6 | 5,052.0 | 4,747.0 | 5,694.0 | 2 | 1 | ok |
-| DuckDB | Q01 | Full Yellow Taxi Row Count | 5/5 | 28.6 | 29.0 | 27.0 | 30.0 | 1 | 1 | ok |
-| DuckDB | Q02 | Yellow Taxi Count For One Partition Month | 5/5 | 12.0 | 13.0 | 9.0 | 14.0 | 1 | 1 | ok |
-| DuckDB | Q03 | Yellow Taxi Trips On One Day | 5/5 | 232.6 | 232.0 | 187.0 | 285.0 | 1 | 1 | ok |
-| DuckDB | Q04 | Long And Expensive Yellow Taxi Trips | 5/5 | 690.6 | 680.0 | 649.0 | 731.0 | 1 | 1 | ok |
-| DuckDB | Q05 | Monthly Yellow Taxi Amount And Distance Summary | 5/5 | 1,169.6 | 1,182.0 | 1,078.0 | 1,262.0 | 37 | 6 | ok |
-| DuckDB | Q06 | Full High-Volume FHV Row Count | 5/5 | 92.4 | 94.0 | 87.0 | 98.0 | 1 | 1 | ok |
-| DuckDB | Q07 | High-Volume FHV Count For One Partition Month | 5/5 | 26.4 | 27.0 | 22.0 | 28.0 | 1 | 1 | ok |
-| DuckDB | Q08 | Long And Expensive High-Volume FHV Trips | 5/5 | 4,990.0 | 5,045.0 | 4,747.0 | 5,134.0 | 1 | 1 | ok |
-| DuckDB | Q09 | Monthly High-Volume FHV Fare And Distance Summary | 5/5 | 9,216.8 | 9,118.0 | 9,026.0 | 9,451.0 | 37 | 7 | ok |
-| DuckDB | Q10 | High-Volume FHV Shared-Ride Flag Distribution | 5/5 | 1,252.6 | 1,279.0 | 1,084.0 | 1,461.0 | 4 | 3 | ok |
-| Apache Spark | Q01 | Full Yellow Taxi Row Count | 5/5 | 1,408.2 | 1,460.0 | 1,148.0 | 1,595.0 | 1 | 1 | ok |
-| Apache Spark | Q02 | Yellow Taxi Count For One Partition Month | 5/5 | 369.4 | 375.0 | 349.0 | 377.0 | 1 | 1 | ok |
-| Apache Spark | Q03 | Yellow Taxi Trips On One Day | 5/5 | 5,150.2 | 4,990.0 | 4,887.0 | 5,861.0 | 1 | 1 | ok |
-| Apache Spark | Q04 | Long And Expensive Yellow Taxi Trips | 5/5 | 3,190.8 | 3,217.0 | 3,009.0 | 3,348.0 | 1 | 1 | ok |
-| Apache Spark | Q05 | Monthly Yellow Taxi Amount And Distance Summary | 5/5 | 4,950.0 | 4,831.0 | 4,737.0 | 5,338.0 | 37 | 6 | ok |
-| Apache Spark | Q06 | Full High-Volume FHV Row Count | 5/5 | 1,705.2 | 1,787.0 | 1,452.0 | 1,807.0 | 1 | 1 | ok |
-| Apache Spark | Q07 | High-Volume FHV Count For One Partition Month | 5/5 | 335.6 | 330.0 | 256.0 | 437.0 | 1 | 1 | ok |
-| Apache Spark | Q08 | Long And Expensive High-Volume FHV Trips | 5/5 | 13,269.0 | 13,234.0 | 12,108.0 | 14,109.0 | 1 | 1 | ok |
-| Apache Spark | Q09 | Monthly High-Volume FHV Fare And Distance Summary | 5/5 | 30,931.6 | 30,810.0 | 30,380.0 | 31,569.0 | 37 | 7 | ok |
-| Apache Spark | Q10 | High-Volume FHV Shared-Ride Flag Distribution | 5/5 | 7,823.6 | 7,779.0 | 7,238.0 | 8,694.0 | 4 | 3 | ok |
+| System                 | Query | Description                                      | Runs | Mean     | Median   | Std Dev | Min      | Max      | Rows | Columns | Status |
+|------------------------|-------|--------------------------------------------------|------|----------|----------|---------|----------|----------|------|---------|--------|
+| Polypheny Relational   | Q01   | Full Yellow Taxi Row Count                       | 5/5  | 12.2     | 12.0     | 3.2     | 8.0      | 17.0     | 1    | 1       | ok     |
+| Polypheny Relational   | Q02   | Yellow Taxi Count For One Partition Month        | 5/5  | 10.4     | 11.0     | 1.5     | 8.0      | 12.0     | 1    | 1       | ok     |
+| Polypheny Relational   | Q03   | Yellow Taxi Trips On One Day                     | 5/5  | 1,304.8  | 1,234.0  | 280.8   | 1,026.0  | 1,614.0  | 1    | 1       | ok     |
+| Polypheny Relational   | Q04   | Long And Expensive Yellow Taxi Trips             | 5/5  | 2,468.8  | 2,446.0  | 293.9   | 2,164.0  | 2,812.0  | 1    | 1       | ok     |
+| Polypheny Relational   | Q05   | Yearly Yellow Taxi Amount And Distance Summary   | 5/5  | 1,841.2  | 1,895.0  | 186.5   | 1,603.0  | 2,065.0  | 4    | 5       | ok     |
+| Polypheny Relational   | Q06   | Full High-Volume FHV Row Count                   | 5/5  | 12.4     | 12.0     | 2.1     | 10.0     | 15.0     | 1    | 1       | ok     |
+| Polypheny Relational   | Q07   | High-Volume FHV Count For One Partition Month    | 5/5  | 14.2     | 15.0     | 1.3     | 12.0     | 15.0     | 1    | 1       | ok     |
+| Polypheny Relational   | Q08   | Long And Expensive High-Volume FHV Trips         | 5/5  | 16,174.2 | 16,362.0 | 475.3   | 15,539.0 | 16,633.0 | 1    | 1       | ok     |
+| Polypheny Relational   | Q09   | Yearly High-Volume FHV Fare And Distance Summary | 5/5  | 15,590.2 | 15,278.0 | 841.6   | 14,999.0 | 17,074.0 | 4    | 6       | ok     |
+| Polypheny Relational   | Q10   | High-Volume FHV Shared-Request Flag Distribution | 5/5  | 5,458.2  | 5,310.0  | 655.8   | 4,858.0  | 6,541.0  | 2    | 2       | ok     |
+| Polypheny Document MQL | Q01   | Full Yellow Taxi Row Count                       | 5/5  | 179.6    | 174.0    | 13.7    | 168.0    | 202.0    | 1    | 1       | ok     |
+| Polypheny Document MQL | Q02   | Yellow Taxi Count For One Partition Month        | 5/5  | 209.0    | 211.0    | 12.5    | 188.0    | 219.0    | 1    | 1       | ok     |
+| Polypheny Document MQL | Q03   | Yellow Taxi Trips On One Day                     | 5/5  | 954.0    | 951.0    | 98.7    | 834.0    | 1,083.0  | 1    | 1       | ok     |
+| Polypheny Document MQL | Q04   | Long And Expensive Yellow Taxi Trips             | 5/5  | 3,223.2  | 3,165.0  | 272.3   | 2,945.0  | 3,588.0  | 1    | 1       | ok     |
+| Polypheny Document MQL | Q05   | Yearly Yellow Taxi Amount And Distance Summary   | 5/5  | 2,007.6  | 1,900.0  | 246.5   | 1,758.0  | 2,382.0  | 4    | 4       | ok     |
+| Polypheny Document MQL | Q06   | Full High-Volume FHV Row Count                   | 5/5  | 177.6    | 173.0    | 18.0    | 162.0    | 204.0    | 1    | 1       | ok     |
+| Polypheny Document MQL | Q07   | High-Volume FHV Count For One Partition Month    | 5/5  | 205.4    | 199.0    | 20.2    | 191.0    | 241.0    | 1    | 1       | ok     |
+| Polypheny Document MQL | Q08   | Long And Expensive High-Volume FHV Trips         | 5/5  | 13,654.2 | 13,636.0 | 185.3   | 13,426.0 | 13,940.0 | 1    | 1       | ok     |
+| Polypheny Document MQL | Q09   | Yearly High-Volume FHV Fare And Distance Summary | 5/5  | 13,427.4 | 13,367.0 | 230.6   | 13,161.0 | 13,783.0 | 4    | 5       | ok     |
+| Polypheny Document MQL | Q10   | High-Volume FHV Shared-Request Flag Distribution | 5/5  | 4,321.0  | 4,405.0  | 223.5   | 3,932.0  | 4,499.0  | 2    | 1       | ok     |
+| DuckDB                 | Q01   | Full Yellow Taxi Row Count                       | 5/5  | 22.6     | 23.0     | 1.7     | 21.0     | 25.0     | 1    | 1       | ok     |
+| DuckDB                 | Q02   | Yellow Taxi Count For One Partition Month        | 5/5  | 9.2      | 9.0      | 0.8     | 8.0      | 10.0     | 1    | 1       | ok     |
+| DuckDB                 | Q03   | Yellow Taxi Trips On One Day                     | 5/5  | 179.0    | 180.0    | 15.2    | 162.0    | 202.0    | 1    | 1       | ok     |
+| DuckDB                 | Q04   | Long And Expensive Yellow Taxi Trips             | 5/5  | 765.4    | 793.0    | 73.3    | 655.0    | 837.0    | 1    | 1       | ok     |
+| DuckDB                 | Q05   | Yearly Yellow Taxi Amount And Distance Summary   | 5/5  | 1,193.0  | 1,170.0  | 92.6    | 1,081.0  | 1,324.0  | 4    | 5       | ok     |
+| DuckDB                 | Q06   | Full High-Volume FHV Row Count                   | 5/5  | 145.4    | 146.0    | 3.8     | 141.0    | 150.0    | 1    | 1       | ok     |
+| DuckDB                 | Q07   | High-Volume FHV Count For One Partition Month    | 5/5  | 34.0     | 36.0     | 4.2     | 29.0     | 38.0     | 1    | 1       | ok     |
+| DuckDB                 | Q08   | Long And Expensive High-Volume FHV Trips         | 5/5  | 4,584.6  | 4,512.0  | 305.2   | 4,216.0  | 5,036.0  | 1    | 1       | ok     |
+| DuckDB                 | Q09   | Yearly High-Volume FHV Fare And Distance Summary | 5/5  | 8,844.2  | 9,076.0  | 369.4   | 8,342.0  | 9,145.0  | 4    | 6       | ok     |
+| DuckDB                 | Q10   | High-Volume FHV Shared-Request Flag Distribution | 5/5  | 435.4    | 456.0    | 42.2    | 384.0    | 480.0    | 2    | 2       | ok     |
+| Apache Spark           | Q01   | Full Yellow Taxi Row Count                       | 5/5  | 1,415.2  | 1,451.0  | 185.0   | 1,162.0  | 1,616.0  | 1    | 1       | ok     |
+| Apache Spark           | Q02   | Yellow Taxi Count For One Partition Month        | 5/5  | 379.2    | 384.0    | 28.9    | 340.0    | 411.0    | 1    | 1       | ok     |
+| Apache Spark           | Q03   | Yellow Taxi Trips On One Day                     | 5/5  | 5,486.4  | 5,474.0  | 536.1   | 4,752.0  | 6,196.0  | 1    | 1       | ok     |
+| Apache Spark           | Q04   | Long And Expensive Yellow Taxi Trips             | 5/5  | 3,825.8  | 3,908.0  | 223.4   | 3,454.0  | 4,034.0  | 1    | 1       | ok     |
+| Apache Spark           | Q05   | Yearly Yellow Taxi Amount And Distance Summary   | 5/5  | 5,447.2  | 5,561.0  | 306.1   | 5,050.0  | 5,721.0  | 4    | 5       | ok     |
+| Apache Spark           | Q06   | Full High-Volume FHV Row Count                   | 5/5  | 2,027.4  | 2,055.0  | 175.4   | 1,822.0  | 2,249.0  | 1    | 1       | ok     |
+| Apache Spark           | Q07   | High-Volume FHV Count For One Partition Month    | 5/5  | 275.6    | 275.0    | 8.3     | 266.0    | 284.0    | 1    | 1       | ok     |
+| Apache Spark           | Q08   | Long And Expensive High-Volume FHV Trips         | 5/5  | 14,250.8 | 14,289.0 | 361.6   | 13,841.0 | 14,638.0 | 1    | 1       | ok     |
+| Apache Spark           | Q09   | Yearly High-Volume FHV Fare And Distance Summary | 5/5  | 34,324.6 | 34,651.0 | 735.7   | 33,487.0 | 35,189.0 | 4    | 6       | ok     |
+| Apache Spark           | Q10   | High-Volume FHV Shared-Request Flag Distribution | 5/5  | 5,407.8  | 5,510.0  | 309.7   | 4,945.0  | 5,713.0  | 2    | 2       | ok     |
diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_duckdb_results.csv b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_duckdb_results.csv
index 1f356e79a9..49693af393 100644
--- a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_duckdb_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_duckdb_results.csv
@@ -1,18 +1,31 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-13T14:42:21.456562600Z","Q01","Filtered full customer rows.","warmup",1,104215,100000,9,true,""
-"2026-06-13T14:44:07.803427300Z","Q01","Filtered full customer rows.","measured",1,106342,100000,9,true,""
-"2026-06-13T14:45:50.530715Z","Q01","Filtered full customer rows.","measured",2,102726,100000,9,true,""
-"2026-06-13T14:47:22.212279100Z","Q01","Filtered full customer rows.","measured",3,91689,100000,9,true,""
-"2026-06-13T14:49:07.150430300Z","Q01","Filtered full customer rows.","measured",4,104938,100000,9,true,""
-"2026-06-13T14:51:37.594162400Z","Q01","Filtered full customer rows.","measured",5,150445,100000,9,true,""
-"2026-06-13T14:51:37.958279800Z","Q02","Filtered explicit customer projection.","warmup",1,349,100000,8,true,""
-"2026-06-13T14:51:38.275145200Z","Q02","Filtered explicit customer projection.","measured",1,328,100000,8,true,""
-"2026-06-13T14:51:38.656120Z","Q02","Filtered explicit customer projection.","measured",2,368,100000,8,true,""
-"2026-06-13T14:51:39.069117100Z","Q02","Filtered explicit customer projection.","measured",3,425,100000,8,true,""
-"2026-06-13T14:51:39.434538700Z","Q02","Filtered explicit customer projection.","measured",4,357,100000,8,true,""
-"2026-06-13T14:51:39.815308200Z","Q02","Filtered explicit customer projection.","measured",5,386,100000,8,true,""
-"2026-06-13T14:55:13.388359700Z","Q03","Filtered order projection.","warmup",1,213562,100000,6,true,""
-"2026-06-13T15:02:26.950192400Z","Q03","Filtered order projection.","measured",1,433558,100000,6,true,""
-"2026-06-13T15:17:07.908231900Z","Q03","Filtered order projection.","measured",2,880957,100000,6,true,""
-"2026-06-13T15:36:12.177990900Z","Q03","Filtered order projection.","measured",3,1144268,100000,6,true,""
-"2026-06-13T16:03:22.285892400Z","Q03","Filtered order projection.","measured",4,1630107,100000,6,true,""
+"2026-06-22T06:58:14.023345800Z","Q01","Filtered full customer rows.","warmup",1,288144,100000,9,true,""
+"2026-06-22T07:03:10.823457600Z","Q01","Filtered full customer rows.","measured",1,296800,100000,9,true,""
+"2026-06-22T07:08:09.594045200Z","Q01","Filtered full customer rows.","measured",2,298765,100000,9,true,""
+"2026-06-22T07:13:27.088063100Z","Q01","Filtered full customer rows.","measured",3,317491,100000,9,true,""
+"2026-06-22T07:36:47.479114400Z","Q01","Filtered full customer rows.","measured",4,1400388,100000,9,true,""
+"2026-06-22T07:40:15.836386700Z","Q01","Filtered full customer rows.","measured",5,208355,100000,9,true,""
+"2026-06-22T07:40:16.475514200Z","Q02","Filtered explicit customer projection.","warmup",1,637,100000,8,true,""
+"2026-06-22T07:40:17.170407700Z","Q02","Filtered explicit customer projection.","measured",1,693,100000,8,true,""
+"2026-06-22T07:40:17.729086100Z","Q02","Filtered explicit customer projection.","measured",2,557,100000,8,true,""
+"2026-06-22T07:40:18.291387900Z","Q02","Filtered explicit customer projection.","measured",3,561,100000,8,true,""
+"2026-06-22T07:40:18.938743300Z","Q02","Filtered explicit customer projection.","measured",4,646,100000,8,true,""
+"2026-06-22T07:40:19.685518400Z","Q02","Filtered explicit customer projection.","measured",5,745,100000,8,true,""
+"2026-06-22T07:41:26.806334100Z","Q03","Filtered order projection.","warmup",1,67118,100000,6,true,""
+"2026-06-22T07:42:53.222554600Z","Q03","Filtered order projection.","measured",1,86416,100000,6,true,""
+"2026-06-22T07:44:22.342716500Z","Q03","Filtered order projection.","measured",2,89117,100000,6,true,""
+"2026-06-22T07:46:00.124162500Z","Q03","Filtered order projection.","measured",3,97781,100000,6,true,""
+"2026-06-22T07:47:44.009356200Z","Q03","Filtered order projection.","measured",4,103882,100000,6,true,""
+"2026-06-22T07:49:27.273733200Z","Q03","Filtered order projection.","measured",5,103261,100000,6,true,""
+"2026-06-22T07:57:39.876338100Z","Q04","Filtered lineitem projection.","warmup",1,492600,100000,8,true,""
+"2026-06-22T08:11:28.231816200Z","Q04","Filtered lineitem projection.","measured",1,828355,100000,8,true,""
+"2026-06-22T08:31:03.685220800Z","Q04","Filtered lineitem projection.","measured",2,1175430,100000,8,true,""
+"2026-06-22T08:59:45.354251600Z","Q04","Filtered lineitem projection.","measured",3,1721657,100000,8,true,""
+"2026-06-22T09:32:17.642743500Z","Q04","Filtered lineitem projection.","measured",4,1952285,100000,8,true,""
+"2026-06-22T10:08:56.070690200Z","Q04","Filtered lineitem projection.","measured",5,2198427,100000,8,true,""
+"2026-06-22T10:54:23.080622500Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","warmup",1,2726986,3,2,true,""
+"2026-06-22T11:46:06.529138500Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",1,3103441,3,2,true,""
+"2026-06-22T12:37:30.736370400Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",2,3084199,3,2,true,""
+"2026-06-22T13:28:20.811228200Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",3,3050067,3,2,true,""
+"2026-06-22T14:22:40.131489100Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",4,3259317,3,2,true,""
+"2026-06-22T16:04:43.210035500Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",5,6123072,3,2,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_mql_results.csv b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_mql_results.csv
index 7c8e1c5cd5..716630c5fa 100644
--- a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_mql_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_mql_results.csv
@@ -1 +1,31 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
+"2026-06-21T11:35:37.715198400Z","Q01","Filtered full customer documents.","warmup",1,229932,100000,10,true,""
+"2026-06-21T11:39:32.946908400Z","Q01","Filtered full customer documents.","measured",1,235220,100000,10,true,""
+"2026-06-21T11:43:23.985183Z","Q01","Filtered full customer documents.","measured",2,231036,100000,10,true,""
+"2026-06-21T11:47:14.532266700Z","Q01","Filtered full customer documents.","measured",3,230546,100000,10,true,""
+"2026-06-21T11:51:07.963678900Z","Q01","Filtered full customer documents.","measured",4,233429,100000,10,true,""
+"2026-06-21T11:55:03.030896400Z","Q01","Filtered full customer documents.","measured",5,235066,100000,10,true,""
+"2026-06-21T11:57:03.599731200Z","Q02","Filtered explicit customer projection.","warmup",1,120567,100000,9,true,""
+"2026-06-21T11:59:07.845898700Z","Q02","Filtered explicit customer projection.","measured",1,124246,100000,9,true,""
+"2026-06-21T12:01:06.072328400Z","Q02","Filtered explicit customer projection.","measured",2,118223,100000,9,true,""
+"2026-06-21T12:03:07.295912Z","Q02","Filtered explicit customer projection.","measured",3,121222,100000,9,true,""
+"2026-06-21T12:05:11.554337300Z","Q02","Filtered explicit customer projection.","measured",4,124260,100000,9,true,""
+"2026-06-21T12:07:16.698649300Z","Q02","Filtered explicit customer projection.","measured",5,125141,100000,9,true,""
+"2026-06-21T12:07:16.937785700Z","Q03","Filtered order projection.","warmup",1,241,0,0,false,"IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0"
+"2026-06-21T12:07:17.056854500Z","Q03","Filtered order projection.","measured",1,117,0,0,false,"IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0"
+"2026-06-21T12:07:17.169261300Z","Q03","Filtered order projection.","measured",2,114,0,0,false,"IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0"
+"2026-06-21T12:07:17.283475600Z","Q03","Filtered order projection.","measured",3,111,0,0,false,"IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0"
+"2026-06-21T12:07:17.405038600Z","Q03","Filtered order projection.","measured",4,121,0,0,false,"IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0"
+"2026-06-21T12:07:17.536943800Z","Q03","Filtered order projection.","measured",5,132,0,0,false,"IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0"
+"2026-06-21T12:07:17.760481900Z","Q04","Filtered lineitem projection.","warmup",1,224,0,0,false,"IOException: Index 0 out of bounds for length 0"
+"2026-06-21T12:07:17.911387600Z","Q04","Filtered lineitem projection.","measured",1,150,0,0,false,"IOException: Index 0 out of bounds for length 0"
+"2026-06-21T12:07:18.075574700Z","Q04","Filtered lineitem projection.","measured",2,163,0,0,false,"IOException: Index 0 out of bounds for length 0"
+"2026-06-21T12:07:18.236246100Z","Q04","Filtered lineitem projection.","measured",3,161,0,0,false,"IOException: Index 0 out of bounds for length 0"
+"2026-06-21T12:07:18.401591700Z","Q04","Filtered lineitem projection.","measured",4,167,0,0,false,"IOException: Index 0 out of bounds for length 0"
+"2026-06-21T12:07:18.569552200Z","Q04","Filtered lineitem projection.","measured",5,166,0,0,false,"IOException: Index 0 out of bounds for length 0"
+"2026-06-21T12:07:18.824085100Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","warmup",1,254,0,0,false,"IOException: Could not convert {} to PolyBigDecimal"
+"2026-06-21T12:07:19.059468600Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",1,235,0,0,false,"IOException: Could not convert {} to PolyBigDecimal"
+"2026-06-21T12:07:19.300013800Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",2,241,0,0,false,"IOException: Could not convert {} to PolyBigDecimal"
+"2026-06-21T12:07:19.555208700Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",3,254,0,0,false,"IOException: Could not convert {} to PolyBigDecimal"
+"2026-06-21T12:07:19.826052600Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",4,269,0,0,false,"IOException: Could not convert {} to PolyBigDecimal"
+"2026-06-21T12:07:20.093675300Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",5,264,0,0,false,"IOException: Could not convert {} to PolyBigDecimal"
diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv
index 7b26f6369d..1d6c9629b9 100644
--- a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv
@@ -1,25 +1,31 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-15T06:35:50.302775Z","Q01","Filtered full customer rows.","warmup",1,3060,100000,9,true,""
-"2026-06-15T06:35:52.955918800Z","Q01","Filtered full customer rows.","measured",1,2645,100000,9,true,""
-"2026-06-15T06:35:54.809585500Z","Q01","Filtered full customer rows.","measured",2,1852,100000,9,true,""
-"2026-06-15T06:35:56.112984200Z","Q01","Filtered full customer rows.","measured",3,1306,100000,9,true,""
-"2026-06-15T06:35:57.360767100Z","Q01","Filtered full customer rows.","measured",4,1248,100000,9,true,""
-"2026-06-15T06:35:58.585865700Z","Q01","Filtered full customer rows.","measured",5,1221,100000,9,true,""
-"2026-06-15T06:35:59.305961900Z","Q02","Filtered explicit customer projection.","warmup",1,719,100000,8,true,""
-"2026-06-15T06:36:00.034878500Z","Q02","Filtered explicit customer projection.","measured",1,727,100000,8,true,""
-"2026-06-15T06:36:00.761478500Z","Q02","Filtered explicit customer projection.","measured",2,728,100000,8,true,""
-"2026-06-15T06:36:01.532056400Z","Q02","Filtered explicit customer projection.","measured",3,768,100000,8,true,""
-"2026-06-15T06:36:02.218668Z","Q02","Filtered explicit customer projection.","measured",4,685,100000,8,true,""
-"2026-06-15T06:36:02.842936Z","Q02","Filtered explicit customer projection.","measured",5,624,100000,8,true,""
-"2026-06-15T06:36:04.823740800Z","Q03","One-join filtered order element projection.","warmup",1,1980,100000,8,true,""
-"2026-06-15T06:36:06.609642500Z","Q03","One-join filtered order element projection.","measured",1,1785,100000,8,true,""
-"2026-06-15T06:36:08.196334800Z","Q03","One-join filtered order element projection.","measured",2,1588,100000,8,true,""
-"2026-06-15T06:36:09.691568200Z","Q03","One-join filtered order element projection.","measured",3,1492,100000,8,true,""
-"2026-06-15T06:36:11.520673800Z","Q03","One-join filtered order element projection.","measured",4,1829,100000,8,true,""
-"2026-06-15T06:36:13.427036900Z","Q03","One-join filtered order element projection.","measured",5,1904,100000,8,true,""
-"2026-06-15T06:36:20.911396300Z","Q04","One-join filtered lineitem element projection.","warmup",1,7487,100000,10,true,""
-"2026-06-15T06:36:28.062280500Z","Q04","One-join filtered lineitem element projection.","measured",1,7145,100000,10,true,""
-"2026-06-15T06:36:35.748171400Z","Q04","One-join filtered lineitem element projection.","measured",2,7684,100000,10,true,""
-"2026-06-15T06:36:43.277546800Z","Q04","One-join filtered lineitem element projection.","measured",3,7528,100000,10,true,""
-"2026-06-15T06:36:50.452826900Z","Q04","One-join filtered lineitem element projection.","measured",4,7178,100000,10,true,""
-"2026-06-15T06:37:00.668515400Z","Q04","One-join filtered lineitem element projection.","measured",5,10213,100000,10,true,""
+"2026-06-21T11:07:20.673930400Z","Q01","Filtered full customer rows.","warmup",1,5021,100000,9,true,""
+"2026-06-21T11:07:24.277267800Z","Q01","Filtered full customer rows.","measured",1,3595,100000,9,true,""
+"2026-06-21T11:07:26.803728700Z","Q01","Filtered full customer rows.","measured",2,2525,100000,9,true,""
+"2026-06-21T11:07:29.319342300Z","Q01","Filtered full customer rows.","measured",3,2515,100000,9,true,""
+"2026-06-21T11:07:31.443130100Z","Q01","Filtered full customer rows.","measured",4,2123,100000,9,true,""
+"2026-06-21T11:07:33.003577100Z","Q01","Filtered full customer rows.","measured",5,1560,100000,9,true,""
+"2026-06-21T11:07:34.451462600Z","Q02","Filtered explicit customer projection.","warmup",1,1445,100000,8,true,""
+"2026-06-21T11:07:35.898124200Z","Q02","Filtered explicit customer projection.","measured",1,1446,100000,8,true,""
+"2026-06-21T11:07:37.068529700Z","Q02","Filtered explicit customer projection.","measured",2,1169,100000,8,true,""
+"2026-06-21T11:07:38.259382Z","Q02","Filtered explicit customer projection.","measured",3,1188,100000,8,true,""
+"2026-06-21T11:07:39.386004300Z","Q02","Filtered explicit customer projection.","measured",4,1128,100000,8,true,""
+"2026-06-21T11:07:40.339852800Z","Q02","Filtered explicit customer projection.","measured",5,953,100000,8,true,""
+"2026-06-21T11:07:45.022400900Z","Q03","One-join filtered order element projection.","warmup",1,4680,100000,8,true,""
+"2026-06-21T11:07:47.422658Z","Q03","One-join filtered order element projection.","measured",1,2400,100000,8,true,""
+"2026-06-21T11:07:50.370564100Z","Q03","One-join filtered order element projection.","measured",2,2943,100000,8,true,""
+"2026-06-21T11:07:52.936674300Z","Q03","One-join filtered order element projection.","measured",3,2565,100000,8,true,""
+"2026-06-21T11:07:56.070075800Z","Q03","One-join filtered order element projection.","measured",4,3132,100000,8,true,""
+"2026-06-21T11:07:58.670837100Z","Q03","One-join filtered order element projection.","measured",5,2599,100000,8,true,""
+"2026-06-21T11:08:10.115552900Z","Q04","One-join filtered lineitem element projection.","warmup",1,11448,100000,10,true,""
+"2026-06-21T11:08:20.494986700Z","Q04","One-join filtered lineitem element projection.","measured",1,10373,100000,10,true,""
+"2026-06-21T11:08:31.384354300Z","Q04","One-join filtered lineitem element projection.","measured",2,10887,100000,10,true,""
+"2026-06-21T11:08:42.479722200Z","Q04","One-join filtered lineitem element projection.","measured",3,11094,100000,10,true,""
+"2026-06-21T11:08:53.340770800Z","Q04","One-join filtered lineitem element projection.","measured",4,10858,100000,10,true,""
+"2026-06-21T11:09:04.354357200Z","Q04","One-join filtered lineitem element projection.","measured",5,11016,100000,10,true,""
+"2026-06-21T11:12:49.648201600Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","warmup",1,225290,3,2,true,""
+"2026-06-21T11:16:15.726822Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",1,206076,3,2,true,""
+"2026-06-21T11:20:16.408652100Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",2,240681,3,2,true,""
+"2026-06-21T11:23:57.207270800Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",3,220801,3,2,true,""
+"2026-06-21T11:27:35.473234600Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",4,218262,3,2,true,""
+"2026-06-21T11:31:21.699664900Z","Q05","Nested lineitem MAX aggregation grouped by return flag.","measured",5,226227,3,2,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_spark_results.csv b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_spark_results.csv
index 0501e8d405..dd69a1b35f 100644
--- a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_spark_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_spark_results.csv
@@ -1,31 +1,31 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-2026-06-13T10:30:41.656029Z,Q01,Count root customer rows after filtering top-level customer fields.,warmup,1,7106,1,1,true,
-2026-06-13T10:30:42.977580Z,Q01,Count root customer rows after filtering top-level customer fields.,measured,1,1320,1,1,true,
-2026-06-13T10:30:44.230603Z,Q01,Count root customer rows after filtering top-level customer fields.,measured,2,1251,1,1,true,
-2026-06-13T10:30:45.507038Z,Q01,Count root customer rows after filtering top-level customer fields.,measured,3,1273,1,1,true,
-2026-06-13T10:30:46.688411Z,Q01,Count root customer rows after filtering top-level customer fields.,measured,4,1179,1,1,true,
-2026-06-13T10:30:48.144617Z,Q01,Count root customer rows after filtering top-level customer fields.,measured,5,1454,1,1,true,
-2026-06-13T10:31:31.276410Z,Q02,Count nested orders after filtering customer and order fields.,warmup,1,42947,1,1,true,
-2026-06-13T10:32:13.935904Z,Q02,Count nested orders after filtering customer and order fields.,measured,1,42537,1,1,true,
-2026-06-13T10:32:47.929137Z,Q02,Count nested orders after filtering customer and order fields.,measured,2,33898,1,1,true,
-2026-06-13T10:33:17.289671Z,Q02,Count nested orders after filtering customer and order fields.,measured,3,29267,1,1,true,
-2026-06-13T10:33:55.135017Z,Q02,Count nested orders after filtering customer and order fields.,measured,4,37746,1,1,true,
-2026-06-13T10:34:32.230284Z,Q02,Count nested orders after filtering customer and order fields.,measured,5,36899,1,1,true,
-2026-06-13T10:35:03.102713Z,Q03,Count deeply nested lineitems after filtering order and lineitem fields.,warmup,1,30748,1,1,true,
-2026-06-13T10:35:36.812003Z,Q03,Count deeply nested lineitems after filtering order and lineitem fields.,measured,1,33604,1,1,true,
-2026-06-13T10:36:06.535773Z,Q03,Count deeply nested lineitems after filtering order and lineitem fields.,measured,2,29609,1,1,true,
-2026-06-13T10:36:37.694455Z,Q03,Count deeply nested lineitems after filtering order and lineitem fields.,measured,3,31070,1,1,true,
-2026-06-13T10:37:07.109566Z,Q03,Count deeply nested lineitems after filtering order and lineitem fields.,measured,4,29315,1,1,true,
-2026-06-13T10:37:40.923054Z,Q03,Count deeply nested lineitems after filtering order and lineitem fields.,measured,5,33689,1,1,true,
-2026-06-13T10:38:19.478809Z,Q04,Sum order prices by nested order status.,warmup,1,38438,3,2,true,
-2026-06-13T10:38:53.103006Z,Q04,Sum order prices by nested order status.,measured,1,33534,3,2,true,
-2026-06-13T10:39:26.127713Z,Q04,Sum order prices by nested order status.,measured,2,32922,3,2,true,
-2026-06-13T10:39:56.184541Z,Q04,Sum order prices by nested order status.,measured,3,29954,3,2,true,
-2026-06-13T10:40:27.645915Z,Q04,Sum order prices by nested order status.,measured,4,31334,3,2,true,
-2026-06-13T10:40:59.041749Z,Q04,Sum order prices by nested order status.,measured,5,31293,3,2,true,
-2026-06-13T10:41:25.807310Z,Q05,Sum line numbers by deeply nested lineitem return flag.,warmup,1,26662,3,2,true,
-2026-06-13T10:41:54.139902Z,Q05,Sum line numbers by deeply nested lineitem return flag.,measured,1,28225,3,2,true,
-2026-06-13T10:42:19.491004Z,Q05,Sum line numbers by deeply nested lineitem return flag.,measured,2,25244,3,2,true,
-2026-06-13T10:42:47.015660Z,Q05,Sum line numbers by deeply nested lineitem return flag.,measured,3,27421,3,2,true,
-2026-06-13T10:43:12.909295Z,Q05,Sum line numbers by deeply nested lineitem return flag.,measured,4,25790,3,2,true,
-2026-06-13T10:43:40.419789Z,Q05,Sum line numbers by deeply nested lineitem return flag.,measured,5,27397,3,2,true,
+2026-06-22T16:08:35.069587Z,Q01,Filtered full customer rows.,warmup,1,84847,100000,9,true,
+2026-06-22T16:09:59.113101Z,Q01,Filtered full customer rows.,measured,1,79733,100000,9,true,
+2026-06-22T16:11:15.212810Z,Q01,Filtered full customer rows.,measured,2,72722,100000,9,true,
+2026-06-22T16:12:37.834099Z,Q01,Filtered full customer rows.,measured,3,77799,100000,9,true,
+2026-06-22T16:14:02.240965Z,Q01,Filtered full customer rows.,measured,4,79575,100000,9,true,
+2026-06-22T16:15:25.580806Z,Q01,Filtered full customer rows.,measured,5,80006,100000,9,true,
+2026-06-22T16:15:27.862993Z,Q02,Filtered explicit customer projection.,warmup,1,2279,100000,8,true,
+2026-06-22T16:15:29.544525Z,Q02,Filtered explicit customer projection.,measured,1,1680,100000,8,true,
+2026-06-22T16:15:32.788722Z,Q02,Filtered explicit customer projection.,measured,2,1590,100000,8,true,
+2026-06-22T16:15:34.646035Z,Q02,Filtered explicit customer projection.,measured,3,1856,100000,8,true,
+2026-06-22T16:15:36.353030Z,Q02,Filtered explicit customer projection.,measured,4,1706,100000,8,true,
+2026-06-22T16:15:38.282318Z,Q02,Filtered explicit customer projection.,measured,5,1928,100000,8,true,
+2026-06-22T16:15:53.188337Z,Q03,Filtered order projection.,warmup,1,14904,100000,6,true,
+2026-06-22T16:16:08.082228Z,Q03,Filtered order projection.,measured,1,13389,100000,6,true,
+2026-06-22T16:16:21.596178Z,Q03,Filtered order projection.,measured,2,13513,100000,6,true,
+2026-06-22T16:16:33.599242Z,Q03,Filtered order projection.,measured,3,12002,100000,6,true,
+2026-06-22T16:16:48.729222Z,Q03,Filtered order projection.,measured,4,13522,100000,6,true,
+2026-06-22T16:17:03.407955Z,Q03,Filtered order projection.,measured,5,14677,100000,6,true,
+2026-06-22T16:17:21.663752Z,Q04,Filtered lineitem projection.,warmup,1,16466,100000,8,true,
+2026-06-22T16:17:36.617055Z,Q04,Filtered lineitem projection.,measured,1,14952,100000,8,true,
+2026-06-22T16:17:55.524815Z,Q04,Filtered lineitem projection.,measured,2,17154,100000,8,true,
+2026-06-22T16:18:14.630275Z,Q04,Filtered lineitem projection.,measured,3,17544,100000,8,true,
+2026-06-22T16:18:32.062433Z,Q04,Filtered lineitem projection.,measured,4,17431,100000,8,true,
+2026-06-22T16:18:50.383855Z,Q04,Filtered lineitem projection.,measured,5,16665,100000,8,true,
+2026-06-22T16:19:06.289721Z,Q05,Nested lineitem MAX aggregation grouped by return flag.,warmup,1,15904,3,2,true,
+2026-06-22T16:19:23.338746Z,Q05,Nested lineitem MAX aggregation grouped by return flag.,measured,1,15246,3,2,true,
+2026-06-22T16:19:38.892225Z,Q05,Nested lineitem MAX aggregation grouped by return flag.,measured,2,15552,3,2,true,
+2026-06-22T16:19:56.024536Z,Q05,Nested lineitem MAX aggregation grouped by return flag.,measured,3,15608,3,2,true,
+2026-06-22T16:20:10.825919Z,Q05,Nested lineitem MAX aggregation grouped by return flag.,measured,4,14800,3,2,true,
+2026-06-22T16:20:27.759679Z,Q05,Nested lineitem MAX aggregation grouped by return flag.,measured,5,15376,3,2,true,
diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_summary.md b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_summary.md
new file mode 100644
index 0000000000..a9b490d0f8
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_summary.md
@@ -0,0 +1,58 @@
+# Nested Data Summary
+
+Phase summarized: `measured`.
+Warmup rows are excluded. Mean, median, and standard deviation values use successful runs only.
+
+## Source Files
+
+| System | CSV |
+| --- | --- |
+| Polypheny Relational Normalized | `plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_normalized_results.csv` |
+| Polypheny Document MQL | `plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_mql_results.csv` |
+| DuckDB | `plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_duckdb_results.csv` |
+| Apache Spark | `plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_spark_results.csv` |
+
+## Mean Elapsed Time (ms)
+
+| Query | Description | Polypheny Relational Normalized | Polypheny Document MQL | DuckDB | Apache Spark | Row counts |
+| --- | --- | --- | --- | --- | --- | --- |
+| Q01 | Filtered full customer rows. | 2,463.6 | 233,059.4 | 504,359.8 | 77,967.0 | 100,000 |
+| Q02 | Filtered explicit customer projection. | 1,176.8 | 122,618.4 | 640.4 | 1,752.0 | 100,000 |
+| Q03 | One-join filtered order element projection. | 2,727.8 |  | 96,091.4 | 13,420.6 | differs |
+| Q04 | One-join filtered lineitem element projection. | 10,845.6 |  | 1,575,230.8 | 16,749.2 | differs |
+| Q05 | Nested lineitem MAX aggregation grouped by return flag. | 222,409.4 |  | 3,724,019.2 | 15,316.4 | differs |
+
+## Result Row Counts
+
+| Query | Description | Polypheny Relational Normalized | Polypheny Document MQL | DuckDB | Apache Spark |
+| --- | --- | --- | --- | --- | --- |
+| Q01 | Filtered full customer rows. | 100,000 | 100,000 | 100,000 | 100,000 |
+| Q02 | Filtered explicit customer projection. | 100,000 | 100,000 | 100,000 | 100,000 |
+| Q03 | One-join filtered order element projection. | 100,000 |  | 100,000 | 100,000 |
+| Q04 | One-join filtered lineitem element projection. | 100,000 |  | 100,000 | 100,000 |
+| Q05 | Nested lineitem MAX aggregation grouped by return flag. | 3 |  | 3 | 3 |
+
+## Detailed Summary (ms)
+
+| System | Query | Description | Runs | Mean | Median | Std Dev | Min | Max | Rows | Columns | Status |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| Polypheny Relational Normalized | Q01 | Filtered full customer rows. | 5/5 | 2,463.6 | 2,515.0 | 744.8 | 1,560.0 | 3,595.0 | 100,000 | 9 | ok |
+| Polypheny Relational Normalized | Q02 | Filtered explicit customer projection. | 5/5 | 1,176.8 | 1,169.0 | 176.9 | 953.0 | 1,446.0 | 100,000 | 8 | ok |
+| Polypheny Relational Normalized | Q03 | One-join filtered order element projection. | 5/5 | 2,727.8 | 2,599.0 | 300.1 | 2,400.0 | 3,132.0 | 100,000 | 8 | ok |
+| Polypheny Relational Normalized | Q04 | One-join filtered lineitem element projection. | 5/5 | 10,845.6 | 10,887.0 | 281.1 | 10,373.0 | 11,094.0 | 100,000 | 10 | ok |
+| Polypheny Relational Normalized | Q05 | Nested lineitem MAX aggregation grouped by return flag. | 5/5 | 222,409.4 | 220,801.0 | 12,599.5 | 206,076.0 | 240,681.0 | 3 | 2 | ok |
+| Polypheny Document MQL | Q01 | Filtered full customer documents. | 5/5 | 233,059.4 | 233,429.0 | 2,193.3 | 230,546.0 | 235,220.0 | 100,000 | 10 | ok |
+| Polypheny Document MQL | Q02 | Filtered explicit customer projection. | 5/5 | 122,618.4 | 124,246.0 | 2,871.3 | 118,223.0 | 125,141.0 | 100,000 | 9 | ok |
+| Polypheny Document MQL | Q03 | Filtered order projection. | 0/5 |  |  |  |  |  |  |  | 0/5 ok; IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0; IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0; IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0; IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0; IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0 |
+| Polypheny Document MQL | Q04 | Filtered lineitem projection. | 0/5 |  |  |  |  |  |  |  | 0/5 ok; IOException: Index 0 out of bounds for length 0; IOException: Index 0 out of bounds for length 0; IOException: Index 0 out of bounds for length 0; IOException: Index 0 out of bounds for length 0; IOException: Index 0 out of bounds for length 0 |
+| Polypheny Document MQL | Q05 | Nested lineitem MAX aggregation grouped by return flag. | 0/5 |  |  |  |  |  |  |  | 0/5 ok; IOException: Could not convert {} to PolyBigDecimal; IOException: Could not convert {} to PolyBigDecimal; IOException: Could not convert {} to PolyBigDecimal; IOException: Could not convert {} to PolyBigDecimal; IOException: Could not convert {} to PolyBigDecimal |
+| DuckDB | Q01 | Filtered full customer rows. | 5/5 | 504,359.8 | 298,765.0 | 502,681.7 | 208,355.0 | 1,400,388.0 | 100,000 | 9 | ok |
+| DuckDB | Q02 | Filtered explicit customer projection. | 5/5 | 640.4 | 646.0 | 82.2 | 557.0 | 745.0 | 100,000 | 8 | ok |
+| DuckDB | Q03 | Filtered order projection. | 5/5 | 96,091.4 | 97,781.0 | 8,018.8 | 86,416.0 | 103,882.0 | 100,000 | 6 | ok |
+| DuckDB | Q04 | Filtered lineitem projection. | 5/5 | 1,575,230.8 | 1,721,657.0 | 563,393.3 | 828,355.0 | 2,198,427.0 | 100,000 | 8 | ok |
+| DuckDB | Q05 | Nested lineitem MAX aggregation grouped by return flag. | 5/5 | 3,724,019.2 | 3,103,441.0 | 1,343,512.3 | 3,050,067.0 | 6,123,072.0 | 3 | 2 | ok |
+| Apache Spark | Q01 | Filtered full customer rows. | 5/5 | 77,967.0 | 79,575.0 | 3,057.8 | 72,722.0 | 80,006.0 | 100,000 | 9 | ok |
+| Apache Spark | Q02 | Filtered explicit customer projection. | 5/5 | 1,752.0 | 1,706.0 | 137.2 | 1,590.0 | 1,928.0 | 100,000 | 8 | ok |
+| Apache Spark | Q03 | Filtered order projection. | 5/5 | 13,420.6 | 13,513.0 | 950.1 | 12,002.0 | 14,677.0 | 100,000 | 6 | ok |
+| Apache Spark | Q04 | Filtered lineitem projection. | 5/5 | 16,749.2 | 17,154.0 | 1,060.4 | 14,952.0 | 17,544.0 | 100,000 | 8 | ok |
+| Apache Spark | Q05 | Nested lineitem MAX aggregation grouped by return flag. | 5/5 | 15,316.4 | 15,376.0 | 322.4 | 14,800.0 | 15,608.0 | 3 | 2 | ok |
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_repartitioned_results.csv b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_repartitioned_results.csv
index 67e4ebea14..55696bc97a 100644
--- a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_repartitioned_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_repartitioned_results.csv
@@ -1,31 +1,31 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-14T08:22:06.970170Z","Q01","Repartitioned full table baseline","warmup",1,73,1,1,true,""
-"2026-06-14T08:22:07.040521500Z","Q01","Repartitioned full table baseline","measured",1,68,1,1,true,""
-"2026-06-14T08:22:07.135844700Z","Q01","Repartitioned full table baseline","measured",2,93,1,1,true,""
-"2026-06-14T08:22:07.217900100Z","Q01","Repartitioned full table baseline","measured",3,81,1,1,true,""
-"2026-06-14T08:22:07.299493400Z","Q01","Repartitioned full table baseline","measured",4,79,1,1,true,""
-"2026-06-14T08:22:07.396249200Z","Q01","Repartitioned full table baseline","measured",5,95,1,1,true,""
-"2026-06-14T08:22:07.455089600Z","Q02","Repartitioned partition by year","warmup",1,56,1,1,true,""
-"2026-06-14T08:22:07.530148500Z","Q02","Repartitioned partition by year","measured",1,71,1,1,true,""
-"2026-06-14T08:22:07.583963100Z","Q02","Repartitioned partition by year","measured",2,52,1,1,true,""
-"2026-06-14T08:22:07.641150500Z","Q02","Repartitioned partition by year","measured",3,55,1,1,true,""
-"2026-06-14T08:22:07.712686500Z","Q02","Repartitioned partition by year","measured",4,70,1,1,true,""
-"2026-06-14T08:22:07.783916800Z","Q02","Repartitioned partition by year","measured",5,69,1,1,true,""
-"2026-06-14T08:22:07.829839200Z","Q03","Repartitioned partition by month","warmup",1,43,1,1,true,""
-"2026-06-14T08:22:07.863879300Z","Q03","Repartitioned partition by month","measured",1,33,1,1,true,""
-"2026-06-14T08:22:07.896069300Z","Q03","Repartitioned partition by month","measured",2,30,1,1,true,""
-"2026-06-14T08:22:07.926855400Z","Q03","Repartitioned partition by month","measured",3,29,1,1,true,""
-"2026-06-14T08:22:07.957042200Z","Q03","Repartitioned partition by month","measured",4,30,1,1,true,""
-"2026-06-14T08:22:07.986544Z","Q03","Repartitioned partition by month","measured",5,27,1,1,true,""
-"2026-06-14T08:22:08.029952600Z","Q04","Repartitioned filtered count, full scan","warmup",1,41,1,1,true,""
-"2026-06-14T08:22:08.075321400Z","Q04","Repartitioned filtered count, full scan","measured",1,44,1,1,true,""
-"2026-06-14T08:22:08.114334Z","Q04","Repartitioned filtered count, full scan","measured",2,37,1,1,true,""
-"2026-06-14T08:22:08.174515900Z","Q04","Repartitioned filtered count, full scan","measured",3,58,1,1,true,""
-"2026-06-14T08:22:08.209018900Z","Q04","Repartitioned filtered count, full scan","measured",4,33,1,1,true,""
-"2026-06-14T08:22:08.247720900Z","Q04","Repartitioned filtered count, full scan","measured",5,37,1,1,true,""
-"2026-06-14T08:22:08.271176700Z","Q05","Repartitioned filtered count, partition by year","warmup",1,27,1,1,true,""
-"2026-06-14T08:22:08.300928300Z","Q05","Repartitioned filtered count, partition by year","measured",1,27,1,1,true,""
-"2026-06-14T08:22:08.331976100Z","Q05","Repartitioned filtered count, partition by year","measured",2,26,1,1,true,""
-"2026-06-14T08:22:08.357783200Z","Q05","Repartitioned filtered count, partition by year","measured",3,25,1,1,true,""
-"2026-06-14T08:22:08.372358Z","Q05","Repartitioned filtered count, partition by year","measured",4,19,1,1,true,""
-"2026-06-14T08:22:08.395158800Z","Q05","Repartitioned filtered count, partition by year","measured",5,16,1,1,true,""
+"2026-06-18T19:48:40.897582600Z","Q1_P","Repartitioned full table baseline","warmup",1,71,1,1,true,""
+"2026-06-18T19:48:40.964769400Z","Q1_P","Repartitioned full table baseline","measured",1,65,1,1,true,""
+"2026-06-18T19:48:41.041661100Z","Q1_P","Repartitioned full table baseline","measured",2,75,1,1,true,""
+"2026-06-18T19:48:41.126731900Z","Q1_P","Repartitioned full table baseline","measured",3,84,1,1,true,""
+"2026-06-18T19:48:41.222858900Z","Q1_P","Repartitioned full table baseline","measured",4,95,1,1,true,""
+"2026-06-18T19:48:41.300459Z","Q1_P","Repartitioned full table baseline","measured",5,75,1,1,true,""
+"2026-06-18T19:48:41.338587700Z","Q2_P","Repartitioned partition by year","warmup",1,37,1,1,true,""
+"2026-06-18T19:48:41.378456600Z","Q2_P","Repartitioned partition by year","measured",1,38,1,1,true,""
+"2026-06-18T19:48:41.414518100Z","Q2_P","Repartitioned partition by year","measured",2,35,1,1,true,""
+"2026-06-18T19:48:41.450219100Z","Q2_P","Repartitioned partition by year","measured",3,33,1,1,true,""
+"2026-06-18T19:48:41.486417600Z","Q2_P","Repartitioned partition by year","measured",4,35,1,1,true,""
+"2026-06-18T19:48:41.523160800Z","Q2_P","Repartitioned partition by year","measured",5,35,1,1,true,""
+"2026-06-18T19:48:41.538896600Z","Q3_P","Repartitioned partition by month","warmup",1,17,1,1,true,""
+"2026-06-18T19:48:41.558626Z","Q3_P","Repartitioned partition by month","measured",1,16,1,1,true,""
+"2026-06-18T19:48:41.579432900Z","Q3_P","Repartitioned partition by month","measured",2,18,1,1,true,""
+"2026-06-18T19:48:41.598481900Z","Q3_P","Repartitioned partition by month","measured",3,20,1,1,true,""
+"2026-06-18T19:48:41.614812400Z","Q3_P","Repartitioned partition by month","measured",4,15,1,1,true,""
+"2026-06-18T19:48:41.632592700Z","Q3_P","Repartitioned partition by month","measured",5,16,1,1,true,""
+"2026-06-18T19:48:41.662878100Z","Q4_P","Repartitioned filtered count, full scan","warmup",1,30,1,1,true,""
+"2026-06-18T19:48:41.696916800Z","Q4_P","Repartitioned filtered count, full scan","measured",1,30,1,1,true,""
+"2026-06-18T19:48:41.728907300Z","Q4_P","Repartitioned filtered count, full scan","measured",2,31,1,1,true,""
+"2026-06-18T19:48:41.756948400Z","Q4_P","Repartitioned filtered count, full scan","measured",3,29,1,1,true,""
+"2026-06-18T19:48:41.792263200Z","Q4_P","Repartitioned filtered count, full scan","measured",4,32,1,1,true,""
+"2026-06-18T19:48:41.822275900Z","Q4_P","Repartitioned filtered count, full scan","measured",5,29,1,1,true,""
+"2026-06-18T19:48:41.842990100Z","Q5_P","Repartitioned filtered count, partition by year","warmup",1,18,1,1,true,""
+"2026-06-18T19:48:41.861605100Z","Q5_P","Repartitioned filtered count, partition by year","measured",1,18,1,1,true,""
+"2026-06-18T19:48:41.881407700Z","Q5_P","Repartitioned filtered count, partition by year","measured",2,19,1,1,true,""
+"2026-06-18T19:48:41.899936500Z","Q5_P","Repartitioned filtered count, partition by year","measured",3,17,1,1,true,""
+"2026-06-18T19:48:41.917304Z","Q5_P","Repartitioned filtered count, partition by year","measured",4,16,1,1,true,""
+"2026-06-18T19:48:41.935321900Z","Q5_P","Repartitioned filtered count, partition by year","measured",5,17,1,1,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_unpartitioned_results.csv b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_unpartitioned_results.csv
index c357d09790..b2da24b6a5 100644
--- a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_unpartitioned_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_unpartitioned_results.csv
@@ -1,31 +1,31 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-14T08:23:06.112269700Z","Q06","Unpartitioned full table baseline","warmup",1,60,1,1,true,""
-"2026-06-14T08:23:06.174293800Z","Q06","Unpartitioned full table baseline","measured",1,59,1,1,true,""
-"2026-06-14T08:23:06.234429100Z","Q06","Unpartitioned full table baseline","measured",2,59,1,1,true,""
-"2026-06-14T08:23:06.291205300Z","Q06","Unpartitioned full table baseline","measured",3,56,1,1,true,""
-"2026-06-14T08:23:06.350725600Z","Q06","Unpartitioned full table baseline","measured",4,58,1,1,true,""
-"2026-06-14T08:23:06.410112100Z","Q06","Unpartitioned full table baseline","measured",5,58,1,1,true,""
-"2026-06-14T08:23:06.489755400Z","Q07","Unpartitioned partition by year","warmup",1,78,1,1,true,""
-"2026-06-14T08:23:06.572798200Z","Q07","Unpartitioned partition by year","measured",1,81,1,1,true,""
-"2026-06-14T08:23:06.659082700Z","Q07","Unpartitioned partition by year","measured",2,84,1,1,true,""
-"2026-06-14T08:23:06.751608900Z","Q07","Unpartitioned partition by year","measured",3,91,1,1,true,""
-"2026-06-14T08:23:06.837184900Z","Q07","Unpartitioned partition by year","measured",4,84,1,1,true,""
-"2026-06-14T08:23:06.927667200Z","Q07","Unpartitioned partition by year","measured",5,89,1,1,true,""
-"2026-06-14T08:23:07.008618300Z","Q08","Unpartitioned partition by month","warmup",1,78,1,1,true,""
-"2026-06-14T08:23:07.088438600Z","Q08","Unpartitioned partition by month","measured",1,79,1,1,true,""
-"2026-06-14T08:23:07.165860100Z","Q08","Unpartitioned partition by month","measured",2,75,1,1,true,""
-"2026-06-14T08:23:07.238305800Z","Q08","Unpartitioned partition by month","measured",3,72,1,1,true,""
-"2026-06-14T08:23:07.312396900Z","Q08","Unpartitioned partition by month","measured",4,72,1,1,true,""
-"2026-06-14T08:23:07.388516500Z","Q08","Unpartitioned partition by month","measured",5,74,1,1,true,""
-"2026-06-14T08:23:07.415603300Z","Q09","Unpartitioned filtered count, full scan","warmup",1,25,1,1,true,""
-"2026-06-14T08:23:07.438613200Z","Q09","Unpartitioned filtered count, full scan","measured",1,22,1,1,true,""
-"2026-06-14T08:23:07.461674500Z","Q09","Unpartitioned filtered count, full scan","measured",2,21,1,1,true,""
-"2026-06-14T08:23:07.483671700Z","Q09","Unpartitioned filtered count, full scan","measured",3,21,1,1,true,""
-"2026-06-14T08:23:07.506671200Z","Q09","Unpartitioned filtered count, full scan","measured",4,22,1,1,true,""
-"2026-06-14T08:23:07.528679100Z","Q09","Unpartitioned filtered count, full scan","measured",5,20,1,1,true,""
-"2026-06-14T08:23:07.547203100Z","Q10","Unpartitioned filtered count, partition by year","warmup",1,17,1,1,true,""
-"2026-06-14T08:23:07.567214100Z","Q10","Unpartitioned filtered count, partition by year","measured",1,19,1,1,true,""
-"2026-06-14T08:23:07.587313Z","Q10","Unpartitioned filtered count, partition by year","measured",2,18,1,1,true,""
-"2026-06-14T08:23:07.606943800Z","Q10","Unpartitioned filtered count, partition by year","measured",3,18,1,1,true,""
-"2026-06-14T08:23:07.625303200Z","Q10","Unpartitioned filtered count, partition by year","measured",4,17,1,1,true,""
-"2026-06-14T08:23:07.644226300Z","Q10","Unpartitioned filtered count, partition by year","measured",5,16,1,1,true,""
+"2026-06-18T19:49:02.776375Z","Q1_NP","Unpartitioned full table baseline","warmup",1,68,1,1,true,""
+"2026-06-18T19:49:02.836927400Z","Q1_NP","Unpartitioned full table baseline","measured",1,59,1,1,true,""
+"2026-06-18T19:49:02.898867600Z","Q1_NP","Unpartitioned full table baseline","measured",2,61,1,1,true,""
+"2026-06-18T19:49:02.959344400Z","Q1_NP","Unpartitioned full table baseline","measured",3,58,1,1,true,""
+"2026-06-18T19:49:03.018183Z","Q1_NP","Unpartitioned full table baseline","measured",4,57,1,1,true,""
+"2026-06-18T19:49:03.080532200Z","Q1_NP","Unpartitioned full table baseline","measured",5,63,1,1,true,""
+"2026-06-18T19:49:03.163949600Z","Q2_NP","Unpartitioned partition by year","warmup",1,82,1,1,true,""
+"2026-06-18T19:49:03.249357Z","Q2_NP","Unpartitioned partition by year","measured",1,81,1,1,true,""
+"2026-06-18T19:49:03.339798700Z","Q2_NP","Unpartitioned partition by year","measured",2,88,1,1,true,""
+"2026-06-18T19:49:03.426448Z","Q2_NP","Unpartitioned partition by year","measured",3,86,1,1,true,""
+"2026-06-18T19:49:03.520746700Z","Q2_NP","Unpartitioned partition by year","measured",4,92,1,1,true,""
+"2026-06-18T19:49:03.617323Z","Q2_NP","Unpartitioned partition by year","measured",5,95,1,1,true,""
+"2026-06-18T19:49:03.697661500Z","Q3_NP","Unpartitioned partition by month","warmup",1,78,1,1,true,""
+"2026-06-18T19:49:03.775593800Z","Q3_NP","Unpartitioned partition by month","measured",1,80,1,1,true,""
+"2026-06-18T19:49:03.847841700Z","Q3_NP","Unpartitioned partition by month","measured",2,70,1,1,true,""
+"2026-06-18T19:49:03.925427500Z","Q3_NP","Unpartitioned partition by month","measured",3,75,1,1,true,""
+"2026-06-18T19:49:03.997736300Z","Q3_NP","Unpartitioned partition by month","measured",4,71,1,1,true,""
+"2026-06-18T19:49:04.064713500Z","Q3_NP","Unpartitioned partition by month","measured",5,65,1,1,true,""
+"2026-06-18T19:49:04.089973400Z","Q4_NP","Unpartitioned filtered count, full scan","warmup",1,25,1,1,true,""
+"2026-06-18T19:49:04.115132300Z","Q4_NP","Unpartitioned filtered count, full scan","measured",1,22,1,1,true,""
+"2026-06-18T19:49:04.135716100Z","Q4_NP","Unpartitioned filtered count, full scan","measured",2,22,1,1,true,""
+"2026-06-18T19:49:04.161700100Z","Q4_NP","Unpartitioned filtered count, full scan","measured",3,20,1,1,true,""
+"2026-06-18T19:49:04.182357300Z","Q4_NP","Unpartitioned filtered count, full scan","measured",4,19,1,1,true,""
+"2026-06-18T19:49:04.203553100Z","Q4_NP","Unpartitioned filtered count, full scan","measured",5,20,1,1,true,""
+"2026-06-18T19:49:04.219128700Z","Q5_NP","Unpartitioned filtered count, partition by year","warmup",1,14,1,1,true,""
+"2026-06-18T19:49:04.234704400Z","Q5_NP","Unpartitioned filtered count, partition by year","measured",1,14,1,1,true,""
+"2026-06-18T19:49:04.251339Z","Q5_NP","Unpartitioned filtered count, partition by year","measured",2,14,1,1,true,""
+"2026-06-18T19:49:04.266102400Z","Q5_NP","Unpartitioned filtered count, partition by year","measured",3,13,1,1,true,""
+"2026-06-18T19:49:04.282152700Z","Q5_NP","Unpartitioned filtered count, partition by year","measured",4,14,1,1,true,""
+"2026-06-18T19:49:04.296691800Z","Q5_NP","Unpartitioned filtered count, partition by year","measured",5,13,1,1,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_polypheny_results.csv b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_polypheny_results.csv
index 758df0b405..bcb00e187c 100644
--- a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_polypheny_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_polypheny_results.csv
@@ -1,61 +1,61 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-14T08:21:51.087884500Z","Q01","Repartitioned full table baseline","warmup",1,1133,1,1,true,""
-"2026-06-14T08:21:51.117551500Z","Q01","Repartitioned full table baseline","measured",1,24,1,1,true,""
-"2026-06-14T08:21:51.137868400Z","Q01","Repartitioned full table baseline","measured",2,19,1,1,true,""
-"2026-06-14T08:21:51.158880400Z","Q01","Repartitioned full table baseline","measured",3,20,1,1,true,""
-"2026-06-14T08:21:51.175875700Z","Q01","Repartitioned full table baseline","measured",4,15,1,1,true,""
-"2026-06-14T08:21:51.202877Z","Q01","Repartitioned full table baseline","measured",5,17,1,1,true,""
-"2026-06-14T08:21:51.478782900Z","Q02","Repartitioned partition by year","warmup",1,273,1,1,true,""
-"2026-06-14T08:21:51.495680900Z","Q02","Repartitioned partition by year","measured",1,15,1,1,true,""
-"2026-06-14T08:21:51.515489700Z","Q02","Repartitioned partition by year","measured",2,18,1,1,true,""
-"2026-06-14T08:21:51.533822600Z","Q02","Repartitioned partition by year","measured",3,17,1,1,true,""
-"2026-06-14T08:21:51.552355300Z","Q02","Repartitioned partition by year","measured",4,17,1,1,true,""
-"2026-06-14T08:21:51.571553300Z","Q02","Repartitioned partition by year","measured",5,17,1,1,true,""
-"2026-06-14T08:21:51.866632100Z","Q03","Repartitioned partition by month","warmup",1,292,1,1,true,""
-"2026-06-14T08:21:51.889162200Z","Q03","Repartitioned partition by month","measured",1,21,1,1,true,""
-"2026-06-14T08:21:51.911982500Z","Q03","Repartitioned partition by month","measured",2,21,1,1,true,""
-"2026-06-14T08:21:51.931861200Z","Q03","Repartitioned partition by month","measured",3,19,1,1,true,""
-"2026-06-14T08:21:51.955056700Z","Q03","Repartitioned partition by month","measured",4,20,1,1,true,""
-"2026-06-14T08:21:51.973058100Z","Q03","Repartitioned partition by month","measured",5,17,1,1,true,""
-"2026-06-14T08:21:54.381224300Z","Q04","Repartitioned filtered count, full scan","warmup",1,2406,1,1,true,""
-"2026-06-14T08:21:54.678133300Z","Q04","Repartitioned filtered count, full scan","measured",1,308,1,1,true,""
-"2026-06-14T08:21:54.958043Z","Q04","Repartitioned filtered count, full scan","measured",2,266,1,1,true,""
-"2026-06-14T08:21:55.244932900Z","Q04","Repartitioned filtered count, full scan","measured",3,285,1,1,true,""
-"2026-06-14T08:21:55.503415700Z","Q04","Repartitioned filtered count, full scan","measured",4,256,1,1,true,""
-"2026-06-14T08:21:55.791166100Z","Q04","Repartitioned filtered count, full scan","measured",5,284,1,1,true,""
-"2026-06-14T08:21:56.174647600Z","Q05","Repartitioned filtered count, partition by year","warmup",1,381,1,1,true,""
-"2026-06-14T08:21:56.261252Z","Q05","Repartitioned filtered count, partition by year","measured",1,85,1,1,true,""
-"2026-06-14T08:21:56.355601800Z","Q05","Repartitioned filtered count, partition by year","measured",2,92,1,1,true,""
-"2026-06-14T08:21:56.439768200Z","Q05","Repartitioned filtered count, partition by year","measured",3,83,1,1,true,""
-"2026-06-14T08:21:56.528297700Z","Q05","Repartitioned filtered count, partition by year","measured",4,86,1,1,true,""
-"2026-06-14T08:21:56.607746600Z","Q05","Repartitioned filtered count, partition by year","measured",5,77,1,1,true,""
-"2026-06-14T08:21:56.666926900Z","Q06","Unpartitioned full table baseline","warmup",1,57,1,1,true,""
-"2026-06-14T08:21:56.681926700Z","Q06","Unpartitioned full table baseline","measured",1,14,1,1,true,""
-"2026-06-14T08:21:56.696928900Z","Q06","Unpartitioned full table baseline","measured",2,13,1,1,true,""
-"2026-06-14T08:21:56.710938300Z","Q06","Unpartitioned full table baseline","measured",3,13,1,1,true,""
-"2026-06-14T08:21:56.725938900Z","Q06","Unpartitioned full table baseline","measured",4,13,1,1,true,""
-"2026-06-14T08:21:56.741209200Z","Q06","Unpartitioned full table baseline","measured",5,13,1,1,true,""
-"2026-06-14T08:21:56.817224800Z","Q07","Unpartitioned partition by year","warmup",1,78,1,1,true,""
-"2026-06-14T08:21:56.835191500Z","Q07","Unpartitioned partition by year","measured",1,14,1,1,true,""
-"2026-06-14T08:21:56.849236100Z","Q07","Unpartitioned partition by year","measured",2,14,1,1,true,""
-"2026-06-14T08:21:56.865149800Z","Q07","Unpartitioned partition by year","measured",3,14,1,1,true,""
-"2026-06-14T08:21:56.881047500Z","Q07","Unpartitioned partition by year","measured",4,13,1,1,true,""
-"2026-06-14T08:21:56.897100900Z","Q07","Unpartitioned partition by year","measured",5,14,1,1,true,""
-"2026-06-14T08:21:56.977498100Z","Q08","Unpartitioned partition by month","warmup",1,87,1,1,true,""
-"2026-06-14T08:21:57.002151400Z","Q08","Unpartitioned partition by month","measured",1,15,1,1,true,""
-"2026-06-14T08:21:57.015868300Z","Q08","Unpartitioned partition by month","measured",2,16,1,1,true,""
-"2026-06-14T08:21:57.035757600Z","Q08","Unpartitioned partition by month","measured",3,17,1,1,true,""
-"2026-06-14T08:21:57.051851900Z","Q08","Unpartitioned partition by month","measured",4,15,1,1,true,""
-"2026-06-14T08:21:57.072529300Z","Q08","Unpartitioned partition by month","measured",5,16,1,1,true,""
-"2026-06-14T08:21:57.382211400Z","Q09","Unpartitioned filtered count, full scan","warmup",1,307,1,1,true,""
-"2026-06-14T08:21:57.679738600Z","Q09","Unpartitioned filtered count, full scan","measured",1,291,1,1,true,""
-"2026-06-14T08:21:57.914067600Z","Q09","Unpartitioned filtered count, full scan","measured",2,228,1,1,true,""
-"2026-06-14T08:21:58.179423700Z","Q09","Unpartitioned filtered count, full scan","measured",3,262,1,1,true,""
-"2026-06-14T08:21:58.428230400Z","Q09","Unpartitioned filtered count, full scan","measured",4,245,1,1,true,""
-"2026-06-14T08:21:58.704018300Z","Q09","Unpartitioned filtered count, full scan","measured",5,274,1,1,true,""
-"2026-06-14T08:21:58.933195600Z","Q10","Unpartitioned filtered count, partition by year","warmup",1,223,1,1,true,""
-"2026-06-14T08:21:59.032699600Z","Q10","Unpartitioned filtered count, partition by year","measured",1,98,1,1,true,""
-"2026-06-14T08:21:59.123420800Z","Q10","Unpartitioned filtered count, partition by year","measured",2,90,1,1,true,""
-"2026-06-14T08:21:59.208227800Z","Q10","Unpartitioned filtered count, partition by year","measured",3,83,1,1,true,""
-"2026-06-14T08:21:59.280231100Z","Q10","Unpartitioned filtered count, partition by year","measured",4,71,1,1,true,""
-"2026-06-14T08:21:59.342796400Z","Q10","Unpartitioned filtered count, partition by year","measured",5,62,1,1,true,""
+"2026-06-18T19:48:23.091987Z","Q1_P","Repartitioned full table baseline","warmup",1,84,1,1,true,""
+"2026-06-18T19:48:23.101094200Z","Q1_P","Repartitioned full table baseline","measured",1,8,1,1,true,""
+"2026-06-18T19:48:23.111347500Z","Q1_P","Repartitioned full table baseline","measured",2,8,1,1,true,""
+"2026-06-18T19:48:23.118789900Z","Q1_P","Repartitioned full table baseline","measured",3,7,1,1,true,""
+"2026-06-18T19:48:23.127250100Z","Q1_P","Repartitioned full table baseline","measured",4,7,1,1,true,""
+"2026-06-18T19:48:23.135604400Z","Q1_P","Repartitioned full table baseline","measured",5,6,1,1,true,""
+"2026-06-18T19:48:23.143591300Z","Q2_P","Repartitioned partition by year","warmup",1,10,1,1,true,""
+"2026-06-18T19:48:23.154089900Z","Q2_P","Repartitioned partition by year","measured",1,8,1,1,true,""
+"2026-06-18T19:48:23.159389900Z","Q2_P","Repartitioned partition by year","measured",2,6,1,1,true,""
+"2026-06-18T19:48:23.164515900Z","Q2_P","Repartitioned partition by year","measured",3,6,1,1,true,""
+"2026-06-18T19:48:23.175208700Z","Q2_P","Repartitioned partition by year","measured",4,7,1,1,true,""
+"2026-06-18T19:48:23.181000700Z","Q2_P","Repartitioned partition by year","measured",5,6,1,1,true,""
+"2026-06-18T19:48:23.190587600Z","Q3_P","Repartitioned partition by month","warmup",1,9,1,1,true,""
+"2026-06-18T19:48:23.201151600Z","Q3_P","Repartitioned partition by month","measured",1,7,1,1,true,""
+"2026-06-18T19:48:23.206632200Z","Q3_P","Repartitioned partition by month","measured",2,9,1,1,true,""
+"2026-06-18T19:48:23.218744300Z","Q3_P","Repartitioned partition by month","measured",3,9,1,1,true,""
+"2026-06-18T19:48:23.226843300Z","Q3_P","Repartitioned partition by month","measured",4,10,1,1,true,""
+"2026-06-18T19:48:23.237276600Z","Q3_P","Repartitioned partition by month","measured",5,8,1,1,true,""
+"2026-06-18T19:48:23.401972900Z","Q4_P","Repartitioned filtered count, full scan","warmup",1,161,1,1,true,""
+"2026-06-18T19:48:23.540192800Z","Q4_P","Repartitioned filtered count, full scan","measured",1,137,1,1,true,""
+"2026-06-18T19:48:23.673994300Z","Q4_P","Repartitioned filtered count, full scan","measured",2,133,1,1,true,""
+"2026-06-18T19:48:23.833697700Z","Q4_P","Repartitioned filtered count, full scan","measured",3,157,1,1,true,""
+"2026-06-18T19:48:23.989901300Z","Q4_P","Repartitioned filtered count, full scan","measured",4,155,1,1,true,""
+"2026-06-18T19:48:24.159242600Z","Q4_P","Repartitioned filtered count, full scan","measured",5,168,1,1,true,""
+"2026-06-18T19:48:24.220792700Z","Q5_P","Repartitioned filtered count, partition by year","warmup",1,60,1,1,true,""
+"2026-06-18T19:48:24.282328100Z","Q5_P","Repartitioned filtered count, partition by year","measured",1,60,1,1,true,""
+"2026-06-18T19:48:24.338283500Z","Q5_P","Repartitioned filtered count, partition by year","measured",2,53,1,1,true,""
+"2026-06-18T19:48:24.392640400Z","Q5_P","Repartitioned filtered count, partition by year","measured",3,53,1,1,true,""
+"2026-06-18T19:48:24.448968700Z","Q5_P","Repartitioned filtered count, partition by year","measured",4,54,1,1,true,""
+"2026-06-18T19:48:24.502612300Z","Q5_P","Repartitioned filtered count, partition by year","measured",5,53,1,1,true,""
+"2026-06-18T19:48:24.513874900Z","Q1_NP","Unpartitioned full table baseline","warmup",1,9,1,1,true,""
+"2026-06-18T19:48:24.525423Z","Q1_NP","Unpartitioned full table baseline","measured",1,10,1,1,true,""
+"2026-06-18T19:48:24.535423800Z","Q1_NP","Unpartitioned full table baseline","measured",2,8,1,1,true,""
+"2026-06-18T19:48:24.546958300Z","Q1_NP","Unpartitioned full table baseline","measured",3,11,1,1,true,""
+"2026-06-18T19:48:24.558740600Z","Q1_NP","Unpartitioned full table baseline","measured",4,9,1,1,true,""
+"2026-06-18T19:48:24.569759700Z","Q1_NP","Unpartitioned full table baseline","measured",5,10,1,1,true,""
+"2026-06-18T19:48:24.581758400Z","Q2_NP","Unpartitioned partition by year","warmup",1,10,1,1,true,""
+"2026-06-18T19:48:24.593947600Z","Q2_NP","Unpartitioned partition by year","measured",1,11,1,1,true,""
+"2026-06-18T19:48:24.604418400Z","Q2_NP","Unpartitioned partition by year","measured",2,9,1,1,true,""
+"2026-06-18T19:48:24.617433400Z","Q2_NP","Unpartitioned partition by year","measured",3,11,1,1,true,""
+"2026-06-18T19:48:24.627965900Z","Q2_NP","Unpartitioned partition by year","measured",4,9,1,1,true,""
+"2026-06-18T19:48:24.637958900Z","Q2_NP","Unpartitioned partition by year","measured",5,8,1,1,true,""
+"2026-06-18T19:48:24.650953400Z","Q3_NP","Unpartitioned partition by month","warmup",1,11,1,1,true,""
+"2026-06-18T19:48:24.661481Z","Q3_NP","Unpartitioned partition by month","measured",1,9,1,1,true,""
+"2026-06-18T19:48:24.672775700Z","Q3_NP","Unpartitioned partition by month","measured",2,10,1,1,true,""
+"2026-06-18T19:48:24.680773900Z","Q3_NP","Unpartitioned partition by month","measured",3,7,1,1,true,""
+"2026-06-18T19:48:24.693774500Z","Q3_NP","Unpartitioned partition by month","measured",4,11,1,1,true,""
+"2026-06-18T19:48:24.706787800Z","Q3_NP","Unpartitioned partition by month","measured",5,12,1,1,true,""
+"2026-06-18T19:48:24.838980300Z","Q4_NP","Unpartitioned filtered count, full scan","warmup",1,130,1,1,true,""
+"2026-06-18T19:48:24.973117600Z","Q4_NP","Unpartitioned filtered count, full scan","measured",1,133,1,1,true,""
+"2026-06-18T19:48:25.104600500Z","Q4_NP","Unpartitioned filtered count, full scan","measured",2,130,1,1,true,""
+"2026-06-18T19:48:25.216567300Z","Q4_NP","Unpartitioned filtered count, full scan","measured",3,110,1,1,true,""
+"2026-06-18T19:48:25.347208900Z","Q4_NP","Unpartitioned filtered count, full scan","measured",4,131,1,1,true,""
+"2026-06-18T19:48:25.500376300Z","Q4_NP","Unpartitioned filtered count, full scan","measured",5,152,1,1,true,""
+"2026-06-18T19:48:25.560658600Z","Q5_NP","Unpartitioned filtered count, partition by year","warmup",1,60,1,1,true,""
+"2026-06-18T19:48:25.635779Z","Q5_NP","Unpartitioned filtered count, partition by year","measured",1,71,1,1,true,""
+"2026-06-18T19:48:25.694812400Z","Q5_NP","Unpartitioned filtered count, partition by year","measured",2,59,1,1,true,""
+"2026-06-18T19:48:25.757000300Z","Q5_NP","Unpartitioned filtered count, partition by year","measured",3,58,1,1,true,""
+"2026-06-18T19:48:25.821822700Z","Q5_NP","Unpartitioned filtered count, partition by year","measured",4,64,1,1,true,""
+"2026-06-18T19:48:25.879809300Z","Q5_NP","Unpartitioned filtered count, partition by year","measured",5,56,1,1,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_results_analysis.md b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_results_analysis.md
index 91b8f6c2f2..9edc106a81 100644
--- a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_results_analysis.md
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_results_analysis.md
@@ -6,35 +6,71 @@ Source summary:
 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
 ```
 
-The partitioning run completed successfully for all systems. Every query has
-`5/5` successful measured runs, and all queries returned one result row.
+The partitioning benchmark completed successfully for all evaluated systems.
+All measured queries have `5/5` successful runs, and every query returned one
+aggregate result row.
+
+## Query Groups
+
+The benchmark uses five logical query groups. Each query is executed on the
+repartitioned Hive-style layout and on the equivalent unpartitioned layout:
+
+- `RP`: repartitioned layout with `year` and `month` represented by directory partitions
+- `UP`: unpartitioned layout with `year` and `month` stored as physical columns
+
+Q1-Q3 run on `yellow_tripdata`. Q4-Q5 run on `green_tripdata`.
+
+## Mean Runtime Overview
+
+All values are mean measured runtimes in milliseconds.
+
+| Query Pair | Operation | Polypheny | DuckDB | Spark |
+| --- | --- | ---: | ---: | ---: |
+| Q1_P / Q1_NP | Full count, repartitioned / unpartitioned | 7.2 / 9.6 | 78.8 / 59.6 | 1681.8 / 1189.6 |
+| Q2_P / Q2_NP | Year predicate, repartitioned / unpartitioned | 6.6 / 9.6 | 35.2 / 88.4 | 641.8 / 2123.2 |
+| Q3_P / Q3_NP | Year and month predicate, repartitioned / unpartitioned | 8.6 / 9.8 | 17.0 / 72.2 | 450.6 / 1326.2 |
+| Q4_P / Q4_NP | Data-column filter, repartitioned / unpartitioned | 150.0 / 131.2 | 30.2 / 20.6 | 1693.0 / 902.6 |
+| Q5_P / Q5_NP | Year plus data-column filter, repartitioned / unpartitioned | 54.6 / 61.6 | 17.4 / 13.6 | 637.8 / 1035.4 |
 
 ## Main Findings
 
-DuckDB and Spark benefit clearly from the repartitioned layout on partition
-predicates. DuckDB improves from 85.8 ms to 63.4 ms for the year filter and from
-74.4 ms to 29.8 ms for the month filter. Spark improves from 1978.8 ms to
-705.4 ms for the year filter and from 1054.2 ms to 426.4 ms for the month
-filter.
+DuckDB and Spark show the clearest benefit from Hive-style partition pruning
+when the predicates match the partition layout. DuckDB improves from 78.8 ms
+for the repartitioned full-count baseline to 35.2 ms with a year predicate and
+17.0 ms with a year-and-month predicate. Spark improves from 1681.8 ms to
+641.8 ms and then to 450.6 ms for the same query sequence.
+
+Polypheny is very fast for the simple count queries in both layouts. The
+repartitioned simple count queries Q1_P-Q3_P run between 6.6 ms and 8.6 ms,
+while the corresponding unpartitioned queries Q1_NP-Q3_NP run between 9.6 ms
+and 9.8 ms. The partitioning effect is therefore present but small in absolute
+terms for these metadata-friendly count queries.
 
-Polypheny is fastest for simple count queries in both layouts. The repartitioned
-count queries Q01-Q03 run around 16.8-19.6 ms, and the unpartitioned count
-queries Q06-Q08 run around 13.2-15.8 ms.
+The clearest Polypheny partitioning signal appears in the filtered-count pair
+over `green_tripdata`. Adding the year partition predicate reduces the
+repartitioned query from 150.0 ms in Q4_P to 54.6 ms in Q5_P, a 2.75x
+improvement. DuckDB improves from 30.2 ms to 17.4 ms, and Spark improves from
+1693.0 ms to 637.8 ms for the same repartitioned query pair.
 
-DuckDB is fastest for the filtered count queries over `green_tripdata`. This is
-true for both repartitioned and unpartitioned layouts.
+DuckDB is fastest for the data-column filtered count queries. Polypheny is
+fastest for the simple count queries, where the counts are computed with very
+low overhead.
 
 ## Interpretation
 
-The partitioning effect is most visible for DuckDB and Spark because their
-runtime changes substantially when `year` and `month` can be used as partition
-columns.
+Partitioning helps most when the query predicate matches the directory layout.
+This is visible in the paired year and year-month comparisons: Q2_P is faster
+than Q2_NP for all systems, and Q3_P is faster than Q3_NP for all systems.
+The effect is strongest for DuckDB and Spark and smaller for Polypheny because
+Polypheny's count queries are already inexpensive.
 
-For Polypheny, simple count queries appear metadata-like and are already very
-cheap, so partition pruning is not strongly visible there. The stronger signal
-is the filtered count pair: repartitioned Q04 takes 279.8 ms, while
-partition-constrained Q05 takes 84.6 ms.
+Partitioning is not universally faster. When no partition predicate is used,
+the unpartitioned layout is faster for DuckDB and Spark on the full-count
+baseline, while Polypheny is slightly faster on the repartitioned layout. For
+the data-column-only filter Q4, the unpartitioned layout is faster for all
+systems because no Hive-style partition pruning can be applied.
 
-Full-table counts are slightly faster on the unpartitioned layout for all
-systems, which is expected when no partition pruning is possible and the
-partitioned directory layout adds overhead.
+For Q5, the repartitioned layout benefits from the year partition predicate in
+Polypheny and Spark. DuckDB remains faster on the unpartitioned version of this
+specific query, which indicates that the benefit of pruning can be outweighed by
+layout and execution overhead for small or already selective workloads.
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_repartitioned_results.csv b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_repartitioned_results.csv
index bbe8df8728..a7ce3afd34 100644
--- a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_repartitioned_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_repartitioned_results.csv
@@ -1,31 +1,31 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-2026-06-14T08:23:44.772895Z,Q01,Repartitioned full table baseline,warmup,1,5950,1,1,true,
-2026-06-14T08:23:46.622161Z,Q01,Repartitioned full table baseline,measured,1,1848,1,1,true,
-2026-06-14T08:23:48.453592Z,Q01,Repartitioned full table baseline,measured,2,1830,1,1,true,
-2026-06-14T08:23:50.120659Z,Q01,Repartitioned full table baseline,measured,3,1666,1,1,true,
-2026-06-14T08:23:51.350358Z,Q01,Repartitioned full table baseline,measured,4,1219,1,1,true,
-2026-06-14T08:23:52.639312Z,Q01,Repartitioned full table baseline,measured,5,1287,1,1,true,
-2026-06-14T08:23:53.714761Z,Q02,Repartitioned partition by year,warmup,1,1018,1,1,true,
-2026-06-14T08:23:54.508928Z,Q02,Repartitioned partition by year,measured,1,792,1,1,true,
-2026-06-14T08:23:55.265645Z,Q02,Repartitioned partition by year,measured,2,755,1,1,true,
-2026-06-14T08:23:55.998682Z,Q02,Repartitioned partition by year,measured,3,731,1,1,true,
-2026-06-14T08:23:56.617615Z,Q02,Repartitioned partition by year,measured,4,618,1,1,true,
-2026-06-14T08:23:57.250702Z,Q02,Repartitioned partition by year,measured,5,631,1,1,true,
-2026-06-14T08:23:57.875402Z,Q03,Repartitioned partition by month,warmup,1,622,1,1,true,
-2026-06-14T08:23:58.406162Z,Q03,Repartitioned partition by month,measured,1,529,1,1,true,
-2026-06-14T08:23:58.816122Z,Q03,Repartitioned partition by month,measured,2,408,1,1,true,
-2026-06-14T08:23:59.217964Z,Q03,Repartitioned partition by month,measured,3,400,1,1,true,
-2026-06-14T08:23:59.621444Z,Q03,Repartitioned partition by month,measured,4,402,1,1,true,
-2026-06-14T08:24:00.015666Z,Q03,Repartitioned partition by month,measured,5,393,1,1,true,
-2026-06-14T08:24:02.806021Z,Q04,"Repartitioned filtered count, full scan",warmup,1,2786,1,1,true,
-2026-06-14T08:24:04.713989Z,Q04,"Repartitioned filtered count, full scan",measured,1,1907,1,1,true,
-2026-06-14T08:24:06.553719Z,Q04,"Repartitioned filtered count, full scan",measured,2,1838,1,1,true,
-2026-06-14T08:24:08.468095Z,Q04,"Repartitioned filtered count, full scan",measured,3,1913,1,1,true,
-2026-06-14T08:24:10.011627Z,Q04,"Repartitioned filtered count, full scan",measured,4,1542,1,1,true,
-2026-06-14T08:24:11.594315Z,Q04,"Repartitioned filtered count, full scan",measured,5,1581,1,1,true,
-2026-06-14T08:24:12.301062Z,Q05,"Repartitioned filtered count, partition by year",warmup,1,704,1,1,true,
-2026-06-14T08:24:13.312396Z,Q05,"Repartitioned filtered count, partition by year",measured,1,1010,1,1,true,
-2026-06-14T08:24:14.695225Z,Q05,"Repartitioned filtered count, partition by year",measured,2,1381,1,1,true,
-2026-06-14T08:24:15.412474Z,Q05,"Repartitioned filtered count, partition by year",measured,3,716,1,1,true,
-2026-06-14T08:24:15.970896Z,Q05,"Repartitioned filtered count, partition by year",measured,4,557,1,1,true,
-2026-06-14T08:24:16.696270Z,Q05,"Repartitioned filtered count, partition by year",measured,5,723,1,1,true,
+2026-06-18T19:49:42.208390Z,Q1_P,Repartitioned full table baseline,warmup,1,4616,1,1,true,
+2026-06-18T19:49:44.247584Z,Q1_P,Repartitioned full table baseline,measured,1,2037,1,1,true,
+2026-06-18T19:49:45.919953Z,Q1_P,Repartitioned full table baseline,measured,2,1670,1,1,true,
+2026-06-18T19:49:47.451548Z,Q1_P,Repartitioned full table baseline,measured,3,1530,1,1,true,
+2026-06-18T19:49:49.042072Z,Q1_P,Repartitioned full table baseline,measured,4,1589,1,1,true,
+2026-06-18T19:49:50.627587Z,Q1_P,Repartitioned full table baseline,measured,5,1583,1,1,true,
+2026-06-18T19:49:52.015322Z,Q2_P,Repartitioned partition by year,warmup,1,1384,1,1,true,
+2026-06-18T19:49:52.698227Z,Q2_P,Repartitioned partition by year,measured,1,682,1,1,true,
+2026-06-18T19:49:53.466217Z,Q2_P,Repartitioned partition by year,measured,2,767,1,1,true,
+2026-06-18T19:49:54.059995Z,Q2_P,Repartitioned partition by year,measured,3,592,1,1,true,
+2026-06-18T19:49:54.643714Z,Q2_P,Repartitioned partition by year,measured,4,582,1,1,true,
+2026-06-18T19:49:55.230687Z,Q2_P,Repartitioned partition by year,measured,5,586,1,1,true,
+2026-06-18T19:49:55.615426Z,Q3_P,Repartitioned partition by month,warmup,1,382,1,1,true,
+2026-06-18T19:49:56.018781Z,Q3_P,Repartitioned partition by month,measured,1,402,1,1,true,
+2026-06-18T19:49:56.375845Z,Q3_P,Repartitioned partition by month,measured,2,355,1,1,true,
+2026-06-18T19:49:56.868018Z,Q3_P,Repartitioned partition by month,measured,3,490,1,1,true,
+2026-06-18T19:49:57.437306Z,Q3_P,Repartitioned partition by month,measured,4,563,1,1,true,
+2026-06-18T19:49:57.882309Z,Q3_P,Repartitioned partition by month,measured,5,443,1,1,true,
+2026-06-18T19:50:01.213310Z,Q4_P,"Repartitioned filtered count, full scan",warmup,1,3329,1,1,true,
+2026-06-18T19:50:02.821014Z,Q4_P,"Repartitioned filtered count, full scan",measured,1,1606,1,1,true,
+2026-06-18T19:50:04.694987Z,Q4_P,"Repartitioned filtered count, full scan",measured,2,1873,1,1,true,
+2026-06-18T19:50:08.649321Z,Q4_P,"Repartitioned filtered count, full scan",measured,3,1501,1,1,true,
+2026-06-18T19:50:10.420282Z,Q4_P,"Repartitioned filtered count, full scan",measured,4,1770,1,1,true,
+2026-06-18T19:50:12.136595Z,Q4_P,"Repartitioned filtered count, full scan",measured,5,1715,1,1,true,
+2026-06-18T19:50:12.946622Z,Q5_P,"Repartitioned filtered count, partition by year",warmup,1,807,1,1,true,
+2026-06-18T19:50:13.684379Z,Q5_P,"Repartitioned filtered count, partition by year",measured,1,736,1,1,true,
+2026-06-18T19:50:14.395795Z,Q5_P,"Repartitioned filtered count, partition by year",measured,2,710,1,1,true,
+2026-06-18T19:50:15.078524Z,Q5_P,"Repartitioned filtered count, partition by year",measured,3,681,1,1,true,
+2026-06-18T19:50:15.639609Z,Q5_P,"Repartitioned filtered count, partition by year",measured,4,560,1,1,true,
+2026-06-18T19:50:16.143143Z,Q5_P,"Repartitioned filtered count, partition by year",measured,5,502,1,1,true,
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_unpartitioned_results.csv b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_unpartitioned_results.csv
index 5ac397498e..dbf8ac7b7c 100644
--- a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_unpartitioned_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_unpartitioned_results.csv
@@ -1,31 +1,31 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-2026-06-14T08:24:16.197698Z,Q06,Unpartitioned full table baseline,warmup,1,5148,1,1,true,
-2026-06-14T08:24:17.916656Z,Q06,Unpartitioned full table baseline,measured,1,1717,1,1,true,
-2026-06-14T08:24:19.799721Z,Q06,Unpartitioned full table baseline,measured,2,1878,1,1,true,
-2026-06-14T08:24:20.839271Z,Q06,Unpartitioned full table baseline,measured,3,1037,1,1,true,
-2026-06-14T08:24:22.046981Z,Q06,Unpartitioned full table baseline,measured,4,1197,1,1,true,
-2026-06-14T08:24:23.095196Z,Q06,Unpartitioned full table baseline,measured,5,1047,1,1,true,
-2026-06-14T08:24:26.347347Z,Q07,Unpartitioned partition by year,warmup,1,3195,1,1,true,
-2026-06-14T08:24:28.247837Z,Q07,Unpartitioned partition by year,measured,1,1898,1,1,true,
-2026-06-14T08:24:30.428185Z,Q07,Unpartitioned partition by year,measured,2,2179,1,1,true,
-2026-06-14T08:24:32.652966Z,Q07,Unpartitioned partition by year,measured,3,2224,1,1,true,
-2026-06-14T08:24:34.377067Z,Q07,Unpartitioned partition by year,measured,4,1723,1,1,true,
-2026-06-14T08:24:36.248432Z,Q07,Unpartitioned partition by year,measured,5,1870,1,1,true,
-2026-06-14T08:24:37.549694Z,Q08,Unpartitioned partition by month,warmup,1,1298,1,1,true,
-2026-06-14T08:24:38.750059Z,Q08,Unpartitioned partition by month,measured,1,1199,1,1,true,
-2026-06-14T08:24:39.853595Z,Q08,Unpartitioned partition by month,measured,2,1102,1,1,true,
-2026-06-14T08:24:40.853512Z,Q08,Unpartitioned partition by month,measured,3,999,1,1,true,
-2026-06-14T08:24:41.805078Z,Q08,Unpartitioned partition by month,measured,4,950,1,1,true,
-2026-06-14T08:24:42.826969Z,Q08,Unpartitioned partition by month,measured,5,1021,1,1,true,
-2026-06-14T08:24:44.352110Z,Q09,"Unpartitioned filtered count, full scan",warmup,1,1522,1,1,true,
-2026-06-14T08:24:45.506055Z,Q09,"Unpartitioned filtered count, full scan",measured,1,1152,1,1,true,
-2026-06-14T08:24:46.322848Z,Q09,"Unpartitioned filtered count, full scan",measured,2,815,1,1,true,
-2026-06-14T08:24:47.041329Z,Q09,"Unpartitioned filtered count, full scan",measured,3,717,1,1,true,
-2026-06-14T08:24:47.880424Z,Q09,"Unpartitioned filtered count, full scan",measured,4,837,1,1,true,
-2026-06-14T08:24:48.889131Z,Q09,"Unpartitioned filtered count, full scan",measured,5,1007,1,1,true,
-2026-06-14T08:24:49.863963Z,Q10,"Unpartitioned filtered count, partition by year",warmup,1,971,1,1,true,
-2026-06-14T08:24:50.720219Z,Q10,"Unpartitioned filtered count, partition by year",measured,1,855,1,1,true,
-2026-06-14T08:24:51.501215Z,Q10,"Unpartitioned filtered count, partition by year",measured,2,780,1,1,true,
-2026-06-14T08:24:52.174468Z,Q10,"Unpartitioned filtered count, partition by year",measured,3,672,1,1,true,
-2026-06-14T08:24:52.793530Z,Q10,"Unpartitioned filtered count, partition by year",measured,4,618,1,1,true,
-2026-06-14T08:24:53.447557Z,Q10,"Unpartitioned filtered count, partition by year",measured,5,615,1,1,true,
+2026-06-18T19:51:21.148637Z,Q1_NP,Unpartitioned full table baseline,warmup,1,4574,1,1,true,
+2026-06-18T19:51:22.568270Z,Q1_NP,Unpartitioned full table baseline,measured,1,1418,1,1,true,
+2026-06-18T19:51:23.590696Z,Q1_NP,Unpartitioned full table baseline,measured,2,1021,1,1,true,
+2026-06-18T19:51:24.808551Z,Q1_NP,Unpartitioned full table baseline,measured,3,1217,1,1,true,
+2026-06-18T19:51:26.128584Z,Q1_NP,Unpartitioned full table baseline,measured,4,1319,1,1,true,
+2026-06-18T19:51:27.103187Z,Q1_NP,Unpartitioned full table baseline,measured,5,973,1,1,true,
+2026-06-18T19:51:31.303359Z,Q2_NP,Unpartitioned partition by year,warmup,1,4197,1,1,true,
+2026-06-18T19:51:33.578528Z,Q2_NP,Unpartitioned partition by year,measured,1,2273,1,1,true,
+2026-06-18T19:51:35.703028Z,Q2_NP,Unpartitioned partition by year,measured,2,2124,1,1,true,
+2026-06-18T19:51:37.819740Z,Q2_NP,Unpartitioned partition by year,measured,3,2115,1,1,true,
+2026-06-18T19:51:39.835658Z,Q2_NP,Unpartitioned partition by year,measured,4,2015,1,1,true,
+2026-06-18T19:51:41.926270Z,Q2_NP,Unpartitioned partition by year,measured,5,2089,1,1,true,
+2026-06-18T19:51:45.987358Z,Q3_NP,Unpartitioned partition by month,warmup,1,1575,1,1,true,
+2026-06-18T19:51:47.181939Z,Q3_NP,Unpartitioned partition by month,measured,1,1193,1,1,true,
+2026-06-18T19:51:48.526311Z,Q3_NP,Unpartitioned partition by month,measured,2,1343,1,1,true,
+2026-06-18T19:51:49.993587Z,Q3_NP,Unpartitioned partition by month,measured,3,1465,1,1,true,
+2026-06-18T19:51:51.268217Z,Q3_NP,Unpartitioned partition by month,measured,4,1273,1,1,true,
+2026-06-18T19:51:52.626166Z,Q3_NP,Unpartitioned partition by month,measured,5,1357,1,1,true,
+2026-06-18T19:51:53.962714Z,Q4_NP,"Unpartitioned filtered count, full scan",warmup,1,1334,1,1,true,
+2026-06-18T19:51:55.117630Z,Q4_NP,"Unpartitioned filtered count, full scan",measured,1,1153,1,1,true,
+2026-06-18T19:51:55.994943Z,Q4_NP,"Unpartitioned filtered count, full scan",measured,2,876,1,1,true,
+2026-06-18T19:51:56.867285Z,Q4_NP,"Unpartitioned filtered count, full scan",measured,3,871,1,1,true,
+2026-06-18T19:51:57.701028Z,Q4_NP,"Unpartitioned filtered count, full scan",measured,4,833,1,1,true,
+2026-06-18T19:51:58.482386Z,Q4_NP,"Unpartitioned filtered count, full scan",measured,5,780,1,1,true,
+2026-06-18T19:52:00.030993Z,Q5_NP,"Unpartitioned filtered count, partition by year",warmup,1,1546,1,1,true,
+2026-06-18T19:52:00.987164Z,Q5_NP,"Unpartitioned filtered count, partition by year",measured,1,955,1,1,true,
+2026-06-18T19:52:02.185426Z,Q5_NP,"Unpartitioned filtered count, partition by year",measured,2,1197,1,1,true,
+2026-06-18T19:52:03.187505Z,Q5_NP,"Unpartitioned filtered count, partition by year",measured,3,1001,1,1,true,
+2026-06-18T19:52:04.257842Z,Q5_NP,"Unpartitioned filtered count, partition by year",measured,4,1069,1,1,true,
+2026-06-18T19:52:05.214857Z,Q5_NP,"Unpartitioned filtered count, partition by year",measured,5,955,1,1,true,
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
index 3ef49f4d1a..6a4cb9928b 100644
--- a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
@@ -1,79 +1,79 @@
 # Partitioning Summary
 
 Phase summarized: `measured`.
-Warmup rows are excluded. Mean and median values use successful runs only.
+Warmup rows are excluded. Mean, median, and standard deviation values use successful runs only.
 
 ## Source Files
 
-| System                     | CSV                                                                                                     |
-|----------------------------|---------------------------------------------------------------------------------------------------------|
-| Polypheny Relational       | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_polypheny_results.csv`            |
-| DuckDB Repartitioned       | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_repartitioned_results.csv` |
-| DuckDB Unpartitioned       | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_unpartitioned_results.csv` |
-| Apache Spark Repartitioned | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_repartitioned_results.csv`  |
-| Apache Spark Unpartitioned | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_unpartitioned_results.csv`  |
+| System | CSV |
+| --- | --- |
+| Polypheny Relational | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_polypheny_results.csv` |
+| DuckDB Repartitioned | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_repartitioned_results.csv` |
+| DuckDB Unpartitioned | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_unpartitioned_results.csv` |
+| Apache Spark Repartitioned | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_repartitioned_results.csv` |
+| Apache Spark Unpartitioned | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_unpartitioned_results.csv` |
 
 ## Mean Elapsed Time (ms)
 
-| Query | Description                                     | Polypheny Relational | DuckDB Repartitioned | DuckDB Unpartitioned | Apache Spark Repartitioned | Apache Spark Unpartitioned | Row counts |
-|-------|-------------------------------------------------|----------------------|----------------------|----------------------|----------------------------|----------------------------|------------|
-| Q01   | Repartitioned full table baseline               | 19.0                 | 83.2                 |                      | 1,570.0                    |                            | 1          |
-| Q02   | Repartitioned partition by year                 | 16.8                 | 63.4                 |                      | 705.4                      |                            | 1          |
-| Q03   | Repartitioned partition by month                | 19.6                 | 29.8                 |                      | 426.4                      |                            | 1          |
-| Q04   | Repartitioned filtered count, full scan         | 279.8                | 41.8                 |                      | 1,756.2                    |                            | 1          |
-| Q05   | Repartitioned filtered count, partition by year | 84.6                 | 22.6                 |                      | 877.4                      |                            | 1          |
-| Q06   | Unpartitioned full table baseline               | 13.2                 |                      | 58.0                 |                            | 1,375.2                    | 1          |
-| Q07   | Unpartitioned partition by year                 | 13.8                 |                      | 85.8                 |                            | 1,978.8                    | 1          |
-| Q08   | Unpartitioned partition by month                | 15.8                 |                      | 74.4                 |                            | 1,054.2                    | 1          |
-| Q09   | Unpartitioned filtered count, full scan         | 260.0                |                      | 21.2                 |                            | 905.6                      | 1          |
-| Q10   | Unpartitioned filtered count, partition by year | 80.8                 |                      | 17.6                 |                            | 708.0                      | 1          |
+| Query | Description | Polypheny Relational | DuckDB Repartitioned | DuckDB Unpartitioned | Apache Spark Repartitioned | Apache Spark Unpartitioned | Row counts |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| Q1_P | Repartitioned full table baseline | 7.2 | 78.8 |  | 1,681.8 |  | 1 |
+| Q1_NP | Unpartitioned full table baseline | 9.6 |  | 59.6 |  | 1,189.6 | 1 |
+| Q2_P | Repartitioned partition by year | 6.6 | 35.2 |  | 641.8 |  | 1 |
+| Q2_NP | Unpartitioned partition by year | 9.6 |  | 88.4 |  | 2,123.2 | 1 |
+| Q3_P | Repartitioned partition by month | 8.6 | 17.0 |  | 450.6 |  | 1 |
+| Q3_NP | Unpartitioned partition by month | 9.8 |  | 72.2 |  | 1,326.2 | 1 |
+| Q4_P | Repartitioned filtered count, full scan | 150.0 | 30.2 |  | 1,693.0 |  | 1 |
+| Q4_NP | Unpartitioned filtered count, full scan | 131.2 |  | 20.6 |  | 902.6 | 1 |
+| Q5_P | Repartitioned filtered count, partition by year | 54.6 | 17.4 |  | 637.8 |  | 1 |
+| Q5_NP | Unpartitioned filtered count, partition by year | 61.6 |  | 13.6 |  | 1,035.4 | 1 |
 
 ## Result Row Counts
 
-| Query | Description                                     | Polypheny Relational | DuckDB Repartitioned | DuckDB Unpartitioned | Apache Spark Repartitioned | Apache Spark Unpartitioned |
-|-------|-------------------------------------------------|----------------------|----------------------|----------------------|----------------------------|----------------------------|
-| Q01   | Repartitioned full table baseline               | 1                    | 1                    |                      | 1                          |                            |
-| Q02   | Repartitioned partition by year                 | 1                    | 1                    |                      | 1                          |                            |
-| Q03   | Repartitioned partition by month                | 1                    | 1                    |                      | 1                          |                            |
-| Q04   | Repartitioned filtered count, full scan         | 1                    | 1                    |                      | 1                          |                            |
-| Q05   | Repartitioned filtered count, partition by year | 1                    | 1                    |                      | 1                          |                            |
-| Q06   | Unpartitioned full table baseline               | 1                    |                      | 1                    |                            | 1                          |
-| Q07   | Unpartitioned partition by year                 | 1                    |                      | 1                    |                            | 1                          |
-| Q08   | Unpartitioned partition by month                | 1                    |                      | 1                    |                            | 1                          |
-| Q09   | Unpartitioned filtered count, full scan         | 1                    |                      | 1                    |                            | 1                          |
-| Q10   | Unpartitioned filtered count, partition by year | 1                    |                      | 1                    |                            | 1                          |
+| Query | Description | Polypheny Relational | DuckDB Repartitioned | DuckDB Unpartitioned | Apache Spark Repartitioned | Apache Spark Unpartitioned |
+| --- | --- | --- | --- | --- | --- | --- |
+| Q1_P | Repartitioned full table baseline | 1 | 1 |  | 1 |  |
+| Q1_NP | Unpartitioned full table baseline | 1 |  | 1 |  | 1 |
+| Q2_P | Repartitioned partition by year | 1 | 1 |  | 1 |  |
+| Q2_NP | Unpartitioned partition by year | 1 |  | 1 |  | 1 |
+| Q3_P | Repartitioned partition by month | 1 | 1 |  | 1 |  |
+| Q3_NP | Unpartitioned partition by month | 1 |  | 1 |  | 1 |
+| Q4_P | Repartitioned filtered count, full scan | 1 | 1 |  | 1 |  |
+| Q4_NP | Unpartitioned filtered count, full scan | 1 |  | 1 |  | 1 |
+| Q5_P | Repartitioned filtered count, partition by year | 1 | 1 |  | 1 |  |
+| Q5_NP | Unpartitioned filtered count, partition by year | 1 |  | 1 |  | 1 |
 
 ## Detailed Summary (ms)
 
-| System                     | Query | Description                                     | Runs | Mean    | Median  | Min     | Max     | Rows | Columns | Status |
-|----------------------------|-------|-------------------------------------------------|------|---------|---------|---------|---------|------|---------|--------|
-| Polypheny Relational       | Q01   | Repartitioned full table baseline               | 5/5  | 19.0    | 19.0    | 15.0    | 24.0    | 1    | 1       | ok     |
-| Polypheny Relational       | Q02   | Repartitioned partition by year                 | 5/5  | 16.8    | 17.0    | 15.0    | 18.0    | 1    | 1       | ok     |
-| Polypheny Relational       | Q03   | Repartitioned partition by month                | 5/5  | 19.6    | 20.0    | 17.0    | 21.0    | 1    | 1       | ok     |
-| Polypheny Relational       | Q04   | Repartitioned filtered count, full scan         | 5/5  | 279.8   | 284.0   | 256.0   | 308.0   | 1    | 1       | ok     |
-| Polypheny Relational       | Q05   | Repartitioned filtered count, partition by year | 5/5  | 84.6    | 85.0    | 77.0    | 92.0    | 1    | 1       | ok     |
-| Polypheny Relational       | Q06   | Unpartitioned full table baseline               | 5/5  | 13.2    | 13.0    | 13.0    | 14.0    | 1    | 1       | ok     |
-| Polypheny Relational       | Q07   | Unpartitioned partition by year                 | 5/5  | 13.8    | 14.0    | 13.0    | 14.0    | 1    | 1       | ok     |
-| Polypheny Relational       | Q08   | Unpartitioned partition by month                | 5/5  | 15.8    | 16.0    | 15.0    | 17.0    | 1    | 1       | ok     |
-| Polypheny Relational       | Q09   | Unpartitioned filtered count, full scan         | 5/5  | 260.0   | 262.0   | 228.0   | 291.0   | 1    | 1       | ok     |
-| Polypheny Relational       | Q10   | Unpartitioned filtered count, partition by year | 5/5  | 80.8    | 83.0    | 62.0    | 98.0    | 1    | 1       | ok     |
-| DuckDB Repartitioned       | Q01   | Repartitioned full table baseline               | 5/5  | 83.2    | 81.0    | 68.0    | 95.0    | 1    | 1       | ok     |
-| DuckDB Repartitioned       | Q02   | Repartitioned partition by year                 | 5/5  | 63.4    | 69.0    | 52.0    | 71.0    | 1    | 1       | ok     |
-| DuckDB Repartitioned       | Q03   | Repartitioned partition by month                | 5/5  | 29.8    | 30.0    | 27.0    | 33.0    | 1    | 1       | ok     |
-| DuckDB Repartitioned       | Q04   | Repartitioned filtered count, full scan         | 5/5  | 41.8    | 37.0    | 33.0    | 58.0    | 1    | 1       | ok     |
-| DuckDB Repartitioned       | Q05   | Repartitioned filtered count, partition by year | 5/5  | 22.6    | 25.0    | 16.0    | 27.0    | 1    | 1       | ok     |
-| DuckDB Unpartitioned       | Q06   | Unpartitioned full table baseline               | 5/5  | 58.0    | 58.0    | 56.0    | 59.0    | 1    | 1       | ok     |
-| DuckDB Unpartitioned       | Q07   | Unpartitioned partition by year                 | 5/5  | 85.8    | 84.0    | 81.0    | 91.0    | 1    | 1       | ok     |
-| DuckDB Unpartitioned       | Q08   | Unpartitioned partition by month                | 5/5  | 74.4    | 74.0    | 72.0    | 79.0    | 1    | 1       | ok     |
-| DuckDB Unpartitioned       | Q09   | Unpartitioned filtered count, full scan         | 5/5  | 21.2    | 21.0    | 20.0    | 22.0    | 1    | 1       | ok     |
-| DuckDB Unpartitioned       | Q10   | Unpartitioned filtered count, partition by year | 5/5  | 17.6    | 18.0    | 16.0    | 19.0    | 1    | 1       | ok     |
-| Apache Spark Repartitioned | Q01   | Repartitioned full table baseline               | 5/5  | 1,570.0 | 1,666.0 | 1,219.0 | 1,848.0 | 1    | 1       | ok     |
-| Apache Spark Repartitioned | Q02   | Repartitioned partition by year                 | 5/5  | 705.4   | 731.0   | 618.0   | 792.0   | 1    | 1       | ok     |
-| Apache Spark Repartitioned | Q03   | Repartitioned partition by month                | 5/5  | 426.4   | 402.0   | 393.0   | 529.0   | 1    | 1       | ok     |
-| Apache Spark Repartitioned | Q04   | Repartitioned filtered count, full scan         | 5/5  | 1,756.2 | 1,838.0 | 1,542.0 | 1,913.0 | 1    | 1       | ok     |
-| Apache Spark Repartitioned | Q05   | Repartitioned filtered count, partition by year | 5/5  | 877.4   | 723.0   | 557.0   | 1,381.0 | 1    | 1       | ok     |
-| Apache Spark Unpartitioned | Q06   | Unpartitioned full table baseline               | 5/5  | 1,375.2 | 1,197.0 | 1,037.0 | 1,878.0 | 1    | 1       | ok     |
-| Apache Spark Unpartitioned | Q07   | Unpartitioned partition by year                 | 5/5  | 1,978.8 | 1,898.0 | 1,723.0 | 2,224.0 | 1    | 1       | ok     |
-| Apache Spark Unpartitioned | Q08   | Unpartitioned partition by month                | 5/5  | 1,054.2 | 1,021.0 | 950.0   | 1,199.0 | 1    | 1       | ok     |
-| Apache Spark Unpartitioned | Q09   | Unpartitioned filtered count, full scan         | 5/5  | 905.6   | 837.0   | 717.0   | 1,152.0 | 1    | 1       | ok     |
-| Apache Spark Unpartitioned | Q10   | Unpartitioned filtered count, partition by year | 5/5  | 708.0   | 672.0   | 615.0   | 855.0   | 1    | 1       | ok     |
+| System | Query | Description | Runs | Mean | Median | Std Dev | Min | Max | Rows | Columns | Status |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| Polypheny Relational | Q1_P | Repartitioned full table baseline | 5/5 | 7.2 | 7.0 | 0.8 | 6.0 | 8.0 | 1 | 1 | ok |
+| Polypheny Relational | Q1_NP | Unpartitioned full table baseline | 5/5 | 9.6 | 10.0 | 1.1 | 8.0 | 11.0 | 1 | 1 | ok |
+| Polypheny Relational | Q2_P | Repartitioned partition by year | 5/5 | 6.6 | 6.0 | 0.9 | 6.0 | 8.0 | 1 | 1 | ok |
+| Polypheny Relational | Q2_NP | Unpartitioned partition by year | 5/5 | 9.6 | 9.0 | 1.3 | 8.0 | 11.0 | 1 | 1 | ok |
+| Polypheny Relational | Q3_P | Repartitioned partition by month | 5/5 | 8.6 | 9.0 | 1.1 | 7.0 | 10.0 | 1 | 1 | ok |
+| Polypheny Relational | Q3_NP | Unpartitioned partition by month | 5/5 | 9.8 | 10.0 | 1.9 | 7.0 | 12.0 | 1 | 1 | ok |
+| Polypheny Relational | Q4_P | Repartitioned filtered count, full scan | 5/5 | 150.0 | 155.0 | 14.6 | 133.0 | 168.0 | 1 | 1 | ok |
+| Polypheny Relational | Q4_NP | Unpartitioned filtered count, full scan | 5/5 | 131.2 | 131.0 | 14.9 | 110.0 | 152.0 | 1 | 1 | ok |
+| Polypheny Relational | Q5_P | Repartitioned filtered count, partition by year | 5/5 | 54.6 | 53.0 | 3.0 | 53.0 | 60.0 | 1 | 1 | ok |
+| Polypheny Relational | Q5_NP | Unpartitioned filtered count, partition by year | 5/5 | 61.6 | 59.0 | 6.0 | 56.0 | 71.0 | 1 | 1 | ok |
+| DuckDB Repartitioned | Q1_P | Repartitioned full table baseline | 5/5 | 78.8 | 75.0 | 11.3 | 65.0 | 95.0 | 1 | 1 | ok |
+| DuckDB Repartitioned | Q2_P | Repartitioned partition by year | 5/5 | 35.2 | 35.0 | 1.8 | 33.0 | 38.0 | 1 | 1 | ok |
+| DuckDB Repartitioned | Q3_P | Repartitioned partition by month | 5/5 | 17.0 | 16.0 | 2.0 | 15.0 | 20.0 | 1 | 1 | ok |
+| DuckDB Repartitioned | Q4_P | Repartitioned filtered count, full scan | 5/5 | 30.2 | 30.0 | 1.3 | 29.0 | 32.0 | 1 | 1 | ok |
+| DuckDB Repartitioned | Q5_P | Repartitioned filtered count, partition by year | 5/5 | 17.4 | 17.0 | 1.1 | 16.0 | 19.0 | 1 | 1 | ok |
+| DuckDB Unpartitioned | Q1_NP | Unpartitioned full table baseline | 5/5 | 59.6 | 59.0 | 2.4 | 57.0 | 63.0 | 1 | 1 | ok |
+| DuckDB Unpartitioned | Q2_NP | Unpartitioned partition by year | 5/5 | 88.4 | 88.0 | 5.4 | 81.0 | 95.0 | 1 | 1 | ok |
+| DuckDB Unpartitioned | Q3_NP | Unpartitioned partition by month | 5/5 | 72.2 | 71.0 | 5.6 | 65.0 | 80.0 | 1 | 1 | ok |
+| DuckDB Unpartitioned | Q4_NP | Unpartitioned filtered count, full scan | 5/5 | 20.6 | 20.0 | 1.3 | 19.0 | 22.0 | 1 | 1 | ok |
+| DuckDB Unpartitioned | Q5_NP | Unpartitioned filtered count, partition by year | 5/5 | 13.6 | 14.0 | 0.5 | 13.0 | 14.0 | 1 | 1 | ok |
+| Apache Spark Repartitioned | Q1_P | Repartitioned full table baseline | 5/5 | 1,681.8 | 1,589.0 | 204.8 | 1,530.0 | 2,037.0 | 1 | 1 | ok |
+| Apache Spark Repartitioned | Q2_P | Repartitioned partition by year | 5/5 | 641.8 | 592.0 | 81.3 | 582.0 | 767.0 | 1 | 1 | ok |
+| Apache Spark Repartitioned | Q3_P | Repartitioned partition by month | 5/5 | 450.6 | 443.0 | 80.2 | 355.0 | 563.0 | 1 | 1 | ok |
+| Apache Spark Repartitioned | Q4_P | Repartitioned filtered count, full scan | 5/5 | 1,693.0 | 1,715.0 | 144.3 | 1,501.0 | 1,873.0 | 1 | 1 | ok |
+| Apache Spark Repartitioned | Q5_P | Repartitioned filtered count, partition by year | 5/5 | 637.8 | 681.0 | 101.5 | 502.0 | 736.0 | 1 | 1 | ok |
+| Apache Spark Unpartitioned | Q1_NP | Unpartitioned full table baseline | 5/5 | 1,189.6 | 1,217.0 | 190.4 | 973.0 | 1,418.0 | 1 | 1 | ok |
+| Apache Spark Unpartitioned | Q2_NP | Unpartitioned partition by year | 5/5 | 2,123.2 | 2,115.0 | 94.1 | 2,015.0 | 2,273.0 | 1 | 1 | ok |
+| Apache Spark Unpartitioned | Q3_NP | Unpartitioned partition by month | 5/5 | 1,326.2 | 1,343.0 | 101.3 | 1,193.0 | 1,465.0 | 1 | 1 | ok |
+| Apache Spark Unpartitioned | Q4_NP | Unpartitioned filtered count, full scan | 5/5 | 902.6 | 871.0 | 145.2 | 780.0 | 1,153.0 | 1 | 1 | ok |
+| Apache Spark Unpartitioned | Q5_NP | Unpartitioned filtered count, partition by year | 5/5 | 1,035.4 | 1,001.0 | 101.7 | 955.0 | 1,197.0 | 1 | 1 | ok |
diff --git a/plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot.pdf b/plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot.pdf
index d683143e1ce0cba03a243ec28fb24f281c00e4e7..bbf8ffe2ef0470b67be15fdbcbd8f03953f56df3 100644
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diff --git a/plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot.png b/plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot.png
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diff --git a/plugins/parquet-adapter/benchmarks/results/plots/aggregation_plot.png b/plugins/parquet-adapter/benchmarks/results/plots/aggregation_plot.png
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diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md
index c015b69115..c1a46cff91 100644
--- a/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md
@@ -92,6 +92,10 @@ python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py
 python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_result_plot_generator.py `
   --title "Access Model Comparison" `
   --name access_model_comparison_tlcp_plot `
+  --query-order "Q01,Q02,Q03,Q04,Q05" `
+  --query-descriptions "Q01=Full access;Q02=Projection;Q03=Filtered count;Q04=Filtered full access;Q05=Filtered projection" `
+  --query-description-wrap-chars 30 `
+  --query-description-max-lines 1 `
   "Polypheny Relational Flat=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rf_tlcp_results.csv" `
   "Polypheny Relational Normalized=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rn_tlcpn_results.csv" `
   "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_mql_tlcp_results.csv" `
diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_aggregation_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_aggregation_bm_pipeline.md
index 818a3ab691..5790caf8da 100644
--- a/plugins/parquet-adapter/benchmarks/run_pipeline/run_aggregation_bm_pipeline.md
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_aggregation_bm_pipeline.md
@@ -40,8 +40,9 @@ PR
 ```powershell
 powershell -ExecutionPolicy Bypass `
   -File plugins\parquet-adapter\benchmarks\scripts\runners\run_polypheny_benchmark.ps1 `
-  -Queries plugins\parquet-adapter\benchmarks\query_lists\arrregation\aggregation_polypheny.sql `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\aggregation\aggregation_polypheny.sql `
   -Output plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_results.csv `
+  -ResultValuesOutput plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_values.jsonl `
   -Warmups 1 `
   -Runs 5 `
   -NoTableNameMapping
@@ -53,8 +54,9 @@ PD
 powershell -ExecutionPolicy Bypass `
   -File plugins\parquet-adapter\benchmarks\scripts\runners\run_polypheny_mql_benchmark.ps1 `
   -Namespace tlcpd_document `
-  -Queries plugins\parquet-adapter\benchmarks\query_lists\arrregation\aggregation_polypheny_mql.sql `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\aggregation\aggregation_polypheny_mql.sql `
   -Output plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_mql_results.csv `
+  -ResultValuesOutput plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_mql_values.jsonl `
   -Warmups 1 `
   -Runs 5
 ```
@@ -65,8 +67,9 @@ DuckDB
 powershell -ExecutionPolicy Bypass `
   -File plugins\parquet-adapter\benchmarks\scripts\runners\run_duckdb_benchmark.ps1 `
   -DataDir C:\PolyData\tlc_partitioned `
-  -Queries plugins\parquet-adapter\benchmarks\query_lists\arrregation\aggregation_sql.sql `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\aggregation\aggregation_sql.sql `
   -Output plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_results.csv `
+  -ResultValuesOutput plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_values.jsonl `
   -Warmups 1 `
   -Runs 5
 ```
@@ -77,12 +80,26 @@ Spark
 powershell -ExecutionPolicy Bypass `
   -File plugins\parquet-adapter\benchmarks\scripts\runners\run_spark_benchmark.ps1 `
   -DataDir C:\PolyData\tlc_partitioned `
-  -Queries plugins\parquet-adapter\benchmarks\query_lists\arrregation\aggregation_sql.sql `
+  -Queries plugins\parquet-adapter\benchmarks\query_lists\aggregation\aggregation_sql.sql `
   -Output plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_results.csv `
+  -ResultValuesOutput plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_values.jsonl `
   -Warmups 1 `
   -Runs 5
 ```
 
+## Create correctness summary
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\compare_aggregation_results.py `
+  --title "Aggregation Correctness Summary" `
+  --output plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_correctness_summary.md `
+  --reference DuckDB `
+  "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_values.jsonl" `
+  "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_mql_values.jsonl" `
+  "DuckDB=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_values.jsonl" `
+  "Apache Spark=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_values.jsonl"
+```
+
 ## Create summary
 
 ```powershell
@@ -99,8 +116,12 @@ python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py
 
 ```powershell
 python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_result_plot_generator.py `
-  --title "Aggregation" `
+  --title "Aggregation on Partitioned TLC Data" `
   --name aggregation_plot `
+  --query-order "Q01,Q02,Q03,Q04,Q05,Q06,Q07,Q08,Q09,Q10" `
+  --query-descriptions "Q01=Yellow total row count;Q02=Yellow one-month count;Q03=Yellow one-day count;Q04=Yellow filtered row count;Q05=Yellow summary by year;Q06=FHV total row count;Q07=FHV one-month count;Q08=FHV filtered row count;Q09=FHV summary by year;Q10=FHV count by shared-request flag" `
+  --query-description-wrap-chars 14 `
+  --query-description-max-lines 3 `
   "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_results.csv" `
   "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_mql_results.csv" `
   "DuckDB=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_results.csv" `
diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md
index 9d2fba639f..efb9cf0a94 100644
--- a/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md
@@ -96,6 +96,10 @@ python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py
 python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_result_plot_generator.py `
   --title "Nested Data" `
   --name nested_data_plot `
+  --query-order "Q01,Q02,Q03,Q04,Q05" `
+  --query-descriptions "Q01=Customer access;Q02=Customer projection;Q03=Order projection;Q04=Lineitem projection;Q05=Lineitem max" `
+  --query-description-wrap-chars 30 `
+  --query-description-max-lines 1 `
   "Polypheny Relational Normalized=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_normalized_results.csv" `
   "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_mql_results.csv" `
   "DuckDB=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_duckdb_results.csv" `
diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md
index bc7e6c4cd4..bedd884052 100644
--- a/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md
@@ -112,8 +112,13 @@ python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py
 
 ```powershell
 python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_result_plot_generator.py `
-  --title "Partitioning" `
+  --title "Partitioning by Year and Month" `
   --name partitioning_plot `
+  --query-order "Q1_P,Q1_NP,Q2_P,Q2_NP,Q3_P,Q3_NP,Q4_P,Q4_NP,Q5_P,Q5_NP" `
+  --query-descriptions "Q1=Full baseline;Q2=Year filter;Q3=Year + month filter;Q4=Data-column filter;Q5=Year + data filter" `
+  --query-description-wrap-chars 30 `
+  --query-description-max-lines 1 `
+  --side-note "P - partitioned;NP - not partitioned" `
   "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_polypheny_results.csv" `
   "DuckDB Repartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_repartitioned_results.csv" `
   "DuckDB Unpartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_unpartitioned_results.csv" `
diff --git a/plugins/parquet-adapter/benchmarks/scripts/compare_aggregation_results.py b/plugins/parquet-adapter/benchmarks/scripts/compare_aggregation_results.py
new file mode 100644
index 0000000000..e18e8e5c60
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/compare_aggregation_results.py
@@ -0,0 +1,386 @@
+#!/usr/bin/env python3
+
+import argparse
+import json
+from decimal import Decimal, InvalidOperation
+from pathlib import Path
+
+
+def main():
+    args = parse_args()
+    inputs = [parse_input(value) for value in args.values]
+    if len(inputs) < 2:
+        raise ValueError("At least two value files are required for comparison.")
+
+    records_by_system = {
+        system: read_records(path)
+        for system, path in inputs
+    }
+    reference_system = args.reference or inputs[0][0]
+    if reference_system not in records_by_system:
+        raise ValueError(f"Reference system not found: {reference_system}")
+
+    output = Path(args.output)
+    output.parent.mkdir(parents=True, exist_ok=True)
+    output.write_text(
+        render_markdown(records_by_system, inputs, reference_system, args),
+        encoding="utf-8",
+    )
+    print(f"Wrote {output}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Compare captured aggregation result values across systems."
+    )
+    parser.add_argument(
+        "values",
+        nargs="+",
+        help="JSONL value file. Use LABEL=path.jsonl to control the system name.",
+    )
+    parser.add_argument(
+        "-o",
+        "--output",
+        required=True,
+        help="Markdown output file.",
+    )
+    parser.add_argument(
+        "--reference",
+        default="",
+        help="Reference system label. Defaults to the first input.",
+    )
+    parser.add_argument(
+        "--abs-tolerance",
+        default="0.000001",
+        help="Absolute tolerance for numeric comparisons. Default: 0.000001.",
+    )
+    parser.add_argument(
+        "--rel-tolerance",
+        default="0.000001",
+        help="Relative tolerance for numeric comparisons. Default: 0.000001.",
+    )
+    parser.add_argument(
+        "--title",
+        default="Aggregation Correctness Summary",
+        help="Markdown title.",
+    )
+    return parser.parse_args()
+
+
+def parse_input(value):
+    if "=" in value:
+        label, path = value.split("=", 1)
+        return label.strip(), Path(path.strip())
+    path = Path(value)
+    return infer_label(path), path
+
+
+def infer_label(path):
+    name = path.stem
+    for suffix in ("_values", "_results"):
+        if name.endswith(suffix):
+            name = name[: -len(suffix)]
+    return name.replace("_", " ").title()
+
+
+def read_records(path):
+    records = {}
+    with path.open(encoding="utf-8") as handle:
+        for line_number, line in enumerate(handle, start=1):
+            line = line.strip()
+            if not line:
+                continue
+            record = json.loads(line)
+            query_id = str(record.get("query_id", "")).strip()
+            if not query_id:
+                raise ValueError(f"{path}:{line_number}: missing query_id")
+            records[query_id] = record
+    return records
+
+
+def render_markdown(records_by_system, inputs, reference_system, args):
+    systems = [system for system, _path in inputs]
+    reference_records = records_by_system[reference_system]
+    query_ids = sorted(
+        {query_id for records in records_by_system.values() for query_id in records},
+        key=natural_key,
+    )
+
+    lines = [
+        f"# {args.title}",
+        "",
+        f"Reference system: `{reference_system}`.",
+        (
+            "Numeric values are compared with absolute tolerance "
+            f"`{args.abs_tolerance}` and relative tolerance `{args.rel_tolerance}`."
+        ),
+        "",
+        "## Source Files",
+        "",
+        "| System | Values file |",
+        "| --- | --- |",
+    ]
+    for system, path in inputs:
+        lines.append(f"| {escape_md(system)} | `{escape_md(str(path))}` |")
+
+    lines.extend(
+        [
+            "",
+            "## Comparison",
+            "",
+            "| Query | " + " | ".join(escape_md(system) for system in systems if system != reference_system) + " |",
+            "| --- | " + " | ".join("---" for system in systems if system != reference_system) + " |",
+        ]
+    )
+
+    abs_tolerance = Decimal(args.abs_tolerance)
+    rel_tolerance = Decimal(args.rel_tolerance)
+
+    for query_id in query_ids:
+        row = [escape_md(query_id)]
+        reference = reference_records.get(query_id)
+        for system in systems:
+            if system == reference_system:
+                continue
+            status, details = compare_records(
+                reference,
+                records_by_system[system].get(query_id),
+                abs_tolerance,
+                rel_tolerance,
+            )
+            row.append(escape_md(status if not details else f"{status}: {details}"))
+        lines.append("| " + " | ".join(row) + " |")
+
+    lines.extend(
+        [
+            "",
+            "## Compared Values",
+            "",
+            "The following tables align captured result values by query.",
+            "Each table includes the reference system so that mismatches can be inspected directly.",
+        ]
+    )
+    for query_id in query_ids:
+        lines.extend(render_query_values(
+            query_id,
+            systems,
+            records_by_system,
+            reference_system,
+        ))
+
+    lines.append("")
+    return "\n".join(lines)
+
+
+def compare_records(reference, candidate, abs_tolerance, rel_tolerance):
+    if reference is None:
+        return "missing reference", ""
+    if candidate is None:
+        return "missing", ""
+    if not reference.get("success", False):
+        return "reference failed", reference.get("error", "")
+    if not candidate.get("success", False):
+        return "failed", candidate.get("error", "")
+
+    reference_columns = [str(column).lower() for column in reference.get("columns", [])]
+    candidate_columns = [str(column).lower() for column in candidate.get("columns", [])]
+    if reference_columns != candidate_columns:
+        return "differs", "columns differ"
+
+    reference_rows = sorted(reference.get("rows", []), key=canonical_row_key)
+    candidate_rows = sorted(candidate.get("rows", []), key=canonical_row_key)
+    if len(reference_rows) != len(candidate_rows):
+        return "differs", f"row count {len(candidate_rows)} != {len(reference_rows)}"
+
+    for row_index, (reference_row, candidate_row) in enumerate(zip(reference_rows, candidate_rows), start=1):
+        if len(reference_row) != len(candidate_row):
+            return "differs", f"row {row_index} column count differs"
+        for column_index, (reference_value, candidate_value) in enumerate(zip(reference_row, candidate_row), start=1):
+            if not values_match(reference_value, candidate_value, abs_tolerance, rel_tolerance):
+                return "differs", f"row {row_index}, column {column_index}"
+
+    return "ok", ""
+
+
+def render_query_values(
+    query_id,
+    systems,
+    records_by_system,
+    reference_system,
+):
+    values_by_system = {
+        system: flatten_record(records_by_system[system].get(query_id))
+        for system in systems
+    }
+    labels = ordered_value_labels(values_by_system, systems, reference_system)
+    lines = [
+        "",
+        f"### {escape_md(query_id)}",
+        "",
+        "| Result | " + " | ".join(escape_md(system) for system in systems) + " |",
+        "| --- | " + " | ".join("---" for _system in systems) + " |",
+    ]
+    if not labels:
+        lines.append("| No captured values | " + " | ".join("" for _system in systems) + " |")
+        return lines
+
+    for label in labels:
+        row = [escape_md(label)]
+        for system in systems:
+            row.append(escape_md(values_by_system[system].get(label, "")))
+        lines.append("| " + " | ".join(row) + " |")
+    return lines
+
+
+def ordered_value_labels(values_by_system, systems, reference_system):
+    ordered = []
+    seen = set()
+
+    def add_labels(system):
+        for label in values_by_system.get(system, {}):
+            if label not in seen:
+                ordered.append(label)
+                seen.add(label)
+
+    add_labels(reference_system)
+    for system in systems:
+        add_labels(system)
+    return ordered
+
+
+def flatten_record(record):
+    if record is None:
+        return {"status": "missing"}
+    if not record.get("success", False):
+        return {"status": "failed: " + str(record.get("error", ""))}
+
+    rows = record.get("rows", [])
+    if not rows:
+        return {"status": "no rows"}
+
+    columns = normalized_columns(record)
+    if len(rows) == 1 and len(columns) == 1:
+        return {columns[0]: rows[0][0]}
+
+    key_indexes = infer_key_indexes(columns, rows)
+    value_indexes = [index for index in range(len(columns)) if index not in key_indexes]
+    flattened = {}
+
+    if not value_indexes:
+        for row_index, row in enumerate(rows, start=1):
+            values = list(row) + [""] * max(0, len(columns) - len(row))
+            label = f"row {row_index}"
+            flattened[label] = ", ".join(
+                f"{columns[index]}={values[index]}"
+                for index in range(len(columns))
+            )
+        return flattened
+
+    for row in rows:
+        values = list(row) + [""] * max(0, len(columns) - len(row))
+        key = format_key(columns, values, key_indexes)
+        for index in value_indexes:
+            label = columns[index] if not key else f"{key} / {columns[index]}"
+            flattened[unique_label(flattened, label)] = values[index]
+    return flattened
+
+
+def infer_key_indexes(columns, rows):
+    if len(columns) == 1:
+        return []
+
+    key_indexes = []
+    for index, column in enumerate(columns):
+        column_lower = str(column).lower()
+        if is_key_column(column_lower):
+            key_indexes.append(index)
+
+    if key_indexes:
+        return key_indexes
+
+    # If the schema is unfamiliar, keep the first column as a row identity and
+    # compare the remaining values as measures.
+    return [0]
+
+
+def is_key_column(column):
+    if column in {"_id", "year", "month"}:
+        return True
+    if column.endswith("_flag"):
+        return True
+    return False
+
+
+def format_key(columns, values, key_indexes):
+    return ", ".join(
+        f"{columns[index]}={values[index]}"
+        for index in key_indexes
+    )
+
+
+def unique_label(values, label):
+    if label not in values:
+        return label
+    counter = 2
+    while f"{label} #{counter}" in values:
+        counter += 1
+    return f"{label} #{counter}"
+
+
+def normalized_columns(record):
+    columns = [str(column) for column in record.get("columns", [])]
+    max_row_width = max((len(row) for row in record.get("rows", [])), default=0)
+    if not columns and max_row_width == 1:
+        columns = ["value"]
+    while len(columns) < max_row_width:
+        columns.append(f"value_{len(columns) + 1}")
+    return columns
+
+
+def values_match(reference, candidate, abs_tolerance, rel_tolerance):
+    if reference is None or candidate is None:
+        return reference is None and candidate is None
+
+    reference_decimal = parse_decimal(reference)
+    candidate_decimal = parse_decimal(candidate)
+    if reference_decimal is not None and candidate_decimal is not None:
+        difference = abs(reference_decimal - candidate_decimal)
+        if difference <= abs_tolerance:
+            return True
+        largest = max(abs(reference_decimal), abs(candidate_decimal), Decimal("1"))
+        return difference / largest <= rel_tolerance
+
+    return str(reference) == str(candidate)
+
+
+def parse_decimal(value):
+    try:
+        return Decimal(str(value))
+    except (InvalidOperation, ValueError):
+        return None
+
+
+def canonical_row_key(row):
+    return tuple("" if value is None else str(value) for value in row)
+
+
+def natural_key(value):
+    parts = []
+    current = ""
+    for char in str(value):
+        if not current or char.isdigit() == current[-1].isdigit():
+            current += char
+        else:
+            parts.append(current)
+            current = char
+    if current:
+        parts.append(current)
+    return [int(part) if part.isdigit() else part for part in parts]
+
+
+def escape_md(value):
+    return str(value).replace("\\", "\\\\").replace("|", "\\|").replace("\n", " ")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/plugins/parquet-adapter/benchmarks/scripts/implementation/DuckDbJdbcBenchmark.java b/plugins/parquet-adapter/benchmarks/scripts/implementation/DuckDbJdbcBenchmark.java
index 9f4caae84e..b7f5f89100 100644
--- a/plugins/parquet-adapter/benchmarks/scripts/implementation/DuckDbJdbcBenchmark.java
+++ b/plugins/parquet-adapter/benchmarks/scripts/implementation/DuckDbJdbcBenchmark.java
@@ -1,5 +1,6 @@
 import java.io.BufferedWriter;
 import java.io.IOException;
+import java.math.BigDecimal;
 import java.nio.charset.StandardCharsets;
 import java.nio.file.Files;
 import java.nio.file.Path;
@@ -29,6 +30,7 @@ private record Config(
             Path dataDir,
             Path queries,
             Path output,
+            Path resultValuesOutput,
             int warmups,
             int runs,
             boolean printRows,
@@ -89,6 +91,8 @@ public static void main( String[] args ) throws Exception {
                     executeAndRecord( connection, writer, query, "measured", i, config );
                 }
             }
+
+            captureResultValues( connection, queries, config );
         }
     }
 
@@ -168,6 +172,66 @@ private static void executeAndRecord( Connection connection, BufferedWriter writ
     }
 
 
+    private static void captureResultValues( Connection connection, List<Query> queries, Config config ) throws IOException {
+        if ( config.resultValuesOutput() == null ) {
+            return;
+        }
+        if ( config.resultValuesOutput().getParent() != null ) {
+            Files.createDirectories( config.resultValuesOutput().getParent() );
+        }
+        try ( BufferedWriter writer = Files.newBufferedWriter( config.resultValuesOutput(), StandardCharsets.UTF_8 ) ) {
+            System.out.printf( "Writing result values to %s%n", config.resultValuesOutput() );
+            for ( Query query : queries ) {
+                writeResultValues( connection, writer, query, config );
+            }
+        }
+    }
+
+
+    private static void writeResultValues( Connection connection, BufferedWriter writer, Query query, Config config ) throws IOException {
+        List<String> columns = new ArrayList<>();
+        List<List<String>> rows = new ArrayList<>();
+        boolean success = false;
+        String error = "";
+
+        try ( Statement statement = connection.createStatement() ) {
+            if ( config.fetchSize() > 0 ) {
+                statement.setFetchSize( config.fetchSize() );
+            }
+            if ( config.queryTimeoutSeconds() > 0 ) {
+                statement.setQueryTimeout( config.queryTimeoutSeconds() );
+            }
+
+            boolean hasResultSet = statement.execute( query.sql() );
+            if ( hasResultSet ) {
+                try ( ResultSet resultSet = statement.getResultSet() ) {
+                    ResultSetMetaData metaData = resultSet.getMetaData();
+                    for ( int i = 1; i <= metaData.getColumnCount(); i++ ) {
+                        columns.add( metaData.getColumnLabel( i ) );
+                    }
+                    while ( resultSet.next() ) {
+                        List<String> row = new ArrayList<>();
+                        for ( int i = 1; i <= metaData.getColumnCount(); i++ ) {
+                            row.add( valueToString( resultSet.getObject( i ) ) );
+                        }
+                        rows.add( row );
+                    }
+                }
+            } else {
+                columns.add( "update_count" );
+                rows.add( List.of( Long.toString( statement.getUpdateCount() ) ) );
+            }
+            success = true;
+        } catch ( SQLException e ) {
+            error = e.getClass().getSimpleName() + ": " + e.getMessage();
+        }
+
+        writer.write( resultValuesJson( query, success, error, columns, rows ) );
+        writer.newLine();
+        writer.flush();
+    }
+
+
     private static void printRow( ResultSet resultSet, ResultSetMetaData metaData ) throws SQLException {
         List<String> values = new ArrayList<>();
         for ( int i = 1; i <= metaData.getColumnCount(); i++ ) {
@@ -201,6 +265,96 @@ private static String csv( String value ) {
     }
 
 
+    private static String resultValuesJson( Query query, boolean success, String error, List<String> columns, List<List<String>> rows ) {
+        StringBuilder json = new StringBuilder();
+        json.append( '{' );
+        appendJsonField( json, "timestamp", Instant.now().toString() );
+        json.append( ',' );
+        appendJsonField( json, "query_id", query.id() );
+        json.append( ',' );
+        appendJsonField( json, "description", query.description() );
+        json.append( ",\"success\":" ).append( success );
+        json.append( ',' );
+        appendJsonField( json, "error", error );
+        json.append( ",\"columns\":" );
+        appendJsonStringArray( json, columns );
+        json.append( ",\"rows\":" );
+        appendJsonRows( json, rows );
+        json.append( '}' );
+        return json.toString();
+    }
+
+
+    private static void appendJsonField( StringBuilder json, String name, String value ) {
+        json.append( '"' ).append( jsonEscape( name ) ).append( "\":" ).append( jsonString( value ) );
+    }
+
+
+    private static void appendJsonStringArray( StringBuilder json, List<String> values ) {
+        json.append( '[' );
+        for ( int i = 0; i < values.size(); i++ ) {
+            if ( i > 0 ) {
+                json.append( ',' );
+            }
+            json.append( jsonString( values.get( i ) ) );
+        }
+        json.append( ']' );
+    }
+
+
+    private static void appendJsonRows( StringBuilder json, List<List<String>> rows ) {
+        json.append( '[' );
+        for ( int rowIndex = 0; rowIndex < rows.size(); rowIndex++ ) {
+            if ( rowIndex > 0 ) {
+                json.append( ',' );
+            }
+            appendJsonStringArray( json, rows.get( rowIndex ) );
+        }
+        json.append( ']' );
+    }
+
+
+    private static String jsonString( String value ) {
+        return value == null ? "null" : "\"" + jsonEscape( value ) + "\"";
+    }
+
+
+    private static String jsonEscape( String value ) {
+        StringBuilder escaped = new StringBuilder();
+        for ( int i = 0; i < value.length(); i++ ) {
+            char ch = value.charAt( i );
+            switch ( ch ) {
+                case '"' -> escaped.append( "\\\"" );
+                case '\\' -> escaped.append( "\\\\" );
+                case '\b' -> escaped.append( "\\b" );
+                case '\f' -> escaped.append( "\\f" );
+                case '\n' -> escaped.append( "\\n" );
+                case '\r' -> escaped.append( "\\r" );
+                case '\t' -> escaped.append( "\\t" );
+                default -> {
+                    if ( ch < 0x20 ) {
+                        escaped.append( String.format( "\\u%04x", (int) ch ) );
+                    } else {
+                        escaped.append( ch );
+                    }
+                }
+            }
+        }
+        return escaped.toString();
+    }
+
+
+    private static String valueToString( Object value ) {
+        if ( value == null ) {
+            return null;
+        }
+        if ( value instanceof BigDecimal decimal ) {
+            return decimal.toPlainString();
+        }
+        return value.toString();
+    }
+
+
     private static List<Query> loadQueries( Path path ) throws IOException {
         List<String> lines = Files.readAllLines( path, StandardCharsets.UTF_8 );
         List<Query> queries = new ArrayList<>();
@@ -282,6 +436,7 @@ private static Config parseArgs( String[] args ) {
                 Path.of( values.getOrDefault( "data-dir", "C:\\tmp\\tlc\\flat_partitioned" ) ),
                 Path.of( values.getOrDefault( "queries", "plugins/parquet-adapter/benchmarks/query_lists/access_model_comparison/access_model_comparison_sql.sql" ) ),
                 Path.of( values.getOrDefault( "output", "plugins/parquet-adapter/benchmarks/results/access_model_comparison/duckdb_results.csv" ) ),
+                optionalPath( values.get( "result-values-output" ) ),
                 parseInt( values, "warmups", 1 ),
                 parseInt( values, "runs", 5 ),
                 parseBoolean( values, "print-rows", false ),
@@ -307,4 +462,9 @@ private static boolean parseBoolean( Map<String, String> values, String key, boo
         return values.containsKey( key ) ? Boolean.parseBoolean( values.get( key ) ) : defaultValue;
     }
 
+
+    private static Path optionalPath( String value ) {
+        return value == null || value.isBlank() ? null : Path.of( value );
+    }
+
 }
diff --git a/plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyJdbcBenchmark.java b/plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyJdbcBenchmark.java
index b5afd47f5a..88faef8544 100644
--- a/plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyJdbcBenchmark.java
+++ b/plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyJdbcBenchmark.java
@@ -30,6 +30,7 @@ private record Config(
             String url,
             Path queries,
             Path output,
+            Path resultValuesOutput,
             int warmups,
             int runs,
             boolean polyphenyTableNames,
@@ -88,6 +89,8 @@ public static void main(String[] args) throws Exception {
                     executeAndRecord(connection, writer, query, "measured", i, config);
                 }
             }
+
+            captureResultValues(connection, queries, config);
         }
     }
 
@@ -144,6 +147,66 @@ private static void executeAndRecord(Connection connection, BufferedWriter write
     }
 
 
+    private static void captureResultValues(Connection connection, List<Query> queries, Config config) throws IOException {
+        if (config.resultValuesOutput() == null) {
+            return;
+        }
+        if (config.resultValuesOutput().getParent() != null) {
+            Files.createDirectories(config.resultValuesOutput().getParent());
+        }
+        try (BufferedWriter writer = Files.newBufferedWriter(config.resultValuesOutput(), StandardCharsets.UTF_8)) {
+            System.out.printf("Writing result values to %s%n", config.resultValuesOutput());
+            for (Query query : queries) {
+                writeResultValues(connection, writer, query, config);
+            }
+        }
+    }
+
+
+    private static void writeResultValues(Connection connection, BufferedWriter writer, Query query, Config config) throws IOException {
+        List<String> columns = new ArrayList<>();
+        List<List<String>> rows = new ArrayList<>();
+        boolean success = false;
+        String error = "";
+
+        try (Statement statement = connection.createStatement()) {
+            if (config.fetchSize() > 0) {
+                statement.setFetchSize(config.fetchSize());
+            }
+            if (config.queryTimeoutSeconds() > 0) {
+                statement.setQueryTimeout(config.queryTimeoutSeconds());
+            }
+
+            boolean hasResultSet = statement.execute(query.sql());
+            if (hasResultSet) {
+                try (ResultSet resultSet = statement.getResultSet()) {
+                    ResultSetMetaData metaData = resultSet.getMetaData();
+                    for (int i = 1; i <= metaData.getColumnCount(); i++) {
+                        columns.add(metaData.getColumnLabel(i));
+                    }
+                    while (resultSet.next()) {
+                        List<String> row = new ArrayList<>();
+                        for (int i = 1; i <= metaData.getColumnCount(); i++) {
+                            row.add(valueToString(resultSet.getObject(i)));
+                        }
+                        rows.add(row);
+                    }
+                }
+            } else {
+                columns.add("update_count");
+                rows.add(List.of(Long.toString(statement.getUpdateCount())));
+            }
+            success = true;
+        } catch (SQLException e) {
+            error = e.getClass().getSimpleName() + ": " + e.getMessage();
+        }
+
+        writer.write(resultValuesJson(query, success, error, columns, rows));
+        writer.newLine();
+        writer.flush();
+    }
+
+
     private static void printRow(ResultSet resultSet, ResultSetMetaData metaData) throws SQLException {
         List<String> values = new ArrayList<>();
         for (int i = 1; i <= metaData.getColumnCount(); i++) {
@@ -177,6 +240,96 @@ private static String csv(String value) {
     }
 
 
+    private static String resultValuesJson(Query query, boolean success, String error, List<String> columns, List<List<String>> rows) {
+        StringBuilder json = new StringBuilder();
+        json.append('{');
+        appendJsonField(json, "timestamp", Instant.now().toString());
+        json.append(',');
+        appendJsonField(json, "query_id", query.id());
+        json.append(',');
+        appendJsonField(json, "description", query.description());
+        json.append(",\"success\":").append(success);
+        json.append(',');
+        appendJsonField(json, "error", error);
+        json.append(",\"columns\":");
+        appendJsonStringArray(json, columns);
+        json.append(",\"rows\":");
+        appendJsonRows(json, rows);
+        json.append('}');
+        return json.toString();
+    }
+
+
+    private static void appendJsonField(StringBuilder json, String name, String value) {
+        json.append('"').append(jsonEscape(name)).append("\":").append(jsonString(value));
+    }
+
+
+    private static void appendJsonStringArray(StringBuilder json, List<String> values) {
+        json.append('[');
+        for (int i = 0; i < values.size(); i++) {
+            if (i > 0) {
+                json.append(',');
+            }
+            json.append(jsonString(values.get(i)));
+        }
+        json.append(']');
+    }
+
+
+    private static void appendJsonRows(StringBuilder json, List<List<String>> rows) {
+        json.append('[');
+        for (int rowIndex = 0; rowIndex < rows.size(); rowIndex++) {
+            if (rowIndex > 0) {
+                json.append(',');
+            }
+            appendJsonStringArray(json, rows.get(rowIndex));
+        }
+        json.append(']');
+    }
+
+
+    private static String jsonString(String value) {
+        return value == null ? "null" : "\"" + jsonEscape(value) + "\"";
+    }
+
+
+    private static String jsonEscape(String value) {
+        StringBuilder escaped = new StringBuilder();
+        for (int i = 0; i < value.length(); i++) {
+            char ch = value.charAt(i);
+            switch (ch) {
+                case '"' -> escaped.append("\\\"");
+                case '\\' -> escaped.append("\\\\");
+                case '\b' -> escaped.append("\\b");
+                case '\f' -> escaped.append("\\f");
+                case '\n' -> escaped.append("\\n");
+                case '\r' -> escaped.append("\\r");
+                case '\t' -> escaped.append("\\t");
+                default -> {
+                    if (ch < 0x20) {
+                        escaped.append(String.format("\\u%04x", (int) ch));
+                    } else {
+                        escaped.append(ch);
+                    }
+                }
+            }
+        }
+        return escaped.toString();
+    }
+
+
+    private static String valueToString(Object value) {
+        if (value == null) {
+            return null;
+        }
+        if (value instanceof BigDecimal decimal) {
+            return decimal.toPlainString();
+        }
+        return value.toString();
+    }
+
+
     private static List<Query> loadQueries(Path path, boolean polyphenyTableNames, String polyphenyTablePrefix) throws IOException {
         List<String> lines = Files.readAllLines(path, StandardCharsets.UTF_8);
         List<Query> queries = new ArrayList<>();
@@ -278,6 +431,7 @@ private static Config parseArgs(String[] args) {
                 values.getOrDefault("url", DEFAULT_URL),
                 Path.of(values.getOrDefault("queries", defaultQueries.toString())),
                 Path.of(values.getOrDefault("output", defaultOutput.toString())),
+                optionalPath(values.get("result-values-output")),
                 parseInt(values, "warmups", 1),
                 parseInt(values, "runs", 5),
                 parseBoolean(values, "polypheny-table-names", true),
@@ -300,6 +454,11 @@ private static String parseTablePrefix(Map<String, String> values) {
     }
 
 
+    private static Path optionalPath(String value) {
+        return value == null || value.isBlank() ? null : Path.of(value);
+    }
+
+
     private static boolean isBooleanFlag(String key) {
         return key.equalsIgnoreCase("polypheny-table-names")
                 || key.equalsIgnoreCase("print-rows");
diff --git a/plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyMqlBenchmark.java b/plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyMqlBenchmark.java
index 2a90559eda..a0ba3db9be 100644
--- a/plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyMqlBenchmark.java
+++ b/plugins/parquet-adapter/benchmarks/scripts/implementation/PolyphenyMqlBenchmark.java
@@ -4,6 +4,8 @@
 import java.io.EOFException;
 import java.io.IOException;
 import java.io.OutputStream;
+import java.math.BigDecimal;
+import java.math.BigInteger;
 import java.net.InetSocketAddress;
 import java.net.Socket;
 import java.nio.ByteBuffer;
@@ -20,6 +22,7 @@
 import java.util.Set;
 import java.util.stream.Collectors;
 import org.polypheny.jdbc.dependency.prism.CloseStatementRequest;
+import org.polypheny.jdbc.dependency.prism.ColumnMeta;
 import org.polypheny.jdbc.dependency.prism.ConnectionProperties;
 import org.polypheny.jdbc.dependency.prism.ConnectionRequest;
 import org.polypheny.jdbc.dependency.prism.DisconnectRequest;
@@ -28,9 +31,12 @@
 import org.polypheny.jdbc.dependency.prism.FetchRequest;
 import org.polypheny.jdbc.dependency.prism.Frame;
 import org.polypheny.jdbc.dependency.prism.ProtoDocument;
+import org.polypheny.jdbc.dependency.prism.ProtoEntry;
+import org.polypheny.jdbc.dependency.prism.ProtoValue;
 import org.polypheny.jdbc.dependency.prism.RelationalFrame;
 import org.polypheny.jdbc.dependency.prism.Request;
 import org.polypheny.jdbc.dependency.prism.Response;
+import org.polypheny.jdbc.dependency.prism.Row;
 import org.polypheny.jdbc.dependency.prism.StatementResponse;
 import org.polypheny.jdbc.dependency.prism.StatementResult;
 
@@ -52,6 +58,7 @@ private record Config(
             int apiMinor,
             Path queries,
             Path output,
+            Path resultValuesOutput,
             int warmups,
             int runs,
             int fetchSize,
@@ -64,6 +71,9 @@ private record Config(
     private record RunResult(long rows, int columns) {
     }
 
+    private record ResultValues(List<String> columns, List<List<String>> rows) {
+    }
+
     private record FrameStats(long rows, int columns) {
     }
 
@@ -111,6 +121,8 @@ public static void main(String[] args) throws Exception {
                     executeAndRecord(client, writer, query, "measured", i, config);
                 }
             }
+
+            captureResultValues(client, queries, config);
         }
     }
 
@@ -148,6 +160,43 @@ private static void executeAndRecord(PrismClient client, BufferedWriter writer,
     }
 
 
+    private static void captureResultValues(PrismClient client, List<Query> queries, Config config) throws IOException {
+        if (config.resultValuesOutput() == null) {
+            return;
+        }
+        if (config.resultValuesOutput().getParent() != null) {
+            Files.createDirectories(config.resultValuesOutput().getParent());
+        }
+        try (BufferedWriter writer = Files.newBufferedWriter(config.resultValuesOutput(), StandardCharsets.UTF_8)) {
+            System.out.printf("Writing result values to %s%n", config.resultValuesOutput());
+            for (Query query : queries) {
+                writeResultValues(client, writer, query, config);
+            }
+        }
+    }
+
+
+    private static void writeResultValues(PrismClient client, BufferedWriter writer, Query query, Config config) throws IOException {
+        List<String> columns = List.of();
+        List<List<String>> rows = List.of();
+        boolean success = false;
+        String error = "";
+
+        try {
+            ResultValues values = client.executeAndCollectValues(query.mql(), config);
+            columns = values.columns();
+            rows = values.rows();
+            success = true;
+        } catch (Exception e) {
+            error = e.getClass().getSimpleName() + ": " + e.getMessage();
+        }
+
+        writer.write(resultValuesJson(query, success, error, columns, rows));
+        writer.newLine();
+        writer.flush();
+    }
+
+
     private static void writeCsvRow(BufferedWriter writer, Query query, String phase, int run, long elapsedMs, long rows, int columns, boolean success, String error) throws IOException {
         writer.write(String.join(",",
                 csv(Instant.now().toString()),
@@ -171,6 +220,85 @@ private static String csv(String value) {
     }
 
 
+    private static String resultValuesJson(Query query, boolean success, String error, List<String> columns, List<List<String>> rows) {
+        StringBuilder json = new StringBuilder();
+        json.append('{');
+        appendJsonField(json, "timestamp", Instant.now().toString());
+        json.append(',');
+        appendJsonField(json, "query_id", query.id());
+        json.append(',');
+        appendJsonField(json, "description", query.description());
+        json.append(",\"success\":").append(success);
+        json.append(',');
+        appendJsonField(json, "error", error);
+        json.append(",\"columns\":");
+        appendJsonStringArray(json, columns);
+        json.append(",\"rows\":");
+        appendJsonRows(json, rows);
+        json.append('}');
+        return json.toString();
+    }
+
+
+    private static void appendJsonField(StringBuilder json, String name, String value) {
+        json.append('"').append(jsonEscape(name)).append("\":").append(jsonString(value));
+    }
+
+
+    private static void appendJsonStringArray(StringBuilder json, List<String> values) {
+        json.append('[');
+        for (int i = 0; i < values.size(); i++) {
+            if (i > 0) {
+                json.append(',');
+            }
+            json.append(jsonString(values.get(i)));
+        }
+        json.append(']');
+    }
+
+
+    private static void appendJsonRows(StringBuilder json, List<List<String>> rows) {
+        json.append('[');
+        for (int rowIndex = 0; rowIndex < rows.size(); rowIndex++) {
+            if (rowIndex > 0) {
+                json.append(',');
+            }
+            appendJsonStringArray(json, rows.get(rowIndex));
+        }
+        json.append(']');
+    }
+
+
+    private static String jsonString(String value) {
+        return value == null ? "null" : "\"" + jsonEscape(value) + "\"";
+    }
+
+
+    private static String jsonEscape(String value) {
+        StringBuilder escaped = new StringBuilder();
+        for (int i = 0; i < value.length(); i++) {
+            char ch = value.charAt(i);
+            switch (ch) {
+                case '"' -> escaped.append("\\\"");
+                case '\\' -> escaped.append("\\\\");
+                case '\b' -> escaped.append("\\b");
+                case '\f' -> escaped.append("\\f");
+                case '\n' -> escaped.append("\\n");
+                case '\r' -> escaped.append("\\r");
+                case '\t' -> escaped.append("\\t");
+                default -> {
+                    if (ch < 0x20) {
+                        escaped.append(String.format("\\u%04x", (int) ch));
+                    } else {
+                        escaped.append(ch);
+                    }
+                }
+            }
+        }
+        return escaped.toString();
+    }
+
+
     private static List<Query> loadQueries(Path path) throws IOException {
         List<String> lines = Files.readAllLines(path, StandardCharsets.UTF_8);
         List<Query> queries = new ArrayList<>();
@@ -273,6 +401,7 @@ private static Config parseArgs(String[] args) {
                 parseInt(values, "api-minor", 9),
                 Path.of(values.getOrDefault("queries", defaultQueries.toString())),
                 Path.of(values.getOrDefault("output", defaultOutput.toString())),
+                optionalPath(values.get("result-values-output")),
                 parseInt(values, "warmups", 1),
                 parseInt(values, "runs", 5),
                 parseInt(values, "fetch-size", 1000),
@@ -288,6 +417,11 @@ private static boolean isBooleanFlag(String key) {
     }
 
 
+    private static Path optionalPath(String value) {
+        return value == null || value.isBlank() ? null : Path.of(value);
+    }
+
+
     private static int parseInt(Map<String, String> values, String key, int defaultValue) {
         String value = values.get(key);
         return value == null ? defaultValue : Integer.parseInt(value);
@@ -312,6 +446,65 @@ private static Set<String> parseOnly(String value) {
     }
 
 
+    private static Map<String, String> documentToMap(ProtoDocument document) {
+        Map<String, String> row = new LinkedHashMap<>();
+        for (ProtoEntry entry : document.getEntriesList()) {
+            String key = entry.hasKey() ? protoValueToString(entry.getKey()) : "";
+            row.put(key, entry.hasValue() ? protoValueToString(entry.getValue()) : null);
+        }
+        return row;
+    }
+
+
+    private static String protoValueToString(ProtoValue value) {
+        return switch (value.getValueCase()) {
+            case BOOLEAN -> Boolean.toString(value.getBoolean().getBoolean());
+            case INTEGER -> Integer.toString(value.getInteger().getInteger());
+            case LONG -> Long.toString(value.getLong().getLong());
+            case BIG_DECIMAL -> protoBigDecimalToString(value);
+            case FLOAT -> Float.toString(value.getFloat().getFloat());
+            case DOUBLE -> Double.toString(value.getDouble().getDouble());
+            case DATE -> Long.toString(value.getDate().getDate());
+            case TIME -> Integer.toString(value.getTime().getTime());
+            case TIMESTAMP -> Long.toString(value.getTimestamp().getTimestamp());
+            case INTERVAL -> value.getInterval().getMonths() + ":" + value.getInterval().getMilliseconds();
+            case STRING -> value.getString().getString();
+            case BINARY -> value.getBinary().getBinary().toString();
+            case NULL, VALUE_NOT_SET -> null;
+            case LIST -> protoListToString(value);
+            case DOCUMENT -> protoDocumentToString(value.getDocument());
+            case FILE -> value.getFile().getBinary().toString();
+        };
+    }
+
+
+    private static String protoBigDecimalToString(ProtoValue value) {
+        return new BigDecimal(
+                new BigInteger(value.getBigDecimal().getUnscaledValue().toByteArray()),
+                value.getBigDecimal().getScale()).toPlainString();
+    }
+
+
+    private static String protoListToString(ProtoValue value) {
+        List<String> values = value.getList().getValuesList().stream()
+                .map(PolyphenyMqlBenchmark::protoValueToString)
+                .map(item -> item == null ? "null" : item)
+                .toList();
+        return "[" + String.join(",", values) + "]";
+    }
+
+
+    private static String protoDocumentToString(ProtoDocument document) {
+        List<String> values = new ArrayList<>();
+        for (ProtoEntry entry : document.getEntriesList()) {
+            String key = entry.hasKey() ? protoValueToString(entry.getKey()) : "";
+            String value = entry.hasValue() ? protoValueToString(entry.getValue()) : null;
+            values.add(key + ":" + (value == null ? "null" : value));
+        }
+        return "{" + String.join(",", values) + "}";
+    }
+
+
     private static final class PrismClient implements Closeable {
 
         private final Socket socket;
@@ -384,6 +577,52 @@ RunResult executeAndDrain(String mql, Config config) throws IOException {
         }
 
 
+        ResultValues executeAndCollectValues(String mql, Config config) throws IOException {
+            int statementId = -1;
+            try {
+                long requestId = nextRequestId++;
+                ExecuteUnparameterizedStatementRequest executeRequest = ExecuteUnparameterizedStatementRequest.newBuilder()
+                        .setLanguageName(config.language())
+                        .setStatement(mql)
+                        .setFetchSize(config.fetchSize())
+                        .setNamespaceName(config.namespace())
+                        .build();
+                send(Request.newBuilder()
+                        .setId(requestId)
+                        .setExecuteUnparameterizedStatementRequest(executeRequest)
+                        .build());
+
+                StatementResult result = null;
+                while (true) {
+                    Response response = receive(requestId);
+                    if (!response.hasStatementResponse()) {
+                        throw new IOException("Expected statement_response, got " + response.getTypeCase());
+                    }
+                    StatementResponse statementResponse = response.getStatementResponse();
+                    statementId = statementResponse.getStatementId();
+                    if (statementResponse.hasResult()) {
+                        result = statementResponse.getResult();
+                    }
+                    if (response.getLast()) {
+                        break;
+                    }
+                }
+
+                if (result == null) {
+                    return new ResultValues(List.of(), List.of());
+                }
+                if (!result.hasFrame()) {
+                    return new ResultValues(List.of("scalar"), List.of(List.of(Long.toString(result.getScalar()))));
+                }
+                return collectFrames(statementId, result.getFrame(), config);
+            } finally {
+                if (statementId > 0) {
+                    closeStatement(statementId);
+                }
+            }
+        }
+
+
         private RunResult drainFrames(int statementId, Frame firstFrame, Config config) throws IOException {
             long rows = 0;
             int columns = 0;
@@ -400,6 +639,57 @@ private RunResult drainFrames(int statementId, Frame firstFrame, Config config)
         }
 
 
+        private ResultValues collectFrames(int statementId, Frame firstFrame, Config config) throws IOException {
+            List<String> columns = new ArrayList<>();
+            List<List<String>> rows = new ArrayList<>();
+            List<Map<String, String>> documentRows = new ArrayList<>();
+            boolean documentMode = false;
+            Frame frame = firstFrame;
+            while (true) {
+                if (frame.hasDocumentFrame()) {
+                    documentMode = true;
+                    DocumentFrame documentFrame = frame.getDocumentFrame();
+                    for (ProtoDocument document : documentFrame.getDocumentsList()) {
+                        Map<String, String> row = documentToMap(document);
+                        for (String column : row.keySet()) {
+                            if (!columns.contains(column)) {
+                                columns.add(column);
+                            }
+                        }
+                        documentRows.add(row);
+                    }
+                } else if (frame.hasRelationalFrame()) {
+                    RelationalFrame relationalFrame = frame.getRelationalFrame();
+                    if (columns.isEmpty()) {
+                        for (ColumnMeta meta : relationalFrame.getColumnMetaList()) {
+                            columns.add(meta.getColumnLabel().isBlank() ? meta.getColumnName() : meta.getColumnLabel());
+                        }
+                    }
+                    for (Row row : relationalFrame.getRowsList()) {
+                        List<String> values = new ArrayList<>();
+                        for (ProtoValue value : row.getValuesList()) {
+                            values.add(protoValueToString(value));
+                        }
+                        rows.add(values);
+                    }
+                }
+                if (frame.getIsLast()) {
+                    if (documentMode) {
+                        for (Map<String, String> documentRow : documentRows) {
+                            List<String> values = new ArrayList<>();
+                            for (String column : columns) {
+                                values.add(documentRow.get(column));
+                            }
+                            rows.add(values);
+                        }
+                    }
+                    return new ResultValues(columns, rows);
+                }
+                frame = fetchFrame(statementId, config.fetchSize());
+            }
+        }
+
+
         private FrameStats countFrame(Frame frame, boolean printRows) {
             if (frame.hasDocumentFrame()) {
                 DocumentFrame documentFrame = frame.getDocumentFrame();
diff --git a/plugins/parquet-adapter/benchmarks/scripts/implementation/SparkSqlBenchmark.py b/plugins/parquet-adapter/benchmarks/scripts/implementation/SparkSqlBenchmark.py
index c18d4f79a2..9cefa95cca 100644
--- a/plugins/parquet-adapter/benchmarks/scripts/implementation/SparkSqlBenchmark.py
+++ b/plugins/parquet-adapter/benchmarks/scripts/implementation/SparkSqlBenchmark.py
@@ -2,6 +2,7 @@
 
 import argparse
 import csv
+import json
 import re
 import sys
 import time
@@ -77,6 +78,7 @@ def parse_args():
     parser.add_argument("--app-name", default="ParquetAdapterSparkBenchmark")
     parser.add_argument("--shuffle-partitions", type=int, default=8)
     parser.add_argument("--console-prefix", default="")
+    parser.add_argument("--result-values-output", default="")
     parser.add_argument(
         "--drain-mode",
         choices=("executor", "driver"),
@@ -154,6 +156,8 @@ def run_queries(spark, queries, args, output):
             for run in range(1, args.runs + 1):
                 execute_and_record(spark, writer, handle, query, "measured", run, args)
 
+    capture_result_values(spark, queries, args)
+
 
 def execute_and_record(spark, writer, handle, query, phase, run, args):
     start = time.perf_counter()
@@ -198,6 +202,40 @@ def execute_and_record(spark, writer, handle, query, phase, run, args):
     handle.flush()
 
 
+def capture_result_values(spark, queries, args):
+    if not args.result_values_output:
+        return
+
+    output = Path(args.result_values_output)
+    if output.parent:
+        output.parent.mkdir(parents=True, exist_ok=True)
+
+    log(spark, f"Writing result values to {output}")
+    with output.open("w", encoding="utf-8") as handle:
+        for query in queries:
+            record = {
+                "timestamp": timestamp_utc(),
+                "query_id": query.query_id,
+                "description": query.description,
+                "success": False,
+                "error": "",
+                "columns": [],
+                "rows": [],
+            }
+            try:
+                result = spark.sql(prepare_sql_for_spark(query.sql))
+                record["columns"] = list(result.columns)
+                record["rows"] = [
+                    [value_to_string(value) for value in row]
+                    for row in result.collect()
+                ]
+                record["success"] = True
+            except Exception as exc:
+                record["error"] = f"{exc.__class__.__name__}: {exc}".replace("\r", " ").replace("\n", " ")
+            handle.write(json.dumps(record, ensure_ascii=False, separators=(",", ":")))
+            handle.write("\n")
+
+
 def load_queries(path):
     lines = path.read_text(encoding="utf-8").splitlines()
     queries = []
@@ -278,6 +316,10 @@ def format_cell(value):
     return "NULL" if value is None else str(value)
 
 
+def value_to_string(value):
+    return None if value is None else str(value)
+
+
 if __name__ == "__main__":
     try:
         main()
diff --git a/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py b/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py
index f21a0840d9..df2201e39b 100644
--- a/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py
+++ b/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py
@@ -10,18 +10,28 @@
 
 
 COLORS = (
-    "#2f6fbb",
-    "#c94c4c",
     "#4f9d55",
-    "#f0a22e",
+    "#c94c4c",
     "#7b61b9",
+    "#f0a22e",
+    "#2f6fbb",
     "#4aa3a1",
 )
 
+SYSTEM_COLORS = (
+    ("Polypheny Relational Normalized", "#c94c4c"),
+    ("Polypheny Relational Flat", "#4f9d55"),
+    ("Polypheny Relational", "#4f9d55"),
+    ("Polypheny Document", "#7b61b9"),
+    ("DuckDB", "#f0a22e"),
+    ("Apache Spark", "#2f6fbb"),
+)
+
 
 @dataclass
 class Series:
     label: str
+    visual_label: str
     color: str
     values: dict[str, float]
 
@@ -39,19 +49,32 @@ class PlotGeometry:
     lower: float
     upper: float
     ticks: list[float]
-    group_width: float
     bar_gap: int
     bar_width: float
+    query_centers: dict[str, float]
 
 
 def main():
     args = parse_args()
     inputs = [parse_input(value) for value in args.csv]
-    series = [
-        Series(label, COLORS[index % len(COLORS)], read_means(path, args.phase))
-        for index, (label, path) in enumerate(inputs)
-    ]
-    query_ids = sorted({query_id for item in series for query_id in item.values}, key=natural_key)
+    query_descriptions = parse_query_descriptions(args.query_descriptions)
+    color_by_label = {}
+    fallback_color_index = 0
+    series = []
+    for label, path in inputs:
+        visual_label = normalize_visual_label(label)
+        if visual_label not in color_by_label:
+            color, fallback_color_index = color_for_label(visual_label, fallback_color_index)
+            color_by_label[visual_label] = color
+        series.append(
+            Series(
+                label,
+                visual_label,
+                color_by_label[visual_label],
+                read_means(path, args.phase),
+            )
+        )
+    query_ids = ordered_query_ids(series, args.query_order)
     if not query_ids:
         raise ValueError("No successful benchmark rows found.")
 
@@ -60,11 +83,40 @@ def main():
     for file_format in parse_formats(args.formats):
         output = output_dir / f"{args.name}.{file_format}"
         if file_format == "svg":
-            output.write_text(render_svg(series, query_ids, args.title), encoding="utf-8")
+            output.write_text(
+                render_svg(
+                    series,
+                    query_ids,
+                    args.title,
+                    query_descriptions,
+                    args.query_description_wrap_chars,
+                    args.query_description_max_lines,
+                    args.side_note,
+                ),
+                encoding="utf-8",
+            )
         elif file_format == "png":
-            write_png(output, series, query_ids, args.title)
+            write_png(
+                output,
+                series,
+                query_ids,
+                args.title,
+                query_descriptions,
+                args.query_description_wrap_chars,
+                args.query_description_max_lines,
+                args.side_note,
+            )
         elif file_format == "pdf":
-            write_pdf(output, series, query_ids, args.title)
+            write_pdf(
+                output,
+                series,
+                query_ids,
+                args.title,
+                query_descriptions,
+                args.query_description_wrap_chars,
+                args.query_description_max_lines,
+                args.side_note,
+            )
         else:
             raise ValueError(f"Unsupported plot format: {file_format}")
         print(f"Wrote {output}")
@@ -96,6 +148,33 @@ def parse_args():
         default="measured",
         help="Benchmark phase to plot. Default: measured.",
     )
+    parser.add_argument(
+        "--query-order",
+        default="",
+        help="Comma-separated query IDs that define the x-axis order. Missing IDs are appended using natural order.",
+    )
+    parser.add_argument(
+        "--query-descriptions",
+        default="",
+        help="Semicolon-separated query descriptions, for example Q1=Full count;Q2=Year filter.",
+    )
+    parser.add_argument(
+        "--query-description-wrap-chars",
+        type=int,
+        default=18,
+        help="Approximate line length for query descriptions. Default: 18.",
+    )
+    parser.add_argument(
+        "--query-description-max-lines",
+        type=int,
+        default=2,
+        help="Maximum number of lines per query description. Default: 2.",
+    )
+    parser.add_argument(
+        "--side-note",
+        default="",
+        help="Semicolon-separated note lines to render below the legend.",
+    )
     return parser.parse_args()
 
 
@@ -122,6 +201,30 @@ def parse_input(value):
     return path.stem, path
 
 
+def parse_query_descriptions(value):
+    descriptions = {}
+    for part in value.split(";"):
+        if "=" not in part:
+            continue
+        query_id, description = part.split("=", 1)
+        query_id = query_id.strip()
+        description = description.strip()
+        if query_id and description:
+            descriptions[query_id] = description
+    return descriptions
+
+
+def parse_side_note(value):
+    return [part.strip() for part in value.split(";") if part.strip()]
+
+
+def side_note_parts(note):
+    if " - " not in note:
+        return note, ""
+    prefix, rest = note.split(" - ", 1)
+    return prefix, " - " + rest
+
+
 def read_means(path, phase):
     elapsed_by_query = {}
     with path.open(newline="", encoding="utf-8-sig") as handle:
@@ -142,8 +245,31 @@ def read_means(path, phase):
     }
 
 
-def render_svg(series, query_ids, title):
-    geometry = make_geometry(series, query_ids)
+def ordered_query_ids(series, query_order):
+    available = {query_id for item in series for query_id in item.values}
+    if not query_order:
+        return sorted(available, key=natural_key)
+
+    requested = [part.strip() for part in query_order.split(",") if part.strip()]
+    ordered = []
+    seen = set()
+    for query_id in requested:
+        if query_id in available and query_id not in seen:
+            ordered.append(query_id)
+            seen.add(query_id)
+
+    ordered.extend(sorted(available - seen, key=natural_key))
+    return ordered
+
+
+def render_svg(series, query_ids, title, query_descriptions, description_wrap_chars, description_max_lines, side_note=""):
+    geometry = make_geometry(
+        series,
+        query_ids,
+        query_descriptions,
+        description_wrap_chars,
+        description_max_lines,
+    )
 
     lines = [
         '<?xml version="1.0" encoding="UTF-8"?>',
@@ -169,28 +295,46 @@ def render_svg(series, query_ids, title):
         )
 
     for query_index, query_id in enumerate(query_ids):
-        center = geometry.left + query_index * geometry.group_width + geometry.group_width / 2
+        center = geometry.query_centers[query_id]
         lines.append(f'<text x="{center:.2f}" y="{geometry.top + geometry.plot_height + 30}" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">{escape(query_id)}</text>')
 
+    for center, description_lines in query_description_items(query_ids, geometry, query_descriptions, description_wrap_chars, description_max_lines):
+        for line_index, description in enumerate(description_lines):
+            y = geometry.top + geometry.plot_height + 55 + line_index * 17
+            lines.append(f'<text x="{center:.2f}" y="{y}" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">{escape(description)}</text>')
+
     legend_x = geometry.left + geometry.plot_width + 35
-    legend_y = geometry.top + 15
-    lines.append(f'<text x="{legend_x}" y="{legend_y - 18}" font-family="Helvetica, Arial, sans-serif" font-size="13" font-weight="700">System</text>')
-    for index, item in enumerate(series):
+    legend_y = geometry.top + 32
+    lines.append(f'<text x="{legend_x}" y="{legend_y - 32}" font-family="Helvetica, Arial, sans-serif" font-size="13" font-weight="700">System</text>')
+    for index, item in enumerate(legend_items(series)):
         y = legend_y + index * 28
-        lines.append(f'<rect x="{legend_x}" y="{y - 12}" width="16" height="16" fill="{item.color}"/>')
-        lines.append(f'<text x="{legend_x + 24}" y="{y + 1}" font-family="Helvetica, Arial, sans-serif" font-size="12">{escape(item.label)}</text>')
+        label, color = item
+        lines.append(f'<rect x="{legend_x}" y="{y - 12}" width="16" height="16" fill="{color}"/>')
+        lines.append(f'<text x="{legend_x + 24}" y="{y + 1}" font-family="Helvetica, Arial, sans-serif" font-size="12">{escape(label)}</text>')
+
+    for index, note in enumerate(parse_side_note(side_note)):
+        y = legend_y + len(legend_items(series)) * 28 + 18 + index * 18
+        prefix, rest = side_note_parts(note)
+        lines.append(f'<text x="{legend_x}" y="{y}" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700" fill="#333">{escape(prefix)}</text>')
+        lines.append(f'<text x="{legend_x + approximate_text_width(prefix, 12, True)}" y="{y}" font-family="Helvetica, Arial, sans-serif" font-size="12" fill="#333">{escape(rest)}</text>')
 
     lines.append("</svg>")
     return "\n".join(lines)
 
 
-def write_png(output, series, query_ids, title):
+def write_png(output, series, query_ids, title, query_descriptions, description_wrap_chars, description_max_lines, side_note=""):
     try:
         from PIL import Image, ImageDraw, ImageFont
     except ImportError as error:
         raise RuntimeError("PNG output requires Pillow.") from error
 
-    geometry = make_geometry(series, query_ids)
+    geometry = make_geometry(
+        series,
+        query_ids,
+        query_descriptions,
+        description_wrap_chars,
+        description_max_lines,
+    )
     image = Image.new("RGB", (geometry.width, geometry.height), "white")
     draw = ImageDraw.Draw(image)
     fonts = {
@@ -198,8 +342,10 @@ def write_png(output, series, query_ids, title):
         "subtitle": load_font(ImageFont, 13),
         "tick": load_font(ImageFont, 11),
         "axis": load_font(ImageFont, 12, bold=True),
+        "description": load_font(ImageFont, 13),
         "legend": load_font(ImageFont, 12),
         "legend_title": load_font(ImageFont, 13, bold=True),
+        "note_bold": load_font(ImageFont, 12, bold=True),
     }
 
     draw.text((geometry.width / 2, 34), title, anchor="mm", font=fonts["title"], fill="#000000")
@@ -217,22 +363,40 @@ def write_png(output, series, query_ids, title):
         draw.rectangle((x, y, x + width, y + height), fill=item.color)
 
     for query_index, query_id in enumerate(query_ids):
-        center = geometry.left + query_index * geometry.group_width + geometry.group_width / 2
+        center = geometry.query_centers[query_id]
         draw.text((center, geometry.top + geometry.plot_height + 30), query_id, anchor="mm", font=fonts["axis"], fill="#000000")
 
+    for center, description_lines in query_description_items(query_ids, geometry, query_descriptions, description_wrap_chars, description_max_lines):
+        for line_index, description in enumerate(description_lines):
+            y = geometry.top + geometry.plot_height + 55 + line_index * 17
+            draw.text((center, y), description, anchor="mm", font=fonts["description"], fill="#333333")
+
     legend_x = geometry.left + geometry.plot_width + 35
-    legend_y = geometry.top + 15
-    draw.text((legend_x, legend_y - 18), "System", anchor="la", font=fonts["legend_title"], fill="#000000")
-    for index, item in enumerate(series):
+    legend_y = geometry.top + 32
+    draw.text((legend_x, legend_y - 32), "System", anchor="la", font=fonts["legend_title"], fill="#000000")
+    for index, item in enumerate(legend_items(series)):
         y = legend_y + index * 28
-        draw.rectangle((legend_x, y - 12, legend_x + 16, y + 4), fill=item.color)
-        draw.text((legend_x + 24, y), item.label, anchor="lm", font=fonts["legend"], fill="#000000")
+        label, color = item
+        draw.rectangle((legend_x, y - 12, legend_x + 16, y + 4), fill=color)
+        draw.text((legend_x + 24, y), label, anchor="lm", font=fonts["legend"], fill="#000000")
+
+    for index, note in enumerate(parse_side_note(side_note)):
+        y = legend_y + len(legend_items(series)) * 28 + 18 + index * 18
+        prefix, rest = side_note_parts(note)
+        draw.text((legend_x, y), prefix, anchor="la", font=fonts["note_bold"], fill="#333333")
+        draw.text((legend_x + draw.textlength(prefix, font=fonts["note_bold"]), y), rest, anchor="la", font=fonts["legend"], fill="#333333")
 
     image.save(output)
 
 
-def write_pdf(output, series, query_ids, title):
-    geometry = make_geometry(series, query_ids)
+def write_pdf(output, series, query_ids, title, query_descriptions, description_wrap_chars, description_max_lines, side_note=""):
+    geometry = make_geometry(
+        series,
+        query_ids,
+        query_descriptions,
+        description_wrap_chars,
+        description_max_lines,
+    )
     commands = []
 
     def py(y):
@@ -286,16 +450,28 @@ def text(x, y, value, size, color="#000000", bold=False, anchor="left"):
         rect(x, y, width, height, item.color)
 
     for query_index, query_id in enumerate(query_ids):
-        center = geometry.left + query_index * geometry.group_width + geometry.group_width / 2
+        center = geometry.query_centers[query_id]
         text(center, geometry.top + geometry.plot_height + 30, query_id, 12, bold=True, anchor="center")
 
+    for center, description_lines in query_description_items(query_ids, geometry, query_descriptions, description_wrap_chars, description_max_lines):
+        for line_index, description in enumerate(description_lines):
+            y = geometry.top + geometry.plot_height + 55 + line_index * 17
+            text(center, y, description, 13, color="#333333", anchor="center")
+
     legend_x = geometry.left + geometry.plot_width + 35
-    legend_y = geometry.top + 15
-    text(legend_x, legend_y - 18, "System", 13, bold=True)
-    for index, item in enumerate(series):
+    legend_y = geometry.top + 32
+    text(legend_x, legend_y - 32, "System", 13, bold=True)
+    for index, item in enumerate(legend_items(series)):
         y = legend_y + index * 28
-        rect(legend_x, y - 12, 16, 16, item.color)
-        text(legend_x + 24, y + 1, item.label, 12)
+        label, color = item
+        rect(legend_x, y - 12, 16, 16, color)
+        text(legend_x + 24, y + 1, label, 12)
+
+    for index, note in enumerate(parse_side_note(side_note)):
+        y = legend_y + len(legend_items(series)) * 28 + 18 + index * 18
+        prefix, rest = side_note_parts(note)
+        text(legend_x, y, prefix, 12, color="#333333", bold=True)
+        text(legend_x + approximate_text_width(prefix, 12, True), y, rest, 12, color="#333333")
 
     write_simple_pdf(output, geometry.width, geometry.height, "\n".join(commands))
 
@@ -370,20 +546,25 @@ def load_font(image_font, size, bold=False):
     return image_font.load_default()
 
 
-def make_geometry(series, query_ids):
-    width = 1180
-    height = 700
+def make_geometry(series, query_ids, query_descriptions=None, description_wrap_chars=18, description_max_lines=2):
+    width = plot_width_for_descriptions(
+        query_ids,
+        query_descriptions,
+        description_wrap_chars,
+        description_max_lines,
+    )
+    height = 720 if query_descriptions else 620
     left = 90
     right = 260
     top = 80
-    bottom = 120
+    bottom = 130 if query_descriptions else 80
     plot_width = width - left - right
     plot_height = height - top - bottom
     lower, upper = plot_bounds(series)
     ticks = nice_log_ticks(lower, upper)
-    group_width = plot_width / len(query_ids)
     bar_gap = 3
-    bar_width = min(24, (group_width - 20) / len(series) - bar_gap)
+    slots = series_slots(series)
+    bar_width, query_centers = query_layout(query_ids, slots, plot_width, left, bar_gap)
     return PlotGeometry(
         width,
         height,
@@ -396,27 +577,180 @@ def make_geometry(series, query_ids):
         lower,
         upper,
         ticks,
-        group_width,
         bar_gap,
         max(1.0, bar_width),
+        query_centers,
+    )
+
+
+def plot_width_for_descriptions(query_ids, query_descriptions, description_wrap_chars, description_max_lines):
+    base_width = 1180
+    left = 90
+    right = 260
+    groups = description_groups(query_ids, query_descriptions)
+    if not groups:
+        return base_width
+
+    longest_line = max(
+        len(line)
+        for _prefix, description in groups
+        for line in wrap_text(description, description_wrap_chars, description_max_lines)
     )
+    group_width = min(145, max(105, longest_line * 7.4 + 26))
+    return max(base_width, int(left + right + len(groups) * group_width))
 
 
 def bars(series, query_ids, geometry):
-    for query_index, query_id in enumerate(query_ids):
-        group_x = geometry.left + query_index * geometry.group_width
-        total_bar_width = len(series) * geometry.bar_width + (len(series) - 1) * geometry.bar_gap
-        start_x = group_x + (geometry.group_width - total_bar_width) / 2
-        for series_index, item in enumerate(series):
+    slots = series_slots(series)
+    slot_indexes = {label: index for index, label in enumerate(slots)}
+    for query_id in query_ids:
+        total_bar_width = len(slots) * geometry.bar_width + (len(slots) - 1) * geometry.bar_gap
+        start_x = geometry.query_centers[query_id] - total_bar_width / 2
+        for item in series:
             value = item.values.get(query_id)
             if value is None:
                 continue
-            x = start_x + series_index * (geometry.bar_width + geometry.bar_gap)
+            x = start_x + slot_indexes[item.visual_label] * (geometry.bar_width + geometry.bar_gap)
             y = y_for(value, geometry)
             height = geometry.top + geometry.plot_height - y
             yield x, y, geometry.bar_width, height, item, query_id, value
 
 
+def query_layout(query_ids, slots, plot_width, left, bar_gap):
+    pair_gap_units = layout_pair_gap_units(query_ids)
+    use_pair_spacing = any(unit == 1 for unit in pair_gap_units)
+    if not use_pair_spacing:
+        group_width = plot_width / len(query_ids)
+        bar_width = max(1.0, min(24, (group_width - 20) / len(slots) - bar_gap))
+        centers = {
+            query_id: left + index * group_width + group_width / 2
+            for index, query_id in enumerate(query_ids)
+        }
+        return bar_width, centers
+
+    fixed_gap_width = len(query_ids) * (len(slots) - 1) * bar_gap
+    bar_units = len(query_ids) * len(slots) + sum(pair_gap_units)
+    bar_width = max(1.0, min(24, (plot_width - fixed_gap_width) / bar_units))
+    group_bar_width = len(slots) * bar_width + (len(slots) - 1) * bar_gap
+    required_width = len(query_ids) * group_bar_width + sum(unit * bar_width for unit in pair_gap_units)
+    cursor = left + max(0, (plot_width - required_width) / 2)
+
+    centers = {}
+    for index, query_id in enumerate(query_ids):
+        centers[query_id] = cursor + group_bar_width / 2
+        cursor += group_bar_width
+        if index < len(pair_gap_units):
+            cursor += pair_gap_units[index] * bar_width
+    return bar_width, centers
+
+
+def layout_pair_gap_units(query_ids):
+    return [
+        1 if paired_layout_prefix(left) is not None and paired_layout_prefix(left) == paired_layout_prefix(right) else 3
+        for left, right in zip(query_ids, query_ids[1:])
+    ]
+
+
+def paired_layout_prefix(query_id):
+    if "_" not in query_id:
+        return None
+    prefix, suffix = query_id.rsplit("_", 1)
+    if suffix in {"P", "NP", "RP", "UP"}:
+        return prefix
+    return None
+
+
+def description_groups(query_ids, query_descriptions):
+    if not query_descriptions:
+        return []
+    groups = []
+    seen = set()
+    for query_id in query_ids:
+        prefix = paired_layout_prefix(query_id) or query_id
+        if prefix in seen:
+            continue
+        seen.add(prefix)
+        description = query_descriptions.get(prefix) or query_descriptions.get(query_id)
+        if description:
+            groups.append((prefix, description))
+    return groups
+
+
+def query_description_items(query_ids, geometry, query_descriptions, description_wrap_chars, description_max_lines):
+    if not query_descriptions:
+        return []
+    items = []
+    seen = set()
+    for query_id in query_ids:
+        prefix = paired_layout_prefix(query_id) or query_id
+        if prefix in seen:
+            continue
+        seen.add(prefix)
+        related = [
+            item
+            for item in query_ids
+            if (paired_layout_prefix(item) or item) == prefix
+        ]
+        description = query_descriptions.get(prefix) or query_descriptions.get(query_id)
+        if not description:
+            continue
+        center = sum(geometry.query_centers[item] for item in related) / len(related)
+        items.append((center, wrap_text(description, description_wrap_chars, description_max_lines)))
+    return items
+
+
+def wrap_text(text, max_chars, max_lines):
+    lines = []
+    current = ""
+    for word in text.split():
+        candidate = word if not current else current + " " + word
+        if len(candidate) <= max_chars:
+            current = candidate
+            continue
+        if current:
+            lines.append(current)
+        current = word
+    if current:
+        lines.append(current)
+    return lines[:max_lines]
+
+
+def normalize_visual_label(label):
+    for suffix in (" Repartitioned", " Unpartitioned"):
+        if label.endswith(suffix):
+            return label[: -len(suffix)]
+    return label
+
+
+def color_for_label(label, fallback_color_index):
+    for prefix, color in SYSTEM_COLORS:
+        if label.startswith(prefix):
+            return color, fallback_color_index
+    return COLORS[fallback_color_index % len(COLORS)], fallback_color_index + 1
+
+
+def series_slots(series):
+    slots = []
+    seen = set()
+    for item in series:
+        if item.visual_label in seen:
+            continue
+        seen.add(item.visual_label)
+        slots.append(item.visual_label)
+    return slots
+
+
+def legend_items(series):
+    items = []
+    seen = set()
+    for item in series:
+        if item.visual_label in seen:
+            continue
+        seen.add(item.visual_label)
+        items.append((item.visual_label, item.color))
+    return items
+
+
 def plot_bounds(series):
     values = [value for item in series for value in item.values.values() if value > 0]
     if not values:
@@ -455,6 +789,12 @@ def format_tick(value):
 
 
 def natural_key(value):
+    suffix_order = {"P": 0, "RP": 0, "NP": 1, "UP": 1}
+    if "_" in value:
+        prefix, suffix = value.rsplit("_", 1)
+        if suffix in suffix_order:
+            return natural_key(prefix) + [suffix_order[suffix]]
+
     parts = []
     current = ""
     for char in value:
diff --git a/plugins/parquet-adapter/benchmarks/scripts/runners/run_duckdb_benchmark.ps1 b/plugins/parquet-adapter/benchmarks/scripts/runners/run_duckdb_benchmark.ps1
index 8ff4b2f948..72d2b8bd55 100644
--- a/plugins/parquet-adapter/benchmarks/scripts/runners/run_duckdb_benchmark.ps1
+++ b/plugins/parquet-adapter/benchmarks/scripts/runners/run_duckdb_benchmark.ps1
@@ -3,6 +3,7 @@ param(
     [string]$DataDir = "C:\PolyData\tlc_partitioned",
     [string]$Queries = (Join-Path $PSScriptRoot "..\..\query_lists\access_model_comparison\access_model_comparison_sql.sql"),
     [string]$Output = (Join-Path $PSScriptRoot "..\..\results\access_model_comparison\duckdb_results.csv"),
+    [string]$ResultValuesOutput = "",
     [int]$Warmups = 1,
     [int]$Runs = 5,
     [int]$FetchSize = 1000,
@@ -73,6 +74,10 @@ if ($SqlFile) {
     $argsList += @("--sql-file", $SqlFile)
 }
 
+if ($ResultValuesOutput) {
+    $argsList += @("--result-values-output", $ResultValuesOutput)
+}
+
 if ($PrintRows) {
     $argsList += "--print-rows"
 }
diff --git a/plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_benchmark.ps1 b/plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_benchmark.ps1
index 113adf9904..0b4204ef44 100644
--- a/plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_benchmark.ps1
+++ b/plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_benchmark.ps1
@@ -2,6 +2,7 @@ param(
     [string]$Url = "jdbc:polypheny://pa:@localhost:20590/public",
     [string]$Queries = (Join-Path $PSScriptRoot "..\..\query_lists\access_model_comparison\access_model_comparison_rf.sql"),
     [string]$Output = (Join-Path $PSScriptRoot "..\..\results\access_model_comparison\polypheny_results.csv"),
+    [string]$ResultValuesOutput = "",
     [int]$Warmups = 1,
     [int]$Runs = 5,
     [int]$FetchSize = 1000,
@@ -71,6 +72,10 @@ if ($SqlFile) {
     $argsList += @("--sql-file", $SqlFile)
 }
 
+if ($ResultValuesOutput) {
+    $argsList += @("--result-values-output", $ResultValuesOutput)
+}
+
 if ($PrintRows) {
     $argsList += "--print-rows"
 }
diff --git a/plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_mql_benchmark.ps1 b/plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_mql_benchmark.ps1
index 7b5d303a54..bd14a348bf 100644
--- a/plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_mql_benchmark.ps1
+++ b/plugins/parquet-adapter/benchmarks/scripts/runners/run_polypheny_mql_benchmark.ps1
@@ -7,6 +7,7 @@ param(
     [string]$Language = "mongo",
     [string]$Queries = (Join-Path $PSScriptRoot "..\..\query_lists\access_model_comparison\access_model_comparison_mql.mql"),
     [string]$Output = (Join-Path $PSScriptRoot "..\..\results\access_model_comparison\polypheny_mql_results.csv"),
+    [string]$ResultValuesOutput = "",
     [int]$Warmups = 1,
     [int]$Runs = 5,
     [int]$FetchSize = 1000,
@@ -79,6 +80,10 @@ if ($MqlFile) {
     $argsList += @("--mql-file", $MqlFile)
 }
 
+if ($ResultValuesOutput) {
+    $argsList += @("--result-values-output", $ResultValuesOutput)
+}
+
 if ($PrintRows) {
     $argsList += "--print-rows"
 }
diff --git a/plugins/parquet-adapter/benchmarks/scripts/runners/run_spark_benchmark.ps1 b/plugins/parquet-adapter/benchmarks/scripts/runners/run_spark_benchmark.ps1
index f5d843ae38..80c5ee855a 100644
--- a/plugins/parquet-adapter/benchmarks/scripts/runners/run_spark_benchmark.ps1
+++ b/plugins/parquet-adapter/benchmarks/scripts/runners/run_spark_benchmark.ps1
@@ -3,6 +3,7 @@ param(
     [string]$NestedCustomerFile = "",
     [string]$Queries = (Join-Path $PSScriptRoot "..\..\query_lists\access_model_comparison\access_model_comparison_sql.sql"),
     [string]$Output = (Join-Path $PSScriptRoot "..\..\results\access_model_comparison\spark_results.csv"),
+    [string]$ResultValuesOutput = "",
     [int]$Warmups = 1,
     [int]$Runs = 5,
     [int]$Threads = 8,
@@ -74,6 +75,16 @@ if ($outputParent) {
     New-Item -ItemType Directory -Force -Path $outputParent | Out-Null
 }
 
+$resultValuesOutputContainer = ""
+if ($ResultValuesOutput) {
+    $resultValuesOutputHost = Resolve-UnderRepo $ResultValuesOutput
+    $resultValuesOutputParent = Split-Path $resultValuesOutputHost -Parent
+    if ($resultValuesOutputParent) {
+        New-Item -ItemType Directory -Force -Path $resultValuesOutputParent | Out-Null
+    }
+    $resultValuesOutputContainer = Convert-ToContainerRepoPath $resultValuesOutputHost
+}
+
 $sqlFileContainer = ""
 if ($SqlFile) {
     $sqlFileHost = Resolve-UnderRepo $SqlFile -MustExist
@@ -156,6 +167,10 @@ if ($sqlFileContainer) {
     $benchmarkArgs += @("--sql-file", $sqlFileContainer)
 }
 
+if ($resultValuesOutputContainer) {
+    $benchmarkArgs += @("--result-values-output", $resultValuesOutputContainer)
+}
+
 if ($PrintRows) {
     $benchmarkArgs += "--print-rows"
 }
diff --git a/plugins/parquet-adapter/benchmarks/scripts/summarize_benchmark_results.py b/plugins/parquet-adapter/benchmarks/scripts/summarize_benchmark_results.py
index a58997965e..1263d3550c 100644
--- a/plugins/parquet-adapter/benchmarks/scripts/summarize_benchmark_results.py
+++ b/plugins/parquet-adapter/benchmarks/scripts/summarize_benchmark_results.py
@@ -125,7 +125,7 @@ def render_markdown(summaries, inputs, args):
         f"# {args.title}",
         "",
         f"Phase summarized: `{args.phase}`.",
-        "Warmup rows are excluded. Mean and median values use successful runs only.",
+        "Warmup rows are excluded. Mean, median, and standard deviation values use successful runs only.",
         "",
         "## Source Files",
         "",
@@ -177,8 +177,8 @@ def render_markdown(summaries, inputs, args):
             "",
             f"## Detailed Summary ({unit_label})",
             "",
-            "| System | Query | Description | Runs | Mean | Median | Min | Max | Rows | Columns | Status |",
-            "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |",
+            "| System | Query | Description | Runs | Mean | Median | Std Dev | Min | Max | Rows | Columns | Status |",
+            "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |",
         ]
     )
     for system in systems:
@@ -196,6 +196,7 @@ def render_markdown(summaries, inputs, args):
                         f"{summary.successful_runs}/{summary.total_runs}",
                         format_stat(summary, statistics.mean, args.unit),
                         format_stat(summary, statistics.median, args.unit),
+                        format_stddev(summary, args.unit),
                         format_stat(summary, min, args.unit),
                         format_stat(summary, max, args.unit),
                         format_values(summary, "rows"),
@@ -230,6 +231,18 @@ def format_stat(summary, fn, unit):
     return f"{value:,.1f}"
 
 
+def format_stddev(summary, unit):
+    if summary is None or len(summary.elapsed_values) < 2:
+        return ""
+    return format_number(statistics.stdev(summary.elapsed_values), unit)
+
+
+def format_number(value, unit):
+    if unit == "s":
+        return f"{value / 1000.0:,.3f}"
+    return f"{value:,.1f}"
+
+
 def format_row_consistency(summaries):
     values = []
     for summary in summaries:
@@ -287,6 +300,11 @@ def format_int(value):
 
 
 def natural_key(value):
+    suffix_order = {"P": 0, "RP": 0, "NP": 1, "UP": 1}
+    if "_" in value:
+        prefix, suffix = value.rsplit("_", 1)
+        if suffix in suffix_order:
+            return natural_key(prefix) + [suffix_order[suffix]]
     return [int(part) if part.isdigit() else part for part in re.split(r"(\d+)", value)]
 
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java
index 3d686f7650..7e2c643b00 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetValueExtractor.java
@@ -6,6 +6,7 @@
 import java.util.Map;
 import org.apache.parquet.example.data.Group;
 import org.apache.parquet.schema.GroupType;
+import org.apache.parquet.schema.LogicalTypeAnnotation;
 import org.apache.parquet.schema.PrimitiveType;
 import org.apache.parquet.schema.Type;
 import org.polypheny.db.adapter.parquet.shared.schema.ParquetNameNormalizer;
@@ -31,6 +32,10 @@ protected PolyValue extractStructuredValue( Group group, int index, Type field )
             return PolyNull.NULL;
         }
 
+        if ( isList( field ) ) {
+            return extractListValue( group, index, field.asGroupType() );
+        }
+
         if ( repetitionCount > 1 ) {
             List<PolyValue> values = new ArrayList<>( repetitionCount );
             for ( int occurrence = 0; occurrence < repetitionCount; occurrence++ ) {
@@ -43,6 +48,43 @@ protected PolyValue extractStructuredValue( Group group, int index, Type field )
     }
 
 
+    private boolean isList( Type field ) {
+        return !field.isPrimitive() && field.getLogicalTypeAnnotation() instanceof LogicalTypeAnnotation.ListLogicalTypeAnnotation;
+    }
+
+
+    private PolyList<PolyValue> extractListValue( Group group, int index, GroupType listType ) {
+        List<PolyValue> values = new ArrayList<>();
+        int listOccurrences = group.getFieldRepetitionCount( index );
+        for ( int listOccurrence = 0; listOccurrence < listOccurrences; listOccurrence++ ) {
+            Group listGroup = group.getGroup( index, listOccurrence );
+            if ( listType.getFieldCount() == 0 ) {
+                continue;
+            }
+            Type repeated = listType.getType( 0 );
+            int elementOccurrences = listGroup.getFieldRepetitionCount( 0 );
+            for ( int elementOccurrence = 0; elementOccurrence < elementOccurrences; elementOccurrence++ ) {
+                values.add( extractListElement( listGroup, repeated, elementOccurrence ) );
+            }
+        }
+        return PolyList.of( values );
+    }
+
+
+    private PolyValue extractListElement( Group listGroup, Type repeated, int occurrence ) {
+        if ( repeated.isPrimitive() ) {
+            return extractStructuredValue( listGroup, 0, repeated, occurrence );
+        }
+
+        Group repeatedGroup = listGroup.getGroup( 0, occurrence );
+        GroupType repeatedType = repeated.asGroupType();
+        if ( repeatedType.getFieldCount() == 1 && "element".equals( repeatedType.getType( 0 ).getName() ) ) {
+            return extractStructuredValue( repeatedGroup, 0, repeatedType.getType( 0 ) );
+        }
+        return extractNestedDocument( repeatedGroup, repeatedType );
+    }
+
+
     protected PolyValue extractStructuredValue( Group group, int index, Type field, int occurrence ) {
         if ( field.isPrimitive() ) {
             return typeConverter.fromObjToPolyValue( field, extractPrimitiveValue( group, index, field.asPrimitiveType(), occurrence ) );
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetPageReader.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetPageReader.java
index eb27cf74af..774e0cfe6a 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetPageReader.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/aggregate/page/ParquetPageReader.java
@@ -83,7 +83,12 @@ protected Object currentValue( ColumnReader reader, ColumnDescriptor descriptor
     protected void consumeRemaining( ColumnReader[] readers, boolean[] consumed ) {
         for ( int i = 0; i < readers.length; i++ ) {
             if ( !consumed[i] ) {
-                readers[i].consume();
+                ColumnReader reader = readers[i];
+                ColumnDescriptor column = columns[i];
+                if ( column.getMaxDefinitionLevel() == 0 || reader.getCurrentDefinitionLevel() == column.getMaxDefinitionLevel() ) {
+                    reader.skip();
+                }
+                reader.consume();
             }
         }
     }
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetCountAggregatePageReaderTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetCountAggregatePageReaderTest.java
index 44d539fd09..e058a8efa4 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetCountAggregatePageReaderTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetCountAggregatePageReaderTest.java
@@ -106,6 +106,36 @@ void evaluatesPredicateForEveryRow() throws Exception {
     }
 
 
+    @Test
+    void keepsUnreadPredicateColumnsAlignedAfterShortCircuit() throws Exception {
+        MessageType schema = Types.buildMessage()
+                .required( PrimitiveTypeName.DOUBLE ).named( "trip_distance" )
+                .required( PrimitiveTypeName.DOUBLE ).named( "total_amount" )
+                .named( "test" );
+        Path file = writeTwoColumnParquet( schema );
+        List<ParquetAdapterFilter<PolyValue>> filters = List.of(
+                new ParquetAdapterFilter<>( 0, Kind.GREATER_THAN_OR_EQUAL, PolyDouble.of( 10D ) ),
+                new ParquetAdapterFilter<>( 1, Kind.GREATER_THAN_OR_EQUAL, PolyDouble.of( 40D ) ) );
+
+        Configuration conf = HadoopConfigurationFactory.create( getClass().getClassLoader() );
+        org.apache.hadoop.fs.Path parquetPath = new org.apache.hadoop.fs.Path( file.toUri() );
+        try ( ParquetFileReader fileReader = ParquetFileReader.open( HadoopInputFile.fromPath( parquetPath, conf ), ParquetReadOptions.builder().build() ) ) {
+            fileReader.setRequestedSchema( schema );
+            try ( PageReadStore pages = fileReader.readNextRowGroup() ) {
+                ColumnReadStore store = new ColumnReadStoreImpl( pages, new NoOpColumnConverter( schema ), schema, fileReader.getFileMetaData().getCreatedBy() );
+                ColumnDescriptor[] descriptors = schema.getColumns().toArray( ColumnDescriptor[]::new );
+                AggregateCallDescriptor[] aggregateCalls = new AggregateCallDescriptor[]{ AggregateCallDescriptor.countStar() };
+                Map<GroupKey, AggregateGroupState> aggregates = new LinkedHashMap<>();
+
+                new ParquetCountAggregatePageReader( pages, store, descriptors, ParquetPrimitivePredicate.compile( schema, filters ) )
+                        .read( aggregates, aggregateCalls, filters, new AtomicBoolean( false ) );
+
+                assertEquals( 0, aggregates.get( GroupKey.Empty ).count( 0 ) );
+            }
+        }
+    }
+
+
     private Path writeParquet( MessageType schema ) throws Exception {
         Path file = tempDir.resolve( "count.parquet" );
         SimpleGroupFactory factory = new SimpleGroupFactory( schema );
@@ -121,6 +151,21 @@ private Path writeParquet( MessageType schema ) throws Exception {
     }
 
 
+    private Path writeTwoColumnParquet( MessageType schema ) throws Exception {
+        Path file = tempDir.resolve( "count_two_columns.parquet" );
+        SimpleGroupFactory factory = new SimpleGroupFactory( schema );
+        try ( ParquetWriter<Group> writer = ExampleParquetWriter.builder( new LocalOutputFile( file ) )
+                .withType( schema )
+                .build() ) {
+            writer.write( factory.newGroup().append( "trip_distance", 1D ).append( "total_amount", 100D ) );
+            writer.write( factory.newGroup().append( "trip_distance", 10D ).append( "total_amount", 20D ) );
+            writer.write( factory.newGroup().append( "trip_distance", 11D ).append( "total_amount", 20D ) );
+            writer.write( factory.newGroup().append( "trip_distance", 12D ).append( "total_amount", 20D ) );
+        }
+        return file;
+    }
+
+
     private record LocalOutputFile( Path path ) implements OutputFile {
 
         @Override
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java
index c3f6412d9b..da004ad490 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java
@@ -35,6 +35,7 @@
 import org.apache.parquet.io.PositionOutputStream;
 import org.apache.parquet.schema.LogicalTypeAnnotation;
 import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.MessageTypeParser;
 import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName;
 import org.apache.parquet.schema.Types;
 import org.jetbrains.annotations.NotNull;
@@ -159,6 +160,40 @@ void extractDocumentConvertsRepeatedNestedGroupsToDocumentLists() {
     }
 
 
+    @Test
+    void extractDocumentUnwrapsStandardParquetListElements() {
+        MessageType schema = MessageTypeParser.parseMessageType( """
+                message doc_schema {
+                  optional group c_orders (LIST) {
+                    repeated group list {
+                      optional group element {
+                        optional int64 o_orderkey;
+                        optional binary o_orderstatus (STRING);
+                      }
+                    }
+                  }
+                }
+                """ );
+        SimpleGroupFactory factory = new SimpleGroupFactory( schema );
+        Group row = factory.newGroup();
+        Group orders = row.addGroup( "c_orders" );
+        orders.addGroup( "list" ).addGroup( "element" )
+                .append( "o_orderkey", 1L )
+                .append( "o_orderstatus", "F" );
+        orders.addGroup( "list" ).addGroup( "element" )
+                .append( "o_orderkey", 2L )
+                .append( "o_orderstatus", "O" );
+
+        PolyDocument document = new ParquetDocValueExtractor().extractDocument( row, schema, PolyString.of( "generated-id" ) );
+
+        PolyList<PolyValue> ordersList = document.get( PolyString.of( "c_orders" ) ).asList();
+        assertEquals( 2, ordersList.size() );
+        assertEquals( 1L, ordersList.get( 0 ).asDocument().get( PolyString.of( "o_orderkey" ) ).asNumber().longValue() );
+        assertEquals( "F", ordersList.get( 0 ).asDocument().get( PolyString.of( "o_orderstatus" ) ).asString().value );
+        assertFalse( ordersList.get( 0 ).asDocument().containsKey( PolyString.of( "element" ) ) );
+    }
+
+
     @Test
     void docFilterTranslatorTranslatesTopLevelNameAndRejectsNestedPaths() {
         AlgDataTypeFactory typeFactory = new PolyTypeFactoryImpl( AlgDataTypeSystem.DEFAULT );

From 5e16663f6b0827a976b2e47d7ee207e1bbb111c6 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Sun, 28 Jun 2026 17:40:06 +0200
Subject: [PATCH 42/46] benchmarks, result analysis, bug fixes

---
 .../benchmarks/benchmark-plan.md              |  52 +++++-
 .../nested_data_polypheny_normalized.sql      |   2 +-
 .../partitioning_query_specification.md       |  24 +--
 ...s_model_comparison_duckdb_tlcp_results.csv |  31 ----
 ..._comparison_polypheny_mql_tlcp_results.csv |  31 ----
 ...l_comparison_polypheny_rf_tlcp_results.csv |  31 ----
 ...ss_model_comparison_spark_tlcp_results.csv |  31 ----
 .../access_model_comparison_tlcp_results.md   | 151 -----------------
 .../access_model_comparison_tlcp_summary.md   |  58 -------
 ...s_model_comparison_tlcp_result_analysis.md |   9 +-
 .../aggregation_correctness_summary.md        |  24 +--
 .../aggregation_polypheny_mql_results.csv     | 120 +++++++-------
 .../aggregation_polypheny_mql_values.jsonl    |  20 +--
 .../aggregation_result_analysis.md            |  41 ++---
 .../aggregation/aggregation_summary.md        | 152 ++++++++++++------
 .../nested_data/Q5_nested_data_query_plan.png | Bin 0 -> 47351 bytes
 .../nested_data_result_analysis.md            |  53 ++++++
 .../nested_data/nested_data_summary.md        | 105 +++++++-----
 .../partitioning_result_analysis.md           |  69 ++++++++
 .../partitioning_results_analysis.md          |  76 ---------
 .../partitioning/partitioning_summary.md      | 149 +++++++++--------
 .../results/plots/aggregation_plot.pdf        | Bin 8367 -> 8367 bytes
 .../results/plots/aggregation_plot.png        | Bin 34568 -> 34561 bytes
 .../results/plots/aggregation_plot.svg        |  20 +--
 .../results/plots/nested_data_plot.pdf        | Bin 5394 -> 5832 bytes
 .../results/plots/nested_data_plot.png        | Bin 25585 -> 28241 bytes
 .../results/plots/nested_data_plot.svg        |  15 +-
 .../results/plots/partitioning_plot.pdf       | Bin 6237 -> 6499 bytes
 .../results/plots/partitioning_plot.png       | Bin 27090 -> 29288 bytes
 .../results/plots/partitioning_plot.svg       |  17 +-
 .../run_nested_data_bm_pipeline.md            |   6 +-
 .../run_partitioning_bm_pipeline.md           |   6 +-
 .../benchmark_result_plot_generator.py        |  60 ++++++-
 .../scripts/summarize_benchmark_results.py    |  75 ++++++---
 .../document/schema/ParquetDocument.java      |  32 ++--
 .../parquet/ParquetDocumentPlanningTest.java  |  49 +++++-
 36 files changed, 743 insertions(+), 766 deletions(-)
 delete mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_duckdb_tlcp_results.csv
 delete mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_mql_tlcp_results.csv
 delete mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_rf_tlcp_results.csv
 delete mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_spark_tlcp_results.csv
 delete mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_results.md
 delete mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_summary.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/nested_data/Q5_nested_data_query_plan.png
 create mode 100644 plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_result_analysis.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_result_analysis.md
 delete mode 100644 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_results_analysis.md

diff --git a/plugins/parquet-adapter/benchmarks/benchmark-plan.md b/plugins/parquet-adapter/benchmarks/benchmark-plan.md
index 4dc4b289a4..2b756d0fb7 100644
--- a/plugins/parquet-adapter/benchmarks/benchmark-plan.md
+++ b/plugins/parquet-adapter/benchmarks/benchmark-plan.md
@@ -126,11 +126,19 @@ plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_
 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_spark_tlcp_results.csv
 ```
 
-The current summary and analysis files are:
+Result summary:
 
 ```text
 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
-plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_results_analysis.md
+```
+
+### Result Interpretation
+
+The generated plot and manual interpretation of the benchmark results are
+documented in:
+
+```text
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_result_analysis.md
 ```
 
 ### Run Pipeline
@@ -195,16 +203,30 @@ Raw and summarized results are stored in:
 plugins/parquet-adapter/benchmarks/results/nested_data/
 ```
 
-Executed raw result and summary files:
+Executed raw result files:
 
 ```text
 plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv
 plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_mql_results.csv
 plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_duckdb_results.csv
 plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_spark_results.csv
+```
+
+Result summary:
+
+```text
 plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_summary.md
 ```
 
+### Result Interpretation
+
+The generated plot and manual interpretation of the benchmark results are
+documented in:
+
+```text
+plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_result_analysis.md
+```
+
 ### Run Pipeline
 
 Runnable commands for this suite are documented in:
@@ -293,12 +315,20 @@ plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_values
 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_values.jsonl
 ```
 
-The current summary, correctness summary, and analysis files are:
+Result and correctness summaries:
 
 ```text
 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_correctness_summary.md
-plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_results_analysis.md
+```
+
+### Result Interpretation
+
+The generated plot and manual interpretation of the benchmark results are
+documented in:
+
+```text
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_result_analysis.md
 ```
 
 ### Run Pipeline
@@ -379,11 +409,19 @@ plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_repar
 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_unpartitioned_results.csv
 ```
 
-The current summary and analysis files are:
+Result summary:
 
 ```text
 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
-plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_results_analysis.md
+```
+
+### Result Interpretation
+
+The generated plot and manual interpretation of the benchmark results are
+documented in:
+
+```text
+plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_result_analysis.md
 ```
 
 ### Query Themes
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_polypheny_normalized.sql b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_polypheny_normalized.sql
index e1842dd9d1..40e90cb323 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_polypheny_normalized.sql
+++ b/plugins/parquet-adapter/benchmarks/query_lists/nested_data/nested_data_polypheny_normalized.sql
@@ -1,4 +1,4 @@
--- Q01: Filtered full customer rows.
+-- Q01: Filtered full customer rows. Use only root level fields
 SELECT *
 FROM ncp__nestedcustomer
 WHERE c_mktsegment = 'BUILDING'
diff --git a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md
index 8e7524082d..848126d512 100644
--- a/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md
+++ b/plugins/parquet-adapter/benchmarks/query_lists/partitioning/partitioning_query_specification.md
@@ -7,18 +7,18 @@ identifier suffix indicates the layout:
 - `P`: repartitioned, Hive-style partitioned layout
 - `NP`: unpartitioned layout with `year` and `month` stored as physical columns
 
-| Query | Layout        | Operation                                      | Predicate                                                            | Returned data   | Purpose                                                               |
-|-------|---------------|------------------------------------------------|----------------------------------------------------------------------|-----------------|-----------------------------------------------------------------------|
-| Q1_P | Repartitioned | Full table baseline                            | None                                                                 | Aggregate count | Measures baseline count over repartitioned `yellow_tripdata`          |
-| Q1_NP | Unpartitioned | Full table baseline                            | None                                                                 | Aggregate count | Measures baseline count over unpartitioned `yellow_tripdata`          |
-| Q2_P | Repartitioned | Filter by year                                 | `year = '2022'`                                                      | Aggregate count | Measures year-level partition pruning                                 |
-| Q2_NP | Unpartitioned | Physical year-column filter                    | `year = '2022'`                                                      | Aggregate count | Measures filtering on the physical year column                        |
-| Q3_P | Repartitioned | Filter by year and month                       | `year = '2022'` and `month = '10'`                                   | Aggregate count | Measures pruning with both partition levels                           |
-| Q3_NP | Unpartitioned | Physical year/month-column filter              | `year = '2022'` and `month = '10'`                                   | Aggregate count | Measures filtering on physical year and month columns                 |
-| Q4_P | Repartitioned | Filtered count, full scan                      | `trip_distance >= 10.0` and `total_amount >= 40.0`                   | Aggregate count | Measures data-column filtering without partition restriction          |
-| Q4_NP | Unpartitioned | Filtered count, full scan                      | `trip_distance >= 10.0` and `total_amount >= 40.0`                   | Aggregate count | Measures data-column filtering on unpartitioned data                  |
-| Q5_P | Repartitioned | Filtered count with year partition             | `year = '2022'`, `trip_distance >= 10.0`, and `total_amount >= 40.0` | Aggregate count | Measures combined partition pruning and data-column filtering         |
-| Q5_NP | Unpartitioned | Filtered count with physical year column       | `year = '2022'`, `trip_distance >= 10.0`, and `total_amount >= 40.0` | Aggregate count | Measures combined physical-column filtering and data-column filtering |
+| Query | Layout        | Operation                                | Predicate                                                            | Returned data   | Purpose                                                               |
+|-------|---------------|------------------------------------------|----------------------------------------------------------------------|-----------------|-----------------------------------------------------------------------|
+| Q1_P  | Repartitioned | Full table baseline                      | None                                                                 | Aggregate count | Measures baseline count over repartitioned `yellow_tripdata`          |
+| Q1_NP | Unpartitioned | Full table baseline                      | None                                                                 | Aggregate count | Measures baseline count over unpartitioned `yellow_tripdata`          |
+| Q2_P  | Repartitioned | Filter by year                           | `year = '2022'`                                                      | Aggregate count | Measures year-level partition pruning                                 |
+| Q2_NP | Unpartitioned | Physical year-column filter              | `year = '2022'`                                                      | Aggregate count | Measures filtering on the physical year column                        |
+| Q3_P  | Repartitioned | Filter by year and month                 | `year = '2022'` and `month = '10'`                                   | Aggregate count | Measures pruning with both partition levels                           |
+| Q3_NP | Unpartitioned | Physical year/month-column filter        | `year = '2022'` and `month = '10'`                                   | Aggregate count | Measures filtering on physical year and month columns                 |
+| Q4_P  | Repartitioned | Filtered count, full scan                | `trip_distance >= 10.0` and `total_amount >= 40.0`                   | Aggregate count | Measures data-column filtering without partition restriction          |
+| Q4_NP | Unpartitioned | Filtered count, full scan                | `trip_distance >= 10.0` and `total_amount >= 40.0`                   | Aggregate count | Measures data-column filtering on unpartitioned data                  |
+| Q5_P  | Repartitioned | Filtered count with year partition       | `year = '2022'`, `trip_distance >= 10.0`, and `total_amount >= 40.0` | Aggregate count | Measures combined partition pruning and data-column filtering         |
+| Q5_NP | Unpartitioned | Filtered count with physical year column | `year = '2022'`, `trip_distance >= 10.0`, and `total_amount >= 40.0` | Aggregate count | Measures combined physical-column filtering and data-column filtering |
 
 Q1_P, Q1_NP, Q2_P, Q2_NP, Q3_P, and Q3_NP run on `yellow_tripdata`.
 Q4_P, Q4_NP, Q5_P, and Q5_NP run on `green_tripdata`.
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_duckdb_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_duckdb_tlcp_results.csv
deleted file mode 100644
index 79160dc352..0000000000
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_duckdb_tlcp_results.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-11T20:06:31.581772500Z","Q01","Full record access","warmup",1,2568,3711544,22,true,""
-"2026-06-11T20:06:34.366902100Z","Q01","Full record access","measured",1,2786,3711544,22,true,""
-"2026-06-11T20:06:36.924498100Z","Q01","Full record access","measured",2,2558,3711544,22,true,""
-"2026-06-11T20:06:39.735769300Z","Q01","Full record access","measured",3,2807,3711544,22,true,""
-"2026-06-11T20:06:42.398329600Z","Q01","Full record access","measured",4,2662,3711544,22,true,""
-"2026-06-11T20:06:45.678604900Z","Q01","Full record access","measured",5,3269,3711544,22,true,""
-"2026-06-11T20:06:45.842128700Z","Q02","Projection","warmup",1,161,3711544,5,true,""
-"2026-06-11T20:06:46.009288800Z","Q02","Projection","measured",1,168,3711544,5,true,""
-"2026-06-11T20:06:46.183710500Z","Q02","Projection","measured",2,173,3711544,5,true,""
-"2026-06-11T20:06:46.369324Z","Q02","Projection","measured",3,183,3711544,5,true,""
-"2026-06-11T20:06:46.550356800Z","Q02","Projection","measured",4,184,3711544,5,true,""
-"2026-06-11T20:06:46.732551300Z","Q02","Projection","measured",5,179,3711544,5,true,""
-"2026-06-11T20:06:46.768651200Z","Q03","Filtered count","warmup",1,32,1,1,true,""
-"2026-06-11T20:06:46.799113100Z","Q03","Filtered count","measured",1,31,1,1,true,""
-"2026-06-11T20:06:46.836965200Z","Q03","Filtered count","measured",2,35,1,1,true,""
-"2026-06-11T20:06:46.883365600Z","Q03","Filtered count","measured",3,46,1,1,true,""
-"2026-06-11T20:06:46.927137900Z","Q03","Filtered count","measured",4,41,1,1,true,""
-"2026-06-11T20:06:46.970439800Z","Q03","Filtered count","measured",5,42,1,1,true,""
-"2026-06-11T20:06:46.983052700Z","Q04","Filtered full record access","warmup",1,11,0,22,true,""
-"2026-06-11T20:06:46.998657800Z","Q04","Filtered full record access","measured",1,13,0,22,true,""
-"2026-06-11T20:06:47.018166900Z","Q04","Filtered full record access","measured",2,17,0,22,true,""
-"2026-06-11T20:06:47.030161300Z","Q04","Filtered full record access","measured",3,12,0,22,true,""
-"2026-06-11T20:06:47.045945500Z","Q04","Filtered full record access","measured",4,13,0,22,true,""
-"2026-06-11T20:06:47.063416700Z","Q04","Filtered full record access","measured",5,18,0,22,true,""
-"2026-06-11T20:06:47.076852700Z","Q05","Filtered projection","warmup",1,13,0,5,true,""
-"2026-06-11T20:06:47.092357100Z","Q05","Filtered projection","measured",1,13,0,5,true,""
-"2026-06-11T20:06:47.107606100Z","Q05","Filtered projection","measured",2,12,0,5,true,""
-"2026-06-11T20:06:47.122789200Z","Q05","Filtered projection","measured",3,16,0,5,true,""
-"2026-06-11T20:06:47.138406400Z","Q05","Filtered projection","measured",4,12,0,5,true,""
-"2026-06-11T20:06:47.148626400Z","Q05","Filtered projection","measured",5,12,0,5,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_mql_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_mql_tlcp_results.csv
deleted file mode 100644
index c3996828ae..0000000000
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_mql_tlcp_results.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-11T18:57:18.950866100Z","Q01","Full document access","warmup",1,146487,3711544,20,true,""
-"2026-06-11T18:59:40.878060600Z","Q01","Full document access","measured",1,141914,3711544,20,true,""
-"2026-06-11T19:02:01.558648500Z","Q01","Full document access","measured",2,140681,3711544,20,true,""
-"2026-06-11T19:04:22.409971200Z","Q01","Full document access","measured",3,140847,3711544,20,true,""
-"2026-06-11T19:06:43.085124100Z","Q01","Full document access","measured",4,140674,3711544,20,true,""
-"2026-06-11T19:09:02.860315100Z","Q01","Full document access","measured",5,139777,3711544,20,true,""
-"2026-06-11T19:10:49.613562100Z","Q02","Projection","warmup",1,106748,3711544,6,true,""
-"2026-06-11T19:12:37.236250Z","Q02","Projection","measured",1,107622,3711544,6,true,""
-"2026-06-11T19:14:24.269443500Z","Q02","Projection","measured",2,107031,3711544,6,true,""
-"2026-06-11T19:16:11.689652200Z","Q02","Projection","measured",3,107420,3711544,6,true,""
-"2026-06-11T19:17:58.630301400Z","Q02","Projection","measured",4,106939,3711544,6,true,""
-"2026-06-11T19:19:50.212246Z","Q02","Projection","measured",5,111583,3711544,6,true,""
-"2026-06-11T19:21:11.230251400Z","Q03","Filtered count","warmup",1,81015,1,1,true,""
-"2026-06-11T19:22:32.692593700Z","Q03","Filtered count","measured",1,81461,1,1,true,""
-"2026-06-11T19:23:54.995095900Z","Q03","Filtered count","measured",2,82302,1,1,true,""
-"2026-06-11T19:25:16.529502700Z","Q03","Filtered count","measured",3,81532,1,1,true,""
-"2026-06-11T19:26:37.617071Z","Q03","Filtered count","measured",4,81087,1,1,true,""
-"2026-06-11T19:27:57.890478900Z","Q03","Filtered count","measured",5,80273,1,1,true,""
-"2026-06-11T19:29:24.111617200Z","Q04","Filtered full document access","warmup",1,86220,283006,20,true,""
-"2026-06-11T19:30:49.465861100Z","Q04","Filtered full document access","measured",1,85352,283006,20,true,""
-"2026-06-11T19:32:15.176393900Z","Q04","Filtered full document access","measured",2,85709,283006,20,true,""
-"2026-06-11T19:33:40.304629900Z","Q04","Filtered full document access","measured",3,85128,283006,20,true,""
-"2026-06-11T19:35:05.504422200Z","Q04","Filtered full document access","measured",4,85199,283006,20,true,""
-"2026-06-11T19:36:30.339635500Z","Q04","Filtered full document access","measured",5,84831,283006,20,true,""
-"2026-06-11T19:37:53.751224200Z","Q05","Filtered projection","warmup",1,83409,283006,6,true,""
-"2026-06-11T19:39:17.115780200Z","Q05","Filtered projection","measured",1,83364,283006,6,true,""
-"2026-06-11T19:40:40.715102800Z","Q05","Filtered projection","measured",2,83598,283006,6,true,""
-"2026-06-11T19:42:03.812402600Z","Q05","Filtered projection","measured",3,83096,283006,6,true,""
-"2026-06-11T19:43:27.234388Z","Q05","Filtered projection","measured",4,83422,283006,6,true,""
-"2026-06-11T19:44:50.397084400Z","Q05","Filtered projection","measured",5,83162,283006,6,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_rf_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_rf_tlcp_results.csv
deleted file mode 100644
index 176802a828..0000000000
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_polypheny_rf_tlcp_results.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-11T20:02:04.322905500Z","Q01","Full record access","warmup",1,27460,3711544,22,true,""
-"2026-06-11T20:02:30.702631600Z","Q01","Full record access","measured",1,26375,3711544,22,true,""
-"2026-06-11T20:02:56.508888700Z","Q01","Full record access","measured",2,25808,3711544,22,true,""
-"2026-06-11T20:03:22.371052800Z","Q01","Full record access","measured",3,25859,3711544,22,true,""
-"2026-06-11T20:03:48.611189800Z","Q01","Full record access","measured",4,26238,3711544,22,true,""
-"2026-06-11T20:04:15.053012Z","Q01","Full record access","measured",5,26432,3711544,22,true,""
-"2026-06-11T20:04:23.672906800Z","Q02","Projection","warmup",1,8618,3711544,5,true,""
-"2026-06-11T20:04:32.827854800Z","Q02","Projection","measured",1,9154,3711544,5,true,""
-"2026-06-11T20:04:41.665483800Z","Q02","Projection","measured",2,8836,3711544,5,true,""
-"2026-06-11T20:04:50.278581600Z","Q02","Projection","measured",3,8612,3711544,5,true,""
-"2026-06-11T20:04:58.725750700Z","Q02","Projection","measured",4,8446,3711544,5,true,""
-"2026-06-11T20:05:07.676704800Z","Q02","Projection","measured",5,8953,3711544,5,true,""
-"2026-06-11T20:05:08.635764Z","Q03","Filtered count","warmup",1,955,1,1,true,""
-"2026-06-11T20:05:09.194810Z","Q03","Filtered count","measured",1,560,1,1,true,""
-"2026-06-11T20:05:09.719193900Z","Q03","Filtered count","measured",2,523,1,1,true,""
-"2026-06-11T20:05:10.229289800Z","Q03","Filtered count","measured",3,508,1,1,true,""
-"2026-06-11T20:05:10.740282Z","Q03","Filtered count","measured",4,509,1,1,true,""
-"2026-06-11T20:05:11.371187100Z","Q03","Filtered count","measured",5,631,1,1,true,""
-"2026-06-11T20:05:11.451489900Z","Q04","Filtered full record access","warmup",1,77,0,22,true,""
-"2026-06-11T20:05:11.463724200Z","Q04","Filtered full record access","measured",1,11,0,22,true,""
-"2026-06-11T20:05:11.476779800Z","Q04","Filtered full record access","measured",2,12,0,22,true,""
-"2026-06-11T20:05:11.488827900Z","Q04","Filtered full record access","measured",3,12,0,22,true,""
-"2026-06-11T20:05:11.502028800Z","Q04","Filtered full record access","measured",4,12,0,22,true,""
-"2026-06-11T20:05:11.512541700Z","Q04","Filtered full record access","measured",5,11,0,22,true,""
-"2026-06-11T20:05:11.590520800Z","Q05","Filtered projection","warmup",1,74,0,5,true,""
-"2026-06-11T20:05:11.599519500Z","Q05","Filtered projection","measured",1,8,0,5,true,""
-"2026-06-11T20:05:11.612525500Z","Q05","Filtered projection","measured",2,12,0,5,true,""
-"2026-06-11T20:05:11.622031100Z","Q05","Filtered projection","measured",3,8,0,5,true,""
-"2026-06-11T20:05:11.633037Z","Q05","Filtered projection","measured",4,10,0,5,true,""
-"2026-06-11T20:05:11.644040600Z","Q05","Filtered projection","measured",5,10,0,5,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_spark_tlcp_results.csv b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_spark_tlcp_results.csv
deleted file mode 100644
index 241abb87c2..0000000000
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_spark_tlcp_results.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-2026-06-11T20:08:11.202281Z,Q01,Full record access,warmup,1,27845,3711544,22,true,
-2026-06-11T20:08:31.149931Z,Q01,Full record access,measured,1,19947,3711544,22,true,
-2026-06-11T20:08:52.997061Z,Q01,Full record access,measured,2,19449,3711544,22,true,
-2026-06-11T20:09:12.591150Z,Q01,Full record access,measured,3,19593,3711544,22,true,
-2026-06-11T20:09:35.059883Z,Q01,Full record access,measured,4,20172,3711544,22,true,
-2026-06-11T20:09:56.709063Z,Q01,Full record access,measured,5,20031,3711544,22,true,
-2026-06-11T20:10:10.017439Z,Q02,Projection,warmup,1,13307,3711544,5,true,
-2026-06-11T20:10:25.359033Z,Q02,Projection,measured,1,13229,3711544,5,true,
-2026-06-11T20:10:38.260598Z,Q02,Projection,measured,2,12901,3711544,5,true,
-2026-06-11T20:10:53.632397Z,Q02,Projection,measured,3,13294,3711544,5,true,
-2026-06-11T20:11:05.793956Z,Q02,Projection,measured,4,12161,3711544,5,true,
-2026-06-11T20:11:18.581505Z,Q02,Projection,measured,5,12787,3711544,5,true,
-2026-06-11T20:11:20.488438Z,Q03,Filtered count,warmup,1,1905,1,1,true,
-2026-06-11T20:11:23.866016Z,Q03,Filtered count,measured,1,1264,1,1,true,
-2026-06-11T20:11:24.992046Z,Q03,Filtered count,measured,2,1125,1,1,true,
-2026-06-11T20:11:25.983035Z,Q03,Filtered count,measured,3,990,1,1,true,
-2026-06-11T20:11:26.877336Z,Q03,Filtered count,measured,4,893,1,1,true,
-2026-06-11T20:11:27.903714Z,Q03,Filtered count,measured,5,1025,1,1,true,
-2026-06-11T20:11:28.029976Z,Q04,Filtered full record access,warmup,1,124,0,22,true,
-2026-06-11T20:11:28.121348Z,Q04,Filtered full record access,measured,1,90,0,22,true,
-2026-06-11T20:11:28.216447Z,Q04,Filtered full record access,measured,2,94,0,22,true,
-2026-06-11T20:11:28.298063Z,Q04,Filtered full record access,measured,3,81,0,22,true,
-2026-06-11T20:11:28.392095Z,Q04,Filtered full record access,measured,4,93,0,22,true,
-2026-06-11T20:11:28.481201Z,Q04,Filtered full record access,measured,5,88,0,22,true,
-2026-06-11T20:11:28.599850Z,Q05,Filtered projection,warmup,1,117,0,5,true,
-2026-06-11T20:11:28.697370Z,Q05,Filtered projection,measured,1,96,0,5,true,
-2026-06-11T20:11:28.811912Z,Q05,Filtered projection,measured,2,113,0,5,true,
-2026-06-11T20:11:28.926642Z,Q05,Filtered projection,measured,3,114,0,5,true,
-2026-06-11T20:11:29.012868Z,Q05,Filtered projection,measured,4,85,0,5,true,
-2026-06-11T20:11:29.096270Z,Q05,Filtered projection,measured,5,82,0,5,true,
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_results.md b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_results.md
deleted file mode 100644
index 762d8ad853..0000000000
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_results.md
+++ /dev/null
@@ -1,151 +0,0 @@
-# Access Model Comparison TLCP Results
-
-This file contains the raw benchmark result rows from the TLCP access model comparison CSV files.
-
-## Polypheny Relational
-
-Source: `access_model_comparison_polypheny_rf_tlcp_results.csv`
-
-| timestamp                      | query_id | description                 | phase    | run | elapsed_ms | rows    | columns | success | error |
-|--------------------------------|----------|-----------------------------|----------|-----|------------|---------|---------|---------|-------|
-| 2026-06-11T20:02:04.322905500Z | Q01      | Full record access          | warmup   | 1   | 27460      | 3711544 | 22      | true    |       |
-| 2026-06-11T20:02:30.702631600Z | Q01      | Full record access          | measured | 1   | 26375      | 3711544 | 22      | true    |       |
-| 2026-06-11T20:02:56.508888700Z | Q01      | Full record access          | measured | 2   | 25808      | 3711544 | 22      | true    |       |
-| 2026-06-11T20:03:22.371052800Z | Q01      | Full record access          | measured | 3   | 25859      | 3711544 | 22      | true    |       |
-| 2026-06-11T20:03:48.611189800Z | Q01      | Full record access          | measured | 4   | 26238      | 3711544 | 22      | true    |       |
-| 2026-06-11T20:04:15.053012Z    | Q01      | Full record access          | measured | 5   | 26432      | 3711544 | 22      | true    |       |
-| 2026-06-11T20:04:23.672906800Z | Q02      | Projection                  | warmup   | 1   | 8618       | 3711544 | 5       | true    |       |
-| 2026-06-11T20:04:32.827854800Z | Q02      | Projection                  | measured | 1   | 9154       | 3711544 | 5       | true    |       |
-| 2026-06-11T20:04:41.665483800Z | Q02      | Projection                  | measured | 2   | 8836       | 3711544 | 5       | true    |       |
-| 2026-06-11T20:04:50.278581600Z | Q02      | Projection                  | measured | 3   | 8612       | 3711544 | 5       | true    |       |
-| 2026-06-11T20:04:58.725750700Z | Q02      | Projection                  | measured | 4   | 8446       | 3711544 | 5       | true    |       |
-| 2026-06-11T20:05:07.676704800Z | Q02      | Projection                  | measured | 5   | 8953       | 3711544 | 5       | true    |       |
-| 2026-06-11T20:05:08.635764Z    | Q03      | Filtered count              | warmup   | 1   | 955        | 1       | 1       | true    |       |
-| 2026-06-11T20:05:09.194810Z    | Q03      | Filtered count              | measured | 1   | 560        | 1       | 1       | true    |       |
-| 2026-06-11T20:05:09.719193900Z | Q03      | Filtered count              | measured | 2   | 523        | 1       | 1       | true    |       |
-| 2026-06-11T20:05:10.229289800Z | Q03      | Filtered count              | measured | 3   | 508        | 1       | 1       | true    |       |
-| 2026-06-11T20:05:10.740282Z    | Q03      | Filtered count              | measured | 4   | 509        | 1       | 1       | true    |       |
-| 2026-06-11T20:05:11.371187100Z | Q03      | Filtered count              | measured | 5   | 631        | 1       | 1       | true    |       |
-| 2026-06-11T20:05:11.451489900Z | Q04      | Filtered full record access | warmup   | 1   | 77         | 0       | 22      | true    |       |
-| 2026-06-11T20:05:11.463724200Z | Q04      | Filtered full record access | measured | 1   | 11         | 0       | 22      | true    |       |
-| 2026-06-11T20:05:11.476779800Z | Q04      | Filtered full record access | measured | 2   | 12         | 0       | 22      | true    |       |
-| 2026-06-11T20:05:11.488827900Z | Q04      | Filtered full record access | measured | 3   | 12         | 0       | 22      | true    |       |
-| 2026-06-11T20:05:11.502028800Z | Q04      | Filtered full record access | measured | 4   | 12         | 0       | 22      | true    |       |
-| 2026-06-11T20:05:11.512541700Z | Q04      | Filtered full record access | measured | 5   | 11         | 0       | 22      | true    |       |
-| 2026-06-11T20:05:11.590520800Z | Q05      | Filtered projection         | warmup   | 1   | 74         | 0       | 5       | true    |       |
-| 2026-06-11T20:05:11.599519500Z | Q05      | Filtered projection         | measured | 1   | 8          | 0       | 5       | true    |       |
-| 2026-06-11T20:05:11.612525500Z | Q05      | Filtered projection         | measured | 2   | 12         | 0       | 5       | true    |       |
-| 2026-06-11T20:05:11.622031100Z | Q05      | Filtered projection         | measured | 3   | 8          | 0       | 5       | true    |       |
-| 2026-06-11T20:05:11.633037Z    | Q05      | Filtered projection         | measured | 4   | 10         | 0       | 5       | true    |       |
-| 2026-06-11T20:05:11.644040600Z | Q05      | Filtered projection         | measured | 5   | 10         | 0       | 5       | true    |       |
-
-## Polypheny Document MQL
-
-Source: `access_model_comparison_polypheny_mql_tlcp_results.csv`
-
-| timestamp                      | query_id | description                   | phase    | run | elapsed_ms | rows    | columns | success | error |
-|--------------------------------|----------|-------------------------------|----------|-----|------------|---------|---------|---------|-------|
-| 2026-06-11T18:57:18.950866100Z | Q01      | Full document access          | warmup   | 1   | 146487     | 3711544 | 20      | true    |       |
-| 2026-06-11T18:59:40.878060600Z | Q01      | Full document access          | measured | 1   | 141914     | 3711544 | 20      | true    |       |
-| 2026-06-11T19:02:01.558648500Z | Q01      | Full document access          | measured | 2   | 140681     | 3711544 | 20      | true    |       |
-| 2026-06-11T19:04:22.409971200Z | Q01      | Full document access          | measured | 3   | 140847     | 3711544 | 20      | true    |       |
-| 2026-06-11T19:06:43.085124100Z | Q01      | Full document access          | measured | 4   | 140674     | 3711544 | 20      | true    |       |
-| 2026-06-11T19:09:02.860315100Z | Q01      | Full document access          | measured | 5   | 139777     | 3711544 | 20      | true    |       |
-| 2026-06-11T19:10:49.613562100Z | Q02      | Projection                    | warmup   | 1   | 106748     | 3711544 | 6       | true    |       |
-| 2026-06-11T19:12:37.236250Z    | Q02      | Projection                    | measured | 1   | 107622     | 3711544 | 6       | true    |       |
-| 2026-06-11T19:14:24.269443500Z | Q02      | Projection                    | measured | 2   | 107031     | 3711544 | 6       | true    |       |
-| 2026-06-11T19:16:11.689652200Z | Q02      | Projection                    | measured | 3   | 107420     | 3711544 | 6       | true    |       |
-| 2026-06-11T19:17:58.630301400Z | Q02      | Projection                    | measured | 4   | 106939     | 3711544 | 6       | true    |       |
-| 2026-06-11T19:19:50.212246Z    | Q02      | Projection                    | measured | 5   | 111583     | 3711544 | 6       | true    |       |
-| 2026-06-11T19:21:11.230251400Z | Q03      | Filtered count                | warmup   | 1   | 81015      | 1       | 1       | true    |       |
-| 2026-06-11T19:22:32.692593700Z | Q03      | Filtered count                | measured | 1   | 81461      | 1       | 1       | true    |       |
-| 2026-06-11T19:23:54.995095900Z | Q03      | Filtered count                | measured | 2   | 82302      | 1       | 1       | true    |       |
-| 2026-06-11T19:25:16.529502700Z | Q03      | Filtered count                | measured | 3   | 81532      | 1       | 1       | true    |       |
-| 2026-06-11T19:26:37.617071Z    | Q03      | Filtered count                | measured | 4   | 81087      | 1       | 1       | true    |       |
-| 2026-06-11T19:27:57.890478900Z | Q03      | Filtered count                | measured | 5   | 80273      | 1       | 1       | true    |       |
-| 2026-06-11T19:29:24.111617200Z | Q04      | Filtered full document access | warmup   | 1   | 86220      | 283006  | 20      | true    |       |
-| 2026-06-11T19:30:49.465861100Z | Q04      | Filtered full document access | measured | 1   | 85352      | 283006  | 20      | true    |       |
-| 2026-06-11T19:32:15.176393900Z | Q04      | Filtered full document access | measured | 2   | 85709      | 283006  | 20      | true    |       |
-| 2026-06-11T19:33:40.304629900Z | Q04      | Filtered full document access | measured | 3   | 85128      | 283006  | 20      | true    |       |
-| 2026-06-11T19:35:05.504422200Z | Q04      | Filtered full document access | measured | 4   | 85199      | 283006  | 20      | true    |       |
-| 2026-06-11T19:36:30.339635500Z | Q04      | Filtered full document access | measured | 5   | 84831      | 283006  | 20      | true    |       |
-| 2026-06-11T19:37:53.751224200Z | Q05      | Filtered projection           | warmup   | 1   | 83409      | 283006  | 6       | true    |       |
-| 2026-06-11T19:39:17.115780200Z | Q05      | Filtered projection           | measured | 1   | 83364      | 283006  | 6       | true    |       |
-| 2026-06-11T19:40:40.715102800Z | Q05      | Filtered projection           | measured | 2   | 83598      | 283006  | 6       | true    |       |
-| 2026-06-11T19:42:03.812402600Z | Q05      | Filtered projection           | measured | 3   | 83096      | 283006  | 6       | true    |       |
-| 2026-06-11T19:43:27.234388Z    | Q05      | Filtered projection           | measured | 4   | 83422      | 283006  | 6       | true    |       |
-| 2026-06-11T19:44:50.397084400Z | Q05      | Filtered projection           | measured | 5   | 83162      | 283006  | 6       | true    |       |
-
-## DuckDB
-
-Source: `access_model_comparison_duckdb_tlcp_results.csv`
-
-| timestamp                      | query_id | description                 | phase    | run | elapsed_ms | rows    | columns | success | error |
-|--------------------------------|----------|-----------------------------|----------|-----|------------|---------|---------|---------|-------|
-| 2026-06-11T20:06:31.581772500Z | Q01      | Full record access          | warmup   | 1   | 2568       | 3711544 | 22      | true    |       |
-| 2026-06-11T20:06:34.366902100Z | Q01      | Full record access          | measured | 1   | 2786       | 3711544 | 22      | true    |       |
-| 2026-06-11T20:06:36.924498100Z | Q01      | Full record access          | measured | 2   | 2558       | 3711544 | 22      | true    |       |
-| 2026-06-11T20:06:39.735769300Z | Q01      | Full record access          | measured | 3   | 2807       | 3711544 | 22      | true    |       |
-| 2026-06-11T20:06:42.398329600Z | Q01      | Full record access          | measured | 4   | 2662       | 3711544 | 22      | true    |       |
-| 2026-06-11T20:06:45.678604900Z | Q01      | Full record access          | measured | 5   | 3269       | 3711544 | 22      | true    |       |
-| 2026-06-11T20:06:45.842128700Z | Q02      | Projection                  | warmup   | 1   | 161        | 3711544 | 5       | true    |       |
-| 2026-06-11T20:06:46.009288800Z | Q02      | Projection                  | measured | 1   | 168        | 3711544 | 5       | true    |       |
-| 2026-06-11T20:06:46.183710500Z | Q02      | Projection                  | measured | 2   | 173        | 3711544 | 5       | true    |       |
-| 2026-06-11T20:06:46.369324Z    | Q02      | Projection                  | measured | 3   | 183        | 3711544 | 5       | true    |       |
-| 2026-06-11T20:06:46.550356800Z | Q02      | Projection                  | measured | 4   | 184        | 3711544 | 5       | true    |       |
-| 2026-06-11T20:06:46.732551300Z | Q02      | Projection                  | measured | 5   | 179        | 3711544 | 5       | true    |       |
-| 2026-06-11T20:06:46.768651200Z | Q03      | Filtered count              | warmup   | 1   | 32         | 1       | 1       | true    |       |
-| 2026-06-11T20:06:46.799113100Z | Q03      | Filtered count              | measured | 1   | 31         | 1       | 1       | true    |       |
-| 2026-06-11T20:06:46.836965200Z | Q03      | Filtered count              | measured | 2   | 35         | 1       | 1       | true    |       |
-| 2026-06-11T20:06:46.883365600Z | Q03      | Filtered count              | measured | 3   | 46         | 1       | 1       | true    |       |
-| 2026-06-11T20:06:46.927137900Z | Q03      | Filtered count              | measured | 4   | 41         | 1       | 1       | true    |       |
-| 2026-06-11T20:06:46.970439800Z | Q03      | Filtered count              | measured | 5   | 42         | 1       | 1       | true    |       |
-| 2026-06-11T20:06:46.983052700Z | Q04      | Filtered full record access | warmup   | 1   | 11         | 0       | 22      | true    |       |
-| 2026-06-11T20:06:46.998657800Z | Q04      | Filtered full record access | measured | 1   | 13         | 0       | 22      | true    |       |
-| 2026-06-11T20:06:47.018166900Z | Q04      | Filtered full record access | measured | 2   | 17         | 0       | 22      | true    |       |
-| 2026-06-11T20:06:47.030161300Z | Q04      | Filtered full record access | measured | 3   | 12         | 0       | 22      | true    |       |
-| 2026-06-11T20:06:47.045945500Z | Q04      | Filtered full record access | measured | 4   | 13         | 0       | 22      | true    |       |
-| 2026-06-11T20:06:47.063416700Z | Q04      | Filtered full record access | measured | 5   | 18         | 0       | 22      | true    |       |
-| 2026-06-11T20:06:47.076852700Z | Q05      | Filtered projection         | warmup   | 1   | 13         | 0       | 5       | true    |       |
-| 2026-06-11T20:06:47.092357100Z | Q05      | Filtered projection         | measured | 1   | 13         | 0       | 5       | true    |       |
-| 2026-06-11T20:06:47.107606100Z | Q05      | Filtered projection         | measured | 2   | 12         | 0       | 5       | true    |       |
-| 2026-06-11T20:06:47.122789200Z | Q05      | Filtered projection         | measured | 3   | 16         | 0       | 5       | true    |       |
-| 2026-06-11T20:06:47.138406400Z | Q05      | Filtered projection         | measured | 4   | 12         | 0       | 5       | true    |       |
-| 2026-06-11T20:06:47.148626400Z | Q05      | Filtered projection         | measured | 5   | 12         | 0       | 5       | true    |       |
-
-## Apache Spark
-
-Source: `access_model_comparison_spark_tlcp_results.csv`
-
-| timestamp                   | query_id | description                 | phase    | run | elapsed_ms | rows    | columns | success | error |
-|-----------------------------|----------|-----------------------------|----------|-----|------------|---------|---------|---------|-------|
-| 2026-06-11T20:08:11.202281Z | Q01      | Full record access          | warmup   | 1   | 27845      | 3711544 | 22      | true    |       |
-| 2026-06-11T20:08:31.149931Z | Q01      | Full record access          | measured | 1   | 19947      | 3711544 | 22      | true    |       |
-| 2026-06-11T20:08:52.997061Z | Q01      | Full record access          | measured | 2   | 19449      | 3711544 | 22      | true    |       |
-| 2026-06-11T20:09:12.591150Z | Q01      | Full record access          | measured | 3   | 19593      | 3711544 | 22      | true    |       |
-| 2026-06-11T20:09:35.059883Z | Q01      | Full record access          | measured | 4   | 20172      | 3711544 | 22      | true    |       |
-| 2026-06-11T20:09:56.709063Z | Q01      | Full record access          | measured | 5   | 20031      | 3711544 | 22      | true    |       |
-| 2026-06-11T20:10:10.017439Z | Q02      | Projection                  | warmup   | 1   | 13307      | 3711544 | 5       | true    |       |
-| 2026-06-11T20:10:25.359033Z | Q02      | Projection                  | measured | 1   | 13229      | 3711544 | 5       | true    |       |
-| 2026-06-11T20:10:38.260598Z | Q02      | Projection                  | measured | 2   | 12901      | 3711544 | 5       | true    |       |
-| 2026-06-11T20:10:53.632397Z | Q02      | Projection                  | measured | 3   | 13294      | 3711544 | 5       | true    |       |
-| 2026-06-11T20:11:05.793956Z | Q02      | Projection                  | measured | 4   | 12161      | 3711544 | 5       | true    |       |
-| 2026-06-11T20:11:18.581505Z | Q02      | Projection                  | measured | 5   | 12787      | 3711544 | 5       | true    |       |
-| 2026-06-11T20:11:20.488438Z | Q03      | Filtered count              | warmup   | 1   | 1905       | 1       | 1       | true    |       |
-| 2026-06-11T20:11:23.866016Z | Q03      | Filtered count              | measured | 1   | 1264       | 1       | 1       | true    |       |
-| 2026-06-11T20:11:24.992046Z | Q03      | Filtered count              | measured | 2   | 1125       | 1       | 1       | true    |       |
-| 2026-06-11T20:11:25.983035Z | Q03      | Filtered count              | measured | 3   | 990        | 1       | 1       | true    |       |
-| 2026-06-11T20:11:26.877336Z | Q03      | Filtered count              | measured | 4   | 893        | 1       | 1       | true    |       |
-| 2026-06-11T20:11:27.903714Z | Q03      | Filtered count              | measured | 5   | 1025       | 1       | 1       | true    |       |
-| 2026-06-11T20:11:28.029976Z | Q04      | Filtered full record access | warmup   | 1   | 124        | 0       | 22      | true    |       |
-| 2026-06-11T20:11:28.121348Z | Q04      | Filtered full record access | measured | 1   | 90         | 0       | 22      | true    |       |
-| 2026-06-11T20:11:28.216447Z | Q04      | Filtered full record access | measured | 2   | 94         | 0       | 22      | true    |       |
-| 2026-06-11T20:11:28.298063Z | Q04      | Filtered full record access | measured | 3   | 81         | 0       | 22      | true    |       |
-| 2026-06-11T20:11:28.392095Z | Q04      | Filtered full record access | measured | 4   | 93         | 0       | 22      | true    |       |
-| 2026-06-11T20:11:28.481201Z | Q04      | Filtered full record access | measured | 5   | 88         | 0       | 22      | true    |       |
-| 2026-06-11T20:11:28.599850Z | Q05      | Filtered projection         | warmup   | 1   | 117        | 0       | 5       | true    |       |
-| 2026-06-11T20:11:28.697370Z | Q05      | Filtered projection         | measured | 1   | 96         | 0       | 5       | true    |       |
-| 2026-06-11T20:11:28.811912Z | Q05      | Filtered projection         | measured | 2   | 113        | 0       | 5       | true    |       |
-| 2026-06-11T20:11:28.926642Z | Q05      | Filtered projection         | measured | 3   | 114        | 0       | 5       | true    |       |
-| 2026-06-11T20:11:29.012868Z | Q05      | Filtered projection         | measured | 4   | 85         | 0       | 5       | true    |       |
-| 2026-06-11T20:11:29.096270Z | Q05      | Filtered projection         | measured | 5   | 82         | 0       | 5       | true    |       |
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_summary.md b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_summary.md
deleted file mode 100644
index a25f686176..0000000000
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/11_6_26/access_model_comparison_tlcp_summary.md
+++ /dev/null
@@ -1,58 +0,0 @@
-# Access Model Comparison TLCP Summary
-
-Phase summarized: `measured`.
-Warmup rows are excluded. Mean and median values use successful runs only.
-
-## Source Files
-
-| System                 | CSV                                                                                                 |
-|------------------------|-----------------------------------------------------------------------------------------------------|
-| Polypheny Relational   | `plugins\parquet-adapter\benchmarks\results\access_model_comparison_polypheny_rf_tlcp_results.csv`  |
-| Polypheny Document MQL | `plugins\parquet-adapter\benchmarks\results\access_model_comparison_polypheny_mql_tlcp_results.csv` |
-| DuckDB                 | `plugins\parquet-adapter\benchmarks\results\access_model_comparison_duckdb_tlcp_results.csv`        |
-| Apache Spark           | `plugins\parquet-adapter\benchmarks\results\access_model_comparison_spark_tlcp_results.csv`         |
-
-## Mean Elapsed Time (ms)
-
-| Query | Description                 | Polypheny Relational | Polypheny Document MQL | DuckDB  | Apache Spark | Row counts |
-|-------|-----------------------------|----------------------|------------------------|---------|--------------|------------|
-| Q01   | Full record access          | 26,142.4             | 140,778.6              | 2,816.4 | 19,838.4     | 3,711,544  |
-| Q02   | Projection                  | 8,800.2              | 108,119.0              | 177.4   | 12,874.4     | 3,711,544  |
-| Q03   | Filtered count              | 546.2                | 81,331.0               | 39.0    | 1,059.4      | 1          |
-| Q04   | Filtered full record access | 11.6                 | 85,243.8               | 14.6    | 89.2         | differs    |
-| Q05   | Filtered projection         | 9.6                  | 83,328.4               | 13.0    | 98.0         | differs    |
-
-## Result Row Counts 
-
-| Query | Description                 | Polypheny Relational | Polypheny Document MQL | DuckDB    | Apache Spark |
-|-------|-----------------------------|----------------------|------------------------|-----------|--------------|
-| Q01   | Full record access          | 3,711,544            | 3,711,544              | 3,711,544 | 3,711,544    |
-| Q02   | Projection                  | 3,711,544            | 3,711,544              | 3,711,544 | 3,711,544    |
-| Q03   | Filtered count              | 1                    | 1                      | 1         | 1            |
-| Q04   | Filtered full record access | 0                    | 283,006                | 0         | 0            |
-| Q05   | Filtered projection         | 0                    | 283,006                | 0         | 0            |
-
-## Detailed Summary (ms)
-
-| System                 | Query | Description                   | Runs | Mean      | Median    | Min       | Max       | Rows      | Columns | Status |
-|------------------------|-------|-------------------------------|------|-----------|-----------|-----------|-----------|-----------|---------|--------|
-| Polypheny Relational   | Q01   | Full record access            | 5/5  | 26,142.4  | 26,238.0  | 25,808.0  | 26,432.0  | 3,711,544 | 22      | ok     |
-| Polypheny Relational   | Q02   | Projection                    | 5/5  | 8,800.2   | 8,836.0   | 8,446.0   | 9,154.0   | 3,711,544 | 5       | ok     |
-| Polypheny Relational   | Q03   | Filtered count                | 5/5  | 546.2     | 523.0     | 508.0     | 631.0     | 1         | 1       | ok     |
-| Polypheny Relational   | Q04   | Filtered full record access   | 5/5  | 11.6      | 12.0      | 11.0      | 12.0      | 0         | 22      | ok     |
-| Polypheny Relational   | Q05   | Filtered projection           | 5/5  | 9.6       | 10.0      | 8.0       | 12.0      | 0         | 5       | ok     |
-| Polypheny Document MQL | Q01   | Full document access          | 5/5  | 140,778.6 | 140,681.0 | 139,777.0 | 141,914.0 | 3,711,544 | 20      | ok     |
-| Polypheny Document MQL | Q02   | Projection                    | 5/5  | 108,119.0 | 107,420.0 | 106,939.0 | 111,583.0 | 3,711,544 | 6       | ok     |
-| Polypheny Document MQL | Q03   | Filtered count                | 5/5  | 81,331.0  | 81,461.0  | 80,273.0  | 82,302.0  | 1         | 1       | ok     |
-| Polypheny Document MQL | Q04   | Filtered full document access | 5/5  | 85,243.8  | 85,199.0  | 84,831.0  | 85,709.0  | 283,006   | 20      | ok     |
-| Polypheny Document MQL | Q05   | Filtered projection           | 5/5  | 83,328.4  | 83,364.0  | 83,096.0  | 83,598.0  | 283,006   | 6       | ok     |
-| DuckDB                 | Q01   | Full record access            | 5/5  | 2,816.4   | 2,786.0   | 2,558.0   | 3,269.0   | 3,711,544 | 22      | ok     |
-| DuckDB                 | Q02   | Projection                    | 5/5  | 177.4     | 179.0     | 168.0     | 184.0     | 3,711,544 | 5       | ok     |
-| DuckDB                 | Q03   | Filtered count                | 5/5  | 39.0      | 41.0      | 31.0      | 46.0      | 1         | 1       | ok     |
-| DuckDB                 | Q04   | Filtered full record access   | 5/5  | 14.6      | 13.0      | 12.0      | 18.0      | 0         | 22      | ok     |
-| DuckDB                 | Q05   | Filtered projection           | 5/5  | 13.0      | 12.0      | 12.0      | 16.0      | 0         | 5       | ok     |
-| Apache Spark           | Q01   | Full record access            | 5/5  | 19,838.4  | 19,947.0  | 19,449.0  | 20,172.0  | 3,711,544 | 22      | ok     |
-| Apache Spark           | Q02   | Projection                    | 5/5  | 12,874.4  | 12,901.0  | 12,161.0  | 13,294.0  | 3,711,544 | 5       | ok     |
-| Apache Spark           | Q03   | Filtered count                | 5/5  | 1,059.4   | 1,025.0   | 893.0     | 1,264.0   | 1         | 1       | ok     |
-| Apache Spark           | Q04   | Filtered full record access   | 5/5  | 89.2      | 90.0      | 81.0      | 94.0      | 0         | 22      | ok     |
-| Apache Spark           | Q05   | Filtered projection           | 5/5  | 98.0      | 96.0      | 82.0      | 114.0     | 0         | 5       | ok     |
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_result_analysis.md b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_result_analysis.md
index 0d301e6594..df4f28f9d6 100644
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_result_analysis.md
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_result_analysis.md
@@ -10,6 +10,7 @@
 - Every query has `5/5` successful measured runs
 - Result row counts match across Polypheny relational flat, Polypheny relational normalized, Polypheny document
 MQL, DuckDB, and Apache Spark.
+- Standard deviations remain within 12.4% of the corresponding mean runtimes, indicating generally stable repeated measurements.
 
 
 ### Main Findings
@@ -26,16 +27,10 @@ MQL, DuckDB, and Apache Spark.
 
 ## Related Documents
 
-Full summary:
+Summary:
 
 ```text
 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
-
-plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rf_tlcp_results.csv
-plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rn_tlcpn_results.csv
-plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_mql_tlcp_results.csv
-plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_duckdb_tlcp_results.csv
-plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_spark_tlcp_results.csv
 ```
 
 Plot:
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_correctness_summary.md b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_correctness_summary.md
index 8bf234d568..76d1cb1158 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_correctness_summary.md
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_correctness_summary.md
@@ -77,17 +77,17 @@ Each table includes the reference system so that mismatches can be inspected dir
 | year=2023 / min_distance | 0.0 |  | 0.0 | 0.0 |
 | year=2023 / max_distance | 258928.15 |  | 258928.15 | 258928.15 |
 | _id=2020 / max_distance |  | 350914.89 |  |  |
-| _id=2020 / min_distance |  | -30.62 |  |  |
 | _id=2020 / gross_amount |  | 454079334.16108584 |  |  |
+| _id=2020 / min_distance |  | -30.62 |  |  |
 | _id=2021 / max_distance |  | 351613.36 |  |  |
-| _id=2021 / min_distance |  | 0.0 |  |  |
 | _id=2021 / gross_amount |  | 608700670.9132242 |  |  |
+| _id=2021 / min_distance |  | 0.0 |  |  |
 | _id=2022 / max_distance |  | 389678.46 |  |  |
-| _id=2022 / min_distance |  | 0.0 |  |  |
 | _id=2022 / gross_amount |  | 859397945.173023 |  |  |
+| _id=2022 / min_distance |  | 0.0 |  |  |
 | _id=2023 / max_distance |  | 258928.15 |  |  |
-| _id=2023 / min_distance |  | 0.0 |  |  |
 | _id=2023 / gross_amount |  | 82865192.2197824 |  |  |
+| _id=2023 / min_distance |  | 0.0 |  |  |
 
 ### Q06
 
@@ -131,22 +131,22 @@ Each table includes the reference system so that mismatches can be inspected dir
 | year=2023 / driver_pay | 310164377.32070553 |  | 3.1016437732070553E8 | 310164377.32070553 |
 | year=2023 / min_miles | 0.0 |  | 0.0 | 0.0 |
 | year=2023 / max_miles | 407.563 |  | 407.563 | 407.563 |
-| _id=2020 / driver_pay |  | 2036707828.5292335 |  |  |
+| _id=2020 / min_miles |  | 0.0 |  |  |
 | _id=2020 / passenger_fare |  | 2527548383.5690694 |  |  |
 | _id=2020 / max_miles |  | 1310.51 |  |  |
-| _id=2020 / min_miles |  | 0.0 |  |  |
-| _id=2021 / driver_pay |  | 3085990462.630042 |  |  |
+| _id=2020 / driver_pay |  | 2036707828.5292335 |  |  |
+| _id=2021 / min_miles |  | 0.0 |  |  |
 | _id=2021 / passenger_fare |  | 3839172144.575017 |  |  |
 | _id=2021 / max_miles |  | 738.95 |  |  |
-| _id=2021 / min_miles |  | 0.0 |  |  |
-| _id=2022 / driver_pay |  | 3966957717.7361007 |  |  |
+| _id=2021 / driver_pay |  | 3085990462.630042 |  |  |
+| _id=2022 / min_miles |  | 0.0 |  |  |
 | _id=2022 / passenger_fare |  | 5029617856.472827 |  |  |
 | _id=2022 / max_miles |  | 634.32 |  |  |
-| _id=2022 / min_miles |  | 0.0 |  |  |
-| _id=2023 / driver_pay |  | 310164377.32070553 |  |  |
+| _id=2022 / driver_pay |  | 3966957717.7361007 |  |  |
+| _id=2023 / min_miles |  | 0.0 |  |  |
 | _id=2023 / passenger_fare |  | 398327570.2230424 |  |  |
 | _id=2023 / max_miles |  | 407.563 |  |  |
-| _id=2023 / min_miles |  | 0.0 |  |  |
+| _id=2023 / driver_pay |  | 310164377.32070553 |  |  |
 
 ### Q10
 
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_results.csv b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_results.csv
index df63d5edae..4d5194ecad 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_results.csv
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_results.csv
@@ -1,61 +1,61 @@
 timestamp,query_id,description,phase,run,elapsed_ms,rows,columns,success,error
-"2026-06-21T10:27:08.705109600Z","Q01","Full Yellow Taxi Row Count","warmup",1,388,1,1,true,""
-"2026-06-21T10:27:08.920209600Z","Q01","Full Yellow Taxi Row Count","measured",1,202,1,1,true,""
-"2026-06-21T10:27:09.094492300Z","Q01","Full Yellow Taxi Row Count","measured",2,174,1,1,true,""
-"2026-06-21T10:27:09.278847900Z","Q01","Full Yellow Taxi Row Count","measured",3,183,1,1,true,""
-"2026-06-21T10:27:09.447981400Z","Q01","Full Yellow Taxi Row Count","measured",4,168,1,1,true,""
-"2026-06-21T10:27:09.620104600Z","Q01","Full Yellow Taxi Row Count","measured",5,171,1,1,true,""
-"2026-06-21T10:27:09.838046Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,217,1,1,true,""
-"2026-06-21T10:27:10.057274800Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,218,1,1,true,""
-"2026-06-21T10:27:10.246639Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,188,1,1,true,""
-"2026-06-21T10:27:10.457196200Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,211,1,1,true,""
-"2026-06-21T10:27:10.677319600Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,219,1,1,true,""
-"2026-06-21T10:27:10.887690700Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,209,1,1,true,""
-"2026-06-21T10:27:11.633505100Z","Q03","Yellow Taxi Trips On One Day","warmup",1,744,1,1,true,""
-"2026-06-21T10:27:12.522231Z","Q03","Yellow Taxi Trips On One Day","measured",1,888,1,1,true,""
-"2026-06-21T10:27:13.358164500Z","Q03","Yellow Taxi Trips On One Day","measured",2,834,1,1,true,""
-"2026-06-21T10:27:14.310113400Z","Q03","Yellow Taxi Trips On One Day","measured",3,951,1,1,true,""
-"2026-06-21T10:27:15.394896500Z","Q03","Yellow Taxi Trips On One Day","measured",4,1083,1,1,true,""
-"2026-06-21T10:27:16.410605500Z","Q03","Yellow Taxi Trips On One Day","measured",5,1014,1,1,true,""
-"2026-06-21T10:27:19.041087100Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,2629,1,1,true,""
-"2026-06-21T10:27:22.208869400Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,3165,1,1,true,""
-"2026-06-21T10:27:25.799706900Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,3588,1,1,true,""
-"2026-06-21T10:27:28.808291900Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,3006,1,1,true,""
-"2026-06-21T10:27:32.222477900Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,3412,1,1,true,""
-"2026-06-21T10:27:35.168557300Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,2945,1,1,true,""
-"2026-06-21T10:27:37.493881400Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","warmup",1,2323,4,4,true,""
-"2026-06-21T10:27:39.375624600Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",1,1879,4,4,true,""
-"2026-06-21T10:27:41.758962700Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",2,2382,4,4,true,""
-"2026-06-21T10:27:43.880701200Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",3,2119,4,4,true,""
-"2026-06-21T10:27:45.780722600Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",4,1900,4,4,true,""
-"2026-06-21T10:27:47.540780400Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",5,1758,4,4,true,""
-"2026-06-21T10:27:47.766526200Z","Q06","Full High-Volume FHV Row Count","warmup",1,227,1,1,true,""
-"2026-06-21T10:27:47.976538700Z","Q06","Full High-Volume FHV Row Count","measured",1,204,1,1,true,""
-"2026-06-21T10:27:48.162732600Z","Q06","Full High-Volume FHV Row Count","measured",2,187,1,1,true,""
-"2026-06-21T10:27:48.336901200Z","Q06","Full High-Volume FHV Row Count","measured",3,173,1,1,true,""
-"2026-06-21T10:27:48.500854100Z","Q06","Full High-Volume FHV Row Count","measured",4,162,1,1,true,""
-"2026-06-21T10:27:48.663371300Z","Q06","Full High-Volume FHV Row Count","measured",5,162,1,1,true,""
-"2026-06-21T10:27:48.859406800Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,194,1,1,true,""
-"2026-06-21T10:27:49.060236800Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,200,1,1,true,""
-"2026-06-21T10:27:49.258164200Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,196,1,1,true,""
-"2026-06-21T10:27:49.458728500Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,199,1,1,true,""
-"2026-06-21T10:27:49.646791100Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,191,1,1,true,""
-"2026-06-21T10:27:49.894462200Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,241,1,1,true,""
-"2026-06-21T10:28:05.125630400Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,15228,1,1,true,""
-"2026-06-21T10:28:19.066893800Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,13940,1,1,true,""
-"2026-06-21T10:28:32.664362100Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,13599,1,1,true,""
-"2026-06-21T10:28:46.303936Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,13636,1,1,true,""
-"2026-06-21T10:28:59.732948800Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,13426,1,1,true,""
-"2026-06-21T10:29:13.404434800Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,13670,1,1,true,""
-"2026-06-21T10:29:27.036915200Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","warmup",1,13631,4,5,true,""
-"2026-06-21T10:29:40.401370800Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",1,13367,4,5,true,""
-"2026-06-21T10:29:53.893779400Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",2,13488,4,5,true,""
-"2026-06-21T10:30:07.675697500Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",3,13783,4,5,true,""
-"2026-06-21T10:30:20.840255200Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",4,13161,4,5,true,""
-"2026-06-21T10:30:34.180043200Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",5,13338,4,5,true,""
-"2026-06-21T10:30:38.733403500Z","Q10","High-Volume FHV Shared-Request Flag Distribution","warmup",1,4552,2,1,true,""
-"2026-06-21T10:30:43.135521300Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",1,4405,2,1,true,""
-"2026-06-21T10:30:47.640880300Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",2,4499,2,1,true,""
-"2026-06-21T10:30:52.054600900Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",3,4413,2,1,true,""
-"2026-06-21T10:30:56.412188500Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",4,4356,2,1,true,""
-"2026-06-21T10:31:00.345306700Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",5,3932,2,1,true,""
+"2026-06-28T13:40:59.774930800Z","Q01","Full Yellow Taxi Row Count","warmup",1,446,1,1,true,""
+"2026-06-28T13:40:59.843934Z","Q01","Full Yellow Taxi Row Count","measured",1,59,1,1,true,""
+"2026-06-28T13:40:59.878088900Z","Q01","Full Yellow Taxi Row Count","measured",2,34,1,1,true,""
+"2026-06-28T13:40:59.909245400Z","Q01","Full Yellow Taxi Row Count","measured",3,32,1,1,true,""
+"2026-06-28T13:40:59.942817600Z","Q01","Full Yellow Taxi Row Count","measured",4,31,1,1,true,""
+"2026-06-28T13:40:59.986269600Z","Q01","Full Yellow Taxi Row Count","measured",5,46,1,1,true,""
+"2026-06-28T13:41:00.074872800Z","Q02","Yellow Taxi Count For One Partition Month","warmup",1,87,1,1,true,""
+"2026-06-28T13:41:00.109857300Z","Q02","Yellow Taxi Count For One Partition Month","measured",1,30,1,1,true,""
+"2026-06-28T13:41:00.156420800Z","Q02","Yellow Taxi Count For One Partition Month","measured",2,46,1,1,true,""
+"2026-06-28T13:41:00.209839500Z","Q02","Yellow Taxi Count For One Partition Month","measured",3,52,1,1,true,""
+"2026-06-28T13:41:00.252886300Z","Q02","Yellow Taxi Count For One Partition Month","measured",4,45,1,1,true,""
+"2026-06-28T13:41:00.292717800Z","Q02","Yellow Taxi Count For One Partition Month","measured",5,36,1,1,true,""
+"2026-06-28T13:41:01.726652800Z","Q03","Yellow Taxi Trips On One Day","warmup",1,1432,1,1,true,""
+"2026-06-28T13:41:02.291769200Z","Q03","Yellow Taxi Trips On One Day","measured",1,565,1,1,true,""
+"2026-06-28T13:41:02.815150200Z","Q03","Yellow Taxi Trips On One Day","measured",2,521,1,1,true,""
+"2026-06-28T13:41:03.351964900Z","Q03","Yellow Taxi Trips On One Day","measured",3,539,1,1,true,""
+"2026-06-28T13:41:03.842633500Z","Q03","Yellow Taxi Trips On One Day","measured",4,488,1,1,true,""
+"2026-06-28T13:41:04.348525700Z","Q03","Yellow Taxi Trips On One Day","measured",5,505,1,1,true,""
+"2026-06-28T13:41:06.246054700Z","Q04","Long And Expensive Yellow Taxi Trips","warmup",1,1895,1,1,true,""
+"2026-06-28T13:41:08.538829700Z","Q04","Long And Expensive Yellow Taxi Trips","measured",1,2290,1,1,true,""
+"2026-06-28T13:41:10.913719600Z","Q04","Long And Expensive Yellow Taxi Trips","measured",2,2372,1,1,true,""
+"2026-06-28T13:41:13.159882700Z","Q04","Long And Expensive Yellow Taxi Trips","measured",3,2247,1,1,true,""
+"2026-06-28T13:41:15.481573400Z","Q04","Long And Expensive Yellow Taxi Trips","measured",4,2319,1,1,true,""
+"2026-06-28T13:41:17.503905800Z","Q04","Long And Expensive Yellow Taxi Trips","measured",5,2019,1,1,true,""
+"2026-06-28T13:41:19.468858500Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","warmup",1,1963,4,4,true,""
+"2026-06-28T13:41:20.979321700Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",1,1508,4,4,true,""
+"2026-06-28T13:41:22.460604700Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",2,1481,4,4,true,""
+"2026-06-28T13:41:23.851575400Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",3,1394,4,4,true,""
+"2026-06-28T13:41:25.347182900Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",4,1489,4,4,true,""
+"2026-06-28T13:41:26.819795900Z","Q05","Yearly Yellow Taxi Amount And Distance Summary","measured",5,1472,4,4,true,""
+"2026-06-28T13:41:26.862430100Z","Q06","Full High-Volume FHV Row Count","warmup",1,45,1,1,true,""
+"2026-06-28T13:41:26.913505200Z","Q06","Full High-Volume FHV Row Count","measured",1,48,1,1,true,""
+"2026-06-28T13:41:26.965735Z","Q06","Full High-Volume FHV Row Count","measured",2,50,1,1,true,""
+"2026-06-28T13:41:27.007823Z","Q06","Full High-Volume FHV Row Count","measured",3,42,1,1,true,""
+"2026-06-28T13:41:27.049584500Z","Q06","Full High-Volume FHV Row Count","measured",4,41,1,1,true,""
+"2026-06-28T13:41:27.100183300Z","Q06","Full High-Volume FHV Row Count","measured",5,46,1,1,true,""
+"2026-06-28T13:41:27.157792700Z","Q07","High-Volume FHV Count For One Partition Month","warmup",1,55,1,1,true,""
+"2026-06-28T13:41:27.209924600Z","Q07","High-Volume FHV Count For One Partition Month","measured",1,55,1,1,true,""
+"2026-06-28T13:41:27.272644800Z","Q07","High-Volume FHV Count For One Partition Month","measured",2,59,1,1,true,""
+"2026-06-28T13:41:27.338261500Z","Q07","High-Volume FHV Count For One Partition Month","measured",3,66,1,1,true,""
+"2026-06-28T13:41:27.395612800Z","Q07","High-Volume FHV Count For One Partition Month","measured",4,52,1,1,true,""
+"2026-06-28T13:41:27.452631300Z","Q07","High-Volume FHV Count For One Partition Month","measured",5,56,1,1,true,""
+"2026-06-28T13:41:40.907491300Z","Q08","Long And Expensive High-Volume FHV Trips","warmup",1,13452,1,1,true,""
+"2026-06-28T13:41:53.023193800Z","Q08","Long And Expensive High-Volume FHV Trips","measured",1,12114,1,1,true,""
+"2026-06-28T13:42:05.127942500Z","Q08","Long And Expensive High-Volume FHV Trips","measured",2,12104,1,1,true,""
+"2026-06-28T13:42:17.812771200Z","Q08","Long And Expensive High-Volume FHV Trips","measured",3,12684,1,1,true,""
+"2026-06-28T13:42:32.125009600Z","Q08","Long And Expensive High-Volume FHV Trips","measured",4,14310,1,1,true,""
+"2026-06-28T13:42:44.318944300Z","Q08","Long And Expensive High-Volume FHV Trips","measured",5,12192,1,1,true,""
+"2026-06-28T13:42:57.897365Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","warmup",1,13577,4,5,true,""
+"2026-06-28T13:43:11.427400400Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",1,13529,4,5,true,""
+"2026-06-28T13:43:24.314374600Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",2,12886,4,5,true,""
+"2026-06-28T13:43:37.346240900Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",3,13030,4,5,true,""
+"2026-06-28T13:43:50.825985600Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",4,13479,4,5,true,""
+"2026-06-28T13:44:03.992390100Z","Q09","Yearly High-Volume FHV Fare And Distance Summary","measured",5,13166,4,5,true,""
+"2026-06-28T13:44:09.114077700Z","Q10","High-Volume FHV Shared-Request Flag Distribution","warmup",1,5122,2,1,true,""
+"2026-06-28T13:44:13.418092200Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",1,4300,2,1,true,""
+"2026-06-28T13:44:17.563772300Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",2,4144,2,1,true,""
+"2026-06-28T13:44:21.700857500Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",3,4137,2,1,true,""
+"2026-06-28T13:44:25.968729500Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",4,4265,2,1,true,""
+"2026-06-28T13:44:30.542594200Z","Q10","High-Volume FHV Shared-Request Flag Distribution","measured",5,4571,2,1,true,""
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_values.jsonl b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_values.jsonl
index 879e060bf6..baa2113187 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_values.jsonl
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_values.jsonl
@@ -1,10 +1,10 @@
-{"timestamp":"2026-06-21T10:31:00.603741600Z","query_id":"Q01","description":"Full Yellow Taxi Row Count","success":true,"error":"","columns":["row_count"],"rows":[["98276264"]]}
-{"timestamp":"2026-06-21T10:31:00.876668700Z","query_id":"Q02","description":"Yellow Taxi Count For One Partition Month","success":true,"error":"","columns":["row_count"],"rows":[["3675411"]]}
-{"timestamp":"2026-06-21T10:31:01.865009700Z","query_id":"Q03","description":"Yellow Taxi Trips On One Day","success":true,"error":"","columns":["row_count"],"rows":[["127002"]]}
-{"timestamp":"2026-06-21T10:31:04.176449300Z","query_id":"Q04","description":"Long And Expensive Yellow Taxi Trips","success":true,"error":"","columns":["row_count"],"rows":[["6438540"]]}
-{"timestamp":"2026-06-21T10:31:05.920949800Z","query_id":"Q05","description":"Yearly Yellow Taxi Amount And Distance Summary","success":true,"error":"","columns":["max_distance","min_distance","_id","gross_amount"],"rows":[["350914.89","-30.62","2020","454079334.16108584"],["351613.36","0.0","2021","608700670.9132242"],["389678.46","0.0","2022","859397945.173023"],["258928.15","0.0","2023","82865192.2197824"]]}
-{"timestamp":"2026-06-21T10:31:06.146784400Z","query_id":"Q06","description":"Full High-Volume FHV Row Count","success":true,"error":"","columns":["row_count"],"rows":[["548801637"]]}
-{"timestamp":"2026-06-21T10:31:06.403439900Z","query_id":"Q07","description":"High-Volume FHV Count For One Partition Month","success":true,"error":"","columns":["row_count"],"rows":[["19306090"]]}
-{"timestamp":"2026-06-21T10:31:20.134493100Z","query_id":"Q08","description":"Long And Expensive High-Volume FHV Trips","success":true,"error":"","columns":["row_count"],"rows":[["42659531"]]}
-{"timestamp":"2026-06-21T10:31:33.677407800Z","query_id":"Q09","description":"Yearly High-Volume FHV Fare And Distance Summary","success":true,"error":"","columns":["driver_pay","passenger_fare","_id","max_miles","min_miles"],"rows":[["2036707828.5292335","2527548383.5690694","2020","1310.51","0.0"],["3085990462.630042","3839172144.575017","2021","738.95","0.0"],["3966957717.7361007","5029617856.472827","2022","634.32","0.0"],["310164377.32070553","398327570.2230424","2023","407.563","0.0"]]}
-{"timestamp":"2026-06-21T10:31:37.565885500Z","query_id":"Q10","description":"High-Volume FHV Shared-Request Flag Distribution","success":true,"error":"","columns":["_id"],"rows":[["N"],["Y"]]}
+{"timestamp":"2026-06-28T13:44:30.599573Z","query_id":"Q01","description":"Full Yellow Taxi Row Count","success":true,"error":"","columns":["row_count"],"rows":[["98276264"]]}
+{"timestamp":"2026-06-28T13:44:30.660049300Z","query_id":"Q02","description":"Yellow Taxi Count For One Partition Month","success":true,"error":"","columns":["row_count"],"rows":[["3675411"]]}
+{"timestamp":"2026-06-28T13:44:31.640458Z","query_id":"Q03","description":"Yellow Taxi Trips On One Day","success":true,"error":"","columns":["row_count"],"rows":[["127002"]]}
+{"timestamp":"2026-06-28T13:44:33.544571900Z","query_id":"Q04","description":"Long And Expensive Yellow Taxi Trips","success":true,"error":"","columns":["row_count"],"rows":[["6438540"]]}
+{"timestamp":"2026-06-28T13:44:35.210522600Z","query_id":"Q05","description":"Yearly Yellow Taxi Amount And Distance Summary","success":true,"error":"","columns":["max_distance","gross_amount","min_distance","_id"],"rows":[["350914.89","454079334.16108584","-30.62","2020"],["351613.36","608700670.9132242","0.0","2021"],["389678.46","859397945.173023","0.0","2022"],["258928.15","82865192.2197824","0.0","2023"]]}
+{"timestamp":"2026-06-28T13:44:35.282061900Z","query_id":"Q06","description":"Full High-Volume FHV Row Count","success":true,"error":"","columns":["row_count"],"rows":[["548801637"]]}
+{"timestamp":"2026-06-28T13:44:35.372285900Z","query_id":"Q07","description":"High-Volume FHV Count For One Partition Month","success":true,"error":"","columns":["row_count"],"rows":[["19306090"]]}
+{"timestamp":"2026-06-28T13:44:48.479892Z","query_id":"Q08","description":"Long And Expensive High-Volume FHV Trips","success":true,"error":"","columns":["row_count"],"rows":[["42659531"]]}
+{"timestamp":"2026-06-28T13:45:01.329864400Z","query_id":"Q09","description":"Yearly High-Volume FHV Fare And Distance Summary","success":true,"error":"","columns":["min_miles","_id","passenger_fare","max_miles","driver_pay"],"rows":[["0.0","2020","2527548383.5690694","1310.51","2036707828.5292335"],["0.0","2021","3839172144.575017","738.95","3085990462.630042"],["0.0","2022","5029617856.472827","634.32","3966957717.7361007"],["0.0","2023","398327570.2230424","407.563","310164377.32070553"]]}
+{"timestamp":"2026-06-28T13:45:05.234518900Z","query_id":"Q10","description":"High-Volume FHV Shared-Request Flag Distribution","success":true,"error":"","columns":["_id"],"rows":[["N"],["Y"]]}
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_result_analysis.md b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_result_analysis.md
index ce1f0987ea..ba1cf1b7d8 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_result_analysis.md
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_result_analysis.md
@@ -6,9 +6,10 @@
 ## Interpretation
 
 ### General
-The aggregation run completed successfully for all evaluated systems. 
-All 10 queries completed successfully, with 5/5 successful measured runs per query for every system. 
-The result row counts match across Polypheny relational, Polypheny document MQL, DuckDB, and Apache Spark.
+- The aggregation run completed successfully for all evaluated systems. 
+- All 10 queries completed successfully, with 5/5 successful measured runs per query for every system. 
+- The result row counts match across Polypheny relational, Polypheny document MQL, DuckDB, and Apache Spark.
+- Standard deviations are generally modest; the largest relative variation occurs in the very short metadata queries and Polypheny Relational Q03, while longer data-reading workloads are more stable.
 
 ### Correctness
 Correctness checks confirm consistent aggregation results across systems: all row counts match, while Polypheny relational, DuckDB, and Apache Spark also produce matching grouping keys and COUNT, SUM, MIN, and MAX results within the configured numeric tolerance. Polypheny document MQL produces the same available aggregate values and groups, but Q05, Q09, and Q10 have intentionally different result schemas because the MQL variants omit some count fields and represent grouping keys through _id. These are output-shape differences rather than conflicting aggregation results.
@@ -16,45 +17,37 @@ Correctness checks confirm consistent aggregation results across systems: all ro
 ### Main Findings
 
 - Polypheny relational is strongest overall on metadata-oriented count queries: Q01, Q02, Q06, and Q07. It completes these metadata-optimized queries in approximately 10-14 ms, leading Q01, Q06, and Q07 and closely matching DuckDB on Q02. 
-- The document adapter also benefits from the metadata optimized count path, although document-processing overhead results in higher runtimes.
+- The document adapter also benefits from the metadata-optimized count path, although additional document-processing overhead results in higher runtimes than the relational path.
 
-DuckDB is fastest for most data-reading aggregation queries. It has the best
+- DuckDB is fastest for most data-reading aggregation queries (filtering, grouping, data aggregation). It has the best
 runtime for Q03, Q04, Q05, Q08, Q09, and Q10. The gap is especially visible for
 Q10, where DuckDB evaluates the shared-request grouping much faster than the
 other systems.
 
-The Polypheny document path is competitive with, and sometimes faster than, the
+- The Polypheny document path is competitive with, and sometimes faster than, the
 relational path for large data-reading aggregation queries. This is visible for
 Q03, Q08, Q09, and Q10. These results show that pushing aggregation into the
 Parquet-backed execution path also benefits document workloads when the
 aggregate can be expressed over primitive Parquet fields.
 
-Spark has substantial overhead on small metadata-oriented count queries. It
-becomes more comparable on the larger high-volume FHV filtered count Q08, but
-it remains slower than DuckDB for every aggregation query in this run.
+- Spark performs worst overall, recording the slowest runtime in eight of ten aggregation queries. 
+Its task scheduling, file discovery, code generation, and shuffle overhead are costly in this single-machine benchmark, 
+especially for short metadata-oriented queries. Spark becomes more competitive on the large Q08 scan and nearly matches 
+Polypheny Relational on Q10, but remains consistently slower than DuckDB.
 
-## Interpretation
-
-The query adjustment makes the grouped aggregation comparison much cleaner:
-Q05, Q09, and Q10 now use the same grouping level across the SQL systems and
-the document adapter. Remaining MQL correctness warnings should therefore be
-interpreted as output-shape differences, not as evidence that a different
-number of groups was produced.
-
-The benchmark confirms that the Parquet adapter's optimized count path is very
-effective, while DuckDB remains the strongest reference point for general
-single-node analytical aggregation over Parquet. The document aggregation
-results are useful because they show that document queries can still benefit
-from relational-style Parquet optimizations when the planner can map document
-fields back to Parquet-backed primitive columns.
+- The benchmark demonstrates that the Parquet adapter's metadata-based count optimization is highly effective for count queries. 
+For general analytical aggregation over Parquet on a single machine, DuckDB remains the strongest reference system, 
+while the optimized Polypheny document execution path delivers competitive performance for several aggregation workloads.
 
 
 ## Related Documents
 
-Source summary:
+Summary:
 
 ```text
 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
+
+
 ```
 
 Source correctness comparison:
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
index 08a18ec21b..76e453c11f 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
@@ -16,16 +16,16 @@ Warmup rows are excluded. Mean, median, and standard deviation values use succes
 
 | Query | Description                                      | Polypheny Relational | Polypheny Document MQL | DuckDB  | Apache Spark | Row counts |
 |-------|--------------------------------------------------|----------------------|------------------------|---------|--------------|------------|
-| Q01   | Full Yellow Taxi Row Count                       | 12.2                 | 179.6                  | 22.6    | 1,415.2      | 1          |
-| Q02   | Yellow Taxi Count For One Partition Month        | 10.4                 | 209.0                  | 9.2     | 379.2        | 1          |
-| Q03   | Yellow Taxi Trips On One Day                     | 1,304.8              | 954.0                  | 179.0   | 5,486.4      | 1          |
-| Q04   | Long And Expensive Yellow Taxi Trips             | 2,468.8              | 3,223.2                | 765.4   | 3,825.8      | 1          |
-| Q05   | Yearly Yellow Taxi Amount And Distance Summary   | 1,841.2              | 2,007.6                | 1,193.0 | 5,447.2      | 4          |
-| Q06   | Full High-Volume FHV Row Count                   | 12.4                 | 177.6                  | 145.4   | 2,027.4      | 1          |
-| Q07   | High-Volume FHV Count For One Partition Month    | 14.2                 | 205.4                  | 34.0    | 275.6        | 1          |
-| Q08   | Long And Expensive High-Volume FHV Trips         | 16,174.2             | 13,654.2               | 4,584.6 | 14,250.8     | 1          |
-| Q09   | Yearly High-Volume FHV Fare And Distance Summary | 15,590.2             | 13,427.4               | 8,844.2 | 34,324.6     | 4          |
-| Q10   | High-Volume FHV Shared-Request Flag Distribution | 5,458.2              | 4,321.0                | 435.4   | 5,407.8      | 2          |
+| Q01   | Full Yellow Taxi Row Count                       | 12.2                 | 40.4                   | 22.6    | 1,415.2      | 1          |
+| Q02   | Yellow Taxi Count For One Partition Month        | 10.4                 | 41.8                   | 9.2     | 379.2        | 1          |
+| Q03   | Yellow Taxi Trips On One Day                     | 1,304.8              | 523.6                  | 179.0   | 5,486.4      | 1          |
+| Q04   | Long And Expensive Yellow Taxi Trips             | 2,468.8              | 2,249.4                | 765.4   | 3,825.8      | 1          |
+| Q05   | Yearly Yellow Taxi Amount And Distance Summary   | 1,841.2              | 1,468.8                | 1,193.0 | 5,447.2      | 4          |
+| Q06   | Full High-Volume FHV Row Count                   | 12.4                 | 45.4                   | 145.4   | 2,027.4      | 1          |
+| Q07   | High-Volume FHV Count For One Partition Month    | 14.2                 | 57.6                   | 34.0    | 275.6        | 1          |
+| Q08   | Long And Expensive High-Volume FHV Trips         | 16,174.2             | 12,680.8               | 4,584.6 | 14,250.8     | 1          |
+| Q09   | Yearly High-Volume FHV Fare And Distance Summary | 15,590.2             | 13,218.0               | 8,844.2 | 34,324.6     | 4          |
+| Q10   | High-Volume FHV Shared-Request Flag Distribution | 5,458.2              | 4,283.4                | 435.4   | 5,407.8      | 2          |
 
 ## Result Row Counts
 
@@ -44,45 +44,93 @@ Warmup rows are excluded. Mean, median, and standard deviation values use succes
 
 ## Detailed Summary (ms)
 
-| System                 | Query | Description                                      | Runs | Mean     | Median   | Std Dev | Min      | Max      | Rows | Columns | Status |
-|------------------------|-------|--------------------------------------------------|------|----------|----------|---------|----------|----------|------|---------|--------|
-| Polypheny Relational   | Q01   | Full Yellow Taxi Row Count                       | 5/5  | 12.2     | 12.0     | 3.2     | 8.0      | 17.0     | 1    | 1       | ok     |
-| Polypheny Relational   | Q02   | Yellow Taxi Count For One Partition Month        | 5/5  | 10.4     | 11.0     | 1.5     | 8.0      | 12.0     | 1    | 1       | ok     |
-| Polypheny Relational   | Q03   | Yellow Taxi Trips On One Day                     | 5/5  | 1,304.8  | 1,234.0  | 280.8   | 1,026.0  | 1,614.0  | 1    | 1       | ok     |
-| Polypheny Relational   | Q04   | Long And Expensive Yellow Taxi Trips             | 5/5  | 2,468.8  | 2,446.0  | 293.9   | 2,164.0  | 2,812.0  | 1    | 1       | ok     |
-| Polypheny Relational   | Q05   | Yearly Yellow Taxi Amount And Distance Summary   | 5/5  | 1,841.2  | 1,895.0  | 186.5   | 1,603.0  | 2,065.0  | 4    | 5       | ok     |
-| Polypheny Relational   | Q06   | Full High-Volume FHV Row Count                   | 5/5  | 12.4     | 12.0     | 2.1     | 10.0     | 15.0     | 1    | 1       | ok     |
-| Polypheny Relational   | Q07   | High-Volume FHV Count For One Partition Month    | 5/5  | 14.2     | 15.0     | 1.3     | 12.0     | 15.0     | 1    | 1       | ok     |
-| Polypheny Relational   | Q08   | Long And Expensive High-Volume FHV Trips         | 5/5  | 16,174.2 | 16,362.0 | 475.3   | 15,539.0 | 16,633.0 | 1    | 1       | ok     |
-| Polypheny Relational   | Q09   | Yearly High-Volume FHV Fare And Distance Summary | 5/5  | 15,590.2 | 15,278.0 | 841.6   | 14,999.0 | 17,074.0 | 4    | 6       | ok     |
-| Polypheny Relational   | Q10   | High-Volume FHV Shared-Request Flag Distribution | 5/5  | 5,458.2  | 5,310.0  | 655.8   | 4,858.0  | 6,541.0  | 2    | 2       | ok     |
-| Polypheny Document MQL | Q01   | Full Yellow Taxi Row Count                       | 5/5  | 179.6    | 174.0    | 13.7    | 168.0    | 202.0    | 1    | 1       | ok     |
-| Polypheny Document MQL | Q02   | Yellow Taxi Count For One Partition Month        | 5/5  | 209.0    | 211.0    | 12.5    | 188.0    | 219.0    | 1    | 1       | ok     |
-| Polypheny Document MQL | Q03   | Yellow Taxi Trips On One Day                     | 5/5  | 954.0    | 951.0    | 98.7    | 834.0    | 1,083.0  | 1    | 1       | ok     |
-| Polypheny Document MQL | Q04   | Long And Expensive Yellow Taxi Trips             | 5/5  | 3,223.2  | 3,165.0  | 272.3   | 2,945.0  | 3,588.0  | 1    | 1       | ok     |
-| Polypheny Document MQL | Q05   | Yearly Yellow Taxi Amount And Distance Summary   | 5/5  | 2,007.6  | 1,900.0  | 246.5   | 1,758.0  | 2,382.0  | 4    | 4       | ok     |
-| Polypheny Document MQL | Q06   | Full High-Volume FHV Row Count                   | 5/5  | 177.6    | 173.0    | 18.0    | 162.0    | 204.0    | 1    | 1       | ok     |
-| Polypheny Document MQL | Q07   | High-Volume FHV Count For One Partition Month    | 5/5  | 205.4    | 199.0    | 20.2    | 191.0    | 241.0    | 1    | 1       | ok     |
-| Polypheny Document MQL | Q08   | Long And Expensive High-Volume FHV Trips         | 5/5  | 13,654.2 | 13,636.0 | 185.3   | 13,426.0 | 13,940.0 | 1    | 1       | ok     |
-| Polypheny Document MQL | Q09   | Yearly High-Volume FHV Fare And Distance Summary | 5/5  | 13,427.4 | 13,367.0 | 230.6   | 13,161.0 | 13,783.0 | 4    | 5       | ok     |
-| Polypheny Document MQL | Q10   | High-Volume FHV Shared-Request Flag Distribution | 5/5  | 4,321.0  | 4,405.0  | 223.5   | 3,932.0  | 4,499.0  | 2    | 1       | ok     |
-| DuckDB                 | Q01   | Full Yellow Taxi Row Count                       | 5/5  | 22.6     | 23.0     | 1.7     | 21.0     | 25.0     | 1    | 1       | ok     |
-| DuckDB                 | Q02   | Yellow Taxi Count For One Partition Month        | 5/5  | 9.2      | 9.0      | 0.8     | 8.0      | 10.0     | 1    | 1       | ok     |
-| DuckDB                 | Q03   | Yellow Taxi Trips On One Day                     | 5/5  | 179.0    | 180.0    | 15.2    | 162.0    | 202.0    | 1    | 1       | ok     |
-| DuckDB                 | Q04   | Long And Expensive Yellow Taxi Trips             | 5/5  | 765.4    | 793.0    | 73.3    | 655.0    | 837.0    | 1    | 1       | ok     |
-| DuckDB                 | Q05   | Yearly Yellow Taxi Amount And Distance Summary   | 5/5  | 1,193.0  | 1,170.0  | 92.6    | 1,081.0  | 1,324.0  | 4    | 5       | ok     |
-| DuckDB                 | Q06   | Full High-Volume FHV Row Count                   | 5/5  | 145.4    | 146.0    | 3.8     | 141.0    | 150.0    | 1    | 1       | ok     |
-| DuckDB                 | Q07   | High-Volume FHV Count For One Partition Month    | 5/5  | 34.0     | 36.0     | 4.2     | 29.0     | 38.0     | 1    | 1       | ok     |
-| DuckDB                 | Q08   | Long And Expensive High-Volume FHV Trips         | 5/5  | 4,584.6  | 4,512.0  | 305.2   | 4,216.0  | 5,036.0  | 1    | 1       | ok     |
-| DuckDB                 | Q09   | Yearly High-Volume FHV Fare And Distance Summary | 5/5  | 8,844.2  | 9,076.0  | 369.4   | 8,342.0  | 9,145.0  | 4    | 6       | ok     |
-| DuckDB                 | Q10   | High-Volume FHV Shared-Request Flag Distribution | 5/5  | 435.4    | 456.0    | 42.2    | 384.0    | 480.0    | 2    | 2       | ok     |
-| Apache Spark           | Q01   | Full Yellow Taxi Row Count                       | 5/5  | 1,415.2  | 1,451.0  | 185.0   | 1,162.0  | 1,616.0  | 1    | 1       | ok     |
-| Apache Spark           | Q02   | Yellow Taxi Count For One Partition Month        | 5/5  | 379.2    | 384.0    | 28.9    | 340.0    | 411.0    | 1    | 1       | ok     |
-| Apache Spark           | Q03   | Yellow Taxi Trips On One Day                     | 5/5  | 5,486.4  | 5,474.0  | 536.1   | 4,752.0  | 6,196.0  | 1    | 1       | ok     |
-| Apache Spark           | Q04   | Long And Expensive Yellow Taxi Trips             | 5/5  | 3,825.8  | 3,908.0  | 223.4   | 3,454.0  | 4,034.0  | 1    | 1       | ok     |
-| Apache Spark           | Q05   | Yearly Yellow Taxi Amount And Distance Summary   | 5/5  | 5,447.2  | 5,561.0  | 306.1   | 5,050.0  | 5,721.0  | 4    | 5       | ok     |
-| Apache Spark           | Q06   | Full High-Volume FHV Row Count                   | 5/5  | 2,027.4  | 2,055.0  | 175.4   | 1,822.0  | 2,249.0  | 1    | 1       | ok     |
-| Apache Spark           | Q07   | High-Volume FHV Count For One Partition Month    | 5/5  | 275.6    | 275.0    | 8.3     | 266.0    | 284.0    | 1    | 1       | ok     |
-| Apache Spark           | Q08   | Long And Expensive High-Volume FHV Trips         | 5/5  | 14,250.8 | 14,289.0 | 361.6   | 13,841.0 | 14,638.0 | 1    | 1       | ok     |
-| Apache Spark           | Q09   | Yearly High-Volume FHV Fare And Distance Summary | 5/5  | 34,324.6 | 34,651.0 | 735.7   | 33,487.0 | 35,189.0 | 4    | 6       | ok     |
-| Apache Spark           | Q10   | High-Volume FHV Shared-Request Flag Distribution | 5/5  | 5,407.8  | 5,510.0  | 309.7   | 4,945.0  | 5,713.0  | 2    | 2       | ok     |
+### Q01 - Full Yellow Taxi Row Count
+
+| System                 | Runs | Mean    | Median  | Std Dev | Min     | Max     | Rows | Columns | Status |
+|------------------------|------|---------|---------|---------|---------|---------|------|---------|--------|
+| Polypheny Relational   | 5/5  | 12.2    | 12.0    | 3.2     | 8.0     | 17.0    | 1    | 1       | ok     |
+| Polypheny Document MQL | 5/5  | 40.4    | 34.0    | 12.0    | 31.0    | 59.0    | 1    | 1       | ok     |
+| DuckDB                 | 5/5  | 22.6    | 23.0    | 1.7     | 21.0    | 25.0    | 1    | 1       | ok     |
+| Apache Spark           | 5/5  | 1,415.2 | 1,451.0 | 185.0   | 1,162.0 | 1,616.0 | 1    | 1       | ok     |
+
+### Q02 - Yellow Taxi Count For One Partition Month
+
+| System                 | Runs | Mean  | Median | Std Dev | Min   | Max   | Rows | Columns | Status |
+|------------------------|------|-------|--------|---------|-------|-------|------|---------|--------|
+| Polypheny Relational   | 5/5  | 10.4  | 11.0   | 1.5     | 8.0   | 12.0  | 1    | 1       | ok     |
+| Polypheny Document MQL | 5/5  | 41.8  | 45.0   | 8.7     | 30.0  | 52.0  | 1    | 1       | ok     |
+| DuckDB                 | 5/5  | 9.2   | 9.0    | 0.8     | 8.0   | 10.0  | 1    | 1       | ok     |
+| Apache Spark           | 5/5  | 379.2 | 384.0  | 28.9    | 340.0 | 411.0 | 1    | 1       | ok     |
+
+### Q03 - Yellow Taxi Trips On One Day
+
+| System                 | Runs | Mean    | Median  | Std Dev | Min     | Max     | Rows | Columns | Status |
+|------------------------|------|---------|---------|---------|---------|---------|------|---------|--------|
+| Polypheny Relational   | 5/5  | 1,304.8 | 1,234.0 | 280.8   | 1,026.0 | 1,614.0 | 1    | 1       | ok     |
+| Polypheny Document MQL | 5/5  | 523.6   | 521.0   | 29.9    | 488.0   | 565.0   | 1    | 1       | ok     |
+| DuckDB                 | 5/5  | 179.0   | 180.0   | 15.2    | 162.0   | 202.0   | 1    | 1       | ok     |
+| Apache Spark           | 5/5  | 5,486.4 | 5,474.0 | 536.1   | 4,752.0 | 6,196.0 | 1    | 1       | ok     |
+
+### Q04 - Long And Expensive Yellow Taxi Trips
+
+| System                 | Runs | Mean    | Median  | Std Dev | Min     | Max     | Rows | Columns | Status |
+|------------------------|------|---------|---------|---------|---------|---------|------|---------|--------|
+| Polypheny Relational   | 5/5  | 2,468.8 | 2,446.0 | 293.9   | 2,164.0 | 2,812.0 | 1    | 1       | ok     |
+| Polypheny Document MQL | 5/5  | 2,249.4 | 2,290.0 | 136.6   | 2,019.0 | 2,372.0 | 1    | 1       | ok     |
+| DuckDB                 | 5/5  | 765.4   | 793.0   | 73.3    | 655.0   | 837.0   | 1    | 1       | ok     |
+| Apache Spark           | 5/5  | 3,825.8 | 3,908.0 | 223.4   | 3,454.0 | 4,034.0 | 1    | 1       | ok     |
+
+### Q05 - Yearly Yellow Taxi Amount And Distance Summary
+
+| System                 | Runs | Mean    | Median  | Std Dev | Min     | Max     | Rows | Columns | Status |
+|------------------------|------|---------|---------|---------|---------|---------|------|---------|--------|
+| Polypheny Relational   | 5/5  | 1,841.2 | 1,895.0 | 186.5   | 1,603.0 | 2,065.0 | 4    | 5       | ok     |
+| Polypheny Document MQL | 5/5  | 1,468.8 | 1,481.0 | 43.9    | 1,394.0 | 1,508.0 | 4    | 4       | ok     |
+| DuckDB                 | 5/5  | 1,193.0 | 1,170.0 | 92.6    | 1,081.0 | 1,324.0 | 4    | 5       | ok     |
+| Apache Spark           | 5/5  | 5,447.2 | 5,561.0 | 306.1   | 5,050.0 | 5,721.0 | 4    | 5       | ok     |
+
+### Q06 - Full High-Volume FHV Row Count
+
+| System                 | Runs | Mean    | Median  | Std Dev | Min     | Max     | Rows | Columns | Status |
+|------------------------|------|---------|---------|---------|---------|---------|------|---------|--------|
+| Polypheny Relational   | 5/5  | 12.4    | 12.0    | 2.1     | 10.0    | 15.0    | 1    | 1       | ok     |
+| Polypheny Document MQL | 5/5  | 45.4    | 46.0    | 3.8     | 41.0    | 50.0    | 1    | 1       | ok     |
+| DuckDB                 | 5/5  | 145.4   | 146.0   | 3.8     | 141.0   | 150.0   | 1    | 1       | ok     |
+| Apache Spark           | 5/5  | 2,027.4 | 2,055.0 | 175.4   | 1,822.0 | 2,249.0 | 1    | 1       | ok     |
+
+### Q07 - High-Volume FHV Count For One Partition Month
+
+| System                 | Runs | Mean  | Median | Std Dev | Min   | Max   | Rows | Columns | Status |
+|------------------------|------|-------|--------|---------|-------|-------|------|---------|--------|
+| Polypheny Relational   | 5/5  | 14.2  | 15.0   | 1.3     | 12.0  | 15.0  | 1    | 1       | ok     |
+| Polypheny Document MQL | 5/5  | 57.6  | 56.0   | 5.3     | 52.0  | 66.0  | 1    | 1       | ok     |
+| DuckDB                 | 5/5  | 34.0  | 36.0   | 4.2     | 29.0  | 38.0  | 1    | 1       | ok     |
+| Apache Spark           | 5/5  | 275.6 | 275.0  | 8.3     | 266.0 | 284.0 | 1    | 1       | ok     |
+
+### Q08 - Long And Expensive High-Volume FHV Trips
+
+| System                 | Runs | Mean     | Median   | Std Dev | Min      | Max      | Rows | Columns | Status |
+|------------------------|------|----------|----------|---------|----------|----------|------|---------|--------|
+| Polypheny Relational   | 5/5  | 16,174.2 | 16,362.0 | 475.3   | 15,539.0 | 16,633.0 | 1    | 1       | ok     |
+| Polypheny Document MQL | 5/5  | 12,680.8 | 12,192.0 | 941.7   | 12,104.0 | 14,310.0 | 1    | 1       | ok     |
+| DuckDB                 | 5/5  | 4,584.6  | 4,512.0  | 305.2   | 4,216.0  | 5,036.0  | 1    | 1       | ok     |
+| Apache Spark           | 5/5  | 14,250.8 | 14,289.0 | 361.6   | 13,841.0 | 14,638.0 | 1    | 1       | ok     |
+
+### Q09 - Yearly High-Volume FHV Fare And Distance Summary
+
+| System                 | Runs | Mean     | Median   | Std Dev | Min      | Max      | Rows | Columns | Status |
+|------------------------|------|----------|----------|---------|----------|----------|------|---------|--------|
+| Polypheny Relational   | 5/5  | 15,590.2 | 15,278.0 | 841.6   | 14,999.0 | 17,074.0 | 4    | 6       | ok     |
+| Polypheny Document MQL | 5/5  | 13,218.0 | 13,166.0 | 279.8   | 12,886.0 | 13,529.0 | 4    | 5       | ok     |
+| DuckDB                 | 5/5  | 8,844.2  | 9,076.0  | 369.4   | 8,342.0  | 9,145.0  | 4    | 6       | ok     |
+| Apache Spark           | 5/5  | 34,324.6 | 34,651.0 | 735.7   | 33,487.0 | 35,189.0 | 4    | 6       | ok     |
+
+### Q10 - High-Volume FHV Shared-Request Flag Distribution
+
+| System                 | Runs | Mean    | Median  | Std Dev | Min     | Max     | Rows | Columns | Status |
+|------------------------|------|---------|---------|---------|---------|---------|------|---------|--------|
+| Polypheny Relational   | 5/5  | 5,458.2 | 5,310.0 | 655.8   | 4,858.0 | 6,541.0 | 2    | 2       | ok     |
+| Polypheny Document MQL | 5/5  | 4,283.4 | 4,265.0 | 176.2   | 4,137.0 | 4,571.0 | 2    | 1       | ok     |
+| DuckDB                 | 5/5  | 435.4   | 456.0   | 42.2    | 384.0   | 480.0   | 2    | 2       | ok     |
+| Apache Spark           | 5/5  | 5,407.8 | 5,510.0 | 309.7   | 4,945.0 | 5,713.0 | 2    | 2       | ok     |
+
diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/Q5_nested_data_query_plan.png b/plugins/parquet-adapter/benchmarks/results/nested_data/Q5_nested_data_query_plan.png
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diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_result_analysis.md b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_result_analysis.md
new file mode 100644
index 0000000000..ac03259fb8
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_result_analysis.md
@@ -0,0 +1,53 @@
+# Nested Data - Benchmark Analysis
+
+## Results Plot
+
+![nested_data_plot.png](../plots/nested_data_plot.png)
+
+## Interpretation
+
+### General
+- Polypheny Relational Normalized, DuckDB, and Apache Spark completed all five queries with `5/5` successful measured runs.
+- Polypheny Document MQL completed Q01 and Q02 successfully, while Q03–Q05 could not be executed because the required nested-data operations are not currently supported by the MQL path.
+- Where comparable, result row counts were consistent across all successful executions.
+- Runtime variability is low to moderate for most successful executions, while DuckDB shows higher variability for Q01, Q04, and Q05.
+
+### Main Findings
+- Q01 should not be interpreted as a direct performance comparison. 
+DuckDB, Spark, and MQL return the complete nested customer record, including `c_orders` and its lineitems. 
+Polypheny Relational Normalized instead returns the root customer fields and a synthetic identifier, while nested records remain accessible through generated child tables.
+
+- Q02 provides the clearest root-level comparison because it explicitly projects only scalar customer fields. 
+DuckDB is fastest at 0.64 seconds, followed by Polypheny Relational Normalized at 1.18 seconds and Spark at 1.75 seconds. 
+MQL is considerably slower at 122.62 seconds because of document construction and transfer overhead. 
+The improvement over Q01 is consistent with avoiding nested payload materialization, although the two queries use different predicates and therefore do not form a controlled projection-only comparison.
+
+- Polypheny Relational Normalized performs best for nested projections. 
+It completes Q03 in 2.73 seconds and Q04 in 10.85 seconds, compared with 13.42 and 16.75 seconds for Spark and 96.09 and 1,575.23 seconds for DuckDB. 
+Polypheny accesses generated order and lineitem relations using a single parent-child join, 
+whereas DuckDB and Spark traverse repeated structures using `UNNEST` or `explode`. 
+The deeper traversal in Q04 particularly affects DuckDB.
+
+- Q05 in Polypheny is executed as an aggregation in the Polypheny engine over a join performed by the adapter, because aggregation over nested fields is not supported by the adapter. 
+This results in worse performance than Apache Spark.
+
+![Q5_nested_data_query_plan.png](Q5_nested_data_query_plan.png)
+
+- DuckDB shows best results when nested structures should not be accessed, otherwise he has the worst performance.
+
+
+## Related Documents
+
+Summary:
+
+```text
+plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_summary.md
+
+```
+
+Plot:
+```text
+plugins/parquet-adapter/benchmarks/results/plots/nested_data_plot.pdf
+plugins/parquet-adapter/benchmarks/results/plots/nested_data_plot.png
+plugins/parquet-adapter/benchmarks/results/plots/nested_data_plot.svg
+```
diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_summary.md b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_summary.md
index a9b490d0f8..8d35ffe4cf 100644
--- a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_summary.md
+++ b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_summary.md
@@ -5,54 +5,77 @@ Warmup rows are excluded. Mean, median, and standard deviation values use succes
 
 ## Source Files
 
-| System | CSV |
-| --- | --- |
+| System                          | CSV                                                                                                   |
+|---------------------------------|-------------------------------------------------------------------------------------------------------|
 | Polypheny Relational Normalized | `plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_normalized_results.csv` |
-| Polypheny Document MQL | `plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_mql_results.csv` |
-| DuckDB | `plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_duckdb_results.csv` |
-| Apache Spark | `plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_spark_results.csv` |
+| Polypheny Document MQL          | `plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_mql_results.csv`        |
+| DuckDB                          | `plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_duckdb_results.csv`               |
+| Apache Spark                    | `plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_spark_results.csv`                |
 
 ## Mean Elapsed Time (ms)
 
-| Query | Description | Polypheny Relational Normalized | Polypheny Document MQL | DuckDB | Apache Spark | Row counts |
-| --- | --- | --- | --- | --- | --- | --- |
-| Q01 | Filtered full customer rows. | 2,463.6 | 233,059.4 | 504,359.8 | 77,967.0 | 100,000 |
-| Q02 | Filtered explicit customer projection. | 1,176.8 | 122,618.4 | 640.4 | 1,752.0 | 100,000 |
-| Q03 | One-join filtered order element projection. | 2,727.8 |  | 96,091.4 | 13,420.6 | differs |
-| Q04 | One-join filtered lineitem element projection. | 10,845.6 |  | 1,575,230.8 | 16,749.2 | differs |
-| Q05 | Nested lineitem MAX aggregation grouped by return flag. | 222,409.4 |  | 3,724,019.2 | 15,316.4 | differs |
+| Query | Description                                             | Polypheny Relational Normalized | Polypheny Document MQL | DuckDB      | Apache Spark | Row counts |
+|-------|---------------------------------------------------------|---------------------------------|------------------------|-------------|--------------|------------|
+| Q01   | Filtered full customer rows.                            | 2,463.6                         | 233,059.4              | 504,359.8   | 77,967.0     | 100,000    |
+| Q02   | Filtered explicit customer projection.                  | 1,176.8                         | 122,618.4              | 640.4       | 1,752.0      | 100,000    |
+| Q03   | One-join filtered order element projection.             | 2,727.8                         |                        | 96,091.4    | 13,420.6     | differs    |
+| Q04   | One-join filtered lineitem element projection.          | 10,845.6                        |                        | 1,575,230.8 | 16,749.2     | differs    |
+| Q05   | Nested lineitem MAX aggregation grouped by return flag. | 222,409.4                       |                        | 3,724,019.2 | 15,316.4     | differs    |
 
 ## Result Row Counts
 
-| Query | Description | Polypheny Relational Normalized | Polypheny Document MQL | DuckDB | Apache Spark |
-| --- | --- | --- | --- | --- | --- |
-| Q01 | Filtered full customer rows. | 100,000 | 100,000 | 100,000 | 100,000 |
-| Q02 | Filtered explicit customer projection. | 100,000 | 100,000 | 100,000 | 100,000 |
-| Q03 | One-join filtered order element projection. | 100,000 |  | 100,000 | 100,000 |
-| Q04 | One-join filtered lineitem element projection. | 100,000 |  | 100,000 | 100,000 |
-| Q05 | Nested lineitem MAX aggregation grouped by return flag. | 3 |  | 3 | 3 |
+| Query | Description                                             | Polypheny Relational Normalized | Polypheny Document MQL | DuckDB  | Apache Spark |
+|-------|---------------------------------------------------------|---------------------------------|------------------------|---------|--------------|
+| Q01   | Filtered full customer rows.                            | 100,000                         | 100,000                | 100,000 | 100,000      |
+| Q02   | Filtered explicit customer projection.                  | 100,000                         | 100,000                | 100,000 | 100,000      |
+| Q03   | One-join filtered order element projection.             | 100,000                         |                        | 100,000 | 100,000      |
+| Q04   | One-join filtered lineitem element projection.          | 100,000                         |                        | 100,000 | 100,000      |
+| Q05   | Nested lineitem MAX aggregation grouped by return flag. | 3                               |                        | 3       | 3            |
 
 ## Detailed Summary (ms)
 
-| System | Query | Description | Runs | Mean | Median | Std Dev | Min | Max | Rows | Columns | Status |
-| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-| Polypheny Relational Normalized | Q01 | Filtered full customer rows. | 5/5 | 2,463.6 | 2,515.0 | 744.8 | 1,560.0 | 3,595.0 | 100,000 | 9 | ok |
-| Polypheny Relational Normalized | Q02 | Filtered explicit customer projection. | 5/5 | 1,176.8 | 1,169.0 | 176.9 | 953.0 | 1,446.0 | 100,000 | 8 | ok |
-| Polypheny Relational Normalized | Q03 | One-join filtered order element projection. | 5/5 | 2,727.8 | 2,599.0 | 300.1 | 2,400.0 | 3,132.0 | 100,000 | 8 | ok |
-| Polypheny Relational Normalized | Q04 | One-join filtered lineitem element projection. | 5/5 | 10,845.6 | 10,887.0 | 281.1 | 10,373.0 | 11,094.0 | 100,000 | 10 | ok |
-| Polypheny Relational Normalized | Q05 | Nested lineitem MAX aggregation grouped by return flag. | 5/5 | 222,409.4 | 220,801.0 | 12,599.5 | 206,076.0 | 240,681.0 | 3 | 2 | ok |
-| Polypheny Document MQL | Q01 | Filtered full customer documents. | 5/5 | 233,059.4 | 233,429.0 | 2,193.3 | 230,546.0 | 235,220.0 | 100,000 | 10 | ok |
-| Polypheny Document MQL | Q02 | Filtered explicit customer projection. | 5/5 | 122,618.4 | 124,246.0 | 2,871.3 | 118,223.0 | 125,141.0 | 100,000 | 9 | ok |
-| Polypheny Document MQL | Q03 | Filtered order projection. | 0/5 |  |  |  |  |  |  |  | 0/5 ok; IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0; IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0; IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0; IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0; IOException: java.lang.IndexOutOfBoundsException: Index 0 out of bounds for length 0 |
-| Polypheny Document MQL | Q04 | Filtered lineitem projection. | 0/5 |  |  |  |  |  |  |  | 0/5 ok; IOException: Index 0 out of bounds for length 0; IOException: Index 0 out of bounds for length 0; IOException: Index 0 out of bounds for length 0; IOException: Index 0 out of bounds for length 0; IOException: Index 0 out of bounds for length 0 |
-| Polypheny Document MQL | Q05 | Nested lineitem MAX aggregation grouped by return flag. | 0/5 |  |  |  |  |  |  |  | 0/5 ok; IOException: Could not convert {} to PolyBigDecimal; IOException: Could not convert {} to PolyBigDecimal; IOException: Could not convert {} to PolyBigDecimal; IOException: Could not convert {} to PolyBigDecimal; IOException: Could not convert {} to PolyBigDecimal |
-| DuckDB | Q01 | Filtered full customer rows. | 5/5 | 504,359.8 | 298,765.0 | 502,681.7 | 208,355.0 | 1,400,388.0 | 100,000 | 9 | ok |
-| DuckDB | Q02 | Filtered explicit customer projection. | 5/5 | 640.4 | 646.0 | 82.2 | 557.0 | 745.0 | 100,000 | 8 | ok |
-| DuckDB | Q03 | Filtered order projection. | 5/5 | 96,091.4 | 97,781.0 | 8,018.8 | 86,416.0 | 103,882.0 | 100,000 | 6 | ok |
-| DuckDB | Q04 | Filtered lineitem projection. | 5/5 | 1,575,230.8 | 1,721,657.0 | 563,393.3 | 828,355.0 | 2,198,427.0 | 100,000 | 8 | ok |
-| DuckDB | Q05 | Nested lineitem MAX aggregation grouped by return flag. | 5/5 | 3,724,019.2 | 3,103,441.0 | 1,343,512.3 | 3,050,067.0 | 6,123,072.0 | 3 | 2 | ok |
-| Apache Spark | Q01 | Filtered full customer rows. | 5/5 | 77,967.0 | 79,575.0 | 3,057.8 | 72,722.0 | 80,006.0 | 100,000 | 9 | ok |
-| Apache Spark | Q02 | Filtered explicit customer projection. | 5/5 | 1,752.0 | 1,706.0 | 137.2 | 1,590.0 | 1,928.0 | 100,000 | 8 | ok |
-| Apache Spark | Q03 | Filtered order projection. | 5/5 | 13,420.6 | 13,513.0 | 950.1 | 12,002.0 | 14,677.0 | 100,000 | 6 | ok |
-| Apache Spark | Q04 | Filtered lineitem projection. | 5/5 | 16,749.2 | 17,154.0 | 1,060.4 | 14,952.0 | 17,544.0 | 100,000 | 8 | ok |
-| Apache Spark | Q05 | Nested lineitem MAX aggregation grouped by return flag. | 5/5 | 15,316.4 | 15,376.0 | 322.4 | 14,800.0 | 15,608.0 | 3 | 2 | ok |
+### Q01 - Filtered full customer rows
+
+| System                          | Runs | Mean      | Median    | Std Dev   | Min       | Max         | Rows    | Columns | Status |
+|---------------------------------|------|-----------|-----------|-----------|-----------|-------------|---------|---------|--------|
+| Polypheny Relational Normalized | 5/5  | 2,463.6   | 2,515.0   | 744.8     | 1,560.0   | 3,595.0     | 100,000 | 9       | ok     |
+| Polypheny Document MQL          | 5/5  | 233,059.4 | 233,429.0 | 2,193.3   | 230,546.0 | 235,220.0   | 100,000 | 10      | ok     |
+| DuckDB                          | 5/5  | 504,359.8 | 298,765.0 | 502,681.7 | 208,355.0 | 1,400,388.0 | 100,000 | 9       | ok     |
+| Apache Spark                    | 5/5  | 77,967.0  | 79,575.0  | 3,057.8   | 72,722.0  | 80,006.0    | 100,000 | 9       | ok     |
+
+### Q02 - Filtered explicit customer projection
+
+| System                          | Runs | Mean      | Median    | Std Dev | Min       | Max       | Rows    | Columns | Status |
+|---------------------------------|------|-----------|-----------|---------|-----------|-----------|---------|---------|--------|
+| Polypheny Relational Normalized | 5/5  | 1,176.8   | 1,169.0   | 176.9   | 953.0     | 1,446.0   | 100,000 | 8       | ok     |
+| Polypheny Document MQL          | 5/5  | 122,618.4 | 124,246.0 | 2,871.3 | 118,223.0 | 125,141.0 | 100,000 | 9       | ok     |
+| DuckDB                          | 5/5  | 640.4     | 646.0     | 82.2    | 557.0     | 745.0     | 100,000 | 8       | ok     |
+| Apache Spark                    | 5/5  | 1,752.0   | 1,706.0   | 137.2   | 1,590.0   | 1,928.0   | 100,000 | 8       | ok     |
+
+### Q03 - One-join filtered order element projection
+
+| System                          | Runs | Mean     | Median   | Std Dev | Min      | Max       | Rows    | Columns | Status        |
+|---------------------------------|------|----------|----------|---------|----------|-----------|---------|---------|---------------|
+| Polypheny Relational Normalized | 5/5  | 2,727.8  | 2,599.0  | 300.1   | 2,400.0  | 3,132.0   | 100,000 | 8       | ok            |
+| Polypheny Document MQL          | 0/5  |          |          |         |          |           |         |         | not supported |
+| DuckDB                          | 5/5  | 96,091.4 | 97,781.0 | 8,018.8 | 86,416.0 | 103,882.0 | 100,000 | 6       | ok            |
+| Apache Spark                    | 5/5  | 13,420.6 | 13,513.0 | 950.1   | 12,002.0 | 14,677.0  | 100,000 | 6       | ok            |
+
+### Q04 - One-join filtered lineitem element projection
+
+| System                          | Runs | Mean        | Median      | Std Dev   | Min       | Max         | Rows    | Columns | Status        |
+|---------------------------------|------|-------------|-------------|-----------|-----------|-------------|---------|---------|---------------|
+| Polypheny Relational Normalized | 5/5  | 10,845.6    | 10,887.0    | 281.1     | 10,373.0  | 11,094.0    | 100,000 | 10      | ok            |
+| Polypheny Document MQL          | 0/5  |             |             |           |           |             |         |         | not supported |
+| DuckDB                          | 5/5  | 1,575,230.8 | 1,721,657.0 | 563,393.3 | 828,355.0 | 2,198,427.0 | 100,000 | 8       | ok            |
+| Apache Spark                    | 5/5  | 16,749.2    | 17,154.0    | 1,060.4   | 14,952.0  | 17,544.0    | 100,000 | 8       | ok            |
+
+### Q05 - Nested lineitem MAX aggregation grouped by return flag
+
+| System                          | Runs | Mean        | Median      | Std Dev     | Min         | Max         | Rows | Columns | Status        |
+|---------------------------------|------|-------------|-------------|-------------|-------------|-------------|------|---------|---------------|
+| Polypheny Relational Normalized | 5/5  | 222,409.4   | 220,801.0   | 12,599.5    | 206,076.0   | 240,681.0   | 3    | 2       | ok            |
+| Polypheny Document MQL          | 0/5  |             |             |             |             |             |      |         | not supported |
+| DuckDB                          | 5/5  | 3,724,019.2 | 3,103,441.0 | 1,343,512.3 | 3,050,067.0 | 6,123,072.0 | 3    | 2       | ok            |
+| Apache Spark                    | 5/5  | 15,316.4    | 15,376.0    | 322.4       | 14,800.0    | 15,608.0    | 3    | 2       | ok            |
+
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_result_analysis.md b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_result_analysis.md
new file mode 100644
index 0000000000..b6db24def4
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_result_analysis.md
@@ -0,0 +1,69 @@
+# Partitioning - Benchmark Analysis
+
+## Results Plot
+![partitioning_plot.png](../plots/partitioning_plot.png)
+
+## Interpretation
+
+### General
+
+- The partitioning benchmark completed successfully for all evaluated systems: Polypheny Relational, DuckDB, and Apache Spark.
+- All benchmark executions completed successfully (`5/5` successful measured runs for every query).
+- Every query produced the expected single aggregate result, confirming consistent execution across all systems.
+- The low standard deviations observed across repeated runs indicate stable benchmark measurements.
+
+## Query Groups
+
+The benchmark consists of five logical query groups. Each query is evaluated on both dataset layouts:
+
+- **RP**: repartitioned Hive-style layout, where `year` and `month` are encoded as directory partitions.
+- **UP**: unpartitioned layout, where `year` and `month` are stored as regular columns in the Parquet files.
+
+Queries **Q1–Q3** are evaluated on the `yellow_tripdata` dataset, while **Q4–Q5** use the `green_tripdata` dataset.
+
+
+## Main Findings
+
+- Q1 serves as the baseline query. Since no predicates are applied to partition columns, both the partitioned and unpartitioned layouts require access to all Parquet files. 
+Consequently, Hive partitioning provides no opportunity for partition pruning, and both layouts exhibit comparable performance. 
+The observed runtime differences are likely attributable to implementation overhead rather than the dataset organization itself.
+
+- Q2 demonstrates the effectiveness of Hive-style partition pruning. 
+The query filters on the partition column year, allowing the partitioned layout to skip all directories except those corresponding to the selected year. 
+In contrast, the unpartitioned layout must inspect every Parquet file because year is stored as a regular column. 
+As a result, all evaluated systems execute the partitioned query substantially faster, with runtime reductions of approximately 31% for Polypheny, 60% for DuckDB, and 70% for Apache Spark.
+- Polypheny shows a smaller difference between the partitioned and unpartitioned layouts in Q2 because the COUNT query is already highly optimized using Parquet metadata.
+
+- Q3 further demonstrates the benefit of Hive-style partitioning. 
+By filtering on both partition columns (year and month), the partitioned layout accesses only a single monthly partition, 
+while the unpartitioned layout must still inspect all Parquet files. As a result, all evaluated systems achieve lower execution times on the partitioned dataset. 
+- As in Q2, the performance gap is smaller for Polypheny because the metadata-based COUNT optimization already minimizes the execution cost of the query.
+- In the current implementation, Polypheny performs planner-level partition pruning only on the first partition level (year). 
+The second partition level (month) is handled within the adapter by filtering the remaining partition directories before reading the Parquet files.
+
+- Q4 query does not filter on the partition columns (year or month), thus Hive-style partition pruning cannot be applied.
+Consequently, both the partitioned and unpartitioned layouts must scan all Parquet files. As expected, partitioning does not provide a performance benefit for this query.
+These results confirm that partitioning provides a performance benefit only when queries contain predicates on the partition columns.
+
+- Q5 combines a partition predicate with row-level filtering. 
+Unlike Q4, the filter on year enables partition pruning, allowing the partitioned layout to skip files belonging to other years. 
+Polypheny and Apache Spark therefore execute the query faster on the partitioned dataset, 
+demonstrating the practical benefit of Hive-style partitioning for selective analytical workloads. 
+Unlike Polypheny and Apache Spark, DuckDB exhibits similar performance for the partitioned and unpartitioned layouts, with the unpartitioned layout being slightly faster in this benchmark. 
+his suggests that Hive-style partitioning provides only a limited benefit for this query in DuckDB
+
+
+## Related Documents
+
+Summary:
+
+```text
+plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
+```
+
+Plot:
+```text
+plugins/parquet-adapter/benchmarks/results/plots/partitioning_plot.png
+plugins/parquet-adapter/benchmarks/results/plots/partitioning_plot.pdf
+plugins/parquet-adapter/benchmarks/results/plots/partitioning_plot.svg
+```
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_results_analysis.md b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_results_analysis.md
deleted file mode 100644
index 9edc106a81..0000000000
--- a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_results_analysis.md
+++ /dev/null
@@ -1,76 +0,0 @@
-# Partitioning Benchmark Analysis
-
-Source summary:
-
-```text
-plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
-```
-
-The partitioning benchmark completed successfully for all evaluated systems.
-All measured queries have `5/5` successful runs, and every query returned one
-aggregate result row.
-
-## Query Groups
-
-The benchmark uses five logical query groups. Each query is executed on the
-repartitioned Hive-style layout and on the equivalent unpartitioned layout:
-
-- `RP`: repartitioned layout with `year` and `month` represented by directory partitions
-- `UP`: unpartitioned layout with `year` and `month` stored as physical columns
-
-Q1-Q3 run on `yellow_tripdata`. Q4-Q5 run on `green_tripdata`.
-
-## Mean Runtime Overview
-
-All values are mean measured runtimes in milliseconds.
-
-| Query Pair | Operation | Polypheny | DuckDB | Spark |
-| --- | --- | ---: | ---: | ---: |
-| Q1_P / Q1_NP | Full count, repartitioned / unpartitioned | 7.2 / 9.6 | 78.8 / 59.6 | 1681.8 / 1189.6 |
-| Q2_P / Q2_NP | Year predicate, repartitioned / unpartitioned | 6.6 / 9.6 | 35.2 / 88.4 | 641.8 / 2123.2 |
-| Q3_P / Q3_NP | Year and month predicate, repartitioned / unpartitioned | 8.6 / 9.8 | 17.0 / 72.2 | 450.6 / 1326.2 |
-| Q4_P / Q4_NP | Data-column filter, repartitioned / unpartitioned | 150.0 / 131.2 | 30.2 / 20.6 | 1693.0 / 902.6 |
-| Q5_P / Q5_NP | Year plus data-column filter, repartitioned / unpartitioned | 54.6 / 61.6 | 17.4 / 13.6 | 637.8 / 1035.4 |
-
-## Main Findings
-
-DuckDB and Spark show the clearest benefit from Hive-style partition pruning
-when the predicates match the partition layout. DuckDB improves from 78.8 ms
-for the repartitioned full-count baseline to 35.2 ms with a year predicate and
-17.0 ms with a year-and-month predicate. Spark improves from 1681.8 ms to
-641.8 ms and then to 450.6 ms for the same query sequence.
-
-Polypheny is very fast for the simple count queries in both layouts. The
-repartitioned simple count queries Q1_P-Q3_P run between 6.6 ms and 8.6 ms,
-while the corresponding unpartitioned queries Q1_NP-Q3_NP run between 9.6 ms
-and 9.8 ms. The partitioning effect is therefore present but small in absolute
-terms for these metadata-friendly count queries.
-
-The clearest Polypheny partitioning signal appears in the filtered-count pair
-over `green_tripdata`. Adding the year partition predicate reduces the
-repartitioned query from 150.0 ms in Q4_P to 54.6 ms in Q5_P, a 2.75x
-improvement. DuckDB improves from 30.2 ms to 17.4 ms, and Spark improves from
-1693.0 ms to 637.8 ms for the same repartitioned query pair.
-
-DuckDB is fastest for the data-column filtered count queries. Polypheny is
-fastest for the simple count queries, where the counts are computed with very
-low overhead.
-
-## Interpretation
-
-Partitioning helps most when the query predicate matches the directory layout.
-This is visible in the paired year and year-month comparisons: Q2_P is faster
-than Q2_NP for all systems, and Q3_P is faster than Q3_NP for all systems.
-The effect is strongest for DuckDB and Spark and smaller for Polypheny because
-Polypheny's count queries are already inexpensive.
-
-Partitioning is not universally faster. When no partition predicate is used,
-the unpartitioned layout is faster for DuckDB and Spark on the full-count
-baseline, while Polypheny is slightly faster on the repartitioned layout. For
-the data-column-only filter Q4, the unpartitioned layout is faster for all
-systems because no Hive-style partition pruning can be applied.
-
-For Q5, the repartitioned layout benefits from the year partition predicate in
-Polypheny and Spark. DuckDB remains faster on the unpartitioned version of this
-specific query, which indicates that the benefit of pruning can be outweighed by
-layout and execution overhead for small or already selective workloads.
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
index 6a4cb9928b..f895277ddd 100644
--- a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
@@ -5,75 +5,98 @@ Warmup rows are excluded. Mean, median, and standard deviation values use succes
 
 ## Source Files
 
-| System | CSV |
-| --- | --- |
-| Polypheny Relational | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_polypheny_results.csv` |
-| DuckDB Repartitioned | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_repartitioned_results.csv` |
-| DuckDB Unpartitioned | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_unpartitioned_results.csv` |
-| Apache Spark Repartitioned | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_repartitioned_results.csv` |
-| Apache Spark Unpartitioned | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_unpartitioned_results.csv` |
+| System                     | CSV                                                                                                     |
+|----------------------------|---------------------------------------------------------------------------------------------------------|
+| Polypheny Relational       | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_polypheny_results.csv`            |
+| DuckDB Repartitioned       | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_repartitioned_results.csv` |
+| DuckDB Unpartitioned       | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_unpartitioned_results.csv` |
+| Apache Spark Repartitioned | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_repartitioned_results.csv`  |
+| Apache Spark Unpartitioned | `plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_unpartitioned_results.csv`  |
 
 ## Mean Elapsed Time (ms)
 
-| Query | Description | Polypheny Relational | DuckDB Repartitioned | DuckDB Unpartitioned | Apache Spark Repartitioned | Apache Spark Unpartitioned | Row counts |
-| --- | --- | --- | --- | --- | --- | --- | --- |
-| Q1_P | Repartitioned full table baseline | 7.2 | 78.8 |  | 1,681.8 |  | 1 |
-| Q1_NP | Unpartitioned full table baseline | 9.6 |  | 59.6 |  | 1,189.6 | 1 |
-| Q2_P | Repartitioned partition by year | 6.6 | 35.2 |  | 641.8 |  | 1 |
-| Q2_NP | Unpartitioned partition by year | 9.6 |  | 88.4 |  | 2,123.2 | 1 |
-| Q3_P | Repartitioned partition by month | 8.6 | 17.0 |  | 450.6 |  | 1 |
-| Q3_NP | Unpartitioned partition by month | 9.8 |  | 72.2 |  | 1,326.2 | 1 |
-| Q4_P | Repartitioned filtered count, full scan | 150.0 | 30.2 |  | 1,693.0 |  | 1 |
-| Q4_NP | Unpartitioned filtered count, full scan | 131.2 |  | 20.6 |  | 902.6 | 1 |
-| Q5_P | Repartitioned filtered count, partition by year | 54.6 | 17.4 |  | 637.8 |  | 1 |
-| Q5_NP | Unpartitioned filtered count, partition by year | 61.6 |  | 13.6 |  | 1,035.4 | 1 |
+| Query | Description                                     | Polypheny Relational | DuckDB Repartitioned | DuckDB Unpartitioned | Apache Spark Repartitioned | Apache Spark Unpartitioned | Row counts |
+|-------|-------------------------------------------------|----------------------|----------------------|----------------------|----------------------------|----------------------------|------------|
+| Q1_P  | Repartitioned full table baseline               | 7.2                  | 78.8                 |                      | 1,681.8                    |                            | 1          |
+| Q1_NP | Unpartitioned full table baseline               | 9.6                  |                      | 59.6                 |                            | 1,189.6                    | 1          |
+| Q2_P  | Repartitioned partition by year                 | 6.6                  | 35.2                 |                      | 641.8                      |                            | 1          |
+| Q2_NP | Unpartitioned partition by year                 | 9.6                  |                      | 88.4                 |                            | 2,123.2                    | 1          |
+| Q3_P  | Repartitioned partition by month                | 8.6                  | 17.0                 |                      | 450.6                      |                            | 1          |
+| Q3_NP | Unpartitioned partition by month                | 9.8                  |                      | 72.2                 |                            | 1,326.2                    | 1          |
+| Q4_P  | Repartitioned filtered count, full scan         | 150.0                | 30.2                 |                      | 1,693.0                    |                            | 1          |
+| Q4_NP | Unpartitioned filtered count, full scan         | 131.2                |                      | 20.6                 |                            | 902.6                      | 1          |
+| Q5_P  | Repartitioned filtered count, partition by year | 54.6                 | 17.4                 |                      | 637.8                      |                            | 1          |
+| Q5_NP | Unpartitioned filtered count, partition by year | 61.6                 |                      | 13.6                 |                            | 1,035.4                    | 1          |
 
 ## Result Row Counts
 
-| Query | Description | Polypheny Relational | DuckDB Repartitioned | DuckDB Unpartitioned | Apache Spark Repartitioned | Apache Spark Unpartitioned |
-| --- | --- | --- | --- | --- | --- | --- |
-| Q1_P | Repartitioned full table baseline | 1 | 1 |  | 1 |  |
-| Q1_NP | Unpartitioned full table baseline | 1 |  | 1 |  | 1 |
-| Q2_P | Repartitioned partition by year | 1 | 1 |  | 1 |  |
-| Q2_NP | Unpartitioned partition by year | 1 |  | 1 |  | 1 |
-| Q3_P | Repartitioned partition by month | 1 | 1 |  | 1 |  |
-| Q3_NP | Unpartitioned partition by month | 1 |  | 1 |  | 1 |
-| Q4_P | Repartitioned filtered count, full scan | 1 | 1 |  | 1 |  |
-| Q4_NP | Unpartitioned filtered count, full scan | 1 |  | 1 |  | 1 |
-| Q5_P | Repartitioned filtered count, partition by year | 1 | 1 |  | 1 |  |
-| Q5_NP | Unpartitioned filtered count, partition by year | 1 |  | 1 |  | 1 |
+| Query | Description                                     | Polypheny Relational | DuckDB Repartitioned | DuckDB Unpartitioned | Apache Spark Repartitioned | Apache Spark Unpartitioned |
+|-------|-------------------------------------------------|----------------------|----------------------|----------------------|----------------------------|----------------------------|
+| Q1_P  | Repartitioned full table baseline               | 1                    | 1                    |                      | 1                          |                            |
+| Q1_NP | Unpartitioned full table baseline               | 1                    |                      | 1                    |                            | 1                          |
+| Q2_P  | Repartitioned partition by year                 | 1                    | 1                    |                      | 1                          |                            |
+| Q2_NP | Unpartitioned partition by year                 | 1                    |                      | 1                    |                            | 1                          |
+| Q3_P  | Repartitioned partition by month                | 1                    | 1                    |                      | 1                          |                            |
+| Q3_NP | Unpartitioned partition by month                | 1                    |                      | 1                    |                            | 1                          |
+| Q4_P  | Repartitioned filtered count, full scan         | 1                    | 1                    |                      | 1                          |                            |
+| Q4_NP | Unpartitioned filtered count, full scan         | 1                    |                      | 1                    |                            | 1                          |
+| Q5_P  | Repartitioned filtered count, partition by year | 1                    | 1                    |                      | 1                          |                            |
+| Q5_NP | Unpartitioned filtered count, partition by year | 1                    |                      | 1                    |                            | 1                          |
 
 ## Detailed Summary (ms)
 
-| System | Query | Description | Runs | Mean | Median | Std Dev | Min | Max | Rows | Columns | Status |
-| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-| Polypheny Relational | Q1_P | Repartitioned full table baseline | 5/5 | 7.2 | 7.0 | 0.8 | 6.0 | 8.0 | 1 | 1 | ok |
-| Polypheny Relational | Q1_NP | Unpartitioned full table baseline | 5/5 | 9.6 | 10.0 | 1.1 | 8.0 | 11.0 | 1 | 1 | ok |
-| Polypheny Relational | Q2_P | Repartitioned partition by year | 5/5 | 6.6 | 6.0 | 0.9 | 6.0 | 8.0 | 1 | 1 | ok |
-| Polypheny Relational | Q2_NP | Unpartitioned partition by year | 5/5 | 9.6 | 9.0 | 1.3 | 8.0 | 11.0 | 1 | 1 | ok |
-| Polypheny Relational | Q3_P | Repartitioned partition by month | 5/5 | 8.6 | 9.0 | 1.1 | 7.0 | 10.0 | 1 | 1 | ok |
-| Polypheny Relational | Q3_NP | Unpartitioned partition by month | 5/5 | 9.8 | 10.0 | 1.9 | 7.0 | 12.0 | 1 | 1 | ok |
-| Polypheny Relational | Q4_P | Repartitioned filtered count, full scan | 5/5 | 150.0 | 155.0 | 14.6 | 133.0 | 168.0 | 1 | 1 | ok |
-| Polypheny Relational | Q4_NP | Unpartitioned filtered count, full scan | 5/5 | 131.2 | 131.0 | 14.9 | 110.0 | 152.0 | 1 | 1 | ok |
-| Polypheny Relational | Q5_P | Repartitioned filtered count, partition by year | 5/5 | 54.6 | 53.0 | 3.0 | 53.0 | 60.0 | 1 | 1 | ok |
-| Polypheny Relational | Q5_NP | Unpartitioned filtered count, partition by year | 5/5 | 61.6 | 59.0 | 6.0 | 56.0 | 71.0 | 1 | 1 | ok |
-| DuckDB Repartitioned | Q1_P | Repartitioned full table baseline | 5/5 | 78.8 | 75.0 | 11.3 | 65.0 | 95.0 | 1 | 1 | ok |
-| DuckDB Repartitioned | Q2_P | Repartitioned partition by year | 5/5 | 35.2 | 35.0 | 1.8 | 33.0 | 38.0 | 1 | 1 | ok |
-| DuckDB Repartitioned | Q3_P | Repartitioned partition by month | 5/5 | 17.0 | 16.0 | 2.0 | 15.0 | 20.0 | 1 | 1 | ok |
-| DuckDB Repartitioned | Q4_P | Repartitioned filtered count, full scan | 5/5 | 30.2 | 30.0 | 1.3 | 29.0 | 32.0 | 1 | 1 | ok |
-| DuckDB Repartitioned | Q5_P | Repartitioned filtered count, partition by year | 5/5 | 17.4 | 17.0 | 1.1 | 16.0 | 19.0 | 1 | 1 | ok |
-| DuckDB Unpartitioned | Q1_NP | Unpartitioned full table baseline | 5/5 | 59.6 | 59.0 | 2.4 | 57.0 | 63.0 | 1 | 1 | ok |
-| DuckDB Unpartitioned | Q2_NP | Unpartitioned partition by year | 5/5 | 88.4 | 88.0 | 5.4 | 81.0 | 95.0 | 1 | 1 | ok |
-| DuckDB Unpartitioned | Q3_NP | Unpartitioned partition by month | 5/5 | 72.2 | 71.0 | 5.6 | 65.0 | 80.0 | 1 | 1 | ok |
-| DuckDB Unpartitioned | Q4_NP | Unpartitioned filtered count, full scan | 5/5 | 20.6 | 20.0 | 1.3 | 19.0 | 22.0 | 1 | 1 | ok |
-| DuckDB Unpartitioned | Q5_NP | Unpartitioned filtered count, partition by year | 5/5 | 13.6 | 14.0 | 0.5 | 13.0 | 14.0 | 1 | 1 | ok |
-| Apache Spark Repartitioned | Q1_P | Repartitioned full table baseline | 5/5 | 1,681.8 | 1,589.0 | 204.8 | 1,530.0 | 2,037.0 | 1 | 1 | ok |
-| Apache Spark Repartitioned | Q2_P | Repartitioned partition by year | 5/5 | 641.8 | 592.0 | 81.3 | 582.0 | 767.0 | 1 | 1 | ok |
-| Apache Spark Repartitioned | Q3_P | Repartitioned partition by month | 5/5 | 450.6 | 443.0 | 80.2 | 355.0 | 563.0 | 1 | 1 | ok |
-| Apache Spark Repartitioned | Q4_P | Repartitioned filtered count, full scan | 5/5 | 1,693.0 | 1,715.0 | 144.3 | 1,501.0 | 1,873.0 | 1 | 1 | ok |
-| Apache Spark Repartitioned | Q5_P | Repartitioned filtered count, partition by year | 5/5 | 637.8 | 681.0 | 101.5 | 502.0 | 736.0 | 1 | 1 | ok |
-| Apache Spark Unpartitioned | Q1_NP | Unpartitioned full table baseline | 5/5 | 1,189.6 | 1,217.0 | 190.4 | 973.0 | 1,418.0 | 1 | 1 | ok |
-| Apache Spark Unpartitioned | Q2_NP | Unpartitioned partition by year | 5/5 | 2,123.2 | 2,115.0 | 94.1 | 2,015.0 | 2,273.0 | 1 | 1 | ok |
-| Apache Spark Unpartitioned | Q3_NP | Unpartitioned partition by month | 5/5 | 1,326.2 | 1,343.0 | 101.3 | 1,193.0 | 1,465.0 | 1 | 1 | ok |
-| Apache Spark Unpartitioned | Q4_NP | Unpartitioned filtered count, full scan | 5/5 | 902.6 | 871.0 | 145.2 | 780.0 | 1,153.0 | 1 | 1 | ok |
-| Apache Spark Unpartitioned | Q5_NP | Unpartitioned filtered count, partition by year | 5/5 | 1,035.4 | 1,001.0 | 101.7 | 955.0 | 1,197.0 | 1 | 1 | ok |
+### Q1
+
+| System                     | Query | Description                       | Runs | Mean    | Median  | Std Dev | Min     | Max     | Rows | Columns | Status |
+|----------------------------|-------|-----------------------------------|------|---------|---------|---------|---------|---------|------|---------|--------|
+| Polypheny Relational       | Q1_P  | Repartitioned full table baseline | 5/5  | 7.2     | 7.0     | 0.8     | 6.0     | 8.0     | 1    | 1       | ok     |
+| Polypheny Relational       | Q1_NP | Unpartitioned full table baseline | 5/5  | 9.6     | 10.0    | 1.1     | 8.0     | 11.0    | 1    | 1       | ok     |
+| DuckDB Repartitioned       | Q1_P  | Repartitioned full table baseline | 5/5  | 78.8    | 75.0    | 11.3    | 65.0    | 95.0    | 1    | 1       | ok     |
+| DuckDB Unpartitioned       | Q1_NP | Unpartitioned full table baseline | 5/5  | 59.6    | 59.0    | 2.4     | 57.0    | 63.0    | 1    | 1       | ok     |
+| Apache Spark Repartitioned | Q1_P  | Repartitioned full table baseline | 5/5  | 1,681.8 | 1,589.0 | 204.8   | 1,530.0 | 2,037.0 | 1    | 1       | ok     |
+| Apache Spark Unpartitioned | Q1_NP | Unpartitioned full table baseline | 5/5  | 1,189.6 | 1,217.0 | 190.4   | 973.0   | 1,418.0 | 1    | 1       | ok     |
+
+### Q2
+
+| System                     | Query | Description                     | Runs | Mean    | Median  | Std Dev | Min     | Max     | Rows | Columns | Status |
+|----------------------------|-------|---------------------------------|------|---------|---------|---------|---------|---------|------|---------|--------|
+| Polypheny Relational       | Q2_P  | Repartitioned partition by year | 5/5  | 6.6     | 6.0     | 0.9     | 6.0     | 8.0     | 1    | 1       | ok     |
+| Polypheny Relational       | Q2_NP | Unpartitioned partition by year | 5/5  | 9.6     | 9.0     | 1.3     | 8.0     | 11.0    | 1    | 1       | ok     |
+| DuckDB Repartitioned       | Q2_P  | Repartitioned partition by year | 5/5  | 35.2    | 35.0    | 1.8     | 33.0    | 38.0    | 1    | 1       | ok     |
+| DuckDB Unpartitioned       | Q2_NP | Unpartitioned partition by year | 5/5  | 88.4    | 88.0    | 5.4     | 81.0    | 95.0    | 1    | 1       | ok     |
+| Apache Spark Repartitioned | Q2_P  | Repartitioned partition by year | 5/5  | 641.8   | 592.0   | 81.3    | 582.0   | 767.0   | 1    | 1       | ok     |
+| Apache Spark Unpartitioned | Q2_NP | Unpartitioned partition by year | 5/5  | 2,123.2 | 2,115.0 | 94.1    | 2,015.0 | 2,273.0 | 1    | 1       | ok     |
+
+### Q3
+
+| System                     | Query | Description                      | Runs | Mean    | Median  | Std Dev | Min     | Max     | Rows | Columns | Status |
+|----------------------------|-------|----------------------------------|------|---------|---------|---------|---------|---------|------|---------|--------|
+| Polypheny Relational       | Q3_P  | Repartitioned partition by month | 5/5  | 8.6     | 9.0     | 1.1     | 7.0     | 10.0    | 1    | 1       | ok     |
+| Polypheny Relational       | Q3_NP | Unpartitioned partition by month | 5/5  | 9.8     | 10.0    | 1.9     | 7.0     | 12.0    | 1    | 1       | ok     |
+| DuckDB Repartitioned       | Q3_P  | Repartitioned partition by month | 5/5  | 17.0    | 16.0    | 2.0     | 15.0    | 20.0    | 1    | 1       | ok     |
+| DuckDB Unpartitioned       | Q3_NP | Unpartitioned partition by month | 5/5  | 72.2    | 71.0    | 5.6     | 65.0    | 80.0    | 1    | 1       | ok     |
+| Apache Spark Repartitioned | Q3_P  | Repartitioned partition by month | 5/5  | 450.6   | 443.0   | 80.2    | 355.0   | 563.0   | 1    | 1       | ok     |
+| Apache Spark Unpartitioned | Q3_NP | Unpartitioned partition by month | 5/5  | 1,326.2 | 1,343.0 | 101.3   | 1,193.0 | 1,465.0 | 1    | 1       | ok     |
+
+### Q4
+
+| System                     | Query | Description                             | Runs | Mean    | Median  | Std Dev | Min     | Max     | Rows | Columns | Status |
+|----------------------------|-------|-----------------------------------------|------|---------|---------|---------|---------|---------|------|---------|--------|
+| Polypheny Relational       | Q4_P  | Repartitioned filtered count, full scan | 5/5  | 150.0   | 155.0   | 14.6    | 133.0   | 168.0   | 1    | 1       | ok     |
+| Polypheny Relational       | Q4_NP | Unpartitioned filtered count, full scan | 5/5  | 131.2   | 131.0   | 14.9    | 110.0   | 152.0   | 1    | 1       | ok     |
+| DuckDB Repartitioned       | Q4_P  | Repartitioned filtered count, full scan | 5/5  | 30.2    | 30.0    | 1.3     | 29.0    | 32.0    | 1    | 1       | ok     |
+| DuckDB Unpartitioned       | Q4_NP | Unpartitioned filtered count, full scan | 5/5  | 20.6    | 20.0    | 1.3     | 19.0    | 22.0    | 1    | 1       | ok     |
+| Apache Spark Repartitioned | Q4_P  | Repartitioned filtered count, full scan | 5/5  | 1,693.0 | 1,715.0 | 144.3   | 1,501.0 | 1,873.0 | 1    | 1       | ok     |
+| Apache Spark Unpartitioned | Q4_NP | Unpartitioned filtered count, full scan | 5/5  | 902.6   | 871.0   | 145.2   | 780.0   | 1,153.0 | 1    | 1       | ok     |
+
+### Q5
+
+| System                     | Query | Description                                     | Runs | Mean    | Median  | Std Dev | Min   | Max     | Rows | Columns | Status |
+|----------------------------|-------|-------------------------------------------------|------|---------|---------|---------|-------|---------|------|---------|--------|
+| Polypheny Relational       | Q5_P  | Repartitioned filtered count, partition by year | 5/5  | 54.6    | 53.0    | 3.0     | 53.0  | 60.0    | 1    | 1       | ok     |
+| Polypheny Relational       | Q5_NP | Unpartitioned filtered count, partition by year | 5/5  | 61.6    | 59.0    | 6.0     | 56.0  | 71.0    | 1    | 1       | ok     |
+| DuckDB Repartitioned       | Q5_P  | Repartitioned filtered count, partition by year | 5/5  | 17.4    | 17.0    | 1.1     | 16.0  | 19.0    | 1    | 1       | ok     |
+| DuckDB Unpartitioned       | Q5_NP | Unpartitioned filtered count, partition by year | 5/5  | 13.6    | 14.0    | 0.5     | 13.0  | 14.0    | 1    | 1       | ok     |
+| Apache Spark Repartitioned | Q5_P  | Repartitioned filtered count, partition by year | 5/5  | 637.8   | 681.0   | 101.5   | 502.0 | 736.0   | 1    | 1       | ok     |
+| Apache Spark Unpartitioned | Q5_NP | Unpartitioned filtered count, partition by year | 5/5  | 1,035.4 | 1,001.0 | 101.7   | 955.0 | 1,197.0 | 1    | 1       | ok     |
+
diff --git a/plugins/parquet-adapter/benchmarks/results/plots/aggregation_plot.pdf b/plugins/parquet-adapter/benchmarks/results/plots/aggregation_plot.pdf
index 0d3b818f49393931d14e2bae7ce9411fef5b5413..ec162b5f5098bae65e3b85498531b4c03ea7ba98 100644
GIT binary patch
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diff --git a/plugins/parquet-adapter/benchmarks/results/plots/partitioning_plot.svg b/plugins/parquet-adapter/benchmarks/results/plots/partitioning_plot.svg
index 954158f32a..c22bd63922 100644
--- a/plugins/parquet-adapter/benchmarks/results/plots/partitioning_plot.svg
+++ b/plugins/parquet-adapter/benchmarks/results/plots/partitioning_plot.svg
@@ -71,11 +71,14 @@
 <text x="716.60" y="620" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q4_NP</text>
 <text x="820.89" y="620" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q5_P</text>
 <text x="892.43" y="620" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q5_NP</text>
-<text x="153.34" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Full baseline</text>
-<text x="329.17" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Year filter</text>
-<text x="505.00" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Year + month filter</text>
-<text x="680.83" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Data-column filter</text>
-<text x="856.66" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Year + data filter</text>
+<text x="153.34" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Full-count baseline</text>
+<text x="329.17" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Year-filtered count</text>
+<text x="505.00" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Year-and-month-filtered</text>
+<text x="505.00" y="662" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">count</text>
+<text x="680.83" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Data-column filtering, no</text>
+<text x="680.83" y="662" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">partition restriction</text>
+<text x="856.66" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Year and data-column</text>
+<text x="856.66" y="662" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">filtering</text>
 <text x="955" y="80" font-family="Helvetica, Arial, sans-serif" font-size="13" font-weight="700">System</text>
 <rect x="955" y="100" width="16" height="16" fill="#4f9d55"/>
 <text x="979" y="113" font-family="Helvetica, Arial, sans-serif" font-size="12">Polypheny Relational</text>
@@ -84,7 +87,7 @@
 <rect x="955" y="156" width="16" height="16" fill="#2f6fbb"/>
 <text x="979" y="169" font-family="Helvetica, Arial, sans-serif" font-size="12">Apache Spark</text>
 <text x="955" y="214" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700" fill="#333">P</text>
-<text x="961.96" y="214" font-family="Helvetica, Arial, sans-serif" font-size="12" fill="#333"> - partitioned</text>
+<text x="963.004" y="214" font-family="Helvetica, Arial, sans-serif" font-size="12" fill="#333"> - partitioned</text>
 <text x="955" y="232" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700" fill="#333">NP</text>
-<text x="968.92" y="232" font-family="Helvetica, Arial, sans-serif" font-size="12" fill="#333"> - not partitioned</text>
+<text x="971.668" y="232" font-family="Helvetica, Arial, sans-serif" font-size="12" fill="#333"> - not partitioned</text>
 </svg>
\ No newline at end of file
diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md
index efb9cf0a94..abdc4351c5 100644
--- a/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md
@@ -97,9 +97,9 @@ python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_resu
   --title "Nested Data" `
   --name nested_data_plot `
   --query-order "Q01,Q02,Q03,Q04,Q05" `
-  --query-descriptions "Q01=Customer access;Q02=Customer projection;Q03=Order projection;Q04=Lineitem projection;Q05=Lineitem max" `
-  --query-description-wrap-chars 30 `
-  --query-description-max-lines 1 `
+  --query-descriptions "Q01=Root-filtered full customer access;Q02=Root-filtered customer projection;Q03=Level-1 nested filtered order projection;Q04=Level-2 nested filtered lineitem projection;Q05=Level-2 nested lineitem MAX aggregation" `
+  --query-description-wrap-chars 24 `
+  --query-description-max-lines 2 `
   "Polypheny Relational Normalized=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_normalized_results.csv" `
   "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_mql_results.csv" `
   "DuckDB=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_duckdb_results.csv" `
diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md
index bedd884052..9e01fac854 100644
--- a/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md
@@ -115,9 +115,9 @@ python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_resu
   --title "Partitioning by Year and Month" `
   --name partitioning_plot `
   --query-order "Q1_P,Q1_NP,Q2_P,Q2_NP,Q3_P,Q3_NP,Q4_P,Q4_NP,Q5_P,Q5_NP" `
-  --query-descriptions "Q1=Full baseline;Q2=Year filter;Q3=Year + month filter;Q4=Data-column filter;Q5=Year + data filter" `
-  --query-description-wrap-chars 30 `
-  --query-description-max-lines 1 `
+  --query-descriptions "Q1=Full-count baseline;Q2=Year-filtered count;Q3=Year-and-month-filtered count;Q4=Data-column filtering, no partition restriction;Q5=Year and data-column filtering" `
+  --query-description-wrap-chars 28 `
+  --query-description-max-lines 2 `
   --side-note "P - partitioned;NP - not partitioned" `
   "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_polypheny_results.csv" `
   "DuckDB Repartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_repartitioned_results.csv" `
diff --git a/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py b/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py
index df2201e39b..2783a642dd 100644
--- a/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py
+++ b/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_plot_generator.py
@@ -492,9 +492,65 @@ def aligned_text_x(x, value, size, bold, anchor):
     raise ValueError(f"Unsupported text anchor: {anchor}")
 
 
+def font_widths(*groups):
+    return {
+        character: width
+        for width, characters in groups
+        for character in characters
+    }
+
+
+# Adobe Font Metrics widths for the built-in PDF Helvetica fonts, expressed in
+# thousandths of an em. Using the real glyph widths keeps independently drawn
+# lines centered on the same x coordinate.
+HELVETICA_WIDTHS = font_widths(
+    (191, "'"),
+    (222, "ijl"),
+    (260, "|"),
+    (278, " !,./:;I[\\]ft"),
+    (333, "()-`r"),
+    (334, "{}"),
+    (355, '"'),
+    (389, "*"),
+    (469, "^"),
+    (500, "Jcksvxyz"),
+    (556, "#$0123456789?_Labdeghnopqu"),
+    (584, "+<=>~"),
+    (611, "FTZ"),
+    (667, "&ABEKPSXY"),
+    (722, "CDHNRUVw"),
+    (778, "GOQ"),
+    (833, "Mm"),
+    (889, "%"),
+    (944, "W"),
+    (1015, "@"),
+)
+
+HELVETICA_BOLD_WIDTHS = font_widths(
+    (238, "'"),
+    (278, " ,./I\\ijl"),
+    (280, "|"),
+    (333, "!()-:;[]`ft"),
+    (389, "*r{}"),
+    (474, '"'),
+    (500, "z"),
+    (556, "#$0123456789J_aceksvxy"),
+    (584, "+<=>^~"),
+    (611, "?FLTZbdghnopqu"),
+    (667, "EPSVXY"),
+    (722, "&ABCDHKNRU"),
+    (778, "GOQw"),
+    (833, "M"),
+    (889, "%m"),
+    (944, "W"),
+    (975, "@"),
+)
+
+
 def approximate_text_width(value, size, bold):
-    factor = 0.58 if bold else 0.52
-    return len(str(value)) * size * factor
+    widths = HELVETICA_BOLD_WIDTHS if bold else HELVETICA_WIDTHS
+    width_units = sum(widths.get(character, 556) for character in str(value))
+    return width_units * size / 1000
 
 
 def pdf_escape(value):
diff --git a/plugins/parquet-adapter/benchmarks/scripts/summarize_benchmark_results.py b/plugins/parquet-adapter/benchmarks/scripts/summarize_benchmark_results.py
index 1263d3550c..c4335eb2f0 100644
--- a/plugins/parquet-adapter/benchmarks/scripts/summarize_benchmark_results.py
+++ b/plugins/parquet-adapter/benchmarks/scripts/summarize_benchmark_results.py
@@ -172,27 +172,41 @@ def render_markdown(summaries, inputs, args):
             row.append(format_values(by_system_query.get((system, query_id)), "rows"))
         lines.append("| " + " | ".join(row) + " |")
 
-    lines.extend(
-        [
-            "",
-            f"## Detailed Summary ({unit_label})",
-            "",
-            "| System | Query | Description | Runs | Mean | Median | Std Dev | Min | Max | Rows | Columns | Status |",
-            "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |",
-        ]
-    )
-    for system in systems:
-        for query_id in query_ids:
-            summary = by_system_query.get((system, query_id))
-            if summary is None:
-                continue
-            lines.append(
-                "| "
-                + " | ".join(
+    lines.extend(["", f"## Detailed Summary ({unit_label})", ""])
+    for group_id, group_query_ids in detailed_query_groups(query_ids):
+        has_variants = len(group_query_ids) > 1
+        heading = f"### {escape_md(group_id)}"
+        if not has_variants:
+            description = description_by_query.get(group_query_ids[0], "").strip().rstrip(".")
+            if description:
+                heading += f" - {escape_md(description)}"
+        lines.extend([heading, ""])
+
+        if has_variants:
+            lines.extend(
+                [
+                    "| System | Query | Description | Runs | Mean | Median | Std Dev | Min | Max | Rows | Columns | Status |",
+                    "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |",
+                ]
+            )
+        else:
+            lines.extend(
+                [
+                    "| System | Runs | Mean | Median | Std Dev | Min | Max | Rows | Columns | Status |",
+                    "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |",
+                ]
+            )
+
+        for system in systems:
+            for query_id in group_query_ids:
+                summary = by_system_query.get((system, query_id))
+                if summary is None:
+                    continue
+                row = [escape_md(system)]
+                if has_variants:
+                    row.extend([escape_md(query_id), escape_md(summary.description)])
+                row.extend(
                     [
-                        escape_md(system),
-                        escape_md(query_id),
-                        escape_md(summary.description),
                         f"{summary.successful_runs}/{summary.total_runs}",
                         format_stat(summary, statistics.mean, args.unit),
                         format_stat(summary, statistics.median, args.unit),
@@ -204,8 +218,8 @@ def render_markdown(summaries, inputs, args):
                         escape_md(status(summary)),
                     ]
                 )
-                + " |"
-            )
+                lines.append("| " + " | ".join(row) + " |")
+        lines.append("")
 
     lines.append("")
     return "\n".join(lines)
@@ -218,6 +232,23 @@ def description_map(summaries):
     return result
 
 
+def detailed_query_groups(query_ids):
+    groups = {}
+    for query_id in query_ids:
+        group_id = logical_query_id(query_id)
+        groups.setdefault(group_id, []).append(query_id)
+    return groups.items()
+
+
+def logical_query_id(query_id):
+    if "_" not in query_id:
+        return query_id
+    prefix, suffix = query_id.rsplit("_", 1)
+    if suffix in {"P", "NP", "RP", "UP"}:
+        return prefix
+    return query_id
+
+
 def format_mean(summary, unit):
     return format_stat(summary, statistics.mean, unit)
 
diff --git a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
index 36fd38d041..a9c8eefea8 100644
--- a/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
+++ b/plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java
@@ -20,6 +20,7 @@
 import java.util.List;
 import java.util.Map;
 import java.util.concurrent.atomic.AtomicBoolean;
+import lombok.AccessLevel;
 import lombok.Getter;
 import org.apache.calcite.linq4j.AbstractEnumerable;
 import org.apache.calcite.linq4j.Enumerable;
@@ -46,6 +47,7 @@
 import org.polypheny.db.algebra.type.AlgDataTypeFieldImpl;
 import org.polypheny.db.algebra.type.DocumentType;
 import org.polypheny.db.catalog.entity.physical.PhysicalCollection;
+import org.polypheny.db.catalog.exceptions.GenericRuntimeException;
 import org.polypheny.db.plan.AlgCluster;
 import org.polypheny.db.plan.AlgTraitSet;
 import org.polypheny.db.schema.types.ScannableEntity;
@@ -63,6 +65,10 @@ public class ParquetDocument extends PhysicalCollection implements ScannableEnti
     private final List<ParquetSourceFile> sourceFiles;
     private final Map<String, List<String>> columnPaths;
     private final AbstractParquetSource parquetSource;
+    @Getter(AccessLevel.NONE)
+    private final List<ExportedColumn> exportedColumns;
+    @Getter(AccessLevel.NONE)
+    private final ParquetDocAggregateExecutor aggregateExecutor;
 
 
     public ParquetDocument( PhysicalCollection collection, DiscoveredTableBinding binding, AbstractParquetSource parquetSource ) {
@@ -77,6 +83,10 @@ public ParquetDocument( PhysicalCollection collection, DiscoveredTableBinding bi
         this.sourceFiles = List.copyOf( binding.sourceFiles() ); // add multi-file handling
         this.columnPaths = Map.copyOf( binding.columnPaths() );
         this.parquetSource = parquetSource;
+        this.exportedColumns = List.copyOf( parquetSource.getExportedColumns().getOrDefault( name, List.of() ) );
+        this.aggregateExecutor = sourceFiles.isEmpty()
+                ? null
+                : new ParquetDocAggregateExecutor( sourceFiles, exportedColumns, columnPaths );
     }
 
 
@@ -151,8 +161,7 @@ public Enumerator<PolyValue[]> enumerator() {
     @SuppressWarnings("unused")
     public Enumerable<PolyValue[]> dataAggregate( DataContext dataContext, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
-        return new ParquetDocAggregateExecutor( sourceFiles, exportedColumns(), columnPaths )
-                .createDataEnumerator( dataContext, fields, filters, groupFields, aggregateKinds, aggregateArgs );
+        return aggregateExecutor().createDataEnumerator( dataContext, fields, filters, groupFields, aggregateKinds, aggregateArgs );
     }
 
 
@@ -170,8 +179,7 @@ public Enumerable<PolyValue[]> dataAggregate( DataContext dataContext, int[] fie
     @SuppressWarnings("unused")
     public Enumerable<PolyValue[]> metadataAggregate( DataContext dataContext, int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, int[] groupFields, String[] aggregateKinds, int[] aggregateArgs ) {
         dataContext.getStatement().getTransaction().registerInvolvedAdapter( parquetSource );
-        return new ParquetDocAggregateExecutor( sourceFiles, exportedColumns(), columnPaths )
-                .createMetadataEnumerator( dataContext, fields, filters, groupFields, aggregateKinds, aggregateArgs );
+        return aggregateExecutor().createMetadataEnumerator( dataContext, fields, filters, groupFields, aggregateKinds, aggregateArgs );
     }
 
 
@@ -184,8 +192,7 @@ public Enumerable<PolyValue[]> metadataAggregate( DataContext dataContext, int[]
      * @return {@code true} if all provided aggregation functions can be applied and {@code false} otherwise.
      */
     public boolean supportsDataAggregate( int[] fields, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
-        return new ParquetDocAggregateExecutor( sourceFiles, exportedColumns(), columnPaths )
-                .supportsDataAggregate( fields, groupSet, aggregateCalls );
+        return aggregateExecutor().supportsDataAggregate( fields, groupSet, aggregateCalls );
     }
 
 
@@ -199,8 +206,7 @@ public boolean supportsDataAggregate( int[] fields, ImmutableBitSet groupSet, Li
      * @return {@code true} if all provided aggregation functions can be applied and {@code false} otherwise.
      */
     public boolean supportsMetadataAggregate( int[] fields, List<ParquetAdapterFilter<PolyValue>> filters, ImmutableBitSet groupSet, List<AggregateCall> aggregateCalls ) {
-        return new ParquetDocAggregateExecutor( sourceFiles, exportedColumns(), columnPaths )
-                .supportsMetadataAggregate( fields, filters, groupSet, aggregateCalls );
+        return aggregateExecutor().supportsMetadataAggregate( fields, filters, groupSet, aggregateCalls );
     }
 
 
@@ -241,7 +247,15 @@ private AlgDataType buildDocumentType( AlgDataTypeFactory typeFactory ) {
 
 
     private List<ExportedColumn> exportedColumns() {
-        return parquetSource.getExportedColumns().getOrDefault( name, List.of() );
+        return exportedColumns;
+    }
+
+
+    private ParquetDocAggregateExecutor aggregateExecutor() {
+        if ( aggregateExecutor == null ) {
+            throw new GenericRuntimeException( "Cannot create a Parquet document aggregate executor without source files." );
+        }
+        return aggregateExecutor;
     }
 
 
diff --git a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java
index 693b7bac77..7e8927eba0 100644
--- a/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java
+++ b/plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java
@@ -111,8 +111,8 @@ private static PhysicalCollection collection( String name ) {
     }
 
 
-    private static ParquetSourceFile sourceFile() {
-        return new ParquetSourceFile( "file:/tmp/orders.parquet", Map.of(), Map.of() );
+    private static ParquetSourceFile sourceFile( Path file ) throws Exception {
+        return ParquetSourceFile.of( file.toUri().toURL().toString(), Map.of() );
     }
 
 
@@ -135,17 +135,20 @@ private static TestParquetSource testSource() throws Exception {
 
     @Test
     void documentBuildsTupleTypeFromExportedColumnsAndFallsBackToDocumentId() throws Exception {
+        Path file = tempDir.resolve( "tuple-type.parquet" );
+        writeOrdersParquet( file );
+        ParquetSourceFile sourceFile = sourceFile( file );
         TestParquetSource.exportedColumns = Map.of(
                 "orders", List.of(
                         exportedColumn( "name", PolyType.VARCHAR, 0, true ),
                         exportedColumn( "score", PolyType.INTEGER, 1, false ) ) );
-        ParquetDocument document = new ParquetDocument( collection( "orders" ), binding( List.of( sourceFile() ), Map.of( "name", List.of( "name" ), "score", List.of( "score" ) ) ), testSource() );
+        ParquetDocument document = new ParquetDocument( collection( "orders" ), binding( List.of( sourceFile ), Map.of( "name", List.of( "name" ), "score", List.of( "score" ) ) ), testSource() );
         AlgDataType tupleType = document.getTupleType( AlgDataTypeFactory.DEFAULT );
 
         assertEquals( List.of( "name", "score" ), tupleType.getFieldNames() );
         assertTrue( tupleType.getFields().get( 0 ).getType().isNullable() );
         assertFalse( tupleType.getFields().get( 1 ).getType().isNullable() );
-        assertEquals( List.of( sourceFile() ), document.getSourceFiles() );
+        assertEquals( List.of( sourceFile ), document.getSourceFiles() );
 
         TestParquetSource.exportedColumns = Map.of();
         ParquetDocument fallback = new ParquetDocument( collection( "missing" ), binding( List.of(), Map.of() ), testSource() );
@@ -155,8 +158,9 @@ void documentBuildsTupleTypeFromExportedColumnsAndFallsBackToDocumentId() throws
 
 
     @Test
-    void docScanCopiesFiltersDerivesDocumentRowAndRegistersFilterRule() {
-        ParquetDocument document = new ParquetDocument( collection( "orders" ), binding( List.of( sourceFile() ), Map.of() ), null );
+    void docScanCopiesFiltersDerivesDocumentRowAndRegistersFilterRule() throws Exception {
+        TestParquetSource.exportedColumns = Map.of();
+        ParquetDocument document = new ParquetDocument( collection( "orders" ), binding( List.of(), Map.of() ), testSource() );
         AlgCluster cluster = cluster();
         ParquetAdapterFilter<PolyValue> filter = new ParquetAdapterFilter<>( 0, Kind.EQUALS, PolyString.of( "Alice" ) );
         ParquetDocScan scan = new ParquetDocScan( cluster, document, List.of( filter ) );
@@ -264,13 +268,15 @@ void documentAggregateExecutorUsesSharedMetadataPath() throws Exception {
 
     @Test
     void documentMetadataScanCopiesAndHasZeroCost() throws Exception {
+        Path file = tempDir.resolve( "metadata-scan.parquet" );
+        writeOrdersParquet( file );
         TestParquetSource.exportedColumns = Map.of(
                 "orders", List.of(
                         exportedColumn( "customer", PolyType.VARCHAR, 0, false ),
                         exportedColumn( "amount", PolyType.DOUBLE, 1, false ) ) );
         ParquetDocument document = new ParquetDocument(
                 collection( "orders" ),
-                binding( List.of( sourceFile() ), Map.of( "customer", List.of( "customer" ), "amount", List.of( "amount" ) ) ),
+                binding( List.of( sourceFile( file ) ), Map.of( "customer", List.of( "customer" ), "amount", List.of( "amount" ) ) ),
                 testSource() );
         AlgCluster cluster = cluster();
         ParquetDocScan scan = new ParquetDocScan( cluster, document, List.of() );
@@ -281,6 +287,35 @@ void documentMetadataScanCopiesAndHasZeroCost() throws Exception {
     }
 
 
+    @Test
+    void documentReusesAggregateSchemaMetadata() throws Exception {
+        Path file = tempDir.resolve( "cached-metadata.parquet" );
+        writeOrdersParquet( file );
+        TestParquetSource.exportedColumns = Map.of(
+                "orders", List.of(
+                        exportedColumn( "customer", PolyType.VARCHAR, 0, false ),
+                        exportedColumn( "amount", PolyType.DOUBLE, 1, false ) ) );
+        ParquetDocument document = new ParquetDocument(
+                collection( "orders" ),
+                binding( List.of( sourceFile( file ) ), Map.of( "customer", List.of( "customer" ), "amount", List.of( "amount" ) ) ),
+                testSource() );
+        Files.delete( file );
+
+        AggregateCall count = AggregateCall.create(
+                aggregateFunction( Kind.COUNT, "COUNT" ),
+                false,
+                false,
+                List.of(),
+                -1,
+                AlgCollations.EMPTY,
+                AlgDataTypeFactory.DEFAULT.createPolyType( PolyType.BIGINT ),
+                "count" );
+
+        assertTrue( document.supportsMetadataAggregate( new int[]{ 0, 1 }, List.of(), ImmutableBitSet.of(), List.of( count ) ) );
+        assertTrue( document.supportsMetadataAggregate( new int[]{ 0, 1 }, List.of(), ImmutableBitSet.of(), List.of( count ) ) );
+    }
+
+
     private static void writeOrdersParquet( Path file ) throws Exception {
         org.apache.parquet.schema.MessageType schema = Types.buildMessage()
                 .required( PrimitiveTypeName.BINARY ).as( LogicalTypeAnnotation.stringType() ).named( "customer" )

From 381b7fcfda6562d43062c86d4464cf14f0b2debc Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Mon, 29 Jun 2026 12:02:18 +0200
Subject: [PATCH 43/46] benchmark documentation

---
 plugins/parquet-adapter/benchmarks/README.md  | 69 +++++++++++--------
 .../benchmarks/benchmark-plan.md              | 31 +++++----
 ...chnologies.md => technology-comparison.md} |  2 +-
 3 files changed, 58 insertions(+), 44 deletions(-)
 rename plugins/parquet-adapter/benchmarks/{compatitive-technologies.md => technology-comparison.md} (99%)

diff --git a/plugins/parquet-adapter/benchmarks/README.md b/plugins/parquet-adapter/benchmarks/README.md
index b22436beaf..58ffd11156 100644
--- a/plugins/parquet-adapter/benchmarks/README.md
+++ b/plugins/parquet-adapter/benchmarks/README.md
@@ -3,23 +3,17 @@
 `plugins/parquet-adapter/benchmarks` folder contains benchmark definitions, execution pipelines, runner code,
 and result artifacts for the Polypheny Parquet adapter comparison.
 
-## Main Files
-
-| Path                          | Purpose                                                                                       |
-|-------------------------------|-----------------------------------------------------------------------------------------------|
-| `benchmark-plan.md`           | Benchmark suite overview: goals, datasets, query files, result locations, and pipeline links. |
-| `compatitive-technologies.md` | Technical comparison of Polypheny, DuckDB, and Apache Spark for Parquet workloads.            |
-| `README.md`                   | Folder structure and file map.                                                                |
-
-## Folder Structure
-
-| Folder          | Contents                                                                                               |
-|-----------------|--------------------------------------------------------------------------------------------------------|
-| `datasets/`     | Dataset descriptions.                                                                                  |
-| `query_lists/`  | Runnable SQL/MQL query files and query specifications.                                                 |
-| `run_pipeline/` | Step-by-step benchmark execution commands. Use these files to run each full suite pipeline.            |
-| `scripts/`      | Benchmark clients, PowerShell runners, preprocessing scripts, summary generation, and plot generation. |
-| `results/`      | Raw CSV results, generated summaries, short analysis notes, and plots.                                 |
+## Folder Structure and Files
+
+| Path                       | Contents                                                                                               |
+|----------------------------|--------------------------------------------------------------------------------------------------------|
+| `benchmark-plan.md`        | Benchmark methodology and suite overview: goals, datasets, query files, result locations, interpretation files, and pipeline links. |
+| `technology-comparison.md` | Technical comparison of Polypheny, DuckDB, and Apache Spark for Parquet workloads.                     |
+| `datasets/`                | Dataset descriptions.                                                                                  |
+| `query_lists/`             | Runnable SQL/MQL query files and query specifications.                                                 |
+| `run_pipeline/`            | Step-by-step benchmark execution commands. Use these files to run each full suite pipeline.            |
+| `scripts/`                 | Benchmark clients, PowerShell runners, preprocessing scripts, summary generation, and plot generation. |
+| `results/`                 | Raw CSV/JSONL results, generated statistical and correctness summaries, manual result interpretations, and plots. |
 
 ## Query Lists
 
@@ -35,9 +29,9 @@ File naming:
 
 - `*_rf.sql`: Polypheny relational flat SQL with explicit adapter-prefixed table names.
 - `*_rn.sql`: Polypheny relational normalized SQL with explicit adapter-prefixed table names.
-- `*_polypheny*.sql`: other Polypheny relational SQL with explicit adapter-prefixed table names.
+- `*_polypheny.sql` and `*_polypheny_normalized.sql`: other Polypheny relational SQL with explicit adapter-prefixed table names.
+- `*_polypheny_mql.sql` and `*.mql`: Polypheny document adapter queries written in MQL.
 - `*_sql.sql`: SQL shared by DuckDB and Spark where both expose the same logical temp views.
-- `*.mql`: Polypheny document adapter queries.
 - `*_query_specification.md`: logical query intent, query groups, and textual query descriptions.
 
 ## Run Pipelines
@@ -56,7 +50,10 @@ run_pipeline/
 | `run_partitioning_bm_pipeline.md`            | Partitioning            |
 
 Each pipeline file contains the commands to build/start Polypheny where needed,
-run the benchmark clients, and generate the suite summary and plot.
+run the benchmark clients, and generate the suite summary and plot. The
+aggregation pipeline additionally captures result values and generates a
+correctness comparison. Result interpretation documents are updated manually
+after the generated artifacts have been reviewed.
 
 ## Scripts
 
@@ -66,7 +63,7 @@ run the benchmark clients, and generate the suite summary and plot.
 | `scripts/runners/nested/`                | Nested-data-specific runner wrappers.                                        |
 | `scripts/implementation/`                | Java/Python benchmark clients called by the runners.                         |
 | `scripts/ds_preprocessing/`              | Dataset materialization and preprocessing utilities.                         |
-| `scripts/summarize_benchmark_results.py` | Creates markdown summaries from benchmark CSV files.                         |
+| `scripts/summarize_benchmark_results.py` | Creates markdown summaries with per-query runtime statistics from benchmark CSV files. |
 | `scripts/compare_aggregation_results.py` | Compares captured aggregation result values across systems.                  |
 | `scripts/plot_generation/`               | Plot generation scripts, including the generic SVG/PDF/PNG benchmark CSV plot generator. |
 
@@ -97,16 +94,30 @@ results/
 Result conventions:
 
 - `*_results.csv`: raw benchmark output from a runner.
-- `*_values.jsonl`: captured query result values used for correctness checks.
+- `*_values.jsonl`: captured query result values used for correctness checks where applicable.
 - `*_summary.md`: generated comparison summary from `scripts/summarize_benchmark_results.py`.
-- `*_results_analysis.md`: short manual interpretation of a generated summary.
+- `*_correctness_summary.md`: generated comparison of captured result values where applicable.
+- `*_result_analysis.md`: manually maintained result interpretation combining the generated plot, findings, and comparability notes.
 - `results/plots/`: generated plot artifacts such as SVG, PDF, and PNG files.
 
+### Artifact Workflow
+
+1. Benchmark runners write raw timing results to CSV files and, where required,
+   captured result values to JSONL files.
+2. Summary scripts calculate per-query mean, median, sample standard deviation,
+   minimum, and maximum runtimes. The aggregation comparison also checks
+   captured grouping keys and aggregate values.
+3. The plot generator creates SVG, PDF, and PNG figures from the raw timing
+   results.
+4. The corresponding `*_result_analysis.md` file is updated manually with the
+   interpretation of the summary and plot.
+
 Current result folders:
 
-| Path                               | Contents                                      |
-|------------------------------------|-----------------------------------------------|
-| `results/access_model_comparison/` | Access-model raw CSVs, summary, and analysis. |
-| `results/aggregation/`             | Aggregation raw CSVs, summary, and analysis.  |
-| `results/nested_data/`             | Nested-data raw CSVs.                         |
-| `results/plots/`                   | Generated benchmark plots.                    |
+| Path                               | Contents                                                                    |
+|------------------------------------|-----------------------------------------------------------------------------|
+| `results/access_model_comparison/` | Access-model raw CSVs, generated summary, and manual result interpretation. |
+| `results/aggregation/`             | Aggregation raw CSV/JSONL files, timing and correctness summaries, and manual result interpretation. |
+| `results/nested_data/`             | Nested-data raw CSVs, generated summary, and manual result interpretation.  |
+| `results/partitioning/`            | Partitioning raw CSVs, generated summary, and manual result interpretation. |
+| `results/plots/`                   | Generated SVG, PDF, and PNG benchmark plots.                                |
diff --git a/plugins/parquet-adapter/benchmarks/benchmark-plan.md b/plugins/parquet-adapter/benchmarks/benchmark-plan.md
index 2b756d0fb7..0dd6277622 100644
--- a/plugins/parquet-adapter/benchmarks/benchmark-plan.md
+++ b/plugins/parquet-adapter/benchmarks/benchmark-plan.md
@@ -13,22 +13,25 @@ The benchmarks evaluate the Parquet adapter from the following perspectives:
 
 ## Common Execution Setup
 
-Unless a benchmark suite states otherwise, each query is executed with:
-
-- one warmup run;
-- five measured runs;
-- elapsed wall-clock time recorded in milliseconds;
-- result row count and column count recorded for correctness checks;
-- failures and timeouts recorded instead of runtime values.
-
-Warmup runs are excluded from reported results. Summary tables report mean,
-median, sample standard deviation, minimum, and maximum elapsed time over
-successful measured runs only. The benchmark clients consume query results
-according to the result boundary described in the report methodology. Polypheny
-and DuckDB use JDBC clients that drain the complete result set. Spark is
+- Unless a benchmark suite states otherwise, each query is executed with:
+
+  - one warmup run;
+  - five measured runs;
+  - elapsed wall-clock time recorded in milliseconds;
+  - result row count and column count recorded for correctness checks;
+  - failures and timeouts recorded instead of runtime values.
+
+- Warmup runs are excluded from reported results. 
+- Summary tables report mean, median, sample standard deviation, minimum, and maximum elapsed time over
+successful measured runs only. 
+- Where comparable, aggregation benchmarks also capture and compare grouping keys and aggregate values across systems. Numeric results are compared using the configured tolerance, while intentional result-schema differences are documented separately.
+- The benchmark clients consume query results
+according to the result boundary described in the report methodology. 
+- Polypheny and DuckDB use JDBC clients that drain the complete result set. Spark is
 executed in local mode inside Docker and consumes rows in the Spark runner.
 
-Raw benchmark outputs are stored as CSV files. Summary tables are generated with:
+- Raw benchmark outputs are stored as CSV files. 
+- Summary tables are generated with:
 
 ```powershell
 python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py
diff --git a/plugins/parquet-adapter/benchmarks/compatitive-technologies.md b/plugins/parquet-adapter/benchmarks/technology-comparison.md
similarity index 99%
rename from plugins/parquet-adapter/benchmarks/compatitive-technologies.md
rename to plugins/parquet-adapter/benchmarks/technology-comparison.md
index 8cfa65e219..b92cd85223 100644
--- a/plugins/parquet-adapter/benchmarks/compatitive-technologies.md
+++ b/plugins/parquet-adapter/benchmarks/technology-comparison.md
@@ -1,4 +1,4 @@
-# Competitive Technologies for Planned Parquet Aggregation Benchmarks
+# Technology Comparison for Parquet Benchmarks
 
 ## Purpose
 

From 92b070e2ef5bbad869a8a439e2de592e8f879b8b Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Fri, 3 Jul 2026 15:51:41 +0200
Subject: [PATCH 44/46] benchmark - add std and welsh test

---
 plugins/parquet-adapter/benchmarks/README.md  |  20 +-
 .../benchmarks/benchmark-plan.md              |  32 +
 ...s_model_comparison_tlcp_result_analysis.md |  38 +-
 .../access_model_comparison_welch_analysis.md | 101 ++
 .../aggregation_result_analysis.md            |  16 +-
 .../aggregation/aggregation_welch_analysis.md | 135 +++
 .../nested_data_result_analysis.md            |  10 +
 .../nested_data/nested_data_welch_analysis.md |  81 ++
 .../partitioning_result_analysis.md           |  23 +-
 .../partitioning_welch_analysis.md            | 146 +++
 .../access_model_comparison_tlcp_plot_std.pdf | Bin 0 -> 10937 bytes
 .../access_model_comparison_tlcp_plot_std.png | Bin 0 -> 32926 bytes
 .../access_model_comparison_tlcp_plot_std.svg | 165 +++
 .../results/plots/aggregation_plot_std.pdf    | Bin 0 -> 16201 bytes
 .../results/plots/aggregation_plot_std.png    | Bin 0 -> 39403 bytes
 .../results/plots/aggregation_plot_std.svg    | 246 +++++
 .../results/plots/nested_data_plot_std.pdf    | Bin 0 -> 9312 bytes
 .../results/plots/nested_data_plot_std.png    | Bin 0 -> 32414 bytes
 .../results/plots/nested_data_plot_std.svg    | 136 +++
 .../results/plots/partitioning_plot_std.pdf   | Bin 0 -> 12395 bytes
 .../results/plots/partitioning_plot_std.png   | Bin 0 -> 33705 bytes
 .../results/plots/partitioning_plot_std.svg   | 187 ++++
 ...run_access_model_comparison_bm_pipeline.md |  32 +
 .../run_aggregation_bm_pipeline.md            |  30 +
 .../run_nested_data_bm_pipeline.md            |  30 +
 .../run_partitioning_bm_pipeline.md           |  50 +
 .../scripts/benchmark_welch_analysis.py       | 536 ++++++++++
 .../benchmark_result_std_plot_generator.py    | 981 ++++++++++++++++++
 .../parquet-adapter/benchmarks/welch_test.md  |  56 +
 29 files changed, 3026 insertions(+), 25 deletions(-)
 create mode 100644 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_welch_analysis.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_welch_analysis.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_welch_analysis.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_welch_analysis.md
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot_std.pdf
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot_std.png
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot_std.svg
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/aggregation_plot_std.pdf
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/aggregation_plot_std.png
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/aggregation_plot_std.svg
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/nested_data_plot_std.pdf
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/nested_data_plot_std.png
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/nested_data_plot_std.svg
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/partitioning_plot_std.pdf
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/partitioning_plot_std.png
 create mode 100644 plugins/parquet-adapter/benchmarks/results/plots/partitioning_plot_std.svg
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/benchmark_welch_analysis.py
 create mode 100644 plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_std_plot_generator.py
 create mode 100644 plugins/parquet-adapter/benchmarks/welch_test.md

diff --git a/plugins/parquet-adapter/benchmarks/README.md b/plugins/parquet-adapter/benchmarks/README.md
index 58ffd11156..2ba8dd80ba 100644
--- a/plugins/parquet-adapter/benchmarks/README.md
+++ b/plugins/parquet-adapter/benchmarks/README.md
@@ -50,7 +50,7 @@ run_pipeline/
 | `run_partitioning_bm_pipeline.md`            | Partitioning            |
 
 Each pipeline file contains the commands to build/start Polypheny where needed,
-run the benchmark clients, and generate the suite summary and plot. The
+run the benchmark clients, and generate the suite summary, exploratory Welch analysis, and plots. The
 aggregation pipeline additionally captures result values and generates a
 correctness comparison. Result interpretation documents are updated manually
 after the generated artifacts have been reviewed.
@@ -65,6 +65,7 @@ after the generated artifacts have been reviewed.
 | `scripts/ds_preprocessing/`              | Dataset materialization and preprocessing utilities.                         |
 | `scripts/summarize_benchmark_results.py` | Creates markdown summaries with per-query runtime statistics from benchmark CSV files. |
 | `scripts/compare_aggregation_results.py` | Compares captured aggregation result values across systems.                  |
+| `scripts/benchmark_welch_analysis.py`    | Creates exploratory pairwise Welch-test reports with confidence intervals and Holm-adjusted p-values. |
 | `scripts/plot_generation/`               | Plot generation scripts, including the generic SVG/PDF/PNG benchmark CSV plot generator. |
 
 PNG plot output requires Pillow. SVG and PDF output use only the Python
@@ -97,6 +98,7 @@ Result conventions:
 - `*_values.jsonl`: captured query result values used for correctness checks where applicable.
 - `*_summary.md`: generated comparison summary from `scripts/summarize_benchmark_results.py`.
 - `*_correctness_summary.md`: generated comparison of captured result values where applicable.
+- `*_welch_analysis.md`: generated exploratory Welch-test report based on successful measured runs.
 - `*_result_analysis.md`: manually maintained result interpretation combining the generated plot, findings, and comparability notes.
 - `results/plots/`: generated plot artifacts such as SVG, PDF, and PNG files.
 
@@ -107,17 +109,19 @@ Result conventions:
 2. Summary scripts calculate per-query mean, median, sample standard deviation,
    minimum, and maximum runtimes. The aggregation comparison also checks
    captured grouping keys and aggregate values.
-3. The plot generator creates SVG, PDF, and PNG figures from the raw timing
+3. The Welch-analysis script compares successful measured runs, reports 95%
+   confidence intervals, and applies Holm adjustment to the p-values.
+4. The plot generators create SVG, PDF, and PNG figures from the raw timing
    results.
-4. The corresponding `*_result_analysis.md` file is updated manually with the
-   interpretation of the summary and plot.
+5. The corresponding `*_result_analysis.md` file is updated manually with the
+   interpretation of the summary, statistical report, and plots.
 
 Current result folders:
 
 | Path                               | Contents                                                                    |
 |------------------------------------|-----------------------------------------------------------------------------|
-| `results/access_model_comparison/` | Access-model raw CSVs, generated summary, and manual result interpretation. |
-| `results/aggregation/`             | Aggregation raw CSV/JSONL files, timing and correctness summaries, and manual result interpretation. |
-| `results/nested_data/`             | Nested-data raw CSVs, generated summary, and manual result interpretation.  |
-| `results/partitioning/`            | Partitioning raw CSVs, generated summary, and manual result interpretation. |
+| `results/access_model_comparison/` | Access-model raw CSVs, generated summary and Welch analysis, and manual result interpretation. |
+| `results/aggregation/`             | Aggregation raw CSV/JSONL files, timing, correctness, and Welch summaries, and manual result interpretation. |
+| `results/nested_data/`             | Nested-data raw CSVs, generated summary and Welch analysis, and manual result interpretation.  |
+| `results/partitioning/`            | Partitioning raw CSVs, generated summary and Welch analysis, and manual result interpretation. |
 | `results/plots/`                   | Generated SVG, PDF, and PNG benchmark plots.                                |
diff --git a/plugins/parquet-adapter/benchmarks/benchmark-plan.md b/plugins/parquet-adapter/benchmarks/benchmark-plan.md
index 0dd6277622..0591710c2f 100644
--- a/plugins/parquet-adapter/benchmarks/benchmark-plan.md
+++ b/plugins/parquet-adapter/benchmarks/benchmark-plan.md
@@ -22,9 +22,11 @@ The benchmarks evaluate the Parquet adapter from the following perspectives:
   - failures and timeouts recorded instead of runtime values.
 
 - Warmup runs are excluded from reported results. 
+- Query and system execution order was not randomized; caching, time trends, or other order effects may therefore influence comparisons.
 - Summary tables report mean, median, sample standard deviation, minimum, and maximum elapsed time over
 successful measured runs only. 
 - Where comparable, aggregation benchmarks also capture and compare grouping keys and aggregate values across systems. Numeric results are compared using the configured tolerance, while intentional result-schema differences are documented separately.
+- Exploratory pairwise Welch tests use the five successful measured runs per compared group. The small samples and non-randomized execution order limit the strength of the conclusions. Reports include unadjusted 95% confidence intervals and Holm-adjusted p-values; non-significant results are not interpreted as proof of equal performance.
 - The benchmark clients consume query results
 according to the result boundary described in the report methodology. 
 - Polypheny and DuckDB use JDBC clients that drain the complete result set. Spark is
@@ -37,6 +39,12 @@ executed in local mode inside Docker and consumes rows in the Spark runner.
 python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py
 ```
 
+Exploratory Welch reports are generated with:
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\benchmark_welch_analysis.py
+```
+
 ## Datasets
 
 | Dataset                     | Local input                                      | Main purpose                                           |
@@ -135,6 +143,12 @@ Result summary:
 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
 ```
 
+Exploratory Welch analysis:
+
+```text
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_welch_analysis.md
+```
+
 ### Result Interpretation
 
 The generated plot and manual interpretation of the benchmark results are
@@ -221,6 +235,12 @@ Result summary:
 plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_summary.md
 ```
 
+Exploratory Welch analysis:
+
+```text
+plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_welch_analysis.md
+```
+
 ### Result Interpretation
 
 The generated plot and manual interpretation of the benchmark results are
@@ -325,6 +345,12 @@ plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_summary.md
 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_correctness_summary.md
 ```
 
+Exploratory Welch analysis:
+
+```text
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_welch_analysis.md
+```
+
 ### Result Interpretation
 
 The generated plot and manual interpretation of the benchmark results are
@@ -418,6 +444,12 @@ Result summary:
 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
 ```
 
+Exploratory Welch analysis:
+
+```text
+plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_welch_analysis.md
+```
+
 ### Result Interpretation
 
 The generated plot and manual interpretation of the benchmark results are
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_result_analysis.md b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_result_analysis.md
index df4f28f9d6..44b3ece5f2 100644
--- a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_result_analysis.md
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_result_analysis.md
@@ -11,19 +11,45 @@
 - Result row counts match across Polypheny relational flat, Polypheny relational normalized, Polypheny document
 MQL, DuckDB, and Apache Spark.
 - Standard deviations remain within 12.4% of the corresponding mean runtimes, indicating generally stable repeated measurements.
+- Exploratory Welch tests use the five existing measurements per group and Holm-adjusted p-values; non-significant results are treated as insufficient evidence of a difference, not as proof of equal performance.
 
 
 ### Main Findings
 
-- DuckDB is fastest for all five access-pattern queries.
-- Polypheny relational Flat and normalized mode are very close, which is logical because only root tables accessed.
+- DuckDB is fastest for all five access-pattern queries, and its differences from both Polypheny relational modes are statistically significant after Holm adjustment.
+- Polypheny relational flat and normalized modes remain in the same performance range. The flat mode is significantly faster for the full-record queries Q01 and Q04 (Holm-adjusted `p=0.0065` and `p=0.0012`), while the five measurements do not establish a significant difference for Q02, Q03, or Q05 (`p=0.3950`).
 
-- Normalized mode is slightly slower for full-record access because it adds and materializes a synthetic row identifier for every record. This produces one additional result column and requires a binding-aware row-extraction path. When the synthetic identifier is not returned, flat and normalized performance is broadly comparable.
+- Normalized mode is slower for full-record access because it adds and materializes a synthetic row identifier for every record. This produces one additional result column and requires a binding-aware row-extraction path. When the synthetic identifier is not returned, the current five-run samples do not establish a performance difference between the relational modes.
 
 - MQL (Polypheny Document) is slower than Polypheny relational because of document materialization.
 
 - Projection consistently improves performance across all tested systems. In both the unfiltered case (Q02 compared with Q01) and the filtered case (Q05 compared with Q04), queries that return only selected fields execute faster than queries that return full records. This indicates that reducing the number of retrieved columns lowers the amount of data that must be read, transferred, and materialized, which has a measurable positive effect for relational, document, DuckDB, and Spark-based access paths.
 
+### Spark vs. Other Systems
+
+For Polypheny and DuckDB:
+
+1. The system executes the query.
+2. Every result row is transferred through JDBC.
+3. The benchmark client reads every field of every row.
+
+For Spark:
+
+1. Spark executes the query.
+2. Rows are processed and consumed inside Spark’s executor tasks.
+3. Only the final row count is returned to the Python driver.
+
+For a projection returning millions of rows, Polypheny and DuckDB therefore measure:
+
+`query execution + row creation + transfer to client + client consumption`
+
+Spark measures approximately:
+
+`query execution + row creation inside Spark + counting`
+
+Spark does not transfer all projected values to the Python driver. Consequently, it avoids a potentially expensive transfer step and may appear faster than it would in a complete result-retrieval comparison.
+
+This matters mainly for Q01, Q02, Q04, and Q05, which return many rows. It matters much less for Q03 because that query returns only one aggregate row. Spark’s results remain useful for comparing execution, but not for comparing the full cost of delivering every result value to an external client.
 
 ## Related Documents
 
@@ -33,6 +59,12 @@ Summary:
 plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_tlcp_summary.md
 ```
 
+Exploratory Welch analysis:
+
+```text
+plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_welch_analysis.md
+```
+
 Plot:
 ```text
 plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot.pdf
diff --git a/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_welch_analysis.md b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_welch_analysis.md
new file mode 100644
index 0000000000..3fb694aa55
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_welch_analysis.md
@@ -0,0 +1,101 @@
+# Access Model Comparison - Exploratory Welch Analysis
+
+> This is an exploratory analysis: each tested group contains only five measurements, and benchmark execution order was not randomized. The results do not provide definitive evidence of performance equivalence or difference.
+
+## Method
+
+- Only `measured` rows with `success=true` are included; warm-up and failed rows are excluded.
+- Tests use the raw `elapsed_ms` measurements rather than summary means.
+- Every comparison is a two-sided Welch t-test, which does not assume equal variances.
+- Execution order was not randomized, so caching, time trends, or other order effects may influence comparisons.
+- The reported difference and unadjusted 95% confidence interval are calculated as system A minus system B.
+- Raw p-values are adjusted together across all 50 comparisons using the Holm method.
+- `Significant` means Holm-adjusted `p <= 0.05`.
+- A non-significant result means that these measurements do not establish a difference; it does not prove equal performance.
+
+## Inputs
+
+| System                          | Result file                                                                                                                 |
+|---------------------------------|-----------------------------------------------------------------------------------------------------------------------------|
+| Polypheny Relational Flat       | `plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rf_tlcp_results.csv`  |
+| Polypheny Relational Normalized | `plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_rn_tlcpn_results.csv` |
+| Polypheny Document MQL          | `plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_polypheny_mql_tlcp_results.csv` |
+| DuckDB                          | `plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_duckdb_tlcp_results.csv`        |
+| Apache Spark                    | `plugins/parquet-adapter/benchmarks/results/access_model_comparison/access_model_comparison_spark_tlcp_results.csv`         |
+
+## Results
+
+### Q01
+
+| Comparison (A vs B)                                          | n (A/B) |    A mean +/- SD (ms) |    B mean +/- SD (ms) | Difference A-B (ms) |              95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|--------------------------------------------------------------|--------:|----------------------:|----------------------:|--------------------:|-------------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational Flat vs Polypheny Relational Normalized |     5/5 |  29,741.8 +/- 1,684.3 |   36,049.6 +/- 280.35 |            -6,307.8 |     [-8,384.8, -4,230.8] |  -8.260 (4.221) |   0.0009 |   0.0065 |     Yes     |
+| Polypheny Relational Flat vs Polypheny Document MQL          |     5/5 |  29,741.8 +/- 1,684.3 | 142,440.8 +/- 7,815.9 |          -112,699.0 | [-122,303.1, -103,094.9] | -31.519 (4.371) | 2.53e-06 | 7.34e-05 |     Yes     |
+| Polypheny Relational Flat vs DuckDB                          |     5/5 |  29,741.8 +/- 1,684.3 |    3,672.4 +/- 281.27 |            26,069.4 |     [23,992.5, 28,146.3] |  34.137 (4.223) | 2.55e-06 | 7.34e-05 |     Yes     |
+| Polypheny Relational Flat vs Apache Spark                    |     5/5 |  29,741.8 +/- 1,684.3 |   21,738.2 +/- 305.76 |             8,003.6 |      [5,928.9, 10,078.3] |  10.455 (4.263) |   0.0003 |   0.0027 |     Yes     |
+| Polypheny Relational Normalized vs Polypheny Document MQL    |     5/5 |   36,049.6 +/- 280.35 | 142,440.8 +/- 7,815.9 |          -106,391.2 |  [-116,092.4, -96,690.0] | -30.418 (4.010) | 6.79e-06 |   0.0002 |     Yes     |
+| Polypheny Relational Normalized vs DuckDB                    |     5/5 |   36,049.6 +/- 280.35 |    3,672.4 +/- 281.27 |            32,377.2 |     [31,967.7, 32,786.7] | 182.304 (8.000) | 9.18e-16 | 4.59e-14 |     Yes     |
+| Polypheny Relational Normalized vs Apache Spark              |     5/5 |   36,049.6 +/- 280.35 |   21,738.2 +/- 305.76 |            14,311.4 |     [13,883.0, 14,739.8] |  77.144 (7.941) | 1.05e-12 | 5.06e-11 |     Yes     |
+| Polypheny Document MQL vs DuckDB                             |     5/5 | 142,440.8 +/- 7,815.9 |    3,672.4 +/- 281.27 |           138,768.4 |   [129,067.3, 148,469.5] |  39.675 (4.010) | 2.35e-06 | 7.04e-05 |     Yes     |
+| Polypheny Document MQL vs Apache Spark                       |     5/5 | 142,440.8 +/- 7,815.9 |   21,738.2 +/- 305.76 |           120,702.6 |   [111,002.1, 130,403.1] |  34.506 (4.012) | 4.08e-06 |   0.0001 |     Yes     |
+| DuckDB vs Apache Spark                                       |     5/5 |    3,672.4 +/- 281.27 |   21,738.2 +/- 305.76 |           -18,065.8 |   [-18,494.8, -17,636.8] | -97.234 (7.945) | 1.66e-13 | 8.12e-12 |     Yes     |
+
+### Q02
+
+| Comparison (A vs B)                                          | n (A/B) |     A mean +/- SD (ms) |     B mean +/- SD (ms) | Difference A-B (ms) |             95% CI (ms) |           t (df) |    Raw p |   Holm p | Significant |
+|--------------------------------------------------------------|--------:|-----------------------:|-----------------------:|--------------------:|------------------------:|-----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational Flat vs Polypheny Relational Normalized |     5/5 |     9,650.6 +/- 395.42 |   10,555.8 +/- 1,123.7 |             -905.20 |      [-2,276.7, 466.33] |   -1.699 (4.976) |   0.1503 |   0.3950 |     No      |
+| Polypheny Relational Flat vs Polypheny Document MQL          |     5/5 |     9,650.6 +/- 395.42 | 106,694.4 +/- 10,386.4 |           -97,043.8 | [-109,934.9, -84,152.7] |  -20.877 (4.012) | 3.04e-05 |   0.0006 |     Yes     |
+| Polypheny Relational Flat vs DuckDB                          |     5/5 |     9,650.6 +/- 395.42 |       218.80 +/- 12.66 |             9,431.8 |      [8,941.0, 9,922.6] |   53.308 (4.008) | 7.24e-07 | 2.39e-05 |     Yes     |
+| Polypheny Relational Flat vs Apache Spark                    |     5/5 |     9,650.6 +/- 395.42 |    13,667.6 +/- 247.62 |            -4,017.0 |    [-4,514.6, -3,519.4] |  -19.252 (6.719) | 3.96e-07 | 1.35e-05 |     Yes     |
+| Polypheny Relational Normalized vs Polypheny Document MQL    |     5/5 |   10,555.8 +/- 1,123.7 | 106,694.4 +/- 10,386.4 |           -96,138.6 | [-108,994.2, -83,283.0] |  -20.577 (4.094) | 2.75e-05 |   0.0005 |     Yes     |
+| Polypheny Relational Normalized vs DuckDB                    |     5/5 |   10,555.8 +/- 1,123.7 |       218.80 +/- 12.66 |            10,337.0 |     [8,941.7, 11,732.3] |   20.568 (4.001) | 3.29e-05 |   0.0006 |     Yes     |
+| Polypheny Relational Normalized vs Apache Spark              |     5/5 |   10,555.8 +/- 1,123.7 |    13,667.6 +/- 247.62 |            -3,111.8 |    [-4,492.2, -1,731.4] |   -6.047 (4.388) |   0.0028 |   0.0139 |     Yes     |
+| Polypheny Document MQL vs DuckDB                             |     5/5 | 106,694.4 +/- 10,386.4 |       218.80 +/- 12.66 |           106,475.6 |   [93,579.1, 119,372.1] |   22.923 (4.000) | 2.15e-05 |   0.0005 |     Yes     |
+| Polypheny Document MQL vs Apache Spark                       |     5/5 | 106,694.4 +/- 10,386.4 |    13,667.6 +/- 247.62 |            93,026.8 |   [80,132.4, 105,921.2] |   20.022 (4.005) | 3.64e-05 |   0.0006 |     Yes     |
+| DuckDB vs Apache Spark                                       |     5/5 |       218.80 +/- 12.66 |    13,667.6 +/- 247.62 |           -13,448.8 |  [-13,756.0, -13,141.6] | -121.288 (4.021) | 2.56e-08 | 1.05e-06 |     Yes     |
+
+### Q03
+
+| Comparison (A vs B)                                          | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |         95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|--------------------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|--------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational Flat vs Polypheny Relational Normalized |     5/5 |   157.40 +/- 13.32 |   170.80 +/- 11.84 |              -13.40 |     [-31.82, 5.020] |  -1.682 (7.892) |   0.1317 |   0.3950 |     No      |
+| Polypheny Relational Flat vs Polypheny Document MQL          |     5/5 |   157.40 +/- 13.32 |   424.80 +/- 23.72 |             -267.40 |  [-296.84, -237.96] | -21.980 (6.293) | 3.45e-07 | 1.21e-05 |     Yes     |
+| Polypheny Relational Flat vs DuckDB                          |     5/5 |   157.40 +/- 13.32 |    35.40 +/- 2.302 |              122.00 |    [105.59, 138.41] |  20.188 (4.239) | 2.25e-05 |   0.0005 |     Yes     |
+| Polypheny Relational Flat vs Apache Spark                    |     5/5 |   157.40 +/- 13.32 | 1,182.4 +/- 142.80 |            -1,025.0 | [-1,201.9, -848.11] | -15.981 (4.070) | 7.97e-05 |   0.0010 |     Yes     |
+| Polypheny Relational Normalized vs Polypheny Document MQL    |     5/5 |   170.80 +/- 11.84 |   424.80 +/- 23.72 |             -254.00 |  [-283.16, -224.84] | -21.423 (5.877) | 8.39e-07 | 2.68e-05 |     Yes     |
+| Polypheny Relational Normalized vs DuckDB                    |     5/5 |   170.80 +/- 11.84 |    35.40 +/- 2.302 |              135.40 |    [120.83, 149.97] |  25.100 (4.302) | 7.88e-06 |   0.0002 |     Yes     |
+| Polypheny Relational Normalized vs Apache Spark              |     5/5 |   170.80 +/- 11.84 | 1,182.4 +/- 142.80 |            -1,011.6 | [-1,188.6, -834.63] | -15.786 (4.055) | 8.58e-05 |   0.0010 |     Yes     |
+| Polypheny Document MQL vs DuckDB                             |     5/5 |   424.80 +/- 23.72 |    35.40 +/- 2.302 |              389.40 |    [360.02, 418.78] |  36.535 (4.075) | 2.77e-06 | 7.49e-05 |     Yes     |
+| Polypheny Document MQL vs Apache Spark                       |     5/5 |   424.80 +/- 23.72 | 1,182.4 +/- 142.80 |             -757.60 |  [-933.70, -581.50] | -11.703 (4.221) |   0.0002 |   0.0020 |     Yes     |
+| DuckDB vs Apache Spark                                       |     5/5 |    35.40 +/- 2.302 | 1,182.4 +/- 142.80 |            -1,147.0 | [-1,324.3, -969.70] | -17.958 (4.002) | 5.63e-05 |   0.0009 |     Yes     |
+
+### Q04
+
+| Comparison (A vs B)                                          | n (A/B) |  A mean +/- SD (ms) |  B mean +/- SD (ms) | Difference A-B (ms) |          95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|--------------------------------------------------------------|--------:|--------------------:|--------------------:|--------------------:|---------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational Flat vs Polypheny Relational Normalized |     5/5 |  6,073.4 +/- 172.93 |  7,594.4 +/- 341.79 |            -1,521.0 | [-1,941.5, -1,100.5] |  -8.879 (5.922) |   0.0001 |   0.0012 |     Yes     |
+| Polypheny Relational Flat vs Polypheny Document MQL          |     5/5 |  6,073.4 +/- 172.93 | 14,752.6 +/- 405.70 |            -8,679.2 | [-9,174.9, -8,183.5] | -44.005 (5.407) | 4.05e-08 | 1.62e-06 |     Yes     |
+| Polypheny Relational Flat vs DuckDB                          |     5/5 |  6,073.4 +/- 172.93 |    480.00 +/- 42.63 |             5,593.4 |   [5,381.4, 5,805.4] |  70.224 (4.484) | 5.40e-08 | 2.10e-06 |     Yes     |
+| Polypheny Relational Flat vs Apache Spark                    |     5/5 |  6,073.4 +/- 172.93 |  2,666.6 +/- 300.58 |             3,406.8 |   [3,032.8, 3,780.8] |  21.968 (6.386) | 2.94e-07 | 1.06e-05 |     Yes     |
+| Polypheny Relational Normalized vs Polypheny Document MQL    |     5/5 |  7,594.4 +/- 341.79 | 14,752.6 +/- 405.70 |            -7,158.2 | [-7,708.0, -6,608.4] | -30.173 (7.776) | 2.44e-09 | 1.05e-07 |     Yes     |
+| Polypheny Relational Normalized vs DuckDB                    |     5/5 |  7,594.4 +/- 341.79 |    480.00 +/- 42.63 |             7,114.4 |   [6,691.8, 7,537.0] |  46.186 (4.124) | 9.35e-07 | 2.90e-05 |     Yes     |
+| Polypheny Relational Normalized vs Apache Spark              |     5/5 |  7,594.4 +/- 341.79 |  2,666.6 +/- 300.58 |             4,927.8 |   [4,457.1, 5,398.5] |  24.209 (7.871) | 1.13e-08 | 4.74e-07 |     Yes     |
+| Polypheny Document MQL vs DuckDB                             |     5/5 | 14,752.6 +/- 405.70 |    480.00 +/- 42.63 |            14,272.6 | [13,770.4, 14,774.8] |  78.234 (4.088) | 1.20e-07 | 4.43e-06 |     Yes     |
+| Polypheny Document MQL vs Apache Spark                       |     5/5 | 14,752.6 +/- 405.70 |  2,666.6 +/- 300.58 |            12,086.0 | [11,557.5, 12,614.5] |  53.523 (7.375) | 7.98e-11 | 3.51e-09 |     Yes     |
+| DuckDB vs Apache Spark                                       |     5/5 |    480.00 +/- 42.63 |  2,666.6 +/- 300.58 |            -2,186.6 | [-2,557.9, -1,815.3] | -16.105 (4.161) | 6.62e-05 |   0.0009 |     Yes     |
+
+### Q05
+
+| Comparison (A vs B)                                          | n (A/B) |  A mean +/- SD (ms) |  B mean +/- SD (ms) | Difference A-B (ms) |           95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|--------------------------------------------------------------|--------:|--------------------:|--------------------:|--------------------:|----------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational Flat vs Polypheny Relational Normalized |     5/5 |  2,528.8 +/- 181.63 |  2,303.0 +/- 286.00 |              225.80 |     [-134.90, 586.50] |   1.490 (6.775) |   0.1812 |   0.3950 |     No      |
+| Polypheny Relational Flat vs Polypheny Document MQL          |     5/5 |  2,528.8 +/- 181.63 | 12,683.2 +/- 274.62 |           -10,154.4 | [-10,503.2, -9,805.6] | -68.963 (6.937) | 4.23e-11 | 1.90e-09 |     Yes     |
+| Polypheny Relational Flat vs DuckDB                          |     5/5 |  2,528.8 +/- 181.63 |    115.20 +/- 3.962 |             2,413.6 |    [2,188.1, 2,639.1] |  29.707 (4.004) | 7.58e-06 |   0.0002 |     Yes     |
+| Polypheny Relational Flat vs Apache Spark                    |     5/5 |  2,528.8 +/- 181.63 |  1,941.4 +/- 224.30 |              587.40 |      [287.50, 887.30] |   4.551 (7.668) |   0.0021 |   0.0125 |     Yes     |
+| Polypheny Relational Normalized vs Polypheny Document MQL    |     5/5 |  2,303.0 +/- 286.00 | 12,683.2 +/- 274.62 |           -10,380.2 | [-10,789.2, -9,971.2] | -58.540 (7.987) | 8.33e-12 | 3.83e-10 |     Yes     |
+| Polypheny Relational Normalized vs DuckDB                    |     5/5 |  2,303.0 +/- 286.00 |    115.20 +/- 3.962 |             2,187.8 |    [1,832.7, 2,542.9] |  17.104 (4.002) | 6.84e-05 |   0.0009 |     Yes     |
+| Polypheny Relational Normalized vs Apache Spark              |     5/5 |  2,303.0 +/- 286.00 |  1,941.4 +/- 224.30 |              361.60 |      [-16.97, 740.17] |   2.225 (7.570) |   0.0586 |   0.2345 |     No      |
+| Polypheny Document MQL vs DuckDB                             |     5/5 | 12,683.2 +/- 274.62 |    115.20 +/- 3.962 |            12,568.0 |  [12,227.0, 12,909.0] | 102.323 (4.002) | 5.44e-08 | 2.10e-06 |     Yes     |
+| Polypheny Document MQL vs Apache Spark                       |     5/5 | 12,683.2 +/- 274.62 |  1,941.4 +/- 224.30 |            10,741.8 |  [10,373.6, 11,110.0] |  67.740 (7.693) | 5.83e-12 | 2.74e-10 |     Yes     |
+| DuckDB vs Apache Spark                                       |     5/5 |    115.20 +/- 3.962 |  1,941.4 +/- 224.30 |            -1,826.2 |  [-2,104.7, -1,547.7] | -18.203 (4.002) | 5.33e-05 |   0.0009 |     Yes     |
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_result_analysis.md b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_result_analysis.md
index ba1cf1b7d8..a38425050a 100644
--- a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_result_analysis.md
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_result_analysis.md
@@ -10,13 +10,14 @@
 - All 10 queries completed successfully, with 5/5 successful measured runs per query for every system. 
 - The result row counts match across Polypheny relational, Polypheny document MQL, DuckDB, and Apache Spark.
 - Standard deviations are generally modest; the largest relative variation occurs in the very short metadata queries and Polypheny Relational Q03, while longer data-reading workloads are more stable.
+- Exploratory Welch tests use the five existing measurements per group and Holm-adjusted p-values; they supplement the runtime ranking but should not be treated as definitive because the samples are small.
 
 ### Correctness
 Correctness checks confirm consistent aggregation results across systems: all row counts match, while Polypheny relational, DuckDB, and Apache Spark also produce matching grouping keys and COUNT, SUM, MIN, and MAX results within the configured numeric tolerance. Polypheny document MQL produces the same available aggregate values and groups, but Q05, Q09, and Q10 have intentionally different result schemas because the MQL variants omit some count fields and represent grouping keys through _id. These are output-shape differences rather than conflicting aggregation results.
 
 ### Main Findings
 
-- Polypheny relational is strongest overall on metadata-oriented count queries: Q01, Q02, Q06, and Q07. It completes these metadata-optimized queries in approximately 10-14 ms, leading Q01, Q06, and Q07 and closely matching DuckDB on Q02. 
+- Polypheny relational is strongest overall on metadata-oriented count queries: Q01, Q02, Q06, and Q07. It completes these metadata-optimized queries in approximately 10-14 ms, leading Q01, Q06, and Q07. On Q02, its 10.4 ms mean is close to DuckDB's 9.2 ms mean, and the existing measurements do not establish a significant difference (Holm-adjusted `p=0.5115`).
 - The document adapter also benefits from the metadata-optimized count path, although additional document-processing overhead results in higher runtimes than the relational path.
 
 - DuckDB is fastest for most data-reading aggregation queries (filtering, grouping, data aggregation). It has the best
@@ -25,15 +26,14 @@ Q10, where DuckDB evaluates the shared-request grouping much faster than the
 other systems.
 
 - The Polypheny document path is competitive with, and sometimes faster than, the
-relational path for large data-reading aggregation queries. This is visible for
-Q03, Q08, Q09, and Q10. These results show that pushing aggregation into the
+relational path for large data-reading aggregation queries. Its lower mean runtimes for
+Q03, Q08, and Q09 are statistically significant after Holm adjustment (`p=0.0290`, `p=0.0057`, and `p=0.0205`). For Q10, the current measurements do not establish a significant difference (`p=0.0838`). These results show that pushing aggregation into the
 Parquet-backed execution path also benefits document workloads when the
 aggregate can be expressed over primitive Parquet fields.
 
 - Spark performs worst overall, recording the slowest runtime in eight of ten aggregation queries. 
 Its task scheduling, file discovery, code generation, and shuffle overhead are costly in this single-machine benchmark, 
-especially for short metadata-oriented queries. Spark becomes more competitive on the large Q08 scan and nearly matches 
-Polypheny Relational on Q10, but remains consistently slower than DuckDB.
+especially for short metadata-oriented queries. Spark becomes more competitive on the large Q08 scan. On Q10, its 5,407.8 ms mean is close to Polypheny relational's 5,458.2 ms mean, and the five measurements do not establish a significant difference (Holm-adjusted `p=0.8819`). Spark nevertheless remains consistently slower than DuckDB.
 
 - The benchmark demonstrates that the Parquet adapter's metadata-based count optimization is highly effective for count queries. 
 For general analytical aggregation over Parquet on a single machine, DuckDB remains the strongest reference system, 
@@ -55,3 +55,9 @@ Source correctness comparison:
 ```text
 plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_correctness_summary.md
 ```
+
+Exploratory Welch analysis:
+
+```text
+plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_welch_analysis.md
+```
diff --git a/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_welch_analysis.md b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_welch_analysis.md
new file mode 100644
index 0000000000..1d33b0489b
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_welch_analysis.md
@@ -0,0 +1,135 @@
+# Aggregation - Exploratory Welch Analysis
+
+> This is an exploratory analysis: each tested group contains only five measurements, and benchmark execution order was not randomized. The results do not provide definitive evidence of performance equivalence or difference.
+
+## Method
+
+- Only `measured` rows with `success=true` are included; warm-up and failed rows are excluded.
+- Tests use the raw `elapsed_ms` measurements rather than summary means.
+- Every comparison is a two-sided Welch t-test, which does not assume equal variances.
+- Execution order was not randomized, so caching, time trends, or other order effects may influence comparisons.
+- The reported difference and unadjusted 95% confidence interval are calculated as system A minus system B.
+- Raw p-values are adjusted together across all 60 comparisons using the Holm method.
+- `Significant` means Holm-adjusted `p <= 0.05`.
+- A non-significant result means that these measurements do not establish a difference; it does not prove equal performance.
+
+## Inputs
+
+| System                 | Result file                                                                                    |
+|------------------------|------------------------------------------------------------------------------------------------|
+| Polypheny Relational   | `plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_results.csv`     |
+| Polypheny Document MQL | `plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_polypheny_mql_results.csv` |
+| DuckDB                 | `plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_duckdb_results.csv`        |
+| Apache Spark           | `plugins/parquet-adapter/benchmarks/results/aggregation/aggregation_spark_results.csv`         |
+
+## Results
+
+### Q01
+
+| Comparison (A vs B)                            | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |          95% CI (ms) |          t (df) |    Raw p | Holm p | Significant |
+|------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|---------------------:|----------------:|---------:|-------:|:-----------:|
+| Polypheny Relational vs Polypheny Document MQL |     5/5 |    12.20 +/- 3.194 |    40.40 +/- 12.01 |              -28.20 |     [-42.91, -13.49] |  -5.073 (4.563) |   0.0050 | 0.0398 |     Yes     |
+| Polypheny Relational vs DuckDB                 |     5/5 |    12.20 +/- 3.194 |    22.60 +/- 1.673 |              -10.40 |     [-14.34, -6.461] |  -6.450 (6.042) |   0.0006 | 0.0096 |     Yes     |
+| Polypheny Relational vs Apache Spark           |     5/5 |    12.20 +/- 3.194 | 1,415.2 +/- 184.97 |            -1,403.0 | [-1,632.6, -1,173.4] | -16.958 (4.002) | 7.06e-05 | 0.0018 |     Yes     |
+| Polypheny Document MQL vs DuckDB               |     5/5 |    40.40 +/- 12.01 |    22.60 +/- 1.673 |               17.80 |       [2.960, 32.64] |   3.282 (4.155) |   0.0288 | 0.1152 |     No      |
+| Polypheny Document MQL vs Apache Spark         |     5/5 |    40.40 +/- 12.01 | 1,415.2 +/- 184.97 |            -1,374.8 | [-1,604.2, -1,145.4] | -16.585 (4.034) | 7.30e-05 | 0.0018 |     Yes     |
+| DuckDB vs Apache Spark                         |     5/5 |    22.60 +/- 1.673 | 1,415.2 +/- 184.97 |            -1,392.6 | [-1,622.3, -1,162.9] | -16.834 (4.001) | 7.29e-05 | 0.0018 |     Yes     |
+
+### Q02
+
+| Comparison (A vs B)                            | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |        95% CI (ms) |          t (df) |    Raw p | Holm p | Significant |
+|------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|-------------------:|----------------:|---------:|-------:|:-----------:|
+| Polypheny Relational vs Polypheny Document MQL |     5/5 |    10.40 +/- 1.517 |    41.80 +/- 8.729 |              -31.40 |   [-42.16, -20.64] |  -7.925 (4.241) |   0.0011 | 0.0137 |     Yes     |
+| Polypheny Relational vs DuckDB                 |     5/5 |    10.40 +/- 1.517 |    9.200 +/- 0.837 |               1.200 |    [-0.679, 3.079] |   1.549 (6.228) |   0.1705 | 0.5115 |     No      |
+| Polypheny Relational vs Apache Spark           |     5/5 |    10.40 +/- 1.517 |   379.20 +/- 28.87 |             -368.80 | [-404.62, -332.98] | -28.522 (4.022) | 8.55e-06 | 0.0003 |     Yes     |
+| Polypheny Document MQL vs DuckDB               |     5/5 |    41.80 +/- 8.729 |    9.200 +/- 0.837 |               32.60 |     [21.79, 43.41] |   8.313 (4.073) |   0.0011 | 0.0137 |     Yes     |
+| Polypheny Document MQL vs Apache Spark         |     5/5 |    41.80 +/- 8.729 |   379.20 +/- 28.87 |             -337.40 | [-372.69, -302.11] | -25.011 (4.725) | 3.33e-06 | 0.0001 |     Yes     |
+| DuckDB vs Apache Spark                         |     5/5 |    9.200 +/- 0.837 |   379.20 +/- 28.87 |             -370.00 | [-405.84, -334.16] | -28.642 (4.007) | 8.71e-06 | 0.0003 |     Yes     |
+
+### Q03
+
+| Comparison (A vs B)                            | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |          95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|---------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational vs Polypheny Document MQL |     5/5 | 1,304.8 +/- 280.80 |   523.60 +/- 29.88 |              781.20 |    [433.61, 1,128.8] |   6.186 (4.091) |   0.0032 |   0.0290 |     Yes     |
+| Polypheny Relational vs DuckDB                 |     5/5 | 1,304.8 +/- 280.80 |   179.00 +/- 15.23 |             1,125.8 |    [777.43, 1,474.2] |   8.952 (4.024) |   0.0008 |   0.0117 |     Yes     |
+| Polypheny Relational vs Apache Spark           |     5/5 | 1,304.8 +/- 280.80 | 5,486.4 +/- 536.08 |            -4,181.6 | [-4,842.7, -3,520.5] | -15.451 (6.041) | 4.38e-06 |   0.0002 |     Yes     |
+| Polypheny Document MQL vs DuckDB               |     5/5 |   523.60 +/- 29.88 |   179.00 +/- 15.23 |              344.60 |     [307.82, 381.38] |  22.975 (5.947) | 4.90e-07 | 2.31e-05 |     Yes     |
+| Polypheny Document MQL vs Apache Spark         |     5/5 |   523.60 +/- 29.88 | 5,486.4 +/- 536.08 |            -4,962.8 | [-5,627.8, -4,297.8] | -20.669 (4.025) | 3.08e-05 |   0.0009 |     Yes     |
+| DuckDB vs Apache Spark                         |     5/5 |   179.00 +/- 15.23 | 5,486.4 +/- 536.08 |            -5,307.4 | [-5,972.9, -4,641.9] | -22.129 (4.006) | 2.44e-05 |   0.0007 |     Yes     |
+
+### Q04
+
+| Comparison (A vs B)                            | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |          95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|---------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational vs Polypheny Document MQL |     5/5 | 2,468.8 +/- 293.89 | 2,249.4 +/- 136.58 |              219.40 |    [-140.62, 579.42] |   1.514 (5.651) |   0.1838 |   0.5115 |     No      |
+| Polypheny Relational vs DuckDB                 |     5/5 | 2,468.8 +/- 293.89 |   765.40 +/- 73.32 |             1,703.4 |   [1,343.1, 2,063.7] |  12.575 (4.496) |   0.0001 |   0.0024 |     Yes     |
+| Polypheny Relational vs Apache Spark           |     5/5 | 2,468.8 +/- 293.89 | 3,825.8 +/- 223.45 |            -1,357.0 |  [-1,742.5, -971.47] |  -8.219 (7.466) | 5.34e-05 |   0.0014 |     Yes     |
+| Polypheny Document MQL vs DuckDB               |     5/5 | 2,249.4 +/- 136.58 |   765.40 +/- 73.32 |             1,484.0 |   [1,315.2, 1,652.8] |  21.407 (6.129) | 5.41e-07 | 2.49e-05 |     Yes     |
+| Polypheny Document MQL vs Apache Spark         |     5/5 | 2,249.4 +/- 136.58 | 3,825.8 +/- 223.45 |            -1,576.4 | [-1,856.6, -1,296.2] | -13.460 (6.623) | 4.69e-06 |   0.0002 |     Yes     |
+| DuckDB vs Apache Spark                         |     5/5 |   765.40 +/- 73.32 | 3,825.8 +/- 223.45 |            -3,060.4 | [-3,333.3, -2,787.5] | -29.099 (4.851) | 1.24e-06 | 5.58e-05 |     Yes     |
+
+### Q05
+
+| Comparison (A vs B)                            | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |          95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|---------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational vs Polypheny Document MQL |     5/5 | 1,841.2 +/- 186.45 |  1,468.8 +/- 43.87 |              372.40 |     [143.61, 601.19] |   4.347 (4.442) |   0.0097 |   0.0676 |     No      |
+| Polypheny Relational vs DuckDB                 |     5/5 | 1,841.2 +/- 186.45 |  1,193.0 +/- 92.59 |              648.20 |     [419.07, 877.33] |   6.962 (5.860) |   0.0005 |   0.0077 |     Yes     |
+| Polypheny Relational vs Apache Spark           |     5/5 | 1,841.2 +/- 186.45 | 5,447.2 +/- 306.09 |            -3,606.0 | [-3,989.6, -3,222.4] | -22.498 (6.609) | 1.71e-07 | 8.37e-06 |     Yes     |
+| Polypheny Document MQL vs DuckDB               |     5/5 |  1,468.8 +/- 43.87 |  1,193.0 +/- 92.59 |              275.80 |     [162.29, 389.31] |   6.019 (5.710) |   0.0011 |   0.0137 |     Yes     |
+| Polypheny Document MQL vs Apache Spark         |     5/5 |  1,468.8 +/- 43.87 | 5,447.2 +/- 306.09 |            -3,978.4 | [-4,356.4, -3,600.4] | -28.769 (4.164) | 5.99e-06 |   0.0002 |     Yes     |
+| DuckDB vs Apache Spark                         |     5/5 |  1,193.0 +/- 92.59 | 5,447.2 +/- 306.09 |            -4,254.2 | [-4,628.3, -3,880.1] | -29.747 (4.726) | 1.47e-06 | 6.47e-05 |     Yes     |
+
+### Q06
+
+| Comparison (A vs B)                            | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |          95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|---------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational vs Polypheny Document MQL |     5/5 |    12.40 +/- 2.074 |    45.40 +/- 3.847 |              -33.00 |     [-37.76, -28.24] | -16.884 (6.143) | 2.22e-06 | 9.54e-05 |     Yes     |
+| Polypheny Relational vs DuckDB                 |     5/5 |    12.40 +/- 2.074 |   145.40 +/- 3.847 |             -133.00 |   [-137.76, -128.24] | -68.049 (6.143) | 4.47e-10 | 2.55e-08 |     Yes     |
+| Polypheny Relational vs Apache Spark           |     5/5 |    12.40 +/- 2.074 | 2,027.4 +/- 175.38 |            -2,015.0 | [-2,232.8, -1,797.2] | -25.689 (4.001) | 1.36e-05 |   0.0004 |     Yes     |
+| Polypheny Document MQL vs DuckDB               |     5/5 |    45.40 +/- 3.847 |   145.40 +/- 3.847 |             -100.00 |    [-105.61, -94.39] | -41.100 (8.000) | 1.35e-10 | 7.98e-09 |     Yes     |
+| Polypheny Document MQL vs Apache Spark         |     5/5 |    45.40 +/- 3.847 | 2,027.4 +/- 175.38 |            -1,982.0 | [-2,199.7, -1,764.3] | -25.264 (4.004) | 1.45e-05 |   0.0005 |     Yes     |
+| DuckDB vs Apache Spark                         |     5/5 |   145.40 +/- 3.847 | 2,027.4 +/- 175.38 |            -1,882.0 | [-2,099.7, -1,664.3] | -23.990 (4.004) | 1.78e-05 |   0.0006 |     Yes     |
+
+### Q07
+
+| Comparison (A vs B)                            | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |        95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|-------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational vs Polypheny Document MQL |     5/5 |    14.20 +/- 1.304 |    57.60 +/- 5.320 |              -43.40 |   [-49.92, -36.88] | -17.718 (4.479) | 2.56e-05 |   0.0007 |     Yes     |
+| Polypheny Relational vs DuckDB                 |     5/5 |    14.20 +/- 1.304 |    34.00 +/- 4.183 |              -19.80 |   [-24.91, -14.69] | -10.104 (4.770) |   0.0002 |   0.0040 |     Yes     |
+| Polypheny Relational vs Apache Spark           |     5/5 |    14.20 +/- 1.304 |   275.60 +/- 8.325 |             -261.40 | [-271.67, -251.13] | -69.368 (4.196) | 1.40e-07 | 6.99e-06 |     Yes     |
+| Polypheny Document MQL vs DuckDB               |     5/5 |    57.60 +/- 5.320 |    34.00 +/- 4.183 |               23.60 |     [16.55, 30.65] |   7.798 (7.579) | 7.04e-05 |   0.0018 |     Yes     |
+| Polypheny Document MQL vs Apache Spark         |     5/5 |    57.60 +/- 5.320 |   275.60 +/- 8.325 |             -218.00 | [-228.51, -207.49] | -49.342 (6.800) | 6.06e-10 | 3.40e-08 |     Yes     |
+| DuckDB vs Apache Spark                         |     5/5 |    34.00 +/- 4.183 |   275.60 +/- 8.325 |             -241.60 | [-251.84, -231.36] | -57.986 (5.899) | 2.33e-09 | 1.24e-07 |     Yes     |
+
+### Q08
+
+| Comparison (A vs B)                            | n (A/B) |  A mean +/- SD (ms) |  B mean +/- SD (ms) | Difference A-B (ms) |           95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|------------------------------------------------|--------:|--------------------:|--------------------:|--------------------:|----------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational vs Polypheny Document MQL |     5/5 | 16,174.2 +/- 475.26 | 12,680.8 +/- 941.70 |             3,493.4 |    [2,335.0, 4,651.8] |   7.405 (5.914) |   0.0003 |   0.0057 |     Yes     |
+| Polypheny Relational vs DuckDB                 |     5/5 | 16,174.2 +/- 475.26 |  4,584.6 +/- 305.19 |            11,589.6 |  [10,989.1, 12,190.1] |  45.883 (6.819) | 9.47e-10 | 5.11e-08 |     Yes     |
+| Polypheny Relational vs Apache Spark           |     5/5 | 16,174.2 +/- 475.26 | 14,250.8 +/- 361.58 |             1,923.4 |    [1,299.8, 2,547.0] |   7.202 (7.469) |   0.0001 |   0.0026 |     Yes     |
+| Polypheny Document MQL vs DuckDB               |     5/5 | 12,680.8 +/- 941.70 |  4,584.6 +/- 305.19 |             8,096.2 |    [6,946.1, 9,246.3] |  18.288 (4.831) | 1.20e-05 |   0.0004 |     Yes     |
+| Polypheny Document MQL vs Apache Spark         |     5/5 | 12,680.8 +/- 941.70 | 14,250.8 +/- 361.58 |            -1,570.0 |   [-2,719.3, -420.73] |  -3.480 (5.154) |   0.0168 |   0.0840 |     No      |
+| DuckDB vs Apache Spark                         |     5/5 |  4,584.6 +/- 305.19 | 14,250.8 +/- 361.58 |            -9,666.2 | [-10,156.6, -9,175.8] | -45.681 (7.781) | 9.76e-11 | 5.86e-09 |     Yes     |
+
+### Q09
+
+| Comparison (A vs B)                            | n (A/B) |  A mean +/- SD (ms) |  B mean +/- SD (ms) | Difference A-B (ms) |            95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|------------------------------------------------|--------:|--------------------:|--------------------:|--------------------:|-----------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational vs Polypheny Document MQL |     5/5 | 15,590.2 +/- 841.62 | 13,218.0 +/- 279.78 |             2,372.2 |     [1,344.6, 3,399.8] |   5.981 (4.873) |   0.0020 |   0.0205 |     Yes     |
+| Polypheny Relational vs DuckDB                 |     5/5 | 15,590.2 +/- 841.62 |  8,844.2 +/- 369.44 |             6,746.0 |     [5,716.9, 7,775.1] |  16.412 (5.486) | 7.13e-06 |   0.0003 |     Yes     |
+| Polypheny Relational vs Apache Spark           |     5/5 | 15,590.2 +/- 841.62 | 34,324.6 +/- 735.65 |           -18,734.4 | [-19,890.8, -17,578.0] | -37.476 (7.859) | 3.82e-10 | 2.21e-08 |     Yes     |
+| Polypheny Document MQL vs DuckDB               |     5/5 | 13,218.0 +/- 279.78 |  8,844.2 +/- 369.44 |             4,373.8 |     [3,889.7, 4,857.9] |  21.104 (7.453) | 6.42e-08 | 3.28e-06 |     Yes     |
+| Polypheny Document MQL vs Apache Spark         |     5/5 | 13,218.0 +/- 279.78 | 34,324.6 +/- 735.65 |           -21,106.6 | [-22,004.4, -20,208.8] | -59.965 (5.133) | 1.66e-08 | 8.65e-07 |     Yes     |
+| DuckDB vs Apache Spark                         |     5/5 |  8,844.2 +/- 369.44 | 34,324.6 +/- 735.65 |           -25,480.4 | [-26,385.1, -24,575.7] | -69.212 (5.897) | 8.27e-10 | 4.55e-08 |     Yes     |
+
+### Q10
+
+| Comparison (A vs B)                            | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |          95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|---------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational vs Polypheny Document MQL |     5/5 | 5,458.2 +/- 655.76 | 4,283.4 +/- 176.21 |             1,174.8 |    [371.85, 1,977.7] |   3.869 (4.575) |   0.0140 |   0.0838 |     No      |
+| Polypheny Relational vs DuckDB                 |     5/5 | 5,458.2 +/- 655.76 |   435.40 +/- 42.24 |             5,022.8 |   [4,209.5, 5,836.1] |  17.092 (4.033) | 6.48e-05 |   0.0017 |     Yes     |
+| Polypheny Relational vs Apache Spark           |     5/5 | 5,458.2 +/- 655.76 | 5,407.8 +/- 309.74 |               50.40 |    [-753.44, 854.24] |   0.155 (5.700) |   0.8819 |   0.8819 |     No      |
+| Polypheny Document MQL vs DuckDB               |     5/5 | 4,283.4 +/- 176.21 |   435.40 +/- 42.24 |             3,848.0 |   [3,631.8, 4,064.2] |  47.487 (4.458) | 3.34e-07 | 1.61e-05 |     Yes     |
+| Polypheny Document MQL vs Apache Spark         |     5/5 | 4,283.4 +/- 176.21 | 5,407.8 +/- 309.74 |            -1,124.4 |  [-1,509.3, -739.51] |  -7.056 (6.344) |   0.0003 |   0.0057 |     Yes     |
+| DuckDB vs Apache Spark                         |     5/5 |   435.40 +/- 42.24 | 5,407.8 +/- 309.74 |            -4,972.4 | [-5,355.1, -4,589.7] | -35.568 (4.149) | 2.58e-06 |   0.0001 |     Yes     |
diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_result_analysis.md b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_result_analysis.md
index ac03259fb8..16901f1726 100644
--- a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_result_analysis.md
+++ b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_result_analysis.md
@@ -11,6 +11,7 @@
 - Polypheny Document MQL completed Q01 and Q02 successfully, while Q03–Q05 could not be executed because the required nested-data operations are not currently supported by the MQL path.
 - Where comparable, result row counts were consistent across all successful executions.
 - Runtime variability is low to moderate for most successful executions, while DuckDB shows higher variability for Q01, Q04, and Q05.
+- Exploratory Welch tests use the available successful measurements with Holm adjustment; Q01 remains unsuitable for direct interpretation because the systems return different result structures.
 
 ### Main Findings
 - Q01 should not be interpreted as a direct performance comparison. 
@@ -20,16 +21,19 @@ Polypheny Relational Normalized instead returns the root customer fields and a s
 - Q02 provides the clearest root-level comparison because it explicitly projects only scalar customer fields. 
 DuckDB is fastest at 0.64 seconds, followed by Polypheny Relational Normalized at 1.18 seconds and Spark at 1.75 seconds. 
 MQL is considerably slower at 122.62 seconds because of document construction and transfer overhead. 
+The pairwise Q02 differences are statistically significant after Holm adjustment, including DuckDB versus Polypheny Relational Normalized (`p=0.0085`) and Polypheny Relational Normalized versus Spark (`p=0.0048`).
 The improvement over Q01 is consistent with avoiding nested payload materialization, although the two queries use different predicates and therefore do not form a controlled projection-only comparison.
 
 - Polypheny Relational Normalized performs best for nested projections. 
 It completes Q03 in 2.73 seconds and Q04 in 10.85 seconds, compared with 13.42 and 16.75 seconds for Spark and 96.09 and 1,575.23 seconds for DuckDB. 
+These Q03 and Q04 differences are statistically significant after Holm adjustment.
 Polypheny accesses generated order and lineitem relations using a single parent-child join, 
 whereas DuckDB and Spark traverse repeated structures using `UNNEST` or `explode`. 
 The deeper traversal in Q04 particularly affects DuckDB.
 
 - Q05 in Polypheny is executed as an aggregation in the Polypheny engine over a join performed by the adapter, because aggregation over nested fields is not supported by the adapter. 
 This results in worse performance than Apache Spark.
+The Polypheny-Spark Q05 difference is statistically significant after Holm adjustment (`p=0.0000453`).
 
 ![Q5_nested_data_query_plan.png](Q5_nested_data_query_plan.png)
 
@@ -45,6 +49,12 @@ plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_summary.md
 
 ```
 
+Exploratory Welch analysis:
+
+```text
+plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_welch_analysis.md
+```
+
 Plot:
 ```text
 plugins/parquet-adapter/benchmarks/results/plots/nested_data_plot.pdf
diff --git a/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_welch_analysis.md b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_welch_analysis.md
new file mode 100644
index 0000000000..84f301da15
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_welch_analysis.md
@@ -0,0 +1,81 @@
+# Nested Data - Exploratory Welch Analysis
+
+> This is an exploratory analysis: each tested group contains only five measurements, and benchmark execution order was not randomized. The results do not provide definitive evidence of performance equivalence or difference.
+
+## Method
+
+- Only `measured` rows with `success=true` are included; warm-up and failed rows are excluded.
+- Tests use the raw `elapsed_ms` measurements rather than summary means.
+- Every comparison is a two-sided Welch t-test, which does not assume equal variances.
+- Execution order was not randomized, so caching, time trends, or other order effects may influence comparisons.
+- The reported difference and unadjusted 95% confidence interval are calculated as system A minus system B.
+- Raw p-values are adjusted together across all 21 comparisons using the Holm method.
+- `Significant` means Holm-adjusted `p <= 0.05`.
+- A non-significant result means that these measurements do not establish a difference; it does not prove equal performance.
+
+## Inputs
+
+| System                          | Result file                                                                                           |
+|---------------------------------|-------------------------------------------------------------------------------------------------------|
+| Polypheny Relational Normalized | `plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_normalized_results.csv` |
+| Polypheny Document MQL          | `plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_polypheny_mql_results.csv`        |
+| DuckDB                          | `plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_duckdb_results.csv`               |
+| Apache Spark                    | `plugins/parquet-adapter/benchmarks/results/nested_data/nested_data_spark_results.csv`                |
+
+## Results
+
+### Q01
+
+| Comparison (A vs B)                                       | n (A/B) |      A mean +/- SD (ms) |      B mean +/- SD (ms) | Difference A-B (ms) |               95% CI (ms) |           t (df) |    Raw p |   Holm p | Significant |
+|-----------------------------------------------------------|--------:|------------------------:|------------------------:|--------------------:|--------------------------:|-----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational Normalized vs Polypheny Document MQL |     5/5 |      2,463.6 +/- 744.81 |   233,059.4 +/- 2,193.3 |          -230,595.8 |  [-233,273.3, -227,918.3] | -222.605 (4.910) | 5.06e-11 | 1.01e-09 |     Yes     |
+| Polypheny Relational Normalized vs DuckDB                 |     5/5 |      2,463.6 +/- 744.81 | 504,359.8 +/- 502,681.7 |          -501,896.2 | [-1,126,057.6, 122,265.2] |   -2.233 (4.000) |   0.0894 |   0.2681 |     No      |
+| Polypheny Relational Normalized vs Apache Spark           |     5/5 |      2,463.6 +/- 744.81 |    77,967.0 +/- 3,057.8 |           -75,503.4 |    [-79,253.5, -71,753.3] |  -53.645 (4.473) | 1.86e-07 | 2.98e-06 |     Yes     |
+| Polypheny Document MQL vs DuckDB                          |     5/5 |   233,059.4 +/- 2,193.3 | 504,359.8 +/- 502,681.7 |          -271,300.4 |   [-895,458.8, 352,858.0] |   -1.207 (4.000) |   0.2940 |   0.2940 |     No      |
+| Polypheny Document MQL vs Apache Spark                    |     5/5 |   233,059.4 +/- 2,193.3 |    77,967.0 +/- 3,057.8 |           155,092.4 |    [151,141.1, 159,043.7] |   92.158 (7.255) | 2.12e-12 | 4.44e-11 |     Yes     |
+| DuckDB vs Apache Spark                                    |     5/5 | 504,359.8 +/- 502,681.7 |    77,967.0 +/- 3,057.8 |           426,392.8 | [-197,762.3, 1,050,547.9] |    1.897 (4.000) |   0.1307 |   0.2681 |     No      |
+
+### Q02
+
+| Comparison (A vs B)                                       | n (A/B) |    A mean +/- SD (ms) |    B mean +/- SD (ms) | Difference A-B (ms) |              95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|-----------------------------------------------------------|--------:|----------------------:|----------------------:|--------------------:|-------------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational Normalized vs Polypheny Document MQL |     5/5 |    1,176.8 +/- 176.86 | 122,618.4 +/- 2,871.3 |          -121,441.6 | [-125,002.9, -117,880.3] | -94.396 (4.030) | 6.80e-08 | 1.29e-06 |     Yes     |
+| Polypheny Relational Normalized vs DuckDB                 |     5/5 |    1,176.8 +/- 176.86 |      640.40 +/- 82.16 |              536.40 |         [319.75, 753.05] |   6.150 (5.649) |   0.0011 |   0.0085 |     Yes     |
+| Polypheny Relational Normalized vs Apache Spark           |     5/5 |    1,176.8 +/- 176.86 |    1,752.0 +/- 137.24 |             -575.20 |       [-808.57, -341.83] |  -5.745 (7.535) |   0.0005 |   0.0048 |     Yes     |
+| Polypheny Document MQL vs DuckDB                          |     5/5 | 122,618.4 +/- 2,871.3 |      640.40 +/- 82.16 |           121,978.0 |   [118,413.7, 125,542.3] |  94.954 (4.007) | 7.21e-08 | 1.30e-06 |     Yes     |
+| Polypheny Document MQL vs Apache Spark                    |     5/5 | 122,618.4 +/- 2,871.3 |    1,752.0 +/- 137.24 |           120,866.4 |   [117,303.6, 124,429.2] |  94.020 (4.018) | 7.20e-08 | 1.30e-06 |     Yes     |
+| DuckDB vs Apache Spark                                    |     5/5 |      640.40 +/- 82.16 |    1,752.0 +/- 137.24 |            -1,111.6 |      [-1,283.2, -940.02] | -15.540 (6.541) | 2.08e-06 | 3.12e-05 |     Yes     |
+
+### Q03
+
+| Comparison (A vs B)                             | n (A/B) |   A mean +/- SD (ms) |   B mean +/- SD (ms) | Difference A-B (ms) |             95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|-------------------------------------------------|--------:|---------------------:|---------------------:|--------------------:|------------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational Normalized vs DuckDB       |     5/5 |   2,727.8 +/- 300.10 | 96,091.4 +/- 8,018.8 |           -93,363.6 | [-103,316.3, -83,410.9] | -26.016 (4.011) | 1.27e-05 |   0.0002 |     Yes     |
+| Polypheny Relational Normalized vs Apache Spark |     5/5 |   2,727.8 +/- 300.10 |  13,420.6 +/- 950.10 |           -10,692.8 |   [-11,853.5, -9,532.1] | -23.997 (4.790) | 3.55e-06 | 4.61e-05 |     Yes     |
+| DuckDB vs Apache Spark                          |     5/5 | 96,091.4 +/- 8,018.8 |  13,420.6 +/- 950.10 |            82,670.8 |    [72,751.5, 92,590.1] |  22.893 (4.112) | 1.71e-05 |   0.0002 |     Yes     |
+
+### Q04
+
+| Comparison (A vs B)                             | n (A/B) |        A mean +/- SD (ms) |        B mean +/- SD (ms) | Difference A-B (ms) |                95% CI (ms) |          t (df) |  Raw p | Holm p | Significant |
+|-------------------------------------------------|--------:|--------------------------:|--------------------------:|--------------------:|---------------------------:|----------------:|-------:|-------:|:-----------:|
+| Polypheny Relational Normalized vs DuckDB       |     5/5 |       10,845.6 +/- 281.05 | 1,575,230.8 +/- 563,393.3 |        -1,564,385.2 | [-2,263,930.4, -864,840.0] |  -6.209 (4.000) | 0.0034 | 0.0240 |     Yes     |
+| Polypheny Relational Normalized vs Apache Spark |     5/5 |       10,845.6 +/- 281.05 |      16,749.2 +/- 1,060.4 |            -5,903.6 |       [-7,202.2, -4,605.0] | -12.034 (4.559) | 0.0001 | 0.0013 |     Yes     |
+| DuckDB vs Apache Spark                          |     5/5 | 1,575,230.8 +/- 563,393.3 |      16,749.2 +/- 1,060.4 |         1,558,481.6 |   [858,937.1, 2,258,026.1] |   6.185 (4.000) | 0.0035 | 0.0240 |     Yes     |
+
+### Q05
+
+| Comparison (A vs B)                             | n (A/B) |          A mean +/- SD (ms) |          B mean +/- SD (ms) | Difference A-B (ms) |                  95% CI (ms) |         t (df) |    Raw p |   Holm p | Significant |
+|-------------------------------------------------|--------:|----------------------------:|----------------------------:|--------------------:|-----------------------------:|---------------:|---------:|---------:|:-----------:|
+| Polypheny Relational Normalized vs DuckDB       |     5/5 |      222,409.4 +/- 12,599.5 | 3,724,019.2 +/- 1,343,512.3 |        -3,501,609.8 | [-5,169,758.3, -1,833,461.3] | -5.828 (4.001) |   0.0043 |   0.0240 |     Yes     |
+| Polypheny Relational Normalized vs Apache Spark |     5/5 |      222,409.4 +/- 12,599.5 |         15,316.4 +/- 322.38 |           207,093.0 |       [191,451.6, 222,734.4] | 36.741 (4.005) | 3.23e-06 | 4.53e-05 |     Yes     |
+| DuckDB vs Apache Spark                          |     5/5 | 3,724,019.2 +/- 1,343,512.3 |         15,316.4 +/- 322.38 |         3,708,702.8 |   [2,040,512.0, 5,376,893.6] |  6.173 (4.000) |   0.0035 |   0.0240 |     Yes     |
+
+## Skipped Data
+
+The following system/query groups had fewer than two successful measured runs and were not used in same-query pairwise tests:
+
+| Query | System                 | Successful runs |
+|-------|------------------------|----------------:|
+| Q03   | Polypheny Document MQL |               0 |
+| Q04   | Polypheny Document MQL |               0 |
+| Q05   | Polypheny Document MQL |               0 |
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_result_analysis.md b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_result_analysis.md
index b6db24def4..a08adaf1cb 100644
--- a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_result_analysis.md
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_result_analysis.md
@@ -11,6 +11,7 @@
 - All benchmark executions completed successfully (`5/5` successful measured runs for every query).
 - Every query produced the expected single aggregate result, confirming consistent execution across all systems.
 - The low standard deviations observed across repeated runs indicate stable benchmark measurements.
+- Exploratory Welch tests compare the five existing measurements and use Holm-adjusted p-values across the report; non-significant results are not interpreted as evidence that two layouts are equivalent.
 
 ## Query Groups
 
@@ -25,32 +26,30 @@ Queries **Q1–Q3** are evaluated on the `yellow_tripdata` dataset, while **Q4
 ## Main Findings
 
 - Q1 serves as the baseline query. Since no predicates are applied to partition columns, both the partitioned and unpartitioned layouts require access to all Parquet files. 
-Consequently, Hive partitioning provides no opportunity for partition pruning, and both layouts exhibit comparable performance. 
-The observed runtime differences are likely attributable to implementation overhead rather than the dataset organization itself.
+Consequently, Hive partitioning provides no opportunity for partition pruning. The exploratory tests find a layout difference for Polypheny and Spark (Holm-adjusted `p=0.0309` and `p=0.0300`), but the DuckDB difference is not significant after adjustment (`p=0.0699`). Because neither layout can prune files, these differences cannot be attributed to partition pruning and are more likely related to layout-specific implementation overhead.
 
 - Q2 demonstrates the effectiveness of Hive-style partition pruning. 
 The query filters on the partition column year, allowing the partitioned layout to skip all directories except those corresponding to the selected year. 
 In contrast, the unpartitioned layout must inspect every Parquet file because year is stored as a regular column. 
 As a result, all evaluated systems execute the partitioned query substantially faster, with runtime reductions of approximately 31% for Polypheny, 60% for DuckDB, and 70% for Apache Spark.
+- The Q2 layout differences are statistically significant for all three systems after Holm adjustment.
 - Polypheny shows a smaller difference between the partitioned and unpartitioned layouts in Q2 because the COUNT query is already highly optimized using Parquet metadata.
 
 - Q3 further demonstrates the benefit of Hive-style partitioning. 
 By filtering on both partition columns (year and month), the partitioned layout accesses only a single monthly partition, 
-while the unpartitioned layout must still inspect all Parquet files. As a result, all evaluated systems achieve lower execution times on the partitioned dataset. 
-- As in Q2, the performance gap is smaller for Polypheny because the metadata-based COUNT optimization already minimizes the execution cost of the query.
+while the unpartitioned layout must still inspect all Parquet files. As a result, all evaluated systems have lower mean execution times on the partitioned dataset.
+- The lower Q3 means are statistically significant for DuckDB and Spark. For Polypheny, the existing measurements do not establish a significant layout difference (Holm-adjusted `p=0.2720`), consistent with its metadata-based COUNT optimization minimizing the execution cost.
 - In the current implementation, Polypheny performs planner-level partition pruning only on the first partition level (year). 
 The second partition level (month) is handled within the adapter by filtering the remaining partition directories before reading the Parquet files.
 
 - Q4 query does not filter on the partition columns (year or month), thus Hive-style partition pruning cannot be applied.
-Consequently, both the partitioned and unpartitioned layouts must scan all Parquet files. As expected, partitioning does not provide a performance benefit for this query.
+Consequently, both the partitioned and unpartitioned layouts must scan all Parquet files. Partitioning provides no measured benefit: the Polypheny difference is not significant (`p=0.1805`), while DuckDB and Spark are significantly slower on the partitioned layout (`p=0.0001` and `p=0.0007`).
 These results confirm that partitioning provides a performance benefit only when queries contain predicates on the partition columns.
 
 - Q5 combines a partition predicate with row-level filtering. 
 Unlike Q4, the filter on year enables partition pruning, allowing the partitioned layout to skip files belonging to other years. 
-Polypheny and Apache Spark therefore execute the query faster on the partitioned dataset, 
-demonstrating the practical benefit of Hive-style partitioning for selective analytical workloads. 
-Unlike Polypheny and Apache Spark, DuckDB exhibits similar performance for the partitioned and unpartitioned layouts, with the unpartitioned layout being slightly faster in this benchmark. 
-his suggests that Hive-style partitioning provides only a limited benefit for this query in DuckDB
+Polypheny and Apache Spark have lower mean runtimes on the partitioned dataset. The Spark difference is statistically significant (`p=0.0030`), while the current Polypheny measurements do not establish a significant difference (`p=0.1805`).
+DuckDB is significantly faster on the unpartitioned layout (`p=0.0050`), indicating that Hive-style partitioning did not benefit this query in DuckDB under the tested conditions.
 
 
 ## Related Documents
@@ -61,6 +60,12 @@ Summary:
 plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_summary.md
 ```
 
+Exploratory Welch analysis:
+
+```text
+plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_welch_analysis.md
+```
+
 Plot:
 ```text
 plugins/parquet-adapter/benchmarks/results/plots/partitioning_plot.png
diff --git a/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_welch_analysis.md b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_welch_analysis.md
new file mode 100644
index 0000000000..2810a30623
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_welch_analysis.md
@@ -0,0 +1,146 @@
+# Partitioning - Exploratory Welch Analysis
+
+> This is an exploratory analysis: each tested group contains only five measurements, and benchmark execution order was not randomized. The results do not provide definitive evidence of performance equivalence or difference.
+
+## Method
+
+- Only `measured` rows with `success=true` are included; warm-up and failed rows are excluded.
+- Tests use the raw `elapsed_ms` measurements rather than summary means.
+- Every comparison is a two-sided Welch t-test, which does not assume equal variances.
+- Execution order was not randomized, so caching, time trends, or other order effects may influence comparisons.
+- The reported difference and unadjusted 95% confidence interval are calculated as system A minus system B.
+- Raw p-values are adjusted together across all 45 comparisons using the Holm method.
+- `Significant` means Holm-adjusted `p <= 0.05`.
+- A non-significant result means that these measurements do not establish a difference; it does not prove equal performance.
+
+## Inputs
+
+| System                     | Result file                                                                                             |
+|----------------------------|---------------------------------------------------------------------------------------------------------|
+| Polypheny Relational       | `plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_polypheny_results.csv`            |
+| DuckDB Repartitioned       | `plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_repartitioned_results.csv` |
+| DuckDB Unpartitioned       | `plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_duckdb_unpartitioned_results.csv` |
+| Apache Spark Repartitioned | `plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_repartitioned_results.csv`  |
+| Apache Spark Unpartitioned | `plugins/parquet-adapter/benchmarks/results/partitioning/partitioning_spark_unpartitioned_results.csv`  |
+
+## Results
+
+### Q1_NP
+
+| Comparison (A vs B)                                | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |         95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|----------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|--------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational vs DuckDB Unpartitioned       |     5/5 |    9.600 +/- 1.140 |    59.60 +/- 2.408 |              -50.00 |    [-52.95, -47.05] | -41.959 (5.707) | 2.50e-08 | 1.10e-06 |     Yes     |
+| Polypheny Relational vs Apache Spark Unpartitioned |     5/5 |    9.600 +/- 1.140 | 1,189.6 +/- 190.40 |            -1,180.0 | [-1,416.4, -943.59] | -13.858 (4.000) |   0.0002 |   0.0027 |     Yes     |
+| DuckDB Unpartitioned vs Apache Spark Unpartitioned |     5/5 |    59.60 +/- 2.408 | 1,189.6 +/- 190.40 |            -1,130.0 | [-1,366.4, -893.60] | -13.270 (4.001) |   0.0002 |   0.0027 |     Yes     |
+
+### Q1_P
+
+| Comparison (A vs B)                                | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |          95% CI (ms) |          t (df) |    Raw p | Holm p | Significant |
+|----------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|---------------------:|----------------:|---------:|-------:|:-----------:|
+| Polypheny Relational vs DuckDB Repartitioned       |     5/5 |    7.200 +/- 0.837 |    78.80 +/- 11.28 |              -71.60 |     [-85.58, -57.62] | -14.157 (4.044) |   0.0001 | 0.0026 |     Yes     |
+| Polypheny Relational vs Apache Spark Repartitioned |     5/5 |    7.200 +/- 0.837 | 1,681.8 +/- 204.77 |            -1,674.6 | [-1,928.9, -1,420.3] | -18.286 (4.000) | 5.26e-05 | 0.0013 |     Yes     |
+| DuckDB Repartitioned vs Apache Spark Repartitioned |     5/5 |    78.80 +/- 11.28 | 1,681.8 +/- 204.77 |            -1,603.0 | [-1,857.0, -1,349.0] | -17.478 (4.024) | 6.02e-05 | 0.0014 |     Yes     |
+
+### Q2_NP
+
+| Comparison (A vs B)                                | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |          95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|----------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|---------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational vs DuckDB Unpartitioned       |     5/5 |    9.600 +/- 1.342 |    88.40 +/- 5.413 |              -78.80 |     [-85.44, -72.16] | -31.596 (4.490) | 1.90e-06 | 7.23e-05 |     Yes     |
+| Polypheny Relational vs Apache Spark Unpartitioned |     5/5 |    9.600 +/- 1.342 |  2,123.2 +/- 94.05 |            -2,113.6 | [-2,230.4, -1,996.8] | -50.244 (4.002) | 9.35e-07 | 3.83e-05 |     Yes     |
+| DuckDB Unpartitioned vs Apache Spark Unpartitioned |     5/5 |    88.40 +/- 5.413 |  2,123.2 +/- 94.05 |            -2,034.8 | [-2,151.5, -1,918.1] | -48.296 (4.026) | 1.02e-06 | 4.08e-05 |     Yes     |
+
+### Q2_P
+
+| Comparison (A vs B)                                | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |        95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|----------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|-------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational vs DuckDB Repartitioned       |     5/5 |    6.600 +/- 0.894 |    35.20 +/- 1.789 |              -28.60 |   [-30.80, -26.40] | -31.976 (5.882) | 8.01e-08 | 3.45e-06 |     Yes     |
+| Polypheny Relational vs Apache Spark Repartitioned |     5/5 |    6.600 +/- 0.894 |   641.80 +/- 81.33 |             -635.20 | [-736.19, -534.21] | -17.462 (4.001) | 6.30e-05 |   0.0014 |     Yes     |
+| DuckDB Repartitioned vs Apache Spark Repartitioned |     5/5 |    35.20 +/- 1.789 |   641.80 +/- 81.33 |             -606.60 | [-707.58, -505.62] | -16.673 (4.004) | 7.53e-05 |   0.0015 |     Yes     |
+
+### Q3_NP
+
+| Comparison (A vs B)                                | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |          95% CI (ms) |          t (df) |    Raw p | Holm p | Significant |
+|----------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|---------------------:|----------------:|---------:|-------:|:-----------:|
+| Polypheny Relational vs DuckDB Unpartitioned       |     5/5 |    9.800 +/- 1.924 |    72.20 +/- 5.630 |              -62.40 |     [-69.27, -55.53] | -23.451 (4.921) | 3.06e-06 | 0.0001 |     Yes     |
+| Polypheny Relational vs Apache Spark Unpartitioned |     5/5 |    9.800 +/- 1.924 | 1,326.2 +/- 101.33 |            -1,316.4 | [-1,442.2, -1,190.6] | -29.045 (4.003) | 8.31e-06 | 0.0003 |     Yes     |
+| DuckDB Unpartitioned vs Apache Spark Unpartitioned |     5/5 |    72.20 +/- 5.630 | 1,326.2 +/- 101.33 |            -1,254.0 | [-1,379.7, -1,128.3] | -27.630 (4.025) | 9.66e-06 | 0.0003 |     Yes     |
+
+### Q3_P
+
+| Comparison (A vs B)                                | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |        95% CI (ms) |          t (df) |  Raw p | Holm p | Significant |
+|----------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|-------------------:|----------------:|-------:|-------:|:-----------:|
+| Polypheny Relational vs DuckDB Repartitioned       |     5/5 |    8.600 +/- 1.140 |    17.00 +/- 2.000 |              -8.400 |   [-10.89, -5.914] |  -8.159 (6.352) | 0.0001 | 0.0026 |     Yes     |
+| Polypheny Relational vs Apache Spark Repartitioned |     5/5 |    8.600 +/- 1.140 |   450.60 +/- 80.23 |             -442.00 | [-541.61, -342.39] | -12.318 (4.002) | 0.0002 | 0.0030 |     Yes     |
+| DuckDB Repartitioned vs Apache Spark Repartitioned |     5/5 |    17.00 +/- 2.000 |   450.60 +/- 80.23 |             -433.60 | [-533.20, -334.00] | -12.082 (4.005) | 0.0003 | 0.0030 |     Yes     |
+
+### Q4_NP
+
+| Comparison (A vs B)                                | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |         95% CI (ms) |          t (df) |    Raw p | Holm p | Significant |
+|----------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|--------------------:|----------------:|---------:|-------:|:-----------:|
+| Polypheny Relational vs DuckDB Unpartitioned       |     5/5 |   131.20 +/- 14.89 |    20.60 +/- 1.342 |              110.60 |     [92.15, 129.05] |  16.543 (4.065) | 6.99e-05 | 0.0015 |     Yes     |
+| Polypheny Relational vs Apache Spark Unpartitioned |     5/5 |   131.20 +/- 14.89 |  902.60 +/- 145.16 |             -771.40 |  [-951.12, -591.68] | -11.821 (4.084) |   0.0003 | 0.0030 |     Yes     |
+| DuckDB Unpartitioned vs Apache Spark Unpartitioned |     5/5 |    20.60 +/- 1.342 |  902.60 +/- 145.16 |             -882.00 | [-1,062.2, -701.77] | -13.586 (4.001) |   0.0002 | 0.0027 |     Yes     |
+
+### Q4_P
+
+| Comparison (A vs B)                                | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |          95% CI (ms) |          t (df) |    Raw p | Holm p | Significant |
+|----------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|---------------------:|----------------:|---------:|-------:|:-----------:|
+| Polypheny Relational vs DuckDB Repartitioned       |     5/5 |   150.00 +/- 14.63 |    30.20 +/- 1.304 |              119.80 |     [101.68, 137.92] |  18.240 (4.064) | 4.73e-05 | 0.0012 |     Yes     |
+| Polypheny Relational vs Apache Spark Repartitioned |     5/5 |   150.00 +/- 14.63 | 1,693.0 +/- 144.26 |            -1,543.0 | [-1,721.6, -1,364.4] | -23.795 (4.082) | 1.56e-05 | 0.0005 |     Yes     |
+| DuckDB Repartitioned vs Apache Spark Repartitioned |     5/5 |    30.20 +/- 1.304 | 1,693.0 +/- 144.26 |            -1,662.8 | [-1,841.9, -1,483.7] | -25.772 (4.001) | 1.34e-05 | 0.0004 |     Yes     |
+
+### Q5_NP
+
+| Comparison (A vs B)                                | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |         95% CI (ms) |          t (df) |    Raw p | Holm p | Significant |
+|----------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|--------------------:|----------------:|---------:|-------:|:-----------:|
+| Polypheny Relational vs DuckDB Unpartitioned       |     5/5 |    61.60 +/- 6.025 |    13.60 +/- 0.548 |               48.00 |      [40.54, 55.46] |  17.741 (4.066) | 5.27e-05 | 0.0013 |     Yes     |
+| Polypheny Relational vs Apache Spark Unpartitioned |     5/5 |    61.60 +/- 6.025 | 1,035.4 +/- 101.68 |             -973.80 | [-1,099.9, -847.67] | -21.378 (4.028) | 2.68e-05 | 0.0007 |     Yes     |
+| DuckDB Unpartitioned vs Apache Spark Unpartitioned |     5/5 |    13.60 +/- 0.548 | 1,035.4 +/- 101.68 |            -1,021.8 | [-1,148.1, -895.55] | -22.470 (4.000) | 2.32e-05 | 0.0007 |     Yes     |
+
+### Q5_P
+
+| Comparison (A vs B)                                | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |        95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|----------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|-------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational vs DuckDB Repartitioned       |     5/5 |    54.60 +/- 3.050 |    17.40 +/- 1.140 |               37.20 |     [33.48, 40.92] |  25.549 (5.097) | 1.41e-06 | 5.50e-05 |     Yes     |
+| Polypheny Relational vs Apache Spark Repartitioned |     5/5 |    54.60 +/- 3.050 |  637.80 +/- 101.51 |             -583.20 | [-709.21, -457.19] | -12.841 (4.007) |   0.0002 |   0.0027 |     Yes     |
+| DuckDB Repartitioned vs Apache Spark Repartitioned |     5/5 |    17.40 +/- 1.140 |  637.80 +/- 101.51 |             -620.40 | [-746.44, -494.36] | -13.665 (4.001) |   0.0002 |   0.0027 |     Yes     |
+
+### Q1_P vs Q1_NP
+
+| Comparison (A vs B)                                                     | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |      95% CI (ms) |         t (df) |  Raw p | Holm p | Significant |
+|-------------------------------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|-----------------:|---------------:|-------:|-------:|:-----------:|
+| Polypheny Relational [Q1_P] vs Polypheny Relational [Q1_NP]             |     5/5 |    7.200 +/- 0.837 |    9.600 +/- 1.140 |              -2.400 | [-3.882, -0.918] | -3.795 (7.339) | 0.0062 | 0.0309 |     Yes     |
+| DuckDB Repartitioned [Q1_P] vs DuckDB Unpartitioned [Q1_NP]             |     5/5 |    78.80 +/- 11.28 |    59.60 +/- 2.408 |               19.20 |   [5.339, 33.06] |  3.723 (4.364) | 0.0175 | 0.0699 |     No      |
+| Apache Spark Repartitioned [Q1_P] vs Apache Spark Unpartitioned [Q1_NP] |     5/5 | 1,681.8 +/- 204.77 | 1,189.6 +/- 190.40 |              492.20 | [203.58, 780.82] |  3.936 (7.958) | 0.0044 | 0.0300 |     Yes     |
+
+### Q2_P vs Q2_NP
+
+| Comparison (A vs B)                                                     | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |          95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|-------------------------------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|---------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational [Q2_P] vs Polypheny Relational [Q2_NP]             |     5/5 |    6.600 +/- 0.894 |    9.600 +/- 1.342 |              -3.000 |     [-4.707, -1.293] |  -4.160 (6.969) |   0.0043 |   0.0300 |     Yes     |
+| DuckDB Repartitioned [Q2_P] vs DuckDB Unpartitioned [Q2_NP]             |     5/5 |    35.20 +/- 1.789 |    88.40 +/- 5.413 |              -53.20 |     [-59.81, -46.59] | -20.867 (4.863) | 6.02e-06 |   0.0002 |     Yes     |
+| Apache Spark Repartitioned [Q2_P] vs Apache Spark Unpartitioned [Q2_NP] |     5/5 |   641.80 +/- 81.33 |  2,123.2 +/- 94.05 |            -1,481.4 | [-1,610.1, -1,352.7] | -26.640 (7.837) | 5.70e-09 | 2.57e-07 |     Yes     |
+
+### Q3_P vs Q3_NP
+
+| Comparison (A vs B)                                                     | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |         95% CI (ms) |          t (df) |    Raw p |   Holm p | Significant |
+|-------------------------------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|--------------------:|----------------:|---------:|---------:|:-----------:|
+| Polypheny Relational [Q3_P] vs Polypheny Relational [Q3_NP]             |     5/5 |    8.600 +/- 1.140 |    9.800 +/- 1.924 |              -1.200 |     [-3.602, 1.202] |  -1.200 (6.502) |   0.2720 |   0.2720 |     No      |
+| DuckDB Repartitioned [Q3_P] vs DuckDB Unpartitioned [Q3_NP]             |     5/5 |    17.00 +/- 2.000 |    72.20 +/- 5.630 |              -55.20 |    [-62.07, -48.33] | -20.658 (4.994) | 4.98e-06 |   0.0002 |     Yes     |
+| Apache Spark Repartitioned [Q3_P] vs Apache Spark Unpartitioned [Q3_NP] |     5/5 |   450.60 +/- 80.23 | 1,326.2 +/- 101.33 |             -875.60 | [-1,010.1, -741.09] | -15.149 (7.600) | 5.96e-07 | 2.50e-05 |     Yes     |
+
+### Q4_P vs Q4_NP
+
+| Comparison (A vs B)                                                     | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |       95% CI (ms) |         t (df) |    Raw p | Holm p | Significant |
+|-------------------------------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|------------------:|---------------:|---------:|-------:|:-----------:|
+| Polypheny Relational [Q4_P] vs Polypheny Relational [Q4_NP]             |     5/5 |   150.00 +/- 14.63 |   131.20 +/- 14.89 |               18.80 |   [-2.727, 40.33] |  2.014 (7.998) |   0.0788 | 0.1805 |     No      |
+| DuckDB Repartitioned [Q4_P] vs DuckDB Unpartitioned [Q4_NP]             |     5/5 |    30.20 +/- 1.304 |    20.60 +/- 1.342 |               9.600 |    [7.670, 11.53] | 11.474 (7.993) | 3.03e-06 | 0.0001 |     Yes     |
+| Apache Spark Repartitioned [Q4_P] vs Apache Spark Unpartitioned [Q4_NP] |     5/5 | 1,693.0 +/- 144.26 |  902.60 +/- 145.16 |              790.40 | [579.35, 1,001.5] |  8.636 (8.000) | 2.51e-05 | 0.0007 |     Yes     |
+
+### Q5_P vs Q5_NP
+
+| Comparison (A vs B)                                                     | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) |        95% CI (ms) |         t (df) |  Raw p | Holm p | Significant |
+|-------------------------------------------------------------------------|--------:|-------------------:|-------------------:|--------------------:|-------------------:|---------------:|-------:|-------:|:-----------:|
+| Polypheny Relational [Q5_P] vs Polypheny Relational [Q5_NP]             |     5/5 |    54.60 +/- 3.050 |    61.60 +/- 6.025 |              -7.000 |    [-14.41, 0.413] | -2.318 (5.923) | 0.0602 | 0.1805 |     No      |
+| DuckDB Repartitioned [Q5_P] vs DuckDB Unpartitioned [Q5_NP]             |     5/5 |    17.40 +/- 1.140 |    13.60 +/- 0.548 |               3.800 |     [2.401, 5.199] |  6.718 (5.753) | 0.0006 | 0.0050 |     Yes     |
+| Apache Spark Repartitioned [Q5_P] vs Apache Spark Unpartitioned [Q5_NP] |     5/5 |  637.80 +/- 101.51 | 1,035.4 +/- 101.68 |             -397.60 | [-545.77, -249.43] | -6.188 (8.000) | 0.0003 | 0.0030 |     Yes     |
diff --git a/plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot_std.pdf b/plugins/parquet-adapter/benchmarks/results/plots/access_model_comparison_tlcp_plot_std.pdf
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+<line x1="801.51" y1="365.50" x2="801.51" y2="371.69" stroke="#222" stroke-width="1.5"/>
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+<line x1="820.89" y1="428.32" x2="820.89" y2="435.58" stroke="#222" stroke-width="1.5"/>
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+<line x1="834.54" y1="242.00" x2="846.01" y2="242.00" stroke="#222" stroke-width="1.5"/>
+<line x1="873.04" y1="356.66" x2="873.04" y2="367.53" stroke="#222" stroke-width="1.5"/>
+<line x1="867.31" y1="356.66" x2="878.78" y2="356.66" stroke="#222" stroke-width="1.5"/>
+<line x1="867.31" y1="367.53" x2="878.78" y2="367.53" stroke="#222" stroke-width="1.5"/>
+<line x1="892.43" y1="443.29" x2="892.43" y2="447.75" stroke="#222" stroke-width="1.5"/>
+<line x1="886.69" y1="443.29" x2="898.16" y2="443.29" stroke="#222" stroke-width="1.5"/>
+<line x1="886.69" y1="447.75" x2="898.16" y2="447.75" stroke="#222" stroke-width="1.5"/>
+<line x1="911.81" y1="200.39" x2="911.81" y2="211.30" stroke="#222" stroke-width="1.5"/>
+<line x1="906.07" y1="200.39" x2="917.54" y2="200.39" stroke="#222" stroke-width="1.5"/>
+<line x1="906.07" y1="211.30" x2="917.54" y2="211.30" stroke="#222" stroke-width="1.5"/>
+<text x="117.57" y="620" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q1_P</text>
+<text x="189.11" y="620" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q1_NP</text>
+<text x="293.40" y="620" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q2_P</text>
+<text x="364.94" y="620" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q2_NP</text>
+<text x="469.23" y="620" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q3_P</text>
+<text x="540.77" y="620" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q3_NP</text>
+<text x="645.06" y="620" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q4_P</text>
+<text x="716.60" y="620" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q4_NP</text>
+<text x="820.89" y="620" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q5_P</text>
+<text x="892.43" y="620" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">Q5_NP</text>
+<text x="153.34" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Full-count baseline</text>
+<text x="329.17" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Year-filtered count</text>
+<text x="505.00" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Year-and-month-filtered</text>
+<text x="505.00" y="662" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">count</text>
+<text x="680.83" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Data-column filtering, no</text>
+<text x="680.83" y="662" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">partition restriction</text>
+<text x="856.66" y="645" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Year and data-column</text>
+<text x="856.66" y="662" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">filtering</text>
+<text x="955" y="80" font-family="Helvetica, Arial, sans-serif" font-size="13" font-weight="700">System</text>
+<rect x="955" y="100" width="16" height="16" fill="#4f9d55"/>
+<text x="979" y="113" font-family="Helvetica, Arial, sans-serif" font-size="12">Polypheny Relational</text>
+<rect x="955" y="128" width="16" height="16" fill="#f0a22e"/>
+<text x="979" y="141" font-family="Helvetica, Arial, sans-serif" font-size="12">DuckDB</text>
+<rect x="955" y="156" width="16" height="16" fill="#2f6fbb"/>
+<text x="979" y="169" font-family="Helvetica, Arial, sans-serif" font-size="12">Apache Spark</text>
+<text x="955" y="214" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700" fill="#333">P</text>
+<text x="963.004" y="214" font-family="Helvetica, Arial, sans-serif" font-size="12" fill="#333"> - partitioned</text>
+<text x="955" y="232" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700" fill="#333">NP</text>
+<text x="971.668" y="232" font-family="Helvetica, Arial, sans-serif" font-size="12" fill="#333"> - not partitioned</text>
+<text x="955" y="250" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700" fill="#333">Bars show mean;</text>
+<text x="1054.36" y="250" font-family="Helvetica, Arial, sans-serif" font-size="12" fill="#333"></text>
+<text x="955" y="268" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700" fill="#333">whiskers show +/-1 sample SD.</text>
+<text x="1132.744" y="268" font-family="Helvetica, Arial, sans-serif" font-size="12" fill="#333"></text>
+</svg>
\ No newline at end of file
diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md
index c1a46cff91..c69b0c04f1 100644
--- a/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_access_model_comparison_bm_pipeline.md
@@ -86,6 +86,19 @@ python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py
 
 ```
 
+## Create exploratory Welch analysis
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\benchmark_welch_analysis.py `
+  --title "Access Model Comparison - Exploratory Welch Analysis" `
+  --output plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_welch_analysis.md `
+  "Polypheny Relational Flat=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rf_tlcp_results.csv" `
+  "Polypheny Relational Normalized=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rn_tlcpn_results.csv" `
+  "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_mql_tlcp_results.csv" `
+  "DuckDB=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_duckdb_tlcp_results.csv" `
+  "Apache Spark=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_spark_tlcp_results.csv"
+```
+
 ## Create plot
 
 ```powershell
@@ -102,3 +115,22 @@ python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_resu
   "DuckDB=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_duckdb_tlcp_results.csv" `
   "Apache Spark=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_spark_tlcp_results.csv"
 ```
+
+## Create plot with standard-deviation whiskers
+
+This creates additional `_std` files and leaves the mean-only plot files unchanged.
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_result_std_plot_generator.py `
+  --title "Access Model Comparison" `
+  --name access_model_comparison_tlcp_plot_std `
+  --query-order "Q01,Q02,Q03,Q04,Q05" `
+  --query-descriptions "Q01=Full access;Q02=Projection;Q03=Filtered count;Q04=Filtered full access;Q05=Filtered projection" `
+  --query-description-wrap-chars 30 `
+  --query-description-max-lines 1 `
+  "Polypheny Relational Flat=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rf_tlcp_results.csv" `
+  "Polypheny Relational Normalized=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_rn_tlcpn_results.csv" `
+  "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_polypheny_mql_tlcp_results.csv" `
+  "DuckDB=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_duckdb_tlcp_results.csv" `
+  "Apache Spark=plugins\parquet-adapter\benchmarks\results\access_model_comparison\access_model_comparison_spark_tlcp_results.csv"
+```
diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_aggregation_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_aggregation_bm_pipeline.md
index 5790caf8da..4d1d7850e7 100644
--- a/plugins/parquet-adapter/benchmarks/run_pipeline/run_aggregation_bm_pipeline.md
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_aggregation_bm_pipeline.md
@@ -112,6 +112,18 @@ python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py
   "Apache Spark=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_results.csv"
 ```
 
+## Create exploratory Welch analysis
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\benchmark_welch_analysis.py `
+  --title "Aggregation - Exploratory Welch Analysis" `
+  --output plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_welch_analysis.md `
+  "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_results.csv" `
+  "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_mql_results.csv" `
+  "DuckDB=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_results.csv" `
+  "Apache Spark=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_results.csv"
+```
+
 ## Create plot
 
 ```powershell
@@ -127,3 +139,21 @@ python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_resu
   "DuckDB=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_results.csv" `
   "Apache Spark=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_results.csv"
 ```
+
+## Create plot with standard-deviation whiskers
+
+This creates additional `_std` files and leaves the mean-only plot files unchanged.
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_result_std_plot_generator.py `
+  --title "Aggregation on Partitioned TLC Data" `
+  --name aggregation_plot_std `
+  --query-order "Q01,Q02,Q03,Q04,Q05,Q06,Q07,Q08,Q09,Q10" `
+  --query-descriptions "Q01=Yellow total row count;Q02=Yellow one-month count;Q03=Yellow one-day count;Q04=Yellow filtered row count;Q05=Yellow summary by year;Q06=FHV total row count;Q07=FHV one-month count;Q08=FHV filtered row count;Q09=FHV summary by year;Q10=FHV count by shared-request flag" `
+  --query-description-wrap-chars 14 `
+  --query-description-max-lines 3 `
+  "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_results.csv" `
+  "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_polypheny_mql_results.csv" `
+  "DuckDB=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_duckdb_results.csv" `
+  "Apache Spark=plugins\parquet-adapter\benchmarks\results\aggregation\aggregation_spark_results.csv"
+```
diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md
index abdc4351c5..eb17dcccab 100644
--- a/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_nested_data_bm_pipeline.md
@@ -90,6 +90,18 @@ python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py
   "Apache Spark=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_spark_results.csv"
 ```
 
+## Create exploratory Welch analysis
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\benchmark_welch_analysis.py `
+  --title "Nested Data - Exploratory Welch Analysis" `
+  --output plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_welch_analysis.md `
+  "Polypheny Relational Normalized=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_normalized_results.csv" `
+  "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_mql_results.csv" `
+  "DuckDB=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_duckdb_results.csv" `
+  "Apache Spark=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_spark_results.csv"
+```
+
 ## Create plot
 
 ```powershell
@@ -105,3 +117,21 @@ python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_resu
   "DuckDB=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_duckdb_results.csv" `
   "Apache Spark=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_spark_results.csv"
 ```
+
+## Create plot with standard-deviation whiskers
+
+This creates additional `_std` files and leaves the mean-only plot files unchanged.
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_result_std_plot_generator.py `
+  --title "Nested Data" `
+  --name nested_data_plot_std `
+  --query-order "Q01,Q02,Q03,Q04,Q05" `
+  --query-descriptions "Q01=Root-filtered full customer access;Q02=Root-filtered customer projection;Q03=Level-1 nested filtered order projection;Q04=Level-2 nested filtered lineitem projection;Q05=Level-2 nested lineitem MAX aggregation" `
+  --query-description-wrap-chars 24 `
+  --query-description-max-lines 2 `
+  "Polypheny Relational Normalized=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_normalized_results.csv" `
+  "Polypheny Document MQL=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_polypheny_mql_results.csv" `
+  "DuckDB=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_duckdb_results.csv" `
+  "Apache Spark=plugins\parquet-adapter\benchmarks\results\nested_data\nested_data_spark_results.csv"
+```
diff --git a/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md b/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md
index 9e01fac854..867c3b817d 100644
--- a/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md
+++ b/plugins/parquet-adapter/benchmarks/run_pipeline/run_partitioning_bm_pipeline.md
@@ -108,6 +108,36 @@ python plugins\parquet-adapter\benchmarks\scripts\summarize_benchmark_results.py
   "Apache Spark Unpartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_unpartitioned_results.csv"
 ```
 
+## Create exploratory Welch analysis
+
+The explicit comparisons evaluate the partitioned and unpartitioned variants of each logical query within each system.
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\benchmark_welch_analysis.py `
+  --title "Partitioning - Exploratory Welch Analysis" `
+  --output plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_welch_analysis.md `
+  --comparison "Polypheny Relational@Q1_P=Polypheny Relational@Q1_NP" `
+  --comparison "DuckDB Repartitioned@Q1_P=DuckDB Unpartitioned@Q1_NP" `
+  --comparison "Apache Spark Repartitioned@Q1_P=Apache Spark Unpartitioned@Q1_NP" `
+  --comparison "Polypheny Relational@Q2_P=Polypheny Relational@Q2_NP" `
+  --comparison "DuckDB Repartitioned@Q2_P=DuckDB Unpartitioned@Q2_NP" `
+  --comparison "Apache Spark Repartitioned@Q2_P=Apache Spark Unpartitioned@Q2_NP" `
+  --comparison "Polypheny Relational@Q3_P=Polypheny Relational@Q3_NP" `
+  --comparison "DuckDB Repartitioned@Q3_P=DuckDB Unpartitioned@Q3_NP" `
+  --comparison "Apache Spark Repartitioned@Q3_P=Apache Spark Unpartitioned@Q3_NP" `
+  --comparison "Polypheny Relational@Q4_P=Polypheny Relational@Q4_NP" `
+  --comparison "DuckDB Repartitioned@Q4_P=DuckDB Unpartitioned@Q4_NP" `
+  --comparison "Apache Spark Repartitioned@Q4_P=Apache Spark Unpartitioned@Q4_NP" `
+  --comparison "Polypheny Relational@Q5_P=Polypheny Relational@Q5_NP" `
+  --comparison "DuckDB Repartitioned@Q5_P=DuckDB Unpartitioned@Q5_NP" `
+  --comparison "Apache Spark Repartitioned@Q5_P=Apache Spark Unpartitioned@Q5_NP" `
+  "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_polypheny_results.csv" `
+  "DuckDB Repartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_repartitioned_results.csv" `
+  "DuckDB Unpartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_unpartitioned_results.csv" `
+  "Apache Spark Repartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_repartitioned_results.csv" `
+  "Apache Spark Unpartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_unpartitioned_results.csv"
+```
+
 ## Create plot
 
 ```powershell
@@ -125,3 +155,23 @@ python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_resu
   "Apache Spark Repartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_repartitioned_results.csv" `
   "Apache Spark Unpartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_unpartitioned_results.csv"
 ```
+
+## Create plot with standard-deviation whiskers
+
+This creates additional `_std` files and leaves the mean-only plot files unchanged.
+
+```powershell
+python plugins\parquet-adapter\benchmarks\scripts\plot_generation\benchmark_result_std_plot_generator.py `
+  --title "Partitioning by Year and Month" `
+  --name partitioning_plot_std `
+  --query-order "Q1_P,Q1_NP,Q2_P,Q2_NP,Q3_P,Q3_NP,Q4_P,Q4_NP,Q5_P,Q5_NP" `
+  --query-descriptions "Q1=Full-count baseline;Q2=Year-filtered count;Q3=Year-and-month-filtered count;Q4=Data-column filtering, no partition restriction;Q5=Year and data-column filtering" `
+  --query-description-wrap-chars 28 `
+  --query-description-max-lines 2 `
+  --side-note "P - partitioned;NP - not partitioned" `
+  "Polypheny Relational=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_polypheny_results.csv" `
+  "DuckDB Repartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_repartitioned_results.csv" `
+  "DuckDB Unpartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_duckdb_unpartitioned_results.csv" `
+  "Apache Spark Repartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_repartitioned_results.csv" `
+  "Apache Spark Unpartitioned=plugins\parquet-adapter\benchmarks\results\partitioning\partitioning_spark_unpartitioned_results.csv"
+```
diff --git a/plugins/parquet-adapter/benchmarks/scripts/benchmark_welch_analysis.py b/plugins/parquet-adapter/benchmarks/scripts/benchmark_welch_analysis.py
new file mode 100644
index 0000000000..974992f9e7
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/benchmark_welch_analysis.py
@@ -0,0 +1,536 @@
+#!/usr/bin/env python3
+
+import argparse
+import csv
+import itertools
+import math
+import statistics
+from collections import OrderedDict
+from dataclasses import dataclass
+from pathlib import Path
+
+
+@dataclass
+class MeasurementInput:
+    label: str
+    path: Path
+    values: dict[str, list[float]]
+    attempted_runs: dict[str, int]
+
+
+@dataclass
+class SampleSummary:
+    count: int
+    mean: float
+    standard_deviation: float
+
+
+@dataclass
+class WelchResult:
+    group: str
+    system_a: str
+    query_a: str
+    system_b: str
+    query_b: str
+    sample_a: SampleSummary
+    sample_b: SampleSummary
+    difference: float
+    confidence_low: float
+    confidence_high: float
+    t_statistic: float
+    degrees_of_freedom: float
+    p_value: float
+    adjusted_p_value: float = math.nan
+
+
+def main():
+    args = parse_args()
+    inputs = [read_measurements(*parse_input(value), args.phase) for value in args.csv]
+    results, skipped_measurements, skipped_comparisons = build_comparisons(
+        inputs,
+        args.comparison,
+        args.alpha,
+    )
+    apply_holm_adjustment(results)
+
+    output = Path(args.output)
+    output.parent.mkdir(parents=True, exist_ok=True)
+    output.write_text(
+        render_report(
+            args.title,
+            inputs,
+            results,
+            skipped_measurements,
+            skipped_comparisons,
+            args.phase,
+            args.alpha,
+        ),
+        encoding="utf-8",
+    )
+    print(f"Wrote {output}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Run exploratory pairwise Welch t-tests on benchmark measurements."
+    )
+    parser.add_argument(
+        "csv",
+        nargs="+",
+        help="CSV input. Use LABEL=path.csv to define the system label.",
+    )
+    parser.add_argument("--title", required=True, help="Report title.")
+    parser.add_argument("--output", required=True, help="Markdown output path.")
+    parser.add_argument(
+        "--phase",
+        default="measured",
+        help="Benchmark phase to analyze. Default: measured.",
+    )
+    parser.add_argument(
+        "--alpha",
+        type=float,
+        default=0.05,
+        help="Family-wise significance level. Default: 0.05.",
+    )
+    parser.add_argument(
+        "--comparison",
+        action="append",
+        default=[],
+        help=(
+            "Additional comparison in LABEL@QUERY=LABEL@QUERY form. "
+            "Useful for comparing different query IDs such as partitioned and unpartitioned variants."
+        ),
+    )
+    args = parser.parse_args()
+    if not 0 < args.alpha < 1:
+        parser.error("--alpha must be between 0 and 1.")
+    return args
+
+
+def parse_input(value):
+    if "=" in value:
+        label, path = value.split("=", 1)
+        return label.strip(), Path(path.strip())
+    path = Path(value)
+    return path.stem, path
+
+
+def read_measurements(label, path, phase):
+    values = {}
+    attempted_runs = {}
+    with path.open(newline="", encoding="utf-8-sig") as handle:
+        for row in csv.DictReader(handle):
+            if row.get("phase") != phase:
+                continue
+            query_id = row.get("query_id", "").strip()
+            if not query_id:
+                continue
+            attempted_runs[query_id] = attempted_runs.get(query_id, 0) + 1
+            if row.get("success", "").strip().lower() != "true":
+                continue
+            elapsed_ms = row.get("elapsed_ms", "").strip()
+            if not elapsed_ms:
+                continue
+            values.setdefault(query_id, []).append(float(elapsed_ms))
+    return MeasurementInput(label, path, values, attempted_runs)
+
+
+def build_comparisons(inputs, explicit_comparisons, alpha):
+    input_by_label = {item.label: item for item in inputs}
+    if len(input_by_label) != len(inputs):
+        raise ValueError("System labels must be unique.")
+
+    query_ids = sorted(
+        {query_id for item in inputs for query_id in item.attempted_runs},
+        key=natural_key,
+    )
+    candidates = []
+    skipped_measurements = []
+    skipped_comparisons = []
+    seen = set()
+
+    for query_id in query_ids:
+        available = []
+        for item in inputs:
+            run_count = len(item.values.get(query_id, []))
+            if run_count >= 2:
+                available.append(item)
+            elif query_id in item.attempted_runs:
+                skipped_measurements.append((query_id, item.label, run_count))
+        for left, right in itertools.combinations(available, 2):
+            add_candidate(
+                candidates,
+                seen,
+                query_id,
+                left.label,
+                query_id,
+                left.values[query_id],
+                right.label,
+                query_id,
+                right.values[query_id],
+            )
+
+    for value in explicit_comparisons:
+        left, right = parse_explicit_comparison(value)
+        system_a, query_a = left
+        system_b, query_b = right
+        if system_a not in input_by_label:
+            raise ValueError(f"Unknown system label in --comparison: {system_a}")
+        if system_b not in input_by_label:
+            raise ValueError(f"Unknown system label in --comparison: {system_b}")
+        values_a = input_by_label[system_a].values.get(query_a, [])
+        values_b = input_by_label[system_b].values.get(query_b, [])
+        group = query_a if query_a == query_b else f"{query_a} vs {query_b}"
+        if len(values_a) < 2 or len(values_b) < 2:
+            skipped_comparisons.append(
+                (system_a, query_a, len(values_a), system_b, query_b, len(values_b))
+            )
+            continue
+        add_candidate(
+            candidates,
+            seen,
+            group,
+            system_a,
+            query_a,
+            values_a,
+            system_b,
+            query_b,
+            values_b,
+        )
+
+    results = [
+        welch_test(group, system_a, query_a, values_a, system_b, query_b, values_b, alpha)
+        for group, system_a, query_a, values_a, system_b, query_b, values_b in candidates
+    ]
+    return results, skipped_measurements, skipped_comparisons
+
+
+def parse_explicit_comparison(value):
+    if "=" not in value:
+        raise ValueError("--comparison must use LABEL@QUERY=LABEL@QUERY format.")
+    left, right = value.split("=", 1)
+    return parse_endpoint(left), parse_endpoint(right)
+
+
+def parse_endpoint(value):
+    if "@" not in value:
+        raise ValueError("Comparison endpoints must use LABEL@QUERY format.")
+    label, query_id = value.rsplit("@", 1)
+    label = label.strip()
+    query_id = query_id.strip()
+    if not label or not query_id:
+        raise ValueError("Comparison labels and query IDs cannot be empty.")
+    return label, query_id
+
+
+def add_candidate(candidates, seen, group, system_a, query_a, values_a, system_b, query_b, values_b):
+    endpoint_a = (system_a, query_a)
+    endpoint_b = (system_b, query_b)
+    key = tuple(sorted((endpoint_a, endpoint_b)))
+    if key in seen:
+        return
+    seen.add(key)
+    candidates.append((group, system_a, query_a, values_a, system_b, query_b, values_b))
+
+
+def summarize(values):
+    return SampleSummary(
+        len(values),
+        statistics.mean(values),
+        statistics.stdev(values),
+    )
+
+
+def welch_test(group, system_a, query_a, values_a, system_b, query_b, values_b, alpha):
+    sample_a = summarize(values_a)
+    sample_b = summarize(values_b)
+    difference = sample_a.mean - sample_b.mean
+    variance_term_a = sample_a.standard_deviation ** 2 / sample_a.count
+    variance_term_b = sample_b.standard_deviation ** 2 / sample_b.count
+    standard_error_squared = variance_term_a + variance_term_b
+
+    if standard_error_squared == 0:
+        t_statistic = 0.0 if difference == 0 else math.copysign(math.inf, difference)
+        degrees_of_freedom = math.inf
+        p_value = 1.0 if difference == 0 else 0.0
+        margin = 0.0
+    else:
+        standard_error = math.sqrt(standard_error_squared)
+        denominator = (
+            variance_term_a ** 2 / (sample_a.count - 1)
+            + variance_term_b ** 2 / (sample_b.count - 1)
+        )
+        degrees_of_freedom = standard_error_squared ** 2 / denominator
+        t_statistic = difference / standard_error
+        p_value = student_t_two_sided_p(t_statistic, degrees_of_freedom)
+        critical_value = student_t_critical_value(alpha, degrees_of_freedom)
+        margin = critical_value * standard_error
+
+    return WelchResult(
+        group,
+        system_a,
+        query_a,
+        system_b,
+        query_b,
+        sample_a,
+        sample_b,
+        difference,
+        difference - margin,
+        difference + margin,
+        t_statistic,
+        degrees_of_freedom,
+        p_value,
+    )
+
+
+def student_t_two_sided_p(t_statistic, degrees_of_freedom):
+    if math.isinf(t_statistic):
+        return 0.0
+    if math.isinf(degrees_of_freedom):
+        normal = statistics.NormalDist()
+        return 2 * (1 - normal.cdf(abs(t_statistic)))
+    x = degrees_of_freedom / (degrees_of_freedom + t_statistic ** 2)
+    return regularized_incomplete_beta(x, degrees_of_freedom / 2, 0.5)
+
+
+def student_t_critical_value(alpha, degrees_of_freedom):
+    if math.isinf(degrees_of_freedom):
+        return statistics.NormalDist().inv_cdf(1 - alpha / 2)
+    lower = 0.0
+    upper = 1.0
+    while student_t_two_sided_p(upper, degrees_of_freedom) > alpha:
+        upper *= 2
+    for _ in range(100):
+        midpoint = (lower + upper) / 2
+        if student_t_two_sided_p(midpoint, degrees_of_freedom) > alpha:
+            lower = midpoint
+        else:
+            upper = midpoint
+    return (lower + upper) / 2
+
+
+def regularized_incomplete_beta(x, a, b):
+    if x <= 0:
+        return 0.0
+    if x >= 1:
+        return 1.0
+    log_beta_factor = (
+        math.lgamma(a + b)
+        - math.lgamma(a)
+        - math.lgamma(b)
+        + a * math.log(x)
+        + b * math.log1p(-x)
+    )
+    beta_factor = math.exp(log_beta_factor)
+    if x < (a + 1) / (a + b + 2):
+        return beta_factor * beta_continued_fraction(a, b, x) / a
+    return 1 - beta_factor * beta_continued_fraction(b, a, 1 - x) / b
+
+
+def beta_continued_fraction(a, b, x):
+    maximum_iterations = 200
+    epsilon = 3e-14
+    minimum = 1e-300
+    qab = a + b
+    qap = a + 1
+    qam = a - 1
+    c = 1.0
+    d = 1 - qab * x / qap
+    if abs(d) < minimum:
+        d = minimum
+    d = 1 / d
+    result = d
+
+    for iteration in range(1, maximum_iterations + 1):
+        doubled = 2 * iteration
+        numerator = iteration * (b - iteration) * x / ((qam + doubled) * (a + doubled))
+        d = 1 + numerator * d
+        if abs(d) < minimum:
+            d = minimum
+        c = 1 + numerator / c
+        if abs(c) < minimum:
+            c = minimum
+        d = 1 / d
+        result *= d * c
+
+        numerator = -(a + iteration) * (qab + iteration) * x / (
+            (a + doubled) * (qap + doubled)
+        )
+        d = 1 + numerator * d
+        if abs(d) < minimum:
+            d = minimum
+        c = 1 + numerator / c
+        if abs(c) < minimum:
+            c = minimum
+        d = 1 / d
+        delta = d * c
+        result *= delta
+        if abs(delta - 1) < epsilon:
+            return result
+    raise ArithmeticError("Incomplete beta calculation did not converge.")
+
+
+def apply_holm_adjustment(results):
+    comparison_count = len(results)
+    running_maximum = 0.0
+    for rank, result in enumerate(sorted(results, key=lambda item: item.p_value)):
+        adjusted = (comparison_count - rank) * result.p_value
+        running_maximum = max(running_maximum, adjusted)
+        result.adjusted_p_value = min(1.0, running_maximum)
+
+
+def render_report(title, inputs, results, skipped_measurements, skipped_comparisons, phase, alpha):
+    lines = [
+        f"# {title}",
+        "",
+        "> This is an exploratory analysis: each tested group contains only five measurements, "
+        "and benchmark execution order was not randomized. The results do not provide definitive "
+        "evidence of performance equivalence or difference.",
+        "",
+        "## Method",
+        "",
+        f"- Only `{phase}` rows with `success=true` are included; warm-up and failed rows are excluded.",
+        "- Tests use the raw `elapsed_ms` measurements rather than summary means.",
+        "- Every comparison is a two-sided Welch t-test, which does not assume equal variances.",
+        "- Execution order was not randomized, so caching, time trends, or other order effects may influence comparisons.",
+        "- The reported difference and unadjusted 95% confidence interval are calculated as system A minus system B.",
+        f"- Raw p-values are adjusted together across all {len(results)} comparisons using the Holm method.",
+        f"- `Significant` means Holm-adjusted `p <= {alpha:g}`.",
+        "- A non-significant result means that these measurements do not establish a difference; it does not prove equal performance.",
+        "",
+        "## Inputs",
+        "",
+        "| System | Result file |",
+        "|---|---|",
+    ]
+    for item in inputs:
+        lines.append(f"| {markdown(item.label)} | `{item.path.as_posix()}` |")
+
+    lines.extend(["", "## Results", ""])
+    if not results:
+        lines.append("No comparisons had at least two successful measurements in both groups.")
+    else:
+        grouped = OrderedDict()
+        for result in results:
+            grouped.setdefault(result.group, []).append(result)
+        for group, group_results in grouped.items():
+            lines.extend(
+                [
+                    f"### {markdown(group)}",
+                    "",
+                    "| Comparison (A vs B) | n (A/B) | A mean +/- SD (ms) | B mean +/- SD (ms) | Difference A-B (ms) | 95% CI (ms) | t (df) | Raw p | Holm p | Significant |",
+                    "|---|---:|---:|---:|---:|---:|---:|---:|---:|:---:|",
+                ]
+            )
+            for result in group_results:
+                comparison = comparison_label(result)
+                lines.append(
+                    "| "
+                    + " | ".join(
+                        (
+                            markdown(comparison),
+                            f"{result.sample_a.count}/{result.sample_b.count}",
+                            format_mean_sd(result.sample_a),
+                            format_mean_sd(result.sample_b),
+                            format_number(result.difference),
+                            f"[{format_number(result.confidence_low)}, {format_number(result.confidence_high)}]",
+                            f"{format_statistic(result.t_statistic)} ({format_statistic(result.degrees_of_freedom)})",
+                            format_p_value(result.p_value),
+                            format_p_value(result.adjusted_p_value),
+                            "Yes" if result.adjusted_p_value <= alpha else "No",
+                        )
+                    )
+                    + " |"
+                )
+            lines.append("")
+
+    if skipped_measurements or skipped_comparisons:
+        lines.extend(["## Skipped Data", ""])
+    if skipped_measurements:
+        lines.extend(
+            [
+                "The following system/query groups had fewer than two successful measured runs and were not used in same-query pairwise tests:",
+                "",
+                "| Query | System | Successful runs |",
+                "|---|---|---:|",
+            ]
+        )
+        for query_id, system, run_count in skipped_measurements:
+            lines.append(f"| {markdown(query_id)} | {markdown(system)} | {run_count} |")
+        lines.append("")
+    if skipped_comparisons:
+        lines.extend(
+            [
+                "The following explicitly requested comparisons could not be calculated:",
+                "",
+                "| Endpoint A | Runs A | Endpoint B | Runs B |",
+                "|---|---:|---|---:|",
+            ]
+        )
+        for system_a, query_a, count_a, system_b, query_b, count_b in skipped_comparisons:
+            lines.append(
+                f"| {markdown(system_a)} [{markdown(query_a)}] | {count_a} | "
+                f"{markdown(system_b)} [{markdown(query_b)}] | {count_b} |"
+            )
+        lines.append("")
+    return "\n".join(lines).rstrip() + "\n"
+
+
+def comparison_label(result):
+    if result.query_a == result.query_b:
+        return f"{result.system_a} vs {result.system_b}"
+    return (
+        f"{result.system_a} [{result.query_a}] vs "
+        f"{result.system_b} [{result.query_b}]"
+    )
+
+
+def format_mean_sd(sample):
+    return f"{format_number(sample.mean)} +/- {format_number(sample.standard_deviation)}"
+
+
+def format_number(value):
+    if math.isinf(value):
+        return "inf" if value > 0 else "-inf"
+    absolute = abs(value)
+    if absolute >= 1000:
+        return f"{value:,.1f}"
+    if absolute >= 10:
+        return f"{value:.2f}"
+    return f"{value:.3f}"
+
+
+def format_statistic(value):
+    if math.isinf(value):
+        return "inf" if value > 0 else "-inf"
+    return f"{value:.3f}"
+
+
+def format_p_value(value):
+    if value == 0:
+        return "<1e-300"
+    if value < 0.0001:
+        return f"{value:.2e}"
+    return f"{value:.4f}"
+
+
+def markdown(value):
+    return str(value).replace("|", "\\|")
+
+
+def natural_key(value):
+    parts = []
+    current = ""
+    for char in value:
+        if char.isdigit() == (current[-1:].isdigit() if current else char.isdigit()):
+            current += char
+        else:
+            parts.append(current)
+            current = char
+    if current:
+        parts.append(current)
+    return [int(part) if part.isdigit() else part for part in parts]
+
+
+if __name__ == "__main__":
+    main()
diff --git a/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_std_plot_generator.py b/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_std_plot_generator.py
new file mode 100644
index 0000000000..cb238cc65e
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/scripts/plot_generation/benchmark_result_std_plot_generator.py
@@ -0,0 +1,981 @@
+#!/usr/bin/env python3
+
+import argparse
+import csv
+import html
+import math
+import statistics
+from dataclasses import dataclass, field
+from pathlib import Path
+
+
+COLORS = (
+    "#4f9d55",
+    "#c94c4c",
+    "#7b61b9",
+    "#f0a22e",
+    "#2f6fbb",
+    "#4aa3a1",
+)
+
+SYSTEM_COLORS = (
+    ("Polypheny Relational Normalized", "#c94c4c"),
+    ("Polypheny Relational Flat", "#4f9d55"),
+    ("Polypheny Relational", "#4f9d55"),
+    ("Polypheny Document", "#7b61b9"),
+    ("DuckDB", "#f0a22e"),
+    ("Apache Spark", "#2f6fbb"),
+)
+
+STANDARD_DEVIATION_NOTE = "Bars show mean; whiskers show +/-1 sample SD."
+
+
+@dataclass
+class Series:
+    label: str
+    visual_label: str
+    color: str
+    values: dict[str, float]
+    standard_deviations: dict[str, float] = field(default_factory=dict)
+    run_counts: dict[str, int] = field(default_factory=dict)
+
+
+@dataclass
+class PlotGeometry:
+    width: int
+    height: int
+    left: int
+    right: int
+    top: int
+    bottom: int
+    plot_width: int
+    plot_height: int
+    lower: float
+    upper: float
+    ticks: list[float]
+    bar_gap: int
+    bar_width: float
+    query_centers: dict[str, float]
+
+
+def main():
+    show_standard_deviation = True
+    args = parse_args()
+    inputs = [parse_input(value) for value in args.csv]
+    query_descriptions = parse_query_descriptions(args.query_descriptions)
+    color_by_label = {}
+    fallback_color_index = 0
+    series = []
+    for label, path in inputs:
+        visual_label = normalize_visual_label(label)
+        if visual_label not in color_by_label:
+            color, fallback_color_index = color_for_label(visual_label, fallback_color_index)
+            color_by_label[visual_label] = color
+        means, standard_deviations, run_counts = read_statistics(path, args.phase)
+        series.append(
+            Series(
+                label,
+                visual_label,
+                color_by_label[visual_label],
+                means,
+                standard_deviations,
+                run_counts,
+            )
+        )
+    query_ids = ordered_query_ids(series, args.query_order)
+    if not query_ids:
+        raise ValueError("No successful benchmark rows found.")
+
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    output_name = args.name
+    if not output_name.endswith("_std"):
+        output_name += "_std"
+    for file_format in parse_formats(args.formats):
+        output = output_dir / f"{output_name}.{file_format}"
+        if file_format == "svg":
+            output.write_text(
+                render_svg(
+                    series,
+                    query_ids,
+                    args.title,
+                    query_descriptions,
+                    args.query_description_wrap_chars,
+                    args.query_description_max_lines,
+                    args.side_note,
+                    show_standard_deviation,
+                ),
+                encoding="utf-8",
+            )
+        elif file_format == "png":
+            write_png(
+                output,
+                series,
+                query_ids,
+                args.title,
+                query_descriptions,
+                args.query_description_wrap_chars,
+                args.query_description_max_lines,
+                args.side_note,
+                show_standard_deviation,
+            )
+        elif file_format == "pdf":
+            write_pdf(
+                output,
+                series,
+                query_ids,
+                args.title,
+                query_descriptions,
+                args.query_description_wrap_chars,
+                args.query_description_max_lines,
+                args.side_note,
+                show_standard_deviation,
+            )
+        else:
+            raise ValueError(f"Unsupported plot format: {file_format}")
+        print(f"Wrote {output}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Generate runtime plots from benchmark result CSV files."
+    )
+    parser.add_argument(
+        "csv",
+        nargs="+",
+        help="CSV input. Use LABEL=path.csv to control the legend label.",
+    )
+    parser.add_argument("--title", required=True, help="Plot title.")
+    parser.add_argument("--name", required=True, help="Output file name without extension.")
+    parser.add_argument(
+        "--output-dir",
+        default="plugins/parquet-adapter/benchmarks/results/plots",
+        help="Directory where plot files are written.",
+    )
+    parser.add_argument(
+        "--formats",
+        default="svg,pdf,png",
+        help="Comma-separated output formats to write. Default: svg,pdf,png.",
+    )
+    parser.add_argument(
+        "--phase",
+        default="measured",
+        help="Benchmark phase to plot. Default: measured.",
+    )
+    parser.add_argument(
+        "--query-order",
+        default="",
+        help="Comma-separated query IDs that define the x-axis order. Missing IDs are appended using natural order.",
+    )
+    parser.add_argument(
+        "--query-descriptions",
+        default="",
+        help="Semicolon-separated query descriptions, for example Q1=Full count;Q2=Year filter.",
+    )
+    parser.add_argument(
+        "--query-description-wrap-chars",
+        type=int,
+        default=18,
+        help="Approximate line length for query descriptions. Default: 18.",
+    )
+    parser.add_argument(
+        "--query-description-max-lines",
+        type=int,
+        default=2,
+        help="Maximum number of lines per query description. Default: 2.",
+    )
+    parser.add_argument(
+        "--side-note",
+        default="",
+        help="Semicolon-separated note lines to render below the legend.",
+    )
+    return parser.parse_args()
+
+
+def parse_formats(value):
+    valid = {"svg", "pdf", "png"}
+    formats = []
+    for part in value.split(","):
+        file_format = part.strip().lower()
+        if not file_format:
+            continue
+        if file_format not in valid:
+            raise ValueError(f"Unsupported plot format: {file_format}")
+        formats.append(file_format)
+    if not formats:
+        raise ValueError("At least one plot format must be selected.")
+    return formats
+
+
+def parse_input(value):
+    if "=" in value:
+        label, path = value.split("=", 1)
+        return label.strip(), Path(path.strip())
+    path = Path(value)
+    return path.stem, path
+
+
+def parse_query_descriptions(value):
+    descriptions = {}
+    for part in value.split(";"):
+        if "=" not in part:
+            continue
+        query_id, description = part.split("=", 1)
+        query_id = query_id.strip()
+        description = description.strip()
+        if query_id and description:
+            descriptions[query_id] = description
+    return descriptions
+
+
+def parse_side_note(value):
+    return [part.strip() for part in value.split(";") if part.strip()]
+
+
+def side_note_parts(note):
+    if " - " not in note:
+        return note, ""
+    prefix, rest = note.split(" - ", 1)
+    return prefix, " - " + rest
+
+
+def read_means(path, phase):
+    means, _standard_deviations, _run_counts = read_statistics(path, phase)
+    return means
+
+
+def read_statistics(path, phase):
+    elapsed_by_query = {}
+    with path.open(newline="", encoding="utf-8-sig") as handle:
+        for row in csv.DictReader(handle):
+            if row.get("phase") != phase:
+                continue
+            if row.get("success", "").strip().lower() != "true":
+                continue
+            query_id = row.get("query_id", "").strip()
+            elapsed_ms = row.get("elapsed_ms", "").strip()
+            if not query_id or not elapsed_ms:
+                continue
+            elapsed_by_query.setdefault(query_id, []).append(float(elapsed_ms) / 1000.0)
+    means = {}
+    standard_deviations = {}
+    run_counts = {}
+    for query_id, values in elapsed_by_query.items():
+        if not values:
+            continue
+        mean, sample_standard_deviation, run_count = sample_statistics(values)
+        means[query_id] = mean
+        run_counts[query_id] = run_count
+        if sample_standard_deviation is not None:
+            standard_deviations[query_id] = sample_standard_deviation
+    return means, standard_deviations, run_counts
+
+
+def sample_statistics(values):
+    if not values:
+        raise ValueError("At least one elapsed value is required.")
+    return (
+        statistics.mean(values),
+        statistics.stdev(values) if len(values) >= 2 else None,
+        len(values),
+    )
+
+
+def ordered_query_ids(series, query_order):
+    available = {query_id for item in series for query_id in item.values}
+    if not query_order:
+        return sorted(available, key=natural_key)
+
+    requested = [part.strip() for part in query_order.split(",") if part.strip()]
+    ordered = []
+    seen = set()
+    for query_id in requested:
+        if query_id in available and query_id not in seen:
+            ordered.append(query_id)
+            seen.add(query_id)
+
+    ordered.extend(sorted(available - seen, key=natural_key))
+    return ordered
+
+
+def render_svg(series, query_ids, title, query_descriptions, description_wrap_chars, description_max_lines, side_note="", show_standard_deviation=False):
+    geometry = make_geometry(
+        series,
+        query_ids,
+        query_descriptions,
+        description_wrap_chars,
+        description_max_lines,
+        show_standard_deviation,
+    )
+
+    lines = [
+        '<?xml version="1.0" encoding="UTF-8"?>',
+        f'<svg xmlns="http://www.w3.org/2000/svg" width="{geometry.width}" height="{geometry.height}" viewBox="0 0 {geometry.width} {geometry.height}">',
+        '<rect width="100%" height="100%" fill="white"/>',
+        f'<text x="{geometry.width / 2}" y="34" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="24" font-weight="700">{escape(title)}</text>',
+        f'<text x="{geometry.left}" y="62" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">Mean execution time (s, log scale)</text>',
+    ]
+
+    for tick in geometry.ticks:
+        y = y_for(tick, geometry)
+        lines.append(f'<line x1="{geometry.left}" y1="{y:.2f}" x2="{geometry.left + geometry.plot_width}" y2="{y:.2f}" stroke="#e2e2e2" stroke-width="1"/>')
+        lines.append(f'<text x="{geometry.left - 12}" y="{y + 4:.2f}" text-anchor="end" font-family="Helvetica, Arial, sans-serif" font-size="11" fill="#333">{format_tick(tick)}</text>')
+
+    lines.append(f'<line x1="{geometry.left}" y1="{geometry.top}" x2="{geometry.left}" y2="{geometry.top + geometry.plot_height}" stroke="#333" stroke-width="1.2"/>')
+    lines.append(f'<line x1="{geometry.left}" y1="{geometry.top + geometry.plot_height}" x2="{geometry.left + geometry.plot_width}" y2="{geometry.top + geometry.plot_height}" stroke="#333" stroke-width="1.2"/>')
+
+    for bar in bars(series, query_ids, geometry):
+        x, y, width, height, item, query_id, value = bar
+        tooltip = bar_tooltip(item, query_id, value, show_standard_deviation)
+        lines.append(
+            f'<rect x="{x:.2f}" y="{y:.2f}" width="{width:.2f}" height="{height:.2f}" fill="{item.color}">'
+            f'<title>{escape(tooltip)}</title></rect>'
+        )
+
+    if show_standard_deviation:
+        for center, lower_y, upper_y, cap_width in standard_deviation_whiskers(series, query_ids, geometry):
+            lines.append(f'<line x1="{center:.2f}" y1="{upper_y:.2f}" x2="{center:.2f}" y2="{lower_y:.2f}" stroke="#222" stroke-width="1.5"/>')
+            lines.append(f'<line x1="{center - cap_width / 2:.2f}" y1="{upper_y:.2f}" x2="{center + cap_width / 2:.2f}" y2="{upper_y:.2f}" stroke="#222" stroke-width="1.5"/>')
+            lines.append(f'<line x1="{center - cap_width / 2:.2f}" y1="{lower_y:.2f}" x2="{center + cap_width / 2:.2f}" y2="{lower_y:.2f}" stroke="#222" stroke-width="1.5"/>')
+
+    for query_index, query_id in enumerate(query_ids):
+        center = geometry.query_centers[query_id]
+        lines.append(f'<text x="{center:.2f}" y="{geometry.top + geometry.plot_height + 30}" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700">{escape(query_id)}</text>')
+
+    for center, description_lines in query_description_items(query_ids, geometry, query_descriptions, description_wrap_chars, description_max_lines):
+        for line_index, description in enumerate(description_lines):
+            y = geometry.top + geometry.plot_height + 55 + line_index * 17
+            lines.append(f'<text x="{center:.2f}" y="{y}" text-anchor="middle" font-family="Helvetica, Arial, sans-serif" font-size="13" fill="#333">{escape(description)}</text>')
+
+    legend_x = geometry.left + geometry.plot_width + 35
+    legend_y = geometry.top + 32
+    lines.append(f'<text x="{legend_x}" y="{legend_y - 32}" font-family="Helvetica, Arial, sans-serif" font-size="13" font-weight="700">System</text>')
+    for index, item in enumerate(legend_items(series)):
+        y = legend_y + index * 28
+        label, color = item
+        lines.append(f'<rect x="{legend_x}" y="{y - 12}" width="16" height="16" fill="{color}"/>')
+        lines.append(f'<text x="{legend_x + 24}" y="{y + 1}" font-family="Helvetica, Arial, sans-serif" font-size="12">{escape(label)}</text>')
+
+    for index, note in enumerate(plot_notes(side_note, show_standard_deviation)):
+        y = legend_y + len(legend_items(series)) * 28 + 18 + index * 18
+        prefix, rest = side_note_parts(note)
+        lines.append(f'<text x="{legend_x}" y="{y}" font-family="Helvetica, Arial, sans-serif" font-size="12" font-weight="700" fill="#333">{escape(prefix)}</text>')
+        lines.append(f'<text x="{legend_x + approximate_text_width(prefix, 12, True)}" y="{y}" font-family="Helvetica, Arial, sans-serif" font-size="12" fill="#333">{escape(rest)}</text>')
+
+    lines.append("</svg>")
+    return "\n".join(lines)
+
+
+def write_png(output, series, query_ids, title, query_descriptions, description_wrap_chars, description_max_lines, side_note="", show_standard_deviation=False):
+    try:
+        from PIL import Image, ImageDraw, ImageFont
+    except ImportError as error:
+        raise RuntimeError("PNG output requires Pillow.") from error
+
+    geometry = make_geometry(
+        series,
+        query_ids,
+        query_descriptions,
+        description_wrap_chars,
+        description_max_lines,
+        show_standard_deviation,
+    )
+    image = Image.new("RGB", (geometry.width, geometry.height), "white")
+    draw = ImageDraw.Draw(image)
+    fonts = {
+        "title": load_font(ImageFont, 24, bold=True),
+        "subtitle": load_font(ImageFont, 13),
+        "tick": load_font(ImageFont, 11),
+        "axis": load_font(ImageFont, 12, bold=True),
+        "description": load_font(ImageFont, 13),
+        "legend": load_font(ImageFont, 12),
+        "legend_title": load_font(ImageFont, 13, bold=True),
+        "note_bold": load_font(ImageFont, 12, bold=True),
+    }
+
+    draw.text((geometry.width / 2, 34), title, anchor="mm", font=fonts["title"], fill="#000000")
+    draw.text((geometry.left, 62), "Mean execution time (s, log scale)", anchor="la", font=fonts["subtitle"], fill="#333333")
+
+    for tick in geometry.ticks:
+        y = y_for(tick, geometry)
+        draw.line((geometry.left, y, geometry.left + geometry.plot_width, y), fill="#e2e2e2", width=1)
+        draw.text((geometry.left - 12, y), format_tick(tick), anchor="rm", font=fonts["tick"], fill="#333333")
+
+    draw.line((geometry.left, geometry.top, geometry.left, geometry.top + geometry.plot_height), fill="#333333", width=1)
+    draw.line((geometry.left, geometry.top + geometry.plot_height, geometry.left + geometry.plot_width, geometry.top + geometry.plot_height), fill="#333333", width=1)
+
+    for x, y, width, height, item, _query_id, _value in bars(series, query_ids, geometry):
+        draw.rectangle((x, y, x + width, y + height), fill=item.color)
+
+    if show_standard_deviation:
+        for center, lower_y, upper_y, cap_width in standard_deviation_whiskers(series, query_ids, geometry):
+            draw.line((center, upper_y, center, lower_y), fill="#222222", width=2)
+            draw.line((center - cap_width / 2, upper_y, center + cap_width / 2, upper_y), fill="#222222", width=2)
+            draw.line((center - cap_width / 2, lower_y, center + cap_width / 2, lower_y), fill="#222222", width=2)
+
+    for query_index, query_id in enumerate(query_ids):
+        center = geometry.query_centers[query_id]
+        draw.text((center, geometry.top + geometry.plot_height + 30), query_id, anchor="mm", font=fonts["axis"], fill="#000000")
+
+    for center, description_lines in query_description_items(query_ids, geometry, query_descriptions, description_wrap_chars, description_max_lines):
+        for line_index, description in enumerate(description_lines):
+            y = geometry.top + geometry.plot_height + 55 + line_index * 17
+            draw.text((center, y), description, anchor="mm", font=fonts["description"], fill="#333333")
+
+    legend_x = geometry.left + geometry.plot_width + 35
+    legend_y = geometry.top + 32
+    draw.text((legend_x, legend_y - 32), "System", anchor="la", font=fonts["legend_title"], fill="#000000")
+    for index, item in enumerate(legend_items(series)):
+        y = legend_y + index * 28
+        label, color = item
+        draw.rectangle((legend_x, y - 12, legend_x + 16, y + 4), fill=color)
+        draw.text((legend_x + 24, y), label, anchor="lm", font=fonts["legend"], fill="#000000")
+
+    for index, note in enumerate(plot_notes(side_note, show_standard_deviation)):
+        y = legend_y + len(legend_items(series)) * 28 + 18 + index * 18
+        prefix, rest = side_note_parts(note)
+        draw.text((legend_x, y), prefix, anchor="la", font=fonts["note_bold"], fill="#333333")
+        draw.text((legend_x + draw.textlength(prefix, font=fonts["note_bold"]), y), rest, anchor="la", font=fonts["legend"], fill="#333333")
+
+    image.save(output)
+
+
+def write_pdf(output, series, query_ids, title, query_descriptions, description_wrap_chars, description_max_lines, side_note="", show_standard_deviation=False):
+    geometry = make_geometry(
+        series,
+        query_ids,
+        query_descriptions,
+        description_wrap_chars,
+        description_max_lines,
+        show_standard_deviation,
+    )
+    commands = []
+
+    def py(y):
+        return geometry.height - y
+
+    def set_fill(color):
+        r, g, b = color_parts(color)
+        commands.append(f"{r:.3f} {g:.3f} {b:.3f} rg")
+
+    def set_stroke(color):
+        r, g, b = color_parts(color)
+        commands.append(f"{r:.3f} {g:.3f} {b:.3f} RG")
+
+    def rect(x, y, width, height, color):
+        set_fill(color)
+        commands.append(f"{x:.2f} {py(y + height):.2f} {width:.2f} {height:.2f} re f")
+
+    def line(x1, y1, x2, y2, color, width=1):
+        set_stroke(color)
+        commands.append(f"{width:.2f} w")
+        commands.append(f"{x1:.2f} {py(y1):.2f} m {x2:.2f} {py(y2):.2f} l S")
+
+    def text(x, y, value, size, color="#000000", bold=False, anchor="left"):
+        set_fill(color)
+        font = "/F2" if bold else "/F1"
+        adjusted_x = aligned_text_x(x, value, size, bold, anchor)
+        commands.append(
+            f"BT {font} {size} Tf 1 0 0 1 {adjusted_x:.2f} {py(y):.2f} Tm ({pdf_escape(value)}) Tj ET"
+        )
+
+    rect(0, 0, geometry.width, geometry.height, "#ffffff")
+    text(geometry.width / 2, 34, title, 24, bold=True, anchor="center")
+    text(geometry.left, 62, "Mean execution time (s, log scale)", 13, color="#333333")
+
+    for tick in geometry.ticks:
+        y = y_for(tick, geometry)
+        line(geometry.left, y, geometry.left + geometry.plot_width, y, "#e2e2e2")
+        text(geometry.left - 12, y + 4, format_tick(tick), 11, color="#333333", anchor="right")
+
+    line(geometry.left, geometry.top, geometry.left, geometry.top + geometry.plot_height, "#333333", 1.2)
+    line(
+        geometry.left,
+        geometry.top + geometry.plot_height,
+        geometry.left + geometry.plot_width,
+        geometry.top + geometry.plot_height,
+        "#333333",
+        1.2,
+    )
+
+    for x, y, width, height, item, _query_id, _value in bars(series, query_ids, geometry):
+        rect(x, y, width, height, item.color)
+
+    if show_standard_deviation:
+        for center, lower_y, upper_y, cap_width in standard_deviation_whiskers(series, query_ids, geometry):
+            line(center, upper_y, center, lower_y, "#222222", 1.5)
+            line(center - cap_width / 2, upper_y, center + cap_width / 2, upper_y, "#222222", 1.5)
+            line(center - cap_width / 2, lower_y, center + cap_width / 2, lower_y, "#222222", 1.5)
+
+    for query_index, query_id in enumerate(query_ids):
+        center = geometry.query_centers[query_id]
+        text(center, geometry.top + geometry.plot_height + 30, query_id, 12, bold=True, anchor="center")
+
+    for center, description_lines in query_description_items(query_ids, geometry, query_descriptions, description_wrap_chars, description_max_lines):
+        for line_index, description in enumerate(description_lines):
+            y = geometry.top + geometry.plot_height + 55 + line_index * 17
+            text(center, y, description, 13, color="#333333", anchor="center")
+
+    legend_x = geometry.left + geometry.plot_width + 35
+    legend_y = geometry.top + 32
+    text(legend_x, legend_y - 32, "System", 13, bold=True)
+    for index, item in enumerate(legend_items(series)):
+        y = legend_y + index * 28
+        label, color = item
+        rect(legend_x, y - 12, 16, 16, color)
+        text(legend_x + 24, y + 1, label, 12)
+
+    for index, note in enumerate(plot_notes(side_note, show_standard_deviation)):
+        y = legend_y + len(legend_items(series)) * 28 + 18 + index * 18
+        prefix, rest = side_note_parts(note)
+        text(legend_x, y, prefix, 12, color="#333333", bold=True)
+        text(legend_x + approximate_text_width(prefix, 12, True), y, rest, 12, color="#333333")
+
+    write_simple_pdf(output, geometry.width, geometry.height, "\n".join(commands))
+
+
+def color_parts(color):
+    value = color.lstrip("#")
+    return tuple(int(value[index:index + 2], 16) / 255.0 for index in (0, 2, 4))
+
+
+def aligned_text_x(x, value, size, bold, anchor):
+    if anchor == "left":
+        return x
+    width = approximate_text_width(value, size, bold)
+    if anchor == "center":
+        return x - width / 2
+    if anchor == "right":
+        return x - width
+    raise ValueError(f"Unsupported text anchor: {anchor}")
+
+
+def font_widths(*groups):
+    return {
+        character: width
+        for width, characters in groups
+        for character in characters
+    }
+
+
+# Adobe Font Metrics widths for the built-in PDF Helvetica fonts, expressed in
+# thousandths of an em. Using the real glyph widths keeps independently drawn
+# lines centered on the same x coordinate.
+HELVETICA_WIDTHS = font_widths(
+    (191, "'"),
+    (222, "ijl"),
+    (260, "|"),
+    (278, " !,./:;I[\\]ft"),
+    (333, "()-`r"),
+    (334, "{}"),
+    (355, '"'),
+    (389, "*"),
+    (469, "^"),
+    (500, "Jcksvxyz"),
+    (556, "#$0123456789?_Labdeghnopqu"),
+    (584, "+<=>~"),
+    (611, "FTZ"),
+    (667, "&ABEKPSXY"),
+    (722, "CDHNRUVw"),
+    (778, "GOQ"),
+    (833, "Mm"),
+    (889, "%"),
+    (944, "W"),
+    (1015, "@"),
+)
+
+HELVETICA_BOLD_WIDTHS = font_widths(
+    (238, "'"),
+    (278, " ,./I\\ijl"),
+    (280, "|"),
+    (333, "!()-:;[]`ft"),
+    (389, "*r{}"),
+    (474, '"'),
+    (500, "z"),
+    (556, "#$0123456789J_aceksvxy"),
+    (584, "+<=>^~"),
+    (611, "?FLTZbdghnopqu"),
+    (667, "EPSVXY"),
+    (722, "&ABCDHKNRU"),
+    (778, "GOQw"),
+    (833, "M"),
+    (889, "%m"),
+    (944, "W"),
+    (975, "@"),
+)
+
+
+def approximate_text_width(value, size, bold):
+    widths = HELVETICA_BOLD_WIDTHS if bold else HELVETICA_WIDTHS
+    width_units = sum(widths.get(character, 556) for character in str(value))
+    return width_units * size / 1000
+
+
+def pdf_escape(value):
+    return str(value).replace("\\", "\\\\").replace("(", "\\(").replace(")", "\\)")
+
+
+def write_simple_pdf(output, width, height, content):
+    content_bytes = content.encode("latin-1", errors="replace")
+    objects = [
+        b"<< /Type /Catalog /Pages 2 0 R >>",
+        b"<< /Type /Pages /Kids [3 0 R] /Count 1 >>",
+        (
+            f"<< /Type /Page /Parent 2 0 R /MediaBox [0 0 {width} {height}] "
+            f"/Resources << /Font << /F1 4 0 R /F2 5 0 R >> >> /Contents 6 0 R >>"
+        ).encode("ascii"),
+        b"<< /Type /Font /Subtype /Type1 /BaseFont /Helvetica >>",
+        b"<< /Type /Font /Subtype /Type1 /BaseFont /Helvetica-Bold >>",
+        b"<< /Length " + str(len(content_bytes)).encode("ascii") + b" >>\nstream\n" + content_bytes + b"\nendstream",
+    ]
+
+    pdf = b"%PDF-1.4\n"
+    offsets = [0]
+    for index, obj in enumerate(objects, start=1):
+        offsets.append(len(pdf))
+        pdf += f"{index} 0 obj\n".encode("ascii") + obj + b"\nendobj\n"
+
+    xref_start = len(pdf)
+    pdf += f"xref\n0 {len(objects) + 1}\n".encode("ascii")
+    pdf += b"0000000000 65535 f \n"
+    for offset in offsets[1:]:
+        pdf += f"{offset:010d} 00000 n \n".encode("ascii")
+    pdf += (
+        f"trailer\n<< /Size {len(objects) + 1} /Root 1 0 R >>\n"
+        f"startxref\n{xref_start}\n%%EOF\n"
+    ).encode("ascii")
+
+    output.write_bytes(pdf)
+
+
+def load_font(image_font, size, bold=False):
+    candidates = (
+        ("arialbd.ttf", "DejaVuSans-Bold.ttf") if bold else ("arial.ttf", "DejaVuSans.ttf")
+    )
+    for candidate in candidates:
+        try:
+            return image_font.truetype(candidate, size)
+        except OSError:
+            continue
+    return image_font.load_default()
+
+
+def make_geometry(series, query_ids, query_descriptions=None, description_wrap_chars=18, description_max_lines=2, show_standard_deviation=False):
+    width = plot_width_for_descriptions(
+        query_ids,
+        query_descriptions,
+        description_wrap_chars,
+        description_max_lines,
+    )
+    height = 720 if query_descriptions else 620
+    left = 90
+    right = 260
+    top = 80
+    bottom = 130 if query_descriptions else 80
+    plot_width = width - left - right
+    plot_height = height - top - bottom
+    lower, upper = plot_bounds(series, show_standard_deviation)
+    ticks = nice_log_ticks(lower, upper)
+    bar_gap = 3
+    slots = series_slots(series)
+    bar_width, query_centers = query_layout(query_ids, slots, plot_width, left, bar_gap)
+    return PlotGeometry(
+        width,
+        height,
+        left,
+        right,
+        top,
+        bottom,
+        plot_width,
+        plot_height,
+        lower,
+        upper,
+        ticks,
+        bar_gap,
+        max(1.0, bar_width),
+        query_centers,
+    )
+
+
+def plot_width_for_descriptions(query_ids, query_descriptions, description_wrap_chars, description_max_lines):
+    base_width = 1180
+    left = 90
+    right = 260
+    groups = description_groups(query_ids, query_descriptions)
+    if not groups:
+        return base_width
+
+    longest_line = max(
+        len(line)
+        for _prefix, description in groups
+        for line in wrap_text(description, description_wrap_chars, description_max_lines)
+    )
+    group_width = min(145, max(105, longest_line * 7.4 + 26))
+    return max(base_width, int(left + right + len(groups) * group_width))
+
+
+def bars(series, query_ids, geometry):
+    slots = series_slots(series)
+    slot_indexes = {label: index for index, label in enumerate(slots)}
+    for query_id in query_ids:
+        total_bar_width = len(slots) * geometry.bar_width + (len(slots) - 1) * geometry.bar_gap
+        start_x = geometry.query_centers[query_id] - total_bar_width / 2
+        for item in series:
+            value = item.values.get(query_id)
+            if value is None:
+                continue
+            x = start_x + slot_indexes[item.visual_label] * (geometry.bar_width + geometry.bar_gap)
+            y = y_for(value, geometry)
+            height = geometry.top + geometry.plot_height - y
+            yield x, y, geometry.bar_width, height, item, query_id, value
+
+
+def standard_deviation_whiskers(series, query_ids, geometry):
+    for x, _y, width, _height, item, query_id, value in bars(series, query_ids, geometry):
+        standard_deviation = item.standard_deviations.get(query_id)
+        if standard_deviation is None:
+            continue
+        lower_value = max(geometry.lower, value - standard_deviation)
+        upper_value = value + standard_deviation
+        yield (
+            x + width / 2,
+            y_for(lower_value, geometry),
+            y_for(upper_value, geometry),
+            min(12, width * 0.7),
+        )
+
+
+def bar_tooltip(item, query_id, value, show_standard_deviation):
+    if not show_standard_deviation:
+        return f"{item.label} {query_id}: {value:.3f} s"
+    standard_deviation = item.standard_deviations.get(query_id)
+    standard_deviation_text = (
+        f"{standard_deviation:.3f} s"
+        if standard_deviation is not None
+        else "n/a"
+    )
+    return (
+        f"{item.label} {query_id}: mean {value:.3f} s; "
+        f"sample SD {standard_deviation_text}; n={item.run_counts.get(query_id, 0)}"
+    )
+
+
+def plot_notes(side_note, show_standard_deviation):
+    notes = parse_side_note(side_note)
+    if show_standard_deviation:
+        first_line, second_line = STANDARD_DEVIATION_NOTE.split("; ", 1)
+        notes.extend((first_line + ";", second_line))
+    return notes
+
+
+def query_layout(query_ids, slots, plot_width, left, bar_gap):
+    pair_gap_units = layout_pair_gap_units(query_ids)
+    use_pair_spacing = any(unit == 1 for unit in pair_gap_units)
+    if not use_pair_spacing:
+        group_width = plot_width / len(query_ids)
+        bar_width = max(1.0, min(24, (group_width - 20) / len(slots) - bar_gap))
+        centers = {
+            query_id: left + index * group_width + group_width / 2
+            for index, query_id in enumerate(query_ids)
+        }
+        return bar_width, centers
+
+    fixed_gap_width = len(query_ids) * (len(slots) - 1) * bar_gap
+    bar_units = len(query_ids) * len(slots) + sum(pair_gap_units)
+    bar_width = max(1.0, min(24, (plot_width - fixed_gap_width) / bar_units))
+    group_bar_width = len(slots) * bar_width + (len(slots) - 1) * bar_gap
+    required_width = len(query_ids) * group_bar_width + sum(unit * bar_width for unit in pair_gap_units)
+    cursor = left + max(0, (plot_width - required_width) / 2)
+
+    centers = {}
+    for index, query_id in enumerate(query_ids):
+        centers[query_id] = cursor + group_bar_width / 2
+        cursor += group_bar_width
+        if index < len(pair_gap_units):
+            cursor += pair_gap_units[index] * bar_width
+    return bar_width, centers
+
+
+def layout_pair_gap_units(query_ids):
+    return [
+        1 if paired_layout_prefix(left) is not None and paired_layout_prefix(left) == paired_layout_prefix(right) else 3
+        for left, right in zip(query_ids, query_ids[1:])
+    ]
+
+
+def paired_layout_prefix(query_id):
+    if "_" not in query_id:
+        return None
+    prefix, suffix = query_id.rsplit("_", 1)
+    if suffix in {"P", "NP", "RP", "UP"}:
+        return prefix
+    return None
+
+
+def description_groups(query_ids, query_descriptions):
+    if not query_descriptions:
+        return []
+    groups = []
+    seen = set()
+    for query_id in query_ids:
+        prefix = paired_layout_prefix(query_id) or query_id
+        if prefix in seen:
+            continue
+        seen.add(prefix)
+        description = query_descriptions.get(prefix) or query_descriptions.get(query_id)
+        if description:
+            groups.append((prefix, description))
+    return groups
+
+
+def query_description_items(query_ids, geometry, query_descriptions, description_wrap_chars, description_max_lines):
+    if not query_descriptions:
+        return []
+    items = []
+    seen = set()
+    for query_id in query_ids:
+        prefix = paired_layout_prefix(query_id) or query_id
+        if prefix in seen:
+            continue
+        seen.add(prefix)
+        related = [
+            item
+            for item in query_ids
+            if (paired_layout_prefix(item) or item) == prefix
+        ]
+        description = query_descriptions.get(prefix) or query_descriptions.get(query_id)
+        if not description:
+            continue
+        center = sum(geometry.query_centers[item] for item in related) / len(related)
+        items.append((center, wrap_text(description, description_wrap_chars, description_max_lines)))
+    return items
+
+
+def wrap_text(text, max_chars, max_lines):
+    lines = []
+    current = ""
+    for word in text.split():
+        candidate = word if not current else current + " " + word
+        if len(candidate) <= max_chars:
+            current = candidate
+            continue
+        if current:
+            lines.append(current)
+        current = word
+    if current:
+        lines.append(current)
+    return lines[:max_lines]
+
+
+def normalize_visual_label(label):
+    for suffix in (" Repartitioned", " Unpartitioned"):
+        if label.endswith(suffix):
+            return label[: -len(suffix)]
+    return label
+
+
+def color_for_label(label, fallback_color_index):
+    for prefix, color in SYSTEM_COLORS:
+        if label.startswith(prefix):
+            return color, fallback_color_index
+    return COLORS[fallback_color_index % len(COLORS)], fallback_color_index + 1
+
+
+def series_slots(series):
+    slots = []
+    seen = set()
+    for item in series:
+        if item.visual_label in seen:
+            continue
+        seen.add(item.visual_label)
+        slots.append(item.visual_label)
+    return slots
+
+
+def legend_items(series):
+    items = []
+    seen = set()
+    for item in series:
+        if item.visual_label in seen:
+            continue
+        seen.add(item.visual_label)
+        items.append((item.visual_label, item.color))
+    return items
+
+
+def plot_bounds(series, show_standard_deviation=False):
+    values = []
+    for item in series:
+        for query_id, value in item.values.items():
+            if value > 0:
+                values.append(value)
+            if not show_standard_deviation:
+                continue
+            standard_deviation = item.standard_deviations.get(query_id)
+            if standard_deviation is None:
+                continue
+            lower_value = value - standard_deviation
+            if lower_value > 0:
+                values.append(lower_value)
+            values.append(value + standard_deviation)
+    if not values:
+        raise ValueError("Log-scale plot requires positive elapsed values.")
+    lower = 10 ** math.floor(math.log10(min(values)))
+    upper = 10 ** math.ceil(math.log10(max(values)))
+    if lower == upper:
+        upper = lower * 10
+    return lower, upper
+
+
+def nice_log_ticks(lower, upper):
+    ticks = []
+    lower_exp = math.floor(math.log10(lower))
+    upper_exp = math.ceil(math.log10(upper))
+    for exp in range(lower_exp, upper_exp + 1):
+        for multiplier in (1, 2, 5):
+            value = multiplier * (10 ** exp)
+            if lower <= value <= upper:
+                ticks.append(value)
+    return ticks
+
+
+def y_for(value, geometry):
+    lower_log = math.log10(geometry.lower)
+    upper_log = math.log10(geometry.upper)
+    value_log = math.log10(value)
+    ratio = (value_log - lower_log) / (upper_log - lower_log)
+    return geometry.top + geometry.plot_height - ratio * geometry.plot_height
+
+
+def format_tick(value):
+    if value >= 1:
+        return f"{value:g}"
+    return f"{value:.3f}".rstrip("0").rstrip(".")
+
+
+def natural_key(value):
+    suffix_order = {"P": 0, "RP": 0, "NP": 1, "UP": 1}
+    if "_" in value:
+        prefix, suffix = value.rsplit("_", 1)
+        if suffix in suffix_order:
+            return natural_key(prefix) + [suffix_order[suffix]]
+
+    parts = []
+    current = ""
+    for char in value:
+        if char.isdigit() == (current[-1:].isdigit() if current else char.isdigit()):
+            current += char
+        else:
+            parts.append(current)
+            current = char
+    if current:
+        parts.append(current)
+    return [int(part) if part.isdigit() else part for part in parts]
+
+
+def escape(value):
+    return html.escape(str(value), quote=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/plugins/parquet-adapter/benchmarks/welch_test.md b/plugins/parquet-adapter/benchmarks/welch_test.md
new file mode 100644
index 0000000000..253b1871c1
--- /dev/null
+++ b/plugins/parquet-adapter/benchmarks/welch_test.md
@@ -0,0 +1,56 @@
+# Welch’s test
+
+Welch’s t-test compares the mean runtimes of two systems without assuming that their runtime variances are equal.
+
+Its null hypothesis is:
+- The two systems have the same mean runtime.
+
+Interpretation:
+- Holm-adjusted p ≤ 0.05: the measured difference is statistically significant.
+- Holm-adjusted p > 0.05: the five runs do not provide enough evidence of a difference.
+- A non-significant result does not prove that performance is equal.
+- Statistical significance does not necessarily mean the difference is practically important.
+
+Because only five measurements are available per group and execution order was not randomized, the results are explicitly described as exploratory. Caching, time trends, or other order effects may influence the comparisons.
+
+
+## benchmark_welch_analysis.py
+`plugins/parquet-adapter/benchmarks/scripts/benchmark_welch_analysis.py`
+
+- Reads the raw benchmark CSV files.
+
+- Selects only successful measured runs, excluding warm-ups and failures.
+
+- Compares systems pairwise for each query using their raw elapsed_ms values.
+
+- Calculates:
+  - number of runs;
+  - mean and sample standard deviation;
+  - mean difference, calculated as system A minus system B;
+  - unadjusted 95% confidence interval;
+  - Welch t-statistic and degrees of freedom;
+  - raw p-value;
+  - Holm-adjusted p-value;
+  - statistical-significance status.
+
+- Applies Holm correction across all comparisons in a benchmark, reducing the risk of false significant results caused by testing many pairs.
+
+- Generates a Markdown report for each benchmark
+  - benchmarks/results/access_model_comparison/access_model_comparison_welch_analysis.md
+  - benchmarks/results/aggregation/aggregation_welch_analysis.md
+  - benchmarks/results/nested_data/nested_data_welch_analysis.md
+  - benchmarks/results/partitioning/partitioning_welch_analysis.md
+
+- For partitioning, additional explicit comparisons test partitioned versus unpartitioned measurements within each system.
+
+The standard-deviation plots and Welch reports answer different questions:
+- standard-deviation whiskers show runtime variability.
+- Welch tests evaluate whether an observed difference between two mean runtimes is statistically supported.
+
+
+Holm correction is used to reduce the risk of false-positive results when many statistical comparisons are performed.
+Holm correction:
+- Sorts all raw p-values from smallest to largest.
+- Adjusts them according to the number of comparisons.
+- Produces the stricter Holm p values.
+- Uses Holm p ≤ 0.05 to determine significance.

From 0cf7ac5f4ffc2126e98f8f9dfed3ee5927d9be49 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Sat, 11 Jul 2026 09:45:57 +0200
Subject: [PATCH 45/46] update documentation

---
 plugins/parquet-adapter/docs/README.md        | 388 ++++----
 .../parquet-adapter/docs/aggregation_flow.md  | 118 ++-
 .../docs/aggregation_planning.md              | 245 +++--
 .../docs/doc_adapter_support_filters.md       |  78 --
 plugins/parquet-adapter/docs/file_pruning.md  | 112 +--
 .../parquet-adapter/docs/filter_overview.md   | 446 ++-------
 plugins/parquet-adapter/docs/future_work.md   |   3 -
 plugins/parquet-adapter/docs/joins.md         | 340 ++-----
 plugins/parquet-adapter/docs/nested_fields.md | 471 +++-------
 .../docs/partitions_changes.md                | 242 -----
 .../docs/partitions_overview.md               | 383 +++-----
 plugins/parquet-adapter/docs/planner.md       | 285 ++----
 .../docs/rel_execution_flows.md               | 220 ++---
 .../docs/simple_nested_column_flat_rel.md     | 142 ---
 plugins/parquet-adapter/docs/statistics.md    | 491 ++--------
 .../docs/test_plan/join_test_list.md          | 852 ------------------
 .../docs/test_plan/partition_test_list.md     | 101 ---
 .../docs/unit_tests/test_coverage.md          | 212 -----
 plugins/parquet-adapter/docs/workflow.md      | 597 +++---------
 19 files changed, 1141 insertions(+), 4585 deletions(-)
 delete mode 100644 plugins/parquet-adapter/docs/doc_adapter_support_filters.md
 delete mode 100644 plugins/parquet-adapter/docs/future_work.md
 delete mode 100644 plugins/parquet-adapter/docs/partitions_changes.md
 delete mode 100644 plugins/parquet-adapter/docs/simple_nested_column_flat_rel.md
 delete mode 100644 plugins/parquet-adapter/docs/test_plan/join_test_list.md
 delete mode 100644 plugins/parquet-adapter/docs/test_plan/partition_test_list.md
 delete mode 100644 plugins/parquet-adapter/docs/unit_tests/test_coverage.md

diff --git a/plugins/parquet-adapter/docs/README.md b/plugins/parquet-adapter/docs/README.md
index 4ba9204faa..96281e213c 100644
--- a/plugins/parquet-adapter/docs/README.md
+++ b/plugins/parquet-adapter/docs/README.md
@@ -1,260 +1,178 @@
-# Parquet Adapter Implementation
-
----
-
-## Overview
-
-The Parquet adapter exposes `.parquet` files as Polypheny source tables.
-At a high level, the adapter is responsible for:
-- discovering Parquet files from the configured source location
-- extracting relational schema metadata from Parquet file footers
-- restoring and registering tables whose data is read from Parquet files
-- executing read queries over Parquet data
-- applying projection pushdown and supported native predicate pushdown during query execution
-
-The adapter is read-only and supports relational and document source integration.
-
-The following sections describe the architecture, execution flow, package responsibilities, supported query behavior, and current limitations in more detail.
-
-## Build and Integration Changes
-
-1\. To integrate the Parquet adapter as a plugin, the Gradle configuration must include the Parquet module and its dependencies.
-
-Affected files:
-- `settings.gradle`
-- `gradle.properties`
-- `plugins/parquet-adapter/build.gradle`
-
-
-2\. Optional: To make Parquet the default data source, `PolyphenyDB.java` sets `defaultSourceName` to `parquet`.<br>
-During `restore()`, `Catalog.defaultSource` is populated from the configured adapter state.
-
-
-## Execution Flow
-
-1. **Plugin registration**
-    - **Responsible class:** `ParquetPlugin`
-    - Registers the Parquet adapter template during startup.
-    - Removes the template during shutdown.
-2. **Schema initialization**
-    - **Responsible classes:** `AbstractParquetSource`, `ParquetFileDiscovery`, `ParquetTypeConverter`
-    - Resolves the configured Parquet directory or file location.
-    - Discovers available `.parquet` files.
-    - Builds relational table or document collection metadata from Parquet file metadata.
-    - Maps Parquet field types to Polypheny types.
-    - Normalizes physical table names and column names.
-    - Initializes the information page from the extracted column definitions.
-3. **Namespace and entity creation**
-    - **Responsible classes:** `ParquetNamespace`, `ParquetRelTable`, `ParquetDocument`
-    - Creates table or collection wrappers for discovered Parquet files.
-    - Registers physical entities in the adapter catalog.
-4. **Restore during startup**
-    - **Responsible classes:** `ParquetRelationalSource`, `ParquetDocumentSource`, `ParquetNamespace`
-    - Recreates relational tables and document collections from persisted adapter state.
-    - Re-registers the wrappers in the adapter catalog.
-5. **Query entry into the adapter**
-    - **Responsible classes:** `ParquetRelScan`, `ParquetDocScan`, `AbstractParquetEnumerator`, `ParquetSourceReader`
-    - The query engine resolves the registered Parquet-backed physical entity.
-    - Projection and supported filter information are passed into the Parquet execution layer.
-    - Rows are read from the Parquet file and converted into Polypheny relational tuples or documents.
-
-## Supported Filter Conditions
-
-The active predicate pushdown path currently supports only simple comparison conditions that can be translated into native Parquet predicates.
-
-Supported operators:
-- `=`
-- `!=`
-- `>`
-- `>=`
-- `<`
-- `<=`
-
-Supported condition shape:
-- `column OP literal`
-- `column OP dynamic-parameter`
-
-Notes:
-- The left-hand side must be a column reference.
-- The right-hand side must be a literal value or dynamic parameter.
-- Simple casts on either side are unwrapped before translation.
-- Only pushdown-safe conditions are translated into adapter-specific filters.
-- Unsupported conditions remain outside the Parquet-specific filter flow.
-
-Supported pushdown types:
-- `BOOLEAN`: `=` and `!=`
-- `INTEGER`, `BIGINT`, `FLOAT`, `DOUBLE`, `DATE`, `TIME`, `TIMESTAMP`: all six comparison operators
-- `VARCHAR`, `CHAR`, `TEXT`: `=` and `!=`
-
-Notes:
-- Pushdown uses Parquet native filter APIs through `FilterCompat.Filter`.
-- Legacy Parquet `INT96` timestamp columns are currently not pushed down.
-
-
-## Parquet Adapter - Package Structure
-
-### `org.polypheny.db.adapter.parquet`
-
-Contains parquet functionality for Relational and Document data source.
-
-- `ParquetPlugin` - plugin entry point. Registers the relational and document adapter templates after catalog initialization and removes them again during shutdown.
-
-### `org.polypheny.db.adapter.parquet.document`
-
-Document-model integration for exposing Parquet files as read-only Polypheny document collections.
-
-- `ParquetDocumentSource` - document adapter implementation. Owns adapter metadata, performs discovery of Parquet-backed document collections, document restore, and delegation into the document scan APIs.
-
-#### `document.execution`
-
-- `ParquetDocEnumerator` - document runtime enumerator. Reads Parquet rows and create from each row a single `PolyDocument`.
-- `ParquetDocFilterTranslator` - translates supported document filter expressions into shared `ParquetAdapterFilter` instances.
-- `ParquetDocValueExtractor` - converts Parquet groups and nested values into Polypheny document values and synthesizes `_id` values when they are missing.
-
-#### `document.planning`
-
-- `ParquetDocFilter` - enumerable filter node used when a document filter can stay inside the Parquet-specific execution path.
-- `ParquetDocFilterRule` - planner rule that recognizes supported document filters and rewrites them into `ParquetDocFilter` plus `ParquetDocScan`.
-- `ParquetDocScan` - document scan algebra node that builds the enumerable execution call for reading Parquet-backed documents.
-
-#### `document.schema`
-
-- `ParquetDocument` - physical collection wrapper for the document model. Represents one Parquet-backed collection inside Polypheny and connects planning and execution to the source adapter.
-
-### `org.polypheny.db.adapter.parquet.relational`
-
-Relational-model integration for exposing Parquet files as source tables.
-
-- `ParquetRelationalSource` - relational adapter implementation. Manages exported table discovery, schema registration, information-page content, and restore of relational Parquet tables.
+# Parquet Adapter Documentation
 
-#### `relational.execution`
+The Parquet adapter exposes Parquet files as read-only Polypheny sources. It
+supports relational tables, document collections, multi-file datasets,
+Hive-style partition folders, projection and filter pushdown, metadata-backed
+statistics, adapter-level structural joins, aggregate pushdown, and workflow
+import/export helpers.
 
-- `ParquetRelEnumerator` - relational runtime enumerator. Reads projected Parquet rows and returns them as relational tuples.
-- `ParquetRelFilterTranslator` - converts adapter-level filters into Parquet native predicates for relational scans.
-- `ParquetRelValueExtractor` - converts Parquet field values into Polypheny relational values with relational-specific scalar handling.
+This folder contains project-code documentation only. The `REPORT` folder is a
+separate report draft area and is not part of the adapter documentation set.
 
-#### `relational.planning`
+## Source Types
 
-- `ParquetRelScan` - relational scan algebra node for Parquet-backed physical tables.
-- `ParquetRelScanRule` - planner rule that rewrites compatible scans and projections to the Parquet-specific relational scan node.
+The plugin registers two source templates in `ParquetPlugin`:
 
-#### `relational.schema`
+- `Parquet Relational`: implemented by `ParquetRelationalSource`
+- `Parquet Document`: implemented by `ParquetDocumentSource`
 
-- `ParquetRelTable` - physical table wrapper for the relational model. Exposes the Parquet-backed table to Polypheny and ties the planner, scanner, and adapter metadata together.
+Both templates accept the same source-location modes:
 
-### `org.polypheny.db.adapter.parquet.shared`
+- `upload`: uploaded Parquet files or folders
+- `link`: a local file or directory path
+- `url`: a URL pointing to Parquet data
 
-Shared infrastructure used by both the relational and document adapters.
+The relational adapter has an additional `schema mode` setting:
 
-- `AbstractParquetSource` - common base class for both source types. Handles settings, file discovery, exported schema derivation, information-page setup, name normalization, and shared restore behavior.
+- `flat`: expose each discovered table as one relational table, flattening
+  non-repeated nested scalar fields into columns where possible
+- `normalized`: expose nested structures as generated parent/child relational
+  tables with synthetic structural columns
 
-#### `shared.execution`
+Adapter-backed entities are read-only. Workflow export to Parquet is handled by
+the workflow engine and shared writer classes; it does not make adapter source
+tables writable.
 
-- `AbstractParquetEnumerator` - common enumerator base. Manages row iteration, projection handling, cancellation support, and reader lifecycle for both models.
-- `AbstractParquetValueExtractor` - shared conversion base for mapping Parquet primitive and structured values into Polypheny values.
-- `ParquetFilterTranslationSupport` - reusable helper for parsing supported Rex predicates and turning them into adapter-level filters.
-- `ParquetValueExtractor` - small interface implemented by value extractors that can convert a Parquet field into a `PolyValue`.
+## Core Runtime Flow
 
-#### `shared.io`
+1. `ParquetPlugin` registers adapter templates and PolyAlg display nodes.
+2. `AbstractParquetSource` resolves the configured source location and delegates
+   discovery to `ParquetFileDiscovery`.
+3. `ParquetSchemaReader` reads Parquet footers and builds `ExportedSchema`
+   objects containing visible columns and source-path metadata.
+4. `ParquetNamespace` creates relational table wrappers or document collection
+   wrappers.
+5. Relational queries enter through `ParquetRelScan`; document queries enter
+   through `ParquetDocScan`.
+6. Runtime execution opens `ParquetSourceReader` instances through the
+   appropriate executor or enumerator and returns Polypheny rows/documents.
 
-- `ParquetSourceReader` - low-level Parquet row-group reader. Opens the file, applies projection and native predicates, and streams groups to the enumerators.
-- `ParquetSourceWriter` - low-level Parquet writer used by workflow export. Owns file writer lifecycle, compression handling, and row writing.
-- `ParquetFileDiscovery` - locates valid `.parquet` files below the configured source location and filters out unsupported files.
+## Relational Planning
 
-#### `shared.filter`
+The active relational planning path uses:
 
-- `ParquetAdapterFilter` - immutable filter description shared across planning and execution. Carries the target column index, comparison operator, literal value, and optional dynamic parameter index.
-- `ParquetNativeFilterBuilder`- creates native Parquet filter predicates for the supported comparison operators and value types.
+- `ParquetRelConvention`
+- `ParquetRelRules`
+- `ParquetRelPatternMatchers`
+- `ParquetAlgOptRule`
+- `EnumerableParquet`
+- `ParquetRelScan`
+- `ParquetRelJoin`
+- `ParquetRelAggregate`
+- `ParquetRelMetadataScan`
+- `ParquetEnumerableUnion`
 
-#### `shared.schema`
+Always-registered rules attach supported projections and filters to scans and
+convert Parquet-convention plans back to enumerable convention. When
+`ParquetOptimizationSettings.isOptimizeAggregation()` is enabled, the planner
+also registers structural join and aggregate rewrite rules.
 
-- `ParquetNamespace` - namespace helper that creates the correct Parquet-backed physical wrapper for either a relational table or a document collection.
-- `ParquetMessageTypeBuilder` - builds Parquet `MessageType` definitions from inferred field schemas for workflow export.
-- `ParquetTypeConverter` - converts Parquet schema types and runtime values into the Polypheny type system.
-- `ParquetFieldNameNormalizer` - creates normalized filed names
+## Document Planning
 
-#### `shared.schema.inference`
+The active document planning path uses:
 
-- `FieldSchema` - internal model for one inferred Parquet field used by workflow export.
-- `SchemaState` - accumulates inferred field definitions while sampled workflow input is inspected.
-- `ValueKind` - enum describing the logical value categories used during schema inference.
-- `ValueSchema` - recursive value-type model used to represent primitive, nested, and repeated Parquet field structures before building the final schema.
+- `ParquetDocConvention`
+- `ParquetDocRules`
+- `ParquetDocPatternMatchers`
+- `ParquetDocScan`
+- `ParquetDocAggregate`
+- `ParquetDocMetadataScan`
+- `ParquetDocFilterTranslator`
 
-#### `shared.util`
+Document filters are stored directly on `ParquetDocScan`. The current
+implementation does not use a separate document-filter planner node.
 
-- `HadoopConfigurationFactory` - builds Hadoop `Configuration` instances that work correctly inside the plugin classloader environment.
+## Feature Documentation
 
+- [Workflow Integration](workflow.md)
+- [Planner Basics](planner.md)
+- [Filter Overview](filter_overview.md)
+- [File Pruning](file_pruning.md)
+- [Partitioned Layouts](partitions_overview.md)
+- [Nested Fields and Normalized Schemas](nested_fields.md)
+- [Adapter-Level Joins](joins.md)
+- [Aggregation Planning](aggregation_planning.md)
+- [Aggregation Runtime Flow](aggregation_flow.md)
+- [Statistics](statistics.md)
+- [Relational and Document Execution Flows](rel_execution_flows.md)
 
-## Schema Mapping Rules
-
-- Table names are derived from Parquet file names and normalized before registration.
-- Column names are derived from Parquet field names and normalized before registration.
-- Column types are mapped from Parquet schema types to Polypheny types.
-- String-like Parquet fields are mapped to `TEXT`.
-- Unlike SQL types with fixed length declarations, Parquet does not impose a fixed maximum string length.
-
-
-## Information Page
-The information page displays the following column metadata:
-
-- Position
-- Column name
-- Type
-- Nullable
-- Filename
-- Primary key flag
-
-
-The following figure shows an example of the displayed schema information.
-![Schema display](images/parquet_schema.png)
-
-## Data Presentation
-![Schema display](images/customers_data.png)
-
-## Query Projection
-![Schema display](images/query_projection.png)
-
-## Query Filter
-![Schema display](images/query_filter.png)
-
-## Workflow Integration
-
-Workflow-specific Parquet extract and load activities are documented separately in:
-- `docs/workflow.md`
-
-## Partitioned Folder Layouts
-
-Hive-style partitioned folder layout support is designed separately in:
-- `docs/partitioned_layouts.md`
-
-## Unit Tests
-___
-
-To run tests for parquet adapter:
-- `.\gradlew.bat :plugins:parquet-adapter:test`
-
-`plugins/parquet-adapter/build.gradle` should contain:
-- DBMS as a test dependency
-- Polypheny JDBC driver dependency
-
-
-## `ParquetPluginTest.java`
-contains the following tests:
-1. `importsAllTablesAndReadsRows()`
-2. `parquetSourceIsReadOnly()`
-3. `readsExpectedRowsFromCustomers()`
-4. `filtersRowsWithWhereClause()`
-5. `projectsOnlyRequestedColumns()`
-6. `supportsGreaterThanFilter()`
-7. `supportsAllComparisonFilterOperations()`
-8. `rejectsUpdateOnParquetSource()`
-9. `projectsAndFiltersOtherTables()`
-10. `returnsNestedShippingAddressAsJSON()`
-
-## `ParquetRelFilterTranslatorTest.java`
-contains focused unit tests for predicate translation behavior.
-
-Current test coverage includes:
-1. `rejectsInt96TimestampPredicatePushdown()`
+## Supported Filtering
 
+Relational filter translation supports:
 
+- `column OP literal`
+- `column OP dynamicParameter`
+- reversed value/column comparisons
+- casts around supported operands
+- `IS NULL` and `IS NOT NULL`
+- `AND`, `OR`, and `NOT` when all operands are translatable
+- `IN` as an `OR` tree of equality filters
+
+Supported binary operators are `=`, `!=`, `>`, `>=`, `<`, and `<=`.
+
+Relational type/operator support:
+
+| Polypheny type family | Supported operators |
+| --- | --- |
+| `BOOLEAN` | `=`, `!=`, `IS NULL`, `IS NOT NULL` |
+| `VARCHAR`, `CHAR`, `TEXT` | `=`, `!=`, `IS NULL`, `IS NOT NULL` |
+| `INTEGER`, `BIGINT`, `FLOAT`, `DOUBLE`, `DATE`, `TIME`, `TIMESTAMP` | all comparison operators plus null checks |
+
+Document filter translation supports top-level document fields referenced as
+`RexNameRef` or lowered `MQL_QUERY_VALUE(document, ARRAY['field'])` expressions.
+It supports the same six binary comparison operators plus logical `AND`, `OR`,
+and `NOT` when all child predicates are translatable. Document null checks,
+`IN`, nested document paths, field-to-field comparisons, and arbitrary
+functions are not pushed into the adapter.
+
+Native Parquet filtering is attempted when the translated filter can be mapped
+to a primitive non-repeated Parquet field. Exact adapter-level filtering remains
+the correctness path when native filtering is unavailable.
+
+## Package Overview
+
+Important packages:
+
+- `org.polypheny.db.adapter.parquet`: plugin entry point
+- `document`: document source, planning, execution, and schema wrappers
+- `relational`: relational source, planning, execution, filters, and schema
+  bindings
+- `shared.execution`: common enumerators, filter translation helpers, writer
+  buffer utilities, and virtual groups
+- `shared.execution.aggregate`: shared aggregate executors and aggregate
+  enumerators
+- `shared.filter`: immutable adapter filter trees and row/file evaluators
+- `shared.io`: file discovery, schema reading, source reading, primitive-row
+  reading, and source writing
+- `shared.optimization`: shared planner matcher wrappers and aggregate
+  decomposition helpers
+- `shared.planning`: shared PolyAlg display helpers and union marker node
+- `shared.schema`: type conversion, name normalization, namespace helpers, and
+  Parquet message building
+- `shared.schema.inference`: workflow export schema inference
+- `shared.statistics`: metadata-backed table and column statistics
+- `shared.util`: Hadoop configuration setup for the plugin classloader
+
+## Build And Tests
+
+Run the Parquet adapter tests with:
+
+```powershell
+.\gradlew.bat :plugins:parquet-adapter:test
+```
+
+Workflow Parquet support has additional tests in the workflow engine module.
+
+## Current Limitations
+
+- Adapter source tables and collections are read-only.
+- Graph model import/export is not supported by the Parquet adapter or Parquet
+  workflow export.
+- Only the first discovered partition column is represented as Polypheny
+  physical partition metadata; deeper partition columns remain adapter-level
+  partition values and can still be used for file pruning.
+- Native Parquet predicate pushdown is an optimization and only applies to
+  primitive non-repeated fields that Parquet can filter safely.
+- Adapter-level structural joins are limited to generated parent/child
+  relationships from the same physical Parquet structure.
+- Aggregate pushdown is controlled by `ParquetOptimizationSettings`.
diff --git a/plugins/parquet-adapter/docs/aggregation_flow.md b/plugins/parquet-adapter/docs/aggregation_flow.md
index 41869c6a16..ad3b44f320 100644
--- a/plugins/parquet-adapter/docs/aggregation_flow.md
+++ b/plugins/parquet-adapter/docs/aggregation_flow.md
@@ -1,41 +1,77 @@
-# Aggregation Flow
-
-## ParquetMetadataAggregateExecutor
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetMetadataAggregateExecutor.java`
-Performs the following:
-1. filter by file level: partition values / column statistics
-2. find group key for each file
-3. update COUNT / MIN / MAX using file metadata
-4. return result as Enumerable
-
-## ParquetDataAggregateExecutor
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/aggregate/ParquetDataAggregateExecutor.java'`
-ParquetAggregateExecutor creates following Enumerators:
-1. ParquetFileGroupedAggregateEnumerator
-   - aggregation can be done per whole file, because each file belongs to exactly one group.
-2. ParquetGroupedAggregateEnumerator 
-   - grouped COUNT(*). For queries like:
-      ```text
-       SELECT category, COUNT(*)
-       FROM parquet_table
-       GROUP BY category;
-       ```
-      Conditions:
-      - all aggregate calls are COUNT(*)
-      - filters are empty or partition filters
-
-    - general grouped aggregate. For queries like:
-      ```text
-      SELECT category, MIN(price), MAX(price), SUM(price)
-      FROM parquet_table
-      WHERE category IN (VALUE1, VALUE2, ...)
-      GROUP BY category;
-      ```
-      Conditions:
-      - filters are supported 
-      - aggregate functions by column
-3. ParquetRowAggregateEnumerator - default
-
-## Flow Diagram 
-
-![aggragation_flow.drawio.png](images/aggregation/aggragation_flow.drawio.png)
+# Aggregation Runtime Flow
+
+Aggregate runtime is shared between relational and document execution through
+`ParquetAggregateSource`.
+
+Relational wrappers:
+
+- `ParquetRelDataAggregateExecutor`
+- `ParquetRelMetadataAggregateExecutor`
+
+Document wrapper:
+
+- `ParquetDocAggregateExecutor`
+
+Shared executors:
+
+- `ParquetDataAggregateExecutor`
+- `ParquetMetadataAggregateExecutor`
+
+## Metadata Aggregate Executor
+
+`ParquetMetadataAggregateExecutor` answers supported aggregate queries from
+source-file partition values and Parquet footer statistics.
+
+Flow:
+
+1. Resolve dynamic filter parameters through `ParquetFilterResolver`.
+2. Build the source-file evaluator chain:
+   `ParquetSourceFilePartitionFilterEvaluator` plus
+   `ParquetSourceFileStatisticsFilterEvaluator`.
+3. Iterate `ParquetAggregateSource.sourceFiles()`.
+4. Skip files proven not to match.
+5. Derive group keys from partition values or file-constant physical columns
+   through `ParquetConstantColumnResolver`.
+6. Accumulate supported `COUNT`, `MIN`, and `MAX` results from metadata.
+7. Return result rows as an enumerable.
+
+Metadata mode is selected only when all filters, groups, and aggregate calls can
+be answered exactly from file-level information.
+
+## Data Aggregate Executor
+
+`ParquetDataAggregateExecutor` scans data inside the adapter when metadata mode
+is not sufficient but the aggregate is still supported.
+
+It chooses among these strategies:
+
+- `ParquetFileGroupedAggregateEnumerator`: file-level grouped aggregate when
+  groups and filters are file-constant
+- `ParquetGroupedAggregateEnumerator`: grouped aggregate over primitive Parquet
+  columns
+- `ParquetRowAggregateEnumerator`: row-level fallback over relational enumerator
+  output when a faster reader strategy is unavailable
+
+The data executor also uses file pruning before opening source files.
+
+## Fast Paths
+
+Fast paths include:
+
+- scalar or grouped `COUNT(*)` with file-decidable filters
+- file-constant partition or physical grouping columns
+- no-filter column aggregates over primitive numeric columns
+- grouped flat-column aggregates that can be read directly from Parquet pages
+
+## Fallback Behavior
+
+The relational path can fall back to row aggregation when direct aggregate
+readers cannot handle a supported aggregate. The document path relies on the
+shared aggregate readers and document field mapping; unsupported document
+aggregate shapes remain outside the adapter aggregate path.
+
+## Diagram
+
+The original implementation diagram is kept as a supporting image:
+
+![Aggregation flow](images/aggregation/aggragation_flow.drawio.png)
diff --git a/plugins/parquet-adapter/docs/aggregation_planning.md b/plugins/parquet-adapter/docs/aggregation_planning.md
index 603ea14952..1491234ff5 100644
--- a/plugins/parquet-adapter/docs/aggregation_planning.md
+++ b/plugins/parquet-adapter/docs/aggregation_planning.md
@@ -1,184 +1,167 @@
-# Aggregation Push-down
+# Aggregation Planning
 
-## Planner
+Parquet aggregate pushdown replaces supported enumerable aggregate plans with
+adapter-specific aggregate nodes. The current implementation supports both the
+relational and document models.
 
-### Nodes
+Aggregate optimization is controlled by `ParquetOptimizationSettings`.
 
-#### 1. ParquetRelMetadataScan - P_Metadata_Scan
+Configuration switches:
 
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelMetadataScan.java`
+- system property: `polypheny.parquet.optimizeAggregation`
+- environment variable: `POLYPHENY_PARQUET_OPTIMIZE_AGGREGATION`
 
-A planner node for “metadata-only Parquet access,” mainly so aggregate queries, such as COUNT, MIN, MAX, can be planned cheaply without pretending they need to read every row.
-Its cost is zero. That tells the optimizer: “this node itself does not scan data rows.”
+The default is enabled.
 
-Aggregates Supported By Metadata:
-- COUNT(*)
-- COUNT(column)
-- MIN(column)
-- MAX(column)
+## Planner Classes
 
-#### 2. ParquetRelAggregate - P_Aggregate
+Relational planning:
 
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java`
+- `ParquetRelAggregate`
+- `ParquetRelMetadataScan`
+- `ParquetRelPatternMatchers.aggregateOnScan`
+- `ParquetRelPatternMatchers.aggregateOnCalcScan`
+- `ParquetRelRules`
 
-Planner node that says, “This aggregate can be handled by the Parquet adapter, either cheaply from file metadata or by scanning Parquet data directly.”
+Document planning:
 
-represents operations like:
-```text
-SELECT COUNT(*)
-SELECT MIN(price)
-SELECT MAX(age)
-SELECT category, COUNT(*) GROUP BY category
-```
+- `ParquetDocAggregate`
+- `ParquetDocMetadataScan`
+- `ParquetDocPatternMatchers.aggregateOnScan`
+- `ParquetDocPatternMatchers.aggregateOnProjectScan`
+- `ParquetDocPatternMatchers.aggregateOnCalcScan`
+- `ParquetDocRules`
 
-This class decides whether an aggregate can be done in one of two ways:
-1. metadataAggregate: use Parquet metadata only, without reading all rows. Example: COUNT(*), MIN(col), MAX(col) may be answerable from Parquet file statistics.
-In this case it wraps Scan to ParquetRelMetadataScan
+Shared planning:
 
-2. dataAggregate: read actual Parquet data and aggregate it.
-   This is the fallback when metadata is not enough, but Parquet can still perform the aggregate directly.
+- `ParquetAggregatePatternMatchers.partialAggregateOnUnion`
+- `ParquetAggregatePatternMatchers.partialAggregateOnCalcUnion`
+- `ParquetEnumerableUnion`
+- `AggregateDecomposition`
+- `PartialAggregate`
 
-The real runtime call is built in `implement`: call "metadataAggregate" or "dataAggregate" depending on the selected mode.
+## Relational Aggregate Rules
 
-`computeSelfCost` makes this node look very cheap to the optimizer.
+`aggregateOnScan` matches:
 
-#### 3. ParquetEnumerableUnion
+```text
+EnumerableAggregate
+  EnumerableParquet
+    ParquetRelScan
+```
 
-Node combines rows from multiple child plans, just like a normal EnumerableUnion, but it has a special class name so planner rules know, 
-that this union was already rewritten for Parquet aggregate optimization.
-It prevents the same optimization rule from firing again and again.
-ParquetEnumerableUnion says: “This union has already been processed for Parquet partial aggregate pushdown.”
+and tries to create:
 
-### Rules
+```text
+EnumerableParquet
+  ParquetRelAggregate
+    ParquetRelScan or ParquetRelMetadataScan
+```
 
-Rules defined in PatternMatchers.java
+`aggregateOnCalcScan` handles the same idea when an `EnumerableCalc` between the
+aggregate and scan carries projection/filter work that can be represented by the
+Parquet scan.
 
-#### 1. aggregateOnScan
+## Document Aggregate Rules
 
-If there is an aggregate directly above a Parquet scan, try to turn it into a Parquet-native aggregate.
+Document aggregate rules map projected top-level document fields to exported
+Parquet columns before creating `ParquetDocAggregate`.
 
-from:
+Supported shapes:
 
-```text
-EnumerableAggregate
-    EnumerableParquet
-        ParquetRelScan
-```
-to:
+- aggregate directly over `ParquetDocScan`
+- aggregate over field projection above `ParquetDocScan`
+- aggregate over a project/filter calc above `ParquetDocScan`
 
-```text
-EnumerableParquet
-    ParquetRelAggregate
-        ParquetRelScan
-```
+Document aggregate pushdown is limited to projected top-level fields that can be
+resolved to exported Parquet columns.
 
-#### 2. aggregateOnCalcScan
-Like aggregateOnScan, but there is a Calc between the aggregate and the Parquet scan.
+## Metadata Aggregate Mode
 
-from:
+`ParquetRelAggregate` and `ParquetDocAggregate` first try metadata mode.
 
-```text
-EnumerableAggregate COUNT(*)
-  EnumerableCalc filter age > 30
-    EnumerableParquet
-      ParquetRelScan
-```
-to:
+Metadata mode is exact-only. It is selected only when:
 
-```text
-EnumerableParquet
-  ParquetRelAggregate COUNT(*)
-    ParquetRelScan with filter age > 30
-```
+- aggregate calls are supported by `ParquetAggregateSupport`
+- grouping fields are file-constant or partition-derived where required
+- filters are decidable from partition values or footer statistics
+- the runtime source confirms support through `supportsMetadataAggregate(...)`
 
-#### 3. partialAggregateOnUnion
+Metadata-supported aggregate calls:
 
-If we see EnumerableUnion under EnumerableAggregate, and the union is UNION ALL, then push partial aggregates below the union, 
-use ParquetEnumerableUnion to union those partial results, then combine those partial results above ParquetEnumerableUnion.
+- `COUNT(*)`
+- `COUNT(column)` when null-count metadata is available
+- `MIN(column)`
+- `MAX(column)`
 
-from:
+Unsupported metadata cases fall through to data mode if data mode can execute
+them.
 
-```text
-EnumerableAggregate
-    EnumerableUnion
-        input1
-        input2
-        input3
-```
-to:
+## Data Aggregate Mode
 
-```text
-EnumerableAggregate          <-- final aggregate: calculates aggregate from already computed values
-  ParquetEnumerableUnion     <-- marker UNION ALL
-    EnumerableAggregate      <-- partial aggregate
-      input1
-    EnumerableAggregate      <-- partial aggregate
-      input2
-    EnumerableAggregate      <-- partial aggregate
-      input3
-```
+Data mode scans Parquet data inside the adapter and aggregates before returning
+rows to the rest of the Polypheny plan.
+
+Supported data aggregate functions:
 
-#### 4. partialAggregateOnCalcUnion
+- `COUNT`
+- `SUM`
+- `MIN`
+- `MAX`
 
-same idea as partialAggregateOnUnion, but there is a Calc between the aggregate and the union.
+Data mode supports numeric aggregation columns and supported grouping/filter
+shapes. When a fast grouped reader path is not possible, the relational path can
+fall back to row aggregation over Parquet enumerator output.
 
-If we see EnumerableUnion under EnumerableCalc under EnumerableAggregate, and the union is UNION ALL, 
-then copy the Calc into each union branch, push partial aggregates below the union, 
-use ParquetEnumerableUnion to union those partial results, then combine those partial results above ParquetEnumerableUnion.
+## Partial Aggregate Over Union
 
-Calc should be copied, because the original plan applies the Calc after the union.
+The shared union rules decompose aggregates above `UNION ALL` into partial
+aggregates below the union and a final aggregate above it.
 
-from:
+Example shape:
 
 ```text
 EnumerableAggregate
-  EnumerableCalc
-    EnumerableUnion
-      input1
-      input2
+  EnumerableUnion
+    input1
+    input2
 ```
 
-to:
+becomes:
 
 ```text
-EnumerableAggregate                 <-- combines partial results
-  ParquetEnumerableUnion             <-- unions partial results
-    EnumerableAggregate              <-- partial aggregate for input1
-      EnumerableCalc                 <-- same calc copied here
-        input1
-    EnumerableAggregate              <-- partial aggregate for input2
-      EnumerableCalc                 <-- same calc copied here
-        input2
+EnumerableAggregate
+  ParquetEnumerableUnion
+    EnumerableAggregate
+      input1
+    EnumerableAggregate
+      input2
 ```
 
-### Other Rules relevant files
+`ParquetEnumerableUnion` marks that the union subtree has already been processed
+for Parquet aggregate optimization.
 
-- `PatternMatchers.java` - defines rewrite rules
-- `ParquetRules.java` - collects all those matcher rules into a list
-- `ParquetAlgOptRule.java` - wraps them as planner rules
-- `ParquetConvention.java` - registers the rules with the planner
+## Runtime Entry Points
 
-## Examples:
+Relational runtime methods on `ParquetRelTable`:
 
-```sql
-SELECT count(*) AS row_count
-FROM tlc__yellow_tripdata;
-```
+- `metadataAggregate(...)`
+- `dataAggregate(...)`
+- `supportsMetadataAggregate(...)`
+- `supportsDataAggregate(...)`
 
-![metadata_scan.png](images/aggregation/metadata_aggregate.png)
+Document runtime methods on `ParquetDocument`:
 
+- `metadataAggregate(...)`
+- `dataAggregate(...)`
+- `supportsMetadataAggregate(...)`
+- `supportsDataAggregate(...)`
 
-```sql
-SELECT
-  "year",
-  "month",
-  count(*) AS trips,
-  sum(total_amount) AS gross_amount,
-  min(trip_distance) AS min_distance,
-  max(trip_distance) AS max_distance
-FROM yellow_tripdata
-GROUP BY "year", "month"
-ORDER BY "year", "month";
-```
+Shared runtime executors:
+
+- `ParquetMetadataAggregateExecutor`
+- `ParquetDataAggregateExecutor`
 
-![data_agregate.png](images/aggregation/data_agregate.png)
+See [Aggregation Runtime Flow](aggregation_flow.md) and
+[Relational and Document Execution Flows](rel_execution_flows.md) for runtime
+details.
diff --git a/plugins/parquet-adapter/docs/doc_adapter_support_filters.md b/plugins/parquet-adapter/docs/doc_adapter_support_filters.md
deleted file mode 100644
index 76a3dd8767..0000000000
--- a/plugins/parquet-adapter/docs/doc_adapter_support_filters.md
+++ /dev/null
@@ -1,78 +0,0 @@
-# Document Adapter Filter Pushdown Changeset
-
-Document Parquet scans now support filter pushdown by translating supported document predicates into `ParquetAdapterFilter` instances stored directly on `ParquetDocScan`.
-For MQL document queries, Polypheny lowers field access in filters to `MQL_QUERY_VALUE(document, ARRAY['field'])`; the translator now recognizes this top-level field access form and can push supported comparison predicates with literals or dynamic parameters into the Parquet scan.
-The implementation moves document filtering from a custom filter node/rule to an `EnumerableCalc`-based planner rule, while keeping the Calc as a residual correctness check.
-
-## Changed Files
-
-## ParquetPlugin.java
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/ParquetPlugin.java` 
-Registers `ParquetDocScan` as `P_DOC_SCAN` in PolyAlg with fields and filters display parameters.
-
-
-## ParquetDocFilterTranslator.java
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java`
-Extends document filter translation to support lowered `MQL_QUERY_VALUE` top-level field access in addition to simple name references.
-
-
-## ParquetDocCalcRule.java
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocCalcRule.java`
-Adds a planner rule that pushes supported `EnumerableCalc` conditions into `ParquetDocScan` filters.
-
-
-## ParquetDocFilter.java
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilter.java`
-Removes the obsolete Parquet-specific document filter node.
-
-
-## ParquetDocFilterRule.java
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocFilterRule.java`
-Removes the obsolete logical filter rewrite rule replaced by calc-based pushdown.
-
-
-## ParquetDocScan.java
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/planning/ParquetDocScan.java`
-Stores typed pushed filters, combines additional filters, exposes them in PolyAlg, adjusts filtered scan cost, and registers the new calc rule.
-
-
-## ParquetRelAggregate.java
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelAggregate.java`
-Switches PolyAlg display support to the shared Parquet display helper.
-
-
-## ParquetRelJoin.java
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelJoin.java`
-Switches PolyAlg display support to the shared Parquet display helper.
-
-
-## ParquetRelScan.java
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java`
-Switches PolyAlg display support to the shared Parquet display helper.
-
-
-## ParquetPolyAlgDisplay.java
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/planning/ParquetPolyAlgDisplay.java`
-Adds a shared helper for formatting Parquet fields and filters in PolyAlg output.
-
-
-## ParquetDocumentPlanningTest.java
-
-`plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetDocumentPlanningTest.java`
-Updates document planning tests for scan-carried filters and removes tests for the deleted filter node/rule.
-
-
-## ParquetSharedDocumentAdapterTest.java
-
-`plugins/parquet-adapter/src/test/java/org/polypheny/db/adapter/parquet/ParquetSharedDocumentAdapterTest.java`
-Adds translator coverage for comparison filters and lowered MQL field access.
diff --git a/plugins/parquet-adapter/docs/file_pruning.md b/plugins/parquet-adapter/docs/file_pruning.md
index 01dcfc1dcb..4b294df24a 100644
--- a/plugins/parquet-adapter/docs/file_pruning.md
+++ b/plugins/parquet-adapter/docs/file_pruning.md
@@ -1,73 +1,77 @@
 # File Pruning
 
-- Not matching file sources is filtered according to parquet statistics.
-- Statistics loaded when adapter created.
-- Statistics stored in Adapter Settings as part of Bindings and reused for Polypheny statistics (`ParquetTableStatisticsReader`)
+File pruning avoids opening Parquet files that cannot satisfy the pushed
+adapter filters. It works for both normal scans and aggregate execution.
 
-## Changes
+## Stored File Metadata
 
-### ParquetColumnStatistics
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetColumnStatistics.java`
+Each physical file is represented by `ParquetSourceFile`:
 
-- per-file statistics
+```text
+fileUrl
+partitionValues
+columnStatistics
+```
 
-### ParquetSourceFile - record
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetSourceFile.java`
+`partitionValues` come from Hive-style `key=value` folders. `columnStatistics`
+are read by `ParquetColumnStatisticsReader` from Parquet footer metadata when
+the source file is discovered.
 
-- Store column statistics per column: `Map<List<String>, ParquetColumnStatistics> columnStatistics`. Column path is a column identifier because field can be nested
-- ParquetSourceFile stored in ParquetTableBindings `Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetTableBinding.java`
+`ParquetTableBinding` stores the list of `ParquetSourceFile` objects and maps
+logical physical columns to `ParquetColumnBinding` instances. A column binding
+knows whether the column is real Parquet data, a partition value, or a synthetic
+normalized-schema column.
 
-### ParquetBindingSerializer
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetBindingSerializer.java`
+## Evaluators
 
-New functions:
-- serializeColumnStatistics()
-- deserializeColumnStatistics()
+Current source-file pruning uses:
 
-### ParquetColumnStatisticsReader
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\statistics\ParquetColumnStatisticsReader.java`
-- Reads column statistics from Parquet footer metadata
-- returns ParquetColumnStatistics
+- `ParquetSourceFilePartitionFilterEvaluator`: evaluates filters over partition
+  columns using `ParquetSourceFile.partitionValues()`
+- `ParquetSourceFileStatisticsFilterEvaluator`: evaluates filters over data
+  columns using per-file `ParquetColumnStatistics`
+- `ParquetMultiFilterEvaluator`: combines evaluator results
+- `ParquetSourceFileFilterReducer`: rejects impossible files and returns
+  residual filters still needed at row level
 
-### ParquetTableStatisticsReader
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\statistics\ParquetTableStatisticsReader.java`
-- load Polypheny statistics from bindings
-- returns ProvidedColumnStatistics
+All source-file evaluators return true, false, or unknown. Unknown means the
+file is kept.
 
-### ParquetSourceFileStatisticsFilterEvaluator
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetSourceFileStatisticsFilterEvaluator.java`
-- evaluateLeaf() function get statistics by column path and evaluates if file should be skipped
-- called from abstract class FilterEvaluator.evaluate() which is called from match()
+## Scan Flow
 
-### ParquetSourceFilePartitionFilterEvaluator
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetSourceFilePartitionFilterEvaluator.java`
-- evaluateLeaf() function evaluate by folder name if file should be skipped
-- called from abstract class FilterEvaluator.evaluate() which is called from match()
+1. Planner rules attach supported filters to `ParquetRelScan`.
+2. `ParquetRelTable.project(...)` resolves dynamic parameters and physical
+   column bindings through `ParquetFilterResolver`.
+3. `ParquetRelExecutorsFactory` creates a `ParquetMultiFileEnumerator`.
+4. For each `ParquetSourceFile`, the file evaluator checks partition values and
+   footer statistics.
+5. If a file is proven false, the enumerator skips it without opening a reader.
+6. If a file is proven true for some filters, those filters can be removed from
+   the residual row-level filter list for that file.
+7. Remaining filters are passed to `ParquetSourceReader` for possible native
+   filtering and to the enumerator for exact row-level evaluation.
 
-### ParquetMultiFileEnumerator
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetMultiFileEnumerator.java`
+## Aggregate Flow
 
-- creates relevant enumerator using factory
+`ParquetDataAggregateExecutor` and `ParquetMetadataAggregateExecutor` use the
+same partition/statistics file-evaluator chain. This allows aggregate plans to:
 
-### ParquetRelTable
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetRelTable.java'`
+- skip files rejected by partition or footer metadata
+- answer file-constant groups from file metadata
+- keep residual filters when a file-level evaluator cannot prove the result
 
-- creates ParquetMultiFileEnumerator
-- ParquetMultiFileEnumerator receives chain of evaluators
-- createParquetSourceFileEvaluatorsChain() - chain contains two evaluators: partition `ParquetSourceFilePartitionFilterEvaluator` and statistics `ParquetSourceFileStatisticsFilterEvaluator`
+## What Can Be Pruned
 
-## Flow
+File pruning is strongest when predicates target:
 
-1. Adapter creation / file discovery
-   Stores partition values + footer stats in ParquetSourceFile
-2. Adapter settings persist those ParquetSourceFiles. 
-3. Adapter/table loading
-   Restore ParquetSourceFiles containing partition values + footer from settings
-4. Query execution calls ParquetRelTable.project(...) / scan(...) / nestedJoin(...),
-   which creates ParquetMultiFileEnumerator.
-5. ParquetMultiFileEnumerator checks each ParquetSourceFile before opening it. Per-file pruning happens inside: `ParquetMultiFileEnumerator.moveNext()`
-6. Partition evaluator compares filters with sourceFile.partitionValues(). 
-7. Statistics evaluator compares filters with sourceFile.columnStatistics(). 
-8. If evaluators prove the file cannot match, the file is skipped. 
-9. If the result is unknown or possibly matching, the file reader is opened. 
-10. Row-level filtering still happens after opening the file.
+- partition columns such as `year` or `month`
+- columns with reliable footer min/max or null-count metadata
+- constant-per-file physical columns that can be proven from statistics
+
+The adapter keeps files for:
+
+- unsupported operators
+- unsupported logical combinations
+- missing or unreliable statistics
+- repeated/nested paths that cannot be decided at file level
+- predicates involving values only known after row expansion
diff --git a/plugins/parquet-adapter/docs/filter_overview.md b/plugins/parquet-adapter/docs/filter_overview.md
index 4479036f54..27dc15bdb5 100644
--- a/plugins/parquet-adapter/docs/filter_overview.md
+++ b/plugins/parquet-adapter/docs/filter_overview.md
@@ -1,273 +1,48 @@
-# Parquet Filter Overview
+# Filter Overview
 
-In particular, the current relational planner path is built around `ParquetConvention`, `ParquetRules`, `PatternMatchers`, `ParquetScan`, and `ParquetJoin`; older `ParquetRelScan` / `ParquetRelJoin` rule classes still exist, but their direct rule registration in `ParquetRelScan.registerRules()` is commented out.
-
-## 1. Short Description
+Filtering is represented once, as immutable `ParquetAdapterFilter` trees, and
+then used by several layers:
 
-Filtering is handled in three layers:
+1. planner translation from Rex predicates
+2. source-file pruning before opening a Parquet file
+3. optional native Parquet predicate pushdown in `ParquetSourceReader`
+4. exact adapter-level row filtering in enumerators
 
-1. Planner translation
-   - Polypheny `RexNode` filter expressions are translated into `ParquetAdapterFilter` objects when the adapter knows how to represent them.
-   - Unsupported filters are not attached to the Parquet adapter plan.
+Native Parquet filtering is an optimization. Exact adapter-level filtering and
+residual-filter handling preserve correctness when native filtering cannot be
+applied.
 
-2. Native Parquet reader filtering
-   - `ParquetNativeFilterBuilder` converts supported adapter filters into Parquet `FilterPredicate` objects.
-   - `ParquetSourceReader` installs the resulting predicate into `ParquetReadOptions`.
-   - This can use Parquet statistics, dictionary, column index, bloom, and record filters.
-   - Native filtering is an optimization. Some adapter filters cannot become native filters.
+## Filter Representation
 
-3. Adapter-level row filtering
-   - `AbstractParquetEnumerator` applies adapter filters after reading a Parquet row and after expanding nested rows.
-   - Evaluators such as `ParquetGroupFilterEvaluator`, `ParquetNestedFilterEvaluator`, and `ParquetNestedJoinFilterEvaluator` decide whether the emitted row should be kept.
-   - Partition values are handled specially because they may come from folder names rather than physical Parquet columns.
+`ParquetAdapterFilter<T>` represents two shapes:
 
-The central representation is `ParquetAdapterFilter`. It can represent:
+- leaf filter: column index, optional Parquet path, operator, literal value, or
+  dynamic parameter index
+- logical filter: `AND`, `OR`, or `NOT` with child filters and `columnIndex = -1`
 
-- a leaf filter: column index, optional Parquet path, operator, literal value, or dynamic parameter index
-- a logical filter tree: `AND`, `OR`, or `NOT` with child filters
+`FiltersContainer` separates adapter filters from native reader filters.
+`JoinFiltersContainer` extends this model with parent-only and child-only
+filters for nested joins. `JoinFiltersSplitter` splits joined-row filters across
+parent, child, native-reader, and residual joined-row evaluation paths.
 
-## 2. Full Flow
+## Relational Translation
 
-### Relational Scan Flow
+`ParquetRelFilterTranslator` translates relational Rex predicates when the
+predicate can be represented by the adapter.
 
-1. A relational Parquet table is exposed as `ParquetScan`.
-   - `ParquetRelationalSource.getRelScan()` registers `FilterSetOpTransposeRule` and the Parquet convention rules.
-   - It returns a `ParquetScan` over a `ParquetRelTable`.
-
-2. `ParquetConvention` registers `ParquetRules`.
-   - The active rule list is created in `ParquetRules.rules()`.
-   - The rule bodies live in `PatternMatchers`.
-
-3. Planner rules attach filters to `ParquetScan`.
-   - `attachFilterToScanUnderLogicalRelFilter`
-   - `attachFilterToScanUnderEnumerableFilter`
-   - `attachProjectedFieldsAndFilterToScanUnderCalc`
-   - Each rule calls `ParquetRelFilterTranslator.translate(...)`.
-   - If translation fails, the rule does not transform the plan.
-
-4. `ParquetScan` stores translated filters.
-   - It stores the projected field indexes.
-   - It stores zero or more `ParquetAdapterFilter` objects.
-   - During enumerable implementation it calls `ParquetRelTable.project(dataContext, fields, filters)`.
-
-5. Runtime filter resolution happens in `ParquetRelTable.project(...)`.
-   - Dynamic parameters are replaced with runtime values from `DataContext`.
-   - Projected output column indexes are mapped to `ParquetColumnBinding`.
-   - Each leaf filter receives the real Parquet `sourcePathElements` from the binding.
-
-6. Source-file pruning runs before reading.
-   - `ParquetSourceFileFilterEvaluator` checks filters against partition values stored in `ParquetSourceFile`.
-   - If a filter targets a partition column and evaluates to false for a source file, that file is skipped.
-   - If the evaluator cannot decide, the file is kept.
-
-7. `ParquetSourceReader` is created for each source file.
-   - It receives the projected fields when a direct projection scan is safe.
-   - It reads the full root schema when a binding/path scan is needed.
-   - It builds a native Parquet filter from `filtersContainer.nativeFilters()`.
-
-8. The relational enumerator applies exact adapter-level filtering.
-   - `ParquetRelEnumerator` is used for direct flat scans.
-   - `ParquetNestedNonRepeatedRelEnumerator` is used when values must be read by binding/path but one root row still maps to one output row.
-   - `ParquetNestedRepeatedRelEnumerator` is used for generated nested repeated tables.
-   - `AbstractParquetEnumerator.accept(...)` applies adapter filters to each row that may be emitted.
-
-### Relational Join Flow
-
-1. Join patterns in `PatternMatchers` detect supported joins over Parquet scans.
-   - The current active join node is `ParquetJoin`.
-   - It is used for supported normalized parent/child joins.
-
-2. `ParquetJoin.create(...)` merges filters already attached to left and right input scans.
-   - Right-side scan filters are shifted so their column indexes match the joined output row.
-
-3. Filters on join inputs can be carried into `ParquetJoin`.
-   - `ParquetJoin.create(...)` merges filters that were already attached to the left and right `ParquetScan` inputs.
-   - In the current convention path, join matching and scan-side filter attachment are handled through `PatternMatchers`.
-   - I did not find an active `ParquetJoin`-specific rule that attaches filters located above an already-created `ParquetJoin`.
-   - Older `ParquetEnumerableFilterJoinRule` and `ParquetEnumerableCalcJoinRule` classes exist for the older `ParquetRelJoin` path, but that direct rule registration is not active in the current `ParquetRelScan.registerRules()` implementation.
-
-4. Runtime execution calls `ParquetRelTable.nestedJoin(...)`.
-   - Dynamic parameters are resolved.
-   - Column indexes are mapped to parent or child `ParquetColumnBinding` objects.
-   - Parent source files may be pruned by partition filters.
-
-5. `JoinFiltersSplitter` splits filters into:
-   - parent filters
-   - child filters
-   - adapter filters over the joined row
-   - native reader filters
-
-6. `ParquetNestedJoinEnumerator` applies the split filters.
-   - Parent filters are evaluated before child rows are expanded.
-   - Child filters are evaluated after child rows are expanded.
-   - Adapter filters are evaluated on the combined parent/child row.
-   - Parent filters with usable Parquet paths may also become native reader filters.
-
-### Document Flow
-
-1. `ParquetDocScan.register(...)` registers `ParquetDocFilterRule`.
-
-2. `ParquetDocFilterRule` matches a `LogicalRelFilter`.
-   - It splits only `AND` conjunctions into individual predicates.
-   - Each predicate is translated by `ParquetDocFilterTranslator`.
-   - If any predicate cannot be translated, the rule does not transform the plan.
-
-3. The rule creates a `ParquetDocScan` containing translated filters.
-   - It wraps the scan in `ParquetDocFilter`.
-   - `ParquetDocFilter.implement(...)` delegates directly to the underlying `ParquetDocScan`.
-
-4. `ParquetDocScan.implement(...)` calls `ParquetDocument.scanFiltered(...)`.
-
-5. `ParquetDocument.scanFiltered(...)` resolves dynamic parameters.
-   - It opens `ParquetSourceReader` with the filters as native and adapter filters.
-   - It does not project fields; document scans read the row as a document shape.
-
-6. `ParquetDocEnumerator` filters and converts rows.
-   - `AbstractParquetEnumerator` applies adapter-level filters.
-   - Accepted Parquet `Group` rows are converted into a single `PolyDocument`.
-
-## 3. Existing Filter Types and Classes
-
-### Filter Representation
-
-- `ParquetAdapterFilter`
-  - Immutable adapter filter representation.
-  - Stores `columnIndex`, `pathElements`, `operator`, `polyValue`, optional `dynamicParamIndex`, and logical `operands`.
-  - Logical filters use operator `AND`, `OR`, or `NOT` and have `columnIndex = -1`.
-
-- `FiltersContainer`
-  - Holds two filter lists:
-    - adapter filters for exact row-level evaluation
-    - native filters for `ParquetSourceReader`
-  - `shared(...)` uses the same list for both layers.
-  - `withoutPathElementsInAdapterFilters()` keeps paths for native filters but removes them from adapter filters. This is used when adapter evaluation should use projected column indexes.
-
-- `JoinFiltersContainer`
-  - Extends `FiltersContainer`.
-  - Adds parent-only and child-only filter lists for nested join execution.
-
-- `JoinFiltersSplitter`
-  - Splits joined-row filters into parent, child, adapter, and native reader filters.
-  - Splits top-level `AND` filters into individual child filters.
-  - Keeps cross-side `OR` and other mixed-side logical filters as adapter-level joined-row filters.
-
-### Translators
-
-- `AbstractFilterTranslator`
-  - Shared parser for simple binary predicates.
-  - Supports `=`, `!=`, `>`, `>=`, `<`, `<=`.
-  - Unwraps casts.
-  - Allows the value to appear on the left side by reversing the comparison.
-  - Converts literals and dynamic parameters into `ParquetAdapterFilter` leaves.
-
-- `ParquetRelFilterTranslator`
-  - Relational translator.
-  - Supports simple comparisons, `IS NULL`, `IS NOT NULL`, `AND`, `OR`, `NOT`, and `IN`.
-  - Validates column indexes, Polypheny data types, and operator support.
-  - Translates `IN` into an `OR` tree of equality filters.
-
-- `ParquetDocFilterTranslator`
-  - Document translator.
-  - Supports only simple single-field comparisons against exported document columns.
-  - Uses a case-insensitive top-level field name match.
-  - Does not translate document `OR`, `NOT`, `IN`, or null checks.
-
-### Native Filter Layer
-
-- `ParquetNativeFilterBuilder`
-  - Converts `ParquetAdapterFilter` objects into Parquet native `FilterPredicate` objects.
-  - Supports top-level primitive fields and non-repeated primitive paths.
-  - Rejects group fields, repeated paths, unresolved paths, and unsupported operator/type combinations.
-  - If no native predicate can be built, returns `FilterCompat.NOOP`.
-
-- `ParquetSourceReader`
-  - Opens a Parquet file.
-  - Builds projection schema.
-  - Installs the native filter into `ParquetReadOptions`.
-  - Reads filtered row groups when a native filter exists.
-
-### Adapter-Level Evaluators
-
-- `FilterEvaluator<C>`
-  - Base tri-state evaluator.
-  - Returns true, false, or null.
-  - `matches(...)` treats null as true, so uncertain pruning/evaluation keeps the data.
-  - Handles logical `AND`, `OR`, and `NOT`.
-
-- `ParquetGroupFilterEvaluator`
-  - Evaluates filters against a Parquet `Group`.
-  - Extracts values by column index or path.
-  - Handles missing values through `PolyNull.NULL`.
-
-- `ParquetPartitionAwareFilterEvaluator`
-  - Extends group filtering with partition-value awareness.
-  - Used when a row has virtual partition columns from folder names.
-
-- `ParquetNestedFilterEvaluator`
-  - Evaluates filters for nested repeated generated tables.
-  - Uses `ParquetPathValueExtractor` and column bindings.
-
-- `ParquetNestedJoinFilterEvaluator`
-  - Evaluates filters on joined parent/child rows represented as `CombinedGroup`.
-  - Also evaluates parent-only filters before child expansion.
-  - Current caveat: its `canApplyFilter(...)` rejects leaf filters whose `polyValue` is null, so joined-row null checks are not enforced by this evaluator except where native parent reader filtering can apply.
-
-- `ParquetSourceFileFilterEvaluator`
-  - Prunes `ParquetSourceFile` objects before reading.
-  - Only decides filters that target partition columns.
-  - Unknown filters keep the file.
-
-### Planner and Runtime Nodes
-
-- `ParquetConvention`
-  - Registers current Parquet convention rules.
-
-- `ParquetRules` and `PatternMatchers`
-  - Current active relational planning path for attaching filters/projections and replacing supported joins.
-
-- `ParquetScan`
-  - Current relational scan node in Parquet convention.
-  - Carries projected fields and filters.
-
-- `ParquetJoin`
-  - Current Parquet convention join node for supported parent/child joins.
-  - Carries joined-row filters.
-
-- `ParquetRelTable`
-  - Runtime table wrapper.
-  - Resolves dynamic parameters.
-  - Maps filter column indexes to `ParquetColumnBinding` and Parquet paths.
-  - Prunes source files and selects the correct enumerator.
-
-- `AbstractParquetEnumerator`
-  - Shared runtime loop.
-  - Expands rows when needed.
-  - Applies adapter-level filters before emitting output rows.
-
-- `ParquetDocFilterRule`, `ParquetDocFilter`, `ParquetDocScan`
-  - Document planning path for supported document filters.
-
-- `ParquetDocument`
-  - Runtime document wrapper.
-  - Resolves dynamic parameters and creates `ParquetDocEnumerator`.
-
-## 4. Currently Supported and Unsupported Filters
-
-### Relational Filters Supported by Translation
-
-Relational translation supports the following filter forms:
+Supported forms:
 
 - `column OP literal`
 - `column OP dynamicParameter`
 - `literal OP column`
 - `dynamicParameter OP column`
-- casts around either side of the predicate
+- casts around supported operands
 - `column IS NULL`
 - `column IS NOT NULL`
 - `condition AND condition`
 - `condition OR condition`
 - `NOT condition`
-- `column IN (literalOrDynamicParameter, ...)`
+- `column IN (value, ...)`
 
 Supported binary operators:
 
@@ -286,138 +61,91 @@ Supported relational type/operator combinations:
 | `VARCHAR`, `CHAR`, `TEXT` | `=`, `!=`, `IS NULL`, `IS NOT NULL` |
 | `INTEGER`, `BIGINT`, `FLOAT`, `DOUBLE`, `DATE`, `TIME`, `TIMESTAMP` | `=`, `!=`, `>`, `>=`, `<`, `<=`, `IS NULL`, `IS NOT NULL` |
 
-`IN` is supported only when:
-
-- the first operand is a column reference
-- there is at least one value
-- every value can be translated as an equality comparison
-- none of the values is a null literal
-
-Logical filters are supported only when every child filter can be translated.
-If any child is unsupported, the whole logical filter is rejected by the translator.
-
-### Relational Native Parquet Pushdown
-
-Native Parquet pushdown is attempted for translated filters, but only a subset can become Parquet native predicates.
+`IN` is translated as an `OR` tree of equality filters. The first operand must
+be a column reference, all values must be supported literal or dynamic
+parameters, and null values are rejected.
 
-Native pushdown can be built for:
+If any child of a logical filter cannot be translated, the whole logical filter
+is rejected by the translator and remains outside the adapter-specific plan.
 
-- top-level primitive columns addressed by column index
-- non-repeated primitive nested paths addressed by `pathElements`
-- logical `AND`, `OR`, and `NOT` when all operands can be converted to native predicates
-- null checks on most primitive types
+## Document Translation
 
-Native pushdown is not built for:
+`ParquetDocFilterTranslator` translates document predicates over exported
+top-level fields.
 
-- group fields
-- repeated paths
-- unresolved paths
-- unsupported physical primitive/operator combinations
-- `INT96` null checks
-- logical filters where any operand cannot become a native predicate
+Supported field references:
 
-For a top-level list of filters, `ParquetNativeFilterBuilder` skips filters that cannot become native predicates and builds an `AND` of the predicates that remain.
+- a single-name `RexNameRef`
+- lowered MQL field access:
+  `MQL_QUERY_VALUE(document, ARRAY['field'])`
 
-### Relational Adapter-Level Filtering
-
-Adapter-level filtering is applied after reading rows.
-It supports the same `ParquetAdapterFilter` tree shape as translation, provided the evaluator can extract the target value.
+Supported document forms:
 
-Current exact-evaluation paths:
+- `field OP literal`
+- `field OP dynamicParameter`
+- reversed value/field comparisons
+- casts around supported operands
+- `AND`, `OR`, and `NOT` when all child predicates are translatable
 
-- flat direct rows: `ParquetRelEnumerator`
-- non-repeated path/binding scans: `ParquetNestedNonRepeatedRelEnumerator`
-- repeated nested generated tables: `ParquetNestedRepeatedRelEnumerator`
-- nested parent/child joins: `ParquetNestedJoinEnumerator`
-- partition source-file pruning: `ParquetSourceFileFilterEvaluator`
-- partition row values: `ParquetPartitionAwareFilterEvaluator`
+Document translation does not support:
 
-Important behavior:
+- `IS NULL` or `IS NOT NULL`
+- `IN`
+- nested document paths such as `address.city`
+- field-to-field comparisons
+- arithmetic expressions or functions as the field side
+- predicates on fields that are not exported Parquet columns
 
-- If an evaluator returns unknown, `matches(...)` treats it as keep.
-- This is intentional for pruning, where uncertain data must not be dropped.
-- It also means exact filtering depends on the selected evaluator being able to evaluate the filter.
+Document filters are stored on `ParquetDocScan`; there is no separate document
+filter planner node.
 
-### Document Filters Supported by Translation
+## Native Parquet Filtering
 
-Document filter support is narrower than relational filter support.
+`ParquetNativeFilterBuilder` converts supported `ParquetAdapterFilter` objects
+into Parquet native predicates.
 
-Supported document forms:
+Native pushdown can be built for:
 
-- `field OP literal`
-- `field OP dynamicParameter`
-- `literal OP field`
-- `dynamicParameter OP field`
-- casts around either side
-- conjunctions with `AND`, but only because `ParquetDocFilterRule` splits `AND` into separate simple predicates
+- top-level primitive columns addressed by column index
+- non-repeated primitive nested paths addressed by `pathElements`
+- logical `AND`, `OR`, and `NOT` when every child can become a native predicate
+- supported null checks on primitive fields
 
-Supported document binary operators:
+Native pushdown is skipped for:
 
-- `=`
-- `!=`
-- `>`
-- `>=`
-- `<`
-- `<=`
+- group fields
+- repeated paths
+- unresolved paths
+- unsupported physical primitive/operator combinations
+- native `INT96` timestamp cases that Parquet cannot safely filter
+- logical filters with a child that cannot become native
 
-Additional document constraints:
+For a top-level list of filters, filters that cannot become native predicates
+are skipped; native predicates that remain are combined with `AND`.
 
-- the field side must be a `RexNameRef`
-- the field reference must contain exactly one name
-- the field name must match an exported column name case-insensitively
-- the value side must be a literal or dynamic parameter
-- null literals are rejected by `toParquetAdapterFilter`
+## Source-File Pruning
 
-Document native pushdown uses the same `ParquetNativeFilterBuilder`, so the native layer may still skip a translated document filter if the Parquet physical type/path is not suitable.
-Adapter-level document filtering then runs in `ParquetDocEnumerator` before converting an accepted row into `PolyDocument`.
+Before opening each file, `ParquetMultiFileEnumerator` and aggregate executors
+use a `ParquetMultiFilterEvaluator<ParquetSourceFile>` composed from:
 
-### Unsupported Relational Filters
+- `ParquetSourceFilePartitionFilterEvaluator`
+- `ParquetSourceFileStatisticsFilterEvaluator`
 
-The relational translator does not support:
+`ParquetSourceFileFilterReducer` removes filters already proven true for a file,
+rejects files proven false, and keeps residual filters for row-level evaluation.
 
-- field-to-field comparisons
-- arithmetic expressions as the column side
-- functions as the column side
-- unsupported Polypheny types outside the listed supported type set
-- string-like ordered comparisons such as `text_col > 'x'`
-- boolean ordered comparisons such as `flag > true`
-- binary comparison against a null literal, such as `col = NULL`
-- `IN` with zero values
-- `IN` with a non-column first operand
-- `IN` containing null or otherwise unsupported values
-- logical `AND`, `OR`, or `NOT` if any child is unsupported
-- `LIKE`
-- `BETWEEN`
-- regex predicates
-- `IS TRUE` / `IS FALSE`
-- subqueries
-- arbitrary user-defined or built-in function predicates
-
-### Unsupported Document Filters
-
-The document translator does not support:
-
-- `OR`
-- `NOT`
-- `IN`
-- `IS NULL`
-- `IS NOT NULL`
-- nested document paths such as `address.city`
-- multi-name `RexNameRef` references
-- field-to-field comparisons
-- arithmetic expressions
-- functions
-- null literal comparisons
-- predicates on fields that are not in the exported column list
+See [File Pruning](file_pruning.md) for details.
 
-### Current Notes on Older Documentation
+## Row-Level Evaluation
 
-The existing filtering documents are likely outdated if they state that:
+Exact row filtering runs after rows are read or expanded:
 
-- relational filtering only supports simple binary comparisons
-- relational filtering does not support logical `AND` / `OR` / `NOT`
-- relational filtering does not support `IN`
-- relational filtering does not support `IS NULL` / `IS NOT NULL`
-- the active relational planning path is centered on registered `ParquetRelScan` / `ParquetRelJoin` rules
+- flat primitive rows: `ParquetPrimitiveValueFilterEvaluator`
+- Parquet groups: `ParquetGroupFilterEvaluator`
+- partition-aware rows: `ParquetPartitionAwareFilterEvaluator`
+- normalized nested tables: `ParquetNestedFilterEvaluator`
+- parent/child joins: `ParquetNestedJoinFilterEvaluator`
 
-They may still be conceptually accurate if they describe the broad separation between native Parquet filtering and adapter-level row filtering.
+`ParquetFilterEvaluator` is tri-state. Unknown evaluations keep the row or file.
+That behavior is intentional for pruning and residual evaluation because the
+adapter must not drop data unless it can prove a predicate is false.
diff --git a/plugins/parquet-adapter/docs/future_work.md b/plugins/parquet-adapter/docs/future_work.md
deleted file mode 100644
index 977d008df9..0000000000
--- a/plugins/parquet-adapter/docs/future_work.md
+++ /dev/null
@@ -1,3 +0,0 @@
-- load statistics when adapter added ? workaround can be recalculated from monitoring -> statistics
-- add adapter - check all options in settings lookup
-- support hierarchical partitions
diff --git a/plugins/parquet-adapter/docs/joins.md b/plugins/parquet-adapter/docs/joins.md
index 493ba04b70..f3ca79697a 100644
--- a/plugins/parquet-adapter/docs/joins.md
+++ b/plugins/parquet-adapter/docs/joins.md
@@ -1,293 +1,119 @@
-# Adapter Level Joins
+# Adapter-Level Structural Joins
 
-## Goal: Perform join on parquet adapter level.
+Adapter-level joins let the Parquet adapter execute direct parent/child joins
+between generated normalized tables without materializing both sides as
+independent generic relational inputs first.
 
-For normalized nested tables, the relation is:
-`child.__polypheny_parent_row_id = parent.__polypheny_row_id`
+## Supported Relationship
 
-That relationship comes from one nested Parquet structure. So the adapter can process it closer to how the file is physically stored.
+The supported relationship is the generated structural key relationship:
 
-
-## Benefits of Adepter Level Join
-Adepter Level Join is faster because the adapter-level join uses file layout and nested-table semantics, 
-while Polypheny-level join treats everything as generic relational rows.
-
-### Polypheny join
-- read parent rows
-- read child rows
-- join outside adapter
-
-### Parquet adapter join:
-- read nested parent/child structure
-- apply filters while expanding
-- return already joined rows
-
-## Supported Joins
-
-We support only joins between virtual tables derived from the same physical parquet file.
-
-## Unsupported Joins
-
-### 1. LOWER(category) filter is not supported
-
-```sql
-SELECT product_id, name, category
-FROM pon__products
-WHERE LOWER(category) IS NOT NULL
-LIMIT 10;
-```
-
-![unsupported_lower.png](images/planner/unsupported_lower.png)
-
-### 2. LIMIT inside the FROM creates E_LIMIT node under the JOIN
-
-```sql
-SELECT i.order_item_id, d.code, d.amount
-FROM (
-    SELECT __polypheny_row_id, order_item_id
-    FROM pon__orders__items
-    LIMIT 100
-) i
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 10;
-```
-![unsupported_limit.png](images/planner/unsupported_limit.png)
-
-### 3. Two JOINs - the second join is not supported
-
-join `orders` with `order_items` was done on adapter level
-
-```sql
-SELECT o.order_id, i.product_id, d.code, d.amount
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 10;
-```
-![unsupported_2_joins.png](images/planner/unsupported_2_joins.png)
-
-### 4. Two JOINs - one join with another physical table
-
-```sql
-SELECT o.order_id, i.quantity, p.name, p.category
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-JOIN pon__products p
-    ON p.product_id = i.product_id
-LIMIT 10;
+```text
+child.__polypheny_parent_row_id = parent.__polypheny_row_id
 ```
-![unsupported_2_joins_one_other_physical.png](images/planner/unsupported_2_joins_one_other_physical.png)
-
-## Main Changes
-
-### 1. Planner Nodes
-
-Two planner nodes were added
-
-#### 1.1. ParquetRelScan
-Planner node that represents reading a Parquet-backed table. It's responsibilities:
-- Represents a Parquet scan in the plan. It is the Parquet convention version of a table scan.
-- Stores projection
-- Stores pushed filters
-- Defines row type
-- Exposes fields and filters in the plan visualization
-- Generates execution call: `ParquetRelTable.project(dataContext, fields, filters)`
-
-#### 1.2 ParquetRelJoin
-Planner node that represents a join the Parquet adapter can execute itself. It determines:
-- If this join be executed inside the Parquet adapter
-- the side of parent table
-- which projected fields and filters belong to each scan
-- which join filters should be applied
-- How should this join be executed
-
-#### 1.3 EnumerableParquet
-Planner node that converts ParquetScan node from Parquet convention to enumerable convention
-
-### 2. Rules
-
-- `ParquetRules` class creates list of 4 rules
-- `PatternMatcher` class describes:
-  - what plan shape to match
-  - what transformation to run
-- `ParquetAlgOptRule` class
-  - wraps PatternMatcher as a real AlgOptRule
-    so Polypheny planner can register and execute it 
-- `PatternMatchers` class contains real rule logic
-
-Four planner rules were added:
 
-#### 2.1 EnumerableParquetRule
+Both tables must come from the same normalized Parquet structure, and the child
+must be a direct generated child of the parent.
 
-Rule that converts a plan from Parquet convention into Enumerable convention.
-Parquet-specific plan wrapped as executable enumerable plan.
-The Parquet adapter first creates nodes (ParquetRelScan, ParquetRelJoin) in ParquetConvention. But Polypheny ultimately needs an executable plan in: EnumerableConvention
-So EnumerableParquetRule adds this wrapper:
-- Input convention: ParquetConvention
-- Output convention: EnumerableConvention
-- Matched node: any AlgNode in ParquetConvention
-- Result: EnumerableParquet wrapping that node
+Examples:
 
-#### 2.2 PatternMatchers.attachFieldsAndFiltersToScanUnderCalc
+- `orders` -> `orders__items`
+- `orders` -> `orders__shipping_address`
+- `orders__items` -> `orders__items__discounts`
 
-Rule that pushes EnumerableCalc projection/filter into a ParquetRelScan.
+## Planning
 
-- finds Calc above Parquet scan
-- extracts simple projection
-- extracts supported filter
-- stores both on ParquetRelScan
-- removes the Calc from this part of the plan
+The active relational planner path uses:
 
-It matches this shape:
-```text
-EnumerableCalc
-  EnumerableParquet
-    ParquetRelScan
-```
-and tries to replace with:
-```text
-EnumerableParquet
-  ParquetRelScan(fields + filters updated)
-```
-If projection is complex or filter translation fails, the rule does nothing.
+- `ParquetRelConvention`
+- `ParquetRelRules`
+- `ParquetRelPatternMatchers`
+- `ParquetAlgOptRule`
+- `EnumerableParquet`
+- `ParquetRelScan`
+- `ParquetRelJoin`
 
-#### 2.3 PatternMatchers.joinWithScanOnLeftAndScanOnRight
-
-Rule that converts a generic enumerable join into a Parquet adapter-level join.
-- finds generic join between two Parquet scans
-- checks if it is a nested parent/child join
-- replaces it with ParquetRelJoin
-- wraps it back into EnumerableParquet
-
-It matches this shape:
-```text
-EnumerableJoin
-    EnumerableParquet
-        ParquetRelScan
+`ParquetRelPatternMatchers.joinWithScanOnLeftAndScanOnRight` recognizes an
+`EnumerableJoin` whose left and right inputs are Parquet scans. It asks
+`ParquetRelJoin.supportedDirection(...)` whether the join condition is a direct
+structural relationship.
 
-    EnumerableParquet
-        ParquetRelScan
-```
-And tries to replace it with:
-```text
-EnumerableParquet
-    ParquetRelJoin
-```
+When supported, the generic join is replaced with `ParquetRelJoin` and wrapped
+back in `EnumerableParquet`.
 
-#### 2.4 PatternMatchers.attachFilterToJoinUnderCalc
+The structural join matcher is registered when
+`ParquetOptimizationSettings.isOptimizeAggregation()` is enabled. This setting
+defaults to enabled unless the corresponding system property or environment
+variable disables it.
 
-Rule that pushes a filter from a Calc above a ParquetRelJoin into the join node.
+## Runtime
 
-- finds Calc above ParquetRelJoin
-- extracts supported filter condition
-- converts it to ParquetAdapterFilter
-- stores it on ParquetRelJoin.joinFilters
-- removes that Calc from this part of the plan
+`ParquetRelJoin.implement(...)` calls:
 
-It matches this shape:
 ```text
-EnumerableCalc
-  EnumerableParquet
-    ParquetRelJoin
+ParquetRelTable.nestedJoin(...)
 ```
-and tries to replace with:
-```text
-EnumerableParquet
-  ParquetRelJoin(joinFilters updated)
-```
-
-### 3. Registration of nodes
 
-`ParquetPlugin.registerPolyAlg()` registers:
-- EnumerableParquet.class
-- ParquetRelScan.class
-- ParquetRelJoin.class
+Runtime execution is handled by:
 
-### 4. Parquet Convention
- 
-`ParquetConvention` is the marker that tells the planner: This part of the plan is executable by the Parquet adapter.
-It defines a separate planner convention named: "PARQUET"
-and says that nodes in this convention must implement: ParquetAlg
+- `ParquetRelNestedJoinExecutor`
+- `JoinFiltersSplitter`
+- `ParquetNestedJoinEnumerator`
+- `CombinedGroup`
+- `VirtualGroup`
 
-Conceptually, a convention tells Polypheny who is responsible for executing a part of the query plan.
+The executor:
 
-For the Parquet adapter, ParquetConvention means:
+1. resolves dynamic parameters
+2. combines filters already attached to the left and right scans
+3. maps filters to parent, child, native-reader, and joined-row residual paths
+4. prunes parent source files using partition/statistics filters
+5. reads parent rows
+6. expands direct child rows from the nested Parquet structure
+7. emits joined rows, including supported outer-join unmatched parent rows
 
-“This part of the plan can be executed by the Parquet adapter.”
-It lets the planner distinguish generic Polypheny operations from Parquet-specific operations.
-It enables adapter-specific nodes such as ParquetRelScan and ParquetRelJoin.
-It allows planner rules to rewrite normal scans, filters, projections, and joins into Parquet-aware operations.
-At the end, the Parquet plan is converted back to EnumerableConvention, so Polypheny can execute it.
+## Filter Handling
 
+Filters may originate from:
 
-## Planner Flow - Query Execution with Join
+- the left scan
+- the right scan
+- a calc above the join
 
-1. ParquetRelScan.register() called
-- All rules from ParquetRules.rules added to Planner
+`ParquetRelPatternMatchers.attachFilterToJoinUnderCalc` can attach supported
+filters above an already-created `ParquetRelJoin`.
 
-2. ParquetRelTable.nestedJoin() called (if no join - project called)
-3. ParquetMultiFileEnumerator created
-4. ParquetMultiFileEnumerator performs file pruning
-5. ParquetMultiFileEnumerator creates ParquetNestedJoinEnumerator
+At runtime, `JoinFiltersSplitter` separates filters into:
 
+- parent-only filters
+- child-only filters
+- joined-row adapter filters
+- native reader filters
 
-## Examples of Physical Plans:
+Parent filters can participate in source-file pruning. Child and joined-row
+filters are evaluated after nested child rows are expanded.
 
-### Node Names
-- P_SCAN - ParquetRelScan
-- PE_CALC - EnumerableParquet
-- P_JOIN - ParquetRelJoin
+## Supported Join Types
 
-### Rule 1: EnumerableParquetRule
-Create Enumerable node PE_CALC above P_SCAN 
+The adapter-level node can represent inner and outer structural joins when the
+planner produces a supported direct parent/child condition. Runtime uses the
+`emitUnmatchedParents` flag to preserve parent rows for parent-preserving outer
+join cases.
 
-No filter, no projection, no join
+## Unsupported Or Fallback Cases
 
-```sql
-SELECT *
-FROM pon__orders o;
-```
-![Schema display](images/planner/rule1.png)
+The adapter does not execute these as structural joins:
 
-### Rule 2: PatternMatchers.attachFieldsAndFiltersToScanUnderCalc
-
-Pushing down of projection/filter from E_CALC into P_SCAN.
-
-```sql
-SELECT o.order_id, o.status
-FROM pon__orders o
-where o.order_id = 2 and total_price > 10;
-```
-![Schema display](images/planner/rule2.png)
-
-
-### Rule 3: PatternMatchers.joinWithScanOnLeftAndScanOnRight
-
-Convert E_JOIN with two P_SCANs under it into P_JOIN.
-
-```sql
-SELECT *
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id;
-```
-![Schema display](images/planner/rule3.png)
-
-
-### Rule 4: PatternMatchers.attachFilterToJoinUnderCalc
-
-Get filters from E_CALC under E_JOIN and push it into the PE_JOIN.
-
-```sql
-SELECT o.order_id, i.product_id
-FROM pon__orders o
-JOIN pon__orders__items i
-ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-WHERE i.__polypheny_elem_ordinal > 0 or i.product_id = 1;
-```
+- root-to-root user-column joins
+- joins between unrelated Parquet tables
+- sibling child joins
+- ancestor-to-grandchild joins that skip the direct parent
+- self joins
+- joins on user columns instead of generated structural keys
+- multi-key structural joins
+- non-equality structural joins
+- disjunctive join conditions
+- joins whose inputs contain operators that cannot be represented inside the
+  Parquet join path, such as a limit below a join input
 
-![Schema display](images/planner/rule4.png)
+Unsupported cases remain available to the normal Polypheny planner and runtime.
diff --git a/plugins/parquet-adapter/docs/nested_fields.md b/plugins/parquet-adapter/docs/nested_fields.md
index 8955bc9645..823cc74301 100644
--- a/plugins/parquet-adapter/docs/nested_fields.md
+++ b/plugins/parquet-adapter/docs/nested_fields.md
@@ -1,20 +1,63 @@
+# Nested Fields And Normalized Relational Schemas
 
-# Relational Schema Normalization
+Parquet files can contain nested groups, repeated groups, lists, and maps. The
+relational adapter exposes these structures in two modes controlled by the
+`schema mode` setting on `ParquetRelationalSource`.
 
-Parquet nested structures are exposed as regular relational tables.
-In FLAT mode, each file becomes one table and nested values remain inside the root row.
-In NORMALIZED mode, Polypheny reads the Parquet schema and recursively splits it as follows:
+## Flat Mode
+
+Flat mode exposes one table per discovered table source.
+
+Primitive top-level fields become normal columns. Non-repeated nested primitive
+fields are flattened into parent-table columns using normalized composite names.
+Repeated/list/map fields are preserved as structured values instead of being
+flattened into repeated scalar columns.
+
+Example Parquet shape:
+
+```text
+customer_id
+shipping_address {
+  city
+  country
+}
+orders repeated group
+```
+
+Flat exposed columns:
+
+```text
+customer_id
+shipping_address_city
+shipping_address_country
+orders
+```
+
+The visible column name is not enough to read the physical value, so
+`ExportedSchema` stores column paths:
+
+```text
+shipping_address_city -> ["shipping_address", "city"]
+shipping_address_country -> ["shipping_address", "country"]
+```
+
+`ParquetSchemaReader.collectFlatColumns(...)` builds these columns and paths.
+`ParquetFileDiscovery` validates that compatible multi-file tables also have
+compatible column paths.
+
+## Normalized Mode
 
-- primitive field -> column
-- non-repeated group -> child table with 0/1 row per parent
-- repeated group -> child table with multiple rows per parent
-- repeated primitive field -> child table with one value column and multiple rows per parent
+Normalized mode splits nested structures into generated relational tables:
 
-All relational Parquet table names are adapter-prefixed, both flat and normalized.
+- primitive field: data column on the current table
+- non-repeated group: child table with at most one row per parent row
+- repeated group: child table with multiple rows per parent row
+- repeated primitive field: child table with one data value column
 
-_Example:_ `pn__orders`, not just `orders`. This allows creating different adapters for flat and normalized schema modes.
+Generated table names are adapter-prefixed to avoid collisions between
+different Parquet source instances and schema modes.
 
-## Example of Parquet shape
+Example:
 
 ```text
 orders.parquet
@@ -29,114 +72,101 @@ root
       |- amount: decimal
 ```
 
-## Flat Mode
-
-- Preserve the Parquet file as one table
-
-**_orders_**
-
-| order_id | customer | items                                                                                                                                                         |
-|----------|----------|---------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| 1001     | Alice    | [{product_id: "A-1", quantity: 2, discounts: [{code: "SUMMER", amount: 5.00}, {code: "VIP", amount: 2.00}]}, {product_id: "B-5", quantity: 1, discounts: []}] |
-| 1002     | Bob      | [{product_id: "C-9", quantity: 4, discounts: []}]                                                                                                             |
+Normalized tables:
 
 ```text
-orders.parquet
--> orders
+pn__orders
+pn__orders__items
+pn__orders__items__discounts
 ```
 
-## Normalized Mode
-
-- Split nested structures into generated relational child tables
-- NEED TO STORE ADDITIONAL INFORMATION
+## Synthetic Columns
 
-**_orders_**
+Normalized relational tables contain generated structural columns.
 
-| __polypheny_row_id | order_id | customer |
-|--------------------|----------|----------|
-| 0                  | 1001     | Alice    |
-| 1                  | 1002     | Bob      |
+Root tables:
 
-**_orders__items_**
+- `__polypheny_row_id`
 
-| __polypheny_row_id | __polypheny_parent_row_id | __polypheny_elem_ordinal | product_id | quantity |
-|--------------------|---------------------------|--------------------------|------------|----------|
-| 0/items[0]         | 0                         | 0                        | A-1        | 2        |
-| 0/items[1]         | 0                         | 1                        | B-5        | 1        |
-| 1/items[0]         | 1                         | 0                        | C-9        | 4        |
+Child tables:
 
-**_orders__items__discounts_**
+- `__polypheny_row_id`
+- `__polypheny_parent_row_id`
+- `__polypheny_elem_ordinal`
 
-| __polypheny_row_id        | __polypheny_parent_row_id | __polypheny_elem_ordinal | code   | amount |
-|---------------------------|---------------------------|--------------------------|--------|--------|
-| 0/items[0]/discounts[0]   | 0/items[0]               | 0                        | SUMMER | 5.00   |
-| 0/items[0]/discounts[1]   | 0/items[0]               | 1                        | VIP    | 2.00   |
+Synthetic row ids are deterministic structural paths:
 
 ```text
-orders.parquet
-    -> orders
-    -> orders__items
-    -> orders__items__discounts
- ```
+0
+0/items[1]
+0/items[1]/discounts[0]
+```
 
-```sql
-SELECT o.order_id, i.product_id, i.quantity
-FROM orders o
-JOIN orders__items i
-  ON i.polypheny_parent_row_id = o.polypheny_row_id;
+These columns are not stored in Parquet. They are generated during scan by
+`VirtualGroup`, `GroupMetadata`, `ParquetNestedRepeatedRelEnumerator`, and
+`ParquetNestedNonRepeatedRelEnumerator`.
 
-```
+## Bindings
 
-## Example with unique table names
+Generated tables are virtual. `ParquetTableBinding` connects each physical
+Polypheny table to the original source files and Parquet paths.
 
-### Flat result
+Important binding objects:
+
+- `DiscoveredTableBinding`: discovery-time binding before physical table ids
+  exist
+- `ParquetTableBinding`: persisted runtime binding for a physical table
+- `ParquetColumnBinding`: maps one physical column to its role and source path
+- `ParquetColumnRole`: `DATA`, `PRIMARY_KEY`, `PARENT_KEY`, `ORDINAL`,
+  `PARTITION`
+- `ParquetBindingSerializer`: persists bindings through adapter settings
+
+For a generated `pn__orders__items` table, the table binding stores:
 
 ```text
-pf__orders -> [order_id, customer_id, status, ...]
+sourceFiles = original Parquet files
+parentTableName = pn__orders
+sourcePathElements = ["items"]
 ```
 
-### Normalized result
+Column bindings describe how each output column is produced:
 
 ```text
-pn__orders -> [order_id, customer_id, status, total_price]
-pn__orders__items -> [__polypheny_row_id, __polypheny_parent_row_id, __polypheny_elem_ordinal, order_item_id, product_id, quantity, price]
-pn__orders__items__discounts -> [__polypheny_row_id, __polypheny_parent_row_id, __polypheny_elem_ordinal, code, amount]
+__polypheny_row_id        -> PRIMARY_KEY
+__polypheny_parent_row_id -> PARENT_KEY
+__polypheny_elem_ordinal  -> ORDINAL
+product_id               -> DATA, ["items", "product_id"]
+quantity                 -> DATA, ["items", "quantity"]
 ```
 
-## Synthetic Columns
+## Scan Strategies
+
+`ParquetRelTable` delegates scans to `ParquetRelExecutorsFactory`, which chooses
+an execution strategy based on table shape and projection:
+
+- `ParquetRowRelEnumerator`: fast primitive-row path for simple flat primitive
+  projections
+- `ParquetRelEnumerator`: generic flat row path
+- `ParquetNestedNonRepeatedRelEnumerator`: path-based one-row-per-root scan
+- `ParquetNestedRepeatedRelEnumerator`: expands repeated nested rows
+- `ParquetNestedJoinEnumerator`: executes supported structural parent/child
+  joins
 
-- Normalized tables include visible _synthetic key columns_:
-  - __polypheny_row_id
-  - __polypheny_parent_row_id
-  - __polypheny_elem_ordinal
-- Root tables have:
-  - __polypheny_row_id
-- Child tables have:
-  - __polypheny_row_id
-  - __polypheny_parent_row_id
-  - __polypheny_elem_ordinal
-- Non-repeated child tables still have:
-  - __polypheny_parent_row_id
-  - __polypheny_elem_ordinal is always 0
-
-Synthetic row ids are generated as deterministic structural path identifiers.
-Examples:
-
-- root row: `0`
-- repeated child row: `0/items[1]`
-- deeper repeated child row: `0/items[1]/discounts[0]`
-
-**_Sql Example:_**
+Path-based scans use `ParquetPathValueExtractor` to read values from the
+original Parquet group and to synthesize structural values.
+
+## Querying Normalized Tables
+
+Generated structural joins use parent and child synthetic keys:
 
 ```sql
 SELECT o.order_id, i.product_id, i.quantity
 FROM pn__orders o
 JOIN pn__orders__items i
   ON i.__polypheny_parent_row_id = o.__polypheny_row_id;
-
 ```
 
-For deeper nesting, the same rule applies at each level. A row in pn__orders__items__discounts points to its parent item row, not directly to the root order row:
+For deeper nesting, join each child to its direct parent:
 
 ```sql
 SELECT i.product_id, d.code, d.amount
@@ -145,270 +175,5 @@ JOIN pn__orders__items__discounts d
   ON d.__polypheny_parent_row_id = i.__polypheny_row_id;
 ```
 
-## BINDINGS
-
-Generated relational tables are virtual: they do not exist as separate Parquet files.
-The data still lives in the original Parquet file.
-To connect virtual tables with physical Parquet data, we store bindings.
-Bindings describe:
-
-- which Parquet file backs each generated table
-- which nested path produces the table rows
-- which Parquet field path provides each column value
-- which synthetic columns are generated by Polypheny
-
-### Example
-
-#### Table Bindings for orders.parquet
-
-| Generated table             | Source Parquet file | Nested path that produces table rows | Parent table      |
-|-----------------------------|---------------------|--------------------------------------|-------------------|
-| `pn_orders`                 | `orders.parquet`    | root row                             | none              |
-| `pn_orders_items`           | `orders.parquet`    | `items[*]`                           | `pn_orders`       |
-| `pn_orders_items_discounts` | `orders.parquet`    | `items[*].discounts[*]`              | `pn_orders_items` |
-
-#### Column Bindings for pn_orders_items_discounts
-
-| Generated column          | Source Parquet field path                              | Column role           |
-|---------------------------|--------------------------------------------------------|-----------------------|
-| `__polypheny_row_id`      | generated by Polypheny from row structure              | synthetic primary key |
-| `__polypheny_parent_row_id` | generated by Polypheny from parent item row          | synthetic parent key  |
-| `__polypheny_elem_ordinal` | generated by Polypheny from repeated discount position | synthetic ordinal     |
-| `code`                    | `items.discounts.code`                                 | data                  |
-| `amount`                  | `items.discounts.amount`                               | data                  |
-
-
-
-
-## Code Changes
-
-### AbstractParquetSource
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\AbstractParquetSource.java`
-
-- Adding shared metadata persistence, safer URL handling, and adapter-prefixed table names
-- Store Parquet Bindings in adapter settings in persistParquetBindings() function
-- Load Parquet Bindings from settings in constructor()
-- Prefix is added to the table names to allow deployment multiple Parquet relational adapters over the same files, for example one flat and one normalized, without logical table name collisions - getPrefixTableName() function
-
-### ParquetColumnRole
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetColumnRole.java`
-
-- Describes what kind of column a relational Parquet column is inside a ParquetTableBinding
-  - DATA - a normal real Parquet value
-  - PRIMARY_KEY - synthetic generated row id
-  - PARENT_KEY - synthetic reference to the parent generated row
-  - ORDINAL - position of a child value inside its parent
-
-### ParquetColumnBinding
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetColumnBinding.java`
-Metadata object that tells the Parquet scanner for specific Polypheny column, where does its value come from. Used for reading data.
-Contains:
-
-- columnId - Polypheny physical column id connected to an actual column in Polypheny's physical table catalog
-- columnName - relational column name
-- role - ParquetColumnRole - what kind of column this is
-- sourcePathElements - the path inside the Parquet schema that should be used to read this column. For example:
-  - flat/root column - path: ["order_id"]
-  - nested - path: ["shipping_address", "city"]
-
-### ParquetTableBinding
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetTableBinding.java`
-Table-level metadata, describes the whole table, immutable record
-Fields:
-
-- sourceUrl - real Parquet file URL to read from
-- parentTableName - generated parent relational table name, or null for root tables
-- sourcePathElements - table-level path inside the Parquet file, For root: List.of(), for nested: List.of("items")
-- columnsByColumnId - `Map<Long, ParquetColumnBinding>` - maps Polypheny physical column ids to column bindings
-
-Example:
-
-```text
-sourceUrl: orders.parquet
-parentTableName: pn__orders
-sourcePathElements: ["items"]
-
-columnPaths:
-product_id -> ["items", "product_id"]
-quantity   -> ["items", "quantity"]
-```
-
-### ParquetUrlResolver
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\io\ParquetUrlResolver.java`
-Utility, Parquet file/directory URL handling
-
-### ParquetSchemaMode
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetSchemaMode.java`
-User-selectable adapter setting that controls how the Parquet schema:
-
-- FLAT
-- NORMALIZED
-
-### ParquetSchemaNormalizer
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetSchemaNormalizer.java`
-Turns Parquet nested schema into multiple relational tables for normalized mode.
-Contains the main normalization logic. It builds ParquetNormalizedSchema with:
-
-- `Map<String, List<ExportedColumn>>` tables
-- `Map<String, DiscoveredTableBinding>` bindings
-- Adds normalized synthetic columns:
-  - `__polypheny_row_id`
-  - `__polypheny_parent_row_id`
-  - `__polypheny_elem_ordinal`
-
-### ParquetNormalizedSchema
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetNormalizedSchema.java`
-contains normalizes schema information for all tables:
-
-- `Map<String, List<ExportedColumn>>` tables - normalized columns per table name
-- `Map<String, DiscoveredTableBinding>` bindings - binding per table name
-
-### DiscoveredTableBinding
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\DiscoveredTableBinding.java`
-temporary discovery metadata before a real ParquetTableBinding created
-for ParquetTableBinding we need physical table ids, that are created after normalization step
-
-### ParquetBindingSerializer
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetBindingSerializer.java`
-Saves and restores ParquetTableBinding metadata through adapter settings.
-
-### AbstractParquetEnumerator
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\execution\AbstractParquetEnumerator.java`
-Shared base for relational scans.
-
-- Manages reader lifecycle, row iteration, and row queueing
-- Supports turning one input Parquet row into zero, one, or many output relational rows
-- Applies exact row-level filtering after native Parquet row-group pruning
-- Reused by flat, non-repeated nested, and repeated nested relational scans
-
-### ParquetNestedRepeatedRelEnumerator
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetNestedRepeatedRelEnumerator.java`
-Used for tables that do not have their own Parquet file, but are created from a nested group inside a Parquet file.
-
-- Expands one root Parquet row into multiple relational rows by following `tablePath`
-- Creates hierarchical synthetic row ids such as `0/items[1]/discounts[0]`
-- Reads data columns by Parquet path and synthetic columns from execution metadata
-
-### ParquetNestedNonRepeatedRelEnumerator
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\execution\ParquetNestedNonRepeatedRelEnumerator.java`
-Used to handle virtual table that was created from nested non-repeated types.
-
-- Keeps one relational row per root Parquet row
-- Wraps rows into execution-time virtual rows with synthetic metadata
-- Reads data columns by Parquet path and synthetic columns from execution metadata
-
-### ParquetRelationalSource
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\ParquetRelationalSource.java`
-changed from simple relational Parquet source that exports one table per file into the controller for flat vs normalized relational Parquet mode.
-
-### ParquetRelTable
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\relational\schema\ParquetRelTable.java`
-
-`ParquetRelTable` changed from a simple wrapper that always scanned Parquet columns by ordinal into a wrapper that chooses the correct scan strategy based on table binding metadata.
-
-Current scan strategies:
-
-- `ParquetRelEnumerator`
-  - used for flat/root tables when relational column positions still match Parquet field positions
-  - keeps the original ordinal-based scan behavior
-
-- `ParquetNestedNonRepeatedRelEnumerator`
-  - used when rows are still produced one-to-one, but values must be read by Parquet source path instead of ordinal position
-  - supports path-based extraction and filtering for non-repeated nested values
-
-- `ParquetNestedRepeatedRelEnumerator`
-  - used for generated repeated child tables
-  - expands one Parquet root row into multiple relational rows by following the configured table path
-
-`ParquetRelTable` uses `ParquetTableBinding` to decide which scanner to create.
-This allows Polypheny to read both flat tables and generated normalized tables from the same original Parquet file.
-Synthetic id columns are not stored in Parquet files; they are exposed during scan through virtual execution rows.
-
-### ParquetNamespace
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\schema\ParquetNamespace.java`
-Make relational table creation binding-aware.
-
-### ParquetAdapterFilter
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\filter\ParquetAdapterFilter.java`
-
-`ParquetAdapterFilter` is the shared immutable filter description used during Parquet scan planning and execution.
-
-It can represent filters in two forms:
-
-- ordinal filter
-  - uses `columnIndex`
-  - used when relational column position still matches Parquet field position
-
-- path-based filter
-  - uses `pathElements`
-  - used for normalized tables and nested fields, where values must be read by Parquet source path
-
-The helper method `makeNested(int startIndex)` creates a nested version of the same filter by trimming the prefix of the Parquet path.
-This is useful when a nested scanner already operates inside a deeper Parquet group and only needs the remaining relative path.
-
-### ParquetNativeFilterBuilder
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\filter\ParquetNativeFilterBuilder.java`
-
-`ParquetNativeFilterBuilder` creates native Parquet predicates for row-group pruning.
-
-It translates `ParquetAdapterFilter` into Parquet filter expressions when the target field can be mapped safely to a primitive Parquet column.
-
-Native Parquet filtering is only supported for primitive non-repeated leaf fields.
-
-If a filter cannot be pushed down safely, it is ignored at the native Parquet level and must still be applied during row scanning.
-
-This means native filtering is used as an optimization, while exact correctness is preserved by enumerator-level filtering.
-
-
-### VirtualGroup
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\execution\VirtualGroup.java`
-
-Execution-time wrapper around a Parquet `Group`.
-
-- Delegates all `Group` operations to the original Parquet row/group
-- Carries `GroupMetadata`
-- Used by normalized scans so data columns can still be read from the original Parquet structure while synthetic columns are generated from metadata
-
-### GroupMetadata
-
-`Polypheny-DB\plugins\parquet-adapter\src\main\java\org\polypheny\db\adapter\parquet\shared\execution\GroupMetadata.java`
-
-Stores synthetic metadata for normalized relational rows:
-
-- `rowId`
-- `parentRowId`
-- `ordinal`
-
-These values are generated during scan and are used to expose:
-
-- `__polypheny_row_id`
-- `__polypheny_parent_row_id`
-- `__polypheny_elem_ordinal`
-
-
-
-
-## Flow
-
-![Schema display](diagrams/nested_fields_flow_simple.png)
-
-![Schema display](diagrams/nested_fields_flow.png)
+The adapter can execute supported direct parent/child joins as `ParquetRelJoin`.
+Other joins fall back to normal Polypheny planning.
diff --git a/plugins/parquet-adapter/docs/partitions_changes.md b/plugins/parquet-adapter/docs/partitions_changes.md
deleted file mode 100644
index 55272ddedd..0000000000
--- a/plugins/parquet-adapter/docs/partitions_changes.md
+++ /dev/null
@@ -1,242 +0,0 @@
-# Partitioning Changed Files
-
-## Discovery and Schema Metadata
-
-### ParquetFileDiscovery.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java`
-
-Discovery layer that converts the physical file/folder layout into logical tables the adapter can create and scan.
-
-### DiscoveredTable.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTable.java`
-
-It represents one logical Polypheny table found during Parquet discovery, before the table is fully registered in Polypheny.
-
-### DiscoveredTableBinding.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/DiscoveredTableBinding.java`
-
-Metadata that connects a discovered logical table back to the real Parquet data. Now contains list of source files.
-
-### ParquetSourceFile.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFile.java`
-
-One physical Parquet file that contributes rows to a Parquet table. Contains partition values.
-
-### ParquetSchemaReader.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java`
-
-Can read many parquet files. Builds one consolidated schema from all file schemas.
-
-### ParquetSchemaNormalizer.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSchemaNormalizer.java`
-Supports normalizing one discovered logical table that may be backed by many files and partitions
-
-### ParquetTableBinding.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetTableBinding.java`
-
-Contains list of source files
-
-### ParquetColumnRole.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetColumnRole.java`
-
-PARTITION was added
-
-### ParquetBindingSerializer.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetBindingSerializer.java`
-
-Saves table binding metadata in adapter settings. It now saves all source files and their partition values, so the adapter can restore partitioned tables after restart.
-
-## Adapter Catalog and Table Exposure
-
-### AbstractParquetSource.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/AbstractParquetSource.java`
-
-Discovers logical tables from the Parquet location. It keeps the discovered tables in memory and exposes their bindings to the relational source.
-
-### ParquetNamespace.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/schema/ParquetNamespace.java`
-
-Creates Parquet table wrappers. Root tables now use `ParquetSourceFile`, so simple tables and multi-file tables use the same file model.
-
-### ParquetRelationalSource.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/ParquetRelationalSource.java`
-
-Creates physical Parquet tables from discovered metadata. If a partition column exists, it creates Polypheny list partitions and stores only the matching files in each partition binding.
-
-### ParquetRelTable.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java`
-
-Runs scans for a Parquet table. It reads all files in the table binding, resolves filters, skips files by partition values, and chooses the needed enumerator.
-
-## Planner and Filter Pushdown
-
-### ParquetRelTableScanRule.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelTableScanRule.java`
-
-Converts a logical scan into `ParquetRelScan` when the table is a Parquet table. This gives the adapter its own scan node.
-
-### ParquetRelScanRuleSupport.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRuleSupport.java`
-
-Contains helper methods used by scan, filter, and calc rules. It finds a `ParquetRelScan` and returns field types for filter translation.
-
-### ParquetRelScan.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScan.java`
-
-Adapter scan node for Parquet tables. It stores selected fields and pushed filters, then calls `ParquetRelTable.project(...)` during execution.
-
-### ParquetRelScanRule.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetRelScanRule.java`
-
-Pushes simple projections into `ParquetRelScan`. This lets the adapter read only selected columns.
-
-### ParquetEnumerableFilterScanRule.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableFilterScanRule.java`
-
-Pushes supported filters into a Parquet scan. If the filter can be translated, the adapter applies it.
-
-### ParquetEnumerableCalcScanRule.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/planning/ParquetEnumerableCalcScanRule.java`
-
-Handles `LogicalCalc`, which may contain both filter and projection. It pushes the filter into Parquet and keeps the projection above the scan.
-
-### AbstractFilterTranslator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractFilterTranslator.java`
-
-Common base for filter translators. It parses simple comparison filters, supports literals and dynamic parameters, and handles reversed comparisons.
-
-### ParquetRelFilterTranslator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelFilterTranslator.java`
-
-Converts relational filters into `ParquetAdapterFilter`. It supports simple comparisons, `AND`, `OR`, `NOT`, `IN`, and dynamic parameters.
-
-## Runtime Filtering and Source Pruning
-
-### FilterEvaluator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/FilterEvaluator.java`
-
-Base class for checking adapter filters. It can return true, false, or unknown; unknown means the data should not be skipped.
-
-### ParquetSourceFileFilterEvaluator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetSourceFileFilterEvaluator.java`
-
-Skips files before reading them. It checks filters against partition values stored in `ParquetSourceFile`.
-
-### ParquetGroupFilterEvaluator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetGroupFilterEvaluator.java`
-
-Checks filters against actual Parquet rows. It can read values by field index or by stored Parquet path.
-
-### ParquetPartitionAwareFilterEvaluator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPartitionAwareFilterEvaluator.java`
-
-Adds support for partition columns during row filtering. Partition values come from the current source file, not from the Parquet row.
-
-### ParquetNestedFilterEvaluator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedFilterEvaluator.java`
-
-Checks filters for generated nested tables. It uses column bindings and table paths to find the real value inside the root Parquet row.
-
-### ParquetNestedJoinFilterEvaluator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/filter/ParquetNestedJoinFilterEvaluator.java`
-
-Checks filters on joined parent-child rows. It also checks parent filters before child rows are expanded.
-
-## Runtime Reading and Enumeration
-
-### ParquetSourceReader.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSourceReader.java`
-
-Low-level reader for one Parquet file. It now accepts filters, can use native Parquet filtering, and exposes the current row number.
-
-### AbstractParquetEnumerator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/AbstractParquetEnumerator.java`
-
-Base enumerator for Parquet rows. It expands rows when needed and applies adapter filters before returning results.
-
-### ParquetMultiFileEnumerator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetMultiFileEnumerator.java`
-
-Reads several files as one table. It runs one enumerator per file and returns all rows as one continuous result.
-
-### ParquetRelEnumerator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetRelEnumerator.java`
-
-Simple enumerator for flat Parquet rows. It now uses the shared filter flow.
-
-### ParquetNestedNonRepeatedRelEnumerator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedNonRepeatedRelEnumerator.java`
-
-Reads rows using column bindings. It can fill partition columns from the source file and data columns from Parquet paths.
-
-### ParquetNestedRepeatedRelEnumerator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedRepeatedRelEnumerator.java`
-
-Reads repeated nested fields as relational rows. It follows the table path and creates one output row per nested item.
-
-### ParquetNestedJoinEnumerator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetNestedJoinEnumerator.java`
-
-Executes supported parent-child joins inside the adapter. It reads one Parquet file, expands child rows under each parent, and returns joined rows.
-
-### ParquetPathValueExtractor.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/execution/ParquetPathValueExtractor.java`
-
-Reads values by Parquet path. It also creates synthetic values like row id, parent row id, and ordinal.
-
-### ParquetValueExtractor.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/ParquetValueExtractor.java`
-
-Common interface for value extractors. It now also supports reading values by path.
-
-### CombinedGroup.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/execution/CombinedGroup.java`
-
-Virtual row for a parent-child join. It knows which output fields come from the parent and which come from the child.
-
-## Statistics and Polypheny Partition Awareness
-
-### ParquetStatisticsReader.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java`
-
-Reads statistics from all files of a table. It also creates statistics for partition columns from folder values.
-
-### StatisticsManagerImpl.java
-`monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java`
-
-Uses statistics provided by the adapter when available. It can combine statistics from several physical partitions of the same logical table.
-
-### AbstractQueryProcessor.java
-`dbms/src/main/java/org/polypheny/db/processing/AbstractQueryProcessor.java`
-
-Uses partition filter values to choose accessed partitions. This helps routing use only the needed Polypheny partitions when possible.
-extractPartitionsForLogicalEntity() new function
-
-## Document Model Support
-
-### ParquetDocument.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/schema/ParquetDocument.java`
-
-Document scans now use the shared filter flow. Dynamic parameters are resolved before reading.
-
-### ParquetDocFilterTranslator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocFilterTranslator.java`
-
-Converts supported document filters into adapter filters. It matches document field names to exported columns.
-
-### ParquetDocEnumerator.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/document/execution/ParquetDocEnumerator.java`
-
-Reads Parquet rows as documents. It now uses the shared filter flow and creates document ids from the file path and row number.
-
-
-
-## Examples
-
-```
-select *
-from prn__yellow_tripdata
-where "year" = '2026'
-```
-
-![Schema display](images/partition_year.png)
diff --git a/plugins/parquet-adapter/docs/partitions_overview.md b/plugins/parquet-adapter/docs/partitions_overview.md
index 6071b4698e..6b28661daf 100644
--- a/plugins/parquet-adapter/docs/partitions_overview.md
+++ b/plugins/parquet-adapter/docs/partitions_overview.md
@@ -1,108 +1,24 @@
-# Parquet Multi-File and Partitioned Folder Layout Design
+# Multi-File And Partitioned Folder Layouts
 
-## Goal
+The relational and document Parquet sources can discover a single Parquet file,
+a folder of compatible Parquet files, a Hive-style partitioned table folder, or
+a dataset root containing multiple table folders.
 
-Add partitions support.
+Partition support is implemented by `ParquetFileDiscovery`,
+`ParquetSourceFile`, `ParquetTableBinding`, `ParquetSchemaNormalizer`,
+`ParquetRelationalSource`, and the source-file pruning evaluators.
 
-## General
+## Supported Source Shapes
 
-Partitioning is a way to organize one logical table into multiple physical files or folders based on column values.
+Single file:
 
-In our Parquet adapter, partition values come from Hive-style folder names, for example:
-
-```text
-yellow_tripdata/
-  year=2025/
-    month=01/
-      yellow_tripdata_2025-01.parquet
-```
-
-This file belongs to the logical table `yellow_tripdata`, and the adapter derives two virtual column values from the path:
-
-```text
-year = '2025'
-month = '01'
-```
-
-These partition columns are exposed together with the real Parquet data columns. When a query filters by partition columns, the adapter can skip files from non-matching folders before opening them. For example, `WHERE "year" = '2025' AND "month" = '01'` reads only files under `year=2025/month=01`.
-
-So partitioning improves query performance by reducing the number of Parquet files that need to be opened and scanned.
-
-## Logical and Physical Tables
-
-If partition integration is triggered, then created:
-- one logical table
-- many physical tables: one physical table per value of the first partition column (one physical table per partition)
-
-### Example:
 ```text
-yellow_tripdata/
-  year=2024/
-    month=01/file.parquet
-    month=02/file.parquet
-  year=2025/
-    month=01/file.parquet
-    month=02/file.parquet
-  year=2026/
-    month=01/file.parquet
+customers.parquet
 ```
 
-Logical table: 
-- yellow_tripdata
-
-Physical tables:
-
-- yellow_tripdata partition year=2024
-- yellow_tripdata partition year=2025
-- yellow_tripdata partition year=2026
-
-## New Functionality
-
-The following functionality was implemented:
-
-### 1. Support multiple Parquet files as one logical table.
-- `ParquetFileDiscovery` discovers multiple files for one logical table.
-- `DiscoveredTableBinding`, `ParquetTableBinding`, `ParquetSourceFile` store the file list.
-- `ParquetRelTable` uses the file list at scan time.
-- `ParquetMultiFileEnumerator` iterates over selected files one by one.
-- `ParquetSchemaReader` can read schemas/footers from multiple files.
-
-### 2. Support one common table schema across those files, including missing fields.
-- `ParquetFileDiscovery` decides whether file schemas can be consolidated.
-- `ParquetSchemaReader` builds a combined schema from multiple file footers.
-- `ParquetSchemaNormalizer` creates normalized tables from discovered multi-file bindings.
-- `ParquetTableBinding` maps logical/physical columns to Parquet source paths.
-- `ParquetRelTable` decides when binding-aware scanning is needed.
-- `ParquetPathValueExtractor` returns NULL when a source path is missing.
+Creates one table or collection backed by one `ParquetSourceFile`.
 
-### 3. Add Hive-style partition discovery from `key=value` folders.
-- `ParquetFileDiscovery` recursively discovers partition folders and parses key=value.
-- `ParquetSourceFile` stores each file URL together with its partition values.
-- `DiscoveredTable`, `DiscoveredTableBinding` carry discovered partitioned table metadata.
-- `ParquetSchemaNormalizer` adds partition columns to normalized root tables.
-- `ParquetTableBinding` marks those columns with `ParquetColumnRole.PARTITION`
-
-### 4. Add file-level pruning from partition filters.
-- `ParquetRelFilterTranslator` translates supported SQL filters into adapter filters.
-- `ParquetRelScan`, `ParquetEnumerableFilterScanRule`, `ParquetEnumerableCalcScanRule` push filters into the scan.
-- `ParquetRelTable` resolves filter columns to `ParquetColumnBinding`.
-- `ParquetSourceFileFilterEvaluator` evaluates partition filters against `ParquetSourceFile.partitionValues()`.
-- `ParquetPartitionAwareFilterEvaluator` evaluates partition filters during row-level scan fallback.
-
-### 5. Integrate Polypheny's logical partition metadata for the first partition column.
-- `ParquetRelationalSource` creates Polypheny partition `groups/partitions/allocations` for the first partition column.
-- `ParquetRelationalSource.firstPolyphenyPartitionColumn` chooses the partition column.
-- `ParquetRelationalSource.createPartitionedTable` creates the catalog partition layout.
-- `AbstractQueryProcessor` maps detected partition filter values to accessed partition IDs.
-- `StatisticsManagerImpl` aggregates statistics from multiple partition allocations.
-- `ParquetStatisticsReader` provides partition-column statistics from folder values.
-
-
-## Examples
-
-### Table Creation
-
-Given this non-partitioned folder:
+Folder with several compatible files:
 
 ```text
 yellow_tripdata/
@@ -110,237 +26,140 @@ yellow_tripdata/
   yellow_tripdata_2025-02.parquet
 ```
 
-Polypheny should expose one table:
+Creates one logical table backed by multiple `ParquetSourceFile` objects when
+the exported schemas can be consolidated.
 
-```text
-parquetrelational1__yellow_tripdata
-```
-
-Given this partitioned folder:
+Partitioned table folder:
 
 ```text
 yellow_tripdata/
-  year=2025/month=01/yellow_tripdata_2025-01.parquet
-  year=2025/month=02/yellow_tripdata_2025-02.parquet
-  year=2026/month=01/yellow_tripdata_2026-01.parquet
-```
-
-Polypheny should also expose one table:
-
-```text
-parquetrelational1__yellow_tripdata
-```
-
-The partitioned table should have:
-
-- data columns from the consolidated Parquet file schemas
-- virtual partition columns from folder names:
-  - `year`
-  - `month`
-
-Example query:
-
-```sql
-SELECT *
-FROM parquetrelational1__yellow_tripdata
-WHERE "year" = '2025' AND "month" = '01';
+  year=2025/
+    month=01/
+      part-1.parquet
+    month=02/
+      part-2.parquet
+  year=2026/
+    month=01/
+      part-3.parquet
 ```
 
-Execution:
-
-1. Translate supported filters as today.
-2. Resolve `year = '2025'` and `month = '01'` as partition-column filters.
-3. Select only files below `year=2025/month=01`.
-4. Open only those files.
-5. Apply existing native Parquet filters inside each selected file for non-partition columns.
-6. Emit rows with `year` and `month` populated from the path values.
-
-## Source and Table Recognition
-
-### Definitions
-
-Configured source location:
+Creates one logical table with virtual partition columns:
 
-- the adapter setting chosen by the user
-- can point to a single `.parquet` file, a table root directory, or a dataset root directory
+- `year`
+- `month`
 
-Table root:
-
-- the directory that represents one logical table
-- all Parquet files below this root belong to that table unless excluded by discovery rules
-- partition directories, when present, are below the table root
-
-Partition root:
-
-- the first `key=value` directory below the table root
-- partition columns are derived from `key=value` directories under the table root, not above it
-
-Example:
+Dataset root:
 
 ```text
-dataset/
+tlc/
   yellow_tripdata/
-    year=2025/
-      month=01/
-        yellow_tripdata_2025-01.parquet
+    year=2025/month=01/part-1.parquet
+  green_tripdata/
+    year=2025/month=01/part-1.parquet
 ```
 
-Here:
+Creates one discovered table per child table folder.
 
-- configured source can be `dataset/` or `dataset/yellow_tripdata/`
-- table root is `dataset/yellow_tripdata/`
-- partition root is `dataset/yellow_tripdata/year=2025/`
-- partition columns are `year`, then `month`
+## Partition Values
 
-### File Naming
+Partition values are parsed from folder names of the form `key=value`.
+`ParquetFileDiscovery` normalizes partition keys with `ParquetNameNormalizer`
+and stores them in `ParquetSourceFile.partitionValues()`.
 
-Files such as:
+Example:
 
 ```text
-yellow_tripdata_2025-01.parquet
-yellow_tripdata_2025-02.parquet
+yellow_tripdata/year=2025/month=01/part-1.parquet
 ```
 
-are valid source files for the same table when they live under the same folder and their schemas consolidate:
+produces:
 
 ```text
-yellow_tripdata/
-  yellow_tripdata_2025-01.parquet
-  yellow_tripdata_2025-02.parquet
+year = "2025"
+month = "01"
 ```
 
-The adapter should not rely on filename prefixes as the main grouping rule. Schema compatibility is the deciding rule for direct files in one folder.
-
-## Logic Flow
-
-1. File/table discovery (ParquetFileDiscovery.discoverTables(...)) checks:
-- one Parquet file
-- folder with several Parquet files
-- partitioned folder
-- dataset root with several table folders
-
-2. Schema creation:
-- ParquetSchemaReader reads Parquet metadata from the discovered files and builds common schema.
-- Before: one file -> one schema; Now: many files -> one common schema
-
-3. Adapter discovery metadata
-- ParquetFileDiscovery creates DiscoveredTable and DiscoveredTableBinding.
-- This metadata describes table name, columns, source files, partition values, and column paths.
-
-4. Logical table creation.
-- Adapter returns columns through getExportedColumns().
-- Polypheny creates the logical table in its catalog.
-
-
-5. Physical tables creation. ParquetRelationalSource.createTable() uses the discovered metadata.
-
+Partition columns are exposed as normal columns. They are not stored inside the
+Parquet row; row projection fills them from the current `ParquetSourceFile`.
 
-6. Add partitions on Polypheny level. ParquetRelationalSource.createPartitionedTable() adds needed information to catalog.
+## Polypheny Physical Partition Metadata
 
+Only the first discovered partition column is mapped to Polypheny physical list
+partition metadata. This is handled by `ParquetRelationalSource`:
 
-7. Query planning happens. ParquetRelScan called. Filters and projections can be pushed into this scan.
+- `firstPolyphenyPartitionColumn(...)` selects the first partition key
+- `createPartitionedTable(...)` groups files by that partition value
+- each partition allocation receives a binding containing only the files for
+  that value
 
+Additional partition columns remain adapter-level partition values. They are
+still visible as columns and can still be used by file pruning.
 
-8. Runtime scan happens. ParquetRelTable:
-- reads metadata
-- applies partition pruning: ParquetSourceFileFilterEvaluator.prune(...) - returns only the files that can match the partition filter
-- and reads only needed files: creates ParquetMultiFileEnumerator
+If there is only one value for the selected first partition column, the adapter
+keeps the normal single physical allocation instead of creating a partitioned
+layout.
 
-9. Statistics updated
-- A logical column can now be spread across several physical Parquet files, and also across several Polypheny physical partitions.
-- During statistics discovery, we may need to inspect all existing files
+## Schema Consolidation
 
-## File/table discovery 
-### 1. one Parquet file
-```text
-yellow_tripdata/
-  yellow_tripdata_2025-01.parquet
-```
-one table backed by one file (old functionality)
+`ParquetSchemaReader.exportedSchema(...)` returns visible columns and a mapping
+from exported column names to physical Parquet paths.
 
-### 2. folder with several Parquet files
-```text
-yellow_tripdata/
-  yellow_tripdata_2025-01.parquet
-  yellow_tripdata_2025-02.parquet
-  yellow_tripdata_2025-03.parquet
-```
-There are no key=value partition folders here.
+`ParquetFileDiscovery` can expose multiple files as one table when:
 
-Result:
-- one logical table
-- one physical table
-- many Parquet files in the binding
+- their exported column lists are compatible enough
+- their exported source paths are compatible
+- partition/data column collisions are either absent or proven safe
 
-This is new functionality compared to old one-file behavior.
+Schema evolution is handled by creating the union of compatible columns. When a
+file does not contain a projected data column, path-based readers return `NULL`
+for that value.
 
-many monthly files -> one table
+## Partition Column Collisions
 
-So the user can query:
+A partition folder may have the same normalized name as a physical Parquet
+column. The adapter allows this only when footer metadata proves that the
+physical column contains the same constant value as the folder partition value.
 
-```text
-select *
-from yellow_tripdata
-```
+If the physical value conflicts with the folder value, discovery rejects the
+file/table because the adapter cannot expose one column name with two different
+meanings.
 
-and get rows from all files.
+## Query Execution
 
-### 3. partitioned folder
+For a query such as:
 
-```text
-yellow_tripdata/
-  year=2025/
-    month=01/
-      yellow_tripdata_2025-01.parquet
-    month=02/
-      yellow_tripdata_2025-02.parquet
-  year=2026/
-    month=01/
-      yellow_tripdata_2026-01.parquet
+```sql
+SELECT *
+FROM "prn__yellow_tripdata"
+WHERE "year" = '2025' AND "month" = '01';
 ```
 
-Here the folders have this form: key=value
+the flow is:
 
-Result:
-- one logical table
-- physical tables per year (Polypheny supports only one partitioning level)
-  - tw0 physical tables in this example
-- many Parquet files in bindings
-- partition columns exposed as normal columns
+1. `ParquetRelFilterTranslator` translates supported predicates.
+2. `ParquetFilterResolver` maps filter indexes to `ParquetColumnBinding`
+   objects and resolves dynamic parameters.
+3. `ParquetSourceFilePartitionFilterEvaluator` checks each file's
+   `partitionValues`.
+4. `ParquetSourceFileStatisticsFilterEvaluator` checks file-level footer
+   statistics where possible.
+5. Files proven not to match are skipped.
+6. Remaining files are opened by `ParquetSourceReader`.
+7. Residual filters are applied by row-level evaluators.
 
-`month` is still stored in file metadata and exposed as a column, but it is not used to create Polypheny physical partitions in current implementation.
-Adapter can skip files by month inside the physical table binding
+## Flat And Normalized Modes
 
-### 4. dataset root with several table folders. Root directory contains multiple partitioned tables
-```text
-tlc/
-    yellow_tripdata/
-        year=2025/
-            month=01/
-                yellow_tripdata_2025-01.parquet
-
-    green_tripdata/
-        year=2025/
-            month=01/
-                green_tripdata_2025-01.parquet
-
-    fhv_tripdata/
-        year=2025/
-            month=01/
-                fhv_tripdata_2025-01.parquet
-```
-Each child folder is treated as a separate table:
+In `flat` schema mode, partition columns are added to the root table.
 
-- yellow_tripdata -> table
-- green_tripdata  -> table
-- fhv_tripdata    -> table
+In `normalized` schema mode, partition columns are added to generated root
+tables. Nested child tables keep their structural synthetic columns and read
+their data through the same source-file bindings as the root table.
 
-Result:
+## Limitations
 
-- multiple logical tables
-  - logical table: yellow_tripdata
-  - logical table: green_tripdata
-  - logical table: fhv_tripdata
-  
-- multiple physical tables 
-- multiple Parquet files
+- Polypheny physical partition metadata is created only for the first partition
+  column.
+- Hierarchical partition folders beyond the first column are represented at the
+  adapter level, not as nested Polypheny physical partitions.
+- File pruning keeps files when a filter cannot be proven true or false from
+  partition values or footer statistics.
diff --git a/plugins/parquet-adapter/docs/planner.md b/plugins/parquet-adapter/docs/planner.md
index 6672750c6d..e6fc1f01ed 100644
--- a/plugins/parquet-adapter/docs/planner.md
+++ b/plugins/parquet-adapter/docs/planner.md
@@ -1,238 +1,131 @@
 # Planner Basics
 
-The planner takes a query and turns it into an executable plan.
+Polypheny turns a query into an executable algebra tree by applying planner
+rules. The Parquet adapter participates by registering Parquet-specific
+conventions, nodes, and rewrite rules.
 
-Polypheny does not execute the first query plan directly.
-The planner searches for a better plan by applying transformation rules.
-
-For the Parquet adapter, we register rules that recognize Parquet-specific opportunities.
-For example, a normal scan can become a ParquetRelScan.
-A filter or projection can be attached directly to that scan.
-A parent-child join between normalized Parquet tables can become a ParquetRelJoin.
-
-The planner compares possible plans using cost estimates.
-Plans that read fewer columns, apply filters earlier, or execute joins inside the adapter are cheaper.
-The selected plan is then converted back into an executable form.
-
-In simple words:
-
-```text
-SQL query
-  -> logical plan
-  -> optimized physical plan
-  -> runtime execution
-```
-
-## Plan Nodes
-
-A plan node is one operation in the query plan.
-
-Examples:
-
-```text
-Scan table
-Filter rows
-Project columns
-Join two inputs
-```
-
-In code, plan nodes are classes such as:
-
-```text
-LogicalRelJoin
-EnumerableJoin
-ParquetRelScan
-ParquetRelJoin
-```
-
-A plan is a tree of plan nodes. For example:
-
-```text
-LogicalRelProject
-  LogicalRelFilter
-    LogicalRelJoin
-      LogicalRelScan(customers)
-      LogicalRelScan(orders)
-```
-
-The node describes what should happen at that point in the tree.
-
-## Logical And Physical Nodes
-
-Logical nodes describe the operation, but not exactly how it will be executed.
-
-Example:
-
-```text
-LogicalRelJoin
-```
-
-means:
-
-```text
-join these two inputs
-```
-
-It does not yet say whether the join will be executed by Polypheny, by an adapter, or by generated enumerable code.
-
-Physical nodes describe a concrete execution strategy.
-
-Examples:
-
-```text
-ParquetRelScan
-ParquetRelJoin
-EnumerableJoin
-```
-
-`ParquetRelJoin` means:
-
-```text
-execute this supported join inside the Parquet adapter
-```
-
-## Rules
-
-A rule is not a plan node.
-
-A rule is a planner transformation. It looks for a specific plan shape and replaces it with another plan shape.
-
-Examples:
-
-```text
-ParquetRelScanRule
-ParquetRelJoinRule
-ParquetEnumerableJoinRule
-ParquetEnumerableFilterJoinRule
-ParquetEnumerableCalcJoinRule
-```
-
-A rule usually extends:
-
-```text
-AlgOptRule
-ConverterRule
-```
-
-Plan nodes usually extend or implement algebra classes, for example:
-
-```text
-ParquetRelScan extends RelScan<ParquetRelTable> implements EnumerableAlg
-ParquetRelJoin extends Join implements EnumerableAlg
-```
-
-So the difference is:
-
-```text
-Rule:
-  changes the plan during optimization
+## Main Concepts
 
 Plan node:
-  is part of the plan that will be optimized or executed
-```
 
-## Example: Projection Pushdown
+- represents one operation in the plan tree
+- examples: `ParquetRelScan`, `ParquetRelJoin`, `ParquetRelAggregate`,
+  `EnumerableParquet`
 
-`ParquetRelScanRule` matches:
+Rule:
 
-```text
-LogicalRelProject
-  ParquetRelScan
-```
+- recognizes a plan shape and replaces it with a cheaper or more specific shape
+- current Parquet rules are wrapped by `ParquetAlgOptRule`
+- relational matchers live in `ParquetRelPatternMatchers`
+- document matchers live in `ParquetDocPatternMatchers`
+- shared aggregate union rewrites live in `ParquetAggregatePatternMatchers`
 
-If the project only selects existing columns, the rule replaces it with:
+Convention:
 
-```text
-ParquetRelScan(projected fields only)
-```
+- marks which runtime owns a physical plan fragment
+- relational Parquet nodes use `ParquetRelConvention`
+- document Parquet nodes use `ParquetDocConvention`
+- Parquet plans are converted back to `EnumerableConvention` by
+  `EnumerableParquet`
 
-So this:
+## Relational Planning Path
 
-```sql
-SELECT name, age FROM customers
-```
+Relational tables are exposed through `ParquetRelTable`.
 
-can become a scan that reads only `name` and `age`.
+When Polypheny asks the source for a scan, `ParquetRelationalSource.getRelScan`
+adds `FilterSetOpTransposeRule` and returns a `ParquetRelScan`. The scan
+registers `ParquetRelConvention`, which registers rules from `ParquetRelRules`.
 
-Here:
+Always-registered relational rules:
 
-- `ParquetRelScanRule` is the rule.
-- `LogicalRelProject` and `ParquetRelScan` are plan nodes.
-- The rule removes the separate project node and creates a new `ParquetRelScan` node with fewer fields.
+- `ParquetRelPatternMatchers.attachFilterToJoinUnderCalc`
+- `ParquetRelPatternMatchers.attachFieldsAndFiltersToScanUnderCalc`
+- `ParquetRelRules.EnumerableParquetRule`
 
-## Example: Adapter-Level Join
+Rules registered when `ParquetOptimizationSettings.isOptimizeAggregation()` is
+enabled:
 
-Before the join rule applies, the planner may have:
+- `ParquetRelPatternMatchers.joinWithScanOnLeftAndScanOnRight`
+- `ParquetRelPatternMatchers.aggregateOnScan`
+- `ParquetRelPatternMatchers.aggregateOnCalcScan`
+- `ParquetAggregatePatternMatchers.partialAggregateOnUnion`
+- `ParquetAggregatePatternMatchers.partialAggregateOnCalcUnion`
 
-```text
-LogicalRelJoin
-  ParquetRelScan(parent)
-  ParquetRelScan(child)
-```
+## Document Planning Path
 
-`ParquetRelJoinRule` checks whether this is a supported Parquet parent-child join.
+Document collections are exposed through `ParquetDocument`.
 
-If it is supported, the rule replaces the logical join with:
+Document plans use `ParquetDocConvention` and rules from `ParquetDocRules`.
+Supported filters are attached directly to `ParquetDocScan`; the current code
+does not have a separate document-filter node.
 
-```text
-ParquetRelJoin
-  ParquetRelScan(parent)
-  ParquetRelScan(child)
-```
+Always-registered document rules:
 
-Here:
+- `ParquetDocPatternMatchers.attachFiltersToScanUnderCalc`
+- `ParquetDocRules.EnumerableParquetDocumentRule`
 
-- `ParquetRelJoinRule` is the rule.
-- `LogicalRelJoin`, `ParquetRelScan`, and `ParquetRelJoin` are plan nodes.
-- `ParquetRelJoin` is the physical node that later generates runtime code.
+Aggregate rules registered when `ParquetOptimizationSettings` enables
+aggregation optimization:
 
-At runtime, `ParquetRelJoin.implement(...)` calls:
+- `ParquetDocPatternMatchers.aggregateOnScan`
+- `ParquetDocPatternMatchers.aggregateOnProjectScan`
+- `ParquetDocPatternMatchers.aggregateOnCalcScan`
+- `ParquetAggregatePatternMatchers.partialAggregateOnUnion`
+- `ParquetAggregatePatternMatchers.partialAggregateOnCalcUnion`
 
-```text
-ParquetRelTable.nestedJoin(...)
-```
+## Projection And Filter Pushdown
 
-which returns rows through:
+The relational scan rule consumes a supported `EnumerableCalc` above an
+`EnumerableParquet -> ParquetRelScan` subtree. It can:
 
-```text
-Enumerable<PolyValue[]>
-```
+- keep simple projected field indexes on `ParquetRelScan`
+- translate supported predicates through `ParquetRelFilterTranslator`
+- remove the calc when all of its work is represented by the updated scan
 
-## Convention
+Document filter pushdown is similar, but the rule works with
+`ParquetDocPatternMatchers.attachFiltersToScanUnderCalc` and
+`ParquetDocFilterTranslator`.
 
-A convention describes how a plan node is implemented physically.
+## Structural Join Planning
 
-`Convention.NONE` means:
+Adapter-level joins use `ParquetRelJoin`. The join matcher recognizes an
+`EnumerableJoin` whose inputs are both Parquet scans and whose condition is a
+generated structural parent/child key comparison:
 
 ```text
-logical node, no concrete execution strategy yet
+child.__polypheny_parent_row_id = parent.__polypheny_row_id
 ```
 
-`EnumerableConvention.INSTANCE` means:
+The matcher checks the bindings through `ParquetRelJoin.supportedDirection` and
+creates a `ParquetRelJoin` when the relationship is a direct generated
+parent/child relationship.
 
-```text
-physical node that can execute through the enumerable row-iterator runtime
-```
+## Aggregate Planning
 
-Enumerable does not mean there are many joins. It means rows are produced one by one through an `Enumerable` / `Enumerator`.
+Aggregate pushdown creates `ParquetRelAggregate` or `ParquetDocAggregate`.
 
-For example, a single `ParquetRelJoin` can be enumerable because it produces joined rows one by one.
+The aggregate node chooses:
 
-## Short Mental Model
+- `metadataAggregate`: exact footer/partition-statistics path for supported
+  `COUNT`, `MIN`, and `MAX` cases
+- `dataAggregate`: adapter-side row scan aggregate path for supported
+  `COUNT`, `SUM`, `MIN`, and `MAX` cases
 
-Use this mental model:
+Shared union rewrites use `ParquetEnumerableUnion` as a marker so partial
+aggregate decomposition is not repeatedly applied to the same union subtree.
 
-```text
-Plan node = an operation in the plan tree
-Rule      = a rewrite that changes the plan tree
-Convention = the execution style of a plan node
-```
+## PolyAlg Names
 
-Example:
+`ParquetPlugin` registers display names for the current planner nodes:
 
-```text
-ParquetRelJoinRule sees LogicalRelJoin
-  -> checks if adapter join is possible
-  -> creates ParquetRelJoin
-  -> ParquetRelJoin later executes the join
-```
+| PolyAlg name | Class |
+| --- | --- |
+| `PE_CALC` | `EnumerableParquet` |
+| `PE_UNION` | `ParquetEnumerableUnion` |
+| `P_SCAN` | `ParquetRelScan` |
+| `P_METADATA_SCAN` | `ParquetRelMetadataScan` |
+| `P_JOIN` | `ParquetRelJoin` |
+| `P_AGGREGATE` | `ParquetRelAggregate` |
+| `P_DOC_SCAN` | `ParquetDocScan` |
+| `P_DOC_METADATA_SCAN` | `ParquetDocMetadataScan` |
+| `P_DOC_AGGREGATE` | `ParquetDocAggregate` |
diff --git a/plugins/parquet-adapter/docs/rel_execution_flows.md b/plugins/parquet-adapter/docs/rel_execution_flows.md
index aeb799c695..ccebe88182 100644
--- a/plugins/parquet-adapter/docs/rel_execution_flows.md
+++ b/plugins/parquet-adapter/docs/rel_execution_flows.md
@@ -1,13 +1,7 @@
-# Parquet Relational And Document Execution Flows
+# Relational And Document Execution Flows
 
-These diagrams describe the current Parquet runtime paths after aggregate
-execution was moved into shared infrastructure and document aggregation pushdown
-was added. They focus on runtime entry points reached from generated planner
-nodes, not on full planner rule matching.
-
-Relational execution starts at `ParquetRelTable`. Document execution starts at
-`ParquetDocument`. Both models use the shared aggregate executors through the
-`ParquetAggregateSource` abstraction.
+This document summarizes the current runtime entry points for relational scans,
+document scans, structural joins, and aggregate execution.
 
 ## Aggregate Entry Points
 
@@ -26,7 +20,7 @@ flowchart TD
     docWrapper["ParquetDocAggregateExecutor"]
 
     relSource["relational ParquetAggregateSource"]
-    docSource["DocumentAggregateSource"]
+    docSource["document ParquetAggregateSource"]
 
     dataExecutor["ParquetDataAggregateExecutor"]
     metadataExecutor["ParquetMetadataAggregateExecutor"]
@@ -42,7 +36,7 @@ flowchart TD
     docMetadataEntry --> docWrapper --> docSource --> metadataExecutor
 ```
 
-## Shared Data Aggregate Flow
+## Data Aggregate Flow
 
 ```mermaid
 flowchart TD
@@ -51,53 +45,23 @@ flowchart TD
     shared["ParquetDataAggregateExecutor.createEnumerator(...)"]
 
     resolveFilters["ParquetFilterResolver.resolveFilters(...)"]
-    strategy{"enumerator strategy"}
-
-    fileProjection["tryBuildAggregateProjection(...)"]
-    fileEnumerator["ParquetFileGroupedAggregateEnumerator"]
-    fileFilter["partition/statistics file filters"]
-    fileGroup["fileGroupKey(...) from ParquetConstantColumnResolver"]
-    countStarRows["COUNT(*) from source row count"]
-    columnReader["ParquetNoFilterColumnAggregateReader"]
-    columnAccumulator["AggregateColumnAccumulator"]
-    fileRows["build aggregate result rows"]
-
-    groupedCountProjection["tryBuildGroupedCountProjection(...)"]
-    groupedProjection["tryBuildGroupedAggregateProjection(...)"]
-    groupedEnumerator["ParquetGroupedAggregateEnumerator"]
-    groupedReader["ParquetGroupedAggregateReader"]
-    pageStrategy{"page strategy"}
-    countPage["ParquetCountAggregatePageReader"]
-    groupedPage["ParquetGroupedAggregatePageReader"]
-
-    fallbackRows["relational fallback rows from ParquetEnumeratorsFactory"]
+    strategy{"strategy"}
+    fileGrouped["ParquetFileGroupedAggregateEnumerator"]
+    grouped["ParquetGroupedAggregateEnumerator"]
+    fallbackRows["relational fallback rows"]
     rowAggregate["ParquetRowAggregateEnumerator"]
-    rowEngine["ParquetRowAggregator"]
-    directProjector["DirectAggregateRowProjector"]
-
-    unsupported["GenericRuntimeException"]
+    unsupported["unsupported aggregate shape"]
 
-    relEntry -->|"with row fallback"| shared
-    docEntry -->|"without row fallback"| shared
+    relEntry --> shared
+    docEntry --> shared
     shared --> resolveFilters --> strategy
-
-    strategy -->|"file-constant groups and exact file filters"| fileProjection --> fileEnumerator
-    fileEnumerator --> fileFilter --> fileGroup
-    fileGroup --> countStarRows --> fileRows
-    fileGroup -->|"aggregate columns projected"| columnReader --> columnAccumulator --> fileRows
-
-    strategy -->|"COUNT(*) grouped by flat data columns"| groupedCountProjection --> groupedEnumerator
-    strategy -->|"flat grouped aggregates with supported filters"| groupedProjection --> groupedEnumerator
-    groupedEnumerator --> groupedReader --> pageStrategy
-    pageStrategy -->|"COUNT(*) only and no group fields"| countPage
-    pageStrategy -->|"default grouped path"| groupedPage
-
-    strategy -->|"no aggregate reader strategy and relational fallback exists"| fallbackRows
-    fallbackRows --> rowAggregate --> rowEngine --> directProjector
-    strategy -->|"no aggregate reader strategy and no fallback"| unsupported
+    strategy -->|"file-constant groups and filters"| fileGrouped
+    strategy -->|"grouped primitive readers"| grouped
+    strategy -->|"relational fallback available"| fallbackRows --> rowAggregate
+    strategy -->|"no supported strategy"| unsupported
 ```
 
-## Shared Metadata Aggregate Flow
+## Metadata Aggregate Flow
 
 ```mermaid
 flowchart TD
@@ -109,78 +73,62 @@ flowchart TD
     evaluator["ParquetMultiFilterEvaluator"]
     partitionEval["ParquetSourceFilePartitionFilterEvaluator"]
     statsEval["ParquetSourceFileStatisticsFilterEvaluator"]
-
     loop["iterate ParquetAggregateSource.sourceFiles()"]
-    matches{"matchesExactly(...)"}
-    skip["skip source file"]
-    groupKey["fileGroupKey(...) from ParquetConstantColumnResolver"]
-    accumulator["MetadataAggregateAccumulator[] per group"]
-    aggregateKind{"aggregate kind"}
-    countStar["COUNT(*) uses sourceRowCount(...)"]
-    countColumn["COUNT(column) uses rowCount - nullCount"]
-    minMax["MIN/MAX uses ParquetColumnStatistics ranges"]
-    emptyGroup["if no groups and no matches, create empty aggregate row"]
-    rows["build PolyValue[] result rows"]
-    enumerable["Linq4j.asEnumerable(rows)"]
-
-    supportGate["supportsMetadataAggregate(...)"]
-    supportChecks["file-constant groups, metadata-decidable filters, COUNT/MIN/MAX support"]
+    match{"file matches exactly?"}
+    skip["skip file"]
+    groupKey["file group key"]
+    accumulate["metadata accumulators"]
+    rows["result rows"]
 
     relEntry --> executor
     docEntry --> executor
     executor --> resolveFilters --> evaluator
     evaluator --> partitionEval
     evaluator --> statsEval
-    evaluator --> loop --> matches
-    matches -->|"false"| skip
-    matches -->|"true"| groupKey --> accumulator --> aggregateKind
-    aggregateKind -->|"COUNT(*)"| countStar --> rows
-    aggregateKind -->|"COUNT(column)"| countColumn --> rows
-    aggregateKind -->|"MIN/MAX"| minMax --> rows
-    loop --> emptyGroup --> rows --> enumerable
-
-    supportGate -.-> supportChecks -.-> executor
+    evaluator --> loop --> match
+    match -->|"false"| skip
+    match -->|"true"| groupKey --> accumulate --> rows
 ```
 
-## Relational Projection And Row Scan Flow
+## Relational Projection Flow
 
 ```mermaid
 flowchart TD
     entry["ParquetRelTable.project(...)"]
-    factory["ParquetRelExecutorsFactory.getExecutor(ParquetRelProjectExecutor.class)"]
+    factory["ParquetRelExecutorsFactory"]
     executor["ParquetRelProjectExecutor.createEnumerator(...)"]
     resolveFilters["ParquetFilterResolver.resolveFilters(...)"]
     enumeratorsFactory["ParquetEnumeratorsFactory.create(...)"]
     multiFile["ParquetMultiFileEnumerator"]
-    fileEvaluator["partition/statistics source-file evaluator"]
-    fileMatches{"source file matches?"}
-    skip["skip source file"]
-    enumForFile["ParquetRelExecutor.enumeratorForFile(...)"]
+    fileEval["partition/statistics file evaluators"]
     shape{"table and projection shape"}
-
-    primitiveReader["ParquetPrimitiveRowReader"]
-    readerFilters["readerFilters(...)"]
-    outputProjection["primitiveRowProjection(...)"]
-    primitiveEnumerator["ParquetRowRelEnumerator"]
-
-    sourceReaderNested["ParquetSourceReader"]
+    primitive["ParquetPrimitiveRowReader + ParquetRowRelEnumerator"]
     nestedRepeated["ParquetNestedRepeatedRelEnumerator"]
-
-    sourceReaderBinding["ParquetSourceReader"]
     nestedNonRepeated["ParquetNestedNonRepeatedRelEnumerator"]
-
-    sourceReaderGeneric["ParquetSourceReader"]
-    relationalEnumerator["ParquetRelEnumerator"]
+    generic["ParquetRelEnumerator"]
 
     entry --> factory --> executor --> resolveFilters --> enumeratorsFactory --> multiFile
-    multiFile --> fileEvaluator --> fileMatches
-    fileMatches -->|"false"| skip
-    fileMatches -->|"true"| enumForFile --> shape
-
-    shape -->|"flat primitive projection"| primitiveReader --> readerFilters --> outputProjection --> primitiveEnumerator
-    shape -->|"nested table"| sourceReaderNested --> nestedRepeated
-    shape -->|"binding scan needed"| sourceReaderBinding --> nestedNonRepeated
-    shape -->|"generic flat row path"| sourceReaderGeneric --> relationalEnumerator
+    multiFile --> fileEval --> shape
+    shape -->|"flat primitive projection"| primitive
+    shape -->|"nested repeated table"| nestedRepeated
+    shape -->|"binding/path scan"| nestedNonRepeated
+    shape -->|"generic flat row path"| generic
+```
+
+## Structural Join Flow
+
+```mermaid
+flowchart TD
+    plan["ParquetRelJoin.implement(...)"]
+    table["ParquetRelTable.nestedJoin(...)"]
+    executor["ParquetRelNestedJoinExecutor.createEnumerator(...)"]
+    split["JoinFiltersSplitter"]
+    files["parent source-file pruning"]
+    reader["ParquetSourceReader"]
+    joinEnum["ParquetNestedJoinEnumerator"]
+    rows["joined PolyValue[] rows"]
+
+    plan --> table --> executor --> split --> files --> reader --> joinEnum --> rows
 ```
 
 ## Document Scan Flow
@@ -189,63 +137,27 @@ flowchart TD
 flowchart TD
     plan["ParquetDocScan.implement(...)"]
     scan["ParquetDocument.scanFiltered(...)"]
-    register["register involved Parquet adapter"]
     resolveDynamic["resolve dynamic filter parameters"]
-    filters["FiltersContainer.shared(...)"]
     multiFile["ParquetMultiFileEnumerator"]
-    sourceReader["ParquetSourceReader"]
-    docEnumerator["ParquetDocEnumerator"]
+    reader["ParquetSourceReader"]
+    docEnum["ParquetDocEnumerator"]
     extractor["ParquetDocValueExtractor"]
     row["PolyValue[] containing one PolyDocument"]
 
-    plan --> scan --> register --> resolveDynamic --> filters --> multiFile
-    multiFile -->|"for each source file"| sourceReader
-    sourceReader --> docEnumerator --> extractor --> row
-```
-
-## Document Aggregate Planning To Runtime
-
-```mermaid
-flowchart TD
-    scan["ParquetDocScan"]
-    filterRule["attachDocumentFiltersToScanUnderCalc"]
-    aggregateRules{"document aggregate rule"}
-    aggregateOnScan["documentAggregateOnScan"]
-    aggregateOnProject["documentAggregateOnProjectScan"]
-    aggregateOnCalc["documentAggregateOnCalcScan"]
-    fieldMapping["map top-level document fields to exported Parquet columns"]
-    aggregateNode["ParquetDocAggregate"]
-    mode{"aggregate mode"}
-    metadataScan["ParquetDocMetadataScan"]
-    metadataRuntime["ParquetDocument.metadataAggregate(...)"]
-    dataRuntime["ParquetDocument.dataAggregate(...)"]
-    sharedMetadata["ParquetMetadataAggregateExecutor"]
-    sharedData["ParquetDataAggregateExecutor"]
-
-    scan --> filterRule --> scan
-    scan --> aggregateRules
-    aggregateRules --> aggregateOnScan
-    aggregateRules --> aggregateOnProject
-    aggregateRules --> aggregateOnCalc
-    aggregateOnScan --> fieldMapping
-    aggregateOnProject --> fieldMapping
-    aggregateOnCalc --> fieldMapping
-    fieldMapping --> aggregateNode --> mode
-    mode -->|"metadata supported"| metadataScan --> metadataRuntime --> sharedMetadata
-    mode -->|"data aggregate supported"| dataRuntime --> sharedData
+    plan --> scan --> resolveDynamic --> multiFile
+    multiFile -->|"for each source file"| reader
+    reader --> docEnum --> extractor --> row
 ```
 
 ## Notes
 
-- Aggregate planner rules are registered only when
-  `ParquetOptimizationSettings.isOptimizeAggregation()` is enabled.
-- Metadata aggregation is exact-only. It requires file-constant group fields,
-  metadata-decidable filters, and supported `COUNT`, `MIN`, or `MAX` aggregate
-  calls.
-- Data aggregation supports the shared fast paths for file-level aggregates,
-  grouped counts, and grouped flat-column aggregates. The relational path also
-  provides a row fallback; the document path relies on the shared aggregate
-  readers.
-- Document aggregate pushdown only maps projected top-level document fields that
-  correspond to exported Parquet columns. More complex document expressions stay
-  outside the adapter aggregate path.
+- `ParquetRelTable` is the relational runtime facade for scans, joins, and
+  aggregates.
+- `ParquetDocument` is the document runtime facade for scans and aggregates.
+- `ParquetSourceReader` is the low-level reader used after source-file pruning.
+- `ParquetMultiFileEnumerator` handles file iteration and residual filter
+  reduction for multi-file tables.
+- Metadata aggregate execution is exact-only and uses partition values plus
+  footer statistics.
+- Data aggregate execution can use direct readers or relational row fallback,
+  depending on the shape of the aggregate.
diff --git a/plugins/parquet-adapter/docs/simple_nested_column_flat_rel.md b/plugins/parquet-adapter/docs/simple_nested_column_flat_rel.md
deleted file mode 100644
index 006d5a7027..0000000000
--- a/plugins/parquet-adapter/docs/simple_nested_column_flat_rel.md
+++ /dev/null
@@ -1,142 +0,0 @@
-# Add separate columns for primitive nested filed in flat mode
-
-Simple nested field becomes a separate column.
-
-Path-aware discovery was added so flat mode can expose non-repeated 
-nested leaf fields as parent-table columns while repeated/list/map fields stay as nested/document columns.
-
-Example:
-```text
-customer {
-  id
-  address {
-    city
-    country
-  }
-  orders[] ...
-}
-```
-Flat mode now exposes:
-```text
-id
-address_city
-address_country
-orders
-```
-
-### ExportedSchema.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ExportedSchema.java`
-
-Contains:
-- columns - what Polypheny should expose in the catalog
-- columnPaths - adapter-private metadata telling the scanner how to read the real value from the Parquet group. Map: column name - column path
-
-```text
-columns[i] = "shipping_address_city_name"
-columnPaths[i] = {"shipping_address_city_name" -> List.of("Shipping Address", "City Name")}
-```
-
-After flattening, the relational column name is no longer enough to know where the value lives in Parquet.
-
-Before: 
-```text
-Polypheny column name == Parquet top-level field name
-```
-After:
-```text
-address_city -> ["address", "city"]
-address_country -> ["address", "country"]
-orders -> ["orders"]
-```
-
-#### Example of column path:
-
-Parquet Schema:
-
-```text
-customer_id
-address {
-  city
-  country
-}
-orders LIST
-```
-Flat exposed columns:
-
-```text
-customer_id
-address_city
-address_country
-orders
-```
-Column path:
-
-```text
-{
-  "customer_id"     -> ["customer_id"],
-  "address_city"    -> ["address", "city"],
-  "address_country" -> ["address", "country"],
-  "orders"          -> ["orders"]
-}
-```
-
-### ParquetSchemaReader.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java`
-
-Create ExportedSchema instead of ExportedColumns.
-
-#### Changes:
-
-- exportedSchema() - returns ExportedSchema, not only columns
-- collectFlatColumns() - recursively walks non-repeated nested groups. Decides whether a Parquet field becomes a column itself, or whether its children become columns on the parent table.
-- addFlatColumn() - creates the ExportedColumn and stores its path.
-- shouldPreserveAsNestedColumn() - prevents repeated/list/map fields from being flattened
-- uniqueColumnName() - avoids name collisions after flattening.
-
-### ParquetFileDiscovery.java
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetFileDiscovery.java`
-
-ParquetFileDiscovery changed from “discover table names and column lists” to 
-“discover table names, column lists, and the binding metadata required to read those columns correctly.”
-
-Discovery now tracks an ExportedSchema per source file instead of only a list of ExportedColumn.
-For each discovered Parquet file we keep:
-- columns      -  visible Polypheny columns
-- columnPaths  - exported column name -> real Parquet path
-
-#### Changes:
-1. exportedSchema(...) instead of exportedColumns(...)
-2. When it creates a DiscoveredTable, it now stores columnPaths in DiscoveredTableBinding
-3. canConsolidateSchemas(...) also calls columnPathsAreCompatible(tables.values()). 
-  - canConsolidateSchemas(...) decides whether several Parquet files can be exposed as one Polypheny table. 
-  - Before flattened nested columns, checking exported column names and types was mostly enough.
-  - After flattening, this is no longer enough.
-  - Example: 
-    - File1: `a { b int }` Flat exported column: `"a_b" -> "a_b" -> ["a", "b"]`
-    - File2: `a_b int` Flat Exported column: `"a_b" -> ["a_b"]`
-  - So we add validation by path: columnPathsAreCompatible 
-4. Partition collision validation now uses the real source path: `exportedSchema.columnPaths().get(column.name())`
-   - Example of partitioned folder:
-   
-     ```text
-     customers/
-         country=CH/
-             part-1.parquet
-         country=US/
-             part-2.parquet
-     ```
-      
-   - Polypheny adds country as a virtual partition column based on the folder name. But sometimes the Parquet file itself also contains a column with the same exported name: `part-1.parquet:
-     country STRING`. 
-   - That is a collision: `folder partition column: country`, `file data column: country`. 
-   - The existing logic allows this only if the file column always has the same value as the folder partition value.
-   - To validate that, we inspect Parquet column statistics. 
-   - But with flattened nested columns, the exported column name may not be the actual Parquet field path.
-   - When validation sees a collision on exported column name, which is flattened nested column `shipping_address_country`, it cannot look for a top-level Parquet column named `shipping_address_country`. That column does not exist.
-   - It must look at the real Parquet path: `exportedSchema.columnPaths().get(column.name())` which gives: `["Shipping Address", "Country"]`
-   - Then findColumnChunk(...) uses that path to find the real Parquet column statistics.
-
-5. Consolidated/partitioned tables rebuild a combined path map
-   - When Polypheny exposes multiple Parquet files as one logical table, it has to combine their discovered schemas.
-   - ParquetFileDiscovery decides: these files are compatible enough, so expose them as one table
-   - `columnPaths(tables.values(), columns)` collects paths from all per-file ExportedSchema objects, but only keeps paths for columns that are actually present in the final combined table.
diff --git a/plugins/parquet-adapter/docs/statistics.md b/plugins/parquet-adapter/docs/statistics.md
index 8c48838a7e..2b93223278 100644
--- a/plugins/parquet-adapter/docs/statistics.md
+++ b/plugins/parquet-adapter/docs/statistics.md
@@ -1,458 +1,121 @@
-# Parquet Statistics
+# Statistics
 
-This document describes the statistics-related changes introduced to avoid loading Parquet data eagerly during statistics reevaluation.
+The Parquet adapter provides metadata-backed statistics so Polypheny does not
+need to scan Parquet rows during normal statistics reevaluation when footer
+metadata is sufficient.
 
-## Goal
+## Core Interfaces
 
-Before this change, the monitoring statistics subsystem computed table and column statistics by executing normal relational queries.
-For Parquet-backed entities, this could trigger full scans during startup statistics reevaluation.
+Core statistics types:
 
-The new goal is:
+- `AdapterStatisticsProvider`
+- `ProvidedEntityStatistics`
+- `ProvidedColumnStatistics`
 
-1. ask the adapter for metadata-backed statistics first
-2. use Parquet footer metadata when available
-3. fall back to the old query-based path only when necessary
+`ParquetRelTable` implements `AdapterStatisticsProvider` and delegates to
+`ParquetTableStatisticsReader`.
 
-## Main Objects
+Current Parquet statistics classes:
 
-### `AdapterStatisticsProvider`
+- `ParquetTableStatisticsReader`
+- `ParquetColumnStatisticsReader`
+- `ParquetColumnStatistics`
+- `ParquetSourceFile`
 
-File:
+## Discovery-Time Statistics
 
-`core/src/main/java/org/polypheny/db/adapter/statistics/AdapterStatisticsProvider.java`
+`ParquetSourceFile.of(...)` reads footer statistics with
+`ParquetColumnStatisticsReader.readAll(...)` and stores the result on the
+source-file record.
 
-Responsibilities:
-
-- generic adapter-side hook for statistics
-- allows an adapter to provide:
-  - entity statistics
-  - column statistics
-
-Methods:
-
-- `getEntityStatistics(long logicalEntityId)`
-- `getColumnStatistics(LogicalColumn column, int uniqueValueLimit)`
-
-Default behavior:
-
-- returns `Optional.empty()`
-- meaning the monitoring subsystem must fall back to query execution
-
-### `ProvidedEntityStatistics`
-
-File:
-
-`core/src/main/java/org/polypheny/db/adapter/statistics/ProvidedEntityStatistics.java`
-
-Responsibilities:
-
-- immutable return type for entity-level statistics
-
-Fields:
-
-- `rowCount`
-  - row count, or `null` if unavailable
-
-### `ProvidedColumnStatistics`
-
-File:
-
-`core/src/main/java/org/polypheny/db/adapter/statistics/ProvidedColumnStatistics.java`
-
-Responsibilities:
-
-- immutable return type for column-level statistics
-
-Fields:
-
-- `count`
-  - non-null value count, or `null` if unavailable
-- `min`
-  - minimum value, or `null` if unavailable
-- `max`
-  - maximum value, or `null` if unavailable
-- `uniqueValues`
-  - bounded unique values, empty if unavailable
-- `full`
-  - whether the unique value list is complete
-
-## Monitoring Integration
-
-### `StatisticsManagerImpl`
-
-File:
-
-`monitoring/src/main/java/org/polypheny/db/monitoring/statistics/StatisticsManagerImpl.java`
-
-Responsibilities after the change:
-
-- still drives startup statistics reevaluation
-- now asks physical entities for adapter-provided statistics before running query-based statistics
-
-New behavior:
-
-- `reevaluateRowCount()`
-  - first calls `getEntityStatistics(...)`
-  - if a provider returns `rowCount`, it uses that value directly
-  - otherwise it falls back to the old `ROW_COUNT_TABLE` query
-
-- `reevaluateField(...)`
-  - first calls `getColumnStatistics(...)`
-  - if a provider returns column statistics, it converts them into the existing `StatisticColumn` model
-  - otherwise it falls back to the old query-based path
-
-Helper methods added:
-
-- `getEntityStatistics(long logicalEntityId)`
-- `getColumnStatistics(QueryResult column)`
-- `getStatisticsProvider(AllocationEntity allocation)`
-- `toStatisticColumn(QueryResult column, ProvidedColumnStatistics provided)`
-
-
-## Parquet Adapter Integration
-
-### `ParquetRelTable`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/relational/schema/ParquetRelTable.java`
-
-Responsibilities after the change:
-
-- still exposes Parquet-backed relational tables to Polypheny
-- now also implements `StatisticsProvider`
-
-New fields:
-
-- `schemaReader`
-  - reusable Parquet footer/schema reader
-- `statisticsReader`
-  - Parquet-specific metadata statistics reader
-
-New behavior:
-
-- `getEntityStatistics(long logicalEntityId)`
-  - returns entity statistics only for the matching logical table
-  - delegates to `statisticsReader.getEntityStatistics(isNestedTable())`
-
-- `getColumnStatistics(LogicalColumn column, int uniqueValueLimit)`
-  - returns column statistics only for columns of the matching logical table
-  - delegates to `statisticsReader.getColumnStatistics(column, uniqueValueLimit)`
-
-This makes Parquet physical entities visible to the monitoring subsystem as statistics providers.
-
-### `ParquetSchemaReader`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/io/ParquetSchemaReader.java`
-
-Responsibilities:
-
-- reads the Parquet footer once
-- stores reusable schema and metadata
-
-Provides:
-
-- `getSchema()`
-  - Parquet schema
-- `getFooter()`
-  - full Parquet footer metadata
-- `getEstimatedRowCount()`
-  - sum of row-group row counts
-
-Purpose:
-
-- centralize footer access for statistics logic
-- avoid reopening or rescanning data when only metadata is needed
-
-### `ParquetStatisticsReader`
-
-File:
-
-`plugins/parquet-adapter/src/main/java/org/polypheny/db/adapter/parquet/shared/statistics/ParquetStatisticsReader.java`
-
-Responsibilities:
-
-- provide entity and column statistics from Parquet metadata
-- use table/column bindings to map logical columns to Parquet source paths
-
-Main methods:
-
-- `getEntityStatistics(boolean nestedTable)`
-- `getColumnStatistics(LogicalColumn column, int uniqueValueLimit)`
-
-
-
-## Full Flow
-
-### Step 1: Startup statistics reevaluation begins
-
-`StatisticsManagerImpl.initializeStatisticSettings()` still triggers:
+The stored statistics are keyed by Parquet source path:
 
 ```text
-asyncReevaluateAllStatistics()
+["total_amount"]
+["shipping_address", "country"]
 ```
 
-when startup statistics are enabled.
-
-### Step 2: Monitoring reevaluates row count and columns
-
-During reevaluation:
-
-- `reevaluateRowCount()` handles table row counts
-- `reevaluateField(...)` handles per-column statistics
-
-### Step 3: Monitoring asks the physical entity for statistics
-
-For each logical entity or column, `StatisticsManagerImpl`:
-
-- resolves allocations from the logical table
-- resolves physical entities from the adapter catalog
-- checks whether the physical entity unwraps to `StatisticsProvider`
-
-If yes:
-
-- entity statistics are requested through `getEntityStatistics(...)`
-- column statistics are requested through `getColumnStatistics(...)`
-
-### Step 4: ParquetRelTable answers the request
-
-If the physical entity is a Parquet relational table:
-
-- `ParquetRelTable` verifies the logical table or column belongs to it
-- delegates the request to `ParquetStatisticsReader`
-
-### Step 5: ParquetStatisticsReader reads footer metadata
-
-`ParquetStatisticsReader` uses:
-
-- `ParquetSchemaReader.getFooter()`
-- `ParquetSchemaReader.getSchema()`
-- `ParquetTableBinding`
-- `ParquetColumnBinding`
-
-to map the logical request to physical Parquet metadata.
-
-### Step 6: Monitoring uses provider statistics or falls back
-
-If a provider result is present:
-
-- `StatisticsManagerImpl` converts it into the existing monitoring statistics model
-
-If not:
-
-- it falls back to the old query-based path through `StatisticQueryProcessor`
+This path-based keying is important because flat mode can expose non-repeated
+nested fields under normalized column names while the physical Parquet path
+remains nested.
 
 ## Entity Statistics
 
-### Root table row count
-
-For a root Parquet table:
-
-```text
-row count = sum of row-group row counts
-```
-
-Implementation:
-
-- `ParquetSchemaReader.getEstimatedRowCount()`
-
-This comes directly from the Parquet footer and does not require row scans.
-
-### Nested table row count
-
-For a generated nested table:
-
-```text
-row count != root row count
-```
-
-because one root row can produce multiple nested rows.
-
-Implementation:
+`ParquetRelTable.getEntityStatistics(logicalEntityId)` returns statistics only
+for the matching logical table.
 
-- `ParquetStatisticsReader.getEntityStatistics(true)`
-  - uses `estimateNestedRowCount()`
-
-### `estimateNestedRowCount()`
-
-Purpose:
-
-- estimate how many rows a normalized nested table will expose
-
-Logic:
-
-1. iterate over all column bindings of the table
-2. keep only `DATA` columns with non-empty Parquet source paths
-3. call `estimateValueCount(...)` for each source path
-4. take the maximum value count across those data columns
-5. if no usable data column exists, fall back to root row count
-
-Reason for using the maximum:
-
-- data columns of the same generated nested table should usually have the same number of values
-- the maximum is used as a robust estimate of the nested row count
-
-### `estimateValueCount()`
-
-Purpose:
-
-- estimate how many values exist for one Parquet leaf path across the whole file
-
-Logic:
-
-1. iterate over all Parquet row groups
-2. find the matching column chunk for the requested Parquet path
-3. sum `column.getValueCount()` across all row groups
-4. if the column is missing in any block, return `0`
-
-This gives a metadata-based estimate for:
-
-```text
-how many values does this Parquet column contain?
-```
+For root tables, `ParquetTableStatisticsReader` estimates row count from source
+file metadata. For nested generated tables, it estimates row count from the
+largest value count among data-column bindings. If nested value counts are not
+available, it falls back to the root row-count estimate.
 
 ## Column Statistics
 
-### What `getColumnStatistics()` provides
-
-For a logical column, `ParquetStatisticsReader.getColumnStatistics()` tries to provide:
-
-- `count`
-- `min`
-- `max`
-- `uniqueValues`
-- `full`
-
-The result is returned as `ProvidedColumnStatistics`.
+`ParquetRelTable.getColumnStatistics(column, uniqueValueLimit)` returns
+statistics only when the logical column belongs to the table.
 
-### Resolution flow
+`ParquetTableStatisticsReader.getColumnStatistics(...)` handles:
 
-1. resolve the `ParquetColumnBinding` for the logical column id
-2. verify the column is a real `DATA` column
-3. verify the binding contains a Parquet source path
-4. resolve the primitive Parquet schema type for that path
-5. read row-group metadata for that exact column path
-6. convert metadata values into `PolyValue`
+- data columns with footer metadata
+- partition columns derived from folder values
+- synthetic normalized columns
+- missing or unreliable statistics
 
-### Metadata source
-
-The actual metadata aggregation happens in:
-
-- `readColumnMetadataStatistics(List<String> sourcePathElements)`
-
-It aggregates across all row groups:
+For data columns, the reader aggregates per-file `ParquetColumnStatistics`:
 
 - row count
+- value count
 - null count when available
-- minimum non-null value
-- maximum non-null value
-
-### Count semantics
-
-If Parquet null statistics are reliable:
-
-```text
-count = rowCount - nullCount
-```
-
-Otherwise:
-
-```text
-count = rowCount
-```
-
-So the count is best understood as:
-
-```text
-best available non-null count estimate from footer metadata
-```
-
-### Min/max semantics
-
-If row-group min/max metadata is available and compatible with the logical Polypheny type:
-
-- `min` is returned
-- `max` is returned
+- min
+- max
+- range reliability
 
-If metadata is missing, unreliable, or conversion fails:
+Values are converted to compatible `PolyValue` instances through
+`ParquetTypeConverter`.
 
-- `min = null`
-- `max = null`
+## Partition Column Statistics
 
-### Type conversion
+Partition columns are not physical Parquet columns. Their statistics are derived
+from `ParquetSourceFile.partitionValues()`.
 
-`toStatisticValue(...)` converts Parquet metadata values into `PolyValue`.
-
-Currently supported families in this conversion path:
-
-- numeric
-- temporal
-- character
-
-## Supported and Unsupported Cases
-
-### Supported well
-
-- root table row count from footer metadata
-- nested table row-count estimation from value counts
-- count/min/max for real Parquet data columns when metadata is available
-- logical-column to Parquet-path mapping through table bindings
-
-### Supported with fallback behavior
-
-- columns whose metadata exists but min/max cannot be converted cleanly
-- columns with missing or unreliable null statistics
-- nested tables whose row count must be estimated from leaf value counts
-
-### Not provided directly from Parquet metadata
-
-- bounded unique value lists
-- exact distinct counts
-- fully semantic statistics for synthetic columns
-
-For these cases, the provider currently returns:
-
-- `uniqueValues = []`
-- `full = true`
-- and may return count-only fallback values with `min = null`, `max = null`
-
-## Synthetic and Non-Data Columns
-
-If a logical column:
-
-- has no binding
-- is not a `DATA` column
-- has an empty source path
-
-then `ParquetStatisticsReader.getColumnStatistics()` does not try to read Parquet leaf metadata for it.
-
-Instead it returns a safe fallback:
+For partition columns, the reader can provide:
 
 - estimated count
-- `min = null`
-- `max = null`
-- empty unique values
+- lexical min/max over partition values
+- bounded unique values if the number of distinct values does not exceed
+  `uniqueValueLimit`
+
+## Synthetic Columns
 
-This is important for synthetic normalized columns such as:
+Synthetic normalized columns include:
 
 - `__polypheny_row_id`
 - `__polypheny_parent_row_id`
 - `__polypheny_elem_ordinal`
 
-## Summary
+They are generated during scan and do not have Parquet footer statistics. The
+statistics provider returns safe fallback statistics for them, such as estimated
+count with no min/max range.
+
+## Fallback Behavior
+
+The adapter returns metadata-backed statistics when it can. If no adapter
+statistics provider is available for an entity or column, Polypheny's monitoring
+statistics subsystem can fall back to its normal query-based path.
 
-The statistics changes introduce a metadata-first path for Parquet-backed entities:
+When a Parquet statistic is partially unavailable or unreliable, the adapter
+keeps the result conservative:
 
-- a new generic `StatisticsProvider` hook in core
-- monitoring-side integration in `StatisticsManagerImpl`
-- Parquet-side footer readers in `ParquetSchemaReader` and `ParquetStatisticsReader`
-- fallback to the old `StatisticQueryProcessor` path when metadata is unavailable
+- count may fall back to estimated row count
+- min/max may be `PolyNull.NULL`
+- unique values may be empty
+- unknown statistics never justify dropping rows or files
 
-This reduces unnecessary Parquet row scans during startup statistics reevaluation, especially for:
+## Related Runtime Use
 
-- table row counts
-- column count/min/max
+The same per-file statistics also support query execution:
 
-while preserving compatibility with the existing monitoring statistics subsystem.
+- `ParquetSourceFileStatisticsFilterEvaluator` uses min/max/null metadata for
+  file pruning.
+- `ParquetMetadataAggregateExecutor` uses footer statistics for exact metadata
+  aggregates when possible.
diff --git a/plugins/parquet-adapter/docs/test_plan/join_test_list.md b/plugins/parquet-adapter/docs/test_plan/join_test_list.md
deleted file mode 100644
index b29ea7f2b9..0000000000
--- a/plugins/parquet-adapter/docs/test_plan/join_test_list.md
+++ /dev/null
@@ -1,852 +0,0 @@
-# PON Planning Query Matrix
-
-This checklist is for exercising different physical-planning shapes over the normalized `pon_` Parquet tables.
-
-The queries assume these table relationships:
-
-- `pon__customers`, `pon__orders`, and `pon__products` are root-level tables.
-- `pon__orders__items` is a direct child of `pon__orders`.
-- `pon__orders__shipping_address` is a direct child of `pon__orders`.
-- `pon__orders__items__discounts` is a direct child of `pon__orders__items`.
-- Supported adapter joins use generated structural keys: child `__polypheny_parent_row_id` to parent `__polypheny_row_id`.
-
-Plan terms below use the short PolyAlg names:
-
-- `P_SCAN`: Parquet scan.
-- `PE_CALC`: converter from Parquet convention to enumerable convention.
-- `P_JOIN`: adapter-level Parquet parent-child join.
-- `E_CALC`, `E_LIMIT`, `E_SORT`, `E_AGGREGATE`, `E_JOIN`: normal enumerable operators above or around Parquet nodes.
-
-For every supported parent-child join, also check that projected scan fields map the join keys correctly. In particular, for child tables the join key `__polypheny_parent_row_id` should map to physical column index `1`, not to `0`.
-
-## Simple Scans And Projections
-
-### Q01. Root table scan with limit
-
-Expected plan: one `P_SCAN` for `pon__orders`, wrapped by `PE_CALC`; limit remains above the scan as `E_LIMIT` unless a future rule pushes it.
-
-Check: baseline root-table planning and limit placement.
-
-```sql
-SELECT *
-FROM pon__orders
-LIMIT 10;
-```
-
-### Q02. Root table projection
-
-Expected plan: `P_SCAN` should read only projected order columns if projection pushdown applies.
-
-Check: simple projection pushdown on a root table.
-
-```sql
-SELECT order_id, status, total_price
-FROM pon__orders
-LIMIT 10;
-```
-
-### Q03. Customer projection
-
-Expected plan: `P_SCAN` for `pon__customers`, with only selected customer fields.
-
-Check: projection over another root table and text/timestamp columns.
-
-```sql
-SELECT customer_id, name, country, signup_date
-FROM pon__customers
-LIMIT 10;
-```
-
-### Q04. Product projection
-
-Expected plan: `P_SCAN` for `pon__products`, with only selected product fields.
-
-Check: projection over numeric and text product columns.
-
-```sql
-SELECT product_id, name, category, price, stock
-FROM pon__products
-LIMIT 10;
-```
-
-### Q05. Repeated child projection
-
-Expected plan: `P_SCAN` for `pon__orders__items`, with projected child fields only.
-
-Check: projection on a nested repeated child table.
-
-```sql
-SELECT order_item_id, product_id, quantity, price
-FROM pon__orders__items
-LIMIT 10;
-```
-
-### Q06. Repeated child structural columns
-
-Expected plan: `P_SCAN` for `pon__orders__items`, keeping the generated row id, parent row id, and ordinal.
-
-Check: scan of generated structural columns used by nested tables.
-
-```sql
-SELECT __polypheny_row_id, __polypheny_parent_row_id, __polypheny_elem_ordinal
-FROM pon__orders__items
-LIMIT 10;
-```
-
-### Q07. Nested grandchild projection
-
-Expected plan: `P_SCAN` for `pon__orders__items__discounts`, with projected discount fields.
-
-Check: projection on a second-level nested repeated child table.
-
-```sql
-SELECT code, amount
-FROM pon__orders__items__discounts
-LIMIT 10;
-```
-
-### Q08. Direct nested shipping-address projection
-
-Expected plan: `P_SCAN` for `pon__orders__shipping_address`, with projected address fields.
-
-Check: projection on a direct nested child that is not the `items` repeated branch.
-
-```sql
-SELECT city, street, zip
-FROM pon__orders__shipping_address
-LIMIT 10;
-```
-
-## Filters Without Joins
-
-### Q09. Root numeric filter
-
-Expected plan: `P_SCAN` with a translatable filter if filter pushdown applies; otherwise `E_CALC` or filter above `PE_CALC`.
-
-Check: root-table numeric predicate.
-
-```sql
-SELECT order_id, total_price
-FROM pon__orders
-WHERE total_price >= 0
-LIMIT 10;
-```
-
-### Q10. Root nullable text filter
-
-Expected plan: `P_SCAN` plus a status filter when supported.
-
-Check: nullable text predicate on root table.
-
-```sql
-SELECT order_id, status
-FROM pon__orders
-WHERE status IS NOT NULL
-LIMIT 10;
-```
-
-### Q11. Root timestamp filter
-
-Expected plan: `P_SCAN` plus a timestamp/nullability filter when supported.
-
-Check: timestamp column handling.
-
-```sql
-SELECT order_id, order_date
-FROM pon__orders
-WHERE order_date IS NOT NULL
-LIMIT 10;
-```
-
-### Q12. Customer text filter
-
-Expected plan: `P_SCAN` for `pon__customers`; text predicate may or may not be pushed.
-
-Check: text filter planning on a root table.
-
-```sql
-SELECT customer_id, name, country
-FROM pon__customers
-WHERE country IS NOT NULL
-LIMIT 10;
-```
-
-### Q13. Product numeric filter
-
-Expected plan: `P_SCAN` for `pon__products` with a numeric filter when supported.
-
-Check: product scan with numeric predicate and projection.
-
-```sql
-SELECT product_id, name, price, stock
-FROM pon__products
-WHERE price >= 0 AND stock >= 0
-LIMIT 10;
-```
-
-### Q14. Repeated child numeric filter
-
-Expected plan: `P_SCAN` for `pon__orders__items`, with `quantity` and `price` filters when supported.
-
-Check: filter pushdown on nested repeated table.
-
-```sql
-SELECT order_item_id, product_id, quantity, price
-FROM pon__orders__items
-WHERE quantity > 0 AND price >= 0
-LIMIT 10;
-```
-
-### Q15. Repeated child ordinal filter
-
-Expected plan: `P_SCAN` for `pon__orders__items`, possibly with a filter on generated ordinal.
-
-Check: generated ordinal field in a filter.
-
-```sql
-SELECT __polypheny_parent_row_id, __polypheny_elem_ordinal, product_id
-FROM pon__orders__items
-WHERE __polypheny_elem_ordinal = 0
-LIMIT 10;
-```
-
-### Q16. Nested grandchild filter
-
-Expected plan: `P_SCAN` for `pon__orders__items__discounts`, with `amount` filter when supported.
-
-Check: filter on second-level nested repeated table.
-
-```sql
-SELECT code, amount
-FROM pon__orders__items__discounts
-WHERE amount > 0
-LIMIT 10;
-```
-
-### Q17. Direct nested shipping-address filter
-
-Expected plan: `P_SCAN` for `pon__orders__shipping_address`; filter may remain as `E_CALC` if text predicates are not pushed.
-
-Check: filter on direct nested child branch different from `items`.
-
-```sql
-SELECT city, street, zip
-FROM pon__orders__shipping_address
-WHERE city IS NOT NULL AND zip IS NOT NULL
-LIMIT 10;
-```
-
-### Q18. Non-pushable expression filter
-
-Expected plan: `P_SCAN` for `pon__products`, with an enumerable calc/filter for the expression if it cannot be pushed.
-
-Check: fallback around a scan when the predicate contains an expression.
-
-```sql
-SELECT product_id, name, category
-FROM pon__products
-WHERE LOWER(category) IS NOT NULL
-LIMIT 10;
-```
-
-## Supported Parent-Child Joins
-
-### Q19. Orders to items, inner join
-
-Expected plan: `P_JOIN` with `pon__orders` as parent and `pon__orders__items` as child.
-
-Check: basic root parent to repeated child join.
-
-```sql
-SELECT o.order_id, i.order_item_id, i.product_id, i.quantity
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q20. Items to orders, inner join with parent on right
-
-Expected plan: `P_JOIN`; direction detection should identify that the right input is the parent.
-
-Check: supported join with swapped input order.
-
-```sql
-SELECT i.order_item_id, i.product_id, o.order_id, o.status
-FROM pon__orders__items i
-JOIN pon__orders o
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q21. Orders to shipping address
-
-Expected plan: `P_JOIN` with `pon__orders` as parent and `pon__orders__shipping_address` as child.
-
-Check: direct nested child branch that is not `items`.
-
-```sql
-SELECT o.order_id, s.city, s.street, s.zip
-FROM pon__orders o
-JOIN pon__orders__shipping_address s
-    ON s.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q22. Shipping address to orders, parent on right
-
-Expected plan: `P_JOIN`; direction detection should identify that the right input is the parent.
-
-Check: swapped direction for a direct nested child branch.
-
-```sql
-SELECT s.city, s.zip, o.order_id
-FROM pon__orders__shipping_address s
-JOIN pon__orders o
-    ON s.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q23. Items to discounts, inner join
-
-Expected plan: `P_JOIN` with `pon__orders__items` as parent and `pon__orders__items__discounts` as child.
-
-Check: nested parent to second-level repeated child.
-
-```sql
-SELECT i.order_item_id, i.product_id, d.code, d.amount
-FROM pon__orders__items i
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q24. Discounts to items, parent on right
-
-Expected plan: `P_JOIN`; direction detection should identify that the right input is the nested parent.
-
-Check: swapped direction for nested parent-child join.
-
-```sql
-SELECT d.code, d.amount, i.order_item_id, i.product_id
-FROM pon__orders__items__discounts d
-JOIN pon__orders__items i
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q25. Reversed equality operands
-
-Expected plan: `P_JOIN`; the equality is written parent key first.
-
-Check: join-key extraction independent of operand order.
-
-```sql
-SELECT i.order_item_id, i.quantity, d.code, d.amount
-FROM pon__orders__items i
-JOIN pon__orders__items__discounts d
-    ON i.__polypheny_row_id = d.__polypheny_parent_row_id
-LIMIT 10;
-```
-
-### Q26. Orders to items, left join
-
-Expected plan: `P_JOIN` with join type `LEFT`.
-
-Check: parent-preserving outer join.
-
-```sql
-SELECT o.order_id, i.order_item_id, i.product_id
-FROM pon__orders o
-LEFT JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q27. Items to orders, right join
-
-Expected plan: `P_JOIN` with join type `RIGHT`.
-
-Check: same parent-preserving semantics as Q26 with swapped input order.
-
-```sql
-SELECT i.order_item_id, i.product_id, o.order_id
-FROM pon__orders__items i
-RIGHT JOIN pon__orders o
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q28. Items to discounts, full join
-
-Expected plan: `P_JOIN` with join type `FULL`.
-
-Check: full outer structural join on nested parent and grandchild.
-
-```sql
-SELECT i.order_item_id, i.quantity, d.code, d.amount
-FROM pon__orders__items i
-FULL JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q29. Projected full join with filter and limit
-
-Expected plan: `P_JOIN` under `E_LIMIT` and final projection/calc. Both scans should carry the projected fields needed for selected columns, filter columns, and join keys.
-
-Check: the child scan projection bug case; child join key should map to physical column `1`.
-
-```sql
-SELECT i.order_item_id, i.quantity, i.price
-FROM pon__orders__items i
-FULL JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE i.quantity = 3
-LIMIT 10;
-```
-
-### Q30. Parent-side filter above structural join
-
-Expected plan: `P_JOIN`; parent filter should be pushed into the scan or join when possible.
-
-Check: filter placement for parent-side predicate.
-
-```sql
-SELECT o.order_id, o.total_price, i.product_id, i.quantity
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-WHERE o.total_price >= 0
-LIMIT 10;
-```
-
-### Q31. Child-side filter above structural join
-
-Expected plan: `P_JOIN`; child filter should be applied on the child branch or in the joined-row filter.
-
-Check: filter placement for child-side predicate.
-
-```sql
-SELECT o.order_id, i.product_id, i.quantity
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-WHERE i.quantity > 1
-LIMIT 10;
-```
-
-### Q32. Filters on both sides of structural join
-
-Expected plan: `P_JOIN`; filters may split between parent and child branches.
-
-Check: filter splitting and field-index shifting for joined rows.
-
-```sql
-SELECT i.order_item_id, i.quantity, d.code, d.amount
-FROM pon__orders__items i
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE i.quantity > 1 AND d.amount > 0
-LIMIT 10;
-```
-
-### Q33. Aggregate above structural join
-
-Expected plan: `E_AGGREGATE` above `P_JOIN`.
-
-Check: join can still be adapter-level when the final result is an aggregate.
-
-```sql
-SELECT count(*)
-FROM pon__orders__items i
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE i.quantity > 0;
-```
-
-### Q34. Group by above structural join
-
-Expected plan: `E_AGGREGATE` above `P_JOIN`.
-
-Check: grouped aggregate after adapter-level join.
-
-```sql
-SELECT i.product_id, count(*) AS discount_rows
-FROM pon__orders__items i
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-GROUP BY i.product_id
-LIMIT 10;
-```
-
-### Q35. Sort above structural join
-
-Expected plan: `E_SORT` or `E_LIMIT` above `P_JOIN`.
-
-Check: sorting after adapter-level join and projection.
-
-```sql
-SELECT o.order_id, i.product_id, i.quantity
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-ORDER BY i.quantity DESC
-LIMIT 10;
-```
-
-## Derived Tables And Calc Permutations
-
-### Q36. Projected parent and child derived tables
-
-Expected plan: ideally `P_JOIN`; derived-table projections should be consumed into `P_SCAN` fields.
-
-Check: projection-only `Calc` or `Project` wrappers on both join inputs.
-
-```sql
-SELECT o.order_id, i.product_id, i.quantity
-FROM (
-    SELECT __polypheny_row_id, order_id
-    FROM pon__orders
-) o
-JOIN (
-    SELECT __polypheny_parent_row_id, product_id, quantity
-    FROM pon__orders__items
-) i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q37. Projected nested parent and grandchild derived tables
-
-Expected plan: `P_JOIN`; child `__polypheny_parent_row_id` must map through the projection to physical column `1`.
-
-Check: projection-only calc on the child side of an `items` to `discounts` join.
-
-```sql
-SELECT i.quantity, i.price, d.code, d.amount
-FROM (
-    SELECT __polypheny_row_id, quantity, price
-    FROM pon__orders__items
-) i
-JOIN (
-    SELECT __polypheny_parent_row_id, code, amount
-    FROM pon__orders__items__discounts
-) d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q38. Filtered derived table on parent branch
-
-Expected plan: `P_JOIN` if the filter can be attached before join recognition; otherwise fallback indicates a rule gap.
-
-Check: filter/calc wrapper on parent input.
-
-```sql
-SELECT o.order_id, i.product_id, i.quantity
-FROM (
-    SELECT __polypheny_row_id, order_id
-    FROM pon__orders
-    WHERE total_price >= 0
-) o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q39. Filtered derived table on child branch
-
-Expected plan: `P_JOIN` if the child filter and projection can be attached to the scan before join recognition.
-
-Check: child-side `EnumerableCalc` projection/filter wrapper.
-
-```sql
-SELECT o.order_id, i.product_id, i.quantity
-FROM pon__orders o
-JOIN (
-    SELECT __polypheny_parent_row_id, product_id, quantity
-    FROM pon__orders__items
-    WHERE quantity > 1
-) i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q40. Final projection hides all child join keys
-
-Expected plan: `P_JOIN`; scan projections still need join keys internally even though final output hides them.
-
-Check: join-key preservation through projection trimming.
-
-```sql
-SELECT i.product_id, d.amount
-FROM pon__orders__items i
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-WHERE d.amount > 0
-LIMIT 10;
-```
-
-### Q41. Limit inside join input
-
-Expected plan: this is a semantic edge case. If the input limit stays below the join, the adapter join should not silently ignore it.
-
-Check: rule ordering around `E_LIMIT` below a structural join input.
-
-```sql
-SELECT i.order_item_id, d.code, d.amount
-FROM (
-    SELECT __polypheny_row_id, order_item_id
-    FROM pon__orders__items
-    LIMIT 100
-) i
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 10;
-```
-
-## Multi-Join And Mixed Join Shapes
-
-### Q42. Orders to items to discounts chain
-
-Expected plan: direct structural joins are individually supported. Depending on rule composition, expect either nested `P_JOIN` use or one `P_JOIN` plus a normal join.
-
-Check: whether adapter-level joins compose across multiple levels.
-
-```sql
-SELECT o.order_id, i.product_id, d.code, d.amount
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = i.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q43. Structural join plus product lookup
-
-Expected plan: `orders` to `items` may become `P_JOIN`; `items` to `products` should be a normal join because it uses user columns.
-
-Check: mixed adapter-level and fallback join planning.
-
-```sql
-SELECT o.order_id, i.quantity, p.name, p.category
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-JOIN pon__products p
-    ON p.product_id = i.product_id
-LIMIT 10;
-```
-
-### Q44. Customer to orders to items
-
-Expected plan: `customers` to `orders` is a normal user-column join; `orders` to `items` is structurally supported if the planner can isolate it.
-
-Check: structural join recognition inside a larger query with a root-root user join.
-
-```sql
-SELECT c.customer_id, c.country, o.order_id, i.product_id
-FROM pon__customers c
-JOIN pon__orders o
-    ON o.customer_id = c.customer_id
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q45. Orders with two direct children
-
-Expected plan: each root-to-child join is structurally valid, but composition may fall back after one `P_JOIN`.
-
-Check: sibling child branches under the same parent.
-
-```sql
-SELECT o.order_id, i.product_id, s.city, s.zip
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-JOIN pon__orders__shipping_address s
-    ON s.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-## Unsupported Or Fallback Plans
-
-### Q46. Customers to orders by user key
-
-Expected plan: no `P_JOIN`; use normal `E_JOIN` or equivalent.
-
-Check: root-root user-column join must not be mistaken for structural join.
-
-```sql
-SELECT c.customer_id, c.name, o.order_id, o.status
-FROM pon__customers c
-JOIN pon__orders o
-    ON o.customer_id = c.customer_id
-LIMIT 10;
-```
-
-### Q47. Items to products by product id
-
-Expected plan: no `P_JOIN`; use normal join.
-
-Check: nested table joined to root lookup table by user column.
-
-```sql
-SELECT i.order_item_id, i.quantity, p.name, p.price
-FROM pon__orders__items i
-JOIN pon__products p
-    ON p.product_id = i.product_id
-LIMIT 10;
-```
-
-### Q48. Orders directly to discounts
-
-Expected plan: no single `P_JOIN`; discounts are not a direct child of orders.
-
-Check: ancestor-to-grandchild direct join is rejected.
-
-```sql
-SELECT o.order_id, d.code, d.amount
-FROM pon__orders o
-JOIN pon__orders__items__discounts d
-    ON d.__polypheny_parent_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q49. Sibling child join
-
-Expected plan: no `P_JOIN`; items and shipping address are siblings, not parent and child.
-
-Check: same-parent sibling relationship is rejected.
-
-```sql
-SELECT i.product_id, s.city, s.zip
-FROM pon__orders__items i
-JOIN pon__orders__shipping_address s
-    ON s.__polypheny_parent_row_id = i.__polypheny_parent_row_id
-LIMIT 10;
-```
-
-### Q50. Same-table self join
-
-Expected plan: no `P_JOIN`; use normal self-join.
-
-Check: self-join on generated parent row id is not a structural parent-child join.
-
-```sql
-SELECT i1.product_id AS product_1, i2.product_id AS product_2
-FROM pon__orders__items i1
-JOIN pon__orders__items i2
-    ON i1.__polypheny_parent_row_id = i2.__polypheny_parent_row_id
-LIMIT 10;
-```
-
-### Q51. Parent-child tables with wrong generated keys
-
-Expected plan: no `P_JOIN`; child primary key is not the parent reference key.
-
-Check: key-role validation in `supportedDirection()`.
-
-```sql
-SELECT o.order_id, i.product_id
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_row_id = o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q52. Parent-child tables joined by user columns
-
-Expected plan: no `P_JOIN`; structural keys are not used.
-
-Check: user-column join between parent and child falls back.
-
-```sql
-SELECT o.order_id, i.order_item_id
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON o.order_id = i.order_item_id
-LIMIT 10;
-```
-
-### Q53. Multi-key join condition
-
-Expected plan: no `P_JOIN` if the extra equality remains part of the join condition.
-
-Check: `supportedDirection()` requires exactly one equi-key on each side.
-
-```sql
-SELECT o.order_id, i.product_id
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-   AND i.__polypheny_elem_ordinal = o.order_id
-LIMIT 10;
-```
-
-### Q54. Non-equi join condition
-
-Expected plan: no `P_JOIN`.
-
-Check: non-equi structural condition is rejected.
-
-```sql
-SELECT o.order_id, i.product_id
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id <> o.__polypheny_row_id
-LIMIT 10;
-```
-
-### Q55. OR join condition
-
-Expected plan: no `P_JOIN`.
-
-Check: disjunctive join condition is rejected.
-
-```sql
-SELECT o.order_id, i.product_id
-FROM pon__orders o
-JOIN pon__orders__items i
-    ON i.__polypheny_parent_row_id = o.__polypheny_row_id
-    OR i.__polypheny_elem_ordinal = 0
-LIMIT 10;
-```
-
-### Q56. EXISTS over structural relationship
-
-Expected plan: no `P_JOIN` unless a future semi-join rule explicitly supports this shape.
-
-Check: correlated/semi-join planning around nested tables.
-
-```sql
-SELECT o.order_id
-FROM pon__orders o
-WHERE EXISTS (
-    SELECT 1
-    FROM pon__orders__items i
-    WHERE i.__polypheny_parent_row_id = o.__polypheny_row_id
-)
-LIMIT 10;
-```
-
-### Q57. NOT EXISTS over structural relationship
-
-Expected plan: no `P_JOIN` unless a future anti-join rule explicitly supports this shape.
-
-Check: anti-join or correlated fallback planning.
-
-```sql
-SELECT o.order_id
-FROM pon__orders o
-WHERE NOT EXISTS (
-    SELECT 1
-    FROM pon__orders__items i
-    WHERE i.__polypheny_parent_row_id = o.__polypheny_row_id
-)
-LIMIT 10;
-```
-
diff --git a/plugins/parquet-adapter/docs/test_plan/partition_test_list.md b/plugins/parquet-adapter/docs/test_plan/partition_test_list.md
deleted file mode 100644
index f9ccfbe716..0000000000
--- a/plugins/parquet-adapter/docs/test_plan/partition_test_list.md
+++ /dev/null
@@ -1,101 +0,0 @@
-# Partition Test Query List
-
-Currently Polypheny does not support hierarchical partitioning
-
-## Test data setup
-
-These tests use the TLC Parquet data under `C:\PolyData\tlc`. The full partition test setup expects this directory layout:
-
-```text
-C:\PolyData\tlc\
-  yellow_tripdata\
-    year=2025\
-      month=01\
-        yellow_tripdata_2025-01.parquet
-  green_tripdata\
-    year=2025\
-      month=01\
-        green_tripdata_2025-01.parquet
-  fhv_tripdata\
-    year=2025\
-      month=01\
-        fhv_tripdata_2025-01.parquet
-  fhvhv_tripdata\
-    year=2025\
-      month=01\
-        fhvhv_tripdata_2025-01.parquet
-```
-
-The SQL examples below assume that the Parquet adapter was loaded with the adapter name `prn`, so the expected tables in the `public` schema are:
-
-- `prn__yellow_tripdata`
-- `prn__green_tripdata`
-- `prn__fhv_tripdata`
-- `prn__fhvhv_tripdata`
-
-If the adapter is created with another name, replace the `prn__` prefix in the queries with that adapter prefix.
-
-### The adapter source location can be provided in four supported forms:
-
-1. One Parquet file, for example `C:\PolyData\tlc\green_tripdata\year=2025\month=01\green_tripdata_2025-01.parquet`. 
-This creates one table backed by one file.
-
-
-2. A folder with several Parquet files and no `key=value` folders. 
-This creates one logical table backed by multiple files.
-
-
-3. A partitioned table folder, for example `C:\PolyData\tlc\green_tripdata`. 
-This creates one logical table with partition columns from folders such as `year=2025` and `month=01`.
-
-
-4. A dataset root with several table folders, for example `C:\PolyData\tlc`. 
-This creates one logical table per child folder, such as `yellow_tripdata`, `green_tripdata`, `fhv_tripdata`, and `fhvhv_tripdata`.
-
-
-For the full test plan, use option 4 (`C:\PolyData\tlc`) so all TLC partitioned tables are available together. 
-The partition columns `year` and `month` should be visible as normal table columns; 
-only the first partition column, `year`, is used for **Polypheny physical partitioning**.
-
-## 1. Simple select by year and month, check that these columns were created
-
-```sql
-SELECT *
-FROM "prn__green_tripdata"
-WHERE "year" = '2025'
-AND "month" = '01';
-```
-
-## 2. Single partition scan on `prn__green_tripdata`, group by year
-
-```sql
-SELECT
-"year",
-COUNT(*) AS trip_count
-FROM "prn__green_tripdata"
-WHERE "year" = '2025'
-GROUP BY "year";
-```
-
-## 3. Simple select by year and month - Polypheny currently does not support logical operators
-
-```sql
-SELECT * from prn__yellow_tripdata WHERE "year"='2025' AND "month"='01' LIMIT 5
-SELECT * from prn__green_tripdata WHERE "year"='2025' AND "month"='01' LIMIT 5
-SELECT * from prn__fhv_tripdata WHERE "year"='2025' AND "month"='01' LIMIT 5
-SELECT * from prn__fhvhv_tripdata WHERE "year"='2025' AND "month"='01' LIMIT 5
-```
-
-## 4. Single partition scan on `prn__green_tripdata` - NOT WORKING
-
-Purpose: verify that Hive-style partition columns are visible and that a filter on both partition columns can prune to one `year=2025/month=01` Parquet file.
-
-```sql
-SELECT
-    "year",
-    "month",
-    COUNT(*) AS trip_count
-FROM "prn__green_tripdata"
-WHERE "year" = '2025'
-GROUP BY "year", "month";
-```
diff --git a/plugins/parquet-adapter/docs/unit_tests/test_coverage.md b/plugins/parquet-adapter/docs/unit_tests/test_coverage.md
deleted file mode 100644
index dbcb9fa085..0000000000
--- a/plugins/parquet-adapter/docs/unit_tests/test_coverage.md
+++ /dev/null
@@ -1,212 +0,0 @@
-# Test Coverage
-
-This document tracks the existing Parquet-related tests. The main inventory is grouped by feature area.
-
-## Discovery And Schema Normalization
-
-| Test                                                                   | File                            | What It Checks                                                                                           |
-|------------------------------------------------------------------------|---------------------------------|----------------------------------------------------------------------------------------------------------|
-| `groupsSchemaEvolvedFilesIntoOneTable`                                 | `ParquetFileDiscoveryTest.java` | Groups compatible schema-evolved Parquet files into one discovered table and keeps the union of columns. |
-| `keepsUnrelatedFilesAsSeparateTables`                                  | `ParquetFileDiscoveryTest.java` | Keeps Parquet files with unrelated schemas as separate discovered tables.                                |
-| `discoversPartitionFoldersAsOneTable`                                  | `ParquetFileDiscoveryTest.java` | Discovers Hive-style partition folders as one table and exposes folder values as columns.                |
-| `discoversPartitionedTableFoldersUnderRoot`                            | `ParquetFileDiscoveryTest.java` | Discovers multiple table folders below a dataset root, including a partitioned table folder.             |
-| `hidesParquetColumnWhenItMatchesPartitionValue`                        | `ParquetFileDiscoveryTest.java` | Hides a physical Parquet column when it duplicates a matching folder partition value.                    |
-| `hidesPhysicalColumnWhenNormalizedPartitionNameCollidesAndValuesMatch`  | `ParquetFileDiscoveryTest.java` | Hides a physical column when a normalized partition folder key collides with it and values match.        |
-| `failsWhenParquetColumnConflictsWithPartitionValue`                    | `ParquetFileDiscoveryTest.java` | Rejects a file when a physical Parquet column conflicts with the folder partition value.                 |
-| `normalizedSchemaUsesDiscoveredMultiFileTable`                         | `ParquetFileDiscoveryTest.java` | Normalizes a multi-file partitioned table and adds the synthetic row-id column.                          |
-| `flatDiscoveryFlattensNonRepeatedNestedColumnsAndKeepsRepeatedColumns` | `ParquetFileDiscoveryTest.java` | Flattens non-repeated nested fields in flat mode and keeps repeated fields as document columns.          |
-| `flatDiscoveryKeepsOrdersDbPrimaryKeys`                                | `ParquetFileDiscoveryTest.java` | Verifies primary-key detection for the bundled `orders_db` flat tables.                                  |
-
-## Filter Translation And Containers
-
-| Test                                                | File                                  | What It Checks                                                                                          |
-|-----------------------------------------------------|---------------------------------------|---------------------------------------------------------------------------------------------------------|
-| `removesOnlyAdapterFilterPaths`                     | `FiltersContainerTest.java`           | Removes path elements only from adapter filters while keeping native filter paths intact.               |
-| `keepsReaderParentFilterForAdapterValidation`       | `JoinFiltersSplitterTest.java`        | Keeps a parent-side filter available both for native Parquet reading and parent-row adapter validation. |
-| `keepsCrossSideOrAsJoinedRowFilter`                 | `JoinFiltersSplitterTest.java`        | Keeps an `OR` spanning parent and child sides as a joined-row adapter filter.                           |
-| `translatesAndOrTree`                               | `ParquetRelFilterTranslatorTest.java` | Translates nested `AND`/`OR` Rex filters into Parquet adapter filter trees.                             |
-| `translatesInAsOrOfEquals`                          | `ParquetRelFilterTranslatorTest.java` | Rewrites `IN` filters as an `OR` of equality filters.                                                   |
-| `translatesNullChecks`                              | `ParquetRelFilterTranslatorTest.java` | Translates `IS NULL` and `IS NOT NULL` predicates.                                                      |
-| `evaluatesNullChecks`                               | `ParquetRelFilterTranslatorTest.java` | Evaluates Parquet null-check filter semantics against null and non-null values.                         |
-| `reversesComparisonWhenLiteralIsOnTheLeft`          | `ParquetRelFilterTranslatorTest.java` | Reverses comparison direction when the literal or dynamic parameter appears on the left side.           |
-| `ignoresUnsupportedInt96TimestampPredicatePushdown` | `ParquetRelFilterTranslatorTest.java` | Avoids failing native predicate creation for unsupported INT96 timestamp pushdown.                      |
-| `buildsNativeNullCheckPredicates`                   | `ParquetRelFilterTranslatorTest.java` | Builds native Parquet predicates for supported null checks.                                             |
-| `docFilterTranslatorTranslatesTopLevelNameAndRejectsNestedPaths` | `ParquetSharedDocumentAdapterTest.java` | Translates top-level document field filters and avoids unsupported nested-path pushdown.                |
-
-## File-Level Filter Pruning
-
-| Test                                                         | File                            | What It Checks                                                                                                  |
-|--------------------------------------------------------------|---------------------------------|-----------------------------------------------------------------------------------------------------------------|
-| `partitionEvaluatorMatchesValuesAndTreatsMissingPartitionsAsNull` | `ParquetFilterPruningTest.java` | Evaluates partition-value filters, mismatches, missing partition values, and non-partition fallback.            |
-| `statisticsEvaluatorPrunesByRangeAndProvesConstantFiles`     | `ParquetFilterPruningTest.java` | Uses footer min/max/null statistics to reject files, prove constant-file matches, and keep unknown cases.       |
-| `reducerDropsExactMatchesRejectsExactFailuresAndKeepsResiduals` | `ParquetFilterPruningTest.java` | Removes exact file-level matches, rejects impossible files, and preserves residual filters for `OR` and `NOT`. |
-| `filterResolverMapsProjectionIndexesAndResolvesDynamicParameters` | `ParquetFilterPruningTest.java` | Rewrites projected/physical filter indexes and resolves dynamic parameters with source path bindings.          |
-| `primitiveValueFilterEvaluatorHandlesScalarComparisonsAndUnknownIndexes` | `ParquetExecutionAndWriterTest.java` | Evaluates primitive row filter comparisons for numeric, boolean, binary, null, default, and unknown-index cases. |
-
-## Primitive Reading And Projection
-
-| Test                                                                       | File                                       | What It Checks                                                                                             |
-|----------------------------------------------------------------------------|--------------------------------------------|------------------------------------------------------------------------------------------------------------|
-| `readsPrimitiveProjectionWithoutGroupMaterialization`                      | `ParquetPrimitiveRowReaderTest.java`       | Reads a flat primitive projection directly, including nullable values, without full group materialization. |
-| `rejectsRepeatedProjection`                                                | `ParquetPrimitiveRowReaderTest.java`       | Rejects repeated fields for the primitive-row fast path.                                                   |
-| `nestedListTableProjectsParquetRootFieldsFromBindings`                     | `ParquetNestedRepeatedProjectionTest.java` | Projects repeated nested list rows using table bindings and generated row/parent/ordinal fields.           |
-| `flatTableProjectsNonRepeatedNestedFieldsAndRepeatedDocumentsFromBindings` | `ParquetNestedRepeatedProjectionTest.java` | Projects flattened nested scalar fields and repeated document fields from binding paths.                   |
-
-## Document Adapter Behavior
-
-| Test                                                    | File                              | What It Checks                                                                                  |
-|---------------------------------------------------------|-----------------------------------|-------------------------------------------------------------------------------------------------|
-| `extractDocumentNormalizesNestedValuesAndGeneratesMissingId` | `ParquetSharedDocumentAdapterTest.java` | Converts Parquet rows into documents, normalizes field names, preserves nested values, and adds `_id`. |
-| `extractDocumentPreservesSourceIdAndGeneratesIdForAllNullRows` | `ParquetSharedDocumentAdapterTest.java` | Preserves source `_id` values and generates `_id` for rows where all optional fields are absent. |
-| `extractDocumentConvertsRepeatedNestedGroupsToDocumentLists` | `ParquetSharedDocumentAdapterTest.java` | Converts repeated nested Parquet groups into `PolyList` values containing nested documents. |
-| `docEnumeratorAppliesFiltersAndGeneratesIds`            | `ParquetSharedDocumentAdapterTest.java` | Enumerates document rows through real Parquet IO, applies filters, and creates generated row ids. |
-
-## Document Planning And Schema
-
-| Test                                                    | File                               | What It Checks                                                                                   |
-|---------------------------------------------------------|------------------------------------|--------------------------------------------------------------------------------------------------|
-| `documentBuildsTupleTypeFromExportedColumnsAndFallsBackToDocumentId` | `ParquetDocumentPlanningTest.java` | Builds document tuple types from exported columns and falls back to the `_id` document type.      |
-| `docScanCopiesFiltersDerivesDocumentRowAndRegistersFilterRule` | `ParquetDocumentPlanningTest.java` | Copies document scan filters, derives the document row shape, includes filters in compare strings, and registers the filter rule. |
-| `docFilterCopyPreservesEntityAndDelegatesToScanInput`  | `ParquetDocumentPlanningTest.java` | Copies Parquet document filter nodes while preserving the entity and input scan.                  |
-| `docFilterRuleSplitsNestedAndConjunctions`             | `ParquetDocumentPlanningTest.java` | Splits nested `AND` Rex conditions into individual document filter predicates.                    |
-
-## Schema And Binding Utilities
-
-| Test                                                       | File                               | What It Checks                                                                                           |
-|------------------------------------------------------------|------------------------------------|----------------------------------------------------------------------------------------------------------|
-| `bindingSerializerRoundTripsSourceFilesPartitionsColumnsAndStatistics` | `ParquetSchemaUtilitiesTest.java` | Round-trips table bindings with encoded paths, partitions, column roles, and metadata statistics.       |
-| `nameNormalizerNormalizesAndUniquifiesNestedFields`        | `ParquetSchemaUtilitiesTest.java`  | Normalizes physical table/field names and resolves duplicate top-level and nested Parquet names.        |
-| `typeConverterConvertsTypedStringsAndComparesNumerically`  | `ParquetSchemaUtilitiesTest.java`  | Converts string statistics into typed PolyValues and compares numeric strings by numeric value.         |
-| `schemaStateSkipsPrimaryKeyAndStringifiesConflictingSampleValues` | `ParquetSchemaUtilitiesTest.java` | Initializes relational schema inference, skips workflow PK export, and stringifies incompatible samples. |
-| `valueSchemaMergesNestedFieldsAndWidensNumericTypes`       | `ParquetSchemaUtilitiesTest.java`  | Merges nested schemas and widens numeric value schemas during inference.                                |
-| `messageTypeBuilderBuildsRepeatedAndNestedFields`          | `ParquetSchemaUtilitiesTest.java`  | Builds repeated and nested Parquet fields and rejects empty Parquet schemas.                            |
-| `namespaceCreatesRootBindingAndEntityWrappers`             | `ParquetTableStatisticsReaderTest.java` | Creates namespace root table bindings from a physical table and wraps them in a `ParquetRelTable`.      |
-
-## IO Utilities
-
-| Test                                                    | File                         | What It Checks                                                                                 |
-|---------------------------------------------------------|------------------------------|------------------------------------------------------------------------------------------------|
-| `cancellationChecksOnlyAtConfiguredRowIntervals`        | `ParquetIoUtilitiesTest.java` | Checks cancellation only at the configured row interval and respects the cancellation flag.     |
-| `urlResolverNormalizesDirectoriesAndKeepsExplicitParquetFiles` | `ParquetIoUtilitiesTest.java` | Normalizes directory URLs, preserves explicit `.parquet` source URLs, and resolves child files. |
-| `bufferedIteratorReplaysSampleRowsBeforeConvertedRemainder` | `ParquetExecutionAndWriterTest.java` | Replays buffered schema-sample rows before converting the remaining iterator values. |
-| `virtualGroupDelegatesToSourceGroupAndKeepsMetadata` | `ParquetExecutionAndWriterTest.java` | Delegates Parquet group access through `VirtualGroup` and preserves synthetic row metadata. |
-| `combinedGroupRoutesFieldsByJoinSideAndRejectsIndexedAccess` | `ParquetExecutionAndWriterTest.java` | Routes joined parent/child fields by join side, detects null join rows, and rejects direct indexed access. |
-| `filterableSourceFileDefensivelyCopiesFilters` | `ParquetExecutionAndWriterTest.java` | Defensively copies filter lists attached to source files. |
-| `sourceWriterRoundTripsRelationalRowsThroughRelEnumerator` | `ParquetExecutionAndWriterTest.java` | Writes relational rows with `ParquetSourceWriter` and reads them back through `ParquetRelEnumerator`. |
-| `sourceWriterRoundTripsDocumentsAndGeneratesMissingIds` | `ParquetExecutionAndWriterTest.java` | Writes document rows, skips source `_id` when requested, reads values back, and generates missing ids. |
-| `sourceWriterReportsCompressionAndValueConversionFailures` | `ParquetExecutionAndWriterTest.java` | Reports unknown compression settings and incompatible value/schema writes as runtime errors. |
-| `compileReturnsAlwaysTrueForEmptyFiltersAndRejectsUnsupportedFilters` | `ParquetPrimitivePredicateTest.java` | Compiles empty primitive predicates and rejects invalid indexes, unsupported operators, null comparisons, and unsupported logical `OR`. |
-| `compiledAndPredicateReadsColumnsOnceAndReusesConsumedValues` | `ParquetPrimitivePredicateTest.java` | Compiles primitive `AND` predicates, reads int/binary/boolean values once, and reuses consumed cached values. |
-| `nullPredicatesMatchAbsentAndPresentValues` | `ParquetPrimitivePredicateTest.java` | Evaluates primitive `IS NULL` and `IS NOT NULL` predicates for absent and present optional values. |
-
-## Aggregates
-
-| Test                                                 | File                                            | What It Checks                                                                                     |
-|------------------------------------------------------|-------------------------------------------------|----------------------------------------------------------------------------------------------------|
-| `readsPrimitiveColumnAggregateResults`               | `ParquetNoFilterColumnAggregateReaderTest.java` | Reads SUM, COUNT, MIN, and MAX for primitive numeric columns without row filters.                  |
-| `rejectsNonNumericProjection`                        | `ParquetNoFilterColumnAggregateReaderTest.java` | Rejects non-numeric projections for the no-filter column aggregate reader.                         |
-| `supportsDistinctOnlyGroupBy`                        | `ParquetGroupedAggregateRelEnumeratorTest.java` | Supports distinct-only grouped output without aggregate calls.                                     |
-| `scalarCountReturnsZeroWhenStatisticsPruneEveryFile` | `ParquetGroupedAggregateRelEnumeratorTest.java` | Returns scalar `COUNT(*) = 0` when statistics/file pruning rejects all files.                      |
-| `supportsCountStarWithSumMinAndMax`                  | `ParquetRowAggregateRelEnumeratorTest.java`     | Computes grouped row aggregates with `COUNT(*)`, `SUM`, `MIN`, and `MAX`, including null handling. |
-| `supportsMultipleGroupingColumnsAndNullGroupKeys`     | `ParquetRowAggregateRelEnumeratorTest.java`     | Aggregates with multiple grouping columns and preserves groups whose key contains null values.     |
-| `evaluatesPredicateForEveryRow`                      | `ParquetCountAggregatePageReaderTest.java`      | Applies a primitive predicate to every row while counting through the page reader.                 |
-| `aggregateInputColumnAddsPresentNumericValuesAndSkipsNulls` | `AggregateAccumulatorTest.java` | Adds present numeric column values to count/sum/min/max slots and skips optional null values. |
-| `aggregateInputColumnSharesConsumedValuesAcrossCalls` | `AggregateAccumulatorTest.java` | Reuses consumed numeric column values across multiple aggregate calls without rereading the column. |
-| `aggregateRowAccumulatorBuildsCountStarAndColumnAggregates` | `AggregateAccumulatorTest.java` | Builds row accumulators from aggregate descriptors and column descriptors. |
-| `aggregateInputColumnRejectsUnsupportedPrimitiveTypes` | `AggregateAccumulatorTest.java` | Rejects unsupported primitive types for numeric aggregate input columns. |
-| `aggregateGroupStateHandlesObjectRowsAndMergesPartialStates` | `AggregateAccumulatorTest.java` | Aggregates object rows, ignores null values, and merges partial aggregate states. |
-| `aggregateDecompositionExposesPartialCallsAndFinalFunctions` | `ParquetAggregateOptimizationTest.java` | Exposes partial aggregate calls and their final aggregation functions for aggregate decomposition. |
-
-## Metadata And File-Constant Aggregates
-
-| Test                                                                      | File                                | What It Checks                                                                                           |
-|---------------------------------------------------------------------------|-------------------------------------|----------------------------------------------------------------------------------------------------------|
-| `metadataAggregateAllowsPartitionFiltersAndGroups`                        | `ParquetMetadataAggregateTest.java` | Accepts metadata aggregates with partition filters/groups and rejects unsupported data-column cases.     |
-| `projectedScanUsesFolderPartitionValuesWhenPhysicalColumnsCollide`        | `ParquetMetadataAggregateTest.java` | Uses folder partition values when physical Parquet columns with the same names contain different values. |
-| `projectedScanReadsMinimalDataColumnWhenOnlyPartitionColumnsAreProjected` | `ParquetMetadataAggregateTest.java` | Reads a minimal backing data column when projecting only partition columns.                              |
-| `metadataAggregateUsesFileConstantPhysicalFilters`                        | `ParquetMetadataAggregateTest.java` | Executes metadata aggregate counts using dynamic filters on file-constant physical columns.              |
-| `metadataAggregateRejectsMixedPhysicalFilterColumns`                      | `ParquetMetadataAggregateTest.java` | Rejects metadata aggregation when a physical column is not constant across the relevant file.            |
-| `metadataAggregateGroupsByFileConstantPhysicalColumns`                    | `ParquetMetadataAggregateTest.java` | Groups metadata aggregate output by file-constant physical columns.                                      |
-| `fileGroupedAggregateGroupsPhysicalConstantColumns`                       | `ParquetMetadataAggregateTest.java` | Groups file-level aggregate results by physical columns that are constant per file.                      |
-| `streamingGroupedAggregateSkipsFileConstantPhysicalFilter`                | `ParquetMetadataAggregateTest.java` | Drops file-constant filters from residual row evaluation after file pruning.                             |
-| `streamingGroupedAggregateRetainsResidualAndFilter`                       | `ParquetMetadataAggregateTest.java` | Keeps residual row filters when an `AND` also contains file-constant predicates.                         |
-| `streamingGroupedAggregateRetainsResidualOrFilter`                        | `ParquetMetadataAggregateTest.java` | Keeps residual row filters for `OR` predicates that cannot be fully decided at file level.               |
-| `fileGroupedScalarCountReturnsZeroWhenFiltersRejectEveryFile`             | `ParquetMetadataAggregateTest.java` | Returns scalar `COUNT(*) = 0` when file-level grouped aggregate filters reject every file.               |
-| `constantColumnResolverRejectsMixedNullAndNonNullValues`                  | `ParquetMetadataAggregateTest.java` | Rejects file-constant resolution when statistics show mixed null and non-null values.                    |
-
-## Statistics Provider
-
-| Test                                                   | File                                  | What It Checks                                                                                         |
-|--------------------------------------------------------|---------------------------------------|--------------------------------------------------------------------------------------------------------|
-| `providedColumnStatisticsDefensivelyCopiesUniqueValues` | `ParquetTableStatisticsReaderTest.java` | Defensively copies adapter-provided unique values and handles null unique-value input.                 |
-| `readerAggregatesDataAndPartitionColumnStatistics`     | `ParquetTableStatisticsReaderTest.java` | Aggregates metadata counts/ranges for data columns and derives bounded partition-column unique values. |
-| `columnStatisticsReaderConvertsTemporalAndBinaryFooterStatistics` | `ParquetTableStatisticsReaderTest.java` | Converts footer statistics for date, time, timestamp, and binary Parquet columns.                       |
-| `readerFallsBackForMissingColumnsAndUnreliableRanges`  | `ParquetTableStatisticsReaderTest.java` | Returns full/null-range statistics for missing columns and type-mismatched or unreliable ranges.       |
-| `nestedEntityStatisticsUsesLargestNestedValueCount`    | `ParquetTableStatisticsReaderTest.java` | Estimates nested-table row count from the largest nested data-column value count.                       |
-| `providedEntityStatisticsAllowsUnknownRowCount`        | `ParquetTableStatisticsReaderTest.java` | Allows adapter-provided entity statistics to represent unknown row counts.                              |
-| `parquetRelTableProvidesStatisticsOnlyForMatchingLogicalEntity` | `ParquetTableStatisticsReaderTest.java` | Exposes entity/column statistics through `ParquetRelTable` only for matching logical table ids.        |
-
-## Nested Join Planning
-
-| Test                                            | File                                 | What It Checks                                                                                |
-|-------------------------------------------------|--------------------------------------|-----------------------------------------------------------------------------------------------|
-| `plansFullJoinWithProjectedFilteredParentInput` | `ParquetNestedJoinPlanningTest.java` | Executes a full parent-child join where the parent input is projected and filtered.           |
-| `appliesLimitAfterFullJoinFilter`               | `ParquetNestedJoinPlanningTest.java` | Applies `LIMIT` correctly after a filtered full parent-child join.                            |
-| `plansFullJoinWithLimitAsParquetJoin`           | `ParquetNestedJoinPlanningTest.java` | Confirms a full parent-child join with limit plans as `ParquetRelJoin`, not `EnumerableJoin`. |
-| `plansLeftJoinWithLimitAsParquetJoin`           | `ParquetNestedJoinPlanningTest.java` | Confirms a left parent-child join with limit plans as `ParquetRelJoin`, not `EnumerableJoin`. |
-
-## Plugin Integration
-
-| Test                                    | File                     | What It Checks                                                                                         |
-|-----------------------------------------|--------------------------|--------------------------------------------------------------------------------------------------------|
-| `importsAllTablesAndReadsRows`          | `ParquetPluginTest.java` | Creates a Parquet source from bundled `orders_db` resources and verifies imported tables contain rows. |
-| `parquetSourceIsReadOnly`               | `ParquetPluginTest.java` | Rejects `DELETE` statements against a Parquet source table.                                            |
-| `readsExpectedRowsFromCustomers`        | `ParquetPluginTest.java` | Reads known customer rows and validates values, including timestamp conversion.                        |
-| `filtersRowsWithWhereClause`            | `ParquetPluginTest.java` | Executes a text equality filter with ordering and limit.                                               |
-| `projectsOnlyRequestedColumns`          | `ParquetPluginTest.java` | Reads only requested columns from a projected query.                                                   |
-| `supportsGreaterThanFilter`             | `ParquetPluginTest.java` | Executes a greater-than filter against the imported customer table.                                    |
-| `supportsAllComparisonFilterOperations` | `ParquetPluginTest.java` | Executes `=`, `!=`, `>`, `>=`, `<`, and `<=` filters through JDBC.                                     |
-| `rejectsUpdateOnParquetSource`          | `ParquetPluginTest.java` | Rejects `UPDATE` statements against a Parquet source table.                                            |
-
-## Workflow Registry
-
-| Test                                 | File                        | What It Checks                                                         |
-|--------------------------------------|-----------------------------|------------------------------------------------------------------------|
-| `extractParquetActivityIsRegistered` | `ActivityRegistryTest.java` | Verifies the workflow registry contains the `extractParquet` activity. |
-| `loadParquetActivityIsRegistered`    | `ActivityRegistryTest.java` | Verifies the workflow registry contains the `loadParquet` activity.    |
-
-## Workflow Parquet Support
-
-| Test                                                                    | File                              | What It Checks                                                                                                        |
-|-------------------------------------------------------------------------|-----------------------------------|-----------------------------------------------------------------------------------------------------------------------|
-| `prepareTargetFileFailsOrDeletesExistingFile`                           | `ParquetWorkflowSupportTest.java` | Covers fail, overwrite, and invalid target-file handling before Parquet export.                                       |
-| `relationalWriteAndExtractRoundTripHonorsPrimaryKeyFileNameAndMaxCount` | `ParquetWorkflowSupportTest.java` | Writes relational workflow rows, skips the workflow PK, extracts rows with file-name column, and enforces `maxCount`. |
-| `documentWriteAndExtractRoundTripHonorsKeepIdNameFieldAndMaxCount`      | `ParquetWorkflowSupportTest.java` | Writes documents without `_id`, extracts generated ids and file-name fields, and enforces `maxCount`.                 |
-| `loadActivityPipeWritesRelationalWithKeptPrimaryKeyAndGzip`             | `ParquetWorkflowSupportTest.java` | Executes `ParquetLoadActivity.pipe` for relational input with `keepPk=true` and gzip compression.                    |
-| `relationalWriterRoundTripsTemporalAndBinaryValues`                     | `ParquetWorkflowSupportTest.java` | Round-trips DATE, TIME, TIMESTAMP, and VARBINARY values through the workflow Parquet writer and extractor.           |
-| `loadActivityPipeRejectsGraphInputAtRuntime`                            | `ParquetWorkflowSupportTest.java` | Verifies runtime graph rejection in `ParquetLoadActivity.pipe`.                                                       |
-| `loadActivityPipeFailsWhenSampleConflictsWithDeclaredRelationalSchema`  | `ParquetWorkflowSupportTest.java` | Verifies `CONFLICT_FAIL` rejects sampled values incompatible with the declared relational schema.                    |
-| `extractActivityLocksOutputTypeAndPipesMultipleSources`                 | `ParquetWorkflowSupportTest.java` | Locks output type and extracts multiple selected Parquet sources through `ParquetExtractActivity.pipe`.              |
-| `documentWriteKeepsOnlyIdWhenRequestedAndRejectsEmptyDroppedIdSchema`   | `ParquetWorkflowSupportTest.java` | Writes documents containing only `_id` when kept and rejects empty schemas when `_id` is dropped.                    |
-| `loadActivityPreviewRejectsEmptyRelationalExportAndBuildsDynamicName`   | `ParquetWorkflowSupportTest.java` | Validates `ParquetLoadActivity` relational preview behavior and dynamic names.                                        |
-| `extractSupportBuildsDynamicNamesAndDocumentOutputType`                 | `ParquetWorkflowSupportTest.java` | Validates extract output types, dynamic names, and tuple-count estimation.                                            |
-
-## To Do
-
-### Coverage Check Summary
-
-The current suite covers the main relational read path, flat and partitioned discovery, primitive projection, primitive and group filter evaluation, file-level pruning, metadata aggregates, row and column aggregate accumulation, aggregate-decomposition data holders, nested join planning, plugin-level relational integration, selected document conversion/enumeration behavior, document tuple-type and planner-node basics, namespace wrapper creation, source writer/enumerator round trips, statistics provider basics, schema utilities, IO utilities, and workflow helper/activity round trips.
-
-The remaining gaps are mostly full integration paths, planner rewrite matrix cases, catalog lifecycle behavior, and hard-to-simulate IO failure paths. Focused unit coverage has been added for the previously listed workflow dispatch, document conversion, document planning helpers, footer statistics, writer/enumerator handling, partition collision, file pruning, aggregate, and IO utility branches.
-
-### Known Remaining Gaps
-
-| Area                                | Representative Classes                                                                                                                                                       | Still Missing                                                                                                                                                                                                                                                       |
-|-------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Document source integration         | `ParquetDocumentSource`, `ParquetDocument`                                                                                                                                   | Creating/restoring document collections through the adapter catalog, imported document collections queried through Polypheny, multi-file document collection scans, document read-only behavior, and `scanFiltered` dynamic-parameter resolution. |
-| Document planner rewrite            | `ParquetDocScan`, `ParquetDocFilter`, `ParquetDocFilterRule`                                                                                                                 | Planner-level checks that supported document filters become `ParquetDocFilter`/filtered `ParquetDocScan`, unsupported predicates stay outside the adapter path, translated `AND` predicates are pushed together through an optimizer rule call, and document scan/filter cost scaling remains stable. |
-| Relational source/catalog lifecycle | `ParquetRelationalSource`, `AbstractParquetSource`, `ParquetNamespace`, `ParquetBindingSerializer`                                                                           | Adapter creation from `upload`/`link`/`url` settings, source URL resolution failures, serialized binding restore into physical tables/collections, table restore/drop behavior, namespace updates, and catalog metadata consistency after restore. |
-| Partition integration               | `ParquetFileDiscovery`, `ParquetSchemaNormalizer`, `ParquetTableBinding`, `ParquetRelTable`                                                                                  | Missing partition columns across files, mixed partition values and data columns beyond collision checks, multi-level folder behavior in Polypheny partition metadata, restore of partitioned tables, and partition values projected through nested join paths. |
-| Planner and optimization matrix     | `ParquetRules`, `PatternMatcher`, `PatternMatchers`, `ParquetRelAggregate`, `ParquetRelMetadataScan`, `ParquetEnumerableUnion` | Matrix coverage from `join_test_list.md` and `partition_test_list.md`, aggregate decomposition/fallback decisions inside `PatternMatchers`, metadata-scan vs data-scan planning, union planning, unsupported join/filter/aggregate fallback to Enumerable, and plan-display stability. |
-| Aggregate stress cases              | `ParquetRowAggregator`, `AggregateColumnAccumulator`, `ParquetGroupedAggregatePageReader`                                                                                    | Decimal/float precision stress, all-null groups, multiple grouping columns combined with row filters, unsupported aggregate rejection beyond current numeric checks, and overflow/large-count behavior. |
-| IO failure paths                    | `ParquetSourceReader`, `ParquetPrimitiveRowReader`, `HadoopConfigurationFactory`                                                                                              | Cancellation during active reads, invalid URL/path handling that requires filesystem/Hadoop failures, projection schema errors, mixed-schema multi-file read failures outside discovery, Hadoop configuration failures, and reader close/error propagation. |
diff --git a/plugins/parquet-adapter/docs/workflow.md b/plugins/parquet-adapter/docs/workflow.md
index f0f453f6c2..3dd81c0bc8 100644
--- a/plugins/parquet-adapter/docs/workflow.md
+++ b/plugins/parquet-adapter/docs/workflow.md
@@ -1,516 +1,155 @@
+# Workflow Integration
 
+The workflow engine provides Parquet extract and load activities. The activities
+live in `plugins/workflow-engine`, while the low-level reader/writer and schema
+conversion utilities are shared with `plugins/parquet-adapter`.
 
-# WORKFLOW
+## Extract Parquet
 
----
+Activity class:
 
-## 1. Extract from Parquet file(s)
+```text
+org.polypheny.db.workflow.dag.activities.impl.extract.ParquetExtractActivity
+```
 
+Support class:
 
-### GOAL
-- read a parquet file
-- convert data according to DOCUMENT / RELATIONAL workflow output model
-- stream output to the workflow pipeline
+```text
+org.polypheny.db.workflow.parquet.ParquetWorkflowExtractSupport
+```
 
-### ACTIVITY SETTINGS
+The activity reads one or more Parquet files and emits either documents or
+relational rows.
 
-#### `file`
-- Display name: `File Location`
-- Type: file setting
-- Required: yes
-- Multiple values: yes
-- Supported source types:
-    - absolute file path
-    - URL
-- Purpose:
-  Select one or more Parquet files, or a folder containing Parquet files, to extract from.
-- Behavior:
-  If multiple files are selected, the activity outputs the union of their rows.
+### Settings
 
-#### `outputModel`
-- Display name: `Output Type`
-- Type: enum
-- Required: yes
-- Default: `document`
-- Available values:
-    - `document`
-    - `relational`
-- Purpose:
-  Defines whether the extracted Parquet rows are exposed as workflow documents or as a relational table.
-
-#### `nameField`
-- Display name: `Add File Name Field`
-- Type: boolean
-- Required: no
-- Default: `false`
-- Purpose:
-  Adds the source file name to the output.
-- Behavior:
-    - in document mode: adds a `fileName` field
-    - in relational mode: adds a `fileName` column
-
-#### `maxCount`
-- Display name: `Maximum Row Count`
-- Type: integer
-- Required: no
-- Default: `-1`
-- Minimum: `-1`
-- Group: advanced
-- Purpose:
-  Limits how many rows are extracted per file.
-- Behavior:
-    - `-1`: extract all rows
-    - `>= 0`: extract at most that many rows from each selected file
-
-#### ***Notes***
+| Key | Meaning |
+| --- | --- |
+| `file` | One or more absolute files, URLs, or folders containing Parquet files |
+| `outputModel` | `document` or `relational` |
+| `nameField` | Adds the source file name as `fileName` |
+| `maxCount` | Maximum rows per selected file; `-1` means no limit |
 
-- The activity has no input ports and one output port of type `ANY`.
-- The concrete output type is determined from `outputModel`.
-- In relational mode, the schema is derived from the selected Parquet file or validated across all selected Parquet files if multiple relational sources are chosen.
-- In document mode, the output is a document stream with generated document IDs where needed.
-- Extract multiple files allowed only for Document model and Relational files with same schema
+### Output Type Preview
 
-![Schema display](images/extract_workflow_act_settings.png)
+Document output:
 
-![Schema display](images/extract_workflow_mult_validation.png)
+- no field-level schema is required
+- if `nameField` is enabled, the preview exposes `fileName`
 
-### CLASSES
+Relational output:
 
-- #### ParquetExtractActivity
+- schema is read from the selected Parquet source
+- multiple selected sources must expose compatible raw Parquet schemas
+- field names are normalized by `ParquetNameNormalizer`
+- field types are mapped by `ParquetTypeConverter`
 
-`org.polypheny.db.workflow.dag.activities.impl.extract.ParquetExtractActivity`
+### Document Extraction Flow
 
-Workflow node definition for reading Parquet files into a workflow output pipe:
-1. It defines how the activity appears in the workflow UI using settings. This provides all needed information for editor to show options.
-2. It decides what type of data the node will output: Relational or Document
-3. It executes the extraction at runtime as follows: the activity resolves the input files, iterates over them, and delegates the actual reading/conversion work to `ParquetWorkflowExtractSupport.java`, which turns Parquet rows into either documents or relational rows and pushes them into the workflow output pipe.
+1. `ParquetExtractActivity.pipe(...)` resolves selected sources.
+2. `ParquetWorkflowExtractSupport.writeDocuments(...)` opens a
+   `ParquetSourceReader` for each source.
+3. `ParquetDocValueExtractor.extractDocument(...)` converts each Parquet
+   `Group` into a `PolyDocument`.
+4. Missing document ids are generated from source file and row number.
+5. `fileName` is added when `nameField` is enabled.
+6. Documents are written to the workflow `OutputPipe`.
 
+### Relational Extraction Flow
 
-- #### ParquetWorkflowExtractSupport
+1. `ParquetWorkflowExtractSupport.writeRows(...)` opens a `ParquetSourceReader`.
+2. Each Parquet row is converted to a `List<PolyValue>`.
+3. `ParquetRelValueExtractor` converts primitive and structured values.
+4. Nested Parquet structures that remain in flat relational output are returned
+   as document-like values.
+5. `fileName` is appended when `nameField` is enabled.
+6. Rows are written to the workflow `OutputPipe`.
 
-`org.polypheny.db.workflow.parquet.ParquetWorkflowExtractSupport`
+## Load To Parquet
 
-Helper class contains Parquet extraction logic for the workflow engine.
-It maps Parquet schemas to workflow output types, estimates row counts, generates dynamic activity names, and converts Parquet rows into either Polypheny documents or relational tuples.
-Calls logic of Parquet Adapter Module
+Activity class:
 
-### FLOW
+```text
+org.polypheny.db.workflow.dag.activities.impl.load.ParquetLoadActivity
+```
 
+Support class:
 
-### 1. Activity entry and configuration
+```text
+org.polypheny.db.workflow.parquet.ParquetWorkflowLoadSupport
+```
 
-- The flow starts in `ParquetExtractActivity`
+The activity writes relational or document workflow input to a Parquet file.
+Graph input is rejected.
 
-#### 1.1 Determine Output Model: DOCUMENT / RELATIONAL
-- call `previewOutTypes(...)`
+### Settings
 
-#### 1.2 Determine workflow-side Output Schema
-- call `lockOutputType(...)`  
-- ##### Make validations:
-  - *DOCUMENT*
-      - validate that source exists
-      - no detailed field schema from the Parquet file needed
-  - *RELATIONAL*
-      - validate that source is a parquet file
-      - for multiple source
-          - `ParquetSourceReader.readSchema(source)` called to read source schemas
-          - all source schemas are read and validated to ensure they are identical
-          - once schema validation succeeds, the first source is used to build the relational output type
-          - if relational sources do not share the same Parquet schema, preview fails.
-- ##### Convert the Parquet schema into a workflow output type - `ParquetWorkflowExtractSupport.getOutputType(...)`
-  - DOCUMENT - treat the output as a document stream with `_id`
-  - RELATIONAL - a relational `AlgDataType` is built from the Parquet fields
-    - call `ParquetFieldNameNormalizer` and `ParquetTypeConverter` functionality for field conversion
+| Key | Meaning |
+| --- | --- |
+| `file` | Target absolute file path |
+| `mode` | `drop` overwrites an existing file, `fail` rejects it |
+| `compression` | `snappy`, `gzip`, or `uncompressed` |
+| `schemaSampleSize` | Number of input tuples sampled for schema inference |
+| `conflictMode` | `stringify` or `fail` for incompatible sampled values |
+| `keepId` | Include document `_id` field when writing documents |
+| `keepPk` | Include workflow primary-key column when writing relational rows |
 
+### Shared Writer Classes
 
-### 2. Determine Extraction flow: Document / Relational
-- call `ParquetExtractActivity.pipe(...)` 
-- Extraction delegated to `ParquetWorkflowExtractSupport`
+Low-level shared classes:
 
-### 3. DOCUMENT Extraction Flow
-- call `ParquetWorkflowExtractSupport.writeDocuments(output, source, addNameField, maxCount)`
+- `ParquetSourceWriter`
+- `ParquetMessageTypeBuilder`
+- `BufferedIterator`
+- `SchemaState`
+- `FieldSchema`
+- `ValueSchema`
+- `ValueKind`
 
-#### 3.1 Read parquet file data
-- `ParquetSourceReader` created for the selected source. It:
-  - opens Parquet file
-  - reads rows as Parquet Group objects
-  - exposes the projection schema
-  - tracks the current row number
+### Relational Export Flow
 
-#### 3.2 Converting a row to a workflow document
-- Each Parquet row is converted into a `PolyDocument` by `ParquetDocValueExtractor.extractDocument(...)`:
-  - convert primitive values into Polypheny values
-  - recursively converts nested groups into nested documents
-  - converts repeated values into lists
-  - synthesizes `_id` if needed. The generated `_id` uses:
-    - source file name
-    - current row number
+1. `ParquetLoadActivity.pipe(...)` reads settings and validates the input model.
+2. `ParquetWorkflowLoadSupport.prepareTargetFile(...)` handles existing-file
+   behavior.
+3. `SchemaState.init(inputType, keepPk)` creates initial fields from the
+   relational input type.
+4. The first `schemaSampleSize` rows are sampled and merged into the schema
+   state with `mergeRelationalRowSchema(...)`.
+5. `conflictMode` decides whether incompatible sampled values become strings or
+   fail the activity.
+6. `ParquetMessageTypeBuilder` builds the final Parquet `MessageType`.
+7. `BufferedIterator` replays sampled rows before the remaining input rows so no
+   sampled data is lost.
+8. `ParquetSourceWriter.writeRows(...)` writes rows to the target file and
+   reports progress through the workflow callback.
 
+### Document Export Flow
 
-#### 3.3 Optional file name column
-- If `nameField = true`: the source file name is added as: `fileName`
+1. Document input has no fixed field schema, so sampling is mandatory.
+2. Sampled documents are merged with `SchemaState.mergeDocumentSchema(...)`.
+3. Nested documents become Parquet groups.
+4. Lists become repeated Parquet fields or repeated groups.
+5. `keepId` controls whether `_id` participates in schema inference and output.
+6. `ParquetSourceWriter.writeDocuments(...)` writes the final file.
 
-#### 3.4 Writing to the workflow output pipe
-- The converted document is passed to the workflow `OutputPipe`.
+## Validation And Limitations
 
-### 4. RELATIONAL Extraction Flow
-- call `ParquetWorkflowExtractSupport.writeRows(output, source, addNameCol, maxCount)`
-
-#### 4.1 Reader creation
-- `ParquetSourceReader` created for the selected source.
-
-#### 4.2 Reading rows
-- loop over Parquet rows
-- For each Parquet row, a workflow tuple is built as `List<PolyValue>`.
-
-#### 4.3 Field conversion
-- Each top-level field is converted using:`ParquetRelValueExtractor.extractValue(row, i, field)`:
-  - nested Parquet structures are stored as PolyDocument
-  - primitive values remain typed scalar columns
-
-
-#### 4.4 Optional file name column
-- If `nameField = true`: the file name is appended as an extra relational column
-
-#### 4.5 Writing to the workflow output pipe
-The relational tuple is passed to the workflow `OutputPipe`.
-
-### 5. Multi-file behavior
-- The activity supports multiple Parquet sources:
-  - In document mode: multiple sources are always allowed, extracted documents are concatenated into one document stream
-  - In relational mode: multiple sources are allowed only if they all share the same raw Parquet schema, schema equality is validated before execution
-
-
-### 6. Row count estimation
-- `estimateTupleCount(...)` gives the workflow engine a rough estimate of the output size, using Parquet metadata.
-
-
-### TESTS:
-
-- Read parquet file as Relational Data Source and store to CSV file
-- Read parquet file as Document Data Source and store to JSON file
-- Add file name as column
-- Add key id column
-- Set Maximum number of rows = 5 in Advanced settings
-- Extract multiple documents
-- Extract multiple relational - same schema
-- Extract multiple relational - different schema - not allowed
-
-
-![Schema display](images/workflow1.png)
-
-![Schema display](images/workflow2.png)
-
-<br>
-
----
-
-## 2. Load to Parquet File
-
-
-### GOAL
-- read data from workflow pipeline
-- convert data according to DOCUMENT / RELATIONAL inout type into Parquet format
-- write output into Parquet file
-
-
-### CLASS DIAGRAM
-
-![Schema display](diagrams/parquet_workflow_load_class_diagram.png)
-
-### ACTIVITY SETTINGS
-
-#### `file`
-- Display name: `Target File`
-- Type: file setting
-- Required: yes
-- Multiple values: no
-- Supported source types:
-    - absolute file path
-- Purpose:
-  Select the Parquet file that should be written.
-
-#### `mode`
-- Display name: `Handling of Existing File`
-- Type: enum
-- Required: yes
-- Default: `fail`
-- Available values:
-    - `drop`
-    - `fail`
-- Purpose:
-  Defines the behavior if the selected output file already exists.
-- Behavior:
-    - `drop`: overwrite the existing file
-    - `fail`: abort the activity
-
-#### `compression`
-- Display name: `Compression`
-- Type: enum
-- Required: yes
-- Default: `snappy`
-- Available values:
-    - `snappy`
-    - `gzip`
-    - `uncompressed`
-- Purpose:
-  Selects the Parquet compression codec used for the generated file.
-
-#### `schemaSampleSize`
-- Display name: `Schema Sample Size`
-- Type: integer
-- Required: yes
-- Default: `100`
-- Minimum: `1`
-- Purpose:
-  Controls how many input tuples are sampled to infer the Parquet schema before writing starts.
-
-#### `conflictMode`
-- Display name: `Conflict Mode Handling`
-- Type: enum
-- Required: yes
-- Default: `stringify`
-- Available values:
-    - `stringify`
-    - `fail`
-- Purpose:
-  Defines how incompatible sampled values should be handled while inferring the Parquet schema.
-- Behavior:
-    - `stringify`: use a shared string field when incompatible sampled values are encountered
-    - `fail`: abort the activity when sampled values are incompatible
-
-#### `keepId`
-- Display name: `Include ID Field`
-- Type: boolean
-- Required: no
-- Default: `true`
-- Purpose:
-  Keeps the `_id` field when the activity receives document input.
-
-#### `keepPk`
-- Display name: `Keep Primary Key Column`
-- Type: boolean
-- Required: no
-- Default: `false`
-- Purpose:
-  Keeps the workflow primary key column when the activity receives relational input.
-
-#### ***Notes***
-
-- The activity has one input port of type `ANY` and no output ports.
-- Relational input and document input are both supported.
-- Graph input is not supported.
-- For relational input, the workflow primary key can optionally be excluded.
-- For document input, the schema is inferred from sampled documents because the document type does not provide field-level schema information.
-
-![Schema display](images/load_workflow_act_settings.png)
-
-### CLASSES
-
-
-- ### ParquetLoadActivity
-`org.polypheny.db.workflow.dag.activities.impl.load.ParquetLoadActivity`
-
-Workflow node definition for exporting workflow input into a Parquet file:<br>
-
-1. It defines how the activity appears in the workflow UI using settings.
-2. It validates whether the connected workflow input is supported.
-3. It executes the export at runtime as follows:
-    - the activity resolves the configured target file and export settings
-    - prepares the output path
-    - delegates the actual schema inference and data conversion work to `ParquetWorkflowLoadSupport.java`.
-   
-
-- ### ParquetWorkflowLoadSupport
-
-`org.polypheny.db.workflow.parquet.ParquetWorkflowLoadSupport`
-
-Helper class that contains Parquet export logic for the workflow engine.
-It prepares output files, builds dynamic activity names, infers Parquet schema from sampled workflow input, maps workflow values to Parquet schema definitions, and delegates low-level file writing to the shared Parquet adapter module.
-
-- ### _Parquet Writing Functionality_
-- #### ParquetSourceWriter
-
-`org.polypheny.db.adapter.parquet.shared.io.ParquetSourceWriter`
-
-Shared low-level Parquet writer used by workflow export functionality.
-It owns the Parquet writer lifecycle, creates Parquet row groups, writes rows to the target file, applies the configured compression codec, and exposes a small progress callback hook without depending on workflow-specific classes.
-
-- #### ParquetMessageTypeBuilder
-
-`org.polypheny.db.adapter.parquet.shared.schema.ParquetMessageTypeBuilder`
-
-Builds a Parquet `MessageType` from inferred workflow field schemas, including primitive, repeated, and nested group fields.
-
-- #### BufferedIterator
-
-`org.polypheny.db.adapter.parquet.shared.execution.BufferedIterator`
-
-Reusable iterator that replays sampled workflow tuples first and then continues with the remaining input iterator. It is used during Parquet export so sampled rows or documents are not lost after schema inference.
-
-
-- ### _Schema Inference Model_
-- ##### FieldSchema
-
-`org.polypheny.db.adapter.parquet.shared.schema.inference.FieldSchema`
-
-Internal model for one exported Parquet field. It tracks the source field name, normalized Parquet field name, optional relational source index, and inferred value schema.
-
-- ##### ValueSchema
-`org.polypheny.db.adapter.parquet.shared.schema.inference.ValueSchema`
-
-Recursive internal model of a field value. It represents primitive values, nested groups, and repeated values before they are converted into a final Parquet schema.
-
-- ##### ValueKind
-`org.polypheny.db.adapter.parquet.shared.schema.inference.ValueKind`
-
-Enum describing the logical value categories used during schema inference.
-
-- ##### SchemaState
-`org.polypheny.db.adapter.parquet.shared.schema.inference.SchemaState`
-
-Container for the currently inferred schema during export. It accumulates field definitions while sampled rows or documents are inspected.
-
-
-
-### FLOW
-
-
-### 1. Entry Point - `ParquetLoadActivity.pipe(...)` 
-- read settings
-- `ParquetWorkflowLoadSupport.prepareTargetFile(...)` checks the output path
-  - file not exists - continue
-  - file exists and mode is `fail` - exception
-  - file exists and mode is `drop` - delete file first
-- `ActivityUtils.getDataModel(input.getType())` - determines the input data model using `ActivityUtils.getDataModel(input.getType())`
-  - Possible cases:
-    - DOCUMENT - continue with Document Input Flow
-    - RELATIONAL - continue with Relational Input Flow
-    - GRAPH - rejected, export to Parquet is not supported
-
-### 2. Relational Input Flow - `ParquetWorkflowLoadSupport.writeRelational(...)` is called
-
-##### 2.1 Initial schema setup from the Pipeline Type
-- The input pipe already provides a relational `AlgDataType`. This type describes the exported workflow row structure. 
-- A new `SchemaState` is created. This object is the mutable schema accumulator used during export. 
-- The first schema shape is initialized from the pipeline type by calling: `schemaState.init(inputType, keepPk)`
-  - creates one `FieldSchema` for every exported column
-  - assigns an initial `ValueSchema` to each field from the declared Polypheny type
-- **_At this point, schema information comes from the pipeline type itself._**
-
-##### 2.2 Refine the schema using sampling rows
-- After initialization, the exporter samples the first `schemaSampleSize` rows from the input iterator.
-- Each sampled row is:
-  - stored in memory 
-  - merged into the schema state using `schemaState.mergeRelationalRowSchema(row, inputType, keepPk)`
-- Runtime values may contain more information than the declared relational type. ***For example:***
-  - the pipeline says a column is TEXT, but the actual data is numeric
-  - one field may hold a list/array rather than a single scalar value - parquet field should be modeled as a repeated field
-  - values may conflict across rows
-- While sampling, each field value is converted into an inferred `ValueSchema` and merged with the existing field schema.
-
-##### 2.3 Conflict Handling   
-If sampled values for the same field are incompatible, the behavior depends on `conflictMode`. Two modes exist:
-- `stringify`
-  - incompatible values are collapsed to `ValueSchema.stringType()`
-  - that field will be written as a Parquet string column
-- `fail`
-  - schema inference throws an exception
-  - export stops before the file is written
-
-#### 2.4 Final Parquet schema creation
-
-The final Parquet schema  `MessageType` is built from the accumulated `SchemaState`: `new ParquetMessageTypeBuilder(schemaState, SCHEMA_NAME).build()`
-- The schema builder converts each inferred `FieldSchema` and `ValueSchema` into Parquet schema elements:
-  - primitive fields become primitive Parquet columns
-  - repeated values become repeated Parquet fields (list)
-  - nested structures become Parquet groups
-
-#### 2.5 Combine sampled rows and remaining input under same Iterator
-
-- The sampled rows were already consumed from the input iterator during inference, so they must not be lost.
-- To solve this produce one continuous iterator `BufferedIterator`:
-  - first the sampled rows are replayed
-  - then the remaining pipeline rows are read normally
-
-#### 2.6 Writing
-- The actual Parquet file writing is delegated to `ParquetSourceWriter`
-- `ParquetSourceWriter.writeRows(...)` iterates over all rows and writes them one by one
-- For each relational row:
-  - a new empty Parquet `Group` is created from the final schema
-  - `populateGroupWithRelationalRow(...)` iterates over all exported `FieldSchema`s
-  - for each field, the source row value is taken using `sourceIndex`
-  - the value is written into the Parquet group using `populateGroupWithValue(...)`
-
-- Then:
-  - repeated values go through `populateGroupWithRepeatedValue(...)`
-  - single values go through `populateGroupWithSingleValue(...)`
-  - primitive values are handled by `addPrimitiveValue(...)` - final conversion from `PolyValue` to a Parquet-compatible primitive value
-
-### 3. Document Input Flow - `ParquetWorkflowLoadSupport.writeDocuments(...)` is called
-
-- No field-level schema comes from the pipeline. 
-- ***Schema inference is mandatory*** for document export.
-
-#### 3.2 Sampling documents
-- Read the first `schemaSampleSize` documents from the input pipe
-- Each sampled document is:
-  - buffered in memory
-  - merged into the schema state using `schemaState.mergeDocumentSchema(document, keepId)`
-
-#### 3.3 Handling nested fields and repeated values
-- Document schema inference supports:
-  - primitive fields
-  - nested documents - Nested documents become `ValueSchema` of kind `GROUP`
-  - repeated values / lists - Lists become repeated `ValueSchema`
-  
-
-- If sampled documents contain incompatible shapes for the same field:
-  - `conflictMode = stringify` collapses the field to string
-  - `conflictMode = fail` aborts the export
-
-#### 3.4 Final Parquet schema creation
-- The accumulated schema state is converted into a Parquet `MessageType` using `ParquetMessageTypeBuilder`.
-
-#### 3.5 Combine sampled rows and remaining input under same Iterator
-- Sampled documents are wrapped together with the remaining input using `BufferedIterator`, so sampled documents are written first and the rest of the pipeline continues after them.
-
-#### 3.6 Writing
-
-- `ParquetSourceWriter`is created
-
-- For each document:
-  - a new empty Parquet `Group` is created
-  - `populateGroupWithDocument(...)` iterates through all inferred fields 
-  - each field value is searched in the input document by `sourceName`
-  - values are written recursively into the Parquet group
-  
-
-- Nested document fields are written as nested groups.
-- Repeated values are written as repeated Parquet fields or repeated groups.
-- Primitive values are converted through `addPrimitiveValue(...)`.
-
-### 4. Progress reporting
-
-Workflow progress is not handled inside the schema builder or the activity itself. It is handled during row writing through the callback passed to `ParquetSourceWriter.writeRows(...)`.
-
-
-
-### TESTS:
-
-- Read relational workflow input and export to Parquet file
-- Read document workflow input and export to Parquet file
-- Keep `_id` for document export
-- Keep workflow primary key for relational export
-- Write with different compression codecs
-- Use `schemaSampleSize = 5`
-- Export incompatible sampled values with `conflictMode = stringify`
-- Fail export on incompatible sampled values with `conflictMode = fail`
-
-![Schema display](images/workflow_parquet_parquet.png)
-
----
-
-## Unit Tests
+- Graph input is not supported for Parquet export.
+- Relational export rejects an input with only the workflow primary-key column
+  when `keepPk` is false.
+- Document export rejects an empty inferred schema.
+- Multiple relational extract sources must have compatible schemas.
+- Progress is reported during row/document writing, not during schema
+  inference.
+
+## Supporting Images
 
+The following images are kept as UI/reference screenshots:
+
+![Extract settings](images/extract_workflow_act_settings.png)
+
+![Extract validation](images/extract_workflow_mult_validation.png)
+
+![Load settings](images/load_workflow_act_settings.png)
 
+![Workflow Parquet round trip](images/workflow_parquet_parquet.png)

From 9b0de05a1cf38f8b293944fe6561a55ffce37c40 Mon Sep 17 00:00:00 2001
From: ycher <julia.neporent@gmail.com>
Date: Sat, 11 Jul 2026 09:49:47 +0200
Subject: [PATCH 46/46] delete unused files

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diff --git a/plugins/parquet-adapter/benchmarks/datasets/flickr_dataset.md b/plugins/parquet-adapter/benchmarks/datasets/flickr_dataset.md
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-# Flickr8k Clean Parquet Nested Benchmark Dataset
-
-## Overview
-
-The Flickr8k Clean dataset is a small real-world image-caption dataset stored
-as a Parquet file. It is intended for nested-data benchmark coverage in the
-Parquet adapter, especially for repeated primitive columns and projection
-behavior around large binary values.
-
-The source dataset is
-[Flickr 8k Dataset (Clean)](https://www.kaggle.com/datasets/habedi/flickr-8k-dataset-clean/data)
-on Kaggle. The dataset page describes it as a cleaned Parquet version of
-Flickr8k with approximately 8,000 images. Each image has five human-written
-captions.
-
-This dataset should be used as a complement to the NYC TLC benchmark dataset.
-The TLC dataset exercises large flat and partitioned tables. Flickr8k exercises
-a different physical shape: one row per image, a repeated captions field, and a
-large binary image field.
-
-## Logical Content
-
-Each row represents one image and its captions.
-
-| Column     | Expected type        | Description |
-|------------|----------------------|-------------|
-| `id`       | string               | Image filename, for example `1000268201_693b08cb0e.jpg`. |
-| `captions` | list of strings      | Five short human-written text descriptions of the image. |
-| `image`    | binary / JPEG bytes  | Raw JPEG image bytes stored inside the Parquet row. SQL benchmarks should treat this as binary data, not as a decoded image. |
-
-The expected Parquet shape is shallow nested data:
-
-```text
-id: string
-captions: list<string>
-image: binary
-```
-
-The important nested field is `captions`. It is a repeated primitive value,
-which makes the dataset useful for testing whether the adapter can read and
-materialize list columns correctly. The `image` field is useful for projection
-benchmarks because it is much larger than the metadata and caption fields.
-
-## Benchmark Purpose
-
-This dataset is useful for the following benchmark categories:
-
-- Flat projection over image metadata, for example selecting only `id`.
-- Nested projection over repeated values, for example selecting `id` and
-  `captions`.
-- Binary projection, for example selecting `id` and `image`.
-- Full-row scans that read `id`, `captions`, and `image` together.
-- Projection pruning checks that compare scans with and without the `image`
-  column.
-- Correctness checks for repeated primitive values.
-
-The most important comparison is between queries that include `image` and
-queries that exclude it. If projection pruning works well, queries over `id`
-and `captions` should avoid paying the full cost of reading JPEG bytes.
-
-## Limitations
-
-Flickr8k Clean is a good starter dataset for nested Parquet behavior, but it is
-not a complete nested-data stress benchmark.
-
-- The nesting is shallow: `captions` is a list of strings. The dataset does not
-  cover structs, maps, deeply nested lists, or `LIST<STRUCT<...>>` shapes.
-- The repeated cardinality is regular: each image has five captions. This does
-  not strongly test variable-length repeated fields.
-- The row count is small compared with the TLC benchmark dataset. It is better
-  for correctness, projection behavior, and regression testing than for large
-  throughput measurements.
-- Full scans that include `image` may be dominated by binary I/O. Those results
-  should not be interpreted as measuring only nested-column reader performance.
-- The dataset is not designed for aggregation benchmarks. It has few numeric
-  columns and no partition layout.
-
-For broader nested coverage, pair this dataset with a synthetic Parquet fixture
-that includes variable-length arrays, nested structs, and arrays of structs.
-
-## Suggested Benchmark Queries
-
-The benchmark query set should keep separate query groups for metadata,
-captions, binary image bytes, and full-row reads.
-
-```sql
--- Metadata-only projection.
-SELECT id
-FROM flickr8k;
-
--- Repeated primitive projection.
-SELECT id, captions
-FROM flickr8k;
-
--- Binary projection.
-SELECT id, image
-FROM flickr8k;
-
--- Full-row projection.
-SELECT id, captions, image
-FROM flickr8k;
-```
-
-If the SQL surface supports nested access or unnesting in the benchmark path,
-additional queries can be added later. Until then, the core value of this
-dataset is measuring whole-list materialization and projection pruning.
-
-## Reporting Notes
-
-Benchmark results should record:
-
-- dataset source and download date
-- local file path
-- Parquet file size
-- row count
-- row group count
-- physical Parquet schema
-- compression codec
-- whether the query selected the `image` column
-
-These details are important because binary payload size, row group layout, and
-compression can dominate the observed runtime.
-
-## License
-
-The Kaggle dataset page lists the license as CC BY-NC 4.0. This is suitable for
-internal, academic, and non-commercial benchmark work, but it should be reviewed
-before using the dataset in a commercial or broadly redistributed benchmark
-package.