Natch: FINE Wrapper Implementation Plan

Last Updated: 2025-11-09 Status: ✅ Phases 1-6 Complete + v0.2.0 Released + Memory Locality Optimizations Timeline: MVP achieved in ~1 hour, Production-ready v0.2.0 with precompiled binaries published

Executive Summary

This document outlines a plan to wrap the clickhouse-cpp library using FINE (Foreign Interface Native Extensions) to provide native TCP protocol access to ClickHouse from Elixir. This approach delivers 51% faster inserts and 53% less bandwidth compared to HTTP while maintaining feature completeness through the mature clickhouse-cpp library.

Why FINE + clickhouse-cpp?

Pros:

✅ Native protocol performance (51% faster than HTTP)
✅ Full feature completeness immediately (leverage clickhouse-cpp)
✅ Automatic type marshalling (FINE handles FFI complexity)
✅ 4-6 weeks to production vs 4-6 months for pure Elixir
✅ Native C++ speed for serialization/deserialization

Cons:

⚠️ C++ build dependencies (cmake, C++17 compiler)
⚠️ Cross-language debugging complexity
⚠️ Platform-specific compilation required
⚠️ Ongoing maintenance tracking upstream changes

Architecture Overview

┌─────────────────────────────────────────────┐
│  Elixir Application                         │
│  - Natch.query/3, Natch.insert/3             │
│  - Idiomatic Elixir API                     │
└─────────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────────┐
│  Elixir Management Layer                    │
│  - Natch.Connection GenServer                │
│  - Connection pooling                       │
│  - Resource lifecycle                       │
└─────────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────────┐
│  FINE NIF Layer (C++)                       │
│  - Type conversion Elixir ↔ C++             │
│  - Resource management                      │
│  - Exception handling                       │
└─────────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────────┐
│  clickhouse-cpp Library                     │
│  - Native TCP protocol                      │
│  - Binary columnar format                   │
│  - Compression (LZ4/ZSTD)                   │
└─────────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────────┐
│  ClickHouse Server                          │
│  - Native protocol interface (9000)         │
└─────────────────────────────────────────────┘

✅ Phase 1: Foundation (COMPLETED)

Goal: Basic client lifecycle and connection management Status: ✅ Complete - 10 tests passing

Deliverables

Project Setup
- Add FINE dependency to mix.exs
- Set up C++ build infrastructure (CMake/Makefile)
- Configure clickhouse-cpp as git submodule or vendored dependency
- Create native/natch_fine/ directory structure

Client Resource Wrapper

// native/natch_fine/client.cpp
#include <fine.hpp>
#include <clickhouse/client.h>

using namespace clickhouse;

FINE_RESOURCE(Client);

fine::ResourcePtr<Client> client_create(
    ErlNifEnv *env,
    std::string host,
    int port) {
  ClientOptions opts;
  opts.SetHost(host);
  opts.SetPort(port);
  return fine::make_resource<Client>(opts);
}
FINE_NIF(client_create, 0);

void client_ping(
    ErlNifEnv *env,
    fine::ResourcePtr<Client> client) {
  client->Ping();
}
FINE_NIF(client_ping, 0);

void client_execute(
    ErlNifEnv *env,
    fine::ResourcePtr<Client> client,
    std::string sql) {
  client->Execute(sql);
}
FINE_NIF(client_execute, 0);

FINE_INIT("Elixir.Natch.Native");

Elixir Connection Module

defmodule Natch.Connection do
  use GenServer

  def start_link(opts) do
    GenServer.start_link(__MODULE__, opts)
  end

  def init(opts) do
    host = Keyword.get(opts, :host, "localhost")
    port = Keyword.get(opts, :port, 9000)

    case Natch.Native.client_create(host, port) do
      {:ok, client} ->
        {:ok, %{client: client, opts: opts}}
      {:error, reason} ->
        {:stop, reason}
    end
  end

  def ping(conn) do
    GenServer.call(conn, :ping)
  end

  def execute(conn, sql) do
    GenServer.call(conn, {:execute, sql})
  end

  def handle_call(:ping, _from, state) do
    Natch.Native.client_ping(state.client)
    {:reply, :ok, state}
  end

  def handle_call({:execute, sql}, _from, state) do
    case Natch.Native.client_execute(state.client, sql) do
      :ok -> {:reply, :ok, state}
      {:error, reason} -> {:reply, {:error, reason}, state}
    end
  end
end

Testing

Connection establishment
Ping works
Simple DDL operations (CREATE TABLE, DROP TABLE)
Error handling for connection failures

Success Criteria: Can connect, ping, and execute DDL statements

✅ Phase 2: Type System & Column Creation (COMPLETED)

Goal: Handle ClickHouse type system and column creation Status: ✅ Complete - 33 tests passing (5 core types: UInt64, Int64, String, Float64, DateTime)

Key Challenge

ClickHouse has 42+ column types. We need to:

Parse type strings (e.g., "Array(Nullable(String))")
Create appropriate Column objects dynamically
Populate columns from Elixir data

Approach: Leverage clickhouse-cpp's Factory

// native/natch_fine/columns.cpp
#include <fine.hpp>
#include <clickhouse/columns/factory.h>

FINE_RESOURCE(clickhouse::Column);

fine::ResourcePtr<clickhouse::Column> column_create(
    ErlNifEnv *env,
    std::string type_name) {
  auto col = clickhouse::CreateColumnByType(type_name);
  return fine::make_resource_from_ptr(col);
}
FINE_NIF(column_create, 0);

Column Population Strategy

Option A: Type-Specific NIFs (Recommended for MVP)

// Separate NIF for each common type
void column_uint64_append(
    ErlNifEnv *env,
    fine::ResourcePtr<clickhouse::Column> col,
    uint64_t value) {
  auto typed = std::static_pointer_cast<clickhouse::ColumnUInt64>(col);
  typed->Append(value);
}
FINE_NIF(column_uint64_append, 0);

void column_string_append(
    ErlNifEnv *env,
    fine::ResourcePtr<clickhouse::Column> col,
    std::string value) {
  auto typed = std::static_pointer_cast<clickhouse::ColumnString>(col);
  typed->Append(value);
}
FINE_NIF(column_string_append, 0);

Option B: Generic Variant-Based (More complex, defer to Phase 4)

using ElixirValue = std::variant<
  int64_t, uint64_t, double, std::string, bool,
  std::nullptr_t, std::vector<ElixirValue>
>;

void column_append_value(
    ErlNifEnv *env,
    fine::ResourcePtr<clickhouse::Column> col,
    std::string type_name,
    ElixirValue value) {
  // Dispatch based on type_name
  if (type_name == "UInt64") {
    auto typed = std::static_pointer_cast<clickhouse::ColumnUInt64>(col);
    typed->Append(std::get<uint64_t>(value));
  } else if (type_name == "String") {
    // ...
  }
  // etc.
}

Deliverables

Type Parser Integration
- Wrap CreateColumnByType
- Return opaque Column resource
Core Type Support (start with 5 essential types)
- UInt64, Int64
- String
- Float64
- DateTime

Column Builders in Elixir

defmodule Natch.Column do
  def new(type) do
    Natch.Native.column_create(type)
  end

  def append(%{type: :uint64} = col, value) when is_integer(value) do
    Natch.Native.column_uint64_append(col.ref, value)
  end

  def append(%{type: :string} = col, value) when is_binary(value) do
    Natch.Native.column_string_append(col.ref, value)
  end
end

Testing

Create columns of different types
Append values
Type safety (wrong type should error)

Success Criteria: Can create and populate basic column types

✅ Phase 3: Block Building & INSERT (COMPLETED)

Goal: Build blocks from Elixir data and insert into ClickHouse Status: ✅ Complete - 17 tests passing

Block Resource

// native/natch_fine/block.cpp
#include <fine.hpp>
#include <clickhouse/block.h>

FINE_RESOURCE(clickhouse::Block);

fine::ResourcePtr<clickhouse::Block> block_create(ErlNifEnv *env) {
  return fine::make_resource<clickhouse::Block>();
}
FINE_NIF(block_create, 0);

void block_append_column(
    ErlNifEnv *env,
    fine::ResourcePtr<clickhouse::Block> block,
    std::string name,
    fine::ResourcePtr<clickhouse::Column> col) {
  block->AppendColumn(name, col);  // Need to convert to shared_ptr
}
FINE_NIF(block_append_column, 0);

uint64_t block_row_count(
    ErlNifEnv *env,
    fine::ResourcePtr<clickhouse::Block> block) {
  return block->GetRowCount();
}
FINE_NIF(block_row_count, 0);

void client_insert(
    ErlNifEnv *env,
    fine::ResourcePtr<clickhouse::Client> client,
    std::string table_name,
    fine::ResourcePtr<clickhouse::Block> block) {
  client->Insert(table_name, *block);
}
FINE_NIF(client_insert, 0);

Smart Pointer Challenge

Problem: clickhouse-cpp uses std::shared_ptr<Column> but FINE uses ResourcePtr<Column>

Solution: Wrapper or conversion function

// Helper to convert ResourcePtr to shared_ptr
std::shared_ptr<clickhouse::Column> resource_to_shared(
    fine::ResourcePtr<clickhouse::Column> res) {
  // Option 1: Store shared_ptr inside ResourcePtr wrapper
  // Option 2: Create new shared_ptr (may need ref counting coordination)
  return std::shared_ptr<clickhouse::Column>(res.get(), [](auto*){});
}

void block_append_column(
    ErlNifEnv *env,
    fine::ResourcePtr<clickhouse::Block> block,
    std::string name,
    fine::ResourcePtr<clickhouse::Column> col) {
  auto shared_col = resource_to_shared(col);
  block->AppendColumn(name, shared_col);
}

Elixir Builder Pattern

defmodule Natch.Insert do
  def build_block(table_schema, rows) do
    block = Natch.Native.block_create()

    # Create columns based on schema
    columns = for {name, type} <- table_schema do
      {name, Natch.Column.new(type)}
    end

    # Populate columns from rows
    for row <- rows do
      for {name, col} <- columns do
        value = Map.get(row, name)
        Natch.Column.append(col, value)
      end
    end

    # Attach columns to block
    for {name, col} <- columns do
      Natch.Native.block_append_column(block, to_string(name), col.ref)
    end

    block
  end

  def insert(conn, table, rows, schema) do
    block = build_block(schema, rows)
    GenServer.call(conn, {:insert, table, block})
  end
end

Schema Inference

Two approaches:

A. Explicit Schema (MVP)

schema = [
  {:id, :uint64},
  {:name, :string},
  {:amount, :float64}
]

Natch.Insert.insert(conn, "test_table", rows, schema)

B. Schema Introspection (Phase 4)

# Query ClickHouse for table schema
schema = Natch.get_table_schema(conn, "test_table")
Natch.Insert.insert(conn, "test_table", rows, schema)

Testing

Build block from Elixir data
Insert into ClickHouse
Verify data with SELECT
Batch inserts (1k, 10k, 100k rows)

Success Criteria: Can insert data from Elixir maps into ClickHouse

✅ Phase 4: SELECT & Data Retrieval (COMPLETED)

Goal: Execute SELECT queries and return results to Elixir Status: ✅ Complete - 12 tests passing

Challenge: Callback Bridge

clickhouse-cpp uses callbacks for SELECT:

void Select(const std::string& query, SelectCallback cb);
// SelectCallback = std::function<void(const Block&)>

We need to bridge this to Elixir.

Approach: Accumulate Results

// native/natch_fine/select.cpp
std::vector<fine::ResourcePtr<clickhouse::Block>> client_select(
    ErlNifEnv *env,
    fine::ResourcePtr<clickhouse::Client> client,
    std::string query) {

  std::vector<fine::ResourcePtr<clickhouse::Block>> results;

  client->Select(query, [&](const clickhouse::Block& block) {
    // Copy block and wrap in ResourcePtr
    auto block_copy = std::make_shared<clickhouse::Block>(block);
    results.push_back(fine::make_resource_from_ptr(block_copy));
  });

  return results;  // FINE will convert vector to Elixir list
}
FINE_NIF(client_select, 0);

Block to Elixir Conversion

// native/natch_fine/convert.cpp
std::vector<std::map<std::string, ElixirValue>> block_to_maps(
    ErlNifEnv *env,
    fine::ResourcePtr<clickhouse::Block> block) {

  std::vector<std::map<std::string, ElixirValue>> rows;
  size_t row_count = block->GetRowCount();

  for (size_t row = 0; row < row_count; ++row) {
    std::map<std::string, ElixirValue> row_map;

    for (size_t col = 0; col < block->GetColumnCount(); ++col) {
      auto col_name = block->GetColumnName(col);
      auto column = block->GetColumn(col);

      // Extract value based on column type
      // This requires type dispatch logic
      row_map[col_name] = extract_value(column, row);
    }

    rows.push_back(row_map);
  }

  return rows;  // FINE converts to list of Elixir maps
}
FINE_NIF(block_to_maps, 0);

Type Extraction

ElixirValue extract_value(
    std::shared_ptr<clickhouse::Column> col,
    size_t row) {

  auto type = col->Type();

  if (type->GetCode() == clickhouse::Type::UInt64) {
    auto typed = std::static_pointer_cast<clickhouse::ColumnUInt64>(col);
    return (*typed)[row];
  } else if (type->GetCode() == clickhouse::Type::String) {
    auto typed = std::static_pointer_cast<clickhouse::ColumnString>(col);
    return std::string((*typed)[row]);
  }
  // ... handle all types

  throw std::runtime_error("Unsupported type");
}

Elixir Query API

defmodule Natch do
  def query(conn, sql) do
    case GenServer.call(conn, {:select, sql}, :infinity) do
      {:ok, blocks} ->
        rows = blocks
        |> Enum.flat_map(&Natch.Native.block_to_maps/1)
        {:ok, rows}
      error ->
        error
    end
  end

  def query!(conn, sql) do
    case query(conn, sql) do
      {:ok, rows} -> rows
      {:error, reason} -> raise "Query failed: #{inspect(reason)}"
    end
  end
end

Testing

Simple SELECT queries
Multiple column types
Large result sets
JOIN queries
Aggregations

Success Criteria: Can execute SELECT and get Elixir maps back

Phase 5: Columnar API & Performance Optimization (CRITICAL - In Progress)

Goal: Redesign insert API for columnar format to match ClickHouse native storage and eliminate performance impedance mismatches Status: 🔄 In Progress Priority: CRITICAL - Current row-oriented API has 10-1000x performance penalty

Performance Problem Discovered

Current Implementation Issue: The row-oriented insert API has a severe performance impedance mismatch:

# Current API (row-oriented)
rows = [
  %{id: 1, name: "Alice", value: 100.0},
  %{id: 2, name: "Bob", value: 200.0},
  # ... 100 rows
]
Natch.insert(conn, "table", rows, schema)  # 100 rows × 100 columns = 10,000 NIF calls!

Performance Analysis:

For N rows × M columns, current implementation makes N × M NIF boundary crossings
Each Column.append/2 call crosses Elixir → C++ → Elixir boundary
100 rows × 100 columns = 10,000 NIF calls (!)
Row-to-column transposition happens in Elixir with map lookups per cell
Significant overhead for bulk analytics workloads

Why This Matters:

ClickHouse is a columnar database - data stored by column, not row
Analytics workloads are columnar by nature (SUM, AVG, GROUP BY operate on columns)
Other high-performance tools (Arrow, Parquet, DuckDB, Polars) use columnar formats
Native protocol performance benefits are negated by API mismatch

Solution: Columnar-First API

New Design Principles:

Columnar as primary API - matches ClickHouse native storage
Bulk operations - 1 NIF call per column (not per value)
Zero transposition - data already in correct format
Performance-obsessed - designed for 100k-1M rows/sec throughput

New API:

# Columnar format (RECOMMENDED)
columns = %{
  id: [1, 2, 3, 4, 5],
  name: ["Alice", "Bob", "Charlie", "Dave", "Eve"],
  value: [100.0, 200.0, 300.0, 400.0, 500.0],
  timestamp: [~U[2024-01-01 10:00:00Z], ~U[2024-01-01 11:00:00Z], ...]
}

schema = [
  id: :uint64,
  name: :string,
  value: :float64,
  timestamp: :datetime
]

Natch.insert(conn, "events", columns, schema)
# Only 4 NIF calls (1 per column) for ANY number of rows!

Performance Improvement:

100 rows × 100 columns: 10,000 NIF calls → 100 NIF calls (100x improvement)
Better memory locality (all values for one column together)
Matches ClickHouse's native columnar format
Vectorization opportunities in C++

Bulk Append NIFs

Replace single-value appends with bulk operations:

// Bulk append - single NIF call for entire column
fine::Atom column_uint64_append_bulk(
    ErlNifEnv *env,
    fine::ResourcePtr<ColumnResource> col_res,
    std::vector<uint64_t> values) {
  try {
    auto typed = std::static_pointer_cast<ColumnUInt64>(col_res->ptr);
    for (auto value : values) {
      typed->Append(value);
    }
    return fine::Atom("ok");
  } catch (const std::exception& e) {
    throw std::runtime_error("UInt64 bulk append failed");
  }
}
FINE_NIF(column_uint64_append_bulk, 0);

// Similar for: int64, string, float64, datetime

FINE Advantage: Automatically converts Elixir lists to std::vector<T> - zero-copy where possible.

Conversion Utilities

For users with row-oriented data sources:

# Helper module for format conversion
defmodule Natch.Conversion do
  def rows_to_columns(rows, schema) do
    # Transpose row-oriented to column-oriented
    for {name, _type} <- schema, into: %{} do
      values = Enum.map(rows, & &1[name])
      {name, values}
    end
  end

  def columns_to_rows(columns, schema) do
    # Convert columnar back to row format (useful for testing)
  end
end

# Usage
rows = [%{id: 1, name: "Alice"}, %{id: 2, name: "Bob"}]
columns = Natch.Conversion.rows_to_columns(rows, schema)
Natch.insert(conn, "users", columns, schema)

Type Safety: Validation happens automatically in Natch.Column.append_bulk/2 and FINE NIFs during block building. No explicit validation calls needed - type errors are caught with helpful error messages, and FINE ensures the VM never crashes.

Note: Streaming insert support was removed. For large datasets, use Natch.insert/4 directly as clickhouse-cpp handles wire-level chunking (64KB compression blocks) automatically. Users can chunk data in their own code before calling Natch.insert/4 if needed for memory management.

✅ Additional Column Types (Phase 5C-D - Complete!)

Status: All 5 advanced types complete (213 tests passing)

✅ Completed Types:

UUID - ✅ Complete
- 128-bit universally unique identifiers
- Flexible parsing: strings (with/without hyphens), 16-byte binaries, mixed case
- Full serialization/deserialization with standard UUID string format
- 11 tests (10 unit + 1 integration)
DateTime64(6) - ✅ Complete
- Microsecond precision timestamps
- DateTime struct support with automatic conversion
- Integer timestamp support for direct microsecond values
- 7 tests (6 unit + 1 integration)
Decimal64(9) - ✅ Complete
- Fixed-point decimals using Decimal library
- Support for Decimal structs, integers, and floats
- Automatic scaling and conversion for financial precision
- 9 tests (8 unit + 1 integration)
Nullable(T) - ✅ Complete
- NULL support for UInt64, Int64, String, Float64
- Natural Elixir nil handling
- Dual-column management (nested data + null bitmap)
- 6 tests (5 unit + 1 integration)
Array(T) - ✅ Complete
- Universal generic path for ALL array types
- Offset-based encoding with ClickHouse's AppendAsColumn
- Recursive support for arbitrary nesting: Array(Array(Array(T)))
- Works for all types including nested: Array(Nullable(String)), Array(Array(Date))
- ~5-10 µs per array operation (very fast!)
- 3 integration tests covering simple arrays, nested arrays, and complex types
- See ALL_TYPES.md for complete architecture documentation

Already Completed (Phase 5B):

✅ Date - Date without time
✅ Bool - Boolean (ClickHouse UInt8)
✅ Float32 - Single precision float
✅ UInt32/16, Int32/16/8 - Additional integer types

✅ Complex Type Nesting (Phase 5E - Complete!)

Status: All complex nesting patterns complete (227 tests passing)

Problem Discovered: During integration testing, discovered critical bugs preventing complex nested types from working:

Map deserialization crash - tuple_col->Size() returned tuple element count (2) instead of key-value pair count
Limited Nullable support - Only handled 4 specific types (UInt64, Int64, String, Float64), missing all others
Missing type handlers - block_to_maps_impl lacked handlers for Map, Tuple, Enum8, Enum16, LowCardinality

Solutions Implemented:

Fixed Map SELECT in select.cpp:
- Changed tuple_col->Size() to keys_col->Size() for correct map size
- Added Map handler to block_to_maps_impl for full SELECT support

Generic Nullable handling:

// Old: Type-specific handling (4 types only)
if (auto uint64_col = nested->As<ColumnUInt64>()) { ... }
else if (auto int64_col = nested->As<ColumnInt64>()) { ... }
// ... only 4 types

// New: Universal generic handling (ALL types)
auto single_value_col = nested->Slice(i, 1);
ERL_NIF_TERM elem_list = column_to_elixir_list(env, single_value_col);
// Extract first element - works for ANY type

Complete type handler coverage in block_to_maps_impl:
- Added Tuple column handler
- Added Enum8/Enum16 handlers
- Added LowCardinality handler
- All nested type combinations now work
Enhanced Block.build_block for INSERT:
- Added transpose_tuples/2 - converts list of tuples to columnar format
- Added transpose_maps/1 - converts list of maps to keys/values arrays
- Enables natural Elixir data structures: [{a, 1}, {b, 2}] and [%{"k" => 1}]

Comprehensive Integration Tests:

Created test/nesting_integration_test.exs with 14 full roundtrip tests:

✅ Array(Nullable(T)) - Arrays with null values
- Array(Nullable(String)) with mixed nulls
- Array(Nullable(UInt64)) with all nulls
✅ LowCardinality(Nullable(String)) - Dictionary encoding with nulls
✅ Tuple with Nullable elements - Tuple(Nullable(String), UInt64)
✅ Map with Nullable values - Map(String, Nullable(UInt64)) (was crashing)
✅ Array(LowCardinality(String)) - Dictionary encoding in arrays
✅ Array(LowCardinality(Nullable(String))) - Triple wrapper type!
✅ Map(String, Array(UInt64)) - Arrays as map values
✅ Map(String, Enum16) - Enums as map values
✅ Tuple(String, Array(UInt64)) - Arrays in tuples
✅ Tuple(Enum8, UInt64) - Enums in tuples
✅ Array(Array(Nullable(UInt64))) - Triple nesting with nulls
✅ Array(Array(Array(UInt64))) - Deep nesting stress test
✅ Array(Enum8) - Enums in arrays

All 14 tests pass with full INSERT→SELECT roundtrip validation.

Impact:

Enables arbitrarily complex nested types
Production-ready support for analytics workloads with complex schemas
Generic patterns ensure future types work automatically

Future: Explorer DataFrame Integration

Status: Documented for future implementation (Phase 6+)

Rationale:

Explorer is Elixir's high-performance DataFrame library
Already columnar format (uses Apache Arrow internally)
Perfect fit for analytics workloads
Near zero-copy potential

Proposed API:

# Future: Direct DataFrame support
df = Explorer.DataFrame.new(
  id: [1, 2, 3],
  name: ["Alice", "Bob", "Charlie"],
  amount: [100.5, 200.75, 300.25]
)

# Schema inference from DataFrame types
Natch.insert(conn, "events", df)

# Or explicit schema
Natch.insert(conn, "events", df, schema: [id: :uint64, name: :string, amount: :float64])

Implementation Notes:

Explorer DataFrames are backed by Rust Polars
Can access underlying Arrow arrays for zero-copy operations
Need to map Explorer types to ClickHouse types
Binary data can be passed directly to C++ without copying

Benefits:

Natural fit: Analytics → DataFrame → Columnar DB
Users work in DataFrames, insert to ClickHouse seamlessly
Potential for SIMD/vectorized operations
Ecosystem integration (Nx, Explorer, Kino)

Breaking Changes

API Changes:

Natch.insert/4 now expects columnar format (map of lists), not row format (list of maps)
Natch.Column.append/2 removed, replaced with append_bulk/2
Single-value append NIFs removed (bulk operations only)

Migration:

# Before (Phases 1-4)
rows = [%{id: 1, name: "Alice"}, %{id: 2, name: "Bob"}]
Natch.insert(conn, "users", rows, schema)

# After (Phase 5+)
columns = %{id: [1, 2], name: ["Alice", "Bob"]}
Natch.insert(conn, "users", columns, schema)

# Or use conversion helper
columns = Natch.Conversion.rows_to_columns(rows, schema)
Natch.insert(conn, "users", columns, schema)

Connection Options & Production Features

Support full ClientOptions:

Authentication (user/password) ✅ Already supported
Compression (LZ4) ✅ Already supported
SSL/TLS ✅ Complete (Phase 5G)
Timeouts ✅ Complete (Phase 6A)
Retry logic - Future consideration

Note: Connection pooling should be investigated in the clickhouse-cpp library itself. If the C++ library handles connection pooling, we should leverage that rather than implementing it at the Elixir level.

Phase 6: Production Polish

Goal: Error handling, testing, and production-readiness Status: 🔄 In Progress (Timeout support + GitHub release preparation complete) Priority: High

✅ Phase 6A: Socket-Level Timeout Configuration (COMPLETED)

Status: Complete - 316 tests passing (8 integration tests added)

Implementation: Added three configurable socket-level timeout options to prevent operations from hanging indefinitely:

{:ok, conn} = Natch.Connection.start_link(
  host: "localhost",
  port: 9000,
  connect_timeout: 5_000,   # TCP connection establishment (default: 5000ms)
  recv_timeout: 60_000,     # Data receive operations (default: 0 = infinite)
  send_timeout: 60_000      # Data send operations (default: 0 = infinite)
)

Key Features:

Three timeout types: connect, recv, send (all in milliseconds)
Conservative defaults: Match clickhouse-cpp library (5000ms, 0ms, 0ms)
Infinite by default: recv_timeout=0 allows long-running analytical queries
Consistent errors: Timeouts raise Natch.ConnectionError with descriptive messages
Integration tests: 8 tests marked with :integration tag, excluded by default

Files Modified:

lib/natch/connection.ex: Added timeout options to type spec, moduledoc, and build_client/1
lib/natch/native.ex: Updated client_create/10 signature (was 7 params, now 10)
native/natch_fine/src/minimal.cpp: Added 3 uint64_t timeout params and ClientOptions setters
test/timeout_integration_test.exs: Created integration test suite
test/test_helper.exs: Added :integration to excluded tags
README.md: Added "Timeout Configuration" section

Design Decisions:

Connection-level only (not per-operation) - matches clickhouse-cpp architecture
Simplified naming (connect_timeout vs connection_connect_timeout) for Elixir ergonomics
Use milliseconds consistently (matching Elixir conventions)
Keep as ConnectionError (no new exception type)

Testing Strategy:

Integration tests use SELECT sleep(seconds) to trigger recv timeout
Connect timeout tested with unreachable IP (TEST-NET-1: 192.0.2.1)
Tests verify timeouts fail within expected duration
All existing 316 tests remain passing

✅ Phase 6B: GitHub Release Preparation (COMPLETED)

Status: Complete - Ready for public release

Deliverables Completed:

Licensing
- ✅ MIT License added (LICENSE file)
- ✅ Third-party notices documented (THIRD_PARTY_NOTICES.md)
- ✅ Apache 2.0 dependency compliance verified
Git Submodule Management
- ✅ clickhouse-cpp converted to git submodule at native/clickhouse-cpp
- ✅ Submodule points to v2.6.0 (commit 69195246)
- ✅ CMakeLists.txt updated for flexible path resolution (env var or submodule)
- ✅ Removed hardcoded absolute paths
- ✅ .gitmodules configured
GitHub Actions CI/CD
- ✅ Comprehensive test workflow (.github/workflows/test.yml)
- ✅ Matrix testing: Elixir 1.18.4/1.19.1 × OTP 27.2/28.1
- ✅ ClickHouse service container with authentication
- ✅ Valgrind memory leak detection job
- ✅ Docker-based valgrind testing (Dockerfile.valgrind)
- ✅ Formatting checks
- ✅ Compiler warnings as errors
- ✅ Artifact uploads for valgrind reports
Package Metadata
- ✅ mix.exs updated with proper package configuration
- ✅ Hex.pm ready with licenses, links, and file filters
- ✅ CHANGELOG.md created (documents v0.2.0 features)
Code Cleanup
- ✅ Removed experimental files (run_.sh, valgrind_.txt, docker_build.log)
- ✅ Updated .gitignore for valgrind artifacts
- ✅ Removed .claude/settings.local.json from git tracking
- ✅ Cleaned up backward compatibility code

GitHub Actions Status:

Automated testing on push to main and pull requests
Comprehensive valgrind testing for memory safety
Multiple Elixir/OTP version combinations verified
Zero memory leaks verified via valgrind

✅ Phase 6D: Prebuilt Binary System (COMPLETED)

Status: Complete - v0.2.0 published to Hex.pm with precompiled binaries

Deliverables Completed:

Precompiler Configuration
- ✅ mix.exs configured with cc_precompiler
- ✅ GitHub release URL template configured
- ✅ NIF version 2.17 (OTP 26-28) specified
- ✅ checksum*.exs added to package files
- ✅ checksum*.exs added to .gitignore
GitHub Actions Workflow
- ✅ Created .github/workflows/precompile.yml
- ✅ Builds on tag push (v*)
- ✅ 7 platform binaries generated:
  - x86_64-linux-gnu
  - aarch64-linux-gnu
  - x86_64-apple-darwin
  - aarch64-apple-darwin
  - armv7l-linux-gnueabihf (cross-compiled)
  - i686-linux-gnu (cross-compiled)
  - riscv64-linux-gnu (cross-compiled)
Release Process
- ✅ v0.2.0 tagged and pushed
- ✅ GitHub Actions built all binaries
- ✅ Checksums generated with mix elixir_make.checksum --all
- ✅ Published to Hex.pm successfully
- ✅ Verified with fresh test installation

Verification: Created test project that successfully:

Downloaded precompiled binary from Hex.pm cache
Connected to ClickHouse
Executed all basic operations (CREATE, INSERT, SELECT)
Tested both row-major and columnar formats
Verified type system and aggregations work

Remaining for Future Releases:

⏳ Phase 6C: Parameterized queries
⏳ Phase 6E: Additional documentation polish

✅ Phase 6E: Memory Locality Optimizations (COMPLETED)

Status: Complete - 36% INSERT performance improvement Date: 2025-11-09

Problem Discovered: Counterintuitive benchmark results showed row-major INSERT (with O(N×M) conversion) was 1.3x faster than direct columnar INSERT for large datasets (1M rows). This contradicted expected algorithmic performance.

Root Cause:

Pre-generated columnar data was fragmented in BEAM's old heap (7 separate comprehensions)
Poor cache locality → 50% cache miss rate (~350M wasted cycles)
NIF list traversal is memory-bound, not computation-bound
Memory locality dominated over algorithmic complexity

Solution Implemented:

Fresh Allocation Helper (bench/helpers.ex):

def fresh_columnar_data(columns) do
  Map.new(columns, fn {name, values} ->
    {name, Enum.to_list(values)}  # Forces young heap allocation
  end)
end

Optimized Single-Pass Generation (bench/helpers.ex):

def generate_test_data_optimized(row_count) do
  initial = %{id: [], user_id: [], event_type: [], ...}

  # Sequential prepends create adjacent cons cells
  columns_reversed = Enum.reduce(1..row_count, initial, fn id, acc ->
    %{id: [id | acc.id], user_id: [rand() | acc.user_id], ...}
  end)

  # Reverse creates contiguous allocation
  Map.new(columns_reversed, fn {name, vals} -> {name, :lists.reverse(vals)} end)
end

Performance Results (1M rows INSERT):

Method	Time	Improvement	Memory
Columnar (original)	2.10s	baseline	940 bytes
Columnar (fresh)	1.80s	14% faster	1.74 KB
Row-major	1.49s	29% faster	997 MB
Columnar (optimized)	1.39s	36% faster	871 MB

Key Findings:

Columnar with optimized generation is now fastest method (beats row-major by 7%)
Sequential allocation in young heap provides excellent cache locality (10% miss rate vs 50%)
Memory locality matters more than algorithmic complexity for NIF boundary crossing
~280M cycles saved from better cache behavior (≈140ms at 2GHz)

Deliverables:

✅ Bench.Helpers.fresh_columnar_data/1 for forcing young heap allocation
✅ Bench.Helpers.generate_test_data_optimized/1 with single-pass reduction pattern
✅ Updated all benchmarks with fresh and optimized-gen variants
✅ Comprehensive documentation in research/PERF.md Phase 6
✅ Updated README with optimized generation pattern and new benchmark stats
✅ Updated API documentation with best practices

Updated Performance Stats (vs Pillar):

INSERT 10k rows: 12.9ms (4.8x faster)
INSERT 100k rows: 156ms (4.0x faster)
INSERT 1M rows: 1,338ms (3.8x faster) ← 36% improvement from previous 2,094ms

Documentation:

Complete analysis in research/PERF.md Phase 6
Best practices added to README Performance Tips section
Cache miss calculations and BEAM GC behavior explained

✅ Phase 6C: Parameterized Queries (COMPLETED)

Goal: Add support for parameterized queries to prevent SQL injection and enable type-safe query building Status: ✅ Complete - 348 tests passing (19 new tests added) Priority: High - Security and usability Date: 2025-11-09

Problem: Current API requires string concatenation for dynamic queries, which is:

Vulnerable to SQL injection
Error-prone (manual escaping)
Lacks type safety

# Current - Unsafe!
user_input = "'; DROP TABLE users; --"
Natch.query(conn, "SELECT * FROM users WHERE name = '#{user_input}'")

Solution: Native ClickHouse Query API with Type Inference

ClickHouse native protocol supports parameterized queries using {name:Type} syntax. We leverage this with automatic type inference for SELECT operations to provide both safety and ergonomics.

Design Decision: Inference for SELECTs, Explicit for Writes

After analysis, we determined:

SELECT queries (90% of use): Types are logically redundant since schema defines them
INSERT queries: Use specialized columnar API (Natch.insert/4) - no params needed
execute/3 (UPDATE/DELETE): Keep explicit types for safety

Implementation:

C++ Query NIFs (native/natch_fine/src/query.cpp)
- query_create/1 - Creates Query resource from SQL
- query_bind_* functions for all ClickHouse types
- query_send/2 - Executes parameterized query via native protocol

Elixir Query Builder (lib/natch/query.ex)

# Builder API - explicit types required
query = Query.new("SELECT * FROM users WHERE id = {id:UInt64}")
|> Query.bind(:id, 42)

# Automatic type inference from Elixir values
defp bind_value(ref, name, value) when is_integer(value) and value >= 0 do
  Natch.Native.query_bind_uint64(ref, name, value)  # UInt64
end

Type Inference for SELECT APIs (lib/natch.ex)

def select_rows(conn, sql, params) when is_binary(sql) do
  # Transform {id} -> {id:UInt64} based on param value
  sql_with_types = add_parameter_types(sql, params)
  query = Query.new(sql_with_types) |> Query.bind_all(params)
  select_rows(conn, query)
end

defp infer_clickhouse_type(v) when is_integer(v) and v >= 0, do: "UInt64"
defp infer_clickhouse_type(v) when is_integer(v), do: "Int64"
defp infer_clickhouse_type(v) when is_float(v), do: "Float64"
defp infer_clickhouse_type(v) when is_binary(v), do: "String"
defp infer_clickhouse_type(%DateTime{}), do: "DateTime"

Usage Patterns:

# SELECT with automatic type inference (RECOMMENDED)
{:ok, rows} = Natch.select_rows(
  conn,
  "SELECT * FROM users WHERE id = {id} AND status = {status}",
  id: 42,          # Inferred as UInt64
  status: "active" # Inferred as String
)

# Builder API with explicit types (advanced control)
query = Query.new("SELECT * FROM users WHERE id = {id:UInt64}")
|> Query.bind(:id, 42)
{:ok, rows} = Natch.select_rows(conn, query)

# execute/3 requires explicit types (safety for writes)
:ok = Natch.execute(
  conn,
  "DELETE FROM users WHERE id = {id:UInt64}",
  id: 42
)

Type Inference Rules:

integer() >= 0 → UInt64
integer() < 0 → Int64
float() → Float64
binary() → String
DateTime.t() → DateTime
Date.t() → Date
nil → raises (must use explicit Nullable(T))

Security:

Parameters sent separately from SQL over wire protocol
No string escaping needed - values cannot be interpreted as SQL
SQL injection prevention verified with malicious input tests

Testing:

19 parameterized query tests added
SQL injection prevention tests
Type inference tests for all supported types
Mixed typed/untyped placeholder tests
NULL handling tests

Implemented Features: ✓ Builder API with explicit types ✓ Simple API with automatic type inference ✓ Both keyword list and map parameter styles ✓ SQL injection protection ✓ Comprehensive type support (integers, floats, strings, DateTime, Date, NULL) ✓ Mixed typed/untyped placeholders ✓ Integration with select_rows, select_cols, and execute

Error Handling

// Custom error types
namespace fine {
  template<>
  struct Encoder<clickhouse::ServerException> {
    static ERL_NIF_TERM encode(ErlNifEnv* env, const clickhouse::ServerException& ex) {
      // Convert to Elixir exception with error code, message, stack trace
    }
  };
}

Testing

Comprehensive error handling tests
Memory leaks (valgrind)
Concurrent operations
Authentication
Connection failures and retries

Success Criteria: Production-ready error handling and reliability

Phase 7: Advanced Query Features (Nice to Have)

Goal: Streaming SELECT, batch operations, advanced query patterns Status: ⏳ Pending Priority: Low - Nice to have

Streaming SELECT

For large result sets, stream data back to Elixir:

void client_select_async(
    ErlNifEnv *env,
    fine::ResourcePtr<Client> client,
    std::string query,
    ErlNifPid receiver_pid) {

  client->Select(query, [&](const Block& block) {
    // Send block to Elixir process
    ErlNifEnv* msg_env = enif_alloc_env();
    ERL_NIF_TERM block_term = encode_block(msg_env, block);
    enif_send(env, &receiver_pid, msg_env, block_term);
    enif_free_env(msg_env);
  });
}

Batch Operations

Support multiple queries in a single transaction or batch.

❌ Phase 8: Ecto Integration (NOT IMPLEMENTING)

Status: ❌ Will not implement Reason: Per user feedback - "I don't think Ecto is a good fit. We should never do this."

ClickHouse is an OLAP database optimized for analytics, not OLTP. Ecto's schema-based approach and transaction model don't align well with ClickHouse's use cases. Users should use Natch's direct query interface instead.

Technical Challenges & Solutions

1. Smart Pointer Lifetime Management

Problem: clickhouse-cpp uses std::shared_ptr, FINE uses ResourcePtr

Solution Options:

A. Store shared_ptr inside a wrapper struct, wrap that with ResourcePtr
B. Use aliasing constructor to keep resources alive
C. Careful manual lifetime management

Recommended: Option A

struct ColumnResource {
  std::shared_ptr<clickhouse::Column> ptr;

  ColumnResource(std::shared_ptr<clickhouse::Column> p) : ptr(p) {}
};

FINE_RESOURCE(ColumnResource);

fine::ResourcePtr<ColumnResource> column_create(
    ErlNifEnv *env, std::string type) {
  auto col = clickhouse::CreateColumnByType(type);
  return fine::make_resource<ColumnResource>(col);
}

2. Type Dispatch for 42 Column Types

Problem: Need to handle 42+ column types dynamically

Solution: Phased implementation

Phase 1: 5 essential types (UInt64, Int64, String, Float64, DateTime)
Phase 2: 10 common types (add Nullable, Array, Date, Bool, Float32)
Phase 3: 20 types (add Decimal, UUID, IPv4/6, Int32/16/8, UInt32/16/8)
Phase 4: All remaining types (Geo, Enum, Map, Tuple, LowCardinality)

Pattern: Use visitor or type code dispatch

ElixirValue extract_value(ColumnRef col, size_t row) {
  switch (col->Type()->GetCode()) {
    case Type::UInt64:
      return (*col->As<ColumnUInt64>())[row];
    case Type::String:
      return std::string((*col->As<ColumnString>())[row]);
    case Type::Array:
      return extract_array_value(col, row);
    // ... etc
  }
}

3. Callback Bridge for SELECT

Problem: C++ callback → Elixir process

Solution: Accumulate in C++, return to Elixir

SELECT accumulates all blocks
Return vector of blocks
Elixir converts blocks to maps

Alternative (Phase 4): Streaming with send to Elixir process

void client_select_async(
    ErlNifEnv *env,
    fine::ResourcePtr<Client> client,
    std::string query,
    ErlNifPid receiver_pid) {

  client->Select(query, [&](const Block& block) {
    // Send block to Elixir process
    ErlNifEnv* msg_env = enif_alloc_env();
    ERL_NIF_TERM block_term = encode_block(msg_env, block);
    enif_send(env, &receiver_pid, msg_env, block_term);
    enif_free_env(msg_env);
  });
}

4. Binary Data Efficiency

Problem: Large strings/binaries copy overhead

Solution: Use ErlNifBinary for zero-copy where possible

// For ColumnString, explore zero-copy paths
ERL_NIF_TERM column_string_get_binary(
    ErlNifEnv *env,
    fine::ResourcePtr<ColumnResource> col,
    size_t index) {

  auto typed = col->ptr->As<ColumnString>();
  auto str_view = (*typed)[index];

  // Ideally zero-copy, but may require copying for safety
  ErlNifBinary bin;
  enif_alloc_binary(str_view.size(), &bin);
  std::memcpy(bin.data, str_view.data(), str_view.size());

  return enif_make_binary(env, &bin);
}

Build System Setup

Directory Structure

natch/
├── lib/
│   ├── natch.ex                 # Public API
│   ├── natch/
│   │   ├── connection.ex       # GenServer
│   │   ├── native.ex          # NIF declarations (minimal)
│   │   ├── column.ex          # Column builders
│   │   └── insert.ex          # Insert helpers
├── native/
│   └── natch_fine/
│       ├── CMakeLists.txt     # Build config
│       ├── Makefile           # Mix integration
│       ├── src/
│       │   ├── client.cpp     # Client NIFs
│       │   ├── block.cpp      # Block NIFs
│       │   ├── column.cpp     # Column NIFs
│       │   ├── select.cpp     # SELECT NIFs
│       │   └── convert.cpp    # Type conversions
│       └── clickhouse-cpp/    # Git submodule or vendored
├── test/
│   ├── natch_test.exs
│   └── integration_test.exs
├── mix.exs
└── README.md

CMakeLists.txt

cmake_minimum_required(VERSION 3.15)
project(natch_fine)

set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)

# Find FINE (from Hex package)
find_package(fine REQUIRED)

# Add clickhouse-cpp
add_subdirectory(clickhouse-cpp)

# Build NIF shared library
add_library(natch_fine SHARED
  src/client.cpp
  src/block.cpp
  src/column.cpp
  src/select.cpp
  src/convert.cpp
)

target_link_libraries(natch_fine
  PRIVATE
    fine::fine
    clickhouse-cpp-lib
)

# Set visibility
set_target_properties(natch_fine PROPERTIES
  CXX_VISIBILITY_PRESET hidden
  PREFIX ""
)

# Output to priv/
set_target_properties(natch_fine PROPERTIES
  LIBRARY_OUTPUT_DIRECTORY ${CMAKE_SOURCE_DIR}/../../priv
)

Makefile (for Mix)

.PHONY: all clean

MIX_ENV ?= dev
BUILD_DIR = _build/$(MIX_ENV)

all:
	@mkdir -p $(BUILD_DIR)
	@cd $(BUILD_DIR) && cmake ../../native/natch_fine
	@cmake --build $(BUILD_DIR)

clean:
	@rm -rf $(BUILD_DIR)
	@rm -rf ../../priv/natch_fine.*

mix.exs

defmodule Natch.MixProject do
  use Mix.Project

  def project do
    [
      app: :natch,
      version: "0.2.0",
      elixir: "~> 1.18",
      compilers: [:elixir_make] ++ Mix.compilers(),
      make_targets: ["all"],
      make_clean: ["clean"],
      deps: deps()
    ]
  end

  def application do
    [extra_applications: [:logger]]
  end

  defp deps do
    [
      {:fine, "~> 0.1"},
      {:elixir_make, "~> 0.6", runtime: false},
      {:ex_doc, "~> 0.34", only: :dev, runtime: false}
    ]
  end
end

Testing Strategy

Unit Tests (per Phase)

defmodule Natch.ConnectionTest do
  use ExUnit.Case

  setup do
    {:ok, conn} = Natch.start_link(host: "localhost", port: 9000)

    on_exit(fn ->
      Natch.stop(conn)
    end)

    {:ok, conn: conn}
  end

  test "ping", %{conn: conn} do
    assert :ok = Natch.ping(conn)
  end

  test "execute DDL", %{conn: conn} do
    assert :ok = Natch.execute(conn, """
      CREATE TABLE IF NOT EXISTS test (
        id UInt64,
        name String
      ) ENGINE = Memory
    """)
  end
end

Integration Tests

defmodule Natch.IntegrationTest do
  use ExUnit.Case

  @moduletag :integration

  setup_all do
    # Start ClickHouse via Docker
    System.cmd("docker-compose", ["up", "-d", "clickhouse"])
    :timer.sleep(2000)

    on_exit(fn ->
      System.cmd("docker-compose", ["down"])
    end)

    :ok
  end

  test "full insert and select cycle" do
    {:ok, conn} = Natch.start_link(host: "localhost", port: 9000)

    # Create table
    Natch.execute(conn, """
      CREATE TABLE test (
        id UInt64,
        name String,
        amount Float64
      ) ENGINE = Memory
    """)

    # Insert data
    columns = %{
      id: [1, 2],
      name: ["Alice", "Bob"],
      amount: [100.5, 200.75]
    }

    schema = [id: :uint64, name: :string, amount: :float64]
    :ok = Natch.insert(conn, "test", columns, schema)

    # Query back
    {:ok, results} = Natch.query(conn, "SELECT * FROM test ORDER BY id")

    assert length(results) == 2
    assert hd(results).name == "Alice"
  end
end

Performance Benchmarks

defmodule Natch.BenchmarkTest do
  use ExUnit.Case

  @tag :benchmark
  test "bulk insert performance" do
    {:ok, conn} = Natch.start_link(host: "localhost", port: 9000)

    # Generate 100k rows
    rows = for i <- 1..100_000 do
      %{id: i, value: :rand.uniform(1000)}
    end

    schema = [id: :uint64, value: :uint64]

    {time_us, :ok} = :timer.tc(fn ->
      Natch.insert(conn, "benchmark", rows, schema)
    end)

    rows_per_sec = 100_000 / (time_us / 1_000_000)
    IO.puts("Inserted #{rows_per_sec} rows/sec")

    # Should be > 50k rows/sec for native protocol to be worthwhile
    assert rows_per_sec > 50_000
  end
end

Memory Leak Testing

# Run with valgrind
MIX_ENV=test valgrind --leak-check=full --track-origins=yes \
  mix test --only memory_leak

# Or use AddressSanitizer
CXXFLAGS="-fsanitize=address" mix compile
mix test

Deployment Considerations

Platform Support

Required Platforms:

Linux x86_64 (primary)
macOS ARM64 (development)
macOS x86_64 (optional)

Build Requirements:

C++17 compiler (GCC 7+, Clang 5+, MSVC 2017+)
CMake 3.15+
ClickHouse-cpp dependencies: abseil, lz4, cityhash, zstd

Docker Development

# Dockerfile.dev
FROM elixir:1.18

# Install C++ build tools
RUN apt-get update && apt-get install -y \
    build-essential \
    cmake \
    git \
    liblz4-dev \
    libzstd-dev

WORKDIR /app

COPY mix.exs mix.lock ./
RUN mix deps.get

COPY . .
RUN mix compile

CMD ["iex", "-S", "mix"]

CI/CD Pipeline

# .github/workflows/ci.yml
name: CI

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest

    services:
      clickhouse:
        image: clickhouse/clickhouse-server:latest
        ports:
          - 9000:9000
          - 8123:8123

    steps:
      - uses: actions/checkout@v3
        with:
          submodules: recursive

      - uses: erlef/setup-beam@v1
        with:
          otp-version: 27.2
          elixir-version: 1.18

      - name: Install C++ dependencies
        run: |
          sudo apt-get update
          sudo apt-get install -y build-essential cmake liblz4-dev libzstd-dev

      - name: Cache dependencies
        uses: actions/cache@v3
        with:
          path: deps
          key: deps-${{ runner.os }}-${{ hashFiles('mix.lock') }}

      - name: Install dependencies
        run: mix deps.get

      - name: Compile
        run: mix compile

      - name: Run tests
        run: mix test

Risk Mitigation

High-Risk Items

Smart Pointer Lifetime Mismatch
- Risk: Segfaults from dangling pointers
- Mitigation: Comprehensive testing, valgrind, wrapper pattern
- Fallback: Use copying instead of sharing
Type Dispatch Complexity
- Risk: Missing type support causes runtime errors
- Mitigation: Explicit error for unsupported types, phased rollout
- Fallback: Support common types only, document limitations
Memory Leaks
- Risk: Resources not freed properly
- Mitigation: RAII, ResourcePtr, valgrind testing
- Fallback: Regular process restarts in production

Medium-Risk Items

Build System Complexity
- Risk: Platform-specific build failures
- Mitigation: Docker-based builds, comprehensive CI
- Fallback: Provide pre-built binaries
Upstream Changes
- Risk: clickhouse-cpp breaking changes
- Mitigation: Pin to specific version/tag, test before upgrading
- Fallback: Fork if necessary

Low-Risk Items

Performance Not Meeting Expectations
- Risk: FFI overhead negates protocol benefits
- Mitigation: Early benchmarking, compare to HTTP baseline
- Fallback: Document actual performance, users decide

Success Metrics

✅ Phase 1 Success (ACHIEVED)

✅ Connection established
✅ Ping works
✅ DDL operations succeed
✅ 10 tests passing

✅ Phase 2 Success (ACHIEVED)

✅ Core 5 types working (UInt64, Int64, String, Float64, DateTime)
✅ Column creation and population
✅ 33 tests passing

✅ Phase 3 Success (ACHIEVED)

✅ Block building from Elixir maps
✅ INSERT operations working
✅ 17 tests passing

✅ Phase 4 Success (ACHIEVED - MVP Reached!)

✅ SELECT queries returning data
✅ Block-to-maps conversion
✅ Large result sets (10k rows tested)
✅ 12 tests passing
✅ Total: 89 tests passing (2 PoC + 10 Phase1 + 33 Phase2 + 17 Phase3 + 12 Phase4 + 15 remaining)

✅ Phase 5A Success (ACHIEVED - Columnar API)

✅ Bulk append NIFs implemented
✅ Columnar insert API with map of lists
✅ 100x performance improvement (N×M NIF calls → M NIF calls)
✅ Zero transposition overhead

✅ Phase 5B Success (ACHIEVED - 8 Additional Types)

✅ Date, Bool, Float32
✅ UInt32/16, Int32/16/8
✅ 160 tests passing total

✅ Phase 5C-D Success (COMPLETE - All 5 Advanced Types)

✅ UUID (128-bit identifiers, flexible parsing)
✅ DateTime64(6) (microsecond precision timestamps)
✅ Decimal64(9) (fixed-point with Decimal library)
✅ Nullable(T) (NULL support for UInt64, Int64, String, Float64)
✅ Array(T) (100% type coverage with universal generic path)
✅ Total: 213 tests passing (53 new tests added)

✅ Phase 5E Success (COMPLETE - Complex Type Nesting)

✅ Enhanced Block.build_block to handle Tuple and Map INSERT
✅ Fixed critical Map deserialization bug (incorrect size calculation)
✅ Made Nullable handling generic via Slice() and recursion
✅ Added missing type handlers: Map, Tuple, Enum8, Enum16, LowCardinality in block_to_maps_impl
✅ Comprehensive integration tests (14 tests) with full INSERT→SELECT roundtrip validation:
- ✅ Array(Nullable(String)) and Array(Nullable(UInt64))
- ✅ LowCardinality(Nullable(String))
- ✅ Tuple(Nullable(String), UInt64)
- ✅ Map(String, Nullable(UInt64))
- ✅ Array(LowCardinality(String))
- ✅ Array(LowCardinality(Nullable(String))) - triple wrapper!
- ✅ Map(String, Array(UInt64))
- ✅ Map(String, Enum16)
- ✅ Tuple(String, Array(UInt64))
- ✅ Tuple(Enum8, UInt64)
- ✅ Array(Array(Nullable(UInt64))) - triple nesting
- ✅ Array(Array(Array(UInt64))) - deep nesting stress test
- ✅ Array(Enum8)
✅ Total: 227 tests passing (14 new integration tests added)

Phase 6A Success (Timeout Configuration)

✅ Socket-level timeout configuration (connect, recv, send)
✅ Conservative defaults matching clickhouse-cpp
✅ Integration test suite (8 tests)
✅ Documentation in README and moduledoc
✅ Total: 316 tests passing (8 integration tests added)

✅ Phase 6D Success (Prebuilt Binary Releases) - COMPLETED

✅ v0.2.0 published to Hex.pm with precompiled binaries
✅ GitHub Actions workflow for 7 platforms
✅ Semi-automated release process
✅ Verification with fresh test installation

✅ Phase 6E Success (Memory Locality Optimizations) - COMPLETED

✅ 36% INSERT performance improvement for large datasets
✅ Optimized data generation helpers implemented
✅ Comprehensive documentation in research/PERF.md
✅ Updated benchmarks and performance stats
✅ All 316 tests passing

Performance Targets (EXCEEDED)

INSERT: ✅ 1,338ms for 1M rows (3.8x faster than Pillar, 36% faster than original)
Latency: ✅ 12.9ms for 10k rows (4.8x faster than Pillar)
Memory: ✅ Minimal BEAM usage (940 bytes columnar, vs 4.3GB Pillar)
SELECT: ✅ 6.2x faster than Pillar for 1M row full scans

Comparison: FINE vs Alternatives

Approach	Effort	Performance	Risk	Maintainability
FINE Wrapper	4-6 weeks	Native C++	Medium	Medium
Pure Elixir	4-6 months	20-40% slower	Low	High
Rustler Wrapper	6-10 weeks	Native	Medium-High	Medium
Pillar (HTTP)	0 (exists)	Baseline	None	High

Open Questions

✅ Resource Lifetime: Best pattern for Column → Block → Insert lifecycle? → Solved: Wrapper pattern with shared_ptr inside ResourcePtr
✅ SELECT callback handling: How to bridge C++ callbacks to Elixir? → Solved: Convert to Erlang terms immediately in callback
Schema Inference: Query ClickHouse for table schema automatically?
Connection Pooling: At Elixir level or leverage clickhouse-cpp? → Investigate clickhouse-cpp capabilities first
Compression Default: Enable LZ4 by default or opt-in?

Next Steps

With MVP achieved (Phases 1-4 complete), all advanced types complete (Phase 5A-G), timeout support complete (Phase 6A), and GitHub release preparation complete (Phase 6B), current priorities:

✅ Phase 5A-B: Columnar API & Performance (COMPLETED)
- ✅ Bulk append NIFs (C++ implementation)
- ✅ Columnar insert API (Elixir layer)
- ✅ 8 additional types (Date, Bool, Float32, UInt32/16, Int32/16/8)
- ✅ 100x performance improvement achieved
- ✅ Breaking change implemented: Row-oriented → Columnar API
✅ Phase 5C-D: All Advanced Types (COMPLETED)
- ✅ UUID (128-bit identifiers)
- ✅ DateTime64(6) (microsecond precision)
- ✅ Decimal64(9) (fixed-point with Decimal library)
- ✅ Nullable(T) (NULL support for 4 types)
- ✅ Array(T) (100% type coverage with universal generic path)
- ✅ 53 new tests added (213 tests passing total)
- ✅ Complete architecture documentation in ALL_TYPES.md
✅ Phase 5E: Complex Type Nesting (COMPLETED)
- ✅ Fixed critical Map deserialization crash
- ✅ Generic Nullable handling for all types
- ✅ Complete type handler coverage in SELECT
- ✅ Enhanced Block.build_block for Tuple and Map INSERT
- ✅ 14 comprehensive integration tests with full roundtrip validation
- ✅ 227 tests passing total
- ✅ Production-ready support for arbitrarily complex nested types
✅ Phase 5F: SELECT API Redesign & Columnar Results (COMPLETED)
- ✅ Implemented client_select_cols in C++ (lines 567-824 in select.cpp)
- ✅ Returns columnar format: %{column_name => [values]}
- ✅ Added select_cols/2 to Elixir Connection API
- ✅ Renamed select/2 to select_rows/2 with backward compatibility
- ✅ Updated benchmarks to compare row-major vs columnar performance
- ✅ Updated README with comprehensive API documentation
- ✅ All 309 tests passing
- Performance characteristics:
  - Columnar format ideal for large result sets and analytics
  - Row-major format maintained for traditional use cases
  - Backward compatible via deprecated select/2 wrapper
✅ Phase 5G: SSL/TLS Support (COMPLETED)
- ✅ OpenSSL integration with clickhouse-cpp
- ✅ System CA certificate support
- ✅ SNI (Server Name Indication) enabled
- ✅ ClickHouse Cloud compatibility on port 9440
- ✅ Comprehensive documentation in README
✅ Phase 6A: Socket-Level Timeout Configuration (COMPLETED)
- ✅ Three timeout types: connect_timeout, recv_timeout, send_timeout
- ✅ Conservative defaults matching clickhouse-cpp (5000ms, 0ms, 0ms)
- ✅ Connection-level configuration (not per-operation)
- ✅ Comprehensive timeout documentation in README and moduledoc
- ✅ Integration test suite (8 tests, excluded by default)
- ✅ All 316 tests passing (8 integration tests added)
✅ Phase 6B: GitHub Release Preparation (COMPLETED)
- ✅ MIT License + third-party notices
- ✅ clickhouse-cpp git submodule conversion
- ✅ GitHub Actions CI/CD with matrix testing
- ✅ Valgrind memory leak detection automation
- ✅ Package metadata for Hex.pm
- ✅ Code cleanup and .gitignore updates
✅ Phase 6D: Prebuilt Binary Releases (COMPLETED)
- ✅ GitHub Actions workflow with 4 parallel jobs (testing + cross-compilation)
- ✅ Tested platforms: Linux x86_64, Linux ARM64, macOS x86_64, macOS ARM64
- ✅ Cross-compiled: Linux ARMv7, RISC-V 64, i686 (best effort, no tests)
- ✅ NIF version matrix: 2.15 (OTP 24), 2.16 (OTP 25), 2.17 (OTP 26-28)
- ✅ ~18-21 precompiled binaries per release
- ✅ Semi-automated workflow: CI builds → manual checksum → manual Hex publish
- ✅ v0.2.0 published to Hex.pm with precompiled binaries
✅ Phase 6E: Memory Locality Optimizations (COMPLETED)
- ✅ 36% INSERT performance improvement for large datasets
- ✅ Fixed memory allocation patterns causing cache misses
- ✅ Added optimized data generation helpers (fresh_columnar_data, generate_test_data_optimized)
- ✅ Comprehensive documentation in research/PERF.md Phase 6
- ✅ Updated benchmarks and README with new performance stats
- ✅ All 316 tests passing
Phase 6C: Parameterized Queries (NEXT PRIORITY)

Support for query parameters to prevent SQL injection
Bind parameter syntax (e.g., SELECT * FROM table WHERE id = ?)
Type-safe parameter binding
Integration with existing query API

Phase 6F: Additional Production Features (FUTURE)

Comprehensive error handling refinement
Performance optimization and profiling
Documentation polish for public consumption

Phase 7: Ecto Integration Investigation (FUTURE - NEEDS RESEARCH)

Status: Under consideration - partial fit at best
Scope: Focus on query execution, not full schema/migration support
Rationale: ClickHouse is OLAP, not OLTP. Investigate limited integration:
- Query builder integration for SELECT operations
- Type casting and result mapping
- Connection pooling via DBConnection
Limitations to document:
- No transactions (ClickHouse doesn't support them)
- No schema migrations (table structures are analytics-focused)
- No associations/preloading (not OLTP use case)
- Limited changesets (bulk inserts don't fit Ecto's row model)
Decision point: Determine if partial Ecto support provides value or creates confusion

Phase 8: Explorer DataFrame Integration (FUTURE)

Direct DataFrame insert support
Zero-copy optimizations with Arrow
Schema inference from DataFrame types
Natural analytics workflow integration

Phase 9: Advanced Query Features (NICE TO HAVE)

Streaming SELECT for large result sets
Batch operations
Async query support

NOT IMPLEMENTING:

❌ Distributed Queries (removed)

References

Appendix A: Minimal Working Example

This example demonstrates the absolute minimum code needed to verify FINE + clickhouse-cpp works:

native/natch_fine/minimal.cpp

#include <fine.hpp>
#include <clickhouse/client.h>

using namespace clickhouse;

FINE_RESOURCE(Client);

fine::ResourcePtr<Client> create_client(ErlNifEnv *env) {
  ClientOptions opts;
  opts.SetHost("localhost");
  opts.SetPort(9000);
  return fine::make_resource<Client>(opts);
}
FINE_NIF(create_client, 0);

std::string ping(ErlNifEnv *env, fine::ResourcePtr<Client> client) {
  try {
    client->Ping();
    return "pong";
  } catch (const std::exception& e) {
    return std::string("error: ") + e.what();
  }
}
FINE_NIF(ping, 0);

FINE_INIT("Elixir.NatchMinimal");

lib/natch_minimal.ex

defmodule NatchMinimal do
  @moduledoc """
  Minimal FINE wrapper proof-of-concept
  """

  @on_load :load_nifs

  def load_nifs do
    path = :filename.join(:code.priv_dir(:natch), 'natch_fine')
    :ok = :erlang.load_nif(path, 0)
  end

  def create_client, do: :erlang.nif_error(:not_loaded)
  def ping(_client), do: :erlang.nif_error(:not_loaded)
end

Test

{:ok, client} = NatchMinimal.create_client()
"pong" = NatchMinimal.ping(client)

If this works, you're ready to proceed with the full implementation!

End of Document

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Natch: FINE Wrapper Implementation Plan

Executive Summary

Why FINE + clickhouse-cpp?

Architecture Overview

✅ Phase 1: Foundation (COMPLETED)

Deliverables

Testing

✅ Phase 2: Type System & Column Creation (COMPLETED)

Key Challenge

Approach: Leverage clickhouse-cpp's Factory

Column Population Strategy

Deliverables

Testing

✅ Phase 3: Block Building & INSERT (COMPLETED)

Block Resource

Smart Pointer Challenge

Elixir Builder Pattern

Schema Inference

Testing

✅ Phase 4: SELECT & Data Retrieval (COMPLETED)

Challenge: Callback Bridge

Approach: Accumulate Results

Block to Elixir Conversion

Type Extraction

Elixir Query API

Testing

Phase 5: Columnar API & Performance Optimization (CRITICAL - In Progress)

Performance Problem Discovered

Solution: Columnar-First API

Bulk Append NIFs

Conversion Utilities

✅ Additional Column Types (Phase 5C-D - Complete!)

✅ Complex Type Nesting (Phase 5E - Complete!)

Future: Explorer DataFrame Integration

Breaking Changes

Connection Options & Production Features

Phase 6: Production Polish

✅ Phase 6A: Socket-Level Timeout Configuration (COMPLETED)

✅ Phase 6B: GitHub Release Preparation (COMPLETED)

✅ Phase 6D: Prebuilt Binary System (COMPLETED)

✅ Phase 6E: Memory Locality Optimizations (COMPLETED)

✅ Phase 6C: Parameterized Queries (COMPLETED)

Error Handling

Testing

Phase 7: Advanced Query Features (Nice to Have)

Streaming SELECT

Batch Operations

❌ Phase 8: Ecto Integration (NOT IMPLEMENTING)

Technical Challenges & Solutions

1. Smart Pointer Lifetime Management

2. Type Dispatch for 42 Column Types

3. Callback Bridge for SELECT

4. Binary Data Efficiency

Build System Setup

Directory Structure

CMakeLists.txt

Makefile (for Mix)

mix.exs

Testing Strategy

Unit Tests (per Phase)

Integration Tests

Performance Benchmarks

Memory Leak Testing

Deployment Considerations

Platform Support

Docker Development

CI/CD Pipeline

Risk Mitigation

High-Risk Items

Medium-Risk Items

Low-Risk Items

Success Metrics

✅ Phase 1 Success (ACHIEVED)

✅ Phase 2 Success (ACHIEVED)