perf: add JSON sidecar for data model loading (~100x faster) - #898
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The merged data model YAML file is re-parsed by every test subprocess using ruamel's pure-Python parser (1.9s per test on a 2.3MB file). This adds a JSON sidecar (.cache.json) written once by the orchestrator and read by tests via json.load() (~0.005s). The orchestrator writes both YAML and JSON at merge time. load_data_model() prefers the JSON sidecar when present and fresh, falling back to YAML if missing or stale. If a test is the first to run (no sidecar), it writes one after parsing YAML so subsequent tests benefit. Measured: 1.9s -> 0.017s per test (112x improvement on data model loading). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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oboehmer
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@ChristopherJHart , please check #931 (comment) for an alternative approach.. TLDR: I would just create merged_data_model_test_variables.json already in main and promote it as a first-class artifcat, with the option to deprecate the .yaml format altogether..
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Closing in favor of #933 |
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Summary
Adds a JSON sidecar cache for the merged data model, eliminating repeated YAML parsing across test subprocesses.
Problem
Every test subprocess re-parses the merged data model YAML file using ruamel's pure-Python parser (
YAML(typ="safe", pure=True)). On a 2.3 MB file, this takes 1.9 seconds per test. For a 169-test device, that's 322 seconds (48% of total setup time) spent re-parsing the same unchanging file.Solution
.cache.jsonsidecar alongside the YAML at merge timeload_data_model()prefers the JSON sidecar when present and fresh (mtime check)Measured Impact
Files Changed
nac_test/data_merger.pynac_test/pyats_core/common/base_test.pyload_data_model()Test plan
🤖 Generated with Claude Code