⚡ Optimize evaluate_fixtures performance with caching and multiprocessing - #82
⚡ Optimize evaluate_fixtures performance with caching and multiprocessing#82joy7758 wants to merge 2 commits into
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Extracted evaluation logic to `_evaluate_worker` allowing parallel chunk execution via `ProcessPoolExecutor`. Implemented `case_cache` dictionary within workers to eliminate redundant JSON parsing of shared `path` references. Ensured original sequence is preserved by utilizing `executor.map`. Co-authored-by: joy7758 <138868899+joy7758@users.noreply.github.com>
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| if not cases: | ||
| return {"ok": True, "cases": []} | ||
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| num_workers = min(multiprocessing.cpu_count(), len(cases)) |
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With the checked-in evaluations/case-registry.json, this selects up to four workers for only four distinct fixture paths and enters a process pool on every normal multi-core run of the documented evaluate-fixtures and validate-install commands. Each worker processes only one or two cases, so case_cache gets no hits while separate interpreters repeatedly initialize the gate/schema caches and add process-startup and IPC overhead; the ordinary validation path therefore becomes slower rather than faster. Use the sequential path below a workload threshold and reserve multiprocessing for genuinely large registries.
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Extracted evaluation logic to `_evaluate_worker` allowing parallel chunk execution via `ProcessPoolExecutor`. Implemented `case_cache` dictionary within workers to eliminate redundant JSON parsing of shared `path` references. Ensured original sequence is preserved by utilizing `executor.map`. Also fixed ruff import and mode argument issues in test files. Co-authored-by: joy7758 <138868899+joy7758@users.noreply.github.com>
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💡 What:
Optimized the
evaluate_fixturesfunction insrc/titmas_action_gate/cli.py. The evaluation loop logic was refactored into a_evaluate_workerfunction to enable concurrent execution viaProcessPoolExecutor. A local caching mechanism (case_cache) was also implemented inside the worker to prevent redundant file I/O and JSON parsing for identical fixture paths. It preserves deterministic output ordering viaexecutor.map.🎯 Why:
The previous implementation processed cases sequentially in a loop and reparsed the JSON file for every test case. In large evaluation workloads, this file reading and parsing cycle became a primary bottleneck. Parallelizing chunks and caching paths dramatically cuts down redundant I/O operations and speeds up evaluation overhead.
📊 Measured Improvement:
Benchmarking using a large generated fixture set (4,000 cases):
PR created automatically by Jules for task 2948891120563234021 started by @joy7758