⚡ Bolt: Optimize Polars metrics calculation by eliminating Python loops - #6218
⚡ Bolt: Optimize Polars metrics calculation by eliminating Python loops#6218SatoryKono wants to merge 2 commits into
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Co-authored-by: SatoryKono <13055362+SatoryKono@users.noreply.github.com>
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Refactors `_count_enriched_records` and `_calculate_field_coverage` in `merger_metrics_mixin.py` to use pure C-level Polars operations (`df.select()` with `expr.sum()`) instead of iterating over columns with a Python loop and repeatedly materializing filtered DataFrames via `df.filter()`. Includes CI fixes (VCR metadata backfill, inventory sync, docs/.gitkeep). Co-authored-by: SatoryKono <13055362+SatoryKono@users.noreply.github.com>
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This PR has been inactive for 14 days and is now marked as stale. It will be automatically closed in 7 days unless there is new activity. |
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Закрыто как устаревшее. Экспериментальная Bolt оптимизация Polars metrics calculation (июль 2026). Вероятно заменена новыми подходами. |
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💡 What: Optimized DataFrame metric calculations in
MergeMetricsRecorderMixinby avoidingdf.filter()materializations and pure-Python column iterations. Replaced with single C-level.select()operations and.sum().🎯 Why: The existing loops repeatedly materialized new DataFrames inside Python, causing measurable execution overhead during large composite merge operations.
📊 Impact: Reduces DataFrame materializations from O(N columns) to O(1) and executes expressions directly in C, resulting in faster composite merge metrics extraction.
🔬 Measurement: Verify by running
uv run pytest tests/unit/application/composite/ -n autoand inspecting performance times.PR created automatically by Jules for task 4554246995758544378 started by @SatoryKono