⚡ Optimize loop in purge_cold over cold_data paths#51
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💡 What: Replaced the enumerate loop checking the index against the length on every iteration with a calculated split index and two list comprehensions using list slicing. 🎯 Why: Iterating over paths_by_age to conditionally append elements individually incurs high loop overhead and redundant computation. 📊 Measured Improvement: Measured using a script generating 1,000,000 dummy paths and purging 950,000. Time decreased from ~0.2057s down to ~0.0886s, netting a ~56.9% performance improvement on large structures. Note: The code handles the edge case where keep_last evaluates to 0 safely to avoid the array[:-0] pitfall that duplicates the entire list. Co-authored-by: dioncx <148190661+dioncx@users.noreply.github.com>
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💡 What: Replaced the loop in
purge_coldinsidejson_memory/memory.pyovercold_datapaths with list comprehensions and slicing. Replaced the individual element checking with a precalculated split index.🎯 Why: Iterating over
paths_by_ageevaluating index math conditionally to append to lists individually incurs high loop overhead. List comprehensions and slicing are evaluated natively by C in Python, bypassing python interpreter overhead.📊 Measured Improvement: The change yields a > 50% performance improvement on large data.
Baseline measured:
Original logic running 1,000,000 paths purging 950,000 paths took 0.2057s.
Optimized logic took 0.0886s.
Improvement: ~56.94%
Important Edge case:
A standard
paths_by_age[:-keep_last]will fail onkeep_last == 0sincepaths_by_age[:-0]evaluates topaths_by_age[:]copying the whole array. It is solved safely viasplit_idx = max(0, len(paths_by_age) - keep_last)calculating the index precisely.PR created automatically by Jules for task 8034458014061861489 started by @dioncx