⚡ Bolt: computeTaskMetrics 루프 최적화 및 배열 캐싱 적용 - #598
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Replaces Array.prototype.reduce and forEach with standard for loops and substitutes the Map cache with an Int32Array in computeTaskMetrics to eliminate JS engine callback allocation, garbage collection, and hash-lookup overhead in hot loops.
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Changes작업 지표 계산 최적화
Estimated code review effort: 2 (Simple) | ~10 minutes Merge Risk: ⚪ Minimal · up to This PR makes a localized performance optimization to task-metric calculation, and no actionable merge-blocking risk remains beyond normal checks and review. 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
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📝 Info: Int32Array substitution is behavior-preserving
Both loops walk state.tasks by identical index over an unchanged length, and calculateDurationDays only returns non-negative integers (app.js), so Int32Array storage loses no precision. Indexing by position also avoids the collision a duplicate task.id would have caused in the previous Map.
(Refers to this code)
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Closing as technically superseded by #508 after exact-head semantic comparison. Current #598 head |
LGTM. Acknowledging that this PR (#598) is technically superseded by #508, which contains the same core computeTaskMetrics() performance optimization (indexed for loops and Int32Array duration caching) alongside the necessary benchmark verification, semantic tests, and CI registration. Stopping work on this duplicate PR to maintain the canonical implementation and preserve the stronger regression evidence in #508. |
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💡 무엇을
app.js의computeTaskMetrics함수 내 배열 처리 로직(reduce,forEach)을 일반for루프로 교체했습니다.id를 키로 사용하던durationCache(Map)를 작업 인덱스를 기반으로 하는Int32Array로 변경했습니다.🎯 왜
Map을 통한 문자열 해시 조회 대신 연속된 메모리를 가진Int32Array와 인덱스 기반 배열 접근(O(1))을 통해 성능을 크게 향상시킬 수 있습니다.📊 영향
🔬 측정
npm run test:e2e및 유닛 테스트(npm run test:unit) 100% 성공PR created automatically by Jules for task 6736333406770692803 started by @seonghobae
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성능 개선
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