⚡ Bolt: Optimize Prisma include dataset iterations#27
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Refactored multiple chained `.reduce()` methods in dashboard session APIs and weekly-report backend logic into single `for...of` loops. This prevents redundant iterations over the same datasets fetched via Prisma `include`, reducing memory allocation and CPU overhead during metric aggregations. Added a journal entry detailing the iteration optimization learning.
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✨ Finishing Touches🧪 Generate unit tests (beta)
✨ Simplify code
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최신 upstream/main 기준 26개 파일의 stale diff를 포함합니다. 일부 성능 변경도 검증 없이 적용하면 usageTimeline 순회 증가, project transfer 순서 변경, P2025 forbidden 처리 제거 등 회귀 위험이 큽니다. 닫고, 필요하면 최신 main에서 안전한 단일 변경으로 다시 추출하겠습니다. |
Understood. Acknowledging that this work is now obsolete and stopping work on this task. |
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💡 What:
Replaced chained
.reduce()loops with a singlefor...ofloop when aggregating metrics from Prismaincludenested relationships across session endpoints and the weekly report generator.🎯 Why:
Processing large in-memory datasets returned by Prisma queries with multiple chained array iterations causes redundant passes over the same data. This leads to unnecessary memory allocation and iteration overhead, which can become a bottleneck when generating reports for thousands of sessions.
📊 Impact:
Reduces loop iterations by a factor of the number of chained operations (e.g., from 3 iterations to 1) for usage record metric summation, leading to lower memory pressure and faster API responses for dashboard metrics.
🔬 Measurement:
The optimization can be verified by profiling the execution time of the
getSessionTotalslogic or the weekly report generator before and after the change on a large organization dataset.Also updated the
.jules/bolt.mdperformance journal with this specific learning to avoid similar patterns.PR created automatically by Jules for task 6189051498280661516 started by @seonghobae