[PERF] 통계 집계 전체 조회 비용 개선 - #258
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No actionable comments were generated in the recent review. 🎉 ℹ️ Recent review info⚙️ Run configurationConfiguration used: Path: .coderabbit.yaml Review profile: CHILL Plan: Advanced Run ID: 📒 Files selected for processing (4)
🚧 Files skipped from review as they are similar to previous changes (1)
Included review availability: Your plan provides up to 1 included review per hour; 0 remain after this review. 📝 WalkthroughWalkthrough주간 통계 집계를 엔티티 전체 조회와 애플리케이션 계산에서 저장소 집계 쿼리 방식으로 변경했습니다. 연습 세션 수, 연습 시간, 평균 점수, 스킬별 평균 점수를 프로젝션으로 반환합니다. 서비스 테스트와 성능 테스트를 새 집계 경로에 맞게 갱신했습니다. Changes주간 통계 집계 최적화
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~25 minutes Severity of issue fixed: Medium Sequence Diagram(s)sequenceDiagram
participant WeeklyPracticeStatisticsAggregationService
participant WeeklySkillStatisticsAggregationService
participant PlayingRepository
participant AnalysisRepository
participant Database
WeeklyPracticeStatisticsAggregationService->>PlayingRepository: aggregateTotalsByUserAndStatusSince 호출
PlayingRepository->>Database: 세션 수와 총 시간 집계
Database-->>PlayingRepository: WeeklyPracticeTotals 반환
WeeklyPracticeStatisticsAggregationService->>AnalysisRepository: aggregateAverageTotalScoreByUserAndStatusSince 호출
AnalysisRepository->>Database: totalScore 평균 집계
Database-->>AnalysisRepository: 평균 점수 반환
WeeklySkillStatisticsAggregationService->>AnalysisRepository: aggregateWeeklySkillAveragesByUserAndStatusSince 호출
AnalysisRepository->>Database: 네 가지 스킬 평균 집계
Database-->>AnalysisRepository: WeeklySkillAverages 반환
Merge Risk: ⚪ Minimal · up to The aggregate-based statistics update preserves the validated scoring and lifecycle behavior while reducing entity loading. No concrete merge-blocking issue is identified. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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Actionable comments posted: 1
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Inline comments:
In
`@src/test/java/com/mr/domain/statistics/service/StatisticsAggregationPerformanceTest.java`:
- Around line 106-107: Update StatisticsAggregationPerformanceTest to assert an
explicit upper bound for Entity loads on the allowed aggregate path, in addition
to assertStableMeasurementCounts(measurements). Base the assertion on the
existing measurement data and configured expected limit, and keep
printBaseline(analysisCount, measurements) unchanged.
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ℹ️ Review info
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Configuration used: Path: .coderabbit.yaml
Review profile: CHILL
Plan: Advanced
Run ID: a3c6d859-6a7e-43b8-9156-cfe7dfe18270
📒 Files selected for processing (7)
src/main/java/com/mr/domain/analysis/repository/AnalysisRepository.javasrc/main/java/com/mr/domain/playing/repository/PlayingRepository.javasrc/main/java/com/mr/domain/statistics/service/AnalysisSkillScoreResolver.javasrc/main/java/com/mr/domain/statistics/service/WeeklyPracticeStatisticsAggregationService.javasrc/main/java/com/mr/domain/statistics/service/WeeklySkillStatisticsAggregationService.javasrc/test/java/com/mr/domain/statistics/service/StatisticsAggregationPerformanceTest.javasrc/test/java/com/mr/domain/statistics/service/StatisticsAggregationServiceTest.java
💤 Files with no reviewable changes (1)
- src/main/java/com/mr/domain/statistics/service/AnalysisSkillScoreResolver.java
Included review availability: Your plan provides up to 1 included review per hour; 0 remain after this review.
- AVG projection 반환 타입을 Double에서 BigDecimal로 변경 - 스킬 평균 계산 과정의 부동소수점 변환 제거 - HALF_UP 반올림 경계값 및 PostgreSQL AVG 매핑 테스트 추가
📍 개요
⛓️💥 관련 이슈
🛠️ 작업 내용
Playing전체 조회를 제거하고COUNT,SUM(durationSec)Aggregate Query로 변경Analysis전체 조회 및 Java Stream 평균 계산을 제거하고AVG(totalScore)Query로 변경Analysis전체 조회 및 스킬별 Java 평균 계산을 제거하고 네 가지 스킬 평균을 한 번에 반환하는 Aggregate Query로 변경HALF_UP, null 스킬 미집계,previousScore유지 정책을 그대로 유지AnalysisSkillScoreResolver를 제거🔥 리뷰 요청 사항
WeeklyPracticeStatisticsAggregationService,WeeklySkillStatisticsAggregationService에서 전체 Entity 조회 제거 범위가 적절한지REQUIRES_NEW트랜잭션 경계와 Listener 재시도 정책은 변경하지 않았으며, 기존 동작이 유지되는지✅ 체크리스트
📎 참고 사항
1) 성능 측정 조건
한 사용자의
Analysis데이터를100 / 1,000 / 5,000 / 10,000건으로 증가시키며 기존 통계가 존재하는 Warm path를 기준으로 측정했습니다. 각 데이터 규모별로 warm-up 이후 10회 측정한 중앙값을 1회 결과로 사용했으며, After 측정은 동일한 과정을 별도 실행으로 3회 반복하고, 세 중앙값의 중앙값을 최종 대표값으로 사용했습니다. Before는 최적화 전 기록한 단일 실행의 중앙값입니다.2) 검증 결과
BUILD SUCCESSFULgit diff --check성공REQUIRES_NEW트랜잭션 및 Listener 재시도 정책 변경 없음Summary by CodeRabbit