[Volume 5] 인덱스와 캐시를 활용한 조회 읽기 성능 최적화#223
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dd-jiny wants to merge 5 commits intoLoopers-dev-lab:dd-jinyfrom
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[Volume 5] 인덱스와 캐시를 활용한 조회 읽기 성능 최적화#223dd-jiny wants to merge 5 commits intoLoopers-dev-lab:dd-jinyfrom
dd-jiny wants to merge 5 commits intoLoopers-dev-lab:dd-jinyfrom
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fix : 예제 테스트 코드 오류 해결을 위한 testcontainers 버전 업
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✨ Finishing Touches🧪 Generate unit tests (beta)
📝 Coding Plan
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📌 Summary
🧭 Context & Decision
문제 정의
인덱스
캐시
선택지와 결정
인덱스
캐시
🏗️ Design Overview
변경 범위
CacheConfig,TwoLevelCache,TwoLevelCacheManager(infrastructure/cache)RedisCacheIntegrationTest,TwoLevelCacheIntegrationTest,CacheBenchmarkTest확정 인덱스 목록 (9개)
idx_products_status_createdProductJpaRepository.findAllForCustomerPaged()idx_products_status_likeProductJpaRepository.findAllForCustomerPaged()idx_orders_status_del_expiresOrderJpaRepository.findExpiredPendingOrders()idx_products_brand_delProductJpaRepository.findAllByBrandId()idx_orders_user_createdOrderJpaRepository.findAllByUserIdAndPeriod()idx_orders_user_statusOrderJpaRepository.countByUserIdAndStatus()idx_orders_del_createdOrderJpaRepository.findAllByPeriod()idx_brands_del_displayBrandJpaRepository.findAllByDelYnAndDisplayStatus()idx_order_items_del_productOrderItemJpaRepository.findAllByOrderIdIn()캐시 적용 클래스 및 메서드
@Cacheable@CacheEvictproductDetailProductService.findById()updateProduct(),deleteProduct(),changeSaleStatus(),incrementLikeCount(),decrementLikeCount()stockAvailableStockService.findByProductId()hold(),release(),commit()brandDetailBrandService.findById()updateBrand(),deleteBrand()authUserUserService.findByLoginId()changePassword()productListProductFacade.getCachedProductList()createProduct(),updateProduct()—allEntries=truestatsOverviewStatsService.getOverview()statsDailyStatsService.getDailyOrderStats()statsTopLikedStatsService.getTopLikedProducts()statsTopOrderedStatsService.getTopOrderedProducts()statsLowStockStatsService.getLowStockProducts()주요 컴포넌트 책임
CacheConfig: 캐시 매니저 구성 (Redis + Caffeine), 캐시별 TTL 설정,CacheErrorHandler로 Redis 장애 시 graceful degradationTwoLevelCache: L1 → L2 순차 조회, 양방향 put/evict 동기화TwoLevelCacheManager: 캐시 이름 기반으로 L1+L2 또는 L2-only 라우팅🔍 리뷰 포인트
캐시 전략에 대해 리뷰 부탁드립니다. 각 조회 패턴별로 어떤 전략을 적용했는지 정리하고, 판단이 어려웠던 부분을 질문으로 남겼습니다.
조회 패턴별 캐시 전략 요약
리뷰 요청 사항
allEntries=true로 목록 캐시를 전부 날리는데, 관리자 수정이 잦을 경우 hit rate가 떨어질 수 있습니다. 이 전략이 적절한지 피드백 부탁드립니다.|
🔁 Flow Diagram
인덱스 실험 프로세스
flowchart TD A[인덱스 없는 상태에서 EXPLAIN] --> B[후보 A 생성 → EXPLAIN → 드랍] B --> C[후보 B 생성 → EXPLAIN → 드랍] C --> D{후보 비교} D -->|filesort 제거 + filtered 100%| E[최적 인덱스 확정] D -->|트레이드오프 존재| F[쓰기 비용 vs 읽기 성능 판단] F --> E E --> G[SHOW PROFILES로 실행시간 비교]캐시 조회 흐름 (L1+L2)
sequenceDiagram autonumber participant Client participant Controller participant Service participant L1 as L1 (Caffeine) participant L2 as L2 (Redis) participant DB as MySQL Client->>Controller: GET /api/v1/products/{id} Controller->>Service: findById(id) Note over Service,L1: @Cacheable("productDetail") Service->>L1: get(key) alt L1 Hit L1-->>Service: cached value else L1 Miss Service->>L2: get(key) alt L2 Hit L2-->>Service: cached value Service->>L1: put(key, value) else L2 Miss Service->>DB: SELECT * FROM products DB-->>Service: result Service->>L2: put(key, value) Service->>L1: put(key, value) end end Service-->>Controller: ProductModel Controller-->>Client: ApiResponse캐시 무효화 흐름
sequenceDiagram autonumber participant Admin participant Controller participant Facade participant Service participant L1 as L1 (Caffeine) participant L2 as L2 (Redis) Admin->>Controller: PUT /api-admin/v1/products/{id} Controller->>Facade: updateProduct(id, command) Note over Facade,L2: @CacheEvict("productList", allEntries=true) Facade->>Service: updateProduct(id, command) Note over Service,L2: @CacheEvict("productDetail", key="#productId") Service->>L1: evict(productDetail::id) Service->>L2: evict(productDetail::id) Facade->>L1: clear(productList) Facade->>L2: clear(productList) Facade-->>Controller: updated Controller-->>Admin: ApiResponse📊 성능 비교 결과
인덱스 실행 시간 (Before → After)
products 쿼리
orders 쿼리
악화 원인
k6 부하 테스트 — L2-only vs L1+L2 비교
비교 분석 요약
테스트 환경 제약 (WSL2)
5배, 디스크 I/O 310배 느림