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jpa-patterns

JPA/Hibernate 模式:實體設計、關聯、查詢最佳化、交易、審計、索引、分頁與連線池管理,適用於 Spring Boot。當需要設計 JPA 實體或關聯,或是修復 Hibernate 查詢、交易、N+1 問題時使用。

jpa-patterns 是什麼?

此技能專注於 Spring Boot 應用程式中 JPA 與 Hibernate 的實戰模式,協助開發者建立高效、可維護的資料存取層。涵蓋實體設計(含索引、審計、枚舉)、關聯對映(@OneToMany、@ManyToOne、@ManyToMany)、N+1 查詢預防(使用 JOIN FETCH 與 DTO 投影)、交易管理(@Transactional 的唯讀最佳化)、分頁與排序、連線池調校(HikariCP)以及第二層快取策略。支援 Spring Data JPA Repository 模式,並提供自訂查詢、投影介面、批次寫入與遷移工具(Flyway/Liquibase)的建議。透過 `@DataJpaTest` 與 Testcontainers 進行資料存取測試,並可調整 Hibernate 日誌層級以分析 SQL 效能。此技能適用於 Claude Code、Cursor、Codex 等 AI 代理,能自動生成符合最佳實踐的程式碼片段,幫助開發者避免常見陷阱(如 N+1、Eager 載入、長時間交易),並維持實體設計的簡潔性與查詢的明確性。

相容平台~Claude Code~Codex CLI✓Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/jpa-patterns

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說明文件

JPA/Hibernate 模式

用於 Spring Boot 中的資料建模、Repository 與效能調校。

啟用時機

  • 設計 JPA 實體與資料表對映
  • 定義關聯(@OneToMany、@ManyToOne、@ManyToMany)
  • 最佳化查詢(N+1 預防、擷取策略、投影)
  • 設定交易、審計或軟刪除
  • 配置分頁、排序或自訂 Repository 方法
  • 調校連線池(HikariCP)或第二層快取

實體設計

@Entity
@Table(name = "markets", indexes = {
  @Index(name = "idx_markets_slug", columnList = "slug", unique = true)
})
@EntityListeners(AuditingEntityListener.class)
public class MarketEntity {
  @Id @GeneratedValue(strategy = GenerationType.IDENTITY)
  private Long id;

  @Column(nullable = false, length = 200)
  private String name;

  @Column(nullable = false, unique = true, length = 120)
  private String slug;

  @Enumerated(EnumType.STRING)
  private MarketStatus status = MarketStatus.ACTIVE;

  @CreatedDate private Instant createdAt;
  @LastModifiedDate private Instant updatedAt;
}

啟用審計功能:

@Configuration
@EnableJpaAuditing
class JpaConfig {}

關聯與 N+1 預防

@OneToMany(mappedBy = "market", cascade = CascadeType.ALL, orphanRemoval = true)
private List<PositionEntity> positions = new ArrayList<>();
  • 預設使用延遲載入;需要時在查詢中使用 JOIN FETCH
  • 避免在集合上使用 EAGER;對讀取路徑使用 DTO 投影
@Query("select m from MarketEntity m left join fetch m.positions where m.id = :id")
Optional<MarketEntity> findWithPositions(@Param("id") Long id);

Repository 模式

public interface MarketRepository extends JpaRepository<MarketEntity, Long> {
  Optional<MarketEntity> findBySlug(String slug);

  @Query("select m from MarketEntity m where m.status = :status")
  Page<MarketEntity> findByStatus(@Param("status") MarketStatus status, Pageable pageable);
}
  • 使用投影進行輕量查詢:
public interface MarketSummary {
  Long getId();
  String getName();
  MarketStatus getStatus();
}
Page<MarketSummary> findAllBy(Pageable pageable);

交易

  • 在 Service 方法上標註 @Transactional
  • 對讀取路徑使用 @Transactional(readOnly = true) 以進行最佳化
  • 謹慎選擇傳播行為;避免長時間交易
@Transactional
public Market updateStatus(Long id, MarketStatus status) {
  MarketEntity entity = repo.findById(id)
      .orElseThrow(() -> new EntityNotFoundException("Market"));
  entity.setStatus(status);
  return Market.from(entity);
}

分頁

PageRequest page = PageRequest.of(pageNumber, pageSize, Sort.by("createdAt").descending());
Page<MarketEntity> markets = repo.findByStatus(MarketStatus.ACTIVE, page);

對於游標式分頁,可在 JPQL 中加入 id > :lastId 並搭配排序。

索引與效能

  • 為常用篩選條件(status、slug、外鍵)增加索引
  • 使用符合查詢模式的複合索引(status, created_at)
  • 避免 select *;只投影需要的欄位
  • 使用 saveAll 與 hibernate.jdbc.batch_size 進行批次寫入

連線池(HikariCP)

建議配置:

spring.datasource.hikari.maximum-pool-size=20
spring.datasource.hikari.minimum-idle=5
spring.datasource.hikari.connection-timeout=30000
spring.datasource.hikari.validation-timeout=5000

針對 PostgreSQL LOB 處理,請加入:

spring.jpa.properties.hibernate.jdbc.lob.non_contextual_creation=true

快取

  • 第一級快取是每個 EntityManager 專屬;避免跨交易保留實體
  • 對於讀取密集的實體,請謹慎考慮第二級快取;務必驗證驅逐策略

遷移

  • 使用 Flyway 或 Liquibase;絕不在生產環境中依賴 Hibernate 自動 DDL
  • 保持遷移的冪等性與增量性;避免未經計畫就移除欄位

測試資料存取

  • 建議使用 @DataJpaTest 搭配 Testcontainers 以模擬生產環境
  • 透過日誌驗證 SQL 效率:設定 logging.level.org.hibernate.SQL=DEBUG 以及 logging.level.org.hibernate.orm.jdbc.bind=TRACE 以檢視參數值

切記:保持實體精簡、查詢明確、交易簡短。使用擷取策略與投影預防 N+1,並根據讀寫路徑建立索引。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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