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jpa-patterns

JPA/Hibernate patterns for entity design, relationships, query optimization, transactions, auditing, indexing, pagination, and pooling in Spring Boot. Use when designing JPA entities or relationships, or when a Hibernate query, transaction, or N+1 problem needs fixing.

jpa-patterns 是什么?

jpa-patterns is a Cursor agent skill that jPA/Hibernate patterns for entity design, relationships, query optimization, transactions, auditing, indexing, pagination, and pooling in Spring Boot. Use when designing JPA entities or relationships, or when a Hibernate query, transaction, or N+1 problem needs fixing.

兼容平台~Claude Code~Codex CLICursor
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JPA/Hibernate Patterns

Use for data modeling, repositories, and performance tuning in Spring Boot.

When to Activate

  • Designing JPA entities and table mappings
  • Defining relationships (@OneToMany, @ManyToOne, @ManyToMany)
  • Optimizing queries (N+1 prevention, fetch strategies, projections)
  • Configuring transactions, auditing, or soft deletes
  • Setting up pagination, sorting, or custom repository methods
  • Tuning connection pooling (HikariCP) or second-level caching

Entity Design

@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;
}

Enable auditing:

@Configuration
@EnableJpaAuditing
class JpaConfig {}

Relationships and N+1 Prevention

@OneToMany(mappedBy = "market", cascade = CascadeType.ALL, orphanRemoval = true)
private List<PositionEntity> positions = new ArrayList<>();
  • Default to lazy loading; use JOIN FETCH in queries when needed
  • Avoid EAGER on collections; use DTO projections for read paths
@Query("select m from MarketEntity m left join fetch m.positions where m.id = :id")
Optional<MarketEntity> findWithPositions(@Param("id") Long id);

Repository Patterns

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);
}
  • Use projections for lightweight queries:
public interface MarketSummary {
  Long getId();
  String getName();
  MarketStatus getStatus();
}
Page<MarketSummary> findAllBy(Pageable pageable);

Transactions

  • Annotate service methods with @Transactional
  • Use @Transactional(readOnly = true) for read paths to optimize
  • Choose propagation carefully; avoid long-running transactions
@Transactional
public Market updateStatus(Long id, MarketStatus status) {
  MarketEntity entity = repo.findById(id)
      .orElseThrow(() -> new EntityNotFoundException("Market"));
  entity.setStatus(status);
  return Market.from(entity);
}

Pagination

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

For cursor-like pagination, include id > :lastId in JPQL with ordering.

Indexing and Performance

  • Add indexes for common filters (status, slug, foreign keys)
  • Use composite indexes matching query patterns (status, created_at)
  • Avoid select *; project only needed columns
  • Batch writes with saveAll and hibernate.jdbc.batch_size

Connection Pooling (HikariCP)

Recommended properties:

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

For PostgreSQL LOB handling, add:

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

Caching

  • 1st-level cache is per EntityManager; avoid keeping entities across transactions
  • For read-heavy entities, consider second-level cache cautiously; validate eviction strategy

Migrations

  • Use Flyway or Liquibase; never rely on Hibernate auto DDL in production
  • Keep migrations idempotent and additive; avoid dropping columns without plan

Testing Data Access

  • Prefer @DataJpaTest with Testcontainers to mirror production
  • Assert SQL efficiency using logs: set logging.level.org.hibernate.SQL=DEBUG and logging.level.org.hibernate.orm.jdbc.bind=TRACE for parameter values

Remember: Keep entities lean, queries intentional, and transactions short. Prevent N+1 with fetch strategies and projections, and index for your read/write paths.

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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