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patrones-jpa

Patrones de JPA/Hibernate para diseño de entidades, relaciones, optimización de consultas, transacciones, auditoría, indexación, paginación y pooling en Spring Boot. Útil al diseñar entidades JPA o relaciones, o cuando se necesita corregir una consulta Hibernate, transacción o problema N+1.

¿Qué es patrones-jpa?

Este skill proporciona una guía completa de patrones y mejores prácticas para trabajar con JPA e Hibernate en aplicaciones Spring Boot. Cubre desde el diseño de entidades y la definición de relaciones (@OneToMany, @ManyToOne, @ManyToMany) hasta la optimización del rendimiento con estrategias de carga perezosa (lazy loading), proyecciones DTO y prevención del problema N+1 mediante JOIN FETCH. También aborda configuración de transacciones, auditoría con @EnableJpaAuditing, paginación eficiente, indexación de bases de datos, pooling de conexiones con HikariCP y caché de segundo nivel. Incluye fragmentos de código Java y propiedades de configuración directamente aplicables. Es ideal para desarrolladores Spring Boot que buscan escribir código JPA/Hibernate eficiente, mantenible y con buen rendimiento, evitando antipatrones comunes.

Compatible con~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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Documentación

Patrones de JPA/Hibernate

Usar para modelado de datos, repositorios y ajuste de rendimiento en Spring Boot.

Cuándo activar

  • Diseñar entidades JPA y mapeos de tablas
  • Definir relaciones (@OneToMany, @ManyToOne, @ManyToMany)
  • Optimizar consultas (prevención de N+1, estrategias de búsqueda, proyecciones)
  • Configurar transacciones, auditoría o borrados suaves
  • Configurar paginación, ordenamiento o métodos de repositorio personalizados
  • Ajustar pool de conexiones (HikariCP) o caché de segundo nivel

Diseño de entidades

@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;
}

Activar auditoría:

@Configuration
@EnableJpaAuditing
class JpaConfig {}

Relaciones y prevención de N+1

@OneToMany(mappedBy = "market", cascade = CascadeType.ALL, orphanRemoval = true)
private List<PositionEntity> positions = new ArrayList<>();
  • Usar carga perezosa por defecto; utilizar JOIN FETCH en consultas cuando sea necesario
  • Evitar EAGER en colecciones; usar proyecciones DTO para rutas de lectura
@Query("select m from MarketEntity m left join fetch m.positions where m.id = :id")
Optional<MarketEntity> findWithPositions(@Param("id") Long id);

Patrones de repositorio

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);
}
  • Usar proyecciones para consultas ligeras:
public interface MarketSummary {
  Long getId();
  String getName();
  MarketStatus getStatus();
}
Page<MarketSummary> findAllBy(Pageable pageable);

Transacciones

  • Anotar métodos de servicio con @Transactional
  • Usar @Transactional(readOnly = true) para rutas de lectura y optimizar
  • Elegir la propagación con cuidado; evitar transacciones de larga duración
@Transactional
public Market updateStatus(Long id, MarketStatus status) {
  MarketEntity entity = repo.findById(id)
      .orElseThrow(() -> new EntityNotFoundException("Market"));
  entity.setStatus(status);
  return Market.from(entity);
}

Paginación

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

Para paginación tipo cursor, incluir id > :lastId en JPQL con ordenamiento.

Indexación y rendimiento

  • Agregar índices para filtros comunes (status, slug, claves foráneas)
  • Usar índices compuestos que coincidan con los patrones de consulta (status, created_at)
  • Evitar select *; proyectar solo las columnas necesarias
  • Escribir en lote con saveAll y hibernate.jdbc.batch_size

Pool de conexiones (HikariCP)

Propiedades recomendadas:

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

Para manejo de LOB en PostgreSQL, agregar:

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

Caché

  • La caché de primer nivel es por EntityManager; evitar mantener entidades a través de transacciones
  • Para entidades con muchas lecturas, considerar la caché de segundo nivel con precaución; validar la estrategia de desalojo

Migraciones

  • Usar Flyway o Liquibase; nunca confiar en DDL automático de Hibernate en producción
  • Mantener las migraciones idempotentes y aditivas; evitar eliminar columnas sin un plan

Pruebas de acceso a datos

  • Preferir @DataJpaTest con Testcontainers para reflejar producción
  • Verificar eficiencia de SQL mediante logs: configurar logging.level.org.hibernate.SQL=DEBUG y logging.level.org.hibernate.orm.jdbc.bind=TRACE para valores de parámetros

Recuerda: Mantén las entidades ligeras, las consultas intencionadas y las transacciones cortas. Prevén N+1 con estrategias de búsqueda y proyecciones, e indexa según tus rutas de lectura/escritura.

Individual skills in this repo

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

accessibility

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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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