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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 o relaciones JPA, o cuando se necesita corregir una consulta de Hibernate, transacción o problema N+1.

¿Qué es patrones-jpa?

Este skill proporciona una guía completa de patrones y mejores prácticas para usar JPA e Hibernate en aplicaciones Spring Boot. Cubre desde el diseño básico de entidades con anotaciones como @Entity, @Table e índices, hasta la configuración de relaciones (OneToMany, ManyToOne, ManyToMany) con estrategias para prevenir el problema N+1 mediante JOIN FETCH y proyecciones DTO. También aborda patrones de repositorio con consultas JPQL optimizadas, paginación con Pageable, manejo transaccional con @Transactional, y configuración de pooling de conexiones con HikariCP. Incluye recomendaciones sobre auditoría, eliminaciones suaves, migraciones con Flyway/Liquibase, y pruebas con @DataJpaTest y Testcontainers. Es útil para cualquier desarrollador que trabaje con la pila Spring Boot + JPA/Hibernate, independientemente de la herramienta agente utilizada (Claude Code, Cursor, Codex, etc.), ya que el contenido es independiente de la plataforma y se centra en el conocimiento técnico de JPA/Hibernate.

Compatible con~Claude Code~Codex CLI✓Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/jpa-patterns

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Documentación

Patrones de JPA/Hibernate

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

Cuándo activarlo

  • Diseñar entidades JPA y mapeos de tablas
  • Definir relaciones (@OneToMany, @ManyToOne, @ManyToMany)
  • Optimizar consultas (prevención de N+1, estrategias de fetch, proyecciones)
  • Configurar transacciones, auditoría o eliminaciones suaves
  • Configurar paginación, ordenamiento o métodos de repositorio personalizados
  • Ajustar pooling 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;
}

Habilitar 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 diferida (lazy loading) por defecto; usar JOIN FETCH en consultas cuando sea necesario
  • Evitar EAGER en colecciones; usar proyecciones DTO para rutas de solo 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 solo lectura, para optimizar
  • Elegir la propagación cuidadosamente; 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
  • Escrituras por lotes con saveAll y hibernate.jdbc.batch_size

Pooling 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 entre transacciones
  • Para entidades con muchas lecturas, considerar la caché de segundo nivel con precaución; validar la estrategia de expulsión

Migraciones

  • Usar Flyway o Liquibase; nunca confiar en el 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 el entorno de producción
  • Verificar la eficiencia de SQL usando registros: 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 intencionales y las transacciones cortas. Prevén el N+1 con estrategias de fetch y proyecciones, e indexa para tus rutas de lectura y escritura.

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/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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