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padrões-jpa

Padrões JPA/Hibernate para design de entidades, relacionamentos, otimização de consultas, transações, auditoria, indexação, paginação e pooling no Spring Boot. Use ao projetar entidades ou relacionamentos JPA, ou quando uma consulta Hibernate, transação ou problema N+1 precisar ser resolvido.

O que é padrões-jpa?

Este skill (padrões-jpa) é um guia abrangente para desenvolvedores que trabalham com JPA e Hibernate no ecossistema Spring Boot. Ele reúne as melhores práticas para modelagem de dados, definição de repositórios, otimização de desempenho e gerenciamento de transações. O conteúdo cobre desde a criação de entidades com anotações como @Entity, @Table e @Column, até o uso de índices, auditoria (com @CreatedDate e @LastModifiedDate) e prevenção do problema N+1 por meio de fetch joins e projeções DTO. Também aborda configurações de pooling de conexão com HikariCP, migrações de banco de dados com Flyway/Liquibase, caching (primeiro e segundo níveis), paginação tradicional e do tipo cursor, e testes de acesso a dados com @DataJpaTest e Testcontainers. Útil tanto para iniciantes que estão projetando suas primeiras entidades quanto para profissionais experientes que precisam ajustar consultas lentas ou configurar transações eficientes em aplicações Spring Boot.

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

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Documentação

Padrões JPA/Hibernate

Use para modelagem de dados, repositórios e ajuste de desempenho no Spring Boot.

Quando Ativar

  • Projetar entidades JPA e mapeamentos de tabelas
  • Definir relacionamentos (@OneToMany, @ManyToOne, @ManyToMany)
  • Otimizar consultas (prevenção N+1, estratégias de fetch, projeções)
  • Configurar transações, auditoria ou exclusões lógicas
  • Configurar paginação, ordenação ou métodos de repositório personalizados
  • Ajustar pooling de conexão (HikariCP) ou cache de segundo nível

Design 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 auditoria:

@Configuration
@EnableJpaAuditing
class JpaConfig {}

Relacionamentos e Prevenção N+1

@OneToMany(mappedBy = "market", cascade = CascadeType.ALL, orphanRemoval = true)
private List<PositionEntity> positions = new ArrayList<>();
  • Prefira carregamento preguiçoso (lazy loading); use JOIN FETCH em consultas quando necessário
  • Evite EAGER em coleções; use projeções DTO para caminhos de leitura
@Query("select m from MarketEntity m left join fetch m.positions where m.id = :id")
Optional<MarketEntity> findWithPositions(@Param("id") Long id);

Padrões de Repositório

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 projeções para consultas leves:
public interface MarketSummary {
  Long getId();
  String getName();
  MarketStatus getStatus();
}
Page<MarketSummary> findAllBy(Pageable pageable);

Transações

  • Anote métodos de serviço com @Transactional
  • Use @Transactional(readOnly = true) para caminhos de leitura e otimizar
  • Escolha a propagação com cuidado; evite transações de longa duração
@Transactional
public Market updateStatus(Long id, MarketStatus status) {
  MarketEntity entity = repo.findById(id)
      .orElseThrow(() -> new EntityNotFoundException("Market"));
  entity.setStatus(status);
  return Market.from(entity);
}

Paginação

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

Para paginação do tipo cursor, inclua id > :lastId no JPQL com ordenação.

Indexação e Desempenho

  • Adicione índices para filtros comuns (status, slug, chaves estrangeiras)
  • Use índices compostos que correspondam aos padrões de consulta (status, created_at)
  • Evite select *; projete apenas as colunas necessárias
  • Realize gravações em lote com saveAll e hibernate.jdbc.batch_size

Pooling de Conexão (HikariCP)

Propriedades 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 manipulação de LOB no PostgreSQL, adicione:

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

Cache

  • O cache de primeiro nível é por EntityManager; evite manter entidades entre transações
  • Para entidades com muitas leituras, considere o cache de segundo nível com cautela; valide a estratégia de evicção

Migrações

  • Use Flyway ou Liquibase; nunca dependa do auto DDL do Hibernate em produção
  • Mantenha as migrações idempotentes e aditivas; evite excluir colunas sem planejamento

Testando Acesso a Dados

  • Prefira @DataJpaTest com Testcontainers para espelhar a produção
  • Avalie a eficiência do SQL usando logs: defina logging.level.org.hibernate.SQL=DEBUG e logging.level.org.hibernate.orm.jdbc.bind=TRACE para valores de parâmetros

Lembre-se: Mantenha as entidades enxutas, consultas intencionais e transações curtas. Previna o N+1 com estratégias de fetch e projeções, e indexe para seus caminhos de leitura/escrita.

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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