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Jpa Patterns

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xu-xiang
jpa-patterns

Spring Boot 中用于实体设计、关联关系、查询优化、事务、审计、索引、分页和连接池的 JPA/Hibernate 模式。

Overview

Publisherxu-xiang
Repositoryeverything-claude-code-zh
Skill namejpa-patterns
Stars
1.9K
Forks
318
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by xu-xiang on GitHub. Read the source before you install it.

Installation

Install the Jpa Patterns AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/xu-xiang/everything-claude-code-zh.git /tmp/everything-claude-code-zh
mkdir -p .claude/skills
cp -r /tmp/everything-claude-code-zh/docs/ja-JP/skills/jpa-patterns .claude/skills/jpa-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Jpa Patterns in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Jpa Patterns on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Jpa Patterns is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

JPA/Hibernate 模式

用于 Spring Boot 中的数据建模、存储库(Repository)和性能调优。

实体设计 (Entity Design)

java
@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;
}

启用审计 (Auditing):

java
@Configuration
@EnableJpaAuditing
class JpaConfig {}

关联关系与 N+1 问题预防

java
@OneToMany(mappedBy = "market", cascade = CascadeType.ALL, orphanRemoval = true)
private List<PositionEntity> positions = new ArrayList<>();
  • 默认使用延迟加载(Lazy Loading)。根据需要,在查询中使用 JOIN FETCH
  • 集合属性应避免使用 EAGER 加载,在读取路径中优先使用 DTO 投影(Projection)。
java
@Query("select m from MarketEntity m left join fetch m.positions where m.id = :id")
Optional<MarketEntity> findWithPositions(@Param("id") Long id);

存储库模式 (Repository Pattern)

java
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);
}
  • 对于轻量级查询,请使用投影(Projection):
java
public interface MarketSummary {
  Long getId();
  String getName();
  MarketStatus getStatus();
}
Page<MarketSummary> findAllBy(Pageable pageable);

事务 (Transactions)

  • 在服务层(Service)方法上添加 @Transactional 注解。
  • 使用 @Transactional(readOnly = true) 优化只读路径。
  • 谨慎选择事务传播(Propagation)行为。避免长时间运行的事务。
java
@Transactional
public Market updateStatus(Long id, MarketStatus status) {
  MarketEntity entity = repo.findById(id)
      .orElseThrow(() -> new EntityNotFoundException("Market"));
  entity.setStatus(status);
  return Market.from(entity);
}

分页 (Pagination)

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

对于类似游标(Cursor)的分页,请在 JPQL 排序中包含 id > :lastId

索引创建与性能优化

  • 为常用过滤器(如 statusslug、外键)添加索引。
  • 使用符合查询模式的复合索引(例如 status, created_at)。
  • 避免使用 select *,仅投影必要的列。
  • 利用 saveAllhibernate.jdbc.batch_size 进行批量写入。

连接池 (Connection Pooling - HikariCP)

推荐属性:

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

对于 PostgreSQL 的 LOB 处理,请添加以下内容:

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

缓存 (Caching)

  • 一级缓存(First-level Cache)是基于 EntityManager 的。不要在事务之间持有实体。
  • 对于读取密集型实体,请谨慎考虑二级缓存(Second-level Cache)。验证淘汰策略(Eviction Strategy)。

数据库迁移 (Migration)

  • 使用 Flyway 或 Liquibase。不要在生产环境中依赖 Hibernate 的自动 DDL。
  • 保持迁移脚本的幂等性(Idempotent)和增量性。不要在未规划的情况下删除列。

数据访问测试

  • 优先使用配合 Testcontainers 的 @DataJpaTest,以真实反映生产环境。
  • 通过日志断言 SQL 效率:将 logging.level.org.hibernate.SQL 设置为 DEBUG,将 logging.level.org.hibernate.orm.jdbc.bind 设置为 TRACE 以查看参数值。

注意:保持实体轻量化,确保查询意图明确,并缩短事务时长。通过抓取策略(Fetch Strategy)和投影(Projection)防止 N+1 问题,并为读/写路径创建索引。

Frequently asked questions

What does the Jpa Patterns AI skill do?

Spring Boot 中用于实体设计、关联关系、查询优化、事务、审计、索引、分页和连接池的 JPA/Hibernate 模式。

Why use Jpa Patterns on TypingMind?

Because you install it once and use it with any model. Jpa Patterns is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Jpa Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xu-xiang/everything-claude-code-zh/tree/main/docs/ja-JP/skills/jpa-patterns. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Jpa Patterns?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Jpa Patterns?

As many as you like. As long as a model supports skills, you can use Jpa Patterns with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Jpa Patterns AI skill free?

Yes. It is published on GitHub by xu-xiang under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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