spring-data-jpa
Spring Data JPA skill provides patterns for implementing persistence layers using Spring Data JPA repositories, entity configuration, custom queries, pagination, auditing, and transaction management. Use this skill when building database access layers with CRUD operations, configuring entity relationships and cascading behavior, writing derived or annotated queries, implementing pagination with Pageable, setting up audit fields like creation timestamps, managing transactions, using UUID primary keys, supporting multiple databases, and creating database indexes.
git clone --depth 1 https://github.com/giuseppe-trisciuoglio/developer-kit /tmp/spring-data-jpa && cp -r /tmp/spring-data-jpa/plugins/developer-kit-java/skills/spring-data-jpa ~/.claude/skills/spring-data-jpaSKILL.md
# Spring Data JPA
## Overview
Provides patterns for Spring Data JPA repositories, entity relationships, queries, pagination, auditing, and transactions.
## When to Use
Creating repositories with CRUD operations, entity relationships, `@Query` annotations, pagination, auditing, or UUID primary keys.
## Instructions
### Create Repository Interfaces
To implement a repository interface:
1. **Extend the appropriate repository interface:**
```java
@Repository
public interface UserRepository extends JpaRepository<User, Long> {
// Custom methods defined here
}
```
2. **Use derived queries for simple conditions:**
```java
Optional<User> findByEmail(String email);
List<User> findByStatusOrderByCreatedDateDesc(String status);
```
3. **Implement custom queries with `@`Query:**
```java
@Query("SELECT u FROM User u WHERE u.status = :status")
List<User> findActiveUsers(@Param("status") String status);
```
### Configure Entities
1. **Define entities with proper annotations:**
```java
@Entity
@Table(name = "users")
public class User {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
private Long id;
@Column(nullable = false, length = 100)
private String email;
}
```
2. **Configure relationships using appropriate cascade types:**
```java
@OneToMany(mappedBy = "user", cascade = CascadeType.ALL, orphanRemoval = true)
private List<Order> orders = new ArrayList<>();
```
**Validation:** Test cascade behavior with a small dataset before applying to production data. Verify delete operations don't cascade unexpectedly.
3. **Set up database auditing:**
```java
@CreatedDate
@Column(nullable = false, updatable = false)
private LocalDateTime createdDate;
```
### Apply Query Patterns
1. **Use derived queries for simple conditions**
2. **Use `@`Query for complex queries**
3. **Return Optional<T> for single results**
4. **Use Pageable for pagination**
5. **Apply `@`Modifying for update/delete operations**
### Manage Transactions
1. **Mark read-only operations with `@`Transactional(readOnly = true)**
2. **Use explicit transaction boundaries for modifying operations**
3. **Specify rollback conditions when needed**
### Validate and Optimize
**1. Verify entity configuration:**
- Test cascade behavior in a transaction before production deployment
- Validate bidirectional relationships sync correctly
**2. Optimize query performance:**
- Run `EXPLAIN ANALYZE` on queries against large tables
- If performance issues detected: add indexes → verify with EXPLAIN → repeat
- Use `@EntityGraph` to prevent N+1 queries
**3. Validate pagination:**
- Ensure indexed columns support pagination queries
- Test with large datasets to verify cursor stability
## Examples
### Basic CRUD Repository
```java
@Repository
public interface ProductRepository extends JpaRepository<Product, Long> {
// Derived query
List<Product> findByCategory(String category);
// Custom query
@Query("SELECT p FROM Product p WHERE p.price > :minPrice")
List<Product> findExpensiveProducts(@Param("minPrice") BigDecimal minPrice);
}
```
### Pagination Implementation
```java
@Service
public class ProductService {
private final ProductRepository repository;
public Page<Product> getProducts(int page, int size) {
Pageable pageable = PageRequest.of(page, size, Sort.by("name").ascending());
return repository.findAll(pageable);
}
}
```
### Entity with Auditing
```java
@Entity
@EntityListeners(AuditingEntityListener.class)
public class Order {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
private Long id;
@CreatedDate
@Column(nullable = false, updatable = false)
private LocalDateTime createdDate;
@LastModifiedDate
private LocalDateTime lastModifiedDate;
@CreatedBy
@Column(nullable = false, updatable = false)
private String createdBy;
}
```
## Best Practices
### Entity Design
- Use constructor injection exclusively (never field injection)
- Prefer immutable fields with `final` modifiers
- Use Java records (16+) or `@Value` for DTOs
- Always provide proper `@Id` and `@GeneratedValue` annotations
- Use explicit `@Table` and `@Column` annotations
### Performance Optimization
- Use appropriate fetch strategies (LAZY vs EAGER)
- Implement pagination for large datasets
- Use database indexes for frequently queried fields
- Consider using `@EntityGraph` to avoid N+1 query problems
### Reference Documentation
For comprehensive examples, detailed patterns, and advanced configurations, see:
- [Examples](references/examples.md) - Complete code examples for common scenarios
- [Reference](references/reference.md) - Detailed patterns and advanced configurations
## Constraints and Warnings
- Never expose JPA entities directly in REST APIs; always use DTOs to prevent lazy loading issues.
- Avoid N+1 query problems by using `@EntityGraph` or `JOIN FETCH` in queries.
- Be cautious with `CascadeType.REMOVE` on large collections as it can cause performance issues.
- Do not use `EAGER` fetch type for collections; it can cause excessive database queries.
- Avoid long-running transactions as they can cause database lock contention.
- Use `@Transactional(readOnly = true)` for read operations to enable optimizations.
- Be aware of the first-level cache; entities may not reflect database changes within the same transaction.
- UUID primary keys can cause index fragmentation; consider using sequential UUIDs or Long IDs.
- Pagination on large datasets requires proper indexing to avoid full table scans.Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
>
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
Provides AWS CloudFormation patterns for Auto Scaling including EC2, ECS, and Lambda. Use when creating Auto Scaling groups, launch configurations, launch templates, scaling policies, lifecycle hooks, and predictive scaling. Covers template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and best practices for high availability and cost optimization.
Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.
Provides AWS CloudFormation patterns for CloudFront distributions, origins (ALB, S3, Lambda@Edge, VPC Origins), CacheBehaviors, Functions, SecurityHeaders, parameters, Outputs and cross-stack references. Use when creating CloudFront distributions with CloudFormation, configuring multiple origins, implementing caching strategies, managing custom domains with ACM, configuring WAF, and optimizing performance.
Provides AWS CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and CloudWatch best practices for monitoring production infrastructure.
Provides AWS CloudFormation patterns for DynamoDB tables, GSIs, LSIs, auto-scaling, and streams. Use when creating DynamoDB tables with CloudFormation, configuring primary keys, local/global secondary indexes, capacity modes (on-demand/provisioned), point-in-time recovery, encryption, TTL, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references.