spring-boot-cache

Overview

6-step workflow for enabling cache abstraction, configuring providers (Caffeine, Redis, Ehcache), annotating service methods, and validating behavior in Spring Boot 3.5+ applications. Apply @Cacheable for reads, @CachePut for writes, @CacheEvict for deletions. Configure TTL/eviction policies and expose metrics via Actuator.

When to Use

• Add @Cacheable, @CachePut, or @CacheEvict to service methods.
• Configure Caffeine, Redis, or Ehcache with TTL and capacity policies.
• Implement eviction strategies for stale data.
• Diagnose cache misses or invalidation issues.
• Expose hit/miss metrics via Actuator or Micrometer.

Instructions

1. Add dependencies — spring-boot-starter-cache plus a provider:

   • Caffeine: caffeine starter
   • Redis: spring-boot-starter-data-redis
   • Ehcache: ehcache starter

2. Enable caching — annotate a @Configuration class with @EnableCaching and define a CacheManager bean.

3. Annotate methods — @Cacheable for reads, @CachePut for writes, @CacheEvict for deletions.

4. Configure TTL/eviction — set spring.cache.caffeine.spec, spring.cache.redis.time-to-live, or spring.cache.ehcache.config.

5. Shape keys — use SpEL in key attributes; guard with condition/unless for selective caching.

6. Validate setup — run integration test to confirm cache hit on second call; check GET /actuator/caches to verify cache manager registration; query GET /actuator/metrics/cache.gets for hit/miss ratios.

Examples

Example 1: Basic @Cacheable Usage

@Service
@CacheConfig(cacheNames = "users")
class UserService {

    @Cacheable(key = "#id", unless = "#result == null")
    User findUser(Long id) { ... }
}

First call → cache miss, repository invoked
Second call → cache hit, repository skipped

Example 2: Conditional Caching with SpEL

@Cacheable(value = "products", key = "#id", condition = "#price > 100")
public Product getProduct(Long id, BigDecimal price) { ... }

// Only expensive products are cached

Example 3: Cache Eviction

@CacheEvict(value = "users", key = "#id")
public void deleteUser(Long id) { ... }

For progressive scenarios (basic product cache, multilevel eviction, Redis integration), load references/cache-examples.md.

Advanced Options

• Use JCache annotations (@CacheResult, @CacheRemove) for providers favoring JSR-107 interoperability; avoid mixing with Spring annotations on the same method.
• Cache reactive return types (Mono, Flux) or CompletableFuture values.
• Apply HTTP CacheControl headers when exposing cached responses via REST.
• Schedule periodic eviction with @Scheduled for time-bound caches.
• Create a CacheManagementService for programmatic cacheManager.getCache(name).

Troubleshooting

If cache misses persist after adding @Cacheable:

1. Verify @EnableCaching is present on a @Configuration class.
2. Confirm the method is public and called from outside the class (Spring uses proxies; self-invocation bypasses the cache).
3. Validate SpEL key expressions resolve correctly.
4. Confirm the cache manager bean is registered as cacheManager or explicitly referenced via cacheManager = "myCacheManager".

References

• references/spring-framework-cache-docs.md: curated excerpts from Spring Framework Reference Guide.
• references/spring-cache-doc-snippet.md: narrative overview from Spring documentation.
• references/cache-core-reference.md: annotation parameters, dependency matrices, property catalogs.
• references/cache-examples.md: end-to-end examples with tests.

Best Practices

• Prefer constructor injection and immutable DTOs for cache entries.
• Separate cache names per aggregate (users, orders) to simplify eviction.
• Log cache hits/misses only at debug; push metrics via Micrometer.
• Tune TTLs based on data staleness tolerance; document rationale in code.
• Guard caches storing PII or credentials with encryption or avoid caching.
• Align cache eviction with transactional boundaries to prevent dirty reads.

Constraints and Warnings

• Avoid caching mutable entities that depend on open persistence contexts.
• Do not mix Spring cache annotations with JCache annotations on the same method.
• Validate serialization compatibility when caching across service instances.
• Monitor memory footprint to prevent OOM with in-memory stores.
• Caffeine + Redis multi-level caches require publish/subscribe invalidation channels.

Related Skills

• ../spring-boot-rest-api-standards
• ../spring-boot-test-patterns
• ../unit-test-caching