spring-boot-cache

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Spring Boot Cache Abstraction

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

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

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

Enable caching — annotate a @Configuration class with @EnableCaching

and define a CacheManager bean.

Annotate methods — @Cacheable for reads, @CachePut for writes,

@CacheEvict for deletions.

Configure TTL/eviction — set spring.cache.caffeine.spec,

spring.cache.redis.time-to-live, or spring.cache.ehcache.config.

Shape keys — use SpEL in key attributes; guard with

condition/unless for selective caching.

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

java

@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

java

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

// Only expensive products are cached

Example 3: Cache Eviction

java

@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:

Verify @EnableCaching is present on a @Configuration class.
Confirm the method is public and called from outside the class (Spring uses

proxies; self-invocation bypasses the cache).

Validate SpEL key expressions resolve correctly.
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.