name: cache_patterns
router_kit: FullStackKit
description: Instruction set for enabling and operating the Spring Cache abstraction in Spring Boot when implementing application-level caching for performance-sensitive workloads.
allowed-tools: Read, Write, Bash
category: backend
tags: [architecture, automation, best practices, cache patterns, cache-managers, cacheable, caching, clean code, coding, collaboration, compliance, debugging, design patterns, development, documentation, efficiency, git, optimization, performance, productivity, programming, project management, quality assurance, refactoring, software engineering, spring-boot, standards, testing, utilities, version control, workflow]
version: 1.1.0
metadata:
skillport:
category: auto-healed
tags:
- cache_patterns
- cache_patterns

Spring Boot Cache Abstraction

Overview

Spring Boot ships with a cache abstraction that wraps expensive service calls
behind annotation-driven caches. This abstraction supports multiple cache
providers (ConcurrentMap, Caffeine, Redis, Ehcache, JCache) without changing
business code. The skill provides a concise workflow for enabling caching,
managing cache lifecycles, and validating behavior in Spring Boot 3.5+ services.

When to Use

• Add @Cacheable, @CachePut, or @CacheEvict to Spring Boot service methods.
• Configure Caffeine, Redis, or JCache cache managers for Spring Boot.
• Diagnose cache invalidation, eviction scheduling, or cache key issues.
• Expose cache management endpoints or scheduled eviction routines.

Use trigger phrases such as "implement service caching", "configure
CaffeineCacheManager", "evict caches on update", or "test Spring cache
behavior" to load this skill.

Prerequisites

• Java 17+ project based on Spring Boot 3.5.x (records encouraged for DTOs).
• Dependency spring-boot-starter-cache; add provider-specific starters as
  needed (spring-boot-starter-data-redis, caffeine, ehcache, etc.).
• Constructor-injected services that expose deterministic method signatures.
• Observability stack (Actuator, Micrometer) when operating caches in
  production.

Quick Start

1. Add dependencies

```xml

org.springframework.boot
spring-boot-starter-cache


com.github.ben-manes.caffeine
caffeine

```

gradle implementation "org.springframework.boot:spring-boot-starter-cache" implementation "com.github.ben-manes.caffeine:caffeine"

1. Enable caching

java @Configuration @EnableCaching class CacheConfig { @Bean CacheManager cacheManager() { return new CaffeineCacheManager("users", "orders"); } }

1. Annotate service methods

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

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

   @CachePut(key = "#user.id")
   User refreshUser(User user) { ... }

   @CacheEvict(key = "#id", beforeInvocation = false)
   void deleteUser(Long id) { ... }

}
```

1. Verify behavior
2. Run focused unit tests that call cached methods twice and assert repository
   invocations.
3. Inspect Actuator cache endpoint (if enabled) for hit/miss counters.

🔄 Workflow

Kaynak: Spring Boot Caching Guide & Caffeine Cache Best Practices

Aşama 1: Strategy & Provider Selection

• [ ] Identifying Hot Paths: En çok beklenen ve nadir değişen veri okuma (I/O) noktalarını belirle.
• [ ] Provider Selection: Bellek içi (Caffeine) veya dağıtık (Redis) cache seçimine karar ver.
• [ ] Key Design: SpEL kullanarak benzersiz ve tahmin edilebilir cache key strategy'si oluştur.

Aşama 2: Annotation Implementation

• [ ] @Cacheable: Veriyi cache'e yaz we sonraki çağrılarda oradan oku.
• [ ] @CachePut: Veri güncellendiğinde cache'i de yenile.
• [ ] @CacheEvict: Silme işlemlerinde veya belirli periyotlarda cache'i temizle (allEntries=true opsiyonunu değerlendir).

Aşama 3: LifeCycle & Monitoring

• [ ] TTL/Eviction: Veri tazeliği (TTL) ve temizleme (Eviction) politikalarını (LRU/LFU) konfigüre et.
• [ ] Actuator Audit: cache endpoint'i üzerinden hit/miss oranlarını izle.
• [ ] Integration Testing: @SpringBootTest ile cache izolasyonunu ve tutarlılığını test et.

Kontrol Noktaları

Cache Patterns v2.0 - With Workflow

Advanced Options

• Integrate JCache annotations when interoperating with providers that favor
  JSR-107 (@CacheResult, @CacheRemove). Avoid mixing with Spring annotations
  on the same method.
• Cache reactive return types (Mono, Flux) or CompletableFuture values.
  Spring stores resolved values and resubscribes on hits; consider TTL alignment
  with publisher semantics.
• Apply HTTP caching headers using CacheControl when exposing cached responses
  via REST.

Examples

• Load references/cache-examples.md for
  progressive scenarios (basic product cache, conditional caching, multilevel
  eviction, Redis integration).
• Load references/cache-core-reference.md
  for annotation matrices, configuration tables, and property samples.

References

• references/spring-framework-cache-docs.md:
  curated excerpts from the Spring Framework Reference Guide (official).
• references/spring-cache-doc-snippet.md:
  narrative overview extracted 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 to avoid noise; push metrics via Micrometer.
• Tune TTLs based on data staleness tolerance; document rationale in code.
• Guard caches that store 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.
• Ensure multi-level caches (e.g. Caffeine + Redis) maintain consistency; prefer
  publish/subscribe invalidation channels.
• Validate serialization compatibility when caching across service instances.
• Monitor memory footprint to prevent OOM when using in-memory stores.

Related Skills

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