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caching-patterns

Configure Spring Cache with Redis and Caffeine for Spring Boot 4 microservices.

1|1|Updated Apr 16, 2026
Search Tags:#caching#redis#spring-boot#ttl#caffeine#cache-annotations#two-level-cache
One-click install
npx skills add https://github.com/iCesofT/awesome-ai --skill caching-patterns-icesoft
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: caching-patterns
Source: https://github.com/iCesofT/awesome-ai/tree/main/skills/03-development-caching-patterns
Command: npx skills add https://github.com/iCesofT/awesome-ai --skill caching-patterns-icesoft

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables developers to implement robust caching strategies in Spring Boot 4 applications by combining Redis distributed caching with a local Caffeine layer to reduce database load and latency.

Core Features & Use Cases

  • Two-level caching (L1 Caffeine + L2 Redis) to minimize latency for hot data.
  • TTL-based eviction policies per data type to balance freshness and performance.
  • Spring Cache annotations (@Cacheable, @CacheEvict, @CachePut) with safe invalidation patterns.
  • DTO-based cache values to avoid serialization issues with JPA entities.
  • Observability hooks using Micrometer and Actuator to monitor cache hit/miss metrics.

Quick Start

Configure Redis and Caffeine caches in your Spring Boot project and apply cache annotations to your services.

Frequently Asked Questions about caching-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I configure two-level caching with Redis and Caffeine in Spring Boot?▼

Configure two-level caching in Spring Boot by using Caffeine as the L1 local cache to store hot data and Redis as the L2 distributed cache to minimize latency and reduce database load.

Why should I cache DTOs instead of JPA entities with Spring Cache annotations?▼

Cache DTOs instead of JPA entities to avoid serialization issues when using Spring Cache annotations. DTO-based cache values ensure safe data transfer across Redis and Caffeine layers without proxy class errors.

How do I apply different TTL eviction policies per data type in Spring Boot microservices?▼

Apply different TTL eviction policies per data type by configuring Spring Cache with Redis and Caffeine. This balances data freshness and performance by matching volatility levels with appropriate expiration times.

What is the best way to monitor cache hit and miss metrics for Redis and Caffeine?▼

Monitor cache hit and miss metrics by integrating Micrometer and Actuator observability hooks. This tracks Redis and Caffeine cache performance to help tune two-level caching strategies effectively.

Can I use Spring Cache annotations for safe cache invalidation in high read throughput scenarios?▼

Use Spring Cache annotations like @Cacheable, @CacheEvict, and @CachePut for safe cache invalidation. They coordinate L1 and L2 toggles to maintain data consistency during high read throughput operations.

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