spring-cache

Configure Redis-backed multi-level caching for Java 21 Spring Boot 3.x services.

Updated Feb 25, 2026

Search Tags:#caching #redis #spring-boot #cache-aside #ttl #micrometer #spring-cache

Ready to run

npx skills add https://github.com/zenobiuszeto/banking-strawman-capabilities --skill spring-cache

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: spring-cache
Source: https://github.com/zenobiuszeto/banking-strawman-capabilities/tree/main/skills/spring-cache
Command: npx skills add https://github.com/zenobiuszeto/banking-strawman-capabilities --skill spring-cache

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Legacy databases suffer latency and load on read-heavy banking operations. Production-grade caching with Redis, L2, and optional L1 (Caffeine) improves response times and reduces DB pressure for Java 21 / Spring Boot 3.x.

Core Features & Use Cases

TTL-based per-cache configuration, cache-aside pattern, and eviction policies to ensure freshness without stale reads.
Multi-level caching (L1 in-process with Caffeine plus L2 Redis) for ultra-fast access of hot data and scalable caching of shared data.
Transaction-aware cache writes and wiring with Spring Cache annotations (@Cacheable, @CachePut, @CacheEvict) to maintain consistency.
Use cases include caching account lookups, configuration data, and frequently accessed reference data across banking modules.

Quick Start

Configure a Redis-backed cache in a Spring Boot 3.3+ application and annotate services with @Cacheable, @CachePut, and @CacheEvict to see caching effects.

Frequently Asked Questions about spring-cache

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

FAQPage Schema

How do I implement Redis caching in a Spring Boot 3.x application to reduce database load?▼

Implement Redis caching in Spring Boot 3.x by configuring a Redis-backed cache and annotating services with @Cacheable, @CachePut, and @CacheEvict. This applies the cache-aside pattern to reduce database round-trips and improve response times for read-heavy operations.

What is multi-level caching in Spring Boot, and when should I use L1 and L2 caches?▼

Multi-level caching uses an in-process L1 cache like Caffeine for ultra-fast access to hot data, alongside an L2 Redis cache for scalable shared data. Use this pattern when read-heavy services require both minimal latency and distributed cache consistency across modules.

Does Spring Cache support transaction-aware cache writes and eviction policies?▼

Yes, Spring Cache supports transaction-aware cache writes and eviction policies through annotations like @Cacheable, @CachePut, and @CacheEvict. Per-cache TTL configuration ensures data freshness without stale reads while maintaining transactional consistency across banking services.

Can I use Micrometer to observe cache hits and misses in a Spring Boot Redis cache?▼

Yes, you can use Micrometer to observe cache performance in a Spring Boot Redis cache. The caching implementation satisfies Micrometer observability requirements, enabling detailed monitoring of cache hits, misses, and latency across read-heavy banking operations and modules.

What is the best way to cache account lookups and reference data across banking modules?▼

The best way to cache account lookups and reference data is using production-grade Redis caching with per-cache TTL configuration and transactional integration. This cache-aside approach dramatically reduces database pressure and improves response times across read-heavy banking modules.