spring-cache

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.

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.

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.

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.

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.