implement-cache-strategy

You can implement multi-tier caching by using a unified reactive CacheAdapter that composes Caffeine and Redis as L1 and L2 backends. This provides consistent cache usage, pluggable persistence, and predictable performance for fast cached reads.

Event-driven cache invalidation in a CQRS setup uses a QueryCacheAdapter to keep reads fresh after writes. It triggers automatic cache invalidation when write events occur, ensuring cached query results remain consistent with the updated data store.

Yes, the unified caching layer supports Hazelcast and JCACHE as pluggable backends alongside Caffeine and Redis. Provider auto-selection allows you to switch cache persistence mechanisms while maintaining consistent get and put semantics.

The best way to monitor cache health is by using SmartCacheAdapter, which supports built-in health checks and metrics. You can create per-use-case CacheManagerFactory instances to track performance and isolate caching behavior for different services.

You should use a write-through cache strategy when your services require fast cached reads alongside workflows that write data back to the primary data store. This ensures data consistency by synchronizing cache updates with database writes.

Yes, the unified caching layer satisfies dependency management for backend providers. It handles provider auto-selection and configuration, allowing you to integrate multiple cache backends without manually resolving their dependencies.