caching

To implement caching in read-heavy microservices, use the cache-aside pattern to store frequently requested aggregate data close to the caller. This reduces database load by serving subsequent reads directly from the cache instead of repeating data fetches.

The best way to handle cache invalidation and avoid stale data is combining TTL-based expiry for automatic eviction with explicit invalidation on writes. This approach clears affected cache entries during updates to maintain data freshness.

Caching patterns for distributed systems requiring event-based coherence include TTL, write-through, cache-aside, and event-based invalidation. These strategies ensure predictable eviction and maintain cache coherence across multiple services.

Yes, this caching approach works for microservices that repeatedly query the same user profiles. You can cache user profiles after the first fetch and explicitly invalidate the cached entries when user updates occur to ensure data freshness.

You should not rely solely on TTL-based expiry when immediate data consistency is required after writes. In these cases, use write-through invalidation or event-based coherence to actively clear affected cache entries instead of waiting for automatic eviction.