How Redis Caching Reduces Database Load in .NET Microservices
A practical guide to using Redis and in-memory caching in .NET microservices to reduce database load, improve API latency, and protect production systems.
Start with the read path
The highest-impact caching work usually starts with static or slow-changing data that appears across many requests. In a .NET microservice, Redis works well as a shared cache while in-memory caching can protect hot paths inside each running instance.
Design cache invalidation before rollout
Caching improves latency only when stale data is controlled. Versioned keys, short TTLs for volatile data, and explicit invalidation for administrative updates keep the cache predictable under production traffic.
Measure database pressure, not only response time
Good caching reduces query volume, connection pressure, and repeated serialization work. Track cache hit rate, database CPU, query count, and API latency together so the optimization is visible end to end.