caching-strategy

Implement caching by identifying expensive or frequently requested operations and storing their results in Redis, in-memory, or HTTP cache layers. This avoids repeated data fetches, reducing latency and backend load for API responses and page renders.

Choose caching layers based on data volatility and access patterns. Redis provides durable cross-process caching, in-memory Maps offer fast local storage, and HTTP Cache-Control handles client-side caching for API responses and pages.

Handle cache invalidation by defining clear cache keys and establishing invalidation rules that trigger on data mutations. This ensures cached responses stay fresh and consistent when underlying data changes across your services.

Avoid caching when data requires strict consistency and cannot tolerate eventual consistency. Operations with highly volatile data that changes frequently or mutations requiring immediate read-through responses are not suitable for caching.

Yes, caching is applicable for session data and external API calls that can tolerate eventual consistency. By caching these operations safely, you avoid repeated expensive data fetches and reduce latency across your services.