Add caching deliberately by defining keys, expiration, and behavior when cached data is missing.
The core idea
A cache stores a result so repeated requests can avoid expensive work. A time-to-live limits how long an entry remains valid, but it does not guarantee that every cached value is fresh for every use case.
A practical way to approach it
Choose a stable key that includes the inputs affecting the result. Set a TTL based on freshness needs, handle cache misses, and decide how updates invalidate or refresh entries. Monitor hit rate, memory, latency, and stale reads.
Try it in a small project
Cache a public product catalogue response for a short period. When an administrator changes a product, invalidate the relevant key and test behavior if Redis is unavailable.
Common mistakes to avoid
Do not cache private responses under shared keys. Plan for stampedes, stale data, and cache outages; a cache should not become the only copy of important data.
Key takeaway
Keep the first version small, make your assumptions explicit, and verify the result with a repeatable test. For production use, check the current official documentation for the tools and versions you choose.