Redis caching strategies that actually move the needle
Where we cache, what we invalidate, and how we kept Redis from becoming a bottleneck in a high-read Laravel app.
Caching everything is rarely the right answer. We started by measuring: which endpoints and queries were slow and read-heavy? Those became our first cache candidates, with clear TTLs and invalidation on write.
We used cache tags in Laravel where we needed to invalidate by section (e.g. “all product listing data”) and avoided storing huge payloads. Serializing minimal data and using Redis memory limits and eviction policies kept the instance predictable.
After a few iterations, we had a small set of cache layers (full-page for a few routes, query results for others) with documented invalidation rules. Response times and database load dropped without turning Redis into a mystery box.