Redis

Redis keeps data in memory rather than on disk, which makes reads and writes fast enough to sit in the request path of latency-sensitive features without becoming the bottleneck. For businesses whose application performance is limited by repeated database queries for the same data — product catalogues, configuration, computed results — caching that data in Redis can materially reduce response times and the load on the primary database.

It's also a common answer to a specific operational problem: keeping user session state consistent when an application runs on more than one server instance. Rather than pinning users to a single server or building custom session-sharing logic, Redis gives multiple instances a shared place to read and write session data, which matters once a system needs to scale horizontally or run behind a load balancer.

Beyond caching and sessions, Redis's pub/sub and list-based data structures make it a reasonable fit for lightweight queueing between services — background job dispatch, simple event notification — in systems that don't yet need the durability and delivery guarantees of a dedicated message broker. We treat that as a genuine but bounded use case, not a replacement for a proper queue where message loss would be a real problem.

We add Redis to an architecture when a concrete need justifies it, not by default. For a small, low-traffic system, running an additional in-memory service to manage can cost more in operational overhead than it saves in performance, and we say so rather than including it as a matter of habit.