Redis for Product Scaling

Write-through updates the cache on every write — the cache and the database stay in sync, and every read hits the cache. Look-aside checks the cache first and populates it on a miss — the cache only holds data that has been read at least once. Write-through suits workloads where stale reads are not acceptable. Look-aside suits read-heavy workloads where not all data needs to be cached — only the data the application actually requests ends up in memory.

Pub/Sub delivers messages to active subscribers only — if a consumer is offline when a message is published, the message is lost. Streams persist messages and allow consumers to read from any point in the stream, including replaying past messages. Pub/Sub works for real-time broadcasts where message loss is acceptable. Streams work for event-driven architectures where every consumer needs to process every message, regardless of when they come online.

A standard inventory check reads the current stock, confirms it is above zero, and decrements it — three operations that are not atomic. Under concurrent load, two requests can both read the same stock value, both confirm it is above zero, and both decrement — producing an oversell. Redis DECR is a single atomic operation — the read and decrement happen in one step with no gap for a concurrent request to intervene. Every decrement either succeeds or fails based on the current value, with no race condition possible.

A database session lookup requires a query on every authenticated request — adding database round-trip latency to every page load. Redis session retrieval is an in-memory key lookup that completes in under a millisecond. For applications with high authenticated traffic, the difference in database load is significant — every session lookup that hits Redis instead of the database is one fewer query the database needs to handle.

AOF persistence with a sync policy matched to the session loss tolerance of the application. For most session workloads, everysec sync — writing to disk once per second — balances write performance with a maximum one-second data loss window on a crash. For workloads where session loss is not acceptable under any failure, synchronous AOF with a replica adds a second layer of durability. The right configuration depends on what losing all active sessions would cost the product.

For workloads that are read-heavy, latency-sensitive, and where the dataset fits in memory — leaderboards, session state, rate limiting counters, feature flags — Redis can serve as the primary data store rather than a cache in front of a database. For workloads that require durability guarantees, complex querying, or datasets that exceed available memory, Redis works best as a performance layer in front of a primary database rather than a replacement for it.

A caching layer implementation — strategy selection, TTL configuration, and cache warming — typically completes within one to two weeks. Adding Pub/Sub or Streams for messaging, Sorted Sets for leaderboards, or atomic counters for inventory management adds one to two weeks per pattern depending on integration complexity. Full session management migration from database to Redis typically completes within two to three weeks.

The implementation is handed over with caching strategy documented, TTL rationale recorded, and operational runbooks covering cache invalidation, memory monitoring, and failover procedures. Your team manages cache configuration changes independently. We do not build on proprietary tooling that requires ongoing GeekyAnts involvement. If a formal support arrangement makes sense post-launch, that is a separate conversation.