Distributed Caching and Redis

Cache API responses versus domain objects

Compares caching response-shaped read models with caching domain entities or raw database objects.

Choose a TTL and expiration strategy for cached data

Explains how TTL choices balance freshness, load reduction, user expectations, and operational safety.

Compare cache-aside, read-through, and write-through caching

Compares cache-aside, read-through, and write-through by read and write ownership, freshness, failure behavior, invalidation, fallback, stampede control, and operational complexity.

Compare in-memory cache and distributed cache

Explains when local process memory is enough and when shared Redis-style caching is worth the extra dependency.

Compare Redis pub/sub and durable queues for cache invalidation

Compares Redis pub/sub with durable queues when broadcasting cache invalidation or refresh events.

Decide whether a slow endpoint should be cached

Explains how to decide if caching is the right fix for a slow API endpoint instead of query tuning or contract changes.

Distributed Caching & Redis

Redis, cache-aside, invalidation, sessions, stampedes, and cache operations.

A practical backend track covering distributed caching with Redis, cache-aside reads, TTLs, invalidation, key scope, stale data, sessions, stampedes, Redis outages, locks, rate limiting, memory pressure, and production observability.