redis-patterns

Redis caching strategies like cache-aside and TTL-based caching store frequently accessed data to reduce database load. You implement them by fetching data from the cache first, falling back to the primary database, and updating the cache with appropriate expiration times.

Rate limiting using Redis sorted sets applies a sliding window algorithm to throttle clients accurately. You implement this by tracking timestamped requests in a sorted set and removing entries outside the current window to count active requests against defined limits.

Redis pub/sub suits real-time notifications where message durability is unnecessary, while streams provide durable event processing. Use pub/sub for fire-and-forget broadcasting and streams when you need reliable data streams with consumer groups and historical message persistence.

Lua scripting executes atomic operations in Redis for critical state transitions without race conditions. You write custom logic that Redis runs as a single, uninterrupted command sequence, ensuring data consistency during complex updates.

Redis event-driven architectures scale backend services by combining streams for durable event processing and pub/sub for real-time notifications. You progressively integrate these patterns to handle reliable data flows and decouple microservices communication.

Redis works well for distributed systems requiring fast data access by providing in-memory data structures and reliable data streams. It supports modern architectures needing high throughput, low latency caching, and real-time event processing across services.