redis-patterns

The cache-aside pattern with Redis uses TTL-based caching to reduce database load and improve response times. You check the cache first, fetch from the database on a miss, and populate the Redis key with an expiration TTL.

Rate limiting with Redis sorted sets enforces per-client request limits by tracking request timestamps as scored members in a sorted set. You remove expired timestamps and count remaining members to block or allow requests.

The canonical Redis key naming convention follows the format {app}:{domain}:{entity}:{id}. This hierarchical structure ensures consistent, searchable keys across distributed services and prevents namespace collisions.

Yes, you can use Redis as a session store for scalable microservices. It provides TTL-managed, in-memory user sessions that enable fast lookups and automatic expiration across distributed service instances.

Use Redis pub/sub for messaging when building real-time applications or microservices requiring fast, in-memory event broadcasting. It allows services to publish messages to channels and subscribers to receive events instantly.

Strings, hashes, and sorted sets are the best Redis data structures for high-performance data access layers. They enable efficient caching, rate limiting, and scalable session management when combined with TTL-based expirations.