redis-patterns

To implement the cache-aside pattern with Redis, you check the cache first, fetch from the database on a miss, and populate the cache. This skill provides Python code templates using asyncio to manage session data efficiently across microservices.

Building a rate-limited API with Redis involves using counters to track requests per user within a time window. This skill offers concrete pattern definitions and runnable templates to quickly prototype rate limiting for high-throughput applications.

Redis Pub/Sub enables real-time messaging by allowing publishers to send messages to channels that multiple subscribers listen to simultaneously. This skill provides templates to wire Pub/Sub for instant updates across distributed services.

You should use Redis Streams instead of Pub/Sub when you need durable event processing and message persistence. While Pub/Sub fires and forgets, Streams provide a durable event buffer, ensuring no data is lost if a consumer disconnects.

Yes, these Redis patterns are designed for high-throughput caching with asyncio in Python. The skill includes scalable data flow templates that integrate Redis with asynchronous Python to handle robust data handling across distributed services.

A key limitation of Redis Pub/Sub is the lack of message persistence; if a subscriber is offline, missed messages are lost. For durable event processing in scalable data flows, Redis Streams should be used instead to buffer events.