redis-deep

A sliding window rate limiter in Redis is commonly implemented using sorted sets to track request timestamps within a specific time frame. This pattern enables precise throttling by automatically expiring outdated entries and counting recent requests to enforce accurate limits.

Redis Cluster provides horizontal scalability and high availability through data sharding across multiple nodes, while Sentinel focuses on high availability through automatic failover monitoring for single-master setups. Choosing between them depends on whether your workload requires scaling memory capacity or simply reliable failover.

The Redlock algorithm implements distributed locking by acquiring locks across multiple independent Redis instances simultaneously. A lock is granted only if the majority of nodes accept it within a validity period, ensuring fault tolerance and preventing single-point failures in distributed systems.

Redis Streams should be used instead of pub/sub when you need persistent event logging and consumer groups, as pub/sub delivers messages fire-and-forget without storing them. Streams allow multiple consumers to read historical data independently, making them suitable for reliable event streaming.

Redis pipeline batching optimizes performance by sending multiple commands in a single network request without waiting for individual responses. This technique significantly reduces network round-trip latency, allowing your application to execute bulk operations and achieve higher throughput.

Redis memory eviction policies dictate how data is removed when the server reaches its memory limit, with common strategies like LRU (Least Recently Used) and LFU (Least Frequently Used). Selecting the right policy ensures optimal cache hit rates and prevents memory overflow during peak usage.