How Redis Powers Distributed API Rate Limiting and Idempotency at Scale

High-throughput public APIs require two core guarantees: protection against traffic spikes and ensuring duplicate client requests execute only once. Naive fixed-window rate limiters expose platforms to boundary burst vulnerabilities, where clients can effectively double their allowed request volume at reset intervals. Redis sorted sets combined with atomic Lua scripts enable sliding-window rate limiting that evaluates request volume in O(log N + M) time, eliminating these edge cases. For idempotency, Redis lock keys cache response payloads for 24 hours, preventing duplicate operations while allowing retries on application errors. Engineers are cautioned to address memory overhead, enforce key expiration policies, and maintain NTP clock synchronization across distributed Redis nodes to avoid common production pitfalls.