Building a Production-Ready Distributed Rate Limiter with Redis

Your API just handled 10x normal traffic during a flash sale, and now your database is melting. You added rate limiting last month, but it's running in-memory on each instance—meaning your 8 replicas each allow 1000 requests per second, effectively giving heavy users 8000 RPS to hammer your backend. Sound familiar? This is the distributed rate limiting problem, and solving it requires more than just adding Redis. You need atomic operations, graceful degradation, multi-tier limits, and observability that actually helps you tune your limits over time. This article walks through building a production-ready distributed rate limiter from first principles, covering the real-world edge cases that tutorials skip. Why Local Rate Limiting Fails at Scale The fundamental problem with per-instance rate limiting is multiplication. When you configure a limit of 1000 requests per minute for a user, you expect that user to hit your backend at most 1000 times. But if that user's requests get distributed