Building Distributed Rate Limiters with Redis and the Token Bucket Algorithm
Introduction In modern web services, protecting APIs from abuse, ensuring fair resource allocation, and maintaining a predictable quality‑of‑service are non‑negotiable requirements. Rate limiting—the practice of restricting how many requests a client can make in a given time window—addresses these concerns. While a simple in‑process limiter works for monolithic applications, today’s micro‑service ecosystems demand a distributed solution that works across multiple instances, data centers, and even cloud regions. This article walks you through the complete design and implementation of a distributed rate limiter built on Redis using the Token Bucket algorithm. We’ll cover the theory behind token buckets, why Redis is a natural fit, practical implementation details, edge‑case handling, scaling strategies, and real‑world patterns you can adopt immediately. ...