A deep dive into token‑bucket and leaky‑bucket rate limiting, covering core math, scaling strategies, and real‑world implementation recipes.
A practical guide to building high‑throughput, distributed rate limiters with token and leaky bucket techniques.
A deep dive into token bucket and leaky bucket algorithms, showing how to choose, implement, and operate them at scale in modern cloud services.
A production‑focused guide that compares token bucket and leaky bucket rate limiters, showing how to choose, implement, and observe them at scale.
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. ...