API throttling: Algorithms, patterns & mistakes to avoid

API throttling and rate limiting are crucial mechanisms for managing traffic and preventing system overloads, yet many teams implement them once without revisiting their effectiveness, leading to potential vulnerabilities. Rate limiting restricts the number of requests a client can make in a specific time window, typically using algorithms like fixed window counters, sliding window logs, sliding window counters, token buckets, and leaky buckets, each with its own trade-offs in terms of memory usage, burst tolerance, and accuracy. Throttling, on the other hand, manages how a system responds when demand exceeds capacity, often overlapping with rate limiting in practice. Effective deployment patterns, such as centralized shared stores, PoP-local with eventual consistency, and API gateway-level throttling, are necessary to ensure consistent enforcement across distributed systems. Common pitfalls include decentralized throttling, local-only counters, and inadequate client communication, which can exacerbate issues like outages or unfair resource distribution. Redis offers robust solutions for implementing rate limiting with its data structures and Lua scripting, providing atomic operations and scalable infrastructure support.

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