Rate Limiting Strategies With Redis: Fixed Window, Sliding Window, and Token Bucket

The Rate Limiting Problem API rate limiting protects services from overload, prevents abuse, and ensures fair resource distribution across clients. Without rate limiting, a single high-volume client can degrade performance for all users, whether through malicious attacks or unintentional bugs causing request loops. Traditional approaches, like in-memory counters, fail in distributed systems. When multiple API servers handle requests, each maintains separate counts, making it impossible to enforce consistent limits. Redis solves this by providing a centralized, fast key-value store that all servers can query atomically.