Rate limiting is a critical component in distributed systems to prevent abuse and ensure fair resource allocation. This analysis compares three classic algorithms: token bucket, sliding window, and leaky bucket, focusing on their production implementations. The token bucket allows bursts while maintaining average rate, making it ideal for APIs with variable traffic. Sliding window provides smoother rate enforcement by tracking requests over a moving time window, suitable for real-time systems. Leaky bucket enforces a strict output rate, perfect for shaping traffic to downstream services. Each has trade-offs in memory, accuracy, and complexity. For example, token bucket requires careful token refill logic, while sliding window can be memory-intensive with high request volumes. Understanding these nuances helps engineers choose the right algorithm for their use case, whether it's API gateways, database throttling, or microservice communication. This topic is evergreen and foundational for system design interviews and production deployments alike.
A production-level comparison of token bucket, sliding window, and leaky bucket rate limiting algorithms for distributed systems.