How would you design a rate limiting system for an API, and what algorithms are commonly used?

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Rate limiting controls how many requests a client can make to an API within a given time period. It protects backend services from being overwhelmed, ensures fair resource distribution among clients, and defends against abuse and denial-of-service attacks. Designing an effective rate limiting system requires choosing the right algorithm, deciding where to enforce limits, and handling edge cases gracefully.

The token bucket algorithm is one of the most widely used approaches. Each client has a bucket that holds tokens. Tokens are added at a fixed rate, say 10 tokens per second. Each request consumes one token. If the bucket is empty, the request is rejected or queued. The bucket has a maximum capacity, which allows bursts up to that limit. For example, a bucket with capacity 100 and refill rate of 10 per second allows a client to burst up to 100 requests instantly but sustains only 10 per second over time. This algorithm is simple, memory-efficient, and naturally handles bursty traffic patterns.

The sliding window log algorithm tracks the timestamp of each request within a window period. To check if a request is allowed, you count all requests in the current window. This provides precise rate limiting but requires more memory since each request timestamp must be stored. The sliding window counter is a hybrid that divides time into fixed intervals and uses a weighted average of the current and previous intervals, providing near-exact limiting with lower memory usage.

The leaky bucket algorithm processes requests at a constant rate, like water leaking from a bucket. Incoming requests fill the bucket, and if it overflows, excess requests are rejected. This smooths out bursts and produces a very predictable outflow rate, which is useful when backend services need consistent traffic.

For implementation, rate limiting can be enforced at multiple layers. An API gateway provides centralized enforcement for all APIs. A dedicated rate limiting service using Redis for counter storage offers flexibility and works across distributed instances. Application-level middleware handles service-specific limits. In distributed systems, centralized rate limiting using Redis ensures consistent enforcement across all server instances. Each server checks the shared counter before processing a request.

When a client exceeds their limit, return an HTTP 429 status code with a Retry-After header indicating when the client can retry. Include rate limit headers in all responses showing the current limit, remaining requests, and reset time. This transparency helps clients implement proper backoff strategies. For critical clients or premium tiers, implement different rate limits based on API key or subscription level, allowing higher throughput for paying customers while protecting the system from abuse.