Design Thread-Safe Rate Limiter
Last Updated: February 3, 2026
Rate limiting is a technique to control how many requests a client can make to a service within a given time window. It protects services from being overwhelmed, ensures fair resource sharing among clients, and prevents abuse.
Why do we need it?
- Protect services from overload: Without rate limiting, a single client (malicious or buggy) could flood your API with millions of requests, crashing your servers or degrading performance for everyone.
- Fair resource sharing: In multi-tenant systems, rate limiting ensures one heavy user doesn't starve others of resources.
- Cost control: Cloud services charge per request. Rate limiting caps costs for both providers and consumers.
- Security: Slows down brute-force attacks, credential stuffing, and scraping.
Common rate limiting algorithms
For this problem, we'll implement the Token Bucket algorithm because it's widely used in production systems. It provides a good balance: it allows short bursts of traffic (up to bucket capacity) while enforcing a sustained average rate.
Loading simulation...
1. Problem Definition
Design a thread-safe rate limiter that caps how many requests a client can make in a given time window.
At first glance, the requirement sounds simple: track request counts and reject when limits are exceeded. But once your API server handles requests on dozens of threads simultaneously, the problem becomes a real concurrency challenge.
Two threads might check the same counter at the exact same moment, both see "1 token remaining," both proceed, and now you have allowed 2 requests when only 1 was permitted.
- Per-client limits: Each client (identified by API key, user ID, or IP) has its own quota that refills over time.
- Atomic check-and-decrement: When a request arrives, checking available tokens and decrementing must happen atomically, or multiple threads can exceed the limit.
- Token refill: Tokens replenish at a configured rate. A bucket with 100 tokens per second should smoothly allow bursts while enforcing the average rate.
- Efficient at scale: The system must handle thousands of concurrent clients without becoming a bottleneck itself.
In short, the system must guarantee that no client exceeds their allowed rate, even under extreme concurrency, while maintaining low latency for legitimate requests.