rate-limiter-interviewer

Designing an API rate limiter for a distributed system interview requires evaluating algorithms like token bucket or sliding window, choosing centralized versus local state, and ensuring atomicity with Redis Lua scripts to handle concurrency.

Redis and Lua handle rate limiting race conditions by executing atomic scripts that read and update state in a single threaded operation, preventing concurrent requests from overflowing token buckets or skewing sliding window counters.

Token bucket rate limiting allows bursty traffic by accumulating tokens over time, while sliding window algorithms provide smoother traffic shaping by calculating the precise rate of requests within a moving time frame.

You need distributed systems experience for API gateway design scenarios, as evaluating latency, scalability, Redis memory management, and race conditions are core requirements for SWE-II backend infrastructure interview preparation.

Use centralized Redis state in an API gateway when you need globally accurate rate limits across distributed nodes, and use local state when you prioritize lower latency and can tolerate approximate rate limiting per server instance.