Knew this one from prep so I wasn't scrambling.
Start by clarifying requirements (scale, rate limit rules, distributed vs single-server, accuracy vs performance) and then propose a high-level design using a suitable algorithm like token bucket or sliding window. Discuss trade-offs, handle follow-up questions on distributed rate limiting, and consider edge cases like race conditions and synchronization.
Pro tip: Demonstrate awareness of real-world constraints by mentioning that rate limiting is often implemented at multiple layers (e.g., API gateway, service mesh) and that you should choose an algorithm based on the specific use case (e.g., token bucket for bursty traffic).
Ask questions to understand the scope: expected QPS, number of users, whether it's per-user or global, distributed environment, and tolerance for inaccuracy.
Select a rate limiting algorithm (e.g., token bucket, leaky bucket, fixed window, sliding window) and justify your choice based on requirements.
Outline the components: where the rate limiter sits (client, server, middleware), data store (e.g., Redis), and how requests are counted and limited.
Address synchronization across multiple servers, race conditions, and consistency (e.g., using Redis with Lua scripts or centralized service).
Compare algorithms, discuss performance vs accuracy, and be prepared for follow-ups on scaling, failure modes, and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.