The interviewer said AI was fair game, so I used it and cranked out a solution pretty fast.
Start by clarifying requirements (scale, accuracy, distributed vs single-node, API-level vs service-level) and then propose a high-level design using a rate limiting algorithm like token bucket or sliding window. Dive into the data structures and trade-offs, and finally discuss distributed implementation with Redis and handling edge cases like race conditions and synchronization.
Pro tip: Demonstrate maturity by proactively discussing how to handle bursts and the trade-off between strictness and user experience, and mention monitoring and dynamic rule updates as part of a production-ready solution.
Ask questions to understand the scope: expected QPS, number of users, whether it's per-user or global, distributed or single server, and tolerance for bursts. This shows you can translate ambiguous problems into concrete constraints.
Compare rate limiting algorithms such as token bucket, leaky bucket, fixed window, and sliding window. Explain their pros and cons (e.g., token bucket allows bursts, sliding window is more accurate but memory-intensive) and select one based on requirements.
Detail how to implement the chosen algorithm: for token bucket, use a counter and timestamp; for sliding window, use a sorted set or ring buffer. Discuss in-memory vs distributed storage (e.g., Redis) and how to ensure atomicity with Lua scripts or transactions.
Address synchronization across multiple nodes, race conditions, and consistency. Explain how to use Redis with atomic operations, and consider trade-offs like eventual consistency vs strict limits. Mention fallback strategies if the rate limiter store is unavailable.
Talk about dynamic rule updates, monitoring (e.g., tracking limit hits), and integration with API gateways. Also mention how to handle edge cases like clock skew and how to test the rate limiter.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.