This started simple enough but kept expanding.
Start by clarifying requirements such as scale, accuracy, and latency, then propose a high-level design using a token bucket or sliding window algorithm. Discuss trade-offs between different algorithms and storage options (e.g., in-memory vs. distributed), and explain how to handle distributed rate limiting with Redis or similar.
Pro tip: Demonstrate awareness of real-world constraints by mentioning how to handle race conditions in distributed environments and the importance of monitoring and alerting on rate limiter effectiveness.
Ask about expected request volume, number of clients, latency requirements, and whether the rate limiter should be centralized or distributed. Also clarify if it's per-user, per-IP, or global.
Select an appropriate rate limiting algorithm (e.g., token bucket, leaky bucket, fixed window, sliding window) based on requirements. Explain the pros and cons of each.
Decide where to store counters (in-memory, Redis, database) and how to handle distributed coordination. Discuss consistency, availability, and partition tolerance trade-offs.
Address race conditions, clock skew, and failure modes. Explain how to ensure atomic operations and handle bursts.
Describe how to monitor rate limiter performance, log violations, and adjust limits dynamically. Mention the importance of metrics and alerts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.