I started with token bucket because it felt like the safest pick and I could explain it clearly, but the interviewer kept pushing on the trade-offs vs sliding window counter.
Start by clarifying requirements (e.g., per-client quota, accuracy, latency, scale) and then propose a layered design: choose a rate limiting algorithm (e.g., token bucket, sliding window) and implement it in a distributed manner using a shared store like Redis with atomic operations. Finally, address multi-region concerns by discussing data replication, consistency trade-offs, and strategies like regional quotas or global synchronization.
Pro tip: Demonstrate awareness of trade-offs by explicitly stating how your design handles burstiness, race conditions, and failure modes (e.g., Redis outage). Also, mention that the choice of algorithm depends on the specific requirements, showing you avoid one-size-fits-all solutions.
Ask questions to understand the scale (requests per second, number of clients), accuracy needs, latency requirements, and whether the quota is global or per-region. This ensures the design meets the actual needs.
Compare algorithms like fixed window, sliding window, token bucket, and leaky bucket, discussing their pros and cons regarding burst handling, memory usage, and accuracy. Choose one that fits the requirements.
Propose using a centralized data store (e.g., Redis) with atomic operations (e.g., Lua scripts) to enforce limits across multiple servers. Discuss sharding, replication, and handling failures.
Explain how to handle rate limiting across regions: options include regional quotas with eventual consistency, global synchronization with higher latency, or a hybrid approach. Discuss trade-offs between consistency and availability.
Mention the importance of monitoring rate limiter performance, adjusting limits dynamically, and handling edge cases like clock skew and hot keys.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.