Classic problem, you've probably seen it a dozen times.
Start by clarifying requirements (e.g., scale, accuracy, distributed vs. single-node) and then present a high-level design using a rate limiting algorithm like token bucket or sliding window. Dive into trade-offs (e.g., memory vs. accuracy, latency) and discuss distributed challenges like synchronization and consistency.
Pro tip: At Bytedance, emphasize scalability and low latency; mention how you'd handle burst traffic and ensure fairness across users. Also, proactively discuss monitoring and dynamic rule updates to show production readiness.
Ask about scale (QPS, number of users), accuracy needs, distributed environment, and whether rate limiting is per-user, per-IP, or global. This ensures you design the right solution.
Compare algorithms like 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).
Outline components: a rate limiter service, a fast data store (e.g., Redis), and integration with API gateways. Discuss where to enforce limits (client-side, server-side, or middleware).
Explain how to handle synchronization across nodes (e.g., using Redis with atomic operations, or a centralized service). Discuss consistency vs. availability trade-offs and potential race conditions.
Cover performance optimizations (e.g., local caching, sharding), handling failures (e.g., fallback to local limits), and monitoring/alerting. Also, mention dynamic rule updates and testing strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.