How would you implement rate limiting in a distributed API?
This question assesses your ability to protect systems from abuse and ensure fair resource usage. It tests your knowledge of algorithms and infrastructure patterns.
Why Interviewers Ask This
Rate limiting is critical for preventing denial-of-service attacks and ensuring service quality. Interviewers want to see if you can design a solution that works across multiple server instances without race conditions. This demonstrates your practical skills in building secure and reliable APIs.
How to Answer This Question
Propose using a token bucket or sliding window algorithm implemented in a shared cache like Redis. Explain how to handle global vs per-user limits. Discuss the importance of atomic operations to prevent race conditions. Mention fallback strategies if the cache fails. Include how to expose rate limit headers to clients.
Key Points to Cover
- Token bucket or sliding window
- Shared state management
- Atomic operations
- Client feedback mechanisms
Sample Answer
I would implement rate limiting using a sliding window counter stored in Redis to ensure consistency across nodes. Each request increments a counter for the user's IP or ID within a time window. If the limit is exceeded,…
Common Mistakes to Avoid
- Implementing rate limiting locally without coordination
- Ignoring edge cases like clock skew
- Forgetting to inform clients about limits
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