How would you implement rate limiting in a distributed system?
This question focuses on protecting system resources from abuse and ensuring fair usage. It tests your knowledge of algorithms and distributed state management.
Why Interviewers Ask This
Interviewers ask this to check if you can prevent denial-of-service attacks and resource exhaustion. They want to see if you understand how to enforce limits across multiple server instances. This demonstrates your ability to build robust, secure systems.
How to Answer This Question
Describe common algorithms like Token Bucket or Leaky Bucket. Explain how to synchronize state across distributed nodes using Redis or similar stores. Discuss handling edge cases like clock skew and burst traffic. Mention sliding window counters for accuracy. Include fallback mechanisms if the rate limiter fails.
Key Points to Cover
- Token bucket algorithm
- Distributed state synchronization
- Sliding window counters
- Fallback strategies
- Atomic operations
Sample Answer
I would implement rate limiting using a token bucket algorithm stored in Redis for fast access. Each user gets a bucket refilled at a fixed rate, allowing controlled bursts. To handle distribution, I'd use a centralized…
Common Mistakes to Avoid
- Using local storage only
- Ignoring clock synchronization
- Not handling burst traffic
- Forgetting fallback mechanisms
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