AdvancedTechnical
5 min
API Rate Limiting Strategy
APIRate LimitingSecurity
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Interview Question
Describe how you would implement rate limiting in a large-scale API to protect against abuse while ensuring fairness.
Key Points to Cover
- Use algorithms like token bucket or leaky bucket
- Apply per-user, per-IP, and global limits
- Implement distributed counters via Redis or similar
- Gracefully reject or throttle excess requests
Evaluation Rubric
Explains rate-limiting algorithms30% weight
Balances fairness and protection30% weight
Implements distributed limit tracking20% weight
Considers user experience on throttling20% weight
Hints
- 💡Consider retry-after headers for clients.
Common Pitfalls to Avoid
- ⚠️Using simple in-memory counters on a single server, which doesn't scale and has no fault tolerance.
- ⚠️Applying only a single type of rate limit (e.g., only per-IP) without considering user-specific needs or global protection.
- ⚠️Not accounting for distributed nature of services, leading to inconsistent rate limiting across instances.
- ⚠️Implementing overly aggressive or static rate limits that inadvertently block legitimate users during peak times.
- ⚠️Failing to monitor rate limit effectiveness and adjust thresholds, leading to either over-blocking or under-protecting the API.
Potential Follow-up Questions
- ❓When to use sliding window counters?
- ❓How do you prevent distributed race conditions?
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