Rate limiting prevents individual clients from overwhelming shared services. Without limits, abusive or buggy clients can degrade service for everyone. Well-designed rate limiting balances protection with usability, providing clear feedback when limits are reached.
Algorithm Selection
Token bucket algorithms allow bursting while enforcing average rates. Sliding window counters provide smoother limiting without burst allowances. Fixed window approaches are simpler but vulnerable to boundary attacks. Choose algorithms matching your traffic patterns and fairness requirements.
- Implement rate limits at multiple levels—per user, per API key, and global
- Return clear rate limit headers showing remaining quota and reset times
- Use 429 status codes with Retry-After headers for proper client handling
- Consider different limits for different endpoints based on cost
- Implement gradual degradation rather than hard cutoffs where appropriate
Distributed Rate Limiting
Multi-instance deployments require coordinated rate limiting. Redis provides fast, distributed counters for rate limit tracking. Eventually consistent approaches reduce latency but allow brief limit overruns. Evaluate tradeoffs between accuracy and performance for your use case.