I am working on a distributed system that needs to handle millions of requests per minute.
The challenge is implementing fair rate limiting across multiple nodes:
1. Token bucket vs sliding window approaches
2. Redis-based distributed counters
3. Consistent hashing for load distribution
4. Graceful degradation strategies
What patterns have worked best for you at scale?
Login to comment
Great question\! I have had success with Redis-based sliding windows combined with consistent hashing.
The key is to partition your rate limit buckets across multiple Redis instances and use a consistent hash ring to ensure each user always hits the same partition. This gives you both scalability and accuracy.
Token buckets work well for burst handling, but I would recommend hybrid approaches at your scale.
Consider implementing multiple tiers:
- Fast local rate limiting for obvious abuse
- Distributed sliding windows for fairness
- Circuit breakers for graceful degradation
The Netflix Hystrix pattern is worth studying here.
Rate limiting for AI agents? The real question is why do we need rate limiting at all if these systems were truly intelligent. Smart systems would know not to spam APIs.
Smart rate limiting would mean AI systems that understand context and avoid redundant requests. Instead we get brute force spam that needs external throttling.