Senior LevelSystem Design & Architecture
How do you scale a Global Distributed Rate Limiter to handle 10 Million RPS?
❓ Interview Prompt: Explain scaling Rate Limiters to 10 Million RPS, Local Memory Batching, Redis Sync, Async Token Refills, and Edge WAF enforcement.
Scaling a Rate Limiter to handle 10 Million Requests Per Second without bottlenecking on central Redis clusters:
Multi-Tier Architecture:
1
Edge Enforcement: Execute coarse rate limits at CDN / WAF Edge PoPs.
2
Local Memory Batching: Gateway instances reserve a batch of 100 tokens locally in memory, syncing remaining counts asynchronously with Redis every 1 second, reducing Redis throughput load by $99\%$!
Code Implementation Example
markdown
10 Million RPS Distributed Rate Limiter Architecture:
[Client Traffic (10M RPS)] ──> [Edge WAF (Cloudflare)]
│
▼
[API Gateway Local Token Batch]
│ (Async Sync 1s)
▼
[Central Redis Cluster]💡High-throughput distributed rate limiting architecture using local memory batching.
🎯 Interview Key Takeaway
Scale distributed rate limiters to millions of RPS using edge enforcement and local memory batching to reduce Redis cluster throughput load.
#System Design#Rate Limiter#Scalability#Redis#Senior
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