Distributed Rate Limiting Strategies
Issue #78: System Design Interview Roadmap
What You'll Master Today
Core Algorithms: Token Bucket burst handling, Sliding Window precision, Fixed Window simplicity
Distributed Coordination: Redis consensus patterns, local fallback strategies, split-brain prevention
Production Patterns: Netflix adaptive limiting, GitHub bucketing optimization, AWS multi-dimensional control
Hands-On Implementation: Build and test all three algorithms with performance comparison tools
When Your API Becomes the Victim of Its Own Success
Picture this: It's Black Friday, and your e-commerce API is handling 50,000 requests per second when suddenly, a rogue client starts hammering your endpoints with 10,000 requests per second. Within minutes, your carefully tuned infrastructure buckles under the load, legitimate customers can't complete purchases, and your revenue tanks. Sound familiar? This scenario plays out daily across the internet, which is why distributed rate limiting isn't just a nice-to-have feature—it's the immune system that keeps your services alive.
The Hidden Complexity Behind "Just Count Requests"
[📊 Rate Limiting Algorithms Comparison]
