Apr 26
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Why does rate limiting fail… even when it’s implemented correctly? In this video, we break down a real-world backend system design failure where a distributed botnet using 1000s of rotating IPs easily bypassed per-IP rate limiting — leading to traffic floods, latency issues, and a massive spike in infra costs. You’ll learn: How per-IP rate limiting actually works Why it fails against distributed botnet attacks The hidden assumption most developers make What a production-grade rate limiting strategy should include We’ll also cover practical approaches used in real systems: User/token-based throttling Behavioral pattern detection Global rate limits for system protection Edge/CDN-level filtering before traffic hits your backend If you're a backend engineer or preparing for system design interviews, this is a must-know concept. 👉 Because in real systems, failures don’t happen due to obvious bugs — they happen due to wrong assumptions at scale. #SystemDesign #BackendEngineering #RateLimiting #DistributedSystems #APIDesign #ProductionFailure #SystemDesignFailure #TechCaseStudy #Debugging #ScalabilityIssues #BotnetAttack #DDoSProtection #RateLimitBypass #TrafficSpike #HighScaleSystems #BackendDeveloper #SoftwareArchitecture #Microservices #CloudEngineering