Why Adding ONE Server Broke Our Entire System 😱 | Consistent Hashing | System Design 101 Series

🚀 *Why can adding just ONE server bring down your entire cache system?* It sounds impossible—but with naive hashing (`hash(key) % N`), adding or removing a server can remap almost every key, causing massive cache misses, database overload, and increased latency. In this video, you'll learn *Consistent Hashing* from first principles using simple animations and real-world examples. 📌 In this video you'll learn:** Why `hash(key) % N` fails at scale What a cache storm is and why it happens How Consistent Hashing minimizes key redistribution Understanding the Hash Ring Why only ~1/N keys move when servers change Virtual Nodes and how they balance load Replication Factor and fault tolerance Why companies like Amazon, Netflix, Redis, Cassandra, DynamoDB, and many distributed systems rely on Consistent Hashing Whether you're preparing for **System Design interviews**, **SDE interviews**, or simply want to understand how large-scale distributed systems work, this video will give you an intuitive understanding of one of the most important backend concepts. 🔥 Subscribe for more videos on: • System Design • Distributed Systems • Backend Engineering • Caching • Databases • Networking • Scalability • Software Engineering Interviews #SystemDesign #ConsistentHashing #DistributedSystems #Caching #BackendEngineering #SoftwareEngineering #Scalability #SystemDesignInterview #Redis #Database #LoadBalancing #Microservices #HighAvailability #FaultTolerance #SoftwareArchitecture #TechExplained #Programming #Coding #Scalekraft