CAP theorem | System design

CAP Theorem Explained 🔥 | The System Design Trade-Off Every Developer Must Understand What happens when two servers lose connection with each other? Should a distributed system reject requests to protect data consistency, or keep responding to users? This is where the CAP Theorem becomes important. 🧠 In this video, you’ll understand one of the most important concepts in System Design through simple explanations and practical examples. 📌 What you’ll learn: Consistency (C): Reads return the latest successful write. Availability (A): Every request receives a response, even if it may not contain the latest data. Partition Tolerance (P): The system continues operating despite network communication failures between nodes. Why a distributed system cannot guarantee all three properties simultaneously during a network partition. CP vs AP: Understanding the trade-off with practical examples. How the CAP Theorem influences database and distributed system design. 💡 The key insight: During a network partition, a system must trade off strong consistency or availability. Partition tolerance is essential when network failures must be handled. 🎯 Perfect for: System Design beginners | B.Tech CSE students | Backend developers | Software engineering interview preparation | Distributed systems learners 🚀 Learning System Design? Follow this playlist to understand APIs, load balancing, caching, scalability, databases, and distributed systems step by step. 💬 INTERVIEW QUESTION: During a network partition, would you prioritize consistency or availability for a banking system? What about a social media feed? 👇 Share your reasoning in the comments. #CAPTheorem #SystemDesign #DistributedSystems #SoftwareEngineering #Database