What is LOAD BALANCING? ⚖️

About this video

- **Introduction to Consistent Hashing**: The discussion is about consistent hashing, a concept relevant to hashing objects, particularly useful in building scalable systems. - **Basic System Setup**: - A server runs an algorithm (e.g., facial recognition) that processes requests from users. - Users send requests to the server, which processes them and sends back responses. - **Scaling Issue**: - As the number of requests increases, a single server may become overloaded. - To handle more requests, additional servers are added, leading to the need for load balancing. - **Load Balancing**: - Load balancing involves distributing incoming requests evenly across multiple servers to prevent any single server from being overwhelmed. - A simple method is to use a hash function on request IDs to determine which server should handle each request. - **Hashing Mechanism**: - Each request ID is hashed, and the result modulo the number of servers determines the target server. - Example: With four servers, a hash result of 10 mod 4 maps to server three. - **Problem with Adding Servers**: - When new servers are added, the hash modulo changes, causing a redistribution of requests. - This can lead to significant changes in request distribution, invalidating cached data and reducing efficiency. - **Impact of Redistribution**: - Adding a fifth server disrupts the balance, requiring reassignment of many requests. - For example, redistributing requests among five servers instead of four causes considerable changes in the served buckets. - **Cache Invalidation Issue**: - Request IDs often encapsulate user information (e.g., user ID), meaning the same user will consistently map to the same server. - Cached data on servers becomes useless when server assignments change drastically, leading to inefficiencies. - **Need for Minimal Change**: - The goal is to minimize the disruption when adding new servers. - Instead of large-scale redistribution, only small adjustments should be made to maintain cache efficiency and system stability. - **Consistent Hashing Solution**: - Consistent hashing offers a solution by ensuring minimal changes when servers are added or removed. - It achieves this by taking small portions from existing servers to allocate to new servers, maintaining overall balance with minimal disruption. - **Conclusion**: - Traditional hashing methods fail in dynamic environments due to significant disruptions during scaling. - Consistent hashing provides an advanced approach to handle such scenarios efficiently, making it essential for scalable systems.

Course: System Design Playlist

**Course Description: System Design Playlist** This comprehensive course, titled "System Design Playlist," is designed to provide students with a deep understanding of system design principles and practices through real-world analogies and technical explanations. The course begins by using the analogy of running a pizza restaurant to illustrate fundamental concepts in system design, such as optimizing processes, scaling resources, and ensuring resilience. Students will learn about vertical scaling—enhancing the capabilities of existing resources—and horizontal scaling—adding more resources to distribute the workload. Through this engaging example, participants will grasp essential strategies for improving throughput, eliminating single points of failure, and implementing backup systems to maintain operational continuity. As the course progresses, students will delve into advanced topics like microservice architecture, where responsibilities within a system are clearly defined and divided among specialized teams or services. This approach allows for efficient scaling and management of different components based on their specific needs. Additionally, the course covers distributed systems, highlighting the importance of fault tolerance and quick response times by strategically placing servers closer to users. Concepts such as load balancing, which intelligently routes requests to optimize performance, and decoupling systems to enhance flexibility and adaptability, are thoroughly explored. Participants will also learn about logging and metrics to monitor system health and make informed decisions. The course wraps up by contrasting high-level system design, which focuses on overarching architectural decisions, with low-level system design, which deals with the actual coding and implementation details. By mapping business scenarios to technical solutions, students will gain insights into designing scalable, reliable, and extensible systems. Whether you're new to system design or looking to deepen your expertise, this course equips you with the knowledge and tools needed to tackle complex design challenges and develop robust systems capable of meeting diverse user demands.