Load balancing is a critical component in modern distributed systems that ensures high availability and reliability by distributing network traffic across multiple servers. Let's explore how it works and why it matters.

Load Balancing Overview

What is Load Balancing?

Load balancing is the process of distributing network traffic across multiple servers to ensure no single server bears too much demand. By spreading the workload, load balancers help

Prevent server overloads
Optimize resource usage
Reduce response time
Ensure high availability
Provide fault tolerance

How Load Balancers Work

Load balancers sit between client devices and backend servers, routing client requests across all available servers capable of fulfilling those requests.

Load Balancing Algorithm

Key Load Balancing Algorithms

1. Round Robin

The simplest method that distributes requests sequentially to each server in the pool. Server 1 gets the first request, Server 2 gets the second, and so on.

Example: A web application with three servers. Request 1 → Server 1, Request 2 → Server 2, Request 3 → Server 3, Request 4 → Server 1, etc.

2. Least Connections

Routes traffic to the server with the fewest active connections, which is particularly useful when requests might require varying processing times.

Example: Server 1 has 15 active connections, Server 2 has 10, and Server 3 has 5. The next request goes to Server 3.

3. IP Hash

Uses the client's IP address to determine which server receives the request. This ensures a client is consistently directed to the same server, which helps maintain session consistency.

Example: User A's IP hash maps to Server 2, so all requests from User A always go to Server 2.

4. Weighted Round Robin

Similar to Round Robin but assigns different weights to servers based on their capacity.

Example: Server 1 (weight: 5), Server 2 (weight: 3), Server 3 (weight: 2). Out of 10 requests, 5 go to Server 1, 3 to Server 2, and 2 to Server 3.

5. Least Response Time

Directs traffic to the server with the quickest response time, which indicates both availability and speed.

Example: Server 1 responds in 15ms, Server 2 in 8ms, and Server 3 in 12ms. The next request goes to Server 2.

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<h1>System Design Explained</h1>

From Beginner to Advanced

</p>

<p>

Welcome to the <strong>System Design Explained</strong> series. This series is designed

to take you from the fundamentals of system design to advanced concepts used by

companies like Google, Netflix, Amazon, Meta, Uber, and WhatsApp.

</p>

<p>

Before diving into complex architectures, it is important to understand the building

blocks that every scalable application is built upon. Every large-scale system is

simply a combination of these fundamental concepts working together.

</p>

<p>

In this overview, we will briefly introduce each concept, understand why it exists,

and see a simple real-world example. Later in the series, each topic will be explained

in depth with animations, diagrams, production use cases, and interview-focused

discussions.

</p>

<h2>Topics Covered</h2>

<h3>1. Client & Server</h3>

<p><strong>Definition:</strong> A client sends requests, while the server processes those requests and returns the required data.</p>

<p><strong>Example:</strong> When you open Instagram, your mobile app (client) requests posts from Instagram's servers, which respond with the feed.</p>

</div>

<h3>2. Scalability</h3>

<p><strong>Definition:</strong> The ability of a system to handle increasing numbers of users or requests without significant performance loss.</p>

<p><strong>Example:</strong> During a cricket World Cup match, millions of users watch live scores simultaneously without the website crashing.</p>

</div>

<h3>3. Load Balancer</h3>

<p><strong>Definition:</strong> A load balancer distributes incoming requests across multiple servers to prevent any single server from becoming overloaded.</p>

<p><strong>Example:</strong> If 10,000 users visit Amazon at the same time, requests are divided among many servers.</p>

</div>

<h3>4. Caching</h3>

<p><strong>Definition:</strong> Frequently accessed data is stored in a faster storage layer to reduce response time.</p>

<p><strong>Example:</strong> Instagram loads previously viewed profile pictures much faster because they are cached.</p>

</div>

<h3>5. Database</h3>

<p><strong>Definition:</strong> A database stores and manages application data permanently.</p>

<p><strong>Example:</strong> Your account details, passwords, and messages are stored inside databases.</p>

</div>

<h3>6. Replication</h3>

<p><strong>Definition:</strong> Multiple copies of the same database are maintained to improve availability and reliability.</p>

<p><strong>Example:</strong> If one database server fails, another replica continues serving users.</p>

</div>

<h3>7. Sharding</h3>

<p><strong>Definition:</strong> Data is divided into smaller pieces and distributed across multiple database servers.</p>

<p><strong>Example:</strong> User IDs 1–1M are stored on one database, while 1M–2M are stored on another.</p>

</div>

<h3>8. Message Queue</h3>

<p><strong>Definition:</strong> A queue allows systems to process tasks asynchronously instead of immediately.</p>

<p><strong>Example:</strong> When uploading a video to YouTube, processing happens in the background through message queues.</p>

</div>

<h3>9. Microservices</h3>

<p><strong>Definition:</strong> An application is divided into smaller independent services that communicate with each other.</p>

<p><strong>Example:</strong> Uber has separate services for rides, payments, maps, and notifications.</p>

</div>

<h3>10. CAP Theorem</h3>

<p><strong>Definition:</strong> A distributed system cannot simultaneously guarantee Consistency, Availability, and Partition Tolerance. It must prioritize two of the three.</p>

<p><strong>Example:</strong> During a network failure, some databases choose availability while others choose consistency.</p>

</div>

<h3>11. Consistent Hashing</h3>

<p><strong>Definition:</strong> A hashing technique that distributes data evenly while minimizing movement when servers are added or removed.</p>

<p><strong>Example:</strong> Distributed cache systems like Redis Cluster use consistent hashing.</p>

</div>

<p><strong>Definition:</strong> A Content Delivery Network stores copies of static content closer to users worldwide.</p>

<p><strong>Example:</strong> Netflix delivers movies from the nearest CDN server instead of a central server.</p>

</div>

<h3>13. API Gateway</h3>

<p><strong>Definition:</strong> A single entry point that routes client requests to the appropriate backend services.</p>

<p><strong>Example:</strong> A mobile app sends all requests to one API Gateway, which forwards them to user, payment, or notification services.</p>

</div>

<h3>14. Rate Limiting</h3>

<p><strong>Definition:</strong> Limits the number of requests a client can make within a specific time period.</p>

<p><strong>Example:</strong> GitHub limits API requests to prevent abuse.</p>

</div>

<h3>15. WebSockets</h3>

<p><strong>Definition:</strong> Enables a persistent two-way connection between client and server for real-time communication.</p>

<p><strong>Example:</strong> WhatsApp messages are delivered instantly using WebSockets.</p>

</div>

<h3>16. WhatsApp System Design</h3>

<p><strong>Definition:</strong> Explains how WhatsApp delivers billions of real-time messages with high reliability.</p>

<p><strong>Example:</strong> Message delivery, online status, media uploads, and notifications.</p>

</div>

<h3>17. Instagram System Design</h3>

<p><strong>Definition:</strong> Covers how Instagram serves feeds, stories, reels, and notifications to millions of users.</p>

<p><strong>Example:</strong> Feed generation, media storage, caching, and recommendation systems.</p>

</div>

<h3>18. Uber System Design</h3>

<p><strong>Definition:</strong> Explains how Uber matches riders with nearby drivers in real time.</p>

<p><strong>Example:</strong> Location tracking, ride matching, surge pricing, and payments.</p>

</div>

<h3>19. Netflix System Design</h3>

<p><strong>Definition:</strong> Demonstrates how Netflix streams high-quality videos to millions of users across the globe.</p>

<p><strong>Example:</strong> CDN usage, recommendation engine, video encoding, and playback optimization.</p>

</div>

<p>

This page serves as an overview of the essential concepts in system design. In the upcoming sections, we will explore each topic individually, starting from the basics and progressing to advanced architectures. Every concept will include detailed explanations, visual animations, real-world examples, and interview-oriented discussions to help you build a strong understanding of designing scalable software systems.

</p>

</section>

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