System Design – Designing a “Like” Button

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Designing a “Like” button system capable of handling a high volume of likes (increment operations) from individual users, while ensuring thread safety and real-time updates, requires a robust and scalable architecture. Let’s delve into a detailed design from a software engineering perspective:

High-Level Architecture

  1. Distributed System Architecture: To handle high volumes of traffic, use a distributed system architecture. This includes load balancers, multiple servers for handling requests, and a distributed database system.
  2. Caching Layer: Implement a caching layer (using technologies like Redis or Memcached) to handle the read-heavy nature of “like” counts. This reduces database load.
  3. Asynchronous Processing: Use message queues (like RabbitMQ or Kafka) to handle “like” updates asynchronously. This ensures that the user experience is not hindered by any processing delays.
  4. Database Design: Use a NoSQL database like Cassandra or MongoDB for horizontal scalability and faster writes. Ensure that the database schema is optimized for quick updates and retrievals.
  5. Real-Time Updates: Implement WebSockets or a similar technology to push real-time updates to clients whenever a “like” count changes.

Front-End User Interface:

  • Interactive “Like” button with a dynamic counter.
  • Real-time updates using WebSockets for a continuous connection with the server.
    API Gateway/Load Balancer:
  • Distributes incoming requests to various server instances.
  • Provides an entry point for rate limiting and user authentication.
    Application Servers:
  • Hosts the business logic for processing “Like” requests.
  • Scales horizontally to manage high request volumes.
    Caching Layer:
  • In-memory data storage (like Redis) for rapid read/write operations.
  • Reduces the load on the primary database and improves response times.
    Message Queue (MQ):
  • Buffers increment requests for batch processing.
  • Helps in decoupling the request handling from processing logic.
    Database:
  • A distributed database for long-term persistence.
  • Can be NoSQL for better write performance and scalability.

Detailed System Components

Front-End

  • Technology: JavaScript (React/Angular/Vue) for a responsive UI.
  • WebSocket Integration: Keeps a persistent connection with the server for real-time updates.
  • Throttling Client Requests: Limits the frequency of requests from the client side to reduce server load.

Load Balancer & API Gateway

  • Functionality: Distributes traffic, enforces SSL termination, and manages CORS.
  • Rate Limiting: Throttles incoming requests per user using algorithms like Token Bucket.

Application Server

  • Framework: Node.js/Java/Spring Boot for backend logic.
  • Concurrency Control: Utilizes thread-safe structures and synchronization mechanisms.
  • Session Management: Identifies unique users and sessions for personalized rate limiting.

Caching Layer

  • Technology: Redis/Memcached for in-memory data storage.
  • Cache Strategy: Implements write-through or write-back caching for like counts.
  • Consistency: Ensures cache is in sync with the database.

Message Queue

  • Choices: RabbitMQ, Kafka, or AWS SQS.
  • Batch Processing: Aggregates like increments for efficient database writes.
  • Asynchronous Processing: Allows the application server to respond to requests without waiting for DB operations.

Database

  • Type: NoSQL (like MongoDB, Cassandra) for horizontal scalability and quick writes.
  • Data Model: Simple key-value pairs or documents storing like counts and related metadata.
  • Replication & Sharding: Ensures data availability and load distribution.

Sample Code Snippets

Node.js Backend (Express Framework)

const express = require('express');
const rateLimit = require('express-rate-limit');
const WebSocket = require('ws');

const app = express();
const wsServer = new WebSocket.Server({ noServer: true });

// Rate limiter middleware
const limiter = rateLimit({
  windowMs: 15 * 60 * 1000, // 15 minutes
  max: 100 // limit each IP to 100 requests per windowMs
});

app.use(limiter);

// Like endpoint
app.post('/like', (req, res) => {
  // Logic to handle like increment
  // Send message to MQ for processing
  res.status(200).send('Like registered');
});

// WebSocket connection for real-time updates
wsServer.on('connection', socket => {
  // Send updates to client
});

app.listen(3000, () => console.log('Server running on port 3000'));

Redis Caching

const redis = require('redis');
const client = redis.createClient();

// Increment like count in cache
client.incr('like_count', (err, reply) => {
  // New like count is in 'reply'
});

// Get like count
client.get('like_count', (err, count) => {
  // Send count to client
});

Additional Considerations

  • Scalability: Design for scalability from the start. Use cloud services (AWS, Azure, GCP) for easy scaling.
  • Monitoring and Logging: Implement extensive monitoring for traffic, server performance, and error rates.
  • Security: Ensure secure data handling, especially for user identification and session management.
  • Testing: Perform load testing to ensure the system can handle peak traffic.
  • Continuous Integration/Continuous Deployment (CI/CD): Automate deployment processes for smoother iterations.
  • Documentation: Maintain clear documentation for system architecture and codebase.

This comprehensive design aims to ensure that the system can handle a high volume of “Like” button interactions in a performant, scalable, and secure manner,