System Design – Designing a “Like” Button

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.

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,