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Twitter System Design

Twitter is a social media platform that allows users to post and interact with messages known as "tweets". Let's analyze its system design.

System Requirements​

Functional Requirements​

  • Users can post tweets (140-280 characters)
  • Users can follow other users
  • Users can view their timeline
  • Users can like and retweet posts
  • Real-time notifications

Non-Functional Requirements​

  • High availability
  • Low latency
  • Scalability for millions of users
  • Data consistency
  • Real-time updates

Architecture​

High-Level Design​

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│  Web/App    │     │  API Layer  │     │  Services   │
│   Clients   │────▶│  (REST/WS)  │────▶│  Layer      │
└─────────────┘     └─────────────┘     └─────────────┘
                           │                   │
                           ▼                   ▼
                    ┌─────────────┐     ┌─────────────┐
                    │  Caching    │     │  Database   │
                    │   Layer     │     │   Layer     │
                    └─────────────┘     └─────────────┘

Components​

  1. Load Balancers

    • Distribute traffic across API servers
    • Handle SSL termination
    • Implement health checks

  2. API Servers

    • Handle user requests
    • Implement rate limiting
    • Manage authentication

  3. Services

    • Tweet Service
    • User Service
    • Timeline Service
    • Notification Service

  4. Caching

    • Redis for timeline caching
    • Memcached for user data
    • CDN for media content

  5. Database

    • MySQL for user data
    • Cassandra for tweets
    • Redis for timelines
    • MongoDB for analytics

Data Models​

User​

{
  "user_id": "123",
  "username": "john_doe",
  "name": "John Doe",
  "email": "[email protected]",
  "followers": ["456", "789"],
  "following": ["789"],
  "created_at": "2023-01-01T00:00:00Z"
}

Tweet​

{
  "tweet_id": "abc123",
  "user_id": "123",
  "content": "Hello, Twitter!",
  "created_at": "2023-01-01T00:00:00Z",
  "likes": 100,
  "retweets": 50,
  "replies": 25
}

Key Features​

Timeline Generation​

  1. Pull Model

    • User requests timeline
    • System fetches tweets from followed users
    • Merge and sort tweets
    • Return to user

  2. Push Model

    • Pre-compute timelines
    • Store in cache
    • Update on new tweets
    • Serve from cache

Real-time Updates​

  • WebSocket connections
  • Fan-out on write
  • Event-driven architecture
  • Message queues

Scaling Strategies​

Database Scaling​

  • Sharding by user_id
  • Read replicas
  • Caching layer
  • Data partitioning

Caching Strategy​

  • Multi-level caching
  • Cache invalidation
  • Write-through cache
  • Cache warming

Challenges​

  1. Timeline Generation

    • Handling celebrity users
    • Real-time updates
    • Consistency vs. Performance

  2. Data Consistency

    • Eventual consistency
    • Conflict resolution
    • Data synchronization

  3. Performance

    • Latency optimization
    • Resource utilization
    • Cost management

Further Reading​