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Netflix System Design

Netflix is a streaming service that provides on-demand video content to millions of users worldwide. Let's analyze its system design.

System Requirements

Functional Requirements

  • User authentication and profiles
  • Video streaming
  • Content recommendations
  • Search functionality
  • User preferences and watch history

Non-Functional Requirements

  • High availability (99.99%)
  • Low latency streaming
  • Global content delivery
  • Scalability for peak loads
  • Data consistency

Architecture

High-Level Design

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│ Client │ │ API │ │ Services │
│ Apps │────▶│ Gateway │────▶│ Layer │
└─────────────┘ └─────────────┘ └─────────────┘
│ │
▼ ▼
┌─────────────┐ ┌─────────────┐
│ CDN │ │ Storage │
│ Network │ │ Layer │
└─────────────┘ └─────────────┘

Components

  1. Client Applications

    • Web browsers
    • Mobile apps
    • Smart TV apps
    • Game consoles
  2. API Gateway

    • Request routing
    • Authentication
    • Rate limiting
    • Load balancing
  3. Microservices

    • User Service
    • Content Service
    • Recommendation Service
    • Search Service
    • Playback Service
  4. Content Delivery

    • AWS CloudFront
    • Netflix Open Connect
    • Edge servers
    • Caching strategy
  5. Storage

    • S3 for video storage
    • Cassandra for user data
    • Redis for caching
    • Elasticsearch for search

Data Models

User Profile

{
"user_id": "123",
"email": "[email protected]",
"profiles": [
{
"profile_id": "p1",
"name": "John",
"preferences": {
"language": "en",
"maturity_level": "PG-13"
},
"watch_history": [
{
"content_id": "movie123",
"timestamp": "2023-01-01T00:00:00Z",
"progress": 3600
}
]
}
]
}

Content

{
"content_id": "movie123",
"title": "Example Movie",
"type": "movie",
"genres": ["Action", "Adventure"],
"languages": ["en", "es"],
"qualities": ["SD", "HD", "4K"],
"segments": [
{
"segment_id": "s1",
"quality": "HD",
"url": "https://cdn.netflix.com/movie123/HD/s1"
}
]
}

Key Features

Video Streaming

  1. Adaptive Bitrate Streaming

    • Multiple quality levels
    • Dynamic switching
    • Bandwidth detection
    • Buffer management
  2. Content Delivery

    • Edge caching
    • Pre-fetching
    • Regional distribution
    • Load balancing

Recommendation System

  • Collaborative filtering
  • Content-based filtering
  • Machine learning models
  • A/B testing

Scaling Strategies

Content Delivery

  • Global CDN network
  • Edge servers
  • Caching at multiple levels
  • Predictive pre-loading

Database Scaling

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

Challenges

  1. Content Delivery

    • Bandwidth management
    • Quality of service
    • Regional restrictions
    • Cost optimization
  2. Personalization

    • Real-time recommendations
    • User privacy
    • Data consistency
    • Performance
  3. Global Scale

    • Regional compliance
    • Content licensing
    • Network latency
    • Infrastructure costs

Further Reading