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Uber System Design

Uber is a ride-sharing platform that connects riders with drivers. Let's analyze its system design.

System Requirements​

Functional Requirements​

  • User registration and authentication
  • Real-time ride matching
  • Location tracking
  • Payment processing
  • Rating system
  • Trip history

Non-Functional Requirements​

  • High availability
  • Low latency
  • Real-time updates
  • Scalability
  • Data consistency

Architecture​

High-Level Design​

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│  Mobile     │     │  API        │     │  Services   │
│  Apps       │────▶│  Gateway    │────▶│  Layer      │
└─────────────┘     └─────────────┘     └─────────────┘
                           │                   │
                           ▼                   ▼
                    ┌─────────────┐     ┌─────────────┐
                    │  Message    │     │  Storage    │
                    │  Queue      │     │  Layer      │
                    └─────────────┘     └─────────────┘

Components​

  1. Mobile Applications

    • Rider app
    • Driver app
    • Real-time location updates
    • Push notifications

  2. API Gateway

    • Request routing
    • Authentication
    • Rate limiting
    • Load balancing

  3. Microservices

    • User Service
    • Location Service
    • Matching Service
    • Payment Service
    • Notification Service

  4. Message Queue

    • Kafka for event streaming
    • RabbitMQ for task queues
    • Real-time updates
    • Event processing

  5. Storage

    • PostgreSQL for user data
    • Redis for caching
    • MongoDB for trip data
    • Cassandra for location data

Data Models​

User​

{
  "user_id": "123",
  "type": "rider",
  "name": "John Doe",
  "email": "john@example.com",
  "phone": "+1234567890",
  "payment_methods": [
    {
      "id": "pm_123",
      "type": "credit_card",
      "last4": "4242"
    }
  ]
}

Trip​

{
  "trip_id": "t123",
  "rider_id": "123",
  "driver_id": "456",
  "status": "completed",
  "pickup_location": {
    "lat": 37.7749,
    "lng": -122.4194
  },
  "dropoff_location": {
    "lat": 37.7833,
    "lng": -122.4167
  },
  "start_time": "2023-01-01T00:00:00Z",
  "end_time": "2023-01-01T00:30:00Z",
  "fare": 25.50,
  "rating": 5
}

Key Features​

Ride Matching​

  1. Location Tracking

    • Real-time GPS updates
    • Geohashing
    • Proximity search
    • Location history

  2. Matching Algorithm

    • Driver availability
    • Distance calculation
    • Rating consideration
    • Surge pricing

Payment Processing​

  • Multiple payment methods
  • Split payments
  • Refund handling
  • Fraud detection

Scaling Strategies​

Location Service​

  • Geohashing for efficient queries
  • Sharding by region
  • Caching hot locations
  • Batch processing

Database Scaling​

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

Challenges​

  1. Real-time Updates

    • Location accuracy
    • Network latency
    • Battery consumption
    • Data consistency

  2. Matching Algorithm

    • Fairness
    • Efficiency
    • Scalability
    • Dynamic pricing

  3. Payment Processing

    • Security
    • Reliability
    • Compliance
    • Fraud prevention

Further Reading​