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Design a Ride-sharing Service (like Uber/Lyft)

Build a scalable ride-sharing platform with real-time matching, geospatial indexing, GPS tracking, dynamic pricing, and global multi-city support.

System Requirements

Functional Requirements

  • Real-time driver-passenger matching
  • GPS location tracking and updates
  • Route optimization and navigation
  • Dynamic pricing (surge pricing)
  • Trip booking and management
  • Payment processing and billing

Non-Functional Requirements

  • Handle 10M+ daily rides
  • Sub-5 second matching time
  • 99.9% availability
  • Support 1M concurrent users
  • Real-time location updates (1-5 seconds)
  • Global multi-city deployment

Geospatial Matching Strategies

QuadTree-based Matching

O(log n) queryOptimal for dense urban areas

Spatial indexing using recursive subdivision of geographic space

Pros

  • • Fast spatial queries
  • • Dynamic updates
  • • Memory efficient

Cons

  • • Complex rebalancing
  • • Hotspot issues
  • • Tree depth variations

Best For

Optimal for dense urban areas

Geohash Grid Matching

O(1) queryGood for uniform coverage

Fixed-size grid cells using geohash for location bucketing

Pros

  • • Simple implementation
  • • Consistent performance
  • • Easy sharding

Cons

  • • Boundary issues
  • • Fixed resolution
  • • Uneven distribution

Best For

Good for uniform coverage

Google S2 Cells

O(log n) queryGlobal scale applications

Hierarchical spherical geometry for precise location indexing

Pros

  • • Accurate on sphere
  • • Multi-resolution
  • • No boundary issues

Cons

  • • Complex implementation
  • • Higher memory
  • • Learning curve

Best For

Global scale applications

System Architecture

Mobile Apps → API Gateway → Load Balancer
↓ ← Location Service (QuadTree/Geohash)
Matching Engine → Driver Assignment → Trip Service
↓ ← Pricing Service → Payment Gateway
Real-time Updates → Push Notifications → Analytics
↓
Database Cluster (Sharded) → Message Queue (Kafka)

Location Service

  • GPS tracking
  • Location indexing
  • Proximity search
  • Route calculation

Matching Engine

  • Driver-rider pairing
  • Optimization algorithms
  • Availability checking
  • Assignment logic

Trip Management

  • Booking workflow
  • Status tracking
  • Route monitoring
  • Completion handling

Pricing Service

  • Base fare calculation
  • Surge pricing
  • Distance/time pricing
  • Promotions handling

Capacity Estimation

Traffic Patterns & Load Distribution

User Types
20%Drivers
80%Riders
Peak vs Average
100%Average
300%Peak Hour
Request Types
10%Booking
90%Location Updates

Scale Metrics

Daily Active Users
15M drivers, 35M riders
50M+
Daily Rides
Peak: 2K rides/second
10M+
Location Updates
Every 4 seconds per driver
1B/hour
Matching Time P95
SLA: < 5 seconds
4.2s

Infrastructure Scale

API Servers
5000+ servers globally
Location Cache
200TB Redis cluster
Database Storage
50TB primary + replicas

Database Schema

users

user_id (UUID, Primary Key) email phone_number name user_type (driver/rider) registration_date status (active/inactive/banned) rating profile_image_url

trips

trip_id (UUID, Primary Key) rider_id (Foreign Key) driver_id (Foreign Key) status (requested/matched/started/completed) pickup_lat_lng dropoff_lat_lng estimated_fare actual_fare created_at completed_at

driver_locations

driver_id (Primary Key) latitude longitude geohash bearing speed accuracy timestamp status (online/offline/busy) vehicle_type

pricing_zones

zone_id (Primary Key) city_id zone_polygon (geometry) base_fare per_mile_rate per_minute_rate surge_multiplier updated_at

Advanced Design Considerations

Dynamic Pricing Algorithm

Surge Pricing Factors:
  • • Supply/demand ratio in real-time
  • • Historical patterns and seasonality
  • • Special events and weather conditions
  • • Competitor pricing and market dynamics

Matching Optimization

Optimization Criteria:
  • • Minimize pickup distance and ETA
  • • Maximize driver utilization
  • • Balance supply across zones
  • • Consider driver and rider ratings

Global Scaling

• Multi-region deployment
• City-specific configurations
• Regulatory compliance
• Currency and payment methods

Safety & Security

• Real-time trip monitoring
• Emergency assistance features
• Driver background checks
• Fraud detection systems

Fault Tolerance

• Service mesh architecture
• Circuit breakers
• Graceful degradation
• Data replication strategies

Practice Questions

1

Design the real-time location tracking system. How do you handle 15M drivers updating location every 4 seconds?

2

Implement the matching algorithm. How do you optimize for pickup time, driver utilization, and rider satisfaction?

3

Design dynamic pricing system. How do you calculate surge pricing in real-time based on supply and demand?

4

Handle global scaling. How do you deploy across multiple cities with different regulations and requirements?

5

Design fault tolerance. How do you ensure the system works during network partitions or service failures?