Design Uber

What is Uber?

Uber is a ride-sharing platform that connects riders with drivers for on-demand transportation. It's similar to Lyft, Grab, or Didi. The service provides real-time matching, location tracking, dynamic pricing, and payment processing.

Real-time driver-rider matching with location tracking and dynamic pricing is what makes systems like Uber unique. By understanding Uber, you can tackle interview questions for similar ride-sharing platforms, since the core design challenges—matching algorithms, location tracking, real-time updates, and dynamic pricing—remain the same.

Functional Requirements

Core (Interview Focussed)

• Ride Request: Users can request rides with pickup and destination locations.
• Driver Matching: Match riders with nearby available drivers.
• Real-time Tracking: Track driver and rider locations in real-time.
• Dynamic Pricing: Adjust pricing based on demand and supply.

Out of Scope

• User authentication and accounts
• Payment processing and billing
• Driver onboarding and verification
• Ride history and analytics
• Mobile app specific features

Non-Functional Requirements

Core (Interview Focussed)

• Low latency: Sub-second response time for ride requests.
• High availability: 99.9% uptime during peak hours.
• Scalability: Handle millions of concurrent users.
• Accuracy: Accurate location tracking and matching.

Out of Scope

• Data retention policies
• Compliance and privacy regulations

💡 Interview Tip: Focus on low latency, high availability, and scalability. Interviewers care most about matching algorithms, location tracking, and real-time updates.

Core Entities

💡 Interview Tip: Focus on Ride, Driver, and Location as they drive matching algorithms, location tracking, and ride management.

Core APIs

Ride Management

• POST /rides { pickup_location, destination, rider_id } – Request a new ride
• GET /rides/{ride_id} – Get ride details
• PUT /rides/{ride_id}/cancel – Cancel a ride
• PUT /rides/{ride_id}/complete – Complete a ride

Driver Management

• POST /drivers/{driver_id}/location { latitude, longitude } – Update driver location
• PUT /drivers/{driver_id}/status { status } – Update driver status
• GET /drivers?location=&radius=&limit= – Find nearby drivers
• POST /drivers/{driver_id}/accept { ride_id } – Accept a ride

Location Services

• GET /location/{user_id} – Get user's current location
• POST /location/{user_id} { latitude, longitude } – Update user location
• GET /location/nearby?latitude=&longitude=&radius= – Find nearby points of interest

Pricing

• GET /pricing?location=&time= – Get current pricing for location
• POST /pricing/calculate { pickup, destination, time } – Calculate ride price
• GET /pricing/surge?location=&time= – Get surge pricing information

High-Level Design

Key Components

• Ride Service: Handle ride requests and lifecycle
• Matching Service: Match riders with drivers
• Location Service: Track and manage locations
• Pricing Service: Calculate dynamic pricing
• Real-time Service: Handle WebSocket connections and real-time updates
• Database: Persistent storage for rides, drivers, and locations

Mapping Core Functional Requirements to Components

Detailed Design

Matching Service

Purpose: Match riders with nearby available drivers.

Key Design Decisions:

• Proximity Search: Use geospatial indexing for efficient proximity search
• Matching Algorithm: Consider distance, driver rating, and availability
• Load Balancing: Distribute rides across available drivers
• Fallback Mechanisms: Handle cases with no available drivers

Algorithm: Driver-rider matching

1. Receive ride request with pickup location
2. Find nearby available drivers:
   - Query geospatial index
   - Filter by driver status
   - Consider driver rating
3. Rank drivers by:
   - Distance to pickup
   - Driver rating
   - Estimated arrival time
4. Select best driver
5. Send ride request to driver
6. If driver accepts:
   - Create ride record
   - Update driver status
   - Notify rider
7. If driver rejects:
   - Try next driver
   - Handle no drivers available

Location Service

Purpose: Track and manage real-time locations of drivers and riders.

Key Design Decisions:

• Geospatial Indexing: Use efficient data structures for location queries
• Real-time Updates: Process location updates in real-time
• Data Validation: Validate location data for accuracy
• Privacy Protection: Protect user location privacy

Algorithm: Location tracking

1. Receive location update
2. Validate location data:
   - Check coordinate accuracy
   - Verify timestamp
   - Check for outliers
3. Update location in geospatial index
4. Broadcast location update:
   - Send to relevant users
   - Update real-time maps
5. Store location history
6. Handle location privacy

Pricing Service

Purpose: Calculate dynamic pricing based on demand and supply.

Key Design Decisions:

• Demand Analysis: Analyze ride demand in different areas
• Supply Tracking: Track available drivers in each area
• Surge Pricing: Implement surge pricing during high demand
• Price Transparency: Provide clear pricing information

Algorithm: Dynamic pricing

1. Analyze current demand:
   - Count active ride requests
   - Calculate demand density
   - Consider time of day
2. Analyze current supply:
   - Count available drivers
   - Calculate supply density
   - Consider driver distribution
3. Calculate surge multiplier:
   - If demand > supply: increase price
   - If supply > demand: decrease price
   - Apply minimum and maximum limits
4. Update pricing for area
5. Notify users of price changes

Real-time Service

Purpose: Handle WebSocket connections and broadcast real-time updates.

Key Design Decisions:

• WebSocket Connections: Maintain persistent connections for real-time updates
• Message Broadcasting: Broadcast updates to relevant users
• Connection Management: Handle connection drops and reconnections
• Update Filtering: Send relevant updates to each user

Algorithm: Real-time update broadcasting

1. User connects to ride stream
2. Send current ride status to user
3. When location updates:
   - Broadcast to relevant users
   - Update real-time maps
4. When ride status changes:
   - Notify rider and driver
   - Update ride tracking
5. Handle connection drops gracefully
6. Reconnect users with missed updates

Database Design

Rides Table

Indexes:

• idx_status on (status) - Active rides
• idx_rider_id on (rider_id) - Rider rides
• idx_driver_id on (driver_id) - Driver rides

Drivers Table

Indexes:

• idx_status on (status) - Available drivers
• idx_location on (current_lat, current_lng) - Geospatial queries

Locations Table

Indexes:

• idx_user_timestamp on (user_id, timestamp) - User location history
• idx_coordinates on (latitude, longitude) - Geospatial queries

Pricing Table

Indexes:

• idx_timestamp on (timestamp) - Time-based queries
• idx_location on (location_lat, location_lng) - Geospatial queries

Scalability Considerations

Horizontal Scaling

• Ride Service: Scale horizontally with load balancers
• Matching Service: Use consistent hashing for geographic partitioning
• Location Service: Scale location tracking with distributed systems
• Database: Shard rides and drivers by geographic regions

Caching Strategy

• Redis: Cache driver locations and ride status
• CDN: Cache static content and maps
• Application Cache: Cache frequently accessed data

Performance Optimization

• Connection Pooling: Efficient database connections
• Batch Processing: Batch location updates for efficiency
• Async Processing: Non-blocking ride processing
• Resource Monitoring: Monitor CPU, memory, and network usage

Monitoring and Observability

Key Metrics

• Ride Request Latency: Average time to process ride requests
• Matching Time: Average time to match rider with driver
• Location Update Latency: Average time to process location updates
• System Health: CPU, memory, and disk usage

Alerting

• High Latency: Alert when ride processing time exceeds threshold
• Matching Failures: Alert when driver matching fails
• Location Errors: Alert when location updates fail
• System Errors: Alert on ride processing failures

Trade-offs and Considerations

Consistency vs. Availability

• Choice: Eventual consistency for location updates, strong consistency for ride status
• Reasoning: Location updates can tolerate slight delays, ride status needs immediate accuracy

Latency vs. Throughput

• Choice: Optimize for latency with real-time processing
• Reasoning: Ride-sharing requires immediate response to requests

Accuracy vs. Performance

• Choice: Use precise location data for accurate matching
• Reasoning: Accurate location tracking is critical for ride-sharing

Common Interview Questions

Q: How would you handle driver availability?

A: Use real-time status tracking, geospatial indexing, and availability management to handle driver availability efficiently.

Q: How do you ensure accurate location tracking?

A: Use multiple location sources, data validation, and real-time updates to ensure accurate location tracking.

Q: How would you scale this system globally?

A: Deploy regional ride servers, use geo-distributed databases, and implement data replication strategies.

Q: How do you handle surge pricing?

A: Use demand analysis, supply tracking, and dynamic pricing algorithms to handle surge pricing effectively.

Key Takeaways

1. Matching Algorithms: Geospatial indexing and proximity search are essential for driver-rider matching
2. Location Tracking: Real-time location updates and geospatial data structures enable accurate tracking
3. Dynamic Pricing: Demand analysis and supply tracking enable effective surge pricing
4. Scalability: Horizontal scaling and geographic partitioning are crucial for handling large-scale ride-sharing
5. Monitoring: Comprehensive monitoring ensures system reliability and performance