Uber

High-Level Architecture

Core Components

1. Dispatch System

Dispatch Features:

• Real-time driver availability
• Multi-factor matching algorithm
• Batched dispatch for efficiency
• Fallback and retry mechanisms
• Technologies: Golang, Ringpop, Redis

2. Location Service (H3 & Geospatial)

H3 Indexing Benefits:

• Efficient neighbor lookups
• Hierarchical aggregation
• Uniform cell shapes
• Fast geospatial queries
• Edge case handling at boundaries

3. Pricing Engine (Surge Pricing)

Surge Pricing Algorithm:

• Real-time supply/demand analysis per H3 cell
• Historical pattern recognition
• Event-based surge triggers
• Price elasticity modeling
• Regulatory compliance caps

4. ETA Prediction Service

5. Payment Service

6. Trip Lifecycle Management

Data Architecture

1. Schemaless (MySQL Sharding)

2. Cassandra (Location & Events)

3. Redis (Real-time State)

4. Kafka (Event Streaming)

Real-time Architecture

1. Ringpop (Consistent Hashing)

2. Real-time Location Tracking

Scalability & Performance

1. City-based Isolation

2. Performance Optimization

Safety & Security

1. Safety Features

2. Security Architecture

Monitoring & Observability

1. Metrics & Dashboards

Deployment and DevOps

1. Continuous Deployment Pipeline

• uDeploy: Uber's internal deployment system
• Canary releases: City-level progressive rollout
• Feature flags: LaunchDarkly for controlled releases
• Automated rollback: Metric-triggered reversal

2. Infrastructure as Code

• Terraform: Infrastructure provisioning
• Kubernetes: Container orchestration (Peloton)
• Helm charts: Service deployment templates

3. Chaos Engineering

• City-level drills: Simulated city outages
• Service injection: Random service failures
• Database failover: Primary/replica switches
• Network partition: Cross-datacenter latency

Analytics and Machine Learning

1. ML Platform (Michelangelo)

2. ML Use Cases

• ETA Prediction: Route time estimation with traffic
• Surge Pricing: Dynamic pricing based on demand
• Fraud Detection: Payment and account fraud
• Driver Matching: Optimal driver-rider pairing
• Demand Forecasting: Supply positioning

Cost Optimization

1. Infrastructure Cost Distribution

2. Cost Optimization Strategies

Future Architecture Considerations

1. Emerging Technologies

Conclusion

Uber's architecture demonstrates expertise in building real-time, location-based services at massive global scale. The system successfully manages:

• Real-time Matching: Sub-second driver-rider matching
• Global Scale: 10,000+ cities, millions of trips daily
• Dynamic Pricing: Real-time supply/demand balancing
• High Availability: 99.99% uptime with graceful degradation
• Safety: Comprehensive safety features for riders and drivers

Key Architectural Principles:

1. City-based Isolation

   • Independent scaling per city
   • Fault isolation boundaries
   • Regulatory compliance
   • Local optimization

2. Real-time Systems

   • H3 hexagonal grid for geospatial
   • Ringpop for consistent hashing
   • Kafka for event streaming
   • Redis for real-time state

3. Data Architecture

   • MySQL Schemaless for flexibility
   • Cassandra for time-series data
   • Redis for hot data
   • HDFS for analytics

4. Reliability

   • Circuit breakers
   • Graceful degradation
   • Multi-region deployment
   • Automated recovery

5. Machine Learning

   • ETA prediction
   • Surge pricing
   • Fraud detection
   • Demand forecasting

The platform continues to evolve with autonomous vehicles, drone delivery, and multimodal transportation, while maintaining the core principles of reliability and real-time responsiveness.

This architecture represents Uber's known systems and best practices. Actual implementation details may vary.