Uber 🚗 Uber serves over 130 million monthly active users across 10,000+ cities, processing millions of rides daily. This document outlines the comprehensive architecture that enables real-time ride matching, dynamic pricing, and seamless transportation at massive scale.
Ringpop
Ringpop
Validation
Fare Estimate
Product Selection
Supply Management
Demand Prediction
Optimization
Dispatch Decision
Rating Match
Vehicle Type
Notify Driver
Driver Accept
Trip Assignment
Validation
Fare Estimate
Product Selection
Supply Management
Demand Prediction
Optimization
Dispatch Decision
Rating Match
Vehicle Type
Notify Driver
Driver Accept
Trip Assignment
Dispatch Features:
Real-time driver availability
Multi-factor matching algorithm
Batched dispatch for efficiency
Fallback and retry mechanisms
Technologies: Golang, Ringpop, Redis
Driver GPS
Batch Updates
H3 Indexing
Resolution Levels
Neighbor Search
Hierarchical
Nearby Search
Geofence Check
Time Series
Driver GPS
Batch Updates
H3 Indexing
Resolution Levels
Neighbor Search
Hierarchical
Nearby Search
Geofence Check
Time Series
H3 Indexing Benefits:
Efficient neighbor lookups
Hierarchical aggregation
Uniform cell shapes
Fast geospatial queries
Edge case handling at boundaries
Supply/Demand Ratio
Surge Multiplier
Upfront Pricing
Price Transparency
Historical Patterns
Event Surge
ML Pricing Model
Price Elasticity
Market Equilibrium
Supply/Demand Ratio
Surge Multiplier
Upfront Pricing
Price Transparency
Historical Patterns
Event Surge
ML Pricing Model
Price Elasticity
Market Equilibrium
Surge Pricing Algorithm:
Real-time supply/demand analysis per H3 cell
Historical pattern recognition
Event-based surge triggers
Price elasticity modeling
Regulatory compliance caps
Pickup ETA
Driver Arrival
Road Network
Contraction Hierarchies
Turn Costs
Historical Traffic
Real-time Traffic
Incidents
Feature Engineering
Gradient Boosting
Ensemble Model
Calibration
Pickup ETA
Driver Arrival
Road Network
Contraction Hierarchies
Turn Costs
Historical Traffic
Real-time Traffic
Incidents
Feature Engineering
Gradient Boosting
Ensemble Model
Calibration
Credit/Debit Card
Digital Wallet
Corporate Account
Authorization
Driver Payout
Payment Processor
Currency Handling
Fare Calculation
Platform Fees
Tax Calculation
Credit/Debit Card
Digital Wallet
Corporate Account
Authorization
Driver Payout
Payment Processor
Currency Handling
Fare Calculation
Platform Fees
Tax Calculation
Arriving
Pickup Event
Dropoff Event
Route Data
Fare Data
Location History
Lost & Found
Arriving
Pickup Event
Dropoff Event
Route Data
Fare Data
Location History
Lost & Found
Schema Migrations
Schema Versioning
Rollback Support
City-based Sharding
User-based Sharding
Trip-based Sharding
Shard Router
Schema Migrations
Schema Versioning
Rollback Support
City-based Sharding
User-based Sharding
Trip-based Sharding
Shard Router
Location Keyspace
Event Keyspace
Analytics Keyspace
driver_locations
city_supply
Time Range Query
Partition Key
Clustering Key
Location Keyspace
Event Keyspace
Analytics Keyspace
driver_locations
city_supply
Time Range Query
Partition Key
Clustering Key
Supply Cluster
Session Cluster
Geo Cluster
GEOADD/GEORADIUS
Online Drivers
Rate Limiting
Supply Cluster
Session Cluster
Geo Cluster
GEOADD/GEORADIUS
Online Drivers
Rate Limiting
location-updates
payment-events
analytics-events
Driver App
Dispatch Service
Payment Service
ETA Service
Analytics
Real-time Processing
location-updates
payment-events
analytics-events
Driver App
Dispatch Service
Payment Service
ETA Service
Analytics
Real-time Processing
Hash Function
Virtual Nodes
Request Forwarding
City Sharding
Driver Sharding
Trip Sharding
Failure Detection
Rebalance
Hash Function
Virtual Nodes
Request Forwarding
City Sharding
Driver Sharding
Trip Sharding
Failure Detection
Rebalance
ETA Service Kafka Redis Location Service API Gateway Driver ETA Service Kafka Redis Location Service API Gateway Driver loop [Every 4 seconds] Batched for efficiency during trips Send GPS Location Process Location Update Driver Position Publish Location Event Consume Location Update ETA Predictions ETA Service Kafka Redis Location Service API Gateway Driver ETA Service Kafka Redis Location Service API Gateway Driver loop [Every 4 seconds] Batched for efficiency during trips Send GPS Location Process Location Update Driver Position Publish Location Event Consume Location Update ETA Predictions
New York City
Los Angeles
Dispatch Service
Supply Service
Surge Service
Fault Isolation
Independent Scaling
Low Latency
Compliance
New York City
Los Angeles
Dispatch Service
Supply Service
Surge Service
Fault Isolation
Independent Scaling
Low Latency
Compliance
Batch GPS Updates
Connection Pooling
Async Processing
Circuit Breaker
Multi-tier Cache
Denormalization
Compression
Dispatch Latency
Location Update
Batch GPS Updates
Connection Pooling
Async Processing
Circuit Breaker
Multi-tier Cache
Denormalization
Compression
Dispatch Latency
Location Update
Trusted Contacts
Emergency Button
Background Check
Face Verification
Speed Alerts
Fatigue Detection
GPS Tracking
Route Deviation
Audio Recording
Crash Detection
Fraud Detection
Content Moderation
Trusted Contacts
Emergency Button
Background Check
Face Verification
Speed Alerts
Fatigue Detection
GPS Tracking
Route Deviation
Audio Recording
Crash Detection
Fraud Detection
Content Moderation
Biometric
Device Binding
Encryption at Rest
Encryption in Transit
PII Masking
Rate Limiting
OAuth Scopes
Audit Logging
DDoS Protection
Biometric
Device Binding
Encryption at Rest
Encryption in Transit
PII Masking
Rate Limiting
OAuth Scopes
Audit Logging
DDoS Protection
Trips/Minute
Completion Rate
Cancellation Rate
DB Connections
Anomaly Detection
Trips/Minute
Completion Rate
Cancellation Rate
DB Connections
Anomaly Detection
main feature-branch Feature Dev Code Changes Unit Tests Integration Tests Build & Package Canary Deploy Production Rollout main feature-branch Feature Dev Code Changes Unit Tests Integration Tests Build & Package Canary Deploy Production Rollout
Canary Deployment
Monitor Metrics
Progressive Rollout
Canary Deployment
Monitor Metrics
Progressive Rollout
uDeploy : Uber's internal deployment systemCanary releases : City-level progressive rolloutFeature flags : LaunchDarkly for controlled releasesAutomated rollback : Metric-triggered reversal
Terraform : Infrastructure provisioningKubernetes : Container orchestration (Peloton)Helm charts : Service deployment templates
City Outage
Service Kill
Network Chaos
DB Failover
City Outage
Service Kill
Network Chaos
DB Failover
City-level drills : Simulated city outagesService injection : Random service failuresDatabase failover : Primary/replica switchesNetwork partition : Cross-datacenter latency
Trip Data
User Data
Driver Data
Market Data
Feature Store
Real-time Features
Distributed Training
Model Evaluation
Model Registry
Online Serving
Streaming
Trip Data
User Data
Driver Data
Market Data
Feature Store
Real-time Features
Distributed Training
Model Evaluation
Model Registry
Online Serving
Streaming
ETA Prediction : Route time estimation with trafficSurge Pricing : Dynamic pricing based on demandFraud Detection : Payment and account fraudDriver Matching : Optimal driver-rider pairingDemand Forecasting : Supply positioning
35% 25% 15% 10% 10% 5% Uber Infrastructure Cost Distribution Compute (Containers, VMs) Database & Storage Mapping & Location Network & CDN Machine Learning Monitoring & Observability 35% 25% 15% 10% 10% 5% Uber Infrastructure Cost Distribution Compute (Containers, VMs) Database & Storage Mapping & Location Network & CDN Machine Learning Monitoring & Observability
Spot Instances
Reserved Capacity
Container Efficiency
Tiered Storage
Compression
Request Batching
Compression
Regional Processing
Spot Instances
Reserved Capacity
Container Efficiency
Tiered Storage
Compression
Request Batching
Compression
Regional Processing
Self-Driving Cars
Sensor Fusion
HD Maps
Simulation
Uber Air
Micromobility
AI Dispatch
Predictive Demand
Real-time ML
Federated Learning
Quantum Optimization
Self-Driving Cars
Sensor Fusion
HD Maps
Simulation
Uber Air
Micromobility
AI Dispatch
Predictive Demand
Real-time ML
Federated Learning
Quantum Optimization
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 matchingGlobal Scale : 10,000+ cities, millions of trips dailyDynamic Pricing : Real-time supply/demand balancingHigh Availability : 99.99% uptime with graceful degradationSafety : Comprehensive safety features for riders and drivers
City-based Isolation
Independent scaling per city
Fault isolation boundaries
Regulatory compliance
Local optimization
Real-time Systems
H3 hexagonal grid for geospatial
Ringpop for consistent hashing
Kafka for event streaming
Redis for real-time state
Data Architecture
MySQL Schemaless for flexibility
Cassandra for time-series data
Redis for hot data
HDFS for analytics
Reliability
Circuit breakers
Graceful degradation
Multi-region deployment
Automated recovery
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.
Masst Docs
