Design Uber/Lyft
Design a ride-sharing application like Uber supporting real-time driver matching, location tracking, and fare calculation.
Solution Overview
Use geospatial indexing for driver matching, real-time location updates with WebSockets, and event-driven architecture.
Use geospatial indexing for driver matching, real-time location updates with WebSockets, and event-driven architecture.
Think about geospatial data structures
How do you handle millions of location updates?
Consider surge pricing algorithms
functional
- •Request ride
- •Match with driver
- •Real-time tracking
- •Fare calculation
- •Payments
- •Ratings
non functional
- •Low latency matching
- •High availability
- •Accurate ETA
- •Fraud prevention
daily rides
20M rides/day
active drivers
5M drivers
location updates
5M drivers × 1 update/4s = 1.25M updates/second
real time
geospatial
QuadTree or Geohash for proximity queries
driver location
WebSocket + Redis Pub/Sub
components
- •Ride Service
- •Matching Service
- •Location Service
- •Pricing Service
- •Payment Service
- •Notification Service
surge pricing
Based on demand/supply ratio in geographic cells
eta calculation
Graph-based routing with real-time traffic
fraud detection
ML models for fake rides, GPS spoofing
storage
Time-series DB for history, Redis for current location
ingestion
Kafka for high-throughput location updates
geospatial index
Update QuadTree as drivers move
approach
- •Find nearby drivers using geospatial index
- •Filter by availability and rating
- •Calculate ETA for each
- •Rank by ETA and other factors
- •Send request to best match
data structure
QuadTree or S2 geometry library