Design a Ride-Sharing Dispatch System (Uber / Lyft)
Track drivers and dispatch rides: Geospatial indexing (Uber H3 Hexagons vs Google S2 vs Geohashes), real-time driver location updates, matching engine, and surge pricing.
01.1. Functional & Non-Functional Requirements
A Real-Time Ride-Sharing Dispatch Platform matches riders with the nearest optimal drivers, tracks real-time driver telemetry, calculates dynamic surge pricing, and manages end-to-end trip lifecycles.
Functional Requirements
- Driver Telemetry: Drivers emit GPS location (latitude, longitude, bearing, speed) every 4 seconds.
- Real-Time Ride Matching: When a rider requests a pickup, locate all available drivers within a
3-5 kmradius, compute actual road travel ETAs, and dispatch a ride offer to the best driver with a 15-second acceptance timer. - Dynamic Surge Pricing: Compute demand vs supply ratios across geographic zones and apply a price multiplier in real time.
- Trip Lifecycle State Machine: Coordinate states:
REQUESTED→OFFERED→ACCEPTED→ARRIVED→IN_PROGRESS→COMPLETED.
Non-Functional Requirements
- High Ingestion Throughput: Ingest
> 250,000 driver GPS updates/secwith sub-second propagation to rider map views. - Ultra-Low Dispatch Latency: Match and send ride offer to candidate driver in
< 1 second(p99). - High Availability & Fault Tolerance:
99.999\%uptime for dispatch operations. - Zero Double-Dispatch: A driver must never be concurrently assigned to two riders.
Uber H3 Hexagonal Geospatial Dispatch Architecture 🚗
Uber H3 Hexagonal Geospatial Dispatch Architecture 🚗
Mapping driver GPS pings into discrete H3 hexagonal cells in Redis, matching nearby riders using 1-ring neighbor scans and real road ETAs.
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