This project is a simplified system design implementation of a ride-hailing platform inspired by Uber. It focuses on real-time matching between riders and drivers, geospatial indexing, and dispatch logic under dynamic conditions.
The goal is to understand how real-world, location-based, real-time matching systems are designed and how they scale under heavy concurrent demand.
Ride-hailing systems must match riders with nearby available drivers in real time while considering location, availability, and system load. These systems must handle constantly changing state, high concurrency, and geographic constraints while maintaining low latency.
This project explores how to design a simplified version of a ride-matching system that supports driver tracking, ride requests, and efficient matching.
- Understand real-time matching systems
- Learn geospatial indexing and proximity search concepts
- Explore dispatch and assignment algorithms
- Understand state changes in real-time systems
- Learn tradeoffs between accuracy and latency in matching
- Explore scalability challenges in location-based services
- Build intuition for dynamic distributed systems
- Request a ride with pickup and drop-off location
- View ride status (requested, matched, in-progress, completed)
- Register driver availability
- Update real-time location
- Accept or decline ride requests
- Match riders to nearest available drivers
- Handle driver availability changes
- Reassign rides if needed (conceptual)
- Store and update driver locations
- Query nearby drivers
- Support geospatial proximity search (conceptual or simplified grid model)
- Handles ride requests
- Tracks ride lifecycle
- Maintains driver availability and location state
- Sends updates to matching system
- Core logic for pairing riders and drivers
- Uses proximity-based selection strategies
- Maintains real-time driver positions
- Supports fast spatial queries
- High-frequency state updates (driver location streaming)
- Tradeoffs between accuracy and latency in matching
- Handling race conditions in driver assignment
- Scaling geospatial queries
- Preventing double-booking of drivers
- System behavior under high demand spikes
- Distributed Systems (real-time coordination layer)
- Rate Limiter (prevent ride spam or abuse)
- Caching system (hot driver/location optimization)
- Message Queue (event-driven location updates)
- Load Testing (high-concurrency matching simulation)
🟡 Planned