Design a ride-hailing platform like Uber

You are asked to design a large-scale ride-hailing platform similar to Uber.

Requirements

Functional requirements

• Riders can:
  • Sign up, log in, and manage their profile.
  • Request a ride from a pickup location to a destination.
  • See nearby available drivers and estimated time of arrival (ETA).
  • See an estimated fare before confirming the ride.
  • Track the driver in real time once a ride is accepted.
  • Pay via stored payment methods (e.g., credit card).
  • Rate and review drivers after the ride.
• Drivers can:
  • Sign up, go through onboarding, and register their vehicle.
  • Go online/offline to indicate availability.
  • Receive ride requests and accept/decline them.
  • Navigate to the rider and then to the destination.
• The system should:
  • Automatically match riders with nearby available drivers.
  • Support surge pricing based on demand and supply in a geographic area.
  • Store trip history (routes, prices, timestamps) for both riders and drivers.
  • Send real-time notifications (push/SMS) about ride status updates.

Non-functional requirements

• High availability: Service should remain usable even if individual components fail.
• Low latency:
  • Rider should see ETA and match results quickly (e.g., within a few hundred ms).
  • Location updates should appear near real time.
• Scalability:
  • Support millions of daily active users in multiple cities/regions.
• Reliability and consistency:
  • Payments and trip records must not be lost or double charged.
  • Some data (e.g., live location) can be eventually consistent.
• Security and privacy:
  • Protect user data and payment information.

Task

Design the backend system for this platform. In your answer, cover:

1. High-level architecture :
   • Main services/components and how they interact (e.g., client apps, API gateway, core services).
2. Data model and storage :
   • Key entities (users, drivers, vehicles, trips, locations, payments) and a rough schema.
   • Choice of databases (SQL / NoSQL / in-memory) and what to store where.
3. Ride request and matching flow :
   • How a rider request flows through the system.
   • How to find and choose a nearby driver efficiently.
4. Real-time location tracking :
   • How drivers send location updates.
   • How the system stores/serves this for ETAs and maps.
5. Scalability and partitioning :
   • How to scale by city/region or other keys.
   • How to handle hotspots during rush hour.
6. Reliability and fault tolerance :
   • How to handle failures of individual services or data centers.
   • How to ensure payments and trip records are durable.
7. Additional considerations (briefly):
   • Surge pricing approach.
   • Monitoring, logging, and key metrics you would track.