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.