Case Study: Designing Uber

Uber's challenge is Geospatial Real-time Data. We need to find the nearest drivers to a rider every 5 seconds for millions of users.

1. Geospatial Indexing (QuadTrees / Google S2)

A normal database index cannot efficiently find "Points within a 2-mile radius." We use **QuadTrees**. A QuadTree recursively splits the map into 4 quadrants. We only search the quadrants that overlap with the rider's location, reducing the search space from millions to hundreds.

2. Real-time Communication (WebSockets)

Drivers send their GPS coordinates every few seconds via **WebSockets**. This high-frequency data is stored in **Redis (GeoSets)** for instant lookup and not persisted to a main DB immediately to save I/O.

3. The Dispatcher

The Dispatcher is an event-driven service that matches Riders to Drivers. It uses a **Ranking Algorithm** based on distance, driver rating, and traffic data. It uses a "Lock" on the driver record for 10 seconds while they accept the ride to prevent double-booking.

4. Interview Mastery

Q: "How do you handle 'Surge Pricing' calculation?"

Architect Answer: "Surge pricing is an **Analytical Problem**. We use a streaming processor like **Apache Flink** to monitor the ratio of 'Riders' to 'Drivers' in each map quadrant in 1-minute windows. If the ratio exceeds a threshold, the system publishes a 'Surge Increase' event to the Pricing service. This is a classic example of Event-Driven Stream Processing."

SYSTEM DESIGN MASTERY COMPLETE.

You are now ready to architect global-scale applications. Go build the next unicorn.