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Design a Ride-Sharing App

LLD Case Studies

Designing a ride-sharing service like Uber or Lyft is a complex LLD problem that touches on real-time data, different user types, and location-based services. The interviewer wants to see how you model a multi-user system and handle state transitions.

1. Clarify Requirements

  • Who are the users? Rider (customers) and Driver.
  • What is the core workflow?
    1. A Rider requests a ride from a pickup location to a destination.
    2. The system finds a nearby, available Driver.
    3. The Driver can accept or reject the ride.
    4. If accepted, the Driver picks up the Rider.
    5. The Driver completes the trip.
    6. Payment is processed.
  • How are drivers matched with riders? The system should find the closest available driver.
  • Can a rider choose the type of ride? Yes (e.g., Economy, Premium).
  • How is the fare calculated? Based on distance, duration, and ride type. A surge pricing mechanism might be mentioned as an extension.
  • What about real-time tracking? Both the rider and driver should be able to see each other's location on a map.

2. Identify the Core Classes and Objects

  • RideSharingApp: The main singleton class to manage the system.
  • User: An abstract base class.
  • Rider, Driver: Subclasses of User. A Driver will have additional properties like vehicle and availabilityStatus.
  • Vehicle: Represents a driver's car.
  • Ride: The central class representing a single trip, containing details like pickup/drop-off locations, rider, driver, and status.
  • RideStatus: An enum to track the state of a ride (REQUESTED, ACCEPTED, IN_PROGRESS, COMPLETED, CANCELLED).
  • Location: A simple class to represent a geographical point (latitude, longitude).
  • RideMatcherStrategy: An interface for the driver matching algorithm (e.g., NearestDriverStrategy).

3. Design the System - Class Diagram

RideSharingAppRideVehicleLocation<<abstract>>UserRiderDriver<<interface>>RideMatcherStrategyNearestDriverStrategy1*1**1*0..1121111

4. Key Design Decisions & Implementation Details

The Ride Class:

This class is the heart of the system, acting as a state machine for a single trip.

public class Ride {
    private final String rideId;
    private final Rider rider;
    private Driver driver;
    private final Location pickupLocation;
    private final Location dropoffLocation;
    private RideStatus status;
    
    public Ride(Rider rider, Location pickup, Location dropoff) {
        this.rideId = UUID.randomUUID().toString();
        this.rider = rider;
        this.pickupLocation = pickup;
        this.dropoffLocation = dropoff;
        this.status = RideStatus.REQUESTED;
    }

    public void accept(Driver driver) {
        this.driver = driver;
        this.status = RideStatus.ACCEPTED;
    }

    public void complete() {
        this.status = RideStatus.COMPLETED;
        // Trigger payment processing
    }
    // ... other state transition methods
}

Driver Matching Strategy:

Using a Strategy Pattern for matching allows you to easily change the algorithm later (e.g., from "nearest" to "highest rated").

public interface RideMatcherStrategy {
    Optional<Driver> findDriver(Ride ride, List<Driver> availableDrivers);
}

public class NearestDriverStrategy implements RideMatcherStrategy {
    @Override
    public Optional<Driver> findDriver(Ride ride, List<Driver> availableDrivers) {
        // Logic to calculate distance between rider's pickup location
        // and each driver's current location.
        // Return the driver with the minimum distance.
        return availableDrivers.stream()
            .min(Comparator.comparing(driver -> 
                calculateDistance(driver.getCurrentLocation(), ride.getPickupLocation())));
    }
    // ...
}

Key Discussion Points

  • Real-Time Location Updates: How does the system handle the constant stream of location data from drivers' phones? This is a system design question, but you can mention that drivers' apps would periodically push their location to a LocationService. This service would update the driver's location in a fast, in-memory database (like Redis) for quick lookups by the RideMatcherStrategy.
  • Concurrency and Race Conditions: What happens if two riders request the same, single available driver at the same time? The findAndAssignDriver process must be atomic. You need to ensure that once a driver is selected, they are marked as "busy" or "assigned" in a transactional way so they cannot be assigned to another ride simultaneously.
  • State Management: The Ride and Driver objects have critical states (RideStatus, DriverAvailability). Using enums and well-defined state transition methods (like ride.accept()) is crucial for maintaining data integrity.
  • Push vs. Pull: How is the rider notified that a driver has been found? The server should push this information to the rider's app (using WebSockets or a push notification service) rather than the app constantly polling the server. This is a more efficient design.
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