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Design a Shared-Ride Matching and Trip Service

WaymoSoftware Engineer · Onsite
MediumSoftware EngineerOnsiteSystem Design

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This problem tests the ability to architect a low-latency geospatial matching system that handles concurrency, real-time vehicle tracking, and the complex state management required for shared-ride optimization.

System Design: Shared-Ride Matching Service

Design a backend system for a shared-ride service (similar to Uber Pool or Waymo Shared) where passengers can request rides that may be combined with other passengers going in a similar direction.

Functional Requirements

  1. Ride Request: Passengers specify origin, destination, and seat count.
  2. Matching: The system must identify available vehicles that can accommodate the request without violating existing passenger arrival time SLAs.
  3. Booking: Atomic reservation of a seat and a driver; the ride state must transition correctly (Requested -> Matched -> PickingUp -> EnRoute -> DroppedOff).
  4. Tracking: Provide real-time vehicle location updates to clients.
  5. Cancellation: Handle mid-trip cancellations and seat re-allocation.

Non-Functional Requirements

  • Latency: Ride matching must occur within 2 seconds.
  • Consistency: Avoid double-booking seats; handle concurrent requests for the same vehicle.
  • Scalability: Support 10,000 requests per second and millions of active drivers.
  • Privacy: Ensure location data is masked or obscured according to platform policies.

Scope of Response

  • Define the high-level architecture and communication patterns (e.g., gRPC, WebSockets, Pub/Sub).
  • Detail the geospatial storage strategy (e.g., Geohashes, Quadtrees, or specialized spatial databases).
  • Explain the matching algorithm logic and how it accounts for constraints (capacity, detour limits).
  • Describe the data model for the ride state machine and how concurrency is managed during booking.

Hints

Hint 1

Use a geospatial indexing service to narrow down candidate vehicles to a manageable subset based on proximity before applying complex route-insertion heuristics.

Hint 2

Implement a distributed locking mechanism or a reservation pattern on the vehicle's state to prevent over-booking when multiple requests target the same vehicle simultaneously.

Hint 3

Consider an event-driven architecture using Kafka or similar queues to decouple trip state transitions from real-time tracking updates.

Follow-up questions

  • How would you handle a scenario where a driver goes offline or loses connectivity while holding active passengers?
  • If you have to optimize for global throughput (total trips served) versus individual wait times, how would you adjust the matching algorithm?
  • How would you store and query historical ride data for long-term analytics and driver behavior patterns while maintaining GDPR-level privacy?