Design a Ride Matching for Square/Block

Question

Design a scalable ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.

Codemia · Accepted Answer

- **Functional Requirements:**
  - **User Registration & Authentication:** Users must be able to sign up and log in securely.
  - **Ride Request Creation:** Users can create ride requests specifying pick-up and drop-off locations, time, and number of passengers.
  - **Real-time Ride Matching:** The system must match drivers to ride requests based on proximity and availability.
  - **In-app Notifications:** Users should receive real-time updates on ride status, including driver arrival and ride completion.
  - **Ride History:** Users can view past rides and their details.

- **Non-Functional Requirements:**
  - **Scalability:** The system should handle millions of ride requests and user interactions concurrently, scaling horizontally.
  - **Low Latency:** Ride matching should occur within 3 seconds to ensure user satisfaction.
  - **High Availability:** The system should maintain 99.9% uptime, even during peak traffic.
  - **Data Consistency:** Ensure that ride requests are processed consistently across distributed systems.

- **User Base:** Assume 10 million active users.
- **Request Rate:** If 10% of users request a ride at peak times, that's 1 million requests per minute.
- **Ride Matching Requests:** Assuming an average of 2 minutes for ride matching, we would have approximately 1 million requests/minute * 2 = 2 million concurrent ride matching processes during peak.
- **Data Storage Needs:** Each ride request and user profile could take around 1KB. For 10 million users, we’d need about 10 GB for user profiles alone. Ride request history could grow significantly, estimating 100 million requests leading to about 100 GB of storage.
- **Total Estimated Load:** 2 million concurrent requests implies a need for at least 2000 concurrent processing units at 1 request per second per unit.

- **Component Diagram:**
  - **API Gateway (AWS API Gateway):** Entry point for all client requests.
  - **Load Balancer (AWS ELB):** Distributes incoming requests to service instances.
  - **Microservices:**
    - **User Service:** Handles user registration, authentication, and profile management (Node.js).
    - **Ride Service:** Manages ride requests and state (Java Spring Boot).
    - **Matching Service:** Real-time algorithm for matching drivers to ride requests (Python with FastAPI).
    - **Notification Service:** Manages real-time updates (WebSocket connections).
  - **Database (Cassandra):** For ride requests and user data, ensuring high availability and partitioning.
  - **Cache Layer (Redis):** For frequently accessed data like user sessions and ride status.
  - **Queue Service (Kafka):** For decoupling services and managing ride requests and notifications.

- **User Table:**
  - `user_id (UUID) PRIMARY KEY`
  - `name (String)`
  - `email (String)`
  - `password_hash (String)`
  - `created_at (Timestamp)`

- **Ride Request Table:**
  - `request_id (UUID) PRIMARY KEY`
  - `user_id (UUID) FOREIGN KEY`
  - `pickup_location (Point)`
  - `dropoff_location (Point)`
  - `status (String)` (pending, matched, completed)
  - `driver_id (UUID) FOREIGN KEY`
  - `created_at (Timestamp)`
  - `updated_at (Timestamp)`

- **Ride History Table:**
  - `history_id (UUID) PRIMARY KEY`
  - `request_id (UUID) FOREIGN KEY`
  - `completed_at (Timestamp)`
  - `fare (Decimal)`

- **Consistency vs. Availability (CAP Theorem):**
  - Opt for eventual consistency in ride matching to improve availability and performance. Users may see slightly outdated ride statuses but will gain faster response times.

- **Data Replication Strategies:**
  - Use a multi-region setup for the database but allow for eventual consistency to ensure that users in different regions can access data quickly without waiting for synchronous updates.

- **Caching Mechanism:**
  - Implement caching for ride requests and user sessions to speed up access. However, this introduces potential staleness, which needs to be managed with appropriate cache invalidation strategies.

- **Operational Concerns:**
  - Monitor system health using SLOs and SLIs to track latency and uptime. Implement chaos engineering practices to test system resilience under failure scenarios.

Design a Ride Matching for Square/Block

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