Design a multi-tenant Ride Matching System

Question

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

Codemia · Accepted Answer

### Functional Requirements:
1. **User Profiles**: Each tenant must have a unique user profile, including preferences for ride types, payment methods, and communication preferences.
2. **Ride Requests**: Users must be able to request rides in real-time, specifying pick-up and drop-off locations.
3. **Ride Matching**: The system should match ride requests with available drivers based on proximity and other factors (e.g., vehicle type).
4. **Multi-Tenant Support**: Ensure that different tenants (e.g., ride-sharing services) can operate independently without interference.
5. **Notifications**: Users and drivers should receive real-time notifications about ride status (e.g., matched, arriving, completed).

### Non-Functional Requirements:
1. **Scalability**: The system must handle millions of requests per second, with the ability to scale horizontally.
2. **Availability**: The system should have a minimum uptime of 99.99% to ensure ride requests are processed without delays.
3. **Latency**: Response times for ride requests must be under 200 milliseconds.
4. **Security**: User data must be stored securely, with compliance to GDPR and other relevant regulations.

Assuming the following:
- **Daily Active Users (DAUs)**: 1 million users.
- **Ride Requests per User**: Average of 2 requests per day.
- **Peak QPS**: 40% of requests occur during peak hours.

### Calculations:
- **Total Daily Requests**: 1,000,000 DAUs * 2 requests = 2,000,000 requests per day.
- **Peak QPS**: 2,000,000 requests / 24 hours = 23,148 requests per second (RPS).
- **Handling Peak Load**: To account for peak hours (let's say 4 hours), we can estimate around 40% of total requests during peak, thus:
- **Peak RPS**: 23,148 * 0.4 = 9,259 requests per second.

This means we need to design the system to handle at least 10,000 RPS during peak loads.

### Component Diagram:
- **Load Balancer**: Distributes incoming ride requests across multiple application servers.
- **Application Servers**: Handle business logic for ride matching and user management, implemented in Node.js for real-time capabilities.
- **Database**: A distributed NoSQL database like Cassandra for high availability and quick write/read access.
- **Caching Layer**: Redis for in-memory caching of frequently accessed data (e.g., active drivers, user preferences).
- **Message Queue**: Kafka for handling notifications and ride status updates asynchronously.
- **Monitoring Tools**: Prometheus and Grafana for real-time monitoring and logging.

### Technology Choices:
- **Load Balancer**: NGINX or AWS Elastic Load Balancing.
- **Application Framework**: Express.js for Node.js.
- **Database**: Cassandra for its wide-column store capabilities and scalability.
- **Caching**: Redis for low-latency access.

### Schema Design:
1. **Users Table**: Stores user profiles.
   - `user_id (PK)`, `tenant_id`, `name`, `email`, `preferences`
2. **Ride Requests Table**: Stores active ride requests.
   - `request_id (PK)`, `user_id`, `pickup_location`, `dropoff_location`, `status`, `timestamp`
3. **Drivers Table**: Stores active drivers.
   - `driver_id (PK)`, `tenant_id`, `location`, `vehicle_type`, `status`
4. **Ride Matches Table**: Stores matched rides.
   - `match_id (PK)`, `request_id`, `driver_id`, `match_timestamp`

### Access Patterns:
- Frequent reads for active rides and nearby drivers, necessitating caching strategies for ride requests and driver availability.

1. **Consistency vs. Availability**: Choosing a NoSQL database like Cassandra allows for high availability and partition tolerance, but we may sacrifice strong consistency. We will implement eventual consistency where appropriate for ride matching.

2. **Caching Strategies**: Using Redis introduces a trade-off between memory usage and speed. While this improves read performance, it adds complexity regarding cache invalidation.

3. **Load Balancing**: Opting for a centralized load balancer can lead to a single point of failure, hence we must consider a distributed load balancing strategy to enhance fault tolerance.

4. **Monitoring and Alerts**: Building a robust monitoring system may introduce additional overhead. However, it's critical for early detection of issues and maintaining the required uptime.

Overall, the architectural decisions will focus on ensuring scalability and reliability while managing the trade-offs in consistency and performance.

Design a multi-tenant Ride Matching System

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