Architect a multi-tenant Ride Matching Engine

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. Multi-tenancy support for different ride-hailing companies.  
  2. Real-time ride matching based on user location, destination, and preferences.  
  3. User profiles for drivers and passengers with rating systems.  
  4. Support for various ride types (e.g., economy, luxury, shared).  
  5. Notifications for users on ride status (matched, confirmed, completed).  
  6. Admin dashboard for monitoring ride requests, matches, and system health.

- **Non-Functional Requirements:**  
  1. High availability (99.99% uptime).  
  2. Low latency (<200ms for ride matching).  
  3. Scalability to handle millions of concurrent users.  
  4. Data consistency across multiple tenants.  
  5. Secure user data handling and compliance with regulations like GDPR.

Assuming Bloomberg's ride matching engine needs to handle 1 million concurrent users at peak times (e.g., rush hour).  
- **Request Rate:**  
  1. Average of 10 requests per user per hour = 10 million requests/hour = 2,777 requests/second.  
- **Data Size:**  
  1. Each ride request has a payload of approximately 1KB (user details, pickup location, destination, etc.).  
- **Data Storage Needs:**  
  1. 10 million requests/hour * 1KB/request = 10GB/hour.  
  2. Over a month, this translates to approximately 7.2TB of data.  
- **Scaling Factor:**  
  1. Assuming a 3x scaling factor for peak usage, we need to plan for 30 million requests/hour = 8,333 requests/second.

The architecture consists of the following components:  
1. **API Gateway (AWS API Gateway):** Routes incoming requests and handles authentication.  
2. **Load Balancer (AWS ELB):** Distributes traffic across ride matching services.  
3. **Ride Matching Service (Kubernetes):** Stateless microservices that handle ride matching logic.  
4. **Database (CockroachDB):** Multi-region, distributed SQL database for strong consistency across tenants.  
5. **Caching (Redis):** Store frequently accessed data (e.g., ride requests, user profiles) to reduce database load.  
6. **Notification Service (AWS SNS):** Sends real-time updates to users via SMS or app notifications.  
7. **Monitoring & Logging (Prometheus & Grafana):** Track system health and performance metrics.

The schema design includes the following tables:  
1. **Users Table:**  
   - user_id (PK)  
   - name  
   - email  
   - phone  
   - rating  
   - tenant_id (FK)

2. **Rides Table:**  
   - ride_id (PK)  
   - user_id (FK)  
   - driver_id (FK)  
   - pickup_location  
   - destination  
   - status (pending, matched, completed)  
   - created_at

3. **Drivers Table:**  
   - driver_id (PK)  
   - user_id (FK)  
   - vehicle_type  
   - availability_status  
   - tenant_id (FK)

**Access Patterns:**  
- Frequent read operations for user profiles and ride statuses.  
- Write-heavy during peak hours as ride requests are submitted.

1. **Consistency vs. Availability (CAP Theorem):**  
   - Opted for strong consistency using CockroachDB to ensure accurate ride matching across tenants, which may introduce latency during partition tolerance scenarios.

2. **Caching Strategies:**  
   - Using Redis for caching ride requests improves performance but may lead to stale data if not invalidated properly.

3. **Operational Excellence:**  
   - Implemented a blue-green deployment strategy for zero-downtime releases and chaos engineering practices to test system resiliency under failure scenarios.

4. **Monitoring and Alerting:**  
   - SLOs set at <200ms for ride matching and automated alerts for latency spikes over 500ms to ensure proactive issue resolution.

Architect a multi-tenant Ride Matching Engine

Bloomberg

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