Architect a scalable Load Balancing Engine

Question

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

Codemia · Accepted Answer

### Functional Requirements:
1. **Request Handling:** The system must distribute incoming requests across multiple servers to ensure optimal resource utilization.
2. **Health Check:** Periodically check the health of each backend service to route traffic only to healthy instances.
3. **Session Persistence:** Maintain session persistence for users to ensure they are routed to the same server for the duration of their session.
4. **Rate Limiting:** Implement rate limiting to control the number of requests a user can make in a given time frame to prevent abuse.
5. **Analytics and Monitoring:** Provide real-time analytics on the load balancer's performance, including request counts, error rates, and server health.

### Non-Functional Requirements:
1. **Scalability:** The system should handle millions of requests per second (QPS) and scale horizontally to manage increased traffic.
2. **Availability:** The system should have an uptime of 99.99%, ensuring minimal disruption to services.
3. **Latency:** The response time should be under 50 milliseconds for 95% of requests.
4. **Consistency:** The system should provide eventual consistency to ensure that requests are routed based on the latest server state.

Assuming Twilio's load balancing engine needs to handle 10 million requests per second (QPS) at peak times:
- **Requests per Second (RPS):** 10,000,000 requests.
- **Average Request Size:** Assume 1 KB per request, leading to a bandwidth requirement of approximately 10 GB/s.
- **Storage Needs:** If we log every request for analytics (e.g., for 30 days), and we save 100 bytes of metadata per request:
  - Total data = 10,000,000 requests/second * 100 bytes/request * 86400 seconds/day * 30 days = ~25 TB.
- **Server Count:** If each server can handle 50,000 RPS, we would need approximately 200 servers at peak capacity (10,000,000 / 50,000).

### Component Diagram:
- **Client:** Sends requests to the load balancer.
- **Load Balancer:** Distributes incoming requests based on algorithms (round-robin, least connections).
- **Health Check Module:** Monitors backend health and updates the status in the registry.
- **Backend Services:** Multiple application servers that handle the actual processing of requests.
- **Database:** A NoSQL database (e.g., DynamoDB) for storing request logs and metadata.
- **Cache Layer:** Redis for caching frequently accessed data.
- **Monitoring & Analytics:** Prometheus for monitoring and Grafana for visualization.

### Technology Choices:
- **Load Balancer:** NGINX or HAProxy for efficient request distribution.
- **Database:** Amazon DynamoDB for scalability and low-latency access.
- **Caching:** Redis for session management and fast data retrieval.

### Schema Design:
1. **Request Log Table (DynamoDB):**
   - **request_id (PK):** Unique identifier for each request.
   - **timestamp:** Time of the request.
   - **user_id:** Identifier of the user making the request.
   - **server_id:** Identifier of the server processing the request.
   - **response_time:** Time taken to process the request.
   - **status_code:** HTTP status code returned.

2. **Session Table (Redis):**
   - **session_id (PK):** Unique session identifier.
   - **user_id:** Identifier of the user.
   - **server_id:** Identifier of the server handling the session.
   - **expiry:** Session expiration time.

### Access Patterns:
- Fast retrieval of request logs for analytics.
- Quick updates and reads for session data to maintain persistence.

### Architectural Decisions:
1. **Consistency vs Availability:** Opted for eventual consistency for request logs to ensure high availability and performance under load. This means some reads may return stale data, but the system remains responsive.
2. **Rate Limiting Implementation:** Implemented at the load balancer level to minimize backend strain, which trades off some user experience for overall system stability.
3. **Caching Strategy:** Using Redis for session data introduces complexity in managing cache invalidation but significantly reduces latency and database load.
4. **Replication and Partitioning:** Chose horizontal partitioning for the NoSQL database to ensure scalability and faster access times, accepting the trade-off of increased complexity in managing partitions.
5. **Health Check Frequency:** Decided on a balance between server health check frequency and overhead; checks every 5 seconds allow for quick recovery of unhealthy servers while minimizing additional load.

Architect a scalable Load Balancing Engine

Twilio

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