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 should be able to accept millions of requests per second and distribute them across multiple back-end servers.
2. **Health Checks**: Implement health checks for servers to route traffic only to healthy instances.
3. **Session Persistence**: Support sticky sessions for user sessions when necessary.
4. **Dynamic Scaling**: Automatically adjust the number of active servers based on real-time traffic.

### Non-Functional Requirements
1. **Scalability**: The load balancer should seamlessly scale horizontally to accommodate growth in traffic.
2. **Performance**: Average request latency should not exceed 100ms.
3. **Availability**: Aim for 99.99% uptime with minimal downtime for maintenance and updates.
4. **Security**: Secure communication (TLS) and authentication to prevent unauthorized access.

Assuming we need to handle 10 million requests per second during peak times:
1. **Requests per hour**: 10 million requests/s * 3600 seconds = 36 billion requests/hour.
2. **Data Size**: Assuming each request is about 1 KB, total data volume = 36 billion KB = ~33.6 TB/hour.
3. **Server Capacity**: If each server can handle 100,000 requests/second, we would need:
   - 10 million requests/s / 100,000 requests/s/server = 100 servers at peak.
4. **Traffic Growth**: With an annual growth rate of 20%, we should plan for 120 servers in the next year.

### Architecture Components
- **Client**: Frontend clients making requests.
- **Load Balancer**: A distributed load balancing layer that uses a round-robin or least-connections strategy to distribute requests.
- **Health Checker**: A service that regularly checks the health of backend servers.
- **Backend Servers**: Stateless application servers that process requests.
- **Data Store**: A distributed database (e.g., PlanetScale) for storing persistent data.

### Technology Choices
- **Load Balancer**: NGINX or Envoy for its high performance and scalability.
- **Health Checking**: Custom service using Kubernetes health checks.
- **Backend Framework**: Node.js or Go for lightweight and efficient handling of concurrent requests.
- **Database**: PlanetScale for its built-in sharding and scaling capabilities.

### Schema Design
- **Users Table**: `user_id (PK)`, `username`, `session_id`, `last_active`.
- **Sessions Table**: `session_id (PK)`, `user_id (FK)`, `timestamp`, `data`.
- **Requests Table**: `request_id (PK)`, `user_id (FK)`, `timestamp`, `response_time`, `status_code`.

### Access Patterns
- Frequent reads for user session data and stats.
- Write-heavy operations for logging requests and maintaining session persistence.

### Trade-offs in Consistency, Availability, and Performance
1. **Consistency vs. Availability**: Using eventual consistency can improve availability but may lead to stale data in user sessions. We could implement a TTL for sessions to mitigate this risk.
2. **Load Balancer Strategy**: Round-robin may be simpler but can lead to uneven load distribution. Using least-connections might improve performance but adds complexity.
3. **Health Checking Frequency**: More frequent health checks ensure only healthy instances are used but may add unnecessary load. Balancing this frequency is crucial.
4. **Session Persistence**: Sticky sessions improve user experience but limit horizontal scaling. Consider using distributed caching (e.g., Redis) for session data to alleviate this.

Architect a scalable Load Balancing Engine

PlanetScale

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