Design a Load Balancing Service

Question

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

Codemia · Accepted Answer

### Functional Requirements
- **Real-time Load Balancing**: Distribute incoming requests across multiple servers based on their current load.
- **Health Monitoring**: Continuously check the health of servers to ensure requests are only sent to healthy instances.
- **Dynamic Scaling**: Ability to add or remove servers based on current load and pre-defined thresholds.
- **Session Persistence**: Ensure that user sessions are not disrupted during load balancing operations.

### Non-Functional Requirements
- **Performance**: Must handle at least 1 million requests per second with sub-50ms latency.
- **Availability**: 99.99% uptime, ensuring the load balancer is always operational.
- **Scalability**: Should effortlessly scale to accommodate up to 10 million requests per second during peak times.
- **Consistency**: Eventual consistency is acceptable, but state should be consistent at the end of each session.

For SpaceX's load balancing service:
- **Daily Request Volume**: Assuming 1 million requests per second, this translates to:
  - 1M * 60 seconds * 60 minutes * 24 hours = 86.4 billion requests per day.
- **Server Capacity**: If one server can handle 10,000 requests per second, then:
  - 1M requests / 10,000 = 100 servers needed at peak.
- **Growth Factor**: To account for future growth (say 50% increase), we should plan for at least 150 servers at peak times.

### Component Diagram
1. **Load Balancer**: A distributed load balancer using **NGINX** or **HAProxy**.
2. **Health Check Service**: A microservice that pings servers every few seconds to check health status, built with **Spring Boot**.
3. **Metrics Storage**: Use **Prometheus** for real-time metrics collection and **Grafana** for monitoring dashboards.
4. **Database**: A **NoSQL database** like **Cassandra** to store server states and request logs for high write throughput.
5. **Scaling Manager**: A service built with **Kubernetes** to manage scaling operations based on metrics.
6. **Session Store**: Use **Redis** for fast access to session data with persistence.

### Schema Design
- **Server** Table:
  - `server_id` (UUID, Primary Key)
  - `status` (Enum: 'healthy', 'unhealthy')
  - `load` (Integer)
  - `last_checked` (Timestamp)

- **Request Log** Table:
  - `request_id` (UUID, Primary Key)
  - `timestamp` (Timestamp)
  - `server_id` (UUID, Foreign Key)
  - `response_time` (Integer)
  - `status_code` (Integer)

### Access Patterns
- Frequent read operations for server health status and load, and write operations for logging requests.

### Architectural Decisions
- **Consistency vs. Availability**: Opting for eventual consistency allows for better availability during high load; however, this may lead to stale data in session handling.
- **Performance vs. Complexity**: Using a microservices architecture introduces complexity in deployment and monitoring but enhances performance through scaling individual components.
- **Dynamic Scaling vs. Cost Efficiency**: While dynamically scaling to meet demand improves performance, it raises operational costs. A hybrid approach with scheduled scaling during known peak times can balance cost and performance.
- **Canary Deployments**: Introducing new algorithms through canary deployments minimizes risk but requires additional monitoring to detect issues quickly.

Design a Load Balancing Service

SpaceX

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