Design Load Balancing Infrastructure for microservices

Question

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

Codemia · Accepted Answer

### Functional Requirements
1. **Traffic Distribution**: The system must efficiently distribute incoming requests across multiple microservices.
2. **Health Checks**: Implement periodic health checks to ensure that only healthy instances receive traffic.
3. **Session Affinity**: Support sticky sessions for user-specific data.
4. **Rate Limiting**: Implement rate limiting to prevent abuse and ensure fair usage of services.
5. **API Gateway**: Provide a unified entry point for APIs that integrates load balancing, authentication, and routing.

### Non-Functional Requirements
1. **Fault Tolerance**: The system must remain operational even in the event of server or network failures.
2. **Scalability**: The load balancer must handle peak loads of millions of requests per minute.
3. **Latency**: Average response time should be under 50ms.
4. **Global Reach**: The system should support multi-region deployment to reduce latency for users worldwide.
5. **Monitoring & Alerts**: Implement monitoring to track performance metrics and alerts for failure conditions.

Assuming Palantir serves approximately 100 million users globally, with an average of 3 requests per user per day, we estimate:

- **Total Requests per Day**: 100 million users * 3 requests/user = 300 million requests/day.
- **Requests per Second (RPS)**: 300 million requests/day / 86400 seconds/day ≈ 3,472 RPS.
- **Peak Load Estimation**: During peak hours, assuming a 10x increase, the system should handle approximately 34,720 RPS.
- **Microservices**: If we have 10 microservices, each service needs to handle approximately 3,472 RPS under normal load and 3,472 RPS during peak load.
- **Load Balancer Redundancy**: At least 3 load balancer instances in an active-active configuration per region for fault tolerance.

### Architecture Components
- **API Gateway**: Utilizes **Kong** or **AWS API Gateway** for routing and rate limiting.
- **Load Balancers**: Employ **NGINX** for load balancing, configured in an active-active setup across multiple regions.
- **Microservices**: Deployed in **Docker** containers orchestrated by **Kubernetes** for scalability and fault tolerance.
- **Data Storage**: Use **Cassandra** for user session data, providing high availability and partition tolerance.
- **Monitoring**: Implement **Prometheus** and **Grafana** for real-time monitoring and alerting.

### Component Diagram
- Load Balancers direct traffic to microservices based on health checks and routing rules, with the API Gateway managing incoming requests.

### Schema Design
- **User Sessions Table**:
  - `user_id` (Primary Key)
  - `session_data` (JSON)
  - `created_at` (Timestamp)
  - `last_accessed` (Timestamp)
- **Rate Limiting Table**:
  - `api_key` (Primary Key)
  - `request_count` (Integer)
  - `last_request_time` (Timestamp)

### Access Patterns
- Frequent reads for user session data to maintain session state.
- Regular writes for updating request counts during rate limiting checks. This design supports efficient querying and updates.

### Architectural Decisions
- **Consistency vs. Availability**: Chose eventual consistency for user session data to ensure high availability across regions, acknowledging that some session data may be stale during replication.
- **Load Balancer Technology**: Opted for NGINX due to its lightweight nature and configurability over heavier solutions, balancing performance with ease of deployment.
- **Health Check Frequency**: Set health checks to every 5 seconds, understanding the trade-off between responsiveness to failures and unnecessary load on services.

### Operational Excellence
- **Deployment Strategy**: Implement blue-green deployments to minimize downtime during updates.
- **Chaos Engineering**: Regularly conduct chaos tests to simulate failures and ensure the system can withstand real-world issues.
- **SLOs/SLIs**: Establish SLOs for response times (<50ms for 95% of requests) and uptime (99.99%) to measure operational performance.

Design Load Balancing Infrastructure for microservices

Palantir

What the Interviewer Expects

Key Topics to Cover

How to Approach This

Possible Follow-up Questions

Practice a Similar Problem on Codemia

Sample Answer

Requirements

Functional Requirements

Capacity Estimation

Submit Your Answer

Palantir Software Engineer Interview Guide

Related Questions

Design a high-throughput Inventory Management System

Design a low-latency Rate Limiting System

Design a fault-tolerant Payment System

Design a fault-tolerant Messaging System

Design Walmart Product Search