Build a multi-tenant Load Balancing Pipeline

Question

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

Codemia · Accepted Answer

### Functional Requirements 1. **Multi-Tenancy**: The system should support multiple tenants, allowing each to manage their own load balancing configurations. 2. **Request Routing**: Efficiently route incoming requests to the appropriate backend service based on tenant-specific rules. 3. **API**: Provide a RESTful API for tenants to configure and monitor their load balancing settings. 4. **Rate Limiting**: Implement tenant-specific rate limiting to prevent abuse and ensure fair resource allocation. 5. **Health Checks**: Monitor the health of backend services and automatically reroute traffic if a service becomes unhealthy. ### Non-Functional Requirements 1. **Scalability**: The system should handle millions of requests per second and scale horizontally as demand grows. 2. **Availability**: Aim for 99.99% uptime to ensure that tenants can rely on the service. 3. **Security**: Implement robust authentication and authorization mechanisms to ensure tenants can only access their configurations. 4. **Latency**: Minimize request routing latency to under 100ms. 5. **Observability**: Provide metrics and logging for monitoring system performance and tenant-specific usage. To estimate the capacity required for the load balancing system, consider the following: 1. **User Base**: Assume 100 tenants, each with an average of 10,000 active requests per second (RPS). 2. **Total RPS**: Total RPS = 100 tenants * 10,000 RPS = **1,000,000 RPS**. 3. **Requests per Day**: 1,000,000 RPS * 86,400 seconds/day = **86.4 billion requests per day**. 4. **Data Throughput**: If each request averages 1 KB of data, total data throughput would be 86.4 TB/day. 5. **Scaling**: Plan for a 3x growth factor to accommodate sudden spikes, leading to a design for **3 million RPS**. ### Components 1. **API Gateway**: Use **AWS API Gateway** to manage incoming requests and provide a single entry point for tenants' API calls. 2. **Load Balancer**: Deploy **NGINX** or **HAProxy** for initial request distribution across multiple backend instances. 3. **Service Registry**: Implement **Consul** for service discovery and health checks. 4. **Caching Layer**: Utilize **Redis** for caching frequently accessed configurations and responses to reduce backend load. 5. **Database**: Choose **PostgreSQL** for relational data storage due to its strong support for complex queries and multi-tenancy. ### Component Diagram - API Gateway -> Load Balancer -> Backend Services (Dockerized microservices) - Load Balancer <-> Caching Layer (Redis) - Backend Services <-> Database (PostgreSQL) ### Schema Design 1. **Tenant Table**: `tenants (id SERIAL PRIMARY KEY, name VARCHAR(255), created_at TIMESTAMP)` 2. **Load Balancer Rules Table**: `rules (id SERIAL PRIMARY KEY, tenant_id INT REFERENCES tenants(id), path_pattern VARCHAR(255), backend_service VARCHAR(255), priority INT)` 3. **Request Logs Table**: `request_logs (id SERIAL PRIMARY KEY, tenant_id INT REFERENCES tenants(id), request_time TIMESTAMP, status_code INT)` ### Access Patterns - Tenants will frequently query their load balancing rules, so ensure the `rules` table is indexed on `tenant_id`. - Log requests to monitor usage and performance; this can be done using time-series tables to improve query efficiency. ### Architectural Decisions 1. **Consistency vs. Availability**: Opted for eventual consistency in the caching layer to maximize availability. This allows tenants to fetch their configurations quickly while making the system resilient to failures. 2. **Rate Limiting**: Implemented rate limiting at the API Gateway level to ensure fair usage but acknowledged that some requests may be dropped during peak loads, affecting user experience. 3. **Caching Strategy**: Chose Redis for in-memory caching to minimize latency but recognized the complexity of cache invalidation strategies that might lead to stale data. 4. **Database Selection**: PostgreSQL was chosen for its robust relational capabilities, but this comes with a trade-off of slower writes compared to NoSQL alternatives, which may impact high-frequency load balancing scenarios.

Build a multi-tenant Load Balancing Pipeline

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