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Design a High-Availability Load Balancer

AmazonSoftware Engineer · Onsite
HardSoftware EngineerOnsiteSystem Design

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This problem tests the ability to architect a highly available, scalable Layer 7 load balancer, focusing on fault tolerance, traffic distribution strategies, and system reliability.

Problem Statement

Design a high-availability (HA) Layer 7 Load Balancer capable of routing HTTP/HTTPS traffic to a large pool of backend microservices. Your system must handle millions of requests per second and ensure zero downtime during infrastructure failures.

Requirements

  • Functional: Support request routing based on URL path or header information (e.g., routing to specific service versions), and implement standard load balancing algorithms (e.g., Round Robin, Least Connections).
  • Non-Functional: Ensure high availability, horizontal scalability, and low latency overhead.
  • Constraints: The system must handle frequent backend service instance lifecycle events (spinning up/shutting down) without dropping active connections.

Deliverables

  1. High-Level Architecture: A diagram or description showing how clients connect, how the entry point is managed (e.g., DNS, Anycast), and how traffic is forwarded.
  2. Health Monitoring: Explain the mechanism for detecting backend health status and how the load balancer maintains an up-to-date view of the service pool.
  3. Failure Handling: Describe what happens if a load balancer node dies and how your system ensures the 'control plane' remains operational.
  4. Scalability Strategy: How do you scale the load balancer itself as traffic grows?

Key Considerations

  • How will you handle session persistence (sticky sessions)?
  • How do you prevent a 'thundering herd' when a backend service recovers?
  • How does the system handle TLS termination versus TLS pass-through?

Hints

Hint 1

Consider using a distributed configuration store (like etcd or Consul) to manage backend membership and propagate health updates to all load balancer nodes.

Hint 2

Think about using Anycast IP with BGP to distribute traffic across multiple global data centers to improve both availability and latency.

Hint 3

For scaling, evaluate the difference between a single-tier load balancer and a multi-tier approach involving a L4 layer (e.g., Maglev or DPDK-based) directing traffic to L7 proxies.

Follow-up questions

  • How would you implement rate limiting per API key or per client IP at the load balancer level?
  • If one of your backend services becomes slow (but not 'dead'), how would your load balancing algorithm adapt to avoid cascading failures?
  • How would you perform zero-downtime updates of the load balancer software itself?