Design a low-latency Load Balancing System
Last updated: September 28, 2025
Quick Overview
Design a low-latency load balancing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Compass
September 28, 202523
13
3,461 solved
Design a low-latency load balancing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This is a common system design question asked during Technical Screen at Compass. The interviewer expects you to demonstrate your ability to design large-scale distributed systems, make well-reasoned trade-offs, and communicate your thought process clearly. Compass values engineers who can think about scalability from day one.
What the Interviewer Expects
- Drive the design discussion proactively with minimal interviewer guidance
- Perform detailed capacity estimation and use it to inform design decisions
- Design for global scale with multi-region deployment and data consistency
- Deep dive into 2-3 critical components with implementation-level detail
- Address complex trade-offs: CAP theorem, eventual consistency, conflict resolution
- Discuss operational excellence: deployment strategy, chaos engineering, SLOs/SLIs
- Propose a phased rollout plan from MVP to full-scale system
Key Topics to Cover
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How would you optimize costs as the system scales?
- How would you migrate from a monolithic to a microservices architecture?
- What monitoring and alerting would you set up on day one?
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Requirements
Functional Requirements
- Request Handling: The system must handle millions of incoming requests per second, distributing them evenly across multiple backend servers.
- Health Checks: Im...
Capacity Estimation
Assuming Compass aims to handle approximately 10 million requests per second (RPS) at peak.
- Request Size: Assuming an average request size of 1 KB.
- Total Data Ingestion: 10 million RPS * ...
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