Design a large-scale Load Balancing Platform
Last updated: April 18, 2026
Quick Overview
Design a fault-tolerant load balancing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Lyft
April 18, 202622
8
1,393 solved
Design a fault-tolerant load balancing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Lyft asks this during the Technical Screen to assess your understanding of the full ML lifecycle. They want to see how you translate a business problem into an ML objective, design the feature pipeline, and plan for model monitoring and retraining.
What the Interviewer Expects
- Map the business problem to a concrete ML objective
- Propose reasonable features and a baseline model
- Discuss basic model evaluation metrics
- Outline a simple serving architecture
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 run A/B tests on different model versions?
- How would you handle the cold start problem?
- How would you handle a 10x increase in prediction requests?
- What is your model retraining strategy?
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Requirements Clarification
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Capacity Estimation
Estimate the scale to drive design decisions. Assume 100M DAU with an average of 10 actions per user per day = 1B requests/day ~ 12K QPS average, ~36K...
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