Architect a geo-distributed Load Balancing Engine
Last updated: May 9, 2026
Quick Overview
Design a geo-distributed load balancing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
MongoDB
May 9, 20269
4
1,752 solved
Design a geo-distributed load balancing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
MongoDB asks this during the Onsite 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
- Design the full ML lifecycle from data collection to model monitoring
- Address cold start, exploration/exploitation, and model freshness
- Discuss multi-objective optimization and ranking systems
- Plan for model debugging, fairness, and bias mitigation
- Design the feature store and training pipeline for scale
- Address model versioning, canary deployments, and rollback strategies
- Discuss the data flywheel and long-term system evolution
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
- What would you do if model performance degrades over time?
- How would you ensure fairness and reduce bias in the model?
- How would you handle a 10x increase in prediction requests?
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Requirements
Functional Requirements
- Request Handling: The system must handle millions of requests per second, directing them to the appropriate backend services based on geographical location.
- **Geo...
Capacity Estimation
Assuming an average of 10 million requests per second (RPS) across a globally distributed user base:
- Each request requires routing logic, estimated to take 1 ms.
- This translates to 10,000 RPS per ...
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