Build a high-throughput Search Pipeline
Last updated: April 2, 2026
Quick Overview
Design a high-throughput search system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
MongoDB
April 2, 202641
7
1,670 solved
Design a high-throughput search system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This ML system design question from MongoDB's Technical Screen tests your ability to think about ML systems at scale. The interviewer expects discussion of data quality, feature stores, model serving infrastructure, and A/B testing strategy.
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 ensure fairness and reduce bias in the model?
- How would you run A/B tests on different model versions?
- What would you do if model performance degrades over time?
- How would you handle a 10x increase in prediction requests?
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