Design Search Infrastructure for IoT devices
Last updated: October 6, 2025
Quick Overview
Design a multi-tenant search system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Robinhood
October 6, 202510
6
2,418 solved
Design a multi-tenant search system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at Robinhood goes beyond model selection. This System Design Round question evaluates your ability to design end-to-end ML pipelines, from data collection to model serving, while considering production constraints like latency and reliability.
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
- How would you ensure fairness and reduce bias in the model?
- How would you handle a 10x increase in prediction requests?
- What is your model retraining strategy?
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Requirements Clarification
Before diving into the architecture, clarify the scope with the interviewer. For Search Infrastructure for IoT devices, key functional requirements in...
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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