Architect a high-throughput Analytics Engine
Last updated: August 24, 2025
Quick Overview
Design a high-throughput analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Robinhood
August 24, 20256
8
2,185 solved
Design a high-throughput analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This ML system design question from Robinhood's System Design Round 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
- Define clear ML objectives with appropriate loss functions and metrics
- Design a comprehensive feature engineering pipeline
- Discuss model selection with trade-offs (complexity vs interpretability vs latency)
- Plan online and offline evaluation strategies including A/B testing
- Address serving infrastructure: batch vs real-time, latency requirements
- Consider data quality, labeling strategy, and feedback loops
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 is your model retraining strategy?
- How would you debug a model that works well offline but poorly online?
- How would you run A/B tests on different model versions?
- What would you do if model performance degrades over time?
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