Build a event-driven Caching Pipeline
Last updated: August 1, 2025
Quick Overview
Design a event-driven caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Optiver
System Design
Software Engineer
Optiver
August 1, 2025Software Engineer
Onsite
System Design
Hard
118
6
3,751 solved
Design a event-driven caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Optiver 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
Training pipeline and infrastructure
Feedback loops and model retraining
Monitoring and model degradation detection
Data collection and labeling strategy
Model serving and latency optimization
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 debug a model that works well offline but poorly online?
- How would you handle the cold start problem?
- How would you handle a 10x increase in prediction requests?
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Requirements
- Functional Requirements:
- An event-driven caching system to store and retrieve model predictions efficiently.
- Support millions of requests per second for both real-time and batch data...
Capacity Estimation
- Assuming Optiver handles around 10 million requests per day.
- Peak traffic could reach 1,000 requests/second during market volatility.
- If each model prediction takes 50ms to process, the ...
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