Architect a high-throughput Task Scheduling Engine
Last updated: August 18, 2025
Quick Overview
Design a high-throughput task scheduling system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
HashiCorp
August 18, 20255
6
4,190 solved
Design a high-throughput task scheduling system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This ML system design question from HashiCorp'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 would you do if model performance degrades over time?
- How would you debug a model that works well offline but poorly online?
- How would you ensure fairness and reduce bias in the model?
- How would you run A/B tests on different model versions?
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Requirements
Functional Requirements
- Task Scheduling: The engine must schedule and manage millions of tasks per second, prioritizing tasks based on user-defined criteria.
- Monitoring and Logging: Re...
Capacity Estimation
Assuming the system needs to handle 1 million requests per second:
- Task Size: Each task is estimated to require 1 KB of storage on average.
- Total Data: 1 million requests/second * 1 KB = 1...
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