Design a distributed Task Scheduling System
Last updated: May 17, 2026
Quick Overview
Design a distributed task scheduling system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Doordash
May 17, 20266
10
3,774 solved
Design a distributed task scheduling system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Doordash asks this during the Technical Screen 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
- 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 run A/B tests on different model versions?
- What is your model retraining strategy?
- How would you ensure fairness and reduce bias in the model?
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Requirements
- Functional Requirements:
- Ability to schedule millions of tasks related to delivery requests across various regions.
- Support for user-defined task priorities and dependencies.
- R...
Capacity Estimation
Assuming DoorDash handles around 30 million orders per month, averaging 1 million tasks scheduled daily:
- Daily Tasks: 1,000,000
- Peak Hour Estimation: Assuming 10% of daily tasks occur ...
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