Architect a real-time Data Pipeline Engine
Last updated: April 15, 2026
Quick Overview
Design a real-time data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Doordash
April 15, 20260
8
2,932 solved
Design a real-time data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Doordash asks this during the Onsite to assess your architectural thinking. They want to see how you decompose a complex problem, choose appropriate technologies, and reason about failure modes. Strong candidates proactively discuss monitoring, alerting, and operational concerns.
What the Interviewer Expects
- Drive the design discussion proactively with minimal interviewer guidance
- Perform detailed capacity estimation and use it to inform design decisions
- Design for global scale with multi-region deployment and data consistency
- Deep dive into 2-3 critical components with implementation-level detail
- Address complex trade-offs: CAP theorem, eventual consistency, conflict resolution
- Discuss operational excellence: deployment strategy, chaos engineering, SLOs/SLIs
- Propose a phased rollout plan from MVP to full-scale system
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 happens if one of your database nodes goes down?
- What monitoring and alerting would you set up on day one?
- How would you handle a region-wide outage?
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Requirements
- Functional Requirements:
- Ingest millions of real-time requests from various sources such as customer orders, restaurant updates, and delivery status changes.
- Process and transform incomi...
Capacity Estimation
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Request Volume Estimation:
- Assume an average of 1.5 requests per second per active user.
- With 5 million active users, peak traffic could reach 10 million requests per minute.
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**Data S...
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