Build a real-time Recommendation Pipeline
Last updated: December 31, 2025
Quick Overview
Design a real-time recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Doordash
December 31, 202565
8
4,286 solved
Design a real-time recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Doordash asks this during the Technical Screen 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
- Clearly define functional and non-functional requirements
- Propose a reasonable high-level architecture with core components
- Choose appropriate data storage solutions with basic justification
- Discuss basic scaling strategies (horizontal scaling, caching)
- Identify potential bottlenecks and suggest simple solutions
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 implement rate limiting to protect the system?
- What monitoring and alerting would you set up on day one?
- How would you migrate from a monolithic to a microservices architecture?
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Requirements
- Functional Requirements:
- Real-time recommendations for users based on their past orders and preferences.
- Ability to update recommendations immediately as new data arrives (e.g., recent o...
Capacity Estimation
Assuming Doordash has around 30 million active users:
- Average number of recommendations requested per user per day: 10
- Total daily recommendation requests: 30 million users * 10 requests = 300 mil...
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