Design a Recommendation for Uber
Last updated: July 26, 2025
Quick Overview
Design a distributed recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Uber
July 26, 2025242
11
2,436 solved
Design a distributed recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at Uber typically last 45-60 minutes. You are expected to drive the conversation, starting from requirements gathering through to a detailed architecture. The interviewer will evaluate your ability to handle ambiguity and make practical engineering decisions.
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 optimize costs as the system scales?
- How do you ensure data consistency across multiple services?
- How would you handle a region-wide outage?
- How would you migrate from a monolithic to a microservices architecture?
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Requirements
Functional Requirements
- User Preferences: The system should allow users to indicate preferences (e.g., food types, ride preferences).
- Real-time Recommendations: Generate real-time re...
Capacity Estimation
Assuming Uber has approximately 100 million active users and each user generates about 10 requests per day for recommendations:
- Daily Requests: 100 million users * 10 requests/user = 1 billion ...
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