Design a multi-tenant Recommendation System
Last updated: April 26, 2026
Quick Overview
Design a multi-tenant recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Bloomberg
System Design
Software Engineer
Bloomberg
April 26, 2026Software Engineer
Onsite
System Design
Easy
0
15
493 solved
Design a multi-tenant recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Bloomberg 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
- 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
Load balancing and horizontal scaling
High-level architecture and component design
Database selection and data modeling
Security and authentication
Consistency models and replication
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 do you ensure data consistency across multiple services?
- How would you handle a 10x increase in traffic overnight?
- What monitoring and alerting would you set up on day one?
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Requirements
- Functional Requirements:
- Provide personalized recommendations for users based on their past interactions, preferences, and trends.
- Support multiple clients (tenants) with distinct ...
Capacity Estimation
- User Base: Assume we have 1 million active users across multiple tenants.
- Requests: Each user makes an average of 10 recommendation requests per hour.
- Total Requests: 1,000,000 u...
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