Architect a geo-distributed Recommendation Engine
Last updated: February 23, 2026
Quick Overview
Design a geo-distributed recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Salesforce
February 23, 20261
6
4,311 solved
Design a geo-distributed recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Salesforce 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
- Systematically gather requirements and estimate capacity (QPS, storage, bandwidth)
- Design a scalable architecture with clear component responsibilities
- Make well-reasoned database and caching decisions with trade-off analysis
- Address consistency vs availability trade-offs specific to the use case
- Discuss partitioning strategy, replication, and data modeling
- Cover failure handling, monitoring, and alerting strategies
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 migrate from a monolithic to a microservices architecture?
- How would you handle a region-wide outage?
- What happens if one of your database nodes goes down?
- How would you handle a 10x increase in traffic overnight?
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Requirements
Functional Requirements
- User Interaction: The recommendation engine will provide personalized product recommendations for users based on their historical interactions and preferences.
- **...
Capacity Estimation
To estimate capacity, let's assume:
- Daily Requests: 10 million recommendations.
- Peak Load: 20% of the total requests occur during peak hours.
- QPS Calculation: (10 million / 24 hours)...
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