Architect a scalable Recommendation Engine
Last updated: October 4, 2025
Quick Overview
Design a scalable recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Square/Block
October 4, 2025151
12
2,093 solved
Design a scalable recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This is a common system design question asked during System Design Round at Square/Block. The interviewer expects you to demonstrate your ability to design large-scale distributed systems, make well-reasoned trade-offs, and communicate your thought process clearly. Square/Block values engineers who can think about scalability from day one.
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
- How would you implement rate limiting to protect the system?
- How would you optimize costs as the system scales?
- What would the deployment pipeline look like for this system?
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Requirements
Functional Requirements
- User Profiles: Store user preferences and past interactions to personalize recommendations.
- Real-time Recommendations: Provide personalized suggestions based ...
Capacity Estimation
To estimate the capacity needs:
- User Base: Assume 10 million active users.
- Requests per User: Each user makes approximately 10 requests per day.
- Total Requests: 10 million users *...
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