Design Recommendation Infrastructure for microservices
Last updated: December 7, 2025
Quick Overview
Design a event-driven recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Rippling
December 7, 20258
13
2,492 solved
Design a event-driven recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This is a common system design question asked during Technical Screen at Rippling. 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. Rippling values engineers who can think about scalability from day one.
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 handle a 10x increase in traffic overnight?
- What would the deployment pipeline look like for this system?
- How would you implement rate limiting to protect the system?
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Requirements
Functional Requirements:
- Event Processing: The system must process user events (like page views, clicks) in real-time to provide recommendations.
- Recommendation Generation: Generate ...
Capacity Estimation
Back-of-Envelope Calculations:
- Traffic Estimate: Assume 1 million users generating 10 events per user per day. This results in 10 million events per day.
- QPS Calculation: If we assum...
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