Build a high-throughput Recommendation Pipeline
Last updated: December 10, 2025
Quick Overview
Design a high-throughput recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Grafana Labs
December 10, 2025247
13
3,736 solved
Design a high-throughput recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This is a common system design question asked during Onsite at Grafana Labs. 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. Grafana Labs 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 migrate from a monolithic to a microservices architecture?
- What monitoring and alerting would you set up on day one?
- How do you ensure data consistency across multiple services?
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Requirements
Functional Requirements
- User Personalization: The system should provide recommendations for users based on their preferences and behavior.
- Real-time Updates: Recommendations should b...
Capacity Estimation
Back-of-Envelope Calculations
- User Base: Assuming 10 million active users.
- Requests Per User: Average of 10 requests per user per day results in 100 million requests per day.
- **Reque...
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