Build a geo-distributed Recommendation Pipeline
Last updated: January 22, 2026
Quick Overview
Design a geo-distributed recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Meta
January 22, 20266
10
4,516 solved
Design a geo-distributed recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at Meta typically last 45-60 minutes. You are expected to drive the conversation, starting from requirements gathering through to a detailed architecture. The interviewer will evaluate your ability to handle ambiguity and make practical engineering decisions.
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
- What happens if one of your database nodes goes down?
- How would you handle a region-wide outage?
- How would you handle schema migrations with zero downtime?
- How would you optimize costs as the system scales?
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Requirements
Functional Requirements:
- The system should provide personalized recommendations based on user behavior and preferences.
- It must support real-time updates to user profiles and recommendation ...
Capacity Estimation
Assuming Meta has approximately 2 billion monthly active users, with around 10% (200 million) of users requesting recommendations daily:
- Requests Per Second (QPS): If we estimate that 10% of da...
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