Build a fault-tolerant Logging Pipeline
Last updated: October 21, 2025
Quick Overview
Design a fault-tolerant logging system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
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Design a fault-tolerant logging system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This ML system design question from LinkedIn's Onsite tests your ability to think about ML systems at scale. The interviewer expects discussion of data quality, feature stores, model serving infrastructure, and A/B testing strategy.
What the Interviewer Expects
- Design the full ML lifecycle from data collection to model monitoring
- Address cold start, exploration/exploitation, and model freshness
- Discuss multi-objective optimization and ranking systems
- Plan for model debugging, fairness, and bias mitigation
- Design the feature store and training pipeline for scale
- Address model versioning, canary deployments, and rollback strategies
- Discuss the data flywheel and long-term system evolution
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 would you do if model performance degrades over time?
- How would you handle a 10x increase in prediction requests?
- How would you handle the cold start problem?
- How would you debug a model that works well offline but poorly online?
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Requirements Clarification
Before diving into the architecture, clarify the scope with the interviewer. For fault-tolerant Logging Pipeline, key functional requirements include:...
Capacity Estimation
Estimate the scale to drive design decisions. Assume 100M DAU with an average of 10 actions per user per day = 1B requests/day ~ 12K QPS average, ~36K...
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