Design a Logging Service
Last updated: May 3, 2026
Quick Overview
Design a distributed logging system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Elastic
System Design
Software Engineer
Elastic
May 3, 2026Software Engineer
Onsite
System Design
Hard
232
6
3,087 solved
Design a distributed logging system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This ML system design question from Elastic'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
A/B testing and experimentation
Model serving and latency optimization
Monitoring and model degradation detection
Feature engineering and feature stores
Data collection and labeling strategy
Training pipeline and infrastructure
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 debug a model that works well offline but poorly online?
- How would you handle a 10x increase in prediction requests?
- How would you handle the cold start problem?
- How would you ensure fairness and reduce bias in the model?
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Requirements
- Functional Requirements:
- Handle millions of log entries per second from various microservices.
- Support structured and unstructured log formats.
- Provide real-time querying capabilitie...
Capacity Estimation
Assuming Elastic's logging service needs to handle:
- 5 million logs per second at peak times.
- Each log entry is around 1 KB in size.
Calculating storage requirements
- Total logs per day...
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