Design a large-scale Monitoring Platform
Last updated: October 2, 2025
Quick Overview
Design a distributed monitoring system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
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Design a distributed monitoring 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 LinkedIn. 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. LinkedIn values engineers who can think about scalability from day one.
What the Interviewer Expects
- Drive the design discussion proactively with minimal interviewer guidance
- Perform detailed capacity estimation and use it to inform design decisions
- Design for global scale with multi-region deployment and data consistency
- Deep dive into 2-3 critical components with implementation-level detail
- Address complex trade-offs: CAP theorem, eventual consistency, conflict resolution
- Discuss operational excellence: deployment strategy, chaos engineering, SLOs/SLIs
- Propose a phased rollout plan from MVP to full-scale system
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 would you handle a 10x increase in traffic overnight?
- What happens if one of your database nodes goes down?
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Requirements
- Functional Requirements:
- Collect and store metrics from various LinkedIn services (e.g., user activity, system performance) in real-time.
- Provide a dashboard for engineers to visua...
Capacity Estimation
To estimate capacity, we consider:
- Daily Active Users (DAU): Assume LinkedIn has 800 million users, with 20% active daily, resulting in 160 million DAUs.
- Metrics per User: Each user ge...
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