LinkedIn·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026

Summary

LinkedIn system design or troubleshooting round, one question about diagnosing a messaging outage. Not much else to go on from what I remember.

Questions Asked (1)

If LinkedIn's messaging feature goes down, how would you go about identifying the root cause?

Root Cause AnalysisSystem DesignProduct Analytics & Metrics

Author's notes

I started with the obvious stuff, check recent deploys, look at error rates, see if it's regional or global.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scope and impact of the outage (e.g., all users vs. a subset, specific regions, or message types). Then systematically check monitoring dashboards, logs, and recent deployments to narrow down the root cause, following a structured troubleshooting process from client to backend.

Pro tip: Demonstrate a bias for action by mentioning that you would first check if a recent deployment or configuration change correlates with the outage, as that's a common cause. Also, emphasize the importance of communication with stakeholders and documenting findings for post-mortem.

1. Assess Impact and Scope

Determine who is affected and how severely: all users or a subset? Is it global or region-specific? Are all message types impacted? This helps prioritize and narrow down potential causes.

2. Check Monitoring and Alerts

Review dashboards for error rates, latency, and throughput in messaging-related services. Look for anomalies in infrastructure metrics (CPU, memory, network) and application logs.

3. Correlate with Recent Changes

Investigate recent deployments, configuration changes, or feature flags that might have introduced the issue. Check if the timing aligns with the outage.

4. Trace the Request Flow

Follow the path of a message from client to backend: load balancers, API gateways, microservices, databases, and third-party dependencies. Identify where failures occur.

5. Mitigate and Verify

Once a likely cause is identified, apply a fix (e.g., rollback, scale up, restart service) and verify that messaging is restored. Monitor to ensure stability.

Key Points to Mention

Use of observability tools (e.g., Grafana, Prometheus, ELK stack) to analyze metrics and logs.
Understanding of distributed systems and common failure modes (e.g., network partitions, database bottlenecks, service dependencies).
Importance of checking recent deployments and feature flags as potential triggers.
Communication with stakeholders and incident response process.
Documentation and post-mortem analysis to prevent recurrence.
Consideration of client-side issues vs. server-side issues.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.