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Discord·Software Engineer·Technical Phone Screen·Senior

Senior
Apr 2026

Summary

Discord SRE-style interview where they drop you into a live CPU spike incident and expect you to work through it out loud. The interviewer plays the role of an on-call engineer feeding you data as you ask for it. Felt more like a real incident than a test, which was both cool and stressful.

Questions Asked (1)

Q1

You get paged: CPU usage on a production service has suddenly spiked. Walk through how you'd respond, from first alert to root cause and fix. You can ask for any metrics or logs you want.

Root Cause AnalysisSystem DesignTechnical Trade-offs
Author's notes

The open-ended format tripped me up at first because there's no single right answer to anchor to.

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AI HintsAI Generated

Suggested Approach

Start by acknowledging the alert, then systematically gather data from metrics, logs, and traces to narrow down the cause. Prioritize mitigation if the impact is severe, then focus on root cause and long-term fix. Communicate clearly with stakeholders throughout.

Pro tip: Always check recent deployments or configuration changes first—most production incidents are caused by recent changes. Also, consider the blast radius: is it one host, one service, or the entire fleet?

1. Acknowledge and Assess Impact

Acknowledge the page, check dashboards for scope (which services, hosts, regions) and user impact. Determine if immediate mitigation is needed.

2. Gather Data

Pull metrics (CPU, memory, I/O, network), logs, and traces for the affected service. Look for patterns: sudden spike vs gradual, correlated with deployments or traffic changes.

3. Form and Test Hypotheses

Based on data, hypothesize likely causes (e.g., traffic surge, infinite loop, inefficient query, resource leak). Validate by querying specific metrics or logs.

4. Mitigate and Fix

If impact is severe, mitigate first (e.g., rollback, scale up, restart). Then implement a proper fix and verify resolution.

5. Follow Up

Document the incident, conduct a post-mortem, and implement preventive measures (e.g., alerts, code changes, capacity planning).

Key Points to Mention

  • Check recent deployments or configuration changes as a common cause.
  • Use metrics like CPU per host, request rate, latency, and error rates to identify patterns.
  • Consider common causes: traffic spike, inefficient code (e.g., N+1 queries, infinite loops), resource leaks, or external dependencies.
  • Prioritize mitigation over root cause if user impact is high (e.g., rollback, scale horizontally).
  • Communicate status updates to stakeholders and document findings for post-mortem.
  • Leverage tracing and profiling tools to pinpoint the exact function or service causing high CPU.

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