Debug and mitigate a CPU spike incident
Production Debugging: CPU Spike Incident
You are on-call for a backend service. An alert fires: CPU usage suddenly spikes on the service’s hosts/pods and stays high.
You can ask for any information you want, such as dashboards, time series metrics, logs, traces, recent deploys, or configuration changes. The interviewer will provide whatever graphs or logs you request.
Tasks
- Immediate mitigation: What are your first steps to reduce user impact?
- Triage plan: What key questions do you ask, and what metrics or logs do you inspect?
- Root cause analysis: Walk through your process for narrowing down hypotheses to a likely root cause.
- Fix + verification: How do you validate the fix and prevent regression?
Assume this is a typical microservice environment, utilizing Kubernetes or VM autoscaling, a load balancer, and centralized logging/metrics.
AI interview prep
Core dilemma
Balancing the urgency of immediate production mitigation to reduce user impact against the need for thorough investigation to identify and permanently resolve the underlying root cause, all while operating under pressure and limited information.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Debug and mitigate a CPU spike incident
Production Debugging: CPU Spike Incident
You are on-call for a backend service. An alert fires: CPU usage suddenly spikes on the service’s hosts/pods and stays high.
You can ask for any information you want, such as dashboards, time series metrics, logs, traces, recent deploys, or configuration changes. The interviewer will provide whatever graphs or logs you request.
Tasks
- Immediate mitigation: What are your first steps to reduce user impact?
- Triage plan: What key questions do you ask, and what metrics or logs do you inspect?
- Root cause analysis: Walk through your process for narrowing down hypotheses to a likely root cause.
- Fix + verification: How do you validate the fix and prevent regression?
Assume this is a typical microservice environment, utilizing Kubernetes or VM autoscaling, a load balancer, and centralized logging/metrics.