Diagnose overloaded Kubernetes cluster
Overview
This question evaluates competency in diagnosing and mitigating production overloads in Kubernetes-based microservice architectures, emphasizing observability, autoscaling behavior, resource constraints, and incident-response reasoning.
Kubernetes Overload: End-to-End Debug + Mitigation Plan
You are given a Kubernetes-based microservices system that is currently overloaded, exhibiting high tail latency (p95/p99), request timeouts, and autoscaling thrash (rapid scale up/down). Design an end-to-end approach to debug and mitigate the incident.
Provide:
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What to inspect
- Metrics (cluster, service, dependencies), logs, and distributed traces to examine.
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Tools and commands
- The tools and concrete commands/queries you would use (e.g., kubectl, Prometheus/Grafana, OpenTelemetry/Jaeger, service mesh/ingress, cloud vendor tools).
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Hypotheses to test
- Specific, testable root-cause hypotheses for overload and how you'd validate or falsify them.
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Immediate mitigations
- Safe, rapid steps to stabilize the system and reduce user impact.
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Longer-term fixes
- Sustainable changes to prevent recurrence (architecture, autoscaling, limits, observability, SLOs).
Assume a typical setup with: Kubernetes, Horizontal Pod Autoscaler (HPA), a metrics stack (e.g., Prometheus/Grafana), logs (e.g., centralized logging), and tracing (e.g., OpenTelemetry + Jaeger/Tempo). If a component is missing, state a pragmatic alternative.
Solution
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