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In this lesson we cover logging and monitoring strategies for Kubernetes clusters and the applications running on them. You’ll learn how to observe cluster components, collect and centralize logs, visualize metrics, and configure alerts so you can maintain reliability and troubleshoot issues effectively in production-grade environments.
A dark-themed slide that reads "Certified Kubernetes Administrator" and "Logging and Monitoring." It features a faint ship's wheel logo in the center and stylized ocean waves along the bottom.
We begin with monitoring the Kubernetes control plane and nodes, then move on to application-level metrics and visualization. Next, we cover logging fundamentals: quick pod-level debugging with kubectl logs, accessing node and kubelet logs, and options for cluster-wide log aggregation. Finally, you’ll see common centralized logging architectures and tools, how to build dashboards, and how to set up alerting rules. What you will learn
  • Cluster component monitoring: observing the API server, scheduler, controller-manager, etcd, kubelets, and node health/resource metrics.
  • Application monitoring: exposing and scraping application metrics, using the Kubernetes metrics-server, Prometheus for metric collection, and Grafana for visualization.
  • Logging fundamentals: using kubectl logs for pod debugging, finding node and kubelet logs, and using cluster-level log collectors.
  • Centralized log aggregation: architectures and tools such as Fluentd / Fluent Bit, Logstash, Elasticsearch / Kibana (EFK), and Loki as an alternative.
  • Alerting and dashboards: integrating Alertmanager, creating alerts, and building Grafana dashboards to maintain cluster reliability.
  • Hands-on exercises: practical labs to practice setup, querying, and troubleshooting.
Summary of key areas and tools Quick troubleshooting commands
Metrics vs logs: use metrics for trend analysis and alerting, and logs for detailed debugging and forensic investigation. Combine both for full observability.
Avoid sending sensitive or PII data to external logging backends without masking, encryption, and strict access controls. Review log retention and access policies before shipping logs off-cluster.
Hands-on exercises and learning path
  • Deploy a metrics-server and validate node metrics with kubectl top.
  • Install Prometheus + Grafana (or use a managed service) and create a dashboard for cluster CPU/memory usage.
  • Instrument a sample app to expose a /metrics endpoint and configure Prometheus to scrape it.
  • Use kubectl logs to debug a failing pod, then forward logs to a Fluent Bit collector and view them in Kibana or Grafana Explore.
  • Configure Alertmanager with basic alert rules (e.g., node memory pressure, pod CrashLoopBackOff) and test notification routing.
Links and references We will walk through these topics with examples and hands-on exercises so you can apply monitoring and logging best practices in real clusters. This lesson is designed to be practical and aligned with real-world CKA-style troubleshooting scenarios.

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