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Monitoring, Logging, Debugging, Troubleshooting in Kubernetes: 10 Powerful Tips

Posted on by Monika
Monitoring, Logging, Debugging, Troubleshooting in Kubernetes

Introduction

Monitoring, logging, debugging, and troubleshooting in Kubernetes are essential practices for ensuring that containerized applications run reliably, securely, and efficiently. K8s orchestrates thousands of workloads in dynamic environments, but without proper observability and Code Analysis workflows, problems can go unnoticed until they affect users. In this blog, we’ll cover how to set up collect logs, debug issues, and troubleshoot production problems in K8s clusters.

1. Monitoring in Kubernetes

It is the continuous collection, processing, and analysis of performance and health data from your cluster, nodes, and applications. It helps detect anomalies, plan scaling, and optimize resource usage.

Key Metrics to Monitor

  • Cluster health: Node readiness, CPU/memory pressure

  • Application performance: Response time, error rates

  • Resource usage: CPU, memory, storage, network

  • Pod lifecycle: Restarts, pending states

Popular Tools

  1. Prometheus – Open-source metrics collection & alerting

  2. Grafana – Visualization dashboards

  3. Kube-State-Metrics – K8s object state metrics

  4. Loki – Log aggregation (works with Grafana)

Example:

kubectl get nodes -o wide

Best Practices:

  • Set up alerts for critical events

  • Use dashboards for real-time visual insights

  • Monitor custom metrics for business logic

2. Logging in Kubernetes

It is the process of recording events and application output for later review. In K8s, logs help you understand system and application behavior.

Types of Logs

  • Application logs: Generated by the app inside a container

  • Node logs: System logs from the host machine

  • Cluster component logs: From kube-apiserver, kubelet, controller-manager, etc.

Viewing Pod Logs:

kubectl logs <pod-name>

To view logs for a specific container in a pod:

kubectl logs <pod-name> -c <container-name>

Centralized Solutions

  • ELK Stack (Elasticsearch, Logstash, Kibana)

  • Fluentd + Grafana Loki

  • OpenSearch Dashboards

Best Practices for:

  • Store logs centrally

  • Use structured Event Storage (JSON) for easy parsing

  • Set retention policies to manage storage

3. Debugging in Kubernetes

It is identifying and fixing errors in applications or K8s configurations.

Common Scenarios

  • Pod stuck in Crash Loop Back Off

  • Service not reachable

  • Containers failing health checks

  • Configuration errors in YAML manifests

Example:

kubectl describe pod <pod-name>

This command provides detailed information about pod events, reasons for restarts, and container status.

Tools and Commands:

  • kubectl exec -it <pod-name> — /bin/sh → Access container shell

  • kubectl port-forward → Test service locally

  • kubectl get events –sort-by=.metadata.creationTimestamp → Check recent cluster events

Best Practices :

  • Always check events before modifying configs

  • Validate YAML manifests with kubectl apply --dry-run=client -f file.yaml

  • Use readiness/liveness probes for early issue detection

4. Troubleshooting in Kubernetes

It is the systematic process of diagnosing and resolving issues in K8s environments, often involving both Observation, data and logs.

Workflow

  1. Identify the Problem – Use Observation alerts/log analysis

  2. Gather Context – Check pod/node status, events, metrics

  3. Form Hypotheses – Possible root causes

  4. Test & Verify Fixes – Apply changes and recheck metrics/logs

  5. Document & Prevent – Update playbooks and automation

Example:

kubectl describe pod my-app
kubectl logs my-app

Possible fixes:

  • Increase resource requests/limits

  • Fix missing Config Maps/Secrets

  • Correct invalid image references

5. Integrating the Four Pillars Together:

  • Observation detects anomalies

  • Event Storage provides context for those anomalies

  • Code Analysis drills down to find the root cause

  • Problem Resolution applies and validates the fix

Example Integration Stack:

  • Prometheus (metrics)

  • Grafana (dashboards)

  • Loki (logs)

  • Kubectl (Code Analysis/Problem Resolution)

6. Security and Reliability Considerations

When implementing Performance Checks, Event Storage, and Problem Resolution in K8s workflows:

  • Ensure logs do not leak sensitive information

  • Use RBAC to control access to Observation, and Event Storagetools

  • Enable audit Event Storage for K8s API calls

  • Backup Observation, dashboards and Event Storage configurations

7. Best Practices

Monitoring:
 Use alerts for CPU, memory, and pod restarts
Monitor application-level metrics

Logging:
Centralize logs with ELK or Loki
 Use structured logs

Debugging:
Use kubectl describe before making changes
 Test YAML files with --dry-run

Troubleshooting:
 Document recurring issues in a runbook
 Automate fixes where possible

8. Conclusion

Monitoring, logging, debugging, and troubleshooting in Kubernetes form the backbone of a healthy and reliable cluster. Without them, teams operate in the dark, risking downtime and poor user experience. By implementing the right tools, workflows, and best practices, DevOps teams can proactively detect issues, diagnose problems quickly, and ensure seamless application performance.

For official guidelines, ]  ps://www.devopsworld.co.in/#

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