> ## Documentation Index
> Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Kubernetes Diagnostics
> Demonstrates using cluster-aware diagnostics to inspect Kubernetes Secrets, ConfigMaps, events and logs to pinpoint and remediate production outages faster than generic external AI
It's 2 a.m. on Black Friday weekend — the busiest sales event of the year.
Sarah, a senior DevOps engineer at a large e-commerce company, is jolted awake by her phone. A P1 critical incident: the payment service is down across all regions. Within seconds, 47 Slack messages arrive. The CEO wants answers immediately.
Every second of downtime costs thousands in lost sales. Sarah is exhausted and alone at mission control in her bedroom-office. She spends nearly two hours combing logs and events. The cluster is noisy: almost 11,000 error messages. At a glance, `kubectl` shows failing pods:
```bash theme={null}
kubectl get pods -n production
```
```text theme={null}
NAME READY STATUS
payment-service-7d4b8c9-x2np 0/1 ImagePullBackOff
payment-service-7d4b8c9-y3mq 0/1 ImagePullBackOff
payment-service-7d4b8c9-z4kr 0/1 ErrImagePull
```
Those pod statuses indicate failures during image pull (e.g., image not found, unauthorized, or registry/network issues). But noisy logs and generic errors make it hard to identify the real cause quickly.
At 5:45 AM, desperate for a quick fix, Sarah tries a public conversational AI (ChatGPT) and pastes the errors. The responses are generic troubleshooting steps — reasonable, but blind to her live cluster state:
```text theme={null}
The Failed ChatGPT attempt
kubernetes ImagePullBackOff help
Try these solutions:
1. Delete and recreate pod
2. Check if image exists
3. Restart deployment
4. Increase memory limits
5. Verify network connectivity
Time Wasted Tracker
ChatGPT Debugging: 23 min
Commands tried: 15
Success rate: 0%
```
Because an external model can't access her cluster, its suggestions are necessarily generic and require manual validation against live resources. Sarah follows several suggestions (delete pods, restart deployments, verify image names) but none address the root cause — precious minutes are lost.
By 6:00 AM, four hours into the incident, Sarah is exhausted. Her team lead Mike suggests using an internal, cluster-aware diagnostic tool instead of more guesswork.
Unlike a public conversational assistant, the cluster-aware tool connects directly (or via authorized credentials) to the Kubernetes control plane, inspects Secrets, ConfigMaps, events, and live logs — then correlates findings and recommends exact remediation steps. In Sarah's case, the tool finds a corrupted Secret used for DB authentication and produces an actionable remediation:
```bash theme={null}
$ # Initializing cluster analysis...
🔎 Initializing cluster analysis...
✓ Connected to k3s-controlplane
✓ Scanning all namespaces...
✓ Checking 47 resources...
CRITICAL ISSUES DETECTED:
1. payment-db-secret [CORRUPTED]
└─ Base64 encoding broken or incorrect value
└─ Password: "cGFzc3dvcmQ=" (mismatch or invalid for DB)
└─ Pods failing authentication
2. frontend-config [MISCONFIGURED]
└─ DB_HOST points to dev server
└─ Should be: payment-db-service
SOLUTION EXECUTING:
$ kubectl delete secret payment-db-secret -n production
$ kubectl create secret generic payment-db-secret \
--from-literal=password='actualPassword' \
-n production
✅ Secret recreated successfully
✅ Pods restarting...
✅ Connections restored
```
After applying the suggested fix, pods restarted and connections to the database were restored. At 6:13 AM — after four intense hours — the payment service is back, metrics are green, and traffic flows again.
Key distinction:
* External AI (e.g., public ChatGPT) can only reason from text and pattern matches; it cannot verify live states or Secrets.
* Cluster-aware diagnostic tools (properly secured and authorized) can inspect the Kubernetes control plane, Secrets, ConfigMaps, events, and pod logs to pinpoint a root cause and provide executable remediation steps.
What this lab demonstrates
* Use a cluster-aware diagnostic workflow to locate issues faster than with generic external advice.
* Correlate Secrets, ConfigMaps, pod authentication errors, and events to find root causes.
* Apply precise `kubectl` commands produced by the diagnostic tool to remediate problems quickly.
Troubleshooting quick-reference table
| Symptom | Likely cause | Quick checks / commands |
| - | -: | - |
| `ImagePullBackOff` / `ErrImagePull` | Bad image name, missing registry auth, or network/registry outage | `kubectl describe pod -n `
`kubectl get events -n `
`kubectl get secret -n ` |
| Pods failing DB auth | Corrupted Secret or wrong `DB_HOST` | `kubectl get secret payment-db-secret -n production -o yaml`
`kubectl logs -n production` |
| Config pointing to dev | Misconfigured ConfigMap or deployment env | `kubectl get configmap frontend-config -n production -o yaml` |
| Excessive noisy logs | Upstream noise masking root cause | Use targeted `kubectl logs` for failing pods and `kubectl describe` to see events |
Security and operational notes:
Cluster-aware diagnostics are powerful. Limit access to only trusted tools and authorized users, audit all actions, and use short-lived credentials (e.g., via Vault, OIDC, or temporary kubeconfigs) when granting tools cluster access.
Never expose full cluster admin credentials to untrusted services. Automated remediation should require explicit approvals (especially in production) and be logged for auditability.
Recommended commands for diagnosing image-pull issues
1. Inspect pod events and description:
```bash theme={null}
kubectl describe pod payment-service-7d4b8c9-x2np -n production
kubectl get events -n production --sort-by='.metadata.creationTimestamp'
```
2. Check image pull secrets and registry access:
```bash theme={null}
kubectl get secret -n production -o yaml
kubectl run --rm -it --restart=Never --image=busybox:1.34 debug-shell -- /bin/sh
# from within pod, try to curl the registry or docker login checks as appropriate
```
3. Inspect Secrets and ConfigMaps for authentication issues:
```bash theme={null}
kubectl get secret payment-db-secret -n production -o jsonpath='{.data.password}' | base64 --decode
kubectl get configmap frontend-config -n production -o yaml
```
References and further reading
* [Kubernetes Basics](https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/)
* [kubectl Cheat Sheet](https://kubernetes.io/docs/reference/kubectl/cheatsheet/)
* [Best practices for managing Secrets](https://kubernetes.io/docs/concepts/configuration/secret/)
Takeaways
* Context matters: tools with live cluster access can validate hypotheses instead of guessing.
* Secure, auditable access is essential when using automated diagnostics or remediation.
* A small investment in cluster-aware diagnostics can save hours and massive revenue loss during critical incidents.
This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.