K8s Troubleshooter

2. Initial Triage

Run cluster health check:

python3 scripts/cluster_health.py

This provides an overview of:

• Node health status
• System pod health
• Pending pods
• Failed pods
• Crash loop pods

3. Deep Dive Investigation

Based on triage results, focus investigation:

For Namespace-Level Issues:

python3 scripts/check_namespace.py <namespace>

This provides comprehensive namespace health:

• Pod status (running, pending, failed, crashlooping)
• Service health and endpoints
• Deployment availability
• PVC status
• Resource quota usage
• Recent events
• Actionable recommendations

For Pod Issues:

python3 scripts/diagnose_pod.py <namespace> <pod-name>

This analyzes:

• Pod phase and readiness
• Container statuses and states
• Restart counts
• Recent events
• Resource usage

For specific investigations:

• Review pod details: kubectl describe pod <pod> -n <namespace>
• Check logs: kubectl logs <pod> -n <namespace>
• Check previous logs if restarting: kubectl logs <pod> -n <namespace> --previous
• Check events: kubectl get events -n <namespace> --sort-by='.lastTimestamp'

4. Identify Root Cause

Consult references/common_issues.md for detailed information on:

• ImagePullBackOff / ErrImagePull
• CrashLoopBackOff
• Pending Pods
• OOMKilled
• Node issues (NotReady, DiskPressure)
• Networking failures
• Storage/PVC issues
• Resource quotas and throttling
• RBAC permission errors

Each issue includes:

• Symptoms
• Common causes
• Diagnostic commands
• Remediation steps
• Prevention strategies

5. Apply Remediation

Follow remediation steps from common_issues.md based on root cause identified.

Always:

• Test fixes in non-production first if possible
• Document actions taken
• Monitor for effectiveness
• Have rollback plan ready

6. Verify & Monitor

After applying fix:

• Verify issue is resolved
• Monitor for recurrence (15-30 minutes minimum)
• Check related systems
• Update documentation

Incident Response

For production incidents, follow structured response in references/incident_response.md:

Severity Assessment:

• SEV-1 (Critical): Complete outage, data loss, security breach
• SEV-2 (High): Major degradation, significant user impact
• SEV-3 (Medium): Minor impairment, workaround available
• SEV-4 (Low): Cosmetic, minimal impact

Incident Phases:

1. Detection - Identify and assess
2. Triage - Determine scope and impact
3. Investigation - Find root cause
4. Resolution - Apply fix
5. Post-Incident - Document and improve

Common Incident Scenarios:

• Complete cluster outage
• Service degradation
• Node failure
• Storage issues
• Security incidents

See references/incident_response.md for detailed playbooks.

Quick Reference Commands

Cluster Overview

kubectl cluster-info
kubectl get nodes
kubectl get pods --all-namespaces | grep -v Running
kubectl get events --all-namespaces --sort-by='.lastTimestamp' | tail -20

Pod Diagnostics

kubectl describe pod <pod> -n <namespace>
kubectl logs <pod> -n <namespace>
kubectl logs <pod> -n <namespace> --previous
kubectl exec -it <pod> -n <namespace> -- /bin/sh
kubectl get pod <pod> -n <namespace> -o yaml

Node Diagnostics

kubectl describe node <node>
kubectl top nodes
kubectl top pods --all-namespaces
ssh <node> "systemctl status kubelet"
ssh <node> "journalctl -u kubelet -n 100"

Service & Network

kubectl describe svc <service> -n <namespace>
kubectl get endpoints <service> -n <namespace>
kubectl get networkpolicies --all-namespaces

Storage

kubectl get pvc,pv --all-namespaces
kubectl describe pvc <pvc> -n <namespace>
kubectl get storageclass

Resource & Configuration

kubectl describe resourcequota -n <namespace>
kubectl describe limitrange -n <namespace>
kubectl get rolebindings,clusterrolebindings -n <namespace>

Diagnostic Scripts

cluster_health.py

Comprehensive cluster health check covering:

• Node status and health
• System pod status (kube-system, etc.)
• Pending pods across all namespaces
• Failed pods
• Pods in crash loops

Usage: python3 scripts/cluster_health.py

Best used as first diagnostic step to get overall cluster health snapshot.

check_namespace.py

Namespace-level health check and diagnostics:

• Pod health (running, pending, failed, crashlooping, image pull errors)
• Service health and endpoints
• Deployment availability status
• PersistentVolumeClaim status
• Resource quota usage and limits
• Recent namespace events
• Health status assessment
• Actionable recommendations

Usage:

# Human-readable output
python3 scripts/check_namespace.py <namespace>

# JSON output for automation
python3 scripts/check_namespace.py <namespace> --json

# Include more events
python3 scripts/check_namespace.py <namespace> --events 20

Best used when troubleshooting issues in a specific namespace or assessing overall namespace health.

diagnose_pod.py

Detailed pod-level diagnostics:

• Pod phase and status
• Container states (waiting, running, terminated)
• Restart counts and patterns
• Resource configuration issues
• Recent events
• Actionable recommendations

Usage: python3 scripts/diagnose_pod.py <namespace> <pod-name>

Best used when investigating specific pod failures or behavior.

Reference Documentation

references/common_issues.md

Comprehensive guide to common Kubernetes issues with:

• Detailed symptom descriptions
• Root cause analysis
• Step-by-step diagnostic procedures
• Remediation instructions
• Prevention strategies

Covers:

• Pod issues (ImagePullBackOff, CrashLoopBackOff, Pending, OOMKilled)
• Node issues (NotReady, DiskPressure)
• Networking issues (pod-to-pod communication, service access)
• Storage issues (PVC pending, volume mount failures)
• Resource issues (quota exceeded, CPU throttling)
• Security issues (vulnerabilities, RBAC)

Read this when you identify a specific issue type but need detailed remediation steps.

references/incident_response.md

Structured incident response framework including:

• Incident response phases (Detection → Triage → Investigation → Resolution → Post-Incident)
• Severity level definitions
• Detailed playbooks for common incident scenarios
• Communication guidelines
• Post-incident review template
• Best practices for prevention, preparedness, response, and recovery

Read this when responding to production incidents or planning incident response procedures.

references/performance_troubleshooting.md

Comprehensive performance diagnosis and optimization guide covering:

• High Latency Issues - API response time, request latency troubleshooting
• CPU Performance - Throttling detection, profiling, optimization
• Memory Performance - OOM issues, leak detection, heap profiling
• Network Performance - Latency, packet loss, DNS resolution
• Storage I/O Performance - Disk performance testing, optimization
• Application-Level Metrics - Prometheus integration, distributed tracing
• Cluster-Wide Performance - Control plane, scheduler, resource utilization

Read this when:

• Investigating slow application response times
• Diagnosing CPU or memory performance issues
• Troubleshooting network latency or connectivity
• Optimizing storage I/O performance
• Setting up performance monitoring

references/helm_troubleshooting.md

Complete guide to Helm troubleshooting including:

• Release Issues - Stuck releases, missing resources, state problems
• Installation Failures - Chart conflicts, validation errors, template rendering
• Upgrade and Rollback - Failed upgrades, immutable field errors, rollback procedures
• Values and Configuration - Values not applied, parsing errors, secret handling
• Chart Dependencies - Dependency updates, version conflicts, subchart values
• Hooks and Lifecycle - Hook failures, cleanup issues
• Repository Issues - Chart access problems, version mismatches

Read this when:

• Working with Helm-deployed applications
• Troubleshooting chart installations or upgrades
• Debugging Helm release states
• Managing chart dependencies

Best Practices

Always:

• Start with high-level health check before deep diving
• Document symptoms and findings as you investigate
• Check recent changes (deployments, config, infrastructure)
• Preserve logs and state before making destructive changes
• Test fixes in non-production when possible
• Monitor after applying fixes to verify resolution

Never:

• Make production changes without understanding impact
• Delete resources without confirming they're safe to remove
• Restart pods repeatedly without investigating root cause
• Apply fixes without documentation
• Skip post-incident review

Key Principles:

• Systematic over random troubleshooting
• Evidence-based diagnosis
• Fix root cause, not symptoms
• Learn and improve from each incident
• Prevention is better than reaction