Debugging Kubernetes Incidents

Debugging Kubernetes Incidents

Overview

Core Principles:

• Read-Only Investigation - Observe and analyze, never modify resources
• Systematic Methodology - Follow structured phases for thorough analysis
• Multi-Source Correlation - Combine logs, events, metrics for complete picture
• Root Cause Focus - Identify underlying cause, not just symptoms

Key Abbreviations:

• RCA - Root Cause Analysis
• MTTR - Mean Time To Resolution
• P1/​P2/​P3/​P4 - Severity levels (Critical/​High/​Medium/​Low)

When to Use

Invoke this skill when:

• ✅ Pods are crashing, restarting, or stuck in CrashLoopBackOff
• ✅ Services are returning errors or experiencing high latency
• ✅ Resources are exhausted (CPU, memory, storage)
• ✅ Certificate errors or TLS handshake failures occur
• ✅ Deployments are failing or stuck in rollout
• ✅ Need to perform incident triage or post-mortem analysis

Quick Reference: Investigation Phases

Common Confusions

Wrong vs. Correct Approaches

❌ WRONG: Jump directly to pod logs without checking events ✅ CORRECT: Check events first to understand context, then investigate logs

❌ WRONG: Only look at the current pod state ✅ CORRECT: Check previous container logs if pod restarted (use --previous flag)

❌ WRONG: Investigate single data source in isolation ✅ CORRECT: Correlate logs + events + metrics for complete picture

❌ WRONG: Assume first error found is root cause ✅ CORRECT: Identify temporal sequence - what happened FIRST?

❌ WRONG: Recommend changes without understanding full impact ✅ CORRECT: Provide read-only recommendations with manual review required

Decision Tree: Incident Type

Q: Is the pod running?

• No → Check pod status and events
  • ImagePullBackOff → Image Pull Issues
  • Pending → Resource Constraints
  • CrashLoopBackOff → Application Crashes
  • OOMKilled → Memory Issues

Q: Is the pod running but unhealthy?

• Yes → Check logs and resource usage
  • High CPU/​Memory → Resource Saturation
  • Application errors → Log Analysis
  • Failed health checks → Liveness/​Readiness Probes

Q: Is this a service-level issue?

• Yes → Check service endpoints and network
  • No endpoints → Pod Selector Issues
  • Connection timeouts → Network Issues
  • Certificate errors → TLS Issues

Rules: Investigation Methodology

Phase 1: Triage

CRITICAL: Assess severity before diving deep

1. Determine incident type:

   • Pod-level: Single pod failure
   • Deployment-level: Multiple pods affected
   • Service-level: API/​service unavailable
   • Cluster-level: Node or system-wide issue

2. Assess severity:

   • P1 (Critical): Production service down, data loss risk
   • P2 (High): Major feature broken, significant user impact
   • P3 (Medium): Minor feature degraded, limited impact
   • P4 (Low): No immediate user impact

3. Identify scope:

   • Single pod, deployment, namespace, or cluster-wide?
   • How many users affected?
   • What services depend on this?

Phase 2: Data Collection

Gather comprehensive data from multiple sources:

Pod Information:

kubectl get pods -n <namespace>
kubectl describe pod <pod-name> -n <namespace>
kubectl get pod <pod-name> -n <namespace> -o yaml

Logs:

# Current logs
kubectl logs <pod-name> -n <namespace> --tail=100

# Previous container (if restarted)
kubectl logs <pod-name> -n <namespace> --previous

# All containers in pod
kubectl logs <pod-name> -n <namespace> --all-containers=true

Events:

# Namespace events
kubectl get events -n <namespace> --sort-by='.lastTimestamp'

# Pod-specific events
kubectl describe pod <pod-name> -n <namespace> | grep -A 10 Events

Resource Usage:

kubectl top pods -n <namespace>
kubectl top nodes

Phase 3: Correlation & Analysis

Create unified timeline:

1. Extract timestamps from logs, events, metrics
2. Align data sources on common timeline
3. Identify temporal patterns:
   • What happened first? (root cause)
   • What happened simultaneously? (correlation)
   • What happened after? (cascading effects)

Look for common patterns:

• Memory spike → OOMKilled → Pod restart
• Image pull failure → Pending → Timeout
• Probe failure → Unhealthy → Traffic removed
• Certificate expiry → TLS errors → Connection failures

Phase 4: Root Cause Determination

CRITICAL: Distinguish correlation from causation

Validate root cause:

1. Temporal precedence: Did it happen BEFORE the symptom?
2. Causal mechanism: Does it logically explain the symptom?
3. Evidence: Is there supporting data from multiple sources?

Common root causes:

• Application bugs (crashes, exceptions)
• Resource exhaustion (CPU, memory, disk)
• Configuration errors (wrong env vars, missing secrets)
• Network issues (DNS failures, timeouts)
• Infrastructure problems (node failures, storage issues)

Phase 5: Remediation Planning

Provide structured recommendations:

Immediate mitigation:

• Rollback deployment
• Scale resources
• Restart pods
• Apply emergency config

Permanent fix:

• Code changes
• Resource limit adjustments
• Configuration updates
• Infrastructure improvements

Prevention:

• Monitoring alerts
• Resource quotas
• Automated testing
• Runbook updates

Common Issue Types

Image Pull Issues

Symptoms:

• Pod status: ImagePullBackOff or ErrImagePull
• Events: "Failed to pull image"

Investigation:

├── Check image name and tag in pod spec
├── Verify image exists in registry
├── Check image pull secrets
└── Validate network connectivity to registry

Common causes:

• Wrong image name/​tag
• Missing/​expired image pull secret
• Private registry authentication failure
• Network connectivity to registry

Resource Constraints

Symptoms:

• Pod status: Pending
• Events: "Insufficient cpu/​memory"

Investigation:

├── Check namespace resource quotas
├── Check pod resource requests/​limits
├── Review node capacity
└── Check for pod priority and preemption

Common causes:

• Namespace quota exceeded
• Insufficient cluster capacity
• Resource requests too high
• No nodes matching pod requirements

Application Crashes

Symptoms:

• Pod status: CrashLoopBackOff
• Container exit code: non-zero
• Frequent restarts

Investigation:

├── Get logs from current container
├── Get logs from previous container (--previous)
├── Check exit code in pod status
├── Review application startup logs
└── Check for environment variable issues

Common causes:

• Application exceptions/​errors
• Missing required environment variables
• Database connection failures
• Invalid configuration

Memory Issues

Symptoms:

• Pod status shows: OOMKilled
• Events: "Container exceeded memory limit"
• High memory usage before restart

Investigation:

├── Check memory limits vs actual usage
├── Review memory usage trends
├── Analyze for memory leaks
└── Check for spike patterns

Common causes:

• Memory leak in application
• Insufficient memory limits
• Unexpected traffic spike
• Large dataset processing

TLS Issues

Symptoms:

• Logs show: "tls: bad certificate" or "x509: certificate has expired"
• Connection failures between services
• HTTP 503 errors

Investigation:

├── Check certificate expiration dates
├── Verify certificate CN/​SAN matches hostname
├── Check CA bundle configuration
└── Review certificate secret in namespace

Common causes:

• Expired certificates
• Certificate CN mismatch
• Missing CA certificate
• Incorrect certificate chain

Real-World Example: Pod Crash Loop Investigation

Scenario: API gateway pods crashing repeatedly

Step 1: Triage

$ kubectl get pods -n production | grep api-gateway
api-gateway-7d8f9b-xyz   0/​1   CrashLoopBackOff   5   10m

Severity: P2 (High) - Production API affected

Step 2: Check Events

$ kubectl describe pod api-gateway-7d8f9b-xyz -n production | grep -A 10 Events
Events:
  10m   Warning   BackOff   Pod   Back-off restarting failed container
  9m    Warning   Failed    Pod   Error: container exceeded memory limit

Key finding: OOMKilled event

Step 3: Check Logs (Previous Container)

$ kubectl logs api-gateway-7d8f9b-xyz -n production --previous --tail=50
...
[ERROR] Database connection pool exhausted: 50/​50 connections in use
[WARN] High memory pressure detected
[CRITICAL] Memory usage at 98%, OOM imminent

Pattern: Connection pool → Memory pressure → OOM

Step 4: Check Resource Limits

$ kubectl get pod api-gateway-7d8f9b-xyz -n production -o yaml | grep -A 5 resources
resources:
  limits:
    memory: "2Gi"
  requests:
    memory: "1Gi"

Step 5: Root Cause Analysis

Timeline:
09:00 - Database query slowdown (from logs)
09:05 - Connection pool exhausted
09:10 - Memory usage spike (connections held)
09:15 - OOM killed
09:16 - Pod restart

Root Cause: Slow database queries → connection pool exhaustion → memory leak → OOM

Recommendations:

Immediate:
1. Increase memory limit to 4Gi (temporary)
2. Add connection timeout (10s)

Permanent:
1. Optimize slow database query
2. Increase connection pool size
3. Implement connection timeout
4. Add memory alerts at 80%

Prevention:
1. Monitor database query performance
2. Add Prometheus alert for connection pool usage
3. Regular load testing

Troubleshooting Decision Matrix

Keywords for Search

kubernetes, pod failure, crashloopbackoff, oomkilled, pending pod, debugging, incident investigation, root cause analysis, pod logs, events, kubectl, container restart, image pull error, resource constraints, memory leak, application crash, service degradation, tls error, certificate expiry, readiness probe, liveness probe, troubleshooting, production incident, cluster debugging, namespace investigation, deployment failure, rollout stuck, error analysis, log correlation