Kubernetes

Top 10 Kubernetes Deployment Mistakes: Causes, Fixes, and Tips

Honghao Wang

— 3 min read
Top 10 Kubernetes Deployment Mistakes: Causes, Fixes, and Tips

Up to 80% of Kubernetes Security & Stability Issues Come from Misconfigurations

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When a Kubernetes deployment fails, it can feel like searching for a needle in a haystack.

A single typo, missing field, or insufficient memory can stop everything. In fact, up to 80% of Kubernetes issues have misconfiguration at their root.

This guide explains why deployment errors happen, how to troubleshoot them, and prevention methods for avoiding the top 10 common problems — including `CrashLoopBackOff`, stuck Pods, YAML issues, and resource mismanagement.

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Guide Overview

  • 3 Primary Causes of Kubernetes Deployment Failures
  • Top 10 Common Deployment Errors & How to Fix Them
  • Universal Troubleshooting Framework
  • Pro Tips to Prevent Future Failures
  • References & Resources

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1. Why Kubernetes Deployment Errors Happen — 3 Key Causes

1. Declarative Configuration Mistakes

  • Kubernetes uses YAML files to define application specs.
  • Even valid YAML can be invalid for Kubernetes — e.g., missing replicas or referencing non‑existent services.
  • Common pitfalls:
  • Typos
  • Indentation errors
  • Missing fields

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2. Image & Resource Limit Issues

  • Incorrect container image names or missing images in the registry block deployment.
  • Insufficient CPU/memory can keep Pods in Pending state.
  • Fix by verifying image registry and adjusting resource requests.

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3. Node & Cluster-Level Problems

  • Nodes can be full, offline, or unhealthy.
  • Network/storage misconfigurations lead to service connectivity failures or crashes.

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> ✅ Tip: Apply structured troubleshooting early — check YAML validity, resources, and logs systematically.

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2. Top 10 Kubernetes Deployment Errors & Troubleshooting

1. CrashLoopBackOff

Application starts then keeps crashing.

  • Check logs: `kubectl logs`
  • Validate startup commands & environment variables
  • Verify dependencies

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2. ImagePullBackOff / ErrImagePull

Kubernetes cannot pull the image.

  • Verify image name/tag
  • Push image to registry
  • Configure `imagePullSecrets` if private

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3. OOMKilled

Pod exceeds memory limit and is killed.

  • Increase memory limits
  • Optimize memory usage
  • Inspect limits: `kubectl describe pod`

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4. CreateContainerConfigError

Pod misconfiguration (Secrets, ConfigMaps, volumes).

  • Debug: `kubectl describe pod`
  • Validate references and paths

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5. Node Not Ready

Node is unavailable.

  • Check: `kubectl get nodes`
  • Describe: `kubectl describe node`
  • Repair/restart node

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6. Pod Pending

Insufficient resources or unassigned volumes.

  • Debug: `kubectl describe pod`
  • Add resources or fix volume configuration

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7. Scheduling Failure

No node matches Pod requirements.

  • Review scheduling events
  • Reduce requirements or adjust selectors/taints

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8. Container Cannot Run

Entrypoint command or permissions issue.

  • Logs: `kubectl logs`
  • Validate commands and file permissions

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9. Exit Code 1 / 125

Immediate container failure.

  • Exit 1: app runtime error
  • Exit 125: Docker start failure
  • Test image locally with `docker run`

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10. Pods Stuck in Init/Waiting

Init containers fail.

  • Debug: `kubectl describe pod`
  • Ensure Init containers complete successfully

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3. Universal Troubleshooting Framework

| Step | Use Case | Command |

|------|----------|---------|

| Describe Resources | Full status & events | `kubectl describe pod` |

| Check Events & Logs | App & cluster behavior | `kubectl get events`, `kubectl logs` |

| Dry Run Config | Validate YAML before applying | `kubectl apply --dry-run=client -f file.yaml` |

| Resource Monitoring | CPU/memory issues | `kubectl top pod` / dashboards |

| Health Probes | Automated readiness checks | Liveness & readiness probes in YAML |

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4. Pro Tips to Prevent Future Failures

1. Automate Linting & Validation

Use tools like:

  • Kubeval
  • kube-linter
  • Datree
  • `kubectl --dry-run`

Integrate into CI/CD pipelines.

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2. Set Resource Requests & Limits Wisely

  • Start small, measure, then adjust
  • Use metrics to fine-tune
  • Prevent one Pod from exhausting cluster resources

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3. Implement Observability

Tools for visibility:

  • Prometheus + Grafana
  • Loki
  • Jaeger
  • Managed monitoring (Datadog, New Relic)

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5. References & Resources

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Would you like me to produce a one‑page visual cheat sheet for these 10 errors?

It could serve as a quick team reference or be shared via platforms like AiToEarn for broader distribution.

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