10 Kubernetes Errors you must know and How to Fix Them

Table of Contents

1. Introduction: Why Kubernetes Errors Happen So Often

2. Understanding Kubernetes Error Patterns

3. Error #1: CrashLoopBackOff

4. Error #2: ImagePullBackOff and ErrImagePull

5. Error #3: Pending Pods (Insufficient Resources)

6. Error #4: OOMKilled (Out of Memory)

7. Error #5: Node NotReady

8. Error #6: Service Not Accessible (Networking Issues)

9. Error #7: Failed Mount / Volume Errors

10. Error #8: CreateContainerConfigError

11. Error #9: Unauthorized or RBAC Permission Errors

12. Error #10: DNS Resolution Failures in Kubernetes

13. Kubernetes Error Prevention Best Practices

14. Frequently Asked Questions (FAQs)

15. Final Thoughts

1. Introduction: Why Kubernetes Errors Happen So Often

Kubernetes is powerful, flexible, and incredibly popular but it is also unforgiving. A single misconfigured YAML file, an incorrect image tag, or a missing permission can bring your workloads to a grinding halt. For beginners, Kubernetes errors feel cryptic. For experienced engineers, they are familiar but still time-consuming.

The reality is simple: Kubernetes is a distributed system. Distributed systems fail in more ways than monolithic applications ever could. Nodes disappear, containers crash, networks flake out, and storage gets detached at the worst possible time.

This article focuses on Kubernetes errors you must know, not obscure edge cases. These are the errors you will see repeatedly in real production clusters. More importantly, you will learn why they happen, how to diagnose them, and how to fix them properly.

If you work with Kubernetes in any serious capacity DevOps engineer, SRE, platform engineer, or backend developer this guide will save you hours of debugging.

2. Understanding Kubernetes Error Patterns

Before jumping into specific issues, it helps to understand how Kubernetes reports errors. Kubernetes rarely tells you exactly what went wrong in one place. Instead, information is scattered across events, pod statuses, logs, and controller messages.

Most Kubernetes troubleshooting starts with three commands:

• kubectl get pods

• kubectl describe pod

• kubectl logs

Errors usually fall into a few broad categories:

• Configuration errors (bad YAML, missing environment variables)

• Resource issues (CPU, memory, disk)

• Image and registry issues

• Networking and DNS failures

• Security and permission problems

Recognizing the pattern early helps you narrow down the root cause quickly.

3. Error #1: CrashLoopBackOff

What is CrashLoopBackOff?

CrashLoopBackOff is one of the most common Kubernetes pod errors. It means your container starts, crashes, restarts, and repeats this cycle continuously.

Kubernetes is doing its job trying to keep your pod alive but the application inside the container keeps failing.

Common Causes

• Application crashes due to bad configuration

• Missing environment variables or secrets

• Incorrect command or entrypoint

• Dependency services not available

How to Diagnose

1. Describe the pod to check events:
   kubectl describe pod

2. View container logs:
   kubectl logs

3. If the container crashes too fast, check previous logs:
   kubectl logs --previous

How to Fix

• Fix application-level errors shown in logs

• Validate environment variables and secrets

• Test the container locally before deploying

• Add readiness and liveness probes carefully

CrashLoopBackOff is rarely a Kubernetes bug—it is almost always an application or configuration issue.

4. Error #2: ImagePullBackOff and ErrImagePull

What This Error Means

These errors indicate Kubernetes cannot pull the container image from the registry. Without the image, the pod cannot start.

Common Causes

• Incorrect image name or tag

• Private registry authentication failure

• Image does not exist

• Network access issues

How to Diagnose

Run:

kubectl describe pod

Look for messages like:

• Wrong image tag

• Authentication required

• Repository not found

How to Fix

• Verify the image name and tag

• Check if the image exists in the registry

• Configure imagePullSecrets for private registries

• Ensure nodes have outbound internet access

ImagePullBackOff is one of the easiest Kubernetes errors to fix once you know where to look.

5. Error #3: Pending Pods (Insufficient Resources)

What Does Pending Mean?

A pod in the Pending state means Kubernetes cannot schedule it on any node. The most common reason is insufficient resources.

Common Causes

• Not enough CPU or memory

• Node selectors or affinity rules too strict

• Taints without matching tolerations

How to Diagnose

Describe the pod:

kubectl describe pod

Look for scheduling errors such as:

• Insufficient CPU

• Insufficient memory

• No nodes match affinity rules

How to Fix

• Reduce resource requests

• Add more nodes to the cluster

• Adjust node affinity and tolerations

• Enable cluster autoscaling

Pending pods are a sign that your cluster capacity planning needs attention.

6. Error #4: OOMKilled (Out of Memory)

What is OOMKilled?

OOMKilled occurs when a container exceeds its memory limit. The Linux kernel kills the process to protect the node.

Common Causes

• Memory limits set too low

• Memory leaks in the application

• Sudden traffic spikes

How to Diagnose

Check pod status:

kubectl get pod -o wide

Then inspect container state:

kubectl describe pod

Look for OOMKilled in the last state.

How to Fix

• Increase memory limits

• Profile application memory usage

• Implement caching limits

• Add horizontal pod autoscaling

OOMKilled errors are performance and stability warnings, not just configuration mistakes.

7. Error #5: Node NotReady

What Does Node NotReady Mean?

A node in NotReady state cannot accept new pods. Existing pods may also be evicted.

Common Causes

• Kubelet stopped or crashed

• Network connectivity issues

• Disk pressure

• Cloud provider interruptions

How to Diagnose

Check node status:

kubectl get nodes

Describe the node:

kubectl describe node

How to Fix

• Restart kubelet

• Check disk and memory usage

• Verify network connectivity

• Replace unhealthy nodes

Node issues often indicate underlying infrastructure problems.

8. Error #6: Service Not Accessible (Networking Issues)

Symptoms

• Service not reachable

• Requests timing out

• Pods can’t talk to each other

Common Causes

• Incorrect Service selector

• Network policies blocking traffic

• Misconfigured Ingress

• CNI plugin issues

How to Diagnose

• Check service endpoints

• Verify pod labels

• Test connectivity using kubectl exec

How to Fix

• Correct service selectors

• Review network policies

• Validate Ingress configuration

• Restart CNI components if needed

Networking issues are among the hardest Kubernetes problems to debug.

9. Error #7: Failed Mount / Volume Errors

What This Error Means

Kubernetes cannot mount a volume into the pod.

Common Causes

• Missing PersistentVolume

• Incorrect StorageClass

• Permission issues

• Cloud storage failures

How to Diagnose

Describe the pod and check events:

kubectl describe pod

How to Fix

• Ensure PV and PVC match

• Verify StorageClass

• Check cloud provider permissions

• Recreate stuck PVCs carefully

Storage issues can block entire applications.

10. Error #8: CreateContainerConfigError

What Causes This?

This error occurs when Kubernetes cannot create the container configuration.

Common Causes

• Missing ConfigMaps or Secrets

• Invalid environment variable references

• Incorrect volume mounts

How to Diagnose

Describe the pod and read events carefully.

How to Fix

• Create missing ConfigMaps or Secrets

• Fix YAML references

• Validate configuration before deployment

This error is almost always configuration-related.

11. Error #9: Unauthorized or RBAC Permission Errors

Symptoms

• Forbidden errors

• Access denied messages

Common Causes

• Missing Role or ClusterRole

• Incorrect RoleBinding

• ServiceAccount misconfiguration

How to Fix

• Audit RBAC policies

• Use least-privilege access

• Test permissions with kubectl auth can-i

Security misconfigurations are common in growing clusters.

12. Error #10: DNS Resolution Failures in Kubernetes

Symptoms

• Services not resolving

• External domains unreachable

Common Causes

• CoreDNS misconfiguration

• Network plugin issues

• Incorrect DNS policies

How to Fix

• Check CoreDNS pods

• Review DNS configuration

• Restart DNS components if necessary

DNS failures can make healthy apps appear broken.

13. Kubernetes Error Prevention Best Practices

• Validate YAML using CI pipelines

• Use resource requests and limits properly

• Monitor cluster health continuously

• Implement logging and observability

• Practice chaos testing

Prevention is cheaper than firefighting.

14. Frequently Asked Questions (FAQs)

Q1. What is the most common Kubernetes error?

CrashLoopBackOff is the most frequently encountered Kubernetes error.

Q2. How do I debug Kubernetes pod errors faster?

Start with kubectl describe and logs, then check events.

Q3. Are Kubernetes errors always configuration-related?

No. Many are caused by infrastructure, networking, or resource issues.

Q4. How can I avoid ImagePullBackOff errors?

Verify image tags and registry authentication before deployment.

Q5. What causes pods to stay in Pending state?

Insufficient resources or scheduling constraints.

Q6. Is OOMKilled a Kubernetes bug?

No. It indicates memory limits being exceeded.

Q7. Why is Kubernetes networking so complex?

Because it spans pods, services, nodes, and external systems.

Q8. How do RBAC errors affect applications?

They block access to required Kubernetes resources.

Q9. Can DNS issues break microservices?

Yes. DNS is critical for service discovery.

Q10. What is the best way to learn Kubernetes troubleshooting?

Hands-on practice and real-world incident analysis.

15. Final Thoughts

Kubernetes errors are not a sign of failure they are part of operating a complex distributed system. The key is not avoiding errors entirely, but recognizing them quickly and fixing them with confidence.

By mastering these 10 Kubernetes errors you must know, you will dramatically reduce downtime, improve reliability, and become far more effective at running production workloads.

The more clusters you run, the more these errors will feel familiar and eventually, routine.