You’ve Encountered CrashLoopBackOff Error – What Now?August 16, 2021 No Comments
Featured article by Jeff Broth
It is inevitable that users will encounter different kinds of errors in any software development environment. The CrashLoopBackOff error is one of the common errors that will occur when dealing with Kubernetes deployments. This article covers how to identify a CrashLoopBackOff error and how to diagnose it effectively in order to find the root cause.
What is CrashLoopBackOff?
You can encounter CrashLoopBackOff error in Kubernetes pods while trying to deploy a containerized application. CrashLoopBackOff error indicates that the pod is repeatedly starting and crashing. This simply means that the pod is stuck in a crash loop.
This issue is directly tied to the lifecycle of pods. The number of restarts depends on the restart policy attached to containers. Every container has an attached restart policy with one of the following options; Always, OnFailure, and Never. The default policy is set to “Always”. Once a failure is detected, “kubelet” will restart the containers according to its restart policy with an exponential back-off delay capped at five minutes.
Identifying CrashLoopBackOff Error
If your application has failed, you first need to look at the pod status. “Kubectl get pods” will indicate the current stats of the pod.
You can use ”kubectl describe pod web-application” to drill down the issue further and get additional information about the pod with the error.
You can get detailed information about the container using the above command. For instance, the above output indicates that the container state is waiting due to the CrashLoopBackOff error, and the last known state is “Terminated” due to the error. The Exit Code value is invaluable when identifying errors as it corresponds to standard Linux interruption signals. If the Exit Code value is equal to zero, that means the container was gracefully exited, while anything else between 1 and 255 corresponds to an error.
Another important section of this output is the events section, which presents a timeline of events until the CrashLoopBackOff error. This allows users to identify the point in the pod lifecycle where the error occurred.
Another way to get the events is to use the “get event” command. It enables users to obtain a more detailed view of the specific event.
Common Causes of CrashLoopBackOff Error and How to Diagnose Them
You now know how to identify a CrashLoopBackOff error, so let us move on to the common causes of this error and how to diagnose them.
The most probable reason for the error is an application-related issue that causes the container to crash. You can identify application issues by looking at the container logs, application code, or container build files.
The “kubectl logs” command can be used to obtain the logs for pods and containers. However, the usefulness of these logs depends on the logging architecture of the cluster and the container.
Some of the common issues related to application failures are as follows.
- Missing scripts or incorrect paths in ENTRYPOINT or CMD in build files, which will lead to the container looking for nonexistent or faulty scripts that will cause issues in the image. Knowing the startup commands are important to understand the behavior of the container and mitigate CrashLoopBackOff errors successfully.
- Permissions issues. This error will occur if the application requires privileged access such as coreDNS without setting the allowPrivilegeEscalation.
Operating system security architectures like SELinux or Apparmor can cause application execution errors. The simplest way to debug such issues is to run the container in a local environment and fix any issues before deploying it in the cluster.
Kubernetes uses liveness, readiness, and startup probes to monitor containers. Thus, the underlying container will be automatically restarted in an event where the liveness or the startup probe fails.
You need to verify the correct configuration of these probes as it depends on the probe configurations. For example, some probes might be configured to use HTTP GET requests, while others are configured to use TCP sockets. You must consider all the aspects of the probe, including endpoints, ports, timeouts, etc., when troubleshooting.
Another reason for probe failures will be connectivity issues within the cluster. The simplest way to diagnose these issues is to spin up ephemeral containers and troubleshoot connectivity using bash commands. This is highly useful if you troubleshoot with distro-less images that do not contain shell environments or debugging tools.
Out of Memory (OOM) Errors
These errors are caused due to insufficient resource allocation to pods. Each pod has a specific memory amount allocated to it, and the pod will encounter crashes when that memory threshold is exceeded. The primary reasons for this will be insufficient memory allocation for the application to function properly or a phantom process in the application itself that consumes the memory.
These issues can be easily identified, as the Reason field in the Last State Section of the pod description will indicate “OOMKilled” signifying a failure due to an OOM error. The first step to mitigate this error is allocating adequate memory to the failing pod and trying to restart the pod. However, if the application still fails to start or consumes excessive memory, you will have to debug the application to find the root cause.
CrashLoopBackOff error indicates repeated crashes in a pod, disabling the applications’ start. There can be multiple reasons for this error. Therefore, the best solution is to peruse the pod events and logs to identify the root cause or using troubleshooting tools such as ephemeral containers to check the container functionality and fix issues that can lead to crash loops.DATA and ANALYTICS , SECURITY