CrashLoopBackOff in Kubernetes: A Common but Solvable Problem

CrashLoopBackOff may sound like the name of a 90s grunge band, or possibly a vague reference to an obscure Ringo Starr song from the 1970s. But in actuality, CrashLoopBackOffs have nothing to do with the music industry and everything to do with Kubernetes performance and reliability. For a variety of reasons, Kubernetes Pods can end up in a state known as CrashLoopBackOff, and until you fix it, any applications that depend on those Pods won't run correctly.

Keep reading for a dive into what CrashLoopBackOff errors mean, why they happen and how to troubleshoot them so you can get your Pods back up and running quickly.

What is a CrashLoopBackOff error?

A CrashLoopBackOff error is a condition where containers inside a Kubernetes Pod restart and subsequently crash in a never-ending loop. When this happens, Kubernetes will begin introducing a delay – known as a backoff period – between restarts, in an effort to give admins time to correct whichever issue is triggering the recurring crashes.

So, although CrashLoopBackOff may sound like a nonsensical term, it actually makes sense when you think about it: It refers to a state where your containers are in a loop of repeated crashes, with backoff periods introduced between the crashes.

Causes of CrashLoopBackOff

Although all CrashLoopBackOff states result in the same type of problem – a never-ending loop of crashes – there are many potential causes:

 • ImagePullBackOff: Your containers might not be starting properly because an image can't be pulled.

 • OutOfMemory (OOM): The containers could be exceeding their allowed memory limits.

 • Configuration errors: Configuration issues like improper environment variables or command arguments could be triggering a crash loop.

 • Application bugs: Errors in application code could be causing the containers to crash shortly after they start.

 • Resource conflicts: Multiple containers may be competing for the same resources in ways that cause them to crash.

 • Persistent storage issues: If there is an issue accessing persistent storage volumes, containers may not be starting properly.

 • Network connectivity problems: Network issues, which could result either from networking configuration problems or a network failure, could be triggering crashes.

In short, basically any condition that could cause containers to crash right after they start has the potential to create a CrashLoopBackOff. If the containers repeatedly try to restart and repeatedly fail, you get this error.

What does a CrashLoopBackOff error look like in Kubernetes?

No red light starts flashing on your Kubernetes console when a CrashLoopBackOff event occurs. Kubernetes doesn't explicitly warn you about the issue.

But you can figure out that it's happening by checking on the state of your Pods with a command like:

(demo-ng is the namespace we’ll be targeting in this guide, as the name suggests it’s a demo setup).
If you see results like the following, you'll know you have a CrashLoopBackOff issue:

Specifically, there are two Pods experiencing this issue – the currencyservice and checkoutservice Pods – in the example above.

You may also have a CrashLoopBackOff error if you see Pods that are not in the Ready condition, or that have been restarted a number of times (which you can check by looking at the restart count in the RESTARTS column of the kubectl output). Although those conditions don't necessarily imply a CrashLoopBackOff error, they may constitute one, even if Kubernetes doesn't explicitly describe the Pod status as being CrashLoopBackOff.

Troubleshooting and fixing CrashLoopBackOff errors

Of course, knowing that you have a CrashLoopBackOff issue is only the first step in resolving the problem. You'll need to dig deeper to troubleshoot the cause of CrashLoopBackOff errors and figure out how to resolve them.

Check Pod description

The first step in this process is to get as much information about the Pod as you can using the kubectl describe pod command. For example, if you were troubleshooting the CrashLoopBackOff errors in the checkoutservice-7db49c4d49-7cv5d Pod we saw above, you'd run:

You'd see output like the following:

In reviewing the output, pay particular attention to the following:

 •  The pod definition.

 •  The container.

 •  The image pulled for the container.

 •  Resources allocated for the container.

 •  Wrong or missing arguments.

In the example above, you can see that the readiness probe failed. Notice also the Warning BackOff…restarting failed container message. This is the event linked to the restart loop. There should be just one line even if multiple restarts have happened.

Check Pod events

It's also possible to get this information using the kubectl getevents command:

Which generates output like:

Notice that these are the same messages we got by describing the Pod.

Dig through container logs

At this point, we have a sense of why the restarts are happening – they're related to a failed readiness probe and recurring failed restarts – but we still don't know exactly why those things are occurring.

So, let's check the container logs to see if that provides a clue. We run:

Which results in:

Sadly,  this could be unhelpful in many cases. But in our case – an application error indicates the root cause of our CrashLoopBackOff – the application is trying to run an unfound script. In this case, that should be focused enough to help us solve the issue, and remediate the CrashLoopBackoff..

Looking at the ReplicaSet

In other cases, where container logs are unhelpful, we’ll need to keep digging. We can look for more clues by checking out the workload that controls our Pod.

The ReplicaSet controlling our Pod in question might contain a misconfiguration that is causing the CrashLoopBackOff.

When we described the Pod, we saw the following:

This line tells us that the Pod is part of a ReplicaSet. So, investigating the ReplicaSet that controls the Pod by describing the ReplicaSet would look like:

The result might help us catch a deeper configuration in the workload that controls our checkoutservice which could be a good way to continue the investigation.

Troubleshooting CrashLoopBackOff with groundcover

The troubleshooting process we just walked through is the one you'd follow if you enjoy suffering and pain.

If, on the other hand, you enjoy sunshine and rainbows, you'd be better suited by using groundcover to troubleshoot CrashLoopBackOff events. Groundcover continuously monitors all applications and Kubernetes infrastructure components inside your clusters. From one place, you can track events that might indicate a CrashloopBackoff error. You can also compare the number of running Pods to the desired state, you can access the logs from Pod containers and you can view infrastructure monitoring metrics that may indicate memory or CPU issues. With this data, you can more easily get to the root cause of CrashLoopBackOff errors.

As an example, here's how an OutOfMemory issue on a Pod (due to misconfigured resource limits) turns into a CrashLoopBackoff event, and how groundcover surfaces the issue:

Best practices for preventing CrashLoopBackOff errors in Kubernetes

Of course, even better than being able to troubleshoot CrashLoopBackOffs easily is to prevent them in the first place. While you can't foresee every possible CrashLoopBackOff cause every time, you can follow some best practices to reduce your risk of this type of problem:

  •  Properly configure resource limits: Make sure you set reasonable resource limits so that lack of resources don't cause containers to fail to start.

  •  Test container images prior to deployment: Testing is a great way to identify issues with pulling container images or their dependencies before you deploy a Pod.

  •  Monitor network connectivity and persistent storage: There are lots of reasons why you should monitor network and storage, but identifying issues that could lead to CrashLoopBackOffs is chief among them.

  •  Implement robust error handling: The better your error handling, the lower the chances that your applications will experience a condition that leads to a CrashLoopBackOff.