Exit Code 139 is also known as a Segmentation Fault, commonly referenced by the shorthand SIGSEGV. Typically, this fault occurs when a Kubernetes container attempts to access a memory repository that it doesn’t have permission to access. Due to this attempt at entering a repository that is not assigned to the particular container, it will then be terminated.
This forceful termination is what produces the SIGSEGV error, returning ‘exit code 139’ on the console. This fault lies within the operating system that you’re using. Since it may notice the memory violation and fail to function by triggering the termination of the process.
Why is SIGSEGV happening in my Kubernetes environment?
If your ecosystem returns with exit code 139, then you’ve got a SIGSEGV error on your hands. Typically, while the exit code returns due to the operating system rejecting a container’s access to particular memory banks, the reason for this rejection isn’t always black and white.
In fact, there are several causes of SIGSEGV that could be the main reason for your segmentation error. Whenever you receive this particular error, you should check the following three areas:
In your low-level system configuration settings, you’re able to allocate different memory subsystems. Typically, if you are repeatedly getting exit code 139 on a range of different libraries, it could be due to you assigning memory libraries to the wrong places. Segment violations occur if the memory repositories on a machine allocate incorrectly or the settings are not correct.
This is by far the most common reason for SIGSEGV errors, and luckily one that is very easy to fix. After updating a library, if you forget to change the version number of that library, then your system may attempt to load the older binary library. If this older binary then tries to access memory addresses assigned to the newer library, then an incompatibility error exists across your binaries and libraries. This is a very common mistake. It can be fixed by simply updating the version number whenever you update a library and its binaries.
Errors in Code
Coding errors are a common reason for segmentation violations. You can typically spot a SIGSEGV error that’s produced due to errors in your code due to the fact that the violation will be within a certain binary file or a particular process. If you haven’t initialized a process properly, then it will attempt to access memory files through any previous configurations. This incompatibility and oversight is the main reason for the error.
Across these three main reasons, you’ll find the vast majority of your segmentation errors.
How do I fix my Kubernetes ecosystem if I get a SIGSEGV error?
Once you’ve identified that you’re currently working against an exit code 139, then you should attempt to fix it as quickly as possible. This ensures that no processes are abandoned when they attempt to access private memory repositories.
Within the Kubernetes environment, you’ll most likely experience this error within a container at some point. This is a common error and one that’s not too difficult to fix, so alarm bells shouldn’t be ringing. The core difference between Kubernetes and how an operating system approaches this error is that Kubernetes will terminate a pod and then try to restart it.
Unless you’ve specified that you don’t want your pods to restart within their configuration files, then Kubernetes will terminate whichever pod is attempting to access a memory bank it should not have access to. Then it tries to restart it. If the error persists, you will then be notified.
If you receive this notification, then you should follow this three-step process:
Check the Container
The first step should always be to check the specific container that is creating the SIGSEGV response. If any documentation has been generated, you can use this to find out more about your issue.
Investigate and Debug
Part of the investigation process should be attempting to intentionally cause the error in order to then retroactively fix it. To ensure you can work through segmentation violations, you should change the configurations of your operating system. This allows them to continue working even after the fault occurs. This will help you spot the bad practice in action and then fix it.
Once you’ve finished the first two steps, it is most likely that you will have already solved your problem. However, if the error persists or you can’t find the location of the particular memory library which is causing the problem, then you can turn to memory configuration. Frustratingly, this is quite a manual process. So, if you get to this step, you might have to spend some time on it. You should work through each memory subsystem within your ecosystem. Do this by checking where they are assigned until you find the error.
Typically, the first two steps are more than enough to fix this issue. However, in extreme cases, the third point will be your last option. While time-consuming, as you’re physically checking each repository, you’re very likely to find the source of the error.
If your Kubernetes ecosystem has started returning exit code 139, then you now know that you have a segmentation error on your hands. This error is fairly easy to fix. So you won’t need to panic as this particular problem has several solutions that you can turn to. Typically, you can go through the process of debugging the exit code 139 in order to generate enough insight to fix the problem.
Additionally, most of the time, your system will outline the concern. And it will create enough documentation for you to use to get to the bottom of your issue. Only in the most extreme cases will you have to do the further manual work of searching for the particular memory violation that is causing the problem.