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Ashok Sharma
Ashok Sharma

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Segmentation Errors in Kubernetes and How to Fix Them

Within the Kubernetes ecosystem, although it can deal with a lot of different circumstances, effectively helping developers to build containerized applications, things can sometimes go wrong. Coding errors are commonplace, with small human mistakes causing breakdowns within your Kubernetes ecosystem.

Among the most common errors that you’ll come across as you begin to code with Kubernetes is what’s known as segmentation errors. These are indicated by an error known as SIGSEGV, often referenced as exit code 139.

If this error comes up within your system, you don’t have to panic; in fact, this is an error that routinely comes up and isn’t much of a hassle to fix. If you’re currently facing down a segmentation error, then this article will help you overcome it.

What Is a Segmentation Error in Kubernetes?
In multipurpose computing ecosystems, like those used and developed within Kubernetes, developers will frequently have to use memory management units, or MMU’s. These MMU’s are what allow operating systems to compartmentalize different functions, allowing things to run simultaneously without impacting other processes' memories. With this function in place, you can create a system that has potentially hundreds or thousands of processes, each of which is totally isolated from the other.

This, on a larger scale, is exactly what Kubernetes does, allowing developers to build containerized applications that run simultaneously to one another.

A segmentation error occurs when one process attempts to access the memory files of a bank that is not assigned to it. This essentially breaks the isolation of the system, flagging the whole system in error and singling out the location where the break has happened.

While related to the MMU and the leakage of this system, there are actually three main causes of segmentation errors in Kubernetes:

  • Wrong Configurations - When assigning MMU’s to different processes, developers need to be careful to create an isolated system. If lots of segmentation errors are occurring within the platform with frequency, then this suggests that there is an underlying problem with how the developer is configuring their system. Always be sure to double-check your processes for assigning MMU’s to ensure there aren’t any wrong configurations.

  • Human Mistake - Everyone makes mistakes, especially when it comes to creating advanced ecosystems of different processes, as is the case with Kubernetes. When a developer accidentally initializes a process badly, using the wrong code, then an error will occur. Ensure that the files you’re accessing have the correct configurations and are initialized correctly.

  • General Incompatibility - Segmentation errors arise from a communication between two things that should not be communicating. One of the leading forms of this is binaries and libraries attempting to communicate. If your process runs a binary that simply isn’t compatible with the shared library you’re directing it to, then it won’t be able to execute its function properly and will result in a memory-based segmentation error.

As a memory-based error, SIGSEGV isn’t the worst possible error to see within Kubernetes, as it is fairly easy to debug and then remedy.

How to Troubleshoot Segmentation Errors in Kubernetes
Within a Linux system, SIGSEGV is fairly easy to track, and accurate and detailed documentation is provided. Kubernetes, on the other hand, runs segmentation errors slightly differently. Foremost, Kubernetes doesn’t instantly trigger the error signal SIGSEGV automatically. Instead, it will continue to run the pod and attempt to resolve the problem itself, often causing lots of errors while doing so.

If you see your terminal returning exit code 139, which is short for segmentation error, then you’ll instantly know that you have a memory configuration issue. Unfortunately, this could mean you have an issue in a range of different areas:

  • Libraries
  • Hardware itself
  • Host management system
  • Within application code

Due to the number of different locations where you may find an error, you should follow a process to debug your system.

How to Debug Segmentation Errors in Kubernetes
Debugging within Kubernetes is fairly simple, only requiring you to follow a range of commands and use some light detective skills.

Within Kubernetes, start off your debugging process for a segmentation error by:

  • On the host machine, take a look through the error logs to find any additional information that has been created around the segmentation error within a certain container. You can search for ‘segmentation violation’ within your code to jump to the error.
  • Within this information, you should identify on which layer the error is occurring. Is it within your application code, or even further down in the container?
  • Use the command docker pull {image-id}, pulling up the specific container that the segmentation error is currently clashing with. Be sure to use the image-id from the first step’s additional information.
  • Using kubectl commands, enter the container and see if you can instantly see which library is having issues.
  • If you can see which library it is, you should attempt to modify anything that attaches to the memory. For example, change your image, or update the library to a newer version. Typically, if there is an incompatibility between the library version and the system itself, this will be the root cause of the error.

If after this debugging process, you still cannot find a singular library that is causing the segmentation error, then you might have a slightly larger problem. In this case, your error isn’t caused by a library misconfiguration, but by the host itself. From there, you’ll need to look at the memory configurations of the host system and hardware to ensure that everything is correctly configured.

From there, you’ll know the root cause of your segmentation error within Kubernetes and will have all the information necessary to then fix your error. After fixing it, exit code 139 should then go away, leaving you with a system that’s working effectively again.

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