The Ultimate Guide to Fix Exit Code 137

Fix Exit Code 137
  Apr 19, 2023      AOL      Admin

Estimated Read Time : 5:00

To Fix Exit Code 137, read this article fully. Exit Code is a special meaning code in Kubernetes Container or Pods which signifies that a process running in a container has terminated due to too much memory consumption. Another name for the exit code is OOMKilled, which terminates the container. The basic resolution is that you increase the computer’s memory. Apart from that, you need to take various other measures based on the root cause that is setting off Code 137. Here we are going to discuss all the possible causes which trigger along with their solutions based on the cause. Read the article further to learn more about Kubernetes Exit Code 137.

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About Kubernetes

Kubernetes is an open-source Operating system Virtualization Model. The primary purpose for using Kubernetes is a software deployment, scaling, and Management. Through Kubernetes, users can automate, configure, and manage computer systems and software in order to establish or manage the consistency of their performance. The Working Architecture of Kubernetes involves the following component:

  • Control Plane: This is the main component which the Kubernetes Master node handles. The master manages the workload and communication across the system. Various parts of the Control Plane include the Key-Value database, scheduler, and controller.
  • Node: A node is a working unit or a machine where workload deployment occurs. Each node consists of Kubelet, Kube Proxy, and Container.
  • Pods: A pod is a basic scheduling unit in Kubernetes comprising one or more containers located on the same node.
  • Service: A set of pod working together comprises of service.

What is Exit Code 137 (OOM Killer Mechanism)

Exit Code 137 is a single reference that the process has been terminated because the memory limit has been exceeded. The code has been executed by the Out of Memory Manager present in the operating system. The OOM manager is an attribute of the Linux Kernel in order to handle container lifecycles. The Out of Memory Manager (OOM) tracks node memory consumption, detecting processes that are using too much memory and terminating them.

The Working Mechanism of OOM involves the OOM score, which for every process should be low in order to avoid any termination. Users can personalize the OOM working through another value called “oom_score_adj” by which they define the Quality of Service of the pod into three categories:

  • Guaranteed
  • Burstable
  • BestEffort

A pod/process which is terminated can also be restarted once the node policy is set to “Always”.

Various Possible Causes Due to Exit Code 137 Occurring in the System:

The first step to resolving any issue is to detect the root cause of the problem and proceed accordingly. Understanding the incitement helps the user to create an effective approach toward the rectification of the problem. The following are various causes that triggers code 137:

  • Limited Memory: As already discussed, this is the primary reason for causing exit code 137 in the system. A pod has a limited memory consumption, which, once increased, will be terminated by the Out of Memory Manager.
  • Memory Leak: Memory leak refers to incorrect memory allocation by the computer program. Memory leaks usually occur when any program does not release the resources it has acquired. Any container which is having certain memory limits can start to leak when it reaches the memory limit. Due to this, the OOM manager terminated the process and showed Exit Code 137.
  • An issue with Network Nodes: A network node is a connection point that serves either as an endpoint for data transmission. Within a Kubernetes environment, there are numerous nodes all together working. However, when the network memory consumption increases the nodes’ memory, it gets the node overloaded, which thus triggers Exit Code 137.

How to fix Exit code 137

Based on the possible cause, various troubleshooting measures is considerable in order to fix exit code 137. The following are the resolution methods for Code 137:

Fix Error Code 137 if the Memory limits Reaches to Higher Level:

The first preventive is to increase the memory limit of the system or for the Kubernetes pod in the container specifications. In order to assign memory resources for Kubernetes Container, follow the given steps:

  • First, you need to have the Kubernetes cluster (having at least two nodes and not acting as control plane hosts) along with Kubectl command-line tool configured to each other. You can create a cluster using a Kubernetes playground like Minikube, Killercoda, or “Play with Kubernetes.”Make sure that each cluster has at least 300MiB of memory.
  • Create a namespace in order to isolate the requested resources from the rest of the cluster.
  • Specify the Memory request.
Resolve/Fix Exit Code 137 in case of an Overloaded Node

A case of overloaded or overcommitted nodes occurs because the pods can plan on a Node in case their memory request limit is less than the available memory on the pod.  Overloading of Nodes results in terminating the pods:

  • First, the node will terminate the pods with no request or limits.
  • Secondly, the pods with memory requests and no limits will terminates.
  • After that, the node will terminate the pods which are consuming more memory than requested within memory limits.
  • Finally, if the memory issue still persists, then the node will terminate those pods that are consuming more than their memory limits.

In order to resolve this, you need to control when to terminate a pod with an OOMKilled Error. Apart from that, you need to specify memory request and limit in order to avoid overload issue with the node.

Wrapping it Up

From the above discussion, we hope that we have provided all the possible measures in order to eliminate Exit Code 137. In case you have any trouble, then you can reach out to us at the given number below. Also, you can connect with us through live chat, where our technical experts are available 24/7 to provide you with further assistance.

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