In order to monitor this, you always have to look at the use of memory compared to the limit. The Exit code 137 is important because it means that the system terminated the container as it tried to use more memory than its limit. Normal Killing 2m56s kubelet, gke-lab-kube-gke-default-pool-02126501-7nqc Killing container with id docker://db:Need to kill Pod Normal SandboxChanged 2m57s kubelet, gke-lab-kube-gke-default-pool-02126501-7nqc Pod sandbox changed, it will be killed and re-created. Normal Scheduled 6m39s default-scheduler Successfully assigned test/frontend to gke-lab-kube-gke-default-pool-02126501-7nqc This usually ends up with a container dying, one pod unhealthy and Kubernetes restarting that pod.ĭescribe pods output would show something like this:

You set a memory limit, one container tries to allocate more memory than that allowed,and it gets an error. This is by far the most simple memory error you can have in a pod. This frees memory to relieve the memory pressure.

These pods are scheduled in a different node if they are managed by a ReplicaSet. When the node is low on memory, Kubernetes eviction policy enters the game and stops pods as failed. Kubernetes OOM management tries to avoid the system running behind trigger its own. When any Unix based system runs out of memory, OOM safeguard kicks in and kills certain processes based on obscure rules only accessible to level 12 dark sysadmins ( chaotic neutral). In this article, we will try to help you detect the most common issues related to the usage of resources. Many issues can arise, possibly due to an incorrect configuration of Kubernetes limits and requests. Managing Kubernetes pod resources can be a challenge. Why is my application struggling if I have plenty of CPU in the node? Security and visibility for cloud applicationsĮxperience Kubernetes OOM kills can be very frustrating.

Quick cpu application crashing