This means that the resources in the CPU are prioritized depending on the value of shares. CPU management is delegated to the system scheduler, and it uses two different mechanisms for the requests and the limits enforcement.ĬPU requests are managed using the shares system. A container using more memory than the limit will most likely die, but using CPU can never be the reason of Kubernetes killing a container. There are many differences on how CPU and memory requests and limits are treated in Kubernetes. In day-to-day operation, this means that in case of overcommitting resources, pods without limits will likely be killed, containers using more resources than requested have some chances to die and guaranteed containers will most likely be fine. This scenario should be avoided as it will probably require a complicated troubleshooting, ending with an RCA based on hypothesis and a node restart. If the system memory fills too quickly, the system can kill Kubernetes control processes, making the node unstable. The pod can be restarted depending on the policy, so that doesn’t mean the pod will be removed entirely.ĭespite this mechanism, we can still finish up with system OOM kills as Kubernetes memory management runs only every several seconds. With these parameters, a blender and some maths, Kubernetes elaborates a score. Memory used by the different containers.Many parameters enter the equation at the same time: Memory management in Kubernetes is complex, as it has many facets. This usually causes the death of some pods in order to free some memory. In practice, if all containers use more memory than requested, it can exhaust the memory in the node. This is called overcommit and it is very common. But limits can be higher than requests, so the sum of all limits can be higher than node capacity. Kubernetes will not allocate pods that sum to more memory requested than memory available in a node. Memory requested is granted to the containers so they can always use that memory, right? Well, it’s complicated. the limit of that container.įind these metrics in Sysdig Monitor in the dashboard: Hosts & containers → Container limits Kubernetes OOM kill due to limit overcommit In Kubernetes, limits are applied to containers, not pods, so monitor the memory usage of a container vs. Percentage of the node memory used by a pod is usually a bad indicator as it gives no indication on how close to the limit the memory usage is. 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.
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