![]() (rate(container_cpu_usage_seconds_total)) > 0.8 Instead, you can check how close a process is to the Kubernetes limits: (sum by (namespace,pod,container) It’s important to track when an OOM kill happens, but you might want to get ahead and have visibility of such an event before it happens. When using node exporter in Prometheus, there’s one metric called node_vmstat_oom_kill. 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. Limits can be higher than requests, so the sum of all limits can be higher than node capacity. A Kubernetes ResourceQuota set on the namespace.A Kubernetes Limit set on the container.Note that in Kubernetes, a process can reach any of these limits: It also features an OOM Killer, which will review the process and terminate those that are using more memory than they should. Additionally, it allows setting a value called oom_score_adj, which is used by Kubernetes to allow Quality of Service. This is a feature present in Linux, where the kernel sets an oom_score value for the process running in the system. This Exit Code 137 means that the process used more memory than the allowed amount and had to be terminated. State: Running Started: Thu, 11 :14:13 +0200 Last State: Terminated Reason: OOMKilled Exit Code: 137 Started: Thu, 11 :04:03 +0200 Finished: Thu, 11 :14:11 +0200 Code language: YAML ( yaml ) This will be marked as Error 137 or OOMKilled. Kubernetes limits are set per container in either a Pod definition or a Deployment definition.Īll modern Unix systems have a way to kill processes in case they need to reclaim memory. In case CPU consumption is higher than the actual limits, the process will start to be throttled.īut, how can you actively monitor how close your Kubernetes Pods to OOM and CPU throttling? Kubernetes OOMĮvery container in a Pod needs memory to run. When a process runs Out Of Memory (OOM), it’s killed since it doesn’t have the required resources. In case a Node doesn’t have enough resources, Pods might get evicted via preemption or node-pressure. With limits and requests, you can configure how your pods should allocate memory and CPU resources in order to prevent resource starvation and adjust cloud costs. Why is that?ĬPU and Memory requirements in cloud applications are ever more important, since they are tied directly to your cloud costs. When working with Kubernetes, Out of Memory (OOM) errors and CPU throttling are the main headaches of resource handling in cloud applications.
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