Chapter 8. Working with clusters 8.1. Viewing system event information in an OpenShift Container Platform cluster Events in OpenShift Container Platform are modeled based on events that happen to API objects in an OpenShift Container Platform cluster. 8.1.1. Understanding events Events allow OpenShift Container Platform to record information about real-world events in a resource-agnostic manner. They also allow developers and administrators to consume information about system components in a unified way. 8.1.2. Viewing events using the CLI You can get a list of events in a given project using the CLI. Procedure To view events in a project use the following command: USD oc get events [-n <project>] 1 1 The name of the project. For example: USD oc get events -n openshift-config Example output LAST SEEN TYPE REASON OBJECT MESSAGE 97m Normal Scheduled pod/dapi-env-test-pod Successfully assigned openshift-config/dapi-env-test-pod to ip-10-0-171-202.ec2.internal 97m Normal Pulling pod/dapi-env-test-pod pulling image "gcr.io/google_containers/busybox" 97m Normal Pulled pod/dapi-env-test-pod Successfully pulled image "gcr.io/google_containers/busybox" 97m Normal Created pod/dapi-env-test-pod Created container 9m5s Warning FailedCreatePodSandBox pod/dapi-volume-test-pod Failed create pod sandbox: rpc error: code = Unknown desc = failed to create pod network sandbox k8s_dapi-volume-test-pod_openshift-config_6bc60c1f-452e-11e9-9140-0eec59c23068_0(748c7a40db3d08c07fb4f9eba774bd5effe5f0d5090a242432a73eee66ba9e22): Multus: Err adding pod to network "openshift-sdn": cannot set "openshift-sdn" ifname to "eth0": no netns: failed to Statfs "/proc/33366/ns/net": no such file or directory 8m31s Normal Scheduled pod/dapi-volume-test-pod Successfully assigned openshift-config/dapi-volume-test-pod to ip-10-0-171-202.ec2.internal To view events in your project from the OpenShift Container Platform console. Launch the OpenShift Container Platform console. Click Home Events and select your project. Move to resource that you want to see events. For example: Home Projects <project-name> <resource-name>. Many objects, such as pods and deployments, have their own Events tab as well, which shows events related to that object. 8.1.3. List of events This section describes the events of OpenShift Container Platform. Table 8.1. Configuration events Name Description FailedValidation Failed pod configuration validation. Table 8.2. Container events Name Description BackOff Back-off restarting failed the container. Created Container created. Failed Pull/Create/Start failed. Killing Killing the container. Started Container started. Preempting Preempting other pods. ExceededGracePeriod Container runtime did not stop the pod within specified grace period. Table 8.3. Health events Name Description Unhealthy Container is unhealthy. Table 8.4. Image events Name Description BackOff Back off Ctr Start, image pull. ErrImageNeverPull The image's NeverPull Policy is violated. Failed Failed to pull the image. InspectFailed Failed to inspect the image. Pulled Successfully pulled the image or the container image is already present on the machine. Pulling Pulling the image. Table 8.5. Image Manager events Name Description FreeDiskSpaceFailed Free disk space failed. InvalidDiskCapacity Invalid disk capacity. Table 8.6. Node events Name Description FailedMount Volume mount failed. HostNetworkNotSupported Host network not supported. HostPortConflict Host/port conflict. KubeletSetupFailed Kubelet setup failed. NilShaper Undefined shaper. NodeNotReady Node is not ready. NodeNotSchedulable Node is not schedulable. NodeReady Node is ready. NodeSchedulable Node is schedulable. NodeSelectorMismatching Node selector mismatch. OutOfDisk Out of disk. Rebooted Node rebooted. Starting Starting kubelet. FailedAttachVolume Failed to attach volume. FailedDetachVolume Failed to detach volume. VolumeResizeFailed Failed to expand/reduce volume. VolumeResizeSuccessful Successfully expanded/reduced volume. FileSystemResizeFailed Failed to expand/reduce file system. FileSystemResizeSuccessful Successfully expanded/reduced file system. FailedUnMount Failed to unmount volume. FailedMapVolume Failed to map a volume. FailedUnmapDevice Failed unmaped device. AlreadyMountedVolume Volume is already mounted. SuccessfulDetachVolume Volume is successfully detached. SuccessfulMountVolume Volume is successfully mounted. SuccessfulUnMountVolume Volume is successfully unmounted. ContainerGCFailed Container garbage collection failed. ImageGCFailed Image garbage collection failed. FailedNodeAllocatableEnforcement Failed to enforce System Reserved Cgroup limit. NodeAllocatableEnforced Enforced System Reserved Cgroup limit. UnsupportedMountOption Unsupported mount option. SandboxChanged Pod sandbox changed. FailedCreatePodSandBox Failed to create pod sandbox. FailedPodSandBoxStatus Failed pod sandbox status. Table 8.7. Pod worker events Name Description FailedSync Pod sync failed. Table 8.8. System Events Name Description SystemOOM There is an OOM (out of memory) situation on the cluster. Table 8.9. Pod events Name Description FailedKillPod Failed to stop a pod. FailedCreatePodContainer Failed to create a pod container. Failed Failed to make pod data directories. NetworkNotReady Network is not ready. FailedCreate Error creating: <error-msg> . SuccessfulCreate Created pod: <pod-name> . FailedDelete Error deleting: <error-msg> . SuccessfulDelete Deleted pod: <pod-id> . Table 8.10. Horizontal Pod AutoScaler events Name Description SelectorRequired Selector is required. InvalidSelector Could not convert selector into a corresponding internal selector object. FailedGetObjectMetric HPA was unable to compute the replica count. InvalidMetricSourceType Unknown metric source type. ValidMetricFound HPA was able to successfully calculate a replica count. FailedConvertHPA Failed to convert the given HPA. FailedGetScale HPA controller was unable to get the target's current scale. SucceededGetScale HPA controller was able to get the target's current scale. FailedComputeMetricsReplicas Failed to compute desired number of replicas based on listed metrics. FailedRescale New size: <size> ; reason: <msg> ; error: <error-msg> . SuccessfulRescale New size: <size> ; reason: <msg> . FailedUpdateStatus Failed to update status. Table 8.11. Network events (openshift-sdn) Name Description Starting Starting OpenShift SDN. NetworkFailed The pod's network interface has been lost and the pod will be stopped. Table 8.12. Network events (kube-proxy) Name Description NeedPods The service-port <serviceName>:<port> needs pods. Table 8.13. Volume events Name Description FailedBinding There are no persistent volumes available and no storage class is set. VolumeMismatch Volume size or class is different from what is requested in claim. VolumeFailedRecycle Error creating recycler pod. VolumeRecycled Occurs when volume is recycled. RecyclerPod Occurs when pod is recycled. VolumeDelete Occurs when volume is deleted. VolumeFailedDelete Error when deleting the volume. ExternalProvisioning Occurs when volume for the claim is provisioned either manually or via external software. ProvisioningFailed Failed to provision volume. ProvisioningCleanupFailed Error cleaning provisioned volume. ProvisioningSucceeded Occurs when the volume is provisioned successfully. WaitForFirstConsumer Delay binding until pod scheduling. Table 8.14. Lifecycle hooks Name Description FailedPostStartHook Handler failed for pod start. FailedPreStopHook Handler failed for pre-stop. UnfinishedPreStopHook Pre-stop hook unfinished. Table 8.15. Deployments Name Description DeploymentCancellationFailed Failed to cancel deployment. DeploymentCancelled Canceled deployment. DeploymentCreated Created new replication controller. IngressIPRangeFull No available Ingress IP to allocate to service. Table 8.16. Scheduler events Name Description FailedScheduling Failed to schedule pod: <pod-namespace>/<pod-name> . This event is raised for multiple reasons, for example: AssumePodVolumes failed, Binding rejected etc. Preempted By <preemptor-namespace>/<preemptor-name> on node <node-name> . Scheduled Successfully assigned <pod-name> to <node-name> . Table 8.17. Daemon set events Name Description SelectingAll This daemon set is selecting all pods. A non-empty selector is required. FailedPlacement Failed to place pod on <node-name> . FailedDaemonPod Found failed daemon pod <pod-name> on node <node-name> , will try to kill it. Table 8.18. LoadBalancer service events Name Description CreatingLoadBalancerFailed Error creating load balancer. DeletingLoadBalancer Deleting load balancer. EnsuringLoadBalancer Ensuring load balancer. EnsuredLoadBalancer Ensured load balancer. UnAvailableLoadBalancer There are no available nodes for LoadBalancer service. LoadBalancerSourceRanges Lists the new LoadBalancerSourceRanges . For example, <old-source-range> <new-source-range> . LoadbalancerIP Lists the new IP address. For example, <old-ip> <new-ip> . ExternalIP Lists external IP address. For example, Added: <external-ip> . UID Lists the new UID. For example, <old-service-uid> <new-service-uid> . ExternalTrafficPolicy Lists the new ExternalTrafficPolicy . For example, <old-policy> <new-policy> . HealthCheckNodePort Lists the new HealthCheckNodePort . For example, <old-node-port> new-node-port> . UpdatedLoadBalancer Updated load balancer with new hosts. LoadBalancerUpdateFailed Error updating load balancer with new hosts. DeletingLoadBalancer Deleting load balancer. DeletingLoadBalancerFailed Error deleting load balancer. DeletedLoadBalancer Deleted load balancer. 8.2. Estimating the number of pods your OpenShift Container Platform nodes can hold As a cluster administrator, you can use the OpenShift Cluster Capacity Tool to view the number of pods that can be scheduled to increase the current resources before they become exhausted, and to ensure any future pods can be scheduled. This capacity comes from an individual node host in a cluster, and includes CPU, memory, disk space, and others. 8.2.1. Understanding the OpenShift Cluster Capacity Tool The OpenShift Cluster Capacity Tool simulates a sequence of scheduling decisions to determine how many instances of an input pod can be scheduled on the cluster before it is exhausted of resources to provide a more accurate estimation. Note The remaining allocatable capacity is a rough estimation, because it does not count all of the resources being distributed among nodes. It analyzes only the remaining resources and estimates the available capacity that is still consumable in terms of a number of instances of a pod with given requirements that can be scheduled in a cluster. Also, pods might only have scheduling support on particular sets of nodes based on its selection and affinity criteria. As a result, the estimation of which remaining pods a cluster can schedule can be difficult. You can run the OpenShift Cluster Capacity Tool as a stand-alone utility from the command line, or as a job in a pod inside an OpenShift Container Platform cluster. Running the tool as job inside of a pod enables you to run it multiple times without intervention. 8.2.2. Running the OpenShift Cluster Capacity Tool on the command line You can run the OpenShift Cluster Capacity Tool from the command line to estimate the number of pods that can be scheduled onto your cluster. You create a sample pod spec file, which the tool uses for estimating resource usage. The pod spec specifies its resource requirements as limits or requests . The cluster capacity tool takes the pod's resource requirements into account for its estimation analysis. Prerequisites Run the OpenShift Cluster Capacity Tool , which is available as a container image from the Red Hat Ecosystem Catalog. Create a sample pod spec file: Create a YAML file similar to the following: apiVersion: v1 kind: Pod metadata: name: small-pod labels: app: guestbook tier: frontend spec: containers: - name: php-redis image: gcr.io/google-samples/gb-frontend:v4 imagePullPolicy: Always resources: limits: cpu: 150m memory: 100Mi requests: cpu: 150m memory: 100Mi Create the cluster role: USD oc create -f <file_name>.yaml For example: USD oc create -f pod-spec.yaml Procedure To use the cluster capacity tool on the command line: From the terminal, log in to the Red Hat Registry: USD podman login registry.redhat.io Pull the cluster capacity tool image: USD podman pull registry.redhat.io/openshift4/ose-cluster-capacity Run the cluster capacity tool: USD podman run -v USDHOME/.kube:/kube:Z -v USD(pwd):/cc:Z ose-cluster-capacity \ /bin/cluster-capacity --kubeconfig /kube/config --<pod_spec>.yaml /cc/<pod_spec>.yaml \ --verbose where: <pod_spec>.yaml Specifies the pod spec to use. verbose Outputs a detailed description of how many pods can be scheduled on each node in the cluster. Example output small-pod pod requirements: - CPU: 150m - Memory: 100Mi The cluster can schedule 88 instance(s) of the pod small-pod. Termination reason: Unschedulable: 0/5 nodes are available: 2 Insufficient cpu, 3 node(s) had taint {node-role.kubernetes.io/master: }, that the pod didn't tolerate. Pod distribution among nodes: small-pod - 192.168.124.214: 45 instance(s) - 192.168.124.120: 43 instance(s) In the above example, the number of estimated pods that can be scheduled onto the cluster is 88. 8.2.3. Running the OpenShift Cluster Capacity Tool as a job inside a pod Running the OpenShift Cluster Capacity Tool as a job inside of a pod allows you to run the tool multiple times without needing user intervention. You run the OpenShift Cluster Capacity Tool as a job by using a ConfigMap object. Prerequisites Download and install OpenShift Cluster Capacity Tool . Procedure To run the cluster capacity tool: Create the cluster role: Create a YAML file similar to the following: kind: ClusterRole apiVersion: rbac.authorization.k8s.io/v1 metadata: name: cluster-capacity-role rules: - apiGroups: [""] resources: ["pods", "nodes", "persistentvolumeclaims", "persistentvolumes", "services", "replicationcontrollers"] verbs: ["get", "watch", "list"] - apiGroups: ["apps"] resources: ["replicasets", "statefulsets"] verbs: ["get", "watch", "list"] - apiGroups: ["policy"] resources: ["poddisruptionbudgets"] verbs: ["get", "watch", "list"] - apiGroups: ["storage.k8s.io"] resources: ["storageclasses"] verbs: ["get", "watch", "list"] Create the cluster role by running the following command: USD oc create -f <file_name>.yaml For example: USD oc create sa cluster-capacity-sa Create the service account: USD oc create sa cluster-capacity-sa -n default Add the role to the service account: USD oc adm policy add-cluster-role-to-user cluster-capacity-role \ system:serviceaccount:<namespace>:cluster-capacity-sa where: <namespace> Specifies the namespace where the pod is located. Define and create the pod spec: Create a YAML file similar to the following: apiVersion: v1 kind: Pod metadata: name: small-pod labels: app: guestbook tier: frontend spec: containers: - name: php-redis image: gcr.io/google-samples/gb-frontend:v4 imagePullPolicy: Always resources: limits: cpu: 150m memory: 100Mi requests: cpu: 150m memory: 100Mi Create the pod by running the following command: USD oc create -f <file_name>.yaml For example: USD oc create -f pod.yaml Created a config map object by running the following command: USD oc create configmap cluster-capacity-configmap \ --from-file=pod.yaml=pod.yaml The cluster capacity analysis is mounted in a volume using a config map object named cluster-capacity-configmap to mount the input pod spec file pod.yaml into a volume test-volume at the path /test-pod . Create the job using the below example of a job specification file: Create a YAML file similar to the following: apiVersion: batch/v1 kind: Job metadata: name: cluster-capacity-job spec: parallelism: 1 completions: 1 template: metadata: name: cluster-capacity-pod spec: containers: - name: cluster-capacity image: openshift/origin-cluster-capacity imagePullPolicy: "Always" volumeMounts: - mountPath: /test-pod name: test-volume env: - name: CC_INCLUSTER 1 value: "true" command: - "/bin/sh" - "-ec" - | /bin/cluster-capacity --podspec=/test-pod/pod.yaml --verbose restartPolicy: "Never" serviceAccountName: cluster-capacity-sa volumes: - name: test-volume configMap: name: cluster-capacity-configmap 1 A required environment variable letting the cluster capacity tool know that it is running inside a cluster as a pod. The pod.yaml key of the ConfigMap object is the same as the Pod spec file name, though it is not required. By doing this, the input pod spec file can be accessed inside the pod as /test-pod/pod.yaml . Run the cluster capacity image as a job in a pod by running the following command: USD oc create -f cluster-capacity-job.yaml Verification Check the job logs to find the number of pods that can be scheduled in the cluster: USD oc logs jobs/cluster-capacity-job Example output small-pod pod requirements: - CPU: 150m - Memory: 100Mi The cluster can schedule 52 instance(s) of the pod small-pod. Termination reason: Unschedulable: No nodes are available that match all of the following predicates:: Insufficient cpu (2). Pod distribution among nodes: small-pod - 192.168.124.214: 26 instance(s) - 192.168.124.120: 26 instance(s) 8.3. Restrict resource consumption with limit ranges By default, containers run with unbounded compute resources on an OpenShift Container Platform cluster. With limit ranges, you can restrict resource consumption for specific objects in a project: pods and containers: You can set minimum and maximum requirements for CPU and memory for pods and their containers. Image streams: You can set limits on the number of images and tags in an ImageStream object. Images: You can limit the size of images that can be pushed to an internal registry. Persistent volume claims (PVC): You can restrict the size of the PVCs that can be requested. If a pod does not meet the constraints imposed by the limit range, the pod cannot be created in the namespace. 8.3.1. About limit ranges A limit range, defined by a LimitRange object, restricts resource consumption in a project. In the project you can set specific resource limits for a pod, container, image, image stream, or persistent volume claim (PVC). All requests to create and modify resources are evaluated against each LimitRange object in the project. If the resource violates any of the enumerated constraints, the resource is rejected. The following shows a limit range object for all components: pod, container, image, image stream, or PVC. You can configure limits for any or all of these components in the same object. You create a different limit range object for each project where you want to control resources. Sample limit range object for a container apiVersion: "v1" kind: "LimitRange" metadata: name: "resource-limits" spec: limits: - type: "Container" max: cpu: "2" memory: "1Gi" min: cpu: "100m" memory: "4Mi" default: cpu: "300m" memory: "200Mi" defaultRequest: cpu: "200m" memory: "100Mi" maxLimitRequestRatio: cpu: "10" 8.3.1.1. About component limits The following examples show limit range parameters for each component. The examples are broken out for clarity. You can create a single LimitRange object for any or all components as necessary. 8.3.1.1.1. Container limits A limit range allows you to specify the minimum and maximum CPU and memory that each container in a pod can request for a specific project. If a container is created in the project, the container CPU and memory requests in the Pod spec must comply with the values set in the LimitRange object. If not, the pod does not get created. The container CPU or memory request and limit must be greater than or equal to the min resource constraint for containers that are specified in the LimitRange object. The container CPU or memory request and limit must be less than or equal to the max resource constraint for containers that are specified in the LimitRange object. If the LimitRange object defines a max CPU, you do not need to define a CPU request value in the Pod spec. But you must specify a CPU limit value that satisfies the maximum CPU constraint specified in the limit range. The ratio of the container limits to requests must be less than or equal to the maxLimitRequestRatio value for containers that is specified in the LimitRange object. If the LimitRange object defines a maxLimitRequestRatio constraint, any new containers must have both a request and a limit value. OpenShift Container Platform calculates the limit-to-request ratio by dividing the limit by the request . This value should be a non-negative integer greater than 1. For example, if a container has cpu: 500 in the limit value, and cpu: 100 in the request value, the limit-to-request ratio for cpu is 5 . This ratio must be less than or equal to the maxLimitRequestRatio . If the Pod spec does not specify a container resource memory or limit, the default or defaultRequest CPU and memory values for containers specified in the limit range object are assigned to the container. Container LimitRange object definition apiVersion: "v1" kind: "LimitRange" metadata: name: "resource-limits" 1 spec: limits: - type: "Container" max: cpu: "2" 2 memory: "1Gi" 3 min: cpu: "100m" 4 memory: "4Mi" 5 default: cpu: "300m" 6 memory: "200Mi" 7 defaultRequest: cpu: "200m" 8 memory: "100Mi" 9 maxLimitRequestRatio: cpu: "10" 10 1 The name of the LimitRange object. 2 The maximum amount of CPU that a single container in a pod can request. 3 The maximum amount of memory that a single container in a pod can request. 4 The minimum amount of CPU that a single container in a pod can request. 5 The minimum amount of memory that a single container in a pod can request. 6 The default amount of CPU that a container can use if not specified in the Pod spec. 7 The default amount of memory that a container can use if not specified in the Pod spec. 8 The default amount of CPU that a container can request if not specified in the Pod spec. 9 The default amount of memory that a container can request if not specified in the Pod spec. 10 The maximum limit-to-request ratio for a container. 8.3.1.1.2. Pod limits A limit range allows you to specify the minimum and maximum CPU and memory limits for all containers across a pod in a given project. To create a container in the project, the container CPU and memory requests in the Pod spec must comply with the values set in the LimitRange object. If not, the pod does not get created. If the Pod spec does not specify a container resource memory or limit, the default or defaultRequest CPU and memory values for containers specified in the limit range object are assigned to the container. Across all containers in a pod, the following must hold true: The container CPU or memory request and limit must be greater than or equal to the min resource constraints for pods that are specified in the LimitRange object. The container CPU or memory request and limit must be less than or equal to the max resource constraints for pods that are specified in the LimitRange object. The ratio of the container limits to requests must be less than or equal to the maxLimitRequestRatio constraint specified in the LimitRange object. Pod LimitRange object definition apiVersion: "v1" kind: "LimitRange" metadata: name: "resource-limits" 1 spec: limits: - type: "Pod" max: cpu: "2" 2 memory: "1Gi" 3 min: cpu: "200m" 4 memory: "6Mi" 5 maxLimitRequestRatio: cpu: "10" 6 1 The name of the limit range object. 2 The maximum amount of CPU that a pod can request across all containers. 3 The maximum amount of memory that a pod can request across all containers. 4 The minimum amount of CPU that a pod can request across all containers. 5 The minimum amount of memory that a pod can request across all containers. 6 The maximum limit-to-request ratio for a container. 8.3.1.1.3. Image limits A LimitRange object allows you to specify the maximum size of an image that can be pushed to an OpenShift image registry. When pushing images to an OpenShift image registry, the following must hold true: The size of the image must be less than or equal to the max size for images that is specified in the LimitRange object. Image LimitRange object definition apiVersion: "v1" kind: "LimitRange" metadata: name: "resource-limits" 1 spec: limits: - type: openshift.io/Image max: storage: 1Gi 2 1 The name of the LimitRange object. 2 The maximum size of an image that can be pushed to an OpenShift image registry. Note To prevent blobs that exceed the limit from being uploaded to the registry, the registry must be configured to enforce quotas. Warning The image size is not always available in the manifest of an uploaded image. This is especially the case for images built with Docker 1.10 or higher and pushed to a v2 registry. If such an image is pulled with an older Docker daemon, the image manifest is converted by the registry to schema v1 lacking all the size information. No storage limit set on images prevent it from being uploaded. The issue is being addressed. 8.3.1.1.4. Image stream limits A LimitRange object allows you to specify limits for image streams. For each image stream, the following must hold true: The number of image tags in an ImageStream specification must be less than or equal to the openshift.io/image-tags constraint in the LimitRange object. The number of unique references to images in an ImageStream specification must be less than or equal to the openshift.io/images constraint in the limit range object. Imagestream LimitRange object definition apiVersion: "v1" kind: "LimitRange" metadata: name: "resource-limits" 1 spec: limits: - type: openshift.io/ImageStream max: openshift.io/image-tags: 20 2 openshift.io/images: 30 3 1 The name of the LimitRange object. 2 The maximum number of unique image tags in the imagestream.spec.tags parameter in imagestream spec. 3 The maximum number of unique image references in the imagestream.status.tags parameter in the imagestream spec. The openshift.io/image-tags resource represents unique image references. Possible references are an ImageStreamTag , an ImageStreamImage and a DockerImage . Tags can be created using the oc tag and oc import-image commands. No distinction is made between internal and external references. However, each unique reference tagged in an ImageStream specification is counted just once. It does not restrict pushes to an internal container image registry in any way, but is useful for tag restriction. The openshift.io/images resource represents unique image names recorded in image stream status. It allows for restriction of a number of images that can be pushed to the OpenShift image registry. Internal and external references are not distinguished. 8.3.1.1.5. Persistent volume claim limits A LimitRange object allows you to restrict the storage requested in a persistent volume claim (PVC). Across all persistent volume claims in a project, the following must hold true: The resource request in a persistent volume claim (PVC) must be greater than or equal the min constraint for PVCs that is specified in the LimitRange object. The resource request in a persistent volume claim (PVC) must be less than or equal the max constraint for PVCs that is specified in the LimitRange object. PVC LimitRange object definition apiVersion: "v1" kind: "LimitRange" metadata: name: "resource-limits" 1 spec: limits: - type: "PersistentVolumeClaim" min: storage: "2Gi" 2 max: storage: "50Gi" 3 1 The name of the LimitRange object. 2 The minimum amount of storage that can be requested in a persistent volume claim. 3 The maximum amount of storage that can be requested in a persistent volume claim. 8.3.2. Creating a Limit Range To apply a limit range to a project: Create a LimitRange object with your required specifications: apiVersion: "v1" kind: "LimitRange" metadata: name: "resource-limits" 1 spec: limits: - type: "Pod" 2 max: cpu: "2" memory: "1Gi" min: cpu: "200m" memory: "6Mi" - type: "Container" 3 max: cpu: "2" memory: "1Gi" min: cpu: "100m" memory: "4Mi" default: 4 cpu: "300m" memory: "200Mi" defaultRequest: 5 cpu: "200m" memory: "100Mi" maxLimitRequestRatio: 6 cpu: "10" - type: openshift.io/Image 7 max: storage: 1Gi - type: openshift.io/ImageStream 8 max: openshift.io/image-tags: 20 openshift.io/images: 30 - type: "PersistentVolumeClaim" 9 min: storage: "2Gi" max: storage: "50Gi" 1 Specify a name for the LimitRange object. 2 To set limits for a pod, specify the minimum and maximum CPU and memory requests as needed. 3 To set limits for a container, specify the minimum and maximum CPU and memory requests as needed. 4 Optional. For a container, specify the default amount of CPU or memory that a container can use, if not specified in the Pod spec. 5 Optional. For a container, specify the default amount of CPU or memory that a container can request, if not specified in the Pod spec. 6 Optional. For a container, specify the maximum limit-to-request ratio that can be specified in the Pod spec. 7 To set limits for an Image object, set the maximum size of an image that can be pushed to an OpenShift image registry. 8 To set limits for an image stream, set the maximum number of image tags and references that can be in the ImageStream object file, as needed. 9 To set limits for a persistent volume claim, set the minimum and maximum amount of storage that can be requested. Create the object: USD oc create -f <limit_range_file> -n <project> 1 1 Specify the name of the YAML file you created and the project where you want the limits to apply. 8.3.3. Viewing a limit You can view any limits defined in a project by navigating in the web console to the project's Quota page. You can also use the CLI to view limit range details: Get the list of LimitRange object defined in the project. For example, for a project called demoproject : USD oc get limits -n demoproject NAME CREATED AT resource-limits 2020-07-15T17:14:23Z Describe the LimitRange object you are interested in, for example the resource-limits limit range: USD oc describe limits resource-limits -n demoproject Name: resource-limits Namespace: demoproject Type Resource Min Max Default Request Default Limit Max Limit/Request Ratio ---- -------- --- --- --------------- ------------- ----------------------- Pod cpu 200m 2 - - - Pod memory 6Mi 1Gi - - - Container cpu 100m 2 200m 300m 10 Container memory 4Mi 1Gi 100Mi 200Mi - openshift.io/Image storage - 1Gi - - - openshift.io/ImageStream openshift.io/image - 12 - - - openshift.io/ImageStream openshift.io/image-tags - 10 - - - PersistentVolumeClaim storage - 50Gi - - - 8.3.4. Deleting a Limit Range To remove any active LimitRange object to no longer enforce the limits in a project: Run the following command: USD oc delete limits <limit_name> 8.4. Configuring cluster memory to meet container memory and risk requirements As a cluster administrator, you can help your clusters operate efficiently through managing application memory by: Determining the memory and risk requirements of a containerized application component and configuring the container memory parameters to suit those requirements. Configuring containerized application runtimes (for example, OpenJDK) to adhere optimally to the configured container memory parameters. Diagnosing and resolving memory-related error conditions associated with running in a container. 8.4.1. Understanding managing application memory It is recommended to fully read the overview of how OpenShift Container Platform manages Compute Resources before proceeding. For each kind of resource (memory, CPU, storage), OpenShift Container Platform allows optional request and limit values to be placed on each container in a pod. Note the following about memory requests and memory limits: Memory request The memory request value, if specified, influences the OpenShift Container Platform scheduler. The scheduler considers the memory request when scheduling a container to a node, then fences off the requested memory on the chosen node for the use of the container. If a node's memory is exhausted, OpenShift Container Platform prioritizes evicting its containers whose memory usage most exceeds their memory request. In serious cases of memory exhaustion, the node OOM killer may select and kill a process in a container based on a similar metric. The cluster administrator can assign quota or assign default values for the memory request value. The cluster administrator can override the memory request values that a developer specifies, to manage cluster overcommit. Memory limit The memory limit value, if specified, provides a hard limit on the memory that can be allocated across all the processes in a container. If the memory allocated by all of the processes in a container exceeds the memory limit, the node Out of Memory (OOM) killer will immediately select and kill a process in the container. If both memory request and limit are specified, the memory limit value must be greater than or equal to the memory request. The cluster administrator can assign quota or assign default values for the memory limit value. The minimum memory limit is 12 MB. If a container fails to start due to a Cannot allocate memory pod event, the memory limit is too low. Either increase or remove the memory limit. Removing the limit allows pods to consume unbounded node resources. 8.4.1.1. Managing application memory strategy The steps for sizing application memory on OpenShift Container Platform are as follows: Determine expected container memory usage Determine expected mean and peak container memory usage, empirically if necessary (for example, by separate load testing). Remember to consider all the processes that may potentially run in parallel in the container: for example, does the main application spawn any ancillary scripts? Determine risk appetite Determine risk appetite for eviction. If the risk appetite is low, the container should request memory according to the expected peak usage plus a percentage safety margin. If the risk appetite is higher, it may be more appropriate to request memory according to the expected mean usage. Set container memory request Set container memory request based on the above. The more accurately the request represents the application memory usage, the better. If the request is too high, cluster and quota usage will be inefficient. If the request is too low, the chances of application eviction increase. Set container memory limit, if required Set container memory limit, if required. Setting a limit has the effect of immediately killing a container process if the combined memory usage of all processes in the container exceeds the limit, and is therefore a mixed blessing. On the one hand, it may make unanticipated excess memory usage obvious early ("fail fast"); on the other hand it also terminates processes abruptly. Note that some OpenShift Container Platform clusters may require a limit value to be set; some may override the request based on the limit; and some application images rely on a limit value being set as this is easier to detect than a request value. If the memory limit is set, it should not be set to less than the expected peak container memory usage plus a percentage safety margin. Ensure application is tuned Ensure application is tuned with respect to configured request and limit values, if appropriate. This step is particularly relevant to applications which pool memory, such as the JVM. The rest of this page discusses this. Additional resources Understanding compute resources and containers 8.4.2. Understanding OpenJDK settings for OpenShift Container Platform The default OpenJDK settings do not work well with containerized environments. As a result, some additional Java memory settings must always be provided whenever running the OpenJDK in a container. The JVM memory layout is complex, version dependent, and describing it in detail is beyond the scope of this documentation. However, as a starting point for running OpenJDK in a container, at least the following three memory-related tasks are key: Overriding the JVM maximum heap size. Encouraging the JVM to release unused memory to the operating system, if appropriate. Ensuring all JVM processes within a container are appropriately configured. Optimally tuning JVM workloads for running in a container is beyond the scope of this documentation, and may involve setting multiple additional JVM options. 8.4.2.1. Understanding how to override the JVM maximum heap size For many Java workloads, the JVM heap is the largest single consumer of memory. Currently, the OpenJDK defaults to allowing up to 1/4 (1/ -XX:MaxRAMFraction ) of the compute node's memory to be used for the heap, regardless of whether the OpenJDK is running in a container or not. It is therefore essential to override this behavior, especially if a container memory limit is also set. There are at least two ways the above can be achieved: If the container memory limit is set and the experimental options are supported by the JVM, set -XX:+UnlockExperimentalVMOptions -XX:+UseCGroupMemoryLimitForHeap . Note The UseCGroupMemoryLimitForHeap option has been removed in JDK 11. Use -XX:+UseContainerSupport instead. This sets -XX:MaxRAM to the container memory limit, and the maximum heap size ( -XX:MaxHeapSize / -Xmx ) to 1/ -XX:MaxRAMFraction (1/4 by default). Directly override one of -XX:MaxRAM , -XX:MaxHeapSize or -Xmx . This option involves hard-coding a value, but has the advantage of allowing a safety margin to be calculated. 8.4.2.2. Understanding how to encourage the JVM to release unused memory to the operating system By default, the OpenJDK does not aggressively return unused memory to the operating system. This may be appropriate for many containerized Java workloads, but notable exceptions include workloads where additional active processes co-exist with a JVM within a container, whether those additional processes are native, additional JVMs, or a combination of the two. The OpenShift Container Platform Jenkins maven slave image uses the following JVM arguments to encourage the JVM to release unused memory to the operating system: -XX:+UseParallelGC -XX:MinHeapFreeRatio=5 -XX:MaxHeapFreeRatio=10 -XX:GCTimeRatio=4 -XX:AdaptiveSizePolicyWeight=90. These arguments are intended to return heap memory to the operating system whenever allocated memory exceeds 110% of in-use memory ( -XX:MaxHeapFreeRatio ), spending up to 20% of CPU time in the garbage collector ( -XX:GCTimeRatio ). At no time will the application heap allocation be less than the initial heap allocation (overridden by -XX:InitialHeapSize / -Xms ). Detailed additional information is available Tuning Java's footprint in OpenShift (Part 1) , Tuning Java's footprint in OpenShift (Part 2) , and at OpenJDK and Containers . 8.4.2.3. Understanding how to ensure all JVM processes within a container are appropriately configured In the case that multiple JVMs run in the same container, it is essential to ensure that they are all configured appropriately. For many workloads it will be necessary to grant each JVM a percentage memory budget, leaving a perhaps substantial additional safety margin. Many Java tools use different environment variables ( JAVA_OPTS , GRADLE_OPTS , MAVEN_OPTS , and so on) to configure their JVMs and it can be challenging to ensure that the right settings are being passed to the right JVM. The JAVA_TOOL_OPTIONS environment variable is always respected by the OpenJDK, and values specified in JAVA_TOOL_OPTIONS will be overridden by other options specified on the JVM command line. By default, to ensure that these options are used by default for all JVM workloads run in the slave image, the OpenShift Container Platform Jenkins maven slave image sets: JAVA_TOOL_OPTIONS="-XX:+UnlockExperimentalVMOptions -XX:+UseCGroupMemoryLimitForHeap -Dsun.zip.disableMemoryMapping=true" Note The UseCGroupMemoryLimitForHeap option has been removed in JDK 11. Use -XX:+UseContainerSupport instead. This does not guarantee that additional options are not required, but is intended to be a helpful starting point. 8.4.3. Finding the memory request and limit from within a pod An application wishing to dynamically discover its memory request and limit from within a pod should use the Downward API. Procedure Configure the pod to add the MEMORY_REQUEST and MEMORY_LIMIT stanzas: Create a YAML file similar to the following: apiVersion: v1 kind: Pod metadata: name: test spec: containers: - name: test image: fedora:latest command: - sleep - "3600" env: - name: MEMORY_REQUEST 1 valueFrom: resourceFieldRef: containerName: test resource: requests.memory - name: MEMORY_LIMIT 2 valueFrom: resourceFieldRef: containerName: test resource: limits.memory resources: requests: memory: 384Mi limits: memory: 512Mi 1 Add this stanza to discover the application memory request value. 2 Add this stanza to discover the application memory limit value. Create the pod by running the following command: USD oc create -f <file-name>.yaml Verification Access the pod using a remote shell: USD oc rsh test Check that the requested values were applied: USD env | grep MEMORY | sort Example output MEMORY_LIMIT=536870912 MEMORY_REQUEST=402653184 Note The memory limit value can also be read from inside the container by the /sys/fs/cgroup/memory/memory.limit_in_bytes file. 8.4.4. Understanding OOM kill policy OpenShift Container Platform can kill a process in a container if the total memory usage of all the processes in the container exceeds the memory limit, or in serious cases of node memory exhaustion. When a process is Out of Memory (OOM) killed, this might result in the container exiting immediately. If the container PID 1 process receives the SIGKILL , the container will exit immediately. Otherwise, the container behavior is dependent on the behavior of the other processes. For example, a container process exited with code 137, indicating it received a SIGKILL signal. If the container does not exit immediately, an OOM kill is detectable as follows: Access the pod using a remote shell: # oc rsh test Run the following command to see the current OOM kill count in /sys/fs/cgroup/memory/memory.oom_control : USD grep '^oom_kill ' /sys/fs/cgroup/memory/memory.oom_control Example output oom_kill 0 Run the following command to provoke an OOM kill: USD sed -e '' </dev/zero Example output Killed Run the following command to view the exit status of the sed command: USD echo USD? Example output 137 The 137 code indicates the container process exited with code 137, indicating it received a SIGKILL signal. Run the following command to see that the OOM kill counter in /sys/fs/cgroup/memory/memory.oom_control incremented: USD grep '^oom_kill ' /sys/fs/cgroup/memory/memory.oom_control Example output oom_kill 1 If one or more processes in a pod are OOM killed, when the pod subsequently exits, whether immediately or not, it will have phase Failed and reason OOMKilled . An OOM-killed pod might be restarted depending on the value of restartPolicy . If not restarted, controllers such as the replication controller will notice the pod's failed status and create a new pod to replace the old one. Use the follwing command to get the pod status: USD oc get pod test Example output NAME READY STATUS RESTARTS AGE test 0/1 OOMKilled 0 1m If the pod has not restarted, run the following command to view the pod: USD oc get pod test -o yaml Example output ... status: containerStatuses: - name: test ready: false restartCount: 0 state: terminated: exitCode: 137 reason: OOMKilled phase: Failed If restarted, run the following command to view the pod: USD oc get pod test -o yaml Example output ... status: containerStatuses: - name: test ready: true restartCount: 1 lastState: terminated: exitCode: 137 reason: OOMKilled state: running: phase: Running 8.4.5. Understanding pod eviction OpenShift Container Platform may evict a pod from its node when the node's memory is exhausted. Depending on the extent of memory exhaustion, the eviction may or may not be graceful. Graceful eviction implies the main process (PID 1) of each container receiving a SIGTERM signal, then some time later a SIGKILL signal if the process has not exited already. Non-graceful eviction implies the main process of each container immediately receiving a SIGKILL signal. An evicted pod has phase Failed and reason Evicted . It will not be restarted, regardless of the value of restartPolicy . However, controllers such as the replication controller will notice the pod's failed status and create a new pod to replace the old one. USD oc get pod test Example output NAME READY STATUS RESTARTS AGE test 0/1 Evicted 0 1m USD oc get pod test -o yaml Example output ... status: message: 'Pod The node was low on resource: [MemoryPressure].' phase: Failed reason: Evicted 8.5. Configuring your cluster to place pods on overcommitted nodes In an overcommitted state, the sum of the container compute resource requests and limits exceeds the resources available on the system. For example, you might want to use overcommitment in development environments where a trade-off of guaranteed performance for capacity is acceptable. Containers can specify compute resource requests and limits. Requests are used for scheduling your container and provide a minimum service guarantee. Limits constrain the amount of compute resource that can be consumed on your node. The scheduler attempts to optimize the compute resource use across all nodes in your cluster. It places pods onto specific nodes, taking the pods' compute resource requests and nodes' available capacity into consideration. OpenShift Container Platform administrators can control the level of overcommit and manage container density on nodes. You can configure cluster-level overcommit using the ClusterResourceOverride Operator to override the ratio between requests and limits set on developer containers. In conjunction with node overcommit and project memory and CPU limits and defaults , you can adjust the resource limit and request to achieve the desired level of overcommit. Note In OpenShift Container Platform, you must enable cluster-level overcommit. Node overcommitment is enabled by default. See Disabling overcommitment for a node . 8.5.1. Resource requests and overcommitment For each compute resource, a container may specify a resource request and limit. Scheduling decisions are made based on the request to ensure that a node has enough capacity available to meet the requested value. If a container specifies limits, but omits requests, the requests are defaulted to the limits. A container is not able to exceed the specified limit on the node. The enforcement of limits is dependent upon the compute resource type. If a container makes no request or limit, the container is scheduled to a node with no resource guarantees. In practice, the container is able to consume as much of the specified resource as is available with the lowest local priority. In low resource situations, containers that specify no resource requests are given the lowest quality of service. Scheduling is based on resources requested, while quota and hard limits refer to resource limits, which can be set higher than requested resources. The difference between request and limit determines the level of overcommit; for instance, if a container is given a memory request of 1Gi and a memory limit of 2Gi, it is scheduled based on the 1Gi request being available on the node, but could use up to 2Gi; so it is 200% overcommitted. 8.5.2. Cluster-level overcommit using the Cluster Resource Override Operator The Cluster Resource Override Operator is an admission webhook that allows you to control the level of overcommit and manage container density across all the nodes in your cluster. The Operator controls how nodes in specific projects can exceed defined memory and CPU limits. You must install the Cluster Resource Override Operator using the OpenShift Container Platform console or CLI as shown in the following sections. During the installation, you create a ClusterResourceOverride custom resource (CR), where you set the level of overcommit, as shown in the following example: apiVersion: operator.autoscaling.openshift.io/v1 kind: ClusterResourceOverride metadata: name: cluster 1 spec: podResourceOverride: spec: memoryRequestToLimitPercent: 50 2 cpuRequestToLimitPercent: 25 3 limitCPUToMemoryPercent: 200 4 # ... 1 The name must be cluster . 2 Optional. If a container memory limit has been specified or defaulted, the memory request is overridden to this percentage of the limit, between 1-100. The default is 50. 3 Optional. If a container CPU limit has been specified or defaulted, the CPU request is overridden to this percentage of the limit, between 1-100. The default is 25. 4 Optional. If a container memory limit has been specified or defaulted, the CPU limit is overridden to a percentage of the memory limit, if specified. Scaling 1Gi of RAM at 100 percent is equal to 1 CPU core. This is processed prior to overriding the CPU request (if configured). The default is 200. Note The Cluster Resource Override Operator overrides have no effect if limits have not been set on containers. Create a LimitRange object with default limits per individual project or configure limits in Pod specs for the overrides to apply. When configured, overrides can be enabled per-project by applying the following label to the Namespace object for each project: apiVersion: v1 kind: Namespace metadata: # ... labels: clusterresourceoverrides.admission.autoscaling.openshift.io/enabled: "true" # ... The Operator watches for the ClusterResourceOverride CR and ensures that the ClusterResourceOverride admission webhook is installed into the same namespace as the operator. 8.5.2.1. Installing the Cluster Resource Override Operator using the web console You can use the OpenShift Container Platform web console to install the Cluster Resource Override Operator to help control overcommit in your cluster. Prerequisites The Cluster Resource Override Operator has no effect if limits have not been set on containers. You must specify default limits for a project using a LimitRange object or configure limits in Pod specs for the overrides to apply. Procedure To install the Cluster Resource Override Operator using the OpenShift Container Platform web console: In the OpenShift Container Platform web console, navigate to Home Projects Click Create Project . Specify clusterresourceoverride-operator as the name of the project. Click Create . Navigate to Operators OperatorHub . Choose ClusterResourceOverride Operator from the list of available Operators and click Install . On the Install Operator page, make sure A specific Namespace on the cluster is selected for Installation Mode . Make sure clusterresourceoverride-operator is selected for Installed Namespace . Select an Update Channel and Approval Strategy . Click Install . On the Installed Operators page, click ClusterResourceOverride . On the ClusterResourceOverride Operator details page, click Create ClusterResourceOverride . On the Create ClusterResourceOverride page, click YAML view and edit the YAML template to set the overcommit values as needed: apiVersion: operator.autoscaling.openshift.io/v1 kind: ClusterResourceOverride metadata: name: cluster 1 spec: podResourceOverride: spec: memoryRequestToLimitPercent: 50 2 cpuRequestToLimitPercent: 25 3 limitCPUToMemoryPercent: 200 4 # ... 1 The name must be cluster . 2 Optional. Specify the percentage to override the container memory limit, if used, between 1-100. The default is 50. 3 Optional. Specify the percentage to override the container CPU limit, if used, between 1-100. The default is 25. 4 Optional. Specify the percentage to override the container memory limit, if used. Scaling 1Gi of RAM at 100 percent is equal to 1 CPU core. This is processed prior to overriding the CPU request, if configured. The default is 200. Click Create . Check the current state of the admission webhook by checking the status of the cluster custom resource: On the ClusterResourceOverride Operator page, click cluster . On the ClusterResourceOverride Details page, click YAML . The mutatingWebhookConfigurationRef section appears when the webhook is called. apiVersion: operator.autoscaling.openshift.io/v1 kind: ClusterResourceOverride metadata: annotations: kubectl.kubernetes.io/last-applied-configuration: | {"apiVersion":"operator.autoscaling.openshift.io/v1","kind":"ClusterResourceOverride","metadata":{"annotations":{},"name":"cluster"},"spec":{"podResourceOverride":{"spec":{"cpuRequestToLimitPercent":25,"limitCPUToMemoryPercent":200,"memoryRequestToLimitPercent":50}}}} creationTimestamp: "2019-12-18T22:35:02Z" generation: 1 name: cluster resourceVersion: "127622" selfLink: /apis/operator.autoscaling.openshift.io/v1/clusterresourceoverrides/cluster uid: 978fc959-1717-4bd1-97d0-ae00ee111e8d spec: podResourceOverride: spec: cpuRequestToLimitPercent: 25 limitCPUToMemoryPercent: 200 memoryRequestToLimitPercent: 50 status: # ... mutatingWebhookConfigurationRef: 1 apiVersion: admissionregistration.k8s.io/v1 kind: MutatingWebhookConfiguration name: clusterresourceoverrides.admission.autoscaling.openshift.io resourceVersion: "127621" uid: 98b3b8ae-d5ce-462b-8ab5-a729ea8f38f3 # ... 1 Reference to the ClusterResourceOverride admission webhook. 8.5.2.2. Installing the Cluster Resource Override Operator using the CLI You can use the OpenShift Container Platform CLI to install the Cluster Resource Override Operator to help control overcommit in your cluster. Prerequisites The Cluster Resource Override Operator has no effect if limits have not been set on containers. You must specify default limits for a project using a LimitRange object or configure limits in Pod specs for the overrides to apply. Procedure To install the Cluster Resource Override Operator using the CLI: Create a namespace for the Cluster Resource Override Operator: Create a Namespace object YAML file (for example, cro-namespace.yaml ) for the Cluster Resource Override Operator: apiVersion: v1 kind: Namespace metadata: name: clusterresourceoverride-operator Create the namespace: USD oc create -f <file-name>.yaml For example: USD oc create -f cro-namespace.yaml Create an Operator group: Create an OperatorGroup object YAML file (for example, cro-og.yaml) for the Cluster Resource Override Operator: apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: clusterresourceoverride-operator namespace: clusterresourceoverride-operator spec: targetNamespaces: - clusterresourceoverride-operator Create the Operator Group: USD oc create -f <file-name>.yaml For example: USD oc create -f cro-og.yaml Create a subscription: Create a Subscription object YAML file (for example, cro-sub.yaml) for the Cluster Resource Override Operator: apiVersion: operators.coreos.com/v1alpha1 kind: Subscription metadata: name: clusterresourceoverride namespace: clusterresourceoverride-operator spec: channel: "4.11" name: clusterresourceoverride source: redhat-operators sourceNamespace: openshift-marketplace Create the subscription: USD oc create -f <file-name>.yaml For example: USD oc create -f cro-sub.yaml Create a ClusterResourceOverride custom resource (CR) object in the clusterresourceoverride-operator namespace: Change to the clusterresourceoverride-operator namespace. USD oc project clusterresourceoverride-operator Create a ClusterResourceOverride object YAML file (for example, cro-cr.yaml) for the Cluster Resource Override Operator: apiVersion: operator.autoscaling.openshift.io/v1 kind: ClusterResourceOverride metadata: name: cluster 1 spec: podResourceOverride: spec: memoryRequestToLimitPercent: 50 2 cpuRequestToLimitPercent: 25 3 limitCPUToMemoryPercent: 200 4 1 The name must be cluster . 2 Optional. Specify the percentage to override the container memory limit, if used, between 1-100. The default is 50. 3 Optional. Specify the percentage to override the container CPU limit, if used, between 1-100. The default is 25. 4 Optional. Specify the percentage to override the container memory limit, if used. Scaling 1Gi of RAM at 100 percent is equal to 1 CPU core. This is processed prior to overriding the CPU request, if configured. The default is 200. Create the ClusterResourceOverride object: USD oc create -f <file-name>.yaml For example: USD oc create -f cro-cr.yaml Verify the current state of the admission webhook by checking the status of the cluster custom resource. USD oc get clusterresourceoverride cluster -n clusterresourceoverride-operator -o yaml The mutatingWebhookConfigurationRef section appears when the webhook is called. Example output apiVersion: operator.autoscaling.openshift.io/v1 kind: ClusterResourceOverride metadata: annotations: kubectl.kubernetes.io/last-applied-configuration: | {"apiVersion":"operator.autoscaling.openshift.io/v1","kind":"ClusterResourceOverride","metadata":{"annotations":{},"name":"cluster"},"spec":{"podResourceOverride":{"spec":{"cpuRequestToLimitPercent":25,"limitCPUToMemoryPercent":200,"memoryRequestToLimitPercent":50}}}} creationTimestamp: "2019-12-18T22:35:02Z" generation: 1 name: cluster resourceVersion: "127622" selfLink: /apis/operator.autoscaling.openshift.io/v1/clusterresourceoverrides/cluster uid: 978fc959-1717-4bd1-97d0-ae00ee111e8d spec: podResourceOverride: spec: cpuRequestToLimitPercent: 25 limitCPUToMemoryPercent: 200 memoryRequestToLimitPercent: 50 status: # ... mutatingWebhookConfigurationRef: 1 apiVersion: admissionregistration.k8s.io/v1 kind: MutatingWebhookConfiguration name: clusterresourceoverrides.admission.autoscaling.openshift.io resourceVersion: "127621" uid: 98b3b8ae-d5ce-462b-8ab5-a729ea8f38f3 # ... 1 Reference to the ClusterResourceOverride admission webhook. 8.5.2.3. Configuring cluster-level overcommit The Cluster Resource Override Operator requires a ClusterResourceOverride custom resource (CR) and a label for each project where you want the Operator to control overcommit. Prerequisites The Cluster Resource Override Operator has no effect if limits have not been set on containers. You must specify default limits for a project using a LimitRange object or configure limits in Pod specs for the overrides to apply. Procedure To modify cluster-level overcommit: Edit the ClusterResourceOverride CR: apiVersion: operator.autoscaling.openshift.io/v1 kind: ClusterResourceOverride metadata: name: cluster spec: podResourceOverride: spec: memoryRequestToLimitPercent: 50 1 cpuRequestToLimitPercent: 25 2 limitCPUToMemoryPercent: 200 3 # ... 1 Optional. Specify the percentage to override the container memory limit, if used, between 1-100. The default is 50. 2 Optional. Specify the percentage to override the container CPU limit, if used, between 1-100. The default is 25. 3 Optional. Specify the percentage to override the container memory limit, if used. Scaling 1Gi of RAM at 100 percent is equal to 1 CPU core. This is processed prior to overriding the CPU request, if configured. The default is 200. Ensure the following label has been added to the Namespace object for each project where you want the Cluster Resource Override Operator to control overcommit: apiVersion: v1 kind: Namespace metadata: # ... labels: clusterresourceoverrides.admission.autoscaling.openshift.io/enabled: "true" 1 # ... 1 Add this label to each project. 8.5.3. Node-level overcommit You can use various ways to control overcommit on specific nodes, such as quality of service (QOS) guarantees, CPU limits, or reserve resources. You can also disable overcommit for specific nodes and specific projects. 8.5.3.1. Understanding compute resources and containers The node-enforced behavior for compute resources is specific to the resource type. 8.5.3.1.1. Understanding container CPU requests A container is guaranteed the amount of CPU it requests and is additionally able to consume excess CPU available on the node, up to any limit specified by the container. If multiple containers are attempting to use excess CPU, CPU time is distributed based on the amount of CPU requested by each container. For example, if one container requested 500m of CPU time and another container requested 250m of CPU time, then any extra CPU time available on the node is distributed among the containers in a 2:1 ratio. If a container specified a limit, it will be throttled not to use more CPU than the specified limit. CPU requests are enforced using the CFS shares support in the Linux kernel. By default, CPU limits are enforced using the CFS quota support in the Linux kernel over a 100ms measuring interval, though this can be disabled. 8.5.3.1.2. Understanding container memory requests A container is guaranteed the amount of memory it requests. A container can use more memory than requested, but once it exceeds its requested amount, it could be terminated in a low memory situation on the node. If a container uses less memory than requested, it will not be terminated unless system tasks or daemons need more memory than was accounted for in the node's resource reservation. If a container specifies a limit on memory, it is immediately terminated if it exceeds the limit amount. 8.5.3.2. Understanding overcomitment and quality of service classes A node is overcommitted when it has a pod scheduled that makes no request, or when the sum of limits across all pods on that node exceeds available machine capacity. In an overcommitted environment, it is possible that the pods on the node will attempt to use more compute resource than is available at any given point in time. When this occurs, the node must give priority to one pod over another. The facility used to make this decision is referred to as a Quality of Service (QoS) Class. A pod is designated as one of three QoS classes with decreasing order of priority: Table 8.19. Quality of Service Classes Priority Class Name Description 1 (highest) Guaranteed If limits and optionally requests are set (not equal to 0) for all resources and they are equal, then the pod is classified as Guaranteed . 2 Burstable If requests and optionally limits are set (not equal to 0) for all resources, and they are not equal, then the pod is classified as Burstable . 3 (lowest) BestEffort If requests and limits are not set for any of the resources, then the pod is classified as BestEffort . Memory is an incompressible resource, so in low memory situations, containers that have the lowest priority are terminated first: Guaranteed containers are considered top priority, and are guaranteed to only be terminated if they exceed their limits, or if the system is under memory pressure and there are no lower priority containers that can be evicted. Burstable containers under system memory pressure are more likely to be terminated once they exceed their requests and no other BestEffort containers exist. BestEffort containers are treated with the lowest priority. Processes in these containers are first to be terminated if the system runs out of memory. 8.5.3.2.1. Understanding how to reserve memory across quality of service tiers You can use the qos-reserved parameter to specify a percentage of memory to be reserved by a pod in a particular QoS level. This feature attempts to reserve requested resources to exclude pods from lower OoS classes from using resources requested by pods in higher QoS classes. OpenShift Container Platform uses the qos-reserved parameter as follows: A value of qos-reserved=memory=100% will prevent the Burstable and BestEffort QoS classes from consuming memory that was requested by a higher QoS class. This increases the risk of inducing OOM on BestEffort and Burstable workloads in favor of increasing memory resource guarantees for Guaranteed and Burstable workloads. A value of qos-reserved=memory=50% will allow the Burstable and BestEffort QoS classes to consume half of the memory requested by a higher QoS class. A value of qos-reserved=memory=0% will allow a Burstable and BestEffort QoS classes to consume up to the full node allocatable amount if available, but increases the risk that a Guaranteed workload will not have access to requested memory. This condition effectively disables this feature. 8.5.3.3. Understanding swap memory and QOS You can disable swap by default on your nodes to preserve quality of service (QOS) guarantees. Otherwise, physical resources on a node can oversubscribe, affecting the resource guarantees the Kubernetes scheduler makes during pod placement. For example, if two guaranteed pods have reached their memory limit, each container could start using swap memory. Eventually, if there is not enough swap space, processes in the pods can be terminated due to the system being oversubscribed. Failing to disable swap results in nodes not recognizing that they are experiencing MemoryPressure , resulting in pods not receiving the memory they made in their scheduling request. As a result, additional pods are placed on the node to further increase memory pressure, ultimately increasing your risk of experiencing a system out of memory (OOM) event. Important If swap is enabled, any out-of-resource handling eviction thresholds for available memory will not work as expected. Take advantage of out-of-resource handling to allow pods to be evicted from a node when it is under memory pressure, and rescheduled on an alternative node that has no such pressure. 8.5.3.4. Understanding nodes overcommitment In an overcommitted environment, it is important to properly configure your node to provide best system behavior. When the node starts, it ensures that the kernel tunable flags for memory management are set properly. The kernel should never fail memory allocations unless it runs out of physical memory. To ensure this behavior, OpenShift Container Platform configures the kernel to always overcommit memory by setting the vm.overcommit_memory parameter to 1 , overriding the default operating system setting. OpenShift Container Platform also configures the kernel not to panic when it runs out of memory by setting the vm.panic_on_oom parameter to 0 . A setting of 0 instructs the kernel to call oom_killer in an Out of Memory (OOM) condition, which kills processes based on priority You can view the current setting by running the following commands on your nodes: USD sysctl -a |grep commit Example output #... vm.overcommit_memory = 0 #... USD sysctl -a |grep panic Example output #... vm.panic_on_oom = 0 #... Note The above flags should already be set on nodes, and no further action is required. You can also perform the following configurations for each node: Disable or enforce CPU limits using CPU CFS quotas Reserve resources for system processes Reserve memory across quality of service tiers 8.5.3.5. Disabling or enforcing CPU limits using CPU CFS quotas Nodes by default enforce specified CPU limits using the Completely Fair Scheduler (CFS) quota support in the Linux kernel. If you disable CPU limit enforcement, it is important to understand the impact on your node: If a container has a CPU request, the request continues to be enforced by CFS shares in the Linux kernel. If a container does not have a CPU request, but does have a CPU limit, the CPU request defaults to the specified CPU limit, and is enforced by CFS shares in the Linux kernel. If a container has both a CPU request and limit, the CPU request is enforced by CFS shares in the Linux kernel, and the CPU limit has no impact on the node. Prerequisites Obtain the label associated with the static MachineConfigPool CRD for the type of node you want to configure by entering the following command: USD oc edit machineconfigpool <name> For example: USD oc edit machineconfigpool worker Example output apiVersion: machineconfiguration.openshift.io/v1 kind: MachineConfigPool metadata: creationTimestamp: "2022-11-16T15:34:25Z" generation: 4 labels: pools.operator.machineconfiguration.openshift.io/worker: "" 1 name: worker 1 The label appears under Labels. Tip If the label is not present, add a key/value pair such as: USD oc label machineconfigpool worker custom-kubelet=small-pods Procedure Create a custom resource (CR) for your configuration change. Sample configuration for a disabling CPU limits apiVersion: machineconfiguration.openshift.io/v1 kind: KubeletConfig metadata: name: disable-cpu-units 1 spec: machineConfigPoolSelector: matchLabels: pools.operator.machineconfiguration.openshift.io/worker: "" 2 kubeletConfig: cpuCfsQuota: false 3 1 Assign a name to CR. 2 Specify the label from the machine config pool. 3 Set the cpuCfsQuota parameter to false . Run the following command to create the CR: USD oc create -f <file_name>.yaml 8.5.3.6. Reserving resources for system processes To provide more reliable scheduling and minimize node resource overcommitment, each node can reserve a portion of its resources for use by system daemons that are required to run on your node for your cluster to function. In particular, it is recommended that you reserve resources for incompressible resources such as memory. Procedure To explicitly reserve resources for non-pod processes, allocate node resources by specifying resources available for scheduling. For more details, see Allocating Resources for Nodes. 8.5.3.7. Disabling overcommitment for a node When enabled, overcommitment can be disabled on each node. Procedure To disable overcommitment in a node run the following command on that node: USD sysctl -w vm.overcommit_memory=0 8.5.4. Project-level limits To help control overcommit, you can set per-project resource limit ranges, specifying memory and CPU limits and defaults for a project that overcommit cannot exceed. For information on project-level resource limits, see Additional resources. Alternatively, you can disable overcommitment for specific projects. 8.5.4.1. Disabling overcommitment for a project When enabled, overcommitment can be disabled per-project. For example, you can allow infrastructure components to be configured independently of overcommitment. Procedure To disable overcommitment in a project: Edit the namespace object to add the following annotation: apiVersion: v1 kind: Namespace metadata: annotations: quota.openshift.io/cluster-resource-override-enabled: "false" 1 # ... 1 Setting this annotation to false disables overcommit for this namespace. 8.5.5. Additional resources Setting deployment resources . Allocating resources for nodes . 8.6. Enabling OpenShift Container Platform features using FeatureGates As an administrator, you can use feature gates to enable features that are not part of the default set of features. 8.6.1. Understanding feature gates You can use the FeatureGate custom resource (CR) to enable specific feature sets in your cluster. A feature set is a collection of OpenShift Container Platform features that are not enabled by default. You can activate the following feature set by using the FeatureGate CR: TechPreviewNoUpgrade . This feature set is a subset of the current Technology Preview features. This feature set allows you to enable these tech preview features on test clusters, where you can fully test them, while leaving the features disabled on production clusters. Enabling this feature set cannot be undone and prevents minor version updates. This feature set is not recommended on production clusters. Warning Enabling the TechPreviewNoUpgrade feature set on your cluster cannot be undone and prevents minor version updates. You should not enable this feature set on production clusters. The following Technology Preview features are enabled by this feature set: Microsoft Azure File CSI Driver Operator. Enables the provisioning of persistent volumes (PVs) by using the Container Storage Interface (CSI) driver for Microsoft Azure File Storage. CSI automatic migration. Enables automatic migration for supported in-tree volume plugins to their equivalent Container Storage Interface (CSI) drivers. Supported for: Amazon Web Services (AWS) Elastic Block Storage (EBS) Google Compute Engine Persistent Disk Azure File VMware vSphere Cluster Cloud Controller Manager Operator. Enables the Cluster Cloud Controller Manager Operator rather than the in-tree cloud controller. Available as a Technology Preview for: Alibaba Cloud Amazon Web Services (AWS) Google Cloud Platform (GCP) IBM Cloud Microsoft Azure Red Hat OpenStack Platform (RHOSP) VMware vSphere Shared resource CSI driver CSI volume support for the OpenShift Container Platform build system Swap memory on nodes Cluster API. Enables the integrated upstream Cluster API in OpenShift Container Platform with the ClusterAPIEnabled feature gate. Available as a Technology Preview for: Amazon Web Services (AWS) Google Cloud Platform (GCP) Managing alerting rules for core platform monitoring Additional resources For more information about the features activated by the TechPreviewNoUpgrade feature gate, see the following topics: CSI automatic migration Cluster Cloud Controller Manager Operator Source-to-image (S2I) build volumes and Docker build volumes Swap memory on nodes Managing alerting rules for core platform monitoring 8.6.2. Enabling feature sets using the web console You can use the OpenShift Container Platform web console to enable feature sets for all of the nodes in a cluster by editing the FeatureGate custom resource (CR). Procedure To enable feature sets: In the OpenShift Container Platform web console, switch to the Administration Custom Resource Definitions page. On the Custom Resource Definitions page, click FeatureGate . On the Custom Resource Definition Details page, click the Instances tab. Click the cluster feature gate, then click the YAML tab. Edit the cluster instance to add specific feature sets: Warning Enabling the TechPreviewNoUpgrade feature set on your cluster cannot be undone and prevents minor version updates. You should not enable this feature set on production clusters. Sample Feature Gate custom resource apiVersion: config.openshift.io/v1 kind: FeatureGate metadata: name: cluster 1 # ... spec: featureSet: TechPreviewNoUpgrade 2 1 The name of the FeatureGate CR must be cluster . 2 Add the feature set that you want to enable: TechPreviewNoUpgrade enables specific Technology Preview features. After you save the changes, new machine configs are created, the machine config pools are updated, and scheduling on each node is disabled while the change is being applied. Verification You can verify that the feature gates are enabled by looking at the kubelet.conf file on a node after the nodes return to the ready state. From the Administrator perspective in the web console, navigate to Compute Nodes . Select a node. In the Node details page, click Terminal . In the terminal window, change your root directory to /host : sh-4.2# chroot /host View the kubelet.conf file: sh-4.2# cat /etc/kubernetes/kubelet.conf Sample output # ... featureGates: InsightsOperatorPullingSCA: true, LegacyNodeRoleBehavior: false # ... The features that are listed as true are enabled on your cluster. Note The features listed vary depending upon the OpenShift Container Platform version. 8.6.3. Enabling feature sets using the CLI You can use the OpenShift CLI ( oc ) to enable feature sets for all of the nodes in a cluster by editing the FeatureGate custom resource (CR). Prerequisites You have installed the OpenShift CLI ( oc ). Procedure To enable feature sets: Edit the FeatureGate CR named cluster : USD oc edit featuregate cluster Warning Enabling the TechPreviewNoUpgrade feature set on your cluster cannot be undone and prevents minor version updates. You should not enable this feature set on production clusters. Sample FeatureGate custom resource apiVersion: config.openshift.io/v1 kind: FeatureGate metadata: name: cluster 1 # ... spec: featureSet: TechPreviewNoUpgrade 2 1 The name of the FeatureGate CR must be cluster . 2 Add the feature set that you want to enable: TechPreviewNoUpgrade enables specific Technology Preview features. After you save the changes, new machine configs are created, the machine config pools are updated, and scheduling on each node is disabled while the change is being applied. Verification You can verify that the feature gates are enabled by looking at the kubelet.conf file on a node after the nodes return to the ready state. From the Administrator perspective in the web console, navigate to Compute Nodes . Select a node. In the Node details page, click Terminal . In the terminal window, change your root directory to /host : sh-4.2# chroot /host View the kubelet.conf file: sh-4.2# cat /etc/kubernetes/kubelet.conf Sample output # ... featureGates: InsightsOperatorPullingSCA: true, LegacyNodeRoleBehavior: false # ... The features that are listed as true are enabled on your cluster. Note The features listed vary depending upon the OpenShift Container Platform version. 8.7. Improving cluster stability in high latency environments using worker latency profiles If the cluster administrator has performed latency tests for platform verification, they can discover the need to adjust the operation of the cluster to ensure stability in cases of high latency. The cluster administrator need change only one parameter, recorded in a file, which controls four parameters affecting how supervisory processes read status and interpret the health of the cluster. Changing only the one parameter provides cluster tuning in an easy, supportable manner. The Kubelet process provides the starting point for monitoring cluster health. The Kubelet sets status values for all nodes in the OpenShift Container Platform cluster. The Kubernetes Controller Manager ( kube controller ) reads the status values every 10 seconds, by default. If the kube controller cannot read a node status value, it loses contact with that node after a configured period. The default behavior is: The node controller on the control plane updates the node health to Unhealthy and marks the node Ready condition`Unknown`. In response, the scheduler stops scheduling pods to that node. The Node Lifecycle Controller adds a node.kubernetes.io/unreachable taint with a NoExecute effect to the node and schedules any pods on the node for eviction after five minutes, by default. This behavior can cause problems if your network is prone to latency issues, especially if you have nodes at the network edge. In some cases, the Kubernetes Controller Manager might not receive an update from a healthy node due to network latency. The Kubelet evicts pods from the node even though the node is healthy. To avoid this problem, you can use worker latency profiles to adjust the frequency that the Kubelet and the Kubernetes Controller Manager wait for status updates before taking action. These adjustments help to ensure that your cluster runs properly if network latency between the control plane and the worker nodes is not optimal. These worker latency profiles contain three sets of parameters that are pre-defined with carefully tuned values to control the reaction of the cluster to increased latency. No need to experimentally find the best values manually. You can configure worker latency profiles when installing a cluster or at any time you notice increased latency in your cluster network. You can configure worker latency profiles when installing a cluster or at any time you notice increased latency in your cluster network. 8.7.1. Understanding worker latency profiles Worker latency profiles are four different categories of carefully-tuned parameters. The four parameters which implement these values are node-status-update-frequency , node-monitor-grace-period , default-not-ready-toleration-seconds and default-unreachable-toleration-seconds . These parameters can use values which allow you control the reaction of the cluster to latency issues without needing to determine the best values using manual methods. Important Setting these parameters manually is not supported. Incorrect parameter settings adversely affect cluster stability. All worker latency profiles configure the following parameters: node-status-update-frequency Specifies how often the kubelet posts node status to the API server. node-monitor-grace-period Specifies the amount of time in seconds that the Kubernetes Controller Manager waits for an update from a kubelet before marking the node unhealthy and adding the node.kubernetes.io/not-ready or node.kubernetes.io/unreachable taint to the node. default-not-ready-toleration-seconds Specifies the amount of time in seconds after marking a node unhealthy that the Kube API Server Operator waits before evicting pods from that node. default-unreachable-toleration-seconds Specifies the amount of time in seconds after marking a node unreachable that the Kube API Server Operator waits before evicting pods from that node. The following Operators monitor the changes to the worker latency profiles and respond accordingly: The Machine Config Operator (MCO) updates the node-status-update-frequency parameter on the worker nodes. The Kubernetes Controller Manager updates the node-monitor-grace-period parameter on the control plane nodes. The Kubernetes API Server Operator updates the default-not-ready-toleration-seconds and default-unreachable-toleration-seconds parameters on the control plane nodes. While the default configuration works in most cases, OpenShift Container Platform offers two other worker latency profiles for situations where the network is experiencing higher latency than usual. The three worker latency profiles are described in the following sections: Default worker latency profile With the Default profile, each Kubelet updates it's status every 10 seconds ( node-status-update-frequency ). The Kube Controller Manager checks the statuses of Kubelet every 5 seconds ( node-monitor-grace-period ). The Kubernetes Controller Manager waits 40 seconds for a status update from Kubelet before considering the Kubelet unhealthy. If no status is made available to the Kubernetes Controller Manager, it then marks the node with the node.kubernetes.io/not-ready or node.kubernetes.io/unreachable taint and evicts the pods on that node. If a pod on that node has the NoExecute taint, the pod is run according to tolerationSeconds . If the pod has no taint, it will be evicted in 300 seconds ( default-not-ready-toleration-seconds and default-unreachable-toleration-seconds settings of the Kube API Server ). Profile Component Parameter Value Default kubelet node-status-update-frequency 10s Kubelet Controller Manager node-monitor-grace-period 40s Kubernetes API Server Operator default-not-ready-toleration-seconds 300s Kubernetes API Server Operator default-unreachable-toleration-seconds 300s Medium worker latency profile Use the MediumUpdateAverageReaction profile if the network latency is slightly higher than usual. The MediumUpdateAverageReaction profile reduces the frequency of kubelet updates to 20 seconds and changes the period that the Kubernetes Controller Manager waits for those updates to 2 minutes. The pod eviction period for a pod on that node is reduced to 60 seconds. If the pod has the tolerationSeconds parameter, the eviction waits for the period specified by that parameter. The Kubernetes Controller Manager waits for 2 minutes to consider a node unhealthy. In another minute, the eviction process starts. Profile Component Parameter Value MediumUpdateAverageReaction kubelet node-status-update-frequency 20s Kubelet Controller Manager node-monitor-grace-period 2m Kubernetes API Server Operator default-not-ready-toleration-seconds 60s Kubernetes API Server Operator default-unreachable-toleration-seconds 60s Low worker latency profile Use the LowUpdateSlowReaction profile if the network latency is extremely high. The LowUpdateSlowReaction profile reduces the frequency of kubelet updates to 1 minute and changes the period that the Kubernetes Controller Manager waits for those updates to 5 minutes. The pod eviction period for a pod on that node is reduced to 60 seconds. If the pod has the tolerationSeconds parameter, the eviction waits for the period specified by that parameter. The Kubernetes Controller Manager waits for 5 minutes to consider a node unhealthy. In another minute, the eviction process starts. Profile Component Parameter Value LowUpdateSlowReaction kubelet node-status-update-frequency 1m Kubelet Controller Manager node-monitor-grace-period 5m Kubernetes API Server Operator default-not-ready-toleration-seconds 60s Kubernetes API Server Operator default-unreachable-toleration-seconds 60s 8.7.2. Using and changing worker latency profiles To change a worker latency profile to deal with network latency, edit the node.config object to add the name of the profile. You can change the profile at any time as latency increases or decreases. You must move one worker latency profile at a time. For example, you cannot move directly from the Default profile to the LowUpdateSlowReaction worker latency profile. You must move from the Default worker latency profile to the MediumUpdateAverageReaction profile first, then to LowUpdateSlowReaction . Similarly, when returning to the Default profile, you must move from the low profile to the medium profile first, then to Default . Note You can also configure worker latency profiles upon installing an OpenShift Container Platform cluster. Procedure To move from the default worker latency profile: Move to the medium worker latency profile: Edit the node.config object: USD oc edit nodes.config/cluster Add spec.workerLatencyProfile: MediumUpdateAverageReaction : Example node.config object apiVersion: config.openshift.io/v1 kind: Node metadata: annotations: include.release.openshift.io/ibm-cloud-managed: "true" include.release.openshift.io/self-managed-high-availability: "true" include.release.openshift.io/single-node-developer: "true" release.openshift.io/create-only: "true" creationTimestamp: "2022-07-08T16:02:51Z" generation: 1 name: cluster ownerReferences: - apiVersion: config.openshift.io/v1 kind: ClusterVersion name: version uid: 36282574-bf9f-409e-a6cd-3032939293eb resourceVersion: "1865" uid: 0c0f7a4c-4307-4187-b591-6155695ac85b spec: workerLatencyProfile: MediumUpdateAverageReaction 1 # ... 1 Specifies the medium worker latency policy. Scheduling on each worker node is disabled as the change is being applied. Optional: Move to the low worker latency profile: Edit the node.config object: USD oc edit nodes.config/cluster Change the spec.workerLatencyProfile value to LowUpdateSlowReaction : Example node.config object apiVersion: config.openshift.io/v1 kind: Node metadata: annotations: include.release.openshift.io/ibm-cloud-managed: "true" include.release.openshift.io/self-managed-high-availability: "true" include.release.openshift.io/single-node-developer: "true" release.openshift.io/create-only: "true" creationTimestamp: "2022-07-08T16:02:51Z" generation: 1 name: cluster ownerReferences: - apiVersion: config.openshift.io/v1 kind: ClusterVersion name: version uid: 36282574-bf9f-409e-a6cd-3032939293eb resourceVersion: "1865" uid: 0c0f7a4c-4307-4187-b591-6155695ac85b spec: workerLatencyProfile: LowUpdateSlowReaction 1 # ... 1 Specifies use of the low worker latency policy. Scheduling on each worker node is disabled as the change is being applied. Verification When all nodes return to the Ready condition, you can use the following command to look in the Kubernetes Controller Manager to ensure it was applied: USD oc get KubeControllerManager -o yaml | grep -i workerlatency -A 5 -B 5 Example output # ... - lastTransitionTime: "2022-07-11T19:47:10Z" reason: ProfileUpdated status: "False" type: WorkerLatencyProfileProgressing - lastTransitionTime: "2022-07-11T19:47:10Z" 1 message: all static pod revision(s) have updated latency profile reason: ProfileUpdated status: "True" type: WorkerLatencyProfileComplete - lastTransitionTime: "2022-07-11T19:20:11Z" reason: AsExpected status: "False" type: WorkerLatencyProfileDegraded - lastTransitionTime: "2022-07-11T19:20:36Z" status: "False" # ... 1 Specifies that the profile is applied and active. To change the medium profile to default or change the default to medium, edit the node.config object and set the spec.workerLatencyProfile parameter to the appropriate value.

Commands:
oc get events [-n <project>] 1
oc get events -n openshift-config
LAST SEEN TYPE REASON OBJECT MESSAGE 97m Normal Scheduled pod/dapi-env-test-pod Successfully assigned openshift-config/dapi-env-test-pod to ip-10-0-171-202.ec2.internal 97m Normal Pulling pod/dapi-env-test-pod pulling image "gcr.io/google_containers/busybox" 97m Normal Pulled pod/dapi-env-test-pod Successfully pulled image "gcr.io/google_containers/busybox" 97m Normal Created pod/dapi-env-test-pod Created container 9m5s Warning FailedCreatePodSandBox pod/dapi-volume-test-pod Failed create pod sandbox: rpc error: code = Unknown desc = failed to create pod network sandbox k8s_dapi-volume-test-pod_openshift-config_6bc60c1f-452e-11e9-9140-0eec59c23068_0(748c7a40db3d08c07fb4f9eba774bd5effe5f0d5090a242432a73eee66ba9e22): Multus: Err adding pod to network "openshift-sdn": cannot set "openshift-sdn" ifname to "eth0": no netns: failed to Statfs "/proc/33366/ns/net": no such file or directory 8m31s Normal Scheduled pod/dapi-volume-test-pod Successfully assigned openshift-config/dapi-volume-test-pod to ip-10-0-171-202.ec2.internal
apiVersion: v1 kind: Pod metadata: name: small-pod labels: app: guestbook tier: frontend spec: containers: - name: php-redis image: gcr.io/google-samples/gb-frontend:v4 imagePullPolicy: Always resources: limits: cpu: 150m memory: 100Mi requests: cpu: 150m memory: 100Mi
oc create -f <file_name>.yaml
oc create -f pod-spec.yaml
podman login registry.redhat.io
podman pull registry.redhat.io/openshift4/ose-cluster-capacity
podman run -v USDHOME/.kube:/kube:Z -v USD(pwd):/cc:Z ose-cluster-capacity /bin/cluster-capacity --kubeconfig /kube/config --<pod_spec>.yaml /cc/<pod_spec>.yaml --verbose
small-pod pod requirements: - CPU: 150m - Memory: 100Mi The cluster can schedule 88 instance(s) of the pod small-pod. Termination reason: Unschedulable: 0/5 nodes are available: 2 Insufficient cpu, 3 node(s) had taint {node-role.kubernetes.io/master: }, that the pod didn't tolerate. Pod distribution among nodes: small-pod - 192.168.124.214: 45 instance(s) - 192.168.124.120: 43 instance(s)
kind: ClusterRole apiVersion: rbac.authorization.k8s.io/v1 metadata: name: cluster-capacity-role rules: - apiGroups: [""] resources: ["pods", "nodes", "persistentvolumeclaims", "persistentvolumes", "services", "replicationcontrollers"] verbs: ["get", "watch", "list"] - apiGroups: ["apps"] resources: ["replicasets", "statefulsets"] verbs: ["get", "watch", "list"] - apiGroups: ["policy"] resources: ["poddisruptionbudgets"] verbs: ["get", "watch", "list"] - apiGroups: ["storage.k8s.io"] resources: ["storageclasses"] verbs: ["get", "watch", "list"]
oc create -f <file_name>.yaml
oc create sa cluster-capacity-sa
oc create sa cluster-capacity-sa -n default
oc adm policy add-cluster-role-to-user cluster-capacity-role system:serviceaccount:<namespace>:cluster-capacity-sa
apiVersion: v1 kind: Pod metadata: name: small-pod labels: app: guestbook tier: frontend spec: containers: - name: php-redis image: gcr.io/google-samples/gb-frontend:v4 imagePullPolicy: Always resources: limits: cpu: 150m memory: 100Mi requests: cpu: 150m memory: 100Mi
oc create -f <file_name>.yaml
oc create -f pod.yaml
oc create configmap cluster-capacity-configmap --from-file=pod.yaml=pod.yaml
apiVersion: batch/v1 kind: Job metadata: name: cluster-capacity-job spec: parallelism: 1 completions: 1 template: metadata: name: cluster-capacity-pod spec: containers: - name: cluster-capacity image: openshift/origin-cluster-capacity imagePullPolicy: "Always" volumeMounts: - mountPath: /test-pod name: test-volume env: - name: CC_INCLUSTER 1 value: "true" command: - "/bin/sh" - "-ec" - | /bin/cluster-capacity --podspec=/test-pod/pod.yaml --verbose restartPolicy: "Never" serviceAccountName: cluster-capacity-sa volumes: - name: test-volume configMap: name: cluster-capacity-configmap
oc create -f cluster-capacity-job.yaml
oc logs jobs/cluster-capacity-job
small-pod pod requirements: - CPU: 150m - Memory: 100Mi The cluster can schedule 52 instance(s) of the pod small-pod. Termination reason: Unschedulable: No nodes are available that match all of the following predicates:: Insufficient cpu (2). Pod distribution among nodes: small-pod - 192.168.124.214: 26 instance(s) - 192.168.124.120: 26 instance(s)
apiVersion: "v1" kind: "LimitRange" metadata: name: "resource-limits" spec: limits: - type: "Container" max: cpu: "2" memory: "1Gi" min: cpu: "100m" memory: "4Mi" default: cpu: "300m" memory: "200Mi" defaultRequest: cpu: "200m" memory: "100Mi" maxLimitRequestRatio: cpu: "10"
apiVersion: "v1" kind: "LimitRange" metadata: name: "resource-limits" 1 spec: limits: - type: "Container" max: cpu: "2" 2 memory: "1Gi" 3 min: cpu: "100m" 4 memory: "4Mi" 5 default: cpu: "300m" 6 memory: "200Mi" 7 defaultRequest: cpu: "200m" 8 memory: "100Mi" 9 maxLimitRequestRatio: cpu: "10" 10
apiVersion: "v1" kind: "LimitRange" metadata: name: "resource-limits" 1 spec: limits: - type: "Pod" max: cpu: "2" 2 memory: "1Gi" 3 min: cpu: "200m" 4 memory: "6Mi" 5 maxLimitRequestRatio: cpu: "10" 6
apiVersion: "v1" kind: "LimitRange" metadata: name: "resource-limits" 1 spec: limits: - type: openshift.io/Image max: storage: 1Gi 2
apiVersion: "v1" kind: "LimitRange" metadata: name: "resource-limits" 1 spec: limits: - type: openshift.io/ImageStream max: openshift.io/image-tags: 20 2 openshift.io/images: 30 3
apiVersion: "v1" kind: "LimitRange" metadata: name: "resource-limits" 1 spec: limits: - type: "PersistentVolumeClaim" min: storage: "2Gi" 2 max: storage: "50Gi" 3
apiVersion: "v1" kind: "LimitRange" metadata: name: "resource-limits" 1 spec: limits: - type: "Pod" 2 max: cpu: "2" memory: "1Gi" min: cpu: "200m" memory: "6Mi" - type: "Container" 3 max: cpu: "2" memory: "1Gi" min: cpu: "100m" memory: "4Mi" default: 4 cpu: "300m" memory: "200Mi" defaultRequest: 5 cpu: "200m" memory: "100Mi" maxLimitRequestRatio: 6 cpu: "10" - type: openshift.io/Image 7 max: storage: 1Gi - type: openshift.io/ImageStream 8 max: openshift.io/image-tags: 20 openshift.io/images: 30 - type: "PersistentVolumeClaim" 9 min: storage: "2Gi" max: storage: "50Gi"
oc create -f <limit_range_file> -n <project> 1
oc get limits -n demoproject
NAME CREATED AT resource-limits 2020-07-15T17:14:23Z
oc describe limits resource-limits -n demoproject
Name: resource-limits Namespace: demoproject Type Resource Min Max Default Request Default Limit Max Limit/Request Ratio ---- -------- --- --- --------------- ------------- ----------------------- Pod cpu 200m 2 - - - Pod memory 6Mi 1Gi - - - Container cpu 100m 2 200m 300m 10 Container memory 4Mi 1Gi 100Mi 200Mi - openshift.io/Image storage - 1Gi - - - openshift.io/ImageStream openshift.io/image - 12 - - - openshift.io/ImageStream openshift.io/image-tags - 10 - - - PersistentVolumeClaim storage - 50Gi - - -
oc delete limits <limit_name>
-XX:+UseParallelGC -XX:MinHeapFreeRatio=5 -XX:MaxHeapFreeRatio=10 -XX:GCTimeRatio=4 -XX:AdaptiveSizePolicyWeight=90.
JAVA_TOOL_OPTIONS="-XX:+UnlockExperimentalVMOptions -XX:+UseCGroupMemoryLimitForHeap -Dsun.zip.disableMemoryMapping=true"
apiVersion: v1 kind: Pod metadata: name: test spec: containers: - name: test image: fedora:latest command: - sleep - "3600" env: - name: MEMORY_REQUEST 1 valueFrom: resourceFieldRef: containerName: test resource: requests.memory - name: MEMORY_LIMIT 2 valueFrom: resourceFieldRef: containerName: test resource: limits.memory resources: requests: memory: 384Mi limits: memory: 512Mi
oc create -f <file-name>.yaml
oc rsh test
env | grep MEMORY | sort
MEMORY_LIMIT=536870912 MEMORY_REQUEST=402653184
oc rsh test
grep '^oom_kill ' /sys/fs/cgroup/memory/memory.oom_control
oom_kill 0
sed -e '' </dev/zero
Killed
echo USD?
137
grep '^oom_kill ' /sys/fs/cgroup/memory/memory.oom_control
oom_kill 1
oc get pod test
NAME READY STATUS RESTARTS AGE test 0/1 OOMKilled 0 1m
oc get pod test -o yaml
status: containerStatuses: - name: test ready: false restartCount: 0 state: terminated: exitCode: 137 reason: OOMKilled phase: Failed
oc get pod test -o yaml
status: containerStatuses: - name: test ready: true restartCount: 1 lastState: terminated: exitCode: 137 reason: OOMKilled state: running: phase: Running
oc get pod test
NAME READY STATUS RESTARTS AGE test 0/1 Evicted 0 1m
oc get pod test -o yaml
status: message: 'Pod The node was low on resource: [MemoryPressure].' phase: Failed reason: Evicted
apiVersion: operator.autoscaling.openshift.io/v1 kind: ClusterResourceOverride metadata: name: cluster 1 spec: podResourceOverride: spec: memoryRequestToLimitPercent: 50 2 cpuRequestToLimitPercent: 25 3 limitCPUToMemoryPercent: 200 4
apiVersion: v1 kind: Namespace metadata: labels: clusterresourceoverrides.admission.autoscaling.openshift.io/enabled: "true"
apiVersion: operator.autoscaling.openshift.io/v1 kind: ClusterResourceOverride metadata: name: cluster 1 spec: podResourceOverride: spec: memoryRequestToLimitPercent: 50 2 cpuRequestToLimitPercent: 25 3 limitCPUToMemoryPercent: 200 4
apiVersion: operator.autoscaling.openshift.io/v1 kind: ClusterResourceOverride metadata: annotations: kubectl.kubernetes.io/last-applied-configuration: | {"apiVersion":"operator.autoscaling.openshift.io/v1","kind":"ClusterResourceOverride","metadata":{"annotations":{},"name":"cluster"},"spec":{"podResourceOverride":{"spec":{"cpuRequestToLimitPercent":25,"limitCPUToMemoryPercent":200,"memoryRequestToLimitPercent":50}}}} creationTimestamp: "2019-12-18T22:35:02Z" generation: 1 name: cluster resourceVersion: "127622" selfLink: /apis/operator.autoscaling.openshift.io/v1/clusterresourceoverrides/cluster uid: 978fc959-1717-4bd1-97d0-ae00ee111e8d spec: podResourceOverride: spec: cpuRequestToLimitPercent: 25 limitCPUToMemoryPercent: 200 memoryRequestToLimitPercent: 50 status: mutatingWebhookConfigurationRef: 1 apiVersion: admissionregistration.k8s.io/v1 kind: MutatingWebhookConfiguration name: clusterresourceoverrides.admission.autoscaling.openshift.io resourceVersion: "127621" uid: 98b3b8ae-d5ce-462b-8ab5-a729ea8f38f3
apiVersion: v1 kind: Namespace metadata: name: clusterresourceoverride-operator
oc create -f <file-name>.yaml
oc create -f cro-namespace.yaml
apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: clusterresourceoverride-operator namespace: clusterresourceoverride-operator spec: targetNamespaces: - clusterresourceoverride-operator
oc create -f <file-name>.yaml
oc create -f cro-og.yaml
apiVersion: operators.coreos.com/v1alpha1 kind: Subscription metadata: name: clusterresourceoverride namespace: clusterresourceoverride-operator spec: channel: "4.11" name: clusterresourceoverride source: redhat-operators sourceNamespace: openshift-marketplace
oc create -f <file-name>.yaml
oc create -f cro-sub.yaml
oc project clusterresourceoverride-operator
apiVersion: operator.autoscaling.openshift.io/v1 kind: ClusterResourceOverride metadata: name: cluster 1 spec: podResourceOverride: spec: memoryRequestToLimitPercent: 50 2 cpuRequestToLimitPercent: 25 3 limitCPUToMemoryPercent: 200 4
oc create -f <file-name>.yaml
oc create -f cro-cr.yaml
oc get clusterresourceoverride cluster -n clusterresourceoverride-operator -o yaml
apiVersion: operator.autoscaling.openshift.io/v1 kind: ClusterResourceOverride metadata: annotations: kubectl.kubernetes.io/last-applied-configuration: | {"apiVersion":"operator.autoscaling.openshift.io/v1","kind":"ClusterResourceOverride","metadata":{"annotations":{},"name":"cluster"},"spec":{"podResourceOverride":{"spec":{"cpuRequestToLimitPercent":25,"limitCPUToMemoryPercent":200,"memoryRequestToLimitPercent":50}}}} creationTimestamp: "2019-12-18T22:35:02Z" generation: 1 name: cluster resourceVersion: "127622" selfLink: /apis/operator.autoscaling.openshift.io/v1/clusterresourceoverrides/cluster uid: 978fc959-1717-4bd1-97d0-ae00ee111e8d spec: podResourceOverride: spec: cpuRequestToLimitPercent: 25 limitCPUToMemoryPercent: 200 memoryRequestToLimitPercent: 50 status: mutatingWebhookConfigurationRef: 1 apiVersion: admissionregistration.k8s.io/v1 kind: MutatingWebhookConfiguration name: clusterresourceoverrides.admission.autoscaling.openshift.io resourceVersion: "127621" uid: 98b3b8ae-d5ce-462b-8ab5-a729ea8f38f3
apiVersion: operator.autoscaling.openshift.io/v1 kind: ClusterResourceOverride metadata: name: cluster spec: podResourceOverride: spec: memoryRequestToLimitPercent: 50 1 cpuRequestToLimitPercent: 25 2 limitCPUToMemoryPercent: 200 3
apiVersion: v1 kind: Namespace metadata: labels: clusterresourceoverrides.admission.autoscaling.openshift.io/enabled: "true" 1
sysctl -a |grep commit
# vm.overcommit_memory = 0 #
sysctl -a |grep panic
# vm.panic_on_oom = 0 #
oc edit machineconfigpool <name>
oc edit machineconfigpool worker
apiVersion: machineconfiguration.openshift.io/v1 kind: MachineConfigPool metadata: creationTimestamp: "2022-11-16T15:34:25Z" generation: 4 labels: pools.operator.machineconfiguration.openshift.io/worker: "" 1 name: worker
oc label machineconfigpool worker custom-kubelet=small-pods
apiVersion: machineconfiguration.openshift.io/v1 kind: KubeletConfig metadata: name: disable-cpu-units 1 spec: machineConfigPoolSelector: matchLabels: pools.operator.machineconfiguration.openshift.io/worker: "" 2 kubeletConfig: cpuCfsQuota: false 3
oc create -f <file_name>.yaml
sysctl -w vm.overcommit_memory=0
apiVersion: v1 kind: Namespace metadata: annotations: quota.openshift.io/cluster-resource-override-enabled: "false" 1
apiVersion: config.openshift.io/v1 kind: FeatureGate metadata: name: cluster 1 spec: featureSet: TechPreviewNoUpgrade 2
sh-4.2# chroot /host
sh-4.2# cat /etc/kubernetes/kubelet.conf
featureGates: InsightsOperatorPullingSCA: true, LegacyNodeRoleBehavior: false
oc edit featuregate cluster
apiVersion: config.openshift.io/v1 kind: FeatureGate metadata: name: cluster 1 spec: featureSet: TechPreviewNoUpgrade 2
sh-4.2# chroot /host
sh-4.2# cat /etc/kubernetes/kubelet.conf
featureGates: InsightsOperatorPullingSCA: true, LegacyNodeRoleBehavior: false
oc edit nodes.config/cluster
apiVersion: config.openshift.io/v1 kind: Node metadata: annotations: include.release.openshift.io/ibm-cloud-managed: "true" include.release.openshift.io/self-managed-high-availability: "true" include.release.openshift.io/single-node-developer: "true" release.openshift.io/create-only: "true" creationTimestamp: "2022-07-08T16:02:51Z" generation: 1 name: cluster ownerReferences: - apiVersion: config.openshift.io/v1 kind: ClusterVersion name: version uid: 36282574-bf9f-409e-a6cd-3032939293eb resourceVersion: "1865" uid: 0c0f7a4c-4307-4187-b591-6155695ac85b spec: workerLatencyProfile: MediumUpdateAverageReaction 1
oc edit nodes.config/cluster
apiVersion: config.openshift.io/v1 kind: Node metadata: annotations: include.release.openshift.io/ibm-cloud-managed: "true" include.release.openshift.io/self-managed-high-availability: "true" include.release.openshift.io/single-node-developer: "true" release.openshift.io/create-only: "true" creationTimestamp: "2022-07-08T16:02:51Z" generation: 1 name: cluster ownerReferences: - apiVersion: config.openshift.io/v1 kind: ClusterVersion name: version uid: 36282574-bf9f-409e-a6cd-3032939293eb resourceVersion: "1865" uid: 0c0f7a4c-4307-4187-b591-6155695ac85b spec: workerLatencyProfile: LowUpdateSlowReaction 1
oc get KubeControllerManager -o yaml | grep -i workerlatency -A 5 -B 5
- lastTransitionTime: "2022-07-11T19:47:10Z" reason: ProfileUpdated status: "False" type: WorkerLatencyProfileProgressing - lastTransitionTime: "2022-07-11T19:47:10Z" 1 message: all static pod revision(s) have updated latency profile reason: ProfileUpdated status: "True" type: WorkerLatencyProfileComplete - lastTransitionTime: "2022-07-11T19:20:11Z" reason: AsExpected status: "False" type: WorkerLatencyProfileDegraded - lastTransitionTime: "2022-07-11T19:20:36Z" status: "False"