Chapter 6. Working with clusters

6.1. 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.

6.1.1. 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.

6.1.2. 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:

    $ oc get events [-n <project>] 1
    1
    The name of the project.

    For example:

    $ 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.

    1. Launch the OpenShift Container Platform console.
    2. Click HomeEvents and select your project.
    3. Move to resource that you want to see events. For example: HomeProjects → <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.

6.1.3. List of events

This section describes the events of OpenShift Container Platform.

Table 6.1. Configuration events

NameDescription

FailedValidation

Failed pod configuration validation.

Table 6.2. Container events

NameDescription

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 6.3. Health events

NameDescription

Unhealthy

Container is unhealthy.

Table 6.4. Image events

NameDescription

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 6.5. Image Manager events

NameDescription

FreeDiskSpaceFailed

Free disk space failed.

InvalidDiskCapacity

Invalid disk capacity.

Table 6.6. Node events

NameDescription

FailedMount

Volume mount failed.

HostNetworkNotSupported

Host network not supported.

HostPortConflict

Host/port conflict.

InsufficientFreeCPU

Insufficient free CPU.

InsufficientFreeMemory

Insufficient free memory.

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 6.7. Pod worker events

NameDescription

FailedSync

Pod sync failed.

Table 6.8. System Events

NameDescription

SystemOOM

There is an OOM (out of memory) situation on the cluster.

Table 6.9. Pod events

NameDescription

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 6.10. Horizontal Pod AutoScaler events

NameDescription

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 6.11. Network events (openshift-sdn)

NameDescription

Starting

Starting OpenShift-SDN.

NetworkFailed

The pod’s network interface has been lost and the pod will be stopped.

Table 6.12. Network events (kube-proxy)

NameDescription

NeedPods

The service-port <serviceName>:<port> needs pods.

Table 6.13. Volume events

NameDescription

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 6.14. Lifecycle hooks

NameDescription

FailedPostStartHook

Handler failed for pod start.

FailedPreStopHook

Handler failed for pre-stop.

UnfinishedPreStopHook

Pre-stop hook unfinished.

Table 6.15. Deployments

NameDescription

DeploymentCancellationFailed

Failed to cancel deployment.

DeploymentCancelled

Canceled deployment.

DeploymentCreated

Created new replication controller.

IngressIPRangeFull

No available Ingress IP to allocate to service.

Table 6.16. Scheduler events

NameDescription

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 6.17. Daemon set events

NameDescription

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 6.18. LoadBalancer service events

NameDescription

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.

6.2. Estimating the number of pods your OpenShift Container Platform nodes can hold

As a cluster administrator, you can use the 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.

6.2.1. Understanding the OpenShift Container Platform cluster capacity tool

The 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 cluster capacity analysis tool as a stand-alone utility from the command line, or as a job in a pod inside an OpenShift Container Platform cluster. Running it as job inside of a pod enables you to run it multiple times without intervention.

6.2.2. Running the cluster capacity tool on the command line

You can run the OpenShift Container Platform cluster capacity tool from the command line to estimate the number of pods that can be scheduled onto your cluster.

Prerequisites

  • Download and install the cluster-capacity tool.
  • Create a sample Pod spec file, which the tool uses for estimating resource usage. The podspec specifies its resource requirements as limits or requests. The cluster capacity tool takes the pod’s resource requirements into account for its estimation analysis.

    An example of the Pod spec input is:

    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

Procedure

To use the tool on the command line:

  1. Run the following command:

    $ ./cluster-capacity --kubeconfig <path-to-kubeconfig> \ 1
        --podspec <path-to-pod-spec> 2
    1
    Specify the path to your Kubernetes configuration file.
    2
    Specify the path to the sample Pod spec file

    You can also add the --verbose option to output a detailed description of how many pods can be scheduled on each node in the cluster:

    $ ./cluster-capacity --kubeconfig <path-to-kubeconfig> \
        --podspec <path-to-pod-spec> --verbose

    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)

    In the above example, the number of estimated pods that can be scheduled onto the cluster is 52.

6.2.3. Running the cluster capacity tool as a job inside a pod

Running the cluster capacity tool as a job inside of a pod has the advantage of being able to be run multiple times without needing user intervention. Running the cluster capacity tool as a job involves using a ConfigMap object.

Prerequisites

Download and install the cluster capacity tool.

Procedure

To run the cluster capacity tool:

  1. Create the cluster role:

    $ cat << EOF| oc create -f -

    Example output

    kind: ClusterRole
    apiVersion: v1
    metadata:
      name: cluster-capacity-role
    rules:
    - apiGroups: [""]
      resources: ["pods", "nodes", "persistentvolumeclaims", "persistentvolumes", "services"]
      verbs: ["get", "watch", "list"]
    EOF

  2. Create the service account:

    $ oc create sa cluster-capacity-sa
  3. Add the role to the service account:

    $ oc adm policy add-cluster-role-to-user cluster-capacity-role \
        system:serviceaccount:default:cluster-capacity-sa
  4. Define and create the Pod spec:

    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
  5. The cluster capacity analysis is mounted in a volume using a ConfigMap object named cluster-capacity-configmap to mount input pod spec file pod.yaml into a volume test-volume at the path /test-pod.

    If you haven’t created a ConfigMap object, create one before creating the job:

    $ oc create configmap cluster-capacity-configmap \
        --from-file=pod.yaml=pod.yaml
  6. Create the job using the below example of a job specification file:

    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.
  7. Run the cluster capacity image as a job in a pod:

    $ oc create -f cluster-capacity-job.yaml
  8. Check the job logs to find the number of pods that can be scheduled in the cluster:

    $ 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)

6.3. 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.

6.3.1. 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"

6.3.1.1. 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.

6.3.1.1.1. 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 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.
6.3.1.1.2. 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.
6.3.1.1.3. Image limits

A LimitRange object allows you to specify the maximum size of an image that can be pushed to an internal registry.

When pushing images to an internal 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 internal 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.

6.3.1.1.4. 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 internal registry. Internal and external references are not distinguished.

6.3.1.1.5. 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.

6.3.2. Creating a Limit Range

To apply a limit range to a project:

  1. 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 internal 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.
  2. Create the object:

    $ 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.

6.3.3. 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:

  1. Get the list of LimitRange object defined in the project. For example, for a project called demoproject:

    $ oc get limits -n demoproject
    NAME              CREATED AT
    resource-limits   2020-07-15T17:14:23Z
  2. Describe the LimitRange object you are interested in, for example the resource-limits limit range:

    $ 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    -               -               -

6.3.4. Deleting a Limit Range

To remove any active LimitRange object to no longer enforce the limits in a project:

  1. Run the following command:

    $ oc delete limits <limit_name>

6.4. 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.

6.4.1. 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, in order 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.

6.4.1.1. Managing application memory strategy

The steps for sizing application memory on OpenShift Container Platform are as follows:

  1. 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?

  2. 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.

  3. 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.

  4. 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.

  5. 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.

6.4.2. 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:

  1. Overriding the JVM maximum heap size.
  2. Encouraging the JVM to release unused memory to the operating system, if appropriate.
  3. 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.

6.4.2.1. 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:

  1. If the container memory limit is set and the experimental options are supported by the JVM, set -XX:+UnlockExperimentalVMOptions -XX:+UseCGroupMemoryLimitForHeap.

    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).

  2. 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.

6.4.2.2. 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.

6.4.2.3. 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"

This does not guarantee that additional options are not required, but is intended to be a helpful starting point.

6.4.3. 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

  1. Configure the pod to add the MEMORY_REQUEST and MEMORY_LIMIT stanzas:

    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.
  2. Create the pod:

    $ oc create -f <file-name>.yaml
  3. Access the pod using a remote shell:

    $ oc rsh test
  4. Check that the requested values were applied:

    $ 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.

6.4.4. 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:

  1. Access the pod using a remote shell:

    # oc rsh test
  2. Run the following command to see the current OOM kill count in /sys/fs/cgroup/memory/memory.oom_control:

    $ grep '^oom_kill ' /sys/fs/cgroup/memory/memory.oom_control
    oom_kill 0
  3. Run the following command to provoke an OOM kill:

    $ sed -e '' </dev/zero

    Example output

    Killed

  4. Run the following command to view the exit status of the sed command:

    $ echo $?

    Example output

    137

    The 137 code indicates the container process exited with code 137, indicating it received a SIGKILL signal.

  5. Run the following command to see that the OOM kill counter in /sys/fs/cgroup/memory/memory.oom_control incremented:

    $ grep '^oom_kill ' /sys/fs/cgroup/memory/memory.oom_control
    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:

    $ 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:

      $ 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:

      $ 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

6.4.5. 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.

$ oc get pod test

Example output

NAME      READY     STATUS    RESTARTS   AGE
test      0/1       Evicted   0          1m

$ oc get pod test -o yaml

Example output

...
status:
  message: 'Pod The node was low on resource: [MemoryPressure].'
  phase: Failed
  reason: Evicted

6.5. 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.

6.5.1. 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.

6.5.2. 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:
   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.

6.5.2.1. 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:

  1. In the OpenShift Container Platform web console, navigate to HomeProjects

    1. Click Create Project.
    2. Specify clusterresourceoverride-operator as the name of the project.
    3. Click Create.
  2. Navigate to OperatorsOperatorHub.

    1. Choose ClusterResourceOverride Operator from the list of available Operators and click Install.
    2. On the Install Operator page, make sure A specific Namespace on the cluster is selected for Installation Mode.
    3. Make sure clusterresourceoverride-operator is selected for Installed Namespace.
    4. Select an Update Channel and Approval Strategy.
    5. Click Install.
  3. On the Installed Operators page, click ClusterResourceOverride.

    1. On the ClusterResourceOverride Operator details page, click Create Instance.
    2. On the Create ClusterResourceOverride page, 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.
    3. Click Create.
  4. Check the current state of the admission webhook by checking the status of the cluster custom resource:

    1. On the ClusterResourceOverride Operator page, click cluster.
    2. On the ClusterResourceOverride Details age, 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/v1beta1
            kind: MutatingWebhookConfiguration
            name: clusterresourceoverrides.admission.autoscaling.openshift.io
            resourceVersion: "127621"
            uid: 98b3b8ae-d5ce-462b-8ab5-a729ea8f38f3
      
      ....
      1
      Reference to the ClusterResourceOverride admission webhook.

6.5.2.2. 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:

  1. Create a namespace for the Cluster Resource Override Operator:

    1. 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
    2. Create the namespace:

      $ oc create -f <file-name>.yaml

      For example:

      $ oc create -f cro-namespace.yaml
  2. Create an Operator group:

    1. 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
    2. Create the Operator Group:

      $ oc create -f <file-name>.yaml

      For example:

      $ oc create -f cro-og.yaml
  3. Create a subscription:

    1. 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: "4.5"
        name: clusterresourceoverride
        source: redhat-operators
        sourceNamespace: openshift-marketplace
    2. Create the subscription:

      $ oc create -f <file-name>.yaml

      For example:

      $ oc create -f cro-sub.yaml
  4. Create a ClusterResourceOverride custom resource (CR) object in the clusterresourceoverride-operator namespace:

    1. Change to the clusterresourceoverride-operator namespace.

      $ oc project clusterresourceoverride-operator
    2. 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.
    3. Create the ClusterResourceOverride object:

      $ oc create -f <file-name>.yaml

      For example:

      $ oc create -f cro-cr.yaml
  5. Verify the current state of the admission webhook by checking the status of the cluster custom resource.

    $ 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/v1beta1
          kind: MutatingWebhookConfiguration
          name: clusterresourceoverrides.admission.autoscaling.openshift.io
          resourceVersion: "127621"
          uid: 98b3b8ae-d5ce-462b-8ab5-a729ea8f38f3
    
    ....

    1
    Reference to the ClusterResourceOverride admission webhook.

6.5.2.3. 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:

  1. Edit the ClusterResourceOverride CR:

    apiVersion: operator.autoscaling.openshift.io/v1
    kind: ClusterResourceOverride
    metadata:
    -   name: cluster
    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.
  2. 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.

6.5.3. 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.

6.5.3.1. Understanding compute resources and containers

The node-enforced behavior for compute resources is specific to the resource type.

6.5.3.1.1. 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.

6.5.3.1.2. 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.

6.5.3.2. 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.

For each compute resource, a container is divided into one of three QoS classes with decreasing order of priority:

Table 6.19. Quality of Service Classes

PriorityClass NameDescription

1 (highest)

Guaranteed

If limits and optionally requests are set (not equal to 0) for all resources and they are equal, then the container 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 container is classified as Burstable.

3 (lowest)

BestEffort

If requests and limits are not set for any of the resources, then the container 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.
6.5.3.2.1. 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.

6.5.3.3. Understanding swap memory and QOS

You can disable swap by default on your nodes in order 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.

6.5.3.4. 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:

$ sysctl -a |grep commit

Example output

vm.overcommit_memory = 1

$ 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

6.5.3.5. 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

  1. Obtain the label associated with the static MachineConfigPool CRD for the type of node you want to configure. Perform one of the following steps:

    1. View the machine config pool:

      $ oc describe machineconfigpool <name>

      For example:

      $ oc describe machineconfigpool worker

      Example output

      apiVersion: machineconfiguration.openshift.io/v1
      kind: MachineConfigPool
      metadata:
        creationTimestamp: 2019-02-08T14:52:39Z
        generation: 1
        labels:
          custom-kubelet: small-pods 1

      1
      If a label has been added it appears under labels.
    2. If the label is not present, add a key/value pair:

      $ oc label machineconfigpool worker custom-kubelet=small-pods

Procedure

  1. 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:
          custom-kubelet: small-pods 2
      kubeletConfig:
        cpuCfsQuota: 3
          - "false"

    1
    Assign a name to CR.
    2
    Specify the label to apply the configuration change.
    3
    Set the cpuCfsQuota parameter to false.

6.5.3.6. 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.

6.5.3.7. 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:

$ sysctl -w vm.overcommit_memory=0

6.5.4. 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.

6.5.4.1. 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:

  1. Edit the project object file
  2. Add the following annotation:

    quota.openshift.io/cluster-resource-override-enabled: "false"
  3. Create the project object:

    $ oc create -f <file-name>.yaml

6.5.5. Additional resources

For information setting per-project resource limits, see Setting deployment resources.

6.6. Enabling OpenShift Container Platform features using FeatureGates

As an administrator, you can turn on features that are Technology Preview features.

6.6.1. Understanding FeatureGates and Technology Preview features

You can use the FeatureGate custom resource to enable Technology Preview features throughout your cluster. This allows you, for example, to enable Technology Preview features on test clusters where you can fully test them while ensuring they are disabled on production clusters.

Important

After turning Technology Preview features on using feature gates, they cannot be turned off and cluster upgrades are prevented.

For more information about the support scope of Red Hat Technology Preview features, see https://access.redhat.com/support/offerings/techpreview/.

This allows you, for example, to ensure that Technology Preview features are off for production clusters while leaving the features on for test clusters where you can fully test them.

6.6.2. Features that are affected by FeatureGates

The following features are affected by FeatureGates:

FeatureGateDescriptionDefault

RotateKubeletServerCertificate

Enables the rotation of the server TLS certificate on the cluster.

True

SupportPodPidsLimit

Enables support for limiting the number of processes (PIDs) running in a pod.

True

MachineHealthCheck

Enables automatically repairing unhealthy machines in a machine pool.

True

LocalStorageCapacityIsolation

Enable the consumption of local ephemeral storage and also the sizeLimit property of an emptyDir volume.

False

You can enable these features by editing the Feature Gate Custom Resource. Turning on these features cannot be undone and prevents the ability to upgrade your cluster.

6.6.3. Enabling Technology Preview features using FeatureGates

You can turn Technology Preview features on and off for all nodes in the cluster by editing the FeatureGates Custom Resource, named cluster, in the openshift-config project.

The following Technology Preview features are enabled by feature gates:

  • RotateKubeletServerCertificate
  • SupportPodPidsLimit
Important

Turning on Technology Preview features using the FeatureGate custom resource cannot be undone and prevents upgrades.

Procedure

To turn on the Technology Preview features for the entire cluster:

  1. Create the FeatureGates instance:

    1. Switch to the AdministrationCustom Resource Definitions page.
    2. On the Custom Resource Definitions page, click FeatureGate.
    3. On the Custom Resource Definitions page, click the Actions Menu and select View Instances.
    4. On the Feature Gates page, click Create Feature Gates.
    5. Replace the code with following sample:

      apiVersion: config.openshift.io/v1
      kind: FeatureGate
      metadata:
        name: cluster
      spec: {}
    6. Click Create.
  2. To turn on the Technology Preview features, change the spec parameter to:

    apiVersion: config.openshift.io/v1
    kind: FeatureGate
    metadata:
      name: cluster
    spec:
      featureSet: TechPreviewNoUpgrade 1
    1
    Add featureSet: TechPreviewNoUpgrade to enable the Technology Preview features that are affected by FeatureGates.