Chapter 2. Working with pods 2.1. Using pods A pod is one or more containers deployed together on one host, and the smallest compute unit that can be defined, deployed, and managed. 2.1.1. Understanding pods Pods are the rough equivalent of a machine instance (physical or virtual) to a Container. Each pod is allocated its own internal IP address, therefore owning its entire port space, and containers within pods can share their local storage and networking. Pods have a lifecycle; they are defined, then they are assigned to run on a node, then they run until their container(s) exit or they are removed for some other reason. Pods, depending on policy and exit code, might be removed after exiting, or can be retained to enable access to the logs of their containers. OpenShift Container Platform treats pods as largely immutable; changes cannot be made to a pod definition while it is running. OpenShift Container Platform implements changes by terminating an existing pod and recreating it with modified configuration, base image(s), or both. Pods are also treated as expendable, and do not maintain state when recreated. Therefore pods should usually be managed by higher-level controllers, rather than directly by users. Note For the maximum number of pods per OpenShift Container Platform node host, see the Cluster Limits. Warning Bare pods that are not managed by a replication controller will be not rescheduled upon node disruption. 2.1.2. Example pod configurations OpenShift Container Platform leverages the Kubernetes concept of a pod , which is one or more containers deployed together on one host, and the smallest compute unit that can be defined, deployed, and managed. The following is an example definition of a pod from a Rails application. It demonstrates many features of pods, most of which are discussed in other topics and thus only briefly mentioned here: Pod object definition (YAML) kind: Pod apiVersion: v1 metadata: name: example namespace: default selfLink: /api/v1/namespaces/default/pods/example uid: 5cc30063-0265780783bc resourceVersion: '165032' creationTimestamp: '2019-02-13T20:31:37Z' labels: app: hello-openshift 1 annotations: openshift.io/scc: anyuid spec: restartPolicy: Always 2 serviceAccountName: default imagePullSecrets: - name: default-dockercfg-5zrhb priority: 0 schedulerName: default-scheduler terminationGracePeriodSeconds: 30 nodeName: ip-10-0-140-16.us-east-2.compute.internal securityContext: 3 seLinuxOptions: level: 's0:c11,c10' containers: 4 - resources: {} terminationMessagePath: /dev/termination-log name: hello-openshift securityContext: capabilities: drop: - MKNOD procMount: Default ports: - containerPort: 8080 protocol: TCP imagePullPolicy: Always volumeMounts: 5 - name: default-token-wbqsl readOnly: true mountPath: /var/run/secrets/kubernetes.io/serviceaccount 6 terminationMessagePolicy: File image: registry.redhat.io/openshift4/ose-ogging-eventrouter:v4.3 7 serviceAccount: default 8 volumes: 9 - name: default-token-wbqsl secret: secretName: default-token-wbqsl defaultMode: 420 dnsPolicy: ClusterFirst status: phase: Pending conditions: - type: Initialized status: 'True' lastProbeTime: null lastTransitionTime: '2019-02-13T20:31:37Z' - type: Ready status: 'False' lastProbeTime: null lastTransitionTime: '2019-02-13T20:31:37Z' reason: ContainersNotReady message: 'containers with unready status: [hello-openshift]' - type: ContainersReady status: 'False' lastProbeTime: null lastTransitionTime: '2019-02-13T20:31:37Z' reason: ContainersNotReady message: 'containers with unready status: [hello-openshift]' - type: PodScheduled status: 'True' lastProbeTime: null lastTransitionTime: '2019-02-13T20:31:37Z' hostIP: 10.0.140.16 startTime: '2019-02-13T20:31:37Z' containerStatuses: - name: hello-openshift state: waiting: reason: ContainerCreating lastState: {} ready: false restartCount: 0 image: openshift/hello-openshift imageID: '' qosClass: BestEffort 1 Pods can be "tagged" with one or more labels, which can then be used to select and manage groups of pods in a single operation. The labels are stored in key/value format in the metadata hash. 2 The pod restart policy with possible values Always , OnFailure , and Never . The default value is Always . 3 OpenShift Container Platform defines a security context for containers which specifies whether they are allowed to run as privileged containers, run as a user of their choice, and more. The default context is very restrictive but administrators can modify this as needed. 4 containers specifies an array of one or more container definitions. 5 The container specifies where external storage volumes are mounted within the container. In this case, there is a volume for storing access to credentials the registry needs for making requests against the OpenShift Container Platform API. 6 Specify the volumes to provide for the pod. Volumes mount at the specified path. Do not mount to the container root, / , or any path that is the same in the host and the container. This can corrupt your host system if the container is sufficiently privileged, such as the host /dev/pts files. It is safe to mount the host by using /host . 7 Each container in the pod is instantiated from its own container image. 8 Pods making requests against the OpenShift Container Platform API is a common enough pattern that there is a serviceAccount field for specifying which service account user the pod should authenticate as when making the requests. This enables fine-grained access control for custom infrastructure components. 9 The pod defines storage volumes that are available to its container(s) to use. In this case, it provides an ephemeral volume for a secret volume containing the default service account tokens. If you attach persistent volumes that have high file counts to pods, those pods can fail or can take a long time to start. For more information, see When using Persistent Volumes with high file counts in OpenShift, why do pods fail to start or take an excessive amount of time to achieve "Ready" state? . Note This pod definition does not include attributes that are filled by OpenShift Container Platform automatically after the pod is created and its lifecycle begins. The Kubernetes pod documentation has details about the functionality and purpose of pods. 2.1.3. Additional resources For more information on pods and storage see Understanding persistent storage and Understanding ephemeral storage . 2.2. Viewing pods As an administrator, you can view the pods in your cluster and to determine the health of those pods and the cluster as a whole. 2.2.1. About pods OpenShift Container Platform leverages the Kubernetes concept of a pod , which is one or more containers deployed together on one host, and the smallest compute unit that can be defined, deployed, and managed. Pods are the rough equivalent of a machine instance (physical or virtual) to a container. You can view a list of pods associated with a specific project or view usage statistics about pods. 2.2.2. Viewing pods in a project You can view a list of pods associated with the current project, including the number of replica, the current status, number or restarts and the age of the pod. Procedure To view the pods in a project: Change to the project: USD oc project <project-name> Run the following command: USD oc get pods For example: USD oc get pods Example output NAME READY STATUS RESTARTS AGE console-698d866b78-bnshf 1/1 Running 2 165m console-698d866b78-m87pm 1/1 Running 2 165m Add the -o wide flags to view the pod IP address and the node where the pod is located. USD oc get pods -o wide Example output NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE console-698d866b78-bnshf 1/1 Running 2 166m 10.128.0.24 ip-10-0-152-71.ec2.internal <none> console-698d866b78-m87pm 1/1 Running 2 166m 10.129.0.23 ip-10-0-173-237.ec2.internal <none> 2.2.3. Viewing pod usage statistics You can display usage statistics about pods, which provide the runtime environments for containers. These usage statistics include CPU, memory, and storage consumption. Prerequisites You must have cluster-reader permission to view the usage statistics. Metrics must be installed to view the usage statistics. Procedure To view the usage statistics: Run the following command: USD oc adm top pods For example: USD oc adm top pods -n openshift-console Example output NAME CPU(cores) MEMORY(bytes) console-7f58c69899-q8c8k 0m 22Mi console-7f58c69899-xhbgg 0m 25Mi downloads-594fcccf94-bcxk8 3m 18Mi downloads-594fcccf94-kv4p6 2m 15Mi Run the following command to view the usage statistics for pods with labels: USD oc adm top pod --selector='' You must choose the selector (label query) to filter on. Supports = , == , and != . For example: USD oc adm top pod --selector='name=my-pod' 2.2.4. Viewing resource logs You can view the log for various resources in the OpenShift CLI ( oc ) and web console. Logs read from the tail, or end, of the log. Prerequisites Access to the OpenShift CLI ( oc ). Procedure (UI) In the OpenShift Container Platform console, navigate to Workloads Pods or navigate to the pod through the resource you want to investigate. Note Some resources, such as builds, do not have pods to query directly. In such instances, you can locate the Logs link on the Details page for the resource. Select a project from the drop-down menu. Click the name of the pod you want to investigate. Click Logs . Procedure (CLI) View the log for a specific pod: USD oc logs -f <pod_name> -c <container_name> where: -f Optional: Specifies that the output follows what is being written into the logs. <pod_name> Specifies the name of the pod. <container_name> Optional: Specifies the name of a container. When a pod has more than one container, you must specify the container name. For example: USD oc logs ruby-58cd97df55-mww7r USD oc logs -f ruby-57f7f4855b-znl92 -c ruby The contents of log files are printed out. View the log for a specific resource: USD oc logs <object_type>/<resource_name> 1 1 Specifies the resource type and name. For example: USD oc logs deployment/ruby The contents of log files are printed out. 2.3. Configuring an OpenShift Container Platform cluster for pods As an administrator, you can create and maintain an efficient cluster for pods. By keeping your cluster efficient, you can provide a better environment for your developers using such tools as what a pod does when it exits, ensuring that the required number of pods is always running, when to restart pods designed to run only once, limit the bandwidth available to pods, and how to keep pods running during disruptions. 2.3.1. Configuring how pods behave after restart A pod restart policy determines how OpenShift Container Platform responds when Containers in that pod exit. The policy applies to all Containers in that pod. The possible values are: Always - Tries restarting a successfully exited Container on the pod continuously, with an exponential back-off delay (10s, 20s, 40s) capped at 5 minutes. The default is Always . OnFailure - Tries restarting a failed Container on the pod with an exponential back-off delay (10s, 20s, 40s) capped at 5 minutes. Never - Does not try to restart exited or failed Containers on the pod. Pods immediately fail and exit. After the pod is bound to a node, the pod will never be bound to another node. This means that a controller is necessary in order for a pod to survive node failure: Condition Controller Type Restart Policy Pods that are expected to terminate (such as batch computations) Job OnFailure or Never Pods that are expected to not terminate (such as web servers) Replication controller Always . Pods that must run one-per-machine Daemon set Any If a Container on a pod fails and the restart policy is set to OnFailure , the pod stays on the node and the Container is restarted. If you do not want the Container to restart, use a restart policy of Never . If an entire pod fails, OpenShift Container Platform starts a new pod. Developers must address the possibility that applications might be restarted in a new pod. In particular, applications must handle temporary files, locks, incomplete output, and so forth caused by runs. Note Kubernetes architecture expects reliable endpoints from cloud providers. When a cloud provider is down, the kubelet prevents OpenShift Container Platform from restarting. If the underlying cloud provider endpoints are not reliable, do not install a cluster using cloud provider integration. Install the cluster as if it was in a no-cloud environment. It is not recommended to toggle cloud provider integration on or off in an installed cluster. For details on how OpenShift Container Platform uses restart policy with failed Containers, see the Example States in the Kubernetes documentation. 2.3.2. Limiting the bandwidth available to pods You can apply quality-of-service traffic shaping to a pod and effectively limit its available bandwidth. Egress traffic (from the pod) is handled by policing, which simply drops packets in excess of the configured rate. Ingress traffic (to the pod) is handled by shaping queued packets to effectively handle data. The limits you place on a pod do not affect the bandwidth of other pods. Procedure To limit the bandwidth on a pod: Write an object definition JSON file, and specify the data traffic speed using kubernetes.io/ingress-bandwidth and kubernetes.io/egress-bandwidth annotations. For example, to limit both pod egress and ingress bandwidth to 10M/s: Limited Pod object definition { "kind": "Pod", "spec": { "containers": [ { "image": "openshift/hello-openshift", "name": "hello-openshift" } ] }, "apiVersion": "v1", "metadata": { "name": "iperf-slow", "annotations": { "kubernetes.io/ingress-bandwidth": "10M", "kubernetes.io/egress-bandwidth": "10M" } } } Create the pod using the object definition: USD oc create -f <file_or_dir_path> 2.3.3. Understanding how to use pod disruption budgets to specify the number of pods that must be up A pod disruption budget allows the specification of safety constraints on pods during operations, such as draining a node for maintenance. PodDisruptionBudget is an API object that specifies the minimum number or percentage of replicas that must be up at a time. Setting these in projects can be helpful during node maintenance (such as scaling a cluster down or a cluster upgrade) and is only honored on voluntary evictions (not on node failures). A PodDisruptionBudget object's configuration consists of the following key parts: A label selector, which is a label query over a set of pods. An availability level, which specifies the minimum number of pods that must be available simultaneously, either: minAvailable is the number of pods must always be available, even during a disruption. maxUnavailable is the number of pods can be unavailable during a disruption. Note Available refers to the number of pods that has condition Ready=True . Ready=True refers to the pod that is able to serve requests and should be added to the load balancing pools of all matching services. A maxUnavailable of 0% or 0 or a minAvailable of 100% or equal to the number of replicas is permitted but can block nodes from being drained. Warning The default setting for maxUnavailable is 1 for all the machine config pools in OpenShift Container Platform. It is recommended to not change this value and update one control plane node at a time. Do not change this value to 3 for the control plane pool. You can check for pod disruption budgets across all projects with the following: USD oc get poddisruptionbudget --all-namespaces Example output NAMESPACE NAME MIN AVAILABLE MAX UNAVAILABLE ALLOWED DISRUPTIONS AGE openshift-apiserver openshift-apiserver-pdb N/A 1 1 121m openshift-cloud-controller-manager aws-cloud-controller-manager 1 N/A 1 125m openshift-cloud-credential-operator pod-identity-webhook 1 N/A 1 117m openshift-cluster-csi-drivers aws-ebs-csi-driver-controller-pdb N/A 1 1 121m openshift-cluster-storage-operator csi-snapshot-controller-pdb N/A 1 1 122m openshift-cluster-storage-operator csi-snapshot-webhook-pdb N/A 1 1 122m openshift-console console N/A 1 1 116m #... The PodDisruptionBudget is considered healthy when there are at least minAvailable pods running in the system. Every pod above that limit can be evicted. Note Depending on your pod priority and preemption settings, lower-priority pods might be removed despite their pod disruption budget requirements. 2.3.3.1. Specifying the number of pods that must be up with pod disruption budgets You can use a PodDisruptionBudget object to specify the minimum number or percentage of replicas that must be up at a time. Procedure To configure a pod disruption budget: Create a YAML file with the an object definition similar to the following: apiVersion: policy/v1 1 kind: PodDisruptionBudget metadata: name: my-pdb spec: minAvailable: 2 2 selector: 3 matchLabels: name: my-pod 1 PodDisruptionBudget is part of the policy/v1 API group. 2 The minimum number of pods that must be available simultaneously. This can be either an integer or a string specifying a percentage, for example, 20% . 3 A label query over a set of resources. The result of matchLabels and matchExpressions are logically conjoined. Leave this parameter blank, for example selector {} , to select all pods in the project. Or: apiVersion: policy/v1 1 kind: PodDisruptionBudget metadata: name: my-pdb spec: maxUnavailable: 25% 2 selector: 3 matchLabels: name: my-pod 1 PodDisruptionBudget is part of the policy/v1 API group. 2 The maximum number of pods that can be unavailable simultaneously. This can be either an integer or a string specifying a percentage, for example, 20% . 3 A label query over a set of resources. The result of matchLabels and matchExpressions are logically conjoined. Leave this parameter blank, for example selector {} , to select all pods in the project. Run the following command to add the object to project: USD oc create -f </path/to/file> -n <project_name> 2.3.3.2. Specifying the eviction policy for unhealthy pods When you use pod disruption budgets (PDBs) to specify how many pods must be available simultaneously, you can also define the criteria for how unhealthy pods are considered for eviction. You can choose one of the following policies: IfHealthyBudget Running pods that are not yet healthy can be evicted only if the guarded application is not disrupted. AlwaysAllow Running pods that are not yet healthy can be evicted regardless of whether the criteria in the pod disruption budget is met. This policy can help evict malfunctioning applications, such as ones with pods stuck in the CrashLoopBackOff state or failing to report the Ready status. Important Specifying the unhealthy pod eviction policy for pod disruption budgets is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process. For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope . To use this Technology Preview feature, you must have enabled the TechPreviewNoUpgrade feature set. 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. Procedure Create a YAML file that defines a PodDisruptionBudget object and specify the unhealthy pod eviction policy: Example pod-disruption-budget.yaml file apiVersion: policy/v1 kind: PodDisruptionBudget metadata: name: my-pdb spec: minAvailable: 2 selector: matchLabels: name: my-pod unhealthyPodEvictionPolicy: AlwaysAllow 1 1 Choose either IfHealthyBudget or AlwaysAllow as the unhealthy pod eviction policy. The default is IfHealthyBudget when the unhealthyPodEvictionPolicy field is empty. Create the PodDisruptionBudget object by running the following command: USD oc create -f pod-disruption-budget.yaml With a PDB that has the AlwaysAllow unhealthy pod eviction policy set, you can now drain nodes and evict the pods for a malfunctioning application guarded by this PDB. Additional resources Enabling features using feature gates Unhealthy Pod Eviction Policy in the Kubernetes documentation 2.3.4. Preventing pod removal using critical pods There are a number of core components that are critical to a fully functional cluster, but, run on a regular cluster node rather than the master. A cluster might stop working properly if a critical add-on is evicted. Pods marked as critical are not allowed to be evicted. Procedure To make a pod critical: Create a Pod spec or edit existing pods to include the system-cluster-critical priority class: apiVersion: v1 kind: Pod metadata: name: my-pdb spec: template: metadata: name: critical-pod priorityClassName: system-cluster-critical 1 1 Default priority class for pods that should never be evicted from a node. Alternatively, you can specify system-node-critical for pods that are important to the cluster but can be removed if necessary. Create the pod: USD oc create -f <file-name>.yaml 2.3.5. Reducing pod timeouts when using persistent volumes with high file counts If a storage volume contains many files (~1,000,000 or greater), you might experience pod timeouts. This can occur because, when volumes are mounted, OpenShift Container Platform recursively changes the ownership and permissions of the contents of each volume in order to match the fsGroup specified in a pod's securityContext . For large volumes, checking and changing the ownership and permissions can be time consuming, resulting in a very slow pod startup. You can reduce this delay by applying one of the following workarounds: Use a security context constraint (SCC) to skip the SELinux relabeling for a volume. Use the fsGroupChangePolicy field inside an SCC to control the way that OpenShift Container Platform checks and manages ownership and permissions for a volume. Use the Cluster Resource Override Operator to automatically apply an SCC to skip the SELinux relabeling. Use a runtime class to skip the SELinux relabeling for a volume. For information, see When using Persistent Volumes with high file counts in OpenShift, why do pods fail to start or take an excessive amount of time to achieve "Ready" state? . 2.4. Automatically scaling pods with the horizontal pod autoscaler As a developer, you can use a horizontal pod autoscaler (HPA) to specify how OpenShift Container Platform should automatically increase or decrease the scale of a replication controller or deployment configuration, based on metrics collected from the pods that belong to that replication controller or deployment configuration. You can create an HPA for any deployment, deployment config, replica set, replication controller, or stateful set. For information on scaling pods based on custom metrics, see Automatically scaling pods based on custom metrics . Note It is recommended to use a Deployment object or ReplicaSet object unless you need a specific feature or behavior provided by other objects. For more information on these objects, see Understanding Deployment and DeploymentConfig objects . 2.4.1. Understanding horizontal pod autoscalers You can create a horizontal pod autoscaler to specify the minimum and maximum number of pods you want to run, as well as the CPU utilization or memory utilization your pods should target. After you create a horizontal pod autoscaler, OpenShift Container Platform begins to query the CPU and/or memory resource metrics on the pods. When these metrics are available, the horizontal pod autoscaler computes the ratio of the current metric utilization with the desired metric utilization, and scales up or down accordingly. The query and scaling occurs at a regular interval, but can take one to two minutes before metrics become available. For replication controllers, this scaling corresponds directly to the replicas of the replication controller. For deployment configurations, scaling corresponds directly to the replica count of the deployment configuration. Note that autoscaling applies only to the latest deployment in the Complete phase. OpenShift Container Platform automatically accounts for resources and prevents unnecessary autoscaling during resource spikes, such as during start up. Pods in the unready state have 0 CPU usage when scaling up and the autoscaler ignores the pods when scaling down. Pods without known metrics have 0% CPU usage when scaling up and 100% CPU when scaling down. This allows for more stability during the HPA decision. To use this feature, you must configure readiness checks to determine if a new pod is ready for use. To use horizontal pod autoscalers, your cluster administrator must have properly configured cluster metrics. 2.4.1.1. Supported metrics The following metrics are supported by horizontal pod autoscalers: Table 2.1. Metrics Metric Description API version CPU utilization Number of CPU cores used. Can be used to calculate a percentage of the pod's requested CPU. autoscaling/v1 , autoscaling/v2 Memory utilization Amount of memory used. Can be used to calculate a percentage of the pod's requested memory. autoscaling/v2 Important For memory-based autoscaling, memory usage must increase and decrease proportionally to the replica count. On average: An increase in replica count must lead to an overall decrease in memory (working set) usage per-pod. A decrease in replica count must lead to an overall increase in per-pod memory usage. Use the OpenShift Container Platform web console to check the memory behavior of your application and ensure that your application meets these requirements before using memory-based autoscaling. The following example shows autoscaling for the image-registry Deployment object. The initial deployment requires 3 pods. The HPA object increases the minimum to 5. If CPU usage on the pods reaches 75%, the pods increase to 7: USD oc autoscale deployment/image-registry --min=5 --max=7 --cpu-percent=75 Example output horizontalpodautoscaler.autoscaling/image-registry autoscaled Sample HPA for the image-registry Deployment object with minReplicas set to 3 apiVersion: autoscaling/v1 kind: HorizontalPodAutoscaler metadata: name: image-registry namespace: default spec: maxReplicas: 7 minReplicas: 3 scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: image-registry targetCPUUtilizationPercentage: 75 status: currentReplicas: 5 desiredReplicas: 0 View the new state of the deployment: USD oc get deployment image-registry There are now 5 pods in the deployment: Example output NAME REVISION DESIRED CURRENT TRIGGERED BY image-registry 1 5 5 config 2.4.2. How does the HPA work? The horizontal pod autoscaler (HPA) extends the concept of pod auto-scaling. The HPA lets you create and manage a group of load-balanced nodes. The HPA automatically increases or decreases the number of pods when a given CPU or memory threshold is crossed. Figure 2.1. High level workflow of the HPA The HPA is an API resource in the Kubernetes autoscaling API group. The autoscaler works as a control loop with a default of 15 seconds for the sync period. During this period, the controller manager queries the CPU, memory utilization, or both, against what is defined in the YAML file for the HPA. The controller manager obtains the utilization metrics from the resource metrics API for per-pod resource metrics like CPU or memory, for each pod that is targeted by the HPA. If a utilization value target is set, the controller calculates the utilization value as a percentage of the equivalent resource request on the containers in each pod. The controller then takes the average of utilization across all targeted pods and produces a ratio that is used to scale the number of desired replicas. The HPA is configured to fetch metrics from metrics.k8s.io , which is provided by the metrics server. Because of the dynamic nature of metrics evaluation, the number of replicas can fluctuate during scaling for a group of replicas. Note To implement the HPA, all targeted pods must have a resource request set on their containers. 2.4.3. About requests and limits The scheduler uses the resource request that you specify for containers in a pod, to decide which node to place the pod on. The kubelet enforces the resource limit that you specify for a container to ensure that the container is not allowed to use more than the specified limit. The kubelet also reserves the request amount of that system resource specifically for that container to use. How to use resource metrics? In the pod specifications, you must specify the resource requests, such as CPU and memory. The HPA uses this specification to determine the resource utilization and then scales the target up or down. For example, the HPA object uses the following metric source: type: Resource resource: name: cpu target: type: Utilization averageUtilization: 60 In this example, the HPA keeps the average utilization of the pods in the scaling target at 60%. Utilization is the ratio between the current resource usage to the requested resource of the pod. 2.4.4. Best practices All pods must have resource requests configured The HPA makes a scaling decision based on the observed CPU or memory utilization values of pods in an OpenShift Container Platform cluster. Utilization values are calculated as a percentage of the resource requests of each pod. Missing resource request values can affect the optimal performance of the HPA. Configure the cool down period During horizontal pod autoscaling, there might be a rapid scaling of events without a time gap. Configure the cool down period to prevent frequent replica fluctuations. You can specify a cool down period by configuring the stabilizationWindowSeconds field. The stabilization window is used to restrict the fluctuation of replicas count when the metrics used for scaling keep fluctuating. The autoscaling algorithm uses this window to infer a desired state and avoid unwanted changes to workload scale. For example, a stabilization window is specified for the scaleDown field: behavior: scaleDown: stabilizationWindowSeconds: 300 In the above example, all desired states for the past 5 minutes are considered. This approximates a rolling maximum, and avoids having the scaling algorithm frequently remove pods only to trigger recreating an equivalent pod just moments later. 2.4.4.1. Scaling policies The autoscaling/v2 API allows you to add scaling policies to a horizontal pod autoscaler. A scaling policy controls how the OpenShift Container Platform horizontal pod autoscaler (HPA) scales pods. Scaling policies allow you to restrict the rate that HPAs scale pods up or down by setting a specific number or specific percentage to scale in a specified period of time. You can also define a stabilization window , which uses previously computed desired states to control scaling if the metrics are fluctuating. You can create multiple policies for the same scaling direction, and determine which policy is used, based on the amount of change. You can also restrict the scaling by timed iterations. The HPA scales pods during an iteration, then performs scaling, as needed, in further iterations. Sample HPA object with a scaling policy apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: hpa-resource-metrics-memory namespace: default spec: behavior: scaleDown: 1 policies: 2 - type: Pods 3 value: 4 4 periodSeconds: 60 5 - type: Percent value: 10 6 periodSeconds: 60 selectPolicy: Min 7 stabilizationWindowSeconds: 300 8 scaleUp: 9 policies: - type: Pods value: 5 10 periodSeconds: 70 - type: Percent value: 12 11 periodSeconds: 80 selectPolicy: Max stabilizationWindowSeconds: 0 ... 1 Specifies the direction for the scaling policy, either scaleDown or scaleUp . This example creates a policy for scaling down. 2 Defines the scaling policy. 3 Determines if the policy scales by a specific number of pods or a percentage of pods during each iteration. The default value is pods . 4 Limits the amount of scaling, either the number of pods or percentage of pods, during each iteration. There is no default value for scaling down by number of pods. 5 Determines the length of a scaling iteration. The default value is 15 seconds. 6 The default value for scaling down by percentage is 100%. 7 Determines which policy to use first, if multiple policies are defined. Specify Max to use the policy that allows the highest amount of change, Min to use the policy that allows the lowest amount of change, or Disabled to prevent the HPA from scaling in that policy direction. The default value is Max . 8 Determines the time period the HPA should look back at desired states. The default value is 0 . 9 This example creates a policy for scaling up. 10 Limits the amount of scaling up by the number of pods. The default value for scaling up the number of pods is 4%. 11 Limits the amount of scaling up by the percentage of pods. The default value for scaling up by percentage is 100%. Example policy for scaling down apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: hpa-resource-metrics-memory namespace: default spec: ... minReplicas: 20 ... behavior: scaleDown: stabilizationWindowSeconds: 300 policies: - type: Pods value: 4 periodSeconds: 30 - type: Percent value: 10 periodSeconds: 60 selectPolicy: Max scaleUp: selectPolicy: Disabled In this example, when the number of pods is greater than 40, the percent-based policy is used for scaling down, as that policy results in a larger change, as required by the selectPolicy . If there are 80 pod replicas, in the first iteration the HPA reduces the pods by 8, which is 10% of the 80 pods (based on the type: Percent and value: 10 parameters), over one minute ( periodSeconds: 60 ). For the iteration, the number of pods is 72. The HPA calculates that 10% of the remaining pods is 7.2, which it rounds up to 8 and scales down 8 pods. On each subsequent iteration, the number of pods to be scaled is re-calculated based on the number of remaining pods. When the number of pods falls below 40, the pods-based policy is applied, because the pod-based number is greater than the percent-based number. The HPA reduces 4 pods at a time ( type: Pods and value: 4 ), over 30 seconds ( periodSeconds: 30 ), until there are 20 replicas remaining ( minReplicas ). The selectPolicy: Disabled parameter prevents the HPA from scaling up the pods. You can manually scale up by adjusting the number of replicas in the replica set or deployment set, if needed. If set, you can view the scaling policy by using the oc edit command: USD oc edit hpa hpa-resource-metrics-memory Example output apiVersion: autoscaling/v1 kind: HorizontalPodAutoscaler metadata: annotations: autoscaling.alpha.kubernetes.io/behavior:\ '{"ScaleUp":{"StabilizationWindowSeconds":0,"SelectPolicy":"Max","Policies":[{"Type":"Pods","Value":4,"PeriodSeconds":15},{"Type":"Percent","Value":100,"PeriodSeconds":15}]},\ "ScaleDown":{"StabilizationWindowSeconds":300,"SelectPolicy":"Min","Policies":[{"Type":"Pods","Value":4,"PeriodSeconds":60},{"Type":"Percent","Value":10,"PeriodSeconds":60}]}}' ... 2.4.5. Creating a horizontal pod autoscaler by using the web console From the web console, you can create a horizontal pod autoscaler (HPA) that specifies the minimum and maximum number of pods you want to run on a Deployment or DeploymentConfig object. You can also define the amount of CPU or memory usage that your pods should target. Note An HPA cannot be added to deployments that are part of an Operator-backed service, Knative service, or Helm chart. Procedure To create an HPA in the web console: In the Topology view, click the node to reveal the side pane. From the Actions drop-down list, select Add HorizontalPodAutoscaler to open the Add HorizontalPodAutoscaler form. Figure 2.2. Add HorizontalPodAutoscaler From the Add HorizontalPodAutoscaler form, define the name, minimum and maximum pod limits, the CPU and memory usage, and click Save . Note If any of the values for CPU and memory usage are missing, a warning is displayed. To edit an HPA in the web console: In the Topology view, click the node to reveal the side pane. From the Actions drop-down list, select Edit HorizontalPodAutoscaler to open the Edit Horizontal Pod Autoscaler form. From the Edit Horizontal Pod Autoscaler form, edit the minimum and maximum pod limits and the CPU and memory usage, and click Save . Note While creating or editing the horizontal pod autoscaler in the web console, you can switch from Form view to YAML view . To remove an HPA in the web console: In the Topology view, click the node to reveal the side panel. From the Actions drop-down list, select Remove HorizontalPodAutoscaler . In the confirmation pop-up window, click Remove to remove the HPA. 2.4.6. Creating a horizontal pod autoscaler for CPU utilization by using the CLI Using the OpenShift Container Platform CLI, you can create a horizontal pod autoscaler (HPA) to automatically scale an existing Deployment , DeploymentConfig , ReplicaSet , ReplicationController , or StatefulSet object. The HPA scales the pods associated with that object to maintain the CPU usage you specify. Note It is recommended to use a Deployment object or ReplicaSet object unless you need a specific feature or behavior provided by other objects. The HPA increases and decreases the number of replicas between the minimum and maximum numbers to maintain the specified CPU utilization across all pods. When autoscaling for CPU utilization, you can use the oc autoscale command and specify the minimum and maximum number of pods you want to run at any given time and the average CPU utilization your pods should target. If you do not specify a minimum, the pods are given default values from the OpenShift Container Platform server. To autoscale for a specific CPU value, create a HorizontalPodAutoscaler object with the target CPU and pod limits. Prerequisites To use horizontal pod autoscalers, your cluster administrator must have properly configured cluster metrics. You can use the oc describe PodMetrics <pod-name> command to determine if metrics are configured. If metrics are configured, the output appears similar to the following, with Cpu and Memory displayed under Usage . USD oc describe PodMetrics openshift-kube-scheduler-ip-10-0-135-131.ec2.internal Example output Name: openshift-kube-scheduler-ip-10-0-135-131.ec2.internal Namespace: openshift-kube-scheduler Labels: <none> Annotations: <none> API Version: metrics.k8s.io/v1beta1 Containers: Name: wait-for-host-port Usage: Memory: 0 Name: scheduler Usage: Cpu: 8m Memory: 45440Ki Kind: PodMetrics Metadata: Creation Timestamp: 2019-05-23T18:47:56Z Self Link: /apis/metrics.k8s.io/v1beta1/namespaces/openshift-kube-scheduler/pods/openshift-kube-scheduler-ip-10-0-135-131.ec2.internal Timestamp: 2019-05-23T18:47:56Z Window: 1m0s Events: <none> Procedure To create a horizontal pod autoscaler for CPU utilization: Perform one of the following: To scale based on the percent of CPU utilization, create a HorizontalPodAutoscaler object for an existing object: USD oc autoscale <object_type>/<name> \ 1 --min <number> \ 2 --max <number> \ 3 --cpu-percent=<percent> 4 1 Specify the type and name of the object to autoscale. The object must exist and be a Deployment , DeploymentConfig / dc , ReplicaSet / rs , ReplicationController / rc , or StatefulSet . 2 Optionally, specify the minimum number of replicas when scaling down. 3 Specify the maximum number of replicas when scaling up. 4 Specify the target average CPU utilization over all the pods, represented as a percent of requested CPU. If not specified or negative, a default autoscaling policy is used. For example, the following command shows autoscaling for the image-registry Deployment object. The initial deployment requires 3 pods. The HPA object increases the minimum to 5. If CPU usage on the pods reaches 75%, the pods will increase to 7: USD oc autoscale deployment/image-registry --min=5 --max=7 --cpu-percent=75 To scale for a specific CPU value, create a YAML file similar to the following for an existing object: Create a YAML file similar to the following: apiVersion: autoscaling/v2 1 kind: HorizontalPodAutoscaler metadata: name: cpu-autoscale 2 namespace: default spec: scaleTargetRef: apiVersion: apps/v1 3 kind: Deployment 4 name: example 5 minReplicas: 1 6 maxReplicas: 10 7 metrics: 8 - type: Resource resource: name: cpu 9 target: type: AverageValue 10 averageValue: 500m 11 1 Use the autoscaling/v2 API. 2 Specify a name for this horizontal pod autoscaler object. 3 Specify the API version of the object to scale: For a Deployment , ReplicaSet , Statefulset object, use apps/v1 . For a ReplicationController , use v1 . For a DeploymentConfig , use apps.openshift.io/v1 . 4 Specify the type of object. The object must be a Deployment , DeploymentConfig / dc , ReplicaSet / rs , ReplicationController / rc , or StatefulSet . 5 Specify the name of the object to scale. The object must exist. 6 Specify the minimum number of replicas when scaling down. 7 Specify the maximum number of replicas when scaling up. 8 Use the metrics parameter for memory utilization. 9 Specify cpu for CPU utilization. 10 Set to AverageValue . 11 Set to averageValue with the targeted CPU value. Create the horizontal pod autoscaler: USD oc create -f <file-name>.yaml Verify that the horizontal pod autoscaler was created: USD oc get hpa cpu-autoscale Example output NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE cpu-autoscale Deployment/example 173m/500m 1 10 1 20m 2.4.7. Creating a horizontal pod autoscaler object for memory utilization by using the CLI Using the OpenShift Container Platform CLI, you can create a horizontal pod autoscaler (HPA) to automatically scale an existing Deployment , DeploymentConfig , ReplicaSet , ReplicationController , or StatefulSet object. The HPA scales the pods associated with that object to maintain the average memory utilization you specify, either a direct value or a percentage of requested memory. Note It is recommended to use a Deployment object or ReplicaSet object unless you need a specific feature or behavior provided by other objects. The HPA increases and decreases the number of replicas between the minimum and maximum numbers to maintain the specified memory utilization across all pods. For memory utilization, you can specify the minimum and maximum number of pods and the average memory utilization your pods should target. If you do not specify a minimum, the pods are given default values from the OpenShift Container Platform server. Prerequisites To use horizontal pod autoscalers, your cluster administrator must have properly configured cluster metrics. You can use the oc describe PodMetrics <pod-name> command to determine if metrics are configured. If metrics are configured, the output appears similar to the following, with Cpu and Memory displayed under Usage . USD oc describe PodMetrics openshift-kube-scheduler-ip-10-0-129-223.compute.internal -n openshift-kube-scheduler Example output Name: openshift-kube-scheduler-ip-10-0-129-223.compute.internal Namespace: openshift-kube-scheduler Labels: <none> Annotations: <none> API Version: metrics.k8s.io/v1beta1 Containers: Name: wait-for-host-port Usage: Cpu: 0 Memory: 0 Name: scheduler Usage: Cpu: 8m Memory: 45440Ki Kind: PodMetrics Metadata: Creation Timestamp: 2020-02-14T22:21:14Z Self Link: /apis/metrics.k8s.io/v1beta1/namespaces/openshift-kube-scheduler/pods/openshift-kube-scheduler-ip-10-0-129-223.compute.internal Timestamp: 2020-02-14T22:21:14Z Window: 5m0s Events: <none> Procedure To create a horizontal pod autoscaler for memory utilization: Create a YAML file for one of the following: To scale for a specific memory value, create a HorizontalPodAutoscaler object similar to the following for an existing object: apiVersion: autoscaling/v2 1 kind: HorizontalPodAutoscaler metadata: name: hpa-resource-metrics-memory 2 namespace: default spec: scaleTargetRef: apiVersion: apps/v1 3 kind: Deployment 4 name: example 5 minReplicas: 1 6 maxReplicas: 10 7 metrics: 8 - type: Resource resource: name: memory 9 target: type: AverageValue 10 averageValue: 500Mi 11 behavior: 12 scaleDown: stabilizationWindowSeconds: 300 policies: - type: Pods value: 4 periodSeconds: 60 - type: Percent value: 10 periodSeconds: 60 selectPolicy: Max 1 Use the autoscaling/v2 API. 2 Specify a name for this horizontal pod autoscaler object. 3 Specify the API version of the object to scale: For a Deployment , ReplicaSet , or Statefulset object, use apps/v1 . For a ReplicationController , use v1 . For a DeploymentConfig , use apps.openshift.io/v1 . 4 Specify the type of object. The object must be a Deployment , DeploymentConfig , ReplicaSet , ReplicationController , or StatefulSet . 5 Specify the name of the object to scale. The object must exist. 6 Specify the minimum number of replicas when scaling down. 7 Specify the maximum number of replicas when scaling up. 8 Use the metrics parameter for memory utilization. 9 Specify memory for memory utilization. 10 Set the type to AverageValue . 11 Specify averageValue and a specific memory value. 12 Optional: Specify a scaling policy to control the rate of scaling up or down. To scale for a percentage, create a HorizontalPodAutoscaler object similar to the following for an existing object: apiVersion: autoscaling/v2 1 kind: HorizontalPodAutoscaler metadata: name: memory-autoscale 2 namespace: default spec: scaleTargetRef: apiVersion: apps/v1 3 kind: Deployment 4 name: example 5 minReplicas: 1 6 maxReplicas: 10 7 metrics: 8 - type: Resource resource: name: memory 9 target: type: Utilization 10 averageUtilization: 50 11 behavior: 12 scaleUp: stabilizationWindowSeconds: 180 policies: - type: Pods value: 6 periodSeconds: 120 - type: Percent value: 10 periodSeconds: 120 selectPolicy: Max 1 Use the autoscaling/v2 API. 2 Specify a name for this horizontal pod autoscaler object. 3 Specify the API version of the object to scale: For a ReplicationController, use v1 . For a DeploymentConfig, use apps.openshift.io/v1 . For a Deployment, ReplicaSet, Statefulset object, use apps/v1 . 4 Specify the type of object. The object must be a Deployment , DeploymentConfig , ReplicaSet , ReplicationController , or StatefulSet . 5 Specify the name of the object to scale. The object must exist. 6 Specify the minimum number of replicas when scaling down. 7 Specify the maximum number of replicas when scaling up. 8 Use the metrics parameter for memory utilization. 9 Specify memory for memory utilization. 10 Set to Utilization . 11 Specify averageUtilization and a target average memory utilization over all the pods, represented as a percent of requested memory. The target pods must have memory requests configured. 12 Optional: Specify a scaling policy to control the rate of scaling up or down. Create the horizontal pod autoscaler: USD oc create -f <file-name>.yaml For example: USD oc create -f hpa.yaml Example output horizontalpodautoscaler.autoscaling/hpa-resource-metrics-memory created Verify that the horizontal pod autoscaler was created: USD oc get hpa hpa-resource-metrics-memory Example output NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE hpa-resource-metrics-memory Deployment/example 2441216/500Mi 1 10 1 20m USD oc describe hpa hpa-resource-metrics-memory Example output Name: hpa-resource-metrics-memory Namespace: default Labels: <none> Annotations: <none> CreationTimestamp: Wed, 04 Mar 2020 16:31:37 +0530 Reference: Deployment/example Metrics: ( current / target ) resource memory on pods: 2441216 / 500Mi Min replicas: 1 Max replicas: 10 ReplicationController pods: 1 current / 1 desired Conditions: Type Status Reason Message ---- ------ ------ ------- AbleToScale True ReadyForNewScale recommended size matches current size ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from memory resource ScalingLimited False DesiredWithinRange the desired count is within the acceptable range Events: Type Reason Age From Message ---- ------ ---- ---- ------- Normal SuccessfulRescale 6m34s horizontal-pod-autoscaler New size: 1; reason: All metrics below target 2.4.8. Understanding horizontal pod autoscaler status conditions by using the CLI You can use the status conditions set to determine whether or not the horizontal pod autoscaler (HPA) is able to scale and whether or not it is currently restricted in any way. The HPA status conditions are available with the v2 version of the autoscaling API. The HPA responds with the following status conditions: The AbleToScale condition indicates whether HPA is able to fetch and update metrics, as well as whether any backoff-related conditions could prevent scaling. A True condition indicates scaling is allowed. A False condition indicates scaling is not allowed for the reason specified. The ScalingActive condition indicates whether the HPA is enabled (for example, the replica count of the target is not zero) and is able to calculate desired metrics. A True condition indicates metrics is working properly. A False condition generally indicates a problem with fetching metrics. The ScalingLimited condition indicates that the desired scale was capped by the maximum or minimum of the horizontal pod autoscaler. A True condition indicates that you need to raise or lower the minimum or maximum replica count in order to scale. A False condition indicates that the requested scaling is allowed. USD oc describe hpa cm-test Example output Name: cm-test Namespace: prom Labels: <none> Annotations: <none> CreationTimestamp: Fri, 16 Jun 2017 18:09:22 +0000 Reference: ReplicationController/cm-test Metrics: ( current / target ) "http_requests" on pods: 66m / 500m Min replicas: 1 Max replicas: 4 ReplicationController pods: 1 current / 1 desired Conditions: 1 Type Status Reason Message ---- ------ ------ ------- AbleToScale True ReadyForNewScale the last scale time was sufficiently old as to warrant a new scale ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from pods metric http_request ScalingLimited False DesiredWithinRange the desired replica count is within the acceptable range Events: 1 The horizontal pod autoscaler status messages. The following is an example of a pod that is unable to scale: Example output Conditions: Type Status Reason Message ---- ------ ------ ------- AbleToScale False FailedGetScale the HPA controller was unable to get the target's current scale: no matches for kind "ReplicationController" in group "apps" Events: Type Reason Age From Message ---- ------ ---- ---- ------- Warning FailedGetScale 6s (x3 over 36s) horizontal-pod-autoscaler no matches for kind "ReplicationController" in group "apps" The following is an example of a pod that could not obtain the needed metrics for scaling: Example output Conditions: Type Status Reason Message ---- ------ ------ ------- AbleToScale True SucceededGetScale the HPA controller was able to get the target's current scale ScalingActive False FailedGetResourceMetric the HPA was unable to compute the replica count: failed to get cpu utilization: unable to get metrics for resource cpu: no metrics returned from resource metrics API The following is an example of a pod where the requested autoscaling was less than the required minimums: Example output Conditions: Type Status Reason Message ---- ------ ------ ------- AbleToScale True ReadyForNewScale the last scale time was sufficiently old as to warrant a new scale ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from pods metric http_request ScalingLimited False DesiredWithinRange the desired replica count is within the acceptable range 2.4.8.1. Viewing horizontal pod autoscaler status conditions by using the CLI You can view the status conditions set on a pod by the horizontal pod autoscaler (HPA). Note The horizontal pod autoscaler status conditions are available with the v2 version of the autoscaling API. Prerequisites To use horizontal pod autoscalers, your cluster administrator must have properly configured cluster metrics. You can use the oc describe PodMetrics <pod-name> command to determine if metrics are configured. If metrics are configured, the output appears similar to the following, with Cpu and Memory displayed under Usage . USD oc describe PodMetrics openshift-kube-scheduler-ip-10-0-135-131.ec2.internal Example output Name: openshift-kube-scheduler-ip-10-0-135-131.ec2.internal Namespace: openshift-kube-scheduler Labels: <none> Annotations: <none> API Version: metrics.k8s.io/v1beta1 Containers: Name: wait-for-host-port Usage: Memory: 0 Name: scheduler Usage: Cpu: 8m Memory: 45440Ki Kind: PodMetrics Metadata: Creation Timestamp: 2019-05-23T18:47:56Z Self Link: /apis/metrics.k8s.io/v1beta1/namespaces/openshift-kube-scheduler/pods/openshift-kube-scheduler-ip-10-0-135-131.ec2.internal Timestamp: 2019-05-23T18:47:56Z Window: 1m0s Events: <none> Procedure To view the status conditions on a pod, use the following command with the name of the pod: USD oc describe hpa <pod-name> For example: USD oc describe hpa cm-test The conditions appear in the Conditions field in the output. Example output Name: cm-test Namespace: prom Labels: <none> Annotations: <none> CreationTimestamp: Fri, 16 Jun 2017 18:09:22 +0000 Reference: ReplicationController/cm-test Metrics: ( current / target ) "http_requests" on pods: 66m / 500m Min replicas: 1 Max replicas: 4 ReplicationController pods: 1 current / 1 desired Conditions: 1 Type Status Reason Message ---- ------ ------ ------- AbleToScale True ReadyForNewScale the last scale time was sufficiently old as to warrant a new scale ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from pods metric http_request ScalingLimited False DesiredWithinRange the desired replica count is within the acceptable range 2.4.9. Additional resources For more information on replication controllers and deployment controllers, see Understanding deployments and deployment configs . For an example on the usage of HPA, see Horizontal Pod Autoscaling of Quarkus Application Based on Memory Utilization . 2.5. Automatically adjust pod resource levels with the vertical pod autoscaler The OpenShift Container Platform Vertical Pod Autoscaler Operator (VPA) automatically reviews the historic and current CPU and memory resources for containers in pods and can update the resource limits and requests based on the usage values it learns. The VPA uses individual custom resources (CR) to update all of the pods associated with a workload object, such as a Deployment , DeploymentConfig , StatefulSet , Job , DaemonSet , ReplicaSet , or ReplicationController , in a project. The VPA helps you to understand the optimal CPU and memory usage for your pods and can automatically maintain pod resources through the pod lifecycle. 2.5.1. About the Vertical Pod Autoscaler Operator The Vertical Pod Autoscaler Operator (VPA) is implemented as an API resource and a custom resource (CR). The CR determines the actions that the VPA Operator should take with the pods associated with a specific workload object, such as a daemon set, replication controller, and so forth, in a project. The VPA Operator consists of three components, each of which has its own pod in the VPA namespace: Recommender The VPA recommender monitors the current and past resource consumption and, based on this data, determines the optimal CPU and memory resources for the pods in the associated workload object. Updater The VPA updater checks if the pods in the associated workload object have the correct resources. If the resources are correct, the updater takes no action. If the resources are not correct, the updater kills the pod so that they can be recreated by their controllers with the updated requests. Admission controller The VPA admission controller sets the correct resource requests on each new pod in the associated workload object, whether the pod is new or was recreated by its controller due to the VPA updater actions. You can use the default recommender or use your own alternative recommender to autoscale based on your own algorithms. The default recommender automatically computes historic and current CPU and memory usage for the containers in those pods and uses this data to determine optimized resource limits and requests to ensure that these pods are operating efficiently at all times. For example, the default recommender suggests reduced resources for pods that are requesting more resources than they are using and increased resources for pods that are not requesting enough. The VPA then automatically deletes any pods that are out of alignment with these recommendations one at a time, so that your applications can continue to serve requests with no downtime. The workload objects then re-deploy the pods with the original resource limits and requests. The VPA uses a mutating admission webhook to update the pods with optimized resource limits and requests before the pods are admitted to a node. If you do not want the VPA to delete pods, you can view the VPA resource limits and requests and manually update the pods as needed. Note By default, workload objects must specify a minimum of two replicas in order for the VPA to automatically delete their pods. Workload objects that specify fewer replicas than this minimum are not deleted. If you manually delete these pods, when the workload object redeploys the pods, the VPA does update the new pods with its recommendations. You can change this minimum by modifying the VerticalPodAutoscalerController object as shown in Changing the VPA minimum value . For example, if you have a pod that uses 50% of the CPU but only requests 10%, the VPA determines that the pod is consuming more CPU than requested and deletes the pod. The workload object, such as replica set, restarts the pods and the VPA updates the new pod with its recommended resources. For developers, you can use the VPA to help ensure your pods stay up during periods of high demand by scheduling pods onto nodes that have appropriate resources for each pod. Administrators can use the VPA to better utilize cluster resources, such as preventing pods from reserving more CPU resources than needed. The VPA monitors the resources that workloads are actually using and adjusts the resource requirements so capacity is available to other workloads. The VPA also maintains the ratios between limits and requests that are specified in initial container configuration. Note If you stop running the VPA or delete a specific VPA CR in your cluster, the resource requests for the pods already modified by the VPA do not change. Any new pods get the resources defined in the workload object, not the recommendations made by the VPA. 2.5.2. Installing the Vertical Pod Autoscaler Operator You can use the OpenShift Container Platform web console to install the Vertical Pod Autoscaler Operator (VPA). Procedure In the OpenShift Container Platform web console, click Operators OperatorHub . Choose VerticalPodAutoscaler from the list of available Operators, and click Install . On the Install Operator page, ensure that the Operator recommended namespace option is selected. This installs the Operator in the mandatory openshift-vertical-pod-autoscaler namespace, which is automatically created if it does not exist. Click Install . Verifiction Verify the installation by listing the VPA Operator components: Navigate to Workloads Pods . Select the openshift-vertical-pod-autoscaler project from the drop-down menu and verify that there are four pods running. Navigate to Workloads Deployments to verify that there are four deployments running. Optional: Verify the installation in the OpenShift Container Platform CLI using the following command: USD oc get all -n openshift-vertical-pod-autoscaler The output shows four pods and four deployments: Example output NAME READY STATUS RESTARTS AGE pod/vertical-pod-autoscaler-operator-85b4569c47-2gmhc 1/1 Running 0 3m13s pod/vpa-admission-plugin-default-67644fc87f-xq7k9 1/1 Running 0 2m56s pod/vpa-recommender-default-7c54764b59-8gckt 1/1 Running 0 2m56s pod/vpa-updater-default-7f6cc87858-47vw9 1/1 Running 0 2m56s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/vpa-webhook ClusterIP 172.30.53.206 <none> 443/TCP 2m56s NAME READY UP-TO-DATE AVAILABLE AGE deployment.apps/vertical-pod-autoscaler-operator 1/1 1 1 3m13s deployment.apps/vpa-admission-plugin-default 1/1 1 1 2m56s deployment.apps/vpa-recommender-default 1/1 1 1 2m56s deployment.apps/vpa-updater-default 1/1 1 1 2m56s NAME DESIRED CURRENT READY AGE replicaset.apps/vertical-pod-autoscaler-operator-85b4569c47 1 1 1 3m13s replicaset.apps/vpa-admission-plugin-default-67644fc87f 1 1 1 2m56s replicaset.apps/vpa-recommender-default-7c54764b59 1 1 1 2m56s replicaset.apps/vpa-updater-default-7f6cc87858 1 1 1 2m56s 2.5.3. About Using the Vertical Pod Autoscaler Operator To use the Vertical Pod Autoscaler Operator (VPA), you create a VPA custom resource (CR) for a workload object in your cluster. The VPA learns and applies the optimal CPU and memory resources for the pods associated with that workload object. You can use a VPA with a deployment, stateful set, job, daemon set, replica set, or replication controller workload object. The VPA CR must be in the same project as the pods you want to monitor. You use the VPA CR to associate a workload object and specify which mode the VPA operates in: The Auto and Recreate modes automatically apply the VPA CPU and memory recommendations throughout the pod lifetime. The VPA deletes any pods in the project that are out of alignment with its recommendations. When redeployed by the workload object, the VPA updates the new pods with its recommendations. The Initial mode automatically applies VPA recommendations only at pod creation. The Off mode only provides recommended resource limits and requests, allowing you to manually apply the recommendations. The off mode does not update pods. You can also use the CR to opt-out certain containers from VPA evaluation and updates. For example, a pod has the following limits and requests: resources: limits: cpu: 1 memory: 500Mi requests: cpu: 500m memory: 100Mi After creating a VPA that is set to auto , the VPA learns the resource usage and deletes the pod. When redeployed, the pod uses the new resource limits and requests: resources: limits: cpu: 50m memory: 1250Mi requests: cpu: 25m memory: 262144k You can view the VPA recommendations using the following command: USD oc get vpa <vpa-name> --output yaml After a few minutes, the output shows the recommendations for CPU and memory requests, similar to the following: Example output ... status: ... recommendation: containerRecommendations: - containerName: frontend lowerBound: cpu: 25m memory: 262144k target: cpu: 25m memory: 262144k uncappedTarget: cpu: 25m memory: 262144k upperBound: cpu: 262m memory: "274357142" - containerName: backend lowerBound: cpu: 12m memory: 131072k target: cpu: 12m memory: 131072k uncappedTarget: cpu: 12m memory: 131072k upperBound: cpu: 476m memory: "498558823" ... The output shows the recommended resources, target , the minimum recommended resources, lowerBound , the highest recommended resources, upperBound , and the most recent resource recommendations, uncappedTarget . The VPA uses the lowerBound and upperBound values to determine if a pod needs to be updated. If a pod has resource requests below the lowerBound values or above the upperBound values, the VPA terminates and recreates the pod with the target values. 2.5.3.1. Changing the VPA minimum value By default, workload objects must specify a minimum of two replicas in order for the VPA to automatically delete and update their pods. As a result, workload objects that specify fewer than two replicas are not automatically acted upon by the VPA. The VPA does update new pods from these workload objects if the pods are restarted by some process external to the VPA. You can change this cluster-wide minimum value by modifying the minReplicas parameter in the VerticalPodAutoscalerController custom resource (CR). For example, if you set minReplicas to 3 , the VPA does not delete and update pods for workload objects that specify fewer than three replicas. Note If you set minReplicas to 1 , the VPA can delete the only pod for a workload object that specifies only one replica. You should use this setting with one-replica objects only if your workload can tolerate downtime whenever the VPA deletes a pod to adjust its resources. To avoid unwanted downtime with one-replica objects, configure the VPA CRs with the podUpdatePolicy set to Initial , which automatically updates the pod only when it is restarted by some process external to the VPA, or Off , which allows you to update the pod manually at an appropriate time for your application. Example VerticalPodAutoscalerController object apiVersion: autoscaling.openshift.io/v1 kind: VerticalPodAutoscalerController metadata: creationTimestamp: "2021-04-21T19:29:49Z" generation: 2 name: default namespace: openshift-vertical-pod-autoscaler resourceVersion: "142172" uid: 180e17e9-03cc-427f-9955-3b4d7aeb2d59 spec: minReplicas: 3 1 podMinCPUMillicores: 25 podMinMemoryMb: 250 recommendationOnly: false safetyMarginFraction: 0.15 1 1 Specify the minimum number of replicas in a workload object for the VPA to act on. Any objects with replicas fewer than the minimum are not automatically deleted by the VPA. 2.5.3.2. Automatically applying VPA recommendations To use the VPA to automatically update pods, create a VPA CR for a specific workload object with updateMode set to Auto or Recreate . When the pods are created for the workload object, the VPA constantly monitors the containers to analyze their CPU and memory needs. The VPA deletes any pods that do not meet the VPA recommendations for CPU and memory. When redeployed, the pods use the new resource limits and requests based on the VPA recommendations, honoring any pod disruption budget set for your applications. The recommendations are added to the status field of the VPA CR for reference. Note By default, workload objects must specify a minimum of two replicas in order for the VPA to automatically delete their pods. Workload objects that specify fewer replicas than this minimum are not deleted. If you manually delete these pods, when the workload object redeploys the pods, the VPA does update the new pods with its recommendations. You can change this minimum by modifying the VerticalPodAutoscalerController object as shown in Changing the VPA minimum value . Example VPA CR for the Auto mode apiVersion: autoscaling.k8s.io/v1 kind: VerticalPodAutoscaler metadata: name: vpa-recommender spec: targetRef: apiVersion: "apps/v1" kind: Deployment 1 name: frontend 2 updatePolicy: updateMode: "Auto" 3 1 The type of workload object you want this VPA CR to manage. 2 The name of the workload object you want this VPA CR to manage. 3 Set the mode to Auto or Recreate : Auto . The VPA assigns resource requests on pod creation and updates the existing pods by terminating them when the requested resources differ significantly from the new recommendation. Recreate . The VPA assigns resource requests on pod creation and updates the existing pods by terminating them when the requested resources differ significantly from the new recommendation. This mode should be used rarely, only if you need to ensure that the pods are restarted whenever the resource request changes. Note Before a VPA can determine recommendations for resources and apply the recommended resources to new pods, operating pods must exist and be running in the project. If a workload's resource usage, such as CPU and memory, is consistent, the VPA can determine recommendations for resources in a few minutes. If a workload's resource usage is inconsistent, the VPA must collect metrics at various resource usage intervals for the VPA to make an accurate recommendation. 2.5.3.3. Automatically applying VPA recommendations on pod creation To use the VPA to apply the recommended resources only when a pod is first deployed, create a VPA CR for a specific workload object with updateMode set to Initial . Then, manually delete any pods associated with the workload object that you want to use the VPA recommendations. In the Initial mode, the VPA does not delete pods and does not update the pods as it learns new resource recommendations. Example VPA CR for the Initial mode apiVersion: autoscaling.k8s.io/v1 kind: VerticalPodAutoscaler metadata: name: vpa-recommender spec: targetRef: apiVersion: "apps/v1" kind: Deployment 1 name: frontend 2 updatePolicy: updateMode: "Initial" 3 1 The type of workload object you want this VPA CR to manage. 2 The name of the workload object you want this VPA CR to manage. 3 Set the mode to Initial . The VPA assigns resources when pods are created and does not change the resources during the lifetime of the pod. Note Before a VPA can determine recommended resources and apply the recommendations to new pods, operating pods must exist and be running in the project. To obtain the most accurate recommendations from the VPA, wait at least 8 days for the pods to run and for the VPA to stabilize. 2.5.3.4. Manually applying VPA recommendations To use the VPA to only determine the recommended CPU and memory values, create a VPA CR for a specific workload object with updateMode set to off . When the pods are created for that workload object, the VPA analyzes the CPU and memory needs of the containers and records those recommendations in the status field of the VPA CR. The VPA does not update the pods as it determines new resource recommendations. Example VPA CR for the Off mode apiVersion: autoscaling.k8s.io/v1 kind: VerticalPodAutoscaler metadata: name: vpa-recommender spec: targetRef: apiVersion: "apps/v1" kind: Deployment 1 name: frontend 2 updatePolicy: updateMode: "Off" 3 1 The type of workload object you want this VPA CR to manage. 2 The name of the workload object you want this VPA CR to manage. 3 Set the mode to Off . You can view the recommendations using the following command. USD oc get vpa <vpa-name> --output yaml With the recommendations, you can edit the workload object to add CPU and memory requests, then delete and redeploy the pods using the recommended resources. Note Before a VPA can determine recommended resources and apply the recommendations to new pods, operating pods must exist and be running in the project. To obtain the most accurate recommendations from the VPA, wait at least 8 days for the pods to run and for the VPA to stabilize. 2.5.3.5. Exempting containers from applying VPA recommendations If your workload object has multiple containers and you do not want the VPA to evaluate and act on all of the containers, create a VPA CR for a specific workload object and add a resourcePolicy to opt-out specific containers. When the VPA updates the pods with recommended resources, any containers with a resourcePolicy are not updated and the VPA does not present recommendations for those containers in the pod. apiVersion: autoscaling.k8s.io/v1 kind: VerticalPodAutoscaler metadata: name: vpa-recommender spec: targetRef: apiVersion: "apps/v1" kind: Deployment 1 name: frontend 2 updatePolicy: updateMode: "Auto" 3 resourcePolicy: 4 containerPolicies: - containerName: my-opt-sidecar mode: "Off" 1 The type of workload object you want this VPA CR to manage. 2 The name of the workload object you want this VPA CR to manage. 3 Set the mode to Auto , Recreate , or Off . The Recreate mode should be used rarely, only if you need to ensure that the pods are restarted whenever the resource request changes. 4 Specify the containers you want to opt-out and set mode to Off . For example, a pod has two containers, the same resource requests and limits: # ... spec: containers: - name: frontend resources: limits: cpu: 1 memory: 500Mi requests: cpu: 500m memory: 100Mi - name: backend resources: limits: cpu: "1" memory: 500Mi requests: cpu: 500m memory: 100Mi # ... After launching a VPA CR with the backend container set to opt-out, the VPA terminates and recreates the pod with the recommended resources applied only to the frontend container: ... spec: containers: name: frontend resources: limits: cpu: 50m memory: 1250Mi requests: cpu: 25m memory: 262144k ... name: backend resources: limits: cpu: "1" memory: 500Mi requests: cpu: 500m memory: 100Mi ... 2.5.3.6. Using an alternative recommender You can use your own recommender to autoscale based on your own algorithms. If you do not specify an alternative recommender, OpenShift Container Platform uses the default recommender, which suggests CPU and memory requests based on historical usage. Because there is no universal recommendation policy that applies to all types of workloads, you might want to create and deploy different recommenders for specific workloads. For example, the default recommender might not accurately predict future resource usage when containers exhibit certain resource behaviors, such as cyclical patterns that alternate between usage spikes and idling as used by monitoring applications, or recurring and repeating patterns used with deep learning applications. Using the default recommender with these usage behaviors might result in significant over-provisioning and Out of Memory (OOM) kills for your applications. Note Instructions for how to create a recommender are beyond the scope of this documentation, Procedure To use an alternative recommender for your pods: Create a service account for the alternative recommender and bind that service account to the required cluster role: apiVersion: v1 1 kind: ServiceAccount metadata: name: alt-vpa-recommender-sa namespace: <namespace_name> --- apiVersion: rbac.authorization.k8s.io/v1 2 kind: ClusterRoleBinding metadata: name: system:example-metrics-reader roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: system:metrics-reader subjects: - kind: ServiceAccount name: alt-vpa-recommender-sa namespace: <namespace_name> --- apiVersion: rbac.authorization.k8s.io/v1 3 kind: ClusterRoleBinding metadata: name: system:example-vpa-actor roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: system:vpa-actor subjects: - kind: ServiceAccount name: alt-vpa-recommender-sa namespace: <namespace_name> --- apiVersion: rbac.authorization.k8s.io/v1 4 kind: ClusterRoleBinding metadata: name: system:example-vpa-target-reader-binding roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: system:vpa-target-reader subjects: - kind: ServiceAccount name: alt-vpa-recommender-sa namespace: <namespace_name> 1 Creates a service account for the recommender in the namespace where the recommender is deployed. 2 Binds the recommender service account to the metrics-reader role. Specify the namespace where the recommender is to be deployed. 3 Binds the recommender service account to the vpa-actor role. Specify the namespace where the recommender is to be deployed. 4 Binds the recommender service account to the vpa-target-reader role. Specify the namespace where the recommender is to be deployed. To add the alternative recommender to the cluster, create a Deployment object similar to the following: apiVersion: apps/v1 kind: Deployment metadata: name: alt-vpa-recommender namespace: <namespace_name> spec: replicas: 1 selector: matchLabels: app: alt-vpa-recommender template: metadata: labels: app: alt-vpa-recommender spec: containers: 1 - name: recommender image: quay.io/example/alt-recommender:latest 2 imagePullPolicy: Always resources: limits: cpu: 200m memory: 1000Mi requests: cpu: 50m memory: 500Mi ports: - name: prometheus containerPort: 8942 securityContext: allowPrivilegeEscalation: false capabilities: drop: - ALL seccompProfile: type: RuntimeDefault serviceAccountName: alt-vpa-recommender-sa 3 securityContext: runAsNonRoot: true 1 Creates a container for your alternative recommender. 2 Specifies your recommender image. 3 Associates the service account that you created for the recommender. A new pod is created for the alternative recommender in the same namespace. USD oc get pods Example output NAME READY STATUS RESTARTS AGE frontend-845d5478d-558zf 1/1 Running 0 4m25s frontend-845d5478d-7z9gx 1/1 Running 0 4m25s frontend-845d5478d-b7l4j 1/1 Running 0 4m25s vpa-alt-recommender-55878867f9-6tp5v 1/1 Running 0 9s Configure a VPA CR that includes the name of the alternative recommender Deployment object. Example VPA CR to include the alternative recommender apiVersion: autoscaling.k8s.io/v1 kind: VerticalPodAutoscaler metadata: name: vpa-recommender namespace: <namespace_name> spec: recommenders: - name: alt-vpa-recommender 1 targetRef: apiVersion: "apps/v1" kind: Deployment 2 name: frontend 1 Specifies the name of the alternative recommender deployment. 2 Specifies the name of an existing workload object you want this VPA to manage. 2.5.4. Using the Vertical Pod Autoscaler Operator You can use the Vertical Pod Autoscaler Operator (VPA) by creating a VPA custom resource (CR). The CR indicates which pods it should analyze and determines the actions the VPA should take with those pods. Prerequisites The workload object that you want to autoscale must exist. If you want to use an alternative recommender, a deployment including that recommender must exist. Procedure To create a VPA CR for a specific workload object: Change to the project where the workload object you want to scale is located. Create a VPA CR YAML file: apiVersion: autoscaling.k8s.io/v1 kind: VerticalPodAutoscaler metadata: name: vpa-recommender spec: targetRef: apiVersion: "apps/v1" kind: Deployment 1 name: frontend 2 updatePolicy: updateMode: "Auto" 3 resourcePolicy: 4 containerPolicies: - containerName: my-opt-sidecar mode: "Off" recommenders: 5 - name: my-recommender 1 Specify the type of workload object you want this VPA to manage: Deployment , StatefulSet , Job , DaemonSet , ReplicaSet , or ReplicationController . 2 Specify the name of an existing workload object you want this VPA to manage. 3 Specify the VPA mode: auto to automatically apply the recommended resources on pods associated with the controller. The VPA terminates existing pods and creates new pods with the recommended resource limits and requests. recreate to automatically apply the recommended resources on pods associated with the workload object. The VPA terminates existing pods and creates new pods with the recommended resource limits and requests. The recreate mode should be used rarely, only if you need to ensure that the pods are restarted whenever the resource request changes. initial to automatically apply the recommended resources when pods associated with the workload object are created. The VPA does not update the pods as it learns new resource recommendations. off to only generate resource recommendations for the pods associated with the workload object. The VPA does not update the pods as it learns new resource recommendations and does not apply the recommendations to new pods. 4 Optional. Specify the containers you want to opt-out and set the mode to Off . 5 Optional. Specify an alternative recommender. Create the VPA CR: USD oc create -f <file-name>.yaml After a few moments, the VPA learns the resource usage of the containers in the pods associated with the workload object. You can view the VPA recommendations using the following command: USD oc get vpa <vpa-name> --output yaml The output shows the recommendations for CPU and memory requests, similar to the following: Example output ... status: ... recommendation: containerRecommendations: - containerName: frontend lowerBound: 1 cpu: 25m memory: 262144k target: 2 cpu: 25m memory: 262144k uncappedTarget: 3 cpu: 25m memory: 262144k upperBound: 4 cpu: 262m memory: "274357142" - containerName: backend lowerBound: cpu: 12m memory: 131072k target: cpu: 12m memory: 131072k uncappedTarget: cpu: 12m memory: 131072k upperBound: cpu: 476m memory: "498558823" ... 1 lowerBound is the minimum recommended resource levels. 2 target is the recommended resource levels. 3 upperBound is the highest recommended resource levels. 4 uncappedTarget is the most recent resource recommendations. 2.5.5. Uninstalling the Vertical Pod Autoscaler Operator You can remove the Vertical Pod Autoscaler Operator (VPA) from your OpenShift Container Platform cluster. After uninstalling, the resource requests for the pods already modified by an existing VPA CR do not change. Any new pods get the resources defined in the workload object, not the recommendations made by the Vertical Pod Autoscaler Operator. Note You can remove a specific VPA CR by using the oc delete vpa <vpa-name> command. The same actions apply for resource requests as uninstalling the vertical pod autoscaler. After removing the VPA Operator, it is recommended that you remove the other components associated with the Operator to avoid potential issues. Prerequisites The Vertical Pod Autoscaler Operator must be installed. Procedure In the OpenShift Container Platform web console, click Operators Installed Operators . Switch to the openshift-vertical-pod-autoscaler project. For the VerticalPodAutoscaler Operator, click the Options menu and select Uninstall Operator . Optional: To remove all operands associated with the Operator, in the dialog box, select Delete all operand instances for this operator checkbox. Click Uninstall . Optional: Use the OpenShift CLI to remove the VPA components: Delete the VPA namespace: USD oc delete namespace openshift-vertical-pod-autoscaler Delete the VPA custom resource definition (CRD) objects: USD oc delete crd verticalpodautoscalercheckpoints.autoscaling.k8s.io USD oc delete crd verticalpodautoscalercontrollers.autoscaling.openshift.io USD oc delete crd verticalpodautoscalers.autoscaling.k8s.io Deleting the CRDs removes the associated roles, cluster roles, and role bindings. Note This action removes from the cluster all user-created VPA CRs. If you re-install the VPA, you must create these objects again. Delete the MutatingWebhookConfiguration object by running the following command: USD oc delete MutatingWebhookConfiguration vpa-webhook-config Delete the VPA Operator: USD oc delete operator/vertical-pod-autoscaler.openshift-vertical-pod-autoscaler 2.6. Providing sensitive data to pods Some applications need sensitive information, such as passwords and user names, that you do not want developers to have. As an administrator, you can use Secret objects to provide this information without exposing that information in clear text. 2.6.1. Understanding secrets The Secret object type provides a mechanism to hold sensitive information such as passwords, OpenShift Container Platform client configuration files, private source repository credentials, and so on. Secrets decouple sensitive content from the pods. You can mount secrets into containers using a volume plugin or the system can use secrets to perform actions on behalf of a pod. Key properties include: Secret data can be referenced independently from its definition. Secret data volumes are backed by temporary file-storage facilities (tmpfs) and never come to rest on a node. Secret data can be shared within a namespace. YAML Secret object definition apiVersion: v1 kind: Secret metadata: name: test-secret namespace: my-namespace type: Opaque 1 data: 2 username: <username> 3 password: <password> stringData: 4 hostname: myapp.mydomain.com 5 1 Indicates the structure of the secret's key names and values. 2 The allowable format for the keys in the data field must meet the guidelines in the DNS_SUBDOMAIN value in the Kubernetes identifiers glossary . 3 The value associated with keys in the data map must be base64 encoded. 4 Entries in the stringData map are converted to base64 and the entry will then be moved to the data map automatically. This field is write-only; the value will only be returned via the data field. 5 The value associated with keys in the stringData map is made up of plain text strings. You must create a secret before creating the pods that depend on that secret. When creating secrets: Create a secret object with secret data. Update the pod's service account to allow the reference to the secret. Create a pod, which consumes the secret as an environment variable or as a file (using a secret volume). 2.6.1.1. Types of secrets The value in the type field indicates the structure of the secret's key names and values. The type can be used to enforce the presence of user names and keys in the secret object. If you do not want validation, use the opaque type, which is the default. Specify one of the following types to trigger minimal server-side validation to ensure the presence of specific key names in the secret data: kubernetes.io/basic-auth : Use with Basic authentication kubernetes.io/dockercfg : Use as an image pull secret kubernetes.io/dockerconfigjson : Use as an image pull secret kubernetes.io/service-account-token : Use to obtain a legacy service account API token kubernetes.io/ssh-auth : Use with SSH key authentication kubernetes.io/tls : Use with TLS certificate authorities Specify type: Opaque if you do not want validation, which means the secret does not claim to conform to any convention for key names or values. An opaque secret, allows for unstructured key:value pairs that can contain arbitrary values. Note You can specify other arbitrary types, such as example.com/my-secret-type . These types are not enforced server-side, but indicate that the creator of the secret intended to conform to the key/value requirements of that type. For examples of creating different types of secrets, see Understanding how to create secrets . 2.6.1.2. Secret data keys Secret keys must be in a DNS subdomain. 2.6.1.3. About automatically generated service account token secrets When a service account is created, a service account token secret is automatically generated for it. This service account token secret, along with an automatically generated docker configuration secret, is used to authenticate to the internal OpenShift Container Platform registry. Do not rely on these automatically generated secrets for your own use; they might be removed in a future OpenShift Container Platform release. Note Prior to OpenShift Container Platform 4.11, a second service account token secret was generated when a service account was created. This service account token secret was used to access the Kubernetes API. Starting with OpenShift Container Platform 4.11, this second service account token secret is no longer created. This is because the LegacyServiceAccountTokenNoAutoGeneration upstream Kubernetes feature gate was enabled, which stops the automatic generation of secret-based service account tokens to access the Kubernetes API. After upgrading to 4.13, any existing service account token secrets are not deleted and continue to function. Workloads are automatically injected with a projected volume to obtain a bound service account token. If your workload needs an additional service account token, add an additional projected volume in your workload manifest. Bound service account tokens are more secure than service account token secrets for the following reasons: Bound service account tokens have a bounded lifetime. Bound service account tokens contain audiences. Bound service account tokens can be bound to pods or secrets and the bound tokens are invalidated when the bound object is removed. For more information, see Configuring bound service account tokens using volume projection . You can also manually create a service account token secret to obtain a token, if the security exposure of a non-expiring token in a readable API object is acceptable to you. For more information, see Creating a service account token secret . Additional resources For information about requesting bound service account tokens, see Using bound service account tokens For information about creating a service account token secret, see Creating a service account token secret . 2.6.2. Understanding how to create secrets As an administrator you must create a secret before developers can create the pods that depend on that secret. When creating secrets: Create a secret object that contains the data you want to keep secret. The specific data required for each secret type is descibed in the following sections. Example YAML object that creates an opaque secret apiVersion: v1 kind: Secret metadata: name: test-secret type: Opaque 1 data: 2 username: <username> password: <password> stringData: 3 hostname: myapp.mydomain.com secret.properties: | property1=valueA property2=valueB 1 Specifies the type of secret. 2 Specifies encoded string and data. 3 Specifies decoded string and data. Use either the data or stringdata fields, not both. Update the pod's service account to reference the secret: YAML of a service account that uses a secret apiVersion: v1 kind: ServiceAccount ... secrets: - name: test-secret Create a pod, which consumes the secret as an environment variable or as a file (using a secret volume): YAML of a pod populating files in a volume with secret data apiVersion: v1 kind: Pod metadata: name: secret-example-pod spec: containers: - name: secret-test-container image: busybox command: [ "/bin/sh", "-c", "cat /etc/secret-volume/*" ] volumeMounts: 1 - name: secret-volume mountPath: /etc/secret-volume 2 readOnly: true 3 volumes: - name: secret-volume secret: secretName: test-secret 4 restartPolicy: Never 1 Add a volumeMounts field to each container that needs the secret. 2 Specifies an unused directory name where you would like the secret to appear. Each key in the secret data map becomes the filename under mountPath . 3 Set to true . If true, this instructs the driver to provide a read-only volume. 4 Specifies the name of the secret. YAML of a pod populating environment variables with secret data apiVersion: v1 kind: Pod metadata: name: secret-example-pod spec: containers: - name: secret-test-container image: busybox command: [ "/bin/sh", "-c", "export" ] env: - name: TEST_SECRET_USERNAME_ENV_VAR valueFrom: secretKeyRef: 1 name: test-secret key: username restartPolicy: Never 1 Specifies the environment variable that consumes the secret key. YAML of a build config populating environment variables with secret data apiVersion: build.openshift.io/v1 kind: BuildConfig metadata: name: secret-example-bc spec: strategy: sourceStrategy: env: - name: TEST_SECRET_USERNAME_ENV_VAR valueFrom: secretKeyRef: 1 name: test-secret key: username from: kind: ImageStreamTag namespace: openshift name: 'cli:latest' 1 Specifies the environment variable that consumes the secret key. 2.6.2.1. Secret creation restrictions To use a secret, a pod needs to reference the secret. A secret can be used with a pod in three ways: To populate environment variables for containers. As files in a volume mounted on one or more of its containers. By kubelet when pulling images for the pod. Volume type secrets write data into the container as a file using the volume mechanism. Image pull secrets use service accounts for the automatic injection of the secret into all pods in a namespace. When a template contains a secret definition, the only way for the template to use the provided secret is to ensure that the secret volume sources are validated and that the specified object reference actually points to a Secret object. Therefore, a secret needs to be created before any pods that depend on it. The most effective way to ensure this is to have it get injected automatically through the use of a service account. Secret API objects reside in a namespace. They can only be referenced by pods in that same namespace. Individual secrets are limited to 1MB in size. This is to discourage the creation of large secrets that could exhaust apiserver and kubelet memory. However, creation of a number of smaller secrets could also exhaust memory. 2.6.2.2. Creating an opaque secret As an administrator, you can create an opaque secret, which allows you to store unstructured key:value pairs that can contain arbitrary values. Procedure Create a Secret object in a YAML file on a control plane node. For example: apiVersion: v1 kind: Secret metadata: name: mysecret type: Opaque 1 data: username: <username> password: <password> 1 Specifies an opaque secret. Use the following command to create a Secret object: USD oc create -f <filename>.yaml To use the secret in a pod: Update the pod's service account to reference the secret, as shown in the "Understanding how to create secrets" section. Create the pod, which consumes the secret as an environment variable or as a file (using a secret volume), as shown in the "Understanding how to create secrets" section. Additional resources For more information on using secrets in pods, see Understanding how to create secrets . 2.6.2.3. Creating a service account token secret As an administrator, you can create a service account token secret, which allows you to distribute a service account token to applications that must authenticate to the API. Note It is recommended to obtain bound service account tokens using the TokenRequest API instead of using service account token secrets. The tokens obtained from the TokenRequest API are more secure than the tokens stored in secrets, because they have a bounded lifetime and are not readable by other API clients. You should create a service account token secret only if you cannot use the TokenRequest API and if the security exposure of a non-expiring token in a readable API object is acceptable to you. See the Additional resources section that follows for information on creating bound service account tokens. Procedure Create a Secret object in a YAML file on a control plane node: Example secret object: apiVersion: v1 kind: Secret metadata: name: secret-sa-sample annotations: kubernetes.io/service-account.name: "sa-name" 1 type: kubernetes.io/service-account-token 2 1 Specifies an existing service account name. If you are creating both the ServiceAccount and the Secret objects, create the ServiceAccount object first. 2 Specifies a service account token secret. Use the following command to create the Secret object: USD oc create -f <filename>.yaml To use the secret in a pod: Update the pod's service account to reference the secret, as shown in the "Understanding how to create secrets" section. Create the pod, which consumes the secret as an environment variable or as a file (using a secret volume), as shown in the "Understanding how to create secrets" section. Additional resources For more information on using secrets in pods, see Understanding how to create secrets . For information on requesting bound service account tokens, see Using bound service account tokens For information on creating service accounts, see Understanding and creating service accounts . 2.6.2.4. Creating a basic authentication secret As an administrator, you can create a basic authentication secret, which allows you to store the credentials needed for basic authentication. When using this secret type, the data parameter of the Secret object must contain the following keys encoded in the base64 format: username : the user name for authentication password : the password or token for authentication Note You can use the stringData parameter to use clear text content. Procedure Create a Secret object in a YAML file on a control plane node: Example secret object apiVersion: v1 kind: Secret metadata: name: secret-basic-auth type: kubernetes.io/basic-auth 1 data: stringData: 2 username: admin password: <password> 1 Specifies a basic authentication secret. 2 Specifies the basic authentication values to use. Use the following command to create the Secret object: USD oc create -f <filename>.yaml To use the secret in a pod: Update the pod's service account to reference the secret, as shown in the "Understanding how to create secrets" section. Create the pod, which consumes the secret as an environment variable or as a file (using a secret volume), as shown in the "Understanding how to create secrets" section. Additional resources For more information on using secrets in pods, see Understanding how to create secrets . 2.6.2.5. Creating an SSH authentication secret As an administrator, you can create an SSH authentication secret, which allows you to store data used for SSH authentication. When using this secret type, the data parameter of the Secret object must contain the SSH credential to use. Procedure Create a Secret object in a YAML file on a control plane node: Example secret object: apiVersion: v1 kind: Secret metadata: name: secret-ssh-auth type: kubernetes.io/ssh-auth 1 data: ssh-privatekey: | 2 MIIEpQIBAAKCAQEAulqb/Y ... 1 Specifies an SSH authentication secret. 2 Specifies the SSH key/value pair as the SSH credentials to use. Use the following command to create the Secret object: USD oc create -f <filename>.yaml To use the secret in a pod: Update the pod's service account to reference the secret, as shown in the "Understanding how to create secrets" section. Create the pod, which consumes the secret as an environment variable or as a file (using a secret volume), as shown in the "Understanding how to create secrets" section. Additional resources Understanding how to create secrets . 2.6.2.6. Creating a Docker configuration secret As an administrator, you can create a Docker configuration secret, which allows you to store the credentials for accessing a container image registry. kubernetes.io/dockercfg . Use this secret type to store your local Docker configuration file. The data parameter of the secret object must contain the contents of a .dockercfg file encoded in the base64 format. kubernetes.io/dockerconfigjson . Use this secret type to store your local Docker configuration JSON file. The data parameter of the secret object must contain the contents of a .docker/config.json file encoded in the base64 format. Procedure Create a Secret object in a YAML file on a control plane node. Example Docker configuration secret object apiVersion: v1 kind: Secret metadata: name: secret-docker-cfg namespace: my-project type: kubernetes.io/dockerconfig 1 data: .dockerconfig:bm5ubm5ubm5ubm5ubm5ubm5ubm5ubmdnZ2dnZ2dnZ2dnZ2dnZ2dnZ2cgYXV0aCBrZXlzCg== 2 1 Specifies that the secret is using a Docker configuration file. 2 The output of a base64-encoded Docker configuration file Example Docker configuration JSON secret object apiVersion: v1 kind: Secret metadata: name: secret-docker-json namespace: my-project type: kubernetes.io/dockerconfig 1 data: .dockerconfigjson:bm5ubm5ubm5ubm5ubm5ubm5ubm5ubmdnZ2dnZ2dnZ2dnZ2dnZ2dnZ2cgYXV0aCBrZXlzCg== 2 1 Specifies that the secret is using a Docker configuration JSONfile. 2 The output of a base64-encoded Docker configuration JSON file Use the following command to create the Secret object USD oc create -f <filename>.yaml To use the secret in a pod: Update the pod's service account to reference the secret, as shown in the "Understanding how to create secrets" section. Create the pod, which consumes the secret as an environment variable or as a file (using a secret volume), as shown in the "Understanding how to create secrets" section. Additional resources For more information on using secrets in pods, see Understanding how to create secrets . 2.6.2.7. Creating a secret using the web console You can create secrets using the web console. Procedure Navigate to Workloads Secrets . Click Create From YAML . Edit the YAML manually to your specifications, or drag and drop a file into the YAML editor. For example: apiVersion: v1 kind: Secret metadata: name: example namespace: <namespace> type: Opaque 1 data: username: <base64 encoded username> password: <base64 encoded password> stringData: 2 hostname: myapp.mydomain.com 1 This example specifies an opaque secret; however, you may see other secret types such as service account token secret, basic authentication secret, SSH authentication secret, or a secret that uses Docker configuration. 2 Entries in the stringData map are converted to base64 and the entry will then be moved to the data map automatically. This field is write-only; the value will only be returned via the data field. Click Create . Click Add Secret to workload . From the drop-down menu, select the workload to add. Click Save . 2.6.3. Understanding how to update secrets When you modify the value of a secret, the value (used by an already running pod) will not dynamically change. To change a secret, you must delete the original pod and create a new pod (perhaps with an identical PodSpec). Updating a secret follows the same workflow as deploying a new Container image. You can use the kubectl rolling-update command. The resourceVersion value in a secret is not specified when it is referenced. Therefore, if a secret is updated at the same time as pods are starting, the version of the secret that is used for the pod is not defined. Note Currently, it is not possible to check the resource version of a secret object that was used when a pod was created. It is planned that pods will report this information, so that a controller could restart ones using an old resourceVersion . In the interim, do not update the data of existing secrets, but create new ones with distinct names. 2.6.4. Creating and using secrets As an administrator, you can create a service account token secret. This allows you to distribute a service account token to applications that must authenticate to the API. Procedure Create a service account in your namespace by running the following command: USD oc create sa <service_account_name> -n <your_namespace> Save the following YAML example to a file named service-account-token-secret.yaml . The example includes a Secret object configuration that you can use to generate a service account token: apiVersion: v1 kind: Secret metadata: name: <secret_name> 1 annotations: kubernetes.io/service-account.name: "sa-name" 2 type: kubernetes.io/service-account-token 3 1 Replace <secret_name> with the name of your service token secret. 2 Specifies an existing service account name. If you are creating both the ServiceAccount and the Secret objects, create the ServiceAccount object first. 3 Specifies a service account token secret type. Generate the service account token by applying the file: USD oc apply -f service-account-token-secret.yaml Get the service account token from the secret by running the following command: USD oc get secret <sa_token_secret> -o jsonpath='{.data.token}' | base64 --decode 1 Example output ayJhbGciOiJSUzI1NiIsImtpZCI6IklOb2dtck1qZ3hCSWpoNnh5YnZhSE9QMkk3YnRZMVZoclFfQTZfRFp1YlUifQ.eyJpc3MiOiJrdWJlcm5ldGVzL3NlcnZpY2VhY2NvdW50Iiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9uYW1lc3BhY2UiOiJkZWZhdWx0Iiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9zZWNyZXQubmFtZSI6ImJ1aWxkZXItdG9rZW4tdHZrbnIiLCJrdWJlcm5ldGVzLmlvL3NlcnZpY2VhY2NvdW50L3NlcnZpY2UtYWNjb3VudC5uYW1lIjoiYnVpbGRlciIsImt1YmVybmV0ZXMuaW8vc2VydmljZWFjY291bnQvc2VydmljZS1hY2NvdW50LnVpZCI6IjNmZGU2MGZmLTA1NGYtNDkyZi04YzhjLTNlZjE0NDk3MmFmNyIsInN1YiI6InN5c3RlbTpzZXJ2aWNlYWNjb3VudDpkZWZhdWx0OmJ1aWxkZXIifQ.OmqFTDuMHC_lYvvEUrjr1x453hlEEHYcxS9VKSzmRkP1SiVZWPNPkTWlfNRp6bIUZD3U6aN3N7dMSN0eI5hu36xPgpKTdvuckKLTCnelMx6cxOdAbrcw1mCmOClNscwjS1KO1kzMtYnnq8rXHiMJELsNlhnRyyIXRTtNBsy4t64T3283s3SLsancyx0gy0ujx-Ch3uKAKdZi5iT-I8jnnQ-ds5THDs2h65RJhgglQEmSxpHrLGZFmyHAQI-_SjvmHZPXEc482x3SkaQHNLqpmrpJorNqh1M8ZHKzlujhZgVooMvJmWPXTb2vnvi3DGn2XI-hZxl1yD2yGH1RBpYUHA 1 Replace <sa_token_secret> with the name of your service token secret. Use your service account token to authenticate with the API of your cluster: USD curl -X GET <openshift_cluster_api> --header "Authorization: Bearer <token>" 1 2 1 Replace <openshift_cluster_api> with the OpenShift cluster API. 2 Replace <token> with the service account token that is output in the preceding command. 2.6.5. About using signed certificates with secrets To secure communication to your service, you can configure OpenShift Container Platform to generate a signed serving certificate/key pair that you can add into a secret in a project. A service serving certificate secret is intended to support complex middleware applications that need out-of-the-box certificates. It has the same settings as the server certificates generated by the administrator tooling for nodes and masters. Service Pod spec configured for a service serving certificates secret. apiVersion: v1 kind: Service metadata: name: registry annotations: service.beta.openshift.io/serving-cert-secret-name: registry-cert 1 # ... 1 Specify the name for the certificate Other pods can trust cluster-created certificates (which are only signed for internal DNS names), by using the CA bundle in the /var/run/secrets/kubernetes.io/serviceaccount/service-ca.crt file that is automatically mounted in their pod. The signature algorithm for this feature is x509.SHA256WithRSA . To manually rotate, delete the generated secret. A new certificate is created. 2.6.5.1. Generating signed certificates for use with secrets To use a signed serving certificate/key pair with a pod, create or edit the service to add the service.beta.openshift.io/serving-cert-secret-name annotation, then add the secret to the pod. Procedure To create a service serving certificate secret : Edit the Pod spec for your service. Add the service.beta.openshift.io/serving-cert-secret-name annotation with the name you want to use for your secret. kind: Service apiVersion: v1 metadata: name: my-service annotations: service.beta.openshift.io/serving-cert-secret-name: my-cert 1 spec: selector: app: MyApp ports: - protocol: TCP port: 80 targetPort: 9376 The certificate and key are in PEM format, stored in tls.crt and tls.key respectively. Create the service: USD oc create -f <file-name>.yaml View the secret to make sure it was created: View a list of all secrets: USD oc get secrets Example output NAME TYPE DATA AGE my-cert kubernetes.io/tls 2 9m View details on your secret: USD oc describe secret my-cert Example output Name: my-cert Namespace: openshift-console Labels: <none> Annotations: service.beta.openshift.io/expiry: 2023-03-08T23:22:40Z service.beta.openshift.io/originating-service-name: my-service service.beta.openshift.io/originating-service-uid: 640f0ec3-afc2-4380-bf31-a8c784846a11 service.beta.openshift.io/expiry: 2023-03-08T23:22:40Z Type: kubernetes.io/tls Data ==== tls.key: 1679 bytes tls.crt: 2595 bytes Edit your Pod spec with that secret. apiVersion: v1 kind: Pod metadata: name: my-service-pod spec: containers: - name: mypod image: redis volumeMounts: - name: my-container mountPath: "/etc/my-path" volumes: - name: my-volume secret: secretName: my-cert items: - key: username path: my-group/my-username mode: 511 When it is available, your pod will run. The certificate will be good for the internal service DNS name, <service.name>.<service.namespace>.svc . The certificate/key pair is automatically replaced when it gets close to expiration. View the expiration date in the service.beta.openshift.io/expiry annotation on the secret, which is in RFC3339 format. Note In most cases, the service DNS name <service.name>.<service.namespace>.svc is not externally routable. The primary use of <service.name>.<service.namespace>.svc is for intracluster or intraservice communication, and with re-encrypt routes. 2.6.6. Troubleshooting secrets If a service certificate generation fails with (service's service.beta.openshift.io/serving-cert-generation-error annotation contains): secret/ssl-key references serviceUID 62ad25ca-d703-11e6-9d6f-0e9c0057b608, which does not match 77b6dd80-d716-11e6-9d6f-0e9c0057b60 The service that generated the certificate no longer exists, or has a different serviceUID . You must force certificates regeneration by removing the old secret, and clearing the following annotations on the service service.beta.openshift.io/serving-cert-generation-error , service.beta.openshift.io/serving-cert-generation-error-num : Delete the secret: USD oc delete secret <secret_name> Clear the annotations: USD oc annotate service <service_name> service.beta.openshift.io/serving-cert-generation-error- USD oc annotate service <service_name> service.beta.openshift.io/serving-cert-generation-error-num- Note The command removing annotation has a - after the annotation name to be removed. 2.7. Creating and using config maps The following sections define config maps and how to create and use them. 2.7.1. Understanding config maps Many applications require configuration by using some combination of configuration files, command line arguments, and environment variables. In OpenShift Container Platform, these configuration artifacts are decoupled from image content to keep containerized applications portable. The ConfigMap object provides mechanisms to inject containers with configuration data while keeping containers agnostic of OpenShift Container Platform. A config map can be used to store fine-grained information like individual properties or coarse-grained information like entire configuration files or JSON blobs. The ConfigMap object holds key-value pairs of configuration data that can be consumed in pods or used to store configuration data for system components such as controllers. For example: ConfigMap Object Definition kind: ConfigMap apiVersion: v1 metadata: creationTimestamp: 2016-02-18T19:14:38Z name: example-config namespace: my-namespace data: 1 example.property.1: hello example.property.2: world example.property.file: |- property.1=value-1 property.2=value-2 property.3=value-3 binaryData: bar: L3Jvb3QvMTAw 2 1 1 Contains the configuration data. 2 Points to a file that contains non-UTF8 data, for example, a binary Java keystore file. Enter the file data in Base 64. Note You can use the binaryData field when you create a config map from a binary file, such as an image. Configuration data can be consumed in pods in a variety of ways. A config map can be used to: Populate environment variable values in containers Set command-line arguments in a container Populate configuration files in a volume Users and system components can store configuration data in a config map. A config map is similar to a secret, but designed to more conveniently support working with strings that do not contain sensitive information. Config map restrictions A config map must be created before its contents can be consumed in pods. Controllers can be written to tolerate missing configuration data. Consult individual components configured by using config maps on a case-by-case basis. ConfigMap objects reside in a project. They can only be referenced by pods in the same project. The Kubelet only supports the use of a config map for pods it gets from the API server. This includes any pods created by using the CLI, or indirectly from a replication controller. It does not include pods created by using the OpenShift Container Platform node's --manifest-url flag, its --config flag, or its REST API because these are not common ways to create pods. 2.7.2. Creating a config map in the OpenShift Container Platform web console You can create a config map in the OpenShift Container Platform web console. Procedure To create a config map as a cluster administrator: In the Administrator perspective, select Workloads Config Maps . At the top right side of the page, select Create Config Map . Enter the contents of your config map. Select Create . To create a config map as a developer: In the Developer perspective, select Config Maps . At the top right side of the page, select Create Config Map . Enter the contents of your config map. Select Create . 2.7.3. Creating a config map by using the CLI You can use the following command to create a config map from directories, specific files, or literal values. Procedure Create a config map: USD oc create configmap <configmap_name> [options] 2.7.3.1. Creating a config map from a directory You can create a config map from a directory by using the --from-file flag. This method allows you to use multiple files within a directory to create a config map. Each file in the directory is used to populate a key in the config map, where the name of the key is the file name, and the value of the key is the content of the file. For example, the following command creates a config map with the contents of the example-files directory: USD oc create configmap game-config --from-file=example-files/ View the keys in the config map: USD oc describe configmaps game-config Example output Name: game-config Namespace: default Labels: <none> Annotations: <none> Data game.properties: 158 bytes ui.properties: 83 bytes You can see that the two keys in the map are created from the file names in the directory specified in the command. The content of those keys might be large, so the output of oc describe only shows the names of the keys and their sizes. Prerequisite You must have a directory with files that contain the data you want to populate a config map with. The following procedure uses these example files: game.properties and ui.properties : USD cat example-files/game.properties Example output enemies=aliens lives=3 enemies.cheat=true enemies.cheat.level=noGoodRotten secret.code.passphrase=UUDDLRLRBABAS secret.code.allowed=true secret.code.lives=30 USD cat example-files/ui.properties Example output color.good=purple color.bad=yellow allow.textmode=true how.nice.to.look=fairlyNice Procedure Create a config map holding the content of each file in this directory by entering the following command: USD oc create configmap game-config \ --from-file=example-files/ Verification Enter the oc get command for the object with the -o option to see the values of the keys: USD oc get configmaps game-config -o yaml Example output apiVersion: v1 data: game.properties: |- enemies=aliens lives=3 enemies.cheat=true enemies.cheat.level=noGoodRotten secret.code.passphrase=UUDDLRLRBABAS secret.code.allowed=true secret.code.lives=30 ui.properties: | color.good=purple color.bad=yellow allow.textmode=true how.nice.to.look=fairlyNice kind: ConfigMap metadata: creationTimestamp: 2016-02-18T18:34:05Z name: game-config namespace: default resourceVersion: "407" selflink: /api/v1/namespaces/default/configmaps/game-config uid: 30944725-d66e-11e5-8cd0-68f728db1985 2.7.3.2. Creating a config map from a file You can create a config map from a file by using the --from-file flag. You can pass the --from-file option multiple times to the CLI. You can also specify the key to set in a config map for content imported from a file by passing a key=value expression to the --from-file option. For example: USD oc create configmap game-config-3 --from-file=game-special-key=example-files/game.properties Note If you create a config map from a file, you can include files containing non-UTF8 data that are placed in this field without corrupting the non-UTF8 data. OpenShift Container Platform detects binary files and transparently encodes the file as MIME . On the server, the MIME payload is decoded and stored without corrupting the data. Prerequisite You must have a directory with files that contain the data you want to populate a config map with. The following procedure uses these example files: game.properties and ui.properties : USD cat example-files/game.properties Example output enemies=aliens lives=3 enemies.cheat=true enemies.cheat.level=noGoodRotten secret.code.passphrase=UUDDLRLRBABAS secret.code.allowed=true secret.code.lives=30 USD cat example-files/ui.properties Example output color.good=purple color.bad=yellow allow.textmode=true how.nice.to.look=fairlyNice Procedure Create a config map by specifying a specific file: USD oc create configmap game-config-2 \ --from-file=example-files/game.properties \ --from-file=example-files/ui.properties Create a config map by specifying a key-value pair: USD oc create configmap game-config-3 \ --from-file=game-special-key=example-files/game.properties Verification Enter the oc get command for the object with the -o option to see the values of the keys from the file: USD oc get configmaps game-config-2 -o yaml Example output apiVersion: v1 data: game.properties: |- enemies=aliens lives=3 enemies.cheat=true enemies.cheat.level=noGoodRotten secret.code.passphrase=UUDDLRLRBABAS secret.code.allowed=true secret.code.lives=30 ui.properties: | color.good=purple color.bad=yellow allow.textmode=true how.nice.to.look=fairlyNice kind: ConfigMap metadata: creationTimestamp: 2016-02-18T18:52:05Z name: game-config-2 namespace: default resourceVersion: "516" selflink: /api/v1/namespaces/default/configmaps/game-config-2 uid: b4952dc3-d670-11e5-8cd0-68f728db1985 Enter the oc get command for the object with the -o option to see the values of the keys from the key-value pair: USD oc get configmaps game-config-3 -o yaml Example output apiVersion: v1 data: game-special-key: |- 1 enemies=aliens lives=3 enemies.cheat=true enemies.cheat.level=noGoodRotten secret.code.passphrase=UUDDLRLRBABAS secret.code.allowed=true secret.code.lives=30 kind: ConfigMap metadata: creationTimestamp: 2016-02-18T18:54:22Z name: game-config-3 namespace: default resourceVersion: "530" selflink: /api/v1/namespaces/default/configmaps/game-config-3 uid: 05f8da22-d671-11e5-8cd0-68f728db1985 1 This is the key that you set in the preceding step. 2.7.3.3. Creating a config map from literal values You can supply literal values for a config map. The --from-literal option takes a key=value syntax, which allows literal values to be supplied directly on the command line. Procedure Create a config map by specifying a literal value: USD oc create configmap special-config \ --from-literal=special.how=very \ --from-literal=special.type=charm Verification Enter the oc get command for the object with the -o option to see the values of the keys: USD oc get configmaps special-config -o yaml Example output apiVersion: v1 data: special.how: very special.type: charm kind: ConfigMap metadata: creationTimestamp: 2016-02-18T19:14:38Z name: special-config namespace: default resourceVersion: "651" selflink: /api/v1/namespaces/default/configmaps/special-config uid: dadce046-d673-11e5-8cd0-68f728db1985 2.7.4. Use cases: Consuming config maps in pods The following sections describe some uses cases when consuming ConfigMap objects in pods. 2.7.4.1. Populating environment variables in containers by using config maps You can use config maps to populate individual environment variables in containers or to populate environment variables in containers from all keys that form valid environment variable names. As an example, consider the following config map: ConfigMap with two environment variables apiVersion: v1 kind: ConfigMap metadata: name: special-config 1 namespace: default 2 data: special.how: very 3 special.type: charm 4 1 Name of the config map. 2 The project in which the config map resides. Config maps can only be referenced by pods in the same project. 3 4 Environment variables to inject. ConfigMap with one environment variable apiVersion: v1 kind: ConfigMap metadata: name: env-config 1 namespace: default data: log_level: INFO 2 1 Name of the config map. 2 Environment variable to inject. Procedure You can consume the keys of this ConfigMap in a pod using configMapKeyRef sections. Sample Pod specification configured to inject specific environment variables apiVersion: v1 kind: Pod metadata: name: dapi-test-pod spec: containers: - name: test-container image: gcr.io/google_containers/busybox command: [ "/bin/sh", "-c", "env" ] env: 1 - name: SPECIAL_LEVEL_KEY 2 valueFrom: configMapKeyRef: name: special-config 3 key: special.how 4 - name: SPECIAL_TYPE_KEY valueFrom: configMapKeyRef: name: special-config 5 key: special.type 6 optional: true 7 envFrom: 8 - configMapRef: name: env-config 9 restartPolicy: Never 1 Stanza to pull the specified environment variables from a ConfigMap . 2 Name of a pod environment variable that you are injecting a key's value into. 3 5 Name of the ConfigMap to pull specific environment variables from. 4 6 Environment variable to pull from the ConfigMap . 7 Makes the environment variable optional. As optional, the pod will be started even if the specified ConfigMap and keys do not exist. 8 Stanza to pull all environment variables from a ConfigMap . 9 Name of the ConfigMap to pull all environment variables from. When this pod is run, the pod logs will include the following output: Note SPECIAL_TYPE_KEY=charm is not listed in the example output because optional: true is set. 2.7.4.2. Setting command-line arguments for container commands with config maps You can use a config map to set the value of the commands or arguments in a container by using the Kubernetes substitution syntax USD(VAR_NAME) . As an example, consider the following config map: apiVersion: v1 kind: ConfigMap metadata: name: special-config namespace: default data: special.how: very special.type: charm Procedure To inject values into a command in a container, you must consume the keys you want to use as environment variables. Then you can refer to them in a container's command using the USD(VAR_NAME) syntax. Sample pod specification configured to inject specific environment variables apiVersion: v1 kind: Pod metadata: name: dapi-test-pod spec: containers: - name: test-container image: gcr.io/google_containers/busybox command: [ "/bin/sh", "-c", "echo USD(SPECIAL_LEVEL_KEY) USD(SPECIAL_TYPE_KEY)" ] 1 env: - name: SPECIAL_LEVEL_KEY valueFrom: configMapKeyRef: name: special-config key: special.how - name: SPECIAL_TYPE_KEY valueFrom: configMapKeyRef: name: special-config key: special.type restartPolicy: Never 1 Inject the values into a command in a container using the keys you want to use as environment variables. When this pod is run, the output from the echo command run in the test-container container is as follows: 2.7.4.3. Injecting content into a volume by using config maps You can inject content into a volume by using config maps. Example ConfigMap custom resource (CR) apiVersion: v1 kind: ConfigMap metadata: name: special-config namespace: default data: special.how: very special.type: charm Procedure You have a couple different options for injecting content into a volume by using config maps. The most basic way to inject content into a volume by using a config map is to populate the volume with files where the key is the file name and the content of the file is the value of the key: apiVersion: v1 kind: Pod metadata: name: dapi-test-pod spec: containers: - name: test-container image: gcr.io/google_containers/busybox command: [ "/bin/sh", "-c", "cat", "/etc/config/special.how" ] volumeMounts: - name: config-volume mountPath: /etc/config volumes: - name: config-volume configMap: name: special-config 1 restartPolicy: Never 1 File containing key. When this pod is run, the output of the cat command will be: You can also control the paths within the volume where config map keys are projected: apiVersion: v1 kind: Pod metadata: name: dapi-test-pod spec: containers: - name: test-container image: gcr.io/google_containers/busybox command: [ "/bin/sh", "-c", "cat", "/etc/config/path/to/special-key" ] volumeMounts: - name: config-volume mountPath: /etc/config volumes: - name: config-volume configMap: name: special-config items: - key: special.how path: path/to/special-key 1 restartPolicy: Never 1 Path to config map key. When this pod is run, the output of the cat command will be: 2.8. Using device plugins to access external resources with pods Device plugins allow you to use a particular device type (GPU, InfiniBand, or other similar computing resources that require vendor-specific initialization and setup) in your OpenShift Container Platform pod without needing to write custom code. 2.8.1. Understanding device plugins The device plugin provides a consistent and portable solution to consume hardware devices across clusters. The device plugin provides support for these devices through an extension mechanism, which makes these devices available to Containers, provides health checks of these devices, and securely shares them. Important OpenShift Container Platform supports the device plugin API, but the device plugin Containers are supported by individual vendors. A device plugin is a gRPC service running on the nodes (external to the kubelet ) that is responsible for managing specific hardware resources. Any device plugin must support following remote procedure calls (RPCs): service DevicePlugin { // GetDevicePluginOptions returns options to be communicated with Device // Manager rpc GetDevicePluginOptions(Empty) returns (DevicePluginOptions) {} // ListAndWatch returns a stream of List of Devices // Whenever a Device state change or a Device disappears, ListAndWatch // returns the new list rpc ListAndWatch(Empty) returns (stream ListAndWatchResponse) {} // Allocate is called during container creation so that the Device // Plug-in can run device specific operations and instruct Kubelet // of the steps to make the Device available in the container rpc Allocate(AllocateRequest) returns (AllocateResponse) {} // PreStartcontainer is called, if indicated by Device Plug-in during // registration phase, before each container start. Device plug-in // can run device specific operations such as resetting the device // before making devices available to the container rpc PreStartcontainer(PreStartcontainerRequest) returns (PreStartcontainerResponse) {} } Example device plugins Nvidia GPU device plugin for COS-based operating system Nvidia official GPU device plugin Solarflare device plugin KubeVirt device plugins: vfio and kvm Kubernetes device plugin for IBM Crypto Express (CEX) cards Note For easy device plugin reference implementation, there is a stub device plugin in the Device Manager code: vendor/k8s.io/kubernetes/pkg/kubelet/cm/deviceplugin/device_plugin_stub.go . 2.8.1.1. Methods for deploying a device plugin Daemon sets are the recommended approach for device plugin deployments. Upon start, the device plugin will try to create a UNIX domain socket at /var/lib/kubelet/device-plugin/ on the node to serve RPCs from Device Manager. Since device plugins must manage hardware resources, access to the host file system, as well as socket creation, they must be run in a privileged security context. More specific details regarding deployment steps can be found with each device plugin implementation. 2.8.2. Understanding the Device Manager Device Manager provides a mechanism for advertising specialized node hardware resources with the help of plugins known as device plugins. You can advertise specialized hardware without requiring any upstream code changes. Important OpenShift Container Platform supports the device plugin API, but the device plugin Containers are supported by individual vendors. Device Manager advertises devices as Extended Resources . User pods can consume devices, advertised by Device Manager, using the same Limit/Request mechanism, which is used for requesting any other Extended Resource . Upon start, the device plugin registers itself with Device Manager invoking Register on the /var/lib/kubelet/device-plugins/kubelet.sock and starts a gRPC service at /var/lib/kubelet/device-plugins/<plugin>.sock for serving Device Manager requests. Device Manager, while processing a new registration request, invokes ListAndWatch remote procedure call (RPC) at the device plugin service. In response, Device Manager gets a list of Device objects from the plugin over a gRPC stream. Device Manager will keep watching on the stream for new updates from the plugin. On the plugin side, the plugin will also keep the stream open and whenever there is a change in the state of any of the devices, a new device list is sent to the Device Manager over the same streaming connection. While handling a new pod admission request, Kubelet passes requested Extended Resources to the Device Manager for device allocation. Device Manager checks in its database to verify if a corresponding plugin exists or not. If the plugin exists and there are free allocatable devices as well as per local cache, Allocate RPC is invoked at that particular device plugin. Additionally, device plugins can also perform several other device-specific operations, such as driver installation, device initialization, and device resets. These functionalities vary from implementation to implementation. 2.8.3. Enabling Device Manager Enable Device Manager to implement a device plugin to advertise specialized hardware without any upstream code changes. Device Manager provides a mechanism for advertising specialized node hardware resources with the help of plugins known as device plugins. Obtain the label associated with the static MachineConfigPool CRD for the type of node you want to configure by entering the following command. Perform one of the following steps: View the machine config: # oc describe machineconfig <name> For example: # oc describe machineconfig 00-worker Example output Name: 00-worker Namespace: Labels: machineconfiguration.openshift.io/role=worker 1 1 Label required for the Device Manager. Procedure Create a custom resource (CR) for your configuration change. Sample configuration for a Device Manager CR apiVersion: machineconfiguration.openshift.io/v1 kind: KubeletConfig metadata: name: devicemgr 1 spec: machineConfigPoolSelector: matchLabels: machineconfiguration.openshift.io: devicemgr 2 kubeletConfig: feature-gates: - DevicePlugins=true 3 1 Assign a name to CR. 2 Enter the label from the Machine Config Pool. 3 Set DevicePlugins to 'true`. Create the Device Manager: USD oc create -f devicemgr.yaml Example output kubeletconfig.machineconfiguration.openshift.io/devicemgr created Ensure that Device Manager was actually enabled by confirming that /var/lib/kubelet/device-plugins/kubelet.sock is created on the node. This is the UNIX domain socket on which the Device Manager gRPC server listens for new plugin registrations. This sock file is created when the Kubelet is started only if Device Manager is enabled. 2.9. Including pod priority in pod scheduling decisions You can enable pod priority and preemption in your cluster. Pod priority indicates the importance of a pod relative to other pods and queues the pods based on that priority. pod preemption allows the cluster to evict, or preempt, lower-priority pods so that higher-priority pods can be scheduled if there is no available space on a suitable node pod priority also affects the scheduling order of pods and out-of-resource eviction ordering on the node. To use priority and preemption, you create priority classes that define the relative weight of your pods. Then, reference a priority class in the pod specification to apply that weight for scheduling. 2.9.1. Understanding pod priority When you use the Pod Priority and Preemption feature, the scheduler orders pending pods by their priority, and a pending pod is placed ahead of other pending pods with lower priority in the scheduling queue. As a result, the higher priority pod might be scheduled sooner than pods with lower priority if its scheduling requirements are met. If a pod cannot be scheduled, scheduler continues to schedule other lower priority pods. 2.9.1.1. Pod priority classes You can assign pods a priority class, which is a non-namespaced object that defines a mapping from a name to the integer value of the priority. The higher the value, the higher the priority. A priority class object can take any 32-bit integer value smaller than or equal to 1000000000 (one billion). Reserve numbers larger than or equal to one billion for critical pods that must not be preempted or evicted. By default, OpenShift Container Platform has two reserved priority classes for critical system pods to have guaranteed scheduling. USD oc get priorityclasses Example output NAME VALUE GLOBAL-DEFAULT AGE system-node-critical 2000001000 false 72m system-cluster-critical 2000000000 false 72m openshift-user-critical 1000000000 false 3d13h cluster-logging 1000000 false 29s system-node-critical - This priority class has a value of 2000001000 and is used for all pods that should never be evicted from a node. Examples of pods that have this priority class are sdn-ovs , sdn , and so forth. A number of critical components include the system-node-critical priority class by default, for example: master-api master-controller master-etcd sdn sdn-ovs sync system-cluster-critical - This priority class has a value of 2000000000 (two billion) and is used with pods that are important for the cluster. Pods with this priority class can be evicted from a node in certain circumstances. For example, pods configured with the system-node-critical priority class can take priority. However, this priority class does ensure guaranteed scheduling. Examples of pods that can have this priority class are fluentd, add-on components like descheduler, and so forth. A number of critical components include the system-cluster-critical priority class by default, for example: fluentd metrics-server descheduler openshift-user-critical - You can use the priorityClassName field with important pods that cannot bind their resource consumption and do not have predictable resource consumption behavior. Prometheus pods under the openshift-monitoring and openshift-user-workload-monitoring namespaces use the openshift-user-critical priorityClassName . Monitoring workloads use system-critical as their first priorityClass , but this causes problems when monitoring uses excessive memory and the nodes cannot evict them. As a result, monitoring drops priority to give the scheduler flexibility, moving heavy workloads around to keep critical nodes operating. cluster-logging - This priority is used by Fluentd to make sure Fluentd pods are scheduled to nodes over other apps. 2.9.1.2. Pod priority names After you have one or more priority classes, you can create pods that specify a priority class name in a Pod spec. The priority admission controller uses the priority class name field to populate the integer value of the priority. If the named priority class is not found, the pod is rejected. 2.9.2. Understanding pod preemption When a developer creates a pod, the pod goes into a queue. If the developer configured the pod for pod priority or preemption, the scheduler picks a pod from the queue and tries to schedule the pod on a node. If the scheduler cannot find space on an appropriate node that satisfies all the specified requirements of the pod, preemption logic is triggered for the pending pod. When the scheduler preempts one or more pods on a node, the nominatedNodeName field of higher-priority Pod spec is set to the name of the node, along with the nodename field. The scheduler uses the nominatedNodeName field to keep track of the resources reserved for pods and also provides information to the user about preemptions in the clusters. After the scheduler preempts a lower-priority pod, the scheduler honors the graceful termination period of the pod. If another node becomes available while scheduler is waiting for the lower-priority pod to terminate, the scheduler can schedule the higher-priority pod on that node. As a result, the nominatedNodeName field and nodeName field of the Pod spec might be different. Also, if the scheduler preempts pods on a node and is waiting for termination, and a pod with a higher-priority pod than the pending pod needs to be scheduled, the scheduler can schedule the higher-priority pod instead. In such a case, the scheduler clears the nominatedNodeName of the pending pod, making the pod eligible for another node. Preemption does not necessarily remove all lower-priority pods from a node. The scheduler can schedule a pending pod by removing a portion of the lower-priority pods. The scheduler considers a node for pod preemption only if the pending pod can be scheduled on the node. 2.9.2.1. Non-preempting priority classes Pods with the preemption policy set to Never are placed in the scheduling queue ahead of lower-priority pods, but they cannot preempt other pods. A non-preempting pod waiting to be scheduled stays in the scheduling queue until sufficient resources are free and it can be scheduled. Non-preempting pods, like other pods, are subject to scheduler back-off. This means that if the scheduler tries unsuccessfully to schedule these pods, they are retried with lower frequency, allowing other pods with lower priority to be scheduled before them. Non-preempting pods can still be preempted by other, high-priority pods. 2.9.2.2. Pod preemption and other scheduler settings If you enable pod priority and preemption, consider your other scheduler settings: Pod priority and pod disruption budget A pod disruption budget specifies the minimum number or percentage of replicas that must be up at a time. If you specify pod disruption budgets, OpenShift Container Platform respects them when preempting pods at a best effort level. The scheduler attempts to preempt pods without violating the pod disruption budget. If no such pods are found, lower-priority pods might be preempted despite their pod disruption budget requirements. Pod priority and pod affinity Pod affinity requires a new pod to be scheduled on the same node as other pods with the same label. If a pending pod has inter-pod affinity with one or more of the lower-priority pods on a node, the scheduler cannot preempt the lower-priority pods without violating the affinity requirements. In this case, the scheduler looks for another node to schedule the pending pod. However, there is no guarantee that the scheduler can find an appropriate node and pending pod might not be scheduled. To prevent this situation, carefully configure pod affinity with equal-priority pods. 2.9.2.3. Graceful termination of preempted pods When preempting a pod, the scheduler waits for the pod graceful termination period to expire, allowing the pod to finish working and exit. If the pod does not exit after the period, the scheduler kills the pod. This graceful termination period creates a time gap between the point that the scheduler preempts the pod and the time when the pending pod can be scheduled on the node. To minimize this gap, configure a small graceful termination period for lower-priority pods. 2.9.3. Configuring priority and preemption You apply pod priority and preemption by creating a priority class object and associating pods to the priority by using the priorityClassName in your pod specs. Note You cannot add a priority class directly to an existing scheduled pod. Procedure To configure your cluster to use priority and preemption: Create one or more priority classes: Create a YAML file similar to the following: apiVersion: scheduling.k8s.io/v1 kind: PriorityClass metadata: name: high-priority 1 value: 1000000 2 preemptionPolicy: PreemptLowerPriority 3 globalDefault: false 4 description: "This priority class should be used for XYZ service pods only." 5 1 The name of the priority class object. 2 The priority value of the object. 3 Optional. Specifies whether this priority class is preempting or non-preempting. The preemption policy defaults to PreemptLowerPriority , which allows pods of that priority class to preempt lower-priority pods. If the preemption policy is set to Never , pods in that priority class are non-preempting. 4 Optional. Specifies whether this priority class should be used for pods without a priority class name specified. This field is false by default. Only one priority class with globalDefault set to true can exist in the cluster. If there is no priority class with globalDefault:true , the priority of pods with no priority class name is zero. Adding a priority class with globalDefault:true affects only pods created after the priority class is added and does not change the priorities of existing pods. 5 Optional. Describes which pods developers should use with this priority class. Enter an arbitrary text string. Create the priority class: USD oc create -f <file-name>.yaml Create a pod spec to include the name of a priority class: Create a YAML file similar to the following: apiVersion: v1 kind: Pod metadata: name: nginx labels: env: test spec: containers: - name: nginx image: nginx imagePullPolicy: IfNotPresent priorityClassName: high-priority 1 1 Specify the priority class to use with this pod. Create the pod: USD oc create -f <file-name>.yaml You can add the priority name directly to the pod configuration or to a pod template. 2.10. Placing pods on specific nodes using node selectors A node selector specifies a map of key-value pairs. The rules are defined using custom labels on nodes and selectors specified in pods. For the pod to be eligible to run on a node, the pod must have the indicated key-value pairs as the label on the node. If you are using node affinity and node selectors in the same pod configuration, see the important considerations below. 2.10.1. Using node selectors to control pod placement You can use node selectors on pods and labels on nodes to control where the pod is scheduled. With node selectors, OpenShift Container Platform schedules the pods on nodes that contain matching labels. You add labels to a node, a compute machine set, or a machine config. Adding the label to the compute machine set ensures that if the node or machine goes down, new nodes have the label. Labels added to a node or machine config do not persist if the node or machine goes down. To add node selectors to an existing pod, add a node selector to the controlling object for that pod, such as a ReplicaSet object, DaemonSet object, StatefulSet object, Deployment object, or DeploymentConfig object. Any existing pods under that controlling object are recreated on a node with a matching label. If you are creating a new pod, you can add the node selector directly to the pod spec. If the pod does not have a controlling object, you must delete the pod, edit the pod spec, and recreate the pod. Note You cannot add a node selector directly to an existing scheduled pod. Prerequisites To add a node selector to existing pods, determine the controlling object for that pod. For example, the router-default-66d5cf9464-m2g75 pod is controlled by the router-default-66d5cf9464 replica set: USD oc describe pod router-default-66d5cf9464-7pwkc Example output kind: Pod apiVersion: v1 metadata: # ... Name: router-default-66d5cf9464-7pwkc Namespace: openshift-ingress # ... Controlled By: ReplicaSet/router-default-66d5cf9464 # ... The web console lists the controlling object under ownerReferences in the pod YAML: apiVersion: v1 kind: Pod metadata: name: router-default-66d5cf9464-7pwkc # ... ownerReferences: - apiVersion: apps/v1 kind: ReplicaSet name: router-default-66d5cf9464 uid: d81dd094-da26-11e9-a48a-128e7edf0312 controller: true blockOwnerDeletion: true # ... Procedure Add labels to a node by using a compute machine set or editing the node directly: Use a MachineSet object to add labels to nodes managed by the compute machine set when a node is created: Run the following command to add labels to a MachineSet object: USD oc patch MachineSet <name> --type='json' -p='[{"op":"add","path":"/spec/template/spec/metadata/labels", "value":{"<key>"="<value>","<key>"="<value>"}}]' -n openshift-machine-api For example: USD oc patch MachineSet abc612-msrtw-worker-us-east-1c --type='json' -p='[{"op":"add","path":"/spec/template/spec/metadata/labels", "value":{"type":"user-node","region":"east"}}]' -n openshift-machine-api Tip You can alternatively apply the following YAML to add labels to a compute machine set: apiVersion: machine.openshift.io/v1beta1 kind: MachineSet metadata: name: xf2bd-infra-us-east-2a namespace: openshift-machine-api spec: template: spec: metadata: labels: region: "east" type: "user-node" # ... Verify that the labels are added to the MachineSet object by using the oc edit command: For example: USD oc edit MachineSet abc612-msrtw-worker-us-east-1c -n openshift-machine-api Example MachineSet object apiVersion: machine.openshift.io/v1beta1 kind: MachineSet # ... spec: # ... template: metadata: # ... spec: metadata: labels: region: east type: user-node # ... Add labels directly to a node: Edit the Node object for the node: USD oc label nodes <name> <key>=<value> For example, to label a node: USD oc label nodes ip-10-0-142-25.ec2.internal type=user-node region=east Tip You can alternatively apply the following YAML to add labels to a node: kind: Node apiVersion: v1 metadata: name: hello-node-6fbccf8d9 labels: type: "user-node" region: "east" # ... Verify that the labels are added to the node: USD oc get nodes -l type=user-node,region=east Example output NAME STATUS ROLES AGE VERSION ip-10-0-142-25.ec2.internal Ready worker 17m v1.26.0 Add the matching node selector to a pod: To add a node selector to existing and future pods, add a node selector to the controlling object for the pods: Example ReplicaSet object with labels kind: ReplicaSet apiVersion: apps/v1 metadata: name: hello-node-6fbccf8d9 # ... spec: # ... template: metadata: creationTimestamp: null labels: ingresscontroller.operator.openshift.io/deployment-ingresscontroller: default pod-template-hash: 66d5cf9464 spec: nodeSelector: kubernetes.io/os: linux node-role.kubernetes.io/worker: '' type: user-node 1 # ... 1 Add the node selector. To add a node selector to a specific, new pod, add the selector to the Pod object directly: Example Pod object with a node selector apiVersion: v1 kind: Pod metadata: name: hello-node-6fbccf8d9 # ... spec: nodeSelector: region: east type: user-node # ... Note You cannot add a node selector directly to an existing scheduled pod. 2.11. Run Once Duration Override Operator 2.11.1. Run Once Duration Override Operator overview You can use the Run Once Duration Override Operator to specify a maximum time limit that run-once pods can be active for. 2.11.1.1. About the Run Once Duration Override Operator OpenShift Container Platform relies on run-once pods to perform tasks such as deploying a pod or performing a build. Run-once pods are pods that have a RestartPolicy of Never or OnFailure . Cluster administrators can use the Run Once Duration Override Operator to force a limit on the time that those run-once pods can be active. After the time limit expires, the cluster will try to actively terminate those pods. The main reason to have such a limit is to prevent tasks such as builds to run for an excessive amount of time. To apply the run-once duration override from the Run Once Duration Override Operator to run-once pods, you must enable it on each applicable namespace. If both the run-once pod and the Run Once Duration Override Operator have their activeDeadlineSeconds value set, the lower of the two values is used. 2.11.2. Run Once Duration Override Operator release notes Cluster administrators can use the Run Once Duration Override Operator to force a limit on the time that run-once pods can be active. After the time limit expires, the cluster tries to terminate the run-once pods. The main reason to have such a limit is to prevent tasks such as builds to run for an excessive amount of time. To apply the run-once duration override from the Run Once Duration Override Operator to run-once pods, you must enable it on each applicable namespace. These release notes track the development of the Run Once Duration Override Operator for OpenShift Container Platform. For an overview of the Run Once Duration Override Operator, see About the Run Once Duration Override Operator . 2.11.2.1. Run Once Duration Override Operator 1.0.2 Issued: 26 November 2024 The following advisory is available for the Run Once Duration Override Operator 1.0.2: RHEA-2024:9999 2.11.2.1.1. Bug fixes This release of the Run Once Duration Override Operator addresses several Common Vulnerabilities and Exposures (CVEs). 2.11.2.2. Run Once Duration Override Operator 1.0.1 Issued: 26 October 2023 The following advisory is available for the Run Once Duration Override Operator 1.0.1: RHSA-2023:5947 2.11.2.2.1. Bug fixes This release of the Run Once Duration Override Operator addresses several Common Vulnerabilities and Exposures (CVEs). 2.11.2.3. Run Once Duration Override Operator 1.0.0 Issued: 18 May 2023 The following advisory is available for the Run Once Duration Override Operator 1.0.0: RHEA-2023:2035 2.11.2.3.1. New features and enhancements This is the initial, generally available release of the Run Once Duration Override Operator. For installation information, see Installing the Run Once Duration Override Operator . 2.11.3. Overriding the active deadline for run-once pods You can use the Run Once Duration Override Operator to specify a maximum time limit that run-once pods can be active for. By enabling the run-once duration override on a namespace, all future run-once pods created or updated in that namespace have their activeDeadlineSeconds field set to the value specified by the Run Once Duration Override Operator. Note If both the run-once pod and the Run Once Duration Override Operator have their activeDeadlineSeconds value set, the lower of the two values is used. 2.11.3.1. Installing the Run Once Duration Override Operator You can use the web console to install the Run Once Duration Override Operator. Prerequisites You have access to the cluster with cluster-admin privileges. You have access to the OpenShift Container Platform web console. Procedure Log in to the OpenShift Container Platform web console. Create the required namespace for the Run Once Duration Override Operator. Navigate to Administration Namespaces and click Create Namespace . Enter openshift-run-once-duration-override-operator in the Name field and click Create . Install the Run Once Duration Override Operator. Navigate to Operators OperatorHub . Enter Run Once Duration Override Operator into the filter box. Select the Run Once Duration Override Operator and click Install . On the Install Operator page: The Update channel is set to stable , which installs the latest stable release of the Run Once Duration Override Operator. Select A specific namespace on the cluster . Choose openshift-run-once-duration-override-operator from the dropdown menu under Installed namespace . Select an Update approval strategy. The Automatic strategy allows Operator Lifecycle Manager (OLM) to automatically update the Operator when a new version is available. The Manual strategy requires a user with appropriate credentials to approve the Operator update. Click Install . Create a RunOnceDurationOverride instance. From the Operators Installed Operators page, click Run Once Duration Override Operator . Select the Run Once Duration Override tab and click Create RunOnceDurationOverride . Edit the settings as necessary. Under the runOnceDurationOverride section, you can update the spec.activeDeadlineSeconds value, if required. The predefined value is 3600 seconds, or 1 hour. Click Create . Verification Log in to the OpenShift CLI. Verify all pods are created and running properly. USD oc get pods -n openshift-run-once-duration-override-operator Example output NAME READY STATUS RESTARTS AGE run-once-duration-override-operator-7b88c676f6-lcxgc 1/1 Running 0 7m46s runoncedurationoverride-62blp 1/1 Running 0 41s runoncedurationoverride-h8h8b 1/1 Running 0 41s runoncedurationoverride-tdsqk 1/1 Running 0 41s 2.11.3.2. Enabling the run-once duration override on a namespace To apply the run-once duration override from the Run Once Duration Override Operator to run-once pods, you must enable it on each applicable namespace. Prerequisites The Run Once Duration Override Operator is installed. Procedure Log in to the OpenShift CLI. Add the label to enable the run-once duration override to your namespace: USD oc label namespace <namespace> \ 1 runoncedurationoverrides.admission.runoncedurationoverride.openshift.io/enabled=true 1 Specify the namespace to enable the run-once duration override on. After you enable the run-once duration override on this namespace, future run-once pods that are created in this namespace will have their activeDeadlineSeconds field set to the override value from the Run Once Duration Override Operator. Existing pods in this namespace will also have their activeDeadlineSeconds value set when they are updated . Verification Create a test run-once pod in the namespace that you enabled the run-once duration override on: apiVersion: v1 kind: Pod metadata: name: example namespace: <namespace> 1 spec: restartPolicy: Never 2 containers: - name: busybox securityContext: allowPrivilegeEscalation: false capabilities: drop: ["ALL"] runAsNonRoot: true seccompProfile: type: "RuntimeDefault" image: busybox:1.25 command: - /bin/sh - -ec - | while sleep 5; do date; done 1 Replace <namespace> with the name of your namespace. 2 The restartPolicy must be Never or OnFailure to be a run-once pod. Verify that the pod has its activeDeadlineSeconds field set: USD oc get pods -n <namespace> -o yaml | grep activeDeadlineSeconds Example output activeDeadlineSeconds: 3600 2.11.3.3. Updating the run-once active deadline override value You can customize the override value that the Run Once Duration Override Operator applies to run-once pods. The predefined value is 3600 seconds, or 1 hour. Prerequisites You have access to the cluster with cluster-admin privileges. You have installed the Run Once Duration Override Operator. Procedure Log in to the OpenShift CLI. Edit the RunOnceDurationOverride resource: USD oc edit runoncedurationoverride cluster Update the activeDeadlineSeconds field: apiVersion: operator.openshift.io/v1 kind: RunOnceDurationOverride metadata: # ... spec: runOnceDurationOverride: spec: activeDeadlineSeconds: 1800 1 # ... 1 Set the activeDeadlineSeconds field to the desired value, in seconds. Save the file to apply the changes. Any future run-once pods created in namespaces where the run-once duration override is enabled will have their activeDeadlineSeconds field set to this new value. Existing run-once pods in these namespaces will receive this new value when they are updated. 2.11.4. Uninstalling the Run Once Duration Override Operator You can remove the Run Once Duration Override Operator from OpenShift Container Platform by uninstalling the Operator and removing its related resources. 2.11.4.1. Uninstalling the Run Once Duration Override Operator You can use the web console to uninstall the Run Once Duration Override Operator. Uninstalling the Run Once Duration Override Operator does not unset the activeDeadlineSeconds field for run-once pods, but it will no longer apply the override value to future run-once pods. Prerequisites You have access to the cluster with cluster-admin privileges. You have access to the OpenShift Container Platform web console. You have installed the Run Once Duration Override Operator. Procedure Log in to the OpenShift Container Platform web console. Navigate to Operators Installed Operators . Select openshift-run-once-duration-override-operator from the Project dropdown list. Delete the RunOnceDurationOverride instance. Click Run Once Duration Override Operator and select the Run Once Duration Override tab. Click the Options menu to the cluster entry and select Delete RunOnceDurationOverride . In the confirmation dialog, click Delete . Uninstall the Run Once Duration Override Operator Operator. Navigate to Operators Installed Operators . Click the Options menu to the Run Once Duration Override Operator entry and click Uninstall Operator . In the confirmation dialog, click Uninstall . 2.11.4.2. Uninstalling Run Once Duration Override Operator resources Optionally, after uninstalling the Run Once Duration Override Operator, you can remove its related resources from your cluster. Prerequisites You have access to the cluster with cluster-admin privileges. You have access to the OpenShift Container Platform web console. You have uninstalled the Run Once Duration Override Operator. Procedure Log in to the OpenShift Container Platform web console. Remove CRDs that were created when the Run Once Duration Override Operator was installed: Navigate to Administration CustomResourceDefinitions . Enter RunOnceDurationOverride in the Name field to filter the CRDs. Click the Options menu to the RunOnceDurationOverride CRD and select Delete CustomResourceDefinition . In the confirmation dialog, click Delete . Delete the openshift-run-once-duration-override-operator namespace. Navigate to Administration Namespaces . Enter openshift-run-once-duration-override-operator into the filter box. Click the Options menu to the openshift-run-once-duration-override-operator entry and select Delete Namespace . In the confirmation dialog, enter openshift-run-once-duration-override-operator and click Delete . Remove the run-once duration override label from the namespaces that it was enabled on. Navigate to Administration Namespaces . Select your namespace. Click Edit to the Labels field. Remove the runoncedurationoverrides.admission.runoncedurationoverride.openshift.io/enabled=true label and click Save .

Commands:
kind: Pod apiVersion: v1 metadata: name: example namespace: default selfLink: /api/v1/namespaces/default/pods/example uid: 5cc30063-0265780783bc resourceVersion: '165032' creationTimestamp: '2019-02-13T20:31:37Z' labels: app: hello-openshift 1 annotations: openshift.io/scc: anyuid spec: restartPolicy: Always 2 serviceAccountName: default imagePullSecrets: - name: default-dockercfg-5zrhb priority: 0 schedulerName: default-scheduler terminationGracePeriodSeconds: 30 nodeName: ip-10-0-140-16.us-east-2.compute.internal securityContext: 3 seLinuxOptions: level: 's0:c11,c10' containers: 4 - resources: {} terminationMessagePath: /dev/termination-log name: hello-openshift securityContext: capabilities: drop: - MKNOD procMount: Default ports: - containerPort: 8080 protocol: TCP imagePullPolicy: Always volumeMounts: 5 - name: default-token-wbqsl readOnly: true mountPath: /var/run/secrets/kubernetes.io/serviceaccount 6 terminationMessagePolicy: File image: registry.redhat.io/openshift4/ose-ogging-eventrouter:v4.3 7 serviceAccount: default 8 volumes: 9 - name: default-token-wbqsl secret: secretName: default-token-wbqsl defaultMode: 420 dnsPolicy: ClusterFirst status: phase: Pending conditions: - type: Initialized status: 'True' lastProbeTime: null lastTransitionTime: '2019-02-13T20:31:37Z' - type: Ready status: 'False' lastProbeTime: null lastTransitionTime: '2019-02-13T20:31:37Z' reason: ContainersNotReady message: 'containers with unready status: [hello-openshift]' - type: ContainersReady status: 'False' lastProbeTime: null lastTransitionTime: '2019-02-13T20:31:37Z' reason: ContainersNotReady message: 'containers with unready status: [hello-openshift]' - type: PodScheduled status: 'True' lastProbeTime: null lastTransitionTime: '2019-02-13T20:31:37Z' hostIP: 10.0.140.16 startTime: '2019-02-13T20:31:37Z' containerStatuses: - name: hello-openshift state: waiting: reason: ContainerCreating lastState: {} ready: false restartCount: 0 image: openshift/hello-openshift imageID: '' qosClass: BestEffort
oc project <project-name>
oc get pods
oc get pods
NAME READY STATUS RESTARTS AGE console-698d866b78-bnshf 1/1 Running 2 165m console-698d866b78-m87pm 1/1 Running 2 165m
oc get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE console-698d866b78-bnshf 1/1 Running 2 166m 10.128.0.24 ip-10-0-152-71.ec2.internal <none> console-698d866b78-m87pm 1/1 Running 2 166m 10.129.0.23 ip-10-0-173-237.ec2.internal <none>
oc adm top pods
oc adm top pods -n openshift-console
NAME CPU(cores) MEMORY(bytes) console-7f58c69899-q8c8k 0m 22Mi console-7f58c69899-xhbgg 0m 25Mi downloads-594fcccf94-bcxk8 3m 18Mi downloads-594fcccf94-kv4p6 2m 15Mi
oc adm top pod --selector=''
oc adm top pod --selector='name=my-pod'
oc logs -f <pod_name> -c <container_name>
oc logs ruby-58cd97df55-mww7r
oc logs -f ruby-57f7f4855b-znl92 -c ruby
oc logs <object_type>/<resource_name> 1
oc logs deployment/ruby
{ "kind": "Pod", "spec": { "containers": [ { "image": "openshift/hello-openshift", "name": "hello-openshift" } ] }, "apiVersion": "v1", "metadata": { "name": "iperf-slow", "annotations": { "kubernetes.io/ingress-bandwidth": "10M", "kubernetes.io/egress-bandwidth": "10M" } } }
oc create -f <file_or_dir_path>
oc get poddisruptionbudget --all-namespaces
NAMESPACE NAME MIN AVAILABLE MAX UNAVAILABLE ALLOWED DISRUPTIONS AGE openshift-apiserver openshift-apiserver-pdb N/A 1 1 121m openshift-cloud-controller-manager aws-cloud-controller-manager 1 N/A 1 125m openshift-cloud-credential-operator pod-identity-webhook 1 N/A 1 117m openshift-cluster-csi-drivers aws-ebs-csi-driver-controller-pdb N/A 1 1 121m openshift-cluster-storage-operator csi-snapshot-controller-pdb N/A 1 1 122m openshift-cluster-storage-operator csi-snapshot-webhook-pdb N/A 1 1 122m openshift-console console N/A 1 1 116m #
apiVersion: policy/v1 1 kind: PodDisruptionBudget metadata: name: my-pdb spec: minAvailable: 2 2 selector: 3 matchLabels: name: my-pod
apiVersion: policy/v1 1 kind: PodDisruptionBudget metadata: name: my-pdb spec: maxUnavailable: 25% 2 selector: 3 matchLabels: name: my-pod
oc create -f </path/to/file> -n <project_name>
apiVersion: policy/v1 kind: PodDisruptionBudget metadata: name: my-pdb spec: minAvailable: 2 selector: matchLabels: name: my-pod unhealthyPodEvictionPolicy: AlwaysAllow 1
oc create -f pod-disruption-budget.yaml
apiVersion: v1 kind: Pod metadata: name: my-pdb spec: template: metadata: name: critical-pod priorityClassName: system-cluster-critical 1
oc create -f <file-name>.yaml
oc autoscale deployment/image-registry --min=5 --max=7 --cpu-percent=75
horizontalpodautoscaler.autoscaling/image-registry autoscaled
apiVersion: autoscaling/v1 kind: HorizontalPodAutoscaler metadata: name: image-registry namespace: default spec: maxReplicas: 7 minReplicas: 3 scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: image-registry targetCPUUtilizationPercentage: 75 status: currentReplicas: 5 desiredReplicas: 0
oc get deployment image-registry
NAME REVISION DESIRED CURRENT TRIGGERED BY image-registry 1 5 5 config
type: Resource resource: name: cpu target: type: Utilization averageUtilization: 60
behavior: scaleDown: stabilizationWindowSeconds: 300
apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: hpa-resource-metrics-memory namespace: default spec: behavior: scaleDown: 1 policies: 2 - type: Pods 3 value: 4 4 periodSeconds: 60 5 - type: Percent value: 10 6 periodSeconds: 60 selectPolicy: Min 7 stabilizationWindowSeconds: 300 8 scaleUp: 9 policies: - type: Pods value: 5 10 periodSeconds: 70 - type: Percent value: 12 11 periodSeconds: 80 selectPolicy: Max stabilizationWindowSeconds: 0
apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: hpa-resource-metrics-memory namespace: default spec: minReplicas: 20 behavior: scaleDown: stabilizationWindowSeconds: 300 policies: - type: Pods value: 4 periodSeconds: 30 - type: Percent value: 10 periodSeconds: 60 selectPolicy: Max scaleUp: selectPolicy: Disabled
oc edit hpa hpa-resource-metrics-memory
apiVersion: autoscaling/v1 kind: HorizontalPodAutoscaler metadata: annotations: autoscaling.alpha.kubernetes.io/behavior: '{"ScaleUp":{"StabilizationWindowSeconds":0,"SelectPolicy":"Max","Policies":[{"Type":"Pods","Value":4,"PeriodSeconds":15},{"Type":"Percent","Value":100,"PeriodSeconds":15}]}, "ScaleDown":{"StabilizationWindowSeconds":300,"SelectPolicy":"Min","Policies":[{"Type":"Pods","Value":4,"PeriodSeconds":60},{"Type":"Percent","Value":10,"PeriodSeconds":60}]}}'
oc describe PodMetrics openshift-kube-scheduler-ip-10-0-135-131.ec2.internal
Name: openshift-kube-scheduler-ip-10-0-135-131.ec2.internal Namespace: openshift-kube-scheduler Labels: <none> Annotations: <none> API Version: metrics.k8s.io/v1beta1 Containers: Name: wait-for-host-port Usage: Memory: 0 Name: scheduler Usage: Cpu: 8m Memory: 45440Ki Kind: PodMetrics Metadata: Creation Timestamp: 2019-05-23T18:47:56Z Self Link: /apis/metrics.k8s.io/v1beta1/namespaces/openshift-kube-scheduler/pods/openshift-kube-scheduler-ip-10-0-135-131.ec2.internal Timestamp: 2019-05-23T18:47:56Z Window: 1m0s Events: <none>
oc autoscale <object_type>/<name> \ 1 --min <number> \ 2 --max <number> \ 3 --cpu-percent=<percent> 4
oc autoscale deployment/image-registry --min=5 --max=7 --cpu-percent=75
apiVersion: autoscaling/v2 1 kind: HorizontalPodAutoscaler metadata: name: cpu-autoscale 2 namespace: default spec: scaleTargetRef: apiVersion: apps/v1 3 kind: Deployment 4 name: example 5 minReplicas: 1 6 maxReplicas: 10 7 metrics: 8 - type: Resource resource: name: cpu 9 target: type: AverageValue 10 averageValue: 500m 11
oc create -f <file-name>.yaml
oc get hpa cpu-autoscale
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE cpu-autoscale Deployment/example 173m/500m 1 10 1 20m
oc describe PodMetrics openshift-kube-scheduler-ip-10-0-129-223.compute.internal -n openshift-kube-scheduler
Name: openshift-kube-scheduler-ip-10-0-129-223.compute.internal Namespace: openshift-kube-scheduler Labels: <none> Annotations: <none> API Version: metrics.k8s.io/v1beta1 Containers: Name: wait-for-host-port Usage: Cpu: 0 Memory: 0 Name: scheduler Usage: Cpu: 8m Memory: 45440Ki Kind: PodMetrics Metadata: Creation Timestamp: 2020-02-14T22:21:14Z Self Link: /apis/metrics.k8s.io/v1beta1/namespaces/openshift-kube-scheduler/pods/openshift-kube-scheduler-ip-10-0-129-223.compute.internal Timestamp: 2020-02-14T22:21:14Z Window: 5m0s Events: <none>
apiVersion: autoscaling/v2 1 kind: HorizontalPodAutoscaler metadata: name: hpa-resource-metrics-memory 2 namespace: default spec: scaleTargetRef: apiVersion: apps/v1 3 kind: Deployment 4 name: example 5 minReplicas: 1 6 maxReplicas: 10 7 metrics: 8 - type: Resource resource: name: memory 9 target: type: AverageValue 10 averageValue: 500Mi 11 behavior: 12 scaleDown: stabilizationWindowSeconds: 300 policies: - type: Pods value: 4 periodSeconds: 60 - type: Percent value: 10 periodSeconds: 60 selectPolicy: Max
apiVersion: autoscaling/v2 1 kind: HorizontalPodAutoscaler metadata: name: memory-autoscale 2 namespace: default spec: scaleTargetRef: apiVersion: apps/v1 3 kind: Deployment 4 name: example 5 minReplicas: 1 6 maxReplicas: 10 7 metrics: 8 - type: Resource resource: name: memory 9 target: type: Utilization 10 averageUtilization: 50 11 behavior: 12 scaleUp: stabilizationWindowSeconds: 180 policies: - type: Pods value: 6 periodSeconds: 120 - type: Percent value: 10 periodSeconds: 120 selectPolicy: Max
oc create -f <file-name>.yaml
oc create -f hpa.yaml
horizontalpodautoscaler.autoscaling/hpa-resource-metrics-memory created
oc get hpa hpa-resource-metrics-memory
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE hpa-resource-metrics-memory Deployment/example 2441216/500Mi 1 10 1 20m
oc describe hpa hpa-resource-metrics-memory
Name: hpa-resource-metrics-memory Namespace: default Labels: <none> Annotations: <none> CreationTimestamp: Wed, 04 Mar 2020 16:31:37 +0530 Reference: Deployment/example Metrics: ( current / target ) resource memory on pods: 2441216 / 500Mi Min replicas: 1 Max replicas: 10 ReplicationController pods: 1 current / 1 desired Conditions: Type Status Reason Message ---- ------ ------ ------- AbleToScale True ReadyForNewScale recommended size matches current size ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from memory resource ScalingLimited False DesiredWithinRange the desired count is within the acceptable range Events: Type Reason Age From Message ---- ------ ---- ---- ------- Normal SuccessfulRescale 6m34s horizontal-pod-autoscaler New size: 1; reason: All metrics below target
oc describe hpa cm-test
Name: cm-test Namespace: prom Labels: <none> Annotations: <none> CreationTimestamp: Fri, 16 Jun 2017 18:09:22 +0000 Reference: ReplicationController/cm-test Metrics: ( current / target ) "http_requests" on pods: 66m / 500m Min replicas: 1 Max replicas: 4 ReplicationController pods: 1 current / 1 desired Conditions: 1 Type Status Reason Message ---- ------ ------ ------- AbleToScale True ReadyForNewScale the last scale time was sufficiently old as to warrant a new scale ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from pods metric http_request ScalingLimited False DesiredWithinRange the desired replica count is within the acceptable range Events:
Conditions: Type Status Reason Message ---- ------ ------ ------- AbleToScale False FailedGetScale the HPA controller was unable to get the target's current scale: no matches for kind "ReplicationController" in group "apps" Events: Type Reason Age From Message ---- ------ ---- ---- ------- Warning FailedGetScale 6s (x3 over 36s) horizontal-pod-autoscaler no matches for kind "ReplicationController" in group "apps"
Conditions: Type Status Reason Message ---- ------ ------ ------- AbleToScale True SucceededGetScale the HPA controller was able to get the target's current scale ScalingActive False FailedGetResourceMetric the HPA was unable to compute the replica count: failed to get cpu utilization: unable to get metrics for resource cpu: no metrics returned from resource metrics API
Conditions: Type Status Reason Message ---- ------ ------ ------- AbleToScale True ReadyForNewScale the last scale time was sufficiently old as to warrant a new scale ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from pods metric http_request ScalingLimited False DesiredWithinRange the desired replica count is within the acceptable range
oc describe PodMetrics openshift-kube-scheduler-ip-10-0-135-131.ec2.internal
Name: openshift-kube-scheduler-ip-10-0-135-131.ec2.internal Namespace: openshift-kube-scheduler Labels: <none> Annotations: <none> API Version: metrics.k8s.io/v1beta1 Containers: Name: wait-for-host-port Usage: Memory: 0 Name: scheduler Usage: Cpu: 8m Memory: 45440Ki Kind: PodMetrics Metadata: Creation Timestamp: 2019-05-23T18:47:56Z Self Link: /apis/metrics.k8s.io/v1beta1/namespaces/openshift-kube-scheduler/pods/openshift-kube-scheduler-ip-10-0-135-131.ec2.internal Timestamp: 2019-05-23T18:47:56Z Window: 1m0s Events: <none>
oc describe hpa <pod-name>
oc describe hpa cm-test
Name: cm-test Namespace: prom Labels: <none> Annotations: <none> CreationTimestamp: Fri, 16 Jun 2017 18:09:22 +0000 Reference: ReplicationController/cm-test Metrics: ( current / target ) "http_requests" on pods: 66m / 500m Min replicas: 1 Max replicas: 4 ReplicationController pods: 1 current / 1 desired Conditions: 1 Type Status Reason Message ---- ------ ------ ------- AbleToScale True ReadyForNewScale the last scale time was sufficiently old as to warrant a new scale ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from pods metric http_request ScalingLimited False DesiredWithinRange the desired replica count is within the acceptable range
oc get all -n openshift-vertical-pod-autoscaler
NAME READY STATUS RESTARTS AGE pod/vertical-pod-autoscaler-operator-85b4569c47-2gmhc 1/1 Running 0 3m13s pod/vpa-admission-plugin-default-67644fc87f-xq7k9 1/1 Running 0 2m56s pod/vpa-recommender-default-7c54764b59-8gckt 1/1 Running 0 2m56s pod/vpa-updater-default-7f6cc87858-47vw9 1/1 Running 0 2m56s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/vpa-webhook ClusterIP 172.30.53.206 <none> 443/TCP 2m56s NAME READY UP-TO-DATE AVAILABLE AGE deployment.apps/vertical-pod-autoscaler-operator 1/1 1 1 3m13s deployment.apps/vpa-admission-plugin-default 1/1 1 1 2m56s deployment.apps/vpa-recommender-default 1/1 1 1 2m56s deployment.apps/vpa-updater-default 1/1 1 1 2m56s NAME DESIRED CURRENT READY AGE replicaset.apps/vertical-pod-autoscaler-operator-85b4569c47 1 1 1 3m13s replicaset.apps/vpa-admission-plugin-default-67644fc87f 1 1 1 2m56s replicaset.apps/vpa-recommender-default-7c54764b59 1 1 1 2m56s replicaset.apps/vpa-updater-default-7f6cc87858 1 1 1 2m56s
resources: limits: cpu: 1 memory: 500Mi requests: cpu: 500m memory: 100Mi
resources: limits: cpu: 50m memory: 1250Mi requests: cpu: 25m memory: 262144k
oc get vpa <vpa-name> --output yaml
status: recommendation: containerRecommendations: - containerName: frontend lowerBound: cpu: 25m memory: 262144k target: cpu: 25m memory: 262144k uncappedTarget: cpu: 25m memory: 262144k upperBound: cpu: 262m memory: "274357142" - containerName: backend lowerBound: cpu: 12m memory: 131072k target: cpu: 12m memory: 131072k uncappedTarget: cpu: 12m memory: 131072k upperBound: cpu: 476m memory: "498558823"
apiVersion: autoscaling.openshift.io/v1 kind: VerticalPodAutoscalerController metadata: creationTimestamp: "2021-04-21T19:29:49Z" generation: 2 name: default namespace: openshift-vertical-pod-autoscaler resourceVersion: "142172" uid: 180e17e9-03cc-427f-9955-3b4d7aeb2d59 spec: minReplicas: 3 1 podMinCPUMillicores: 25 podMinMemoryMb: 250 recommendationOnly: false safetyMarginFraction: 0.15
apiVersion: autoscaling.k8s.io/v1 kind: VerticalPodAutoscaler metadata: name: vpa-recommender spec: targetRef: apiVersion: "apps/v1" kind: Deployment 1 name: frontend 2 updatePolicy: updateMode: "Auto" 3
apiVersion: autoscaling.k8s.io/v1 kind: VerticalPodAutoscaler metadata: name: vpa-recommender spec: targetRef: apiVersion: "apps/v1" kind: Deployment 1 name: frontend 2 updatePolicy: updateMode: "Initial" 3
apiVersion: autoscaling.k8s.io/v1 kind: VerticalPodAutoscaler metadata: name: vpa-recommender spec: targetRef: apiVersion: "apps/v1" kind: Deployment 1 name: frontend 2 updatePolicy: updateMode: "Off" 3
oc get vpa <vpa-name> --output yaml
apiVersion: autoscaling.k8s.io/v1 kind: VerticalPodAutoscaler metadata: name: vpa-recommender spec: targetRef: apiVersion: "apps/v1" kind: Deployment 1 name: frontend 2 updatePolicy: updateMode: "Auto" 3 resourcePolicy: 4 containerPolicies: - containerName: my-opt-sidecar mode: "Off"
spec: containers: - name: frontend resources: limits: cpu: 1 memory: 500Mi requests: cpu: 500m memory: 100Mi - name: backend resources: limits: cpu: "1" memory: 500Mi requests: cpu: 500m memory: 100Mi
spec: containers: name: frontend resources: limits: cpu: 50m memory: 1250Mi requests: cpu: 25m memory: 262144k name: backend resources: limits: cpu: "1" memory: 500Mi requests: cpu: 500m memory: 100Mi
apiVersion: v1 1 kind: ServiceAccount metadata: name: alt-vpa-recommender-sa namespace: <namespace_name> --- apiVersion: rbac.authorization.k8s.io/v1 2 kind: ClusterRoleBinding metadata: name: system:example-metrics-reader roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: system:metrics-reader subjects: - kind: ServiceAccount name: alt-vpa-recommender-sa namespace: <namespace_name> --- apiVersion: rbac.authorization.k8s.io/v1 3 kind: ClusterRoleBinding metadata: name: system:example-vpa-actor roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: system:vpa-actor subjects: - kind: ServiceAccount name: alt-vpa-recommender-sa namespace: <namespace_name> --- apiVersion: rbac.authorization.k8s.io/v1 4 kind: ClusterRoleBinding metadata: name: system:example-vpa-target-reader-binding roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: system:vpa-target-reader subjects: - kind: ServiceAccount name: alt-vpa-recommender-sa namespace: <namespace_name>
apiVersion: apps/v1 kind: Deployment metadata: name: alt-vpa-recommender namespace: <namespace_name> spec: replicas: 1 selector: matchLabels: app: alt-vpa-recommender template: metadata: labels: app: alt-vpa-recommender spec: containers: 1 - name: recommender image: quay.io/example/alt-recommender:latest 2 imagePullPolicy: Always resources: limits: cpu: 200m memory: 1000Mi requests: cpu: 50m memory: 500Mi ports: - name: prometheus containerPort: 8942 securityContext: allowPrivilegeEscalation: false capabilities: drop: - ALL seccompProfile: type: RuntimeDefault serviceAccountName: alt-vpa-recommender-sa 3 securityContext: runAsNonRoot: true
oc get pods
NAME READY STATUS RESTARTS AGE frontend-845d5478d-558zf 1/1 Running 0 4m25s frontend-845d5478d-7z9gx 1/1 Running 0 4m25s frontend-845d5478d-b7l4j 1/1 Running 0 4m25s vpa-alt-recommender-55878867f9-6tp5v 1/1 Running 0 9s
apiVersion: autoscaling.k8s.io/v1 kind: VerticalPodAutoscaler metadata: name: vpa-recommender namespace: <namespace_name> spec: recommenders: - name: alt-vpa-recommender 1 targetRef: apiVersion: "apps/v1" kind: Deployment 2 name: frontend
apiVersion: autoscaling.k8s.io/v1 kind: VerticalPodAutoscaler metadata: name: vpa-recommender spec: targetRef: apiVersion: "apps/v1" kind: Deployment 1 name: frontend 2 updatePolicy: updateMode: "Auto" 3 resourcePolicy: 4 containerPolicies: - containerName: my-opt-sidecar mode: "Off" recommenders: 5 - name: my-recommender
oc create -f <file-name>.yaml
oc get vpa <vpa-name> --output yaml
status: recommendation: containerRecommendations: - containerName: frontend lowerBound: 1 cpu: 25m memory: 262144k target: 2 cpu: 25m memory: 262144k uncappedTarget: 3 cpu: 25m memory: 262144k upperBound: 4 cpu: 262m memory: "274357142" - containerName: backend lowerBound: cpu: 12m memory: 131072k target: cpu: 12m memory: 131072k uncappedTarget: cpu: 12m memory: 131072k upperBound: cpu: 476m memory: "498558823"
oc delete namespace openshift-vertical-pod-autoscaler
oc delete crd verticalpodautoscalercheckpoints.autoscaling.k8s.io
oc delete crd verticalpodautoscalercontrollers.autoscaling.openshift.io
oc delete crd verticalpodautoscalers.autoscaling.k8s.io
oc delete MutatingWebhookConfiguration vpa-webhook-config
oc delete operator/vertical-pod-autoscaler.openshift-vertical-pod-autoscaler
apiVersion: v1 kind: Secret metadata: name: test-secret namespace: my-namespace type: Opaque 1 data: 2 username: <username> 3 password: <password> stringData: 4 hostname: myapp.mydomain.com 5
apiVersion: v1 kind: Secret metadata: name: test-secret type: Opaque 1 data: 2 username: <username> password: <password> stringData: 3 hostname: myapp.mydomain.com secret.properties: | property1=valueA property2=valueB
apiVersion: v1 kind: ServiceAccount secrets: - name: test-secret
apiVersion: v1 kind: Pod metadata: name: secret-example-pod spec: containers: - name: secret-test-container image: busybox command: [ "/bin/sh", "-c", "cat /etc/secret-volume/*" ] volumeMounts: 1 - name: secret-volume mountPath: /etc/secret-volume 2 readOnly: true 3 volumes: - name: secret-volume secret: secretName: test-secret 4 restartPolicy: Never
apiVersion: v1 kind: Pod metadata: name: secret-example-pod spec: containers: - name: secret-test-container image: busybox command: [ "/bin/sh", "-c", "export" ] env: - name: TEST_SECRET_USERNAME_ENV_VAR valueFrom: secretKeyRef: 1 name: test-secret key: username restartPolicy: Never
apiVersion: build.openshift.io/v1 kind: BuildConfig metadata: name: secret-example-bc spec: strategy: sourceStrategy: env: - name: TEST_SECRET_USERNAME_ENV_VAR valueFrom: secretKeyRef: 1 name: test-secret key: username from: kind: ImageStreamTag namespace: openshift name: 'cli:latest'
apiVersion: v1 kind: Secret metadata: name: mysecret type: Opaque 1 data: username: <username> password: <password>
oc create -f <filename>.yaml
apiVersion: v1 kind: Secret metadata: name: secret-sa-sample annotations: kubernetes.io/service-account.name: "sa-name" 1 type: kubernetes.io/service-account-token 2
oc create -f <filename>.yaml
apiVersion: v1 kind: Secret metadata: name: secret-basic-auth type: kubernetes.io/basic-auth 1 data: stringData: 2 username: admin password: <password>
oc create -f <filename>.yaml
apiVersion: v1 kind: Secret metadata: name: secret-ssh-auth type: kubernetes.io/ssh-auth 1 data: ssh-privatekey: | 2 MIIEpQIBAAKCAQEAulqb/Y
oc create -f <filename>.yaml
apiVersion: v1 kind: Secret metadata: name: secret-docker-cfg namespace: my-project type: kubernetes.io/dockerconfig 1 data: .dockerconfig:bm5ubm5ubm5ubm5ubm5ubm5ubm5ubmdnZ2dnZ2dnZ2dnZ2dnZ2dnZ2cgYXV0aCBrZXlzCg== 2
apiVersion: v1 kind: Secret metadata: name: secret-docker-json namespace: my-project type: kubernetes.io/dockerconfig 1 data: .dockerconfigjson:bm5ubm5ubm5ubm5ubm5ubm5ubm5ubmdnZ2dnZ2dnZ2dnZ2dnZ2dnZ2cgYXV0aCBrZXlzCg== 2
oc create -f <filename>.yaml
apiVersion: v1 kind: Secret metadata: name: example namespace: <namespace> type: Opaque 1 data: username: <base64 encoded username> password: <base64 encoded password> stringData: 2 hostname: myapp.mydomain.com
oc create sa <service_account_name> -n <your_namespace>
apiVersion: v1 kind: Secret metadata: name: <secret_name> 1 annotations: kubernetes.io/service-account.name: "sa-name" 2 type: kubernetes.io/service-account-token 3
oc apply -f service-account-token-secret.yaml
oc get secret <sa_token_secret> -o jsonpath='{.data.token}' | base64 --decode 1
ayJhbGciOiJSUzI1NiIsImtpZCI6IklOb2dtck1qZ3hCSWpoNnh5YnZhSE9QMkk3YnRZMVZoclFfQTZfRFp1YlUifQ.eyJpc3MiOiJrdWJlcm5ldGVzL3NlcnZpY2VhY2NvdW50Iiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9uYW1lc3BhY2UiOiJkZWZhdWx0Iiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9zZWNyZXQubmFtZSI6ImJ1aWxkZXItdG9rZW4tdHZrbnIiLCJrdWJlcm5ldGVzLmlvL3NlcnZpY2VhY2NvdW50L3NlcnZpY2UtYWNjb3VudC5uYW1lIjoiYnVpbGRlciIsImt1YmVybmV0ZXMuaW8vc2VydmljZWFjY291bnQvc2VydmljZS1hY2NvdW50LnVpZCI6IjNmZGU2MGZmLTA1NGYtNDkyZi04YzhjLTNlZjE0NDk3MmFmNyIsInN1YiI6InN5c3RlbTpzZXJ2aWNlYWNjb3VudDpkZWZhdWx0OmJ1aWxkZXIifQ.OmqFTDuMHC_lYvvEUrjr1x453hlEEHYcxS9VKSzmRkP1SiVZWPNPkTWlfNRp6bIUZD3U6aN3N7dMSN0eI5hu36xPgpKTdvuckKLTCnelMx6cxOdAbrcw1mCmOClNscwjS1KO1kzMtYnnq8rXHiMJELsNlhnRyyIXRTtNBsy4t64T3283s3SLsancyx0gy0ujx-Ch3uKAKdZi5iT-I8jnnQ-ds5THDs2h65RJhgglQEmSxpHrLGZFmyHAQI-_SjvmHZPXEc482x3SkaQHNLqpmrpJorNqh1M8ZHKzlujhZgVooMvJmWPXTb2vnvi3DGn2XI-hZxl1yD2yGH1RBpYUHA
curl -X GET <openshift_cluster_api> --header "Authorization: Bearer <token>" 1 2
apiVersion: v1 kind: Service metadata: name: registry annotations: service.beta.openshift.io/serving-cert-secret-name: registry-cert 1
kind: Service apiVersion: v1 metadata: name: my-service annotations: service.beta.openshift.io/serving-cert-secret-name: my-cert 1 spec: selector: app: MyApp ports: - protocol: TCP port: 80 targetPort: 9376
oc create -f <file-name>.yaml
oc get secrets
NAME TYPE DATA AGE my-cert kubernetes.io/tls 2 9m
oc describe secret my-cert
Name: my-cert Namespace: openshift-console Labels: <none> Annotations: service.beta.openshift.io/expiry: 2023-03-08T23:22:40Z service.beta.openshift.io/originating-service-name: my-service service.beta.openshift.io/originating-service-uid: 640f0ec3-afc2-4380-bf31-a8c784846a11 service.beta.openshift.io/expiry: 2023-03-08T23:22:40Z Type: kubernetes.io/tls Data ==== tls.key: 1679 bytes tls.crt: 2595 bytes
apiVersion: v1 kind: Pod metadata: name: my-service-pod spec: containers: - name: mypod image: redis volumeMounts: - name: my-container mountPath: "/etc/my-path" volumes: - name: my-volume secret: secretName: my-cert items: - key: username path: my-group/my-username mode: 511
secret/ssl-key references serviceUID 62ad25ca-d703-11e6-9d6f-0e9c0057b608, which does not match 77b6dd80-d716-11e6-9d6f-0e9c0057b60
oc delete secret <secret_name>
oc annotate service <service_name> service.beta.openshift.io/serving-cert-generation-error-
oc annotate service <service_name> service.beta.openshift.io/serving-cert-generation-error-num-
kind: ConfigMap apiVersion: v1 metadata: creationTimestamp: 2016-02-18T19:14:38Z name: example-config namespace: my-namespace data: 1 example.property.1: hello example.property.2: world example.property.file: |- property.1=value-1 property.2=value-2 property.3=value-3 binaryData: bar: L3Jvb3QvMTAw 2
oc create configmap <configmap_name> [options]
oc create configmap game-config --from-file=example-files/
oc describe configmaps game-config
Name: game-config Namespace: default Labels: <none> Annotations: <none> Data game.properties: 158 bytes ui.properties: 83 bytes
cat example-files/game.properties
enemies=aliens lives=3 enemies.cheat=true enemies.cheat.level=noGoodRotten secret.code.passphrase=UUDDLRLRBABAS secret.code.allowed=true secret.code.lives=30
cat example-files/ui.properties
color.good=purple color.bad=yellow allow.textmode=true how.nice.to.look=fairlyNice
oc create configmap game-config --from-file=example-files/
oc get configmaps game-config -o yaml
apiVersion: v1 data: game.properties: |- enemies=aliens lives=3 enemies.cheat=true enemies.cheat.level=noGoodRotten secret.code.passphrase=UUDDLRLRBABAS secret.code.allowed=true secret.code.lives=30 ui.properties: | color.good=purple color.bad=yellow allow.textmode=true how.nice.to.look=fairlyNice kind: ConfigMap metadata: creationTimestamp: 2016-02-18T18:34:05Z name: game-config namespace: default resourceVersion: "407" selflink: /api/v1/namespaces/default/configmaps/game-config uid: 30944725-d66e-11e5-8cd0-68f728db1985
oc create configmap game-config-3 --from-file=game-special-key=example-files/game.properties
cat example-files/game.properties
enemies=aliens lives=3 enemies.cheat=true enemies.cheat.level=noGoodRotten secret.code.passphrase=UUDDLRLRBABAS secret.code.allowed=true secret.code.lives=30
cat example-files/ui.properties
color.good=purple color.bad=yellow allow.textmode=true how.nice.to.look=fairlyNice
oc create configmap game-config-2 --from-file=example-files/game.properties --from-file=example-files/ui.properties
oc create configmap game-config-3 --from-file=game-special-key=example-files/game.properties
oc get configmaps game-config-2 -o yaml
apiVersion: v1 data: game.properties: |- enemies=aliens lives=3 enemies.cheat=true enemies.cheat.level=noGoodRotten secret.code.passphrase=UUDDLRLRBABAS secret.code.allowed=true secret.code.lives=30 ui.properties: | color.good=purple color.bad=yellow allow.textmode=true how.nice.to.look=fairlyNice kind: ConfigMap metadata: creationTimestamp: 2016-02-18T18:52:05Z name: game-config-2 namespace: default resourceVersion: "516" selflink: /api/v1/namespaces/default/configmaps/game-config-2 uid: b4952dc3-d670-11e5-8cd0-68f728db1985
oc get configmaps game-config-3 -o yaml
apiVersion: v1 data: game-special-key: |- 1 enemies=aliens lives=3 enemies.cheat=true enemies.cheat.level=noGoodRotten secret.code.passphrase=UUDDLRLRBABAS secret.code.allowed=true secret.code.lives=30 kind: ConfigMap metadata: creationTimestamp: 2016-02-18T18:54:22Z name: game-config-3 namespace: default resourceVersion: "530" selflink: /api/v1/namespaces/default/configmaps/game-config-3 uid: 05f8da22-d671-11e5-8cd0-68f728db1985
oc create configmap special-config --from-literal=special.how=very --from-literal=special.type=charm
oc get configmaps special-config -o yaml
apiVersion: v1 data: special.how: very special.type: charm kind: ConfigMap metadata: creationTimestamp: 2016-02-18T19:14:38Z name: special-config namespace: default resourceVersion: "651" selflink: /api/v1/namespaces/default/configmaps/special-config uid: dadce046-d673-11e5-8cd0-68f728db1985
apiVersion: v1 kind: ConfigMap metadata: name: special-config 1 namespace: default 2 data: special.how: very 3 special.type: charm 4
apiVersion: v1 kind: ConfigMap metadata: name: env-config 1 namespace: default data: log_level: INFO 2
apiVersion: v1 kind: Pod metadata: name: dapi-test-pod spec: containers: - name: test-container image: gcr.io/google_containers/busybox command: [ "/bin/sh", "-c", "env" ] env: 1 - name: SPECIAL_LEVEL_KEY 2 valueFrom: configMapKeyRef: name: special-config 3 key: special.how 4 - name: SPECIAL_TYPE_KEY valueFrom: configMapKeyRef: name: special-config 5 key: special.type 6 optional: true 7 envFrom: 8 - configMapRef: name: env-config 9 restartPolicy: Never
SPECIAL_LEVEL_KEY=very log_level=INFO
apiVersion: v1 kind: ConfigMap metadata: name: special-config namespace: default data: special.how: very special.type: charm
apiVersion: v1 kind: Pod metadata: name: dapi-test-pod spec: containers: - name: test-container image: gcr.io/google_containers/busybox command: [ "/bin/sh", "-c", "echo USD(SPECIAL_LEVEL_KEY) USD(SPECIAL_TYPE_KEY)" ] 1 env: - name: SPECIAL_LEVEL_KEY valueFrom: configMapKeyRef: name: special-config key: special.how - name: SPECIAL_TYPE_KEY valueFrom: configMapKeyRef: name: special-config key: special.type restartPolicy: Never
very charm
apiVersion: v1 kind: ConfigMap metadata: name: special-config namespace: default data: special.how: very special.type: charm
apiVersion: v1 kind: Pod metadata: name: dapi-test-pod spec: containers: - name: test-container image: gcr.io/google_containers/busybox command: [ "/bin/sh", "-c", "cat", "/etc/config/special.how" ] volumeMounts: - name: config-volume mountPath: /etc/config volumes: - name: config-volume configMap: name: special-config 1 restartPolicy: Never
very
apiVersion: v1 kind: Pod metadata: name: dapi-test-pod spec: containers: - name: test-container image: gcr.io/google_containers/busybox command: [ "/bin/sh", "-c", "cat", "/etc/config/path/to/special-key" ] volumeMounts: - name: config-volume mountPath: /etc/config volumes: - name: config-volume configMap: name: special-config items: - key: special.how path: path/to/special-key 1 restartPolicy: Never
very
service DevicePlugin { // GetDevicePluginOptions returns options to be communicated with Device // Manager rpc GetDevicePluginOptions(Empty) returns (DevicePluginOptions) {} // ListAndWatch returns a stream of List of Devices // Whenever a Device state change or a Device disappears, ListAndWatch // returns the new list rpc ListAndWatch(Empty) returns (stream ListAndWatchResponse) {} // Allocate is called during container creation so that the Device // Plug-in can run device specific operations and instruct Kubelet // of the steps to make the Device available in the container rpc Allocate(AllocateRequest) returns (AllocateResponse) {} // PreStartcontainer is called, if indicated by Device Plug-in during // registration phase, before each container start. Device plug-in // can run device specific operations such as resetting the device // before making devices available to the container rpc PreStartcontainer(PreStartcontainerRequest) returns (PreStartcontainerResponse) {} }
oc describe machineconfig <name>
oc describe machineconfig 00-worker
Name: 00-worker Namespace: Labels: machineconfiguration.openshift.io/role=worker 1
apiVersion: machineconfiguration.openshift.io/v1 kind: KubeletConfig metadata: name: devicemgr 1 spec: machineConfigPoolSelector: matchLabels: machineconfiguration.openshift.io: devicemgr 2 kubeletConfig: feature-gates: - DevicePlugins=true 3
oc create -f devicemgr.yaml
kubeletconfig.machineconfiguration.openshift.io/devicemgr created
oc get priorityclasses
NAME VALUE GLOBAL-DEFAULT AGE system-node-critical 2000001000 false 72m system-cluster-critical 2000000000 false 72m openshift-user-critical 1000000000 false 3d13h cluster-logging 1000000 false 29s
apiVersion: scheduling.k8s.io/v1 kind: PriorityClass metadata: name: high-priority 1 value: 1000000 2 preemptionPolicy: PreemptLowerPriority 3 globalDefault: false 4 description: "This priority class should be used for XYZ service pods only." 5
oc create -f <file-name>.yaml
apiVersion: v1 kind: Pod metadata: name: nginx labels: env: test spec: containers: - name: nginx image: nginx imagePullPolicy: IfNotPresent priorityClassName: high-priority 1
oc create -f <file-name>.yaml
oc describe pod router-default-66d5cf9464-7pwkc
kind: Pod apiVersion: v1 metadata: Name: router-default-66d5cf9464-7pwkc Namespace: openshift-ingress Controlled By: ReplicaSet/router-default-66d5cf9464
apiVersion: v1 kind: Pod metadata: name: router-default-66d5cf9464-7pwkc ownerReferences: - apiVersion: apps/v1 kind: ReplicaSet name: router-default-66d5cf9464 uid: d81dd094-da26-11e9-a48a-128e7edf0312 controller: true blockOwnerDeletion: true
oc patch MachineSet <name> --type='json' -p='[{"op":"add","path":"/spec/template/spec/metadata/labels", "value":{"<key>"="<value>","<key>"="<value>"}}]' -n openshift-machine-api
oc patch MachineSet abc612-msrtw-worker-us-east-1c --type='json' -p='[{"op":"add","path":"/spec/template/spec/metadata/labels", "value":{"type":"user-node","region":"east"}}]' -n openshift-machine-api
apiVersion: machine.openshift.io/v1beta1 kind: MachineSet metadata: name: xf2bd-infra-us-east-2a namespace: openshift-machine-api spec: template: spec: metadata: labels: region: "east" type: "user-node"
oc edit MachineSet abc612-msrtw-worker-us-east-1c -n openshift-machine-api
apiVersion: machine.openshift.io/v1beta1 kind: MachineSet spec: template: metadata: spec: metadata: labels: region: east type: user-node
oc label nodes <name> <key>=<value>
oc label nodes ip-10-0-142-25.ec2.internal type=user-node region=east
kind: Node apiVersion: v1 metadata: name: hello-node-6fbccf8d9 labels: type: "user-node" region: "east"
oc get nodes -l type=user-node,region=east
NAME STATUS ROLES AGE VERSION ip-10-0-142-25.ec2.internal Ready worker 17m v1.26.0
kind: ReplicaSet apiVersion: apps/v1 metadata: name: hello-node-6fbccf8d9 spec: template: metadata: creationTimestamp: null labels: ingresscontroller.operator.openshift.io/deployment-ingresscontroller: default pod-template-hash: 66d5cf9464 spec: nodeSelector: kubernetes.io/os: linux node-role.kubernetes.io/worker: '' type: user-node 1
apiVersion: v1 kind: Pod metadata: name: hello-node-6fbccf8d9 spec: nodeSelector: region: east type: user-node
oc get pods -n openshift-run-once-duration-override-operator
NAME READY STATUS RESTARTS AGE run-once-duration-override-operator-7b88c676f6-lcxgc 1/1 Running 0 7m46s runoncedurationoverride-62blp 1/1 Running 0 41s runoncedurationoverride-h8h8b 1/1 Running 0 41s runoncedurationoverride-tdsqk 1/1 Running 0 41s
oc label namespace <namespace> \ 1 runoncedurationoverrides.admission.runoncedurationoverride.openshift.io/enabled=true
apiVersion: v1 kind: Pod metadata: name: example namespace: <namespace> 1 spec: restartPolicy: Never 2 containers: - name: busybox securityContext: allowPrivilegeEscalation: false capabilities: drop: ["ALL"] runAsNonRoot: true seccompProfile: type: "RuntimeDefault" image: busybox:1.25 command: - /bin/sh - -ec - | while sleep 5; do date; done
oc get pods -n <namespace> -o yaml | grep activeDeadlineSeconds
activeDeadlineSeconds: 3600
oc edit runoncedurationoverride cluster
apiVersion: operator.openshift.io/v1 kind: RunOnceDurationOverride metadata: spec: runOnceDurationOverride: spec: activeDeadlineSeconds: 1800 1