Chapter 16. Scheduling

Pod scheduling is an internal process that determines placement of new pods onto nodes within the cluster.

The scheduler code has a clean separation that watches new pods as they get created and identifies the most suitable node to host them. It then creates bindings (pod to node bindings) for the pods using the master API.

OpenShift Container Platform comes with a default scheduler that serves the needs of most users. The default scheduler uses both inherent and customizable tools to determine the best fit for a pod.

For information on how the default scheduler determines pod placement and available customizable parameters, see Default Scheduling.

In situations where you might want more control over where new pods are placed, the OpenShift Container Platform advanced scheduling features allow you to configure a pod so that the pod is required to (or has a preference to) run on a particular node or alongside a specific pod. Advanced scheduling also allows you to prevent pods from being placed on a node or with another pod.

For information about advanced scheduling, see Advanced Scheduling.

OpenShift Container Platform also allows you to use your own or third-party schedulers by editing the pod specification.

For more information, see Custom Schedulers.

The default OpenShift Container Platform pod scheduler is responsible for determining placement of new pods onto nodes within the cluster. It reads data from the pod and tries to find a node that is a good fit based on configured policies. It is completely independent and exists as a standalone/pluggable solution. It does not modify the pod and just creates a binding for the pod that ties the pod to the particular node.

The existing generic scheduler is the default platform-provided scheduler engine that selects a node to host the pod in a three-step operation:

The available nodes are filtered based on the constraints or requirements specified. This is done by running each node through the list of filter functions called predicates.

The selection of the predicate and priorities defines the policy for the scheduler.

The scheduler configuration file is a JSON file that specifies the predicates and priorities the scheduler will consider.

In the absence of the scheduler policy file, the default configuration file, /etc/origin/master/scheduler.json, gets applied.

{
    "apiVersion": "v1",
    "kind": "Policy",
    "predicates": [
        {
            "name": "NoVolumeZoneConflict"
        },
        {
            "name": "MaxEBSVolumeCount"
        },
        {
            "name": "MaxGCEPDVolumeCount"
        },
        {
            "name": "MaxAzureDiskVolumeCount"
        },
        {
            "name": "MatchInterPodAffinity"
        },
        {
            "name": "NoDiskConflict"
        },
        {
            "name": "GeneralPredicates"
        },
        {
            "name": "PodToleratesNodeTaints"
        },
        {
            "argument": {
                "serviceAffinity": {
                    "labels": [
                        "region"
                    ]
                }
            },
            "name": "Region"

         }
    ],
    "priorities": [
        {
            "name": "SelectorSpreadPriority",
            "weight": 1
        },
        {
            "name": "InterPodAffinityPriority",
            "weight": 1
        },
        {
            "name": "LeastRequestedPriority",
            "weight": 1
        },
        {
            "name": "BalancedResourceAllocation",
            "weight": 1
        },
        {
            "name": "NodePreferAvoidPodsPriority",
            "weight": 10000
        },
        {
            "name": "NodeAffinityPriority",
            "weight": 1
        },
        {
            "name": "TaintTolerationPriority",
            "weight": 1
        },
        {
            "argument": {
                "serviceAntiAffinity": {
                    "label": "zone"
                }
            },
            "name": "Zone",
            "weight": 2
        }
    ]
}

kind: "Policy"
version: "v1"
"predicates": [
        {
            "name": "PodFitsResources"
        },
        {
            "name": "NoDiskConflict"
        },
        {
            "name": "MatchNodeSelector"
        },
        {
            "name": "HostName"
        },
        {
            "argument": {
                "serviceAffinity": {
                    "labels": [
                        "region"
                    ]
                }
            },
            "name": "Region"
        }
    ],
    "priorities": [
        {
            "name": "LeastRequestedPriority",
            "weight": 1
        },
        {
            "name": "BalancedResourceAllocation",
            "weight": 1
        },
        {
            "name": "ServiceSpreadingPriority",
            "weight": 1
        },
        {
            "argument": {
                "serviceAntiAffinity": {
                    "label": "zone"
                }
            },
            "name": "Zone",
            "weight": 2
        }
    ]

Predicates are rules that filter out unqualified nodes.

There are several predicates provided by default in OpenShift Container Platform. Some of these predicates can be customized by providing certain parameters. Multiple predicates can be combined to provide additional filtering of nodes.

{"name" : "NoVolumeZoneConflict"}

{"name" : "MaxEBSVolumeCount"}

{"name" : "MaxGCEPDVolumeCount"}

{"name" : "MatchInterPodAffinity"}

{"name" : "NoDiskConflict"}

{"name" : "PodToleratesNodeTaints"}

{"name" : "CheckVolumeBinding"}

{"name" : "CheckNodeCondition"}

{"name" : "PodToleratesNodeNoExecuteTaints"}

{"name" : "CheckNodeLabelPresence"}

{"name" : "checkServiceAffinity"}

{"name" : "MaxAzureDiskVolumeCount"}

{"name" : "PodFitsResources"}

{"name" : "PodFitsHostPorts"}

{"name" : "HostName"}

{"name" : "MatchNodeSelector"}

"predicates":[
      {
         "name":"<name>", 1
         "argument":{
            "serviceAffinity":{
               "labels":[
                  "<label>" 2
               ]
            }
         }
      }
   ],

        "name":"ZoneAffinity",
        "argument":{
            "serviceAffinity":{
                "labels":[
                    "rack"
                ]
            }
        }

"predicates":[
      {
         "name":"<name>", 1
         "argument":{
            "labelsPresence":{
               "labels":[
                  "<label>" 2
                ],
                "presence": true 3
            }
         }
      }
   ],

        "name":"RackPreferred",
        "argument":{
            "labelsPresence":{
                "labels":[
                    "rack",
                    "region"
                ],
                "presence": true
            }
        }

Priorities are rules that rank remaining nodes according to preferences.

A custom set of priorities can be specified to configure the scheduler. There are several priorities provided by default in OpenShift Container Platform. Other priorities can be customized by providing certain parameters. Multiple priorities can be combined and different weights can be given to each in order to impact the prioritization.

{"name" : "SelectorSpreadPriority", "weight" : 1}

{"name" : "InterPodAffinityPriority", "weight" : 1}

{"name" : "LeastRequestedPriority", "weight" : 1}

{"name" : "BalancedResourceAllocation", "weight" : 1}

{"name" : "NodePreferAvoidPodsPriority", "weight" : 10000}

{"name" : "NodeAffinityPriority", "weight" : 1}

{"name" : "TaintTolerationPriority", "weight" : 1}

{"name" : "EqualPriority", "weight" : 1}

{"name" : "MostRequestedPriority", "weight" : 1}

{"name" : "ImageLocalityPriority", "weight" : 1}

{"name" : "ServiceSpreadingPriority", "weight" : 1}

"priorities":[
    {
        "name":"<name>", 1
        "weight" : 1 2
        "argument":{
            "serviceAntiAffinity":{
                "label":[
                    "<label>" 3
                ]
            }
        }
    }
]

        "name":"RackSpread", 1
        "weight" : 1 2
        "argument":{
            "serviceAntiAffinity":{
                "label": "rack" 3
            }
        }

"priorities":[
    {
        "name":"<name>", 1
        "weight" : 1, 2
        "argument":{
            "labelPreference":{
                "label": "<label>", 3
                "presence": true 4
            }
        }
    }
]

One of the important use cases for scheduling within OpenShift Container Platform is to support flexible affinity and anti-affinity policies.

The configuration below specifies the default scheduler configuration, if it were to be specified via the scheduler policy file.

kind: "Policy"
version: "v1"
predicates:
...
  - name: "RegionZoneAffinity" 1
    argument:
      serviceAffinity: 2
        labels: 3
          - "region"
          - "zone"
priorities:
...
  - name: "RackSpread" 4
    weight: 1
    argument:
      serviceAntiAffinity: 5
        label: "rack" 6

In all of the sample configurations below, the list of predicates and priority functions is truncated to include only the ones that pertain to the use case specified. In practice, a complete/meaningful scheduler policy should include most, if not all, of the default predicates and priorities listed above.

The following example defines three topological levels, region (affinity) zone (affinity) rack (anti-affinity):

kind: "Policy"
version: "v1"
predicates:
...
  - name: "RegionZoneAffinity"
    argument:
      serviceAffinity:
        labels:
          - "region"
          - "zone"
priorities:
...
  - name: "RackSpread"
    weight: 1
    argument:
      serviceAntiAffinity:
        label: "rack"

The following example defines three topological levels, city (affinity) building (anti-affinity) room (anti-affinity):

kind: "Policy"
version: "v1"
predicates:
...
  - name: "CityAffinity"
    argument:
      serviceAffinity:
        labels:
          - "city"
priorities:
...
  - name: "BuildingSpread"
    weight: 1
    argument:
      serviceAntiAffinity:
        label: "building"
  - name: "RoomSpread"
    weight: 1
    argument:
      serviceAntiAffinity:
        label: "room"

The following example defines a policy to only use nodes with the 'region' label defined and prefer nodes with the 'zone' label defined:

kind: "Policy"
version: "v1"
predicates:
...
  - name: "RequireRegion"
    argument:
      labelsPresence:
        labels:
          - "region"
        presence: true
priorities:
...
  - name: "ZonePreferred"
    weight: 1
    argument:
      labelPreference:
        label: "zone"
        presence: true

The following example combines both static and configurable predicates and also priorities:

kind: "Policy"
version: "v1"
predicates:
...
  - name: "RegionAffinity"
    argument:
      serviceAffinity:
        labels:
          - "region"
  - name: "RequireRegion"
    argument:
      labelsPresence:
        labels:
          - "region"
        presence: true
  - name: "BuildingNodesAvoid"
    argument:
      labelsPresence:
        labels:
          - "building"
        presence: false
  - name: "PodFitsPorts"
  - name: "MatchNodeSelector"
priorities:
...
  - name: "ZoneSpread"
    weight: 2
    argument:
      serviceAntiAffinity:
        label: "zone"
  - name: "ZonePreferred"
    weight: 1
    argument:
      labelPreference:
        label: "zone"
        presence: true
  - name: "ServiceSpreadingPriority"
    weight: 1

Descheduling involves evicting pods based on specific policies so that the pods can be rescheduled onto more appropriate nodes.

Your cluster can benefit from descheduling and rescheduling already-running pods for various reasons:

The descheduler does not schedule replacement of evicted pods. The scheduler automatically performs this task for the evicted pods.

It is important to note that there are a number of core components, such as DNS, that are critical to a fully functional cluster, but, run on a regular cluster node rather than the master. A cluster may stop working properly if the component is evicted. To prevent the descheduler from removing these pods, configure the pod as a critical pod by adding the scheduler.alpha.kubernetes.io/critical-pod annotation to the pod specification.

The descheduler job and descheduler pod are created in the kube-system project, which is created by default.

The descheduler does not evict the following types of pods:

The following sections describe the process to configure and run the descheduler:

To configure the necessary permissions for the descheduler to work in a pod:

You can configure the descheduler to remove pods from nodes that violate rules defined by strategies in a YAML policy file. You then create a configuration map that includes a path to the policy file and a job specification using that configuration map to apply the specific descheduling strategy.

apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
  "RemoveDuplicates":
     enabled: false
  "LowNodeUtilization":
     enabled: true
     params:
       nodeResourceUtilizationThresholds:
         thresholds:
           "cpu" : 20
           "memory": 20
           "pods": 20
         targetThresholds:
           "cpu" : 50
           "memory": 50
           "pods": 50
         numberOfNodes: 3
  "RemovePodsViolatingInterPodAntiAffinity":
     enabled: true
  "RemovePodsViolatingNodeAffinity":
    enabled: true
    params:
      nodeAffinityType:
      - "requiredDuringSchedulingIgnoredDuringExecution"

There are three default strategies that can be used with the descheduler:

You can configure and disable parameters associated with strategies as needed.

apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
  "RemoveDuplicates":
     enabled: false 1

apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
  "LowNodeUtilization":
     enabled: true
     params:
       nodeResourceUtilizationThresholds:
         thresholds: 1
           "cpu" : 20
           "memory": 20
           "pods": 20
         targetThresholds: 2
           "cpu" : 50
           "memory": 50
           "pods": 50
         numberOfNodes: 3 3

apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
  "RemovePodsViolatingInterPodAntiAffinity": 1
     enabled: true

apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
  "RemovePodsViolatingNodeAffinity": 1
    enabled: true
    params:
      nodeAffinityType:
      - "requiredDuringSchedulingIgnoredDuringExecution" 2

Create a configuration map for the descheduler policy file in the kube-system project, so that it can be referenced by the descheduler job.

# oc create configmap descheduler-policy-configmap \
     -n kube-system --from-file=<path-to-policy-dir/policy.yaml> 1

Create a job configuration for the descheduler.

apiVersion: batch/v1
kind: Job
metadata:
  name: descheduler-job
  namespace: kube-system
spec:
  parallelism: 1
  completions: 1
  template:
    metadata:
      name: descheduler-pod 1
      annotations:
        scheduler.alpha.kubernetes.io/critical-pod: "true" 2
    spec:
        containers:
        - name: descheduler
          image: registry.access.redhat.com/openshift3/ose-descheduler
          volumeMounts: 3
          - mountPath: /policy-dir
            name: policy-volume
          command:
          - "/bin/sh"
          - "-ec"
          - |
            /bin/descheduler --policy-config-file /policy-dir/policy.yaml 4
        restartPolicy: "Never"
        serviceAccountName: descheduler-sa 5
        volumes:
        - name: policy-volume
          configMap:
            name: descheduler-policy-configmap

The policy file is mounted as a volume from the configuration map.

To run the descheduler as a job in a pod:

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

For example:

# oc create -f descheduler-job.yaml

You can run multiple, custom schedulers alongside the default scheduler and configure which scheduler to use for each pods.

To schedule a given pod using a specific scheduler, specify the name of the scheduler in that pod specification.

The general process for including a custom scheduler in your cluster involves creating an image and including that image in a deployment.

After your custom scheduler is deployed in your cluster, you can configure pods to use that scheduler instead of the default scheduler.

As a cluster administrator, you can set a policy to prevent application developers with certain roles from targeting specific nodes when scheduling pods.

The Pod Node Constraints admission controller ensures that pods are deployed onto only specified node hosts using labels and prevents users without a specific role from using the nodeSelector field to schedule pods.

Use the Pod Node Constraints admission controller to ensure a pod is deployed onto only a specified node host by assigning it a label and specifying this in the nodeName setting in a pod configuration.

Using node selectors, you can ensure that pods are only placed onto nodes with specific labels. As a cluster administrator, you can use the Pod Node Constraints admission controller to set a policy that prevents users without the pods/binding permission from using node selectors to schedule pods.

The nodeSelectorLabelBlacklist field of a master configuration file gives you control over the labels that certain roles can specify in a pod configuration’s nodeSelector field. Users, service accounts, and groups that have the pods/binding permission role can specify any node selector. Those without the pods/binding permission are prohibited from setting a nodeSelector for any label that appears in nodeSelectorLabelBlacklist.

For example, an OpenShift Container Platform cluster might consist of five data centers spread across two regions. In the U.S., us-east, us-central, and us-west; and in the Asia-Pacific region (APAC), apac-east and apac-west. Each node in each geographical region is labeled accordingly. For example, region: us-east.

As a cluster administrator, you can create an infrastructure where application developers should be deploying pods only onto the nodes closest to their geographical location. You can create a node selector, grouping the U.S. data centers into superregion: us and the APAC data centers into superregion: apac.

To maintain an even loading of resources per data center, you can add the desired region to the nodeSelectorLabelBlacklist section of a master configuration. Then, whenever a developer located in the U.S. creates a pod, it is deployed onto a node in one of the regions with the superregion: us label. If the developer tries to target a specific region for their pod (for example, region: us-east), they receive an error. If they try again, without the node selector on their pod, it can still be deployed onto the region they tried to target, because superregion: us is set as the project-level node selector, and nodes labeled region: us-east are also labeled superregion: us.

The Pod Node Selector admission controller allows you to force pods onto nodes associated with a specific project and prevent pods from being scheduled in those nodes.

The Pod Node Selector admission controller determines where a pod can be placed using labels on projects and node selectors specified in pods. A new pod will be placed on a node associated with a project only if the node selectors in the pod match the labels in the project.

After the pod is created, the node selectors are merged into the pod so that the pod specification includes the labels originally included in the specification and any new labels from the node selectors. The example below illustrates the merging effect.

The Pod Node Selector admission controller also allows you to create a list of labels that are permitted in a specific project. This list acts as a whitelist that lets developers know what labels are acceptable to use in a project and gives administrators greater control over labeling in a cluster.

To activate the Pod Node Selector admission controller:

If you create a pod with a label that is not specified in the project specification, the pod is not scheduled on the node.

For example, here the label env: production is not in any project specification:

nodeSelector:
 "env: production"
 "infra": "fedora",
 "os": "fedora"

If there is a node that does not have a node selector annotation, the pod will be scheduled there.

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.

Preemption is controlled by the disablePreemption parameter in the scheduler configuration file, which is set to false by default.

When the Pod Priority and Preemption feature is enabled, 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.

When a developer creates a pod, the pod goes into a queue. When the Pod Priority and Preemption feature is enabled, 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 specification 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 specification 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.

Pod priority and preemption assigns a priority to pods for scheduling. The scheduler will preempt (evict) lower-priority pods in order to schedule higher-priority pods.

You apply pod priority and preemption by creating a priority class objects and associating pods to the priority using the priorityClassName in your pod specifications.

apiVersion: scheduling.k8s.io/v1beta1
kind: PriorityClass
metadata:
  name: high-priority 1
value: 1000000 2
globalDefault: false 3
description: "This priority class should be used for XYZ service pods only." 4

apiVersion: v1
kind: Pod
metadata:
  name: nginx
  labels:
    env: test
spec:
  containers:
  - name: nginx
    image: nginx
    imagePullPolicy: IfNotPresent
  priorityClassName: high-priority 1

To configure your cluster to use priority and preemption:

You can disable the pod priority and preemption feature.

After the feature is disabled, the existing pods keep their priority fields, but preemption is disabled, and priority fields are ignored. If the feature is disabled, you cannot set a priority class name in new pods.

To disable the preemption for the cluster:

Advanced scheduling involves configuring a pod so that the pod is required to run on particular nodes or has a preference to run on particular nodes.

Generally, advanced scheduling is not necessary, as the OpenShift Container Platform automatically places pods in a reasonable manner. For example, the default scheduler attempts to distribute pods across the nodes evenly and considers the available resources in a node. However, you might want more control over where a pod is placed.

If a pod needs to be on a machine with a faster disk speed (or prevented from being placed on that machine) or pods from two different services need to be located so they can communicate, you can use advanced scheduling to make that happen.

To ensure that appropriate new pods are scheduled on a dedicated group of nodes and prevent other new pods from being scheduled on those nodes, you can combine these methods as needed.

There are several ways to invoke advanced scheduling in your cluster:

Node affinity is a set of rules used by the scheduler to determine where a pod can be placed. The rules are defined using custom labels on nodes and label selectors specified in pods. Node affinity allows a pod to specify an affinity (or anti-affinity) towards a group of nodes it can be placed on. The node does not have control over the placement.

For example, you could configure a pod to only run on a node with a specific CPU or in a specific availability zone.

There are two types of node affinity rules: required and preferred.

Required rules must be met before a pod can be scheduled on a node. Preferred rules specify that, if the rule is met, the scheduler tries to enforce the rules, but does not guarantee enforcement.

You configure node affinity through the pod specification file. You can specify a required rule, a preferred rule, or both. If you specify both, the node must first meet the required rule, then attempts to meet the preferred rule.

The following example is a pod specification with a rule that requires the pod be placed on a node with a label whose key is e2e-az-NorthSouth and whose value is either e2e-az-North or e2e-az-South:

apiVersion: v1
kind: Pod
metadata:
  name: with-node-affinity
spec:
  affinity:
    nodeAffinity: 1
      requiredDuringSchedulingIgnoredDuringExecution: 2
        nodeSelectorTerms:
        - matchExpressions:
          - key: e2e-az-NorthSouth 3
            operator: In 4
            values:
            - e2e-az-North 5
            - e2e-az-South 6
  containers:
  - name: with-node-affinity
    image: docker.io/ocpqe/hello-pod

The following example is a node specification with a preferred rule that a node with a label whose key is e2e-az-EastWest and whose value is either e2e-az-East or e2e-az-West is preferred for the pod:

apiVersion: v1
kind: Pod
metadata:
  name: with-node-affinity
spec:
  affinity:
    nodeAffinity: 1
      preferredDuringSchedulingIgnoredDuringExecution: 2
      - weight: 1 3
        preference:
          matchExpressions:
          - key: e2e-az-EastWest 4
            operator: In 5
            values:
            - e2e-az-East 6
            - e2e-az-West 7
  containers:
  - name: with-node-affinity
    image: docker.io/ocpqe/hello-pod

There is no explicit node anti-affinity concept, but using the NotIn or DoesNotExist operator replicates that behavior.

The following examples demonstrate node affinity.

Pod affinity and pod anti-affinity allow you to specify rules about how pods should be placed relative to other pods. The rules are defined using custom labels on nodes and label selectors specified in pods. Pod affinity/anti-affinity allows a pod to specify an affinity (or anti-affinity) towards a group of pods it can be placed with. The node does not have control over the placement.

For example, using affinity rules, you could spread or pack pods within a service or relative to pods in other services. Anti-affinity rules allow you to prevent pods of a particular service from scheduling on the same nodes as pods of another service that are known to interfere with the performance of the pods of the first service. Or, you could spread the pods of a service across nodes or availability zones to reduce correlated failures.

Pod affinity/anti-affinity allows you to constrain which nodes your pod is eligible to be scheduled on based on the labels on other pods. A label is a key/value pair.

There are two types of pod affinity rules: required and preferred.

Required rules must be met before a pod can be scheduled on a node. Preferred rules specify that, if the rule is met, the scheduler tries to enforce the rules, but does not guarantee enforcement.

You configure pod affinity/anti-affinity through the pod specification files. You can specify a required rule, a preferred rule, or both. If you specify both, the node must first meet the required rule, then attempts to meet the preferred rule.

The following example shows a pod specification configured for pod affinity and anti-affinity.

In this example, the pod affinity rule indicates that the pod can schedule onto a node only if that node has at least one already-running pod with a label that has the key security and value S1. The pod anti-affinity rule says that the pod prefers to not schedule onto a node if that node is already running a pod with label having key security and value S2.

apiVersion: v1
kind: Pod
metadata:
  name: with-pod-affinity
spec:
  affinity:
    podAffinity: 1
      requiredDuringSchedulingIgnoredDuringExecution: 2
      - labelSelector:
          matchExpressions:
          - key: security 3
            operator: In 4
            values:
            - S1 5
        topologyKey: failure-domain.beta.kubernetes.io/zone
  containers:
  - name: with-pod-affinity
    image: docker.io/ocpqe/hello-pod

apiVersion: v1
kind: Pod
metadata:
  name: with-pod-antiaffinity
spec:
  affinity:
    podAntiAffinity: 1
      preferredDuringSchedulingIgnoredDuringExecution: 2
      - weight: 100  3
        podAffinityTerm:
          labelSelector:
            matchExpressions:
            - key: security 4
              operator: In 5
              values:
              - S2
          topologyKey: kubernetes.io/hostname
  containers:
  - name: with-pod-affinity
    image: docker.io/ocpqe/hello-pod

The following examples demonstrate pod affinity and pod anti-affinity.

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.

Using nodeSelector in a pod configuration, you can ensure that pods are only placed onto nodes with specific labels.

Taints and tolerations allow the node to control which pods should (or should not) be scheduled on them.

A taint allows a node to refuse pod to be scheduled unless that pod has a matching toleration.

You apply taints to a node through the node specification (NodeSpec) and apply tolerations to a pod through the pod specification (PodSpec). A taint on a node instructs the node to repel all pods that do not tolerate the taint.

Taints and tolerations consist of a key, value, and effect. An operator allows you to leave one of these parameters empty.

A toleration matches a taint:

You add a taint to a node using the oc adm taint command with the parameters described in the Taint and toleration components table:

$ oc adm taint nodes <node-name> <key>=<value>:<effect>

For example:

$ oc adm taint nodes node1 key1=value1:NoExecute

The example places a taint on node1 that has key key1, value value1, and taint effect NoExecute.

To add a toleration to a pod, edit the pod specification to include a tolerations section:

tolerations:
- key: "key1" 1
  operator: "Equal" 2
  value: "value1" 3
  effect: "NoExecute" 4
  tolerationSeconds: 3600 5

tolerations:
- key: "key1"
  operator: "Exists"
  effect: "NoExecute"
  tolerationSeconds: 3600

Both of these tolerations match the taint created by the oc adm taint command above. A pod with either toleration would be able to schedule onto node1.

tolerations:
- key: "key1"
  operator: "Equal"
  value: "value1"
  effect: "NoExecute"
  tolerationSeconds: 3600

OpenShift Container Platform can be configured to represent node unreachable and node not ready conditions as taints. This allows per-pod specification of how long to remain bound to a node that becomes unreachable or not ready, rather than using the default of five minutes.

When the Taint Based Evictions feature is enabled, the taints are automatically added by the node controller and the normal logic for evicting pods from Ready nodes is disabled.

To enable Taint Based Evictions:

DaemonSet pods are created with NoExecute tolerations for node.kubernetes.io/unreachable and node.kubernetes.io/not-ready with no tolerationSeconds to ensure that DaemonSet pods are never evicted due to these problems, even when the Default Toleration Seconds feature is disabled.

Taints and tolerations are a flexible way to steer pods away from nodes or evict pods that should not be running on a node. A few of typical scenrios are: