Assigning Pods to Nodes

You can constrain a Pod so that it is restricted to run on particular node(s), or to prefer to run on particular nodes. There are several ways to do this and the recommended approaches all use label selectors to facilitate the selection. Often, you do not need to set any such constraints; the scheduler will automatically do a reasonable placement (for example, spreading your Pods across nodes so as not place Pods on a node with insufficient free resources). However, there are some circumstances where you may want to control which node the Pod deploys to, for example, to ensure that a Pod ends up on a node with an SSD attached to it, or to co-locate Pods from two different services that communicate a lot into the same availability zone.

You can use any of the following methods to choose where Kubernetes schedules specific Pods:

• nodeSelector field matching against node labels
• Affinity and anti-affinity
• nodeName field
• Pod topology spread constraints

Node labels

Like many other Kubernetes objects, nodes have labels. You can attach labels manually. Kubernetes also populates a standard set of labels on all nodes in a cluster. See Well-Known Labels, Annotations and Taints for a list of common node labels.

Node isolation/restriction

Adding labels to nodes allows you to target Pods for scheduling on specific nodes or groups of nodes. You can use this functionality to ensure that specific Pods only run on nodes with certain isolation, security, or regulatory properties.

If you use labels for node isolation, choose label keys that the kubelet cannot modify. This prevents a compromised node from setting those labels on itself so that the scheduler schedules workloads onto the compromised node.

The NodeRestriction admission plugin prevents the kubelet from setting or modifying labels with a node-restriction.kubernetes.io/ prefix.

To make use of that label prefix for node isolation:

1. Ensure you are using the Node authorizer and have enabled the NodeRestriction admission plugin.
2. Add labels with the node-restriction.kubernetes.io/ prefix to your nodes, and use those labels in your node selectors. For example, example.com.node-restriction.kubernetes.io/fips=true or example.com.node-restriction.kubernetes.io/pci-dss=true.

nodeSelector

nodeSelector is the simplest recommended form of node selection constraint. You can add the nodeSelector field to your Pod specification and specify the node labels you want the target node to have. Kubernetes only schedules the Pod onto nodes that have each of the labels you specify.

See Assign Pods to Nodes for more information.

Affinity and anti-affinity

nodeSelector is the simplest way to constrain Pods to nodes with specific labels. Affinity and anti-affinity expands the types of constraints you can define. Some of the benefits of affinity and anti-affinity include:

• The affinity/anti-affinity language is more expressive. nodeSelector only selects nodes with all the specified labels. Affinity/anti-affinity gives you more control over the selection logic.
• You can indicate that a rule is soft or preferred, so that the scheduler still schedules the Pod even if it can't find a matching node.
• You can constrain a Pod using labels on other Pods running on the node (or other topological domain), instead of just node labels, which allows you to define rules for which Pods can be co-located on a node.

The affinity feature consists of two types of affinity:

• Node affinity functions like the nodeSelector field but is more expressive and allows you to specify soft rules.
• Inter-pod affinity/anti-affinity allows you to constrain Pods against labels on other Pods.

Node affinity

Node affinity is conceptually similar to nodeSelector, allowing you to constrain which nodes your Pod can be scheduled on based on node labels. There are two types of node affinity:

• requiredDuringSchedulingIgnoredDuringExecution: The scheduler can't schedule the Pod unless the rule is met. This functions like nodeSelector, but with a more expressive syntax.
• preferredDuringSchedulingIgnoredDuringExecution: The scheduler tries to find a node that meets the rule. If a matching node is not available, the scheduler still schedules the Pod.

You can specify node affinities using the .spec.affinity.nodeAffinity field in your Pod spec.

For example, consider the following Pod spec:

pods/pod-with-node-affinity.yaml

apiVersion: v1
kind: Pod
metadata:
  name: with-node-affinity
spec:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: topology.kubernetes.io/zone
            operator: In
            values:
            - antarctica-east1
            - antarctica-west1
      preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 1
        preference:
          matchExpressions:
          - key: another-node-label-key
            operator: In
            values:
            - another-node-label-value
  containers:
  - name: with-node-affinity
    image: k8s.gcr.io/pause:2.0

In this example, the following rules apply:

• The node must have a label with the key topology.kubernetes.io/zone and the value of that label must be either antarctica-east1 or antarctica-west1.
• The node preferably has a label with the key another-node-label-key and the value another-node-label-value.

You can use the operator field to specify a logical operator for Kubernetes to use when interpreting the rules. You can use In, NotIn, Exists, DoesNotExist, Gt and Lt.

NotIn and DoesNotExist allow you to define node anti-affinity behavior. Alternatively, you can use node taints to repel Pods from specific nodes.

See Assign Pods to Nodes using Node Affinity for more information.

Node affinity weight

You can specify a weight between 1 and 100 for each instance of the preferredDuringSchedulingIgnoredDuringExecution affinity type. When the scheduler finds nodes that meet all the other scheduling requirements of the Pod, the scheduler iterates through every preferred rule that the node satisfies and adds the value of the weight for that expression to a sum.

The final sum is added to the score of other priority functions for the node. Nodes with the highest total score are prioritized when the scheduler makes a scheduling decision for the Pod.

For example, consider the following Pod spec:

pods/pod-with-affinity-anti-affinity.yaml

apiVersion: v1
kind: Pod
metadata:
  name: with-affinity-anti-affinity
spec:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: kubernetes.io/os
            operator: In
            values:
            - linux
      preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 1
        preference:
          matchExpressions:
          - key: label-1
            operator: In
            values:
            - key-1
      - weight: 50
        preference:
          matchExpressions:
          - key: label-2
            operator: In
            values:
            - key-2
  containers:
  - name: with-node-affinity
    image: k8s.gcr.io/pause:2.0

If there are two possible nodes that match the preferredDuringSchedulingIgnoredDuringExecution rule, one with the label-1:key-1 label and another with the label-2:key-2 label, the scheduler considers the weight of each node and adds the weight to the other scores for that node, and schedules the Pod onto the node with the highest final score.

Node affinity per scheduling profile

When configuring multiple scheduling profiles, you can associate a profile with a node affinity, which is useful if a profile only applies to a specific set of nodes. To do so, add an addedAffinity to the args field of the NodeAffinity plugin in the scheduler configuration. For example:

apiVersion: kubescheduler.config.k8s.io/v1beta3
kind: KubeSchedulerConfiguration

profiles:
  - schedulerName: default-scheduler
  - schedulerName: foo-scheduler
    pluginConfig:
      - name: NodeAffinity
        args:
          addedAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
              - matchExpressions:
                - key: scheduler-profile
                  operator: In
                  values:
                  - foo

The addedAffinity is applied to all Pods that set .spec.schedulerName to foo-scheduler, in addition to the NodeAffinity specified in the PodSpec. That is, in order to match the Pod, nodes need to satisfy addedAffinity and the Pod's .spec.NodeAffinity.

Since the addedAffinity is not visible to end users, its behavior might be unexpected to them. Use node labels that have a clear correlation to the scheduler profile name.

Inter-pod affinity and anti-affinity

Inter-pod affinity and anti-affinity allow you to constrain which nodes your Pods can be scheduled on based on the labels of Pods already running on that node, instead of the node labels.

Inter-pod affinity and anti-affinity rules take the form "this Pod should (or, in the case of anti-affinity, should not) run in an X if that X is already running one or more Pods that meet rule Y", where X is a topology domain like node, rack, cloud provider zone or region, or similar and Y is the rule Kubernetes tries to satisfy.

You express these rules (Y) as label selectors with an optional associated list of namespaces. Pods are namespaced objects in Kubernetes, so Pod labels also implicitly have namespaces. Any label selectors for Pod labels should specify the namespaces in which Kubernetes should look for those labels.

You express the topology domain (X) using a topologyKey, which is the key for the node label that the system uses to denote the domain. For examples, see Well-Known Labels, Annotations and Taints.

Types of inter-pod affinity and anti-affinity

Similar to node affinity are two types of Pod affinity and anti-affinity as follows:

• requiredDuringSchedulingIgnoredDuringExecution
• preferredDuringSchedulingIgnoredDuringExecution

For example, you could use requiredDuringSchedulingIgnoredDuringExecution affinity to tell the scheduler to co-locate Pods of two services in the same cloud provider zone because they communicate with each other a lot. Similarly, you could use preferredDuringSchedulingIgnoredDuringExecution anti-affinity to spread Pods from a service across multiple cloud provider zones.

To use inter-pod affinity, use the affinity.podAffinity field in the Pod spec. For inter-pod anti-affinity, use the affinity.podAntiAffinity field in the Pod spec.

Pod affinity example

Consider the following Pod spec:

pods/pod-with-pod-affinity.yaml

apiVersion: v1
kind: Pod
metadata:
  name: with-pod-affinity
spec:
  affinity:
    podAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
      - labelSelector:
          matchExpressions:
          - key: security
            operator: In
            values:
            - S1
        topologyKey: topology.kubernetes.io/zone
    podAntiAffinity:
      preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 100
        podAffinityTerm:
          labelSelector:
            matchExpressions:
            - key: security
              operator: In
              values:
              - S2
          topologyKey: topology.kubernetes.io/zone
  containers:
  - name: with-pod-affinity
    image: k8s.gcr.io/pause:2.0

This example defines one Pod affinity rule and one Pod anti-affinity rule. The Pod affinity rule uses the "hard" requiredDuringSchedulingIgnoredDuringExecution, while the anti-affinity rule uses the "soft" preferredDuringSchedulingIgnoredDuringExecution.

The affinity rule says that the scheduler can only schedule a Pod onto a node if the node is in the same zone as one or more existing Pods with the label security=S1. More precisely, the scheduler must place the Pod on a node that has the topology.kubernetes.io/zone=V label, as long as there is at least one node in that zone that currently has one or more Pods with the Pod label security=S1.

The anti-affinity rule says that the scheduler should try to avoid scheduling the Pod onto a node that is in the same zone as one or more Pods with the label security=S2. More precisely, the scheduler should try to avoid placing the Pod on a node that has the topology.kubernetes.io/zone=R label if there are other nodes in the same zone currently running Pods with the Security=S2 Pod label.

To get yourself more familiar with the examples of Pod affinity and anti-affinity, refer to the design proposal.

You can use the In, NotIn, Exists and DoesNotExist values in the operator field for Pod affinity and anti-affinity.

In principle, the topologyKey can be any allowed label key with the following exceptions for performance and security reasons:

• For Pod affinity and anti-affinity, an empty topologyKey field is not allowed in both requiredDuringSchedulingIgnoredDuringExecution and preferredDuringSchedulingIgnoredDuringExecution.
• For requiredDuringSchedulingIgnoredDuringExecution Pod anti-affinity rules, the admission controller LimitPodHardAntiAffinityTopology limits topologyKey to kubernetes.io/hostname. You can modify or disable the admission controller if you want to allow custom topologies.

In addition to labelSelector and topologyKey, you can optionally specify a list of namespaces which the labelSelector should match against using the namespaces field at the same level as labelSelector and topologyKey. If omitted or empty, namespaces defaults to the namespace of the Pod where the affinity/anti-affinity definition appears.

Namespace selector

You can also select matching namespaces using namespaceSelector, which is a label query over the set of namespaces. The affinity term is applied to namespaces selected by both namespaceSelector and the namespaces field. Note that an empty namespaceSelector ({}) matches all namespaces, while a null or empty namespaces list and null namespaceSelector matches the namespace of the Pod where the rule is defined.

More practical use-cases

Inter-pod affinity and anti-affinity can be even more useful when they are used with higher level collections such as ReplicaSets, StatefulSets, Deployments, etc. These rules allow you to configure that a set of workloads should be co-located in the same defined topology; for example, preferring to place two related Pods onto the same node.

For example: imagine a three-node cluster. You use the cluster to run a web application and also an in-memory cache (such as Redis). For this example, also assume that latency between the web application and the memory cache should be as low as is practical. You could use inter-pod affinity and anti-affinity to co-locate the web servers with the cache as much as possible.

In the following example Deployment for the Redis cache, the replicas get the label app=store. The podAntiAffinity rule tells the scheduler to avoid placing multiple replicas with the app=store label on a single node. This creates each cache in a separate node.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: redis-cache
spec:
  selector:
    matchLabels:
      app: store
  replicas: 3
  template:
    metadata:
      labels:
        app: store
    spec:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
          - labelSelector:
              matchExpressions:
              - key: app
                operator: In
                values:
                - store
            topologyKey: "kubernetes.io/hostname"
      containers:
      - name: redis-server
        image: redis:3.2-alpine

The following example Deployment for the web servers creates replicas with the label app=web-store. The Pod affinity rule tells the scheduler to place each replica on a node that has a Pod with the label app=store. The Pod anti-affinity rule tells the scheduler never to place multiple app=web-store servers on a single node.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: web-server
spec:
  selector:
    matchLabels:
      app: web-store
  replicas: 3
  template:
    metadata:
      labels:
        app: web-store
    spec:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
          - labelSelector:
              matchExpressions:
              - key: app
                operator: In
                values:
                - web-store
            topologyKey: "kubernetes.io/hostname"
        podAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
          - labelSelector:
              matchExpressions:
              - key: app
                operator: In
                values:
                - store
            topologyKey: "kubernetes.io/hostname"
      containers:
      - name: web-app
        image: nginx:1.16-alpine

Creating the two preceding Deployments results in the following cluster layout, where each web server is co-located with a cache, on three separate nodes.

The overall effect is that each cache instance is likely to be accessed by a single client, that is running on the same node. This approach aims to minimize both skew (imbalanced load) and latency.

You might have other reasons to use Pod anti-affinity. See the ZooKeeper tutorial for an example of a StatefulSet configured with anti-affinity for high availability, using the same technique as this example.

nodeName

nodeName is a more direct form of node selection than affinity or nodeSelector. nodeName is a field in the Pod spec. If the nodeName field is not empty, the scheduler ignores the Pod and the kubelet on the named node tries to place the Pod on that node. Using nodeName overrules using nodeSelector or affinity and anti-affinity rules.

Some of the limitations of using nodeName to select nodes are:

• If the named node does not exist, the Pod will not run, and in some cases may be automatically deleted.
• If the named node does not have the resources to accommodate the Pod, the Pod will fail and its reason will indicate why, for example OutOfmemory or OutOfcpu.
• Node names in cloud environments are not always predictable or stable.

Here is an example of a Pod spec using the nodeName field:

apiVersion: v1
kind: Pod
metadata:
  name: nginx
spec:
  containers:
  - name: nginx
    image: nginx
  nodeName: kube-01

The above Pod will only run on the node kube-01.

Pod topology spread constraints

You can use topology spread constraints to control how Pods are spread across your cluster among failure-domains such as regions, zones, nodes, or among any other topology domains that you define. You might do this to improve performance, expected availability, or overall utilization.

Read Pod topology spread constraints to learn more about how these work.

What's next

• Read more about taints and tolerations .
• Read the design docs for node affinity and for inter-pod affinity/anti-affinity.
• Learn about how the topology manager takes part in node-level resource allocation decisions.
• Learn how to use nodeSelector.
• Learn how to use affinity and anti-affinity.