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, and other user-defined topology domains. This can help to achieve high availability as well as efficient resource utilization.

You can set cluster-level constraints as a default, or configure topology spread constraints for individual workloads.

Motivation

Imagine that you have a cluster of up to twenty nodes, and you want to run a workload that automatically scales how many replicas it uses. There could be as few as two Pods or as many as fifteen. When there are only two Pods, you'd prefer not to have both of those Pods run on the same node: you would run the risk that a single node failure takes your workload offline.

In addition to this basic usage, there are some advanced usage examples that enable your workloads to benefit on high availability and cluster utilization.

As you scale up and run more Pods, a different concern becomes important. Imagine that you have three nodes running five Pods each. The nodes have enough capacity to run that many replicas; however, the clients that interact with this workload are split across three different datacenters (or infrastructure zones). Now you have less concern about a single node failure, but you notice that latency is higher than you'd like, and you are paying for network costs associated with sending network traffic between the different zones.

You decide that under normal operation you'd prefer to have a similar number of replicas scheduled into each infrastructure zone, and you'd like the cluster to self-heal in the case that there is a problem.

Pod topology spread constraints offer you a declarative way to configure that.

topologySpreadConstraints field

The Pod API includes a field, spec.topologySpreadConstraints. The usage of this field looks like the following:

---
apiVersion: v1
kind: Pod
metadata:
  name: example-pod
spec:
  # Configure a topology spread constraint
  topologySpreadConstraints:
    - maxSkew: <integer>
      minDomains: <integer> # optional; beta since v1.25
      topologyKey: <string>
      whenUnsatisfiable: <string>
      labelSelector: <object>
      matchLabelKeys: <list> # optional; alpha since v1.25
      nodeAffinityPolicy: [Honor|Ignore] # optional; alpha since v1.25
      nodeTaintsPolicy: [Honor|Ignore] # optional; alpha since v1.25
  ### other Pod fields go here

You can read more about this field by running kubectl explain Pod.spec.topologySpreadConstraints or refer to scheduling section of the API reference for Pod.

Spread constraint definition

You can define one or multiple topologySpreadConstraints entries to instruct the kube-scheduler how to place each incoming Pod in relation to the existing Pods across your cluster. Those fields are:

• maxSkew describes the degree to which Pods may be unevenly distributed. You must specify this field and the number must be greater than zero. Its semantics differ according to the value of whenUnsatisfiable:

  • if you select whenUnsatisfiable: DoNotSchedule, then maxSkew defines the maximum permitted difference between the number of matching pods in the target topology and the global minimum (the minimum number of matching pods in an eligible domain or zero if the number of eligible domains is less than MinDomains). For example, if you have 3 zones with 2, 2 and 1 matching pods respectively, MaxSkew is set to 1 then the global minimum is 1.
  • if you select whenUnsatisfiable: ScheduleAnyway, the scheduler gives higher precedence to topologies that would help reduce the skew.

• minDomains indicates a minimum number of eligible domains. This field is optional. A domain is a particular instance of a topology. An eligible domain is a domain whose nodes match the node selector.

  Note: The minDomains field is a beta field and disabled by default in 1.25. You can enable it by enabling the MinDomainsInPodTopologySpread feature gate.
  • The value of minDomains must be greater than 0, when specified. You can only specify minDomains in conjunction with whenUnsatisfiable: DoNotSchedule.
  • When the number of eligible domains with match topology keys is less than minDomains, Pod topology spread treats global minimum as 0, and then the calculation of skew is performed. The global minimum is the minimum number of matching Pods in an eligible domain, or zero if the number of eligible domains is less than minDomains.
  • When the number of eligible domains with matching topology keys equals or is greater than minDomains, this value has no effect on scheduling.
  • If you do not specify minDomains, the constraint behaves as if minDomains is 1.

• topologyKey is the key of node labels. Nodes that have a label with this key and identical values are considered to be in the same topology. We call each instance of a topology (in other words, a <key, value> pair) a domain. The scheduler will try to put a balanced number of pods into each domain. Also, we define an eligible domain as a domain whose nodes meet the requirements of nodeAffinityPolicy and nodeTaintsPolicy.

• whenUnsatisfiable indicates how to deal with a Pod if it doesn't satisfy the spread constraint:

  • DoNotSchedule (default) tells the scheduler not to schedule it.
  • ScheduleAnyway tells the scheduler to still schedule it while prioritizing nodes that minimize the skew.

• labelSelector is used to find matching Pods. Pods that match this label selector are counted to determine the number of Pods in their corresponding topology domain. See Label Selectors for more details.

• matchLabelKeys is a list of pod label keys to select the pods over which spreading will be calculated. The keys are used to lookup values from the pod labels, those key-value labels are ANDed with labelSelector to select the group of existing pods over which spreading will be calculated for the incoming pod. Keys that don't exist in the pod labels will be ignored. A null or empty list means only match against the labelSelector.

  With matchLabelKeys, users don't need to update the pod.spec between different revisions. The controller/operator just needs to set different values to the same label key for different revisions. The scheduler will assume the values automatically based on matchLabelKeys. For example, if users use Deployment, they can use the label keyed with pod-template-hash, which is added automatically by the Deployment controller, to distinguish between different revisions in a single Deployment.

      topologySpreadConstraints:
          - maxSkew: 1
            topologyKey: kubernetes.io/hostname
            whenUnsatisfiable: DoNotSchedule
            matchLabelKeys:
              - app
              - pod-template-hash
  

  Note: The matchLabelKeys field is an alpha field added in 1.25. You have to enable the MatchLabelKeysInPodTopologySpread feature gate in order to use it.

• nodeAffinityPolicy indicates how we will treat Pod's nodeAffinity/nodeSelector when calculating pod topology spread skew. Options are:

  • Honor: only nodes matching nodeAffinity/nodeSelector are included in the calculations.
  • Ignore: nodeAffinity/nodeSelector are ignored. All nodes are included in the calculations.

  If this value is null, the behavior is equivalent to the Honor policy.

  Note: The nodeAffinityPolicy is an alpha-level field added in 1.25. You have to enable the NodeInclusionPolicyInPodTopologySpread feature gate in order to use it.

• nodeTaintsPolicy indicates how we will treat node taints when calculating pod topology spread skew. Options are:

  • Honor: nodes without taints, along with tainted nodes for which the incoming pod has a toleration, are included.
  • Ignore: node taints are ignored. All nodes are included.

  If this value is null, the behavior is equivalent to the Ignore policy.

  Note: The nodeTaintsPolicy is an alpha-level field added in 1.25. You have to enable the NodeInclusionPolicyInPodTopologySpread feature gate in order to use it.

When a Pod defines more than one topologySpreadConstraint, those constraints are combined using a logical AND operation: the kube-scheduler looks for a node for the incoming Pod that satisfies all the configured constraints.

Node labels

Topology spread constraints rely on node labels to identify the topology domain(s) that each node is in. For example, a node might have labels:

  region: us-east-1
  zone: us-east-1a

Suppose you have a 4-node cluster with the following labels:

NAME    STATUS   ROLES    AGE     VERSION   LABELS
node1   Ready    <none>   4m26s   v1.16.0   node=node1,zone=zoneA
node2   Ready    <none>   3m58s   v1.16.0   node=node2,zone=zoneA
node3   Ready    <none>   3m17s   v1.16.0   node=node3,zone=zoneB
node4   Ready    <none>   2m43s   v1.16.0   node=node4,zone=zoneB

Then the cluster is logically viewed as below:

Consistency

You should set the same Pod topology spread constraints on all pods in a group.

Usually, if you are using a workload controller such as a Deployment, the pod template takes care of this for you. If you mix different spread constraints then Kubernetes follows the API definition of the field; however, the behavior is more likely to become confusing and troubleshooting is less straightforward.

You need a mechanism to ensure that all the nodes in a topology domain (such as a cloud provider region) are labelled consistently. To avoid you needing to manually label nodes, most clusters automatically populate well-known labels such as topology.kubernetes.io/hostname. Check whether your cluster supports this.

Topology spread constraint examples

Example: one topology spread constraint

Suppose you have a 4-node cluster where 3 Pods labelled foo: bar are located in node1, node2 and node3 respectively:

If you want an incoming Pod to be evenly spread with existing Pods across zones, you can use a manifest similar to:

pods/topology-spread-constraints/one-constraint.yaml

kind: Pod
apiVersion: v1
metadata:
  name: mypod
  labels:
    foo: bar
spec:
  topologySpreadConstraints:
  - maxSkew: 1
    topologyKey: zone
    whenUnsatisfiable: DoNotSchedule
    labelSelector:
      matchLabels:
        foo: bar
  containers:
  - name: pause
    image: registry.k8s.io/pause:3.1

From that manifest, topologyKey: zone implies the even distribution will only be applied to nodes that are labelled zone: <any value> (nodes that don't have a zone label are skipped). The field whenUnsatisfiable: DoNotSchedule tells the scheduler to let the incoming Pod stay pending if the scheduler can't find a way to satisfy the constraint.

If the scheduler placed this incoming Pod into zone A, the distribution of Pods would become [3, 1]. That means the actual skew is then 2 (calculated as 3 - 1), which violates maxSkew: 1. To satisfy the constraints and context for this example, the incoming Pod can only be placed onto a node in zone B:

OR

You can tweak the Pod spec to meet various kinds of requirements:

• Change maxSkew to a bigger value - such as 2 - so that the incoming Pod can be placed into zone A as well.
• Change topologyKey to node so as to distribute the Pods evenly across nodes instead of zones. In the above example, if maxSkew remains 1, the incoming Pod can only be placed onto the node node4.
• Change whenUnsatisfiable: DoNotSchedule to whenUnsatisfiable: ScheduleAnyway to ensure the incoming Pod to be always schedulable (suppose other scheduling APIs are satisfied). However, it's preferred to be placed into the topology domain which has fewer matching Pods. (Be aware that this preference is jointly normalized with other internal scheduling priorities such as resource usage ratio).

Example: multiple topology spread constraints

This builds upon the previous example. Suppose you have a 4-node cluster where 3 existing Pods labeled foo: bar are located on node1, node2 and node3 respectively:

You can combine two topology spread constraints to control the spread of Pods both by node and by zone:

pods/topology-spread-constraints/two-constraints.yaml

kind: Pod
apiVersion: v1
metadata:
  name: mypod
  labels:
    foo: bar
spec:
  topologySpreadConstraints:
  - maxSkew: 1
    topologyKey: zone
    whenUnsatisfiable: DoNotSchedule
    labelSelector:
      matchLabels:
        foo: bar
  - maxSkew: 1
    topologyKey: node
    whenUnsatisfiable: DoNotSchedule
    labelSelector:
      matchLabels:
        foo: bar
  containers:
  - name: pause
    image: registry.k8s.io/pause:3.1

In this case, to match the first constraint, the incoming Pod can only be placed onto nodes in zone B; while in terms of the second constraint, the incoming Pod can only be scheduled to the node node4. The scheduler only considers options that satisfy all defined constraints, so the only valid placement is onto node node4.

Example: conflicting topology spread constraints

Multiple constraints can lead to conflicts. Suppose you have a 3-node cluster across 2 zones:

If you were to apply two-constraints.yaml (the manifest from the previous example) to this cluster, you would see that the Pod mypod stays in the Pending state. This happens because: to satisfy the first constraint, the Pod mypod can only be placed into zone B; while in terms of the second constraint, the Pod mypod can only schedule to node node2. The intersection of the two constraints returns an empty set, and the scheduler cannot place the Pod.

To overcome this situation, you can either increase the value of maxSkew or modify one of the constraints to use whenUnsatisfiable: ScheduleAnyway. Depending on circumstances, you might also decide to delete an existing Pod manually - for example, if you are troubleshooting why a bug-fix rollout is not making progress.

Interaction with node affinity and node selectors

The scheduler will skip the non-matching nodes from the skew calculations if the incoming Pod has spec.nodeSelector or spec.affinity.nodeAffinity defined.

Example: topology spread constraints with node affinity

Suppose you have a 5-node cluster ranging across zones A to C:

and you know that zone C must be excluded. In this case, you can compose a manifest as below, so that Pod mypod will be placed into zone B instead of zone C. Similarly, Kubernetes also respects spec.nodeSelector.

pods/topology-spread-constraints/one-constraint-with-nodeaffinity.yaml

kind: Pod
apiVersion: v1
metadata:
  name: mypod
  labels:
    foo: bar
spec:
  topologySpreadConstraints:
  - maxSkew: 1
    topologyKey: zone
    whenUnsatisfiable: DoNotSchedule
    labelSelector:
      matchLabels:
        foo: bar
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: zone
            operator: NotIn
            values:
            - zoneC
  containers:
  - name: pause
    image: registry.k8s.io/pause:3.1

Implicit conventions

There are some implicit conventions worth noting here:

• Only the Pods holding the same namespace as the incoming Pod can be matching candidates.

• The scheduler bypasses any nodes that don't have any topologySpreadConstraints[*].topologyKey present. This implies that:

  1. any Pods located on those bypassed nodes do not impact maxSkew calculation - in the above example, suppose the node node1 does not have a label "zone", then the 2 Pods will be disregarded, hence the incoming Pod will be scheduled into zone A.
  2. the incoming Pod has no chances to be scheduled onto this kind of nodes - in the above example, suppose a node node5 has the mistyped label zone-typo: zoneC (and no zone label set). After node node5 joins the cluster, it will be bypassed and Pods for this workload aren't scheduled there.

• Be aware of what will happen if the incoming Pod's topologySpreadConstraints[*].labelSelector doesn't match its own labels. In the above example, if you remove the incoming Pod's labels, it can still be placed onto nodes in zone B, since the constraints are still satisfied. However, after that placement, the degree of imbalance of the cluster remains unchanged - it's still zone A having 2 Pods labelled as foo: bar, and zone B having 1 Pod labelled as foo: bar. If this is not what you expect, update the workload's topologySpreadConstraints[*].labelSelector to match the labels in the pod template.

Cluster-level default constraints

It is possible to set default topology spread constraints for a cluster. Default topology spread constraints are applied to a Pod if, and only if:

• It doesn't define any constraints in its .spec.topologySpreadConstraints.
• It belongs to a Service, ReplicaSet, StatefulSet or ReplicationController.

Default constraints can be set as part of the PodTopologySpread plugin arguments in a scheduling profile. The constraints are specified with the same API above, except that labelSelector must be empty. The selectors are calculated from the Services, ReplicaSets, StatefulSets or ReplicationControllers that the Pod belongs to.

An example configuration might look like follows:

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

profiles:
  - schedulerName: default-scheduler
    pluginConfig:
      - name: PodTopologySpread
        args:
          defaultConstraints:
            - maxSkew: 1
              topologyKey: topology.kubernetes.io/zone
              whenUnsatisfiable: ScheduleAnyway
          defaultingType: List

Built-in default constraints

If you don't configure any cluster-level default constraints for pod topology spreading, then kube-scheduler acts as if you specified the following default topology constraints:

defaultConstraints:
  - maxSkew: 3
    topologyKey: "kubernetes.io/hostname"
    whenUnsatisfiable: ScheduleAnyway
  - maxSkew: 5
    topologyKey: "topology.kubernetes.io/zone"
    whenUnsatisfiable: ScheduleAnyway

Also, the legacy SelectorSpread plugin, which provides an equivalent behavior, is disabled by default.

If you don't want to use the default Pod spreading constraints for your cluster, you can disable those defaults by setting defaultingType to List and leaving empty defaultConstraints in the PodTopologySpread plugin configuration:

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

profiles:
  - schedulerName: default-scheduler
    pluginConfig:
      - name: PodTopologySpread
        args:
          defaultConstraints: []
          defaultingType: List

Comparison with podAffinity and podAntiAffinity

In Kubernetes, inter-Pod affinity and anti-affinity control how Pods are scheduled in relation to one another - either more packed or more scattered.

podAffinity

attracts Pods; you can try to pack any number of Pods into qualifying topology domain(s).

podAntiAffinity

repels Pods. If you set this to requiredDuringSchedulingIgnoredDuringExecution mode then only a single Pod can be scheduled into a single topology domain; if you choose preferredDuringSchedulingIgnoredDuringExecution then you lose the ability to enforce the constraint.

For finer control, you can specify topology spread constraints to distribute Pods across different topology domains - to achieve either high availability or cost-saving. This can also help on rolling update workloads and scaling out replicas smoothly.

For more context, see the Motivation section of the enhancement proposal about Pod topology spread constraints.

Known limitations

• There's no guarantee that the constraints remain satisfied when Pods are removed. For example, scaling down a Deployment may result in imbalanced Pods distribution.

  You can use a tool such as the Descheduler to rebalance the Pods distribution.

• Pods matched on tainted nodes are respected. See Issue 80921.

• The scheduler doesn't have prior knowledge of all the zones or other topology domains that a cluster has. They are determined from the existing nodes in the cluster. This could lead to a problem in autoscaled clusters, when a node pool (or node group) is scaled to zero nodes, and you're expecting the cluster to scale up, because, in this case, those topology domains won't be considered until there is at least one node in them.

  You can work around this by using an cluster autoscaling tool that is aware of Pod topology spread constraints and is also aware of the overall set of topology domains.

What's next

• The blog article Introducing PodTopologySpread explains maxSkew in some detail, as well as covering some advanced usage examples.
• Read the scheduling section of the API reference for Pod.