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Pod Topology Spread Constraints

You can use topology spread constraints to control how PodsThe smallest and simplest Kubernetes object. A Pod represents a set of running containers on your cluster. 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.

• Prerequisites
• Spread Constraints for Pods
• Comparison with PodAffinity/PodAntiAffinity
• Known Limitations

Prerequisites

Enable Feature Gate

The EvenPodsSpread feature gate must be enabled for the API ServerControl plane component that serves the Kubernetes API. and schedulerControl plane component that watches for newly created pods with no assigned node, and selects a node for them to run on. .

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: node=node1,zone=us-east-1a,region=us-east-1

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:

+---------------+---------------+
|     zoneA     |     zoneB     |
+-------+-------+-------+-------+
| node1 | node2 | node3 | node4 |
+-------+-------+-------+-------+

Instead of manually applying labels, you can also reuse the well-known labels that are created and populated automatically on most clusters.

Spread Constraints for Pods

API

The field pod.spec.topologySpreadConstraints is introduced in 1.16 as below:

apiVersion: v1
kind: Pod
metadata:
  name: mypod
spec:
  topologySpreadConstraints:
  - maxSkew: <integer>
    topologyKey: <string>
    whenUnsatisfiable: <string>
    labelSelector: <object>

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

• maxSkew describes the degree to which Pods may be unevenly distributed. It’s the maximum permitted difference between the number of matching Pods in any two topology domains of a given topology type. It must be greater than zero.
• topologyKey is the key of node labels. If two Nodes are labelled with this key and have identical values for that label, the scheduler treats both Nodes as being in the same topology. The scheduler tries to place a balanced number of Pods into each topology domain.
• 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.

You can read more about this field by running kubectl explain Pod.spec.topologySpreadConstraints.

Example: One TopologySpreadConstraint

Suppose you have a 4-node cluster where 3 Pods labeled foo:bar are located in node1, node2 and node3 respectively (P represents Pod):

+---------------+---------------+
|     zoneA     |     zoneB     |
+-------+-------+-------+-------+
| node1 | node2 | node3 | node4 |
+-------+-------+-------+-------+
|   P   |   P   |   P   |       |
+-------+-------+-------+-------+

If we want an incoming Pod to be evenly spread with existing Pods across zones, the spec can be given as:

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: k8s.gcr.io/pause:3.1

topologyKey: zone implies the even distribution will only be applied to the nodes which have label pair “zone:” present. whenUnsatisfiable: DoNotSchedule tells the scheduler to let it stay pending if the incoming Pod can’t satisfy the constraint.

If the scheduler placed this incoming Pod into “zoneA”, the Pods distribution would become [3, 1], hence the actual skew is 2 (3 - 1) - which violates maxSkew: 1. In this example, the incoming Pod can only be placed onto “zoneB”:

+---------------+---------------+      +---------------+---------------+
|     zoneA     |     zoneB     |      |     zoneA     |     zoneB     |
+-------+-------+-------+-------+      +-------+-------+-------+-------+
| node1 | node2 | node3 | node4 |  OR  | node1 | node2 | node3 | node4 |
+-------+-------+-------+-------+      +-------+-------+-------+-------+
|   P   |   P   |   P   |   P   |      |   P   |   P   |  P P  |       |
+-------+-------+-------+-------+      +-------+-------+-------+-------+

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

• Change maxSkew to a bigger value like “2” so that the incoming Pod can be placed onto “zoneA” 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 “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 onto the topology domain which has fewer matching Pods. (Be aware that this preferability is jointly normalized with other internal scheduling priorities like resource usage ratio, etc.)

Example: Multiple TopologySpreadConstraints

This builds upon the previous example. Suppose you have a 4-node cluster where 3 Pods labeled foo:bar are located in node1, node2 and node3 respectively (P represents Pod):

+---------------+---------------+
|     zoneA     |     zoneB     |
+-------+-------+-------+-------+
| node1 | node2 | node3 | node4 |
+-------+-------+-------+-------+
|   P   |   P   |   P   |       |
+-------+-------+-------+-------+

You can use 2 TopologySpreadConstraints to control the Pods spreading on both zone and node:

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: k8s.gcr.io/pause:3.1

In this case, to match the first constraint, the incoming Pod can only be placed onto “zoneB”; while in terms of the second constraint, the incoming Pod can only be placed onto “node4”. Then the results of 2 constraints are ANDed, so the only viable option is to place on “node4”.

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

+---------------+-------+
|     zoneA     | zoneB |
+-------+-------+-------+
| node1 | node2 | node3 |
+-------+-------+-------+
|  P P  |   P   |  P P  |
+-------+-------+-------+

If you apply “two-constraints.yaml” to this cluster, you will notice “mypod” stays in Pending state. This is because: to satisfy the first constraint, “mypod” can only be put to “zoneB”; while in terms of the second constraint, “mypod” can only put to “node2”. Then a joint result of “zoneB” and “node2” returns nothing.

To overcome this situation, you can either increase the maxSkew or modify one of the constraints to use whenUnsatisfiable: ScheduleAnyway.

Conventions

There are some implicit conventions worth noting here:

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

• Nodes without topologySpreadConstraints[*].topologyKey present will be bypassed. It implies that:

  1. the Pods located on those nodes do not impact maxSkew calculation - in the above example, suppose “node1” does not have label “zone”, then the 2 Pods will be disregarded, hence the incomingPod will be scheduled into “zoneA”.
  2. the incoming Pod has no chances to be scheduled onto this kind of nodes - in the above example, suppose a “node5” carrying label {zone-typo: zoneC} joins the cluster, it will be bypassed due to the absence of label key “zone”.

• Be aware of what will happen if the incomingPod’s topologySpreadConstraints[*].labelSelector doesn’t match its own labels. In the above example, if we remove the incoming Pod’s labels, it can still be placed onto “zoneB” since the constraints are still satisfied. However, after the placement, the degree of imbalance of the cluster remains unchanged - it’s still zoneA having 2 Pods which hold label {foo:bar}, and zoneB having 1 Pod which holds label {foo:bar}. So if this is not what you expect, we recommend the workload’s topologySpreadConstraints[*].labelSelector to match its own labels.

• If the incoming Pod has spec.nodeSelector or spec.affinity.nodeAffinity defined, nodes not matching them will be bypassed.

  Suppose you have a 5-node cluster ranging from zoneA to zoneC:

  +---------------+---------------+-------+
  |     zoneA     |     zoneB     | zoneC |
  +-------+-------+-------+-------+-------+
  | node1 | node2 | node3 | node4 | node5 |
  +-------+-------+-------+-------+-------+
  |   P   |   P   |   P   |       |       |
  +-------+-------+-------+-------+-------+
  

  and you know that “zoneC” must be excluded. In this case, you can compose the yaml as below, so that “mypod” will be placed onto “zoneB” instead of “zoneC”. Similarly spec.nodeSelector is also respected.

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: k8s.gcr.io/pause:3.1

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, replication controller, replica set or stateful set.

Default constraints can be set as part of the PodTopologySpread plugin args 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, replication controllers, replica sets or stateful sets that the Pod belongs to.

An example configuration might look like follows:

apiVersion: kubescheduler.config.k8s.io/v1alpha2
kind: KubeSchedulerConfiguration

profiles:
  pluginConfig:
  - name: PodTopologySpread
    args:
      defaultConstraints:
      - maxSkew: 1
        topologyKey: failure-domain.beta.kubernetes.io/zone
        whenUnsatisfiable: ScheduleAnyway

Note: The score produced by default scheduling constraints might conflict with the score produced by the DefaultPodTopologySpread plugin. It is recommended that you disable this plugin in the scheduling profile when using default constraints for PodTopologySpread.

Comparison with PodAffinity/PodAntiAffinity

In Kubernetes, directives related to “Affinity” control how Pods are scheduled - more packed or more scattered.

• For PodAffinity, you can try to pack any number of Pods into qualifying topology domain(s)
• For PodAntiAffinity, only one Pod can be scheduled into a single topology domain.

The “EvenPodsSpread” feature provides flexible options to distribute Pods evenly across different topology domains - to achieve high availability or cost-saving. This can also help on rolling update workloads and scaling out replicas smoothly. See Motivation for more details.

Known Limitations

As of 1.18, at which this feature is Beta, there are some known limitations:

• Scaling down a Deployment may result in imbalanced Pods distribution.
• Pods matched on tainted nodes are respected. See Issue 80921

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