# Pod Topology Spread Constraints LLMS index: [llms.txt](/llms.txt) --- 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](#cluster-level-default-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](/docs/concepts/scheduling-eviction/) 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: ```yaml --- apiVersion: v1 kind: Pod metadata: name: example-pod spec: # Configure a topology spread constraint topologySpreadConstraints: - maxSkew: minDomains: # optional topologyKey: whenUnsatisfiable: labelSelector: matchLabelKeys: # optional; beta since v1.27 nodeAffinityPolicy: [Honor|Ignore] # optional; beta since v1.26 nodeTaintsPolicy: [Honor|Ignore] # optional; beta since v1.26 ### other Pod fields go here ```

Note:

There can only be one topologySpreadConstraint for a given topologyKey and whenUnsatisfiable value. For example, if you have defined a topologySpreadConstraint that uses the topologyKey "kubernetes.io/hostname" and whenUnsatisfiable value "DoNotSchedule", you can only add another topologySpreadConstraint for the topologyKey "kubernetes.io/hostname" if you use a different whenUnsatisfiable value.
You can read more about this field by running `kubectl explain Pod.spec.topologySpreadConstraints` or refer to the [scheduling](/docs/reference/kubernetes-api/workload-resources/pod-v1/#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:

Before Kubernetes v1.30, the minDomains field was only available if the MinDomainsInPodTopologySpread feature gate was enabled (default since v1.28). In older Kubernetes clusters it might be explicitly disabled or the field might not be available.
- 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](#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 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](/docs/concepts/overview/working-with-objects/labels/#label-selectors) for more details. - **matchLabelKeys** is a list of pod label keys to select the group of pods over which the spreading skew will be calculated. At a pod creation, the kube-apiserver uses those keys to lookup values from the incoming pod labels, and those key-value labels will be merged with any existing `labelSelector`. The same key is forbidden to exist in both `matchLabelKeys` and `labelSelector`. `matchLabelKeys` cannot be set when `labelSelector` isn't set. Keys that don't exist in the pod labels will be ignored. A null or empty list means only match against the `labelSelector`.

Caution:

It's not recommended to use matchLabelKeys with labels that might be updated directly on pods. Even if you edit the pod's label that is specified at matchLabelKeys directly, (that is, you edit the Pod and not a Deployment), kube-apiserver doesn't reflect the label update onto the merged labelSelector.
With `matchLabelKeys`, you 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. For example, if you are configuring a Deployment, you can use the label keyed with [pod-template-hash](/docs/concepts/workloads/controllers/deployment/#pod-template-hash-label), which is added automatically by the Deployment controller, to distinguish between different revisions in a single Deployment. ```yaml topologySpreadConstraints: - maxSkew: 1 topologyKey: kubernetes.io/hostname whenUnsatisfiable: DoNotSchedule labelSelector: matchLabels: app: foo matchLabelKeys: - pod-template-hash ```

Note:

The matchLabelKeys field is a beta-level field and enabled by default in 1.27. You can disable it by disabling the MatchLabelKeysInPodTopologySpread feature gate.

Before v1.34, matchLabelKeys was handled implicitly. Since v1.34, key-value labels corresponding to matchLabelKeys are explicitly merged into labelSelector. You can disable it and revert to the previous behavior by disabling the MatchLabelKeysInPodTopologySpreadSelectorMerge feature gate of kube-apiserver.

- **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 became beta in 1.26 and graduated to GA in 1.33. It's enabled by default in beta, you can disable it by disabling the NodeInclusionPolicyInPodTopologySpread feature gate.
- **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 became beta in 1.26 and graduated to GA in 1.33. It's enabled by default in beta, you can disable it by disabling the NodeInclusionPolicyInPodTopologySpread feature gate.
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: ```yaml region: us-east-1 zone: us-east-1a ```

Note:

For brevity, this example doesn't use the well-known label keys topology.kubernetes.io/zone and topology.kubernetes.io/region. However, those registered label keys are nonetheless recommended rather than the private (unqualified) label keys region and zone that are used here.

You can't make a reliable assumption about the meaning of a private label key between different contexts.

Suppose you have a 4-node cluster with the following labels: ``` NAME STATUS ROLES AGE VERSION LABELS node1 Ready 4m26s v1.16.0 node=node1,zone=zoneA node2 Ready 3m58s v1.16.0 node=node2,zone=zoneA node3 Ready 3m17s v1.16.0 node=node3,zone=zoneB node4 Ready 2m43s v1.16.0 node=node4,zone=zoneB ``` Then the cluster is logically viewed as below:
graph TB subgraph "zoneB" n3(Node3) n4(Node4) end subgraph "zoneA" n1(Node1) n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4 k8s; class zoneA,zoneB cluster;
## 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 labeled consistently. To avoid you needing to manually label nodes, most clusters automatically populate well-known labels such as `kubernetes.io/hostname`. Check whether your cluster supports this. ## Topology spread constraint examples ### Example: one topology spread constraint {#example-one-topologyspreadconstraint} Suppose you have a 4-node cluster where 3 Pods labeled `foo: bar` are located in node1, node2 and node3 respectively:
graph BT subgraph "zoneB" p3(Pod) --> n3(Node3) n4(Node4) end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3 k8s; class zoneA,zoneB cluster;
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 labeled `zone: ` (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`:
graph BT subgraph "zoneB" p3(Pod) --> n3(Node3) p4(mypod) --> n4(Node4) end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3 k8s; class p4 plain; class zoneA,zoneB cluster;
OR
graph BT subgraph "zoneB" p3(Pod) --> n3(Node3) p4(mypod) --> n3 n4(Node4) end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3 k8s; class p4 plain; class zoneA,zoneB cluster;
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 {#example-multiple-topologyspreadconstraints} 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:
graph BT subgraph "zoneB" p3(Pod) --> n3(Node3) n4(Node4) end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3 k8s; class p4 plain; class zoneA,zoneB cluster;
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 {#example-conflicting-topologyspreadconstraints} Multiple constraints can lead to conflicts. Suppose you have a 3-node cluster across 2 zones:
graph BT subgraph "zoneB" p4(Pod) --> n3(Node3) p5(Pod) --> n3 end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n1 p3(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3,p4,p5 k8s; class zoneA,zoneB cluster;
If you were to apply [`two-constraints.yaml`](https://raw.githubusercontent.com/kubernetes/website/main/content/en/examples/pods/topology-spread-constraints/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 {#example-topologyspreadconstraints-with-nodeaffinity} Suppose you have a 5-node cluster ranging across zones A to C:
graph BT subgraph "zoneB" p3(Pod) --> n3(Node3) n4(Node4) end subgraph "zoneA" p1(Pod) --> n1(Node1) p2(Pod) --> n2(Node2) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n1,n2,n3,n4,p1,p2,p3 k8s; class p4 plain; class zoneA,zoneB cluster;
graph BT subgraph "zoneC" n5(Node5) end classDef plain fill:#ddd,stroke:#fff,stroke-width:4px,color:#000; classDef k8s fill:#326ce5,stroke:#fff,stroke-width:4px,color:#fff; classDef cluster fill:#fff,stroke:#bbb,stroke-width:2px,color:#326ce5; class n5 k8s; class zoneC cluster;
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 only considers nodes that have all `topologySpreadConstraints[*].topologyKey` present at the same time. Nodes missing any of these `topologyKeys` are bypassed. This implies that: 1. any Pods located on those bypassed nodes do not impact `maxSkew` calculation - in the above [example](#example-conflicting-topologyspreadconstraints), 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 labeled as `foo: bar`, and zone `B` having 1 Pod labeled 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](/docs/reference/scheduling/config/#profiles). The constraints are specified with the same [API above](#topologyspreadconstraints-field), 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: ```yaml apiVersion: kubescheduler.config.k8s.io/v1 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 {#internal-default-constraints}
FEATURE STATE: Kubernetes v1.24 [stable]
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: ```yaml 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.

Note:

The PodTopologySpread plugin does not score the nodes that don't have the topology keys specified in the spreading constraints. This might result in a different default behavior compared to the legacy SelectorSpread plugin when using the default topology constraints.

If your nodes are not expected to have both kubernetes.io/hostname and topology.kubernetes.io/zone labels set, define your own constraints instead of using the Kubernetes defaults.

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: ```yaml apiVersion: kubescheduler.config.k8s.io/v1 kind: KubeSchedulerConfiguration profiles: - schedulerName: default-scheduler pluginConfig: - name: PodTopologySpread args: defaultConstraints: [] defaultingType: List ``` ## Comparison with podAffinity and podAntiAffinity {#comparison-with-podaffinity-podantiaffinity} In Kubernetes, [inter-Pod affinity and anti-affinity](/docs/concepts/scheduling-eviction/assign-pod-node/#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](https://github.com/kubernetes/enhancements/tree/master/keps/sig-scheduling/895-pod-topology-spread#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](https://github.com/kubernetes-sigs/descheduler) to rebalance the Pods distribution. - Pods matched on tainted nodes are respected. See [Issue 80921](https://github.com/kubernetes/kubernetes/issues/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 a Node autoscaler that is aware of Pod topology spread constraints and is also aware of the overall set of topology domains. - Pods that don't match their own labelSelector create "ghost pods". If a pod's labels don't match the `labelSelector` in its topology spread constraint, the pod won't count itself in spread calculations. This means: - Multiple such pods can just accumulate on the same topology (until matching pods are newly created/deleted) because those pod's schedule don't change a spreading calculation result. - The spreading constraint works in an unintended way, most likely not matching your expectations Ensure your pod's labels match the `labelSelector` in your spread constraints. Typically, a pod should match its own topology spread constraint selector. ## What's next - The blog article [Introducing PodTopologySpread](/blog/2020/05/introducing-podtopologyspread/) explains `maxSkew` in some detail, as well as covering some advanced usage examples. - Read the [scheduling](/docs/reference/kubernetes-api/workload-resources/pod-v1/#scheduling) section of the API reference for Pod.