---
title: Pod 拓扑分布约束
content_type: concept
weight: 40
---

<!--
title: Pod Topology Spread Constraints
content_type: concept
weight: 40
-->

{{< feature-state for_k8s_version="v1.19" state="stable" >}}
<!--
leave this shortcode in place until the note about EvenPodsSpread is obsolete
-->

<!-- overview -->

<!--
You can use _topology spread constraints_ to control how {{< glossary_tooltip text="Pods" term_id="Pod" >}} 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.
-->
你可以使用 _拓扑分布约束（Topology Spread Constraints）_ 来控制
{{< glossary_tooltip text="Pods" term_id="Pod" >}} 在集群内故障域
之间的分布，例如区域（Region）、可用区（Zone）、节点和其他用户自定义拓扑域。
这样做有助于实现高可用并提升资源利用率。

<!--
{{< note >}}
In versions of Kubernetes before v1.19, you must enable the `EvenPodsSpread`
[feature gate](/docs/reference/command-line-tools-reference/feature-gates/) on
the [API server](/docs/concepts/overview/components/#kube-apiserver) and the
[scheduler](/docs/reference/generated/kube-scheduler/) in order to use Pod
topology spread constraints.
{{< /note >}}
-->

{{< note >}}
在 v1.19 之前的 Kubernetes 版本中，如果要使用 Pod 拓扑扩展约束，你必须在 [API 服务器](/zh/docs/concepts/overview/components/#kube-apiserver) 
和[调度器](/zh/docs/reference/command-line-tools-reference/kube-scheduler/)
中启用 `EvenPodsSpread` [特性门控](/zh/docs/reference/command-line-tools-reference/feature-gates/)。
{{< /note >}}

<!-- body -->

<!--
## Prerequisites

### Node Labels
-->
## 先决条件   {#prerequisites}

### 节点标签   {#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`
-->
拓扑分布约束依赖于节点标签来标识每个节点所在的拓扑域。
例如，某节点可能具有标签：`node=node1,zone=us-east-1a,region=us-east-1`

<!--
Suppose you have a 4-node cluster with the following labels:
-->
假设你拥有具有以下标签的一个 4 节点集群：

```
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:
-->
然后从逻辑上看集群如下：

{{<mermaid>}}
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;
{{< /mermaid >}}

<!--
Instead of manually applying labels, you can also reuse the [well-known labels](/docs/reference/kubernetes-api/labels-annotations-taints/) that are created and populated automatically on most clusters.
-->
你可以复用在大多数集群上自动创建和填充的
[常用标签](/zh/docs/reference/kubernetes-api/labels-annotations-taints/)，
而不是手动添加标签。

<!--
## Spread Constraints for Pods
-->
## Pod 的分布约束    {#spread-constraints-for-pods}

### API

<!--
The API field `pod.spec.topologySpreadConstraints` is defined as below:
-->
`pod.spec.topologySpreadConstraints` 字段定义如下所示：

```yaml
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:
-->
你可以定义一个或多个 `topologySpreadConstraint` 来指示 kube-scheduler
如何根据与现有的 Pod 的关联关系将每个传入的 Pod 部署到集群中。字段包括：

<!--
- **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. Its semantics differs according to the value of `whenUnsatisfiable`:
  - when `whenUnsatisfiable` equals to "DoNotSchedule", `maxSkew` is the maximum
    permitted difference between the number of matching pods in the target
    topology and the global minimum.
  - when `whenUnsatisfiable` equals to "ScheduleAnyway", scheduler gives higher
    precedence to topologies that would help reduce the skew.
- **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](/docs/concepts/overview/working-with-objects/labels/#label-selectors) for more details.
-->

- **maxSkew** 描述 Pod 分布不均的程度。这是给定拓扑类型中任意两个拓扑域中
  匹配的 pod 之间的最大允许差值。它必须大于零。取决于 `whenUnsatisfiable` 的
  取值，其语义会有不同。
  - 当 `whenUnsatisfiable` 等于 "DoNotSchedule" 时，`maxSkew` 是目标拓扑域
    中匹配的 Pod 数与全局最小值之间可存在的差异。
  - 当 `whenUnsatisfiable` 等于 "ScheduleAnyway" 时，调度器会更为偏向能够降低
    偏差值的拓扑域。
- **topologyKey** 是节点标签的键。如果两个节点使用此键标记并且具有相同的标签值，
  则调度器会将这两个节点视为处于同一拓扑域中。调度器试图在每个拓扑域中放置数量
  均衡的 Pod。
- **whenUnsatisfiable** 指示如果 Pod 不满足分布约束时如何处理：
  - `DoNotSchedule`（默认）告诉调度器不要调度。
  - `ScheduleAnyway` 告诉调度器仍然继续调度，只是根据如何能将偏差最小化来对
    节点进行排序。
- **labelSelector** 用于查找匹配的 pod。匹配此标签的 Pod 将被统计，以确定相应
  拓扑域中 Pod 的数量。
  有关详细信息，请参考[标签选择算符](/zh/docs/concepts/overview/working-with-objects/labels/#label-selectors)。

<!--
You can read more about this field by running `kubectl explain Pod.spec.topologySpreadConstraints`.
-->
你可以执行 `kubectl explain Pod.spec.topologySpreadConstraints` 命令以
了解关于 topologySpreadConstraints 的更多信息。

<!--
### Example: One TopologySpreadConstraint

Suppose you have a 4-node cluster where 3 Pods labeled `foo:bar` are located in node1, node2 and node3 respectively:
-->
### 例子：单个 TopologySpreadConstraint

假设你拥有一个 4 节点集群，其中标记为 `foo:bar` 的 3 个 Pod 分别位于
node1、node2 和 node3 中：

{{<mermaid>}}
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;
{{< /mermaid >}}

<!--
If we want an incoming Pod to be evenly spread with existing Pods across zones, the spec can be given as:
-->
如果希望新来的 Pod 均匀分布在现有的可用区域，则可以按如下设置其规约：

{{< codenew file="pods/topology-spread-constraints/one-constraint.yaml" >}}

<!--
`topologyKey: zone` implies the even distribution will only be applied to the nodes which have label pair "zone:&lt;any value&gt;" present. `whenUnsatisfiable: DoNotSchedule` tells the scheduler to let it stay pending if the incoming Pod can’t satisfy the constraint.
-->
`topologyKey: zone` 意味着均匀分布将只应用于存在标签键值对为
"zone:&lt;any value&gt;" 的节点。
`whenUnsatisfiable: DoNotSchedule` 告诉调度器如果新的 Pod 不满足约束，
则让它保持悬决状态。

<!--
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":
-->
如果调度器将新的 Pod 放入 "zoneA"，Pods 分布将变为 [3, 1]，因此实际的偏差
为 2（3 - 1）。这违反了 `maxSkew: 1` 的约定。此示例中，新 Pod 只能放置在
"zoneB" 上：

{{<mermaid>}}
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;
{{< /mermaid >}}

或者

{{<mermaid>}}
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;
{{< /mermaid >}}

<!--
You can tweak the Pod spec to meet various kinds of requirements:
-->
你可以调整 Pod 规约以满足各种要求：

<!--
- 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.)
-->
- 将 `maxSkew` 更改为更大的值，比如 "2"，这样新的 Pod 也可以放在 "zoneA" 上。
- 将 `topologyKey` 更改为 "node"，以便将 Pod 均匀分布在节点上而不是区域中。
  在上面的例子中，如果 `maxSkew` 保持为 "1"，那么传入的 Pod 只能放在 "node4" 上。
- 将 `whenUnsatisfiable: DoNotSchedule` 更改为 `whenUnsatisfiable: ScheduleAnyway`，
  以确保新的 Pod 始终可以被调度（假设满足其他的调度 API）。
  但是，最好将其放置在匹配 Pod 数量较少的拓扑域中。
  （请注意，这一优先判定会与其他内部调度优先级（如资源使用率等）排序准则一起进行标准化。）

<!--
### Example: Multiple TopologySpreadConstraints
-->
### 例子：多个 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):
-->
下面的例子建立在前面例子的基础上。假设你拥有一个 4 节点集群，其中 3 个标记为 `foo:bar` 的
Pod 分别位于 node1、node2 和 node3 上：

{{<mermaid>}}
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;
{{< /mermaid >}}

<!--
You can use 2 TopologySpreadConstraints to control the Pods spreading on both zone and node:
-->
可以使用 2 个 TopologySpreadConstraint 来控制 Pod 在 区域和节点两个维度上的分布：

{{< codenew file="pods/topology-spread-constraints/two-constraints.yaml" >}}

<!--
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".
-->
在这种情况下，为了匹配第一个约束，新的 Pod 只能放置在 "zoneB" 中；而在第二个约束中，
新的 Pod 只能放置在 "node4" 上。最后两个约束的结果加在一起，唯一可行的选择是放置
在 "node4" 上。

<!--
Multiple constraints can lead to conflicts. Suppose you have a 3-node cluster across 2 zones:
-->
多个约束之间可能存在冲突。假设有一个跨越 2 个区域的 3 节点集群：

{{<mermaid>}}
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;
{{< /mermaid >}}


<!--
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.
-->
如果对集群应用 "two-constraints.yaml"，会发现 "mypod" 处于 `Pending` 状态。
这是因为：为了满足第一个约束，"mypod" 只能放在 "zoneB" 中，而第二个约束要求
"mypod" 只能放在 "node2" 上。Pod 调度无法满足两种约束。

<!--
To overcome this situation, you can either increase the `maxSkew` or modify one of the constraints to use `whenUnsatisfiable: ScheduleAnyway`.
-->
为了克服这种情况，你可以增加 `maxSkew` 或修改其中一个约束，让其使用
`whenUnsatisfiable: ScheduleAnyway`。

<!--
### Conventions

There are some implicit conventions worth noting here:
-->
### 约定   {#conventions}

这里有一些值得注意的隐式约定：

<!--
- 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".
-->
- 只有与新的 Pod 具有相同命名空间的 Pod 才能作为匹配候选者。
- 没有 `topologySpreadConstraints[*].topologyKey` 的节点将被忽略。这意味着：
  1. 位于这些节点上的 Pod 不影响 `maxSkew` 的计算。
     在上面的例子中，假设 "node1" 没有标签 "zone"，那么 2 个 Pod 将被忽略，
     因此传入的 Pod 将被调度到 "zoneA" 中。

  2. 新的 Pod 没有机会被调度到这类节点上。
     在上面的例子中，假设一个带有标签 `{zone-typo: zoneC}` 的 "node5" 加入到集群，
     它将由于没有标签键 "zone" 而被忽略。

<!--
- 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.
-->
- 注意，如果新 Pod 的 `topologySpreadConstraints[*].labelSelector` 与自身的
  标签不匹配，将会发生什么。
  在上面的例子中，如果移除新 Pod 上的标签，Pod 仍然可以调度到 "zoneB"，因为约束仍然满足。
  然而，在调度之后，集群的不平衡程度保持不变。zoneA 仍然有 2 个带有 {foo:bar} 标签的 Pod，
  zoneB 有 1 个带有 {foo:bar} 标签的 Pod。
  因此，如果这不是你所期望的，建议工作负载的 `topologySpreadConstraints[*].labelSelector`
  与其自身的标签匹配。

<!--
- 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:
-->
- 如果新 Pod 定义了 `spec.nodeSelector` 或 `spec.affinity.nodeAffinity`，则
  不匹配的节点会被忽略。

  假设你有一个跨越 zoneA 到 zoneC 的 5 节点集群：

  {{<mermaid>}}
  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;
  {{< /mermaid >}}

  {{<mermaid>}}
  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;
  {{< /mermaid >}}

  <!--
  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.
  -->
  而且你知道 "zoneC" 必须被排除在外。在这种情况下，可以按如下方式编写 yaml，
  以便将 "mypod" 放置在 "zoneB" 上，而不是 "zoneC" 上。同样，`spec.nodeSelector`
  也要一样处理。

  {{< codenew file="pods/topology-spread-constraints/one-constraint-with-nodeaffinity.yaml" >}}

<!--
### 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.
-->
### 集群级别的默认约束   {#cluster-level-default-constraints}

为集群设置默认的拓扑分布约束也是可能的。默认拓扑分布约束在且仅在以下条件满足
时才会应用到 Pod 上：

- Pod 没有在其 `.spec.topologySpreadConstraints` 设置任何约束；
- Pod 隶属于某个服务、副本控制器、ReplicaSet 或 StatefulSet。

<!--
Default constraints can be set as part of the `PodTopologySpread` plugin args
in a [scheduling profile](/docs/reference/scheduling/config/#profiles).
The constraints are specified with the same [API above](#api), 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:
-->
你可以在 [调度方案（Schedulingg Profile）](/zh/docs/reference/scheduling/config/#profiles)
中将默认约束作为 `PodTopologySpread` 插件参数的一部分来设置。
约束的设置采用[如前所述的 API](#api)，只是 `labelSelector` 必须为空。
选择算符是根据 Pod 所属的服务、副本控制器、ReplicaSet 或 StatefulSet 来设置的。

配置的示例可能看起来像下面这个样子：

```yaml
apiVersion: kubescheduler.config.k8s.io/v1beta1
kind: KubeSchedulerConfiguration

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

{{< note >}}
<!--
The score produced by default scheduling constraints might conflict with the
score produced by the
[`SelectorSpread` plugin](/docs/reference/scheduling/config/#scheduling-plugins).
It is recommended that you disable this plugin in the scheduling profile when
using default constraints for `PodTopologySpread`.
-->
默认调度约束所生成的评分可能与
[`SelectorSpread` 插件](/zh/docs/reference/scheduling/config/#scheduling-plugins).
所生成的评分有冲突。
建议你在为 `PodTopologySpread` 设置默认约束是禁用调度方案中的该插件。
{{< /note >}}

<!--
#### Internal default constraints
-->
#### 内部默认约束    {#internal-default-constraints}

{{< feature-state for_k8s_version="v1.20" state="beta" >}}

<!--
With the `DefaultPodTopologySpread` feature gate, enabled by default, the
legacy `SelectorSpread` plugin is disabled.
kube-scheduler uses the following default topology constraints for the
`PodTopologySpread` plugin configuration:
-->
当你使用了默认启用的 `DefaultPodTopologySpread` 特性门控时，原来的
`SelectorSpread` 插件会被禁用。
kube-scheduler 会使用下面的默认拓扑约束作为 `PodTopologySpread` 插件的
配置：

```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.
-->
此外，原来用于提供等同行为的 `SelectorSpread` 插件也会被禁用。

<!--
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.

The `PodTopologySpread` plugin does not score the nodes that don't have
the topology keys specified in the spreading constraints.
-->
{{< note >}}
如果你的节点不会 **同时** 设置 `kubernetes.io/hostname` 和
`topology.kubernetes.io/zone` 标签，你应该定义自己的约束而不是使用
Kubernetes 的默认约束。

插件 `PodTopologySpread` 不会为未设置分布约束中所给拓扑键的节点评分。
{{< /note >}}

<!--
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:
-->
如果你不想为集群使用默认的 Pod 分布约束，你可以通过设置 `defaultingType` 参数为 `List` 和
将 `PodTopologySpread` 插件配置中的 `defaultConstraints` 参数置空来禁用默认 Pod 分布约束。

```yaml
apiVersion: kubescheduler.config.k8s.io/v1beta1
kind: KubeSchedulerConfiguration

profiles:
  - pluginConfig:
      - name: PodTopologySpread
        args:
          defaultConstraints: []
          defaultingType: List
```

<!--
## Comparison with PodAffinity/PodAntiAffinity

In Kubernetes, directives related to "Affinity" control how Pods are
scheduled - more packed or more scattered.
-->
## 与 PodAffinity/PodAntiAffinity 相比较

在 Kubernetes 中，与“亲和性”相关的指令控制 Pod 的调度方式（更密集或更分散）。

<!--
- 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.
-->
- 对于 `PodAffinity`，你可以尝试将任意数量的 Pod 集中到符合条件的拓扑域中。
- 对于 `PodAntiAffinity`，只能将一个 Pod 调度到某个拓扑域中。

<!--
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. See
[Motivation](https://github.com/kubernetes/enhancements/tree/master/keps/sig-scheduling/895-pod-topology-spread#motivation)
for more details.


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](https://github.com/kubernetes/enhancements/blob/master/keps/sig-scheduling/20190221-pod-topology-spread.md#motivation) for more details.
-->
要实现更细粒度的控制，你可以设置拓扑分布约束来将 Pod 分布到不同的拓扑域下，
从而实现高可用性或节省成本。这也有助于工作负载的滚动更新和平稳地扩展副本规模。
有关详细信息，请参考
[动机](https://github.com/kubernetes/enhancements/blob/master/keps/sig-scheduling/20190221-pod-topology-spread.md#motivation)文档。

<!--
## Known Limitations

- Scaling down a Deployment may result in imbalanced Pods distribution.
- Pods matched on tainted nodes are respected. See [Issue 80921](https://github.com/kubernetes/kubernetes/issues/80921)
-->
## 已知局限性

- Deployment 缩容操作可能导致 Pod 分布不平衡。
- 具有污点的节点上的 Pods 也会被统计。
  参考 [Issue 80921](https://github.com/kubernetes/kubernetes/issues/80921)。

## {{% heading "whatsnext" %}}

<!--
- [Blog: Introducing PodTopologySpread](https://kubernetes.io/blog/2020/05/introducing-podtopologyspread/)
  explains `maxSkew` in details, as well as bringing up some advanced usage examples.
-->
- [博客: PodTopologySpread介绍](https://kubernetes.io/blog/2020/05/introducing-podtopologyspread/)
  详细解释了 `maxSkew`，并给出了一些高级的使用示例。