---
title: Pod 水平自动扩缩
feature:
  title: 水平扩缩 
  description: >
    使用一个简单的命令、一个 UI 或基于 CPU 使用情况自动对应用程序进行扩缩。

content_type: concept
weight: 90
---

<!-- overview -->

<!--
In Kubernetes, a _HorizontalPodAutoscaler_ automatically updates a workload resource (such as
a {{< glossary_tooltip text="Deployment" term_id="deployment" >}} or
{{< glossary_tooltip text="StatefulSet" term_id="statefulset" >}}), with the
aim of automatically scaling the workload to match demand.
-->
在 Kubernetes 中，_HorizontalPodAutoscaler_ 自动更新工作负载资源
（例如 {{< glossary_tooltip text="Deployment" term_id="deployment" >}} 或者 
{{< glossary_tooltip text="StatefulSet" term_id="statefulset" >}}），
目的是自动扩缩工作负载以满足需求。

<!--
Horizontal scaling means that the response to increased load is to deploy more
{{< glossary_tooltip text="Pods" term_id="pod" >}}.
This is different from _vertical_ scaling, which for Kubernetes would mean
assigning more resources (for example: memory or CPU) to the Pods that are already
running for the workload.

If the load decreases, and the number of Pods is above the configured minimum,
the HorizontalPodAutoscaler instructs the workload resource (the Deployment, StatefulSet,
or other similar resource) to scale back down.
-->
水平扩缩意味着对增加的负载的响应是部署更多的 {{< glossary_tooltip text="Pods" term_id="pod" >}}。
这与 “垂直（Vertical）” 扩缩不同，对于 Kubernetes，
垂直扩缩意味着将更多资源（例如：内存或 CPU）分配给已经为工作负载运行的 Pod。

如果负载减少，并且 Pod 的数量高于配置的最小值，
HorizontalPodAutoscaler 会指示工作负载资源（ Deployment、StatefulSet 或其他类似资源）缩减。

<!--
Horizontal pod autoscaling does not apply to objects that can't be scaled (for example:
a {{< glossary_tooltip text="DaemonSet" term_id="daemonset" >}}.)
-->
水平 Pod 自动扩缩不适用于无法扩缩的对象（例如：{{< glossary_tooltip text="DaemonSet" term_id="daemonset" >}}。）

<!--
The HorizontalPodAutoscaler is implemented as a Kubernetes API resource and a
{{< glossary_tooltip text="controller" term_id="controller" >}}.
The resource determines the behavior of the controller.
The horizontal pod autoscaling controller, running within the Kubernetes
{{< glossary_tooltip text="control plane" term_id="control-plane" >}}, periodically adjusts the
desired scale of its target (for example, a Deployment) to match observed metrics such as average
CPU utilization, average memory utilization, or any other custom metric you specify.
 -->
HorizontalPodAutoscaler 被实现为 Kubernetes API 资源和{{< glossary_tooltip text="控制器" term_id="controller" >}}。

资源决定了控制器的行为。在 Kubernetes {{< glossary_tooltip text="控制平面" term_id="control-plane" >}}内运行的水平
Pod 自动扩缩控制器会定期调整其目标（例如：Deployment）的所需规模，以匹配观察到的指标，
例如，平均 CPU 利用率、平均内存利用率或你指定的任何其他自定义指标。

<!--
There is [walkthrough example](/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/) of using
horizontal pod autoscaling.
-->
使用水平 Pod 自动扩缩[演练示例](/zh/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/)。

<!-- body -->

<!--
## How does a HorizontalPodAutoscaler work?
-->
## HorizontalPodAutoscaler 是如何工作的？ {#how-does-a-horizontalpodautoscaler-work}

{{< figure src="/images/docs/horizontal-pod-autoscaler.svg" caption="HorizontalPodAutoscaler 控制 Deployment 及其 ReplicaSet 的规模" class="diagram-medium">}}

<!--
Kubernetes implements horizontal pod autoscaling as a control loop that runs intermittently
(it is not a continuous process). The interval is set by the
`--horizontal-pod-autoscaler-sync-period` parameter to the
[`kube-controller-manager`](/docs/reference/command-line-tools-reference/kube-controller-manager/)
(and the default interval is 15 seconds).
-->
Kubernetes 将水平 Pod 自动扩缩实现为一个间歇运行的控制回路（它不是一个连续的过程）。间隔由 
[`kube-controller-manager`](/zh/docs/reference/command-line-tools-reference/kube-controller-manager/) 
的 `--horizontal-pod-autoscaler-sync-period` 参数设置（默认间隔为 15 秒）。

<!--
Once during each period, the controller manager queries the resource utilization against the
metrics specified in each HorizontalPodAutoscaler definition.  The controller manager 
finds the target resource defined by the `scaleTargetRef`,
then selects the pods based on the target resource's `.spec.selector` labels, and obtains the metrics from either the resource metrics API (for per-pod resource metrics),
or the custom metrics API (for all other metrics).
-->
在每个时间段内，控制器管理器都会根据每个 HorizontalPodAutoscaler 定义中指定的指标查询资源利用率。
控制器管理器找到由 `scaleTargetRef` 定义的目标资源，然后根据目标资源的 `.spec.selector` 标签选择 Pod，
并从资源指标 API（针对每个 Pod 的资源指标）或自定义指标获取指标 API（适用于所有其他指标）。

<!--
* For per-pod resource metrics (like CPU), the controller fetches the metrics
  from the resource metrics API for each Pod targeted by the HorizontalPodAutoscaler.
  Then, if a target utilization value is set, the controller calculates the utilization
  value as a percentage of the equivalent [resource request](/docs/concepts/configuration/manage-resources-containers/#requests-and-limits) on the containers in
  each Pod.  If a target raw value is set, the raw metric values are used directly.
  The controller then takes the mean of the utilization or the raw value (depending on the type
  of target specified) across all targeted Pods, and produces a ratio used to scale
  the number of desired replicas.
-->
* 对于按 Pod 统计的资源指标（如 CPU），控制器从资源指标 API 中获取每一个
  HorizontalPodAutoscaler 指定的 Pod 的度量值，如果设置了目标使用率，
  控制器获取每个 Pod 中的容器[资源使用](/zh/docs/concepts/configuration/manage-resources-containers/#requests-and-limits) 情况，
  并计算资源使用率。如果设置了 target 值，将直接使用原始数据（不再计算百分比）。
  接下来，控制器根据平均的资源使用率或原始值计算出扩缩的比例，进而计算出目标副本数。

  <!--
  Please note that if some of the Pod's containers do not have the relevant resource request set,
  CPU utilization for the Pod will not be defined and the autoscaler will
  not take any action for that metric. See the [algorithm
  details](#algorithm-details) section below for more information about
  how the autoscaling algorithm works.
  -->
  需要注意的是，如果 Pod 某些容器不支持资源采集，那么控制器将不会使用该 Pod 的 CPU 使用率。
  下面的[算法细节](#algorithm-details)章节将会介绍详细的算法。

<!--
* For per-pod custom metrics, the controller functions similarly to per-pod resource metrics,
  except that it works with raw values, not utilization values.
-->
* 如果 Pod 使用自定义指示，控制器机制与资源指标类似，区别在于自定义指标只使用
  原始值，而不是使用率。

<!--
* For object metrics and external metrics, a single metric is fetched, which describes
  the object in question. This metric is compared to the target
  value, to produce a ratio as above. In the `autoscaling/v2beta2` API
  version, this value can optionally be divided by the number of Pods before the
  comparison is made.
-->
* 如果 Pod 使用对象指标和外部指标（每个指标描述一个对象信息）。
  这个指标将直接根据目标设定值相比较，并生成一个上面提到的扩缩比例。
  在 `autoscaling/v2beta2` 版本 API 中，这个指标也可以根据 Pod 数量平分后再计算。

<!--
The common use for HorizontalPodAutoscaler is to configure it to fetch metrics from
{{< glossary_tooltip text="aggregated APIs" term_id="aggregation-layer" >}}
(`metrics.k8s.io`, `custom.metrics.k8s.io`, or `external.metrics.k8s.io`).  The `metrics.k8s.io` API is
usually provided by an add-on named Metrics Server, which needs to be launched separately.
For more information about resource metrics, see
[Metrics Server](/docs/tasks/debug/debug-cluster/resource-metrics-pipeline/#metrics-server).
-->
HorizontalPodAutoscaler 的常见用途是将其配置为从{{< glossary_tooltip text="聚合 API" term_id="aggregation-layer" >}}
（`metrics.k8s.io`、`custom.metrics.k8s.io` 或 `external.metrics.k8s.io`）获取指标。
`metrics.k8s.io` API 通常由名为 Metrics Server 的插件提供，需要单独启动。有关资源指标的更多信息，
请参阅 [Metrics Server](/zh/docs/tasks/debug/debug-cluster/resource-metrics-pipeline/#metrics-server)。

<!--
[Support for metrics APIs](#support-for-metrics-apis) explains the stability guarantees and support status for these
different APIs.

The HorizontalPodAutoscaler controller accesses corresponding workload resources that support scaling (such as Deployments
and StatefulSet). These resources each have a subresource named `scale`, an interface that allows you to dynamically set the
number of replicas and examine each of their current states.
For general information about subresources in the Kubernetes API, see
[Kubernetes API Concepts](/docs/reference/using-api/api-concepts/).
-->
对 [Metrics API 的支持](#support-for-metrics-apis)解释了这些不同 API 的稳定性保证和支持状态

HorizontalPodAutoscaler 控制器访问支持扩缩的相应工作负载资源（例如：Deployments 和 StatefulSet）。
这些资源每个都有一个名为 `scale` 的子资源，该接口允许你动态设置副本的数量并检查它们的每个当前状态。
有关 Kubernetes API 子资源的一般信息，
请参阅 [Kubernetes API 概念](/zh/docs/reference/using-api/api-concepts/)。

<!--
### Algorithm Details

From the most basic perspective, the Horizontal Pod Autoscaler controller
operates on the ratio between desired metric value and current metric
value:
-->
### 算法细节   {#algorithm-details}

从最基本的角度来看，Pod 水平自动扩缩控制器根据当前指标和期望指标来计算扩缩比例。

<!--
```
desiredReplicas = ceil[currentReplicas * ( currentMetricValue / desiredMetricValue )]
```
-->
```
期望副本数 = ceil[当前副本数 * (当前指标 / 期望指标)]
```

<!--
For example, if the current metric value is `200m`, and the desired value
is `100m`, the number of replicas will be doubled, since `200.0 / 100.0 ==
2.0` If the current value is instead `50m`, you'll halve the number of
replicas, since `50.0 / 100.0 == 0.5`.  The control plane skips any scaling
action if the ratio is sufficiently close to 1.0 (within a globally-configurable
tolerance, 0.1 by default).
-->
例如，如果当前指标值为 `200m`，而期望值为 `100m`，则副本数将加倍，
因为 `200.0 / 100.0 == 2.0` 如果当前值为 `50m`，则副本数将减半，
因为 `50.0 / 100.0 == 0.5`。如果比率足够接近 1.0（在全局可配置的容差范围内，默认为 0.1），
则控制平面会跳过扩缩操作。

<!--
When a `targetAverageValue` or `targetAverageUtilization` is specified,
the `currentMetricValue` is computed by taking the average of the given
metric across all Pods in the HorizontalPodAutoscaler's scale target.

Before checking the tolerance and deciding on the final values, the control
plane also considers whether any metrics are missing, and how many Pods
are [`Ready`](/docs/concepts/workloads/pods/pod-lifecycle/#pod-conditions).
-->
如果 HorizontalPodAutoscaler 指定的是 `targetAverageValue` 或 `targetAverageUtilization`，
那么将会把指定 Pod 度量值的平均值做为 `currentMetricValue`。

在检查容差并决定最终值之前，控制平面还会考虑是否缺少任何指标，
以及有多少 Pod [`已就绪`](/zh/docs/concepts/workloads/pods/pod-lifecycle/#pod-conditions)。

<!--
All Pods with a deletion timestamp set (objects with a deletion timestamp are
in the process of being shut down / removed) are ignored, and all failed Pods
are discarded.

If a particular Pod is missing metrics, it is set aside for later; Pods
with missing metrics will be used to adjust the final scaling amount.
-->
所有设置了删除时间戳的 Pod（带有删除时间戳的对象正在关闭/移除的过程中）都会被忽略，
所有失败的 Pod 都会被丢弃。

如果某个 Pod 缺失度量值，它将会被搁置，只在最终确定扩缩数量时再考虑。

<!--
When scaling on CPU, if any pod has yet to become ready (it's still
initializing, or possibly is unhealthy) *or* the most recent metric point for the pod was before it
became ready, that pod is set aside as well.
-->
当使用 CPU 指标来扩缩时，任何还未就绪（还在初始化，或者可能是不健康的）状态的 Pod **或** 
最近的指标度量值采集于就绪状态前的 Pod，该 Pod 也会被搁置。

<!--
Due to technical constraints, the HorizontalPodAutoscaler controller
cannot exactly determine the first time a pod becomes ready when
determining whether to set aside certain CPU metrics. Instead, it
considers a Pod "not yet ready" if it's unready and transitioned to
unready within a short, configurable window of time since it started.
This value is configured with the `--horizontal-pod-autoscaler-initial-readiness-delay` flag, and its default is 30
seconds.  Once a pod has become ready, it considers any transition to
ready to be the first if it occurred within a longer, configurable time
since it started. This value is configured with the `--horizontal-pod-autoscaler-cpu-initialization-period` flag, and its
default is 5 minutes.
-->
由于技术限制，HorizontalPodAutoscaler 控制器在确定是否保留某些 CPU 指标时无法准确确定 Pod 首次就绪的时间。
相反，如果 Pod 未准备好并在其启动后的一个可配置的短时间窗口内转换为未准备好，它会认为 Pod “尚未准备好”。
该值使用 `--horizontal-pod-autoscaler-initial-readiness-delay` 标志配置，默认值为 30 秒。
一旦 Pod 准备就绪，如果它发生在自启动后较长的、可配置的时间内，它就会认为任何向准备就绪的转换都是第一个。
该值由 `-horizontal-pod-autoscaler-cpu-initialization-period` 标志配置，默认为 5 分钟。

<!--
The `currentMetricValue / desiredMetricValue` base scale ratio is then
calculated using the remaining pods not set aside or discarded from above.
-->
在排除掉被搁置的 Pod 后，扩缩比例就会根据 `currentMetricValue/desiredMetricValue`
计算出来。

<!--
If there were any missing metrics, the control plane recomputes the average more
conservatively, assuming those pods were consuming 100% of the desired
value in case of a scale down, and 0% in case of a scale up.  This dampens
the magnitude of any potential scale.
-->
如果缺失某些度量值，控制平面会更保守地重新计算平均值，在需要缩小时假设这些 Pod 消耗了目标值的 100%，
在需要放大时假设这些 Pod 消耗了 0% 目标值。这可以在一定程度上抑制扩缩的幅度。

<!--
Furthermore, if any not-yet-ready pods were present, and the workload would have
scaled up without factoring in missing metrics or not-yet-ready pods,
the controller conservatively assumes that the not-yet-ready pods are consuming 0%
of the desired metric, further dampening the magnitude of a scale up.
-->
此外，如果存在任何尚未就绪的 Pod，工作负载会在不考虑遗漏指标或尚未就绪的 Pod 的情况下进行扩缩，
控制器保守地假设尚未就绪的 Pod 消耗了期望指标的 0%，从而进一步降低了扩缩的幅度。

<!--
After factoring in the not-yet-ready pods and missing metrics, the
controller recalculates the usage ratio.  If the new ratio reverses the scale
direction, or is within the tolerance, the controller doesn't take any scaling
action. In other cases, the new ratio is used to decide any change to the
number of Pods.
-->
考虑到尚未准备好的 Pod 和缺失的指标后，控制器会重新计算使用率。
如果新的比率与扩缩方向相反，或者在容差范围内，则控制器不会执行任何扩缩操作。
在其他情况下，新比率用于决定对 Pod 数量的任何更改。

<!--
Note that the *original* value for the average utilization is reported
back via the HorizontalPodAutoscaler status, without factoring in the
not-yet-ready pods or missing metrics, even when the new usage ratio is
used.
-->
注意，平均利用率的 **原始** 值是通过 HorizontalPodAutoscaler 状态体现的，
而不考虑尚未准备好的 Pod 或缺少的指标，即使使用新的使用率也是如此。

<!--
If multiple metrics are specified in a HorizontalPodAutoscaler, this
calculation is done for each metric, and then the largest of the desired
replica counts is chosen. If any of these metrics cannot be converted
into a desired replica count (e.g. due to an error fetching the metrics
from the metrics APIs) and a scale down is suggested by the metrics which
can be fetched, scaling is skipped. This means that the HPA is still capable
of scaling up if one or more metrics give a `desiredReplicas` greater than
the current value.
-->
如果创建 HorizontalPodAutoscaler 时指定了多个指标，
那么会按照每个指标分别计算扩缩副本数，取最大值进行扩缩。
如果任何一个指标无法顺利地计算出扩缩副本数（比如，通过 API 获取指标时出错），
并且可获取的指标建议缩容，那么本次扩缩会被跳过。
这表示，如果一个或多个指标给出的 `desiredReplicas` 值大于当前值，HPA 仍然能实现扩容。

<!--
Finally, right before HPA scales the target, the scale recommendation is recorded.  The
controller considers all recommendations within a configurable window choosing the
highest recommendation from within that window. This value can be configured using the `--horizontal-pod-autoscaler-downscale-stabilization` flag, which defaults to 5 minutes.
This means that scaledowns will occur gradually, smoothing out the impact of rapidly
fluctuating metric values.
-->
最后，在 HPA 控制器执行扩缩操作之前，会记录扩缩建议信息。
控制器会在操作时间窗口中考虑所有的建议信息，并从中选择得分最高的建议。
这个值可通过 `kube-controller-manager` 服务的启动参数
`--horizontal-pod-autoscaler-downscale-stabilization` 进行配置，
默认值为 5 分钟。
这个配置可以让系统更为平滑地进行缩容操作，从而消除短时间内指标值快速波动产生的影响。

<!--
## API Object

The Horizontal Pod Autoscaler is an API resource in the Kubernetes
`autoscaling` API group.  The current stable version can be found in
the `autoscaling/v2` API version which includes support for scaling on
memory and custom metrics. The new fields introduced in
`autoscaling/v2` are preserved as annotations when working with
`autoscaling/v1`.
-->
## API 对象   {#api-object}

HorizontalPodAutoscaler 是 Kubernetes `autoscaling` API 组中的 API 资源。
当前的稳定版本可以在 `autoscaling/v2` API 版本中找到，其中包括对基于内存和自定义指标执行扩缩的支持。
在使用 `autoscaling/v1` 时，`autoscaling/v2` 中引入的新字段作为注释保留。

<!--
When you create a HorizontalPodAutoscaler API object, make sure the name specified is a valid
[DNS subdomain name](/docs/concepts/overview/working-with-objects/names#dns-subdomain-names).
More details about the API object can be found at
[HorizontalPodAutoscaler Object](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#horizontalpodautoscaler-v2-autoscaling).
-->
创建 HorizontalPodAutoscaler 对象时，需要确保所给的名称是一个合法的
[DNS 子域名](/zh/docs/concepts/overview/working-with-objects/names#dns-subdomain-names)。
有关 API 对象的更多信息，请查阅
[HorizontalPodAutoscaler 对象设计文档](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#horizontalpodautoscaler-v2-autoscaling)。

<!--
## Stability of workload scale {#flapping}

When managing the scale of a group of replicas using the HorizontalPodAutoscaler,
it is possible that the number of replicas keeps fluctuating frequently due to the
dynamic nature of the metrics evaluated. This is sometimes referred to as *thrashing*,
or *flapping*. It's similar to the concept of *hysteresis* in cybernetics.
-->
## 工作量规模的稳定性 {#flapping}

在使用 HorizontalPodAutoscaler 管理一组副本的规模时，由于评估的指标的动态特性，
副本的数量可能会经常波动。这有时被称为 **抖动（thrashing）** 或 **波动（flapping）**。它类似于控制论中的 **滞后（hysteresis）** 概念。

<!--
## Autoscaling during rolling update

Kubernetes lets you perform a rolling update on a Deployment. In that
case, the Deployment manages the underlying ReplicaSets for you.
When you configure autoscaling for a Deployment, you bind a
HorizontalPodAutoscaler to a single Deployment. The HorizontalPodAutoscaler
manages the `replicas` field of the Deployment. The deployment controller is responsible
for setting the `replicas` of the underlying ReplicaSets so that they add up to a suitable
number during the rollout and also afterwards.
-->
## 滚动升级时扩缩   {#autoscaling-during-rolling-update}

Kubernetes 允许你在 Deployment 上执行滚动更新。在这种情况下，Deployment 为你管理下层的 ReplicaSet。
当你为一个 Deployment 配置自动扩缩时，你要为每个 Deployment 绑定一个 HorizontalPodAutoscaler。
HorizontalPodAutoscaler 管理 Deployment 的 `replicas` 字段。
Deployment Controller 负责设置下层 ReplicaSet 的 `replicas` 字段，
以便确保在上线及后续过程副本个数合适。

<!--
If you perform a rolling update of a StatefulSet that has an autoscaled number of
replicas, the StatefulSet directly manages its set of Pods (there is no intermediate resource
similar to ReplicaSet).
-->
如果你对一个副本个数被自动扩缩的 StatefulSet 执行滚动更新， 该 StatefulSet
会直接管理它的 Pod 集合 （不存在类似 ReplicaSet 这样的中间资源）。

<!--
## Support for resource metrics

Any HPA target can be scaled based on the resource usage of the pods in the scaling target.
When defining the pod specification the resource requests like `cpu` and `memory` should
be specified. This is used to determine the resource utilization and used by the HPA controller
to scale the target up or down. To use resource utilization based scaling specify a metric source
like this:
-->
## 对资源指标的支持   {#support-for-resource-metrics}

HPA 的任何目标资源都可以基于其中的 Pods 的资源用量来实现扩缩。
在定义 Pod 规约时，类似 `cpu` 和 `memory` 这类资源请求必须被设定。
这些设定值被用来确定资源利用量并被 HPA 控制器用来对目标资源完成扩缩操作。
要使用基于资源利用率的扩缩，可以像下面这样指定一个指标源：

```yaml
type: Resource
resource:
  name: cpu
  target:
    type: Utilization
    averageUtilization: 60
```

<!--
With this metric the HPA controller will keep the average utilization of the pods in the scaling
target at 60%. Utilization is the ratio between the current usage of resource to the requested
resources of the pod. See [Algorithm](#algorithm-details) for more details about how the utilization
is calculated and averaged.
-->
基于这一指标设定，HPA 控制器会维持扩缩目标中的 Pods 的平均资源利用率在 60%。
利用率是 Pod 的当前资源用量与其请求值之间的比值。关于如何计算利用率以及如何计算平均值
的细节可参考[算法](#algorithm-details)小节。

{{< note >}}
<!--
Since the resource usages of all the containers are summed up the total pod utilization may not
accurately represent the individual container resource usage. This could lead to situations where
a single container might be running with high usage and the HPA will not scale out because the overall
pod usage is still within acceptable limits.
-->
由于所有的容器的资源用量都会被累加起来，Pod 的总体资源用量值可能不会精确体现
各个容器的资源用量。这一现象也会导致一些问题，例如某个容器运行时的资源用量非常
高，但因为 Pod 层面的资源用量总值让人在可接受的约束范围内，HPA 不会执行扩大
目标对象规模的操作。
{{< /note >}}

<!--
### Container Resource Metrics
-->
### 容器资源指标   {#container-resource-metrics}

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

<!--
The HorizontalPodAutoscaler API also supports a container metric source where the HPA can track the
resource usage of individual containers across a set of Pods, in order to scale the target resource.
This lets you configure scaling thresholds for the containers that matter most in a particular Pod.
For example, if you have a web application and a logging sidecar, you can scale based on the resource
use of the web application, ignoring the sidecar container and its resource use.
-->
HorizontalPodAutoscaler API 也支持容器指标源，这时 HPA 可以跟踪记录一组 Pods 中各个容器的
资源用量，进而触发扩缩目标对象的操作。
容器资源指标的支持使得你可以为特定 Pod 中最重要的容器配置规模扩缩阈值。
例如，如果你有一个 Web 应用和一个执行日志操作的边车容器，你可以基于 Web 应用的
资源用量来执行扩缩，忽略边车容器的存在及其资源用量。

<!--
If you revise the target resource to have a new Pod specification with a different set of containers,
you should revise the HPA spec if that newly added container should also be used for
scaling. If the specified container in the metric source is not present or only present in a subset
of the pods then those pods are ignored and the recommendation is recalculated. See [Algorithm](#algorithm-details)
for more details about the calculation. To use container resources for autoscaling define a metric
source as follows:
-->
如果你更改扩缩目标对象，令其使用新的、包含一组不同的容器的 Pod 规约，你就需要
修改 HPA 的规约才能基于新添加的容器来执行规模扩缩操作。
如果指标源中指定的容器不存在或者仅存在于部分 Pods 中，那么这些 Pods 会被忽略，
HPA 会重新计算资源用量值。参阅[算法](#algorithm-details)小节进一步了解计算细节。
要使用容器资源用量来完成自动扩缩，可以像下面这样定义指标源：

```yaml
type: ContainerResource
containerResource:
  name: cpu
  container: application
  target:
    type: Utilization
    averageUtilization: 60
```

<!--
In the above example the HPA controller scales the target such that the average utilization of the cpu
in the `application` container of all the pods is 60%.
-->
在上面的例子中，HPA 控制器会对目标对象执行扩缩操作以确保所有 Pods 中
`application` 容器的平均 CPU 用量为 60%。

{{< note >}}
<!--
If you change the name of a container that a HorizontalPodAutoscaler is tracking, you can
make that change in a specific order to ensure scaling remains available and effective
whilst the change is being applied. Before you update the resource that defines the container
(such as a Deployment), you should update the associated HPA to track both the new and
old container names. This way, the HPA is able to calculate a scaling recommendation
throughout the update process.
-->
如果你要更改 HorizontalPodAutoscaler 所跟踪记录的容器的名称，你可以按一定顺序
来执行这一更改，确保在应用更改的过程中用来判定扩缩行为的容器可用。
在更新定义容器的资源（如 Deployment）之前，你需要更新相关的 HPA，使之能够同时
跟踪记录新的和老的容器名称。这样，HPA 就能够在整个更新过程中继续计算并提供扩缩操作建议。

<!--
Once you have rolled out the container name change to the workload resource, tidy up by removing
the old container name from the HPA specification.
-->
一旦你已经将容器名称变更这一操作应用到整个负载对象至上，就可以从 HPA
的规约中去掉老的容器名称，完成清理操作。
{{< /note >}}

<!--
## Scaling on custom metrics

(the `autoscaling/v2beta2` API version previously provided this ability as a beta feature)

Provided that you use the `autoscaling/v2` API version, you can configure a HorizontalPodAutoscaler
to scale based on a custom metric (that is not built in to Kubernetes or any Kubernetes component).
The HorizontalPodAutoscaler controller then queries for these custom metrics from the Kubernetes
API.

See [Support for metrics APIs](#support-for-metrics-apis) for the requirements.
-->
## 扩展自定义指标 {#scaling-on-custom-metrics}

{{< feature-state for_k8s_version="v1.23" state="stable" >}}

（之前的 `autoscaling/v2beta2` API 版本将此功能作为 beta 功能提供）

如果你使用 `autoscaling/v2` API 版本，则可以将 HorizontalPodAutoscaler 
配置为基于自定义指标（未内置于 Kubernetes 或任何 Kubernetes 组件）进行扩缩。
HorizontalPodAutoscaler 控制器能够从 Kubernetes API 查询这些自定义指标。

有关要求，请参阅对 [Metrics APIs 的支持](#support-for-metrics-apis)。

<!--
## Scaling on multiple metrics

(the `autoscaling/v2beta2` API version previously provided this ability as a beta feature)

Provided that you use the `autoscaling/v2` API version, you can specify multiple metrics for a
HorizontalPodAutoscaler to scale on. Then, the HorizontalPodAutoscaler controller evaluates each metric,
and proposes a new scale based on that metric. The HorizontalPodAutoscaler takes the maximum scale
recommended for each metric and sets the workload to that size (provided that this isn't larger than the
overall maximum that you configured).
-->
## 基于多个指标来执行扩缩 {#scaling-on-multiple-metrics}

{{< feature-state for_k8s_version="v1.23" state="stable" >}}

（之前的 `autoscaling/v2beta2` API 版本将此功能作为 beta 功能提供）

如果你使用 `autoscaling/v2` API 版本，你可以为 HorizontalPodAutoscaler 指定多个指标以进行扩缩。
HorizontalPodAutoscaler 控制器评估每个指标，并根据该指标提出一个新的比例。
HorizontalPodAutoscaler 采用为每个指标推荐的最大比例，
并将工作负载设置为该大小（前提是这不大于你配置的总体最大值）。

<!--
## Support for metrics APIs

By default, the HorizontalPodAutoscaler controller retrieves metrics from a series of APIs.  In order for it to access these
APIs, cluster administrators must ensure that:
-->
## 对 Metrics API 的支持   {#support-for-metrics-apis}

默认情况下，HorizontalPodAutoscaler 控制器会从一系列的 API 中检索度量值。
集群管理员需要确保下述条件，以保证 HPA 控制器能够访问这些 API：

<!--
* The [API aggregation layer](/docs/tasks/extend-kubernetes/configure-aggregation-layer/) is enabled.

* The corresponding APIs are registered:

   * For resource metrics, this is the `metrics.k8s.io` API, generally provided by [metrics-server](https://github.com/kubernetes-sigs/metrics-server).
     It can be launched as a cluster addon.

   * For custom metrics, this is the `custom.metrics.k8s.io` API.  It's provided by "adapter" API servers provided by metrics solution vendors.
     Check with your metrics pipeline to see if there is a Kubernetes metrics adapter available.

   * For external metrics, this is the `external.metrics.k8s.io` API.  It may be provided by the custom metrics adapters provided above.
-->
* 启用了 [API 聚合层](/zh/docs/tasks/extend-kubernetes/configure-aggregation-layer/)

* 相应的 API 已注册：

   * 对于资源指标，将使用 `metrics.k8s.io` API，一般由 [metrics-server](https://github.com/kubernetes-incubator/metrics-server) 提供。
     它可以作为集群插件启动。
    
   * 对于自定义指标，将使用 `custom.metrics.k8s.io` API。
    它由其他度量指标方案厂商的“适配器（Adapter）” API 服务器提供。
    检查你的指标管道以查看是否有可用的 Kubernetes 指标适配器。
   
   * 对于外部指标，将使用 `external.metrics.k8s.io` API。可能由上面的自定义指标适配器提供。

<!--  
For more information on these different metrics paths and how they differ please see the relevant design proposals for
[the HPA V2](https://github.com/kubernetes/design-proposals-archive/blob/main/autoscaling/hpa-v2.md),
[custom.metrics.k8s.io](https://github.com/kubernetes/design-proposals-archive/blob/main/instrumentation/custom-metrics-api.md)
and [external.metrics.k8s.io](https://github.com/kubernetes/design-proposals-archive/blob/main/instrumentation/external-metrics-api.md).
-->
关于指标来源以及其区别的更多信息，请参阅相关的设计文档，
[HPA V2](https://github.com/kubernetes/design-proposals-archive/blob/main/autoscaling/hpa-v2.md)，
[custom.metrics.k8s.io](https://github.com/kubernetes/design-proposals-archive/blob/main/instrumentation/custom-metrics-api.md) 和 
[external.metrics.k8s.io](https://github.com/kubernetes/design-proposals-archive/blob/main/instrumentation/external-metrics-api.md)。

<!--
For examples of how to use them see [the walkthrough for using custom metrics](/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/#autoscaling-on-multiple-metrics-and-custom-metrics)
and [the walkthrough for using external metrics](/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/#autoscaling-on-metrics-not-related-to-kubernetes-objects).
-->
关于如何使用它们的示例，请参考 
[使用自定义指标的教程](/zh/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/#autoscaling-on-multiple-metrics-and-custom-metrics)
和[使用外部指标的教程](/zh/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/#autoscaling-on-metrics-not-related-to-kubernetes-objects)。

<!--  
## Configurable scaling behavior

(the `autoscaling/v2beta2` API version previously provided this ability as a beta feature)

If you use the `v2` HorizontalPodAutoscaler API, you can use the `behavior` field
(see the [API reference](/docs/reference/kubernetes-api/workload-resources/horizontal-pod-autoscaler-v2/#HorizontalPodAutoscalerSpec))
to configure separate scale-up and scale-down behaviors.
You specify these behaviours by setting `scaleUp` and / or `scaleDown`
under the `behavior` field.
-->
## 可配置的扩缩行为 {#configurable-scaling-behavior}

{{< feature-state for_k8s_version="v1.23" state="stable" >}}

（之前的 `autoscaling/v2beta2` API 版本将此功能作为 beta 功能提供）

如果你使用 `v2` HorizontalPodAutoscaler API，你可以使用 `behavior` 字段
（请参阅 [API 参考](/zh/docs/reference/kubernetes-api/workload-resources/horizontal-pod-autoscaler-v2/#HorizontalPodAutoscalerSpec)）
来配置单独的放大和缩小行为。你可以通过在行为字段下设置 `scaleUp` 和/或 `scaleDown` 来指定这些行为。

<!--
You can specify a _stabilization window_ that prevents [flapping](#flapping)
the replica count for a scaling target. Scaling policies also let you controls the
rate of change of replicas while scaling.
-->

你可以指定一个 “稳定窗口” ，以防止扩缩目标的副本计数发生[波动](#flapping)。
扩缩策略还允许你在扩缩时控制副本的变化率。

<!--  
### Scaling Policies

One or more scaling policies can be specified in the `behavior` section of the spec.
When multiple policies are specified the policy which allows the highest amount of
change is the policy which is selected by default. The following example shows this behavior
while scaling down:
-->
### 扩缩策略 {#scaling-policies}
可以在规约的 `behavior` 部分中指定一个或多个扩缩策略。当指定多个策略时，
允许最大更改量的策略是默认选择的策略。以下示例显示了缩小时的这种行为：

```yaml
behavior:
  scaleDown:
    policies:
    - type: Pods
      value: 4
      periodSeconds: 60
    - type: Percent
      value: 10
      periodSeconds: 60
```

<!--  
`periodSeconds` indicates the length of time in the past for which the policy must hold true.
The first policy _(Pods)_ allows at most 4 replicas to be scaled down in one minute. The second policy
_(Percent)_ allows at most 10% of the current replicas to be scaled down in one minute.

Since by default the policy which allows the highest amount of change is selected, the second policy will
only be used when the number of pod replicas is more than 40. With 40 or less replicas, the first policy will be applied.
For instance if there are 80 replicas and the target has to be scaled down to 10 replicas
then during the first step 8 replicas will be reduced. In the next iteration when the number
of replicas is 72, 10% of the pods is 7.2 but the number is rounded up to 8. On each loop of
the autoscaler controller the number of pods to be change is re-calculated based on the number
of current replicas. When the number of replicas falls below 40 the first policy _(Pods)_ is applied
and 4 replicas will be reduced at a time.
-->
`periodSeconds` 表示在过去的多长时间内要求策略值为真。
第一个策略（Pods）允许在一分钟内最多缩容 4 个副本。第二个策略（Percent）
允许在一分钟内最多缩容当前副本个数的百分之十。

由于默认情况下会选择容许更大程度作出变更的策略，只有 Pod 副本数大于 40 时，
第二个策略才会被采用。如果副本数为 40 或者更少，则应用第一个策略。
例如，如果有 80 个副本，并且目标必须缩小到 10 个副本，那么在第一步中将减少 8 个副本。
在下一轮迭代中，当副本的数量为 72 时，10% 的 Pod 数为 7.2，但是这个数字向上取整为 8。
在 autoscaler 控制器的每个循环中，将根据当前副本的数量重新计算要更改的 Pod 数量。
当副本数量低于 40 时，应用第一个策略（Pods），一次减少 4 个副本。

<!--  
The policy selection can be changed by specifying the `selectPolicy` field for a scaling
direction. By setting the value to `Min` which would select the policy which allows the
smallest change in the replica count. Setting the value to `Disabled` completely disables
scaling in that direction.
-->
可以指定扩缩方向的 `selectPolicy` 字段来更改策略选择。
通过设置 `Min` 的值，它将选择副本数变化最小的策略。
将该值设置为 `Disabled` 将完全禁用该方向的扩缩。

<!--  
### Stabilization Window

The stabilization window is used to restrict the [flapping](#flapping) of
replicas count when the metrics used for scaling keep fluctuating. The autoscaling algorithm
uses this window to infer a previous desired state and avoid unwanted changes to workload
scale.

For example, in the following example snippet, a stabilization window is specified for `scaleDown`.
-->
### 稳定窗口 {#stabilization-window}

当用于扩缩的指标不断波动时，稳定窗口用于限制副本计数的[波动](#flapping)。
自动扩缩算法使用此窗口来推断先前的期望状态并避免对工作负载规模进行不必要的更改。

例如，在以下示例代码段中，为 `scaleDown` 指定了稳定窗口。

```yaml
behavior:
  scaleDown:
    stabilizationWindowSeconds: 300
```

<!--  
When the metrics indicate that the target should be scaled down the algorithm looks
into previously computed desired states, and uses the highest value from the specified
interval. In the above example, all desired states from the past 5 minutes will be considered.
-->
当指标显示目标应该缩容时，自动扩缩算法查看之前计算的期望状态，并使用指定时间间隔内的最大值。
在上面的例子中，过去 5 分钟的所有期望状态都会被考虑。

<!--
This approximates a rolling maximum, and avoids having the scaling algorithm frequently
remove Pods only to trigger recreating an equivalent Pod just moments later.
-->
这近似于滚动最大值，并避免了扩缩算法频繁删除 Pod 而又触发重新创建等效 Pod。

<!--  
### Default Behavior

To use the custom scaling not all fields have to be specified. Only values which need to be
customized can be specified. These custom values are merged with default values. The default values
match the existing behavior in the HPA algorithm.
-->
### 默认行为 {#default-behavior}

要使用自定义扩缩，不必指定所有字段。
只有需要自定义的字段才需要指定。
这些自定义值与默认值合并。
默认值与 HPA 算法中的现有行为匹配。

```yaml
behavior:
  scaleDown:
    stabilizationWindowSeconds: 300
    policies:
    - type: Percent
      value: 100
      periodSeconds: 15
  scaleUp:
    stabilizationWindowSeconds: 0
    policies:
    - type: Percent
      value: 100
      periodSeconds: 15
    - type: Pods
      value: 4
      periodSeconds: 15
    selectPolicy: Max
```

<!--  
For scaling down the stabilization window is _300_ seconds (or the value of the
`--horizontal-pod-autoscaler-downscale-stabilization` flag if provided). There is only a single policy
for scaling down which allows a 100% of the currently running replicas to be removed which
means the scaling target can be scaled down to the minimum allowed replicas.
For scaling up there is no stabilization window. When the metrics indicate that the target should be
scaled up the target is scaled up immediately. There are 2 policies where 4 pods or a 100% of the currently
running replicas will be added every 15 seconds till the HPA reaches its steady state.
-->
用于缩小稳定窗口的时间为 _300_  秒(或是 `--horizontal-pod-autoscaler-downscale-stabilization`
参数设定值)。
只有一种缩容的策略，允许 100% 删除当前运行的副本，这意味着扩缩目标可以缩小到允许的最小副本数。
对于扩容，没有稳定窗口。当指标显示目标应该扩容时，目标会立即扩容。
这里有两种策略，每 15 秒添加 4 个 Pod 或 100% 当前运行的副本数，直到 HPA 达到稳定状态。

<!--  
### Example: change downscale stabilization window

To provide a custom downscale stabilization window of 1 minute, the following
behavior would be added to the HPA:
--> 
### 示例：更改缩容稳定窗口 {#example-change-downscale-stabilization-window}

将下面的 behavior 配置添加到 HPA 中，可提供一个 1 分钟的自定义缩容稳定窗口：

```yaml
behavior:
  scaleDown:
    stabilizationWindowSeconds: 60
```

<!--  
### Example: limit scale down rate

To limit the rate at which pods are removed by the HPA to 10% per minute, the
following behavior would be added to the HPA:
-->
### 示例：限制缩容速率 {#example-limit-scale-down-rate}

将下面的 behavior 配置添加到 HPA 中，可限制 Pod 被 HPA 删除速率为每分钟 10%：

```yaml
behavior:
  scaleDown:
    policies:
    - type: Percent
      value: 10
      periodSeconds: 60
```

<!--  
To ensure that no more than 5 Pods are removed per minute, you can add a second scale-down
policy with a fixed size of 5, and set `selectPolicy` to minimum. Setting `selectPolicy` to `Min` means
that the autoscaler chooses the policy that affects the smallest number of Pods:
-->
为了确保每分钟删除的 Pod 数不超过 5 个，可以添加第二个缩容策略，大小固定为 5，并将 `selectPolicy` 设置为最小值。
将 `selectPolicy` 设置为 `Min` 意味着 autoscaler 会选择影响 Pod 数量最小的策略:

```yaml
behavior:
  scaleDown:
    policies:
    - type: Percent
      value: 10
      periodSeconds: 60
    - type: Pods
      value: 5
      periodSeconds: 60
    selectPolicy: Min
```

<!--  
### Example: disable scale down

The `selectPolicy` value of `Disabled` turns off scaling the given direction.
So to prevent downscaling the following policy would be used:
-->

### 示例：禁用缩容 {#example-disable-scale-down}

`selectPolicy` 的值 `Disabled` 会关闭对给定方向的缩容。
因此使用以下策略，将会阻止缩容：

```yaml
behavior:
  scaleDown:
    selectPolicy: Disabled
```
<!--
## Support for HorizontalPodAutoscaler in kubectl

HorizontalPodAutoscaler, like every API resource, is supported in a standard way by `kubectl`.
You can create a new autoscaler using `kubectl create` command.
You can list autoscalers by `kubectl get hpa` or get detailed description by `kubectl describe hpa`.
Finally, you can delete an autoscaler using `kubectl delete hpa`.
-->
## kubectl 对 HorizontalPodAutoscaler 的支持 {#support-for-horizontalpodautoscaler-in-kubectl}

与每个 API 资源一样，HorizontalPodAutoscaler 都被 `kubectl` 以标准方式支持。
你可以使用 `kubectl create` 命令创建一个新的自动扩缩器。
你可以通过 `kubectl get hpa` 列出自动扩缩器或通过 `kubectl describe hpa` 获取详细描述。
最后，你可以使用 `kubectl delete hpa` 删除自动扩缩器。

<!--
In addition, there is a special `kubectl autoscale` command for creating a HorizontalPodAutoscaler object.
For instance, executing `kubectl autoscale rs foo --min=2 --max=5 --cpu-percent=80`
will create an autoscaler for ReplicaSet *foo*, with target CPU utilization set to `80%`
and the number of replicas between 2 and 5.
-->
此外，还有一个特殊的 `kubectl autoscale` 命令用于创建 HorizontalPodAutoscaler 对象。
例如，执行 `kubectl autoscale rs foo --min=2 --max=5 --cpu-percent=80` 
将为 ReplicaSet *foo* 创建一个自动扩缩器，目标 CPU 利用率设置为 `80%`，副本数在 2 到 5 之间。

<!--
## Implicit maintenance-mode deactivation

You can implicitly deactivate the HPA for a target without the
need to change the HPA configuration itself. If the target's desired replica count
is set to 0, and the HPA's minimum replica count is greater than 0, the HPA 
stops adjusting the target (and sets the `ScalingActive` Condition on itself
to `false`) until you reactivate it by manually adjusting the target's desired
replica count or HPA's minimum replica count.
-->
## 隐式维护状态禁用 {#implicit-maintenance-mode-deactivation}

你可以在不必更改 HPA 配置的情况下隐式地为某个目标禁用 HPA。
如果此目标的期望副本个数被设置为 0，而 HPA 的最小副本个数大于 0，
则 HPA 会停止调整目标（并将其自身的 `ScalingActive` 状况设置为 `false`），
直到你通过手动调整目标的期望副本个数或 HPA 的最小副本个数来重新激活。

<!--
### Migrating Deployments and StatefulSets to horizontal autoscaling

When an HPA is enabled, it is recommended that the value of `spec.replicas` of
the Deployment and / or StatefulSet be removed from their
{{< glossary_tooltip text="manifest(s)" term_id="manifest" >}}.  If this isn't done, any time
a change to that object is applied, for example via `kubectl apply -f
deployment.yaml`, this will instruct Kubernetes to scale the current number of Pods
to the value of the `spec.replicas` key. This may not be
desired and could be troublesome when an HPA is active.
-->

### 将 Deployment 和 StatefulSet 迁移到水平自动扩缩 {#migrating-deployments-and-statefulsets-to-horizontal-autoscaling}

当启用 HPA 时，建议从它们的{{< glossary_tooltip text="清单" term_id="manifest" >}}中
删除 Deployment 和/或 StatefulSet 的 `spec.replicas` 的值。
如果不这样做，则只要应用对该对象的更改，例如通过 `kubectl apply -f deployment.yaml`，
这将指示 Kubernetes 将当前 Pod 数量扩缩到 `spec.replicas` 键的值。这可能不是所希望的，
并且当 HPA 处于活动状态时可能会很麻烦。

<!--
Keep in mind that the removal of `spec.replicas` may incur a one-time
degradation of Pod counts as the default value of this key is 1 (reference
[Deployment Replicas](/docs/concepts/workloads/controllers/deployment#replicas)).
Upon the update, all Pods except 1 will begin their termination procedures.  Any
deployment application afterwards will behave as normal and respect a rolling
update configuration as desired.  You can avoid this degradation by choosing one of the following two
methods based on how you are modifying your deployments:
-->
请记住，删除 `spec.replicas` 可能会导致 Pod 计数一次性降级，因为此键的默认值为 1
（参考 [Deployment Replicas](/zh/docs/concepts/workloads/controllers/deployment#replicas)）。
更新后，除 1 之外的所有 Pod 都将开始其终止程序。之后的任何部署应用程序都将正常运行，
并根据需要遵守滚动更新配置。你可以根据修改部署的方式选择以下两种方法之一来避免这种降级：

{{< tabs name="fix_replicas_instructions" >}}
{{% tab name="客户端 apply 操作（默认行为）" %}}

<!--
1. `kubectl apply edit-last-applied deployment/<deployment_name>`
2. In the editor, remove `spec.replicas`. When you save and exit the editor, `kubectl`
    applies the update.  No changes to Pod counts happen at this step.
3. You can now remove `spec.replicas` from the manifest. If you use source code management,
    also commit your changes or take whatever other steps for revising the source code
    are appropriate for how you track updates.
4. From here on out you can run `kubectl apply -f deployment.yaml`
-->
1. `kubectl apply edit-last-applied deployment/<Deployment 名称>`
2. 在编辑器中，删除 `spec.replicas`。当你保存并退出编辑器时，`kubectl` 会应用更新。
  在此步骤中不会更改 Pod 计数。
3. 你现在可以从清单中删除 `spec.replicas`。如果你使用源代码管理，
  还应提交你的更改或采取任何其他步骤来修改源代码，以适应你如何跟踪更新。
4. 从这里开始，你可以运行 `kubectl apply -f deployment.yaml`

{{% /tab %}}
{{% tab name="服务器端 apply 操作" %}}

<!--
When using the [Server-Side Apply](/docs/reference/using-api/server-side-apply/)
you can follow the [transferring ownership](/docs/reference/using-api/server-side-apply/#transferring-ownership)
guidelines, which cover this exact use case.
-->
使用[服务器端 Apply](/zh/docs/reference/using-api/server-side-apply/) 机制，
你可以遵循[交出所有权](/zh/docs/reference/using-api/server-side-apply/#transferring-ownership) 说明，
该指南涵盖了这个确切的用例。

{{% /tab %}}
{{< /tabs >}}
## {{% heading "whatsnext" %}}

<!--
If you configure autoscaling in your cluster, you may also want to consider running a
cluster-level autoscaler such as [Cluster Autoscaler](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler).

For more information on HorizontalPodAutoscaler:
-->
如果你在集群中配置自动扩缩，你可能还需要考虑运行集群级别的自动扩缩器，
例如 [Cluster Autoscaler](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler)。

有关 HorizontalPodAutoscaler 的更多信息：

<!--
* Read a [walkthrough example](/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/) for horizontal pod autoscaling.
* Read documentation for [`kubectl autoscale`](/docs/reference/generated/kubectl/kubectl-commands/#autoscale).
* If you would like to write your own custom metrics adapter, check out the
  [boilerplate](https://github.com/kubernetes-sigs/custom-metrics-apiserver) to get started.
* Read the [API reference](/docs/reference/kubernetes-api/workload-resources/horizontal-pod-autoscaler-v2/) for HorizontalPodAutoscaler.
-->
* 阅读水平 Pod 自动扩缩的[演练示例](/zh/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/)。
* 阅读 [`kubectl autoscale`](/zh/docs/reference/generated/kubectl/kubectl-commands/#autoscale) 的文档。
* 如果你想编写自己的自定义指标适配器，
  请查看 [boilerplate](https://github.com/kubernetes-sigs/custom-metrics-apiserver) 以开始使用。
* 阅读 [API 参考](/docs/reference/kubernetes-api/workload-resources/horizontal-pod-autoscaler-v2/)。