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docs/tasks/run-application/horizontal-pod-autoscale-walkthrough.
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+---
+reviewers:
+- fgrzadkowski
+- jszczepkowski
+- justinsb
+- directxman12
+title: Horizontal Pod Autoscaler演练
+content_template: templates/task
+weight: 100
+---
+
+{{% capture overview %}}
+
+<!--
+Horizontal Pod Autoscaler automatically scales the number of pods
+in a replication controller, deployment or replica set based on observed CPU utilization
+(or, with beta support, on some other, application-provided metrics).
+-->
+Horizontal Pod Autoscaler 可以根据CPU利用率自动伸缩 replication controller、deployment 或者 replica set 中的Pod数量
+（也可以基于其他应用程序提供的度量指标，目前这一功能处于 beta 版本）。
+
+<!--
+This document walks you through an example of enabling Horizontal Pod Autoscaler for the php-apache server.  For more information on how Horizontal Pod Autoscaler behaves, see the [Horizontal Pod Autoscaler user guide](/docs/tasks/run-application/horizontal-pod-autoscale/).
+-->
+本文将引导您了解如何为 php-apache 服务器配置和使用 Horizontal Pod Autoscaler。
+更多 Horizontal Pod Autoscaler 的信息请参阅 [Horizontal Pod Autoscaler user guide](/docs/tasks/run-application/horizontal-pod-autoscale/)。
+
+{{% /capture %}}
+
+
+
+{{% capture prerequisites %}}
+
+<!--
+This example requires a running Kubernetes cluster and kubectl, version 1.2 or later.
+[metrics-server](https://github.com/kubernetes-incubator/metrics-server/) monitoring needs to be deployed in the cluster
+to provide metrics via the resource metrics API, as Horizontal Pod Autoscaler uses this API to collect metrics. The instructions for deploying this are on the GitHub repository of [metrics-server](https://github.com/kubernetes-incubator/metrics-server/), if you followed [getting started on GCE guide](/docs/setup/production-environment/turnkey/gce/),
+metrics-server monitoring will be turned-on by default.
+-->
+本文示例需要一个1.2或者更高版本的可运行的 Kubernetes 集群以及 kubectl。 
+[metrics-server](https://github.com/kubernetes-incubator/metrics-server/) 也需要部署到集群中，
+它可以通过 resource metrics API 对外提供度量数据，Horizontal Pod Autoscaler 正是根据此 API 来获取度量数据，部署方法请参考 [metrics-server](https://github.com/kubernetes-incubator/metrics-server/) 。
+如果你正在使用GCE，按照 [getting started on GCE guide](/docs/setup/production-environment/turnkey/gce/) 操作，metrics-server 会默认启动。
+
+<!--
+To specify multiple resource metrics for a Horizontal Pod Autoscaler, you must have a Kubernetes cluster
+and kubectl at version 1.6 or later.  Furthermore, in order to make use of custom metrics, your cluster
+must be able to communicate with the API server providing the custom metrics API. Finally, to use metrics
+not related to any Kubernetes object you must have a Kubernetes cluster at version 1.10 or later, and
+you must be able to communicate with the API server that provides the external metrics API.
+See the [Horizontal Pod Autoscaler user guide](/docs/tasks/run-application/horizontal-pod-autoscale/#support-for-custom-metrics) for more details.
+-->
+如果需要为 Horizontal Pod Autoscaler 指定多种资源度量指标，您的 Kubernetes 集群以及 kubectl 至少需要达到1.6版本。 
+此外，如果要使用自定义度量指标，您的Kubernetes 集群还必须能够与提供这些自定义指标的API服务器通信。
+最后，如果要使用与 Kubernetes 对象无关的度量指标，则 Kubernetes 集群版本至少需要达到1.10版本，同样，需要保证集群能够与提供这些外部指标的API服务器通信。
+更多详细信息，请参阅[Horizontal Pod Autoscaler user guide](/docs/tasks/run-application/horizontal-pod-autoscale/#support-for-custom-metrics)。
+
+{{% /capture %}}
+
+{{% capture steps %}}
+
+<!--
+## Run & expose php-apache server
+-->
+## 第一步：运行 php-apache 服务器并暴露服务
+
+<!--
+To demonstrate Horizontal Pod Autoscaler we will use a custom docker image based on the php-apache image.
+The Dockerfile has the following content:
+-->
+为了演示 Horizontal Pod Autoscaler，我们将使用一个基于 php-apache 镜像的定制 Docker 镜像。
+Dockerfile 内容如下： 
+
+```
+FROM php:5-apache
+ADD index.php /var/www/html/index.php
+RUN chmod a+rx index.php
+```
+<!--
+It defines an index.php page which performs some CPU intensive computations:
+-->
+它定义一个 index.php 页面来执行一些 CPU 密集型计算：
+
+```
+<?php
+  $x = 0.0001;
+  for ($i = 0; $i <= 1000000; $i++) {
+    $x += sqrt($x);
+  }
+  echo "OK!";
+?>
+```
+<!--
+First, we will start a deployment running the image and expose it as a service:
+-->
+首先，我们先启动一个 deployment 来运行这个镜像并暴露一个服务:
+
+```shell
+kubectl run php-apache --image=k8s.gcr.io/hpa-example --requests=cpu=200m --expose --port=80
+```
+```
+service/php-apache created
+deployment.apps/php-apache created
+```
+
+<!--
+## Create Horizontal Pod Autoscaler
+-->
+## 创建 Horizontal Pod Autoscaler
+
+<!--
+Now that the server is running, we will create the autoscaler using
+[kubectl autoscale](/docs/reference/generated/kubectl/kubectl-commands#autoscale).
+The following command will create a Horizontal Pod Autoscaler that maintains between 1 and 10 replicas of the Pods
+controlled by the php-apache deployment we created in the first step of these instructions.
+Roughly speaking, HPA will increase and decrease the number of replicas
+(via the deployment) to maintain an average CPU utilization across all Pods of 50%
+(since each pod requests 200 milli-cores by [kubectl run](https://github.com/kubernetes/kubernetes/blob/{{< param "githubbranch" >}}/docs/user-guide/kubectl/kubectl_run.md), this means average CPU usage of 100 milli-cores).
+See [here](https://git.k8s.io/community/contributors/design-proposals/autoscaling/horizontal-pod-autoscaler.md#autoscaling-algorithm) for more details on the algorithm.
+-->
+现在，php-apache服务器已经运行，我们将通过 [kubectl autoscale](/docs/reference/generated/kubectl/kubectl-commands#autoscale) 命令创建 Horizontal Pod Autoscaler。 
+以下命令将创建一个 Horizontal Pod Autoscaler 用于控制我们上一步骤中创建的 deployment，使 Pod 的副本数量在维持在1到10之间。
+大致来说，HPA 将通过增加或者减少 Pod 副本的数量（通过 Deployment ）以保持所有 Pod 的平均CPU利用率在50%以内
+（由于每个 Pod 通过 [kubectl run](https://github.com/kubernetes/kubernetes/blob/{{< param "githubbranch" >}}/docs/user-guide/kubectl/kubectl_run.md)
+申请了200 milli-cores CPU，所以50%的 CPU 利用率意味着平均 CPU 利用率为100 milli-cores）。 
+相关算法的详情请参阅[here](https://git.k8s.io/community/contributors/design-proposals/autoscaling/horizontal-pod-autoscaler.md#autoscaling-algorithm)。
+
+```shell
+kubectl autoscale deployment php-apache --cpu-percent=50 --min=1 --max=10
+```
+```
+horizontalpodautoscaler.autoscaling/php-apache autoscaled
+```
+
+<!--
+We may check the current status of autoscaler by running:
+-->
+我们可以通过以下命令查看 autoscaler 的状态：
+
+```shell
+kubectl get hpa
+```
+```
+NAME         REFERENCE                     TARGET    MINPODS   MAXPODS   REPLICAS   AGE
+php-apache   Deployment/php-apache/scale   0% / 50%  1         10        1          18s
+
+```
+
+<!--
+Please note that the current CPU consumption is 0% as we are not sending any requests to the server
+(the ``CURRENT`` column shows the average across all the pods controlled by the corresponding deployment).
+-->
+请注意在上面的命令输出中，当前的CPU利用率是0%，这是由于我们尚未发送任何请求到服务器
+（``CURRENT`` 列显示了相应 deployment 所控制的所有 Pod 的平均 CPU 利用率）。
+
+<!--
+## Increase load
+-->
+## 增加负载
+
+<!--
+Now, we will see how the autoscaler reacts to increased load.
+We will start a container, and send an infinite loop of queries to the php-apache service (please run it in a different terminal):
+-->
+现在，我们将看到 autoscaler 如何对增加负载作出反应。 
+我们将启动一个容器，并通过一个循环向 php-apache 服务器发送无限的查询请求（请在另一个终端中运行以下命令）：
+
+```shell
+kubectl run -i --tty load-generator --image=busybox /bin/sh
+
+Hit enter for command prompt
+
+while true; do wget -q -O- http://php-apache.default.svc.cluster.local; done
+```
+
+<!--
+Within a minute or so, we should see the higher CPU load by executing:
+-->
+在几分钟时间内，通过以下命令，我们可以看到CPU负载升高了：
+
+```shell
+kubectl get hpa
+```
+```
+NAME         REFERENCE                     TARGET      CURRENT   MINPODS   MAXPODS   REPLICAS   AGE
+php-apache   Deployment/php-apache/scale   305% / 50%  305%      1         10        1          3m
+
+```
+
+<!--
+Here, CPU consumption has increased to 305% of the request.
+As a result, the deployment was resized to 7 replicas:
+-->
+这时，由于请求增多，CPU利用率已经升至305%。 可以看到，deployment 的副本数量已经增长到了7：
+
+```shell
+kubectl get deployment php-apache
+```
+```
+NAME         DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
+php-apache   7         7         7            7           19m
+```
+
+<!--
+{{< note >}}
+It may take a few minutes to stabilize the number of replicas. Since the amount
+of load is not controlled in any way it may happen that the final number of replicas
+will differ from this example.
+{{< /note >}}
+-->
+{{< note >}}
+有时最终副本的数量可能需要几分钟才能稳定下来。 由于环境的差异，不同环境中最终的副本数量可能与本示例中的数量不同。
+{{< /note >}}
+
+<!--
+## Stop load
+-->
+## 停止负载
+
+<!--
+We will finish our example by stopping the user load.
+
+In the terminal where we created the container with `busybox` image, terminate
+the load generation by typing `<Ctrl> + C`.
+
+Then we will verify the result state (after a minute or so):
+-->
+我们将通过停止负载来结束我们的示例。
+
+在我们创建 busybox 容器的终端中，输入`<Ctrl> + C`来终止负载的产生。
+
+然后我们可以再次查看负载状态（等待几分钟时间）：
+
+```shell
+kubectl get hpa
+```
+```
+NAME         REFERENCE                     TARGET       MINPODS   MAXPODS   REPLICAS   AGE
+php-apache   Deployment/php-apache/scale   0% / 50%     1         10        1          11m
+```
+
+```shell
+kubectl get deployment php-apache
+```
+```
+NAME         DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
+php-apache   1         1         1            1           27m
+```
+
+<!--
+Here CPU utilization dropped to 0, and so HPA autoscaled the number of replicas back down to 1.
+-->
+这时，CPU利用率已经降到0，所以 HPA 将自动缩减副本数量至1。
+
+<!--
+{{< note >}}
+Autoscaling the replicas may take a few minutes.
+{{< /note >}}
+-->
+{{< note >}}
+自动伸缩完成副本数量的改变可能需要几分钟的时间。
+{{< /note >}}
+
+{{% /capture %}}
+
+{{% capture discussion %}}
+
+<!--
+## Autoscaling on multiple metrics and custom metrics
+-->
+## 基于多项度量指标和自定义度量指标自动伸缩
+
+<!--
+You can introduce additional metrics to use when autoscaling the `php-apache` Deployment
+by making use of the `autoscaling/v2beta2` API version.
+-->
+利用`autoscaling/v2beta2`API版本，您可以在自动伸缩 php-apache 这个 Deployment 时引入其他度量指标。
+
+<!--
+First, get the YAML of your HorizontalPodAutoscaler in the `autoscaling/v2beta2` form:
+-->
+首先，获取`autoscaling/v2beta2`格式的 HorizontalPodAutoscaler 的YAML文件：
+
+```shell
+kubectl get hpa.v2beta2.autoscaling -o yaml > /tmp/hpa-v2.yaml
+```
+
+<!--
+Open the `/tmp/hpa-v2.yaml` file in an editor, and you should see YAML which looks like this:
+-->
+在编辑器中打开`/tmp/hpa-v2.yaml`：
+
+```yaml
+apiVersion: autoscaling/v2beta2
+kind: HorizontalPodAutoscaler
+metadata:
+  name: php-apache
+  namespace: default
+spec:
+  scaleTargetRef:
+    apiVersion: apps/v1
+    kind: Deployment
+    name: php-apache
+  minReplicas: 1
+  maxReplicas: 10
+  metrics:
+  - type: Resource
+    resource:
+      name: cpu
+      target:
+        type: Utilization
+        averageUtilization: 50
+status:
+  observedGeneration: 1
+  lastScaleTime: <some-time>
+  currentReplicas: 1
+  desiredReplicas: 1
+  currentMetrics:
+  - type: Resource
+    resource:
+      name: cpu
+      current:
+        averageUtilization: 0
+        averageValue: 0
+```
+
+<!--
+Notice that the `targetCPUUtilizationPercentage` field has been replaced with an array called `metrics`.
+The CPU utilization metric is a *resource metric*, since it is represented as a percentage of a resource
+specified on pod containers.  Notice that you can specify other resource metrics besides CPU.  By default,
+the only other supported resource metric is memory.  These resources do not change names from cluster
+to cluster, and should always be available, as long as the `metrics.k8s.io` API is available.
+-->
+需要注意的是，`targetCPUUtilizationPercentage` 字段已经被名为 `metrics` 的数组所取代。
+CPU利用率这个度量指标是一个*resource metric*(资源度量指标)，因为它表示容器上指定资源的百分比。
+除CPU外，您还可以指定其他资源度量指标。默认情况下，目前唯一支持的其他资源度量指标为内存。
+只要`metrics.k8s.io` API存在，这些资源度量指标就是可用的，并且他们不会在不同的Kubernetes集群中改变名称。
+
+<!--
+You can also specify resource metrics in terms of direct values, instead of as percentages of the
+requested value, by using a `target` type of `AverageValue` instead of `AverageUtilization`, and
+setting the corresponding `target.averageValue` field instead of the `target.averageUtilization`.
+-->
+您还可以指定资源度量指标使用绝对数值，而不是百分比，你需要将`target`类型`AverageUtilization`替换成`AverageValue`，同时
+将`target.averageUtilization`替换成`target.averageValue`并设定相应的值。
+
+<!--
+There are two other types of metrics, both of which are considered *custom metrics*: pod metrics and
+object metrics.  These metrics may have names which are cluster specific, and require a more
+advanced cluster monitoring setup.
+-->
+还有两种其他类型的度量指标，他们被认为是*custom metrics*（自定义度量指标）：
+即 Pod 度量指标和对象度量指标（pod metrics and object metrics）。
+这些度量指标可能具有特定于集群的名称，并且需要更高级的集群监控设置。
+
+<!--
+The first of these alternative metric types is *pod metrics*.  These metrics describe pods, and
+are averaged together across pods and compared with a target value to determine the replica count.
+They work much like resource metrics, except that they *only* support a `target` type of `AverageValue`.
+-->
+第一种可选的度量指标类型是 Pod 度量指标。这些指标从某一方面描述了Pod，在不同Pod之间进行平均，并通过与一个目标值比对来确定副本的数量。
+它们的工作方式与资源度量指标非常相像，差别是它们仅支持`target` 类型为`AverageValue`。
+
+<!--
+Pod metrics are specified using a metric block like this:
+-->
+Pod 度量指标通过如下代码块定义：
+
+```yaml
+type: Pods
+pods:
+  metric:
+    name: packets-per-second
+  target:
+    type: AverageValue
+    averageValue: 1k
+```
+
+<!--
+The second alternative metric type is *object metrics*. These metrics describe a different
+object in the same namespace, instead of describing pods. The metrics are not necessarily
+fetched from the object; they only describe it. Object metrics support `target` types of
+both `Value` and `AverageValue`.  With `Value`, the target is compared directly to the returned
+metric from the API. With `AverageValue`, the value returned from the custom metrics API is divided
+by the number of pods before being compared to the target. The following example is the YAML
+representation of the `requests-per-second` metric.
+-->
+第二种可选的度量指标类型是对象度量指标。相对于描述 Pod，这些度量指标用于描述一个在相同名字空间(namespace)中的其他对象。
+请注意这些度量指标用于描述这些对象，并非从对象中获取。
+对象度量指标支持的`target`类型包括`Value`和`AverageValue`。如果是`Value`类型，target值将直接与API返回的度量指标比较，
+而`AverageValue`类型，API返回的度量指标将按照 Pod 数量拆分，然后再与target值比较。
+下面的 YAML 文件展示了一个表示`requests-per-second`的度量指标。
+
+```yaml
+type: Object
+object:
+  metric:
+    name: requests-per-second
+  describedObject:
+    apiVersion: networking.k8s.io/v1beta1
+    kind: Ingress
+    name: main-route
+  target:
+    type: Value
+    value: 2k
+```
+
+<!--
+If you provide multiple such metric blocks, the HorizontalPodAutoscaler will consider each metric in turn.
+The HorizontalPodAutoscaler will calculate proposed replica counts for each metric, and then choose the
+one with the highest replica count.
+-->
+如果您指定了多个上述类型的度量指标，HorizontalPodAutoscaler 将会依次考量各个指标。
+HorizontalPodAutoscaler 将会计算每一个指标所提议的副本数量，然后最终选择一个最高值。
+
+<!--
+For example, if you had your monitoring system collecting metrics about network traffic,
+you could update the definition above using `kubectl edit` to look like this:
+-->
+比如，如果您的监控系统能够提供网络流量数据，您可以通过`kubectl edit`命令将上述 Horizontal Pod Autoscaler 的定义更改为：
+
+```yaml
+apiVersion: autoscaling/v2beta1
+kind: HorizontalPodAutoscaler
+metadata:
+  name: php-apache
+  namespace: default
+spec:
+  scaleTargetRef:
+    apiVersion: apps/v1
+    kind: Deployment
+    name: php-apache
+  minReplicas: 1
+  maxReplicas: 10
+  metrics:
+  - type: Resource
+    resource:
+      name: cpu
+      target:
+        type: AverageUtilization
+        averageUtilization: 50
+  - type: Pods
+    pods:
+      metric:
+        name: packets-per-second
+      targetAverageValue: 1k
+  - type: Object
+    object:
+      metric:
+        name: requests-per-second
+      describedObject:
+        apiVersion: networking.k8s.io/v1beta1
+        kind: Ingress
+        name: main-route
+      target:
+        kind: Value
+        value: 10k
+status:
+  observedGeneration: 1
+  lastScaleTime: <some-time>
+  currentReplicas: 1
+  desiredReplicas: 1
+  currentMetrics:
+  - type: Resource
+    resource:
+      name: cpu
+    current:
+      averageUtilization: 0
+      averageValue: 0
+  - type: Object
+    object:
+      metric:
+        name: requests-per-second
+      describedObject:
+        apiVersion: networking.k8s.io/v1beta1
+        kind: Ingress
+        name: main-route
+      current:
+        value: 10k
+```
+
+<!--
+Then, your HorizontalPodAutoscaler would attempt to ensure that each pod was consuming roughly
+50% of its requested CPU, serving 1000 packets per second, and that all pods behind the main-route
+Ingress were serving a total of 10000 requests per second.
+-->
+然后，您的 HorizontalPodAutoscaler 将会尝试确保每个Pod的CPU利用率在50%以内，每秒能够服务1000个数据包请求，
+并确保所有在Ingress后的Pod每秒能够服务的请求总数达到10000个。
+
+<!--
+### Autoscaling on more specific metrics
+-->
+### 多个度量指标下伸缩
+
+<!--
+Many metrics pipelines allow you to describe metrics either by name or by a set of additional
+descriptors called _labels_. For all non-resource metric types (pod, object, and external,
+described below), you can specify an additional label selector which is passed to your metric
+pipeline. For instance, if you collect a metric `http_requests` with the `verb`
+label, you can specify the following metric block to scale only on GET requests:
+-->
+许多度量管道允许您通过名称或附加的_labels_来描述度量指标。对于所有非资源类型度量指标(pod、object和后面将介绍的external)，
+，可以额外指定一个标签选择器。例如，如果你希望收集包含`verb`标签的`http_requests`度量指标，
+你可以在 GET 请求中指定需要的度量指标，如下所示：
+
+```yaml
+type: Object
+object:
+  metric:
+    name: `http_requests`
+    selector: `verb=GET`
+```
+
+<!--
+This selector uses the same syntax as the full Kubernetes label selectors. The monitoring pipeline
+determines how to collapse multiple series into a single value, if the name and selector
+match multiple series. The selector is additive, and cannot select metrics
+that describe objects that are **not** the target object (the target pods in the case of the `Pods`
+type, and the described object in the case of the `Object` type).
+-->
+这个选择器使用与 Kubernetes 标签选择器相同的语法。
+如果名称和标签选择器匹配到多个系列，监测管道会决定如何将多个系列合并成单个值。
+选择器是附加的，它不会选择目标以外的对象（类型为`Pods`的目标和类型为`Object`的目标）。
+
+<!--
+### Autoscaling on metrics not related to Kubernetes objects
+-->
+### 基于Kubernetes以外的度量指标伸缩
+
+<!--
+Applications running on Kubernetes may need to autoscale based on metrics that don't have an obvious
+relationship to any object in the Kubernetes cluster, such as metrics describing a hosted service with
+no direct correlation to Kubernetes namespaces. In Kubernetes 1.10 and later, you can address this use case
+with *external metrics*.
+-->
+运行在 Kubernetes 上的应用程序可能需要基于与 Kubernetes 集群中的任何对象没有明显关系的度量指标进行自动伸缩，
+例如那些描述不在 Kubernetes 任何 namespaces 服务的度量指标。
+
+<!--
+Using external metrics requires knowledge of your monitoring system; the setup is
+similar to that required when using custom metrics. External metrics allow you to autoscale your cluster
+based on any metric available in your monitoring system. Just provide a `metric` block with a
+`name` and `selector`, as above, and use the `External` metric type instead of `Object`.
+If multiple time series are matched by the `metricSelector`,
+the sum of their values is used by the HorizontalPodAutoscaler.
+External metrics support both the `Value` and `AverageValue` target types, which function exactly the same
+as when you use the `Object` type.
+-->
+使用外部的度量指标，需要了解你使用的监控系统，相关的设置与使用自定义试题指标类似。
+External metrics 可以使用你的监控系统的任何指标来自动伸缩你的集群。你只需要在`metric`块中提供`name` 和 `selector`，同时将类型由`Object`改为`External`。
+如果`metricSelector`匹配到多个度量指标，HorizontalPodAutoscaler 将会把它们加和。
+External metrics 同时支持`Value`和`AverageValue`类型，这与`Object`类型的度量指标相同。
+
+<!--
+For example if your application processes tasks from a hosted queue service, you could add the following
+section to your HorizontalPodAutoscaler manifest to specify that you need one worker per 30 outstanding tasks.
+-->
+例如，如果你的应用程序处理主机上的消息队列，
+为了让每30个任务有1个worker，你可以将下面的内容添加到 HorizontalPodAutoscaler 的配置中。
+
+```yaml
+- type: External
+  external:
+    metric:
+      name: queue_messages_ready
+      selector: "queue=worker_tasks"
+    target:
+      type: AverageValue
+      averageValue: 30
+```
+
+<!--
+When possible, it's preferable to use the custom metric target types instead of external metrics, since it's
+easier for cluster administrators to secure the custom metrics API.  The external metrics API potentially allows
+access to any metric, so cluster administrators should take care when exposing it.
+-->
+如果可能，还是推荐 custom metric 而不是 external metrics，因为这便于让系统管理员加固 custom metrics API。
+而 external metrics API 可以允许访问所有的度量指标，当暴露这些服务时，系统管理员需要仔细考虑这个问题。
+
+<!--
+## Appendix: Horizontal Pod Autoscaler Status Conditions
+-->
+## 附录：Horizontal Pod Autoscaler状态条件
+
+<!--
+When using the `autoscaling/v2beta2` form of the HorizontalPodAutoscaler, you will be able to see
+*status conditions* set by Kubernetes on the HorizontalPodAutoscaler.  These status conditions indicate
+whether or not the HorizontalPodAutoscaler is able to scale, and whether or not it is currently restricted
+in any way.
+-->
+当使用`autoscaling/v2beta2`格式的 HorizontalPodAutoscaler 时，您将可以看到 Kubernetes 为 HorizongtalPodAutoscaler 设置的状态条件（status conditions）。
+这些状态条件可以显示当前 HorizontalPodAutoscaler 是否能够执行伸缩以及是否受到一定的限制。
+
+<!--
+The conditions appear in the `status.conditions` field.  To see the conditions affecting a HorizontalPodAutoscaler,
+we can use `kubectl describe hpa`:
+-->
+`status.conditions`字段展示了这些状态条件。
+可以通过`kubectl describe hpa`命令查看当前影响 HorizontalPodAutoscaler 的各种状态条件信息：
+
+```shell
+kubectl describe hpa cm-test
+```
+```shell
+Name:                           cm-test
+Namespace:                      prom
+Labels:                         <none>
+Annotations:                    <none>
+CreationTimestamp:              Fri, 16 Jun 2017 18:09:22 +0000
+Reference:                      ReplicationController/cm-test
+Metrics:                        ( current / target )
+  "http_requests" on pods:      66m / 500m
+Min replicas:                   1
+Max replicas:                   4
+ReplicationController pods:     1 current / 1 desired
+Conditions:
+  Type                  Status  Reason                  Message
+  ----                  ------  ------                  -------
+  AbleToScale           True    ReadyForNewScale        the last scale time was sufficiently old as to warrant a new scale
+  ScalingActive         True    ValidMetricFound        the HPA was able to successfully calculate a replica count from pods metric http_requests
+  ScalingLimited        False   DesiredWithinRange      the desired replica count is within the acceptable range
+Events:
+```
+
+<!--
+For this HorizontalPodAutoscaler, we can see several conditions in a healthy state.  The first,
+`AbleToScale`, indicates whether or not the HPA is able to fetch and update scales, as well as
+whether or not any backoff-related conditions would prevent scaling.  The second, `ScalingActive`,
+indicates whether or not the HPA is enabled (i.e. the replica count of the target is not zero) and
+is able to calculate desired scales. When it is `False`, it generally indicates problems with
+fetching metrics.  Finally, the last condition, `ScalingLimited`, indicates that the desired scale
+was capped by the maximum or minimum of the HorizontalPodAutoscaler.  This is an indication that
+you may wish to raise or lower the minimum or maximum replica count constraints on your
+HorizontalPodAutoscaler.
+-->
+对于上面展示的这个 HorizontalPodAutoscaler，我们可以看出有若干状态条件处于健康状态。
+首先，`AbleToScale` 表明 HPA 是否可以获取和更新伸缩信息，以及是否存在阻止伸缩的各种回退条件。
+其次，`ScalingActive` 表明HPA是否被启用（即目标的副本数量不为零） 以及是否能够完成伸缩计算。
+当这一状态为 `False` 时，通常表明获取度量指标存在问题。
+最后一个条件 `ScalingLimitted` 表明所需伸缩的值被 HorizontalPodAutoscaler 所定义的最大或者最小值所限制（即已经达到最大或者最小伸缩值）。
+这通常表明您可能需要调整 HorizontalPodAutoscaler 所定义的最大或者最小副本数量的限制了。
+
+<!--
+## Appendix: Quantities
+-->
+## 附录：Quantities
+
+<!--
+All metrics in the HorizontalPodAutoscaler and metrics APIs are specified using
+a special whole-number notation known in Kubernetes as a *quantity*.  For example,
+the quantity `10500m` would be written as `10.5` in decimal notation.  The metrics APIs
+will return whole numbers without a suffix when possible, and will generally return
+quantities in milli-units otherwise.  This means you might see your metric value fluctuate
+between `1` and `1500m`, or `1` and `1.5` when written in decimal notation.  See the
+[glossary entry on quantities](/docs/reference/glossary?core-object=true#term-quantity) for more information.
+-->
+HorizontalPodAutoscaler 和 metrics api 中的所有的度量指标使用 Kubernetes 中称为 *quantity* （）殊整数表示。
+例如，数量`10500m`用十进制表示为`10.5`。
+如果可能的话，metrics api 将返回没有后缀的整数，否则返回以千分单位的数量。
+这意味着您可能会看到您的度量指标在`1`和`1500m`之间波动，或者在十进制记数法中的`1`和`1.5`。
+更多信息，请参阅[度量术语](/docs/reference/glossary?core-object=true#term-quantity)
+
+<!--
+## Appendix: Other possible scenarios
+-->
+## 附录：其他可能的情况
+
+<!--
+### Creating the autoscaler declaratively
+-->
+### 使用YAML文件创建 autoscaler
+
+<!--
+Instead of using `kubectl autoscale` command to create a HorizontalPodAutoscaler imperatively we
+can use the following file to create it declaratively:
+-->
+除了使用 `kubectl autoscale` 命令，也可以文件创建 HorizontalPodAutoscaler ：
+
+{{< codenew file="application/hpa/php-apache.yaml" >}}
+
+<!--
+We will create the autoscaler by executing the following command:
+-->
+使用如下命令创建 autoscaler：
+
+
+
+```shell
+kubectl create -f https://k8s.io/examples/application/hpa/php-apache.yaml
+```
+```
+horizontalpodautoscaler.autoscaling/php-apache created
+```
+
+{{% /capture %}}