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Merge pull request #43282 from my-git9/path-18297 

[zh-cn] sync tasks/run-application/*
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Kubernetes Prow Robot 2023-10-02 05:38:46 -07:00 committed by GitHub
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6
content/zh-cn/docs/tasks/run-application/configure-pdb.md
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@ -311,14 +311,14 @@ Example PDB Using minAvailable:
-->
使用 minAvailable 的 PDB 示例：
{{% code file="policy/zookeeper-pod-disruption-budget-minavailable.yaml" %}}
{{% code_sample file="policy/zookeeper-pod-disruption-budget-minavailable.yaml" %}}
<!--
Example PDB Using maxUnavailable:
-->
使用 maxUnavailable 的 PDB 示例：
{{% code file="policy/zookeeper-pod-disruption-budget-maxunavailable.yaml" %}}
{{% code_sample file="policy/zookeeper-pod-disruption-budget-maxunavailable.yaml" %}}
<!--
For example, if the above `zk-pdb` object selects the pods of a StatefulSet of size 3, both
@ -535,7 +535,7 @@ You can use a PDB with pods controlled by another resource, by an
- only `.spec.minAvailable` can be used, not `.spec.maxUnavailable`.
- only an integer value can be used with `.spec.minAvailable`, not a percentage.
-->
- 只能够使用 `.spec.minAvailable`，而不能够使用 `.spec.maxUnavailable。`
- 只能够使用 `.spec.minAvailable`，而不能够使用 `.spec.maxUnavailable`。
- 只能够使用整数作为 `.spec.minAvailable` 的值，而不能使用百分比。
<!--

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content/zh-cn/docs/tasks/run-application/delete-stateful-set.md
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@ -62,10 +62,10 @@ You may need to delete the associated headless service separately after the Stat
kubectl delete service <service-name>
```
-->
删除 StatefulSet 之后，你可能需要单独删除关联的无头服务。
删除 StatefulSet 之后，你可能需要单独删除关联的无头服务（Headless Service）。
```shell
kubectl delete service <服务名称>
kubectl delete service <Service 名称>
```
<!--

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content/zh-cn/docs/tasks/run-application/force-delete-stateful-set-pod.md
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@ -74,7 +74,7 @@ You can perform a graceful pod deletion with the following command:
-->
## 删除 Pod {#delete-pods}
你可以使用下面的命令执行体面地删除 Pod:
你可以使用下面的命令执行体面地删除 Pod：
```shell
kubectl delete pods <pod>
@ -187,7 +187,7 @@ kubectl delete pods <pod> --grace-period=0
If even after these commands the pod is stuck on `Unknown` state, use the following command to
remove the pod from the cluster:
-->
如果在执行这些命令后 Pod 仍处于 `Unknown` 状态，请使用以下命令从集群中删除 Pod:
如果在执行这些命令后 Pod 仍处于 `Unknown` 状态，请使用以下命令从集群中删除 Pod：
```shell
kubectl patch pod <pod> -p '{"metadata":{"finalizers":null}}'

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content/zh-cn/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough.md
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@ -104,7 +104,7 @@ using the following manifest:
为了演示 HorizontalPodAutoscaler，你将首先启动一个 Deployment 用 `hpa-example` 镜像运行一个容器，
然后使用以下清单文件将其暴露为一个 {{< glossary_tooltip term_id="service">}}：
{{% code file="application/php-apache.yaml" %}}
{{% code_sample file="application/php-apache.yaml" %}}
<!--
To do so, run the following command:
@ -775,7 +775,7 @@ can use the following manifest to create it declaratively:
-->
除了使用 `kubectl autoscale` 命令，也可以使用以下清单以声明方式创建 HorizontalPodAutoscaler：
{{% code file="application/hpa/php-apache.yaml" %}}
{{% code_sample file="application/hpa/php-apache.yaml" %}}
<!--
Then, create the autoscaler by executing the following command:

62
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@ -46,7 +46,7 @@ the HorizontalPodAutoscaler instructs the workload resource (the Deployment, Sta
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="Pod" term_id="pod" >}}。
这与 “垂直（Vertical）” 扩缩不同，对于 Kubernetes，
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，
@ -131,7 +131,8 @@ Kubernetes 将水平 Pod 自动扩缩实现为一个间歇运行的控制回路
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),
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。
@ -277,8 +278,8 @@ 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。
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
@ -286,10 +287,12 @@ 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
ready 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
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
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。
@ -340,7 +343,7 @@ 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，
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。
<!--
@ -362,7 +365,8 @@ the current value.
<!--
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.
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.
-->
@ -439,7 +443,7 @@ replicas, the StatefulSet directly manages its set of Pods (there is no intermed
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）。
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
@ -452,7 +456,7 @@ 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。
HPA 的任何目标资源都可以基于其中的 Pod 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：
@ -614,8 +618,8 @@ HorizontalPodAutoscaler 采用为每个指标推荐的最大比例，
<!--
## 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:
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}
@ -627,26 +631,31 @@ APIs, cluster administrators must ensure that:
* 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 add-on.
* For resource metrics, this is the `metrics.k8s.io` [API](/docs/reference/external-api/metrics.v1beta1/),
generally provided by [metrics-server](https://github.com/kubernetes-sigs/metrics-server).
It can be launched as a cluster add-on.
* 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 custom metrics, this is the `custom.metrics.k8s.io` [API](/docs/reference/external-api/metrics.v1beta1/).
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.
* For external metrics, this is the `external.metrics.k8s.io` [API](/docs/reference/external-api/metrics.v1beta1/).
It may be provided by the custom metrics adapters provided above.
-->
* 启用了 [API 聚合层](/zh-cn/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。
* 对于资源指标，将使用 `metrics.k8s.io` [API](/zh-cn/docs/reference/external-api/metrics.v1beta1/)，
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。
* 对于自定义指标，将使用 `custom.metrics.k8s.io` [API](/zh-cn/docs/reference/external-api/metrics.v1beta1/)。
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。
* 对于外部指标，将使用 `external.metrics.k8s.io` [API](/zh-cn/docs/reference/external-api/metrics.v1beta1/)。
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
@ -660,7 +669,8 @@ and [external.metrics.k8s.io](https://git.k8s.io/design-proposals-archive/instru
[external.metrics.k8s.io](https://git.k8s.io/design-proposals-archive/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)
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，
@ -696,7 +706,7 @@ the replica count for a scaling target. Scaling policies also let you control th
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)。
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。
<!--

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@ -59,7 +59,7 @@ MySQL 设置都使用的是不安全的默认值，这是因为我们想把重
[PersistentVolumes](/zh-cn/docs/concepts/storage/persistent-volumes/)
与 [StatefulSet](/zh-cn/docs/concepts/workloads/controllers/statefulset/)，
以及其他核心概念，例如 [Pod](/zh-cn/docs/concepts/workloads/pods/)、
[服务](/zh-cn/docs/concepts/services-networking/service/)与
[Service](/zh-cn/docs/concepts/services-networking/service/)与
[ConfigMap](/zh-cn/docs/tasks/configure-pod-container/configure-pod-configmap/)。
- 熟悉 MySQL 会有所帮助，但是本教程旨在介绍对其他系统应该有用的常规模式。
- 你正在使用默认命名空间或不包含任何冲突对象的另一个命名空间。
@ -98,7 +98,7 @@ Create the ConfigMap from the following YAML configuration file:
使用以下的 YAML 配置文件创建 ConfigMap ：
{{% code file="application/mysql/mysql-configmap.yaml" %}}
{{% code_sample file="application/mysql/mysql-configmap.yaml" %}}
```shell
kubectl apply -f https://k8s.io/examples/application/mysql/mysql-configmap.yaml
@ -132,7 +132,7 @@ Create the Services from the following YAML configuration file:
使用以下 YAML 配置文件创建服务：
{{% code file="application/mysql/mysql-services.yaml" %}}
{{% code_sample file="application/mysql/mysql-services.yaml" %}}
```shell
kubectl apply -f https://k8s.io/examples/application/mysql/mysql-services.yaml
@ -180,7 +180,7 @@ Finally, create the StatefulSet from the following YAML configuration file:
最后，使用以下 YAML 配置文件创建 StatefulSet：
{{% code file="application/mysql/mysql-statefulset.yaml" %}}
{{% code_sample file="application/mysql/mysql-statefulset.yaml" %}}
```shell
kubectl apply -f https://k8s.io/examples/application/mysql/mysql-statefulset.yaml
@ -247,7 +247,7 @@ In addition, the controller assigns each Pod a unique, stable name of the form
`mysql-1`, and `mysql-2`.
-->
此外，控制器为每个 Pod 分配一个唯一、稳定的名称，形如 `<statefulset 名称>-<序数索引>`，
其结果是 Pods 名为 `mysql-0`、`mysql-1` 和 `mysql-2`。
其结果是 Pod 名为 `mysql-0`、`mysql-1` 和 `mysql-2`。
<!--
The Pod template in the above StatefulSet manifest takes advantage of these
@ -688,7 +688,7 @@ Now uncordon the Node to return it to a normal state:
kubectl uncordon <node-name>
```
-->
现在去掉节点保护（Uncordon），使其恢复为正常模式:
现在去掉节点保护（Uncordon），使其恢复为正常模式：
```shell
kubectl uncordon <节点名称>

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@ -58,8 +58,8 @@ for a secure solution.
注意：在配置的 YAML 文件中定义密码的做法是不安全的。具体安全解决方案请参考
[Kubernetes Secrets](/zh-cn/docs/concepts/configuration/secret/)。
{{% code file="application/mysql/mysql-deployment.yaml" %}}
{{% code file="application/mysql/mysql-pv.yaml" %}}
{{% code_sample file="application/mysql/mysql-deployment.yaml" %}}
{{% code_sample file="application/mysql/mysql-pv.yaml" %}}
<!--
1. Deploy the PV and PVC of the YAML file:

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@ -42,7 +42,7 @@ a Deployment that runs the nginx:1.14.2 Docker image:
YAML 文件中描述 Deployment。例如，下面这个 YAML 文件描述了一个运行 nginx:1.14.2
Docker 镜像的 Deployment：
{{% code file="application/deployment.yaml" %}}
{{% code_sample file="application/deployment.yaml" %}}
<!--
1. Create a Deployment based on the YAML file:
@ -142,7 +142,7 @@ specifies that the deployment should be updated to use nginx 1.16.1.
你可以通过应用一个新的 YAML 文件来更新 Deployment。下面的 YAML 文件指定该
Deployment 镜像更新为 nginx 1.16.1。
{{% code file="application/deployment-update.yaml" %}}
{{% code_sample file="application/deployment-update.yaml" %}}
<!--
1. Apply the new YAML file:
@ -174,7 +174,7 @@ should have four Pods:
你可以通过应用新的 YAML 文件来增加 Deployment 中 Pod 的数量。
下面的 YAML 文件将 `replicas` 设置为 4，指定该 Deployment 应有 4 个 Pod：
{{% code file="application/deployment-scale.yaml" %}}
{{% code_sample file="application/deployment-scale.yaml" %}}
<!--
1. Apply the new YAML file:

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@ -184,4 +184,4 @@ Stateful 应用的集群是完全健康时才执行扩缩操作。
<!--
- Learn more about [deleting a StatefulSet](/docs/tasks/run-application/delete-stateful-set/).
-->
- 进一步了解[删除 StatefulSet](/zh-cn/docs/tasks/run-application/delete-stateful-set/)
- 进一步了解[删除 StatefulSet](/zh-cn/docs/tasks/run-application/delete-stateful-set/)。