[zh] sync content/zh/docs/tasks/debug-application-cluster/resource-metrics-pipeline.md
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---
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title: 资源指标管道
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content_type: concept
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---
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<!--
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reviewers:
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- fgrzadkowski
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- piosz
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title: Resource metrics pipeline
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content_type: concept
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-->
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<!-- overview -->
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<!--
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For Kubernetes, the _Metrics API_ offers a basic set of metrics to support automatic scaling and
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similar use cases. This API makes information available about resource usage for node and pod,
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including metrics for CPU and memory. If you deploy the Metrics API into your cluster, clients of
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the Kubernetes API can then query for this information, and you can use Kubernetes' access control
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mechanisms to manage permissions to do so.
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The [HorizontalPodAutoscaler](/docs/tasks/run-application/horizontal-pod-autoscale/) (HPA) and
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[VerticalPodAutoscaler](https://github.com/kubernetes/autoscaler/tree/master/vertical-pod-autoscaler#readme) (VPA)
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use data from the metrics API to adjust workload replicas and resources to meet customer demand.
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You can also view the resource metrics using the
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[`kubectl top`](/docs/reference/generated/kubectl/kubectl-commands#top)
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command.
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-->
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对于 Kubernetes，_Metrics API_ 提供了一组基本的指标，以支持自动伸缩和类似的用例。
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该 API 提供有关节点和 Pod 的资源使用情况的信息，
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包括 CPU 和内存的指标。如果将 Metrics API 部署到集群中，
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那么 Kubernetes API 的客户端就可以查询这些信息，并且可以使用 Kubernetes 的访问控制机制来管理权限。
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[HorizontalPodAutoscaler](/zh/docs/tasks/run-application/horizontal-pod-autoscale/) (HPA) 和
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[VerticalPodAutoscaler](https://github.com/kubernetes/autoscaler/tree/master/vertical-pod-autoscaler#readme) (VPA)
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使用 metrics API 中的数据调整工作负载副本和资源，以满足客户需求。
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你也可以通过 [`kubectl top`](/zh/docs/reference/generated/kubectl/kubectl-commands#top) 命令来查看资源指标。
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{{< note >}}
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<!--
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The Metrics API, and the metrics pipeline that it enables, only offers the minimum
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CPU and memory metrics to enable automatic scaling using HPA and / or VPA.
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If you would like to provide a more complete set of metrics, you can complement
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the simpler Metrics API by deploying a second
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[metrics pipeline](/docs/tasks/debug-application-cluster/resource-usage-monitoring/#full-metrics-pipeline)
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that uses the _Custom Metrics API_.
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-->
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Metrics API 及其启用的指标管道仅提供最少的 CPU 和内存指标，以启用使用 HPA 和/或 VPA 的自动扩展。
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如果你想提供更完整的指标集，你可以通过部署使用 _Custom Metrics API_ 的第二个
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[指标管道](/zh/docs/tasks/debug-application-cluster/resource-usage-monitoring/#full-metrics-pipeline) 来作为简单的 Metrics API 的补充。
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{{< /note >}}
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<!--
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Figure 1 illustrates the architecture of the resource metrics pipeline.
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-->
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图 1 说明了资源指标管道的架构。
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{{< mermaid >}}
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flowchart RL
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subgraph cluster[Cluster]
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direction RL
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S[ <br><br> ]
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A[Metrics-<br>Server]
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subgraph B[Nodes]
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direction TB
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D[cAdvisor] --> C[kubelet]
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E[Container<br>runtime] --> D
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E1[Container<br>runtime] --> D
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P[pod data] -.- C
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end
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L[API<br>server]
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W[HPA]
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C ---->|Summary<br>API| A -->|metrics<br>API| L --> W
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end
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L ---> K[kubectl<br>top]
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classDef box fill:#fff,stroke:#000,stroke-width:1px,color:#000;
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class W,B,P,K,cluster,D,E,E1 box
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classDef spacewhite fill:#ffffff,stroke:#fff,stroke-width:0px,color:#000
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class S spacewhite
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classDef k8s fill:#326ce5,stroke:#fff,stroke-width:1px,color:#fff;
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class A,L,C k8s
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{{< /mermaid >}}
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<!--
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Figure 1. Resource Metrics Pipeline
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The architecture components, from right to left in the figure, consist of the following:
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* [cAdvisor](https://github.com/google/cadvisor): Daemon for collecting, aggregating and exposing
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container metrics included in Kubelet.
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* [kubelet](/docs/concepts/overview/components/#kubelet): Node agent for managing container
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resources. Resource metrics are accessible using the `/metrics/resource` and `/stats` kubelet
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API endpoints.
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* [Summary API](#summary-api-source): API provided by the kubelet for discovering and retrieving
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per-node summarized stats available through the `/stats` endpoint.
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* [metrics-server](#metrics-server): Cluster addon component that collects and aggregates resource
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metrics pulled from each kubelet. The API server serves Metrics API for use by HPA, VPA, and by
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the `kubectl top` command. Metrics Server is a reference implementation of the Metrics API.
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* [Metrics API](#metrics-api): Kubernetes API supporting access to CPU and memory used for
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workload autoscaling. To make this work in your cluster, you need an API extension server that
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provides the Metrics API.
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-->
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图 1. 资源指标管道
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图中从右到左的架构组件包括以下内容：
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* [cAdvisor](https://github.com/google/cadvisor): 用于收集、聚合和公开 Kubelet 中包含的容器指标的守护程序。
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* [kubelet](/zh/docs/concepts/overview/components/#kubelet): 用于管理容器资源的节点代理。
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可以使用 `/metrics/resource` 和 `/stats` kubelet API 端点访问资源指标。
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* [Summary API](#summary-api-source): kubelet 提供的 API，用于发现和检索可通过 `/stats` 端点获得的每个节点的汇总统计信息。
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* [metrics-server](#metrics-server): 集群插件组件，用于收集和聚合从每个 kubelet 中提取的资源指标。
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API 服务器提供 Metrics API 以供 HPA、VPA 和 `kubectl top` 命令使用。 Metrics Server 是 Metrics API 的参考实现。
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* [Metrics API](#metrics-api): Kubernetes API 支持访问用于工作负载自动缩放的 CPU 和内存。
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要在你的集群中进行这项工作，你需要一个提供 Metrics API 的 API 扩展服务器。
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<!--
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cAdvisor supports reading metrics from cgroups, which works with typical container runtimes on Linux.
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If you use a container runtime that uses another resource isolation mechanism, for example
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virtualization, then that container runtime must support
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[CRI Container Metrics](https://github.com/kubernetes/community/blob/master/contributors/devel/sig-node/cri-container-stats.md)
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in order for metrics to be available to the kubelet.
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-->
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{{< note >}}
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cAdvisor 支持从 cgroups 读取指标，它适用于 Linux 上的典型容器运行时。
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如果你使用基于其他资源隔离机制的容器运行时，例如虚拟化，那么该容器运行时必须支持
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[CRI 容器指标](https://github.com/kubernetes/community/blob/master/contributors/devel/sig-node/cri-container-stats.md)
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以便 kubelet 可以使用指标。
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{{< /note >}}
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<!-- body -->
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<!--
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## Metrics API
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The metrics-server implements the Metrics API. This API allows you to access CPU and memory usage
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for the nodes and pods in your cluster. Its primary role is to feed resource usage metrics to K8s
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autoscaler components.
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Here is an example of the Metrics API request for a `minikube` node piped through `jq` for easier
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reading:
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-->
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## Metrics API {#the-metrics-api}
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{{< feature-state for_k8s_version="1.8" state="beta" >}}
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metrics-server 实现了 Metrics API。此 API 允许你访问集群中节点和 Pod 的 CPU 和内存使用情况。
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它的主要作用是将资源使用指标提供给 K8s 自动缩放器组件。
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下面是一个 `minikube` 节点的 Metrics API 请求示例，通过 `jq` 管道处理以便于阅读：
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```shell
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kubectl get --raw "/apis/metrics.k8s.io/v1beta1/nodes/minikube" | jq '.'
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```
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<!-- Here is the same API call using `curl`: -->
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这是使用 `curl` 来执行的相同 API 调用：
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```shell
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curl http://localhost:8080/apis/metrics.k8s.io/v1beta1/nodes/minikube
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```
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<!-- Sample response: -->
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响应示例：
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```json
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{
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"kind": "NodeMetrics",
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"apiVersion": "metrics.k8s.io/v1beta1",
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"metadata": {
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"name": "minikube",
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"selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/minikube",
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"creationTimestamp": "2022-01-27T18:48:43Z"
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},
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"timestamp": "2022-01-27T18:48:33Z",
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"window": "30s",
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"usage": {
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"cpu": "487558164n",
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"memory": "732212Ki"
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}
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}
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```
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<!--
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Here is an example of the Metrics API request for a `kube-scheduler-minikube` pod contained in the
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`kube-system` namespace and piped through `jq` for easier reading:
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-->
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下面是一个 `kube-system` 命名空间中的 `kube-scheduler-minikube` Pod 的 Metrics API 请求示例，
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通过 `jq` 管道处理以便于阅读：
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```shell
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kubectl get --raw "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube-scheduler-minikube" | jq '.'
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```
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<!-- Here is the same API call using `curl`: -->
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这是使用 `curl` 来完成的相同 API 调用：
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```shell
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curl http://localhost:8080/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube-scheduler-minikube
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```
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<!-- Sample response: -->
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响应示例：
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```json
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{
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"kind": "PodMetrics",
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"apiVersion": "metrics.k8s.io/v1beta1",
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"metadata": {
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"name": "kube-scheduler-minikube",
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"namespace": "kube-system",
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"selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube-scheduler-minikube",
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"creationTimestamp": "2022-01-27T19:25:00Z"
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},
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"timestamp": "2022-01-27T19:24:31Z",
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"window": "30s",
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"containers": [
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{
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"name": "kube-scheduler",
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"usage": {
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"cpu": "9559630n",
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"memory": "22244Ki"
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}
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}
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]
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}
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```
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<!--
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The Metrics API is defined in the [k8s.io/metrics](https://github.com/kubernetes/metrics)
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repository. You must enable the [API aggregation layer](/docs/tasks/extend-kubernetes/configure-aggregation-layer/)
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and register an [APIService](/docs/reference/kubernetes-api/cluster-resources/api-service-v1/)
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for the `metrics.k8s.io` API.
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To learn more about the Metrics API, see [resource metrics API design](https://github.com/kubernetes/design-proposals-archive/blob/main/instrumentation/resource-metrics-api.md),
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the [metrics-server repository](https://github.com/kubernetes-sigs/metrics-server) and the
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[resource metrics API](https://github.com/kubernetes/metrics#resource-metrics-api).
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-->
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Metrics API 在 [k8s.io/metrics](https://github.com/kubernetes/metrics) 代码库中定义。
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你必须启用 [API 聚合层](/zh/docs/tasks/extend-kubernetes/configure-aggregation-layer/)并为
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`metrics.k8s.io` API 注册一个 [APIService](/zh/docs/reference/kubernetes-api/cluster-resources/api-service-v1/)。
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要了解有关 Metrics API 的更多信息，
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请参阅资源 [Resource Metrics API Design](https://github.com/kubernetes/design-proposals-archive/blob/main/instrumentation/resource-metrics-api.md)、
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[metrics-server 代码库](https://github.com/kubernetes-sigs/metrics-server) 和
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[Resource Metrics API](https://github.com/kubernetes/metrics#resource-metrics-api)。
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<!--
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You must deploy the metrics-server or alternative adapter that serves the Metrics API to be able
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to access it.
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-->
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{{< note >}}
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你必须部署提供 Metrics API 服务的 metrics-server 或其他适配器才能访问它。
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{{< /note >}}
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<!--
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## Measuring Resource Usage
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### CPU
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CPU is reported as the average core usage measured in cpu units. One cpu, in Kubernetes, is
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equivalent to 1 vCPU/Core for cloud providers, and 1 hyper-thread on bare-metal Intel processors.
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This value is derived by taking a rate over a cumulative CPU counter provided by the kernel (in
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both Linux and Windows kernels). The time window used to calculate CPU is shown under window field
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in Metrics API.
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To learn more about how Kubernetes allocates and measures CPU resources, see
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[meaning of CPU](/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu).
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-->
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## 度量资源用量 {#measuring-resource-usage}
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### CPU
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CPU 报告为以 cpu 为单位测量的平均核心使用率。在 Kubernetes 中，
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一个 cpu 相当于云提供商的 1 个 vCPU/Core，以及裸机 Intel 处理器上的 1 个超线程。
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该值是通过对内核提供的累积 CPU 计数器（在 Linux 和 Windows 内核中）取一个速率得出的。
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用于计算 CPU 的时间窗口显示在 Metrics API 的窗口字段下。
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要了解更多关于 Kubernetes 如何分配和测量 CPU 资源的信息，请参阅
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[CPU 的含义](/zh/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu)。
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<!--
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### Memory
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Memory is reported as the working set, measured in bytes, at the instant the metric was collected.
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In an ideal world, the "working set" is the amount of memory in-use that cannot be freed under
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memory pressure. However, calculation of the working set varies by host OS, and generally makes
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heavy use of heuristics to produce an estimate.
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The Kubernetes model for a container's working set expects that the container runtime counts
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anonymous memory associated with the container in question. The working set metric typically also
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includes some cached (file-backed) memory, because the host OS cannot always reclaim pages.
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To learn more about how Kubernetes allocates and measures memory resources, see
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[meaning of memory](/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory).
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-->
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### 内存 {#memory}
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内存报告为在收集度量标准的那一刻的工作集大小，以字节为单位。
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在理想情况下，“工作集”是在内存压力下无法释放的正在使用的内存量。
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然而，工作集的计算因主机操作系统而异，并且通常大量使用启发式算法来产生估计。
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Kubernetes 模型中，容器工作集是由容器运行时计算的与相关容器关联的匿名内存。
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工作集指标通常还包括一些缓存（文件支持）内存，因为主机操作系统不能总是回收页面。
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要了解有关 Kubernetes 如何分配和测量内存资源的更多信息，
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请参阅[内存的含义](/zh/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory)。
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<!--
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## Metrics Server
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The metrics-server fetches resource metrics from the kubelets and exposes them in the Kubernetes
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API server through the Metrics API for use by the HPA and VPA. You can also view these metrics
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using the `kubectl top` command.
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The metrics-server uses the Kubernetes API to track nodes and pods in your cluster. The
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metrics-server queries each node over HTTP to fetch metrics. The metrics-server also builds an
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internal view of pod metadata, and keeps a cache of pod health. That cached pod health information
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is available via the extension API that the metrics-server makes available.
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For example with an HPA query, the metrics-server needs to identify which pods fulfill the label
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selectors in the deployment.
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-->
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## Metrics 服务器 {#metrics-server}
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metrics-server 从 kubelet 中获取资源指标，并通过 Metrics API 在 Kubernetes API 服务器中公开它们，以供 HPA 和 VPA 使用。
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你还可以使用 `kubectl top` 命令查看这些指标。
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metrics-server 使用 Kubernetes API 来跟踪集群中的节点和 Pod。metrics-server 服务器通过 HTTP 查询每个节点以获取指标。
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metrics-server 还构建了 Pod 元数据的内部视图，并维护 Pod 健康状况的缓存。
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缓存的 Pod 健康信息可通过 metrics-server 提供的扩展 API 获得。
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例如，对于 HPA 查询，metrics-server 需要确定哪些 Pod 满足 Deployment 中的标签选择器。
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<!--
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The metrics-server calls the [kubelet](/docs/reference/command-line-tools-reference/kubelet/) API
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to collect metrics from each node. Depending on the metrics-server version it uses:
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* Metrics resource endpoint `/metrics/resource` in version v0.6.0+ or
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* Summary API endpoint `/stats/summary` in older versions
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-->
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metrics-server 调用 [kubelet](/zh/docs/reference/command-line-tools-reference/kubelet/) API
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从每个节点收集指标。根据它使用的度量服务器版本：
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* 版本 v0.6.0+ 中，使用指标资源端点 `/metrics/resource`
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* 旧版本中使用 Summary API 端点 `/stats/summary`
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<!--
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To learn more about the metrics-server, see the
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[metrics-server repository](https://github.com/kubernetes-sigs/metrics-server).
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You can also check out the following:
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* [metrics-server design](https://github.com/kubernetes/community/blob/master/contributors/design-proposals/instrumentation/metrics-server.md)
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* [metrics-server FAQ](https://github.com/kubernetes-sigs/metrics-server/blob/master/FAQ.md)
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* [metrics-server known issues](https://github.com/kubernetes-sigs/metrics-server/blob/master/KNOWN_ISSUES.md)
|
||||
* [metrics-server releases](https://github.com/kubernetes-sigs/metrics-server/releases)
|
||||
* [Horizontal Pod Autoscaling](/docs/tasks/run-application/horizontal-pod-autoscale/)
|
||||
-->
|
||||
|
||||
了解更多 metrics-server，参阅 [metrics-server 代码库](https://github.com/kubernetes-sigs/metrics-server)。
|
||||
|
||||
你还可以查看以下内容：
|
||||
|
||||
* [metrics-server 设计](https://github.com/kubernetes/community/blob/master/contributors/design-proposals/instrumentation/metrics-server.md)
|
||||
* [metrics-server FAQ](https://github.com/kubernetes-sigs/metrics-server/blob/master/FAQ.md)
|
||||
* [metrics-server known issues](https://github.com/kubernetes-sigs/metrics-server/blob/master/KNOWN_ISSUES.md)
|
||||
* [metrics-server releases](https://github.com/kubernetes-sigs/metrics-server/releases)
|
||||
* [Horizontal Pod Autoscaling](/zh/docs/tasks/run-application/horizontal-pod-autoscale/)
|
||||
|
||||
<!--
|
||||
### Summary API Source
|
||||
|
||||
The [Kubelet](/docs/reference/command-line-tools-reference/kubelet/) gathers stats at node, volume, pod and container level, and emits
|
||||
them in the [Summary API](https://github.com/kubernetes/kubernetes/blob/7d309e0104fedb57280b261e5677d919cb2a0e2d/staging/src/k8s.io/kubelet/pkg/apis/stats/v1alpha1/types.go)
|
||||
for consumers to read.
|
||||
-->
|
||||
|
||||
### Summary API 来源
|
||||
|
||||
[Kubelet](/zh/docs/reference/command-line-tools-reference/kubelet/) 在节点、卷、Pod 和容器级别收集统计信息，
|
||||
并在[Summary API](https://github.com/kubernetes/kubernetes/blob/7d309e0104fedb57280b261e5677d919cb2a0e2d/staging/src/k8s.io/kubelet/pkg/apis/stats/v1alpha1/types.go)
|
||||
中提供它们的统计信息供消费者阅读。
|
||||
|
||||
<!--
|
||||
Here is an example of a Summary API request for a `minikube` node:
|
||||
-->
|
||||
|
||||
下面是一个 `minikube` 节点的 Summary API 请求示例：
|
||||
|
||||
```shell
|
||||
kubectl get --raw "/api/v1/nodes/minikube/proxy/stats/summary"
|
||||
```
|
||||
|
||||
<!-- Here is the same API call using `curl`: -->
|
||||
这是使用 `curl` 来执行的相同 API 调用：
|
||||
|
||||
```shell
|
||||
curl http://localhost:8080/api/v1/nodes/minikube/proxy/stats/summary
|
||||
```
|
||||
|
||||
{{< note >}}
|
||||
<!--
|
||||
The summary API `/stats/summary` endpoint will be replaced by the `/metrics/resource` endpoint
|
||||
beginning with metrics-server 0.6.x.
|
||||
-->
|
||||
从 metrics-server 0.6.x 开始，Summary API `/stats/summary` 端点被 `/metrics/resource` 端点替换。
|
||||
{{< /note >}}
|
||||
|
|
@ -1,6 +1,7 @@
|
|||
---
|
||||
title: 资源指标管道
|
||||
content_type: concept
|
||||
weight: 15
|
||||
---
|
||||
<!--
|
||||
reviewers:
|
||||
|
|
@ -8,6 +9,7 @@ reviewers:
|
|||
- piosz
|
||||
title: Resource metrics pipeline
|
||||
content_type: concept
|
||||
weight: 15
|
||||
-->
|
||||
|
||||
<!-- overview -->
|
||||
|
|
|
|||
Loading…
Reference in New Issue