106 lines
5.5 KiB
Markdown
106 lines
5.5 KiB
Markdown
---
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reviewers:
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- mikedanese
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content_template: templates/concept
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title: 资源监控工具
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---
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<!--
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---
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reviewers:
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- mikedanese
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content_template: templates/concept
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title: Tools for Monitoring Resources
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---
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-->
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{{% capture overview %}}
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<!--
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To scale an application and provide a reliable service, you need to
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understand how the application behaves when it is deployed. You can examine
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application performance in a Kubernetes cluster by examining the containers,
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[pods](/docs/user-guide/pods), [services](/docs/user-guide/services), and
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the characteristics of the overall cluster. Kubernetes provides detailed
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information about an application's resource usage at each of these levels.
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This information allows you to evaluate your application's performance and
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where bottlenecks can be removed to improve overall performance.
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-->
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要扩展应用程序并提供可靠的服务，您需要了解应用程序在部署时的行为。
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您可以通过检测容器检查 Kubernetes 集群中的应用程序性能，[pods](/docs/user-guide/pods), [服务](/docs/user-guide/services)和整个集群的特征。
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Kubernetes 在每个级别上提供有关应用程序资源使用情况的详细信息。
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此信息使您可以评估应用程序的性能，以及在何处可以消除瓶颈以提高整体性能。
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{{% /capture %}}
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{{% capture body %}}
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<!--
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In Kubernetes, application monitoring does not depend on a single monitoring solution. On new clusters, you can use [resource metrics](#resource-metrics-pipeline) or [full metrics](#full-metrics-pipeline) pipelines to collect monitoring statistics.
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-->
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在 Kubernetes 中，应用程序监控不依赖单个监控解决方案。
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在新集群上，您可以使用[资源度量](#资源度量管道)或[完整度量](#完整度量管道)管道来收集监视统计信息。
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<!--
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## Resource metrics pipeline
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-->
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## 资源度量管道
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<!--
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The resource metrics pipeline provides a limited set of metrics related to
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cluster components such as the [Horizontal Pod Autoscaler](/docs/tasks/run-application/horizontal-pod-autoscale) controller, as well as the `kubectl top` utility.
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These metrics are collected by the lightweight, short-term, in-memory
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[metrics-server](https://github.com/kubernetes-incubator/metrics-server) and
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are exposed via the `metrics.k8s.io` API.
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-->
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资源指标管道提供了一组与集群组件，例如[Horizontal Pod Autoscaler]控制器(/docs/tasks/run-application/horizontal-pod-autoscale)，以及 `kubectl top` 实用程序相关的有限度量。
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这些指标是由轻量级的、短期内存[度量服务器](https://github.com/kubernetes-incubator/metrics-server)收集的，通过 `metrics.k8s.io` 公开。
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<!--
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metrics-server discovers all nodes on the cluster and
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queries each node's
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[kubelet](/docs/reference/command-line-tools-reference/kubelet) for CPU and
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memory usage. The kubelet acts as a bridge between the Kubernetes master and
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the nodes, managing the pods and containers running on a machine. The kubelet
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translates each pod into its constituent containers and fetches individual
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container usage statistics from the container runtime through the container
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runtime interface. The kubelet fetches this information from the integrated
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cAdvisor for the legacy Docker integration. It then exposes the aggregated pod
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resource usage statistics through the metrics-server Resource Metrics API.
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This API is served at `/metrics/resource/v1beta1` on the kubelet's authenticated and
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read-only ports.
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-->
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度量服务器发现集群中的所有节点，并且查询每个节点的[kubelet](/docs/reference/command-line-tools-reference/kubelet)以获取 CPU 和内存使用情况。
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Kubelet 充当 Kubernetes 主节点与节点之间的桥梁，管理机器上运行的 Pod 和容器。
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kubelet 将每个 pod 转换为其组成的容器，并在容器运行时通过容器运行时界面获取各个容器使用情况统计信息。
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kubelet 从集成的 cAdvisor 获取此信息，以进行旧式 Docker 集成。
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然后，它通过 metrics-server Resource Metrics API 公开聚合的 pod 资源使用情况统计信息。
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该 API 在 kubelet 的经过身份验证和只读的端口上的`/metrics/resource/v1beta1`中提供。
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<!--
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## Full metrics pipeline
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-->
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## 完整度量管道
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<!--
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A full metrics pipeline gives you access to richer metrics. Kubernetes can
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respond to these metrics by automatically scaling or adapting the cluster
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based on its current state, using mechanisms such as the Horizontal Pod
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Autoscaler. The monitoring pipeline fetches metrics from the kubelet and
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then exposes them to Kubernetes via an adapter by implementing either the
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`custom.metrics.k8s.io` or `external.metrics.k8s.io` API.
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-->
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一个完整度量管道可以让您访问更丰富的度量。
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Kubernetes 还可以根据集群的当前状态，使用 Pod 水平自动扩缩器等机制，通过自动调用扩展或调整集群来响应这些度量。
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监控管道从 kubelet 获取度量，然后通过适配器将它们公开给 Kubernetes，方法是实现 `custom.metrics.k8s.io` 或 `external.metrics.k8s.io` API。
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<!--
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[Prometheus](https://prometheus.io), a CNCF project, can natively monitor Kubernetes, nodes, and Prometheus itself.
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Full metrics pipeline projects that are not part of the CNCF are outside the scope of Kubernetes documentation.
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-->
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[Prometheus](https://prometheus.io)，一个 CNCF 项目，可以原生监控 Kubernetes、节点和
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Prometheus 本身。
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完整度量管道项目不属于 CNCF 的一部分，不在 Kubernetes 文档的范围之内。
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{{% /capture %}}
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