114 lines
5.5 KiB
Markdown
114 lines
5.5 KiB
Markdown
---
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content_type: concept
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title: 资源监控工具
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---
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<!--
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reviewers:
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- mikedanese
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content_type: concept
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title: Tools for Monitoring Resources
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-->
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<!-- 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/concepts/workloads/pods/),
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[services](/docs/concepts/services-networking/service/), 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，你需要了解应用程序在部署时的行为。
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你可以通过检测容器检查 Kubernetes 集群中的应用程序性能，
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[Pods](/zh/docs/concepts/workloads/pods),
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[服务](/zh/docs/concepts/services-networking/service/)
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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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此信息使你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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<!-- body -->
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<!--
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In Kubernetes, application monitoring does not depend on a single monitoring solution.
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On new clusters, you can use [resource metrics](#resource-metrics-pipeline) or
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[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，你可以使用[资源度量](#resource-metrics-pipeline)或
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[完整度量](#full-metrics-pipeline)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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The resource metrics pipeline provides a limited set of metrics related to
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cluster components such as the
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[Horizontal Pod Autoscaler](/docs/tasks/run-application/horizontal-pod-autoscale)
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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-sigs/metrics-server) and
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are exposed via the `metrics.k8s.io` API.
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-->
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## 资源度量管道 {#resource-metrics-pipeline}
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资源指标管道提供了一组与集群组件，例如
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[Horizontal Pod Autoscaler](/zh/docs/tasks/run-application/horizontal-pod-autoscale/)
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控制器以及 `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、内存存储的
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[metrics-server](https://github.com/kubernetes-sigs/metrics-server) 收集的，
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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
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[kubelet](/zh/docs/reference/command-line-tools-reference/kubelet/)
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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接口
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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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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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## 完整度量管道 {#full-metrics-pipeline}
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一个完整度量管道可以让你访问更丰富的度量。
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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，
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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 获取度量值，然后通过适配器将它们公开给 Kubernetes，
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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) 是一个 CNCF 项目，可以原生监控 Kubernetes、
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节点和 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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