Reorg the monitoring task section (#32823)

* reorg the monitoring task section Signed-off-by: Paul S. Schweigert <paulschw@us.ibm.com> * reorg from review comments Signed-off-by: Paul S. Schweigert <paulschw@us.ibm.com> * review comments Signed-off-by: Paul S. Schweigert <paulschw@us.ibm.com> * review fixes Signed-off-by: Paul S. Schweigert <paulschw@us.ibm.com>
2022-04-26 00:30:51 -04:00
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+---
+reviewers:
+- mikedanese
+content_type: concept
+title: Tools for Monitoring Resources
+weight: 15
+---
+
+<!-- overview -->
+
+To scale an application and provide a reliable service, you need to
+understand how the application behaves when it is deployed. You can examine
+application performance in a Kubernetes cluster by examining the containers,
+[pods](/docs/concepts/workloads/pods/),
+[services](/docs/concepts/services-networking/service/), and
+the characteristics of the overall cluster. Kubernetes provides detailed
+information about an application's resource usage at each of these levels.
+This information allows you to evaluate your application's performance and
+where bottlenecks can be removed to improve overall performance.
+
+<!-- body -->
+
+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.
+
+## Resource metrics pipeline
+
+The resource metrics pipeline provides a limited set of metrics related to
+cluster components such as the
+[Horizontal Pod Autoscaler](/docs/tasks/run-application/horizontal-pod-autoscale/)
+controller, as well as the `kubectl top` utility.
+These  metrics are collected by the lightweight, short-term, in-memory 
+[metrics-server](https://github.com/kubernetes-sigs/metrics-server) and
+ are exposed via the `metrics.k8s.io` API. 
+
+metrics-server discovers all nodes on the cluster and 
+queries each node's 
+[kubelet](/docs/reference/command-line-tools-reference/kubelet/) for CPU and 
+memory usage. The kubelet acts as a bridge between the Kubernetes master and 
+the nodes, managing the pods and containers running on a machine. The kubelet 
+translates each pod into its constituent containers and fetches individual 
+container usage statistics from the container runtime through the container 
+runtime interface. The kubelet fetches this information from the integrated 
+cAdvisor for the legacy Docker integration.  It then exposes the aggregated pod 
+resource usage statistics through the metrics-server Resource Metrics API.
+This API is served at `/metrics/resource/v1beta1` on the kubelet's authenticated and 
+read-only ports. 
+
+## Full metrics pipeline
+
+A full metrics pipeline gives you access to richer metrics. Kubernetes can
+respond to these metrics by  automatically scaling or adapting the cluster
+based on its current state, using mechanisms such as the Horizontal Pod
+Autoscaler. The monitoring pipeline fetches metrics from the kubelet and
+then exposes them to Kubernetes via an adapter by implementing either the
+`custom.metrics.k8s.io` or `external.metrics.k8s.io` API. 
+
+[Prometheus](https://prometheus.io), a CNCF project, can natively monitor Kubernetes, nodes, and Prometheus itself.
+Full metrics pipeline projects that are not part of the CNCF are outside the scope of Kubernetes documentation.  
+
+## {{% heading "whatsnext" %}}
+
+
+Learn about additional debugging tools, including:
+
+* [Logging](/docs/concepts/cluster-administration/logging/)
+* [Monitoring](/docs/tasks/debug-application-cluster/resource-usage-monitoring/)
+* [Getting into containers via `exec`](/docs/tasks/debug-application-cluster/applications/get-shell-running-container/)
+* [Connecting to containers via proxies](/docs/tasks/extend-kubernetes/http-proxy-access-api/)
+* [Connecting to containers via port forwarding](/docs/tasks/access-application-cluster/port-forward-access-application-cluster/)
+* [Inspect Kubernetes node with crictl](/docs/tasks/debug-application-cluster/monitoring/crictl/)