62 lines
2.9 KiB
Markdown
62 lines
2.9 KiB
Markdown
---
|
|
reviewers:
|
|
- mikedanese
|
|
content_type: concept
|
|
title: Tools for Monitoring Resources
|
|
---
|
|
|
|
<!-- 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.
|
|
|
|
|