99 lines
5.2 KiB
Markdown
99 lines
5.2 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: Tools for Monitoring Resources
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---
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{{% capture overview %}}
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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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{{< toc >}}
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In Kubernetes, application monitoring does not depend on a single monitoring
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solution. On new clusters, you can use two separate pipelines to collect
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monitoring statistics by default:
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- The [**resource metrics pipeline**](#resource-metrics-pipeline) provides a limited set of metrics related
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to cluster components such as the HorizontalPodAutoscaler controller, as well
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as the `kubectl top` utility. These metrics are collected by
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[metrics-server](https://github.com/kubernetes-incubator/metrics-server)
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and are exposed via the `metrics.k8s.io` API. `metrics-server` discovers
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all nodes on the cluster and queries each node's [Kubelet](/docs/admin/kubelet)
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for CPU and memory usage. The Kubelet fetches the data from
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[cAdvisor](https://github.com/google/cadvisor). `metrics-server` is a
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lightweight short-term in-memory store.
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- A [**full metrics pipeline**](#full-metrics-pipelines), such as Prometheus, gives you access to richer
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metrics. In addition, Kubernetes can respond to these metrics by automatically
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scaling or adapting the cluster based on its current state, using mechanisms
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such as the Horizontal Pod Autoscaler. The monitoring pipeline fetches
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metrics from the Kubelet, and then exposes them to Kubernetes via an adapter
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by implementing either the `custom.metrics.k8s.io` or
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`external.metrics.k8s.io` API.
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## Resource metrics pipeline
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### Kubelet
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The Kubelet acts as a bridge between the Kubernetes master and the nodes. It manages the pods and containers running on a machine. Kubelet translates each pod into its constituent containers and fetches individual container usage statistics from cAdvisor. It then exposes the aggregated pod resource usage statistics via a REST API.
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### cAdvisor
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cAdvisor is an open source container resource usage and performance analysis agent. It is purpose-built for containers and supports Docker containers natively. In Kubernetes, cAdvisor is integrated into the Kubelet binary. cAdvisor auto-discovers all containers in the machine and collects CPU, memory, filesystem, and network usage statistics. cAdvisor also provides the overall machine usage by analyzing the 'root' container on the machine.
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On most Kubernetes clusters, cAdvisor exposes a simple UI for on-machine containers on port 4194. Here is a snapshot of part of cAdvisor's UI that shows the overall machine usage:
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## Full metrics pipelines
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Many full metrics solutions exist for Kubernetes.
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### Prometheus
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[Prometheus](https://prometheus.io) can natively monitor kubernetes, nodes, and prometheus itself.
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The [Prometheus Operator](https://coreos.com/operators/prometheus/docs/latest/)
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simplifies Prometheus setup on Kubernetes, and allows you to serve the
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custom metrics API using the
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[Prometheus adapter](https://github.com/directxman12/k8s-prometheus-adapter).
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Prometheus provides a robust query language and a built-in dashboard for
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querying and visualizing your data. Prometheus is also a supported
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data source for [Grafana](https://prometheus.io/docs/visualization/grafana/).
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### Google Cloud Monitoring
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Google Cloud Monitoring is a hosted monitoring service you can use to
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visualize and alert on important metrics in your application. can collect
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metrics from Kubernetes, and you can access them
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using the [Cloud Monitoring Console](https://app.google.stackdriver.com/).
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You can create and customize dashboards to visualize the data gathered
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from your Kubernetes cluster.
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This video shows how to configure and run a Google Cloud Monitoring backed Heapster:
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[](https://www.youtube.com/watch?v=xSMNR2fcoLs)
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{{< figure src="/images/docs/gcm.png" alt="Google Cloud Monitoring dashboard example" title="Google Cloud Monitoring dashboard example" caption="This dashboard shows cluster-wide resource usage." >}}
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### Dynatrace Kubernetes monitoring
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With [Dynatrace Kubernetes monitoring](https://www.dynatrace.com/technologies/cloud-and-microservices/kubernetes-monitoring/), you can monitor application and cluster health in highly-dynamic Kubernetes environments.
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Dynatrace automatically discovers all containers running on Kubernetes and presents you with a real-time view of all the connections between your containerized processes, hosts, and cloud instances. Dynatrace includes root cause analysis and the ability to replay problems to see how they evolved over time.
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