Tools for Monitoring Resources

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, services, 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.

In Kubernetes, application monitoring does not depend on a single monitoring solution. On new clusters, you can use resource metrics or full metrics 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 controller, as well as the kubectl top utility. These metrics are collected by the lightweight, short-term, in-memory 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 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. If you use a container runtime that uses Linux cgroups and namespaces to implement containers, and the container runtime does not publish usage statistics, then the kubelet can look up those statistics directly (using code from cAdvisor). No matter how those statistics arrive, the kubelet 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, 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.

What's next

Learn about additional debugging tools, including:

• Logging
• Monitoring
• Getting into containers via exec
• Connecting to containers via proxies
• Connecting to containers via port forwarding
• Inspect Kubernetes node with crictl