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 two separate pipelines to collect monitoring statistics by default:
The resource metrics pipeline provides a limited set of metrics related
to cluster components such as the HorizontalPodAutoscaler controller, as well
kubectl top utility. These metrics are collected by
and are exposed via the
all nodes on the cluster and queries each node’s Kubelet
for CPU and memory usage. The Kubelet fetches the data from
metrics-server is a
lightweight short-term in-memory store.
A full metrics pipeline, such as Prometheus, gives you access to richer
metrics. In addition, 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
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.
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.
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:
Many full metrics solutions exist for Kubernetes.
Prometheus can natively monitor kubernetes, nodes, and prometheus itself. The Prometheus Operator simplifies Prometheus setup on Kubernetes, and allows you to serve the custom metrics API using the Prometheus adapter. Prometheus provides a robust query language and a built-in dashboard for querying and visualizing your data. Prometheus is also a supported data source for Grafana.
Google Cloud Monitoring is a hosted monitoring service you can use to visualize and alert on important metrics in your application. can collect metrics from Kubernetes, and you can access them using the Cloud Monitoring Console. You can create and customize dashboards to visualize the data gathered from your Kubernetes cluster.
This video shows how to configure and run a Google Cloud Monitoring backed Heapster:
With the Kubernetes Job Monitor dashboard a Cluster Administrator can see which jobs are running and view the status of completed jobs.
New Relic Kubernetes integration provides increased visibility into the performance of your Kubernetes environment. New Relic’s Kubernetes integration instruments the container orchestration layer by reporting metrics from Kubernetes objects. The integration gives you insight into your Kubernetes nodes, namespaces, deployments, replica sets, pods, and containers.
Marquee capabilities: View your data in pre-built dashboards for immediate insight into your Kubernetes environment. Create your own custom queries and charts in Insights from automatically reported data. Create alert conditions on Kubernetes data. Learn more on this page.