Kubernetes Monitoring pages mirror the hierarchy of Kubernetes objects, so you can begin at any level above containers. Main pages include lists of Clusters, namespaces, workloads, and Nodes.
For example, the Cluster main page shows the list of your Clusters. When you click on a Cluster in the list, it opens the Cluster detail page. That page shows the details for the Cluster along with a list of Nodes within that Cluster.
You can continue to drill into a Node and see the list of Pods for that Node, all the way to the container level.
There are also main pages for Cluster configuration as well as managing alerts, cost, and efficiency.
For additional navigation tips, refer to Navigation tips for Kubernetes Monitoring.
Start with high-level snapshot
The Kubernetes Overview page gives you a high-level view of your Clusters, usage, and alerts. This page brings to the forefront key data about your infrastructure.
Refine counts of Kubernetes objects
Adjust the time range and filter by Cluster and namespace to narrow and include historical data for:
Clusters, Nodes, namespaces, workloads, Pods, and containers
Use the time range selector to focus on a time period while looking for patterns or spikes in CPU and memory usage in your Clusters.
When spikes occur:
Zoom in on the graph to narrow the time selection.
Hover over and click the peak of the spike to see the percentage of use compared to capacity. In the following example, the spike shows 46.5% of CPU usage compared to capacity.
Click the link to view the Cluster. The Cluster page shows the time range you set when zooming in on the graph.
On the Cost page, use the Overview and Savings tabs to gain an understanding what Kubernetes is costing and how you can save.
You can see the cost of each item in a list view as well as on the detail pages.
Throughout Kubernetes Monitoring, resource usage statistics are available for Kubernetes objects.
Learn what’s predicted
CPU and memory prediction can help you ensure resources are available during spikes in usage, as well as help you decrease the amount of unused resources due to over provisioning.
To use prediction tools, first enable the Machine Learning plugin.
The following buttons are available in various views. Click them to show a prediction for Clusters, namespaces, workloads, Nodes, Pods, and containers:
Predict Mem Usage: Shows a predictive graph for memory usage one week in the future. Calculations are based on metrics from the previous week.
Predict CPU: Shows a predictive graph for CPU usage one week in the future. Calculations are based on metrics from the previous week.
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This feature can be seen on this Node details page.
Identify any Pods that have CPU usage different from other Pods.
To do so, on a workload detail page, click the Detect Outlier CPU Usage amongst Pods button.
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This feature can be seen on this namespace details page.
Click Explore this query in the Machine Learning plugin to view the raw data and troubleshoot issues. Here you can adjust parameters and see a more detailed graph of the findings.
Select a time range to see your historical data for any time frame you choose. As you navigate from page to page, the time range remains the same for period you set until you change it again.
As an example, the Pod optimization section of the Pod detail page shows a time range over several hours. You can use this to understand the historical pattern of CPU usage and memory usage.
Zoom into an area of any graph on the detail pages to narrow the time range selector even further. The time range remains selected until you click Back to default.
You can find deleted Clusters, namespaces, workloads, Nodes, Pods, and containers to understand what occurred in the past. To do so, set the time range selector to a past time period.
The following example shows a time range of the previous 30 days, and then filtering for Nodes with the condition of “No data”. The Node detail page shows a graph depicting when the Node expired.
Grafana Cloud has a default 30-day limit for queries. If your Kubernetes object was deleted 30 days beyond the current date, use the time range selector to choose a specific 30-day time frame in the past.
Use the network panels to understand when bandwidth limits are causing network saturation, which can lead to dropped packets.
On any detail page for Cluster, namespace, workload, Node, or Pod, click the Network tab to view:
Network Bandwidth Rx/Tx: Shows the rate of received and transmitted bytes
Network Saturation Rx/Tx dropped packets: Shows rate of received and transmitted packets dropped
Network Bandwidth and Network Saturation by Node, workload, or Pod: Shows the bandwidth and saturation by object
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This feature can be seen on the Network tab of this namespace details page.
Navigate easily from Kubernetes Monitoring to other capabilities in Grafana Cloud to analyze, troubleshoot, and solve issues.
Start an automated diagnostic
From a Pod, Cluster, namespace, or workload detail view, you can begin an automated investigation by clicking Run Sift investigation.
Sift performs a set of automated system checks, and surfaces potential issues in your Kubernetes environment.
It then works to identify the root cause of an incident.
To access root cause analysis tools, enable Asserts on your stack.
You can take troubleshooting deeper by understanding relationships between components and what is occurring between them.
Within Kubernetes Monitoring, access Asserts Workbench to perform root cause analysis.
From any list of Clusters, Nodes, workloads, namespaces, or Pods you choose, select the box to the left of the list item, and click the Compare in Asserts Workbench button.
The RCA Workbench opens in a new tab.
To return to Kubernetes Monitoring, click the browser back button.
View queries to troubleshoot with Explore
To further query data, use any of the Explore buttons available throughout the interface (such as Explore namespaces or Explore alerts). You see a view that provides additional query tools for troubleshooting.
Here are some tips and shortcuts for getting around in Kubernetes Monitoring.
Give it a try using Grafana Play
With Grafana Play, you can explore and see how it works, learning from practical examples to accelerate your development.
This feature can be seen on the Kubernetes Monitoring Overview.
Throughout the views in Kubernetes Monitoring, you see color used as an additional means of indicating status or condition.
For example, sometimes text is a different color for Pod status:
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