The new Kubernetes experience is optimized for DevOps Platform Engineers and Site Reliability Engineers (SREs), focusing on the health and performance optimization of multicloud Kubernetes environments. The centerpiece of this experience is the new Kubernetes app .
The underlying metrics, events, and logs are all powered by Grail, which supports flexible analytics through the Dynatrace Query Language in Notebooks, Dashboards, and Workflows.
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Set up Kubernetes monitoring with Dynatrace.
Explore cluster, node, and workload insights.
Analyze health status with Davis AI.
Detect and troubleshoot Kubernetes issues.
Dynatrace SaaS environment powered by Grail and AppEngine
DPS license with the Kubernetes Platform Monitoring capability on your Rate Card
The new Kubernetes experience is not available for Managed or SaaS on non-Grail environments—you can continue to use Kubernetes Classic (accessible from the previous Dynatrace via Kubernetes).
Kubernetes app provides a comprehensive view of your environment, enabling you to automate monitoring and optimize the health and performance of your Kubernetes clusters and workloads. This guide walks you through the main concepts underlying the Kubernetes app.
1 of 5High-level overview of all your Kubernetes clusters, independent of the cloud service they run on.
Setup and reference
Use the following guides to set up and configure Kubernetes monitoring in Dynatrace.
The Kubernetes app offers insights into your entire Kubernetes environment, presenting valuable information across primary areas as indicated in the picture below.
On the left side (1), you can find a sidebar with all Kubernetes objects grouped by type, such as clusters, nodes, and workloads. In the center of the app (2), the main view lists all objects of the selected type, serving as the starting point for analysis and drill-down for your observability use-cases. Above this table (3), there's an aggregated health status of the displayed objects and their child objects. Finally, the filter bar located below the app header (4) allows you to refine the information in the list view, focusing on specific objects or health statuses.
Selecting a Kubernetes object from the list opens a detail view focusing on the specific object.
The detail view is divided into two primary sections. At the top (1), you get the health and security status of the selected object and its child objects. The main section (2) provides detailed insights of the given object, featuring tabs for analyzing health and utilization, as well as for exploring logs, events, ownership, and vulnerabilities. The data presented in the detailed view remains consistent regardless of any filters applied in the main interface.
Perspectives
Perspectives support various use cases, such as health monitoring or resource optimization.
Selecting a perspective (1) determines the columns displayed in the table view. For example, the Health perspective provides health-related information, whereas the Utilization perspective focuses on resource utilization data. Each perspective can be tailored to your own requirements (2) by adding or removing columns as desired. Your personal configuration persists in your browser, and you can reset to the default layout at any time by selecting More (…) next to the list of available perspectives (1).
Davis AI health status
The health status is based on the Kubernetes-focused anomaly detectors. Health indicators aggregate the states of these anomaly detectors per resource.
A Kubernetes object (such as a cluster) is considered unhealthy if any of its associated anomaly detectors are in an unhealthy state. By selecting a specific health indicator, you can gain further insights into the underlying reasons for this status.
In this example, you can see that, out of 32 nodes, 2 are currently considered unhealthy.
Select the red numbers displayed within the health status area to drill down to the list of currently unhealthy nodes.
Select any node to open the details view of the problematic node, including key metrics and events that led to their current state.
Anomaly detection for Kubernetes comes with effective defaults, but you can customize them to suit your environment's needs. Additionally, the Anomaly detectors page within the app provides a quick overview of your current configuration status
Warning signals
In addition to health status, the Kubernetes app surfaces active problematic conditions of workloads and nodes, as well as warning signals that occurred in the last 10 minutes. These warning signals combine both problematic conditions and warning events, providing insight into potential upcoming problems or existing misconfigurations.
While they may not always represent active health issues at the moment, frequent Unhealthy signals, for instance, might indicate misconfigured readiness probes, inappropriate CPU limits, or unusually high workload.
There are two types of warning signals. They're organized as follows:
Problematic conditions affect the health of the node or workload (for example, DiskPressure, MemoryPressure).
Listed first
Sorted alphabetically within each category
Warning events are less critical, and often signal temporary issues (for example, OOMKilled, PodEviction).
Listed after problematic conditions
Sorted by their frequency
The Kubernetes app provides several interaction options:
Context menu actions:
Go to affected nodes or Go to affected workloads: Navigates directly to the nodes or workloads experiencing the selected condition. This opens a filtered view displaying only the affected nodes or workloads.
Explore events: Opens a detailed log view showing the events associated with the warning signal.
Filter for: Automatically applies a filter to show only the entities impacted by the specific condition or event.
Filtering from the menu bar:
You can apply filters directly from the menu bar by selecting either general categories such as Any problematic condition or individual signals like MemoryPressure:True or FailedMount. Once filtered, the view updates to focus on the entities affected by the selected filter.
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