Welcome to Dynatrace!
This interactive product tour explores Kubernetes observability. Dynatrace automatically discovers and maps workloads, containers, pods, and nodes in real-time. So, you can collaborate across teams to optimize your platform and applications and improve user experiences.
The Kubernetes cluster overview provides you immediate insight into your cluster health, so you know where to prioritize your efforts.
Here we see that while the cluster nodes are healthy, 116 workloads have recently failed.
Dashboards are easily customized to provide you the key data you need.
* This experience is optimized for desktop devices
The Kubernetes cluster page provides you an overview of the cluster utilization, workloads and events.
Achieve immediate visibility across your Kubernetes infrastructure
Here, we automatically see basic information on our cluster and workloads. Nothing appears wrong, so let’s scroll down to the Events.
It looks like a payment service is responsible for causing the previous workload failures, and may not be working properly.
Let’s investigate that workload a bit more.
The cluster workload page shows relevant information at a glance.
Quickly identify problematic workloads and filter by cluster, namespace or type
Related to the event we saw on the previous screen, here we see there is a problem with the online-boutique 'frontend', which has a single service running on it.
Intuitive filtering gets you to the most relevant workloads faster using filters such as cluster, namespace, pod, pod state, and much more.
The service overview page automatically shows which applications or services use the 'frontend' service and any outgoing calls to other services or databases.
View the service details of your workloads to understand the broader context
Click next to analyze the failure rate.
Even though the response time appears to be normal, we see a high failure rate of 3.46% that Dynatrace’s Davis AI has associated to a single problem.
Specifically, the called service 'checkout' has a high failure rate.
Immediately see any problem associated with the 'frontend' service.
Diving into the details of the failure rate, we can see failures across time. The most relevant timeframe is automatically determined by the Davis AI or can be easily manually set. Let’s analyze the backtrace to determine any other impacted applications.
Gain additional context for failures across relevant timeframes to understand business impact
Click next to analyze the backtrace.
See the types and groups of failed requests.
See the types and groups of failed requests.
A backtrace shows the upstream services called from the request.
Trace the call back upstream to identify the impact to application frontends and users
Click next to continue to the application page.
Details of specific user actions, including the number of users and failed calls provide context and impact to the user experience.
In this case, we see that only the ‘online-boutique’ application calls the ‘frontend’ service. It’s clear that the increased service failure rate will impact end users and the business, with the details of those impacts highlighted.
The application summary page shows both performance and user behavior information.
Explore application performance and user experience impact associated with the ‘frontend’ problem
Instead of attempting to manually diagnose, let’s let Dynatrace’s AI solve this. Click next to continue to the problem page.
The out-of-the-box user experience summary shows user demographics, behavior, and experience, as well as associated resources and services consumed by those users.
In this case the service failures on the application have resulted in 18 errors/minute and are impacting user experience. Again, we see the associated Problem that Dynatrace’s Davis AI engine identified, this time in the context of the application.
The problem card automatically delivers the precise root cause and business impact. Davis automatically identifies the issue and provides all the context you need to quickly remediate and free up time to innovate. This automatic and intelligent observability provides you the context of how Kubernetes workloads fit into the broader application and end user environment.
Davis AI automatically pinpoints root cause and business impact
Let’s navigate to the root cause section to understand the causation. It appears the issue concerns the ‘frontend’ deployment. We can roll this back to a previous deployment and fix the problem.
The automatically generated business impact analysis shows you that 190 users, and over 450k service calls are affected. The severity of the impact prioritizes your remediation actions.
Click next to close the problem and continue to deployment.
Activating Dynatrace on your Kubernetes cluster is lightning fast and easy. Select a few options, copy the deployment script, and watch Kubernetes monitoring rollout and connect to all your applications in Dynatrace. After the deployment is finished, our out-of-the-box dashboards show you everything you need to see on a single pane of glass.
Automatic and Simple Deployment
Watch this quick tutorial to see how to deploy Dynatrace on your Kubernetes cluster.
The Dynatrace platform delivers automatic and intelligent observability for dynamic, multi cloud environments. So, you can stop wasting time and resources manually stitching together data from disparate tools, mitigate risk, speed innovation, and drive better outcomes for your business.
Want to see how Dynatrace can radically transform how you work?
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