![]() ![]() To do this, log into your environment, and click on your profile picture. Keptn version Now that the CLI is installed, you need to link it to your Dynatrace Cloud Automation instance. Now run the version command to make sure everything is installed properly: Basically, you don’t want to hear a lot of Keptn noise about your Kubernetes instance if you don’t have one! Do that with this command: If you are not running Keptn within your own Kubernetes environment, you will probably want to suppress Keptn CLI messages related to kubectl configuration. The Keptn CLI is available from the Keptn website with the following command:Ĭurl -sL | KEPTN_VERSION=0.10.0 sudo -E bash First, however, you’ll need to get Keptn setup and configured! Keptn is built into the Dynatrace Cloud Automation platform - no setup or installation required! However, to interact with the Keptn backbone, you will need to set up and configure the Keptn Command Line Interface (CLI). Once inside your trial, you’ll be able to setup and configure new projects. This will provide the dashboard and reporting views of your project and allow you to see how a sequence stage can automatically trigger a performance test. Setup and configuration of a simple Cloud Automation instanceįirst, you’ll need to sign up for a free trial of Cloud Automation from Dynatrace. This ensures that the stakeholders interested in reviewing the data can do so in their solution of choice. ![]() NeoLoad and Dynatrace have a long history of collaboration and integration, and your performance test data can be viewed in both NeoLoad reports as well as within the Dynatrace platform. ![]() Using NeoLoad by Tricentis, we can simulate hundreds, thousands, or even millions of production users in the context of a performance test.īy leveraging the existing and seamless integration between the Dynatrace monitoring solutions and NeoLoad, we are now an active participant in the Cloud Automation story, allowing new builds to be automatically performance tested to simulate production-level traffic, and expose any weaknesses that may exist prior to release into a production environment. The simple truth is that prior to release into production environments, the ability to understand the performance characteristics of an application are sharply limited, because there are no production users. When you link that data to the different versions being deployed within the cloud automation platform, you gain insight into the operational context of the applications and how they’re going to run in your infrastructure. If you examine only metrics from the servers like memory and CPU, you might see a sharp increase at a moment in time. This is extremely useful to teams when looking at the historical context of the data they have. As subsequent versions of an application are ready for deployment, Keptn provides a historical context of how the application changes, and ties that to the monitoring data. That’s a complex way of saying that it manages the automation of deployments and tests and can extend your deployment visibility by enforcing quality gates and tying into monitoring platforms. Keptn is an open-source enterprise-grade control plane for cloud-native continuous delivery. What does it even mean from a quality assurance perspective when an application microservice fails, but the application infrastructure automatically remediated the failure? The business process was still successful, so was this even a failure? These and similar questions are driving a new generation of AI-powered deterministic engines to monitor and triage these complex environments. We can no longer manage or monitor these dynamic environments in real-time using traditional monitoring techniques. New microservices-based applications that self-heal when components fail and that provision additional copies of themselves in response to increased demand are driving this complexity. Unfortunately, cloud environments are also growing in complexity so quickly that they’re exceeding the capacity of the human brain to manage. The cloud is complex, dynamic, and hyper fast.
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