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Landscape

The Landscape informs your Chaos Engineering journey by being able to analyze the discovered infrastructure component in more detail. You can easily navigate in potential targets and, e.g., search for new experiments that can be used to approach a team or verify whether other components may be affected by a revealed reliability issue.

Drill-Down Targets

When using the Landscape, you have the following capabilities at your hand to better understand discovered targets
  • Environment to define the upper amount of targets that you would like to explore
  • Filter targets via Query to include targets you are interested in and exclude all targets that do not match
  • Group by to group targets by an attribute value and bring them next to each other. You can even do endless subgroupings.
  • Size by to analyze the target's attributes that have multiple different values assigned
  • Color by to highlight and differentiate attribute's values
Explorer Landscape to analyze your system
For every target or group of targets, you can view the discovered target types and attributes.
Landscape to show targets grouped by type and target's attributes

Create Experiments

Once you have identified a relevant group of targets for an experiment, you can click on that group and choose to use the target selection for an experiment. The Landscape provides the exact Query so that you can copy it into an experiment design.
Creating an experiment from the Landscape.png

Saved Views

You can save all views you have created with the Landscape to share them automatically with your team members. In addition, you can always share a view with someone else using a deep link to that particular view. Whenever you have made changes to a saved view, the Landscape will ask you whether you want to keep them or you can save them as a new view.
In addition, Steadybit shares some predefined views that will help you to get started with the Landscape. They are dependent on the actual discovered targets, showing e.g. a Kubernetes map only when at least one Kubernetes cluster is discovered.
Landscape views
Last modified 1mo ago