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Testkube Core Open Source

Welcome to the Open Source version of Testkube!

Designed to integrate seamlessly with your Kubernetes clusters, Testkube offers flexibility and power. For those searching for a quicker and streamlined experience, we suggest signing up for Testkube Pro. However, for organizations that prefer the hands-on approach, diving deep into the Open Source version could be the ideal choice.

Visit Open Source or Pro to see a comparison of features to help you with your choice.

Also, take a look at our Quick Setup Video.

Getting Started with Testkube Core Open Source​

Installation via Helm: Dive into detailed installation instructions with Helm in our official documentation.

Installing Using Testkube CLI: Our CLI tool is here to simplify your deployment experience:

  • 1st - Make sure your KUBECONFIG is pointing to the desired location for the Testkube installation.
  • 2nd - Execute the command: 'testkube init'.
note

Testkube deploys in the testkube namespace.

This command will set up the following components in your Kubernetes cluster:

  • Create a Testkube namespace.
  • Deploy the Testkube API.
  • Use MongoDB for test results and Minio for artifact storage (optional; disable with --no-minio).
  • Testkube will listen and manage all the CRDs for Tests, TestSuites, Executors, etc… inside the Testkube namespace.

Verify Your Installation: Ensure that Testkube is up and running with: kubectl get all -n testkube.

Once set up, you're ready to unleash the full potential of Testkube in your environment. Whether you opt for the Open Source or Pro variant, Testkube is committed to powering your development and testing workflows seamlessly.

Upgrade Testkube Core Open Source​

See upgrade for instructions on how to upgrade your Testkube Core Open Source components.

Minimum Resource Requirements​

To ensure optimal performance, the initial setup requires a minimum of 2 CPU cores and 8 GB of RAM. This configuration is suitable for basic operations.

For environments with higher demands or fluctuating workloads, we recommend implementing an autoscaler. This allows for dynamic scaling of resources based on actual usage, ensuring both efficient performance and cost-effectiveness. Users can configure the autoscaler according to their specific needs, adapting to varying workloads seamlessly.