Installing Release on GCP GKE

This section describes how to install the Release application on GCP GKE.

Audience

This guide is intended for administrators with cluster administrator credentials who are responsible for application deployment.

Before You Begin

The following are the prerequisites required to install Deploy using Kubernetes Operator installer:

  • Docker version 17.03 or later
  • The kubectl command-line tool
  • Access to a Kubernetes cluster version 1.19 or later
  • Kubernetes cluster configuration

Keycloak as the Default Authentication Manager for Release

  • Keycloak is the default authentication manager with Release 22.1 and later.
  • This is defined by the spec.keycloak.install parameter that is set to true by default in the dairelease_cr.yaml file.
  • If you want to disable Keycloak as the default authentication manager for Digitial.ai Release, set the spec.keycloak.install parameter to false.
  • After you disable the Keycloak authentication, the default login credentials (admin/admin) will be applicable when you log in to the Digital.ai Release interface.
  • For more information about how to configure Keycloak for Kubernetes Operator-based Installer for Release, see Keycloak Configuration for Kubernetes Operator Installer.

Step 1—Create a Folder for Installation Tasks

Create a folder on your workstation from where you will execute the installation tasks, for example, ReleaseInstallation.

Step 2—Download the Operator ZIP

  1. Download the release-operator-gcp-gke-22.2.0.zip file from the Release Software Distribution site.
  2. Extract the ZIP file to the ReleaseInstallation folder.

Step 3—Update the GCP GKE Cluster Resource Files

To deploy the Deploy application on the Kubernetes cluster, update the infrastructure.yaml file parameters (Infrastructure File Parameters) in DeployInstallation folder with the parameters corresponding to the kubeconfig file (GCP GKE Kubernetes Cluster Configuration File Parameters) as described in the table below. You can find the Kubernetes cluster information in the default location ~/.kube/config. Ensure the location of the kubeconfig configuration file is your home directory.

Note: The deployment will not proceed further if the infrastructure.yaml is updated with wrong details.

Infrastructure File Parameters GCP GKE Kubernetes Cluster Configuration File Parameters Steps to Follow
apiServerURL server Enter the server details of the cluster.
caCert certificate-authority-data Before updating the parameter value, decode to base 64 format.
token access token Enter the access token details.

Step 4—Update the Daideploy_Cr.Yaml File with the License and Keystore Details

  1. Run the following command to get the storage class list:

    kubectl get sc
  2. Convert the Release license and the repository keystore files to the base 64 format.
  3. Run the following commands:
  4. To convert the xlrLicense into base 64 format, run:

    cat <License.lic> | base64 -w 0
  5. To convert RepositoryKeystore to base64 format, run:

    cat <keystore.jks> | base64 -w 0

    Note: The above commands are for Linux-based systems. For Windows, there is no built-in command to directly perform Base64 encoding and decoding. But you can use the built-in command certutil -encode/-decode to indirectly perform Base64 encoding and decoding.

Step 5—Update the Default Digitial.Ai Deploy Custom Resource Definitions

  1. Update the mandatory parameters as described in the following table:

    Note: For deployments on test environments, you can use most of the parameters with their default values in the daideploy_cr.yaml file.

    Parameter Description
    AdminPassword Admin password for xl-release
    KeystorePassphrase The passphrase for the RepositoryKeystore.
    Persistence.StorageClass The storage class that must be defined as GKE cluster
    RepositoryKeystore Convert the repository keystore file for Digital.ai Release to the base64 format.
    ingress.hosts DNS name for accessing UI of Digital.ai Release.
    postgresql.persistence.storageClass The storage Class that needs to be defined as PostgreSQL
    rabbitmq.persistence.storageClass The storage class that must be defined as RabbitMQ
    xlrLicense Release license

    Note: For deployments on production environments, you must configure all the relevant/required parameters for your GCP GKE production setup, in the dairelease_cr.yaml file. See Default Parameters to know more about the parameters available in the Digital.ai release’s dairelease_cr.yaml file and their default values. You must update the default values for the parameters per your requirements.

    To configure the Keycloak parameters for OIDC authentication, see Keycloak Configuration for Kubernetes Operator Installer.

  2. Update the relevant/required parameters for your GCP GKE production setup in the dairelease_cr.yaml file. See Default Parameters.

    If you want to use your own database and messaging queue, refer Using Existing DB and Using Existing MQ topics, and update the daideploy_cr.yaml file. For information on how to configure SSL/TLS with Digital.ai Release, see Configuring SSL/TLS.

Step 6—Download and Set up the Xl Cli

Check following link for details: Install the XL-CLI

Note: Use the version that matches your product version in the public folder.

Step 7—Set up the Namespace

You can use any namespace for the installation. By default, digitalai namespace is used. First you need to create namespace, replace digitalai with your custom name if you would like to use some other name:

kubectl create namespace digitalai

In case you are not using digitalai as namespace or if you would like to install multiple release instances on the same cluster you need to use custom namespace setup. Got to the following document to see how to install the release operator to use custom namespace.

Step 8—Set up the Digital.Ai Deploy Container Instance

  1. Run the following command to download and start the Digital.ai Deploy instance:

    Note: A local instance of Digital.ai Deploy is used to automate the product installation on the Kubernetes cluster.

    docker run -d -e "ADMIN_PASSWORD=admin" -e "ACCEPT_EULA=Y" -p 4516:4516 --name xld xebialabs/xl-deploy:22.2.0

    Note: Before running the command check if there is already running docker containers with name xld or the same port with docker ps command. Stop and delete the container with commands, for example with name xld: docker stop xld; doecker rm xld.

  2. Wait Deploy has started and access the Deploy interface, go to:
    http://<host IP address>:4516/

Step 9—Start the Deployment

Go to the release-operator-gcp-gke folder of the extracted ZIP file and run the following command:

xl apply -v -f digital-ai.yaml

Step 10—Verify the Deployment Status

  1. Check the deployment job completion using XL CLI.
    The deployment job starts the execution of various tasks as defined in the digital-ai.yaml file in a sequential manner. If you encounter an execution error while running the scripts, the system displays error messages. The average time to complete the job is around 10 minutes.

    Note: The runtime depends on the environment.

    Deployment Status

    To troubleshoot runtime errors, see Troubleshooting Operator Based Installer.

Verify the deployment succeeded, do one of the following:

  • Open the local Deploy application, go to the Explorer tab, and from Library, click Monitoring > Deployment tasks

    Deployment Status

  • Run the following command in a terminal or command prompt:

    Deployment Status Using CLI Command

Step 11—Perform Sanity Checks

Open the Release application and perform the required deployment sanity checks.

Configure the User Permissions