Create Azure Arc data controller using the CLI
Prerequisites
Review the topic Plan an Azure Arc-enabled data services deployment for overview information.
Install tools
Before you begin, install the arcdata extension for Azure (az) CLI.
Install the (arcdata) extension for Azure (az) CLI
Regardless of which target platform you choose, you need to set the following environment variables prior to the creation for the data controller. These environment variables become the credentials used for accessing the metrics and logs dashboards after data controller creation.
Set environment variables
Following are two sets of environment variables needed to access the metrics and logs dashboards.
The environment variables include passwords for log and metric services. The passwords must be at least eight characters long and contain characters from three of the following four categories: Latin uppercase letters, Latin lowercase letters, numbers, and non-alphanumeric characters.
## variables for Metrics and Monitoring dashboard credentials
export AZDATA_LOGSUI_USERNAME=<username for Kibana dashboard>
export AZDATA_LOGSUI_PASSWORD=<password for Kibana dashboard>
export AZDATA_METRICSUI_USERNAME=<username for Grafana dashboard>
export AZDATA_METRICSUI_PASSWORD=<password for Grafana dashboard>
Connect to Kubernetes cluster
Connect and authenticate to a Kubernetes cluster and have an existing Kubernetes context selected prior to beginning the creation of the Azure Arc data controller. How you connect to a Kubernetes cluster or service varies. See the documentation for the Kubernetes distribution or service that you are using on how to connect to the Kubernetes API server.
You can check to see that you have a current Kubernetes connection and confirm your current context with the following commands.
kubectl cluster-info
kubectl config current-context
Create the Azure Arc data controller
The following sections provide instructions for specific types of Kubernetes platforms. Follow the instructions for your platform.
- Azure Kubernetes Service (AKS)
- AKS on Azure Stack HCI
- Azure Red Hat OpenShift (ARO)
- Red Hat OpenShift Container Platform (OCP)
- Open source, upstream Kubernetes (kubeadm)
- AWS Elastic Kubernetes Service (EKS)
- Google Cloud Kubernetes Engine Service (GKE)
Tip
If you have no Kubernetes cluster, you can create one on Azure. Follow the instructions at Quickstart: Deploy Azure Arc-enabled data services - directly connected mode - Azure portal to walk through the entire process.
Then follow the instructions under Create on Azure Kubernetes Service (AKS).
Create on Azure Kubernetes Service (AKS)
By default, the AKS deployment profile uses the managed-premium storage class. The managed-premium storage class only works if you have VMs that were deployed using VM images that have premium disks.
If you are going to use managed-premium as your storage class, then you can run the following command to create the data controller. Substitute the placeholders in the command with your resource group name, subscription ID, and Azure location.
az arcdata dc create --profile-name azure-arc-aks-premium-storage --k8s-namespace <namespace> --name arc --subscription <subscription id> --resource-group <resource group name> --location <location> --connectivity-mode indirect --use-k8s
#Example:
#az arcdata dc create --profile-name azure-arc-aks-premium-storage --k8s-namespace arc --name arc --subscription xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx --resource-group my-resource-group --location eastus --connectivity-mode indirect --use-k8s
If you are not sure what storage class to use, you should use the default storage class which is supported regardless of which VM type you are using. It just won't provide the fastest performance.
If you want to use the default storage class, then you can run this command:
az arcdata dc create --profile-name azure-arc-aks-default-storage --k8s-namespace <namespace> --use-k8s --name arc --subscription <subscription id> --resource-group <resource group name> --location <location> --connectivity-mode indirect
#Example:
#az arcdata dc create --profile-name azure-arc-aks-default-storage --k8s-namespace arc --use-k8s --name arc --subscription xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx --resource-group my-resource-group --location eastus --connectivity-mode indirect
Once you have run the command, continue on to Monitoring the creation status.
Create on AKS on Azure Stack HCI
Configure storage (Azure Stack HCI with AKS-HCI)
If you are using Azure Stack HCI with AKS-HCI, create a custom storage class with fsType.
fsType: ext4
Use this type to deploy the data controller. See the complete instructions at Create a custom storage class for an AKS on Azure Stack HCI disk.
By default, the deployment profile uses a storage class named default and the service type LoadBalancer.
You can run the following command to create the data controller using the default storage class and service type LoadBalancer.
az arcdata dc create --profile-name azure-arc-aks-hci --k8s-namespace <namespace> --use-k8s --name arc --subscription <subscription id> --resource-group <resource group name> --location <location> --connectivity-mode indirect
#Example:
#az arcdata dc create --profile-name azure-arc-aks-hci --k8s-namespace arc --use-k8s --name arc --subscription xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx --resource-group my-resource-group --location eastus --connectivity-mode indirect
Once you have run the command, continue on to Monitoring the creation status.
Create on Azure Red Hat OpenShift (ARO)
Create custom deployment profile
Use the profile azure-arc-azure-openshift for Azure RedHat Open Shift.
az arcdata dc config init --source azure-arc-azure-openshift --path ./custom
Create data controller
You can run the following command to create the data controller:
az arcdata dc create --profile-name azure-arc-azure-openshift --k8s-namespace <namespace> --use-k8s --name arc --subscription <subscription id> --resource-group <resource group name> --location <location> --connectivity-mode indirect
#Example
#az arcdata dc create --profile-name azure-arc-azure-openshift --k8s-namespace arc --use-k8s --name arc --subscription xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx --resource-group my-resource-group --location eastus --connectivity-mode indirect
Once you have run the command, continue on to Monitoring the creation status.
Create on Red Hat OpenShift Container Platform (OCP)
Determine storage class
To determine which storage class to use, run the following command.
kubectl get storageclass
Create custom deployment profile
Create a new custom deployment profile file based on the azure-arc-openshift deployment profile by running the following command. This command creates a directory custom in your current working directory and a custom deployment profile file control.json in that directory.
Use the profile azure-arc-openshift for OpenShift Container Platform.
az arcdata dc config init --source azure-arc-openshift --path ./custom
Set storage class
Now, set the desired storage class by replacing <storageclassname> in the command below with the name of the storage class that you want to use that was determined by running the kubectl get storageclass command above.
az arcdata dc config replace --path ./custom/control.json --json-values "spec.storage.data.className=<storageclassname>"
az arcdata dc config replace --path ./custom/control.json --json-values "spec.storage.logs.className=<storageclassname>"
#Example:
#az arcdata dc config replace --path ./custom/control.json --json-values "spec.storage.data.className=mystorageclass"
#az arcdata dc config replace --path ./custom/control.json --json-values "spec.storage.logs.className=mystorageclass"
Set LoadBalancer (optional)
By default, the azure-arc-openshift deployment profile uses NodePort as the service type. If you are using an OpenShift cluster that is integrated with a load balancer, you can change the configuration to use the LoadBalancer service type using the following command:
az arcdata dc config replace --path ./custom/control.json --json-values "$.spec.services[*].serviceType=LoadBalancer"
Create data controller
Now you are ready to create the data controller using the following command.
Note
The --path parameter should point to the directory containing the control.json file not to the control.json file itself.
Note
When deploying to OpenShift Container Platform, specify the --infrastructure parameter value. Options are: aws, azure, alibaba, gcp, onpremises.
az arcdata dc create --path ./custom --k8s-namespace <namespace> --use-k8s --name arc --subscription <subscription id> --resource-group <resource group name> --location <location> --connectivity-mode indirect --infrastructure <infrastructure>
#Example:
#az arcdata dc create --path ./custom --k8s-namespace arc --use-k8s --name arc --subscription xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx --resource-group my-resource-group --location eastus --connectivity-mode indirect --infrastructure onpremises
Once you have run the command, continue on to Monitoring the creation status.
Create on open source, upstream Kubernetes (kubeadm)
By default, the kubeadm deployment profile uses a storage class called local-storage and service type NodePort. If this is acceptable you can skip the instructions below that set the desired storage class and service type and immediately run the az arcdata dc create command below.
If you want to customize your deployment profile to specify a specific storage class and/or service type, start by creating a new custom deployment profile file based on the kubeadm deployment profile by running the following command. This command creates a directory custom in your current working directory and a custom deployment profile file control.json in that directory.
az arcdata dc config init --source azure-arc-kubeadm --path ./custom
You can look up the available storage classes by running the following command.
kubectl get storageclass
Now, set the desired storage class by replacing <storageclassname> in the command below with the name of the storage class that you want to use that was determined by running the kubectl get storageclass command above.
az arcdata dc config replace --path ./custom/control.json --json-values "spec.storage.data.className=<storageclassname>"
az arcdata dc config replace --path ./custom/control.json --json-values "spec.storage.logs.className=<storageclassname>"
#Example:
#az arcdata dc config replace --path ./custom/control.json --json-values "spec.storage.data.className=mystorageclass"
#az arcdata dc config replace --path ./custom/control.json --json-values "spec.storage.logs.className=mystorageclass"
By default, the kubeadm deployment profile uses NodePort as the service type. If you are using a Kubernetes cluster that is integrated with a load balancer, you can change the configuration using the following command.
az arcdata dc config replace --path ./custom/control.json --json-values "$.spec.services[*].serviceType=LoadBalancer"
Now you are ready to create the data controller using the following command.
Note
When deploying to OpenShift Container Platform, specify the --infrastructure parameter value. Options are: aws, azure, alibaba, gcp, onpremises.
az arcdata dc create --path ./custom --k8s-namespace <namespace> --use-k8s --name arc --subscription <subscription id> --resource-group <resource group name> --location <location> --connectivity-mode indirect --infrastructure <infrastructure>
#Example:
#az arcdata dc create --path ./custom - --k8s-namespace <namespace> --use-k8s --name arc --subscription xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx --resource-group my-resource-group --location eastus --connectivity-mode indirect --infrastructure onpremises
Once you have run the command, continue on to Monitoring the creation status.
Create on AWS Elastic Kubernetes Service (EKS)
By default, the EKS storage class is gp2 and the service type is LoadBalancer.
Run the following command to create the data controller using the provided EKS deployment profile.
az arcdata dc create --profile-name azure-arc-eks --k8s-namespace <namespace> --use-k8s --name arc --subscription <subscription id> --resource-group <resource group name> --location <location> --connectivity-mode indirect
#Example:
#az arcdata dc create --profile-name azure-arc-eks --k8s-namespace <namespace> --use-k8s --name arc --subscription xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx --resource-group my-resource-group --location eastus --connectivity-mode indirect
Once you have run the command, continue on to Monitoring the creation status.
Create on Google Cloud Kubernetes Engine Service (GKE)
By default, the GKE storage class is standard and the service type is LoadBalancer.
Run the following command to create the data controller using the provided GKE deployment profile.
az arcdata dc create --profile-name azure-arc-gke --k8s-namespace <namespace> --use-k8s --name arc --subscription <subscription id> --resource-group <resource group name> --location <location> --connectivity-mode indirect
#Example:
#az arcdata dc create --profile-name azure-arc-gke --k8s-namespace <namespace> --use-k8s --name arc --subscription xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx --resource-group my-resource-group --location eastus --connectivity-mode indirect
Once you have run the command, continue on to Monitoring the creation status.
Monitor the creation status
It takes a few minutes to create the controller completely. You can monitor the progress in another terminal window with the following commands:
Note
The example commands below assume that you created a data controller named arc-dc and Kubernetes namespace named arc. If you used different values update the script accordingly.
kubectl get datacontroller/arc-dc --namespace arc
kubectl get pods --namespace arc
You can also check on the creation status of any particular pod by running a command like below. This is especially useful for troubleshooting any issues.
kubectl describe po/<pod name> --namespace arc
#Example:
#kubectl describe po/control-2g7bl --namespace arc
Troubleshooting creation problems
If you encounter any troubles with creation, see the troubleshooting guide.
Feedback
Coming soon: Throughout 2024 we will be phasing out GitHub Issues as the feedback mechanism for content and replacing it with a new feedback system. For more information see: https://aka.ms/ContentUserFeedback.
Submit and view feedback for