Deploy Azure SQL Managed Instance in directly connected mode on ARO using an ARM Template

The following Jumpstart scenario will guide you on how to deploy a “Ready to Go” environment so you can start using Azure Arc-enabled data services and SQL Managed Instance deployed on Azure Red Hat OpenShift (ARO) cluster using Azure ARM Template.

By the end of this scenario, you will have an ARO cluster deployed with an Azure Arc Data Controller, SQL Managed Instance, and a Microsoft Windows Server 2022 (Datacenter) Azure client VM, installed & pre-configured with all the required tools needed to work with Azure Arc-enabled data services.

Prerequisites

  • Clone the Azure Arc Jumpstart repository

    git clone https://github.com/microsoft/azure_arc.git
    
  • Install or update Azure CLI to version 2.36.0 and above. Use the below command to check your current installed version.

    az --version
    
  • Generate SSH Key (or use existing ssh key).

  • Create Azure service principal (SP). To deploy this scenario, an Azure service principal assigned with multiple RBAC roles is required:

    • “Contributor” - Required for provisioning Azure resources

    • “Security admin” - Required for installing Cloud Defender Azure-Arc enabled Kubernetes extension and dismiss alerts

    • “Security reader” - Required for being able to view Azure-Arc enabled Kubernetes Cloud Defender extension findings

    • “Monitoring Metrics Publisher” - Required for being Azure Arc-enabled data services billing, monitoring metrics, and logs management

      To create it login to your Azure account run the below command (this can also be done in Azure Cloud Shell.

      az login
      subscriptionId=$(az account show --query id --output tsv)
      az ad sp create-for-rbac -n "<Unique SP Name>" --role "Contributor" --scopes /subscriptions/$subscriptionId
      az ad sp create-for-rbac -n "<Unique SP Name>" --role "Security admin" --scopes /subscriptions/$subscriptionId
      az ad sp create-for-rbac -n "<Unique SP Name>" --role "Security reader" --scopes /subscriptions/$subscriptionId
      az ad sp create-for-rbac -n "<Unique SP Name>" --role "Monitoring Metrics Publisher" --scopes /subscriptions/$subscriptionId
      

      For example:

      az login
      subscriptionId=$(az account show --query id --output tsv)
      az ad sp create-for-rbac -n "JumpstartArcDataSvc" --role "Contributor" --scopes /subscriptions/$subscriptionId
      az ad sp create-for-rbac -n "JumpstartArcDataSvc" --role "Security admin" --scopes /subscriptions/$subscriptionId
      az ad sp create-for-rbac -n "JumpstartArcDataSvc" --role "Security reader" --scopes /subscriptions/$subscriptionId
      az ad sp create-for-rbac -n "JumpstartArcDataSvc" --role "Monitoring Metrics Publisher" --scopes /subscriptions/$subscriptionId
      

      Output should look like this:

      {
      "appId": "XXXXXXXXXXXXXXXXXXXXXXXXXXXX",
      "displayName": "JumpstartArcDataSvc",
      "password": "XXXXXXXXXXXXXXXXXXXXXXXXXXXX",
      "tenant": "XXXXXXXXXXXXXXXXXXXXXXXXXXXX"
      }
      

      NOTE: If you create multiple subsequent role assignments on the same service principal, your client secret (password) will be destroyed and recreated each time. Therefore, make sure you grab the correct password.

      NOTE: The Jumpstart scenarios are designed with as much ease of use in-mind and adhering to security-related best practices whenever possible. It is optional but highly recommended to scope the service principal to a specific Azure subscription and resource group as well considering using a less privileged service principal account

  • Check your subscription quota for the DSv3 family.

    NOTE: Azure Red Hat OpenShift requires a minimum of 40 cores to create and run an OpenShift cluster.

    LOCATION=eastus
    az vm list-usage -l $LOCATION --query "[?contains(name.value, 'standardDSv3Family')]" -o table
    

    Screenshot of checking DSV3 family cores usage

  • Get the Azure Red Hat OpenShift resource provider Id which needs to be assigned with the “Contributor” role.

    az ad sp list --filter "displayname eq 'Azure Red Hat OpenShift RP'" --query "[?appDisplayName=='Azure Red Hat OpenShift RP'].{name: appDisplayName, objectId: objectId}"
    

    Screenshot of Azure resource provider for Aro

Automation Flow

For you to get familiar with the automation and deployment flow, below is an explanation.

  • User is editing the ARM template parameters file (1-time edit). These parameters values are being used throughout the deployment.

  • Main azuredeploy ARM template will initiate the deployment of the linked ARM templates:

    • VNET - Deploys a Virtual Network with a single subnet to be used by the Client virtual machine.

    • ARO - Deploys the ARO cluster where all the Azure Arc data services will be deployed.

    • clientVm - Deploys the client Windows VM. This is where all user interactions with the environment are made from.

    • logAnalytics - Deploys Azure Log Analytics workspace to support Azure Arc-enabled data services logs uploads.

    • User remotes into client Windows VM, which automatically kicks off the DataServicesLogonScript PowerShell script that deploy and configure Azure Arc-enabled data services on the ARO cluster including the data controller and SQL Managed Instance.

    • In addition to deploying the data controller and SQL Managed Instance, the sample AdventureWorks database will restored automatically for you as well.

Deployment

As mentioned, this deployment will leverage ARM templates. You will deploy a single template that will initiate the entire automation for this scenario.

  • The deployment is using the ARM template parameters file. Before initiating the deployment, edit the azuredeploy.parameters.json file located in your local cloned repository folder. An example parameters file is located here.

    • sshRSAPublicKey - Your SSH public key

    • spnClientId - Your Azure service principal id

    • spnClientSecret - Your Azure service principal secret

    • spnTenantId - Your Azure tenant id

    • windowsAdminUsername - Client Windows VM Administrator name

    • windowsAdminPassword - Client Windows VM Password. Password must have 3 of the following: 1 lower case character, 1 upper case character, 1 number, and 1 special character. The value must be between 12 and 123 characters long.

    • myIpAddress - Your local public IP address. This is used to allow remote RDP and SSH connections to the client Windows VM and ARO cluster.

    • logAnalyticsWorkspaceName - Unique name for the deployment log analytics workspace.

    • deploySQLMI - Boolean that sets whether or not to deploy SQL Managed Instance, for this Azure Arc-enabled SQL Managed Instance scenario we will set it to true.

    • SQLMIHA - Boolean that sets whether or not to deploy SQL Managed Instance with high-availability (business continuity) configurations, set this to either true or false.

    • deployPostgreSQL - Boolean that sets whether or not to deploy PostgreSQL, for this scenario we leave it set to false.

    • deployBastion - Choice (true | false) to deploy Azure Bastion or not to connect to the client VM.

    • bastionHostName - Azure Bastion host name.

    • AroProviderId - ARO resource provider Id

      NOTE: In case you decided to deploy SQL Managed Instance in an highly-available fashion, refer to the “High Availability” section in this scenario. Also note that this capability is currently in preview.

  • To deploy the ARM template, navigate to the local cloned deployment folder and run the below command:

    az group create --name <Name of the Azure resource group> --location <Azure Region>
    az deployment group create \
    --resource-group <Name of the Azure resource group> \
    --name <The name of this deployment> \
    --template-uri https://raw.githubusercontent.com/microsoft/azure_arc/main/azure_arc_data_jumpstart/aro/ARM/azuredeploy.json \
    --parameters <The *azuredeploy.parameters.json* parameters file location>
    

    NOTE: Make sure that you are using the same Azure resource group name as the one you’ve just used in the azuredeploy.parameters.json file

    For example:

    az group create --name Arc-Data-Demo --location "East US"
    az deployment group create \
    --resource-group Arc-Data-Demo \
    --name arcdata \
    --template-uri https://raw.githubusercontent.com/microsoft/azure_arc/main/azure_arc_data_jumpstart/aro/ARM/azuredeploy.json \
    --parameters azuredeploy.parameters.json
    

    NOTE: The deployment time for this scenario can take ~15-20min

  • Once Azure resources have been provisioned, you will be able to see them in the Azure portal. At this point, the resource group should have 8 various Azure resources deployed (If you chose to deploy Azure Bastion, you will have 9 Azure resources).

    Screenshot showing ARM template deployment completed

    Screenshot showing the new Azure resource group with all resources

Windows Login & Post Deployment

  • Now that the first phase of the automation is completed, it is time to RDP to the client VM. If you have not chosen to deploy Azure Bastion in the ARM template, RDP to the VM using its public IP.

    Screenshot showing the Client VM public IP

  • If you have chosen to deploy Azure Bastion in the ARM template, use it to connect to the VM.

    Screenshot showing connecting using Azure Bastion

  • At first login, as mentioned in the “Automation Flow” section above, the DataServicesLogonScript PowerShell logon script will start it’s run.

  • Let the script to run its course and do not close the PowerShell session, this will be done for you once completed. Once the script will finish it’s run, the logon script PowerShell session will be closed, the Windows wallpaper will change and both the Azure Arc Data Controller and SQL Managed Instance will be deployed on the cluster and be ready to use.

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the PowerShell logon script run

    Screenshot showing the post-run desktop

  • Since this scenario is deploying the Azure Arc Data Controller and SQL Managed Instance, you will also notice additional newly deployed Azure resources in the resources group (at this point you should have 12 various Azure resources deployed. The important ones to notice are:

    • Azure Arc-enabled Kubernetes cluster - Azure Arc-enabled data services deployed in directly connected are using this type of resource in order to deploy the data services cluster extension as well as for using Azure Arc Custom locations.

    • Custom location - provides a way for tenant administrators to use their Azure Arc-enabled Kubernetes clusters as target locations for deploying Azure services instances.

    • Azure Arc Data Controller - The data controller that is now deployed on the Kubernetes cluster.

    • Azure Arc-enabled SQL Managed Instance - The SQL Managed Instance that is now deployed on the Kubernetes cluster.

      Screenshot showing additional Azure resources in the resource group

  • As part of the automation, Azure Data Studio is installed along with the Azure Data CLI, Azure CLI, Azure Arc and the PostgreSQL extensions. Using the Desktop shortcut created for you, open Azure Data Studio and click the Extensions settings to see the installed extensions.

    Screenshot showing Azure Data Studio shortcut

    Screenshot showing Azure Data Studio extensions

  • Additionally, the SQL Managed Instance connection will be configured automatically for you. As mentioned, the sample AdventureWorks database was restored as part of the automation.

    Screenshot showing Azure Data Studio SQL MI connection

Cluster extensions

In this scenario, two Azure Arc-enabled Kubernetes cluster extensions were installed:

In order to view these cluster extensions, click on the Azure Arc-enabled Kubernetes resource Extensions settings.

Screenshot showing the Azure Arc-enabled Kubernetes cluster extensions settings

Screenshot showing the Azure Arc-enabled Kubernetes installed extensions

High Availability with SQL Always-On availability groups

Azure Arc-enabled SQL Managed Instance is deployed on Kubernetes as a containerized application and uses kubernetes constructs such as stateful sets and persistent storage to provide built-in health monitoring, failure detection, and failover mechanisms to maintain service health. For increased reliability, you can also configure Azure Arc-enabled SQL Managed Instance to deploy with extra replicas in a high availability configuration.

For showcasing and testing SQL Managed Instance with Always On availability groups, a dedicated Jumpstart scenario is available to help you simulate failures and get hands-on experience with this deployment model.

Operations

Azure Arc-enabled SQL Managed Instance stress simulation

Included in this scenario, is a dedicated SQL stress simulation tool named SqlQueryStress automatically installed for you on the Client VM. SqlQueryStress will allow you to generate load on the Azure Arc-enabled SQL Managed Instance that can be done used to showcase how the SQL database and services are performing as well to highlight operational practices described in the next section.

  • To start with, open the SqlQueryStress desktop shortcut and connect to the SQL Managed Instance primary endpoint IP address. This can be found in the SQLMI Endpoints text file desktop shortcut that was also created for you alongside the username and password you used to deploy the environment.

    Screenshot showing opened SqlQueryStress

    Screenshot showing SQLMI Endpoints text file

NOTE: Secondary SQL Managed Instance endpoint will be available only when using the HA deployment model (“Business Critical”).

  • To connect, use “SQL Server Authentication” and select the deployed sample AdventureWorks database (you can use the “Test” button to check the connection).

    Screenshot showing SqlQueryStress connected

  • To generate some load, we will be running a simple stored procedure. Copy the below procedure and change the number of iterations you want it to run as well as the number of threads to generate even more load on the database. In addition, change the delay between queries to 1ms for allowing the stored procedure to run for a while.

    exec [dbo].[uspGetEmployeeManagers] @BusinessEntityID = 8
    
  • As you can see from the example below, the configuration settings are 100,000 iterations, five threads per iteration, and a 1ms delay between queries. These configurations should allow you to have the stress test running for a while.

    Screenshot showing SqlQueryStress settings

    Screenshot showing SqlQueryStress running

Azure Arc-enabled SQL Managed Instance monitoring using Grafana

When deploying Azure Arc-enabled data services, a Grafana instance is also automatically deployed on the same Kubernetes cluster and include built-in dashboards for both Kubernetes infrastructure as well SQL Managed Instance monitoring (PostgreSQL dashboards are included as well but we will not be covering these in this section).

  • Now that you have the SqlQueryStress stored procedure running and generating load, we can look how this is shown in the the built-in Grafana dashboard. As part of the automation, a new URL desktop shortcut simply named “Grafana” was created.

    Screenshot showing Grafana desktop shortcut

  • [Optional] The IP address for this instance represents the Kubernetes LoadBalancer external IP that was provision as part of Azure Arc-enabled data services. Use the kubectl get svc -n arc command to view the metricsui external service IP address.

    Screenshot showing metricsui Kubernetes service

  • To log in, use the same username and password that is in the SQLMI Endpoints text file desktop shortcut.

    Screenshot showing Grafana username and password

  • Navigate to the built-in “SQL Managed Instance Metrics” dashboard.

    Screenshot showing Grafana dashboards

    Screenshot showing Grafana “SQL Managed Instance Metrics” dashboard

  • Change the dashboard time range to “Last 5 minutes” and re-run the stress test using SqlQueryStress (in case it was already finished).

    Screenshot showing “Last 5 minutes” time range

  • You can now see how the SQL graphs are starting to show increased activity and load on the database instance.

    Screenshot showing increased load activity

    Screenshot showing increased load activity

Cleanup

  • If you want to delete the entire environment, simply delete the deployment resource group from the Azure portal.

    Screenshot showing Azure resource group deletion