Deploy Azure SQL Managed Instance in directly connected mode on EKS using a Terraform

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 Elastic Kubernetes Service (EKS) cluster using Terraform

By the end of this scenario, you will have an EKS 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
    
  • Install and Configure AWS CLI.

  • Create a free Amazon Web Services account if you don’t already have one.

  • Install Terraform >=1.0

  • 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

  • Follow the steps here or run the command below to retrieve your AAD Tenant Specific ObjectID for the “Custom Locations RP” Enterprise Application needed to onboard Custom Locations on EKS:

    # Note that the APPLICATION ID: bc313c14-388c-4e7d-a58e-70017303ee3b is constant across all tenants
    az ad sp show --id 'bc313c14-388c-4e7d-a58e-70017303ee3b' --query id -o tsv
    
  • Create a resource group

    az group create --name "Arc-Data-Demo" --location "eastus"
    

Create a new AWS IAM Role & Key

Create AWS User IAM Key. An access key grants programmatic access to your resources which we will be using later in this scenario.

  • Navigate to the IAM Access page.

    Screenshot showing creating an AWS IAM Role & Key

  • Select the Users from the side menu.

    Screenshot showing creating an AWS IAM Role & Key

  • Select the User you want to create the access key for.

    Screenshot showing creating an AWS IAM Role & Key

  • Select Security credentials of the User selected.

    Screenshot showing creating an AWS IAM Role & Key

  • Under Access Keys select Create Access Keys.

    Screenshot showing creating an AWS IAM Role & Key

  • In the popup window it will show you the Access key ID and Secret access key. Save both of these values to configure the Terraform plan variables later.

    Screenshot showing creating an AWS IAM Role & Key

  • In order to open a RDP session to the Windows Client EC2 instance, an EC2 Key Pair is required. From the Services menu, click on “EC2”, enter the Key Pairs settings from the left sidebar (under the Network & Security section) and click on “Create key pair” (top-right corner) to create a new key pair.

    Screenshot showing creating an EC2 Key Pair

    Screenshot showing creating an EC2 Key Pair

    Screenshot showing creating an EC2 Key Pair

  • Provide a meaningful name, for example terraform, and click on “Create key pair” which will then automatically download the created pem file.

    Screenshot showing creating an EC2 Key Pair

    Screenshot showing creating an EC2 Key Pair

    Screenshot showing creating an EC2 Key Pair

  • Copy the downloaded pem file to where the terraform binaries are located (in your cloned repository directory).

    Screenshot showing creating an EC2 Key Pair

    NOTE: EC2 Key Pairs are regional.

Automation Flow

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

  • User is editing the Terraform runtime environment variables in the terraform.tfvars file (1-time edit). The variables are being used throughout the deployment.

  • Screenshot showing creating the main Terraform plan will initiate the deployment of the other modules:

    • clientVM - Deploys the client Windows VM. This is where all user interactions with the environment are made from.
    • cluster - Deploys the EKS cluster where all the Azure Arc data services will be deployed.
    • workers - Deploys the EKS cluster’s worker nodes.
  • 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 EKS 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, the Terraform plan will deploy an EKS cluster, the Azure Arc Data Controller and the SQL Managed Instance on that cluster and an EC2 Windows Server 2022 Client instance.

  • Before running the Terraform plan, create a terraform.tfvars file in the root of the terraform folder and supply some values for your environment.

     AWS_ACCESS_KEY_ID      = "ZFTIFC443FTFDEZ5TKNR"
     AWS_SECRET_ACCESS_KEY  = "fakeSecretValue1dfd343sd5712adfddjh"
     AWS_DEFAULT_REGION     = "us-west-1"
     azureLocation          = "eastus"
     spnClientId            = "1414133c-9786-53a4-b231-f87c143ebdb1"
     spnClientSecret        = "fakeSecretValue123458125712ahjeacjh"
     spnTenantId            = "33572583-d294-5b56-c4e6-dcf9a297ec17"
     subscriptionId         = "33987583-A984-5C87-T4e3-POf7a397ec17"
     resourceGroup          = "Arc-Data-Demo"
     workspaceName          = "la-arc-001"
     deploySQLMI            = true
     SQLMIHA                = false
     deployPostgreSQL       = false
     customLocationObjectId = "649cb28f-bc13-492a-9470-c8bf01fa8eeb"
    
  • Variable reference:

    • AWS_ACCESS_KEY_ID - Your AWS access key.
    • AWS_SECRET_ACCESS_KEY - Your AWS secret access key.
    • AWS_DEFAULT_REGION - AWS location code (e.g. ‘us-west-1’, ‘us-east-2’, etc.).
    • azureLocation - Azure location code (e.g. ‘eastus’, ‘westus2’, etc.).
    • spnClientId - Your Azure service principal id.
    • spnClientSecret - Your Azure service principal secret.
    • spnTenantId - Your Azure tenant id.
    • subscriptionId - Your Azure subscription Id.
    • resourceGroup - Resource group which will contain all of the Azure Arc data services resources.
    • workspaceName - Unique name for the ArcBox Log Analytics workspace.
    • deploySQLMI - Boolean that sets whether or not to deploy SQL Managed Instance, for this data controller vanilla scenario we leave it set to false.
    • SQLMIHA - Boolean that sets whether or not to deploy SQL Managed Instance with high-availability (business continuity) configurations, for this data controller vanilla scenario we leave it set to false.
    • deployPostgreSQL - Boolean that sets whether or not to deploy PostgreSQL, for this data controller vanilla scenario we leave it set to false.
    • customLocationObjectId - The Azure AD application used by Azure Arc service retrieved in the prerequisites section.

NOTE: Any variables in bold are required. If any optional parameters are not provided, defaults will be used.

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.

  • Now you will deploy the Terraform file. Navigate to the local cloned deployment folder and run the commands below:

    terraform init
    terraform plan -out=infra.out
    terraform apply "infra.out"
    

NOTE: The deployment time for this scenario can take ~20-35min

  • Example output from terraform init:

    Screenshot showing creating the terraform init command output

  • Example output from terraform plan -out=infra.out:

    Screenshot showing creating the terraform plan command output

  • Once completed, the plan will output a decrypted password for your Windows Client instance that you will use to RDP into it. Before connecting to the Client instance, you can review the EKS cluster and the EC2 instances created. Notice how 4 instances were created; 3 EKS nodes and the Client instance.

    Screenshot showing creating the terraform apply command output

    Screenshot showing creating the new EKS cluster

    Screenshot showing creating the new EKS cluster

    Screenshot showing creating the new EC2 instances

    Screenshot showing creating the new EC2 instances

    Screenshot showing creating the new EC2 instances

Windows Login & Post Deployment

  • Now that the first phase of the automation is completed, it is time to RDP to the client VM. Select the Windows instance, click “Connect” and download the Remote Desktop file.

    Screenshot showing starting an RDP session to the Client instance

    Screenshot showing starting an RDP session to the Client instance

  • Using the decrypted password generated from the plan output, RDP the Windows instance. In case you need to get the password later, use the terraform output command to re-present the plan output.

  • 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 the Azure Arc Data Controller 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 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 5 various Azure resources deployed.

    • 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

Delete the deployment

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

    Screenshot showing Azure resource group deletion

  • If you want to delete the entire environment, use the terraform destroy to delete all of the AWS resources.

    terraform destroy --auto-approve
    

    Screenshot showing the deletion of all AWS resources

    NOTE: Because the following resources were created by EKS that creates internal AWS dependencies that Terraform has no knowledge of from our plan, we need to delete the resources from AWS console as terraform destroy is cleaning up - this allows us to avoid dependency conflicts and ongoing billing from orphaned resources such as EKS Volumes.

  • While the destroy command is running, delete any new Load Balancers created as EKS Services (EC2 > Load Balancing > Load Balancers) that are deployed in AWS from the Console:

    Screenshot showing the Deletion of Load Balancers

  • While the destroy command is running, delete any new Elastic Block Stores, created as EKS Persistent Volumes (EC2 > Elastic Block Store > Volumes) that are deployed in AWS from the Console:

    Screenshot showing the Deletion of Elastic Block Stores