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"source": [
"\n",
" \n",
"## Deploy SQL Server 2019 big data cluster on an existing Azure Kubernetes Service (AKS) cluster\n",
" \n",
"This notebook walks through the process of deploying a SQL Server 2019 big data cluster on an existing AKS cluster.\n",
" \n",
"* Follow the instructions in the **Prerequisites** cell to install the tools if not already installed.\n",
"* Make sure you have the target cluster set as the current context in your kubectl config file.\n",
" The config file would typically be under C:\\Users\\(userid)\\.kube on Windows, and under ~/.kube/ for macOS and Linux for a default installation.\n",
" In the kubectl config file, look for \"current-context\" and ensure it is set to the AKS cluster that the SQL Server 2019 big data cluster will be deployed to.\n",
"* The **Required information** cell will prompt you for password that will be used to access the cluster controller, SQL Server, and Knox.\n",
"* The values in the **Default settings** cell can be changed as appropriate.\n",
"\n",
"Please press the \"Run Cells\" button to run the notebook"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### **Prerequisites** \n",
"Ensure the following tools are installed and added to PATH before proceeding.\n",
" \n",
"|Tools|Description|Installation|\n",
"|---|---|---|\n",
"|kubectl | Command-line tool for monitoring the underlying Kuberentes cluster | [Installation](https://kubernetes.io/docs/tasks/tools/install-kubectl/#install-kubectl-binary-using-native-package-management) |\n",
"|azdata | Command-line tool for installing and managing a big data cluster |[Installation](https://docs.microsoft.com/en-us/sql/big-data-cluster/deploy-install-azdata?view=sqlallproducts-allversions) |"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": "### **Check dependencies**",
"metadata": {}
},
{
"cell_type": "code",
"source": [
"import pandas,sys,os,json,html,getpass,time\r\n",
"pandas_version = pandas.__version__.split('.')\r\n",
"pandas_major = int(pandas_version[0])\r\n",
"pandas_minor = int(pandas_version[1])\r\n",
"pandas_patch = int(pandas_version[2])\r\n",
"if not (pandas_major > 0 or (pandas_major == 0 and pandas_minor > 24) or (pandas_major == 0 and pandas_minor == 24 and pandas_patch >= 2)):\r\n",
" sys.exit('Please upgrade the Notebook dependency before you can proceed, you can do it by running the \"Reinstall Notebook dependencies\" command in command palette (View menu -> Command Palette…).')\r\n",
"def run_command():\r\n",
" print(\"Executing: \" + cmd)\r\n",
" !{cmd}\r\n",
" if _exit_code != 0:\r\n",
" sys.exit(f'Command execution failed with exit code: {str(_exit_code)}.\\n\\t{cmd}\\n')\r\n",
" print(f'Successfully executed: {cmd}')\r\n",
"\r\n",
"cmd = 'kubectl version --client=true'\r\n",
"run_command()\r\n",
"cmd = 'azdata --version'\r\n",
"run_command()"
],
"metadata": {},
"outputs": [],
"execution_count": 1
},
{
"cell_type": "markdown",
"source": "### **Show current context**",
"metadata": {}
},
{
"cell_type": "code",
"source": [
"cmd = ' kubectl config current-context'\r\n",
"run_command()"
],
"metadata": {},
"outputs": [],
"execution_count": 2
},
{
"cell_type": "markdown",
"source": "### **Required information**",
"metadata": {}
},
{
"cell_type": "code",
"source": [
"env_var_flag = \"AZDATA_NB_VAR_BDC_CONTROLLER_PASSWORD\" in os.environ\n",
"if env_var_flag:\n",
" mssql_password = os.environ[\"AZDATA_NB_VAR_BDC_CONTROLLER_PASSWORD\"]\n",
"else: \n",
" mssql_password = getpass.getpass(prompt = 'SQL Server 2019 big data cluster controller password')\n",
" if mssql_password == \"\":\n",
" sys.exit(f'Password is required.')\n",
" confirm_password = getpass.getpass(prompt = 'Confirm password')\n",
" if mssql_password != confirm_password:\n",
" sys.exit(f'Passwords do not match.')\n",
"print('You can also use the same password to access Knox and SQL Server.')"
],
"metadata": {},
"outputs": [],
"execution_count": 3
},
{
"cell_type": "markdown",
"source": "### **Default settings**",
"metadata": {}
},
{
"cell_type": "code",
"source": [
"if env_var_flag:\n",
" mssql_cluster_name = os.environ[\"AZDATA_NB_VAR_BDC_NAME\"]\n",
" mssql_controller_username = os.environ[\"AZDATA_NB_VAR_BDC_CONTROLLER_USERNAME\"]\n",
"else:\n",
" mssql_cluster_name = 'mssql-cluster'\n",
" mssql_controller_username = 'admin'\n",
"configuration_profile = 'aks-dev-test'\n",
"configuration_folder = 'mssql-bdc-configuration'\n",
"print(f'SQL Server big data cluster name: {mssql_cluster_name}')\n",
"print(f'SQL Server big data cluster controller user name: {mssql_controller_username}')\n",
"print(f'Deployment configuration profile: {configuration_profile}')\n",
"print(f'Deployment configuration: {configuration_folder}')"
],
"metadata": {},
"outputs": [],
"execution_count": 4
},
{
"cell_type": "markdown",
"source": "### **Create a deployment configuration file**",
"metadata": {}
},
{
"cell_type": "code",
"source": [
"os.environ[\"ACCEPT_EULA\"] = 'yes'\n",
"cmd = f'azdata bdc config init --source {configuration_profile} --target {configuration_folder} --force'\n",
"run_command()\n",
"cmd = f'azdata bdc config replace -c {configuration_folder}/bdc.json -j metadata.name={mssql_cluster_name}'\n",
"run_command()"
],
"metadata": {},
"outputs": [],
"execution_count": 6
},
{
"cell_type": "markdown",
"source": "### **Create SQL Server 2019 big data cluster**",
"metadata": {}
},
{
"cell_type": "code",
"source": [
"print (f'Creating SQL Server 2019 big data cluster: {mssql_cluster_name} using configuration {configuration_folder}')\n",
"os.environ[\"CONTROLLER_USERNAME\"] = mssql_controller_username\n",
"os.environ[\"CONTROLLER_PASSWORD\"] = mssql_password\n",
"os.environ[\"MSSQL_SA_PASSWORD\"] = mssql_password\n",
"os.environ[\"KNOX_PASSWORD\"] = mssql_password\n",
"cmd = f'azdata bdc create -c {configuration_folder}'\n",
"run_command()"
],
"metadata": {},
"outputs": [],
"execution_count": 7
},
{
"cell_type": "markdown",
"source": "### **Login to SQL Server 2019 big data cluster**",
"metadata": {}
},
{
"cell_type": "code",
"source": [
"cmd = f'azdata login --cluster-name {mssql_cluster_name}'\n",
"run_command()"
],
"metadata": {},
"outputs": [],
"execution_count": 8
},
{
"cell_type": "markdown",
"source": "### **Show SQL Server 2019 big data cluster endpoints**",
"metadata": {}
},
{
"cell_type": "code",
"source": [
"from IPython.display import *\n",
"pandas.set_option('display.max_colwidth', -1)\n",
"cmd = f'azdata bdc endpoint list'\n",
"cmdOutput = !{cmd}\n",
"endpoints = json.loads(''.join(cmdOutput))\n",
"endpointsDataFrame = pandas.DataFrame(endpoints)\n",
"endpointsDataFrame.columns = [' '.join(word[0].upper() + word[1:] for word in columnName.split()) for columnName in endpoints[0].keys()]\n",
"display(HTML(endpointsDataFrame.to_html(index=False, render_links=True)))"
],
"metadata": {},
"outputs": [],
"execution_count": 9
},
{
"cell_type": "markdown",
"source": [
"### **Connect to master SQL Server instance in Azure Data Studio**\r\n",
"Click the link below to connect to the master SQL Server instance of the SQL Server 2019 big data cluster."
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"sqlEndpoints = [x for x in endpoints if x['name'] == 'sql-server-master']\r\n",
"if sqlEndpoints and len(sqlEndpoints) == 1:\r\n",
" connectionParameter = '{\"serverName\":\"' + sqlEndpoints[0]['endpoint'] + '\",\"providerName\":\"MSSQL\",\"authenticationType\":\"SqlLogin\",\"userName\":\"sa\",\"password\":' + json.dumps(mssql_password) + '}'\r\n",
" display(HTML('
Click here to connect to master SQL Server instance
'))\r\n",
"else:\r\n",
" sys.exit('Could not find the master SQL Server instance endpoint')"
],
"metadata": {},
"outputs": [],
"execution_count": 10
}
]
}