mirror of
https://github.com/ckaczor/azuredatastudio.git
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738ca479e4
* saving untested work * fixes for #8165 and #8167 * minor fixes * fix for #8260 * minor quoting fixes * fix for #8264 * minor fixes * minor fixes. * move tools constants to their own files * remove execution cell results from notebooks. * remove extraneous changes * move ensuring of StoragePath to platformservice * remove fix for #8264 pending pm input
329 lines
14 KiB
Plaintext
329 lines
14 KiB
Plaintext
{
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"metadata": {
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python",
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"version": "3.6.6",
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"mimetype": "text/x-python",
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"pygments_lexer": "ipython3",
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"nbconvert_exporter": "python",
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"file_extension": ".py"
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}
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},
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"nbformat_minor": 2,
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"nbformat": 4,
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"\n",
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" \n",
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"## Deploy SQL Server 2019 Big Data Cluster on an existing Azure Kubernetes Service (AKS) cluster\n",
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" \n",
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"This notebook walks through the process of deploying a <a href=\"https://docs.microsoft.com/sql/big-data-cluster/big-data-cluster-overview?view=sqlallproducts-allversions\">SQL Server 2019 Big Data Cluster</a> on an existing AKS cluster.\n",
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" \n",
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"* Follow the instructions in the **Prerequisites** cell to install the tools if not already installed.\n",
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"* The **Required information** will check and prompt you for password if it is not set in the environment variable. The password can be used to access the cluster controller, SQL Server, and Knox.\n",
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"\n",
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"<span style=\"color:red\"><font size=\"3\">Please press the \"Run Cells\" button to run the notebook</font></span>"
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],
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"metadata": {
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"azdata_cell_guid": "82e60c1a-7acf-47ee-877f-9e85e92e11da"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### **Prerequisites** \n",
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"Ensure the following tools are installed and added to PATH before proceeding.\n",
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" \n",
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"|Tools|Description|Installation|\n",
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"|---|---|---|\n",
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"|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",
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"|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) |"
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],
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"metadata": {
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"azdata_cell_guid": "714582b9-10ee-409e-ab12-15a4825c9471"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### **Setup**"
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],
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"metadata": {
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"azdata_cell_guid": "e3dd8e75-e15f-44b4-81fc-1f54d6f0b1e2"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"import pandas,sys,os,json,html,getpass,time\n",
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"pandas_version = pandas.__version__.split('.')\n",
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"pandas_major = int(pandas_version[0])\n",
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"pandas_minor = int(pandas_version[1])\n",
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"pandas_patch = int(pandas_version[2])\n",
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"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)):\n",
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" 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…).')\n",
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"def run_command(command):\n",
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" print(\"Executing: \" + command)\n",
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" !{command}\n",
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" if _exit_code != 0:\n",
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" sys.exit(f'Command execution failed with exit code: {str(_exit_code)}.\\n\\t{command}\\n')\n",
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" print(f'Successfully executed: {command}')"
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],
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"metadata": {
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"azdata_cell_guid": "d973d5b4-7f0a-4a9d-b204-a16480f3940d",
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"tags": [
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"hide_input"
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]
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},
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"outputs": [],
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"execution_count": 1
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},
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{
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"cell_type": "markdown",
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"source": [
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"### **Set variables**\n",
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"Generated by Azure Data Studio using the values collected in the Deploy Big Data Cluster wizard"
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],
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"metadata": {
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"azdata_cell_guid": "4b266b2d-bd1b-4565-92c9-3fc146cdce6d"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### **Check dependencies**"
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],
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"metadata": {
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"azdata_cell_guid": "2544648b-59c9-4ce5-a3b6-87086e214d4c"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"run_command('kubectl version --client=true')\n",
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"run_command('azdata --version')"
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],
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"metadata": {
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"azdata_cell_guid": "691671d7-3f05-406c-a183-4cff7d17f83d",
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"tags": [
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"hide_input"
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]
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},
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"outputs": [],
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"execution_count": 0
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},
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{
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"cell_type": "markdown",
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"source": [
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"### **Required information**"
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],
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"metadata": {
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"azdata_cell_guid": "0bb02e76-fee8-4dbc-a75b-d5b9d1b187d0"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"invoked_by_wizard = \"AZDATA_NB_VAR_BDC_ADMIN_PASSWORD\" in os.environ\n",
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"if invoked_by_wizard:\n",
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" mssql_password = os.environ[\"AZDATA_NB_VAR_BDC_ADMIN_PASSWORD\"]\n",
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"else:\n",
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" mssql_password = getpass.getpass(prompt = 'SQL Server 2019 Big Data Cluster controller password')\n",
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" if mssql_password == \"\":\n",
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" sys.exit(f'Password is required.')\n",
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" confirm_password = getpass.getpass(prompt = 'Confirm password')\n",
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" if mssql_password != confirm_password:\n",
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" sys.exit(f'Passwords do not match.')\n",
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"print('You can also use the controller password to access Knox and SQL Server.')"
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],
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"metadata": {
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"azdata_cell_guid": "e7e10828-6cae-45af-8c2f-1484b6d4f9ac",
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"tags": [
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"hide_input"
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]
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},
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"outputs": [],
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"execution_count": 3
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},
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{
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"cell_type": "markdown",
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"source": [
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"### **Set and show current context**"
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],
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"metadata": {
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"azdata_cell_guid": "127c8042-181f-4862-a390-96e59c181d09"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"run_command(f'kubectl config use-context {mssql_cluster_context}')\n",
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"run_command('kubectl config current-context')"
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],
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"metadata": {
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"azdata_cell_guid": "7d1a03d4-1df8-48eb-bff0-0042603b95b1",
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"tags": [
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"hide_input"
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]
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},
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"outputs": [],
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"execution_count": 0
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},
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{
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"cell_type": "markdown",
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"source": [
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"### **Create deployment configuration files**"
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],
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"metadata": {
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"azdata_cell_guid": "138536c3-1db6-428f-9e5c-8269a02fb52e"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"mssql_target_profile = 'ads-bdc-custom-profile'\n",
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"if not os.path.exists(mssql_target_profile):\n",
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" os.mkdir(mssql_target_profile)\n",
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"bdcJsonObj = json.loads(bdc_json)\n",
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"controlJsonObj = json.loads(control_json)\n",
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"bdcJsonFile = open(f'{mssql_target_profile}/bdc.json', 'w')\n",
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"bdcJsonFile.write(json.dumps(bdcJsonObj, indent = 4))\n",
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"bdcJsonFile.close()\n",
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"controlJsonFile = open(f'{mssql_target_profile}/control.json', 'w')\n",
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"controlJsonFile.write(json.dumps(controlJsonObj, indent = 4))\n",
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"controlJsonFile.close()\n",
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"print(f'Created deployment configuration folder: {mssql_target_profile}')"
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],
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"metadata": {
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"azdata_cell_guid": "2ff82c8a-4bce-449c-9d91-3ac7dd272021",
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"tags": [
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"hide_input"
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]
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},
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"outputs": [],
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"execution_count": 6
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},
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{
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"cell_type": "markdown",
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"source": [
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"### **Create SQL Server 2019 Big Data Cluster**"
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],
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"metadata": {
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"azdata_cell_guid": "efe78cd3-ed73-4c9b-b586-fdd6c07dd37f"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"print (f'Creating SQL Server 2019 Big Data Cluster: {mssql_cluster_name} using configuration {mssql_target_profile}')\n",
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"os.environ[\"ACCEPT_EULA\"] = 'yes'\n",
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"os.environ[\"AZDATA_USERNAME\"] = mssql_username\n",
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"os.environ[\"AZDATA_PASSWORD\"] = mssql_password\n",
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"if os.name == 'nt':\n",
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" print(f'If you don\\'t see output produced by azdata, you can run the following command in a terminal window to check the deployment status:\\n\\t{os.environ[\"AZDATA_NB_VAR_KUBECTL\"]} get pods -n {mssql_cluster_name} ')\n",
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"run_command(f'azdata bdc create -c {mssql_target_profile}')"
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],
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"metadata": {
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"azdata_cell_guid": "373947a1-90b9-49ee-86f4-17a4c7d4ca76",
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"tags": [
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"hide_input"
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]
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},
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"outputs": [],
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"execution_count": 7
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},
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{
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"cell_type": "markdown",
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"source": [
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"### **Login to SQL Server 2019 Big Data Cluster**"
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],
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"metadata": {
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"azdata_cell_guid": "4e026d39-12d4-4c80-8e30-de2b782f2110"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"run_command(f'azdata login -n {mssql_cluster_name}')"
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],
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"metadata": {
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"azdata_cell_guid": "79adda27-371d-4dcb-b867-db025f8162a5",
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"tags": [
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"hide_input"
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]
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},
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"outputs": [],
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"execution_count": 8
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},
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{
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"cell_type": "markdown",
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"source": [
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"### **Show SQL Server 2019 Big Data Cluster endpoints**"
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],
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"metadata": {
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"azdata_cell_guid": "c1921288-ad11-40d8-9aea-127a722b3df8"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"from IPython.display import *\n",
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"pandas.set_option('display.max_colwidth', -1)\n",
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"cmd = f'azdata bdc endpoint list'\n",
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"cmdOutput = !{cmd}\n",
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"endpoints = json.loads(''.join(cmdOutput))\n",
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"endpointsDataFrame = pandas.DataFrame(endpoints)\n",
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"endpointsDataFrame.columns = [' '.join(word[0].upper() + word[1:] for word in columnName.split()) for columnName in endpoints[0].keys()]\n",
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"display(HTML(endpointsDataFrame.to_html(index=False, render_links=True)))"
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],
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"metadata": {
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"azdata_cell_guid": "a2202494-fd6c-4534-987d-15c403a5096f",
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"tags": [
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"hide_input"
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]
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},
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"outputs": [],
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"execution_count": 9
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},
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{
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"cell_type": "markdown",
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"source": [
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"### **Connect to SQL Server Master instance in Azure Data Studio**\n",
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"Click the link below to connect to the SQL Server Master instance of the SQL Server 2019 Big Data Cluster."
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],
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"metadata": {
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"azdata_cell_guid": "621863a2-aa61-46f4-a9d0-717f41c009ee"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"sqlEndpoints = [x for x in endpoints if x['name'] == 'sql-server-master']\n",
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"if sqlEndpoints and len(sqlEndpoints) == 1:\n",
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" connectionParameter = '{\"serverName\":\"' + sqlEndpoints[0]['endpoint'] + '\",\"providerName\":\"MSSQL\",\"authenticationType\":\"SqlLogin\",\"userName\":' + json.dumps(mssql_username) + ',\"password\":' + json.dumps(mssql_password) + '}'\n",
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" display(HTML('<br/><a href=\"command:azdata.connect?' + html.escape(connectionParameter)+'\"><font size=\"3\">Click here to connect to SQL Server Master instance</font></a><br/>'))\n",
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"else:\n",
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" sys.exit('Could not find the SQL Server Master instance endpoint.')"
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],
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"metadata": {
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"azdata_cell_guid": "48342355-9d2b-4fa6-b1aa-3bc77d434dfa",
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"tags": [
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"hide_input"
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]
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},
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"outputs": [],
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"execution_count": 10
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}
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]
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}
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