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fixed the windows save issue and updated the cluster status notebook with Vicky's updates (#6475)

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Maddy
2019-07-23 16:43:37 -07:00
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extensions/mssql/notebooks/TSG/cluster-status.ipynb
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"cells": [
{
"cell_type": "markdown",
"source": "![11811317_10153406249401648_2787740058697948111_n](https://raw.githubusercontent.com/Microsoft/sqlworkshops/master/graphics/solutions-microsoft-logo-small.png)\n\n# View the status of your SQL Server big data cluster\nThis notebook allows you to see the status of the controller, master instance, and pools in your SQL Server big data cluster.\n\n## <span style=\"color:red\">Important Instructions</span>\n### **Before you begin, you will need:**\n* Big data cluster name\n* Controller username\n* Controller password\n* Controller endpoint \n\nYou can find the controller endpoint from the SQL Server big data cluster dashboard in the Service Endpoints table. The endpoint is listed as **Cluster Management Service.**\n\nIf you do not know the credentials, ask the admin who deployed your cluster.\n\n### **Prerequisites**\nEnsure 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 SQL Server big data cluster |[Installation](https://docs.microsoft.com/en-us/sql/big-data-cluster/deploy-install-azdata?view=sqlallproducts-allversions) |\n\n### **Instructions**\n* For the best experience, click **Run Cells** on the toolbar above. This will automatically execute all code cells below and show the cluster status in each table.\n* When you click **Run Cells** for this Notebook, you will be prompted at the *Log in to your SQL Server big data cluster* code cell to provide your login credentials. Follow the prompts and press enter to proceed.\n* **You won't need to modify any of the code cell contents** in this Notebook. If you accidentally made a change, you can reopen this Notebook from the bdc dashboard.\n\n\n",
"source": "![11811317_10153406249401648_2787740058697948111_n](https://raw.githubusercontent.com/Microsoft/sqlworkshops/master/graphics/solutions-microsoft-logo-small.png)\n\n# View the status of your SQL Server big data cluster\nThis notebook allows you to see the status of the controller, master instance, and pools in your SQL Server big data cluster.\n\n> ## **Important Instructions**\n> ### **Before you begin, you will need:**\n>* Big data cluster name\n>* Controller username\n>* Controller password\n>* Controller endpoint \n\nYou can find the controller endpoint from the big data cluster dashboard in the Service Endpoints table. The endpoint is listed as **Cluster Management Service.**\n\nIf you do not know the credentials, ask the admin who deployed your cluster.\n\n### **Prerequisites**\nEnsure 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 Kubernetes 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) |\n|Pandas Package | Python package for data manipulation | Will be installed by the notebook if not present |\n\n\n### **Instructions**\n* For the best experience, click **Run Cells** on the toolbar above. This will automatically execute all code cells below and show the cluster status in each table.\n* When you click **Run Cells** for this Notebook, you will be prompted at the *Log in to your big data cluster* code cell to provide your login credentials. Follow the prompts and press enter to proceed.\n* **You won't need to modify any of the code cell contents** in this Notebook. If you accidentally made a change, you can reopen this Notebook from the cluster dashboard.\n\n\n",
"metadata": {}
},
{
"cell_type": "markdown",
"source": "### **azdata version**",
"source": "### **Check azdata version**",
"metadata": {}
},
{
@@ -51,24 +51,24 @@
},
{
"cell_type": "markdown",
"source": "## **Log in to your SQL Server big data cluster**\r\nTo view cluster status, you will need to connect to your SQL Server big data cluster through mssqlctl. \r\n\r\nWhen you run this code cell, you will be prompted for:\r\n- Cluster name\r\n- Controller username\r\n- Controller password\r\n\r\nTo proceed:\r\n- **Click** on the input box\r\n- **Type** the login info\r\n- **Press** enter.\r\n\r\nIf your cluster is missing a configuration file, you will be asked to provide your controller endpoint. (Format: **https://00.00.00.000:00000**)",
"source": "## **Log in to your big data cluster**\r\nTo view cluster status, you will need to connect to your big data cluster through mssqlctl. \r\n\r\nWhen you run this code cell, you will be prompted for:\r\n- Cluster name\r\n- Controller username\r\n- Controller password\r\n\r\nTo proceed:\r\n- **Click** on the input box\r\n- **Type** the login info\r\n- **Press** enter.\r\n\r\nIf your cluster is missing a configuration file, you will be asked to provide your controller endpoint. (Format: **https://00.00.00.000:00000**) You can find the controller endpoint from the big data cluster dashboard in the Service Endpoints table. The endpoint is listed as **Cluster Management Service.**\r\n",
"metadata": {}
},
{
"cell_type": "code",
"source": "import os, getpass, json\nimport pandas as pd\nimport numpy as np\nfrom IPython.display import *\n\ndef PromptForInfo(promptMsg, isPassword, errorMsg):\n if isPassword:\n promptResponse = getpass.getpass(prompt=promptMsg)\n else:\n promptResponse = input(promptMsg)\n if promptResponse == \"\":\n raise SystemExit(errorMsg + '\\n')\n return promptResponse\n\n# Prompt user inputs:\ncluster_name = PromptForInfo('Please provide your Cluster Name: ', False, 'Cluster Name is required!')\n\ncontroller_username = PromptForInfo('Please provide your Controller Username for login: ', False, 'Controller Username is required!')\n\ncontroller_password = PromptForInfo('Controller Password: ', True, 'Password is required!')\nprint('***********')\n\n!azdata logout\n# Login in to your SQL Server big data cluster \ncmd = f'azdata login -n {cluster_name} -u {controller_username} -a yes'\nprint(\"Start \" + cmd)\nos.environ['CONTROLLER_USERNAME'] = controller_username\nos.environ['CONTROLLER_PASSWORD'] = controller_password\nos.environ['ACCEPT_EULA'] = 'yes'\n\nloginResult = !{cmd}\nif 'ERROR: Please check your kube config or specify the correct controller endpoint with: --controller-endpoint https://<ip>:<port>.' in loginResult[0] or 'ERROR' in loginResult[0]:\n controller_ip = input('Please provide your Controller endpoint: ')\n if controller_ip == \"\":\n raise SystemExit(f'Controller IP is required!' + '\\n')\n else:\n cmd = f'azdata login -n {cluster_name} -e {controller_ip} -u {controller_username} -a yes'\n loginResult = !{cmd}\nprint(loginResult)",
"source": "import os, getpass, json\nimport pandas as pd\nimport numpy as np\nfrom IPython.display import *\n\ndef PromptForInfo(promptMsg, isPassword, errorMsg):\n if isPassword:\n promptResponse = getpass.getpass(prompt=promptMsg)\n else:\n promptResponse = input(promptMsg)\n if promptResponse == \"\":\n raise SystemExit(errorMsg + '\\n')\n return promptResponse\n\n# Prompt user inputs:\ncluster_name = PromptForInfo('Please provide your Cluster Name: ', False, 'Cluster Name is required!')\n\ncontroller_username = PromptForInfo('Please provide your Controller Username for login: ', False, 'Controller Username is required!')\n\ncontroller_password = PromptForInfo('Controller Password: ', True, 'Password is required!')\nprint('***********')\n\n!azdata logout\n# Login in to your big data cluster \ncmd = f'azdata login -n {cluster_name} -u {controller_username} -a yes'\nprint(\"Start \" + cmd)\nos.environ['CONTROLLER_USERNAME'] = controller_username\nos.environ['CONTROLLER_PASSWORD'] = controller_password\nos.environ['ACCEPT_EULA'] = 'yes'\n\nloginResult = !{cmd}\nif 'ERROR: Please check your kube config or specify the correct controller endpoint with: --controller-endpoint https://<ip>:<port>.' in loginResult[0] or 'ERROR' in loginResult[0]:\n controller_ip = input('Please provide your Controller endpoint: ')\n if controller_ip == \"\":\n raise SystemExit(f'Controller IP is required!' + '\\n')\n else:\n cmd = f'azdata login -n {cluster_name} -e {controller_ip} -u {controller_username} -a yes'\n loginResult = !{cmd}\nprint(loginResult)",
"metadata": {},
"outputs": [],
"execution_count": 0
},
{
"cell_type": "markdown",
"source": "## **Status of SQL Server big data cluster**\r\nAfter you successfully login to your bdc, you can view the overall status of each container before drilling down into each component.",
"source": "## **Status of big data cluster**\r\nAfter you successfully login to your bdc, you can view the overall status of each container before drilling down into each component.",
"metadata": {}
},
{
"cell_type": "code",
"source": "# Display status of SQL Server big data cluster\ndef formatColumnNames(column):\n return ' '.join(word[0].upper() + word[1:] for word in column.split())\n\npd.set_option('display.max_colwidth', -1)\ndef show_results(input):\n input = ''.join(input)\n results = json.loads(input)\n df = pd.DataFrame(results)\n df.columns = [formatColumnNames(n) for n in results[0].keys()]\n mydata = HTML(df.to_html(render_links=True))\n display(mydata)\n\nresults = !azdata bdc status show\nstrRes = ''.join(results)\njsonRes = json.loads(strRes)\ndtypes = '{'\nspark = [x for x in jsonRes if x['kind'] == 'Spark']\nif spark:\n spark_exists = True\nelse:\n spark_exists = False\nshow_results(results)",
"source": "# Display status of big data cluster\ndef formatColumnNames(column):\n return ' '.join(word[0].upper() + word[1:] for word in column.split())\n\npd.set_option('display.max_colwidth', -1)\ndef show_results(input):\n input = ''.join(input)\n results = json.loads(input)\n df = pd.DataFrame(results)\n df.columns = [formatColumnNames(n) for n in results[0].keys()]\n mydata = HTML(df.to_html(render_links=True))\n display(mydata)\n\nresults = !azdata bdc status show\nstrRes = ''.join(results)\nif 'ERROR: (404)' in strRes or 'Not Found' in strRes:\n print('\\n\\n \\033[1m \\033[91m' + f'Cluster {cluster_name} could not be found! Make sure you\\'re logged in to the correct cluster. \\n' + '\\033[0m')\n raise SystemExit(f'Error: !azdata bdc status show')\njsonRes = json.loads(strRes)\ndtypes = '{'\nspark = [x for x in jsonRes if x['kind'] == 'Spark']\nif spark:\n spark_exists = True\nelse:\n spark_exists = False\nshow_results(results)",
"metadata": {},
"outputs": [],
"execution_count": 0