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first draft : connector with AD mode
This commit is contained in:
ShivSood
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{
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"metadata": {
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"kernelspec": {
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"name": "pysparkkernel",
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"display_name": "PySpark"
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},
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"language_info": {
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"name": "pyspark",
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"mimetype": "text/x-python",
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"codemirror_mode": {
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"name": "python",
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"version": 2
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},
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"pygments_lexer": "python2"
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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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"# Using MsSQLSparkConnector with Integrated AD Auth\r\n",
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"\r\n",
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"This sample shows how to use the MsSQLSparkConnector with integrated AD Auth when using use principal and keytab instead of username/password. \r\n",
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"\r\n",
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"## PreReq\r\n",
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"-------\r\n",
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"- SQL Server 2019 big data cluster is deployed with AD\r\n",
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"- Have access to AD controller to create keytabs that we will use in this sample. \r\n",
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"- Download [AdultCensusIncome.csv]( https://amldockerdatasets.azureedge.net/AdultCensusIncome.csv ) to your local machine. Upload this file to hdfs folder named *spark_data*. \r\n",
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"- The sample uses a SQL database *spark_sql_db* to create/update tables. The database needs to be created before the sample is run.\r\n",
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" "
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],
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"metadata": {
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"azdata_cell_guid": "2757df21-8174-4bb1-a52c-66eaf94f6b96"
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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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"\r\n",
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""
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],
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"metadata": {
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"azdata_cell_guid": "48874729-541f-4b01-888f-3fdd2aeb59da"
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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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"# Creating KeyTab file\r\n",
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"The following section shows how to generate principal and keytab. This assumes you have a SS19 Big Data Cluster installed with Windows AD contoller for domain AZDATA.LOCAL. One of the users is testusera1@AZDATA.LOCAL and the user is part of Domain Admin group.\r\n",
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"\r\n",
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"## Create KeyTab file using ktpass\r\n",
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"1. Login to the Windows AD controller with user1 credentials.\r\n",
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"2. Open command prompt in Administrator mode.\r\n",
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"3. Use ktpass to create a key tab. Refer [here](https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/ktpass) for documentation on using ktpass. \r\n",
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"\r\n",
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"```sh\r\n",
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"ktpass -out testusera1.keytab -mapUser testusera1@AZDATA.LOCAL -pass <testusera1 password> -mapOp set +DumpSalt -crypto AES256-SHA1 -ptype KRB5_NT_PRINCIPAL -princ testusera1@AZDATA.LOCAL\r\n",
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"```\r\n",
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"\r\n",
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"The command above should generate a keytab file named testusera1.keytab. Transfer this file to hdfs folder in Big Data Cluster. In this sample we transfer the file to /user/testusera1/testusera1.keytab\r\n",
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"\r\n",
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"## Create KeyTab file using kinit\r\n",
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"\r\n",
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"If you are on a linux machine kinit can be used as follows to create keytab. Note that you linux machine shoud be connected to the domain controler.\r\n",
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"\r\n",
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"``` sh\r\n",
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"ktutil\r\n",
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"ktutil : add_entry -password -p testusera1@AZDATA.LOCAL -k 1 -e arcfour-hmac-md5\r\n",
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"Password for testusera1@myDomain:\r\n",
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"ktutil : add_entry -password -p testusera1@AZDATA.LOCAL -k 1 -e des-cbc-md4\r\n",
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"ktutil : wkt testusera1@AZDATA.keytab \r\n",
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"```\r\n",
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"\r\n",
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"``` sh\r\n",
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"## Check if keytab generated properly. Any error implies that keytab is not generated right.\r\n",
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"kinit -kt testusera1.keytab testusera1@AZDATA.LOCAL\r\n",
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"```\r\n",
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"\r\n",
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"Load Keytab to HDFS for use\r\n",
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"\r\n",
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"```sh\r\n",
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"hadoop fs -mkdir -p /user/testusera1/\r\n",
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"hadoop fs -copyFromLocal -f testusera1.keytab /user/testusera1/testusera1.keytab\r\n",
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"```\r\n",
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"\r\n",
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"\r\n",
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"\r\n",
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" "
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],
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"metadata": {
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"azdata_cell_guid": "5e733ddd-cbf8-4aa1-9f97-9fff40c28332"
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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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"# Create a database user\r\n",
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"\r\n",
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"``` sql\r\n",
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"IF NOT EXISTS (select name from sys.server_principals where name='AZDATA.LOCAL\\testusera1')\r\n",
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"BEGIN\r\n",
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" CREATE LOGIN [AZDATA.LOCAL\\testusera1] FROM WINDOWS\r\n",
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"END\r\n",
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"\r\n",
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"ALTER SERVER ROLE dbcreator ADD MEMBER [AZDATA.LOCAL\\testusera1]\r\n",
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"GRANT VIEW SERVER STATE to [AZDATA.LOCAL\\testusera1]\r\n",
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"\r\n",
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"# Create a database named \"spark_mssql_db\"\r\n",
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"IF NOT EXISTS (SELECT * FROM sys.databases WHERE name = N'spark_mssql_db')\r\n",
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" CREATE DATABASE spark_mssql_db\r\n",
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"```"
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],
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"metadata": {
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"azdata_cell_guid": "453e7b2f-e590-4b95-9fbd-5dc8b9d1f02c"
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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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"# Configure Spark applicaion to point to the key tab file\r\n",
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"Here we configure spark to use the keytab file once the keytab is created and uploaded to HDFS (/user/testusera1/testusera1.keytab). \r\n",
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"Note the usage of \"spark.files\" : \"/user/testusera1/testusera1.keytab\". As a result of this configuration Spark driver distributes the file to all executors. \r\n",
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"\r\n",
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"Run the cell below to start the spark application.\r\n",
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""
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],
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"metadata": {
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"azdata_cell_guid": "8ecbc487-996a-4bd1-b4f0-bcbf439decea"
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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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"%%configure -f\r\n",
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"{\"conf\": {\r\n",
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" \"spark.files\" : \"/user/testusera1/testusera1.keytab\",\r\n",
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" \"spark.executor.memory\": \"4g\",\r\n",
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" \"spark.driver.memory\": \"4g\",\r\n",
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" \"spark.executor.cores\": 2,\r\n",
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" \"spark.driver.cores\": 1,\r\n",
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" \"spark.executor.instances\": 4\r\n",
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" }\r\n",
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"}"
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],
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"metadata": {
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"azdata_cell_guid": "32acad02-f758-4a0a-a4e3-e64a52948986",
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"tags": []
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "Starting Spark application\n"
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<IPython.core.display.HTML object>",
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"text/html": "<table>\n<tr><th>ID</th><th>YARN Application ID</th><th>Kind</th><th>State</th><th>Spark UI</th><th>Driver log</th><th>Current session?</th></tr><tr><td>12</td><td>application_1581458669418_0041</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"https://10.193.21.202:30443/gateway/default/yarn/proxy/application_1581458669418_0041/\">Link</a></td><td><a target=\"_blank\" href=\"https://10.193.21.202:30443/gateway/default/yarn/container/container_1581458669418_0041_01_000001/root\">Link</a></td><td>✔</td></tr></table>"
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},
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"metadata": {}
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},
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{
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"output_type": "display_data",
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"data": {
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}
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},
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"metadata": {}
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},
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "SparkSession available as 'spark'.\n"
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<IPython.core.display.HTML object>",
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"text/html": "Current session configs: <tt>{'conf': {'spark.files': '/user/user1/user1.keytab', 'spark.executor.memory': '4g', 'spark.driver.memory': '4g', 'spark.executor.cores': 2, 'spark.driver.cores': 1, 'spark.executor.instances': 4}, 'kind': 'pyspark'}</tt><br>"
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},
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"metadata": {}
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<IPython.core.display.HTML object>",
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"text/html": "<table>\n<tr><th>ID</th><th>YARN Application ID</th><th>Kind</th><th>State</th><th>Spark UI</th><th>Driver log</th><th>Current session?</th></tr><tr><td>6</td><td>application_1581458669418_0035</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"https://10.193.21.202:30443/gateway/default/yarn/proxy/application_1581458669418_0041/\">Link</a></td><td><a target=\"_blank\" href=\"https://10.193.21.202:30443/gateway/default/yarn/container/container_1581458669418_0041_01_000001/root\">Link</a></td><td>✔</td></tr></table>"
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},
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"metadata": {}
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}
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],
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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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"# Read CSV into a data frame\r\n",
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"In this step we read the CSV into a data frame. This dataframe would then be written to SQL table using MSSQL Spark Connector \r\n",
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"\r\n",
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"\r\n",
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""
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],
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"metadata": {
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"azdata_cell_guid": "ed8b58e0-3607-4a71-8dc8-034bc0180ee4"
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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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"#spark = SparkSession.builder.getOrCreate()\r\n",
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"sc.setLogLevel(\"INFO\")\r\n",
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"\r\n",
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"#Read a file and then write it to the SQL table\r\n",
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"datafile = \"/spark_data/AdultCensusIncome.csv\"\r\n",
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"df = spark.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\r\n",
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"df.show(5)\r\n",
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"\r\n",
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"\r\n",
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"#Process this data. Very simple data cleanup steps. Replacing \"-\" with \"_\" in column names\r\n",
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"columns_new = [col.replace(\"-\", \"_\") for col in df.columns]\r\n",
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"df = df.toDF(*columns_new)\r\n",
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"df.show(5)\r\n",
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""
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],
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"metadata": {
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"azdata_cell_guid": "813bbfa3-2613-45dd-9556-94faba602977"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "Starting Spark application\n"
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<IPython.core.display.HTML object>",
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"text/html": "<table>\n<tr><th>ID</th><th>YARN Application ID</th><th>Kind</th><th>State</th><th>Spark UI</th><th>Driver log</th><th>Current session?</th></tr><tr><td>7</td><td>application_1581458669418_0036</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"https://10.193.21.202:30443/gateway/default/yarn/proxy/application_1581458669418_0036/\">Link</a></td><td><a target=\"_blank\" href=\"https://10.193.21.202:30443/gateway/default/yarn/container/container_1581458669418_0036_01_000001/root\">Link</a></td><td>✔</td></tr></table>"
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},
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"metadata": {}
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…",
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}
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"metadata": {}
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},
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "SparkSession available as 'spark'.\n"
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…",
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"metadata": {}
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},
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n|age| workclass|fnlwgt|education|education-num| marital-status| occupation| relationship| race| sex|capital-gain|capital-loss|hours-per-week|native-country|income|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n| 39| State-gov| 77516|Bachelors| 13| Never-married| Adm-clerical|Not-in-family|White| Male| 2174| 0| 40| United-States| <=50K|\n| 50|Self-emp-not-inc| 83311|Bachelors| 13|Married-civ-spouse| Exec-managerial| Husband|White| Male| 0| 0| 13| United-States| <=50K|\n| 38| Private|215646| HS-grad| 9| Divorced|Handlers-cleaners|Not-in-family|White| Male| 0| 0| 40| United-States| <=50K|\n| 53| Private|234721| 11th| 7|Married-civ-spouse|Handlers-cleaners| Husband|Black| Male| 0| 0| 40| United-States| <=50K|\n| 28| Private|338409|Bachelors| 13|Married-civ-spouse| Prof-specialty| Wife|Black|Female| 0| 0| 40| Cuba| <=50K|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\nonly showing top 5 rows\n\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n|age| workclass|fnlwgt|education|education_num| marital_status| occupation| relationship| race| sex|capital_gain|capital_loss|hours_per_week|native_country|income|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n| 39| State-gov| 77516|Bachelors| 13| Never-married| Adm-clerical|Not-in-family|White| Male| 2174| 0| 40| United-States| <=50K|\n| 50|Self-emp-not-inc| 83311|Bachelors| 13|Married-civ-spouse| Exec-managerial| Husband|White| Male| 0| 0| 13| United-States| <=50K|\n| 38| Private|215646| HS-grad| 9| Divorced|Handlers-cleaners|Not-in-family|White| Male| 0| 0| 40| United-States| <=50K|\n| 53| Private|234721| 11th| 7|Married-civ-spouse|Handlers-cleaners| Husband|Black| Male| 0| 0| 40| United-States| <=50K|\n| 28| Private|338409|Bachelors| 13|Married-civ-spouse| Prof-specialty| Wife|Black|Female| 0| 0| 40| Cuba| <=50K|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\nonly showing top 5 rows"
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||||
}
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],
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"execution_count": 5
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Write and READ to/from SQL Table ( using Integrated Auth)\r\n",
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"- Write dataframe to SQL table to Master instance\r\n",
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"- Read SQL Table to Spark dataframe\r\n",
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"\r\n",
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"In both scenarions here we use integrated auth with principal\\keytab file rather than username\\password of the user."
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||||
],
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"metadata": {
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"azdata_cell_guid": "a6afceb2-6fbc-435b-af88-e9f5cc784f5d"
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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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"#Write from Spark to SQL table using MSSQL Spark Connector\r\n",
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"print(\"MSSQL Spark Connector write(overwrite) start \")\r\n",
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"\r\n",
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"servername = \"jdbc:sqlserver://master-p-svc:1433\"\r\n",
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"dbname = \"mssql_spark_db\"\r\n",
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"security_spec = \";integratedSecurity=true;authenticationScheme=JavaKerberos;\"\r\n",
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"url = servername + \";\" + \"databaseName=\" + dbname + security_spec\r\n",
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"\r\n",
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"dbtable = \"AdultCensus_test\"\r\n",
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"principal = \"testusera1@AZDATA.LOCAL\"\r\n",
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"keytab = \"/user/testusera1/testusera1.keytab\" \r\n",
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"\r\n",
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"try:\r\n",
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" df.write \\\r\n",
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" .format(\"com.microsoft.sqlserver.jdbc.spark\") \\\r\n",
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" .mode(\"overwrite\") \\\r\n",
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||||
" .option(\"url\", url) \\\r\n",
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" .option(\"dbtable\", dbtable) \\\r\n",
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" .option(\"principal\", principal) \\\r\n",
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||||
" .option(\"keytab\", keytab) \\\r\n",
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||||
" .save()\r\n",
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||||
"except ValueError as error :\r\n",
|
||||
" print(\"MSSQL Spark Connector write(overwrite) failed\", error)\r\n",
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||||
"\r\n",
|
||||
"print(\"MSSQL Connector write(overwrite) done \")"
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||||
],
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"metadata": {
|
||||
"azdata_cell_guid": "b851fe61-6e85-4e46-a20f-4063fc6586e0"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "display_data",
|
||||
"data": {
|
||||
"text/plain": "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…",
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"version_major": 2,
|
||||
"version_minor": 0,
|
||||
"model_id": "644bfe3ba48142f38f52717cab9eb19d"
|
||||
}
|
||||
},
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": "Use MSSQL connector to write to master SQL instance \nMSSQL Connector write(overwrite) succeeded"
|
||||
}
|
||||
],
|
||||
"execution_count": 7
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#Read from SQL table using MSSQ Connector\r\n",
|
||||
"print(\"MSSQL Spark Connector read start \")\r\n",
|
||||
"jdbcDF = spark.read \\\r\n",
|
||||
" .format(\"com.microsoft.sqlserver.jdbc.spark\") \\\r\n",
|
||||
" .option(\"url\", url) \\\r\n",
|
||||
" .option(\"dbtable\", dbtable) \\\r\n",
|
||||
" .option(\"url\", url) \\\r\n",
|
||||
" .option(\"dbtable\", dbtable) \\\r\n",
|
||||
" .option(\"principal\", user) \\\r\n",
|
||||
" .option(\"keytab\", keytab).load()\r\n",
|
||||
"\r\n",
|
||||
"jdbcDF.show(5)\r\n",
|
||||
"\r\n",
|
||||
"print(\"MSSQL Spark Connector read done\")"
|
||||
],
|
||||
"metadata": {
|
||||
"azdata_cell_guid": "e3e19e1f-1325-47ea-87d1-1170f316d2d8"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": "read data from SQL server table \n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n|age| workclass|fnlwgt|education|education_num| marital_status| occupation| relationship| race| sex|capital_gain|capital_loss|hours_per_week|native_country|income|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n| 39| State-gov| 77516|Bachelors| 13| Never-married| Adm-clerical|Not-in-family|White| Male| 2174| 0| 40| United-States| <=50K|\n| 50|Self-emp-not-inc| 83311|Bachelors| 13|Married-civ-spouse| Exec-managerial| Husband|White| Male| 0| 0| 13| United-States| <=50K|\n| 38| Private|215646| HS-grad| 9| Divorced|Handlers-cleaners|Not-in-family|White| Male| 0| 0| 40| United-States| <=50K|\n| 53| Private|234721| 11th| 7|Married-civ-spouse|Handlers-cleaners| Husband|Black| Male| 0| 0| 40| United-States| <=50K|\n| 28| Private|338409|Bachelors| 13|Married-civ-spouse| Prof-specialty| Wife|Black|Female| 0| 0| 40| Cuba| <=50K|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\nonly showing top 5 rows"
|
||||
}
|
||||
],
|
||||
"execution_count": 7
|
||||
}
|
||||
]
|
||||
}
|
||||
Reference in New Issue
Block a user