{ "metadata": { "kernelspec": { "name": "pyspark3kernel", "display_name": "PySpark3" }, "language_info": { "name": "pyspark3", "mimetype": "text/x-python", "codemirror_mode": { "name": "python", "version": 3 }, "pygments_lexer": "python3" } }, "nbformat_minor": 2, "nbformat": 4, "cells": [ { "cell_type": "markdown", "source": "# Read and write from Spark to SQL\r\nA typical big data scenario is large scale ETL in Spark and post processing the data is written out to SQLServer for access to LOB applications. This sample shows how to write to SQLServer from Spark. The main steps in the sample are \r\n- Reading a HDFS file, \r\n- Basic processing on it and \r\n- Then writing processed data to SQL Server table using JDBC\r\n\r\nPreReq : \r\n- The sample uses a SQL database named \"MyTestDatabase\". Create this before you run this sample. The database can be created as follows\r\n ``` sql\r\n Create DATABASE MyTestDatabase\r\n GO \r\n ``` \r\n- Download [AdultCensusIncome.csv]( https://amldockerdatasets.azureedge.net/AdultCensusIncome.csv ) to your local machine. Create a hdfs folder named spark_data and upload the file there. \r\n\r\n \r\n ", "metadata": {} }, { "cell_type": "code", "source": "\r\n#Read a file and then write it to the SQL table\r\ndatafile = \"/spark_data/AdultCensusIncome.csv\"\r\ndf = spark.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\r\ndf.show(5)\r\n", "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": "Starting Spark application\n" }, { "output_type": "display_data", "data": { "text/plain": "", "text/html": "\n
IDYARN Application IDKindStateSpark UIDriver logCurrent session?
2application_1554755839506_0003pyspark3idleLinkLink✔
" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": "SparkSession available as 'spark'.\n" }, { "output_type": "stream", "name": "stdout", "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" } ], "execution_count": 3 }, { "cell_type": "code", "source": "\r\n#Process this data. Very simple data cleanup steps. Replacing \"-\" with \"_\" in column names\r\ncolumns_new = [col.replace(\"-\", \"_\") for col in df.columns]\r\ndf = df.toDF(*columns_new)\r\ndf.show(5)\r\n\r\n", "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "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" } ], "execution_count": 4 }, { "cell_type": "code", "source": "#Write from Spark to SQL table using JDBC\r\nprint(\"Use build in JDBC connector to write to SQLServer master instance in Big data \")\r\n\r\nservername = \"jdbc:sqlserver://master-0.master-svc\"\r\ndbname = \"MyTestDatabase\"\r\nurl = servername + \";\" + \"databaseName=\" + dbname + \";\"\r\n\r\ndbtable = \"dbo.AdultCensus\"\r\nuser = \"***\"\r\npassword = \"****\"\r\n\r\nprint(\"url is \", url)\r\n\r\ntry:\r\n df.write \\\r\n .format(\"jdbc\") \\\r\n .mode(\"overwrite\") \\\r\n .option(\"url\", url) \\\r\n .option(\"dbtable\", dbtable) \\\r\n .option(\"user\", user) \\\r\n .option(\"password\", password)\\\r\n .save()\r\nexcept ValueError as error :\r\n print(\"JDBC Write failed\", error)\r\n\r\nprint(\"JDBC Write done \")\r\n\r\n\r\n", "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": "Use build in JDBC connector to write to SQLServer master instance in Big data \nurl is jdbc:sqlserver://master-0.master-svc;databaseName=MyTestDatabase;\nJDBC Write done" } ], "execution_count": 9 }, { "cell_type": "code", "source": "#Read to Spark from SQL table using JDBC\r\nprint(\"read data from SQL server table \")\r\njdbcDF = spark.read \\\r\n .format(\"jdbc\") \\\r\n .option(\"url\", url\r\n ) \\\r\n .option(\"dbtable\", dbtable) \\\r\n .option(\"user\", user) \\\r\n .option(\"password\", password) \\\r\n .load()\r\n\r\njdbcDF.show(5)", "metadata": {}, "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": 11 } ] }