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Re-organized spark samples

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Umachandar Jayachandran
2019-04-18 14:26:45 -07:00
parent 954fb3cd58
commit 92ae53e05b
7 changed files with 169 additions and 49 deletions
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samples/features/sql-big-data-cluster/spark/data-virtualization/spark_to_sql_jdbc.ipynb
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{
"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 writing the processed data to SQLServer. The following samples shows \r\n- reading a HDFS file, \r\n- some basic processing on it and \r\n- then processed data to SQL Server table.\r\n\r\nNeed a database precreated in SQL for this sample. Here we are using database name \"MyTestDatabase\" that can be created using SQL statements below.\r\n\r\n``` sql\r\nCreate DATABASE MyTestDatabase\r\nGO \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": "+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\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": 8
},
{
"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": 9
},
{
"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://mssql-master-pool-0.service-master-pool\"\r\ndbname = \"MyTestDatabase\"\r\nurl = servername + \";\" + \"databaseName=\" + dbname + \";\"\r\n\r\nc = \"dbo.AdultCensus\"\r\nuser = \"sa\"\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://mssql-master-pool-0.service-master-pool;databaseName=MyTestDatabase;\nJDBC Write done"
}
],
"execution_count": 10
},
{
"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": 13
}
]
}