I am trying to write classic sql query using scala to insert some information into a sql server database table.
The connection to my database works perfectly and I succeed to read data from JDBC, from a table recently created called "textspark" which has only 1 column called "firstname" create table textspark(firstname varchar(10)).
However, when I try to write data into the table , I get the following error:
Exception in thread "main" org.apache.spark.sql.AnalysisException: Table or view not found: textspark
this is my code:
//Step 1: Check that the JDBC driver is available
Class.forName("com.microsoft.sqlserver.jdbc.SQLServerDriver")
//Step 2: Create the JDBC URL
val jdbcHostname = "localhost"
val jdbcPort = 1433
val jdbcDatabase ="mydatabase"
val jdbcUsername = "mylogin"
val jdbcPassword = "mypwd"
// Create the JDBC URL without passing in the user and password parameters.
val jdbcUrl = s"jdbc:sqlserver://${jdbcHostname}:${jdbcPort};database=${jdbcDatabase}"
// Create a Properties() object to hold the parameters.
import java.util.Properties
val connectionProperties = new Properties()
connectionProperties.put("user", s"${jdbcUsername}")
connectionProperties.put("password", s"${jdbcPassword}")
//Step 3: Check connectivity to the SQLServer database
val driverClass = "com.microsoft.sqlserver.jdbc.SQLServerDriver"
connectionProperties.setProperty("Driver", driverClass)
//Read data from JDBC
val textspark_table = spark.read.jdbc(jdbcUrl, "textspark", connectionProperties)
textspark_table.show()
//the read operation works perfectly!!
//Write data to JDBC
import org.apache.spark.sql.SaveMode
spark.sql("insert into textspark values('test') ")
.write
.mode(SaveMode.Append) // <--- Append to the existing table
.jdbc(jdbcUrl, "textspark", connectionProperties)
//the write operation generates error!!
Can anyone help me please to fix this error?
You don't use insert statement in Spark. You specified the append mode what is ok. You shouldn't insert data, you should select / create it. Try something like this:
spark.sql("select 'text'")
.write
.mode(SaveMode.Append)
.jdbc(jdbcUrl, "textspark", connectionProperties)
or
Seq("test").toDS
.write
.mode(SaveMode.Append)
.jdbc(jdbcUrl, "textspark", connectionProperties)
Related
I am quite a newbie to Spark and Scala ;)
Code summary :
Reading data from CSV files --> Creating A simple inner join on 2 Files --> Writing data to Hive table --> Submitting the job on the cluster
Can you please help to identify what went wrong.
The code is not really complex.
The job is executed well on cluster.
Therefore when I try to visualize data written on hive table I am facing issue.
hive> select * from Customers limit 10;
Failed with exception java.io.IOException:java.io.IOException: hdfs://m01.itversity.com:9000/user/itv000666/warehouse/updatedcustomers.db/customers/part-00000-348a54cf-aa0c-45b4-ac49-3a881ae39702_00000.c000 .csv not a SequenceFile
object LapeyreSparkDemo extends App {
//Getting spark ready
val sparkConf = new SparkConf()
sparkConf.set("spark.app.name","Spark for Lapeyre")
//Creating Spark Session
val spark = SparkSession.builder()
.config(sparkConf)
.enableHiveSupport()
.config("spark.sql.warehouse.dir","/user/itv000666/warehouse")
.getOrCreate()
Logger.getLogger(getClass.getName).info("Spark Session Created Successfully")
//Reading
Logger.getLogger(getClass.getName).info("Data loading in DF started")
val ordersSchema = "orderid Int, customerName String, orderDate String, custId Int, orderStatus
String, age String, amount Int"
val orders2019Df = spark.read
.format("csv")
.option("header",true)
.schema(ordersSchema)
.option("path","/user/itv0006666/lapeyrePoc/orders2019.csv")
.load
val newOrder = orders2019Df.withColumnRenamed("custId", "oldCustId")
.withColumnRenamed("customername","oldCustomerName")
val orders2020Df = spark.read
.format("csv")
.option("header",true)
.schema(ordersSchema)
.option("path","/user/itv000666/lapeyrePoc/orders2020.csv")
.load
Logger.getLogger(getClass.getName).info("Data loading in DF complete")
//processing
Logger.getLogger(getClass.getName).info("Processing Started")
val joinCondition = newOrder.col("oldCustId") === orders2020Df.col("custId")
val joinType = "inner"
val joinData = newOrder.join(orders2020Df, joinCondition, joinType)
.select("custId","customername")
//Writing
spark.sql("create database if not exists updatedCustomers")
joinData.write
.format("csv")
.mode(SaveMode.Overwrite)
.bucketBy(4, "custId")
.sortBy("custId")
.saveAsTable("updatedCustomers.Customers")
//Stopping Spark Session
spark.stop()
}
Please let me know in case more information required.
Thanks in advance.
This is the culprit
joinData.write
.format("csv")
Instead used this and it worked.
joinData.write
.format("Hive")
Since I am writing data to hive table (orc format), the format should be "Hive" and not csv.
Also, do not forget to enable hive support while creating spark session.
Also, In spark 2, bucketby & sortby is not supported. Maybe it does in Spark 3.
I'm using Azure's Databricks and want to pushdown a query to a Azure SQL using PySpark. I've tried many ways and found a solution using Scala (code below), but doing this I need to convert part of my code to scala then bring back to PySpark again.
%scala
import java.util.Properties
import java.sql.DriverManager
val jdbcUsername = username
val jdbcPassword = password
val driverClass = "com.microsoft.sqlserver.jdbc.SQLServerDriver"
// Create the JDBC URL without passing in the user and password parameters.
val jdbcUrl = "entire-string-connection-to-Azure-SQL"
// Create a Properties() object to hold the parameters.
val connectionProperties = new Properties()
connectionProperties.put("user", s"${jdbcUsername}")
connectionProperties.put("password", s"${jdbcPassword}")
connectionProperties.setProperty("Driver", driverClass)
val connection = DriverManager.getConnection(jdbcUrl, jdbcUsername, jdbcPassword)
val stmt = connection.createStatement()
val sql = "TRUNCATE TABLE dbo.table"
stmt.execute(sql)
connection.close()
Is there a way to achieve the pushdown of a DML code using PySpark instead of Scala language?
Found something related but only works to read data and DDL commands:
jdbcUrl = "jdbc:mysql://{0}:{1}/{2}".format(jdbcHostname, jdbcPort, jdbcDatabase)
connectionProperties = {
"user" : jdbcUsername,
"password" : jdbcPassword,
"driver" : "com.mysql.jdbc.Driver"
}
pushdown_query = "(select * from employees where emp_no < 10008) emp_alias"
df = spark.read.jdbc(url=jdbcUrl, table=pushdown_query, properties=connectionProperties)
You can actually achieve the same thing as the Scala example you provided in Python.
driver_manager = spark._sc._gateway.jvm.java.sql.DriverManager
connection = driver_manager.getConnection(jdbcUrl, jdbcUsername, jdbcPassword)
query = "YOUR SQL QUERY"
exec_statement = connection.prepareCall(query)
exec_statement.execute()
exec_statement.close()
connection.close()
For your case I would try
driver_manager = spark._sc._gateway.jvm.java.sql.DriverManager
connection = driver_manager.getConnection(jdbcUrl, jdbcUsername, jdbcPassword)
stmt = connection.createStatement()
sql = "TRUNCATE TABLE dbo.table"
stmt.execute(sql)
connection.close()
I have a table in Azure SQL database from which I want to either delete selected rows based on some criteria or entire table from Azure Databricks. Currently I am using the truncate property of JDBC to truncate the entire table without dropping it and then re-write it with new dataframe.
df.write \
.option('user', jdbcUsername) \
.option('password', jdbcPassword) \
.jdbc('<connection_string>', '<table_name>', mode = 'overwrite', properties = {'truncate' : 'true'} )
But going forward I don't want to truncate and overwrite the entire table every time but rather use delete command. I was not able to achieve this using pushdown query either. Any help on this would be greatly appreciated.
You can also drop down to scala to do this, as the SQL Server JDBC driver is already installed. EG:
%scala
import java.util.Properties
import java.sql.DriverManager
val jdbcUsername = "xxxxx"
val jdbcPassword = "xxxxxx"
val driverClass = "com.microsoft.sqlserver.jdbc.SQLServerDriver"
// Create the JDBC URL without passing in the user and password parameters.
val jdbcUrl = s"jdbc:sqlserver://xxxxxx.database.windows.net:1433;database=AdventureWorks;encrypt=true;trustServerCertificate=false;hostNameInCertificate=*.database.windows.net;loginTimeout=30;"
// Create a Properties() object to hold the parameters.
val connectionProperties = new Properties()
connectionProperties.put("user", s"${jdbcUsername}")
connectionProperties.put("password", s"${jdbcPassword}")
connectionProperties.setProperty("Driver", driverClass)
val connection = DriverManager.getConnection(jdbcUrl, jdbcUsername, jdbcPassword)
val stmt = connection.createStatement()
val sql = "delete from sometable where someColumn > 4"
stmt.execute(sql)
connection.close()
Use pyodbc to execute a SQL Statement.
import pyodbc
conn = pyodbc.connect( 'DRIVER={ODBC Driver 17 for SQL Server};'
'SERVER=mydatabe.database.azure.net;'
'DATABASE=AdventureWorks;UID=jonnyFast;'
'PWD=MyPassword')
conn.execute('DELETE TableBlah WHERE 1=2')
It's a bit of a pain to get pyodbc working on Databricks - see details here: https://datathirst.net/blog/2018/10/12/executing-sql-server-stored-procedures-on-databricks-pyspark
I stated using AWS Glue to read data using data catalog and GlueContext and transform as per requirement.
val spark: SparkContext = new SparkContext()
val glueContext: GlueContext = new GlueContext(spark)
val sparkSession = glueContext.getSparkSession
// Data Catalog: database and table name
val dbName = "abcdb"
val tblName = "xyzdt_2017_12_05"
// S3 location for output
val outputDir = "s3://output/directory/abc"
// Read data into a DynamicFrame using the Data Catalog metadata
val stGBDyf = glueContext.getCatalogSource(database = dbName, tableName = tblName).getDynamicFrame()
val revisedDF = stGBDyf.toDf() // This line getting error
While executing above code I got following error,
Error : Syntax Error: error: value toDf is not a member of
com.amazonaws.services.glue.DynamicFrame val revisedDF =
stGBDyf.toDf() one error found.
I followed this example to convert DynamicFrame to Spark dataFrame.
Please suggest what will be the best way to resolve this problem
There's a typo. It should work fine with capital F in toDF:
val revisedDF = stGBDyf.toDF()
I am trying to insert data into Hive table like this:
val partfile = sc.textFile("partfile")
val partdata = partfile.map(p => p.split(","))
val partSchema = StructType(Array(StructField("id",IntegerType,true),StructField("name",StringType,true),StructField("salary",IntegerType,true),StructField("dept",StringType,true),StructField("location",StringType,true)))
val partRDD = partdata.map(p => Row(p(0).toInt,p(1),p(2).toInt,p(3),p(4)))
val partDF = sqlContext.createDataFrame(partRDD, partSchema)
Packages I imported:
import org.apache.spark.sql.Row
import org.apache.spark.sql.Row
import org.apache.spark.sql.types.{StructType,StructField,StringType,IntegerType}
import org.apache.spark.sql.types._
This is how I tried to insert the dataframe into Hive partition:
partDF.write.mode(saveMode.Append).partitionBy("location").insertInto("parttab")
Im getting the below error even though I have the Hive Table:
org.apache.spark.sql.AnalysisException: Table not found: parttab;
Could anyone tell me what is the mistake I am doing here and how can I correct it ?
To write data to Hive warehouse, you need to initialize hiveContext instance.
Upon doing that, it will take confs from Hive-Site.xml (from classpath); and connects to underlying Hive warehouse.
HiveContext is an extension to SQLContext to support and connect to hive.
To do so, try this::
val hc = new HiveContext(sc)
And perform your append-query onn this instance.
partDF.registerAsTempTable("temp")
hc.sql(".... <normal sql query to pick data from table `temp`; and insert in to Hive table > ....")
Please make sure that the table parttab is under db - default.
If the table in under another db, table name should be specified as : <db-name>.parttab
If you need to directly save the dataframe in to hive; use this:
df.saveAsTable("<db-name>.parttab")