DataFrameWrite.jdbc() does not work?
From: Felix Cheung [mailto:felixcheun...@hotmail.com]
Sent: Sunday, February 7, 2016 9:54 AM
To: Andrew Holway <andrew.hol...@otternetworks.de>; dev@spark.apache.org
Subject: Re: Fwd: Writing to jdbc database from SparkR (1.5.2)
Unfortunately I couldn't find a simple workaround. It seems to be an issue with
DataFrameWriter.save() that does not work with jdbc source/format
For instance, this does not work in Scala either
df1.write.format("jdbc").mode("overwrite").option("url",
"jdbc:mysql://something.rds.amazonaws.com<http://something.rds.amazonaws.com>:3306?user=user&password=password").option("dbtable",
"table").save()
For Spark 1.5.x, it seems the best option would be to write a JVM wrapper and
call it from R.
_____________________________
From: Andrew Holway
<andrew.hol...@otternetworks.de<mailto:andrew.hol...@otternetworks.de>>
Sent: Saturday, February 6, 2016 11:22 AM
Subject: Fwd: Writing to jdbc database from SparkR (1.5.2)
To: <dev@spark.apache.org<mailto:dev@spark.apache.org>>
Hi,
I have a thread on u...@spark.apache.org<mailto:u...@spark.apache.org> but I
think this might require developer attention.
I'm reading data from a database: This is working well.
> df <- read.df(sqlContext, source="jdbc",
> url="jdbc:mysql://database.foo.eu-west-1.rds.amazonaws.com:3306?user=user&password=pass<http://database.foo.eu-west-1.rds.amazonaws.com:3306/?user=user&password=pass>")
When I try and write something back to the DB I see this following error:
> write.df(fooframe, path="NULL", source="jdbc",
> url="jdbc:mysql://database.foo.eu-west-1.rds.amazonaws.com:3306?user=user&password=pass<http://database.foo.eu-west-1.rds.amazonaws.com:3306?user=user&password=pass>",
> dbtable="db.table", mode="append")
16/02/06 19:05:43 ERROR RBackendHandler: save on 2 failed
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
java.lang.RuntimeException:
org.apache.spark.sql.execution.datasources.jdbc.DefaultSource does not allow
create table as select.
at scala.sys.package$.error(package.scala:27)
at
org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:200)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:146)
at org.apache.spark.sql.DataFrame.save(DataFrame.scala:1855)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:497)
at
org.apache.spark.api.r.RBackendHandler.handleMethodCall(RBackendHandler.scala:132)
at org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:79)
at org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:38)
at io.netty.channel.SimpleChannelIn
Any ideas on a workaround?
Thanks,
Andrew
