This looks more like a matter for Databricks support than spark-user.
On Tue, May 9, 2017 at 2:02 PM, lucas.g...@gmail.com <lucas.g...@gmail.com>
wrote:
> df = spark.sqlContext.read.csv('out/df_in.csv')
>>
>
>
>> 17/05/09 15:51:29 WARN ObjectStore: Version information not found in
>> metastore. hive.metastore.schema.verification is not enabled so
>> recording the schema version 1.2.0
>> 17/05/09 15:51:29 WARN ObjectStore: Failed to get database default,
>> returning NoSuchObjectException
>> 17/05/09 15:51:30 WARN ObjectStore: Failed to get database global_temp,
>> returning NoSuchObjectException
>>
>
>
>> Py4JJavaError: An error occurred while calling o72.csv.
>> : java.lang.RuntimeException: Multiple sources found for csv
>> (*com.databricks.spark.csv.DefaultSource15,
>> org.apache.spark.sql.execution.datasources.csv.CSVFileFormat*), please
>> specify the fully qualified class name.
>> at scala.sys.package$.error(package.scala:27)
>> at org.apache.spark.sql.execution.datasources.
>> DataSource$.lookupDataSource(DataSource.scala:591)
>> at org.apache.spark.sql.execution.datasources.DataSource.providingClass$
>> lzycompute(DataSource.scala:86)
>> at org.apache.spark.sql.execution.datasources.DataSource.providingClass(
>> DataSource.scala:86)
>> at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(
>> DataSource.scala:325)
>> at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:152)
>> at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:415)
>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>> at sun.reflect.NativeMethodAccessorImpl.invoke(
>> NativeMethodAccessorImpl.java:57)
>> at sun.reflect.DelegatingMethodAccessorImpl.invoke(
>> DelegatingMethodAccessorImpl.java:43)
>> at java.lang.reflect.Method.invoke(Method.java:606)
>> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
>> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
>> at py4j.Gateway.invoke(Gateway.java:280)
>> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
>> at py4j.commands.CallCommand.execute(CallCommand.java:79)
>> at py4j.GatewayConnection.run(GatewayConnection.java:214) at
>> java.lang.Thread.run(Thread.java:745)
>
>
> When I change our call to:
>
> df = spark.hiveContext.read \
> .format('org.apache.spark.sql.execution.datasources.csv.CSVFileFormat')
> \
> .load('df_in.csv)
>
> No such issue, I was under the impression (obviously wrongly) that spark
> would automatically pick the local lib. We have the databricks library
> because other jobs still explicitly call it.
>
> Is the 'correct answer' to go through and modify so as to remove the
> databricks lib / remove it from our deploy? Or should this just work?
>
> One of the things I find less helpful in the spark docs are when there's
> multiple ways to do it but no clear guidance on what those methods are
> intended to accomplish.
>
> Thanks!
>
