Looks to me like it is a conflict between a Databricks library and Spark
2.1. That's an issue for Databricks to resolve or provide guidance.
On Tue, May 9, 2017 at 2:36 PM, lucas.g...@gmail.com <lucas.g...@gmail.com>
wrote:
> I'm a bit confused by that answer, I'm assuming it's spark deciding which
> lib to use.
>
> On 9 May 2017 at 14:30, Mark Hamstra <m...@clearstorydata.com> wrote:
>
>> 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$.error(package.scala:27)
>>>> at org.apache.spark.sql.execution.datasources.DataSource$.looku
>>>> pDataSource(DataSource.scala:591)
>>>> at org.apache.spark.sql.execution.datasources.DataSource.provid
>>>> ingClass$lzycompute(DataSource.scala:86)
>>>> at org.apache.spark.sql.execution.datasources.DataSource.provid
>>>> ingClass(DataSource.scala:86)
>>>> at org.apache.spark.sql.execution.datasources.DataSource.resolv
>>>> eRelation(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(NativeMethodAcce
>>>> ssorImpl.java:57)
>>>> at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMe
>>>> thodAccessorImpl.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!
>>>
>>
>>
>
