+1 Having an rc1 would help me get stable feedback on using my library with
Spark, compared to relying on 2.0.0-SNAPSHOT.
On Fri, 20 May 2016 at 05:57 Xiao Li <gatorsm...@gmail.com> wrote:
> Changed my vote to +1. Thanks!
>
> 2016-05-19 13:28 GMT-07:00 Xiao Li <gatorsm...@gmail.com>:
>
>> Will do. Thanks!
>>
>> 2016-05-19 13:26 GMT-07:00 Reynold Xin <r...@databricks.com>:
>>
>>> Xiao thanks for posting. Please file a bug in JIRA. Again as I said in
>>> the email this is not meant to be a functional release and will contain
>>> bugs.
>>>
>>> On Thu, May 19, 2016 at 1:20 PM, Xiao Li <gatorsm...@gmail.com> wrote:
>>>
>>>> -1
>>>>
>>>> Unable to use Hive meta-store in pyspark shell. Tried both HiveContext
>>>> and SparkSession. Both failed. It always uses in-memory catalog. Anybody
>>>> else hit the same issue?
>>>>
>>>>
>>>> Method 1: SparkSession
>>>>
>>>> >>> from pyspark.sql import SparkSession
>>>>
>>>> >>> spark = SparkSession.builder.enableHiveSupport().getOrCreate()
>>>>
>>>> >>>
>>>>
>>>> >>> spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
>>>>
>>>> DataFrame[]
>>>>
>>>> >>> spark.sql("LOAD DATA LOCAL INPATH
>>>> 'examples/src/main/resources/kv1.txt' INTO TABLE src")
>>>>
>>>> Traceback (most recent call last):
>>>>
>>>> File "<stdin>", line 1, in <module>
>>>>
>>>> File
>>>> "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/session.py",
>>>> line 494, in sql
>>>>
>>>> return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
>>>>
>>>> File
>>>> "/Users/xiaoli/IdeaProjects/sparkDelivery/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py",
>>>> line 933, in __call__
>>>>
>>>> File
>>>> "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/utils.py",
>>>> line 57, in deco
>>>>
>>>> return f(*a, **kw)
>>>>
>>>> File
>>>> "/Users/xiaoli/IdeaProjects/sparkDelivery/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py",
>>>> line 312, in get_return_value
>>>>
>>>> py4j.protocol.Py4JJavaError: An error occurred while calling o21.sql.
>>>>
>>>> : java.lang.UnsupportedOperationException: loadTable is not implemented
>>>>
>>>> at
>>>> org.apache.spark.sql.catalyst.catalog.InMemoryCatalog.loadTable(InMemoryCatalog.scala:297)
>>>>
>>>> at
>>>> org.apache.spark.sql.catalyst.catalog.SessionCatalog.loadTable(SessionCatalog.scala:280)
>>>>
>>>> at org.apache.spark.sql.execution.command.LoadData.run(tables.scala:263)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:57)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:55)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:69)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:115)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:115)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:136)
>>>>
>>>> at
>>>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:133)
>>>>
>>>> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:114)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:85)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:85)
>>>>
>>>> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:187)
>>>>
>>>> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:168)
>>>>
>>>> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:63)
>>>>
>>>> at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:541)
>>>>
>>>> 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:237)
>>>>
>>>> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
>>>>
>>>> at py4j.Gateway.invoke(Gateway.java:280)
>>>>
>>>> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)
>>>>
>>>> at py4j.commands.CallCommand.execute(CallCommand.java:79)
>>>>
>>>> at py4j.GatewayConnection.run(GatewayConnection.java:211)
>>>>
>>>> at java.lang.Thread.run(Thread.java:745)
>>>>
>>>>
>>>> Method 2: Using HiveContext:
>>>>
>>>> >>> from pyspark.sql import HiveContext
>>>>
>>>> >>> sqlContext = HiveContext(sc)
>>>>
>>>> >>> sqlContext.sql("CREATE TABLE IF NOT EXISTS src (key INT, value
>>>> STRING)")
>>>>
>>>> DataFrame[]
>>>>
>>>> >>> sqlContext.sql("LOAD DATA LOCAL INPATH
>>>> 'examples/src/main/resources/kv1.txt' INTO TABLE src")
>>>>
>>>> Traceback (most recent call last):
>>>>
>>>> File "<stdin>", line 1, in <module>
>>>>
>>>> File
>>>> "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/context.py",
>>>> line 346, in sql
>>>>
>>>> return self.sparkSession.sql(sqlQuery)
>>>>
>>>> File
>>>> "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/session.py",
>>>> line 494, in sql
>>>>
>>>> return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
>>>>
>>>> File
>>>> "/Users/xiaoli/IdeaProjects/sparkDelivery/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py",
>>>> line 933, in __call__
>>>>
>>>> File
>>>> "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/utils.py",
>>>> line 57, in deco
>>>>
>>>> return f(*a, **kw)
>>>>
>>>> File
>>>> "/Users/xiaoli/IdeaProjects/sparkDelivery/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py",
>>>> line 312, in get_return_value
>>>>
>>>> py4j.protocol.Py4JJavaError: An error occurred while calling o21.sql.
>>>>
>>>> : java.lang.UnsupportedOperationException: loadTable is not implemented
>>>>
>>>> at
>>>> org.apache.spark.sql.catalyst.catalog.InMemoryCatalog.loadTable(InMemoryCatalog.scala:297)
>>>>
>>>> at
>>>> org.apache.spark.sql.catalyst.catalog.SessionCatalog.loadTable(SessionCatalog.scala:280)
>>>>
>>>> at org.apache.spark.sql.execution.command.LoadData.run(tables.scala:263)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:57)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:55)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:69)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:115)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:115)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:136)
>>>>
>>>> at
>>>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:133)
>>>>
>>>> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:114)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:85)
>>>>
>>>> at
>>>> org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:85)
>>>>
>>>> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:187)
>>>>
>>>> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:168)
>>>>
>>>> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:63)
>>>>
>>>> at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:541)
>>>>
>>>> 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:237)
>>>>
>>>> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
>>>>
>>>> at py4j.Gateway.invoke(Gateway.java:280)
>>>>
>>>> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)
>>>>
>>>> at py4j.commands.CallCommand.execute(CallCommand.java:79)
>>>>
>>>> at py4j.GatewayConnection.run(GatewayConnection.java:211)
>>>>
>>>> at java.lang.Thread.run(Thread.java:745)
>>>>
>>>> 2016-05-19 12:49 GMT-07:00 Herman van Hövell tot Westerflier <
>>>> hvanhov...@questtec.nl>:
>>>>
>>>>> +1
>>>>>
>>>>>
>>>>> 2016-05-19 18:20 GMT+02:00 Xiangrui Meng <m...@databricks.com>:
>>>>>
>>>>>> +1
>>>>>>
>>>>>> On Thu, May 19, 2016 at 9:18 AM Joseph Bradley <jos...@databricks.com>
>>>>>> wrote:
>>>>>>
>>>>>>> +1
>>>>>>>
>>>>>>> On Wed, May 18, 2016 at 10:49 AM, Reynold Xin <r...@databricks.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Hi Ovidiu-Cristian ,
>>>>>>>>
>>>>>>>> The best source of truth is change the filter with target version
>>>>>>>> to 2.1.0. Not a lot of tickets have been targeted yet, but I'd imagine
>>>>>>>> as
>>>>>>>> we get closer to 2.0 release, more will be retargeted at 2.1.0.
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> On Wed, May 18, 2016 at 10:43 AM, Ovidiu-Cristian MARCU <
>>>>>>>> ovidiu-cristian.ma...@inria.fr> wrote:
>>>>>>>>
>>>>>>>>> Yes, I can filter..
>>>>>>>>> Did that and for example:
>>>>>>>>>
>>>>>>>>> https://issues.apache.org/jira/browse/SPARK-15370?jql=project%20%3D%20SPARK%20AND%20resolution%20%3D%20Unresolved%20AND%20affectedVersion%20%3D%202.0.0
>>>>>>>>> <https://issues.apache.org/jira/browse/SPARK-15370?jql=project%20=%20SPARK%20AND%20resolution%20=%20Unresolved%20AND%20affectedVersion%20=%202.0.0>
>>>>>>>>>
>>>>>>>>> To rephrase: for 2.0 do you have specific issues that are not a
>>>>>>>>> priority and will released maybe with 2.1 for example?
>>>>>>>>>
>>>>>>>>> Keep up the good work!
>>>>>>>>>
>>>>>>>>> On 18 May 2016, at 18:19, Reynold Xin <r...@databricks.com> wrote:
>>>>>>>>>
>>>>>>>>> You can find that by changing the filter to target version =
>>>>>>>>> 2.0.0. Cheers.
>>>>>>>>>
>>>>>>>>> On Wed, May 18, 2016 at 9:00 AM, Ovidiu-Cristian MARCU <
>>>>>>>>> ovidiu-cristian.ma...@inria.fr> wrote:
>>>>>>>>>
>>>>>>>>>> +1 Great, I see the list of resolved issues, do you have a list
>>>>>>>>>> of known issue you plan to stay with this release?
>>>>>>>>>>
>>>>>>>>>> with
>>>>>>>>>> build/mvn -Pyarn -Phadoop-2.6 -Dhadoop.version=2.7.1 -Phive
>>>>>>>>>> -Phive-thriftserver -DskipTests clean package
>>>>>>>>>>
>>>>>>>>>> mvn -version
>>>>>>>>>> Apache Maven 3.3.9 (bb52d8502b132ec0a5a3f4c09453c07478323dc5;
>>>>>>>>>> 2015-11-10T17:41:47+01:00)
>>>>>>>>>> Maven home: /Users/omarcu/tools/apache-maven-3.3.9
>>>>>>>>>> Java version: 1.7.0_80, vendor: Oracle Corporation
>>>>>>>>>> Java home:
>>>>>>>>>> /Library/Java/JavaVirtualMachines/jdk1.7.0_80.jdk/Contents/Home/jre
>>>>>>>>>> Default locale: en_US, platform encoding: UTF-8
>>>>>>>>>> OS name: "mac os x", version: "10.11.5", arch: "x86_64", family:
>>>>>>>>>> “mac"
>>>>>>>>>>
>>>>>>>>>> [INFO] Reactor Summary:
>>>>>>>>>> [INFO]
>>>>>>>>>> [INFO] Spark Project Parent POM ...........................
>>>>>>>>>> SUCCESS [ 2.635 s]
>>>>>>>>>> [INFO] Spark Project Tags .................................
>>>>>>>>>> SUCCESS [ 1.896 s]
>>>>>>>>>> [INFO] Spark Project Sketch ...............................
>>>>>>>>>> SUCCESS [ 2.560 s]
>>>>>>>>>> [INFO] Spark Project Networking ...........................
>>>>>>>>>> SUCCESS [ 6.533 s]
>>>>>>>>>> [INFO] Spark Project Shuffle Streaming Service ............
>>>>>>>>>> SUCCESS [ 4.176 s]
>>>>>>>>>> [INFO] Spark Project Unsafe ...............................
>>>>>>>>>> SUCCESS [ 4.809 s]
>>>>>>>>>> [INFO] Spark Project Launcher .............................
>>>>>>>>>> SUCCESS [ 6.242 s]
>>>>>>>>>> [INFO] Spark Project Core .................................
>>>>>>>>>> SUCCESS [01:20 min]
>>>>>>>>>> [INFO] Spark Project GraphX ...............................
>>>>>>>>>> SUCCESS [ 9.148 s]
>>>>>>>>>> [INFO] Spark Project Streaming ............................
>>>>>>>>>> SUCCESS [ 22.760 s]
>>>>>>>>>> [INFO] Spark Project Catalyst .............................
>>>>>>>>>> SUCCESS [ 50.783 s]
>>>>>>>>>> [INFO] Spark Project SQL ..................................
>>>>>>>>>> SUCCESS [01:05 min]
>>>>>>>>>> [INFO] Spark Project ML Local Library .....................
>>>>>>>>>> SUCCESS [ 4.281 s]
>>>>>>>>>> [INFO] Spark Project ML Library ...........................
>>>>>>>>>> SUCCESS [ 54.537 s]
>>>>>>>>>> [INFO] Spark Project Tools ................................
>>>>>>>>>> SUCCESS [ 0.747 s]
>>>>>>>>>> [INFO] Spark Project Hive .................................
>>>>>>>>>> SUCCESS [ 33.032 s]
>>>>>>>>>> [INFO] Spark Project HiveContext Compatibility ............
>>>>>>>>>> SUCCESS [ 3.198 s]
>>>>>>>>>> [INFO] Spark Project REPL .................................
>>>>>>>>>> SUCCESS [ 3.573 s]
>>>>>>>>>> [INFO] Spark Project YARN Shuffle Service .................
>>>>>>>>>> SUCCESS [ 4.617 s]
>>>>>>>>>> [INFO] Spark Project YARN .................................
>>>>>>>>>> SUCCESS [ 7.321 s]
>>>>>>>>>> [INFO] Spark Project Hive Thrift Server ...................
>>>>>>>>>> SUCCESS [ 16.496 s]
>>>>>>>>>> [INFO] Spark Project Assembly .............................
>>>>>>>>>> SUCCESS [ 2.300 s]
>>>>>>>>>> [INFO] Spark Project External Flume Sink ..................
>>>>>>>>>> SUCCESS [ 4.219 s]
>>>>>>>>>> [INFO] Spark Project External Flume .......................
>>>>>>>>>> SUCCESS [ 6.987 s]
>>>>>>>>>> [INFO] Spark Project External Flume Assembly ..............
>>>>>>>>>> SUCCESS [ 1.465 s]
>>>>>>>>>> [INFO] Spark Integration for Kafka 0.8 ....................
>>>>>>>>>> SUCCESS [ 6.891 s]
>>>>>>>>>> [INFO] Spark Project Examples .............................
>>>>>>>>>> SUCCESS [ 13.465 s]
>>>>>>>>>> [INFO] Spark Project External Kafka Assembly ..............
>>>>>>>>>> SUCCESS [ 2.815 s]
>>>>>>>>>> [INFO]
>>>>>>>>>> ------------------------------------------------------------------------
>>>>>>>>>> [INFO] BUILD SUCCESS
>>>>>>>>>> [INFO]
>>>>>>>>>> ------------------------------------------------------------------------
>>>>>>>>>> [INFO] Total time: 07:04 min
>>>>>>>>>> [INFO] Finished at: 2016-05-18T17:55:33+02:00
>>>>>>>>>> [INFO] Final Memory: 90M/824M
>>>>>>>>>> [INFO]
>>>>>>>>>> ------------------------------------------------------------------------
>>>>>>>>>>
>>>>>>>>>> On 18 May 2016, at 16:28, Sean Owen <so...@cloudera.com> wrote:
>>>>>>>>>>
>>>>>>>>>> I think it's a good idea. Although releases have been preceded
>>>>>>>>>> before
>>>>>>>>>> by release candidates for developers, it would be good to get a
>>>>>>>>>> formal
>>>>>>>>>> preview/beta release ratified for public consumption ahead of a
>>>>>>>>>> new
>>>>>>>>>> major release. Better to have a little more testing in the wild to
>>>>>>>>>> identify problems before 2.0.0 is finalized.
>>>>>>>>>>
>>>>>>>>>> +1 to the release. License, sigs, etc check out. On Ubuntu 16 +
>>>>>>>>>> Java
>>>>>>>>>> 8, compilation and tests succeed for "-Pyarn -Phive
>>>>>>>>>> -Phive-thriftserver -Phadoop-2.6".
>>>>>>>>>>
>>>>>>>>>> On Wed, May 18, 2016 at 6:40 AM, Reynold Xin <r...@apache.org>
>>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>> Hi,
>>>>>>>>>>
>>>>>>>>>> In the past the Apache Spark community have created preview
>>>>>>>>>> packages (not
>>>>>>>>>> official releases) and used those as opportunities to ask
>>>>>>>>>> community members
>>>>>>>>>> to test the upcoming versions of Apache Spark. Several people in
>>>>>>>>>> the Apache
>>>>>>>>>> community have suggested we conduct votes for these preview
>>>>>>>>>> packages and
>>>>>>>>>> turn them into formal releases by the Apache foundation's
>>>>>>>>>> standard. Preview
>>>>>>>>>> releases are not meant to be functional, i.e. they can and highly
>>>>>>>>>> likely
>>>>>>>>>> will contain critical bugs or documentation errors, but we will
>>>>>>>>>> be able to
>>>>>>>>>> post them to the project's website to get wider feedback. They
>>>>>>>>>> should
>>>>>>>>>> satisfy the legal requirements of Apache's release policy
>>>>>>>>>> (http://www.apache.org/dev/release.html) such as having proper
>>>>>>>>>> licenses.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> Please vote on releasing the following candidate as Apache Spark
>>>>>>>>>> version
>>>>>>>>>> 2.0.0-preview. The vote is open until Friday, May 20, 2015 at
>>>>>>>>>> 11:00 PM PDT
>>>>>>>>>> and passes if a majority of at least 3 +1 PMC votes are cast.
>>>>>>>>>>
>>>>>>>>>> [ ] +1 Release this package as Apache Spark 2.0.0-preview
>>>>>>>>>> [ ] -1 Do not release this package because ...
>>>>>>>>>>
>>>>>>>>>> To learn more about Apache Spark, please see
>>>>>>>>>> http://spark.apache.org/
>>>>>>>>>>
>>>>>>>>>> The tag to be voted on is 2.0.0-preview
>>>>>>>>>> (8f5a04b6299e3a47aca13cbb40e72344c0114860)
>>>>>>>>>>
>>>>>>>>>> The release files, including signatures, digests, etc. can be
>>>>>>>>>> found at:
>>>>>>>>>>
>>>>>>>>>> http://home.apache.org/~pwendell/spark-releases/spark-2.0.0-preview-bin/
>>>>>>>>>>
>>>>>>>>>> Release artifacts are signed with the following key:
>>>>>>>>>> https://people.apache.org/keys/committer/pwendell.asc
>>>>>>>>>>
>>>>>>>>>> The documentation corresponding to this release can be found at:
>>>>>>>>>>
>>>>>>>>>> http://home.apache.org/~pwendell/spark-releases/spark-2.0.0-preview-docs/
>>>>>>>>>>
>>>>>>>>>> The list of resolved issues are:
>>>>>>>>>>
>>>>>>>>>> https://issues.apache.org/jira/browse/SPARK-15351?jql=project%20%3D%20SPARK%20AND%20fixVersion%20%3D%202.0.0
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> If you are a Spark user, you can help us test this release by
>>>>>>>>>> taking an
>>>>>>>>>> existing Apache Spark workload and running on this candidate,
>>>>>>>>>> then reporting
>>>>>>>>>> any regressions.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> ---------------------------------------------------------------------
>>>>>>>>>> To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org
>>>>>>>>>> For additional commands, e-mail: dev-h...@spark.apache.org
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>
>>>>
>>>
>>
>
