This is isn't yet a release candidate since, as Reynold mention in his
opening post, preview releases are "not meant to be functional, i.e. they
can and highly likely will contain critical bugs or documentation errors."
Once we're at the point where we expect there not to be such bugs and
errors, then the release candidates will start.
On Fri, May 20, 2016 at 4:40 AM, Ross Lawley <ross.law...@gmail.com> wrote:
> +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
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
