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
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>
>>
>
