@Xiao,
It is tracked in SPARK-15345
<https://issues.apache.org/jira/browse/SPARK-15345>
On Fri, May 20, 2016 at 4:20 AM, 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
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>
>
--
Best Regards
Jeff Zhang
