-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 >>>>>> >>>>>> >>>>>> >>>>> >>>>> >>>> >