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Posted to issues@spark.apache.org by "Reynold Xin (JIRA)" <ji...@apache.org> on 2016/11/21 19:12:59 UTC
[jira] [Resolved] (SPARK-17765)
org.apache.spark.mllib.linalg.VectorUDT cannot be cast to
org.apache.spark.sql.types.StructType
[ https://issues.apache.org/jira/browse/SPARK-17765?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Reynold Xin resolved SPARK-17765.
---------------------------------
Resolution: Fixed
Assignee: Hyukjin Kwon
Fix Version/s: 2.1.0
> org.apache.spark.mllib.linalg.VectorUDT cannot be cast to org.apache.spark.sql.types.StructType
> -----------------------------------------------------------------------------------------------
>
> Key: SPARK-17765
> URL: https://issues.apache.org/jira/browse/SPARK-17765
> Project: Spark
> Issue Type: Bug
> Components: MLlib, PySpark, SQL
> Affects Versions: 1.6.1, 2.0.0
> Reporter: Alexander Shorin
> Assignee: Hyukjin Kwon
> Fix For: 2.1.0
>
>
> The issue in subject happens on attempt to transform DataFrame in Parquet format into ORC while DF contains SparseVector/DenseVector data.
> In [sources|https://github.com/apache/spark/blob/v1.6.1/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala#L192] it looks like that there shouldn't be any serialization issues, but they happens.
> {code}
> In[4] pqtdf = hqlctx.read.parquet(pqt_feature)
> In[5] pqtdf.take(1)
> Out[5]: [Row(foo=u'abc, bar=SparseVector(100, {74: 1.0}))]
> In[6]: pqtdf.write.format('orc').save('/tmp/orc')
> ---------------------------------------------------------------------------
> Py4JJavaError Traceback (most recent call last)
> <ipython-input-5-57e68fd0c5cb> in <module>()
> ----> pqtdf.write.format('orc').save('/tmp/orc')
> /usr/local/share/spark/python/pyspark/sql/readwriter.pyc in save(self, path, format, mode, partitionBy, **options)
> 395 self._jwrite.save()
> 396 else:
> --> 397 self._jwrite.save(path)
> 398
> 399 @since(1.4)
> /usr/local/lib/python2.7/site-packages/py4j/java_gateway.pyc in __call__(self, *args)
> 811 answer = self.gateway_client.send_command(command)
> 812 return_value = get_return_value(
> --> 813 answer, self.gateway_client, self.target_id, self.name)
> 814
> 815 for temp_arg in temp_args:
> /usr/local/share/spark/python/pyspark/sql/utils.pyc in deco(*a, **kw)
> 43 def deco(*a, **kw):
> 44 try:
> ---> 45 return f(*a, **kw)
> 46 except py4j.protocol.Py4JJavaError as e:
> 47 s = e.java_exception.toString()
> /usr/local/lib/python2.7/site-packages/py4j/protocol.pyc in get_return_value(answer, gateway_client, target_id, name)
> 306 raise Py4JJavaError(
> 307 "An error occurred while calling {0}{1}{2}.\n".
> --> 308 format(target_id, ".", name), value)
> 309 else:
> 310 raise Py4JError(
> Py4JJavaError: An error occurred while calling o62.save.
> : org.apache.spark.SparkException: Job aborted.
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply$mcV$sp(InsertIntoHadoopFsRelation.scala:156)
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply(InsertIntoHadoopFsRelation.scala:108)
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply(InsertIntoHadoopFsRelation.scala:108)
> at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(InsertIntoHadoopFsRelation.scala:108)
> at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:58)
> at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:56)
> at org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:70)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
> at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
> at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
> at org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:256)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:148)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:139)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
> at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:606)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
> at py4j.Gateway.invoke(Gateway.java:259)
> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.GatewayConnection.run(GatewayConnection.java:209)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 3.0 failed 4 times, most recent failure: Lost task 0.3 in stage 3.0 (TID 185, node123.example.com): org.apache.spark.SparkException: Task failed while writing rows.
> at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:272)
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: java.lang.ClassCastException: org.apache.spark.mllib.linalg.VectorUDT cannot be cast to org.apache.spark.sql.types.StructType
> at org.apache.spark.sql.hive.HiveInspectors$class.wrap(HiveInspectors.scala:554)
> at org.apache.spark.sql.hive.orc.OrcOutputWriter.wrap(OrcRelation.scala:66)
> at org.apache.spark.sql.hive.orc.OrcOutputWriter.wrapOrcStruct(OrcRelation.scala:128)
> at org.apache.spark.sql.hive.orc.OrcOutputWriter.writeInternal(OrcRelation.scala:139)
> at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:264)
> ... 8 more
> Driver stacktrace:
> at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
> at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
> at scala.Option.foreach(Option.scala:236)
> at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1922)
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply$mcV$sp(InsertIntoHadoopFsRelation.scala:150)
> ... 27 more
> Caused by: org.apache.spark.SparkException: Task failed while writing rows.
> at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:272)
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> ... 1 more
> Caused by: java.lang.ClassCastException: org.apache.spark.mllib.linalg.VectorUDT cannot be cast to org.apache.spark.sql.types.StructType
> at org.apache.spark.sql.hive.HiveInspectors$class.wrap(HiveInspectors.scala:554)
> at org.apache.spark.sql.hive.orc.OrcOutputWriter.wrap(OrcRelation.scala:66)
> at org.apache.spark.sql.hive.orc.OrcOutputWriter.wrapOrcStruct(OrcRelation.scala:128)
> at org.apache.spark.sql.hive.orc.OrcOutputWriter.writeInternal(OrcRelation.scala:139)
> at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:264)
> ... 8 more
> {code}
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