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https://issues.apache.org/jira/browse/SPARK-3231?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen updated SPARK-3231:
-----------------------------
Assignee: Alex Rovner
> select on a table in parquet format containing smallint as a field type does
> not work
> -------------------------------------------------------------------------------------
>
> Key: SPARK-3231
> URL: https://issues.apache.org/jira/browse/SPARK-3231
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.1.0
> Environment: The table is created through Hive-0.13.
> SparkSql 1.1 is used.
> Reporter: chirag aggarwal
> Assignee: Alex Rovner
> Fix For: 1.5.0
>
>
> A table is created through hive. This table has a field of type smallint. The
> format of the table is parquet.
> select on this table works perfectly on hive shell.
> But, when the select is run on this table from spark-sql, then the query
> fails.
> Steps to reproduce the issue:
> --------------------------------------
> hive> create table abct (a smallint, b int) row format delimited fields
> terminated by '|' stored as textfile;
> A text file is stored in hdfs for this table.
> hive> create table abc (a smallint, b int) stored as parquet;
> hive> insert overwrite table abc select * from abct;
> hive> select * from abc;
> 2 1
> 2 2
> 2 3
> spark-sql> select * from abc;
> 10:08:46 ERROR CliDriver: org.apache.spark.SparkException: Job aborted due to
> stage failure: Task 0.0 in stage 33.0 (TID 2340) had a not serializable
> result: org.apache.hadoop.io.IntWritable
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1158)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1147)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1146)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1146)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:685)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:685)
> at scala.Option.foreach(Option.scala:236)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:685)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1364)
> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
> at akka.actor.ActorCell.invoke(ActorCell.scala:456)
> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
> at akka.dispatch.Mailbox.run(Mailbox.scala:219)
> at
> akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
> at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
> at
> scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
> at
> scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
> at
> scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
> But, if the type of this table is now changed to int, then spark-sql gives
> the correct results.
> hive> alter table abc change a a int;
> spark-sql> select * from abc;
> 2 1
> 2 2
> 2 3
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