[jira] [Updated] (SPARK-3231) select on a table in parquet format containing smallint as a field type does not work

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-3231?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen updated SPARK-3231:
-----------------------------
    Component/s:     (was: Spark Core)
                 SQL

> 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
>
> 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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