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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-17689) _temporary files breaks the Spark SQL streaming job.
Date Wed, 15 Feb 2017 21:00:44 GMT

    [ https://issues.apache.org/jira/browse/SPARK-17689?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15868552#comment-15868552
] 

Sean Owen commented on SPARK-17689:
-----------------------------------

This is created by for example HDFS copy jobs to hold the files before they are fully written.
It exists transiently and could stick around if something failed.

> _temporary files breaks the Spark SQL streaming job.
> ----------------------------------------------------
>
>                 Key: SPARK-17689
>                 URL: https://issues.apache.org/jira/browse/SPARK-17689
>             Project: Spark
>          Issue Type: Bug
>          Components: Structured Streaming
>            Reporter: Prashant Sharma
>
> Steps to reproduce:
> 1) Start a streaming job which reads from HDFS location hdfs://xyz/*
> 2) Write content to hdfs://xyz/a
> .
> .
> repeat a few times.
> And then job breaks as follows.
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 49 in stage 304.0
failed 1 times, most recent failure: Lost task 49.0 in stage 304.0 (TID 14794, localhost):
java.io.FileNotFoundException: File does not exist: hdfs://localhost:9000/input/t5/_temporary
> 	at org.apache.hadoop.hdfs.DistributedFileSystem$22.doCall(DistributedFileSystem.java:1309)
> 	at org.apache.hadoop.hdfs.DistributedFileSystem$22.doCall(DistributedFileSystem.java:1301)
> 	at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)
> 	at org.apache.hadoop.hdfs.DistributedFileSystem.getFileStatus(DistributedFileSystem.java:1317)
> 	at org.apache.spark.sql.execution.datasources.HadoopFsRelation$$anonfun$7$$anonfun$apply$4.apply(fileSourceInterfaces.scala:464)
> 	at org.apache.spark.sql.execution.datasources.HadoopFsRelation$$anonfun$7$$anonfun$apply$4.apply(fileSourceInterfaces.scala:462)
> 	at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
> 	at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
> 	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> 	at scala.collection.Iterator$class.foreach(Iterator.scala:893)
> 	at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
> 	at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
> 	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
> 	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
> 	at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
> 	at scala.collection.AbstractIterator.to(Iterator.scala:1336)
> 	at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
> 	at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
> 	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
> 	at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
> 	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:912)
> 	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:912)
> 	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1919)
> 	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1919)
> 	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
> 	at org.apache.spark.scheduler.Task.run(Task.scala:86)
> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
> 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> 	at java.lang.Thread.run(Thread.java:745)



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