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https://issues.apache.org/jira/browse/SPARK-4835?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14260654#comment-14260654
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Apache Spark commented on SPARK-4835:
-------------------------------------
User 'JoshRosen' has created a pull request for this issue:
https://github.com/apache/spark/pull/3832
> Streaming saveAs*HadoopFiles() methods may throw FileAlreadyExistsException
> during checkpoint recovery
> ------------------------------------------------------------------------------------------------------
>
> Key: SPARK-4835
> URL: https://issues.apache.org/jira/browse/SPARK-4835
> Project: Spark
> Issue Type: Bug
> Components: Streaming
> Affects Versions: 1.3.0
> Reporter: Josh Rosen
> Assignee: Tathagata Das
> Priority: Critical
>
> While running (a slightly modified version of) the "recovery with
> saveAsHadoopFiles operation" test in the streaming CheckpointSuite, I noticed
> the following error message in the streaming driver log:
> {code}
> 14/12/12 17:42:50.687 pool-1-thread-1-ScalaTest-running-CheckpointSuite INFO
> JobScheduler: Added jobs for time 1500 ms
> 14/12/12 17:42:50.687 pool-1-thread-1-ScalaTest-running-CheckpointSuite INFO
> RecurringTimer: Started timer for JobGenerator at time 2000
> 14/12/12 17:42:50.688 sparkDriver-akka.actor.default-dispatcher-3 INFO
> JobScheduler: Starting job streaming job 1500 ms.0 from job set of time 1500
> ms
> 14/12/12 17:42:50.688 pool-1-thread-1-ScalaTest-running-CheckpointSuite INFO
> JobGenerator: Restarted JobGenerator at 2000 ms
> 14/12/12 17:42:50.688 pool-1-thread-1-ScalaTest-running-CheckpointSuite INFO
> JobScheduler: Started JobScheduler
> 14/12/12 17:42:50.689 sparkDriver-akka.actor.default-dispatcher-3 INFO
> JobScheduler: Starting job streaming job 1500 ms.1 from job set of time 1500
> ms
> 14/12/12 17:42:50.689 sparkDriver-akka.actor.default-dispatcher-3 ERROR
> JobScheduler: Error running job streaming job 1500 ms.0
> org.apache.hadoop.mapred.FileAlreadyExistsException: Output directory
> file:/var/folders/0k/2qp2p2vs7bv033vljnb8nk1c0000gn/T/1418434967213-0/-1500.result
> already exists
> at
> org.apache.hadoop.mapred.FileOutputFormat.checkOutputSpecs(FileOutputFormat.java:121)
> at
> org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopDataset(PairRDDFunctions.scala:1045)
> at
> org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopFile(PairRDDFunctions.scala:944)
> at
> org.apache.spark.streaming.dstream.PairDStreamFunctions$$anonfun$9.apply(PairDStreamFunctions.scala:677)
> at
> org.apache.spark.streaming.dstream.PairDStreamFunctions$$anonfun$9.apply(PairDStreamFunctions.scala:675)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:42)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:40)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:40)
> at scala.util.Try$.apply(Try.scala:161)
> at org.apache.spark.streaming.scheduler.Job.run(Job.scala:32)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:171)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> 14/12/12 17:42:50.691 pool-12-thread-1 INFO SparkContext: Starting job: apply
> at Transformer.scala:22
> {code}
> Spark Streaming's {{saveAsHadoopFiles}} method calls Spark's
> {{rdd.saveAsHadoopFile}} method. The Spark method, in turn, called
> {{PairRDDFunctions.saveAsHadoopDataset()}}, which has error-checking to
> ensure that the output directory does not already exist:
> {code}
> if (self.conf.getBoolean("spark.hadoop.validateOutputSpecs", true)) {
> // FileOutputFormat ignores the filesystem parameter
> val ignoredFs = FileSystem.get(hadoopConf)
> hadoopConf.getOutputFormat.checkOutputSpecs(ignoredFs, hadoopConf)
> }
> {code}
> If Spark Streaming recovers from a checkpoint and re-runs the last batch in
> the checkpoint, then {{saveAsHadoopDataset}} will have been called twice with
> the same output path. If the output path exists from the first, pre-recovery
> run, then the recovery will fail.
> This seems like it could be a pretty serious issue: imagine that a streaming
> job fails partway through a save() operation, then recovers: in this case,
> the existing directory will prevent us from ever recovering and finishing the
> save().
> Fortunately, this should be simple to fix: we should disable the existing
> directory checks for output operations called by streaming jobs.
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