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From "Jason Lowe (Updated) (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (MAPREDUCE-3790) Broken pipe on streaming job can lead to truncated output for a successful job
Date Thu, 09 Feb 2012 21:34:57 GMT

     [ https://issues.apache.org/jira/browse/MAPREDUCE-3790?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel

Jason Lowe updated MAPREDUCE-3790:

         Component/s: mrv2
    Target Version/s: 0.23.1, 0.24.0  (was: 0.24.0, 0.23.1)
> Broken pipe on streaming job can lead to truncated output for a successful job
> ------------------------------------------------------------------------------
>                 Key: MAPREDUCE-3790
>                 URL: https://issues.apache.org/jira/browse/MAPREDUCE-3790
>             Project: Hadoop Map/Reduce
>          Issue Type: Bug
>          Components: contrib/streaming, mrv2
>    Affects Versions: 0.23.1, 0.24.0
>            Reporter: Jason Lowe
>            Assignee: Jason Lowe
>         Attachments: MAPREDUCE-3790.patch
> If a streaming job doesn't consume all of its input then the job can be marked successful
even though the job's output is truncated.
> Here's a simple setup that can exhibit the problem.  Note that the job output will most
likely be truncated compared to the same job run with a zero-length input file.
> {code}
> $ hdfs dfs -cat in
> foo
> $ yarn jar ./share/hadoop/tools/lib/hadoop-streaming-0.24.0-SNAPSHOT.jar -Dmapred.map.tasks=1
-Dmapred.reduce.tasks=1 -mapper /bin/env -reducer NONE -input in -output out
> {code}
> Examining the map task log shows this:
> {code:title=Excerpt from map task stdout log}
> 2012-02-02 11:27:25,054 WARN [main] org.apache.hadoop.streaming.PipeMapRed: java.io.IOException:
Broken pipe
> 2012-02-02 11:27:25,054 INFO [main] org.apache.hadoop.streaming.PipeMapRed: mapRedFinished
> 2012-02-02 11:27:25,056 WARN [Thread-12] org.apache.hadoop.streaming.PipeMapRed: java.io.IOException:
Bad file descriptor
> 2012-02-02 11:27:25,124 INFO [main] org.apache.hadoop.mapred.Task: Task:attempt_1328203555769_0001_m_000000_0
is done. And is in the process of commiting
> 2012-02-02 11:27:25,127 WARN [Thread-11] org.apache.hadoop.streaming.PipeMapRed: java.io.IOException:
DFSOutputStream is closed
> 2012-02-02 11:27:25,199 INFO [main] org.apache.hadoop.mapred.Task: Task attempt_1328203555769_0001_m_000000_0
is allowed to commit now
> 2012-02-02 11:27:25,225 INFO [main] org.apache.hadoop.mapred.FileOutputCommitter: Saved
output of task 'attempt_1328203555769_0001_m_000000_0' to hdfs://localhost:9000/user/somebody/out/_temporary/1
> 2012-02-02 11:27:27,834 INFO [main] org.apache.hadoop.mapred.Task: Task 'attempt_1328203555769_0001_m_000000_0'
> {code}
> In PipeMapRed.mapRedFinished() we can see it will eat IOExceptions and return without
waiting for the output threads or throwing a runtime exception to fail the job.  Net result
is that the DFS streams could be shutdown too early if the output threads are still busy and
we could lose job output.
> Fixing this brings up the bigger question of what *should* happen when a streaming job
doesn't consume all of its input.  Should we have grabbed all of the output from the job and
still marked it successful or should we have failed the job?  If the former then we need to
fix some other places in the code as well, since feeding a much larger input file (e.g.: 600K)
to the same sample streaming job results in the job failing with the exception below.  It
wouldn't be consistent to fail the job that doesn't consume a lot of input but pass the job
that leaves just a few leftovers.
> {code}
> 2012-02-02 10:29:37,220 INFO  mapreduce.Job (Job.java:monitorAndPrintJob(1270)) - Running
job: job_1328200108174_0001
> 2012-02-02 10:29:44,354 INFO  mapreduce.Job (Job.java:monitorAndPrintJob(1291)) - Job
job_1328200108174_0001 running in uber mode : false
> 2012-02-02 10:29:44,355 INFO  mapreduce.Job (Job.java:monitorAndPrintJob(1298)) -  map
0% reduce 0%
> 2012-02-02 10:29:46,394 INFO  mapreduce.Job (Job.java:printTaskEvents(1386)) - Task Id
: attempt_1328200108174_0001_m_000000_0, Status : FAILED
> Error: java.io.IOException: Broken pipe
> 	at java.io.FileOutputStream.writeBytes(Native Method)
> 	at java.io.FileOutputStream.write(FileOutputStream.java:282)
> 	at java.io.BufferedOutputStream.write(BufferedOutputStream.java:105)
> 	at java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:65)
> 	at java.io.BufferedOutputStream.write(BufferedOutputStream.java:109)
> 	at java.io.DataOutputStream.write(DataOutputStream.java:90)
> 	at org.apache.hadoop.streaming.io.TextInputWriter.writeUTF8(TextInputWriter.java:72)
> 	at org.apache.hadoop.streaming.io.TextInputWriter.writeValue(TextInputWriter.java:51)
> 	at org.apache.hadoop.streaming.PipeMapper.map(PipeMapper.java:106)
> 	at org.apache.hadoop.mapred.MapRunner.run(MapRunner.java:54)
> 	at org.apache.hadoop.streaming.PipeMapRunner.run(PipeMapRunner.java:34)
> 	at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:394)
> 	at org.apache.hadoop.mapred.MapTask.run(MapTask.java:329)
> 	at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:147)
> 	at java.security.AccessController.doPrivileged(Native Method)
> 	at javax.security.auth.Subject.doAs(Subject.java:396)
> 	at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1177)
> 	at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:142)
> {code}
> Assuming the job returns a successful exit code, I think we should allow the job to complete
successfully even though it doesn't consume all of its inputs.  Part of the reasoning is that
there's already this comment in PipeMapper.java that implies we desire that behavior:
> {code:title=PipeMapper.java}
>         // terminate with success:
>         // swallow input records although the stream processor failed/closed
> {code}

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