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Posted to mapreduce-issues@hadoop.apache.org by "Jason Lowe (Commented) (JIRA)" <ji...@apache.org> on 2012/02/04 00:07:54 UTC
[jira] [Commented] (MAPREDUCE-3790) Broken pipe on streaming job
can lead to truncated output for a successful job
[ https://issues.apache.org/jira/browse/MAPREDUCE-3790?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13200135#comment-13200135 ]
Jason Lowe commented on MAPREDUCE-3790:
---------------------------------------
Upon closer investigation of the code, there's already a config option, stream.minRecWrittenToEnableSkip_, to specify input errors should be skipped beyond a certain number of records output. This can be set to 0 to ignore any errors on the input such as broken pipe.
That still leaves the race condition in mapRedFinished() where we can close the DFSOutputStream before the output thread has finished, but there's existing support for allowing streaming jobs to ignore input.
> 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
> Affects Versions: 0.23.1, 0.24.0
> Reporter: Jason Lowe
>
> 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' done.
> {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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