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https://issues.apache.org/jira/browse/HADOOP-3149?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12611854#action_12611854
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Hadoop QA commented on HADOOP-3149:
-----------------------------------
+1 overall. Here are the results of testing the latest attachment
http://issues.apache.org/jira/secure/attachment/12385462/patch3149.txt
against trunk revision 674932.
+1 @author. The patch does not contain any @author tags.
+1 tests included. The patch appears to include 6 new or modified tests.
+1 javadoc. The javadoc tool did not generate any warning messages.
+1 javac. The applied patch does not increase the total number of javac
compiler warnings.
+1 findbugs. The patch does not introduce any new Findbugs warnings.
+1 release audit. The applied patch does not increase the total number of
release audit warnings.
+1 core tests. The patch passed core unit tests.
+1 contrib tests. The patch passed contrib unit tests.
Test results:
http://hudson.zones.apache.org/hudson/job/Hadoop-Patch/2817/testReport/
Findbugs warnings:
http://hudson.zones.apache.org/hudson/job/Hadoop-Patch/2817/artifact/trunk/build/test/findbugs/newPatchFindbugsWarnings.html
Checkstyle results:
http://hudson.zones.apache.org/hudson/job/Hadoop-Patch/2817/artifact/trunk/build/test/checkstyle-errors.html
Console output:
http://hudson.zones.apache.org/hudson/job/Hadoop-Patch/2817/console
This message is automatically generated.
> supporting multiple outputs for M/R jobs
> ----------------------------------------
>
> Key: HADOOP-3149
> URL: https://issues.apache.org/jira/browse/HADOOP-3149
> Project: Hadoop Core
> Issue Type: New Feature
> Components: mapred
> Environment: all
> Reporter: Alejandro Abdelnur
> Assignee: Alejandro Abdelnur
> Attachments: patch3149.txt, patch3149.txt, patch3149.txt,
> patch3149.txt, patch3149.txt, patch3149.txt, patch3149.txt, patch3149.txt,
> patch3149.txt, patch3149.txt, patch3149.txt, patch3149.txt, patch3149.txt,
> patch3149.txt, patch3149.txt, patch3149.txt
>
>
> The outputcollector supports writing data to a single output, the 'part'
> files in the output path.
> We found quite common that our M/R jobs have to write data to different
> output. For example when classifying data as NEW, UPDATE, DELETE, NO-CHANGE
> to later do different processing on it.
> Handling the initialization of additional outputs from within the M/R code
> complicates the code and is counter intuitive with the notion of job
> configuration.
> It would be desirable to:
> # Configure the additional outputs in the jobconf, potentially specifying
> different outputformats, key and value classes for each one.
> # Write to the additional outputs in a similar way as data is written to the
> outputcollector.
> # Support the speculative execution semantics for the output files, only
> visible in the final output for promoted tasks.
> To support multiple outputs the following classes would be added to
> mapred/lib:
> * {{MOJobConf}} : extends {{JobConf}} adding methods to define named outputs
> (name, outputformat, key class, value class)
> * {{MOOutputCollector}} : extends {{OutputCollector}} adding a
> {{collect(String outputName, WritableComparable key, Writable value)}} method.
> * {{MOMapper}} and {{MOReducer}}: implement {{Mapper}} and {{Reducer}} adding
> a new {{configure}}, {{map}} and {{reduce}} signature that take the
> corresponding {{MO}} classes and performs the proper initialization.
> The data flow behavior would be: key/values written to the default (unnamed)
> output (using the original OutputCollector {{collect}} signature) take part
> of the shuffle/sort/reduce processing phases. key/values written to a named
> output from within a map don't.
> The named output files would be named using the task type and task ID to
> avoid collision among tasks (i.e. 'new-m-00002' and 'new-r-00001').
> Together with the setInputPathFilter feature introduced by HADOOP-2055 it
> would become very easy to chain jobs working on particular named outputs
> within a single directory.
> We are using heavily this pattern and it greatly simplified our M/R code as
> well as chaining different M/R.
> We wanted to contribute this back to Hadoop as we think is a generic feature
> many could benefit from.
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