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From "Alejandro Abdelnur (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HADOOP-3149) supporting multiple outputs for M/R jobs
Date Tue, 03 Jun 2008 16:16:45 GMT

    [ https://issues.apache.org/jira/browse/HADOOP-3149?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12601990#action_12601990

Alejandro Abdelnur commented on HADOOP-3149:

For our requirements the MultipleOutputFormat shortcomings (that are handled by the proposed
patch) are:

* It does not support K & V of different types for the different output names.
* A Map/Reduce job cannot use it from both Map and Reduce.
* If used from a Map the job cannot have a Reduce.
* It is an abstract class and it requires different implementations for different OutputFormats.

>From the M/R developer usage perspective:

* By looking at the M/R code is not obvious that data is not written to a different output
and it falls outside of the M/R I/O flow.
* It is not clear to which output the data is being written unless there is a clear understanding
of the MOF implementation.

IMO, the current MOF has great flexibility but that comes with a complexity cost in usage
and understanding it.

On your commento on not needing the {{InternalFileOutputFormat}}, you are right, I could make
{{MultipleOutputs}} to extends {{FileOutputFormat}} and have the {{getRecordWriter()}} method

On your comment on using the default file system, I did that because the {{OutputFormat}}
interface indicates that is ignored and because the file to be created goes in the same place
of the job output, thus using the job conf for it.

> 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
>             Fix For: 0.18.0
>         Attachments: 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
> 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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