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https://issues.apache.org/jira/browse/DATAFU-51?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Matthew Hayes closed DATAFU-51.
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Resolution: Won't Do
Closing this as it is quite old and there have been no updates.
> Add DataFu MR project, a lightweight for implementing Java/Scala MapReduce
> jobs
> --------------------------------------------------------------------------------
>
> Key: DATAFU-51
> URL: https://issues.apache.org/jira/browse/DATAFU-51
> Project: DataFu
> Issue Type: New Feature
> Reporter: Mathieu Bastian
> Assignee: Mathieu Bastian
> Priority: Major
> Attachments: DATAFU-51-v10.patch, DATAFU-51-v11.patch,
> DATAFU-51-v2.patch, DATAFU-51-v3.patch, DATAFU-51-v4.patch,
> DATAFU-51-v5.patch, DATAFU-51-v6.patch, DATAFU-51-v7.patch,
> DATAFU-51-v8.patch, DATAFU-51-v9.patch, DATAFU-51.patch
>
>
> New lightweight framework to develop Java/Scala MapReduce jobs. Inspired from
> Matt's work on Hourglass and my experience in developing Java jobs on Hadoop.
> It's a thin layer on top of the Hadoop API which mostly reduces boilerplate
> code and automate configuration.
> The core feature is the `AbstractJob` abstract class. Developers get all the
> benefits of DataFu MR simply by using this abstract class for each MapReduce
> job.
> * If nested, Mapper and Reducer classes are automatically inferred
> * Mapper, reducer and intermediate key/value classes are inferred when
> possible
> * Estimate the number of reducers needed if not provided
> * Staged output to avoid deleting the existing files if the job fails
> DataFu MR also plays well with Avro input/output and provide additional
> features through the `AbstractAvroJob` abstract class.
> * Built-in support for Avro input and output formats
> * Avro schemas are inferred when using POJO objects
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