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From "Runping Qi (JIRA)" <j...@apache.org>
Subject [jira] Assigned: (HADOOP-1120) Contribute some code helping implement map/reduce apps for joining data from multiple sources
Date Thu, 15 Mar 2007 00:11:09 GMT

     [ https://issues.apache.org/jira/browse/HADOOP-1120?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel

Runping Qi reassigned HADOOP-1120:

    Assignee: Runping Qi

> Contribute some code helping implement map/reduce apps for joining data from multiple
> ---------------------------------------------------------------------------------------------
>                 Key: HADOOP-1120
>                 URL: https://issues.apache.org/jira/browse/HADOOP-1120
>             Project: Hadoop
>          Issue Type: New Feature
>          Components: contrib/streaming
>            Reporter: Runping Qi
>         Assigned To: Runping Qi
>         Attachments: data_join.patch
> With the current Hadoop, it is a bit hard for the user to implement data joining apps.

> HADOOP-475/485 attempt to provide some support for data joining jobs, but it seems to
be had to implement.
> This Jira rather calls for a application level support. 
> The idea is to provide a generic map/reduce classes implementing data join jobs, 
> and allows the user to extend those classes to add their special logic. 
> In particular, the user needs to define a mapper class 
> that extends DataJoinMapperBase class  to implement methods for the
> following functionalities:
> 1. Compute the source tag of input values 
> 2. Compute the map output value object 
> 3. Compute the map output key object
> The source tag will be used by the reducer to determine from which source
> (which table in SQL terminology) a value comes. Computing the map output
> value object amounts to performing projecting/filtering work in a SQL
> statement (through the select/where clauses). Computing the map output key
> amounts to choosing the join key. This class provides the appropriate plugin
> points for the user defined subclasses to implement the appropriate logic.
> The the user needs to define a reducer class 
> that extends DataJoinReduceBase class  to implement the following:
>     protected abstract TaggedMapOutput combine(Object[] tags, Object[] values);
> The above method is expected to produce one output value from an array of
> records of different sources. The user code can also perform filtering here.
> It can return null if it decides to the records do not meet certain conditions.
> That is pretty much the user need to do in order to create a map/reduce job to join data

> from different sources.

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