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https://issues.apache.org/jira/browse/MAHOUT-612?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen updated MAHOUT-612:
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Component/s: Collaborative Filtering
Classification
Fix Version/s: 0.6
Looking good, marking for 0.6
> Simplify configuring and running Mahout MapReduce jobs from Java using Java
> bean configuration
> ----------------------------------------------------------------------------------------------
>
> Key: MAHOUT-612
> URL: https://issues.apache.org/jira/browse/MAHOUT-612
> Project: Mahout
> Issue Type: Improvement
> Components: Classification, Clustering, Collaborative Filtering
> Affects Versions: 0.4, 0.5
> Reporter: Frank Scholten
> Assignee: Sean Owen
> Fix For: 0.6
>
> Attachments: MAHOUT-612-canopy.patch, MAHOUT-612-kmeans.patch,
> MAHOUT-612-v2.patch, MAHOUT-612.patch
>
>
> Most of the Mahout features require running several jobs in sequence. This
> can be done via the command line or using one of the driver classes.
> Running and configuring a Mahout job from Java requires using either the
> Driver's static methods or creating a String array of parameters and pass
> them to the main method of the job. If we can instead configure jobs through
> a Java bean or factory we it will be type safe and easier to use in by DI
> frameworks such as Spring and Guice.
> I have added a patch where I factored out a KMeans MapReduce job plus a
> configuration Java bean, from KMeansDriver.buildClustersMR(...)
> * The KMeansMapReduceConfiguration takes care of setting up the correct
> values in the Hadoop Configuration object and initializes defaults. I copied
> the config keys from KMeansConfigKeys.
> * The KMeansMapReduceJob contains the code for the actual algorithm running
> all iterations of KMeans and returns the KMeansMapReduceConfiguration, which
> contains the cluster path for the final iteration.
> I like to extend this approach to other Hadoop jobs for instance the job for
> creating points in KMeansDriver, but I first want some feedback on this.
> One of the benefits of this approach is that it becomes easier to chain jobs.
> For instance we can chain Canopy to KMeans by connecting the output dir of
> Canopy's configuration to the input dir of the configuration of the KMeans
> job next in the chain. Hadoop's JobControl class can then be used to connect
> and execute the entire chain.
> This approach can be further improved by turning the configuration bean into
> a factory for creating MapReduce or sequential jobs. This would probably
> remove some duplicated code in the KMeansDriver.
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