[jira] [Updated] (MAHOUT-1286) Memory-efficient DataModel, supporting fast online updates and element-wise iteration

["Gokhan Capan (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MAHOUT-1286?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Gokhan Capan updated MAHOUT-1286:
---------------------------------

    Attachment: benchmark.patch

Peng,

I am attaching a patch -not to be committed- that includes some benchmarking code in case you need one, and 2 in-memory data models as a baseline.

> Memory-efficient DataModel, supporting fast online updates and element-wise iteration
> -------------------------------------------------------------------------------------
>
>                 Key: MAHOUT-1286
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-1286
>             Project: Mahout
>          Issue Type: Improvement
>          Components: Collaborative Filtering
>    Affects Versions: 0.9
>            Reporter: Peng Cheng
>              Labels: collaborative-filtering, datamodel, patch, recommender
>             Fix For: 0.9
>
>         Attachments: benchmark.patch, InMemoryDataModel.java, InMemoryDataModelTest.java, Semifinal-implementation-added.patch
>
>   Original Estimate: 336h
>  Remaining Estimate: 336h
>
> Most DataModel implementation in current CF component use hash map to enable fast 2d indexing and update. This is not memory-efficient for big data set. e.g. Netflix prize dataset takes 11G heap space as a FileDataModel.
> Improved implementation of DataModel should use more compact data structure (like arrays), this can trade a little of time complexity in 2d indexing for vast improvement in memory efficiency. In addition, any online recommender or online-to-batch converted recommender will not be affected by this in training process.



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