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Posted to dev@mahout.apache.org by "Peng Cheng (JIRA)" <ji...@apache.org> on 2013/08/12 17:40:48 UTC
[jira] [Commented] (MAHOUT-1286) Memory-efficient DataModel,
supporting fast online updates and element-wise iteration
[ https://issues.apache.org/jira/browse/MAHOUT-1286?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13736962#comment-13736962 ]
Peng Cheng commented on MAHOUT-1286:
------------------------------------
The idea of ArrayMap has been discarded due to its impractical time consumption of insertion (O(n) for a batch insertion) and query (O(logn)). I have moved back to HashMap. Due to the same reason, I feel that using Sparse Row/Column matrix may have the same problem.
> 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
> Assignee: Sean Owen
> 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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