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https://issues.apache.org/jira/browse/MAHOUT-1286?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13715906#comment-13715906
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Peng Cheng edited comment on MAHOUT-1286 at 7/23/13 12:26 AM:
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In another hand, I try to solve the problem by implementing FastByIDArrayMap, a
slightly more compact Map implementation than FastByIDMap, it uses binary
search to arrange all entries into a tight array, so its worst-case time
complexity for get, put and delete is log( n ) (much slower than double
hashing's average O(1)). But has a (marginally) smaller memory footprint and
faster iteration. It has no problem passing all unit tests. But its real
performance can only be shown when embedded in FileDataModel. I'll post the
result shortly.
However, I don't feel this is the right direction. If Sean Owen did everything
right in his FastByIDMap, then reducing memory footage to 0.66 times doesn't
worth the speed loss.
was (Author: peng):
In another hand, I try to solve the problem by implementing
FastByIDArrayMap, a slightly more compact Map implementation than FastByIDMap,
it uses binary search to arrange all entries into a tight array, so its
worst-case time complexity for get, put and delete is log(n) (much slower than
double hashing's average O(1)). But has a (marginally) smaller memory footprint
and faster iteration. It has no problem passing all unit tests. But its real
performance can only be shown when embedded in FileDataModel. I'll post the
result shortly.
However, I don't feel this is the right direction. If Sean Owen did everything
right in his FastByIDMap, then reducing memory footage to 0.66 times doesn't
worth the speed loss.
> 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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