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https://issues.apache.org/jira/browse/SPARK-19208?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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zhengruifeng updated SPARK-19208:
---------------------------------
    Description: 
Now, {{MaxAbsScaler}} and {{MinMaxScaler}} are using 
{{MultivariateOnlineSummarizer}} to compute the min/max.
However {{MultivariateOnlineSummarizer}} will also compute extra unused 
statistics. It slows down the task, moreover it is more prone to cause OOM.

For example:
env : --driver-memory 4G --executor-memory 1G --num-executors 4
data: 
[http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#kdd2010%20(bridge%20to%20algebra)]
 748401 instances,   and 29,890,095 features
{{MaxAbsScaler.fit}} fail because OOM

{{MultivariateOnlineSummarizer}} maintain 8 arrays
For {{MaxAbsScaler}}, only one array is needed (max of abs value)
For {{MinMaxScaler}}, only 3 arrays are needed (max, min, nnz)

  was:
Now, {{MaxAbsScaler}} and {{MinMaxScaler}} are using 
{{MultivariateOnlineSummarizer}} to compute the min/max.
However {{MultivariateOnlineSummarizer}} will also compute extra unused 
statistics. It slows down the task, moreover it is more prone to cause OOM.

For example:
env : --driver-memory 4G --executor-memory 1G --num-executors 4
data: 748401 instances,   and 3,000,000 features
{{MaxAbsScaler.fit}} fail because OOM

{{MultivariateOnlineSummarizer}} maintain 8 arrays
For {{MaxAbsScaler}}, only one array is needed (max of abs value)
For {{MinMaxScaler}}, only 3 arrays are needed (max, min, nnz)


> MaxAbsScaler and MinMaxScaler are very inefficient
> --------------------------------------------------
>
>                 Key: SPARK-19208
>                 URL: https://issues.apache.org/jira/browse/SPARK-19208
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>            Reporter: zhengruifeng
>         Attachments: WechatIMG2621.jpeg
>
>
> Now, {{MaxAbsScaler}} and {{MinMaxScaler}} are using 
> {{MultivariateOnlineSummarizer}} to compute the min/max.
> However {{MultivariateOnlineSummarizer}} will also compute extra unused 
> statistics. It slows down the task, moreover it is more prone to cause OOM.
> For example:
> env : --driver-memory 4G --executor-memory 1G --num-executors 4
> data: 
> [http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#kdd2010%20(bridge%20to%20algebra)]
>  748401 instances,   and 29,890,095 features
> {{MaxAbsScaler.fit}} fail because OOM
> {{MultivariateOnlineSummarizer}} maintain 8 arrays
> For {{MaxAbsScaler}}, only one array is needed (max of abs value)
> For {{MinMaxScaler}}, only 3 arrays are needed (max, min, nnz)



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