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https://issues.apache.org/jira/browse/SPARK-19208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15855090#comment-15855090
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Joseph K. Bradley commented on SPARK-19208:
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You're right that sharing intermediate results will be necessary.

I'm happy with [~mlnick]'s VectorSummarizer API.  I also think that, if we 
wanted to use the API I suggested above, the version returning a single struct 
col would work: {{df.select(VectorSummary.summary("features", "weights"))}}.  
The new column could be constructed from intermediate columns which would not 
show up in the final output.  (Is this essentially the "private UDAF" 
[~podongfeng] is mentioning above?)

I'm OK either way.

> MultivariateOnlineSummarizer performance optimization
> -----------------------------------------------------
>
>                 Key: SPARK-19208
>                 URL: https://issues.apache.org/jira/browse/SPARK-19208
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>            Reporter: zhengruifeng
>         Attachments: Tests.pdf, 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}} fails because of OOM
> {{MultivariateOnlineSummarizer}} maintains 8 arrays:
> {code}
> private var currMean: Array[Double] = _
>   private var currM2n: Array[Double] = _
>   private var currM2: Array[Double] = _
>   private var currL1: Array[Double] = _
>   private var totalCnt: Long = 0
>   private var totalWeightSum: Double = 0.0
>   private var weightSquareSum: Double = 0.0
>   private var weightSum: Array[Double] = _
>   private var nnz: Array[Long] = _
>   private var currMax: Array[Double] = _
>   private var currMin: Array[Double] = _
> {code}
> For {{MaxAbsScaler}}, only 1 array is needed (max of abs value)
> For {{MinMaxScaler}}, only 3 arrays are needed (max, min, nnz)
> After modication in the pr, the above example run successfully.



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