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https://issues.apache.org/jira/browse/SPARK-19208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15821549#comment-15821549
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Apache Spark commented on SPARK-19208:
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User 'zhengruifeng' has created a pull request for this issue:
https://github.com/apache/spark/pull/16571
> 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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