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https://issues.apache.org/jira/browse/SPARK-30178?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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zhengruifeng resolved SPARK-30178.
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Fix Version/s: 3.0.0
Resolution: Fixed
Issue resolved by pull request 26803
[https://github.com/apache/spark/pull/26803]
> RobustScaler support bigger numFeatures
> ---------------------------------------
>
> Key: SPARK-30178
> URL: https://issues.apache.org/jira/browse/SPARK-30178
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 3.0.0
> Reporter: zhengruifeng
> Assignee: zhengruifeng
> Priority: Minor
> Fix For: 3.0.0
>
>
> It is a bottleneck to collect the whole Array[QuantileSummaries] from
> executors,
> since a QuantileSummaries is a large object, which maintains large arrays of
> size
> 10k({color:#93a6f5}defaultCompressThreshold{color})/50k({color:#93a6f5}defaultHeadSize{color}).
> So we need to compute the ranges/medians more distributedly.
> In Spark-Shell with default params, I processed dataset with
> numFeatures=69,200, and current impl fail due to OOM.
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