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https://issues.apache.org/jira/browse/SPARK-23333?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16358634#comment-16358634
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V Luong commented on SPARK-23333:
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[~cloud_fan] alternatively, is there any way that
VectorAssembler.transform(...) can get the "numAttributes"
([https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala#L88)]
metadata from somewhere else instead of materializing a row? Does the current
need to materialize a row mean that some metadata is lacking somewhere?
> SparkML VectorAssembler.transform slow when needing to invoke .first() on
> sorted DataFrame
> ------------------------------------------------------------------------------------------
>
> Key: SPARK-23333
> URL: https://issues.apache.org/jira/browse/SPARK-23333
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib, SQL
> Affects Versions: 2.2.1
> Reporter: V Luong
> Priority: Minor
>
> Under certain circumstances, newDF = vectorAssembler.transform(oldDF) invokes
> oldDF.first() in order to establish some metadata/attributes:
> [https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala#L88.]
> When oldDF is sorted, the above triggering of oldDF.first() can be very slow.
> For the purpose of establishing metadata, taking an arbitrary row from oldDF
> will be just as good as taking oldDF.first(). Is there hence a way we can
> speed up a great deal by somehow grabbing a random row, instead of relying on
> oldDF.first()?
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