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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2018/02/09 14:44:00 UTC
[jira] [Updated] (SPARK-23333) SparkML VectorAssembler.transform
slow when needing to invoke .first() on sorted DataFrame
[ https://issues.apache.org/jira/browse/SPARK-23333?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen updated SPARK-23333:
------------------------------
Priority: Minor (was: Major)
> 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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