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https://issues.apache.org/jira/browse/SPARK-6761?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16138809#comment-16138809
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poplav edited comment on SPARK-6761 at 8/23/17 6:22 PM:
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Question: Say I have a DataFrame of 1000 columns. I want approximate
quantiles for all 1000 columns of that DataFrame. I am seeing that this method
takes in a parameter for one column, thus I am having to map over all 1000
columns and run this sequentially. Is it possible for this to accept a
sequence of columns and improve performance?
[Edit nevermind I just saw import
org.apache.spark.sql.execution.stat.StatFunctions.multipleApproxQuantiles, but
it looks like there are performance issues when running on many columns see
https://issues.apache.org/jira/browse/SPARK-18656]
was (Author: poplav):
Question: Say I have a DataFrame of 1000 columns. I want approximate
quantiles for all 1000 columns of that DataFrame. I am seeing that this method
takes in a parameter for one column, thus I am having to map over all 1000
columns and run this sequentially. Is it possible for this to accept a
sequence of columns and improve performance?
> Approximate quantile
> --------------------
>
> Key: SPARK-6761
> URL: https://issues.apache.org/jira/browse/SPARK-6761
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Reporter: Reynold Xin
> Assignee: Liang-Chi Hsieh
> Fix For: 2.0.0
>
>
> See mailing list discussion:
> http://apache-spark-developers-list.1001551.n3.nabble.com/Approximate-rank-based-statistics-median-95-th-percentile-etc-for-Spark-td11414.html
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