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https://issues.apache.org/jira/browse/SPARK-37711?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-37711:
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Labels: pull-request-available (was: )
> Create a plan for top & frequency for pandas-on-Spark optimization
> ------------------------------------------------------------------
>
> Key: SPARK-37711
> URL: https://issues.apache.org/jira/browse/SPARK-37711
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.3.0
> Reporter: Haejoon Lee
> Priority: Major
> Labels: pull-request-available
>
> When invoking the DataFrame.describe() in pandas API on Spark, the multiple
> Spark job is run as much as number of columns to retrieve the `top` and
> `freq` status.
> Top is the most common value in column, and the freq is the count of the most
> common value.
> We should write a util in Scala side, and make it return key value (count) in
> one Spark job. e.g.) Dataset.mapPartitions and calculate the summation of the
> key and value (count). Such APIs are missing in PySpark so we would have to
> write one in Scala side.
> See the [https://github.com/apache/spark/pull/34931#discussion_r772220260]
> for more detail.
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