[jira] [Updated] (SPARK-32112) Easier way to repartition/coalesce DataFrames based on the number of parallel tasks that Spark can process at the same time

["Noritaka Sekiyama (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-32112?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Noritaka Sekiyama updated SPARK-32112:
--------------------------------------
    Summary: Easier way to repartition/coalesce DataFrames based on the number of parallel tasks that Spark can process at the same time  (was: Add a method to calculate the number of parallel tasks that Spark can process at the same time)

> Easier way to repartition/coalesce DataFrames based on the number of parallel tasks that Spark can process at the same time
> ---------------------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-32112
>                 URL: https://issues.apache.org/jira/browse/SPARK-32112
>             Project: Spark
>          Issue Type: Improvement
>          Components: Spark Core
>    Affects Versions: 3.0.0
>            Reporter: Noritaka Sekiyama
>            Priority: Major
>
> Repartition/coalesce is very important to optimize Spark application's performance, however, a lot of users are struggling with determining the number of partitions.
> This issue is to add a method to calculate the number of parallel tasks that Spark can process at the same time.
> It will help Spark users to determine the optimal number of partitions.
> Expected use-cases:
> - repartition with the calculated parallel tasks



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