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https://issues.apache.org/jira/browse/SPARK-31841?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17118287#comment-17118287
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Yuming Wang commented on SPARK-31841:
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I try to fix this issue before: https://github.com/apache/spark/pull/27986
> Dataset.repartition leverage adaptive execution
> -----------------------------------------------
>
> Key: SPARK-31841
> URL: https://issues.apache.org/jira/browse/SPARK-31841
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.0.0
> Environment: spark branch-3.0 from may 1 this year
> Reporter: koert kuipers
> Priority: Minor
>
> hello,
> we are very happy users of adaptive query execution. its a great feature to
> now have to think about and tune the number of partitions anymore in a
> shuffle.
> i noticed that Dataset.groupBy consistently uses adaptive execution when its
> enabled (e.g. i don't see the default 200 partitions) but when i do
> Dataset.repartition it seems i am back to a hardcoded number of partitions.
> Should adaptive execution also be used for repartition? It would be nice to
> be able to repartition without having to think about optimal number of
> partitions.
> An example:
> {code:java}
> $ spark-shell --conf spark.sql.adaptive.enabled=true --conf
> spark.sql.adaptive.advisoryPartitionSizeInBytes=100000
> Welcome to
> ____ __
> / __/__ ___ _____/ /__
> _\ \/ _ \/ _ `/ __/ '_/
> /___/ .__/\_,_/_/ /_/\_\ version 3.0.0-SNAPSHOT
> /_/
>
> Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_252)
> Type in expressions to have them evaluated.
> Type :help for more information.
> scala> val x = (1 to 1000000).toDF
> x: org.apache.spark.sql.DataFrame = [value: int]
> scala> x.rdd.getNumPartitions
> res0: Int = 2scala> x.repartition($"value").rdd.getNumPartitions
> res1: Int = 200
> scala> x.groupBy("value").count.rdd.getNumPartitions
> res2: Int = 67
> {code}
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