[jira] [Updated] (SPARK-30101) spark.sql.shuffle.partitions is not in Configuration docs, but a very critical parameter

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

     [ https://issues.apache.org/jira/browse/SPARK-30101?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

sam updated SPARK-30101:
------------------------
    Summary: spark.sql.shuffle.partitions is not in Configuration docs, but a very critical parameter  (was: Dataset distinct does not respect spark.default.parallelism)

> spark.sql.shuffle.partitions is not in Configuration docs, but a very critical parameter
> ----------------------------------------------------------------------------------------
>
>                 Key: SPARK-30101
>                 URL: https://issues.apache.org/jira/browse/SPARK-30101
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 2.4.0, 2.4.4
>            Reporter: sam
>            Priority: Major
>
> I'm creating a `SparkSession` like this:
> ```
> SparkSession
>       .builder().appName("foo").master("local")
>       .config("spark.default.parallelism", 2).getOrCreate()
> ```
> when I run
> ```
> ((1 to 10) ++ (1 to 10)).toDS().distinct().count()
> ```
> I get 200 partitions
> ```
> 19/12/02 10:29:34 INFO TaskSchedulerImpl: Adding task set 1.0 with 200 tasks
> ...
> 19/12/02 10:29:34 INFO TaskSetManager: Finished task 0.0 in stage 1.0 (TID 2) in 46 ms on localhost (executor driver) (1/200)
> ```
> It is the `distinct` that is broken since `ds.rdd.getNumPartitions` gives `2`, while `ds.distinct().rdd.getNumPartitions` gives `200`.  `ds.rdd.groupBy(identity).map(_._2.head)` and `ds.rdd.distinct()` work correctly.
> Finally I notice that the good old `RDD` interface has a `distinct` that accepts `numPartitions` partitions, while `Dataset` does not.



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