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https://issues.apache.org/jira/browse/SPARK-30101?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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sam reopened SPARK-30101:
-------------------------
What is expected, is what is documented.
> Dataset distinct does not respect spark.default.parallelism
> -----------------------------------------------------------
>
> 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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