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https://issues.apache.org/jira/browse/SPARK-26024?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Julien Peloton updated SPARK-26024:
-----------------------------------
Affects Version/s: 2.3.0
2.3.1
> Dataset API: repartitionByRange(...) has inconsistent behaviour
> ---------------------------------------------------------------
>
> Key: SPARK-26024
> URL: https://issues.apache.org/jira/browse/SPARK-26024
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.3.0, 2.3.1, 2.3.2
> Environment: Spark version 2.3.2
> Reporter: Julien Peloton
> Priority: Major
> Labels: dataFrame, partitioning, repartition, spark-sql
>
> Hi,
> I recently played with the {{repartitionByRange}} method for DataFrame
> introduced in SPARK-22614. For DataFrames larger than the one tested in the
> code (which has only 10 elements), the code sends back random results.
> As a test for showing the inconsistent behaviour, I start as the unit code
> used to test {{repartitionByRange}}
> ([here|https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala#L352])
> but I increase the size of the initial array to 1000, repartition using 3
> partitions, and count the number of element per-partitions:
>
> {code}
> // Shuffle numbers from 0 to 1000, and make a DataFrame
> val df = Random.shuffle(0.to(1000)).toDF("val")
> // Repartition it using 3 partitions
> // Sum up number of elements in each partition, and collect it.
> // And do it several times
> for (i <- 0 to 9) {
> var counts = df.repartitionByRange(3, col("val"))
> .mapPartitions{part => Iterator(part.size)}
> .collect()
> println(counts.toList)
> }
> // -> the number of elements in each partition varies...
> {code}
> I do not know whether it is expected (I will dig further in the code), but it
> sounds like a bug.
> Or I just misinterpret what {{repartitionByRange}} is for?
> Any ideas?
> Thanks!
> Julien
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