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https://issues.apache.org/jira/browse/SPARK-51710?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-51710:
-----------------------------------
Labels: pull-request-available (was: )
> Using Dataframe.dropDuplicates with an empty array as argument behaves
> unexpectedly
> -----------------------------------------------------------------------------------
>
> Key: SPARK-51710
> URL: https://issues.apache.org/jira/browse/SPARK-51710
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 3.5.5
> Reporter: David Kunzmann
> Priority: Major
> Labels: pull-request-available
>
> When using PySpark DataFrame.dropDuplicates with an empty array as the subset
> argument, the resulting DataFrame contains a single row (the first row). This
> behavior is different than using DataFrame.dropDuplicates without any
> parameters or with None as the subset argument.
>
> {code:java}
> from pyspark.sql import SparkSession
>
> spark = SparkSession.builder.getOrCreate()
> data = [
> (1, "Alice"),
> (2, "Bob"),
> (3, "Alice"),
> (3, "Alice"),
> (2, "Bob")
> ]
> df = spark.createDataFrame(data, ["id", "name"])
> df_dedup = df.dropDuplicates([])
> df_dedup.show()
> {code}
> The above snippet will show the following DataFrame:
> {code:java}
> +---+-----+
> | id| name|
> +---+-----+
> | 1|Alice|
> +---+-----+ {code}
> I would expect the behavior to be the same as df.dropDuplicates() which
> returns:
> {code:java}
> +---+-----+
> | id| name|
> +---+-----+
> | 1|Alice|
> | 2| Bob|
> | 3|Alice|
> +---+-----+ {code}
>
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