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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/08/02 10:49:00 UTC

[jira] [Resolved] (SPARK-28445) Inconsistency between Scala and Python/Panda udfs when groupby with udf() is used

     [ https://issues.apache.org/jira/browse/SPARK-28445?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-28445.
----------------------------------
       Resolution: Fixed
    Fix Version/s: 3.0.0

Issue resolved by pull request 25215
[https://github.com/apache/spark/pull/25215]

> Inconsistency between Scala and Python/Panda udfs when groupby with udf() is used
> ---------------------------------------------------------------------------------
>
>                 Key: SPARK-28445
>                 URL: https://issues.apache.org/jira/browse/SPARK-28445
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark, SQL
>    Affects Versions: 3.0.0
>            Reporter: Stavros Kontopoulos
>            Assignee: Liang-Chi Hsieh
>            Priority: Major
>             Fix For: 3.0.0
>
>
> Python:
> {code}
> from pyspark.sql.functions import pandas_udf, PandasUDFType
> @pandas_udf("int", PandasUDFType.SCALAR)
> def noop(x):
>  return x
> spark.udf.register("udf", noop)
> sql("""
>  CREATE OR REPLACE TEMPORARY VIEW testData AS SELECT * FROM VALUES
>  (1, 1), (1, 2), (2, 1), (2, 2), (3, 1), (3, 2), (null, 1), (3, null), (null, null)
>  AS testData(a, b)""")
> sql("""SELECT udf(a + 1), udf(COUNT(b)) FROM testData GROUP BY udf(a + 1)""").show()
> {code}
> {code}
> : org.apache.spark.sql.AnalysisException: expression 'testdata.`a`' is neither present in the group by, nor is it an aggregate function. Add to group by or wrap in first() (or first_value) if you don't care which value you get.;;
> Aggregate [udf((a#0 + 1))], [udf((a#0 + 1)) AS udf((a + 1))#10, udf(count(b#1)) AS udf(count(b))#12]
> +- SubqueryAlias `testdata`
>  +- Project [a#0, b#1]
>  +- SubqueryAlias `testData`
>  +- LocalRelation [a#0, b#1]
> {code}
> Scala:
> {code}
> spark.udf.register("udf", (input: Int) => input)
> sql("""
>  CREATE OR REPLACE TEMPORARY VIEW testData AS SELECT * FROM VALUES
>  (1, 1), (1, 2), (2, 1), (2, 2), (3, 1), (3, 2), (null, 1), (3, null), (null, null)
>  AS testData(a, b)""")
> sql("""SELECT udf(a + 1), udf(COUNT(b)) FROM testData GROUP BY udf(a + 1)""").show()
> {code}
> {code}
> +------------+-------------+
> |udf((a + 1))|udf(count(b))|
> +------------+-------------+
> |        null|            1|
> |           3|            2|
> |           4|            2|
> |           2|            2|
> +------------+-------------+
> {code}



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