[jira] [Commented] (SPARK-19728) PythonUDF with multiple parents shouldn't be pushed down when used as a predicate

["Sergey Bahchissaraitsev (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-19728?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16602040#comment-16602040 ] 

Sergey Bahchissaraitsev commented on SPARK-19728:
-------------------------------------------------

This is still happening in 2.3.1 when using the UDF inside the join "on" condition.
e.g.
{quote}df1.join(df2, pred(df1.col_a, df2.col_b)).show()
{quote}

I have opened for this: SPARK-25314

>  PythonUDF with multiple parents shouldn't be pushed down when used as a predicate
> ----------------------------------------------------------------------------------
>
>                 Key: SPARK-19728
>                 URL: https://issues.apache.org/jira/browse/SPARK-19728
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark, SQL
>    Affects Versions: 2.0.0, 2.1.0
>            Reporter: Maciej Szymkiewicz
>            Priority: Major
>             Fix For: 2.2.0
>
>
> Prior to Spark 2.0 it was possible to use Python UDF output as a predicate:
> {code}
> from pyspark.sql.functions import udf
> from pyspark.sql.types import BooleanType
> df1 = sc.parallelize([(1, ), (2, )]).toDF(["col_a"])
> df2 = sc.parallelize([(2, ), (3, )]).toDF(["col_b"])
> pred = udf(lambda x, y: x == y, BooleanType())
> df1.join(df2).where(pred("col_a", "col_b")).show()
> {code}
> In Spark 2.0 this is no longer possible:
> {code}
> spark.conf.set("spark.sql.crossJoin.enabled", True)
> df1.join(df2).where(pred("col_a", "col_b")).show()
> ## ...
> ## Py4JJavaError: An error occurred while calling o731.showString.
> : java.lang.RuntimeException: Invalid PythonUDF <lambda>(col_a#132L, col_b#135L), requires attributes from more than one child.
> ## ...
> {code}



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