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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-22347) UDF is evaluated when 'F.when' condition is false
Date Fri, 27 Oct 2017 05:14:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-22347?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-22347:
------------------------------------

    Assignee:     (was: Apache Spark)

> UDF is evaluated when 'F.when' condition is false
> -------------------------------------------------
>
>                 Key: SPARK-22347
>                 URL: https://issues.apache.org/jira/browse/SPARK-22347
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 2.2.0
>            Reporter: Nicolas Porter
>            Priority: Minor
>
> Here's a simple example on how to reproduce this:
> {code}
> from pyspark.sql import functions as F, Row, types
> def Divide10():
>     def fn(value): return 10 / int(value)
>     return F.udf(fn, types.IntegerType())
> df = sc.parallelize([Row(x=5), Row(x=0)]).toDF()
> x = F.col('x')
> df2 = df.select(F.when((x > 0), Divide10()(x)))
> df2.show(200)
> {code}
> This raises a division by zero error, even if `F.when` is trying to filter out all cases
where `x <= 0`. I believe the correct behavior should be not to evaluate the UDF when the
`F.when` condition is false.
> Interestingly enough, when the `F.when` condition is set to `F.lit(False)`, then the
error is not raised and all rows resolve to `null`, which is the expected result.



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