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https://issues.apache.org/jira/browse/SPARK-13301?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15152150#comment-15152150
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Simone commented on SPARK-13301:
--------------------------------
Maybe could it be dataset dependent?
I am not able to reproduce the problem with a few record dataset generated with
a parallelize() because in this way the problem disappear.
I can try to give you another point of view:
I have 66258 records in my Dataframe:
myDF.count()
66258
If I redefine my function myFunc to do nothing in this way:
myFunc = functions.udf(lambda s: s)
This code:
myDF2 = myDF.select("col1", "col2").withColumn("col3", myFunc(myDF["col1"]))
myDF2.where(myDF2["col1"] != myDF2["col3"]).count()
Return a different number for each invocation:
1774
1548
1709
...
Very strange...
> PySpark Dataframe return wrong results with custom UDF
> ------------------------------------------------------
>
> Key: SPARK-13301
> URL: https://issues.apache.org/jira/browse/SPARK-13301
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.5.0
> Environment: PySpark in yarn-client mode - CDH 5.5.1
> Reporter: Simone
> Priority: Critical
>
> Using a User Defined Function in PySpark inside the withColumn() method of
> Dataframe, gives wrong results.
> Here an example:
> from pyspark.sql import functions
> import string
> myFunc = functions.udf(lambda s: string.lower(s))
> myDF.select("col1", "col2").withColumn("col3", myFunc(myDF["col1"])).show()
> | col1| col2| col3|
> |1265AB4F65C05740E...| Ivo|4f00ae514e7c015be...|
> |1D94AB4F75C83B51E...| Raffaele|4f00dcf6422100c0e...|
> |4F008903600A0133E...| Cristina|4f008903600a0133e...|
> The results are wrong and seem to be random: some record are OK (for example
> the third) some others NO (for example the first 2).
> The problem seems not occur with Spark built-in functions:
> from pyspark.sql.functions import *
> myDF.select("col1", "col2").withColumn("col3", lower(myDF["col1"])).show()
> Without the withColumn() method, results seems to be always correct:
> myDF.select("col1", "col2", myFunc(myDF["col1"])).show()
> This can be considered only in part a workaround because you have to list
> each time all column of your Dataframe.
> Also in Scala/Java the problems seems not occur.
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