Simone created SPARK-13301:
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Summary: 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 - 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:
# UDF the returs the lower version of a string
from pyspark.sql import functions
import string
myFunc = functions.udf(lambda x: string.lower(x))
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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