[ https://issues.apache.org/jira/browse/SPARK-13301?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Simone updated SPARK-13301: --------------------------- Description: 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 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. was: 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. > 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: > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org