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https://issues.apache.org/jira/browse/SPARK-34545?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17291954#comment-17291954
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Baohe Zhang commented on SPARK-34545:
-------------------------------------
A simpler code to reproduce the error:
{code:python}
>>> from pyspark.sql.functions import udf
>>> from pyspark.sql.types import *
>>>
>>> def udf1(data_type):
... def u1(e):
... return e[0]
... return udf(u1, data_type)
...
>>> df = spark.createDataFrame(
... [((1.0, 1.0), (1, 1))],
... ['c1', 'c2'])
>>>
>>>
>>> df = df.withColumn("c3", udf1(DoubleType())("c1"))
>>> df = df.withColumn("c4", udf1(IntegerType())("c2"))
>>>
>>> # Show the results
... df.select("c3").show()
+---+
| c3|
+---+
|1.0|
+---+
>>> df.select("c4").show()
+---+
| c4|
+---+
| 1|
+---+
>>> df.select("c3", "c4").show()
+---+----+
| c3| c4|
+---+----+
|1.0|null|
+---+----+
{code}
> PySpark Python UDF return inconsistent results when applying 2 UDFs with
> different return type to 2 columns together
> --------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-34545
> URL: https://issues.apache.org/jira/browse/SPARK-34545
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 3.0.0
> Reporter: Baohe Zhang
> Priority: Blocker
>
> Python UDF returns inconsistent results between evaluating 2 columns together
> and evaluating one by one.
> The issue occurs after we upgrading to spark3, so seems it doesn't exist in
> spark2.
> How to reproduce it?
> {code:python}
> df = spark.createDataFrame([([(1.0, "1"), (1.0, "2"), (1.0, "3")], [(1, "1"),
> (1, "2"), (1, "3")]), ([(2.0, "1"), (2.0, "2"), (2.0, "3")], [(2, "1"), (2,
> "2"), (2, "3")]), ([(3.1, "1"), (3.1, "2"), (3.1, "3")], [(3, "1"), (3, "2"),
> (3, "3")])], ['c1', 'c2'])
> from pyspark.sql.functions import udf
> from pyspark.sql.types import *
> def getLastElementWithTimeMaster(data_type):
> def getLastElementWithTime(list_elm):
> # x should be a list of (val, time)
> y = sorted(list_elm, key=lambda x: x[1]) # default is ascending
> return y[-1][0]
> return udf(getLastElementWithTime, data_type)
> # Add 2 columns whcih apply Python UDF
> df = df.withColumn("c3", getLastElementWithTimeMaster(DoubleType())("c1"))
> df = df.withColumn("c4", getLastElementWithTimeMaster(IntegerType())("c2"))
> # Show the results
> df.select("c3").show()
> df.select("c4").show()
> df.select("c3", "c4").show()
> {code}
> Results:
> {noformat}
> >>> df.select("c3").show()
> +---+
>
> | c3|
> +---+
> |1.0|
> |2.0|
> |3.1|
> +---+
> >>> df.select("c4").show()
> +---+
> | c4|
> +---+
> | 1|
> | 2|
> | 3|
> +---+
> >>> df.select("c3", "c4").show()
> +---+----+
> | c3| c4|
> +---+----+
> |1.0|null|
> |2.0|null|
> |3.1| 3|
> +---+----+
> {noformat}
> The test was done in branch-3.1 local mode.
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