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Posted to issues@spark.apache.org by "Baohe Zhang (Jira)" <ji...@apache.org> on 2021/02/25 17:31:00 UTC
[jira] [Updated] (SPARK-34545) PySpark Python UDF return
inconsistent results when applying UDFs to 2 columns together
[ https://issues.apache.org/jira/browse/SPARK-34545?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Baohe Zhang updated SPARK-34545:
--------------------------------
Description:
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), val can be a single element or a list
"""
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.
was:
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), val can be a single element or a list
"""
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|{color:red}null{color}|
|2.0|{color:red}null{color}|
|3.1| 3|
+---+----+
{noformat}
The test was done in branch-3.1 local mode.
> PySpark Python UDF return inconsistent results when applying UDFs to 2 columns together
> ---------------------------------------------------------------------------------------
>
> Key: SPARK-34545
> URL: https://issues.apache.org/jira/browse/SPARK-34545
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 3.0.0
> Reporter: Baohe Zhang
> Priority: Major
>
> 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), val can be a single element or a list
> """
> 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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