[
https://issues.apache.org/jira/browse/SPARK-26611?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Alberto updated SPARK-26611:
----------------------------
Description:
The following snippet crashes with error: org.apache.spark.SparkException:
Python worker exited unexpectedly (crashed)
{code:java}
from pyspark.sql.functions import pandas_udf, PandasUDFType
df = spark.createDataFrame([("1","2"),("2","2"),("2","3"), ("3","4"),
("5","6")], ("first","second"))
@pandas_udf("first string, second string", PandasUDFType.GROUPED_MAP)
def filter_pandas(df):
return df[df['first']=="9"]
df.groupby("second").apply(filter_pandas).count()
{code}
while this one does not:
{code:java}
from pyspark.sql.functions import pandas_udf, PandasUDFType
df = spark.createDataFrame([(1,2),(2,2),(2,3), (3,4), (5,6)],
("first","second"))
@pandas_udf("first string, second string", PandasUDFType.GROUPED_MAP)
def filter_pandas(df):
return df[df['first']==9]
df.groupby("second").apply(filter_pandas).count()
{code}
and niether this:
{code:java}
from pyspark.sql.functions import pandas_udf, PandasUDFType
df = spark.createDataFrame([("1","2"),("2","2"),("2","3"), ("3","4"),
("5","6")], ("first","second"))
@pandas_udf("first string, second string", PandasUDFType.GROUPED_MAP)
def filter_pandas(df):
if len(df)>0:
return df
else:
return pd.DataFrame({"first":[],"second":[]})
df.groupby("second").apply(filter_pandas).count()
{code}
See stacktrace
[here|https://gist.github.com/afumagallireply/02d4c1355bc64a9d2129cdd6d0e9d9f3]
was:
The following snippet crashes with error: org.apache.spark.SparkException:
Python worker exited unexpectedly (crashed)
{code:java}
df = spark.createDataFrame([("1","2"),("2","2"),("2","3"), ("3","4"),
("5","6")], ("first","second"))
@pandas_udf("first string, second string", PandasUDFType.GROUPED_MAP)
def filter_pandas(df):
return df[df['first']=="9"]
df.groupby("second").apply(filter_pandas).count()
{code}
while this one does not:
{code:java}
df = spark.createDataFrame([(1,2),(2,2),(2,3), (3,4), (5,6)],
("first","second"))
@pandas_udf("first string, second string", PandasUDFType.GROUPED_MAP)
def filter_pandas(df):
return df[df['first']==9]
df.groupby("second").apply(filter_pandas).count()
{code}
and niether this:
{code:java}
df = spark.createDataFrame([("1","2"),("2","2"),("2","3"), ("3","4"),
("5","6")], ("first","second"))
@pandas_udf("first string, second string", PandasUDFType.GROUPED_MAP)
def filter_pandas(df):
if len(df)>0:
return df
else:
return pd.DataFrame({"first":[],"second":[]})
df.groupby("second").apply(filter_pandas).count()
{code}
See stacktrace
[here|https://gist.github.com/afumagallireply/02d4c1355bc64a9d2129cdd6d0e9d9f3]
> GROUPED_MAP pandas_udf crashing "Python worker exited unexpectedly"
> -------------------------------------------------------------------
>
> Key: SPARK-26611
> URL: https://issues.apache.org/jira/browse/SPARK-26611
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 2.4.0
> Reporter: Alberto
> Priority: Major
> Labels: UDF, pandas, pyspark
>
> The following snippet crashes with error: org.apache.spark.SparkException:
> Python worker exited unexpectedly (crashed)
> {code:java}
> from pyspark.sql.functions import pandas_udf, PandasUDFType
> df = spark.createDataFrame([("1","2"),("2","2"),("2","3"), ("3","4"),
> ("5","6")], ("first","second"))
> @pandas_udf("first string, second string", PandasUDFType.GROUPED_MAP)
> def filter_pandas(df):
> return df[df['first']=="9"]
> df.groupby("second").apply(filter_pandas).count()
> {code}
> while this one does not:
> {code:java}
> from pyspark.sql.functions import pandas_udf, PandasUDFType
> df = spark.createDataFrame([(1,2),(2,2),(2,3), (3,4), (5,6)],
> ("first","second"))
> @pandas_udf("first string, second string", PandasUDFType.GROUPED_MAP)
> def filter_pandas(df):
> return df[df['first']==9]
> df.groupby("second").apply(filter_pandas).count()
> {code}
> and niether this:
> {code:java}
> from pyspark.sql.functions import pandas_udf, PandasUDFType
> df = spark.createDataFrame([("1","2"),("2","2"),("2","3"), ("3","4"),
> ("5","6")], ("first","second"))
> @pandas_udf("first string, second string", PandasUDFType.GROUPED_MAP)
> def filter_pandas(df):
> if len(df)>0:
> return df
> else:
> return pd.DataFrame({"first":[],"second":[]})
> df.groupby("second").apply(filter_pandas).count()
> {code}
>
>
> See stacktrace
> [here|https://gist.github.com/afumagallireply/02d4c1355bc64a9d2129cdd6d0e9d9f3]
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