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https://issues.apache.org/jira/browse/ARROW-6382?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Jan updated ARROW-6382:
-----------------------
Description:
When PyArrow is enabled, Pandas UDF exceptions raised by the Executor become
impossible to catch: see example below. Is this expected behavior?
If so, what is the rationale. If not, how do I fix this?
Confirmed behavior in PyArrow 0.11 and 0.14.1 (latest) and PySpark 2.4.0 and
2.4.3. Python 3.6.5.
To reproduce:
{code:java}
import pandas as pd
from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
spark = SparkSession.builder.getOrCreate()
# setting this to false will allow the exception to be caught
spark.conf.set("spark.sql.execution.arrow.enabled", "true")
@udfdef disrupt:
raise Exception("Test EXCEPTION")
data = spark.createDataFrame(pd.DataFrame({"A": [1, 2, 3]}))
try:
test = data.withColumn("test", disrupt("A")).toPandas()
except:
print("exception caught")
print('end'){code}
I would hope there's a way to catch the exception with the general except
clause.
was:
When PyArrow is enabled, Pandas UDF exceptions raised by the Executor become
impossible to catch: see example below. Is this expected behavior?
If so, what is the rationale. If not, how do I fix this?
Confirmed behavior in PyArrow 0.11 and 0.14.1 (latest) and PySpark 2.4.0 and
2.4.3. Python 3.6.5.
To reproduce:
{{import pandas as pdfrom pyspark.sql import SparkSessionfrom
pyspark.sql.functions import udf
spark = SparkSession.builder.getOrCreate()# setting this to false will allow
the exception to be caughtspark.conf.set("spark.sql.execution.arrow.enabled",
"true")@udfdef disrupt(x):raise Exception("Test EXCEPTION")data =
spark.createDataFrame(pd.DataFrame({"A": [1, 2, 3]}))try: test =
data.withColumn("test", disrupt("A")).toPandas()except:print("exception
caught")print('end')}}
I would hope there's a way to catch the exception with the general except
clause.
> Unable to catch Python UDF exceptions when using PyArrow
> --------------------------------------------------------
>
> Key: ARROW-6382
> URL: https://issues.apache.org/jira/browse/ARROW-6382
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 0.14.1
> Environment: Ubuntu 18.04
> Reporter: Jan
> Priority: Minor
>
> When PyArrow is enabled, Pandas UDF exceptions raised by the Executor become
> impossible to catch: see example below. Is this expected behavior?
> If so, what is the rationale. If not, how do I fix this?
> Confirmed behavior in PyArrow 0.11 and 0.14.1 (latest) and PySpark 2.4.0 and
> 2.4.3. Python 3.6.5.
> To reproduce:
> {code:java}
> import pandas as pd
> from pyspark.sql import SparkSession
> from pyspark.sql.functions import udf
> spark = SparkSession.builder.getOrCreate()
> # setting this to false will allow the exception to be caught
> spark.conf.set("spark.sql.execution.arrow.enabled", "true")
> @udfdef disrupt:
> raise Exception("Test EXCEPTION")
> data = spark.createDataFrame(pd.DataFrame({"A": [1, 2, 3]}))
> try:
> test = data.withColumn("test", disrupt("A")).toPandas()
> except:
> print("exception caught")
> print('end'){code}
> I would hope there's a way to catch the exception with the general except
> clause.
>
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