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https://issues.apache.org/jira/browse/SPARK-22674?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16277424#comment-16277424
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Jonas Amrich commented on SPARK-22674:
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Sure, you're right that pickle won't unpickle it without class definition. As
far as I know PySpark uses pickle serializer as default and the hijack is there
to enable namedtuple pickling and unpickling with regular pickle.
Do you propose removing the hijack? Removing it would mean that regular pickle
won't be able to unpickle namedtuples anymore. And therefore cloudpickle will
have to be used as default, which is quite big change (and IMHO not very good
for performance).
> PySpark breaks serialization of namedtuple subclasses
> -----------------------------------------------------
>
> Key: SPARK-22674
> URL: https://issues.apache.org/jira/browse/SPARK-22674
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.2.0
> Reporter: Jonas Amrich
>
> Pyspark monkey patches the namedtuple class to make it serializable, however
> this breaks serialization of its subclasses. With current implementation, any
> subclass will be serialized (and deserialized) as it's parent namedtuple.
> Consider this code, which will fail with {{AttributeError: 'Point' object has
> no attribute 'sum'}}:
> {code}
> from collections import namedtuple
> Point = namedtuple("Point", "x y")
> class PointSubclass(Point):
> def sum(self):
> return self.x + self.y
> rdd = spark.sparkContext.parallelize([[PointSubclass(1, 1)]])
> rdd.collect()[0][0].sum()
> {code}
> Moreover, as PySpark hijacks all namedtuples in the main module, importing
> pyspark breaks serialization of namedtuple subclasses even in code which is
> not related to spark / distributed execution. I don't see any clean solution
> to this; a possible workaround may be to limit serialization hack only to
> direct namedtuple subclasses like in
> https://github.com/JonasAmrich/spark/commit/f3efecee28243380ecf6657fe54e1a165c1b7204
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