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https://issues.apache.org/jira/browse/SPARK-20580?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16001607#comment-16001607
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Fernando Pereira commented on SPARK-20580:
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I understand that at some point it will be better to fully implement
serialization of our objects.
To be more precise in our use case, the objects are instances of Python
extension types (implemented in Cython). Apparently by default they will
serialize and deserialize with their basic structures, except not non-python
data, like buffers, and therefore the "deserialized" objects are not valid.
My discussion here started since I found counter-intuitive that in some
situations cache() may lead to the program to beak, I was looking for
confirmation whether any operation following a map() will induce data
deserialization (instead of trying to use the previous RDD data).
Any chance this behavior changes?
Thanks
> Allow RDD cache with unserializable objects
> -------------------------------------------
>
> Key: SPARK-20580
> URL: https://issues.apache.org/jira/browse/SPARK-20580
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 1.3.0
> Reporter: Fernando Pereira
> Priority: Minor
>
> In my current scenario we load complex Python objects in the worker nodes
> that are not completely serializable. We then apply map certain operations to
> the RDD which at some point we collect. In this basic usage all works well.
> However, if we cache() the RDD (which defaults to memory) suddenly it fails
> to execute the transformations after the caching step. Apparently caching
> serializes the RDD data and deserializes it whenever more transformations are
> required.
> It would be nice to avoid serialization of the objects if they are to be
> cached to memory, and keep the original object
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