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https://issues.apache.org/jira/browse/SPARK-20580?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-20580.
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Resolution: Not A Problem
Although map() wouldn't generally entail serializing anything, it could cause
other computations to trigger, which do. Think of earlier stages which might
entail shuffles, or caching, or checkpointing. This is generally an
implementation detail. I don't think it would be possible to support
unserializable objects in Spark, as most operations wouldn't work, and
committing to make even a subset work is difficult and probably still confusing
semantics for a user. In Java-land you can always implement your own
serialization of complex third-party types anyway, if you must.
> 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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