[SPARK-20580] Allow RDD cache with unserializable objects

• Type: Improvement
• Status: Resolved
• Priority: Minor
• Resolution: Not A Problem
• Affects Version/s: 1.3.0
• Component/s: Spark Core
• Assignee: Unassigned
• Reporter: Fernando Pereira
• Created: 03/May/17 14:12
• Updated: 09/May/17 09:40
• Resolved: 09/May/17 09:40

Description

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