Github user jerryshao commented on the issue:
https://github.com/apache/spark/pull/19586
I tend to agree with @cloud-fan , I think you can implement your own
serializer out of Spark to be more specialized for your application, that will
definitely be more efficient than the built-in one. But for the Spark's default
solution, it should be general enough to cover all cases. Setting a flag or a
configuration is not intuitive enough from my understanding.
And for ML, can you please provide an example about how this could be
improved with your approach. From my understanding you approach is more useful
when leverage custom class definition, like `Person` in your example. But for
ML/SQL cases, all the types should be predefined or primitives, will that
improved a lot?
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