Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/14124
@viirya Thanks for your comment! Actually, that's I want to have some
feedback for from @marmbrus .
It seems forcing to a nullable schema all is already happening when you
read/write data via `read`/`write` API (but not for structured streaming and
another API for json).
So, actually, the reason of this PR is, to make all consistent. The reason
to make them consistent in a way that the schema is forced as nullable is what
he said in the mailing list.
>Sure, but a traditional RDBMS has the opportunity to do validation before
>loading data in. Thats not really an option when you are reading random
>files from S3. This is why Hive and many other systems in this space treat
>all columns as nullable.
Actually, Parquet also reads and writes the schema with nullability
correctly if we get rid of `asNullable` (I tested this before) but it seems
that's prevented due to (I assume) the reason above.
@marmbrus Do you mind if I ask to clarify here please?
I think we may have to deal with this as datasource-specific problem.
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