Github user MaxGekk commented on the issue:
https://github.com/apache/spark/pull/22379
> Out of curiosity, is this one related with an actual usecase Maxim? or is
this proposed for API consistency?
This is actual use case when users received CSV content dumped from another
DBs and stored as one of columns (in Kafka for example). When they read the
data back by Spark, they need to parse strings in the columns somehow. Usually
they do that manually by using string column functions which is error prone
especially in the cases of quoted values. In general you can extract the column
and convert it to `Dataset[String]`, and use `def csv(csvDataset:
Dataset[String]): DataFrame` for parsing, but joining the resulted dataframe
with original one is inconvenient and just slow down execution.
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