Github user marmbrus commented on the issue:
https://github.com/apache/spark/pull/13604
I'm not sure I agree with all of the reasoning here. Here are my thoughts:
- `SQLContext` should probably not break any APIs (its only there for
compatibility anyway).
- In `SparkSession`, we should consider that many of the users will be
coming from SparkContext and will expect similar semantics (i.e. `sc.textFile`).
- We should think about the experience of new users who are just getting
started with the API. Several people, who are not as familiar with Spark as
you guys, did comment they were surprised that `range`/`text` were returning
`Row`, which is why its the way it is today.
> spark.read.text() silently discards partitioned columns when reading a
partitioned table in text format since Dataset[String] only contains a single
field. Users have to use spark.read.format("text").load() to workaround this,
which is pretty confusing and error-prone.
The behavior is pretty clearly documented. Have users actually complained
about this?
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