Github user HyukjinKwon commented on the pull request:
https://github.com/apache/spark/pull/13105#issuecomment-219588474
Just for a note, JSON data source is doing this in via
[`JacsonGenerator.apply()`](https://github.com/apache/spark/blob/d6dc12ef0146ae409834c78737c116050961f350/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala#L36-L86)
which is equivalent with
[`CSVRelation.rowToString()`](https://github.com/apache/spark/blob/d6dc12ef0146ae409834c78737c116050961f350/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVRelation.scala#L159-L165).
However, it seems JSON is doing this because it needs to traverse the schema
as a tree to verify if each type is supported or not. So, it seems this is
doing this while writing each row.
For CSV, it does not need to traverse a tree but just look the root types
because it does not support nested types. So, schema can be checked faster.
Therefore, it seems reasonable to me.
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