Github user HyukjinKwon commented on the pull request:
https://github.com/apache/spark/pull/11756#issuecomment-197624257
@cloud-fan Actually, I have a question.
So, in JSON data source, I thought JSON data format itself can have a
flexible schema so it does not necessarily have the same data unlike CSV data.
So, I thought the range of "malformed" rows does not include some rows
having different schema for JSON data source (whereas for CSV the range of
"malformed" rows includes some rows having different schema).
For the differences, it lead to some different actions for each parse mode
comparing to CSV data source.
- **CSV**
- `FAILFAST` : **It throws an exception if any row does not have a same
schema** or if any row could not be converted into the user-given schema.
- `DROPMALFORMED` : : **It drops every row that does not have a same
schema** or could not be converted into the user-given schema.
- **JSON**
- `FAILFAST` : **It throws an exception if any row has a corrupted
format** or if any row could not be converted into the user-given schema.
- `DROPMALFORMED` : **It drops every row that has a corrupted format** or
could not be converted into the user-given schema.
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