Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/16928#discussion_r120021921
--- Diff: python/pyspark/sql/readwriter.py ---
@@ -191,10 +191,13 @@ def json(self, path, schema=None,
primitivesAsString=None, prefersDecimal=None,
:param mode: allows a mode for dealing with corrupt records during
parsing. If None is
set, it uses the default value, ``PERMISSIVE``.
- * ``PERMISSIVE`` : sets other fields to ``null`` when it
meets a corrupted \
- record and puts the malformed string into a new field
configured by \
- ``columnNameOfCorruptRecord``. When a schema is set by
user, it sets \
- ``null`` for extra fields.
+ * ``PERMISSIVE`` : sets other fields to ``null`` when it
meets a corrupted \
+ record, and puts the malformed string into a field
configured by \
+ ``columnNameOfCorruptRecord``. To keep corrupt records,
an user can set \
+ a string type field named ``columnNameOfCorruptRecord``
in an user-defined \
+ schema. If a schema does not have the field, it drops
corrupt records during \
+ parsing. When inferring a schema, it implicitly adds a \
+ ``columnNameOfCorruptRecord`` field in an output schema.
--- End diff --
@maropu For JSON, we implicitly adds the ``columnNameOfCorruptRecord``
field in schema inference. What is the reason we are not doing the same thing
for CSV?
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