Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/16928#discussion_r120023340
--- 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 --
(Sorry for interrupting) yea, it should be consistent and we probably
should change. Probably, we should also consider the records with tokens less
or more than the schema as malformed records in PERMISSIVE mode rafher than
filling some of it. @cloud-fan raised this issue before and I had a talk with
some data analysists. It looked some agree and others do not. So, I just
decided to not change the current behaviour for now.
To cut it short, the reason (I assume) is I could not imagine a simple
common case that fails to parse CSV (not during conversion) for the current
implementation. If there are, we should match the behaviour.
I am currently outside and this is my phone. I will double check this when
I get to my computer but this will be correct if I haven't missed some changes
in this code path.
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