Github user maropu commented on a diff in the pull request:
https://github.com/apache/spark/pull/16928#discussion_r102665176
--- Diff: python/pyspark/sql/readwriter.py ---
@@ -193,8 +193,9 @@ def json(self, path, schema=None,
primitivesAsString=None, prefersDecimal=None,
* ``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.
+ ``columnNameOfCorruptRecord``. An user-defined schema can
include \
+ a string type field named ``columnNameOfCorruptRecord``
for corrupt records. \
+ When a schema is set by user, it sets ``null`` for extra
fields.
--- End diff --
Ah..., a bit different I think. As @HyukjinKwon said
above(https://github.com/apache/spark/pull/16928#discussion_r102645047), CSV
formats depend on a length of parsed tokens (if the length shorter, fills
`null`, and if longer, drops them in permissive mode). One the other hand, in
JSON formats, fields in a required schema are mapped by `key`. In case of
missing keys in JSON formats, it just sets `null` in these fields with the keys
in all the three mode. cc: @HyukjinKwon
e.x.)
```
import org.apache.spark.sql.types._
scala> Seq("""{"a": "a", "b" : 1}""", """{"a":
"a"}""").toDF().write.text("/Users/maropu/Desktop/data")
scala> val dataSchema = StructType(StructField("a", StringType, true) ::
StructField("b", IntegerType, true) :: Nil)
scala> spark.read.schema(dataSchema).option("mode",
"PERMISSIVE").json("/Users/maropu/Desktop/data").show()
+---+----+
| a| b|
+---+----+
| a| 1|
| a|null|
+---+----+
scala> spark.read.schema(dataSchema).option("mode",
"FAILFAST").json("/Users/maropu/Desktop/data").show()
+---+----+
| a| b|
+---+----+
| a| 1|
| a|null|
+---+----+
scala> spark.read.schema(dataSchema).option("mode",
"DROPMALFORMED").json("/Users/maropu/Desktop/data").show()
+---+----+
| a| b|
+---+----+
| a| 1|
| a|null|
+---+----+
```
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