Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/22237#discussion_r220788994
--- Diff: docs/sql-programming-guide.md ---
@@ -1879,6 +1879,10 @@ working with timestamps in `pandas_udf`s to get the
best performance, see
# Migration Guide
+## Upgrading From Spark SQL 2.4 to 2.5
+
+ - Since Spark 3.0, the `from_json` functions supports two modes -
`PERMISSIVE` and `FAILFAST`. The modes can be set via the `mode` option. The
default mode became `PERMISSIVE`. In previous versions, behavior of `from_json`
did not conform to either `PERMISSIVE` nor `FAILFAST`, especially in processing
of malformed JSON records. For example, the JSON string `{"a" 1}` with the
schema `a INT` is converted to `null` by previous versions but Spark 3.0
converts it to `Row(null)`. In version 2.4 and earlier, arrays of JSON objects
are considered as invalid and converted to `null` if specified schema is
`StructType`. Since Spark 3.0, the input is considered as a valid JSON array
and only its first element is parsed if it conforms to the specified
`StructType`.
--- End diff --
`Since Spark 3.0` -> `Since Spark 2.5` :-)
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