Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/23253#discussion_r240026245
--- Diff: docs/sql-migration-guide-upgrade.md ---
@@ -35,7 +35,9 @@ displayTitle: Spark SQL Upgrading Guide
- Since Spark 3.0, CSV datasource uses java.time API for parsing and
generating CSV content. New formatting implementation supports date/timestamp
patterns conformed to ISO 8601. To switch back to the implementation used in
Spark 2.4 and earlier, set `spark.sql.legacy.timeParser.enabled` to `true`.
- - In Spark version 2.4 and earlier, CSV datasource converts a malformed
CSV string to a row with all `null`s in the PERMISSIVE mode. Since Spark 3.0,
returned row can contain non-`null` fields if some of CSV column values were
parsed and converted to desired types successfully.
+ - In Spark version 2.4 and earlier, CSV datasource converts a malformed
CSV string to a row with all `null`s in the PERMISSIVE mode. Since Spark 3.0,
the returned row can contain non-`null` fields if some of CSV column values
were parsed and converted to desired types successfully.
+
+ - In Spark version 2.4 and earlier, JSON datasource and JSON functions
like `from_json` convert a bad JSON record to a row with all `null`s in the
PERMISSIVE mode when specified schema is `StructType`. Since Spark 3.0, the
returned row can contain non-`null` fields if some of JSON column values were
parsed and converted to desired types successfully.
--- End diff --
does `from_csv` support it?
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