MaxGekk commented on a change in pull request #32343:
URL: https://github.com/apache/spark/pull/32343#discussion_r620265656
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File path: docs/sql-migration-guide.md
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@@ -83,6 +83,8 @@ license: |
- In Spark 3.2, the unit-to-unit interval literals like `INTERVAL '1-1' YEAR
TO MONTH` are converted to ANSI interval types: `YearMonthIntervalType` or
`DayTimeIntervalType`. In Spark 3.1 and earlier, such interval literals are
converted to `CalendarIntervalType`. To restore the behavior before Spark 3.2,
you can set `spark.sql.legacy.interval.enabled` to `true`.
+ - In Spark 3.2, Spark supports `DayTimeIntervalType` and
`YearMonthIntervalType` as inputs and outputs of `TRANSFORM` clause in Hive
`SERDE` mode, the behavior is different between Hive `SERDE` mode and `ROW
FORMAT DELIMITED` mode when this two types are used as inputs. In Hive `SERDE`
mode, `DayTimeIntervalType` column is converted to `HiveIntervalDayTime`, its
string format is `[+|-]?d h:m:s.n`, but in `ROW FORMAT DELIMITED` mode the
format is `INTERVAL [-|+]?'[-|+]?d h:m:s.n' DAY TO TIME`. In Hive `SERDE` mode,
`YearMonthIntervalType` column is converted to `HiveIntervalYearMonth`, its
string format is `[+|-]?y-m`, but in `ROW FORMAT DELIMITED` mode the format is
`INTERVAL [-|+]?'[-|+]?y-m' YEAR TO MONTH`.
Review comment:
Sorry, one more iteration:
this two types -> these two types
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