Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/22732#discussion_r226156400
--- Diff: docs/sql-programming-guide.md ---
@@ -1978,6 +1978,7 @@ working with timestamps in `pandas_udf`s to get the
best performance, see
- Since Spark 2.4, empty strings are saved as quoted empty strings `""`.
In version 2.3 and earlier, empty strings are equal to `null` values and do not
reflect to any characters in saved CSV files. For example, the row of `"a",
null, "", 1` was writted as `a,,,1`. Since Spark 2.4, the same row is saved as
`a,,"",1`. To restore the previous behavior, set the CSV option `emptyValue` to
empty (not quoted) string.
- Since Spark 2.4, The LOAD DATA command supports wildcard `?` and `*`,
which match any one character, and zero or more characters, respectively.
Example: `LOAD DATA INPATH '/tmp/folder*/'` or `LOAD DATA INPATH
'/tmp/part-?'`. Special Characters like `space` also now work in paths.
Example: `LOAD DATA INPATH '/tmp/folder name/'`.
- In Spark version 2.3 and earlier, HAVING without GROUP BY is treated
as WHERE. This means, `SELECT 1 FROM range(10) HAVING true` is executed as
`SELECT 1 FROM range(10) WHERE true` and returns 10 rows. This violates SQL
standard, and has been fixed in Spark 2.4. Since Spark 2.4, HAVING without
GROUP BY is treated as a global aggregate, which means `SELECT 1 FROM range(10)
HAVING true` will return only one row. To restore the previous behavior, set
`spark.sql.legacy.parser.havingWithoutGroupByAsWhere` to `true`.
+ - Since Spark 2.4, use of the method `def udf(f: AnyRef, dataType:
DataType): UserDefinedFunction` should not expect any automatic null handling
of the input parameters, thus a null input of a Scala primitive type will be
converted to the type's corresponding default value in the UDF. All other UDF
declaration and registration methods remain the same behavior as before.
--- End diff --
yes, this fixes a bug introduced by a commit in 2.4
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