Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/21074#discussion_r182484023
--- Diff: docs/sql-programming-guide.md ---
@@ -1810,6 +1810,7 @@ working with timestamps in `pandas_udf`s to get the
best performance, see
- Since Spark 2.4, writing a dataframe with an empty or nested empty
schema using any file formats (parquet, orc, json, text, csv etc.) is not
allowed. An exception is thrown when attempting to write dataframes with empty
schema.
- Since Spark 2.4, Spark compares a DATE type with a TIMESTAMP type after
promotes both sides to TIMESTAMP. To set `false` to
`spark.sql.hive.compareDateTimestampInTimestamp` restores the previous
behavior. This option will be removed in Spark 3.0.
- Since Spark 2.4, creating a managed table with nonempty location is not
allowed. An exception is thrown when attempting to create a managed table with
nonempty location. To set `true` to
`spark.sql.allowCreatingManagedTableUsingNonemptyLocation` restores the
previous behavior. This option will be removed in Spark 3.0.
+ - Since Spark 2.4, finding the widest common type for the arguments of a
variadic function(e.g. IN/COALESCE) should always success when each of the
types of arguments is either StringType or can be promoted to StringType.
Previously this may throw an exception for some specific arguments ordering.
--- End diff --
> - Since Spark 2.4, the type coercion rules can automatically promote the
argument types of the variadic SQL functions (e.g., IN/COALESCE) to the widest
common type, no matter how the input arguments order. In prior Spark versions,
the promotion could fail in some specific orders (e.g., TimestampType,
IntegerType and StringType) and throw an exception.
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