Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/19858#discussion_r154453799
--- Diff: docs/sql-programming-guide.md ---
@@ -1776,6 +1776,8 @@ options.
Note that, for <b>DecimalType(38,0)*</b>, the table above
intentionally does not cover all other combinations of scales and precisions
because currently we only infer decimal type like `BigInteger`/`BigInt`. For
example, 1.1 is inferred as double type.
- In PySpark, now we need Pandas 0.19.2 or upper if you want to use
Pandas related functionalities, such as `toPandas`, `createDataFrame` from
Pandas DataFrame, etc.
- In PySpark, the behavior of timestamp values for Pandas related
functionalities was changed to respect session timezone. If you want to use the
old behavior, you need to set a configuration
`spark.sql.execution.pandas.respectSessionTimeZone` to `False`. See
[SPARK-22395](https://issues.apache.org/jira/browse/SPARK-22395) for details.
+
+ - Since Spark 2.3, broadcast behaviour changed to broadcast the join side
with an explicit broadcast hint first. See
[SPARK-22489](https://issues.apache.org/jira/browse/SPARK-22489) for details.
--- End diff --
```
Since Spark 2.3, when either broadcast hash join or broadcast nested loop
join is applicable, we prefer to broadcasting the table that is explicitly
specified in a broadcast hint. For details, see the section
[JDBC/ODBC](#broadcast-hint-for-sql-queries) and
[SPARK-22489](https://issues.apache.org/jira/browse/SPARK-22489) for details.
```
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