[SPARK-31319][SQL][FOLLOW-UP] Add a SQL example for UDAF

### What changes were proposed in this pull request?
Add a SQL example for UDAF

### Why are the changes needed?
To make SQL Reference complete

### Does this PR introduce any user-facing change?
Yes.
Add the following page, also change ```Sql``` to ```SQL``` in the example tab for all the sql examples.
<img width="1110" alt="Screen Shot 2020-04-13 at 6 09 24 PM" src="https://user-images.githubusercontent.com/13592258/79175240-06cd7400-7db2-11ea-8f3e-af71a591a64b.png">

### How was this patch tested?
Manually build and check

Closes #28209 from huaxingao/udf_followup.

Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>

docs/sql-data-sources-jdbc.md

@@ -231,7 +231,7 @@ the following case-insensitive options:
 {% include_example jdbc_dataset r/RSparkSQLExample.R %}
 </div>
 
-<div data-lang="sql"  markdown="1">
+<div data-lang="SQL"  markdown="1">
 
 {% highlight sql %}
 

docs/sql-data-sources-json.md

@@ -77,7 +77,7 @@ For a regular multi-line JSON file, set a named parameter `multiLine` to `TRUE`.
 
 </div>
 
-<div data-lang="sql"  markdown="1">
+<div data-lang="SQL"  markdown="1">
 
 {% highlight sql %}
 

docs/sql-data-sources-load-save-functions.md

@@ -127,7 +127,7 @@ visit the official Apache ORC/Parquet websites.
 {% include_example manual_save_options_orc r/RSparkSQLExample.R %}
 </div>
 
-<div data-lang="sql"  markdown="1">
+<div data-lang="SQL"  markdown="1">
 
 {% highlight sql %}
 CREATE TABLE users_with_options (
@@ -257,7 +257,7 @@ Bucketing and sorting are applicable only to persistent tables:
 {% include_example write_sorting_and_bucketing python/sql/datasource.py %}
 </div>
 
-<div data-lang="sql"  markdown="1">
+<div data-lang="SQL"  markdown="1">
 
 {% highlight sql %}
 
@@ -291,7 +291,7 @@ while partitioning can be used with both `save` and `saveAsTable` when using the
 {% include_example write_partitioning python/sql/datasource.py %}
 </div>
 
-<div data-lang="sql"  markdown="1">
+<div data-lang="SQL"  markdown="1">
 
 {% highlight sql %}
 
@@ -323,7 +323,7 @@ It is possible to use both partitioning and bucketing for a single table:
 {% include_example write_partition_and_bucket python/sql/datasource.py %}
 </div>
 
-<div data-lang="sql"  markdown="1">
+<div data-lang="SQL"  markdown="1">
 
 {% highlight sql %}
 

docs/sql-data-sources-parquet.md

@@ -52,7 +52,7 @@ Using the data from the above example:
 
 </div>
 
-<div data-lang="sql"  markdown="1">
+<div data-lang="SQL"  markdown="1">
 
 {% highlight sql %}
 
@@ -242,7 +242,7 @@ refreshTable("my_table")
 
 </div>
 
-<div data-lang="sql"  markdown="1">
+<div data-lang="SQL"  markdown="1">
 
 {% highlight sql %}
 REFRESH TABLE my_table;

docs/sql-getting-started.md

@@ -205,7 +205,7 @@ refer it, e.g. `SELECT * FROM global_temp.view1`.
 {% include_example global_temp_view python/sql/basic.py %}
 </div>
 
-<div data-lang="sql"  markdown="1">
+<div data-lang="SQL"  markdown="1">
 
 {% highlight sql %}
 

docs/sql-performance-tuning.md

@@ -169,7 +169,7 @@ head(join(src, hint(records, "broadcast"), src$key == records$key))
 
 </div>
 
-<div data-lang="sql"  markdown="1">
+<div data-lang="SQL"  markdown="1">
 
 {% highlight sql %}
 -- We accept BROADCAST, BROADCASTJOIN and MAPJOIN for broadcast hint

docs/sql-ref-datatypes.md

@@ -631,7 +631,7 @@ from pyspark.sql.types import *
 </table>
 </div>
 
-<div data-lang="sql"  markdown="1">
+<div data-lang="SQL"  markdown="1">
 
 The following table shows the type names as well as aliases used in Spark SQL parser for each data type.
 

docs/sql-ref-functions-udf-aggregate.md

@@ -94,8 +94,45 @@ For example, a user-defined average for untyped DataFrames can look like:
 <div data-lang="java"  markdown="1">
   {% include_example untyped_custom_aggregation java/org/apache/spark/examples/sql/JavaUserDefinedUntypedAggregation.java%}
 </div>
+<div data-lang="SQL"  markdown="1">
+{% highlight sql %}
+-- Compile and place UDAF MyAverage in a JAR file called `MyAverage.jar` in /tmp.
+CREATE FUNCTION myAverage AS 'MyAverage' USING JAR '/tmp/MyAverage.jar';
+
+SHOW USER FUNCTIONS;
+-- +------------------+
+-- |          function|
+-- +------------------+
+-- | default.myAverage|
+-- +------------------+
+
+CREATE TEMPORARY VIEW employees
+USING org.apache.spark.sql.json
+OPTIONS (
+    path "examples/src/main/resources/employees.json"
+);
+
+SELECT * FROM employees;
+-- +-------+------+
+-- |   name|salary|
+-- +-------+------+
+-- |Michael|  3000|
+-- |   Andy|  4500|
+-- | Justin|  3500|
+-- |  Berta|  4000|
+-- +-------+------+
+
+SELECT myAverage(salary) as average_salary FROM employees;
+-- +--------------+
+-- |average_salary|
+-- +--------------+
+-- |        3750.0|
+-- +--------------+
+{% endhighlight %}
+</div>
 </div>
 
 ### Related Statements
+
  * [Scalar User Defined Functions (UDFs)](sql-ref-functions-udf-scalar.html)
  * [Integration with Hive UDFs/UDAFs/UDTFs](sql-ref-functions-udf-hive.html)