[SPARK-29458][SQL][DOCS] Add a paragraph for scalar function in sql getting started

### What changes were proposed in this pull request?
Add a paragraph for scalar function in sql getting started

### Why are the changes needed?
To make 3.0 doc complete.

### Does this PR introduce any user-facing change?
before:
<img width="870" alt="Screen Shot 2020-04-21 at 10 11 12 PM" src="https://user-images.githubusercontent.com/13592258/79943182-16d1fd00-841d-11ea-9744-9cdd58d83f81.png">

after:
<img width="865" alt="Screen Shot 2020-04-22 at 11 49 59 PM" src="https://user-images.githubusercontent.com/13592258/80068256-26704500-84f4-11ea-9845-c835927c027e.png">

<img width="1033" alt="Screen Shot 2020-04-23 at 6 22 53 PM" src="https://user-images.githubusercontent.com/13592258/80165100-82d47280-858f-11ea-8c84-1ef702cc1bff.png">

### How was this patch tested?

Closes #28290 from huaxingao/scalar.

Authored-by: Huaxin Gao <[email protected]>
Signed-off-by: Sean Owen <[email protected]>

Author: huaxingao

docs/sql-getting-started.md

@@ -347,16 +347,13 @@ For example:
 </div>
 
 ## Scalar Functions
-(to be filled soon)
 
-## Aggregations
+Scalar functions are functions that return a single value per row, as opposed to aggregation functions, which return a value for a group of rows. Spark SQL supports a variety of [Built-in Scalar Functions](sql-ref-functions.html#scalar-functions). It also supports [User Defined Scalar Functions](sql-ref-functions-udf-scalar.html).
 
-The [built-in DataFrames functions](api/scala/org/apache/spark/sql/functions$.html) provide common
-aggregations such as `count()`, `countDistinct()`, `avg()`, `max()`, `min()`, etc.
-While those functions are designed for DataFrames, Spark SQL also has type-safe versions for some of them in
-[Scala](api/scala/org/apache/spark/sql/expressions/scalalang/typed$.html) and
-[Java](api/java/org/apache/spark/sql/expressions/javalang/typed.html) to work with strongly typed Datasets.
-Moreover, users are not limited to the predefined aggregate functions and can create their own. For more details
+## Aggregate Functions
+
+Aggregate functions are functions that return a single value on a group of rows. The [Built-in Aggregation Functions](sql-ref-functions-builtin.html#aggregate-functions) provide common aggregations such as `count()`, `countDistinct()`, `avg()`, `max()`, `min()`, etc.
+Users are not limited to the predefined aggregate functions and can create their own. For more details
 about user defined aggregate functions, please refer to the documentation of
 [User Defined Aggregate Functions](sql-ref-functions-udf-aggregate.html).
 

docs/sql-ref-functions.md

@@ -27,13 +27,16 @@ Built-in functions are commonly used routines that Spark SQL predefines and a co
 Spark SQL has some categories of frequently-used built-in functions for aggregtion, arrays/maps, date/timestamp, and JSON data.
 This subsection presents the usages and descriptions of these functions.
 
- * [Aggregate Functions](sql-ref-functions-builtin.html#aggregate-functions)
- * [Window Functions](sql-ref-functions-builtin.html#window-functions)
+#### Scalar Functions
  * [Array Functions](sql-ref-functions-builtin.html#array-functions)
  * [Map Functions](sql-ref-functions-builtin.html#map-functions)
  * [Date and Timestamp Functions](sql-ref-functions-builtin.html#date-and-timestamp-functions)
  * [JSON Functions](sql-ref-functions-builtin.html#json-functions)
 
+#### Aggregate-like Functions
+ * [Aggregate Functions](sql-ref-functions-builtin.html#aggregate-functions)
+ * [Window Functions](sql-ref-functions-builtin.html#window-functions)
+
 ### UDFs (User-Defined Functions)
 
 User-Defined Functions (UDFs) are a feature of Spark SQL that allows users to define their own functions when the system's built-in functions are not enough to perform the desired task. To use UDFs in Spark SQL, users must first define the function, then register the function with Spark, and finally call the registered function. The User-Defined Functions can act on a single row or act on multiple rows at once. Spark SQL also supports integration of existing Hive implementations of UDFs, UDAFs and UDTFs.