[SPARK-31383][SQL][DOC] Clean up the SQL documents in docs/sql-ref*

docs/sql-ref-ansi-compliance.md

@@ -69,18 +69,15 @@ When `spark.sql.ansi.enabled` is set to `true` and an overflow occurs in numeric
 {% highlight sql %}
 -- `spark.sql.ansi.enabled=true`
 SELECT 2147483647 + 1;
-
   java.lang.ArithmeticException: integer overflow
 
 -- `spark.sql.ansi.enabled=false`
 SELECT 2147483647 + 1;
-
   +----------------+
   |(2147483647 + 1)|
   +----------------+
   |     -2147483648|
   +----------------+
-
 {% endhighlight %}
 
 ### Type Conversion
@@ -97,24 +94,20 @@ In future releases, the behaviour of type coercion might change along with the o
 
 -- `spark.sql.ansi.enabled=true`
 SELECT CAST('a' AS INT);
-
   java.lang.NumberFormatException: invalid input syntax for type numeric: a
 
 SELECT CAST(2147483648L AS INT);
-
   java.lang.ArithmeticException: Casting 2147483648 to int causes overflow
 
 -- `spark.sql.ansi.enabled=false` (This is a default behaviour)
 SELECT CAST('a' AS INT);
-
   +--------------+
   |CAST(a AS INT)|
   +--------------+
   |          null|
   +--------------+
 
 SELECT CAST(2147483648L AS INT);
-
   +-----------------------+
   |CAST(2147483648 AS INT)|
   +-----------------------+
@@ -126,20 +119,17 @@ CREATE TABLE t (v INT);
 
 -- `spark.sql.storeAssignmentPolicy=ANSI`
 INSERT INTO t VALUES ('1');
-
   org.apache.spark.sql.AnalysisException: Cannot write incompatible data to table '`default`.`t`':
   - Cannot safely cast 'v': StringType to IntegerType;
 
 -- `spark.sql.storeAssignmentPolicy=LEGACY` (This is a legacy behaviour until Spark 2.x)
 INSERT INTO t VALUES ('1');
 SELECT * FROM t;
-
   +---+
   |  v|
   +---+
   |  1|
   +---+
-
 {% endhighlight %}
 
 ### SQL Functions

docs/sql-ref-datatypes.md

@@ -19,7 +19,6 @@ license: |
   limitations under the License.
 ---
 
-
 Spark SQL and DataFrames support the following data types:
 
 * Numeric types

docs/sql-ref-functions-builtin.md

@@ -21,6 +21,6 @@ license: |
 
 Spark SQL defines built-in functions to use, a complete list of which can be found [here](api/sql/). Among them, Spark SQL has several special categories of built-in functions: [Aggregate Functions](sql-ref-functions-builtin-aggregate.html) to operate on a group of rows, [Array Functions](sql-ref-functions-builtin-array.html) to operate on Array columns, and [Date and Time Functions](sql-ref-functions-builtin-date-time.html) to operate on Date and Time.
 
-* [Aggregate Functions](sql-ref-functions-builtin-aggregate.html)
-* [Array Functions](sql-ref-functions-builtin-array.html)
-* [Date and Time Functions](sql-ref-functions-builtin-date-time.html)
+ * [Aggregate Functions](sql-ref-functions-builtin-aggregate.html)
+ * [Array Functions](sql-ref-functions-builtin-array.html)
+ * [Date and Time Functions](sql-ref-functions-builtin-date-time.html)

docs/sql-ref-functions-udf.md

@@ -21,6 +21,6 @@ license: |
 
 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.
 
-* [Scalar User-Defined Functions (UDFs)](sql-ref-functions-udf-scalar.html)
-* [User-Defined Aggregate Functions (UDAFs)](sql-ref-functions-udf-aggregate.html)
-* [Integration with Hive UDFs/UDAFs/UDTFs](sql-ref-functions-udf-hive.html)
+ * [Scalar User-Defined Functions (UDFs)](sql-ref-functions-udf-scalar.html)
+ * [User-Defined Aggregate Functions (UDAFs)](sql-ref-functions-udf-aggregate.html)
+ * [Integration with Hive UDFs/UDAFs/UDTFs](sql-ref-functions-udf-hive.html)