Code review

Author: dilipbiswal

docs/sql-ref-ansi-compliance.md

@@ -28,7 +28,7 @@ The casting behaviours are defined as store assignment rules in the standard.
 When `spark.sql.storeAssignmentPolicy` is set to `ANSI`, Spark SQL complies with the ANSI store assignment rules. This is a separate configuration because its default value is `ANSI`, while the configuration `spark.sql.ansi.enabled` is disabled by default.
 
 |Property Name|Default|Meaning|Since Version|
-|--- |--- |--- |--- |
+|-------------|-------|-------|-------------|
 |`spark.sql.ansi.enabled`|false|(Experimental) When true, Spark tries to conform to the ANSI SQL specification: <br> 1. Spark will throw a runtime exception if an overflow occurs in any operation on integral/decimal field. <br> 2. Spark will forbid using the reserved keywords of ANSI SQL as identifiers in the SQL parser.|3.0.0|
 |`spark.sql.storeAssignmentPolicy`|ANSI|(Experimental) When inserting a value into a column with different data type, Spark will perform type coercion.  Currently, we support 3 policies for the type coercion rules: ANSI, legacy and strict. With ANSI policy, Spark performs the type coercion as per ANSI SQL. In practice, the behavior is mostly the same as PostgreSQL.  It disallows certain unreasonable type conversions such as converting string to int or double to boolean.  With legacy policy, Spark allows the type coercion as long as it is a valid Cast, which is very loose.  e.g. converting string to int or double to boolean is allowed.  It is also the only behavior in Spark 2.x and it is compatible with Hive.  With strict policy, Spark doesn't allow any possible precision loss or data truncation in type coercion, e.g. converting double to int or decimal to double is not allowed.|3.0.0|
 
@@ -128,7 +128,7 @@ By default `spark.sql.ansi.enabled` is false.
 Below is a list of all the keywords in Spark SQL.
 
 |Keyword|Spark SQL<br>ANSI Mode|Spark SQL<br>Default Mode|SQL-2011|
-|--- |--- |--- |--- |
+|-------|----------------------|-------------------------|--------|
 |ADD|non-reserved|non-reserved|non-reserved|
 |AFTER|non-reserved|non-reserved|non-reserved|
 |ALL|reserved|non-reserved|reserved|

docs/sql-ref-datatypes.md

@@ -72,7 +72,7 @@ You can access them by doing
 {% include_example data_types scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}
 
 |Data type|Value type in Scala|API to access or create a data type|
-|--- |--- |--- |
+|---------|-------------------|-----------------------------------|
 |**ByteType**|Byte|ByteType|
 |**ShortType**|Short|ShortType|
 |**IntegerType**|Int|IntegerType|
@@ -100,7 +100,7 @@ please use factory methods provided in
 `org.apache.spark.sql.types.DataTypes`.
 
 |Data type|Value type in Java|API to access or create a data type|
-|--- |--- |--- |
+|---------|------------------|-----------------------------------|
 |**ByteType**|byte or Byte|DataTypes.ByteType|
 |**ShortType**|short or Short|DataTypes.ShortType|
 |**IntegerType**|int or Integer|DataTypes.IntegerType|
@@ -129,7 +129,7 @@ from pyspark.sql.types import *
 {% endhighlight %}
 
 |Data type|Value type in Python|API to access or create a data type|
-|--- |--- |--- |
+|---------|--------------------|-----------------------------------|
 |**ByteType**|int or long<br>**Note:** Numbers will be converted to 1-byte signed integer numbers at runtime. Please make sure that numbers are within the range of -128 to 127.|ByteType()|
 |**ShortType**|int or long<br>**Note:** Numbers will be converted to 2-byte signed integer numbers at runtime. Please make sure that numbers are within the range of -32768 to 32767.|ShortType()|
 |**IntegerType**|int or long|IntegerType()|
@@ -151,7 +151,7 @@ from pyspark.sql.types import *
 
 <div data-lang="r"  markdown="1">
 |Data type|Value type in R|API to access or create a data type|
-|--- |--- |--- |
+|---------|---------------|-----------------------------------|
 |**ByteType**|integer <br>**Note:** Numbers will be converted to 1-byte signed integer numbers at runtime.  Please make sure that numbers are within the range of -128 to 127.|"byte"|
 |**ShortType**|integer <br>**Note:** Numbers will be converted to 2-byte signed integer numbers at runtime.  Please make sure that numbers are within the range of -32768 to 32767.|"short"|
 |**IntegerType**|integer|"integer"|
@@ -176,7 +176,7 @@ from pyspark.sql.types import *
 The following table shows the type names as well as aliases used in Spark SQL parser for each data type.
 
 |Data type|SQL name|
-|--- |--- |
+|---------|--------|
 |**BooleanType**|BOOLEAN|
 |**ByteType**|BYTE, TINYINT|
 |**ShortType**|SHORT, SMALLINT|

docs/sql-ref-datetime-pattern.md

@@ -29,7 +29,7 @@ There are several common scenarios for datetime usage in Spark:
 Spark uses pattern letters in the following table for date and timestamp parsing and formatting:
 
 |Symbol|Meaning|Presentation|Examples|
-|--- |--- |--- |--- |
+|------|-------|------------|--------|
 |**G**|era|text|AD; Anno Domini; A|
 |**y**|year|year|2020; 20|
 |**D**|day-of-year|number|189|
@@ -58,8 +58,8 @@ Spark uses pattern letters in the following table for date and timestamp parsing
 |**Z**|zone-offset|offset-Z|+0000; -0800; -08:00;|
 |**'**|escape for text|delimiter||
 |**''**|single quote|literal|'|
-|**[**|optional section start|||
-|**]**|optional section end|||
+|**[**|optional section start| | |
+|**]**|optional section end| | |
 
 The count of pattern letters determines the format.
 

docs/sql-ref-syntax-qry-select-tvf.md

@@ -51,7 +51,7 @@ function_name ( expression [ , ... ] ) [ table_alias ]
 ### Supported Table-valued Functions
 
 |Function|Argument Type(s)|Description|
-|--- |--- |--- |
+|--------|----------------|-----------|
 |**range** ( end )|Long|Creates a table with a single *LongType* column named *id*, containing<br> rows in a range from 0 to *end* (exclusive) with step value 1.|
 |**range** (  start, end )|Long, Long|Creates a table with a single *LongType* column named *id*, containing<br> rows in a range from *start* to *end* (exclusive) with step value 1.|
 |**range** (  start, end, step )|Long, Long, Long|Creates a table with a single *LongType* column named *id*, containing<br> rows in a range from *start* to *end* (exclusive) with *step* value.|