srowen commented on a change in pull request #28433:
URL: https://github.com/apache/spark/pull/28433#discussion_r418974993
##########
File path: docs/sql-ref-syntax-qry-select-tvf.md
##########
@@ -50,31 +50,12 @@ function_name ( expression [ , ... ] ) [ table_alias ]
### Supported Table-valued Functions
-<table class="table">
- <thead>
- <tr><th style="width:25%">Function</th><th>Argument
Type(s)</th><th>Description</th></tr>
- </thead>
- <tr>
- <td><b> range </b>( <i>end</i> )</td>
- <td> Long </td>
- <td>Creates a table with a single <code>LongType</code> column named
<code>id</code>, containing rows in a range from 0 to <code>end</code>
(exclusive) with step value 1.</td>
- </tr>
- <tr>
- <td><b> range </b>( <i> start, end</i> )</td>
- <td> Long, Long </td>
- <td width="60%">Creates a table with a single <code>LongType</code>
column named <code>id</code>, containing rows in a range from
<code>start</code> to <code>end</code> (exclusive) with step value 1.</td>
- </tr>
- <tr>
- <td><b> range </b>( <i> start, end, step</i> )</td>
- <td> Long, Long, Long </td>
- <td width="60%">Creates a table with a single <code>LongType</code>
column named <code>id</code>, containing rows in a range from
<code>start</code> to <code>end</code> (exclusive) with <code>step</code>
value.</td>
- </tr>
- <tr>
- <td><b> range </b>( <i> start, end, step, numPartitions</i> )</td>
- <td> Long, Long, Long, Int </td>
- <td width="60%">Creates a table with a single <code>LongType</code>
column named <code>id</code>, containing rows in a range from
<code>start</code> to <code>end</code> (exclusive) with <code>step</code>
value, with partition number <code>numPartitions</code> specified. </td>
- </tr>
-</table>
+|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.|
Review comment:
Nit, but can we preserve the italics on _end_ for example?
##########
File path: docs/sql-ref-ansi-compliance.md
##########
@@ -27,35 +27,10 @@ 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.
-<table class="table">
-<tr><th>Property Name</th><th>Default</th><th>Meaning</th><th>Since
Version</th></tr>
-<tr>
- <td><code>spark.sql.ansi.enabled</code></td>
- <td>false</td>
- <td>
- (Experimental) When true, Spark tries to conform to the ANSI SQL
specification:
- 1. Spark will throw a runtime exception if an overflow occurs in any
operation on integral/decimal field.
- 2. Spark will forbid using the reserved keywords of ANSI SQL as
identifiers in the SQL parser.
- </td>
- <td>3.0.0</td>
-</tr>
-<tr>
- <td><code>spark.sql.storeAssignmentPolicy</code></td>
- <td>ANSI</td>
- <td>
- (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.
- </td>
- <td>3.0.0</td>
-</tr>
-</table>
+|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|
Review comment:
Also not a big deal, but if you can search/replace `<br>` for `<br/>`
that's slightly more correct.
##########
File path: docs/sql-ref-ansi-compliance.md
##########
@@ -27,35 +27,10 @@ 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.
-<table class="table">
-<tr><th>Property Name</th><th>Default</th><th>Meaning</th><th>Since
Version</th></tr>
-<tr>
- <td><code>spark.sql.ansi.enabled</code></td>
- <td>false</td>
- <td>
- (Experimental) When true, Spark tries to conform to the ANSI SQL
specification:
- 1. Spark will throw a runtime exception if an overflow occurs in any
operation on integral/decimal field.
- 2. Spark will forbid using the reserved keywords of ANSI SQL as
identifiers in the SQL parser.
- </td>
- <td>3.0.0</td>
-</tr>
-<tr>
- <td><code>spark.sql.storeAssignmentPolicy</code></td>
- <td>ANSI</td>
- <td>
- (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.
- </td>
- <td>3.0.0</td>
-</tr>
-</table>
+|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|
Review comment:
For long table cells is there any standard way to wrap the markdown? the
markdown source is harder to read now, but not terrible.
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