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[GitHub] [spark] dilipbiswal opened a new pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
dilipbiswal opened a new pull request #28433:
URL: https://github.com/apache/spark/pull/28433
### What changes were proposed in this pull request?
This PR is to clean up the markdown file in remaining pages in sql reference. The first one was done by @gatorsmile in this [28415](https://github.com/apache/spark/pull/28415)
- Replace HTML table by MD table
- **sql-ref-ansi-compliance.md**
<img width="967" alt="Screen Shot 2020-05-01 at 4 36 35 PM" src="https://user-images.githubusercontent.com/14225158/80848981-1cbca080-8bca-11ea-8a5d-63174b31c800.png">
- **sql-ref-datatypes.md (Scala)**
<img width="967" alt="Screen Shot 2020-05-01 at 4 37 30 PM" src="https://user-images.githubusercontent.com/14225158/80849057-6a390d80-8bca-11ea-8866-ab08bab31432.png">
<img width="967" alt="Screen Shot 2020-05-01 at 4 39 18 PM" src="https://user-images.githubusercontent.com/14225158/80849061-6c9b6780-8bca-11ea-834c-eb93d3ab47ae.png">
- **sql-ref-datatypes.md (Java)**
<img width="967" alt="Screen Shot 2020-05-01 at 4 41 24 PM" src="https://user-images.githubusercontent.com/14225158/80849138-b3895d00-8bca-11ea-9d3b-555acad2086c.png">
<img width="967" alt="Screen Shot 2020-05-01 at 4 41 39 PM" src="https://user-images.githubusercontent.com/14225158/80849140-b6844d80-8bca-11ea-9ca9-1812b6a76c02.png">
- **sql-ref-datatypes.md (Python)**
<img width="967" alt="Screen Shot 2020-05-01 at 4 43 36 PM" src="https://user-images.githubusercontent.com/14225158/80849202-0400ba80-8bcb-11ea-96a5-7caecbf9dbbf.png">
<img width="967" alt="Screen Shot 2020-05-01 at 4 43 54 PM" src="https://user-images.githubusercontent.com/14225158/80849205-06fbab00-8bcb-11ea-8f00-6df52b151684.png">
- **sql-ref-datatypes.md (R)**
<img width="967" alt="Screen Shot 2020-05-01 at 4 45 16 PM" src="https://user-images.githubusercontent.com/14225158/80849288-5fcb4380-8bcb-11ea-8277-8589b5bb31bc.png">
<img width="967" alt="Screen Shot 2020-05-01 at 4 45 36 PM" src="https://user-images.githubusercontent.com/14225158/80849294-62c63400-8bcb-11ea-9438-b4f1193bc757.png">
- **sql-ref-datatypes.md (SQL)**
<img width="967" alt="Screen Shot 2020-05-01 at 4 48 02 PM" src="https://user-images.githubusercontent.com/14225158/80849336-986b1d00-8bcb-11ea-9736-5fb40496b681.png">
- **sql-ref-syntax-qry-select-tvf.md**
<img width="967" alt="Screen Shot 2020-05-01 at 4 49 32 PM" src="https://user-images.githubusercontent.com/14225158/80849399-d10af680-8bcb-11ea-8dc2-e3e750e21a59.png">
### Why are the changes needed?
Make the doc cleaner and easily editable by MD editors
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually using jekyll serve
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[GitHub] [spark] maropu commented on pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
maropu commented on pull request #28433:
URL: https://github.com/apache/spark/pull/28433#issuecomment-622655461
> Replace HTML table by MD table
All the fixes in `docs/sql-ref-*` addressed in this PR? I found the tag in `sql-ref-null-semantics.md`, too;
```
$ls sql-ref* | xargs grep "<table " .
sql-ref-ansi-compliance.md:<table class="table">
sql-ref-ansi-compliance.md:<table class="table">
sql-ref-datatypes.md:<table class="table">
sql-ref-datatypes.md:<table class="table">
sql-ref-datatypes.md:<table class="table">
sql-ref-datatypes.md:<table class="table">
sql-ref-datatypes.md:<table class="table">
sql-ref-datetime-pattern.md:<table class="table">
sql-ref-null-semantics.md:<table class="tsclass" border="1">
sql-ref-null-semantics.md:<table class="tsclass" border="1">
sql-ref-null-semantics.md:<table class="tsclass" border="1">
sql-ref-null-semantics.md:<table class="tsclass" border="1">
sql-ref-syntax-qry-select-tvf.md:<table class="table">
```
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[GitHub] [spark] dilipbiswal commented on pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
dilipbiswal commented on pull request #28433:
URL: https://github.com/apache/spark/pull/28433#issuecomment-623799161
@maropu @srowen Can this get in now, if there are no other comments ? The reason i ask is @huaxingao has a big PR which is changing a lot of files. If this can get in first then she can rebase and push ?
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Posted by GitBox <gi...@apache.org>.
SparkQA commented on pull request #28433:
URL: https://github.com/apache/spark/pull/28433#issuecomment-622617070
**[Test build #122182 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/122182/testReport)** for PR 28433 at commit [`4debc83`](https://github.com/apache/spark/commit/4debc83e53b256cec6395bbf2526577b9bc9b431).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] [spark] srowen commented on a change in pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
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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[GitHub] [spark] dilipbiswal commented on pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
dilipbiswal commented on pull request #28433:
URL: https://github.com/apache/spark/pull/28433#issuecomment-622649549
cc @maropu
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[GitHub] [spark] dilipbiswal commented on a change in pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
dilipbiswal commented on a change in pull request #28433:
URL: https://github.com/apache/spark/pull/28433#discussion_r419015360
##########
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:
@srowen Replaced `<br>` with `<br/>` for all the files in this PR. Will look at other files and follow-up if needed. About the wrapping in markdown, i couldn't find any markup syntax to control the cell width. I tried specifying a style for table in the doc and that seems to work ok.
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[GitHub] [spark] dilipbiswal commented on a change in pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
dilipbiswal commented on a change in pull request #28433:
URL: https://github.com/apache/spark/pull/28433#discussion_r418887140
##########
File path: docs/sql-ref-datetime-pattern.md
##########
@@ -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| | |
Review comment:
@maropu @gatorsmile OK.
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[GitHub] [spark] dilipbiswal commented on a change in pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
dilipbiswal commented on a change in pull request #28433:
URL: https://github.com/apache/spark/pull/28433#discussion_r418887456
##########
File path: docs/sql-ref-datatypes.md
##########
@@ -824,4 +312,4 @@ SELECT COUNT(*), c2 FROM test GROUP BY c2;
| 2|-Infinity|
| 3| Infinity|
+---------+---------+
-{% endhighlight %}
\ No newline at end of file
+{% endhighlight %}
Review comment:
@maropu Fixed.
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[GitHub] [spark] SparkQA commented on pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
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* This patch passes all tests.
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maropu commented on pull request #28433:
URL: https://github.com/apache/spark/pull/28433#issuecomment-623876997
Thanks! Merged to master/3.0. If @srowen has more comments, please ping @huaxingao so that she can include more fixes in her PR (#28451).
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maropu commented on pull request #28433:
URL: https://github.com/apache/spark/pull/28433#issuecomment-623877304
Ur, the jira ID is incorrect? @dilipbiswal
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[GitHub] [spark] dilipbiswal commented on a change in pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
dilipbiswal commented on a change in pull request #28433:
URL: https://github.com/apache/spark/pull/28433#discussion_r418816535
##########
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|
+|--- |--- |--- |--- |
Review comment:
@huaxingao Done.
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[GitHub] [spark] dilipbiswal commented on a change in pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
dilipbiswal commented on a change in pull request #28433:
URL: https://github.com/apache/spark/pull/28433#discussion_r419015360
##########
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:
@srowen Replaced <br> with <br/> for all the files in this PR. Will look at other files and follow-up if needed. About the wrapping in markdown, i couldn't find any markup syntax to control the cell width. I tried specifying a style for table in the doc and that seems to work ok.
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[GitHub] [spark] gatorsmile commented on a change in pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
gatorsmile commented on a change in pull request #28433:
URL: https://github.com/apache/spark/pull/28433#discussion_r418870517
##########
File path: docs/sql-ref-datetime-pattern.md
##########
@@ -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| | |
Review comment:
it would be nicer if we have an extra space between | and |
Yes. Line 59 is needed too
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[GitHub] [spark] maropu commented on a change in pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
maropu commented on a change in pull request #28433:
URL: https://github.com/apache/spark/pull/28433#discussion_r418834997
##########
File path: docs/sql-ref-datetime-pattern.md
##########
@@ -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| | |
Review comment:
Need the space between `|`s? But, no space between `|`s in line 59?
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dilipbiswal commented on pull request #28433:
URL: https://github.com/apache/spark/pull/28433#issuecomment-623876888
Thanks a lot @maropu @huaxingao @srowen @gatorsmile
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dilipbiswal commented on pull request #28433:
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@maropu Thanks for fixing the title.
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maropu commented on pull request #28433:
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Looks fine and I'll merge this first.
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dilipbiswal commented on pull request #28433:
URL: https://github.com/apache/spark/pull/28433#issuecomment-622614587
cc @huaxingao @gatorsmile
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[GitHub] [spark] AmplabJenkins removed a comment on pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
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URL: https://github.com/apache/spark/pull/28433#issuecomment-622615289
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[GitHub] [spark] SparkQA commented on pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
SparkQA commented on pull request #28433:
URL: https://github.com/apache/spark/pull/28433#issuecomment-622651055
**[Test build #122186 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/122186/testReport)** for PR 28433 at commit [`c419b93`](https://github.com/apache/spark/commit/c419b931b96e00f6787137032fde8b211fb39def).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] [spark] maropu commented on a change in pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
maropu commented on a change in pull request #28433:
URL: https://github.com/apache/spark/pull/28433#discussion_r418834443
##########
File path: docs/sql-ref-datatypes.md
##########
@@ -824,4 +312,4 @@ SELECT COUNT(*), c2 FROM test GROUP BY c2;
| 2|-Infinity|
| 3| Infinity|
+---------+---------+
-{% endhighlight %}
\ No newline at end of file
+{% endhighlight %}
Review comment:
What's a difference here?
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[GitHub] [spark] dilipbiswal commented on pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
dilipbiswal commented on pull request #28433:
URL: https://github.com/apache/spark/pull/28433#issuecomment-622667364
@maropu sorry, i missed sql-ref-null-semantics.md. I have added it now.
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[GitHub] [spark] huaxingao commented on a change in pull request #28433: [SPARK-31030] [DOCS] [FOLLOWUP] Replace HTML Table by Markdown Table
Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #28433:
URL: https://github.com/apache/spark/pull/28433#discussion_r418801878
##########
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|
+|--- |--- |--- |--- |
Review comment:
Could you align the --- with the column header?
```
|Property Name|Default|Meaning|Since Version|
|-------------|-------|-------|-------------|
```
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