maropu commented on a change in pull request #28220: [SPARK-31390][SQL][DOCS]
Document Window Function in SQL Syntax Section
URL: https://github.com/apache/spark/pull/28220#discussion_r409208518
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File path: docs/sql-ref-syntax-qry-window.md
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@@ -19,4 +19,208 @@ license: |
limitations under the License.
---
-**This page is under construction**
+### Description
+
+Window functions operate on a group of rows, referred to as a window, and
calculate an aggregated value for each row based on the specified window.
Window functions are useful for processing tasks such as calculating a moving
average, computing a cumulative statistic, or accessing the value of rows given
the relative position of the current row. Spark SQL supports three types of
window functions:
+
+ * Ranking Functions
+ * Analytic Functions
+ * Aggregate Functions
+
+### Syntax
+
+{% highlight sql %}
+window_function OVER ( [ partition_spec ] order_spec [ window_frame ] )
+{% endhighlight %}
+
+### Parameters
+
+<dl>
+ <dt><code><em>window_function</em></code></dt>
+ <dd>
+ <ul>
+ <li> Ranking Functions </li>
+ <br>
+ <b>Syntax:</b>
+ <code>
+ RANK | DENSE_RANK | PERCENT_RANK | NTILE | ROW_NUMBER
+ </code>
+ </ul>
+ <ul>
+ <li> Analytic Functions </li>
+ <br>
+ <b>Syntax:</b>
+ <code>
+ CUME_DIST | LAG | LEAD
+ </code>
+ </ul>
+ <ul>
+ <li> Aggregate Functions </li>
+ <br>
+ <b>Syntax:</b>
+ <code>
+ MAX | MIN | COUNT | SUM | AVG | ...
+ </code>
+ <br>
+ Please refer <a href="api/sql/">here</a> for a complete list of Spark
Aggregate Functions.
+ </ul>
+ </dd>
+</dl>
+<dl>
+ <dt><code><em>partition_spec</em></code></dt>
+ <dd>
+ Specifies a comma separated list of key and value pairs for
partitions.<br><br>
+ <b>Syntax:</b><br>
+ <code>
+ { PARTITION | DISTRIBUTE } BY partition_col_name = partition_col_val
( [ , ... ] )
Review comment:
nit: I found the double spaces in this line.
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