kevinyu98 commented on a change in pull request #28120:
[SPARK-31349][SQL][DOCS] Sql ref buildin-aggregate
URL: https://github.com/apache/spark/pull/28120#discussion_r403766350
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File path: docs/sql-ref-functions-builtin-aggregate.md
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@@ -19,4 +19,626 @@ license: |
limitations under the License.
---
-Aggregate functions
\ No newline at end of file
+Spark SQL provides build-in Aggregate functions defines in dataset API and SQL
interface. Aggregate functions
+operate on a group of rows and return a single value.
+
+Spark SQL Aggregate functions are grouped as <code>agg_funcs</code> in spark
SQL. Below is the list of functions.
+
+**Note:** Every below function has another signature which take String as a
column name instead of Column.
+
+* Table of contents
+{:toc}
+<table class="table">
+ <thead>
+ <tr><th
style="width:25%">Function</th><th>Parameters</th><th>Description</th></tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td> <b>{avg | mean}</b>(<i>e: Column</i>)</td>
+ <td>Column name</td>
+ <td> Returns the average of values in the input column.</td>
+ </tr>
+ <tr>
+ <td> <b>{bool_and | every}</b>(<i>e: Column</i>)</td>
+ <td>Column name</td>
+ <td>Returns true if all values are true</td>
+ </tr>
+ <tr>
+ <td> <b>{any | some | bool_or}</b>(<i>e: Column</i>)</td>
+ <td>Column name</td>
+ <td>Returns true if at least one value is true</td>
+ </tr>
+ <tr>
+ <td> <b>approx_count_distinct</b>(<i>e: Column</i>)</td>
+ <td>Column name</td>
+ <td>Returns the estimated cardinality by HyperLogLog++</td>
+ </tr>
+ <tr>
+ <td> <b>corr</b>(<i>e1: Column, e2: Column</i>)</td>
+ <td>Column name</td>
+ <td>Returns Pearson coefficient of correlation between a set of number
pairs</td>
+ </tr>
+ <tr>
+ <td> <b>count</b>(<i>*</i>)</td>
+ <td>None</td>
+ <td>Returns the total number of retrieved rows, including rows
containing null</td>
+ </tr>
+ <tr>
+ <td> <b>count</b>(<i>e: Column[, e: Column]</i>)</td>
+ <td>Column name</td>
+ <td>Returns the number of rows for which the supplied column(s) are all
not null</td>
+ </tr>
+ <tr>
+ <td> <b>count</b>(<b>DISTINCT</b> <i> e: Column[, e: Column</i>])</td>
+ <td>Column name</td>
+ <td>Returns the number of rows for which the supplied column(s) are
unique and not null</td>
+ </tr>
+ <tr>
+ <td> <b>count_if</b>(<i>Predicate</i>)</td>
+ <td>Expression that will be used for aggregation calculation</td>
+ <td>Returns the count number from the predicate evaluate to `TRUE`
values</td>
+ </tr>
+ <tr>
+ <td> <b>covar_pop</b>(<i>e1: Column, e2: Column</i>)</td>
+ <td>Column name</td>
+ <td>Returns the population covariance of a set of number pairs</td>
+ </tr>
+ <tr>
+ <td> <b>covar_samp</b>(<i>e1: Column, e2: Column</i>)</td>
+ <td>Column name</td>
+ <td>Returns the sample covariance of a set of number pairs</td>
+ </tr>
+ <tr>
+ <td> <b>{first | first_value}</b>(<i>e: Column[, isIgnoreNull]</i>)</td>
+ <td>Column name[, True/False(default)]</td>
+ <td>Returns the first value of column for a group of rows. If
`isIgnoreNull` is true, returns only non-null values, default is false. This
function is non-deterministic</td>
Review comment:
done
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