From 18ff1eddc145cf23d197da0c0b5c55d6ea2e7bd1 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Mon, 1 Dec 2014 00:48:18 -0800
Subject: [PATCH] [DOC] Date type

---
 docs/sql-programming-guide.md | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 5500da83b2b66..59bd2c347e061 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1048,6 +1048,7 @@ Spark SQL supports the vast majority of Hive features, such as:
   * `STRING`
   * `BINARY`
   * `TIMESTAMP`
+  * `DATE`
   * `ARRAY<>`
   * `MAP<>`
   * `STRUCT<>`
@@ -1148,6 +1149,7 @@ evaluated by the SQL execution engine.  A full list of the functions supported c
 * Datetime type
     - `TimestampType`: Represents values comprising values of fields year, month, day,
     hour, minute, and second.
+    - `DateType`: Represents values comprising values of fields year, month, day.
 * Complex types
     - `ArrayType(elementType, containsNull)`: Represents values comprising a sequence of
     elements with the type of `elementType`. `containsNull` is used to indicate if
@@ -1255,6 +1257,13 @@ import  org.apache.spark.sql._
   TimestampType
   </td>
 </tr>
+<tr>
+  <td> <b>DateType</b> </td>
+  <td> java.sql.Date </td>
+  <td>
+  DateType
+  </td>
+</tr>
 <tr>
   <td> <b>ArrayType</b> </td>
   <td> scala.collection.Seq </td>
@@ -1381,6 +1390,13 @@ please use factory methods provided in
   DataType.TimestampType
   </td>
 </tr>
+<tr>
+  <td> <b>DateType</b> </td>
+  <td> java.sql.Date </td>
+  <td>
+  DataType.DateType
+  </td>
+</tr>
 <tr>
   <td> <b>ArrayType</b> </td>
   <td> java.util.List </td>
@@ -1528,6 +1544,13 @@ from pyspark.sql import *
   TimestampType()
   </td>
 </tr>
+<tr>
+  <td> <b>DateType</b> </td>
+  <td> datetime.date </td>
+  <td>
+  DateType()
+  </td>
+</tr>
 <tr>
   <td> <b>ArrayType</b> </td>
   <td> list, tuple, or array </td>