@@ -1048,6 +1048,7 @@ Spark SQL supports the vast majority of Hive features, such as:
10481048 * ` STRING `
10491049 * ` BINARY `
10501050 * ` TIMESTAMP `
1051+ * ` DATE `
10511052 * ` ARRAY<> `
10521053 * ` MAP<> `
10531054 * ` STRUCT<> `
@@ -1148,6 +1149,7 @@ evaluated by the SQL execution engine. A full list of the functions supported c
11481149* Datetime type
11491150 - ` TimestampType ` : Represents values comprising values of fields year, month, day,
11501151 hour, minute, and second.
1152+ - ` DateType ` : Represents values comprising values of fields year, month, day.
11511153* Complex types
11521154 - ` ArrayType(elementType, containsNull) ` : Represents values comprising a sequence of
11531155 elements with the type of ` elementType ` . ` containsNull ` is used to indicate if
@@ -1255,6 +1257,13 @@ import org.apache.spark.sql._
12551257 TimestampType
12561258 </td >
12571259</tr >
1260+ <tr >
1261+ <td > <b >DateType</b > </td >
1262+ <td > java.sql.Date </td >
1263+ <td >
1264+ DateType
1265+ </td >
1266+ </tr >
12581267<tr >
12591268 <td > <b >ArrayType</b > </td >
12601269 <td > scala.collection.Seq </td >
@@ -1381,6 +1390,13 @@ please use factory methods provided in
13811390 DataType.TimestampType
13821391 </td >
13831392</tr >
1393+ <tr >
1394+ <td > <b >DateType</b > </td >
1395+ <td > java.sql.Date </td >
1396+ <td >
1397+ DataType.DateType
1398+ </td >
1399+ </tr >
13841400<tr >
13851401 <td > <b >ArrayType</b > </td >
13861402 <td > java.util.List </td >
@@ -1528,6 +1544,13 @@ from pyspark.sql import *
15281544 TimestampType()
15291545 </td >
15301546</tr >
1547+ <tr >
1548+ <td > <b >DateType</b > </td >
1549+ <td > datetime.date </td >
1550+ <td >
1551+ DateType()
1552+ </td >
1553+ </tr >
15311554<tr >
15321555 <td > <b >ArrayType</b > </td >
15331556 <td > list, tuple, or array </td >
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