DATE_ADD命令格式、参数和返回值_云原生大数据计算服务 MaxCompute(MaxCompute)-阿里云帮助中心

按照delta幅度增减startdate日期的天数。

注意事项

若您需要获取在当前时间基础上，指定变动幅度的日期，请参见GETDATE函数。
若您需要精确到时、分或秒更细的幅度增减，请参见DATEADD函数。
本命令与DATE_SUB的增减逻辑相反。

命令格式

DATE DATE_ADD(DATE|TIMESTAMP|STRING <startdate>, BIGINT <delta>)

参数说明

参数

是否必填

说明

startdate

是

起始日期值。支持DATE、DATETIME或STRING类型。

如果参数为STRING类型，且MaxCompute项目的数据类型版本是1.0，则会隐式转换为DATE类型后参与运算，且STRING参数格式至少要包含'yyyy-mm-dd'。例如'2025-07-27'。

delta

是

修改幅度。BIGINT类型。如果delta大于0，则增；delta小于0，则减；delta等于0，不增不减。

返回值说明

返回DATE类型，格式为yyyy-mm-dd。返回规则如下：

startdate非DATE、DATETIME或STRING类型时，返回报错。
startdate值为NULL时，返回报错。
delta值为NULL时，返回NULL。

示例数据

为便于理解各函数的使用方法，本文为您提供源数据，基于源数据提供函数相关示例。创建表mf_date_fun_t，并添加数据，命令示例如下。

create table if not exists mf_date_fun_t(
    id      int,
    date1   date,
    datetime1   datetime,
    timestamp1 timestamp,
    date2   date,
    datetime2   datetime,
    timestamp2 timestamp,
    date3 string,
    date4 bigint);
insert into mf_date_fun_t values
(1,DATE'2021-11-29',DATETIME'2021-11-29 00:01:00',TIMESTAMP'2021-01-11 00:00:00.123456789',DATE'2021-10-29',DATETIME'2021-10-29 00:00:00',TIMESTAMP'2021-10-11 00:00:00.123456789','2021-11-20',123456780),
(2,DATE'2021-11-28',DATETIME'2021-11-28 00:02:00',TIMESTAMP'2021-02-11 00:00:00.123456789',DATE'2021-10-29',DATETIME'2021-10-29 00:00:00',TIMESTAMP'2021-10-11 00:00:00.123456789','2021-11-21',123456781),
(3,DATE'2021-11-27',DATETIME'2021-11-27 00:03:00',TIMESTAMP'2021-03-11 00:00:00.123456789',DATE'2021-10-29',DATETIME'2021-10-29 00:00:00',TIMESTAMP'2021-10-11 00:00:00.123456789','2021-11-22',123456782),
(4,DATE'2021-11-26',DATETIME'2021-11-26 00:04:00',TIMESTAMP'2021-04-11 00:00:00.123456789',DATE'2021-10-29',DATETIME'2021-10-29 00:00:00',TIMESTAMP'2021-10-11 00:00:00.123456789','2021-11-23',123456783),
(5,DATE'2021-11-25',DATETIME'2021-11-25 00:05:00',TIMESTAMP'2021-05-11 00:00:00.123456789',DATE'2021-10-29',DATETIME'2021-10-29 00:00:00',TIMESTAMP'2021-10-11 00:00:00.123456789','2021-11-24',123456784),
(6,DATE'2021-11-24',DATETIME'2021-11-24 00:06:00',TIMESTAMP'2021-06-11 00:00:00.123456789',DATE'2021-10-29',DATETIME'2021-10-29 00:00:00',TIMESTAMP'2021-10-11 00:00:00.123456789','2021-11-25',123456785),
(7,DATE'2021-11-23',DATETIME'2021-11-23 00:07:00',TIMESTAMP'2021-07-11 00:00:00.123456789',DATE'2021-10-29',DATETIME'2021-10-29 00:00:00',TIMESTAMP'2021-10-11 00:00:00.123456789','2021-11-26',123456786),
(8,DATE'2021-11-22',DATETIME'2021-11-22 00:08:00',TIMESTAMP'2021-08-11 00:00:00.123456789',DATE'2021-10-29',DATETIME'2021-10-29 00:00:00',TIMESTAMP'2021-10-11 00:00:00.123456789','2021-11-27',123456787),
(9,DATE'2021-11-21',DATETIME'2021-11-21 00:09:00',TIMESTAMP'2021-09-11 00:00:00.123456789',DATE'2021-10-29',DATETIME'2021-10-29 00:00:00',TIMESTAMP'2021-10-11 00:00:00.123456789','2021-11-28',123456788),
(10,DATE'2021-11-20',DATETIME'2021-11-20 00:10:00',TIMESTAMP'2021-10-11 00:00:00.123456789',DATE'2021-10-29',DATETIME'2021-10-29 00:00:00',TIMESTAMP'2021-10-11 00:00:00.123456789','2021-11-29',123456789);

查询表mf_date_fun_t中的数据，命令示例如下：

select * from mf_date_fun_t;
--返回结果。
+------+-------+------------+------------+-------+------------+------------+-------+------------+
| id   | date1 | datetime1  | timestamp1 | date2 | datetime2  | timestamp2 | date3 | date4      |
+------+-------+------------+------------+-------+------------+------------+-------+------------+
| 1    | 2021-11-29 | 2021-11-29 00:01:00 | 2021-01-11 00:00:00.123456789 | 2021-10-29 | 2021-10-29 00:00:00 | 2021-10-11 00:00:00.123456789 | 2021-11-20 | 123456780  |
| 2    | 2021-11-28 | 2021-11-28 00:02:00 | 2021-02-11 00:00:00.123456789 | 2021-10-29 | 2021-10-29 00:00:00 | 2021-10-11 00:00:00.123456789 | 2021-11-21 | 123456781  |
| 3    | 2021-11-27 | 2021-11-27 00:03:00 | 2021-03-11 00:00:00.123456789 | 2021-10-29 | 2021-10-29 00:00:00 | 2021-10-11 00:00:00.123456789 | 2021-11-22 | 123456782  |
| 4    | 2021-11-26 | 2021-11-26 00:04:00 | 2021-04-11 00:00:00.123456789 | 2021-10-29 | 2021-10-29 00:00:00 | 2021-10-11 00:00:00.123456789 | 2021-11-23 | 123456783  |
| 5    | 2021-11-25 | 2021-11-25 00:05:00 | 2021-05-11 00:00:00.123456789 | 2021-10-29 | 2021-10-29 00:00:00 | 2021-10-11 00:00:00.123456789 | 2021-11-24 | 123456784  |
| 6    | 2021-11-24 | 2021-11-24 00:06:00 | 2021-06-11 00:00:00.123456789 | 2021-10-29 | 2021-10-29 00:00:00 | 2021-10-11 00:00:00.123456789 | 2021-11-25 | 123456785  |
| 7    | 2021-11-23 | 2021-11-23 00:07:00 | 2021-07-11 00:00:00.123456789 | 2021-10-29 | 2021-10-29 00:00:00 | 2021-10-11 00:00:00.123456789 | 2021-11-26 | 123456786  |
| 8    | 2021-11-22 | 2021-11-22 00:08:00 | 2021-08-11 00:00:00.123456789 | 2021-10-29 | 2021-10-29 00:00:00 | 2021-10-11 00:00:00.123456789 | 2021-11-27 | 123456787  |
| 9    | 2021-11-21 | 2021-11-21 00:09:00 | 2021-09-11 00:00:00.123456789 | 2021-10-29 | 2021-10-29 00:00:00 | 2021-10-11 00:00:00.123456789 | 2021-11-28 | 123456788  |
| 10   | 2021-11-20 | 2021-11-20 00:10:00 | 2021-10-11 00:00:00.123456789 | 2021-10-29 | 2021-10-29 00:00:00 | 2021-10-11 00:00:00.123456789 | 2021-11-29 | 123456789  |
+------+-------+------------+------------+-------+------------+------------+-------+------------+

使用示例：静态数据示例

--返回2005-03-01。加1天，结果超出当年2月份的最后1天，实际值为下个月的第1天。
SELECT DATE_ADD(DATETIME '2005-02-28 00:00:00', 1);

--返回2005-02-27。减1天。
SELECT DATE_ADD(DATE '2005-02-28', -1);

--返回2005-03-20。
SET odps.sql.type.system.odps2=false; 
SELECT DATE_ADD('2005-02-28 00:00:00', 20);

--假设当前时间为2020-11-17 16:31:44，返回2020-11-16。
SELECT DATE_ADD(getdate(),-1);

--返回NULL。
SELECT DATE_ADD('2005-02-28 00:00:00', null);

使用示例：表数据示例

基于示例数据，对date1、datetime1和timestamp1列按照幅度变化天数，命令示例如下。

-- 开启2.0新类型。此命令需要与SQL语句一起提交。
SET odps.sql.type.system.odps2=true;
SELECT date1, 
       DATE_ADD(date1, 1) AS date1_date_add, 
       datetime1, 
       DATE_ADD(datetime1, -1) AS datetime1_date_add, 
       timestamp1, 
       DATE_ADD(timestamp1, 0) AS timestamp1_date_add 
FROM mf_date_fun_t;

返回结果如下。

+------------+----------------+---------------------+-------------------+-------------------------------+--------------------+
| date1      | date1_date_add | datetime1           | datetime1_date_add| timestamp1                    | timestamp1_date_add|
+------------+----------------+---------------------+-------------------+-------------------------------+--------------------+
| 2021-11-29 | 2021-11-30     | 2021-11-29 00:01:00 | 2021-11-28        | 2021-01-11 00:00:00.123456789 | 2021-01-11         |
| 2021-11-28 | 2021-11-29     | 2021-11-28 00:02:00 | 2021-11-27        | 2021-02-11 00:00:00.123456789 | 2021-02-11         |
| 2021-11-27 | 2021-11-28     | 2021-11-27 00:03:00 | 2021-11-26        | 2021-03-11 00:00:00.123456789 | 2021-03-11         |
| 2021-11-26 | 2021-11-27     | 2021-11-26 00:04:00 | 2021-11-25        | 2021-04-11 00:00:00.123456789 | 2021-04-11         |
| 2021-11-25 | 2021-11-26     | 2021-11-25 00:05:00 | 2021-11-24        | 2021-05-11 00:00:00.123456789 | 2021-05-11         |
| 2021-11-24 | 2021-11-25     | 2021-11-24 00:06:00 | 2021-11-23        | 2021-06-11 00:00:00.123456789 | 2021-06-11         |
| 2021-11-23 | 2021-11-24     | 2021-11-23 00:07:00 | 2021-11-22        | 2021-07-11 00:00:00.123456789 | 2021-07-11         |
| 2021-11-22 | 2021-11-23     | 2021-11-22 00:08:00 | 2021-11-21        | 2021-08-11 00:00:00.123456789 | 2021-08-11         |
| 2021-11-21 | 2021-11-22     | 2021-11-21 00:09:00 | 2021-11-20        | 2021-09-11 00:00:00.123456789 | 2021-09-11         |
| 2021-11-20 | 2021-11-21     | 2021-11-20 00:10:00 | 2021-11-19        | 2021-10-11 00:00:00.123456789 | 2021-10-11         |
+------------+----------------+---------------------+-------------------+-------------------------------+--------------------+