我在SQL Server中有2个表，我希望按ID / Name（对我而言合适）将某些行转换为列顺序，并按Date排序。
[dbo].[USERINFO]：
+--------+-------------+---------+
| USERID | BADGENUMBER | NAME |
+--------+-------------+---------+
| 1 | 1000 | BEN |
+--------+-------------+---------+
| 2 | 1111 | ANNE |
+--------+-------------+---------+
[dbo].[CHECKINOUT]：
+--------+-------------------------+
| USERID | CHECKTIME |
+--------+-------------------------+
| 1 | 2019-02-16 08:01:39.000 |
+--------+-------------------------+
| 1 | 2019-02-16 13:05:21.000 |
+--------+-------------------------+
| 1 | 2019-02-16 14:42:23.000 |
+--------+-------------------------+
| 1 | 2019-02-16 17:07:55.000 |
+--------+-------------------------+
| 1 | 2019-02-18 07:56:23.000 |
+--------+-------------------------+
| 1 | 2019-02-18 19:48:23.000 |
+--------+-------------------------+
| 2 | 2019-02-16 07:43:57.000 |
+--------+-------------------------+
| 2 | 2019-02-16 12:30:04.000 |
+--------+-------------------------+
| 2 | 2019-02-18 06:52:55.000 |
+--------+-------------------------+
| 2 | 2019-02-18 18:01:41.000 |
+--------+-------------------------+
| 2 | 2019-02-19 07:55:17.000 |
+--------+-------------------------+
| 2 | 2019-02-19 12:30:08.000 |
+--------+-------------------------+
| 2 | 2019-02-20 07:52:15.000 |
+--------+-------------------------+
| 2 | 2019-02-20 17:51:49.000 |
+--------+-------------------------+
我期望这样的结果。
+------+------+------------+----------+----------+----------+----------+--------+
| ID | Name | Date | Time1 | Time2 | Time3 | Time4 | Time5 |
+------+------+------------+----------+----------+----------+----------+--------+
| 1111 | ANNE | 16/02/2019 | 07:43:57 | 12:30:04 | NULL | NULL | NULL |
+------+------+------------+----------+----------+----------+----------+--------+
| 1111 | ANNE | 18/02/2019 | 06:52:55 | 18:01:41 | NULL | NULL | NULL |
+------+------+------------+----------+----------+----------+----------+--------+
| 1111 | ANNE | 19/02/2019 | 07:55:17 | 12:30:08 | NULL | NULL | NULL |
+------+------+------------+----------+----------+----------+----------+--------+
| 1111 | ANNE | 20/02/2019 | 07:52:15 | 17:51:49 | NULL | NULL | NULL |
+------+------+------------+----------+----------+----------+----------+--------+
| 1000 | BEN | 16/02/2019 | 08:01:39 | 13:05:21 | 14:42:23 | 17:07:55 | NULL |
+------+------+------------+----------+----------+----------+----------+--------+
| 1000 | BEN | 18/02/2019 | 07:56:23 | 19:48:23 | NULL | NULL | NULL |
+------+------+------------+----------+----------+----------+----------+--------+
按ID排序或按名称排序都可以。
到目前为止，我已经尝试过了
SELECT *
INTO #Temp
FROM (
SELECT U.BADGENUMBER as ID, U.[NAME] as Name,
CONVERT(VARCHAR(10),C.CHECKTIME, 103) [Date],
CONVERT(VARCHAR(8), C.CHECKTIME, 108) [Time]
FROM [CHECKINOUT] as C JOIN [USERINFO] as U
ON C.USERID = U.USERID
) AS x
SELECT ID, Name, Date, [1] as Time1, [2] as Time2, [3] as Time3,
[4] as Time4, [5] as Time5, [6] as Time6, [7] as Time7, [8] as Time8, [9] as Time9
FROM ( SELECT
ID, Name, Date, Time,
row_number() over (partition by Name order by Date) as rn
from #Temp
) s
PIVOT (
MAX([Time]) for rn in ([1], [2], [3], [4], [5], [6], [7], [8], [9])
) as pvt
ORDER BY ID
DROP TABLE #Temp
基于此link。
相反，我得到了这样的结果，
+------+------+------------+----------+----------+----------+----------+----------+
| ID | Name | Date | Time1 | Time2 | Time3 | Time4 | Time5 |
+------+------+------------+----------+----------+----------+----------+----------+
| 1111 | ANNE | 16/02/2019 | 07:43:57 | 12:30:04 | NULL | NULL | NULL |
+------+------+------------+----------+----------+----------+----------+----------+
| 1111 | ANNE | 18/02/2019 | NULL | NULL | 06:52:55 | 18:01:41 | NULL |
+------+------+------------+----------+----------+----------+----------+----------+
| 1111 | ANNE | 19/02/2019 | NULL | NULL | NULL | NULL | 07:55:17 |
+------+------+------------+----------+----------+----------+----------+----------+
| 1111 | ANNE | 20/02/2019 | NULL | NULL | NULL | NULL | NULL |
+------+------+------------+----------+----------+----------+----------+----------+
| 1000 | BEN | 16/02/2019 | 08:01:39 | 13:05:21 | 14:42:23 | 17:07:55 | NULL |
+------+------+------------+----------+----------+----------+----------+----------+
| 1000 | BEN | 18/02/2019 | NULL | NULL | NULL | NULL | 07:56:23 |
+------+------+------------+----------+----------+----------+----------+----------+
我哪部分做错了？请为我指出。提前致谢。 问候。
答案 0 :(得分：1)
该问题来自OVER()函数的ROW_NUMBER()子句。您还需要按[Date]进行分区，而不仅仅是按用户进行分区。另外，您还想按[Time]订购。
您需要更改此内容
row_number() over (partition by Name order by Date) as rn
收件人：
row_number() over (partition by [Date], Name order by [Time]) as rn as rn
ID | Name | Date | Time1 | Time2 | Time3 | Time4 | Time5 | Time6 | Time7 | Time8 | Time9 ---: | :--- | :--------- | :------- | :------- | :------- | :------- | :---- | :---- | :---- | :---- | :---- 1000 | BEN | 16/02/2019 | 08:01:39 | 13:05:21 | 14:42:23 | 17:07:55 | null | null | null | null | null 1000 | BEN | 18/02/2019 | 07:56:23 | 19:48:23 | null | null | null | null | null | null | null 1111 | ANNE | 16/02/2019 | 07:43:57 | 12:30:04 | null | null | null | null | null | null | null 1111 | ANNE | 18/02/2019 | 06:52:55 | 18:01:41 | null | null | null | null | null | null | null 1111 | ANNE | 19/02/2019 | 07:55:17 | 12:30:08 | null | null | null | null | null | null | null 1111 | ANNE | 20/02/2019 | 07:52:15 | 17:51:49 | null | null | null | null | null | null | null
此外，我将针对此问题提出另一种解决方案，该解决方案使用条件聚合代替PIVOT。后者是特定于供应商的，而大多数RDBMS支持前者。如果还发现此语法更易于阅读：
SELECT
badgenumber,
name,
[Date],
MAX(CASE WHEN rn = 1 THEN [Time] END) AS Time1,
MAX(CASE WHEN rn = 2 THEN [Time] END) AS Time2,
MAX(CASE WHEN rn = 3 THEN [Time] END) AS Time3,
MAX(CASE WHEN rn = 4 THEN [Time] END) AS Time4,
MAX(CASE WHEN rn = 5 THEN [Time] END) AS Time5
FROM (
SELECT
u.badgenumber,
u.name,
CAST(checktime AS DATE) as [Date],
CAST(checktime AS TIME) as [Time],
ROW_NUMBER() OVER(PARTITION BY u.badgenumber, CAST(checktime AS DATE) ORDER BY c.checktime) rn
FROM userinfo u
INNER JOIN checkinout c ON c.userid = u.userid
) x
GROUP BY badgenumber, name, [Date]
答案 1 :(得分：0)
这应该有效
DECLARE @USERINFO TABLE (USERID INT,BADGENUMBER INT, [NAME] VARCHAR(50))
DECLARE @CHECKINOUT TABLE (USERID INT,CHECKTIME DATETIME)
INSERT INTO @USERINFO VALUES
(1,1000, 'BEN '),
(2,1111, 'ANNE')
INSERT INTO @CHECKINOUT VALUES
(1,'2019-02-16 08:01:39.000'),
(1,'2019-02-16 13:05:21.000'),
(1,'2019-02-16 14:42:23.000'),
(1,'2019-02-16 17:07:55.000'),
(1,'2019-02-18 07:56:23.000'),
(1,'2019-02-18 19:48:23.000'),
(2,'2019-02-16 07:43:57.000'),
(2,'2019-02-16 12:30:04.000'),
(2,'2019-02-18 06:52:55.000'),
(2,'2019-02-18 18:01:41.000'),
(2,'2019-02-19 07:55:17.000'),
(2,'2019-02-19 12:30:08.000'),
(2,'2019-02-20 07:52:15.000'),
(2,'2019-02-20 17:51:49.000')
SELECT *
INTO #Temp
FROM (
SELECT U.BADGENUMBER as ID, U.[NAME] as Name,
CONVERT(VARCHAR(10),C.CHECKTIME, 103) [Date],
CONVERT(VARCHAR(8), C.CHECKTIME, 108) [Time]
FROM @CHECKINOUT as C JOIN @USERINFO as U
ON C.USERID = U.USERID
) AS x
SELECT ID, [Name], Date, [1] as Time1, [2] as Time2, [3] as Time3,
[4] as Time4, [5] as Time5, [6] as Time6, [7] as Time7, [8] as Time8, [9] as Time9
FROM ( SELECT
ID, Name, Date, Time,
row_number() over (partition by [Date], Name order by [Time]) as rn
from #Temp
) s
PIVOT (
MAX([Time]) for rn in ([1], [2], [3], [4], [5], [6], [7], [8], [9])
) as pvt
ORDER BY ID DESC;
DROP TABLE #Temp