我想透视" Person_Log"表数据.. 其专栏如下:
EmployeeID-> Foreign key
Log-> DateTime type
格式" Log"是"1/22/2013 2:02:34 PM"
我想基于对日志日期列的检查创建枢轴,然后显示每个日期的时间的最小值和最大值... 它是一种出勤报告.. 必填列类似于......
EmployeeID, 01-Jan IN, 01-Jan OUT, 02-Jan IN, 02-Jan OUT, 03-Jan IN, 03-Jan OUT.....and so on..
除了EmployeeID之外的列应该只包含从" Log"中提取的时间。柱.. 对于提取,我使用转换(char(10),Log,101)for Date和convert(char(5),Log,108)进行时间提取目的..
我达到一天的最好成绩是:
SELECT dbo.DoorLog.EmployeeID,
CONVERT(char(10),
MIN(dbo.DoorLog.DateTime), 101) AS Date,
CONVERT(char(8), MIN(dbo.DoorLog.DateTime), 108) AS INTime,
CONVERT(char(8), MAX(dbo.DoorLog.DateTime), 108) AS OUTTime,
dbo.Person.Name, dbo.Person.Department, dbo.Person.Sex,
dbo.Person.WorkUnit,
dbo.Person.Position
FROM dbo.DoorLog
INNER JOIN dbo.Person ON dbo.DoorLog.EmployeeID = dbo.Person.EmployeeID
GROUP BY CONVERT(char(10), dbo.DoorLog.DateTime, 101),
dbo.DoorLog.EmployeeID, dbo.Person.Name, dbo.Person.Department,
dbo.Person.Sex, dbo.Person.WorkUnit, dbo.Person.Position;
请回复,因为我在两天的截止日期前运行。 提前致谢
正如你问的......样本数据......
Log EmployeeID
2013/01/31 12:31 11
2013/01/25 10:31 10
2013/01/23 13:29 8
2013/01/20 11:49 4
答案 0 :(得分:5)
此数据转换为PIVOT
。在SQL Server 2005+中,有一个函数可以为您旋转数据。有几种方法可以获得您想要的结果。这两个版本都将实现UNPIVOT
和PIVOT
函数。
示例数据:
CREATE TABLE Person ([EmployeeId] int, [Name] varchar(4));
INSERT INTO Person ([EmployeeId], [Name])
VALUES
(11, 'Jim'),
(10, 'John'),
(8, 'Mary'),
(4, 'Tim');
CREATE TABLE DoorLog([EmployeeId] int, [DoorDate] datetime);
INSERT INTO DoorLog ([EmployeeId], [DoorDate])
VALUES
(11, '2013-01-31 12:31:00'),
(11, '2013-01-31 16:50:00'),
(11, '2013-01-31 17:50:00'),
(10, '2013-01-25 10:31:00'),
(10, '2013-01-25 16:45:00'),
(8, '2013-01-23 13:29:00'),
(8, '2013-01-23 18:25:00'),
(4, '2013-01-20 11:49:00'),
(4, '2013-01-20 19:10:00'),
(11, '2013-01-15 11:15:00'),
(11, '2013-01-15 16:25:00'),
(10, '2013-01-10 09:21:00'),
(10, '2013-01-10 15:45:00'),
(8, '2013-01-08 01:29:00'),
(8, '2013-01-08 02:25:00'),
(4, '2013-01-06 10:17:00'),
(4, '2013-01-06 19:10:00');
您的查询首先获取具有每个日期的最小/最大值的员工列表:
select p.employeeid,
p.name,
convert(char(10),d.doordate, 101) date,
min(d.doordate) [In],
max(d.doordate) [Out]
from person p
left join doorlog d
on p.employeeid = d.employeeid
group by p.employeeid, p.name,
convert(char(10),d.doordate, 101)
下一步是UNPIVOT
,它将为IN / OUT时间采用单独的列并将它们分成多行:
select employeeid, name,
convert(char(8), doortime, 108) DoorTime,
date + '_'+ col as col_names
from
(
select p.employeeid,
p.name,
convert(char(10),d.doordate, 101) date,
min(d.doordate) [In],
max(d.doordate) [Out]
from person p
left join doorlog d
on p.employeeid = d.employeeid
group by p.employeeid, p.name,
convert(char(10),d.doordate, 101)
) src
unpivot
(
doortime
for col in ([In], [Out])
) unpiv
见SQL Fiddle with Demo。结果将如下所示:
| EMPLOYEEID | NAME | DOORTIME | COL_NAMES |
-------------------------------------------------
| 4 | Tim | 10:17:00 | 01/06/2013_In |
| 4 | Tim | 19:10:00 | 01/06/2013_Out |
| 4 | Tim | 11:49:00 | 01/20/2013_In |
| 4 | Tim | 19:10:00 | 01/20/2013_Out |
获得此结果后,即可应用数据透视表。如果您提前知道日期值,那么您可以硬编码与此类似的值:
select *
from
(
select employeeid, name,
convert(char(8), doortime, 108) DoorTime,
date + '_'+ col as col_names
from
(
select p.employeeid,
p.name,
convert(char(10),d.doordate, 101) date,
min(d.doordate) [In],
max(d.doordate) [Out]
from person p
left join doorlog d
on p.employeeid = d.employeeid
group by p.employeeid, p.name,
convert(char(10),d.doordate, 101)
) src
unpivot
(
doortime
for col in ([In], [Out])
) unpiv
) p
pivot
(
max(doortime)
for col_names in ([01/06/2013_In], [01/06/2013_Out],
[01/08/2013_In], [01/08/2013_Out],
[01/10/2013_In], [01/10/2013_Out],
[01/15/2013_In], [01/15/2013_Out],
[01/20/2013_In], [01/20/2013_Out],
[01/23/2013_In], [01/23/2013_Out],
[01/31/2013_In], [01/31/2013_Out])
) piv
但是对于您的情况,您可能需要使用动态SQL来生成结果,因为您很可能希望在任何月份都能获得结果。这个动态SQL版本是:
DECLARE @cols AS NVARCHAR(MAX),
@query AS NVARCHAR(MAX)
select @cols = STUFF((SELECT ',' + QUOTENAME(date +'_'+Logname)
from
(
select doordate,
convert(char(10),doordate, 101) date,
LogName
from DoorLog
cross apply
(
select 'In' LogName
union all
select 'Out'
) l
) s
group by convert(char(10), doordate, 112), date, Logname
order by convert(char(10), doordate, 112)
FOR XML PATH(''), TYPE
).value('.', 'NVARCHAR(MAX)')
,1,1,'')
set @query
= 'select employeeid, name, '+@cols+'
from
(
select employeeid, name,
convert(char(8), doortime, 108) DoorTime,
date + ''_''+ col col_names
from
(
select p.employeeid,
p.name,
convert(char(10),d.doordate, 101) date,
min(d.doordate) [In],
max(d.doordate) [Out]
from person p
left join doorlog d
on p.employeeid = d.employeeid
group by p.employeeid, p.name,
convert(char(10),d.doordate, 101)
)src
unpivot
(
doortime
for col in ([In], [Out])
) unpiv
) p
pivot
(
max(doortime)
for col_names in('+@cols+')
) piv'
execute(@query)
两个查询的结果是:
| EMPLOYEEID | NAME | 01/06/2013_IN | 01/06/2013_OUT | 01/08/2013_IN | 01/08/2013_OUT | 01/10/2013_IN | 01/10/2013_OUT | 01/15/2013_IN | 01/15/2013_OUT | 01/20/2013_IN | 01/20/2013_OUT | 01/23/2013_IN | 01/23/2013_OUT | 01/25/2013_IN | 01/25/2013_OUT | 01/31/2013_IN | 01/31/2013_OUT |
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
| 11 | Jim | (null) | (null) | (null) | (null) | (null) | (null) | 11:15:00 | 16:25:00 | (null) | (null) | (null) | (null) | (null) | (null) | 12:31:00 | 17:50:00 |
| 10 | John | (null) | (null) | (null) | (null) | 09:21:00 | 15:45:00 | (null) | (null) | (null) | (null) | (null) | (null) | 10:31:00 | 16:45:00 | (null) | (null) |
| 8 | Mary | (null) | (null) | 01:29:00 | 02:25:00 | (null) | (null) | (null) | (null) | (null) | (null) | 13:29:00 | 18:25:00 | (null) | (null) | (null) | (null) |
| 4 | Tim | 10:17:00 | 19:10:00 | (null) | (null) | (null) | (null) | (null) | (null) | 11:49:00 | 19:10:00 | (null) | (null) | (null) | (null) | (null) | (null) |