1

我想透视“Person_Log”表数据。它的列如下：

EmployeeID-> Foreign key
Log-> DateTime type

“日志”的格式是"1/22/2013 2:02:34 PM"

我想根据日志列中的日期检查创建数据透视，然后显示每个日期时间的最小值和最大值......它是一种出勤报告......所需的列就像......

EmployeeID, 01-Jan IN, 01-Jan OUT, 02-Jan IN, 02-Jan OUT, 03-Jan IN, 03-Jan OUT.....and so on..

EmployeeID 以外的列应该只包含从“Log”列中提取的时间。对于提取，我使用 convert(char(10), Log, 101) 作为 Date 和 convert(char(5), Log, 108)时间提取目的..

我一天达到的最好成绩是这样的：

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，因为我在两天的最后期限内运行..提前谢谢

正如你所问...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
4

1 回答 1

5

此数据转换是一个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。两个版本都将实现 theUNPIVOT和 thenPIVOT函数。

样本数据：

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');

您的查询从获取每个日期的最小值/最大值的员工列表开始：

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)

请参阅带有演示的 SQL Fiddle

下一步是UNPIVOT将输入/输出时间的单独列放入多行中：

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

请参阅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 |

一旦你得到这个结果，你就可以应用枢轴。如果您提前知道日期值，则可以对这些值进行硬编码，如下所示：

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

请参阅SQL Fiddle with Demo。

但是对于您的情况，您可能需要使用动态 SQL 来生成结果，因为您很可能希望任何一个月的动态结果。它的动态 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)

请参阅SQL Fiddle with Demo。

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) |
于 2013-01-31T17:02:28.240 回答