SQL Server:动态透视 5 列-数据库问题

SQL Server : dynamic pivot over 5 columns(SQL Server:动态透视 5 列)

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问题描述

我很难弄清楚如何在 SQL Server 2008 中使用多列进行动态数据透视.

I'm having a very tough time trying to figure out how to do a dynamic pivot in SQL Server 2008 with multiple columns.

我的示例表如下:

ID  YEAR  TYPE  TOTAL   VOLUME
DD1 2008    A   1000    10
DD1 2008    B   2000    20
DD1 2008    C   3000    30
DD1 2009    A   4000    40
DD1 2009    B   5000    50
DD1 2009    C   6000    60
DD2 2008    A   7000    70
DD2 2008    B   8000    80
DD2 2008    C   9000    90
DD2 2009    A   10000   100
DD2 2009    B   11000   110
DD2 2009    C   12000   120

我正在尝试如下所示:

ID  2008_A_TOTAL    2008_A_VOLUME   2008_B_TOTAL    2008_B_VOLUME   2008_C_TOTAL    2008_C_VOLUME   2009_A_TOTAL    2009_A_VOLUME   2009_B_TOTAL    2009_B_VOLUME   2009_C_TOTAL    2009_C_VOLUME
DD1 1000            10              2000            20              3000            30              4000            40              5000            50              6000            60
DD2 7000            70              8000            80              9000            90              10000           100             11000           110             12000           120

我的SQL Server 2008查询创建表如下:

My SQL Server 2008 query is as follows to create the table:

CREATE TABLE ATM_TRANSACTIONS 
(
 ID varchar(5),
 T_YEAR varchar(4),
 T_TYPE varchar(3), 
 TOTAL int,
 VOLUME int
);

INSERT INTO ATM_TRANSACTIONS
(ID,T_YEAR,T_TYPE,TOTAL,VOLUME)

VALUES
('DD1','2008','A',1000,10),
('DD1','2008','B',2000,20),
('DD1','2008','C',3000,30),
('DD1','2009','A',4000,40),
('DD1','2009','B',5000,50),
('DD1','2009','C',6000,60),
('DD2','2008','A',7000,70),
('DD2','2008','B',8000,80),
('DD2','2008','C',9000,90),
('DD2','2009','A',10000,100),
('DD2','2009','B',11000,110),
('DD2','2009','C',1200,120);

T_Year 列将来可能会改变，但 T_TYPE 列一般是已知的，所以我不确定是否可以在中使用 PIVOT 函数的组合带有动态代码的 SQL Server?

The T_Year column may change in the future but the T_TYPE column is generally know, so I'm not sure if I can use a combination of the PIVOT function in SQL Server with dynamic code?

我尝试按照此处的示例进行操作:

I tried following the example here:

http://social.technet.microsoft.com/wiki/contents/articles/17510.t-sql-dynamic-pivot-on-multiple-columns.aspx

但我最终得到了奇怪的结果.

but I ended up with with weird results.

推荐答案

为了得到结果，你需要查看 Total 和 Volume 列.我的建议是首先编写查询的硬编码版本，然后将其转换为动态 SQL.


In order to get the result, you will need to look at unpivoting the data in the Total and Volume columns first before applying the PIVOT function to get the final result.  My suggestion would be to first write a hard-coded version of the query then convert it to dynamic SQL.
UNPIVOT 进程将这些多列转换为行.UNPIVOT 有几种方法，您可以使用 UNPIVOT 功能，也可以使用 CROSS APPLY.取消透视数据的代码类似于:
The UNPIVOT process converts these multiple columns into rows. There are a few ways to UNPIVOT, you can use the UNPIVOT function or you can use CROSS APPLY.   The code to unpivot the data will be similar to:
select id, 
    col = cast(t_year as varchar(4))+'_'+t_type+'_'+col, 
    value
from ATM_TRANSACTIONS t
cross apply
(
    select 'total', total union all
    select 'volume', volume
) c (col, value);

这将为您提供以下格式的数据:
This gives you data in the format:
+-----+---------------+-------+
| id  |      col      | value |
+-----+---------------+-------+
| DD1 | 2008_A_total  |  1000 |
| DD1 | 2008_A_volume |    10 |
| DD1 | 2008_B_total  |  2000 |
| DD1 | 2008_B_volume |    20 |
| DD1 | 2008_C_total  |  3000 |
| DD1 | 2008_C_volume |    30 |
+-----+---------------+-------+

然后就可以应用 PIVOT 函数了:
Then you can apply the PIVOT function:
select ID, 
    [2008_A_total], [2008_A_volume], [2008_B_total], [2008_B_volume],
    [2008_C_total], [2008_C_volume], [2009_A_total], [2009_A_volume]
from
(
    select id, 
        col = cast(t_year as varchar(4))+'_'+t_type+'_'+col, 
        value
    from ATM_TRANSACTIONS t
    cross apply
    (
        select 'total', total union all
        select 'volume', volume
    ) c (col, value)
) d
pivot
(
    max(value)
    for col in ([2008_A_total], [2008_A_volume], [2008_B_total], [2008_B_volume],
                [2008_C_total], [2008_C_volume], [2009_A_total], [2009_A_volume])
) piv;

既然你有正确的逻辑，你可以把它转换成动态 SQL:
Now that you have the correct logic, you can convert this to dynamic SQL:
DECLARE @cols AS NVARCHAR(MAX),
    @query  AS NVARCHAR(MAX)

select @cols = STUFF((SELECT ',' + QUOTENAME(cast(t_year as varchar(4))+'_'+t_type+'_'+col) 
                    from ATM_TRANSACTIONS t
                    cross apply
                    (
                        select 'total', 1 union all
                        select 'volume', 2
                    ) c (col, so)
                    group by col, so, T_TYPE, T_YEAR
                    order by T_YEAR, T_TYPE, so
            FOR XML PATH(''), TYPE
            ).value('.', 'NVARCHAR(MAX)') 
        ,1,1,'')

set @query = 'SELECT id,' + @cols + ' 
            from 
            (
                select id, 
                    col = cast(t_year as varchar(4))+''_''+t_type+''_''+col, 
                    value
                from ATM_TRANSACTIONS t
                cross apply
                (
                    select ''total'', total union all
                    select ''volume'', volume
                ) c (col, value)
            ) x
            pivot 
            (
                max(value)
                for col in (' + @cols + ')
            ) p '

execute sp_executesql @query;

这会给你一个结果:
+-----+--------------+---------------+--------------+---------------+--------------+---------------+--------------+---------------+--------------+---------------+--------------+---------------+
| id  | 2008_A_total | 2008_A_volume | 2008_B_total | 2008_B_volume | 2008_C_total | 2008_C_volume | 2009_A_total | 2009_A_volume | 2009_B_total | 2009_B_volume | 2009_C_total | 2009_C_volume |
+-----+--------------+---------------+--------------+---------------+--------------+---------------+--------------+---------------+--------------+---------------+--------------+---------------+
| DD1 |         1000 |            10 |         2000 |            20 |         3000 |            30 |         4000 |            40 |         5000 |            50 |         6000 |            60 |
| DD2 |         7000 |            70 |         8000 |            80 |         9000 |            90 |        10000 |           100 |        11000 |           110 |         1200 |           120 |
+-----+--------------+---------------+--------------+---------------+--------------+---------------+--------------+---------------+--------------+---------------+--------------+---------------+


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