将垂直结果转换为水平模式 (T-SQL)-数据库问题

Transform vertical result into horizontal mode (T-SQL)(将垂直结果转换为水平模式 (T-SQL))

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问题描述

以下是示例数据:

计算日期PLResult
2014-01-02     100
2014-01-03     200
2014-02-03    300
2014-02-04     400
2014-02-27    500

这是预期的结果(以逻辑格式):

Here are the expected result (in logical format) :

一月 二月 ;
CalculationDatePLResultCalculationDatePLResult
2014-01-02 100 2014-02-03 300 2014-01-03 200 2014-02-04 400 ; 2014-02-27 500

这是预期的结果(使用 T-SQL 查询):

Here are the expected result (using T-SQL Query) :

Jan-CalculationDateJan-PLResultFeb-CalculationDateFeb-PLResult
2014-01-02        100           2014-02-03     ;   300
2014-01-03        200           2014-02-04     ;    400
               ;                     2014-02-27      ;   500

目标:

按月份对结果进行分类.在上面的示例中，一月份的结果放在一月份的细分中.
月数可以是动态的.在上面的例子中，它只显示了 1 月和 2 月，因为只有 2 个月的结果
结果将通过 Excel 显示.其实我可以查询多个查询表来汇总不同月份的结果，但是如果可以通过一个查询返回所有结果，那么维护和调试会更容易.

以下是填充示例数据的脚本:

Here are the scripts to populate the sample data :

CREATE TABLE #PLResultPerDay ( CalculationDate DATETIME, PLResult DECIMAL(18,8) )
INSERT INTO #PLResultPerDay ( CalculationDate, PLResult ) VALUES ('2014-01-02' , 100 )
INSERT INTO #PLResultPerDay ( CalculationDate, PLResult ) VALUES ('2014-01-03' , 200 )
INSERT INTO #PLResultPerDay ( CalculationDate, PLResult ) VALUES ('2014-02-03' , 300 )
INSERT INTO #PLResultPerDay ( CalculationDate, PLResult ) VALUES ('2014-02-04' , 400 )

到目前为止，这是我构建查询的尝试:

So far here is my attempt in building the query :

SELECT 
    CalculationDate, [January], CalculationDate, [February]
FROM 
(
    SELECT CalculationDate, PLResult, DATENAME(MONTH, CalculationDate) AS [MTH]
    FROM #PLResultPerDay
) x
PIVOT
( 
    MIN(PLResult)
    FOR [MTH] IN ([January], [February])
) p

推荐答案

正如人们所说，这实际上是不可能的，你能得到的最接近的是:

As has been said this isn't actually possible, the closest you could get is:

January2014CalculationDate | January2014PLResult | February2014CalculationDate | February2014PLResult
---------------------------+---------------------+-----------------------------+------------------
    2014-01-02             |       100           |       2014-02-03            |       300
    2014-01-03             |       200           |       2014-02-04            |       400
    NULL                   |       NULL          |       2014-02-27            |       500

即使这并不简单，我仍然建议在 sql 之外处理这样的格式.第一步是按月对数据进行分区，然后对每个月的日期进行排序:

And even that is not simple and I would still advise handling formatting like this outside of sql. The first step is to partition the data by month, and then rank the dates in each month:

SELECT  CalculationDate,
        PLResult,
        CalculationMonth,
        DenseRank = DENSE_RANK() OVER(PARTITION BY CalculationMonth ORDER BY CalculationDate)
FROM    (   SELECT  CalculationDate,
                    PLResult,
                    CalculationMonth = DATEADD(MONTH, DATEDIFF(MONTH, 0, CalculationDate), 0)
            FROM    #PLResultPerDay
        ) pl;

这给出:

CalculationDate PLResult    CalculationMonth    DenseRank
2014-01-02      100         2014-01-01          1
2014-01-03      200         2014-01-01          2
2014-02-03      300         2014-02-01          1
2014-02-04      400         2014-02-01          2
2014-02-27      500         2014-02-01          3

然后您可以旋转这些数据:

You can then pivot this data:

WITH Data AS
(   SELECT  CalculationDate,
            PLResult,
            CalculationMonth,
            DenseRank = DENSE_RANK() OVER(PARTITION BY CalculationMonth ORDER BY CalculationDate)
    FROM    (   SELECT  CalculationDate,
                        PLResult,
                        CalculationMonth = DATEADD(MONTH, DATEDIFF(MONTH, 0, CalculationDate), 0)
                FROM    #PLResultPerDay
            ) pl
)
SELECT  Jan2014CalcDate = MIN(CASE WHEN CalculationMonth = '20140101' THEN CalculationDate END),
        Jan2014Result = SUM(CASE WHEN CalculationMonth = '20140101' THEN PLResult END),
        Feb2014CalcDate = MIN(CASE WHEN CalculationMonth = '20140201' THEN CalculationDate END),
        Feb2014Result = SUM(CASE WHEN CalculationMonth = '20140201' THEN PLResult END)
FROM    Data
GROUP BY DenseRank
ORDER BY DenseRank;

这给出:

Jan2014CalcDate Jan2014Result   Feb2014CalcDate Feb2014Result
2014-01-02      100             2014-02-03      300
2014-01-03      200             2014-02-04      400
NULL            NULL            2014-02-27      500

那么由于您有一个动态的月份数，您需要动态构建上述语句并使用 SP_EXECUTESQL 来运行它:

Then since you have a dynamic number of months you need to build the above statement dynamically and use SP_EXECUTESQL to run it:

DECLARE @SQL NVARCHAR(MAX) = '';

WITH Months AS
(   SELECT  M,
            ColName = DATENAME(MONTH, M) + DATENAME(YEAR, M),
            CharFormat = CONVERT(VARCHAR(8), M, 112)
    FROM    (   SELECT  DISTINCT M = DATEADD(MONTH, DATEDIFF(MONTH, 0, CalculationDate), 0)
                FROM    #PLResultPerDay
            ) m
)
SELECT  @SQL = 'WITH Data AS
                (   SELECT  CalculationDate,
                            PLResult,
                            CalculationMonth,
                            DenseRank = DENSE_RANK() OVER(PARTITION BY CalculationMonth ORDER BY CalculationDate)
                    FROM    (   SELECT  CalculationDate,
                                        PLResult,
                                        CalculationMonth = DATEADD(MONTH, DATEDIFF(MONTH, 0, CalculationDate), 0)
                                FROM    #PLResultPerDay
                            ) pl
                )
                SELECT  ' + 
                STUFF(( SELECT  ', ' + ColName + 'CalculationDate = MIN(CASE WHEN CalculationMonth = ''' + CharFormat + ''' THEN CalculationDate END), ' + 
                                ColName + 'PLResult = SUM(CASE WHEN CalculationMonth = ''' + CharFormat + ''' THEN PLResult END)'
                        FROM    Months
                        ORDER BY M
                        FOR XML PATH(''), TYPE
                    ).value('.', 'NVARCHAR(MAX)'), 1, 2, '') + 
                'FROM   Data
                GROUP BY DenseRank
                ORDER BY DenseRank;';

EXECUTE SP_EXECUTESQL @SQL;

SQL Fiddle 示例

请注意，我仍然建议不要使用这种技术，并认为 SQL 应该用于存储/检索数据，以及用于格式化数据的表示层

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