我正在使用MS Sql2008，
Table1: Plan.Plan
Table2: Plan.PlanFeature
Table3:Plan.PlanDetails
Plan.Plan
PlanID_PK PlanName AnnualPrice MonthlyPrice
1 Plan1 Free Free
2 Plan2 $50.00 $4.99
3 Plan3 $100.00 $9.99
Plan.PlanFeature
PlanFeatureID_PK FeatureName
- - - - - - - - - - - - - - - - - - - - - - - - - - -
1 Feature1
2 Feature2
3 Feature3
4 Feature4
Plan.PlanDetails
PlanDetailsID_PK PlanID_FK PlanFeatureID_FK Quantity Quantity_Type
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - -
1 1 1 0 Included
2 1 2 0 Unlimited
3 1 3 2 None
4 1 4 0 Unlimited
5 2 1 0 Included
6 2 2 0 Unlimited
7 2 3 20 None
8 2 4 0 Unlimited
9 3 1 0 Included
10 3 2 0 Unlimited
11 3 3 >20 None
12 3 4 0 Unlimited
Output :
FeatureName Plan1 Plan2 Plan3
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Feature1 Included Included Included
Feature2 Unlimited Unlimited Unlimited
Feature3 2 20 >20
Feature4 Unlimited Unlimited Unlimited
AnnualPrice Free $50.00 $100.00
MonthlyPrice Free $4.99 $9.99
所以我们在这里，为了将行转换为列，我使用了COALESCE和Pivot
这是我正在尝试的存储过程，
DECLARE @cols NVARCHAR(2000)
DECLARE @SubjectQuery NVARCHAR(MAX)
SELECT @cols = COALESCE(@cols + ',[' + PlanNames + ']',
'[' + PlanNames + ']')
FROM Plans.Plans
ORDER BY PlanNames
SET @SubjectQuery = 'SELECT FeatureName, ' + @cols + '
FROM
(select p.PlanId_PK, p.PlanNames,pf.FeatureName,pd.Quantity from Plans.PlanDetails pd
join Plans.Plans p on pd.PlanId_FK=p.PlanId_PK
join Plans.PlanFeatures pf on pd.PlanFeatureId_FK=pf.PlanFeatureId_PK ) S
PIVOT
(
Count(Quantity) --Dont know which aggregate functions i have to use here according to my output
FOR PlanNames IN
(' + @cols + ')) AS pvt'
exec @SubjectQuery
对于数量，我必须检查此MAX(CASE WHEN pd.Quantity = '0' THEN pd.Quantity_Type)as Quantity之类的条件，如果数量值为0，那么我必须显示Quantity_type值
在枢轴块内部根据我的输出不知道我必须使用哪种聚合功能，你能想出来吗？
答案 0 :(得分：3)
根据您的示例数据和期望输出，我认为您需要在case之前使用子查询内部的pivot：
DECLARE @cols NVARCHAR(2000)
DECLARE @SubjectQuery NVARCHAR(MAX)
SELECT @cols = COALESCE(@cols + ',[' + PlanName + ']',
'[' + PlanName + ']')
FROM [Plans]
ORDER BY PlanName
SET @SubjectQuery
= 'SELECT FeatureName, ' + @cols + '
FROM
(
select p.PlanName,
pf.FeatureName,
MAX(CASE WHEN pd.Quantity = ''0'' THEN pd.Quantity_Type else quantity end)as Quantity
from PlanDetails pd
join [Plans] p
on pd.PlanId_FK=p.PlanId_PK
join PlanFeature pf
on pd.PlanFeatureId_FK=pf.PlanFeatureId_PK
group by p.PlanName, pf.FeatureName
) S
PIVOT
(
max(Quantity)
FOR PlanName IN (' + @cols + ')
) AS pvt'
exec(@SubjectQuery);
结果是：
| FEATURENAME | PLAN1 | PLAN2 | PLAN3 |
---------------------------------------------------
| Feature1 | Included | Included | Included |
| Feature2 | Unlimited | Unlimited | Unlimited |
| Feature3 | 2 | 20 | >20 |
| Feature4 | Unlimited | Unlimited | Unlimited |
编辑，如果您需要年度价格和月度价格，那么您需要合并unpivot和pivot函数：
DECLARE @cols NVARCHAR(2000)
DECLARE @SubjectQuery NVARCHAR(MAX)
SELECT @cols = COALESCE(@cols + ',[' + PlanName + ']',
'[' + PlanName + ']')
FROM [Plans]
ORDER BY PlanName
SET @SubjectQuery
= 'SELECT FeatureName,'+@cols+'
FROM
(
select PlanName, FeatureName,
MAX(CASE WHEN Quantity = ''0'' THEN Quantity_Type else quantity end)as Quantity,
SortOrder
from
(
select p.PlanName, pf.FeatureName, pd.Quantity, pd.Quantity_Type, 1 Sortorder
from PlanDetails pd
join [Plans] p
on pd.PlanId_FK=p.PlanId_PK
join PlanFeature pf
on pd.PlanFeatureId_FK=pf.PlanFeatureId_PK
union all
select PlanName, col, ''0'', value, 2 SortOrder
from
(
select PlanID_PK, PlanName,
AnnualPrice,
cast(MonthlyPrice as varchar(6)) MonthlyPrice
from plans
) src
unpivot
(
value
for col in (annualprice, monthlyprice)
) unpiv
) pl
group by PlanName, FeatureName, SortOrder
) d
PIVOT
(
max(Quantity)
FOR PlanName IN ('+@cols+')
) AS pvt
order by SortOrder'
exec(@SubjectQuery);
结果：
| FEATURENAME | PLAN1 | PLAN2 | PLAN3 |
----------------------------------------------------
| Feature1 | Included | Included | Included |
| Feature2 | Unlimited | Unlimited | Unlimited |
| Feature3 | 2 | 20 | >20 |
| Feature4 | Unlimited | Unlimited | Unlimited |
| AnnualPrice | Free | 50.00 | 100.00 |
| MonthlyPrice | Free | 4.99 | 9.99 |