SQL Query的性能問題
[英]Performance issue with SQL Query
問題描述
我從兩個表中提取數據 - 預測和訂單來計算銷售預測准確性。
我正在采取的步驟：
識別產品 - 區域 - 需求月份的所有詳盡組合，然后兩個數據集......讓我們稱之為（1）
識別預測數據中的不同預測快照...讓我們稱之為（2）
執行（1）和（2）的交叉連接......讓我們稱之為（3）
對於訂單和預測，在（3）的行上執行“SUMIF（）”等價物。 例如，如果我將預測與2月份的實際訂單進行比較，
Jan“INDPOR”預測--->部分產品/地區 - 2月交付組合：2月預測（1月生成）與1月1日之后預訂的訂單以及2月交付時間表
2月“INDPOR”預測--->對於同一產品/地區 - 2月交貨日期組合：2月預測（2月生成）與1月27日之后訂購的訂單* 2月交貨時間表
注1：為同一月生成多個預測
注2：使用的財政日歷定義; 這就是為什么2月27日開始的原因
輸出正確生成。 但是，它很慢（1小時+）。 請幫助我對其進行微調並使其更快，因為我還需要將它用於更大的數據集。
其他詳情：
- 我在我的桌面本地在SQL Server 2014上運行它。使用SQL數據導入向導將其從當前的Excel文件中導入SQL
- 輸入預測數據：ForecastAggTable
- 輸入訂單數據：OrderAggTable
碼：
Select *
from
(
Select *,
(Select isnull(sum([Forecast Qty]),0) from ForecastAggTable t2 where t2.LOB=D.LOB and
t2.[Demand Month]=D.[Demand Month] and t2.Class=D.Class
and t2.[Item Type]=D.[Item Type] and t2.[LoB Region]=D.[LoB Region] and
t2.[Key Account]=D.[Key Account] and t2.Country=D.Country
and t2.[Master Customer]=D.[Master Customer] and t2.[INDPOR Version]=D.[INDPOR Version])[Forecast Qty],
(
Select isnull(sum([Order Qty]),0) from OrderAggTable t1 where t1.LOB=D.LOB and
t1.[SAD Month]=D.[Demand Month] and t1.Class=D.Class
and t1.[Item Type]=D.[Item Type] and t1.[LoB Region]=D.[LoB Region] and
t1.[Key Account]=D.[Key Account] and t1.Country=D.Country
and t1.[Master Customer]=D.[Master Customer] and t1.[Book Date]>=D.[INDPOR Timestamp]
)[SAD-OrderQty],
(
Select isnull(sum([Order Revenue]),0) from OrderAggTable t1 where t1.LOB=D.LOB and
t1.[SAD Month]=D.[Demand Month] and t1.Class=D.Class
and t1.[Item Type]=D.[Item Type] and t1.[LoB Region]=D.[LoB Region] and
t1.[Key Account]=D.[Key Account] and t1.Country=D.Country
and t1.[Master Customer]=D.[Master Customer] and t1.[Book Date]>=D.[INDPOR Timestamp]
)[SAD-OrderRevenue],
(
Select isnull(sum([Order Qty]),0) from OrderAggTable t1 where t1.LOB=D.LOB and
t1.[RDD Month]=D.[Demand Month] and t1.Class=D.Class
and t1.[Item Type]=D.[Item Type] and t1.[LoB Region]=D.[LoB Region] and
t1.[Key Account]=D.[Key Account] and t1.Country=D.Country
and t1.[Master Customer]=D.[Master Customer] and t1.[Book Date]>=D.[INDPOR Timestamp]
)[RDD-OrderQty],
(
Select isnull(sum([Order Revenue]),0) from OrderAggTable t1 where t1.LOB=D.LOB and
t1.[RDD Month]=D.[Demand Month] and t1.Class=D.Class
and t1.[Item Type]=D.[Item Type] and t1.[LoB Region]=D.[LoB Region] and
t1.[Key Account]=D.[Key Account] and t1.Country=D.Country
and t1.[Master Customer]=D.[Master Customer] and t1.[Book Date]>=D.[INDPOR Timestamp]
)[RDD-OrderRevenue]
from
(
Select distinct LOB,[INDPOR Version],[INDPOR Timestamp],[Demand Month],
[Demand Quarter],[Min Date],Class,[Item Type],[Offer PF],
[LoB Region],[Key Account],Country,[Master Customer]
from
(
Select V.LOB,V.[SAD Month][Demand Month],V.[SAD Quarter][Demand Quarter],V.[SAD Min Date][Min Date],V.Class,
[Item Type],[Offer PF],[LoB Region],[Key Account],Country,[Master Customer]
from OrderAggTable V
union
(
Select Z.LOB,Z.[RDD Month][Demand Month],Z.[RDD Quarter][Demand Quarter],Z.[RDD Min Date][Min Date],Z.Class,
[Item Type],[Offer PF],[LoB Region],[Key Account],Country,[Master Customer]
from OrderAggTable Z
)
union
(
Select LOB,[Demand Month],[Demand Quarter],[Min Date],Class[Class],[Item Type],[Offer PF],[LoB Region],
[Key Account],Country,[Master Customer] from ForecastAggTable
)
)A
cross join
(
select distinct [INDPOR Version],[INDPOR Timestamp]
from ForecastAggTable
)B
)D
where [Min Date]>=[INDPOR Timestamp]
)E
where ([SAD-OrderQty] + [RDD-OrderQty] + [Forecast Qty]<>0)
1 個解決方案
解決方案1
0 已采納 2019-06-24 11:26:23
如何簡化和減少表的傳遞。
在這個例子中，我對預測表進行了兩次掃描，一次針對distinct，一次針對union，一次掃描orders表。
with cte as
(
select distinct [INDPOR Version],[INDPOR Timestamp]
from ForecastAggTable
)
,cte2 as
(
Select
V.LOB
,iif(DUP=0,V.[SAD Month] ,V.[RDD Month] ) [Demand Month]
,iif(DUP=0,V.[SAD Quarter] ,V.[RDD Quarter] ) [Demand Quarter]
,iif(DUP=0,V.[SAD Min Date] ,V.[RDD Min Date] ) [Min Date]
,V.[Book Date]
,V.Class
,V.[Item Type]
,V.[Offer PF]
,V.[LoB Region]
,V.[Key Account]
,V.Country
,V.[Master Customer]
,null [INDPOR Version]
,null [Forecast Qty]
,iif(DUP=0,v.[Order Qty] ,null ) [SAD-OrderQty]
,iif(DUP=0,V.[Order Revenue] ,null ) [SAD-OrderRevenue]
,iif(DUP=1,V.[Order Qty] ,null ) [RDD-OrderQty]
,iif(DUP=1,V.[Order Revenue] ,null ) [RDD-OrderRevenue]
from OrderAggTable V
cross join (select dup from (values (0),(1))a(dup)) a
union all
Select
LOB
,[Demand Month]
,[Demand Quarter]
,[Min Date]
,[Min Date]
,Class
,[Item Type]
,[Offer PF]
,[LoB Region]
,[Key Account]
,Country
,[Master Customer]
,[INDPOR Version]
,[Forecast Qty]
,null[SAD-OrderQty]
,null[SAD-OrderRevenue]
,null[RDD-OrderQty]
,null[RDD-OrderRevenue]
from ForecastAggTable
)
select
cte2.LOB
,cte.[INDPOR Version]
,cte.[INDPOR Timestamp]
,cte2.[Demand Month]
,cte2.[Demand Quarter]
,cte2.[Min Date]
,cte2.Class
,cte2.[Item Type]
,cte2.[Offer PF]
,cte2.[LoB Region]
,cte2.[Key Account]
,cte2.Country
,cte2.[Master Customer]
,isnull(sum(cte2.[Forecast Qty] ),0) [Forecast Qty]
,isnull(sum(cte2.[SAD-OrderQty] ),0) [SAD-OrderQty]
,isnull(sum(cte2.[SAD-OrderRevenue]) ,0) [SAD-OrderRevenue]
,isnull(sum(cte2.[RDD-OrderQty] ),0) [RDD-OrderQty]
,isnull(sum(cte2.[RDD-OrderRevenue]) ,0) [RDD-OrderRevenue]
from cte2
inner join cte
on cte2.[Book Date]>=cte.[INDPOR Timestamp]
where isnull(cte2.[INDPOR Version],cte.[INDPOR Version])=cte.[INDPOR Version]
group by
cte2.LOB
,cte2.[Demand Month]
,cte2.[Demand Quarter]
,cte2.[Min Date]
,cte2.Class
,cte2.[Item Type]
,cte2.[Offer PF]
,cte2.[LoB Region]
,cte2.[Key Account]
,cte2.Country
,cte2.[Master Customer]
,cte.[INDPOR Version]
,cte.[INDPOR Timestamp]
having
isnull(sum(cte2.[Forecast Qty] ),0) +
isnull(sum(cte2.[SAD-OrderQty] ),0) +
isnull(sum(cte2.[RDD-OrderQty] ),0)
!=0
SqL查詢性能問題
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