sql - 涉及 SQL 中的分组和平均的复杂查询的逻辑

Question

我有 2 张桌子。
以下是我需要遵循的步骤才能获得所需的输出。我可以按照步骤 3 进行操作。请帮助我，因为它有点复杂，我无法理解如何进一步进行。

表格1

Site  Code FailFlag  Comments  ModifiedDate  ModifiedBy
ABT   A01     F      Dfasdf    10/11/2011    Anna
ABT   A01     F      dsfsdf    15/12/2012    Mand
ABT   A01            Rds       30/03/2011    Tim
ABT   A01            GHDs      02/12/2012    Andy
ABT   A02     F      dfd       09/05/2012    Anna
ABT   A02            sdada     11/02/2013    Kathy
ABT   A02            Dfg       15/05/2011    Rob
AFL   A02     F      asda      13/02/2011    Dan
AFL   A02            dsaa      24/12/2010    Ryan
TRG   A01            sdasd     16/04/2010    Richard
TRG   K05            jksdh     23/04/2012    Mark
KLD   K05     F      sd        18/05/2013    Jim
KLD   K05            dsfsd     10/03/2012    James
KLD   K05            sdsd      12/05/2011    Luther
KTY   K05     F      saq       09/09/2012    Ryan
KTY   K05            asd       04/04/2010    Kathy
KMD   C02     F      nas       29/02/2012    Rob
KMD   C02            asda      11/11/2011    Andy

表 2：

Site  Code   Freq     StartDate   EndDate
ABT   A01    43       01/01/2011  01/02/2012
ABT   A02    254      01/01/2011  19/02/2011
ABT   A02    109      20/02/2011  01/01/2012
ABT   A02    12       02/01/2012  01/01/2013
AFL   A02    13       01/01/2011  01/02/2012
TRG   A01    122      01/01/2011  01/02/2012
TRG   K05    61       01/01/2011  01/02/2012
KLD   KO5    33       01/01/2011  15/05/2012
KLD   K05    79       16/05/2012  01/01/2013
KTY   K05    52       01/01/2011  01/02/2012
KMD   C02    78       01/01/2011  01/02/2012
ZYT   G01    11       01/01/2011  01/02/2012
PYN   A01    15       01/01/2011  01/02/2012
DYN   F08    122      01/01/2011  01/02/2012

脚步：

表 1 中的“站点”和“代码”两列的组合是在表 2 中的“站点”和“代码”列的组合中查看的。

在“失败”列中过滤相同的内容并找出失败的次数

以下是查询和输出：

SELECT Site,Code,COUNT(*) as [Count],
FailFlagCount= SUM(CASE WHEN F = 'F' THEN 1 ELSE 0 END) FROM Table1 
GROUP BY Site,Det


Site Code    Count   FailFlagCount
ABT   A01     4      2
ABT   A02     3      1
AFL   A02     2      1
TRG   A01     1      0
TRG   K05     1      0      
KLD   KO5     3      1
KTY   K05     2      1
KMD   C02     2      1

我们在表 2 中检查相同的组合。即，在表 2 中查看步骤 2 输出的站点和代码以获得其频率
计算：

一个。CC % = [1-(FailCount / Count)]*100 = [1-(2/4)]*100

湾。BPO % = [1-(FailCount / Freq)]*100 = [1-(2/43)]*100

c.Forecast% =

让我们假设当前月份是 3 月。

计算过去 3 年的失败次数并找到平均值，假设为“X”。
计算剩余月份的故障，'Y' = (X * 剩余月份/12)
12 个月内的总故障 = 当前故障 + Y

因此 Forecast% = [1-(12 个月内的总故障/频率)]*100。

对于我们的示例，让我们假设 X = 2 的值因此 Y = (2 * 9/12)

预测百分比 = [1-(1+1.5)]*100

将对站点和代码的所有可能组合进行上述计算。

Site    Code    CC  B.P.O   Forecast   StartDate    EndDate
ABT   A01                                  01/01/2011  01/02/2012
ABT   A02                                  01/01/2011  19/02/2011
ABT   A02                                  20/02/2011  01/01/2012
ABT   A02                                  02/01/2012  01/01/2013
AFL   A02                                  01/01/2011  01/02/2012
TRG   A01                                  01/01/2011  01/02/2012
TRG   K05                                  01/01/2011  01/02/2012
KLD   K05                                  01/01/2011  15/05/2012
KLD   K05                                  16/05/2012  01/01/2013
KTY   K05                                  01/01/2011  01/02/2012
KMD   C02                                  01/01/2011  01/02/2012

上表的分组是在站点类型上完成的，即站点的第一个字母。然后在分组后完成所有计算（cc，BPO，Forecast）的平均值例如： - 'A' 代表'ABT'，'T' 代表'TRG'。（我假设我们为多种类型创建多个表，然后进行联合以获取以下查询）
示例：-
```
Site     Code                             CC     B.P.O    Forecast          
 A    A01  
 A    A02 
    [i.e.,Avg value of (ABT and A02) 
         and (AFL and A02)]  
 T    A01 
 T    K05 avg
    [i.e., Avg value of (KLD and K05) 
         and (KTY and K05)]  
 K    K05 
 K    C02 
```

这应该是我的最终输出。请帮忙。

为表格添加脚本以便更容易提供帮助：

表 1 脚本：

CREATE TABLE Table1
    (
    ID INT IDENTITY(1,1) PRIMARY KEY CLUSTERED,
    Site VARCHAR(5),
    Code VARCHAR(5),
    FailFlag CHAR(1),
    Comments VARCHAR(100),
    ModifiedDate  DATETIME,
    ModifiedBy   VARCHAR(50)
    );

INSERT INTO Table1 
      (Site,  Code,  FailFlag, Comments, ModifiedDate, ModifiedBy)
SELECT 'ABT', 'A01',   'F',    'Dfasdf', '10/11/2011', 'Anna'    UNION ALL
SELECT 'ABT', 'A01',   'F',    'dsfsdf', '15/12/2012', 'Mand'    UNION ALL
SELECT 'ABT', 'A01',   NULL,   'Rds',    '30/03/2011', 'Tim'     UNION ALL
SELECT 'ABT', 'A01',   NULL,   'GHDs',   '02/12/2012', 'Andy'    UNION ALL
SELECT 'ABT', 'A02',   'F',    'dfd',    '09/05/2012', 'Anna'    UNION ALL
SELECT 'ABT', 'A02',   NULL,   'sdada',  '11/02/2013', 'Kathy'   UNION ALL
SELECT 'ABT', 'A02',   NULL,   'Dfg',    '15/05/2011', 'Rob'     UNION ALL
SELECT 'AFL', 'A02',   'F',    'asda',   '13/02/2011', 'Dan'     UNION ALL
SELECT 'AFL', 'A02',   NULL,   'dsaa',   '24/12/2010', 'Ryan'    UNION ALL
SELECT 'TRG', 'A01',   NULL,   'sdasd',  '16/04/2010', 'Richard' UNION ALL
SELECT 'TRG', 'K05',   NULL,   'jksdh',  '23/04/2012', 'Mark'    UNION ALL
SELECT 'KLD', 'K05',   'F',    'sd',     '18/05/2013', 'Jim'     UNION ALL
SELECT 'KLD', 'K05',   NULL,   'dsfsd',  '10/03/2012', 'James'   UNION ALL
SELECT 'KLD', 'K05',   NULL,   'sdsd',   '12/05/2011', 'Luther'  UNION ALL
SELECT 'KTY', 'K05',   'F',    'saq',    '09/09/2012', 'Ryan'    UNION ALL
SELECT 'KTY', 'K05',   NULL,   'asd',    '04/04/2010', 'Kathy'   UNION ALL
SELECT 'KMD', 'C02',   'F',    'nas',    '29/02/2012', 'Rob'     UNION ALL
SELECT 'KMD', 'C02',   NULL,   'asda',   '11/11/2011', 'Andy';

表 2 脚本：

CREATE TABLE Table2
    (
    ID INT IDENTITY(1,1) PRIMARY KEY CLUSTERED,
    Site VARCHAR(5),
    Code VARCHAR(5),
    Freq  int,
    StartDate  DATETIME,
    EndDate  DATETIME
    );

INSERT INTO Table2 (Site, Code, Freq, StartDate, EndDate)
SELECT 'ABT', 'A01', 43,  '01/01/2011', '01/02/2012' UNION ALL
SELECT 'ABT', 'A02', 254, '01/01/2011', '19/02/2011' UNION ALL
SELECT 'ABT', 'A02', 109, '20/02/2011', '01/01/2012' UNION ALL
SELECT 'ABT', 'A02', 12,  '02/01/2012', '01/01/2013' UNION ALL
SELECT 'AFL', 'A02', 13,  '01/01/2011', '01/02/2012' UNION ALL
SELECT 'TRG', 'A01', 122, '01/01/2011', '01/02/2012' UNION ALL
SELECT 'TRG', 'K05', 61,  '01/01/2011', '01/02/2012' UNION ALL
SELECT 'KLD', 'KO5', 33,  '01/01/2011', '15/05/2012' UNION ALL
SELECT 'KLD', 'K05', 79,  '16/05/2012', '01/01/2013' UNION ALL
SELECT 'KTY', 'K05', 52,  '01/01/2011', '01/02/2012' UNION ALL
SELECT 'KMD', 'C02', 78,  '01/01/2011', '01/02/2012' UNION ALL
SELECT 'ZYT', 'G01', 11,  '01/01/2011', '01/02/2012' UNION ALL
SELECT 'PYN', 'A01', 15,  '01/01/2011', '01/02/2012' UNION ALL
SELECT 'DYN', 'F08', 122, '01/01/2011', '01/02/2012';

score 1 · Accepted Answer

“有点复杂。” 是的。

从顶部开始，这是您的第 2 步。这可能有一个关键缺陷，因为它没有将任何内容限制为持续 3 年或持续 12 个月。从您的问题中不清楚这是意图，所以我们将使用它。

SELECT Site,Code,COUNT(*) as [Count],
FailFlagCount= SUM(CASE WHEN FailFlag = 'F' THEN 1 ELSE 0 END)
FROM Table1 
GROUP BY Site,Code;

需要将其与 Table2 连接以获取频率字段。我已将其包装在一个子选择中并称为该子查询 S2，因为它是第 2 步。

SELECT s2.Site, s2.Code,s2.[Count], S2.FailFlagCount, Table2.Freq
FROM
(
SELECT Site,Code,COUNT(*) as [Count],
FailFlagCount= SUM(CASE WHEN FailFlag = 'F' THEN 1 ELSE 0 END)
FROM Table1 
GROUP BY Site,Code
) S2
LEFT JOIN Table2 on S2.Site = table2.Site and S2.Code = table2.Code
;

计算 CC 和 BPO 就相对容易了。上面变成了一个名为 S3 的子查询。

请注意，我在这里防止了除以零错误。

SELECT Site, Code, [Count], FailFlagCount, Freq,
CASE WHEN [Count]=0 THEN 0 ELSE  ( 1-(FailFlagCount / [Count]))*100 END  CC,
CASE WHEN Freq = 0 THEN 0 ELSE ( 1-(FailFlagCount / Freq))*100 END BPO

FROM
(
SELECT s2.Site, s2.Code,s2.[Count], S2.FailFlagCount, ISNULL(Table2.Freq, 1) AS Freq
FROM   
(
SELECT Site,Code,COUNT(*) as [Count],
FailFlagCount= SUM(CASE WHEN FailFlag = 'F' THEN 1 ELSE 0 END)
FROM Table1 
GROUP BY Site,Code
) S2
LEFT JOIN Table2 on S2.Site = table2.Site and S2.Code = table2.Code
) s3
;

好的，这里有点实验，围绕 SQL 月份函数，如何计算还剩多少个月，以及今年的 1 号。

select GETDATE(), month(GETDATE()), 12-MONTH(GETDATE()),
DATEADD(YEAR, -3, GETDATE()),
DATEFROMPARTS (YEAR(GETDATE()),1,1)
;

所以现在我们可以计算 Y 数字——这又是整个 table1，但过滤了过去 36 个月，然后计算每月平均值。当您在月中运行此程序时，隐式错误可能会在这里蔓延 - 它不会打破月份边界，它需要过去 3 年完成而不是过去 36 个月完成，不包括本月。

请注意，我必须将 count(*) 转换为浮点数，否则它是一个整数，在除法中四舍五入为 0。

SELECT SITE, CODE, count(*) tot, cast(count(*) as float)/36 avg, (cast(count(*) as float)/36) * ((12-MONTH(GETDATE()))/12) Y
FROM Table1 
WHERE FailFlag = 'F'
AND ModifiedDate >= DATEADD(YEAR, -3, GETDATE())
GROUP BY SITE, CODE;

我们以同样的方式计算今年的失败。

SELECT SITE, CODE, COUNT(*) currFails 
FROM Table1 
WHERE FailFlag = 'F'
AND ModifiedDate >= DATEFROMPARTS (YEAR(GETDATE()),1,1)
GROUP BY SITE, CODE;

所以现在我们可以结合上面的 3 个步骤来得到一个带有 Y 并且当前年份失败的表。请注意那里的 ISNULL 和 LEFT JOIN 以补偿今年没有失败的站点。

SELECT s3.Site, s3.Code, [Count], FailFlagCount, Freq,
CASE WHEN [Count]=0 THEN 0 ELSE  ( 1-(FailFlagCount / [Count]))*100 END  CC,
CASE WHEN Freq = 0 THEN 0 ELSE ( 1-(FailFlagCount / Freq))*100 END BPO,
ISNULL(YBIt.Y, 0) Y,
isnull(currBit.currFails, 0) as currFails

FROM
(
SELECT s2.Site, s2.Code,s2.[Count], S2.FailFlagCount, ISNULL(Table2.Freq, 1) AS Freq
FROM   
(
SELECT Site,Code,COUNT(*) as [Count],
FailFlagCount= SUM(CASE WHEN FailFlag = 'F' THEN 1 ELSE 0 END)
FROM Table1 
GROUP BY Site,Code
) S2
LEFT JOIN Table2 on S2.Site = table2.Site and S2.Code = table2.Code
) s3

LEFT JOIN
(
SELECT SITE, CODE, count(*) tot, cast(count(*) as float)/36 avg, (cast(count(*) as float)/36) * ((12-MONTH(GETDATE()))/12) Y
FROM Table1 
WHERE FailFlag = 'F'
AND ModifiedDate >= DATEADD(YEAR, -3, GETDATE())
GROUP BY SITE, CODE
) YBit on S3.Site = Ybit.site AND S3.code = YBit.code 

LEFT JOIN
(
SELECT SITE, CODE, COUNT(*) currFails 
FROM Table1 
WHERE FailFlag = 'F'
AND ModifiedDate >= DATEFROMPARTS (YEAR(GETDATE()),1,1)
GROUP BY SITE, CODE
) currBit ON s3.site = currBit.site and s3.code = currBit.code;

;

所以我们最终可以计算预测。

SELECT Site, code, [Count], FAilFlagCount, Freq,
   CC, BPO, Y, currFails, Y+currFails totF12,
    CASE WHEN Freq = 0 then 0 else (1-(( Y+currFails)/ Freq))*100 END Forecast
 FROM 
(
SELECT s3.Site, s3.Code, [Count], FailFlagCount, Freq,
CASE WHEN [Count]=0 THEN 0 ELSE  ( 1-(FailFlagCount / [Count]))*100 END  CC,
CASE WHEN Freq = 0 THEN 0 ELSE ( 1-(FailFlagCount / Freq))*100 END BPO,
ISNULL(YBIt.Y, 0) Y,
isnull(currBit.currFails, 0) as currFails

FROM
(
SELECT s2.Site, s2.Code,s2.[Count], S2.FailFlagCount, ISNULL(Table2.Freq, 1) AS Freq
FROM   
(
SELECT Site,Code,COUNT(*) as [Count],
FailFlagCount= SUM(CASE WHEN FailFlag = 'F' THEN 1 ELSE 0 END)
FROM Table1 
GROUP BY Site, Code
) S2
LEFT JOIN Table2 on S2.Site = table2.Site and S2.Code = table2.Code
) s3

LEFT JOIN
(
SELECT SITE, CODE, count(*) tot, cast(count(*) as float)/36 avg, (cast(count(*) as float)/36) * ((12-MONTH(GETDATE()))/12) Y
FROM Table1 
WHERE FailFlag = 'F'
AND ModifiedDate >= DATEADD(YEAR, -3, GETDATE())
GROUP BY SITE, CODE
) YBit on S3.Site = Ybit.site AND S3.code = YBit.code 

LEFT JOIN
(
SELECT SITE, CODE, COUNT(*) currFails 
FROM Table1 
WHERE FailFlag = 'F'
AND ModifiedDate >= DATEFROMPARTS (YEAR(GETDATE()),1,1)
GROUP BY SITE, CODE
) currBit ON s3.site = currBit.site and s3.code = currBit.code

) S4A
;

好的，突然我们又需要开始日期和结束日期了。它们在表 2 中，这似乎也可能是站点 + 代码的主列表。因此，让我们将其设为主要并使用左连接将我们的计算加入到该计算中。我们还将计算站点类型。

这是您对每个站点 + 代码的详细计算。

select left (t2.site, 1) siteType, t2.site, t2.code, 
  [Count], FAilFlagCount, s4.Freq,
   CC, BPO, Forecast,
    T2.StartDate, T2.EndDAte

FROM 
 Table2 T2 
LEFT JOIN
(

SELECT Site, code, [Count], FAilFlagCount, Freq,
   CC, BPO, Y, currFails, Y+currFails totF12,
    CASE WHEN Freq = 0 then 0 else (1-(( Y+currFails)/ Freq))*100 END Forecast
 FROM 
(
SELECT s3.Site, s3.Code, [Count], FailFlagCount, Freq,
CASE WHEN [Count]=0 THEN 0 ELSE  ( 1-(FailFlagCount / [Count]))*100 END  CC,
CASE WHEN Freq = 0 THEN 0 ELSE ( 1-(FailFlagCount / Freq))*100 END BPO,
ISNULL(YBIt.Y, 0) Y,
isnull(currBit.currFails, 0) as currFails

FROM
(
SELECT s2.Site, s2.Code,s2.[Count], S2.FailFlagCount, ISNULL(Table2.Freq, 1) AS Freq
FROM   
(
SELECT Site,Code,COUNT(*) as [Count],
FailFlagCount= SUM(CASE WHEN FailFlag = 'F' THEN 1 ELSE 0 END)
FROM Table1 
GROUP BY Site, Code
) S2
LEFT JOIN Table2 on S2.Site = table2.Site and S2.Code = table2.Code
) s3

LEFT JOIN
(
SELECT SITE, CODE, count(*) tot, cast(count(*) as float)/36 avg, (cast(count(*) as float)/36) * ((12-MONTH(GETDATE()))/12) Y
FROM Table1 
WHERE FailFlag = 'F'
AND ModifiedDate >= DATEADD(YEAR, -3, GETDATE())
GROUP BY SITE, CODE
) YBit on S3.Site = Ybit.site AND S3.code = YBit.code 

LEFT JOIN
(
SELECT SITE, CODE, COUNT(*) currFails 
FROM Table1 
WHERE FailFlag = 'F'
AND ModifiedDate >= DATEFROMPARTS (YEAR(GETDATE()),1,1)
GROUP BY SITE, CODE
) currBit ON s3.site = currBit.site and s3.code = currBit.code

) S4A
) S4 
on t2.site = s4.site and t2.code = s4.code
;

从那里，我们可以轻松地创建平均值。

SELECT siteType, '' as site, '' as code, avg([count]) [count], avg(FailFlagCount) FailFlagCount, avg(Freq) Freq, 
    avg (CC) CC, avg(BPO) BPO, avg(Forecast) Forecast, '' StartDAte, '' EndDate
FROM 
(

select left (t2.site, 1) siteType, t2.site, t2.code, 
  [Count], FAilFlagCount, s4.Freq,
   CC, BPO, Forecast,
    T2.StartDate, T2.EndDAte

FROM 
 Table2 T2 
LEFT JOIN
(

SELECT Site, code, [Count], FAilFlagCount, Freq,
   CC, BPO, Y, currFails, Y+currFails totF12,
    CASE WHEN Freq = 0 then 0 else (1-(( Y+currFails)/ Freq))*100 END Forecast
 FROM 
(
SELECT s3.Site, s3.Code, [Count], FailFlagCount, Freq,
CASE WHEN [Count]=0 THEN 0 ELSE  ( 1-(FailFlagCount / [Count]))*100 END  CC,
CASE WHEN Freq = 0 THEN 0 ELSE ( 1-(FailFlagCount / Freq))*100 END BPO,
ISNULL(YBIt.Y, 0) Y,
isnull(currBit.currFails, 0) as currFails

FROM
(
SELECT s2.Site, s2.Code,s2.[Count], S2.FailFlagCount, ISNULL(Table2.Freq, 1) AS Freq
FROM   
(
SELECT Site,Code,COUNT(*) as [Count],
FailFlagCount= SUM(CASE WHEN FailFlag = 'F' THEN 1 ELSE 0 END)
FROM Table1 
GROUP BY Site, Code
) S2
LEFT JOIN Table2 on S2.Site = table2.Site and S2.Code = table2.Code
) s3

LEFT JOIN
(
SELECT SITE, CODE, count(*) tot, cast(count(*) as float)/36 avg, (cast(count(*) as float)/36) * ((12-MONTH(GETDATE()))/12) Y
FROM Table1 
WHERE FailFlag = 'F'
AND ModifiedDate >= DATEADD(YEAR, -3, GETDATE())
GROUP BY SITE, CODE
) YBit on S3.Site = Ybit.site AND S3.code = YBit.code 

LEFT JOIN
(
SELECT SITE, CODE, COUNT(*) currFails 
FROM Table1 
WHERE FailFlag = 'F'
AND ModifiedDate >= DATEFROMPARTS (YEAR(GETDATE()),1,1)
GROUP BY SITE, CODE
) currBit ON s3.site = currBit.site and s3.code = currBit.code

) S4A
) S4 
on t2.site = s4.site and t2.code = s4.code
) S5
GROUP BY siteType
;

或者你可以用详细的计算填充一个表格，然后计算平均值，而不是使用我的最终计算。

我会把最后的联合留给你。

sql - 涉及 SQL 中的分组和平均的复杂查询的逻辑

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