将行转为列 - 按 Main_IDs 聚类
Transposing rows into columns - clustered by Main_IDs
我正在尝试找出一种方法来将我的数据运行放置到按特定集群分组的行中。我已经 运行 一个垂直显示数据的查询，但我想知道如何才能 运行 处理这个问题。
查询后的数据如下（我将其放入临时 table）：
App Old_Status_ID New_Status_ID Status_Change_Date UserID
A 1 2 2015_01_01 22
A 2 3 2015_02_01 20
A 3 4 2015_03_20 51
B 1 2 2015_01_25 84
B 2 3 2015_02_11 22
C 1 2 2015_01_02 35
C 2 3 2015_03_10 01
C 3 4 2015_04_05 55
....
上面提到的table有数百个不同的应用程序，7个不同的状态和数百个用户。
我想要做的是在一行中显示应用程序中的所有更改。此外，我想包括以天为单位的状态变化之间经过的时间差 (ΔStatus_Change_Date) = ΔSCD.
这是数据表的示例：
App Status1A Status1B User1 ΔSCP_1 Status_2A Status_2B User2 ΔSCP_2 ...
A 1 2 22 0 2 3 20 31 ...
B 1 2 84 0 2 3 22 17 ...
遗憾的是，并非所有内容都适合这里的行，但我希望您能通过示例理解概念和我的目标。
如何运行设置或编写查询来实现一个应用程序的关联数据在一行中？
非常感谢您的帮助！！！
这是一些示例数据：
+-------+-------------+-------------+------------------+--------+
| app | OldStatusId | NewStatusId | StatusChangeDate | userid |
+-------+-------------+-------------+------------------+--------+
| 16195 | 1 | 32 | 2017-10-03 | 2137 |
| 16195 | 32 | 32 | 2017-10-03 | 2137 |
| 16195 | 32 | 8 | 2018-01-10 | 6539 |
| 16195 | 8 | 2 | 2018-01-12 | 3452 |
| 16505 | 1 | 1 | 2017-04-26 | 3551 |
| 16505 | 1 | 32 | 2017-05-24 | 2063 |
| 16505 | 32 | 32 | 2017-05-24 | 2063 |
| 16505 | 1 | 1 | 2017-06-23 | 3551 |
| 16505 | 32 | 4 | 2017-06-23 | 5291 |
| 16505 | 4 | 32 | 2017-06-26 | 2063 |
| 16505 | 32 | 8 | 2017-06-26 | 5291 |
| 16505 | 2 | 2 | 2017-06-28 | 3438 |
| 16505 | 8 | 2 | 2017-06-28 | 3438 |
| 16505 | 1 | 32 | 2017-08-28 | 2063 |
| 16505 | 32 | 4 | 2017-10-03 | 5291 |
| 16505 | 4 | 32 | 2017-10-04 | 2063 |
| 16505 | 2 | 2 | 2017-10-25 | 3438 |
| 16505 | 8 | 2 | 2017-10-25 | 3438 |
| 16505 | 32 | 8 | 2017-10-25 | 5291 |
| 16515 | 1 | 32 | 2017-06-01 | 2456 |
| 16515 | 32 | 32 | 2017-06-01 | 2456 |
| 16515 | 4 | 4 | 2017-07-25 | 5291 |
| 16515 | 32 | 4 | 2017-07-25 | 5291 |
| 16515 | 4 | 32 | 2017-07-27 | 2456 |
| 16515 | 32 | 4 | 2017-08-09 | 5291 |
| 16515 | 4 | 32 | 2017-08-10 | 2456 |
| 16515 | 32 | 8 | 2017-08-24 | 5291 |
| 16515 | 2 | 2 | 2017-08-28 | 3438 |
| 16515 | 8 | 2 | 2017-08-28 | 3438 |
| 16515 | 1 | 32 | 2017-10-06 | 2456 |
| 16515 | 32 | 32 | 2017-10-06 | 2456 |
| 16515 | 1 | 1 | 2017-10-17 | 2456 |
| 16515 | 32 | 128 | 2017-11-20 | 5291 |
| 16515 | 32 | 8 | 2017-11-29 | 5291 |
| 16515 | 128 | 32 | 2017-11-29 | 5291 |
| 16515 | 8 | 2 | 2017-12-07 | 3611 |
+-------+-------------+-------------+------------------+--------+
您可以使用 PIVOT 和 UNPIVOT 关系运算符将 table 值的表达式更改为另一个 table。 PIVOT 通过将表达式中一列的唯一值转换为输出中的多列来旋转 table 值表达式，并在需要时对最终输出中所需的任何剩余列值执行聚合
我会把订购问题留给你。正如我之前所说，当您有两行具有相同日期时，您无法知道哪一行将首先列出，因为您没有任何方法可以对您的数据执行此操作。您在这里需要的是一些非常丑陋的动态 sql 来生成所有这些列。在这段代码中，我将使用计数 table。在我的系统中，我将其保留为一个视图。这是我的理货代码 table.
create View [dbo].[cteTally] as
WITH
E1(N) AS (select 1 from (values (1),(1),(1),(1),(1),(1),(1),(1),(1),(1))dt(n)),
E2(N) AS (SELECT 1 FROM E1 a, E1 b), --10E+2 or 100 rows
E4(N) AS (SELECT 1 FROM E2 a, E2 b), --10E+4 or 10,000 rows max
cteTally(N) AS
(
SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) FROM E4
)
select N from cteTally
GO
现在我们需要利用动态 sql 和这个计数 table。我们将 sql 为我们构建 sql。像这样。
if OBJECT_ID('tempdb..#Something') is not null
drop table #Something
create table #Something
(
app int
, OldStatusId int
, NewStatusId int
, StatusChangeDate date
, userid int
)
insert #Something
(
app
, OldStatusId
, NewStatusId
, StatusChangeDate
, userid
) VALUES
(16195, 1, 32, '2017-10-03', 2137)
, (16195, 32, 32, '2017-10-03', 2137)
, (16195, 32, 8, '2018-01-10', 6539)
, (16195, 8, 2, '2018-01-12', 3452)
, (16505, 1, 1, '2017-04-26', 3551)
, (16505, 1, 32, '2017-05-24', 2063)
, (16505, 32, 32, '2017-05-24', 2063)
, (16505, 1, 1, '2017-06-23', 3551)
, (16505, 32, 4, '2017-06-23', 5291)
, (16505, 4, 32, '2017-06-26', 2063)
, (16505, 32, 8, '2017-06-26', 5291)
, (16505, 2, 2, '2017-06-28', 3438)
, (16505, 8, 2, '2017-06-28', 3438)
, (16505, 1, 32, '2017-08-28', 2063)
, (16505, 32, 4, '2017-10-03', 5291)
, (16505, 4, 32, '2017-10-04', 2063)
, (16505, 2, 2, '2017-10-25', 3438)
, (16505, 8, 2, '2017-10-25', 3438)
, (16505, 32, 8, '2017-10-25', 5291)
, (16515, 1, 32, '2017-06-01', 2456)
, (16515, 32, 32, '2017-06-01', 2456)
, (16515, 4, 4, '2017-07-25', 5291)
, (16515, 32, 4, '2017-07-25', 5291)
, (16515, 4, 32, '2017-07-27', 2456)
, (16515, 32, 4, '2017-08-09', 5291)
, (16515, 4, 32, '2017-08-10', 2456)
, (16515, 32, 8, '2017-08-24', 5291)
, (16515, 2, 2, '2017-08-28', 3438)
, (16515, 8, 2, '2017-08-28', 3438)
, (16515, 1, 32, '2017-10-06', 2456)
, (16515, 32, 32, '2017-10-06', 2456)
, (16515, 1, 1, '2017-10-17', 2456)
, (16515, 32, 28, '2017-11-20', 5291)
, (16515, 32, 8, '2017-11-29', 5291)
, (16515, 128, 32, '2017-11-29', 5291)
, (16515, 8, 2, '2017-12-07', 3611)
declare @StaticPortion nvarchar(2000) =
'with OrderedResults as
(
select *, ROW_NUMBER() over(partition by app order by StatusChangeDate) as RowNum
from #Something
)
select app';
declare @DynamicPortion nvarchar(max) = '';
select @DynamicPortion = @DynamicPortion +
', MAX(Case when RowNum = ' + CAST(N as varchar(6)) + ' then OldStatusId end) as OldStatus' + CAST(N as varchar(6)) + CHAR(10)
+ ', MAX(Case when RowNum = ' + CAST(N as varchar(6)) + ' then NewStatusId end) as NewStatus' + CAST(N as varchar(6)) + CHAR(10)
+ ', MAX(Case when RowNum = ' + CAST(N as varchar(6)) + ' then StatusChangeDate end) as StatusChangeDate' + CAST(N as varchar(6)) + CHAR(10)
+ ', MAX(Case when RowNum = ' + CAST(N as varchar(6)) + ' then userid end) as userid' + CAST(N as varchar(6)) + CHAR(10)
from cteTally t
where t.N <=
(
select top 1 Count(*)
from #Something
group by app
order by COUNT(*) desc
)
declare @FinalStaticPortion nvarchar(2000) = ' from OrderedResults Group by app order by app';
declare @SqlToExecute nvarchar(max) = @StaticPortion + @DynamicPortion + @FinalStaticPortion;
exec sp_executesql @SqlToExecute
我在这里没有演示的唯一部分是获取行与行之间的更改。你可以用临时 table 来做到这一点。但是您必须使用全局临时文件 table，因为列是动态生成的，临时文件 table 的范围不允许我们在执行动态查询后查看它。一旦您理解了这段代码的作用，您应该能够自己添加最后一部分。但是，如果您遇到困难 post，我们将看看我们能做些什么。
您可以简单地使用STRING_AGG函数来实现您的目标。
示例数据：
create table clean (
app VARCHAR(50),
old_status INT,
new_status INT,
start_date DATETIME,
end_date DATETIME,
user_id INT
);
INSERT INTO CLEAN VALUES('14595', 2, 2, '9/12/2017 16:14:33', '11/1/2017 15:37:58', 3470);
INSERT INTO CLEAN VALUES('14595', 1, 2, '9/12/2017 16:14:33', '11/1/2017 15:37:58', 3470);
INSERT INTO CLEAN VALUES('14595', 2, 64, '11/1/2017 15:21:49', '11/1/2017 15:37:58', 3470);
INSERT INTO CLEAN VALUES('14595', 2, 2, '11/1/2017 15:37:58', NULL, 3470);
INSERT INTO CLEAN VALUES('14595', 64, 2, '11/1/2017 15:37:58', NULL, 3470);
INSERT INTO CLEAN VALUES('14595', 32, 8, '9/27/2017 10:19:48', '1/26/2018 10:50:18', 5291);
INSERT INTO CLEAN VALUES('14595', 32, 8, '1/26/2018 10:50:18', NULL, 5291);
INSERT INTO CLEAN VALUES('14595', 1, 32, '9/13/2017 15:18:24', NULL, 5297);
INSERT INTO CLEAN VALUES('14595', 1, 1, '7/14/2017 14:29:51', '1/19/2018 14:15:13', 5327);
INSERT INTO CLEAN VALUES('14595', 1, 32, '1/19/2018 14:15:13', NULL, 5327);
INSERT INTO CLEAN VALUES('14595', 2, 2, '9/27/2017 10:40:06', '1/26/2018 10:52:54', 6509);
INSERT INTO CLEAN VALUES('14595', 8, 2, '9/27/2017 10:40:06', '1/26/2018 10:52:54', 6509);
INSERT INTO CLEAN VALUES('14595', 8, 2, '1/26/2018 10:52:54', NULL, 6509);
INSERT INTO CLEAN VALUES('14596', 32, 4, '10/9/2017 14:28:10', '12/14/2017 14:45:59', 5290);
INSERT INTO CLEAN VALUES('14596', 32, 4, '10/11/2017 11:57:05', '12/14/2017 14:45:59', 5290);
INSERT INTO CLEAN VALUES('14596', 8, 8, '10/11/2017 15:02:23', '12/14/2017 14:45:59', 5290);
INSERT INTO CLEAN VALUES('14596', 32, 8, '10/11/2017 15:02:23', '12/14/2017 14:45:59', 5290);
INSERT INTO CLEAN VALUES('14596', 32, 4, '12/13/2017 10:51:30', '12/14/2017 14:45:59', 5290);
INSERT INTO CLEAN VALUES('14596', 32, 8, '12/14/2017 14:45:59', NULL, 5290);
INSERT INTO CLEAN VALUES('14596', 1, 1, '8/11/2017 12:17:49', '1/12/2018 16:06:16', 5298);
INSERT INTO CLEAN VALUES('14596', 1, 32, '9/19/2017 16:00:36', '1/12/2018 16:06:16', 5298);
INSERT INTO CLEAN VALUES('14596', 4, 32, '10/9/2017 15:45:59', '1/12/2018 16:06:16', 5298);
INSERT INTO CLEAN VALUES('14596', 4, 32, '10/11/2017 12:43:21', '1/12/2018 16:06:16', 5298);
INSERT INTO CLEAN VALUES('14596', 1, 32, '11/9/2017 16:05:44', '1/12/2018 16:06:16', 5298);
INSERT INTO CLEAN VALUES('14596', 32, 32, '11/9/2017 16:05:44', '1/12/2018 16:06:16', 5298);
INSERT INTO CLEAN VALUES('14596', 4, 32, '12/14/2017 10:38:19', '1/12/2018 16:06:16', 5298);
INSERT INTO CLEAN VALUES('14596', 1, 32, '1/12/2018 16:06:16', NULL, 5298);
INSERT INTO CLEAN VALUES('14596', 8, 2, '12/13/2017 13:36:56', '1/4/2018 16:47:43', 6506);
INSERT INTO CLEAN VALUES('14596', 8, 2, '1/4/2018 16:47:43', NULL, 6506);
INSERT INTO CLEAN VALUES('15980', 8, 2, '1/18/2018 16:11:46', '1/19/2018 10:27:44', 3441);
INSERT INTO CLEAN VALUES('15980', 8, 2, '1/19/2018 10:27:44', NULL, 3441);
INSERT INTO CLEAN VALUES('15980', 32, 8, '1/17/2018 11:11:40', '1/18/2018 10:22:32', 5290);
INSERT INTO CLEAN VALUES('15980', 32, 128, '1/17/2018 15:54:36', '1/18/2018 10:22:32', 5290);
INSERT INTO CLEAN VALUES('15980', 128, 32, '1/18/2018 10:22:28', '1/18/2018 10:22:32', 5290);
INSERT INTO CLEAN VALUES('15980', 32, 8, '1/18/2018 10:22:32', NULL, 5290);
INSERT INTO CLEAN VALUES('15980', 1, 1, '10/1/2017 21:54:45', '12/27/2017 0:11:12', 5467);
INSERT INTO CLEAN VALUES('15980', 1, 32, '12/27/2017 0:00:18', '12/27/2017 0:11:12', 5467);
INSERT INTO CLEAN VALUES('15980', 1, 32, '12/27/2017 0:11:12', NULL, 5467);
INSERT INTO CLEAN VALUES('15998', 1, 32, '6/1/2017 13:32:49', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 32, 32, '6/1/2017 13:32:49', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 4, 32, '7/24/2017 9:51:27', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 4, 32, '7/27/2017 13:26:39', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 4, 32, '8/10/2017 13:19:22', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 1, 32, '10/6/2017 13:43:21', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 32, 32, '10/6/2017 13:43:21', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 1, 1, '10/17/2017 12:51:12', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 1, 32, '12/12/2017 12:52:16', NULL, 2456);
INSERT INTO CLEAN VALUES('15998', 8, 2, '8/18/2017 13:26:22', NULL, 3438);
INSERT INTO CLEAN VALUES('15998', 2, 2, '8/18/2017 13:26:22', NULL, 3438);
INSERT INTO CLEAN VALUES('15998', 2, 2, '11/10/2017 13:15:40', NULL, 3611);
INSERT INTO CLEAN VALUES('15998', 8, 2, '11/10/2017 13:15:40', NULL, 3611);
INSERT INTO CLEAN VALUES('15998', 4, 4, '7/21/2017 11:19:39', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 32, 4, '7/21/2017 11:19:39', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 32, 4, '7/25/2017 13:15:59', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 4, 4, '7/25/2017 13:15:59', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 32, 4, '8/9/2017 16:36:43', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 32, 8, '8/10/2017 13:46:16', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 32, 128, '11/7/2017 16:42:24', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 128, 32, '11/10/2017 12:20:43', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 32, 8, '11/10/2017 12:20:50', NULL, 5291);
查询：
with a as (
select app, user_id,
old_status, new_status,
start_date, end_date, datediff(day, start_date, end_date) as delta
from clean
), b as (
select app, user_id,
old_status, new_status,
start_date, end_date, ISNULL(delta, 0) as delta
from a
where old_status != new_status
), c as (
select app, user_id,
concat('[', old_status, '-', new_status, ' ', delta, ' days]') as column_2
from b
), d as (
select c.app, concat('{USER: ', c.user_id, ' ', STRING_AGG(c.column_2, ' | '), '}') as concat
from c
group by c.app, c.user_id
)
select d.app, STRING_AGG(d.concat, '; ') as user_activity from d
group by d.app
order by d.app;
结果：
+-------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| app | user_activity |
+-------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 14595 | {USER: 3470 [1-2 50 days] | [2-64 0 days] | [64-2 0 days]}; {USER: 5291 [32-8 121 days] | [32-8 0 days]}; {USER: 5297 [1-32 0 days]}; {USER: 5327 [1-32 0 days]}; {USER: 6509 [8-2 121 days] | [8-2 0 days]}
| 14596 | {USER: 5290 [32-4 66 days] | [32-4 64 days] | [32-8 64 days] | [32-4 1 days] | [32-8 0 days]}; {USER: 5298 [1-32 115 days] | [4-32 95 days] | [4-32 93 days] | [1-32 64 days] | [4-32 29 days] | [1-32 0 days]}; {USER: 6506 [8-2 22 days] | [8-2 0 days]}
| 15980 | {USER: 3441 [8-2 1 days] | [8-2 0 days]}; {USER: 5290 [32-8 1 days] | [32-128 1 days] | [128-32 0 days] | [32-8 0 days]}; {USER: 5467 [1-32 0 days] | [1-32 0 days]}
| 15998 | {USER: 2456 [1-32 194 days] | [4-32 141 days] | [4-32 138 days] | [4-32 124 days] | [1-32 67 days] | [1-32 0 days]}; {USER: 3438 [8-2 0 days]}; {USER: 3611 [8-2 0 days]}; {USER: 5291 [32-4 112 days] | [32-4 108 days] | [32-4 93 days] | [32-8 92 days] | [32-128 3 days] | [128-32 0 days] | [32-8 0 days]}
如果更改的顺序和用户的顺序很重要，这里是带有 WITHIN GROUP 子句的第二种解决方案：
with a as (
select app, user_id,
old_status, new_status,
start_date, end_date, datediff(day, start_date, end_date) as delta
from clean
), b as (
select app, user_id,
old_status, new_status,
start_date, end_date, ISNULL(delta, 0) as delta
from a
where old_status != new_status
), c as (
select app, user_id, start_date,
concat('[', old_status, '-', new_status, ' ', delta, ' days]') as column_2
from b
), d as (
select c.app, concat('{USER: ', c.user_id, ' ', STRING_AGG(c.column_2, ' | ') WITHIN GROUP (ORDER BY c.start_date ASC), '}') as concat
from c
group by c.app, c.user_id
)
select d.app, STRING_AGG(d.concat, '; ') WITHIN GROUP (ORDER BY d.concat ASC) as user_activity from d
group by d.app
order by d.app;
结果：
+-------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| app | user_activity |
+-------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 14595 | {USER: 3470 [1-2 50 days] | [2-64 0 days] | [64-2 0 days]}; {USER: 5291 [32-8 121 days] | [32-8 0 days]}; {USER: 5297 [1-32 0 days]}; {USER: 5327 [1-32 0 days]}; {USER: 6509 [8-2 121 days] | [8-2 0 days]}
| 14596 | {USER: 5290 [32-4 66 days] | [32-4 64 days] | [32-8 64 days] | [32-4 1 days] | [32-8 0 days]}; {USER: 5298 [1-32 115 days] | [4-32 95 days] | [4-32 93 days] | [1-32 64 days] | [4-32 29 days] | [1-32 0 days]}; {USER: 6506 [8-2 22 days] | [8-2 0 days]}
| 15980 | {USER: 3441 [8-2 1 days] | [8-2 0 days]}; {USER: 5290 [32-8 1 days] | [32-128 1 days] | [128-32 0 days] | [32-8 0 days]}; {USER: 5467 [1-32 0 days] | [1-32 0 days]}
| 15998 | {USER: 2456 [1-32 194 days] | [4-32 141 days] | [4-32 138 days] | [4-32 124 days] | [1-32 67 days] | [1-32 0 days]}; {USER: 3438 [8-2 0 days]}; {USER: 3611 [8-2 0 days]}; {USER: 5291 [32-4 112 days] | [32-4 108 days] | [32-4 93 days] | [32-8 92 days] | [32-128 3 days] | [128-32 0 days] | [32-8 0 days]}
我正在尝试找出一种方法来将我的数据运行放置到按特定集群分组的行中。我已经 运行 一个垂直显示数据的查询，但我想知道如何才能 运行 处理这个问题。
查询后的数据如下（我将其放入临时 table）：
App Old_Status_ID New_Status_ID Status_Change_Date UserID
A 1 2 2015_01_01 22
A 2 3 2015_02_01 20
A 3 4 2015_03_20 51
B 1 2 2015_01_25 84
B 2 3 2015_02_11 22
C 1 2 2015_01_02 35
C 2 3 2015_03_10 01
C 3 4 2015_04_05 55
....
上面提到的table有数百个不同的应用程序，7个不同的状态和数百个用户。 我想要做的是在一行中显示应用程序中的所有更改。此外，我想包括以天为单位的状态变化之间经过的时间差 (ΔStatus_Change_Date) = ΔSCD.
这是数据表的示例：
App Status1A Status1B User1 ΔSCP_1 Status_2A Status_2B User2 ΔSCP_2 ...
A 1 2 22 0 2 3 20 31 ...
B 1 2 84 0 2 3 22 17 ...
遗憾的是，并非所有内容都适合这里的行，但我希望您能通过示例理解概念和我的目标。
如何运行设置或编写查询来实现一个应用程序的关联数据在一行中？
非常感谢您的帮助！！！
这是一些示例数据：
+-------+-------------+-------------+------------------+--------+
| app | OldStatusId | NewStatusId | StatusChangeDate | userid |
+-------+-------------+-------------+------------------+--------+
| 16195 | 1 | 32 | 2017-10-03 | 2137 |
| 16195 | 32 | 32 | 2017-10-03 | 2137 |
| 16195 | 32 | 8 | 2018-01-10 | 6539 |
| 16195 | 8 | 2 | 2018-01-12 | 3452 |
| 16505 | 1 | 1 | 2017-04-26 | 3551 |
| 16505 | 1 | 32 | 2017-05-24 | 2063 |
| 16505 | 32 | 32 | 2017-05-24 | 2063 |
| 16505 | 1 | 1 | 2017-06-23 | 3551 |
| 16505 | 32 | 4 | 2017-06-23 | 5291 |
| 16505 | 4 | 32 | 2017-06-26 | 2063 |
| 16505 | 32 | 8 | 2017-06-26 | 5291 |
| 16505 | 2 | 2 | 2017-06-28 | 3438 |
| 16505 | 8 | 2 | 2017-06-28 | 3438 |
| 16505 | 1 | 32 | 2017-08-28 | 2063 |
| 16505 | 32 | 4 | 2017-10-03 | 5291 |
| 16505 | 4 | 32 | 2017-10-04 | 2063 |
| 16505 | 2 | 2 | 2017-10-25 | 3438 |
| 16505 | 8 | 2 | 2017-10-25 | 3438 |
| 16505 | 32 | 8 | 2017-10-25 | 5291 |
| 16515 | 1 | 32 | 2017-06-01 | 2456 |
| 16515 | 32 | 32 | 2017-06-01 | 2456 |
| 16515 | 4 | 4 | 2017-07-25 | 5291 |
| 16515 | 32 | 4 | 2017-07-25 | 5291 |
| 16515 | 4 | 32 | 2017-07-27 | 2456 |
| 16515 | 32 | 4 | 2017-08-09 | 5291 |
| 16515 | 4 | 32 | 2017-08-10 | 2456 |
| 16515 | 32 | 8 | 2017-08-24 | 5291 |
| 16515 | 2 | 2 | 2017-08-28 | 3438 |
| 16515 | 8 | 2 | 2017-08-28 | 3438 |
| 16515 | 1 | 32 | 2017-10-06 | 2456 |
| 16515 | 32 | 32 | 2017-10-06 | 2456 |
| 16515 | 1 | 1 | 2017-10-17 | 2456 |
| 16515 | 32 | 128 | 2017-11-20 | 5291 |
| 16515 | 32 | 8 | 2017-11-29 | 5291 |
| 16515 | 128 | 32 | 2017-11-29 | 5291 |
| 16515 | 8 | 2 | 2017-12-07 | 3611 |
+-------+-------------+-------------+------------------+--------+
您可以使用 PIVOT 和 UNPIVOT 关系运算符将 table 值的表达式更改为另一个 table。 PIVOT 通过将表达式中一列的唯一值转换为输出中的多列来旋转 table 值表达式，并在需要时对最终输出中所需的任何剩余列值执行聚合
我会把订购问题留给你。正如我之前所说，当您有两行具有相同日期时，您无法知道哪一行将首先列出，因为您没有任何方法可以对您的数据执行此操作。您在这里需要的是一些非常丑陋的动态 sql 来生成所有这些列。在这段代码中，我将使用计数 table。在我的系统中，我将其保留为一个视图。这是我的理货代码 table.
create View [dbo].[cteTally] as
WITH
E1(N) AS (select 1 from (values (1),(1),(1),(1),(1),(1),(1),(1),(1),(1))dt(n)),
E2(N) AS (SELECT 1 FROM E1 a, E1 b), --10E+2 or 100 rows
E4(N) AS (SELECT 1 FROM E2 a, E2 b), --10E+4 or 10,000 rows max
cteTally(N) AS
(
SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) FROM E4
)
select N from cteTally
GO
现在我们需要利用动态 sql 和这个计数 table。我们将 sql 为我们构建 sql。像这样。
if OBJECT_ID('tempdb..#Something') is not null
drop table #Something
create table #Something
(
app int
, OldStatusId int
, NewStatusId int
, StatusChangeDate date
, userid int
)
insert #Something
(
app
, OldStatusId
, NewStatusId
, StatusChangeDate
, userid
) VALUES
(16195, 1, 32, '2017-10-03', 2137)
, (16195, 32, 32, '2017-10-03', 2137)
, (16195, 32, 8, '2018-01-10', 6539)
, (16195, 8, 2, '2018-01-12', 3452)
, (16505, 1, 1, '2017-04-26', 3551)
, (16505, 1, 32, '2017-05-24', 2063)
, (16505, 32, 32, '2017-05-24', 2063)
, (16505, 1, 1, '2017-06-23', 3551)
, (16505, 32, 4, '2017-06-23', 5291)
, (16505, 4, 32, '2017-06-26', 2063)
, (16505, 32, 8, '2017-06-26', 5291)
, (16505, 2, 2, '2017-06-28', 3438)
, (16505, 8, 2, '2017-06-28', 3438)
, (16505, 1, 32, '2017-08-28', 2063)
, (16505, 32, 4, '2017-10-03', 5291)
, (16505, 4, 32, '2017-10-04', 2063)
, (16505, 2, 2, '2017-10-25', 3438)
, (16505, 8, 2, '2017-10-25', 3438)
, (16505, 32, 8, '2017-10-25', 5291)
, (16515, 1, 32, '2017-06-01', 2456)
, (16515, 32, 32, '2017-06-01', 2456)
, (16515, 4, 4, '2017-07-25', 5291)
, (16515, 32, 4, '2017-07-25', 5291)
, (16515, 4, 32, '2017-07-27', 2456)
, (16515, 32, 4, '2017-08-09', 5291)
, (16515, 4, 32, '2017-08-10', 2456)
, (16515, 32, 8, '2017-08-24', 5291)
, (16515, 2, 2, '2017-08-28', 3438)
, (16515, 8, 2, '2017-08-28', 3438)
, (16515, 1, 32, '2017-10-06', 2456)
, (16515, 32, 32, '2017-10-06', 2456)
, (16515, 1, 1, '2017-10-17', 2456)
, (16515, 32, 28, '2017-11-20', 5291)
, (16515, 32, 8, '2017-11-29', 5291)
, (16515, 128, 32, '2017-11-29', 5291)
, (16515, 8, 2, '2017-12-07', 3611)
declare @StaticPortion nvarchar(2000) =
'with OrderedResults as
(
select *, ROW_NUMBER() over(partition by app order by StatusChangeDate) as RowNum
from #Something
)
select app';
declare @DynamicPortion nvarchar(max) = '';
select @DynamicPortion = @DynamicPortion +
', MAX(Case when RowNum = ' + CAST(N as varchar(6)) + ' then OldStatusId end) as OldStatus' + CAST(N as varchar(6)) + CHAR(10)
+ ', MAX(Case when RowNum = ' + CAST(N as varchar(6)) + ' then NewStatusId end) as NewStatus' + CAST(N as varchar(6)) + CHAR(10)
+ ', MAX(Case when RowNum = ' + CAST(N as varchar(6)) + ' then StatusChangeDate end) as StatusChangeDate' + CAST(N as varchar(6)) + CHAR(10)
+ ', MAX(Case when RowNum = ' + CAST(N as varchar(6)) + ' then userid end) as userid' + CAST(N as varchar(6)) + CHAR(10)
from cteTally t
where t.N <=
(
select top 1 Count(*)
from #Something
group by app
order by COUNT(*) desc
)
declare @FinalStaticPortion nvarchar(2000) = ' from OrderedResults Group by app order by app';
declare @SqlToExecute nvarchar(max) = @StaticPortion + @DynamicPortion + @FinalStaticPortion;
exec sp_executesql @SqlToExecute
我在这里没有演示的唯一部分是获取行与行之间的更改。你可以用临时 table 来做到这一点。但是您必须使用全局临时文件 table，因为列是动态生成的，临时文件 table 的范围不允许我们在执行动态查询后查看它。一旦您理解了这段代码的作用，您应该能够自己添加最后一部分。但是，如果您遇到困难 post，我们将看看我们能做些什么。
您可以简单地使用STRING_AGG函数来实现您的目标。
示例数据：
create table clean (
app VARCHAR(50),
old_status INT,
new_status INT,
start_date DATETIME,
end_date DATETIME,
user_id INT
);
INSERT INTO CLEAN VALUES('14595', 2, 2, '9/12/2017 16:14:33', '11/1/2017 15:37:58', 3470);
INSERT INTO CLEAN VALUES('14595', 1, 2, '9/12/2017 16:14:33', '11/1/2017 15:37:58', 3470);
INSERT INTO CLEAN VALUES('14595', 2, 64, '11/1/2017 15:21:49', '11/1/2017 15:37:58', 3470);
INSERT INTO CLEAN VALUES('14595', 2, 2, '11/1/2017 15:37:58', NULL, 3470);
INSERT INTO CLEAN VALUES('14595', 64, 2, '11/1/2017 15:37:58', NULL, 3470);
INSERT INTO CLEAN VALUES('14595', 32, 8, '9/27/2017 10:19:48', '1/26/2018 10:50:18', 5291);
INSERT INTO CLEAN VALUES('14595', 32, 8, '1/26/2018 10:50:18', NULL, 5291);
INSERT INTO CLEAN VALUES('14595', 1, 32, '9/13/2017 15:18:24', NULL, 5297);
INSERT INTO CLEAN VALUES('14595', 1, 1, '7/14/2017 14:29:51', '1/19/2018 14:15:13', 5327);
INSERT INTO CLEAN VALUES('14595', 1, 32, '1/19/2018 14:15:13', NULL, 5327);
INSERT INTO CLEAN VALUES('14595', 2, 2, '9/27/2017 10:40:06', '1/26/2018 10:52:54', 6509);
INSERT INTO CLEAN VALUES('14595', 8, 2, '9/27/2017 10:40:06', '1/26/2018 10:52:54', 6509);
INSERT INTO CLEAN VALUES('14595', 8, 2, '1/26/2018 10:52:54', NULL, 6509);
INSERT INTO CLEAN VALUES('14596', 32, 4, '10/9/2017 14:28:10', '12/14/2017 14:45:59', 5290);
INSERT INTO CLEAN VALUES('14596', 32, 4, '10/11/2017 11:57:05', '12/14/2017 14:45:59', 5290);
INSERT INTO CLEAN VALUES('14596', 8, 8, '10/11/2017 15:02:23', '12/14/2017 14:45:59', 5290);
INSERT INTO CLEAN VALUES('14596', 32, 8, '10/11/2017 15:02:23', '12/14/2017 14:45:59', 5290);
INSERT INTO CLEAN VALUES('14596', 32, 4, '12/13/2017 10:51:30', '12/14/2017 14:45:59', 5290);
INSERT INTO CLEAN VALUES('14596', 32, 8, '12/14/2017 14:45:59', NULL, 5290);
INSERT INTO CLEAN VALUES('14596', 1, 1, '8/11/2017 12:17:49', '1/12/2018 16:06:16', 5298);
INSERT INTO CLEAN VALUES('14596', 1, 32, '9/19/2017 16:00:36', '1/12/2018 16:06:16', 5298);
INSERT INTO CLEAN VALUES('14596', 4, 32, '10/9/2017 15:45:59', '1/12/2018 16:06:16', 5298);
INSERT INTO CLEAN VALUES('14596', 4, 32, '10/11/2017 12:43:21', '1/12/2018 16:06:16', 5298);
INSERT INTO CLEAN VALUES('14596', 1, 32, '11/9/2017 16:05:44', '1/12/2018 16:06:16', 5298);
INSERT INTO CLEAN VALUES('14596', 32, 32, '11/9/2017 16:05:44', '1/12/2018 16:06:16', 5298);
INSERT INTO CLEAN VALUES('14596', 4, 32, '12/14/2017 10:38:19', '1/12/2018 16:06:16', 5298);
INSERT INTO CLEAN VALUES('14596', 1, 32, '1/12/2018 16:06:16', NULL, 5298);
INSERT INTO CLEAN VALUES('14596', 8, 2, '12/13/2017 13:36:56', '1/4/2018 16:47:43', 6506);
INSERT INTO CLEAN VALUES('14596', 8, 2, '1/4/2018 16:47:43', NULL, 6506);
INSERT INTO CLEAN VALUES('15980', 8, 2, '1/18/2018 16:11:46', '1/19/2018 10:27:44', 3441);
INSERT INTO CLEAN VALUES('15980', 8, 2, '1/19/2018 10:27:44', NULL, 3441);
INSERT INTO CLEAN VALUES('15980', 32, 8, '1/17/2018 11:11:40', '1/18/2018 10:22:32', 5290);
INSERT INTO CLEAN VALUES('15980', 32, 128, '1/17/2018 15:54:36', '1/18/2018 10:22:32', 5290);
INSERT INTO CLEAN VALUES('15980', 128, 32, '1/18/2018 10:22:28', '1/18/2018 10:22:32', 5290);
INSERT INTO CLEAN VALUES('15980', 32, 8, '1/18/2018 10:22:32', NULL, 5290);
INSERT INTO CLEAN VALUES('15980', 1, 1, '10/1/2017 21:54:45', '12/27/2017 0:11:12', 5467);
INSERT INTO CLEAN VALUES('15980', 1, 32, '12/27/2017 0:00:18', '12/27/2017 0:11:12', 5467);
INSERT INTO CLEAN VALUES('15980', 1, 32, '12/27/2017 0:11:12', NULL, 5467);
INSERT INTO CLEAN VALUES('15998', 1, 32, '6/1/2017 13:32:49', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 32, 32, '6/1/2017 13:32:49', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 4, 32, '7/24/2017 9:51:27', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 4, 32, '7/27/2017 13:26:39', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 4, 32, '8/10/2017 13:19:22', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 1, 32, '10/6/2017 13:43:21', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 32, 32, '10/6/2017 13:43:21', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 1, 1, '10/17/2017 12:51:12', '12/12/2017 12:52:16', 2456);
INSERT INTO CLEAN VALUES('15998', 1, 32, '12/12/2017 12:52:16', NULL, 2456);
INSERT INTO CLEAN VALUES('15998', 8, 2, '8/18/2017 13:26:22', NULL, 3438);
INSERT INTO CLEAN VALUES('15998', 2, 2, '8/18/2017 13:26:22', NULL, 3438);
INSERT INTO CLEAN VALUES('15998', 2, 2, '11/10/2017 13:15:40', NULL, 3611);
INSERT INTO CLEAN VALUES('15998', 8, 2, '11/10/2017 13:15:40', NULL, 3611);
INSERT INTO CLEAN VALUES('15998', 4, 4, '7/21/2017 11:19:39', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 32, 4, '7/21/2017 11:19:39', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 32, 4, '7/25/2017 13:15:59', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 4, 4, '7/25/2017 13:15:59', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 32, 4, '8/9/2017 16:36:43', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 32, 8, '8/10/2017 13:46:16', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 32, 128, '11/7/2017 16:42:24', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 128, 32, '11/10/2017 12:20:43', '11/10/2017 12:20:50', 5291);
INSERT INTO CLEAN VALUES('15998', 32, 8, '11/10/2017 12:20:50', NULL, 5291);
查询：
with a as (
select app, user_id,
old_status, new_status,
start_date, end_date, datediff(day, start_date, end_date) as delta
from clean
), b as (
select app, user_id,
old_status, new_status,
start_date, end_date, ISNULL(delta, 0) as delta
from a
where old_status != new_status
), c as (
select app, user_id,
concat('[', old_status, '-', new_status, ' ', delta, ' days]') as column_2
from b
), d as (
select c.app, concat('{USER: ', c.user_id, ' ', STRING_AGG(c.column_2, ' | '), '}') as concat
from c
group by c.app, c.user_id
)
select d.app, STRING_AGG(d.concat, '; ') as user_activity from d
group by d.app
order by d.app;
结果：
+-------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| app | user_activity |
+-------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 14595 | {USER: 3470 [1-2 50 days] | [2-64 0 days] | [64-2 0 days]}; {USER: 5291 [32-8 121 days] | [32-8 0 days]}; {USER: 5297 [1-32 0 days]}; {USER: 5327 [1-32 0 days]}; {USER: 6509 [8-2 121 days] | [8-2 0 days]}
| 14596 | {USER: 5290 [32-4 66 days] | [32-4 64 days] | [32-8 64 days] | [32-4 1 days] | [32-8 0 days]}; {USER: 5298 [1-32 115 days] | [4-32 95 days] | [4-32 93 days] | [1-32 64 days] | [4-32 29 days] | [1-32 0 days]}; {USER: 6506 [8-2 22 days] | [8-2 0 days]}
| 15980 | {USER: 3441 [8-2 1 days] | [8-2 0 days]}; {USER: 5290 [32-8 1 days] | [32-128 1 days] | [128-32 0 days] | [32-8 0 days]}; {USER: 5467 [1-32 0 days] | [1-32 0 days]}
| 15998 | {USER: 2456 [1-32 194 days] | [4-32 141 days] | [4-32 138 days] | [4-32 124 days] | [1-32 67 days] | [1-32 0 days]}; {USER: 3438 [8-2 0 days]}; {USER: 3611 [8-2 0 days]}; {USER: 5291 [32-4 112 days] | [32-4 108 days] | [32-4 93 days] | [32-8 92 days] | [32-128 3 days] | [128-32 0 days] | [32-8 0 days]}
如果更改的顺序和用户的顺序很重要，这里是带有 WITHIN GROUP 子句的第二种解决方案：
with a as (
select app, user_id,
old_status, new_status,
start_date, end_date, datediff(day, start_date, end_date) as delta
from clean
), b as (
select app, user_id,
old_status, new_status,
start_date, end_date, ISNULL(delta, 0) as delta
from a
where old_status != new_status
), c as (
select app, user_id, start_date,
concat('[', old_status, '-', new_status, ' ', delta, ' days]') as column_2
from b
), d as (
select c.app, concat('{USER: ', c.user_id, ' ', STRING_AGG(c.column_2, ' | ') WITHIN GROUP (ORDER BY c.start_date ASC), '}') as concat
from c
group by c.app, c.user_id
)
select d.app, STRING_AGG(d.concat, '; ') WITHIN GROUP (ORDER BY d.concat ASC) as user_activity from d
group by d.app
order by d.app;
结果：
+-------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| app | user_activity |
+-------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 14595 | {USER: 3470 [1-2 50 days] | [2-64 0 days] | [64-2 0 days]}; {USER: 5291 [32-8 121 days] | [32-8 0 days]}; {USER: 5297 [1-32 0 days]}; {USER: 5327 [1-32 0 days]}; {USER: 6509 [8-2 121 days] | [8-2 0 days]}
| 14596 | {USER: 5290 [32-4 66 days] | [32-4 64 days] | [32-8 64 days] | [32-4 1 days] | [32-8 0 days]}; {USER: 5298 [1-32 115 days] | [4-32 95 days] | [4-32 93 days] | [1-32 64 days] | [4-32 29 days] | [1-32 0 days]}; {USER: 6506 [8-2 22 days] | [8-2 0 days]}
| 15980 | {USER: 3441 [8-2 1 days] | [8-2 0 days]}; {USER: 5290 [32-8 1 days] | [32-128 1 days] | [128-32 0 days] | [32-8 0 days]}; {USER: 5467 [1-32 0 days] | [1-32 0 days]}
| 15998 | {USER: 2456 [1-32 194 days] | [4-32 141 days] | [4-32 138 days] | [4-32 124 days] | [1-32 67 days] | [1-32 0 days]}; {USER: 3438 [8-2 0 days]}; {USER: 3611 [8-2 0 days]}; {USER: 5291 [32-4 112 days] | [32-4 108 days] | [32-4 93 days] | [32-8 92 days] | [32-128 3 days] | [128-32 0 days] | [32-8 0 days]}