如何解决如何使用Windows功能将付款的每个部分按付款的最短日期进行分组
我有一个表，用于存储付款已更改的记录。 因此，每次更改付款方式时，都会使用所使用的付款方式并存储日期。数据是成批存储的，但是我只掌握使用新付款的第一天。
CREATE TABLE #payments
(
pay_ID uniqueidentifier,pay_type int,pay_account varchar(max),pay_routing varchar(max),pay_date datetime
);
DECLARE @payID uniqueidentifier = newid();
--Actual payments made
INSERT INTO #payments (pay_ID,pay_type,pay_account,pay_routing,pay_date) VALUES
(@payID,1,'e121','0101','09/18/2020'),(@payID,'09/19/2020'),'09/20/2020'),2,'e122','0102','09/21/2020'),'09/22/2020'),'09/23/2020'),'09/24/2020'),'09/25/2020'),'09/26/2020'),'09/27/2020'),3,'e123','0103','09/28/2020'),'09/29/2020'),'09/30/2020'),'10/01/2020'),'10/02/2020')
SELECT *
FROM #payments
ORDER BY pay_ID ASC,pay_date ASC;
此代码可用于为每次付款更改创建一个集合，但是我不确定如何获取此日期的开始日期和结束日期。
SELECT
p.*
FROM
(SELECT
p.*,LAG(pay_date) OVER (PARTITION BY pay_id,ORDER BY pay_date) AS prev_pd,pay_routing ORDER BY pay_date) AS prev_pd_grp
FROM
#payments p) p
WHERE
prev_pd_grp IS NULL OR prev_pd_grp <> prev_pd
理想的结果是，使第一笔付款在更改付款的每个部分都有一个开始和结束日期的戳记。
ID PayType account routing CreatedDate start end
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 1 e121 0101 2020-09-18 00:00:00.000 2020-09-18 00:00:00.000 2020-09-20 00:00:00.000
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 1 e121 0101 2020-09-19 00:00:00.000 NULL NULL
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 1 e121 0101 2020-09-20 00:00:00.000 NULL NULL
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 2 e122 0102 2020-09-21 00:00:00.000 2020-09-21 00:00:00.000 2020-09-22 00:00:00.000
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 2 e122 0102 2020-09-22 00:00:00.000 NULL NULL
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 1 e121 0101 2020-09-23 00:00:00.000 2020-09-23 00:00:00.000 2020-09-25 00:00:00.000
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 1 e121 0101 2020-09-24 00:00:00.000 NULL NULL
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 1 e121 0101 2020-09-25 00:00:00.000 NULL NULL
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 2 e122 0102 2020-09-26 00:00:00.000 2020-09-26 00:00:00.000 2020-09-27 00:00:00.000
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 2 e122 0102 2020-09-27 00:00:00.000 NULL NULL
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 3 e123 0103 2020-09-28 00:00:00.000 2020-09-28 00:00:00.000 2020-09-28 00:00:00.000
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 1 e121 0101 2020-09-29 00:00:00.000 2020-09-29 00:00:00.000 2020-10-02 00:00:00.000
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 1 e121 0101 2020-09-30 00:00:00.000 NULL NULL
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 1 e121 0101 2020-10-01 00:00:00.000 NULL NULL
FB4FE2A7-3609-4E35-AFB9-908B2D3072E9 1 e121 0101 2020-10-02 00:00:00.000 NULL NULL
解决方法
这是一个孤岛问题。这是一种使用行号之间的差异来标识组的方法。然后，您可以在外部查询中再次使用row_number()来识别每组的第一条记录，并使用窗口min()和max()来显示相应的日期范围：
select pay_id,pay_type,pay_account,pay_routing,pay_date,case when row_number() over(partition by pay_id,rn1 - rn2 order by pay_date) = 1
then min(pay_date) over(partition by pay_id,rn1 - rn2)
end as pay_date_start,rn1 - rn2 order by pay_date) = 1
then max(pay_date) over(partition by pay_id,rn1 - rn2)
end as pay_date_end
from (
select p.*,row_number() over(partition by pay_id order by pay_date) rn1,row_number() over(partition by pay_id,pay_type order by pay_date) rn2
from #payments p
) p
order by pay_id,pay_date
pay_id | pay_type | pay_account | pay_routing | pay_date | pay_date_start | pay_date_end :----------------------------------- | -------: | :---------- | :---------- | :---------------------- | :---------------------- | :---------------------- 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 1 | e121 | 0101 | 2020-09-18 00:00:00.000 | 2020-09-18 00:00:00.000 | 2020-09-20 00:00:00.000 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 1 | e121 | 0101 | 2020-09-19 00:00:00.000 | null | null 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 1 | e121 | 0101 | 2020-09-20 00:00:00.000 | null | null 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 2 | e122 | 0102 | 2020-09-21 00:00:00.000 | 2020-09-21 00:00:00.000 | 2020-09-22 00:00:00.000 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 2 | e122 | 0102 | 2020-09-22 00:00:00.000 | null | null 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 1 | e121 | 0101 | 2020-09-23 00:00:00.000 | 2020-09-23 00:00:00.000 | 2020-09-25 00:00:00.000 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 1 | e121 | 0101 | 2020-09-24 00:00:00.000 | null | null 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 1 | e121 | 0101 | 2020-09-25 00:00:00.000 | null | null 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 2 | e122 | 0102 | 2020-09-26 00:00:00.000 | 2020-09-26 00:00:00.000 | 2020-09-27 00:00:00.000 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 2 | e122 | 0102 | 2020-09-27 00:00:00.000 | null | null 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 3 | e123 | 0103 | 2020-09-28 00:00:00.000 | 2020-09-28 00:00:00.000 | 2020-09-28 00:00:00.000 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 1 | e121 | 0101 | 2020-09-29 00:00:00.000 | 2020-09-29 00:00:00.000 | 2020-10-02 00:00:00.000 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 1 | e121 | 0101 | 2020-09-30 00:00:00.000 | null | null 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 1 | e121 | 0101 | 2020-10-01 00:00:00.000 | null | null 2c1a463f-198b-41bd-a1a4-30aafda21d4f | 1 | e121 | 0101 | 2020-10-02 00:00:00.000 | null | null
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