SQL Server填充月分组差距

Question

我有一个类似下面的表格：

declare @test table(Aid int, Bid int, CheckMonth date, Avalue decimal(18,2))

insert into @test (Aid, Bid, CheckMonth, Avalue)
values (1, 4, '2014-07-05', 123.00)
      ,(1, 4, '2014-08-01', 467.00)
      ,(1, 4, '2014-11-03', 876.00)
      ,(1, 4, '2014-12-01', 876.00)
      ,(2, 6, '2016-01-02', 23.00)
      ,(2, 6, '2016-03-14', 56.00)
      ,(2, 6, '2016-04-17', 98.00)
      ,(2, 6, '2016-07-01', 90.00)

我希望用0.00值（在Avalue列中）填补月份（在上面的CheckMonth列中）的空白。数据按“援助”和“出价”列分组。

结果应如下图所示：

Aid   Bid    CheckMonth     Avalue
1     4      '2014-07-05'   123.00
1     4      '2014-08-01'   467.00
1     4      '2014-09-01'   0.00    -->inserted
1     4      '2014-10-01'   0.00    -->inserted
1     4      '2014-11-03'   876.00
1     4      '2014-12-01'   876.00
2     6      '2016-01-02'   23.00
2     6      '2016-02-01'   0.00    -->inserted
2     6      '2016-03-14'   56.00
2     6      '2016-04-17'   98.00
2     6      '2016-05-01'   0.00    -->inserted
2     6      '2016-06-01'   0.00    -->inserted
2     6      '2016-07-01'   90.00

感谢您的帮助。谢谢。

Answer 1

[一个选项使用递归查询为每个(aid, bid)元组生成月份开始；然后可以用原始表来left join生成的结果集：

with cte as (
    select 
        aid, 
        bid, 
        datefromparts(year(min(checkMonth)), month(min(checkMonth)), 1) dt, 
        datefromparts(year(max(checkMonth)), month(max(checkMonth)), 1) maxDt 
        from @test
        group by aid, bid
    union all
    select 
        aid, 
        bid, 
        dateadd(month, 1, dt),
        maxDt
    from cte
    where dt < maxDt
)
select c.aid, c.bid, coalesce(t.checkMonth, c.dt) checkMonth, coalesce(t.avalue, 0) avalue 
from cte c
left join @test t 
    on  t.aid = c.aid
    and t.bid = c.bid
    and t.checkMonth >= c.dt
    and t.checkMonth <  dateadd(month, 1, c.dt)
order by c.aid, c.bid, c.dt

Demo on DB Fiddle：

援助|出价| checkMonth |一个值-：| -：| ：--------- | ：-----1 | 4 | 2014-07-05 | 123.001 | 4 | 2014-08-01 | 467.001 | 4 | 2014-09-01 | 0.001 | 4 | 2014-10-01 | 0.001 | 4 | 2014-11-03 | 876.001 | 4 | 2014-12-01 | 876.002 | 6 | 2016-01-02 | 23.002 | 6 | 2016-02-01 | 0.002 | 6 | 2016-03-14 | 56.002 | 6 | 2016-04-17 | 98.002 | 6 | 2016-05-01 | 0.002 | 6 | 2016-06-01 | 0.002 | 6 | 2016-07-01 | 90.00