示例数据可能有助于解释我正在尝试做的事情而不是解释它，所以我将从那开始。
以下是我目前正在处理的数据：
+-------------------------+--------------+
| CallStart | CallDuration |
+-------------------------+--------------+
| 2017-09-15 09:15:15.313 | 00:01:28 |
| 2017-09-15 09:15:15.317 | 00:01:45 |
| 2017-09-15 09:16:45.603 | 00:01:31 |
| 2017-09-15 09:17:00.637 | 00:01:24 |
| 2017-09-15 09:18:20.853 | 00:01:42 |
| 2017-09-15 09:18:25.870 | 00:01:24 |
| 2017-09-15 11:27:05.117 | 00:00:59 |
| 2017-09-15 11:31:16.053 | 00:01:18 |
| 2017-09-15 11:34:41.627 | 00:01:00 |
| 2017-09-15 12:16:45.413 | 00:01:01 |
| 2017-09-15 12:18:15.820 | 00:01:05 |
| 2017-09-15 12:30:43.607 | 00:01:04 |
| 2017-09-15 12:31:48.817 | 00:00:55 |
| 2017-09-15 12:35:14.563 | 00:00:59 |
| 2017-09-15 12:42:10.947 | 00:00:43 |
| 2017-09-15 12:56:28.807 | 00:01:14 |
| 2017-09-15 13:05:10.643 | 00:00:37 |
| 2017-09-15 13:20:08.400 | 00:00:37 |
| 2017-09-15 14:30:12.607 | 00:00:59 |
| 2017-09-15 14:31:22.807 | 00:00:49 |
| 2017-09-15 15:19:47.240 | 00:01:07 |
| 2017-09-15 16:04:47.753 | 00:00:55 |
| 2017-09-15 16:58:08.080 | 00:00:55 |
| 2017-09-15 17:05:04.557 | 00:00:50 |
| 2017-09-15 17:20:42.753 | 00:00:58 |
| 2017-09-15 17:28:09.140 | 00:01:05 |
| 2017-09-15 17:39:46.690 | 00:00:38 |
| 2017-09-15 17:40:21.957 | 00:01:05 |
| 2017-09-15 17:43:47.570 | 00:01:08 |
| 2017-09-15 17:47:23.390 | 00:01:05 |
| 2017-09-15 17:47:28.410 | 00:00:56 |
| 2017-09-15 17:51:59.380 | 00:01:04 |
+-------------------------+--------------+
我试图在15分钟的时间范围内对此数据中的出现次数进行滚动COUNT(*)。该数据的预期结果如下：
+-------------------------+--------------+------------------+
| CallStart | CallDuration | DropsIn15Minutes |
+-------------------------+--------------+------------------+
| 2017-09-15 09:15:15.313 | 00:01:28 | 1 |
| 2017-09-15 09:15:15.317 | 00:01:45 | 2 |
| 2017-09-15 09:16:45.603 | 00:01:31 | 3 |
| 2017-09-15 09:17:00.637 | 00:01:24 | 4 |
| 2017-09-15 09:18:20.853 | 00:01:42 | 5 |
| 2017-09-15 09:18:25.870 | 00:01:24 | 6 |
| 2017-09-15 11:27:05.117 | 00:00:59 | 1 |
| 2017-09-15 11:31:16.053 | 00:01:18 | 2 |
| 2017-09-15 11:34:41.627 | 00:01:00 | 3 |
| 2017-09-15 12:16:45.413 | 00:01:01 | 1 |
| 2017-09-15 12:18:15.820 | 00:01:05 | 2 |
| 2017-09-15 12:30:43.607 | 00:01:04 | 3 |
| 2017-09-15 12:31:48.817 | 00:00:55 | 3 |
| 2017-09-15 12:35:14.563 | 00:00:59 | 3 |
| 2017-09-15 12:42:10.947 | 00:00:43 | 4 |
| 2017-09-15 12:56:28.807 | 00:01:14 | 2 |
| 2017-09-15 13:05:10.643 | 00:00:37 | 2 |
| 2017-09-15 13:20:08.400 | 00:00:37 | 2 |
| 2017-09-15 14:30:12.607 | 00:00:59 | 1 |
| 2017-09-15 14:31:22.807 | 00:00:49 | 2 |
| 2017-09-15 15:19:47.240 | 00:01:07 | 1 |
| 2017-09-15 16:04:47.753 | 00:00:55 | 1 |
| 2017-09-15 16:58:08.080 | 00:00:55 | 1 |
| 2017-09-15 17:05:04.557 | 00:00:50 | 2 |
| 2017-09-15 17:20:42.753 | 00:00:58 | 1 |
| 2017-09-15 17:28:09.140 | 00:01:05 | 2 |
| 2017-09-15 17:39:46.690 | 00:00:38 | 2 |
| 2017-09-15 17:40:21.957 | 00:01:05 | 3 |
| 2017-09-15 17:43:47.570 | 00:01:08 | 3 |
| 2017-09-15 17:47:23.390 | 00:01:05 | 4 |
| 2017-09-15 17:47:28.410 | 00:00:56 | 5 |
| 2017-09-15 17:51:59.380 | 00:01:04 | 6 |
+-------------------------+--------------+------------------+
示例数据：
Create Table #Calls
(
CallStart DateTime,
CallDuration Time(0)
);
Insert Into #Calls
Values (N'2017-09-15T09:15:15.313', N'00:01:28'),
(N'2017-09-15T09:15:15.317', N'00:01:45'),
(N'2017-09-15T09:16:45.603', N'00:01:31'),
(N'2017-09-15T09:17:00.637', N'00:01:24'),
(N'2017-09-15T09:18:20.853', N'00:01:42'),
(N'2017-09-15T09:18:25.87', N'00:01:24'),
(N'2017-09-15T11:27:05.117', N'00:00:59'),
(N'2017-09-15T11:31:16.053', N'00:01:18'),
(N'2017-09-15T11:34:41.627', N'00:01:00'),
(N'2017-09-15T12:16:45.413', N'00:01:01'),
(N'2017-09-15T12:18:15.82', N'00:01:05'),
(N'2017-09-15T12:30:43.607', N'00:01:04'),
(N'2017-09-15T12:31:48.817', N'00:00:55'),
(N'2017-09-15T12:35:14.563', N'00:00:59'),
(N'2017-09-15T12:42:10.947', N'00:00:43'),
(N'2017-09-15T12:56:28.807', N'00:01:14'),
(N'2017-09-15T13:05:10.643', N'00:00:37'),
(N'2017-09-15T13:20:08.4', N'00:00:37'),
(N'2017-09-15T14:30:12.607', N'00:00:59'),
(N'2017-09-15T14:31:22.807', N'00:00:49'),
(N'2017-09-15T15:19:47.24', N'00:01:07'),
(N'2017-09-15T16:04:47.753', N'00:00:55'),
(N'2017-09-15T16:58:08.08', N'00:00:55'),
(N'2017-09-15T17:05:04.557', N'00:00:50'),
(N'2017-09-15T17:20:42.753', N'00:00:58'),
(N'2017-09-15T17:28:09.14', N'00:01:05'),
(N'2017-09-15T17:39:46.69', N'00:00:38'),
(N'2017-09-15T17:40:21.957', N'00:01:05'),
(N'2017-09-15T17:43:47.57', N'00:01:08'),
(N'2017-09-15T17:47:23.39', N'00:01:05'),
(N'2017-09-15T17:47:28.41', N'00:00:56'),
(N'2017-09-15T17:51:59.38', N'00:01:04');
我可以稍微通过以下方式使其工作：
Select CallStart,
CallDuration,
DropsIn15Minutes =
(
Select Count(*)
From #Calls C2
Where C2.CallStart Between DateAdd(Minute, -15, C1.CallStart)
And C1.CallStart
)
From #Calls C1
但是，我想避免使用子查询，而是支持COUNT(*) OVER ()（或任何其他解决方案，如果可能的话）解决方案。
这可能吗？或者子查询是否是正确的解决方案？
答案 0 :(得分：2)
另外两个选择：
使用cross apply()：
select
CallStart
, CallDuration
, DropsIn15Minutes
from calls c
outer apply (
select DropsIn15Minutes = count(*)
from calls i
where i.callstart >= dateadd(minute,-15,c.CallStart)
and i.callstart <= c.CallStart
) x
order by c.CallStart
使用inner join：
select
c.CallStart
, c.CallDuration
, DropsIn15Minutes = count(i.CallStart)
from calls c
inner join calls i
on i.callstart >= dateadd(minute,-15,c.CallStart)
and i.callstart <= c.CallStart
group by c.CallStart, c.CallDuration
order by c.CallStart
rextester演示：http://rextester.com/PFCE43712
比较所有3个（包括你的）的执行计划：dbfiddle.uk demo
答案 1 :(得分：2)
一种方法 - 如果表格很大，可能比嵌套循环连接在一个范围上更好 - 将首先创建一个数字表......
CREATE TABLE dbo.Numbers
(
N INT PRIMARY KEY
);
WITH E1(N) AS
(
SELECT 1 UNION ALL SELECT 1 UNION ALL SELECT 1 UNION ALL
SELECT 1 UNION ALL SELECT 1 UNION ALL SELECT 1 UNION ALL
SELECT 1 UNION ALL SELECT 1 UNION ALL SELECT 1 UNION ALL SELECT 1
) -- 1*10^1 or 10 rows
, E2(N) AS (SELECT 1 FROM E1 a, E1 b) -- 1*10^2 or 100 rows
, E4(N) AS (SELECT 1 FROM E2 a, E2 b) -- 1*10^4 or 10,000 rows
, E8(N) AS (SELECT 1 FROM E4 a, E4 b) -- 1*10^8 or 100,000,000 rows
INSERT INTO dbo.Numbers
SELECT TOP (60*60*24) -1 + ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS N FROM E8;
然后使用以下几行内容。
WITH Calls
AS (SELECT *,
--pre-truncate all call starts to second precision
CallStart_sec = DATEADD(SECOND, DATEDIFF(SECOND, '20000101', CallStart), '20000101')
FROM #Calls),
PreAgg
AS (SELECT CallStart_sec,
COUNT(*) AS Cnt
FROM Calls
GROUP BY CallStart_sec),
Dates(D)
--Todo - something else other than hardcoding the dates
AS (SELECT CAST('2017-09-15' AS DATETIME2)),
RT
AS (SELECT *,
Cume = SUM(Cnt) OVER (ORDER BY DATEADD(SECOND, N.N, Dates.D)
ROWS BETWEEN 900 PRECEDING AND CURRENT ROW)
FROM Dates
INNER JOIN dbo.Numbers N
ON N.N BETWEEN 0 AND 86399
LEFT JOIN PreAgg P
ON P.CallStart_sec = DATEADD(SECOND, N.N, Dates.D))
SELECT C.CallStart_sec AS CallStart,
CallDuration,
DropsIn15Minutes = Cume
FROM Calls C
JOIN RT
ON RT.CallStart_sec = C.CallStart_sec
答案 2 :(得分：-1)
Select CallStart,
CallDuration,
COUNT(*) OVER (PARTITION BY trunc(CallStart,'mi') -
numtodsinterval(mod(to_char(CallStart,'mi'),15),'minute'))
as DropsIn15Minutes
From #Calls C1
让我们试一试，看看它是如何运作的。
泰德