Question

我有一个如下的SQL Server表。

CREATE TABLE [dbo].[ChannelData](
[Id] [int] IDENTITY(1,1) NOT NULL,
[ChannelId] [int] NOT NULL,
[ChannelValue] [decimal](10, 2) NULL,
[ChannelDataLogTime] [datetime] NOT NULL,
[Active] [bit] NULL,CONSTRAINT [PK_ChannelData] PRIMARY KEY CLUSTERED ( [Id] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]) ON [PRIMARY]

样本数据如下：::

+----+-----------+--------------+-------------------------+--------+
| Id | ChannelId | ChannelValue |   ChannelDataLogTime    | Active |
+----+-----------+--------------+-------------------------+--------+
|  1 |         9 |         5.46 | 2015-06-09 14:00:11.463 |      1 |
|  2 |         9 |         8.46 | 2015-06-09 14:01:11.503 |      1 |
|  3 |         9 |         3.46 | 2015-06-09 14:02:27.747 |      1 |
|  4 |         9 |         6.46 | 2015-06-09 14:03:11.503 |      1 |
|  5 |         9 |         1.46 | 2015-06-09 14:04:11.530 |      1 |
|  6 |         9 |         4.46 | 2015-06-09 14:05:11.537 |      1 |
|  7 |         9 |         7.46 | 2015-06-09 14:06:11.547 |      1 |
|  8 |         9 |         2.46 | 2015-06-09 14:07:33.983 |      1 |
|  9 |         9 |         5.46 | 2015-06-09 14:08:11.570 |      1 |
| 10 |         9 |         8.46 | 2015-06-09 14:09:11.603 |      1 |
| 11 |         9 |         3.46 | 2015-06-09 14:10:11.613 |      1 |
| 12 |         9 |         6.47 | 2015-06-09 14:11:11.623 |      1 |
| 13 |         9 |         1.47 | 2015-06-09 14:12:24.497 |      1 |
| 14 |         9 |         4.47 | 2015-06-09 14:13:11.623 |      1 |
| 15 |         9 |         7.47 | 2015-06-09 14:14:11.650 |      1 |
| 16 |         9 |         2.47 | 2015-06-09 14:15:11.707 |      1 |
| 17 |         9 |         5.47 | 2015-06-09 14:16:11.707 |      1 |
| 18 |         9 |         8.47 | 2015-06-09 14:17:25.647 |      1 |
| 19 |         9 |         3.47 | 2015-06-09 14:18:11.707 |      1 |
| 20 |         9 |         6.47 | 2015-06-09 14:19:11.753 |      1 |
| 21 |         9 |         1.47 | 2015-06-09 14:20:11.760 |      1 |
| 22 |         9 |         4.47 | 2015-06-09 14:21:11.790 |      1 |
| 23 |         9 |         7.47 | 2015-06-09 14:22:29.500 |      1 |
| 24 |         9 |         2.47 | 2015-06-09 14:23:11.907 |      1 |
| 25 |         9 |         5.47 | 2015-06-09 14:24:12.057 |      1 |
| 26 |         9 |         8.47 | 2015-06-09 14:25:11.817 |      1 |
| 27 |         9 |         3.47 | 2015-06-09 14:26:11.837 |      1 |
| 28 |         9 |         6.47 | 2015-06-09 14:27:32.253 |      1 |
| 29 |         9 |         1.47 | 2015-06-09 14:28:11.870 |      1 |
| 30 |         9 |         4.47 | 2015-06-09 14:29:11.870 |      1 |
| 31 |         9 |         7.50 | 2015-06-09 16:00:13.313 |      1 |
| 32 |         9 |         2.50 | 2015-06-09 16:01:13.260 |      1 |
| 33 |         9 |         5.50 | 2015-06-09 16:02:13.290 |      1 |
| 34 |         9 |         8.50 | 2015-06-09 16:03:13.270 |      1 |
| 35 |         9 |         3.50 | 2015-06-09 16:04:32.827 |      1 |
| 36 |         9 |         6.50 | 2015-06-09 16:05:13.323 |      1 |
| 37 |         9 |         1.50 | 2015-06-09 16:06:13.330 |      1 |
| 38 |         9 |         4.50 | 2015-06-09 16:07:13.337 |      1 |
| 39 |         9 |         7.50 | 2015-06-09 16:08:13.313 |      1 |
| 40 |         9 |         2.50 | 2015-06-09 16:09:28.497 |      1 |
| 41 |         9 |         5.50 | 2015-06-09 16:10:13.370 |      1 |
| 42 |         9 |         8.50 | 2015-06-09 16:11:13.417 |      1 |
| 43 |         9 |         3.50 | 2015-06-09 16:12:13.540 |      1 |
| 44 |         9 |         6.50 | 2015-06-09 16:13:13.577 |      1 |
| 45 |         9 |         1.50 | 2015-06-09 16:14:33.880 |      1 |
| 46 |         9 |         4.50 | 2015-06-09 16:15:13.453 |      1 |
| 47 |         9 |         7.50 | 2015-06-09 16:16:13.500 |      1 |
| 48 |         9 |         2.50 | 2015-06-09 16:17:13.497 |      1 |
| 49 |         9 |         5.50 | 2015-06-09 16:18:13.503 |      1 |
| 50 |         9 |         8.50 | 2015-06-09 16:19:38.717 |      1 |
| 51 |         9 |         3.50 | 2015-06-09 16:21:13.567 |      1 |
| 52 |         9 |         6.50 | 2015-06-09 16:22:13.557 |      1 |
| 53 |         9 |         1.50 | 2015-06-09 16:23:14.163 |      1 |
| 54 |         9 |         4.50 | 2015-06-09 16:24:13.607 |      1 |
| 55 |         9 |         7.50 | 2015-06-09 16:25:38.783 |      1 |
| 56 |         9 |         2.50 | 2015-06-09 16:27:13.660 |      1 |
| 57 |         9 |         5.51 | 2015-06-09 16:28:13.710 |      1 |
| 58 |         9 |         8.51 | 2015-06-09 16:29:13.703 |      1 |
| 59 |         9 |         3.51 | 2015-06-09 16:30:13.713 |      1 |
+----+-----------+--------------+-------------------------+--------+

现在，我将在一段时间内生成15分钟的平均数据，包括开始日期和结束日期。可以很好地解决任何问题。

我遇到的情况是数据会丢失一段时间。由于缺少15分钟时隙的数据，因此又错过了15分钟时隙。我需要的是列出15分钟的时间段，即使使用SQL查询在该时间段内数据不可用。

SELECT
                            Avg(chnldata.ChannelValue)  AS ChannelValue,
                            DATEADD(minute,FLOOR(DATEDIFF(minute,0,ChannelDataLogTime)/15)*15,0) as HourlyDateTime,
                           chnldata.ChannelId as Id
                    FROM ChannelData as chnldata
                    WHERE  chnldata.ChannelId in (9) AND chnldata.ChannelDataLogTime >= '06/09/2015'  AND chnldata.ChannelDataLogTime < '06/11/2015 23:59:50' 
                     GROUP BY chnldata.ChannelId, DATEADD(minute,FLOOR(DATEDIFF(minute,0,ChannelDataLogTime)/15)*15,0)

这是现有的15分钟平均查询。但是它不会显示缺少15分钟的广告位。当前输出为：::

+--------------+-------------------------+----+
| ChannelValue |     HourlyDateTime      | Id |
+--------------+-------------------------+----+
|     5.129333 | 2015-06-09 14:00:00.000 |  9 |
|     4.803333 | 2015-06-09 14:15:00.000 |  9 |
|     5.033333 | 2015-06-09 16:00:00.000 |  9 |
|     5.270769 | 2015-06-09 16:15:00.000 |  9 |
|     3.510000 | 2015-06-09 16:30:00.000 |  9 |
+--------------+-------------------------+----+

必需的输出是：::

+--------------+-------------------------+----+
| ChannelValue |     HourlyDateTime      | Id |
+--------------+-------------------------+----+
| 5.129333     | 2015-06-09 14:00:00.000 |  9 |
| 4.803333     | 2015-06-09 14:15:00.000 |  9 |
| NULL         | 2015-06-09 14:30:00.000 |  9 |
| NULL         | 2015-06-09 14:45:00.000 |  9 |
| NULL         | 2015-06-09 15:00:00.000 |  9 |
| NULL         | 2015-06-09 15:15:00.000 |  9 |
| NULL         | 2015-06-09 15:30:00.000 |  9 |
| NULL         | 2015-06-09 15:45:00.000 |  9 |
| 5.033333     | 2015-06-09 16:00:00.000 |  9 |
| 5.270769     | 2015-06-09 16:15:00.000 |  9 |
| 3.510000     | 2015-06-09 16:30:00.000 |  9 |
+--------------+-------------------------+----+

Answer 1

右外加入CTE，该CTE在您的时间范围内具有所有可能的15分钟间隔。

Answer 2

建立时间范围的CTE，可以用多种方法完成，但是笛卡尔积方法可能比许多方法快如果您想提高速度，最好建立一个静态的日期表，也许还有一个日期和时间表

;WITH mins as (SELECT 0 as q union select 15 union select 30 union select 45),
      dats as (SELECT MIN(ChannelDataLogTime) as t1, max(ChannelDataLogTime) as t2 from channeldata),
      ranges as (SELECT CAST(t1 as date) s1 FROM dats
                union all
                SELECT dateadd(day,1,r.s1) from ranges r where r.s1< (select t2 from dats)
                ),
      hrs as (select 0 h union all select  h + 1 from hrs where h < 23),    --hours 0 to 23
      slots as (select dateadd(MINUTE,mins.q,dateadd(hour,hrs.h,cast(ranges.s1 as datetime2))) as strt from mins,ranges,hrs      ),
      ids as (SELECT distinct  ChannelId from  ChannelData),
      allslot as (select channelid, strt from slots,ids)
        SELECT    count(0) as x,
                            coalesce(Avg(chnldata.ChannelValue)   , 0) AS ChannelValue,
                            s.strt HourlyDateTime,
                           s.ChannelId as Id
                    FROM ChannelData as chnldata
                    RIGHT JOIN  allslot s on s.strt <= ChannelDataLogTime and ChannelDataLogTime < dateadd(minute,15,s.strt) and s.ChannelId = chnldata.ChannelId 
                    WHERE  chnldata.ChannelId is null or chnldata.ChannelId in (9) AND chnldata.ChannelDataLogTime >= '20150906'  AND chnldata.ChannelDataLogTime < '20151123' 
                     GROUP BY s.ChannelId, s.strt

Answer 3

请考虑maxrecursion选项的局限性。

DECLARE @StartDT DATETIME = '2015-06-09';
DECLARE @EndDT DATETIME = '2015-06-12'; -- moved to the next day to use >= and < operators correctly

;WITH
[Interval]
AS
(
    SELECT
         [Start] = @StartDT
        ,[End] = DATEADD(MINUTE, 15, @StartDT)

    UNION ALL

    SELECT
         [Start] = [End]
        ,[End] = DATEADD(MINUTE, 15, [End])
    FROM [Interval]
    WHERE (1 = 1)
        AND ([End] < @EndDT)
),
[Available]
AS
(
    SELECT
         [Start] = CONVERT(SMALLDATETIME, MIN([CD].[ChannelDataLogTime]))
        ,[End] = CONVERT(SMALLDATETIME, MAX([CD].[ChannelDataLogTime]))
    FROM [dbo].[ChannelData] AS [CD]
    WHERE (1 = 1)
        AND (@StartDT <= [CD].[ChannelDataLogTime] AND [CD].[ChannelDataLogTime] < @EndDT)
)
SELECT
     [ChannelValue] = AVG([CD].[ChannelValue])
    ,[HourlyDateTime] = [I].[Start]
    ,[Id] = [CD].[ChannelId]
FROM [Available] AS [A]
INNER JOIN [Interval] AS [I]
    ON ([A].[Start] <= [I].[Start] AND [I].[Start] <= [A].[End])
LEFT OUTER JOIN [dbo].[ChannelData] AS [CD]
    ON
    (
            ([CD].[ChannelId] IN (9))
        AND ([I].[Start] <= [CD].[ChannelDataLogTime] AND [CD].[ChannelDataLogTime] < [I].[End])
    )
GROUP BY
     [I].[Start]
    ,[CD].[ChannelId]
ORDER BY
     [I].[Start]
OPTION (MAXRECURSION 32767);

使用SQL Server生成15分钟插槽的数据平均值

3 个答案: