通过聚合SQL审计记录来衡量应用程序性能

假设有一个包含两列的简单审计表(在生产中有更多列)：

ID | Date

处理请求时,我们在此表中添加一条记录.
请求分批处理,批处理中可以有任意数量的项目.对于每个项目,我们将添加一条记录.批次之间将存在至少2秒的延迟(该数量是可配置的).

性能是通过每单位时间(例如每秒)处理请求的速度来衡量的.考虑这个示例数据(2个集群,项目数量相同,仅用于演示目的)：

--2016-01-29 10:27:25.603
--2016-01-29 10:27:25.620
--2016-01-29 10:27:25.637
--2016-01-29 10:27:25.653
--2016-01-29 10:27:25.723
--Avg time between requests = 24ms

--2016-01-29 10:27:34.647
--2016-01-29 10:27:34.667
--2016-01-29 10:27:34.680
--2016-01-29 10:27:34.690
--2016-01-29 10:27:34.707
--Avg time = 12ms

我们可以说,在最坏的情况下,每秒可以处理41.67个请求,最多可以处理83.33个请求.很高兴知道平均批次性能.

题.是否可以单独使用T-SQL获取这些指标以及如何使用？

编辑：要使结果具有统计显着性,丢弃批次可能比小于10个项目(可配置)更有用.

也许我已经过度简化了您的请求,但请考虑以下内容

Declare @YourTable table (ID int,Date datetime)
Insert Into @YourTable values
( 1,'2016-01-29 10:27:25.603'),
( 2,'2016-01-29 10:27:25.620'),
( 3,'2016-01-29 10:27:25.637'),
( 4,'2016-01-29 10:27:25.653'),
( 5,'2016-01-29 10:27:25.723'),
( 6,'2016-01-29 10:27:34.647'),
( 7,'2016-01-29 10:27:34.667'),
( 8,'2016-01-29 10:27:34.680'),
( 9,'2016-01-29 10:27:34.690'),
(10,'2016-01-29 10:27:34.707')


Declare @BatchSecondsGap int = 2  -- Seconds Between Batches
Declare @MinObservations int = 5  -- Batch must n or greater

;with cte as (
      Select *,Cnt = sum(1) over (Partition By Batch)
       From  (
              Select *,Batch = sum(Flg) over (Order By Date)
               From (
                     Select ID,Date
                           ,Flg = case when DateDiff(SECOND,Lag(Date,1,null) over (Order By Date),Date)>=@BatchSecondsGap then 1 else 0 end
                           ,MS  = case when DateDiff(SECOND,Lag(Date,1,Date) over (Order By Date),Date)>=@BatchSecondsGap then 0 else DateDiff(MILLISECOND,Lag(Date,1,Date) over (Order By Date),Date) end
                      From  @YourTable
                     ) A
             ) B
 )
Select Title    = 'Total'
      ,DateR1   = min(Date)
      ,DateR2   = max(Date)
      ,BatchCnt = count(Distinct Batch)
      ,TransCnt = count(*)
      ,MS_Ttl   = sum(MS)
      ,MS_Avg   = avg(MS*1.0)
      ,MS_Std   = stdev(MS)
 From  cte
 Where Cnt>=@MinObservations
Union All
Select Title    = concat('Batch ',Batch)
      ,DateR1   = min(Date)
      ,DateR2   = max(Date)
      ,BatchCnt = count(Distinct Batch)
      ,TransCnt = count(*)
      ,MS_Ttl   = sum(MS)
      ,MS_Avg   = avg(MS*1.0)
      ,MS_Std   = stdev(MS)
 From  cte
 Where Cnt>=@MinObservations
 Group By Batch

返回

下图说明您不会因批次之间的时间而受到惩罚,因此它将成为最终结果的简单聚合