Monday, January 4, 2010

Response time statistics using SQL

I typically use Excel for statistical analysis of response times. Occasionally, for very large data sets, I use SQL. Here is some handy SQL to calculate min, max, average, std deviation, and 95th percentiles from response time data. The response time data for this example came from a test where I had established steady state periods of activity at 300, 600, 900, and 1200 virtual users.

This method doesn't do any sort of interpolation and therefore requires at least 100 samples to give a reasonable result for percentiles. The base table of timers consists of the columns timername (type varchar), activeusers (number of active users at the time the timer was recorded, type int), and the 'elapsed time' of the event timed as a floating point number.


    timername as 'Timer Name',
    activeusers as 'Active Users',
    count(elapsedtime) as 'Count',
    avg(elapsedtime) as 'Average',
    stdev(elapsedtime) as 'StdDev',
    min(elapsedtime) as 'Min',
    max(elapsedtime) as 'Max',
    (select max(elapsedtime) from
        (select top 95 percent elapsedtime
         from timers b
             where b.timername = a.timername and b.activeusers = a.activeusers
             order by b.elapsedtime asc) as elapsedtime) as '95th Percentile'
from timers a
    where a.activeusers in (300,600,900,1200)
    group by a.timername, a.activeusers
    order by a.timername asc, a.activeusers asc


After executing the query, I cut and paste the output to Excel.

Graphing count vs VUs gives show how throughput varies with load. Here is a graph of home page throughput.

And a graph of 95th percentile homepage response time vs VUs which indicates some sort of bottleneck above 900 VUs.

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