Peter/Michael
Thanks that explains the number and the rational.
One more related question, I am running the tests in non GUI mode, is there a
way to get the aggregate report/aggregate graph report in non GUI mode?
Matt
Peter Lin <[EMAIL PROTECTED]> wrote:
as mike already explained, the 90% is "90% of the samples finished within
0-x time".
Since I instigated this feature, I'll explain the purpose for it. In my line
work, requirements are expressed in terms of "90% of the requests must
finish within X milliseconds". This is driven by SLA (service level
agreements) contracts, which tell the customer "you can expect 90% of the
requests to finish within x time." if 11% of the requests for a given month
exceed the gaurantee, the customer gets a discount.
The average response time is a good number to know, but from a performance
requirement perspective, it normally doesn't drive SLA's. In a
business-to-business service application, it doesn't matter if the average
response time is 500ms, if the 90% line is 1.5 seconds.
from a developer perspective, if the 50% and 90% line are far appart, ie
(50% - 550ms, 90% - 5000ms), it tells you the application fluctuates a lot.
In an ideal situtation, the 90% is less than double the 50% line. Depending
on the application, this might not be possible. hope that helps
peter lin
On 10/10/05, Michael Stover wrote:
>
> The 90% line tells you that 90% of the samples fell at or below that
> number. It works like median rather than like mean. The advantage of
> such a measure is it allows you to assert something like "90% of
> requests were handled in x amount of time". With an average, you can
> make no such assertion about what users experience - you have no handle
> on what users experience during busy periods.
>
> -Mike
>
> On Mon, 2005-10-10 at 12:15 -0700, m mat wrote:
> > I want to understand the explaination of the 90% line in aggregate
> report.
> >
> > What I think it should be: Average of samples with 90% significance, i.e.
> if you assume a bell shaped distribution around the mean, this number should
> be an average of the middle 90% numbers (as in average after top 5% and
> bottom 5% outliers are removed). So that this number could be inferred as
> "the response time 90% of your users are likely to see". For example if you
> have 10 samples 1,2,3,4,5,6,7,8,9,10. 90th percentile should be
> (2+3+4+5+6+7+8+9)/8 = 5.5
> >
> > it seems to me JMeter reports this number as the 90% value in the sample
> set. So for the above sample set JMeter would report 9
> >
> > Which one of the above two is correct for JMeter?
> > Matt
> >
> >
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