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Abstract
Thoughts on how best to set sampling intervals for long-running performance tester load tests.
Content
Gathering statistics is a vital component of performance test reports. The gathering of them is also a frequent contributor to product performance issues and, sometimes, apparent "hangs" at the end of test runs.
As a schedule runs, it periodically gathers samples. The playback engine collects samples in bit buckets. For the sake of argument, consider a bit bucket to be a file. To that end, every sample bit bucket consumes system resources and network bandwidth when it is transmitted back to the performance tester workbench at the end of a run.
The default sample interval is 5 seconds. As the expected execution time of a schedule increases, the number of bit buckets increases. If that number gets too high, it can have noticeable, even fatal effect on a schedule run.
It is recommended that testers consider the sample interval with every schedule created. Ideally, settle upon a sample interval that generates *approximately* 100 bit buckets.
Some quick math as an example:
* 2-hour schedule
* 5 second sample interval
That schedule will run for 7200 seconds (60 seconds in an minute x 60 minutes in an hour x 2 hours).
With a 5 second sample interval, it will generate 1440 bit buckets.
This can be a burden on the system.
How do we to approximately 100 bit buckets, instead of 1440?
7200 seconds/100 samples = 72 seconds per sample
This is where "approximately" comes into play. 72 seconds is not a time interval that feels natural; no one thinks in 72-second intervals. A minute, though, that is a commonly understood interval.
* 2 hour schedule
* 60 second sample interval
That will generate 120 bit buckets (7200 seconds/60 seconds per sample = 120 samples).
120 is a far cry smaller than 1440.
The important thing to remember when working through the math, is that no statistical data is lost in these scenarios. It is simply gathered less often.
Original Publication Date
01 October 2019
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Document Information
Modified date:
01 October 2019
UID
ibm11077489