>> >> of servers (operators) is fixed and gate entry into the system. However, >> I'm often simulating open systems which means I do not want rate of entry >> into the system to be controlled by the performance of the system (rate of >> exit). > > What makes you think JMeter does this ?
math (queuing theory) and observation from profiling JMeter. > > > >> and the quality of the results > > I disagree with this assertion . We have been using JMeter for load testing > all kind of applications Intranets / Large ECommerce Systems / Backoffice > systems / , and quality of results is good provided you configure it > properly. > Particularly when using Remote Testing. Lot of users in this mailing list > use it and are satisfied (I think). I'm happy to engage JMeter project developers in a constructive conversation if they are interested and I shan't be offended if they choose to ignore me. What I don't feel is a the need to get into this type of a long protracted discussion. As I've said, JMeter has a lot of admiral qualities which is why I use it and have been introducing people how to use it for years. All I'm suggesting here are ways in which the tool can be improved so that scales better and has less of an artificial influence on profiling results. For example, I've tried to use it to drive a websocket app that was driving traffic to 1,000,000 simulated end users. We tried using JMeter but the threading model simply prevented it so I took on the arduous task of custom rolling an injector (not a route that I wanted to take). As any of the JMeter developers will tell you, load injectors are not trivial applications that can simply be thrown together and they require a boat load of testing. It took a number of iterations to understand the load pattern it was producing and then be able to tweak it to remove artificial artifacts that were simply a result of internal mechanics of the injector. To see this you have to study the output of the injector. Without this study of the output of the injector the bench would have failed. To their credit, the client in this case learned quite a bit from the exercise and went on to use that information to their advantage in some pretty tough competitive situations. So, with all due respect, I doubt the vast majority of JMeter users ever bother to do any analysis of the load generated by the tool. They just simply use it and if that works, good for them. That said, the vast majority of benchmarks I deal with are busted in some fundamental way and one common way in which they are busted is in how the load injector pushed load into the unit of test. In this case JMeter's thread model can artificially bottleneck the test due to it's threading model and when that happens you can't rely on the results of that bench.. IOWs, the bench is busted and it's busted because of how the load is generated. Regards, Kirk --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@jmeter.apache.org For additional commands, e-mail: user-h...@jmeter.apache.org
