Are you talking about 200-1000 jobs configured, or running simultaneously?

Clients feed log data and artifacts to the Jenkins server in real time.  If 
you’re running hundreds of these simultaneously, you are using tons of threads, 
tons of RAM for buffers, and tons of disk revolutions.  At this point, you can 
start thinking of your Jenkins server as a database server and determine your 
hardware requirements accordingly.  It’s fairly easy to become I/O bound, and 
you might become network bound if you’re using older, slower networking.

Also, how is your polling?  If you have hundreds of jobs polling, think of 
those as hundreds of “mini-jobs” querying source control on a regular basis.  
They don’t show up in the regular build logs, but they have similar resource 
needs.  If there’s a way to avoid polling (we use GitLab and have a plugin 
which lets GitLab push updates to Jenkins rather than have Jenkins poll), 
consider it.

The guys at Cloudbees would be willing to sell you a way to do this using 
multiple Jenkins servers and have them all centrally managed; you may want to 
talk to them if this is that big of a project.

--Rob

From: jenkinsci-users@googlegroups.com 
[mailto:jenkinsci-users@googlegroups.com] On Behalf Of Tomasz Majchrowski
Sent: Friday, January 16, 2015 12:47 PM
To: jenkinsci-users@googlegroups.com
Subject: Schedule large numbers of building jobs - perfromance issue

Dear All
The way in which Jenkins manages tasks and machines is very powerful. We are 
thinking about moving our python written system to distribute builds across 
server farm in to the Jenkins.

The challenge that I recognize will be a number of parallel tasks that needs to 
be handle by Jenkins. Basically I create project which based on dependency 
triggers the downstream project with using of groovy system scripts and 
schedule2(Task, int, 
List)<http://javadoc.jenkins-ci.org/hudson/model/Queue.html#schedule2%28hudson.model.Queue.Task,%20int,%20java.util.List%29>.
 Since the number of downstream builds of the same project (with different 
parameter) may achieve range (200-1000) I recognize performance issue.

It's really hard to say if the bottle neck is coming from:
* the bad design of one of the plugin installed in the chain or
* basically the Jenkins is not the right tool for such a scale of parallel task 
due to its' overhead.

I debug a bit with visualvm to found a bottle neck, however couldn't found any 
interesting. Anyone is having experience with such a large build queue. Could 
you share hints/best practice?

Best regards, Tomasz Majchrowski.
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