Graphing Jenkins Statistics
Like many people, we use Jenkins at work as our continuous integration server and we require that all changes that are committed go through being built in CI before they can get deployed. Yesterday, someone asked if we could add another jenkins slave to try to reduce the amount of time spent waiting on builds. While the slaves are fully puppetized and so it’s not much work to bring an additional slave online, my own anecdotal experience made me think that we weren’t really held up often in a way that additional slaves would help. I had a vague memory of some graphs within jenkins so eventually found them but didn’t really find them that enlightening. The scale is funky, it’s a weird exponential moving average and I just didn’t find it that easy to get any insight from them.
So last night, I sat down and wrote a quick little script to run via cron and pull some statistics and throw them into graphite. Already with less than a day of data, I’m better able to tell that we end up with a few periods of about ten minutes where having more executors could help that are correlated with when someone does a commit to one of the projects at the base of our dependency tree. So that gives us a lot better idea of whether or not the cost of an additional machine is worth the few minutes that we’d be able to save in those cases.
Since it didn’t look like anyone else had done anything along these lines yet, I put the code up on github. There are a lot more stats that could be pulled out via the jenkins api, this is really just a starting point for what I needed today.
