Hi all,
I have become increasingly concerned with CI performance lately, and have been raising those concerns with various people. Most specifically, I'm worried about our turnaround time or "time to get a result", which has been creeping up lately. Right after the beginning of the year, we had a really bad week where the turnaround time was well over 24 hours. That means if you submit a patch on Tuesday afternoon, you might not get a test result until Thursday. That is, IMHO, a real problem and massively hurts our ability to quickly merge priority fixes as well as just general velocity and morale. If people won't review my code until they see a +1 from Zuul, and that is two days after I submitted it, that's bad.
Things have gotten a little better since that week, due in part to getting past a rush of new year submissions (we think) and also due to some job trimming in various places (thanks Neutron!). However, things are still not great. Being in almost the last timezone of the day, the queue is usually so full when I wake up that it's quite often I don't get to see a result before I stop working that day.
I would like to ask that projects review their jobs for places where they can cut out redundancy, as well as turn their eyes towards optimizations that can be made. I've been looking at both Nova and Glance jobs and have found some things I think we can do less of. I also wanted to get an idea of who is "using too much" in the way of resources, so I've been working on trying to characterize the weight of the jobs we run for a project, based on the number of worker nodes required to run all the jobs, as well as the wall clock time of how long we tie those up. The results are interesting, I think, and may help us to identify where we see some gains.
The idea here is to figure out[1] how many "node hours" it takes to run all the normal jobs on a Nova patch compared to, say, a Neutron one. If the jobs were totally serialized, this is the number of hours a single computer (of the size of a CI worker) would take to do all that work. If the number is 24 hours, that means a single computer could only check *one* patch in a day, running around the clock. I chose the top five projects in terms of usage[2] to report here, as they represent 70% of the total amount of resources consumed. The next five only add up to 13%, so the "top five" seems like a good target group. Here are the results, in order of total consumption:
Project % of total Node Hours Nodes ------------------------------------------ 1. TripleO 38% 31 hours 20 2. Neutron 13% 38 hours 32 3. Nova 9% 21 hours 25 4. Kolla 5% 12 hours 18 5. OSA 5% 22 hours 17
What that means is that a single computer (of the size of a CI worker) couldn't even process the jobs required to run on a single patch for Neutron or TripleO in a 24-hour period. Now, we have lots of workers in the gate, of course, but there is also other potential overhead involved in that parallelism, like waiting for nodes to be available for dependent jobs. And of course, we'd like to be able to check more than patch per day. Most projects have smaller gate job sets than check, but assuming they are equivalent, a Neutron patch from submission to commit would undergo 76 hours of testing, not including revisions and not including rechecks. That's an enormous amount of time and resource for a single patch!
Now, obviously nobody wants to run fewer tests on patches before they land, and I'm not really suggesting that we take that approach necessarily. However, I think there are probably a lot of places that we can cut down the amount of *work* we do. Some ways to do this are:
1. Evaluate whether or not you need to run all of tempest on two configurations of a devstack on each patch. Maybe having a stripped-down tempest (like just smoke) to run on unique configs, or even specific tests. 2. Revisit your "irrelevant_files" lists to see where you might be able to avoid running heavy jobs on patches that only touch something small. 3. Consider moving some jobs to the experimental queue and run them on-demand for patches that touch particular subsystems or affect particular configurations. 4. Consider some periodic testing for things that maybe don't need to run on every single patch. 5. Re-examine tests that take a long time to run to see if something can be done to make them more efficient. 6. Consider performance improvements in the actual server projects, which also benefits the users.
If you're a project that is not in the top ten then your job configuration probably doesn't matter that much, since your usage is dwarfed by the heavy projects. If the heavy projects would consider making changes to decrease their workload, even small gains have the ability to multiply into noticeable improvement. The higher you are on the above list, the more impact a small change will have on the overall picture.
Also, thanks to Neutron and TripleO, both of which have already addressed this in some respect, and have other changes on the horizon.
Thanks for listening!
--Dan
1: https://gist.github.com/kk7ds/5edbfacb2a341bb18df8f8f32d01b37c 2; http://paste.openstack.org/show/C4pwUpdgwUDrpW6V6vnC/