Ian Wienand: Zuul and Ansible in OpenStack CI
(It is a gross oversimplification, but for the purposes of OpenStack CI, Jenkins is pretty much used as a glorified ssh/scp wrapper. Zuul Version 3, under development, is working to remove the need for Jenkins to be involved at all).Well some recent security issues with Jenkins and other changes has led to a roll-out of what is being called Zuul 2.5, which has indeed removed Jenkins and makes extensive use of Ansible as the basis for running CI tests in OpenStack. Since I already had the diagram, it seems worth updating it for the new reality.
The process starts when a developer uploads their code to gerrit via the git-review tool. There is no further action required on their behalf and the developer simply waits for results of their jobs.
Gerrit provides a JSON-encoded "fire-hose" output of everything happening to it. New reviews, votes, updates and more all get sent out over this pipe. Zuul is the overall scheduler that subscribes itself to this information and is responsible for managing the CI jobs appropriate for each change.
Zuul has a configuration that tells it what jobs to run for what projects. Zuul can do lots of interesting things, but for the purposes of this discussion we just consider that it puts the jobs it wants run into gearman for a launcher to consume. gearman is a job-server; as they explain it "[gearman] provides a generic application framework to farm out work to other machines or processes that are better suited to do the work". Zuul puts into gearman basically a tuple (job-name, node-type) for each job it wants run, specifying the unique job name to run and what type of node it should be run on.
A group of Zuul launchers are subscribed to gearman as workers. It is these Zuul launchers that will consume the job requests from the queue and actually get the tests running. However, a launcher needs two things to be able to run a job a job definition (what to actually do) and a worker node (somewhere to do it). The first part what to do is provided by job-definitions stored in external YAML files. The Zuul launcher knows how to process these files (with some help from Jenkins Job Builder, which despite the name is not outputting XML files for Jenkins to consume, but is being used to help parse templates and macros within the generically defined job definitions). Each Zuul launcher gets these definitions pushed to it constantly by Puppet, thus each launcher knows about all the jobs it can run automatically. Of course Zuul also knows about these same job definitions; this is the job-name part of the tuple we said it put into gearman. The second part somewhere to run the test takes some more explaining. To the next point...
Several cloud companies donate capacity in their clouds for OpenStack to run CI tests. Overall, this capacity is managed by a customized management tool called nodepool (you can see the details of this capacity at any given time by checking the nodepool configuration). Nodepool watches the gearman queue and sees what requests are coming out of Zuul. It looks at node-type of jobs in the queue (i.e. what platform the job has requested to run on) and decides what types of nodes need to start and which cloud providers have capacity to satisfy demand. Nodepool will start fresh virtual machines (from images built daily as described in the prior post), monitor their start-up and, when they're ready, put a new "assignment job" back into gearman with the details of the fresh node. One of the active Zuul launchers will pick up this assignment job and register the new node to itself.
At this point, the Zuul launcher has what it needs to actually get jobs started. With an fresh node registered to it and waiting for something to do, the Zuul launcher can advertise its ability to consume one of the waiting jobs from the gearman queue. For example, if a ubuntu-trusty node is provided to the Zuul launcher, the launcher can now consume from gearman any job it knows about that is intended to run on an ubuntu-trusty node type. If you're looking at the launcher code this is driven by the NodeWorker class you can see this being created in response to an assignment via LaunchServer.assignNode. To actually run the job where the "job hits the metal" as it were the Zuul launcher will dynamically construct an Ansible playbook to run. This playbook is a concatenation of common setup and teardown operations along with the actual test scripts the jobs wants to run. Using Ansible to run the job means all the flexibility an orchestration tool provides is now available to the launcher. For example, there is a custom console streamer library that allows us to live-stream the console output for the job over a plain TCP connection, and there is the possibility to use projects like ARA for visualisation of CI runs. In the future, Ansible will allow for better coordination when running multiple-node testing jobs after all, this is what orchestration tools such as Ansible are made for! While the Ansible run can be fairly heavyweight (especially when you're talking about launching thousands of jobs an hour), the system scales horizontally with more launchers able to consume more work easily. When checking your job results on logs.openstack.org you will see a _zuul_ansible directory now which contains copies of the inventory, playbooks and other related files that the launcher used to do the test run.
Eventually, the test will finish. The Zuul launcher will put the result back into gearman, which Zuul will consume (log copying is interesting but a topic for another day). The testing node will be released back to nodepool, which destroys it and starts all over again nodes are not reused and also have no sensitive details on them, as they are essentially publicly accessible. Zuul will wait for the results of all jobs for the change and post the result back to Gerrit; it either gives a positive vote or the dreaded negative vote if required jobs failed (it also handles merges to git, but that is also a topic for another day).




This week I have been gathered with 38 KDE people in Randa, Switzerland. Randa is a place in a valley in the middle of the Alps close to various peaks like Matterhorn. It has been a week of intense hacking, bugfixing, brainstorming and a bit of enjoying the nature.
R is for Reproducible builds
I spent the first couple of days trying to get the Qt Documentation generation tool to reproducible generate documentation. Some of the fixes were of the usual put data in an randomized datastructure, then iterate over it and create output , where the fix is similar well known: Sort the datastructure first. Others were a bit more severe bugs that lead to the documentation to shuffle around the obsolete bit, and the inheritance chains. Most of these fixes have been reviewed and submitted to the Qt 5.6 branch, one is still 
TL;DR: I put a $5
You can still pull out the little platform to increase the space
that can be used to put something on top.
But, most conveniently,
they've designed in an empty compartment, roughly the size of a modern
smartphone, beneath the platform. The original idea was to actually
put a smartphone inside, but this has turned out (at least to me)
to not be very feasible. Fortunately, they thought about the
need for electricity and added a Micro USB cable terminating within the
newly created, empty compartment.
My first idea was to put a conventional Raspberry Pi inside.
When I received the braille display, however, we immediately
noticed that a standard-sized rpi is roughly 3mm too high
to fit into the empty compartment.
Fortunately, though, a co-worker noticed that the
I also learned (everything I now know) about Apache websites, redirects, the website/navigation/directory structure of tests.reproducible-builds.org, and the roles of many of the reproducible scripts in
I'll tune in to the post-based conversation being held on Planet Debian:
In order to monitor some of the
Quite a lot has happened in xdg-app since

How to manage your photos? That is probably the biggest question for anyone doing anything with a photo camera. As resolutions of cameras grow, the data we have to manage is growing ever. In my case I am talking about more than 50000 photos and videos measuring up to about 200Gb of disk space, constantly growing. There are several photo management softwares out there, I guess the most commonly used ones are
There is no clear winner here, unfortunately. Both have their strength and their weaknesses. And as a consequence I am using both in parallel.
Before I start a clear declaration: I have been using Shotwell for many years, and have myself contributed considerable code to Shotwell, in particular the whole comment system (comments for photos and events), as well as improved the
So in short Shotwell distinguishes between disk layout and album/event names.
In digiKam there is a strict connection between disk layout and album names 1:1. Albums are directories. One can adjust the viewer to show all photos of sub-albums in the main album, and by this one can achieve the same effect of merging all photos of my friend and myself. The good thing in this approach is that one can easily have sub-albums: Imagine a trip to three different islands of Hawaii during one trip. This is something easy to achieve in digiKam, but hard in Shotwell.
Other organization methods
Both Shotwell and digiKam support tags, including hierarchical tags and rating (0-5 stars). Shotwell has in addition a quick flag action that I used quite often for initial selecting photos, as well as accepted and rejected. digiKam also has so called picks (no pick, reject, pending, accepted), and colors (not used by now). Both programs have some face detection support, but also this I haven t used.
So in the organization respect there is no clear winner. I like the Event idea of Shotwell, or better, the separation of events from the disk structure. But on the other hand, Shotwell does not allow for sub-albums, which is also a pain.
No clear winner draw
Offline storage
That is simple: Shotwell: forget it, not reasonably possible. One can move parts to an external HD, then unplug it, and Shotwell will tell you that all the photos are missings. And when you plug the external HD in, it will redetect them. But this is not proper support, just a consequence of hash sum storage. Also separation into several libraries (online and offline) is not supported.
On the other hand, digiKam supports multiple libraries, partly offline, without a hinch. I would love to have this feature in Shotwell, because I need to free disk space, urgently!!!
Clear winner: digiKam
Sharing
Again here my testing is very restricted I am using 
On the other hand digiKam has a very barebone interface to Piwigo you can upload photos to an existing album and resize them, but that is already everything. One also needs to say that the list of supported services in digiKam is by far longer than the one in Shotwell, but the main services (usual suspects) are supported in both.
Clear winner: Shotwell (but I haven t tested other upload services).
Editing
The editing capabilities of Shotwell are again very restricted: red eyes, resize, levels, .
Clear winner: digiKam
Conclusions
While I am still working with both, I actually would love to move completely to digiKam. I simply cannot stand the Gnome 3 philosophy of reducing functionality to a minimal for dummy users. There is a market for that, sure, but I am not one of it. Unfortunately, the missing Event support, and much more the completely minimal support for Piwigo sharing in digiKam is a big show stopper at the moment. Even after testing the upcoming digiKam 5.0 version for some time, I didn t see any improvement wrt to sharing.
That leaves me with the only option to continue working with both programs, and hope to get a new bigger SSD before the current one runs out of space. Of course I could start hacking into the digiKam source maybe I do this when I have a bit more time to add proper Piwigo support.