The book is composed of a series of essays, which discuss the trials of the OS/360 team in the mid-1960s, and uses those experiences to attempt to form a series of more general observations on the art of software development and systems engineering.
I want to be able to see the level of change between OpenStack releases. However, there are a relatively small number of changes with simply huge amounts of delta in them — they’re generally large refactors or the delete which happens when part of a repository is spun out into its own project.
I therefore wanted to explore what was a reasonable size for a change in OpenStack so that I could decide what maximum size to filter away as likely to be a refactor. After playing with a couple of approaches, including just randomly picking a number, it seems the logical way to decide is to simply plot a histogram of the various sizes, and then pick a reasonable place on the curve as the cutoff. Due to the large range of values (from zero lines of change to over a million!), I ended up deciding a logarithmic axis was the way to go.
For the projects listed in the OpenStack compute starter kit reference set, that produces the following histogram:I feel that filtering out commits over 10,000 lines of delta feels justified based on that graph. For reference, the raw histogram buckets are:
This proposal was submitted for FOSDEM 2021. Given that acceptances were meant to be sent out on 25 December and its basically a week later I think we can assume that its been rejected. I’ve recently been writing up my rejected proposals, partially because I’ve put in the effort to write them and they might be useful elsewhere, but also because I think its important to demonstrate that its not unusual for experienced speakers to be rejected from these events.
OpenStack today is a complicated beast — not only does it try to perform well for large clusters, but it also embraces a diverse set of possible implementations from hypervisors, storage, networking, and more. This was a deliberate tactical choice made by the OpenStack community years ago, forming a so called “Big Tent” for vendors to collaborate in to build Open Source cloud options. It made a lot of sense at the time to be honest. However, OpenStack today finds itself constrained by the large number of permutations it must support, ten years of software and backwards compatability legacy, and a decreasing investment from those same vendors that OpenStack courted so actively.
Shaken Fist makes a series of simplifying assumptions that allow it to achieve a surprisingly large amount in not a lot of code. For example, it supports only one hypervisor, one hypervisor OS, one networking implementation, and lacks an image service. It tries hard to be respectful of compute resources while idle, and as fast as possible to deploy resources when requested — its entirely possible to deploy a new VM and start it booting in less than a second for example (if the boot image is already held in cache). Shaken Fist is likely a good choice for small deployments such as home labs and telco edge applications. It is unlikely to be a good choice for large scale compute however.
I was recently contacted about availability problems with the code for pngtools. Frankly, I’m mildly surprised anyone still uses this code, but I am happy for them to do so. I have resurrected the code, placed it on github, and included the note below on all relevant posts:
A historical note from November 2020: this code is quite old, but still actively used. I have therefore converted the old subversion repository to git and it is hosted at https://github.com/mikalstill/pngtools. I will monitor there for issues and patches and try my best to remember what I was thinking 20 years ago…
The other day we released Shaken Fist version 0.2, and I never got around to announcing it here. In fact, we’ve done a minor release since then and have another minor release in the wings ready to go out in the next day or so.
So what’s changed in Shaken Fist between version 0.1 and 0.2? Well, actually kind of a lot…
We moved from MySQL to etcd for storage of persistant state. This was partially done because we wanted distributed locking, but it was also because MySQL was a pain to work with.
Some work has gone into making the API service more production grade, although there is still some work to be done there probably in the 0.3 release — specifically there is a timeout if a response takes more than 300 seconds, which can be the case in launch large VMs where the disk images are not in cache.
There were also some important features added:
Authentication of API requests.
Namespaces (a bit like Kubernetes namespaces or OpenStack projects).
Resource tagging, called metadata.
Support for local mirroring of common disk images.
…and a large number of bug fixes.
Shaken Fist is also now packaged on pypi, and the deployment tooling knows how to install from packages as well as source if that’s a thing you’re interested in. You can read more at shakenfist.com, but that site is a bit of a work in progress at the moment. The new github organisation is at github.com/shakenfist.
I spent much of yesterday playing with KSM (Kernel Shared Memory, or Kernel Samepage Merging depending on which universe you come from). Unix kernels store memory in “pages” which are moved in and out of memory as a single block. On most Linux architectures pages are 4,096 bytes long.
KSM is a Linux Kernel feature which scans memory looking for identical pages, and then de-duplicating them. So instead of having two pages, we just have one and have two processes point at that same page. This has obvious advantages if you’re storing lots of repeating data. Why would you be doing such a thing? Well the traditional answer is virtual machines.
Take my employer’s systems for example. We manage virtual learning environments for students, where every student gets a set of virtual machines to do their learning thing on. So, if we have 50 students in a class, we have 50 sets of the same virtual machine. That’s a lot of duplicated memory. The promise of KSM is that instead of storing the same thing 50 times, we can store it once and therefore fit more virtual machines onto a single physical machine.
For my experiments I used libvirt / KVM on Ubuntu 18.04. To ensure KSM was turned on, I needed to:
Ensure KSM is turned on. /sys/kernel/mm/ksm/run should contain a “1” if it is enabled. If it is not, just write “1” to that file to enable it.
Ensure libvirt is enabling KSM. The KSM value in /etc/defaults/qemu-kvm should be set to “AUTO”.
My lab machines are currently setup with Shaken Fist, so I just quickly launched a few hundred identical VMs. This first graph is that experiment. Its a little hard to see here but on three machines I consumed about about 40gb of RAM with indentical VMs and then waited. After three or so hours I had saved about 2,500 pages of memory.
To be honest, that’s a pretty disappointing result. 2,5000 4kb pages is only about 10mb of RAM, which isn’t very much at all. Also, three hours is a really long time for our workload, where students often fire up their labs for a couple of hours at a time before shutting them down again. If this was as good as KSM gets, it wasn’t for us.
After some pondering, I realised that KSM is configured by default to not work very well. The default value for pages_to_scan is 100, which means each scan run only inspects about half a megabyte of RAM. It would take a very very long time to scan a modern machine that way. So I tried setting pages_to_scan to 1,000,000,000 instead. One billion is an unreasonably large number for the real world, but hey. You update this number by writing a new value to /sys/kernel/mm/ksm/pages_to_scan.
This time we get a much better result — I launched as many VMs as would fit on each machine, and the sat back and waited (well, went to bed acutally). Again the graph is a bit hard to read, but what it is saying is that after 90 minutes KSM had saved me over 300gb of RAM across the three machines. Its still a little too slow for our workload, but for workloads where the VMs are relatively static that’s a real saving.
Now it should be noted that setting pages_to_scan to 1,000,000,000 comes at a cost — each of these machines now has one of its 48 cores dedicated to scanning memory and deduplicating. For my workload that’s something I am ok with because my workload is not CPU bound, but it might not work for you.
A lot has happened in that time — OpenStack has gone from being two separate Open Source projects to a whole ecosystem, developers have come and gone (and passed away), and OpenStack has weathered the cloud wars of the last decade. OpenStack survived its early growth phase by deliberately offering a “big tent” to the community and associated vendors, with an expansive definition of what should be included. This has resulted in most developers being associated with a corporate sponser, and hence the decrease in the number of developers today as corporate interest wanes — OpenStack has never been great at attracting or retaining hobbist contributors.
My personal involvement with OpenStack started in November 2011, so while I missed the very early days I was around for a lot and made many of the mistakes that I now see in OpenStack.
What do I see as mistakes in OpenStack in hindsight? Well, embracing vendors who later lose interest has been painful, and has increased the complexity of the code base significantly. Nova itself is now nearly 400,000 lines of code, and that’s after splitting off many of the original features of Nova such as block storage and networking. Additionally, a lot of our initial assumptions are no longer true — for example in many cases we had to write code to implement things, where there are now good libraries available from third parties.
That’s not to say that OpenStack is without value — I am a daily user of OpenStack to this day, and use at least three OpenStack public clouds at the moment. That said, OpenStack is a complicated beast with a lot of legacy that makes it hard to maintain and slow to change.
For at least six months I’ve felt the desire for a simpler cloud orchestration layer — both for my own personal uses, and also as a test bed for ideas for what a smaller, simpler cloud might look like. My personal use case involves a relatively small environment which echos what we now think of as edge compute — less than 10 RU of machines with a minimum of orchestration and management overhead.
At the time that I was thinking about these things, the Australian bushfires and COVID-19 came along, and presented me with a lot more spare time than I had expected to have. While I’m still blessed to be employed, all of my social activities have been cancelled, so I find myself at home at a loose end on weekends and evenings at lot more than before.
Thus Shaken Fist was born — named for a Simpson’s meme, Shaken Fist is a deliberately small and highly opinionated cloud implementation aimed at working well in small deployments such as homes, labs, edge compute locations, deployed systems, and so forth.
I’d taken a bit of trouble with each feature in Shaken Fist to think through what the simplest and highest value way of doing something is. For example, instances always get a config drive and there is no metadata server. There is also only one supported type of virtual networking, and one supported hypervisor. That said, this means Shaken Fist is less than 5,000 lines of code, and small enough that new things can be implemented very quickly by a single middle aged developer.
Shaken Fist definitely has feature gaps — API authentication and scheduling are the most obvious at the moment — but I have plans to fill those when the time comes.
I’m not sure if Shaken Fist is useful to others, but you never know. Its apache2 licensed, and available on github if you’re interested.
I’m not sure what it was like in other parts of the world, but during the COVID-19 pandemic Australia suffered a bunch of shortages — toilet paper, flour, and yeast were among those things stores simply didn’t have any stock of. Luckily we’d only just done a costco shop so were ok for toilet paper and flour, but we were definitely getting low on yeast. The obvious answer is a sour dough starter, but I’d never done that thing before.
In the end my answer was to cheat and use this recipe. However, I found the instructions unclear, so here’s what I ended up doing:
2 cups of warm water
2 teaspoons of dry yeast
2 cups of bakers flour
Mix these three items together in a plastic container with enough space for the mix to double in size. Place in a warm place (on the bench on top of the dish washer was our answer), and cover with cloth secured with a rubber band.
Once a day you should feed your starter with 1 cup of flour and 1 cup of warm water. Stir throughly.
The recipe online says to feed for five days, but the size of my starter was getting out of hand by a couple of days, so I started baking at that point. I’ll describe the baking process in a later post. The early loaves definitely weren’t as good as the more recent ones, but they were still edible.
Once the starter is going, you feed daily and probably need to bake daily to keep the starters size under control. That obviously doesn’t work so great if you can’t eat an entire loaf of bread a day. You can hybernate the starter by putting it in the fridge, which means you only need to feed it once a week.
To wake a hybernated starter up, take it out of the fridge and feed it. I do this at 8am. That means I can then start the loaf for baking at about noon, and the starter can either go back in the fridge until next time or stay on the bench being fed daily.
I have noticed that sometimes the starter comes out of the fridge with a layer of dark water on top. Its worked out ok for us to just ignore that and stir it into the mix as part of the feeding process. Hopefully we wont die.
So here we were, in the middle of a pandemic which closed bakeries and cancelled almost all of my non-work activities. I found this animated GIF on Reddit for a super simple no-kneed bread and decided to give it a go. It turns out that a few things are true:
animated GIFs are a super terrible way store recipes
and that I only learned these things while to trying and work out who to credit for this recipe
The basic recipe is really easy — chuck the following into a big bowl, stir, and then cover with a plate. Leave resting a warm place for a long time (three or four hours), then turn out onto a floured bench. Fold into a ball with flour, and then bake. You can see a more detailed version in the YouTube video above.
3 cups of bakers flour (not plain white flour)
2 tea spoons of yeast
2 tea spooons of salt
1.5 cups of warm water (again, I use 42 degrees from my gas hot water system)
The dough will seem really dry when you first mix it, but gets wetter as it rises. Don’t panic if it seems tacky and dry.
I think the key here is the baking process, which is how the oven loaf in my previous post about bread maker white loaves was baked. I use a cast iron camp oven (sometimes called a dutch oven), because thermal mass is key. If I had a fancy enamelized cast iron camp oven I’d use that, but I don’t and I wasn’t going shopping during the shutdown to get one. Oh, and they can be crazy expensive at up to $500 AUD.
Warm the oven with the camp oven inside for at least 30 minutes at 230 degrees celsius. Then place the dough inside the camp oven on some baking paper — I tend to use a triffet as well, but I think you could skip that if you didn’t have one. Bake for 30 minutes with the lid on — this helps steam the bread a little and forms a nice crust. Then bake for another 12 minutes with the camp over lid off — this darkens the crust up nicely.
Oh, and I’ve noticed a bit of variation in how wet the dough seems to be when I turn it out and form it in flour, but it doesn’t really seem to change the outcome once baked, so that’s nice.
The original blogger for this receipe also recommends chilling the dough overnight in the fridge before baking, but I haven’t tried that yet.