Hacker News Reader

Comment by c0l0 | Hacker News Reader

I don't see how appending

    --cpus 2 --memory 500mb

to the argument vector liberates these options' definer from "understanding [...] the subsystems that power it", and reflect on their potential impact. And I'd argue that THIS is the real complexity incurred by these kinds of resource constraints that seem so simple on the surface - not the specific syntax or location you have to use to introduce them. All of which makes systemd unit files and their settings' implications (which are amazingly well-documented btw) as good as any other option, imho.

View on HN

Systemd is great software -- it is useful, and powerful. A systemd unit file, the idea of what a unit is, when they run, how they run, what shells they use, what permissions they run under, and lots of other complexities are expressed as options in the unit file, configuration files on the system, and in other places.

If I want to run a useful piece of software like redis let's say, but I want to run it with a resource constraint to make sure that it doesn't take more than 2CPUs and 500MB of memory, it is far easier to do that with the following command line:

   docker run --rm redis --cpus 2 --memory 500mb -p 6379:6379

Than to write the equivalent systemd unit file, set up the isolated filesystems that docker would let you easily bind mount in, etc. This is like comparing systemd-nspawn to systemd -- if systemd-nspawn isn't simpler than systemd then what are we even doing.

Docker won because of it's developer ergonomics (containers weren't new), systemd won because of it's feature set, convenience and sturdiness. They're different tools with different primary use-cases.

Then you will want to manage the configuration file and to browse logs, and you ll have to mount files to you container. And your deployment start to reimplement (a subset to your needs) what distros did System integration complexity didn't disappear thanks to containers. It was just moved to another place. I dd add that, being convenient for developers doesn't mean it's convenient for hosting management. At the end, when one side convenience is taken in consideration, it often translate to complexity and pain at the other side. K8S become in some companies more a mandatory runtime requirement than a hosting commodity/facility.

As a (former) developer I d rather ran redis in a container during development, but for production I d rather rely on boring VMs unless some scaling is required. (Managed k8s case set apart)

> Then you will want to manage the configuration file and to browse logs, and you ll have to mount files to you container. And your deployment start to reimplement (a subset to your needs) what distros did System integration complexity didn't disappear thanks to containers.

Agree, but my view on this is that the implicit answer of how you do all that (i.e. the file system, syslog) is now gone. There will be pain (complexity) in the short term, but at the end of the day, we're going to be able to build much better orchestration and systems. To kind of restate that, before you had to worry where a process wrote out it's output (stdout? /var/log/<program>? /etc/<program>/logs? /home/<user>/<program>/logs? syslog?), now you know want to get the non-stdout/stderr logs of the thing you're running, you'd better give it a volume to write to (which may be fake, and actually write everything to some remote storage or something), and I think that's a step forward.

Of course, I'm not saying containers should go everywhere -- relying on boring VMs over containers is fine too -- but I think rich world of functionality available to container-driven workflows is popular for good and bad reasons, and the good reasons are worth exploring/beneficial to me.

can anyone explain why you might need to limit memory consumption of your service?

I thought the kernel has virtual memory and if you consume more it will swap some memory and thats it.

Couldnt you just manage memory from inside your app? if its redis, then check the db size and shutdown/cleanup gracefully, and not crash redis with OOM?

> can anyone explain why you might need to limit memory consumption of your service?

> I thought the kernel has virtual memory and if you consume more it will swap some memory and thats it.

Well just to make sure the right memory goes to the right places -- if someone uploads a large file and you've made a mistake in your code that tries to hold it all in memory instead of buffering it straight to disk for example, you'd want that process to crash, and not your machine.

Also you generally don't want to swap, so much so that Kubernetes disables it immediately[0]. Not that Google is the only group with the right answer but they seem to think nothing can come of a machine having to swap. Maybe they're right. Even if they're not, A world where one service swaps[1] (I've never done this with docker to try it though) is probably better than one where it uses all the memory and everything swaps.

> Couldnt you just manage memory from inside your app? if its redis, then check the db size and shutdown/cleanup gracefully, and not crash redis with OOM?

You'd be surprised -- some languages just don't have a way to very easily get feedback from GC[2]. It's also something that I don't think most people think about, messing with the -XmXx<setting>s in Java is definitely year 2/3/4 java development for most people.

[0]: https://github.com/kubernetes/kubernetes/issues/53533

[1]: https://docs.docker.com/config/containers/resource_constrain...

[2]: https://github.com/golang/go/issues/29696

Also thank you very much for kubernetes link.

Wow, given how expensive RAM is it is no surprise they will never allow kubernetes work with swap, becausr it directly translates to $$$ for GCP and other cloud providers. Also since everyone loves using Java/Spring/Node and other memory hungry frameworks - it print enormous $ for cloud providers to require users overallocate RAM and disable swap.

or am I just spitballing conspiracy theory here and there is no conflict btw decisions like these and vendors' revenue streams?

For years, nay decades, the standard for data centers is no swapping. An incident caused by machine went slow due to swapping is almost as embarrassing as one caused by disk filling. Better to orchestrate a restart every night on your leaky code.

I think it is just different thinking: DEV where software is a pet, where I want full control of my memory and take care of it carefully, and dont want anyone shutting down my service violently.

contrast it to Ops mentality of software is a cattle (here is your memory quota and if its OOM, just kill/restart the service and hope next run it wont run OOM)

Reply on news.ycombinator.com