So I am not a professional programmer, but I do like to tinker with projects and just teach myself stuff (in python and now rust).
I currently just install stuff on my linux distro off of the repos or anaconda for python. I’ve never had any particular issue or anything. I was thinking about maybe moving projects into a container just so that they are more cleanly separated from my base install.
I am mostly wondering about how the community uses containers and when they are most appropriate and when they are more issue than they are worth. I think it will be good for learning, but want to hear from people who do it for a living.
Containers are used for a whole bunch of reasons. I’ll address just
one: process isolation. I’ll only do one because I’ve ran into times
when containers were not helpful. And it may lead to some funny stories
and interesting discussion from others!
A rule of thumb for me is that if the process is well-behaved, has its
dependencies under control and doesn’t keep uneccesary state, then it
may not need the isolation provided by a container and all the tooling
that comes with it.
On one extreme, should we run ls in a container? Probably not.
It doesn’t write to the filesystem and has only a handful of
dependencies available on pretty much any Unix-like/Linux system.
But on the other extreme, what about that big bad internal Node.JS
application which requires some weird outdated Python dependencies
that has many hardcoded paths and package versions?
The original developer is long gone.
It dumps all sorts of shit to the filesystem.
Nobody is really sure whether those files are used as a cache or they contain some critical state management.
Who wants to spend the time and money to tidy that thing up?
In this scenario containers can be used to hermetically seal a fragile thing.
This can come back to bite you.
Instead of actually improving the software to be portable and robust
enough to work in varied execution environments (different operating
systems, on a laptop, as a library…), you kick the can down the road.
And FYI to OP, if you can’t install two versions of the same library at the same time (ex: numpy 1.25 and numpy 1.19) then the answer to “has its dependencies under control?” is generally “no”.
Deno (successor to NodeJS) is “yes” by default, and has very very few exceptions
Rust can by default, and has few but notable/relevant exceptions
Python (without venv) cannot (even with venv, each project can be different, but 1 project still can’t reliably have two versions of numpy)
NodeJS can, but it was kind of an afterthought, and it has tons modules that are notable exceptions
Sadly it still causes system instability even if you NEVER need the feature.
You might not need numpy at all, but Pandas needs numpy and Opencv needs numpy. Sometimes pandas needs one version and Opencv needs a different version. Well… python only allows one global verison of numpy, so pandas and opencv fight over which one they want installed, and the looser is forced to use a numpy they were not designed/tested for. Upgrading pandas might also upgrade numpy and break opencv. That causes system instability.
Stable systems like cargo coupld upgrade pandas, have pandas use numpy 1.29 without touching/breaking opencv (opencv would still importing/using using numpy 1.19 or whatever). That stability is only possible if the system is capable of having two versions of the same dependency at the same time.
No worries! Writing that down actually helped clarify some of my thoughts.
Something extra: distributed computing.
Let’s say you have 3 processes that need to communicate with one
another. There’s heaps of tooling available in OSs to manage those
processes. Logging, networking, filesystem access, privilege
separation, resource allocation… all provided by the host OS without
installing anything. But what if those 3 processes can’t run on one
“machine”? Which process should go where? What if it needs 8GB memory
but there’s only 6GB available on some of the machines? Who controls
that?
Systems like Kubernetes, Nomad, Docker Swarm etc. offer a way to
manage this. They let us say something like:
run this process (by specifying a container image),
give it at least these resources (xGB memory, xvCPUs)
let it communicate with these other processes (e.g. pods, overlay networks…)
These systems manage containers. If you want to do distributed
computing and want to take advantage of those systems to manage it,
stuff needs to be run in containers.
Containers are not the only way to do distributed computing - far from
it! But over the past few years this particular approach has become
popular in the, umm… “commercial software development industry”.
Opinion. Are Linux containers something to look into as someone who doesn’t work in the industry?
Unless you’re interested in how containers themselves work and/or distributed computing;
frankly - no. Computers are still getting faster and cheaper.
So why is all this stuff so popular in the commercial world?
I’ll end with some tongue-in-cheek.
Partly it’s because the software development industry is made up of
actual human beings who have their own emotions and desires.
Distributed computing is a fun idea because tech people are faced with
challenges tech people are interested in.
Boring: can we increase our real estate agency brand recognition by 200%?
We could provide property listings as both a CSV and PDF to our partners!
Our logo could go on the PDF! Wow! Who knows how popular our brand could be?
Fun: can we increase throughput in this part of the system by 200%? We
might need to break that component out to run on a separate machine!
Wow! Who knows who fast it could go?
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Containers are used for a whole bunch of reasons. I’ll address just one: process isolation. I’ll only do one because I’ve ran into times when containers were not helpful. And it may lead to some funny stories and interesting discussion from others!
A rule of thumb for me is that if the process is well-behaved, has its dependencies under control and doesn’t keep uneccesary state, then it may not need the isolation provided by a container and all the tooling that comes with it.
On one extreme, should we run
lsin a container? Probably not. It doesn’t write to the filesystem and has only a handful of dependencies available on pretty much any Unix-like/Linux system.But on the other extreme, what about that big bad internal Node.JS application which requires some weird outdated Python dependencies that has many hardcoded paths and package versions? The original developer is long gone. It dumps all sorts of shit to the filesystem. Nobody is really sure whether those files are used as a cache or they contain some critical state management. Who wants to spend the time and money to tidy that thing up? In this scenario containers can be used to hermetically seal a fragile thing. This can come back to bite you. Instead of actually improving the software to be portable and robust enough to work in varied execution environments (different operating systems, on a laptop, as a library…), you kick the can down the road.
And FYI to OP, if you can’t install two versions of the same library at the same time (ex: numpy 1.25 and numpy 1.19) then the answer to “has its dependencies under control?” is generally “no”.
I have not gotten to the point where I would want two versions of the same library, but that is good to know.
Sadly it still causes system instability even if you NEVER need the feature.
You might not need numpy at all, but Pandas needs numpy and Opencv needs numpy. Sometimes pandas needs one version and Opencv needs a different version. Well… python only allows one global verison of numpy, so pandas and opencv fight over which one they want installed, and the looser is forced to use a numpy they were not designed/tested for. Upgrading pandas might also upgrade numpy and break opencv. That causes system instability.
Stable systems like cargo coupld upgrade pandas, have pandas use numpy 1.29 without touching/breaking opencv (opencv would still importing/using using numpy 1.19 or whatever). That stability is only possible if the system is capable of having two versions of the same dependency at the same time.
That feels like it makes way too much sense, thank you for the answer!
No worries! Writing that down actually helped clarify some of my thoughts.
Something extra: distributed computing.
Let’s say you have 3 processes that need to communicate with one another. There’s heaps of tooling available in OSs to manage those processes. Logging, networking, filesystem access, privilege separation, resource allocation… all provided by the host OS without installing anything. But what if those 3 processes can’t run on one “machine”? Which process should go where? What if it needs 8GB memory but there’s only 6GB available on some of the machines? Who controls that?
Systems like Kubernetes, Nomad, Docker Swarm etc. offer a way to manage this. They let us say something like:
These systems manage containers. If you want to do distributed computing and want to take advantage of those systems to manage it, stuff needs to be run in containers.
Containers are not the only way to do distributed computing - far from it! But over the past few years this particular approach has become popular in the, umm… “commercial software development industry”.
Opinion. Are Linux containers something to look into as someone who doesn’t work in the industry? Unless you’re interested in how containers themselves work and/or distributed computing; frankly - no. Computers are still getting faster and cheaper. So why is all this stuff so popular in the commercial world? I’ll end with some tongue-in-cheek.
Partly it’s because the software development industry is made up of actual human beings who have their own emotions and desires. Distributed computing is a fun idea because tech people are faced with challenges tech people are interested in.
Boring: can we increase our real estate agency brand recognition by 200%? We could provide property listings as both a CSV and PDF to our partners! Our logo could go on the PDF! Wow! Who knows how popular our brand could be?
Fun: can we increase throughput in this part of the system by 200%? We might need to break that component out to run on a separate machine! Wow! Who knows who fast it could go?