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.
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.
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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.