Should I Pin My Python Dependencies Versions?

I am about to release a Python library I've been working on the past few weeks. I've read a lot about Python dependencies but something is not quite clear yet: Some people pretend

Solution 1:

The reason the two other answers contradict each other is that they're both right (and worth reading), but they apply to different situations.

If you're releasing a library on PyPI, you should declare whatever dependencies you know about, but not pin to a specific version. For example, if you know you need >= 1.2, but 1.4 is broken, then you can write something like somepkg >= 1.2, != 1.4. If one of the things you know is that somepkg follows SemVer, then you can add a < 2.

If you're building something like a web app that you deploy yourself, then you should pin all of your exact dependencies, and use a service like pyup.io or requires.io to notify you when new versions are released. This way you can stay up to date, while making sure that the versions you deploy are the same as the versions you tested against.

Notice that these two pieces of advice complement each other: it's just a fact that if app A that uses library B, then either the author of A or the author of B can pin B's dependencies, but not both. So we have to pick one. The underlying principle here is that this is best done as late as possible, i.e., by the author of A, who can see their whole system; the job of library B is to pass on some useful hints to help A make these decisions. In particular, if all of the libraries that A depends on record exactly what they know about their underlying dependencies, then it's possible for A to make sensible decisions about what to do when they overlap. Like if dependency B depends on requests >= 1.0, != 1.2, and dependency C depends on requests >= 1.1, then we can guess that 1.1 or 1.3 might be good versions to pin. If dependency B depends on requests == 1.1 and dependency C depends on requests == 1.2, then we're just stuck.

Solution 2:

Pinning can be problematic and lead to security risks. Especially for a library, as in your case, it can lead to more dependency conflicts if it will typically be used in combination with other PyPI packages which themselves will have dependencies.

Why? A detailed study of Python Dependency Resolution, after analyzing tens of thousands of PyPI packages and their current rates of dependency conflicts, discusses this issue. It explains that:

if the distribution is not installed into its own, empty, single-purpose environment, then the likelihood of dependency conflicts is substantially increased if dependency versions are all pinned instead of leaving the ranges flexible.

and notes that pinning can exacerbate security problems by interfering with upgrading.

It advises:

If a project pins dependencies, then it must be prepared to issue a new release every time there is an important release of anything the project depends on directly or indirectly, all the way down the dependency chain.