Why update software dependencies? Don’t forget that even docker has a dependency on the operating system, and the operating system depends on the existence of the computer and its hardware, and the hardware exists because it depends on people who manufactured it and put it there. A dependency chain has no base case, except perhaps the big bang. The deeper you go the more stable dependencies tend to get (the length of time they have been pinned) though this is not a guaranteed or reliable rule.
These other dependencies may even update before you update your software. Your hardware is probably
pinned for 5-8 years, though it depends (unless you e.g. install more RAM, which won’t break
anything). Your operating system is probably only pinned for a few weeks at a time; you need to be
able to regularly run
sudo apt upgrade for security reasons and to keep the whole system up to
date in general. You don’t pin system packages because you often need your system for many other
tasks besides some single application. Consider all dependencies, including humans, businesses,
and hardware. Software may depend on an operating system, a docker image, system-installed packages,
manually-installed binaries, user-installed packages, conda packages, pip packages, and shell
scripts. That is, try to look beyond the first tier of your Supply chain:
The major advantage of libraries is that you do not need to write them yourself, effectively reinventing the wheel. Reading library code is often more enjoyable than reading your own or your team’s code, because you know it is more generally useful outside the context of your current job or project.
For all the same reasons, you probably want to upgrade regularly so you can work with the same open source (more general) software out there on the internet. Going through an upgrade of your dependencies often gives you an opportunity to learn about what is happening in the outside world rather than chasing after your project’s idiosyncratic needs.
More than ever, the software you need to perform a task is being created quickly in an open domain on the internet. This is driven not only by an increasing number of software developers, but by the simple fact that there has been more time for consumers to replace proprietry with open source dependencies. Software developers should spend more and more time evaluating libraries and incorporating them than writing their own version, assuming this trend continues.
Sitting on the old side of a library’s stable version means you’ll always be looking up the old documentation online, because you know the “stable” version may be different than what you’re using. For example, here is a link to PyTorch 1.3.1. It’s often through accidentally reading the stable documentation on a project that we discover there are features we want from it.
To some extent, this a question of whether to understand concepts first in your own words and then in the words of others, or to understand the words of others and then incorporate them into your own. The latter approach implies staying near the bleeding edge; the former implies staying in stable territory longer (and avoiding excessive dependencies).
A library or package is upgraded to a newer version. Said another way, you “rebase” your code (or logic, in general) onto a newer version of a library or package and all the tests you are currently monitoring still pass.
An upgrade is often triggered by the desire for an upgrade in a single library or package, even if in the end it is often necessary to update a set of libraries at a time to find a new working combination of dependencies. Alternatively, you can adopt a policy of regular updates that ensures you have the latest version of a library before you need it.
We’ll use the term “stable” in this article to mean the fraction of tests we expect to pass for our particular top-level application (or library) for a given dependency configurations, or the sum of the fraction we expect to pass across a range of dependency configurations. Usually this is a belief statement, that is, a prior given what we know about a particular library, only because it is too expensive to check everything. For example, we assume a dependency labeled “stable” is best for our particular application because it apparently passes the largest number of tests for those who evaluate it.
Let’s say a team of maintainers of Library A have avoided all dependencies (excluding the standard libraries). They hear about some Library B that does something they are already doing. The other library has some great ideas and is essentially doing what the team is already doing in a better way. Said another way, they are going in the wrong direction with their own code. They decide it’s OK to take this whole library as a dependency and refactor the code to depend on Library B.
A library maintainer knows that you can’t test everything. You have many users, sometimes with unique needs, and they aren’t sharing all their tests with you. Often their tests are manual rather than automatic. It’s possible the library’s automatic tests are known to be incomplete in important areas. Typically a “bleeding edge” release identifies where your package is unstable, but this is a fuzzy line. See also When you should switch to Python 3.10.
The team also doesn’t maintain old branches indefinitely even when a few users ask. It’s not valuable enough to backport bug fixes back years when most users are asking for features on newer versions. See also Software maintenance and Backporting.
Library B doesn’t explicitly indicate which versions are maintenance, stable, bleeding edge. etc. so you guess which version is the most stable. Let’s plot the mean of these priors, assuming a library with integer versions:
import matplotlib.pyplot as plt
import numpy as np
def stability_1d(versions, time_or_version):
return np.maximum(1 - np.abs((versions - time_or_version) / 4), 0)
def plot_1d_stability(versions, lib_stab):
fig, ax = plt.subplots(figsize=(10,10))
ax.set_xlim(-0.2, time_or_version + 1.2)
versions = np.arange(11)
time_or_version = 5
lib_stab = stability_1d(versions, time_or_version)
You refactor your code, making a version of Library A work with what you believe is the highest stability version of Library B. If you refactor everything at once, this version of Library A may be a “bleeding edge” version for longer than normal. If you incrementally adopt the library, however, you’ll have to come in and out of focused work on the topic.
Let’s say someone were to ask how hard it would be to backport your refactor to an older version of your source code, perhaps to release a feature enabled by the library on an older branch. They’re also curious how hard it would be upgrade or downgrade Library B, in case a user already has an older version installed.
We’ll assume your own library also uses integer versions. In this plot, the alpha channel represents the fraction of your tests you believe would pass if you merged your changes to use Library B’s API, for various versions of Library A and B. Assume a duck-typed language, where the whole build wouldn’t fail.
def stability_2d(versions, time_or_version):
lib_stab_b = stability_1d(versions, time_or_version)
lib_stab_a = stability_1d(versions, time_or_version + 1)
return np.maximum(lib_stab_b[:, np.newaxis] + lib_stab_a[np.newaxis, :] - 1, 0)
def plot_stability(libs_stab, time_or_version):
fig, ax = plt.subplots(figsize=(10,10))
version_a, version_b = np.meshgrid(versions, versions)
ax.scatter(version_a, version_b, alpha=libs_stab, color="black")
ax.set_xlim(-0.2, time_or_version + 1.2)
ax.set_ylim(-0.2, time_or_version + 1.2)
libs_stab = stability_2d(versions, time_or_version)
Notice the update is applied to the “bleeding edge” version of Library A. If you had a dependency on Library A, your ability to upgrade to the latest version of Library B would depend on how quickly Library A was updating.
There are (generally speaking) two update strategies: when the need arises, or on a regular schedule.
Let’s say the developers of Library A want to update their dependency on Library B, but it has been so long that the API has changed in every source file where they used the previous version of the library. Said another way, they have to do as much work as they did to adopt the library in the first place.
This upgrade is necessarily a large (non-incremental) refactor because you can’t depend on two versions of a library at once. A major downside of non-incremental refactors is that the change is likely to conflict with other developer’s work on features in the library during the course of development. Any feature you plan to release based on your upgrade will also take longer to get out the door not only because the refactor took a long time, but because it was large and therefore also introduces more risk. That is, the release will stay in the “bleeding edge” stage for longer.
time_or_version_v2 = time_or_version + 4
new_work = stability_2d(versions, time_or_version_v2)
libs_stab_v2 = np.maximum(libs_stab, new_work)
One downside to this approach is that is often valuable to be able to say you depend on a range of versions of a library rather than a single versions. Some dependency stating systems do not have a way to say you can depend on e.g. v5 and v7 but not v6, but they do have a way to state you can depend on a range of versions. If you never have a release on v6 and instead only run your tests on it once, you may be missing issues it temporarily introduces.
Your users may complain you only support one version of the library at a time, rather than a range. If they depend on the same library, they’ll have to pin it to the exact same version that you use. Even if they don’t pin it at all but depend on another library that states a dependency on an incompatible version of the library you depend on, their dependency resolver isn’t going to be able to find a solution that includes your library. If you state you can support v6 but in practice didn’t get enough time testing on it, they’re going to file a defect when it doesn’t work.
This approach effectively creates stable islands that your users must jump between during upgrades. An advantage to this approach is that if there is a large space between islands you can avoid dealing with any back and forth that happened between the islands. Since you aren’t jumping on either the bleeding edge or even the stable release of your dependency, you don’t have to deal with any temporary defects happening on either of those channels.
Companies that create versioned software generally require their users to jump between versions. For example, there are multiple versions of Ubuntu (18.04, 20.04) that users must jump between for upgrades, and less-stable islands between these islands (18.10, 19.04, 19.10). Unlike the situation described above, Canonical developers are constantly working on keeping the distribution stable between releases but only commit to keeping checkpoints that have been given more time (like 20.04) stable.
Another strategy is to simply stay on the latest or near the latest version of a library, even if you don’t immediately need the features provided by it. This creates a stable tunnel rather than islands:
def update_stab(libs_stab, time_or_version):
new_work = stability_2d(versions, time_or_version)
return np.maximum(libs_stab, new_work)
update_range = range(time_or_version, time_or_version + 4)
libs_stab_reg = functools.reduce(update_stab, update_range, libs_stab)
A major advantage to this approach is that because you likely don’t immediately need any features for the library you are upgrading, you can file an issue and leave time for the developers of your dependency to fix it before you upgrade. That is, check if the update is easy, and if it isn’t say something and go back to what you were doing.
It’ll help in the course of the manual work associated with an upgrade to be able to quickly see your dependency tree. Start with text-based solutions for both recursive and reverse dependency checks. See also:
The number of additional packages you are installing naturally increases the risk you will fail to find a usable combination. It’s likely the decrease in stability is faster than linear, however. This is related to the Curse of dimensionality; there’s simply more “space” in larger dimensions so that individual samples represent a smaller fraction of the total. Imagine taking the 2-dimensional graphs above to three dimensions.
One of the first steps you should take when you run into an issue is to understand your dependencies
and delete as many as possible. It’s often easy to maintain a Linux installation on a machine for
years if you take time to delete dependencies when you run into a problem with
If you can’t remove dependencies, an understanding of them can help you pin them less tightly. This gives your dependency resolver more “space” to find a working sample. See Loosen your top-level requirements.
Try to pin all packages, or none of them. Version numbers of course always increase together, with time. If you pin only one package and not another, one package may continue to get upgraded until it’s years apart from another (leading to failing tests). Even an existing stable “tunnel” between two packages won’t help with packages that have never been automatically or manually tested together.
If you pin many packages, you have to manually update all your pins. Tools like
conda-lock can help, but they often simply upgrade you to the latest version of every dependency.
If that configuration doesn’t work you have to manually edit your pinning, anyways. Still, these
tools can provide a starting point. These tools also can’t intelligently set dependency ranges, etc.
Pinning may include freezing external dependencies, such as the source you depend on in a
file. Saving these artifacts as files is manual work as well, work you must do every you time you
Generally speaking, the more reproducible your builds, the more work it is to upgrade them. You can take on this cost through repeated manual work or through setting up an automatic process. It’s likely best to do the manual work a few times until it becomes clear you’ll be doing the work indefinitely.
To take a dependency on a library you can either install a package (with a pin) or copy and paste only the source code you need from the library. If you only need one or a few functions from a library, the latter solution may make more sense because you can avoid pulling in unnecessary dependencies the rest of the package uses.
You can see every source file in your repository as a dependency. These dependencies are effectively pinned; the pin is e.g. a git SHA. You can copy and paste whole libraries into your source tree as a way of pinning your dependency on them, for example if you want to:
Get some bleeding edge-feature, but not them all.
Include one bug fix but not another.
Use your own build system rather than the library maintainer’s build system.
Remove features that are causing dependency issues for you.
To copy and paste the source with modifications is to fork the project; you may want to let the library maintainers see your work. Your next upgrade will continue to be difficult in your changes never make it upstream.
Copy-paste pinning can be partially automated with a script that updates a git branch then copies across git branches.
It’s common to take a dependency on a library for one or two functions. If you don’t copy and paste in only the functions you need, stick to these functions you really care about rather than trying out random functions only because they’re available. If you do have an issue upgrading the dependency in the future, perhaps because it is incompatible with another library you use, you can change how you depend on fewer functions (e.g. by copying and pasting them in).
If you are writing a library yourself, stick to the part of your dependency’s API that you expect to change the least. In some cases, this may mean copying and pasting in new functions marked “alpha” from the library that only combine other features from the library. If you do have a user that depends on an older or newer version of the library than you’ve tested with, it will be more likely that your code will work with their version.
You should upgrade dependencies that relate to security immediately. If a particular package is using Semantic Versioning, you often want to leave the last digit unpinned. One exception might be production environments that aren’t exposed to users outside your company or team.
To produce a numerical estimate of the actual time it will take to perform an upgrade, use reference class forecasting. That is, check how long it took to perform the last upgrade or the last few upgrades. Typically you can find this information in version control.
How long it takes to perform an upgrade can vary widely however; sometimes you run into an issue and sometimes it just works.