> But… CRAN had also rerun the tests for all packages that depend on mine, even if they don’t belong to me!
When you propose a change to something that other things depend on, it makes sense to test those dependents for a regression; this is not earth shattering.
If you want to change something which breaks them, you have to then do it in a different way. First provide a new way of doing something. Then get all the dependencies that use the old way to migrate to the new way. Then when the dependents are no longer relying on the old way, you can push out a change which removes it.
>If you want to change something which breaks them, you have to then do it in a different way.
Almost every other package repo works differently and publishing packages which would break other packages is more than common, it is the standards way to publish updates.
>Then get all the dependencies that use the old way to migrate to the new way. Then when the dependents are no longer relying on the old way, you can push out a change which removes it.
This is actually how no software repo works as it is actually insane.
> When declaring dependencies, most packages don’t specify any version requirements, and if they do, it’s usually just a lower bound like ‘grf >= 1.0’.
I like the perspective presented in this article, I think CRAN is taking an interesting approach. But this is nuts and bolts. Explicitly saying you're compatible with any future breaking changes!? You can't possibly know that!
I get that a lot of R programmers might be data scientists first and programmers second, so many of them probably don't know semver, but I feel like the language should guide them to a safe choice here. If CRAN is going to email you about reverse dependencies, maybe publishing a package with a crazy semver expression should also trigger an email.
> Explicitly saying you're compatible with any future breaking changes!? You can't possibly know that!
I kind of like it in a way. In a lot of eco systems it's easy for package publishers to be a bit lazy with compatibility which can push a huge amount of work on package consumers. R seems similar to go in this regard, where there is a big focus on not breaking compatibility which then means they are conservative about adding new stuff until they're happy to support it for a long time.
CRAN’s approach here sounds like it has all the disadvantages of a monorepo without any of the advantages.
In a true monorepo — the one for the FreeBSD base system, say — if you make a PR that updates some low-level code, then the expectation is that you 1. compile the tree and run all the tests (so far so good), 2. update the high-level code so the tests pass (hmm), and 3. include those updates in your PR. In a true centralized monorepo, a single atomic commit can affect vertical-slice change through a dependency and all of its transitive dependents.
I don’t know what the equivalent would be in distributed “meta-monorepo” development ala CRAN, but it’s not what they’re currently doing.
(One hypothetical approach I could imagine, is that a dependency major-version release of a package can ship with AST-rewriting-algorithm code migrations, which automatically push both “dependency-computed” PRs to the dependents’ repos, while also pushing those same patches as temporary forced overlays onto releases of dependent packages until such time as the related PRs get merged. So your dependents’ tests still have to pass before you can release your package — but you can iteratively update things on your end until those tests do pass, and then trigger a simultaneous release of your package and your dependent packages. It’s then in your dependents’ court to modify + merge your PR to undo the forced overlay, asynchronously, as they wish.)
There is a parallel with database transactions: it's great if you can do everything in a single database/transaction (atomic monorepo commit). But that only scales so far (on both dimensions: single database and single transaction). You can try
distributed transactions (multiple coordinated commits) but that also has limits. The next step is eventual consistency, which would be equivalent to releasing a new version of the component while preserving the old one and with dependents eventually migrating to it at their own pace.
This is why in microservice architectures you try to have data stores encapsulated via each service. So the APIs can support some kind backward compatibility path as you roll out changes to clients that interact with the service (presuming the db migration has public API implications).
I agree, more automated tools for API migration would be a good next step, but I think that's missing the point a bit.
Read the actionable part of the "dependency error" mail again:
> Please reply-all and explain: Is this expected or do you need to fix anything in your package? If expected, have all maintainers of affected packages been informed well in advance? Are there false positives in our results?
This is not a hard fail and demand that you go back and rewrite your package. It's also not a demand for you to go out on your own and write pull requests for all the dependent packages.
The only strict requirement is to notify the dependents and explain the reason of that change. Depending on the nature of the change, it's then something the dependents can easily fix themselves - or, if they can't, you will likely get feedback what you'd have to change in your package to make the migration feasible.
In the end, it's a request for developers to get up and talk to their users and figure out a solution together, instead of just relying on automation and deciding everything unilaterally. It's sad that this is indeed a novel concept.
(And hey, as a side effect: If breaking changes suddenly have a cost for the author, this might give momentum to actually develop those automated migration systems. In a traditional package repository, no one might even have seen the need for them in the first place)
This was an interesting article, but it made me even more interested in the author's larger take on R as a language:
> In the years since, my discomfort has given away to fascination. I’ve come to respect R’s bold choices, its clarity of focus, and the R community’s continued confidence to ‘do their own thing’.
I would love to see a follow-up article about the key insights that the author took away from diving more deeply into R.
I genuinely enjoy R. I use it for calculations daily. In comparison using Python feels tedious and clunky even though I know it better.
> CRAN had also rerun the tests for all packages that depend on mine, even if they don’t belong to me!
Another way to frame this is these are the customers of your package's API. If you broke them you are required to ship a fix.
I see why this isn't the default (e.g. on GitHub you have no idea how many people depend on you). But the developer experience is much nicer like this. Google, for example, makes this promise with some of their public tools.
Outside the word of professional software developers, R is used by many academics in statistics, economics, social sciences etc. This rule makes it less likely that their research breaks because of some obscure dependency they don't understand.
I've never written a line of R but it seems slightly underrated from what I've seen.
Maybe there are some massive footguns I'm not aware of but python is mostly oriented around variables rather than pipelines so it never seems to flow as well as R
The author is a little confused. A system that blocks releases on defects and doesn't pin versions is continuous integration, not a monorepo. The two are not synonymous. Monorepos often use continuous integration to ensure their integrity, but you can use continuous integration without a monorepo, and monorepos can be used without continuous integration.
> But the migration had a steep cost: over 6 years later, there are thousands of projects still stuck on an older version.
This is a feature, not a bug. The pinning of versions allows systems to independently maintain their own dependency trees. This is how your Linux distribution actually remains stable (or used to, before the onslaught of "rolling release" distributions, and the infection of the "automatically updating application" into product development culture, which constantly leaves me with non-functional Mobile applications whereupon I am forced to update them once a week). You set the versions, and nothing changes, so you can keep using the same software, and it doesn't break. Until you choose to upgrade it and deal with all the breaking shit.
Every decision in life is a tradeoff. Do you go with no version numbers at all, always updating, always fixing things? Or do you always require version numbers, keeping things stable, but having difficulty updating because of a lack of compatible versions? Or do you find some middle ground? There are pros and cons to all these decisions. There is no one best way, only different ways.
I think that the laws of physics dictate that there is. If your developers are spawning the galaxy, the speed of development is slower with continuous development than with pinning deps.
The problem with pinning dependencies is clashing transitive dependencies over a bunch of dependencies. For me this happens in python every third time I try to run sth new even though version numbers are pinned (things can still fail in your system, or you may want to include dependencies with incompatible transitive dependencies). I have never happened with R, and now I know why.
The actual trade off is end user experience and ease, vs package developer experience and ease. It is not about updating R or a package; it is when somebody tries to create or run a project not getting into a clash of dependencies for reasons that can hardly be controlled by either them or the package developer.
I feel like if more package repositories did this, you would end up just finding more and more workarounds and alternative distribution methods.
I mean, just look at how many projects use “curl and bash” as their distribution method even though the project repositories they could use instead don’t even require anything nearly as onerous as the reverse dependency checks described in this article. If the minimal requirements the current repos have are enough to push projects to alternate distribution, I can’t imagine what would happen if it was added.
Eventually, yes I guess. But long before that the breaker and breakee both are notified, and the breakage hopefully is fixed. As it should be.
I would hope the other aspirational software distribution systems (pip, npm, et al) ALSO do that, but according to this article, I guess they don't? Not shocked , to be honest
This is awesome. I run a team that uses software I produce and i have a rule that i can’t deliver breaking changes, and i cant force migrations. I can do the migration myself, or i have to emulate the old behavior next to the new. It makes you think really hard about releasing new APIs. I wish this was standard practice.
To be honest, I don't know what is worse. Installing a R library that require re-installing a bunch of updates, and being stuck in R installation hell or exerpiencing conda install that is stuck in "Resolving Dependencies" hell. The only thing I've learned to mitigate both is just containerize everything.
What people have to understand is that this is not a repository for software developers. It is a repository for people needing tools for statistical analysis and scientific computing.
The way a software developer thinks about a package is totally different to the way someone trying to perform statistical analysis thinks about packages.
This is the same for CTAN, the name is no coincidence. The packages are for users and not developers.
I think there's tooling for doing something like this reverse dependency trick in nixpkgs. I made a change to pre-commit and somebody more in-the-know than I stopped by the PR and pointed out the two python packages that my changes broke.
Zero wouldn't have been surprising to me, nor would several hundred, but two... what a conveniently actionable number.
It has me wanting to give names to some of my hacks and publish them as packages so that people are more more aware when their changes are breaking changes. On the other hand, if I do something weird, I don't necessity want to burden others with maintaining it. Tradeoffs...
> Taking advantage of the major version bump, we had snuck in a small API change. This change then caused a test failure in policytree
Wait a second. Another package failed your MAJOR version upgrade because you changed your API? Not following semver is crazy for any package manager to enforce.
My wife is having to learn R as part of her Masters - she's not got a technical background and my impression is it's throwing people into programming at the deep end, and likely after the course she will never actually use it.
Meanwhile I read the material and t absolutely feels like a cult - "R is fun" is like something they say to persuade themselves they are not in a cult
I recently started using python packages for some statistical work.
I then discovered that there are often bugs with many of the python stats packages. Many python numerical packages also have the reputation of changing how things work "under the hood" from version to version. This means that you can't trust your output after a version change.
Given all of the above, I can see why "serious" data scientists stick with R and this article is just another reason why.
37 comments
[ 3.1 ms ] story [ 55.0 ms ] threadThat's the objective function of Hastie et al's GLM. I had a good chuckle when I realized the author's last name is Tibshirani. If you know you know.
When you propose a change to something that other things depend on, it makes sense to test those dependents for a regression; this is not earth shattering.
If you want to change something which breaks them, you have to then do it in a different way. First provide a new way of doing something. Then get all the dependencies that use the old way to migrate to the new way. Then when the dependents are no longer relying on the old way, you can push out a change which removes it.
Almost every other package repo works differently and publishing packages which would break other packages is more than common, it is the standards way to publish updates.
>Then get all the dependencies that use the old way to migrate to the new way. Then when the dependents are no longer relying on the old way, you can push out a change which removes it.
This is actually how no software repo works as it is actually insane.
I like the perspective presented in this article, I think CRAN is taking an interesting approach. But this is nuts and bolts. Explicitly saying you're compatible with any future breaking changes!? You can't possibly know that!
I get that a lot of R programmers might be data scientists first and programmers second, so many of them probably don't know semver, but I feel like the language should guide them to a safe choice here. If CRAN is going to email you about reverse dependencies, maybe publishing a package with a crazy semver expression should also trigger an email.
I kind of like it in a way. In a lot of eco systems it's easy for package publishers to be a bit lazy with compatibility which can push a huge amount of work on package consumers. R seems similar to go in this regard, where there is a big focus on not breaking compatibility which then means they are conservative about adding new stuff until they're happy to support it for a long time.
In a true monorepo — the one for the FreeBSD base system, say — if you make a PR that updates some low-level code, then the expectation is that you 1. compile the tree and run all the tests (so far so good), 2. update the high-level code so the tests pass (hmm), and 3. include those updates in your PR. In a true centralized monorepo, a single atomic commit can affect vertical-slice change through a dependency and all of its transitive dependents.
I don’t know what the equivalent would be in distributed “meta-monorepo” development ala CRAN, but it’s not what they’re currently doing.
(One hypothetical approach I could imagine, is that a dependency major-version release of a package can ship with AST-rewriting-algorithm code migrations, which automatically push both “dependency-computed” PRs to the dependents’ repos, while also pushing those same patches as temporary forced overlays onto releases of dependent packages until such time as the related PRs get merged. So your dependents’ tests still have to pass before you can release your package — but you can iteratively update things on your end until those tests do pass, and then trigger a simultaneous release of your package and your dependent packages. It’s then in your dependents’ court to modify + merge your PR to undo the forced overlay, asynchronously, as they wish.)
Read the actionable part of the "dependency error" mail again:
> Please reply-all and explain: Is this expected or do you need to fix anything in your package? If expected, have all maintainers of affected packages been informed well in advance? Are there false positives in our results?
This is not a hard fail and demand that you go back and rewrite your package. It's also not a demand for you to go out on your own and write pull requests for all the dependent packages.
The only strict requirement is to notify the dependents and explain the reason of that change. Depending on the nature of the change, it's then something the dependents can easily fix themselves - or, if they can't, you will likely get feedback what you'd have to change in your package to make the migration feasible.
In the end, it's a request for developers to get up and talk to their users and figure out a solution together, instead of just relying on automation and deciding everything unilaterally. It's sad that this is indeed a novel concept.
(And hey, as a side effect: If breaking changes suddenly have a cost for the author, this might give momentum to actually develop those automated migration systems. In a traditional package repository, no one might even have seen the need for them in the first place)
Automated tests, compilation by the package publisher, and enforcement of portability flags and SemVer semantics.
> In the years since, my discomfort has given away to fascination. I’ve come to respect R’s bold choices, its clarity of focus, and the R community’s continued confidence to ‘do their own thing’.
I would love to see a follow-up article about the key insights that the author took away from diving more deeply into R.
> CRAN had also rerun the tests for all packages that depend on mine, even if they don’t belong to me!
Another way to frame this is these are the customers of your package's API. If you broke them you are required to ship a fix.
I see why this isn't the default (e.g. on GitHub you have no idea how many people depend on you). But the developer experience is much nicer like this. Google, for example, makes this promise with some of their public tools.
Outside the word of professional software developers, R is used by many academics in statistics, economics, social sciences etc. This rule makes it less likely that their research breaks because of some obscure dependency they don't understand.
Maybe there are some massive footguns I'm not aware of but python is mostly oriented around variables rather than pipelines so it never seems to flow as well as R
> But the migration had a steep cost: over 6 years later, there are thousands of projects still stuck on an older version.
This is a feature, not a bug. The pinning of versions allows systems to independently maintain their own dependency trees. This is how your Linux distribution actually remains stable (or used to, before the onslaught of "rolling release" distributions, and the infection of the "automatically updating application" into product development culture, which constantly leaves me with non-functional Mobile applications whereupon I am forced to update them once a week). You set the versions, and nothing changes, so you can keep using the same software, and it doesn't break. Until you choose to upgrade it and deal with all the breaking shit.
Every decision in life is a tradeoff. Do you go with no version numbers at all, always updating, always fixing things? Or do you always require version numbers, keeping things stable, but having difficulty updating because of a lack of compatible versions? Or do you find some middle ground? There are pros and cons to all these decisions. There is no one best way, only different ways.
I think that the laws of physics dictate that there is. If your developers are spawning the galaxy, the speed of development is slower with continuous development than with pinning deps.
The actual trade off is end user experience and ease, vs package developer experience and ease. It is not about updating R or a package; it is when somebody tries to create or run a project not getting into a clash of dependencies for reasons that can hardly be controlled by either them or the package developer.
I mean, just look at how many projects use “curl and bash” as their distribution method even though the project repositories they could use instead don’t even require anything nearly as onerous as the reverse dependency checks described in this article. If the minimal requirements the current repos have are enough to push projects to alternate distribution, I can’t imagine what would happen if it was added.
I would hope the other aspirational software distribution systems (pip, npm, et al) ALSO do that, but according to this article, I guess they don't? Not shocked , to be honest
This system is unworkable.
The downside is that dependees have to manually change their dependency and you get proliferation of packages with informal relationships.
The way a software developer thinks about a package is totally different to the way someone trying to perform statistical analysis thinks about packages.
This is the same for CTAN, the name is no coincidence. The packages are for users and not developers.
That essentially makes the high level project a monorepo while giving you the option to work on the submodule on its own.
Zero wouldn't have been surprising to me, nor would several hundred, but two... what a conveniently actionable number.
It has me wanting to give names to some of my hacks and publish them as packages so that people are more more aware when their changes are breaking changes. On the other hand, if I do something weird, I don't necessity want to burden others with maintaining it. Tradeoffs...
Wait a second. Another package failed your MAJOR version upgrade because you changed your API? Not following semver is crazy for any package manager to enforce.
Meanwhile I read the material and t absolutely feels like a cult - "R is fun" is like something they say to persuade themselves they are not in a cult
I then discovered that there are often bugs with many of the python stats packages. Many python numerical packages also have the reputation of changing how things work "under the hood" from version to version. This means that you can't trust your output after a version change.
Given all of the above, I can see why "serious" data scientists stick with R and this article is just another reason why.