>Our plan is to provide paid services that are better and easier to use than the alternatives by integrating our open-source offerings directly. Our goal is for these services to be as impactful as Ruff itself — but you may choose not to use them. Either way, Ruff will remain free and open-source, just as it is today.
Yeah, I love their work but I am concerned that the VC death-spiral will be painful. I wonder whether they will offer support plans or similar, except that past experience makes me cynically assume that most developers will be “$5? haha, no!” until pivoting to complain about whatever they end up doing to make payroll.
Every time I hear "new system faster than old system!", my first thought is: what features does the new system not implement, what corner-cases does it ignore?
Being 15 years old pip is limited by backwards compatibility constraints. The biggest performance gains actually don’t come from uv being written in rust as much as reworking the dependency resolver algorithm from the ground up.
As a result the full feature set of pip isn’t supported yet, but since it’s totally interoperable with your regular venv that’s not much of an issue (you can always fall back to pip).
I’ve been playing with it for a month and I can’t imagine going back to vanilla pip.
In my experience, most performance gains I've implemented have been moving to better data structures and not iterating the same thing a bunch of times.
While uv supports a large subset of the pip interface, it does not support the entire feature set. In some cases, those differences are intentional; in others, they're a result of uv's early stage of development.
Rust, the language, has rather successfully worked towards and demonstrated that, in many areas, you CAN have your pie and eat it too. But that doesn't carry over to applications, where it must be demonstrated again.
I’m doubtful the fact that uv is written in rust is directly responsible for the speed increase, it’s probably just a better dependency determination algorthm.
Stop talking about fancy pies, you don’t need it even if it smells nice. What you actually need is to eat healthy and nutritious foods.
Feels like a mixed message, because my natural inclination would be to think of Rust as healthy and nutritious foods (it takes dedication and consistency to stick with it) and a better dependency algorithm as a fancy pie, since it's a on-off nice gain that feels good, but doesn't necessarily lead to additional benefits later.
Having done a fair amount of rust porting I imagine it’s a mix a both. Rust really is remarkably faster than Python at everything. But these results are indicative of a better algorithm as well.
Yep. Reminds me of pnpm/npm/yarn. And then of every deprecation of a core pattern with a new™ and shinier™ replacement that lacks essential functionality deemed "a useless edge-case nobody uses because analytics say so".
Is it really a drop-in replacement for pip? If it's so that's great, then why not also officially replace pip?
The comments here betray a significant misunderstanding of both the Python ecosystem and open source work.
Getting to a consensus is hard work. They have a vision for how Python packaging and tooling could be better (I assume cargo-like), and a roadmap for improving that. They could have tried to convince the pip maintainers their roadmap was best, but with large fragmented communities like Python it is impossible to reason about how the community will receive changes to tooling. It’s better to build something and get feedback to prove the hypothesis.
The choice of Rust is just because they like the developer ergonomics in that language (I suspect a big reason is that they wanted to ship a single binary independent of any Python env). CPython is written in C because… Guido started to write it in C.
Many comments here don’t understand that their competitors are actually NOT just pip but the conda ecosystem including micromamba (written in C++) which does not attempt to maintain pip interop at all. Conda realized that there are millions of Python programmers who are not developers and could not debug a GCC error message when pip installing scipy.
There are thousands of Python packages that are basically tested on whatever version of Python and libraries the author had installed at the time they wrote it, and literally millions of Github issues that are something like “this doesn’t work because I installed version Y of package X and package Z requires < Y” all vaguely for solveable reasons. Many of these include C/C++ code that builds via custom setup.py scripts.
If you haven’t lost days of work to solving stupid dependency issues in Python I’m not sure you can relate.
I guess I don't have enough context to really understand this. It takes 1.5 hours to calculate what dependencies Home Assistant needs? Wouldn't you normally amortize that out for each top-level dependency you add to the project and write it to a lock file or something, so that builds just become fetching the relevant artifacts?
Even then, I'm not sure how it would take 1.5 hours even on pen and paper.
> Wouldn't you normally amortize that out for each top-level dependency you add to the project and write it to a lock file or something
Plain pip doesn't really have a lockfile. You can generate a freeze file, but that's something different in practice.
I haven't really been in the Python ecosystem in several years, but my surprise was mostly how there was no mention of Poetry or pipenv. Didn't they address that need before? Are they already out of favor?
I don't think this is anything like poetry. The dependency resolution doesnt seem to handle much variation in arch or Python version or other variables I at least have to deal with. I generally use poetry for dependency management (only runs in dev) then use pip or venv-update to install in prod from a generated requirements.txt.
uv looks like a good and fast replacement for pip or venv-update but those only take a fraction of a second to run (on a noop, my main use case) so I personally haven't seen where uv would fit in my workflow yet. Maybe building in containers from scratch each time, though again pip is performant enough for me when not doing the hard work of dependency resolution, and for some reason it's not cached.
I am not sure why dependency resolution outside of dev or build systems would be done. But maybe I have escaped the loop.
I've spent an embarrassing amount of time trying to figure out why Docker refuses to use my cached layers 80-90% of the time, even though every dependency is properly pinned and build commands properly ordered. I said f-it, gave up and replaced pip with uv. It's nowhere near the speed of a cached build but it's a free 3 minutes.
Would the authors not have been better off adding their performance improvements to pip rather than creating their own tool? I remember this being a complaint from Node maintainers when Bun was released: they had multiple open performance-related issues with no one available to work on them.
Much like how people underestimate the time and effort it takes to build mature software from scratch, people also seem to under-estimate greatly the complexity and effort involved in trying to make huge improvements to existing tools, both technical and political in nature. There's absolutely no chance that the vision the Bun folks had could be carried out in the Node.JS project in my opinion, the resulting attempt would have less impressive results and probably would leave everyone unsatisfied. One of the biggest benefits and most powerful features of open source is undoubtedly the fork, but just as important is knowing when not to do that, and that's definitely the case when the position you want to be is more different from any existing starting point than it is from scratch; I'd argue writing a 'fast' Pip-compatible package manager from first-principles is a perfect case for that.
Re: politics. Note that sometimes forks and competitors are so successful, they make it possible for the upstream to "unblock" by virtue of convincing them to adjust their vision. For Node.JS, consider io.js and Yarn in particular.
Yeah but I suspect there’s something more going on than Rust magic. They’re claiming a savings of about an hour and a half. (4% of the original time) That sounds like an architecture change, or more likely a whole lot of caching.
Your assignment, should you choose to accept it, is to write a small, simple Python program. Maybe something that builds a list of five or six elements, runs a simple map over it, then converts it to a dict somehow. Something simple.
Then run it through gdb and watch every single CPU opcode that is executed for the code you wrote. Not Python opcode; CPU. Print something first and trigger off of that or something, I'm not worried about Python startup costs here.
Then do the same with a roughly equivalent compiled program. Doesn't have to be Rust, C or whatever you know would do just fine.
Yes. You can get a big speed improvement over pure Python simply by moving it to a compiled language and making effectively no other changes. Python is slow. This is not a value judgment. It is a big mistake to think that this is somehow an emotional judgment about Python stemming from anger or hatred or something and that anyone talking about Python's speed issues are just haters or something. I like Python just fine. But you will understand how it is not a value judgment when you are running the Python program through gdb. It is simply an engineering reality that anyone using Python needs to understand about Python. You can get yourself into a lot of trouble not understanding this about Python.
Is there more things going on than a straight port? Possibly. But I don't find the difference unbelievable for it to be coming from a straight port with no major changes.
If the first step of your build process is creating a new pip venv from scratch, it's entirely believable to me. Pip resolves packages sequentially, so there's a lot of low hanging fruit even without yoinking a state of the art constraint solver.
As a concrete example, I have a raspberry pi 4 I use for "what's the most pathetic hardware a user might try". It takes 5-10 minutes to build up a fresh venv even when it has all of the packages cached. If I nuke the caches and make it download, it's 20+ minutes. For comparison a modern laptop takes ~1 minute.
I threw in a moderately fast USB drive (~90 mb/s after pi4 overhead) because I got sick of SD cards dying every 6-12 months. Pi4 is very speed limited. Even if you tried to use an ssd, I think the highest I've seen is around 250mb/s with an nvme drive that gets a few gb/s in a real computer. In theory it should get to like 500mb/s for sata limits.
I don’t know how you can seriously imply that Python could be just as fast as Rust if the authors of uv cared enough.
That seems like a disingenuous framing of why UV exists, let alone doesn’t pass the smell test that Python can just be as fast as a compiled, native machine code language. Python and Ruby and all that are just so god awful slow that it’s completely obvious that’s a rewrite in native code would have these sorts of benefits.
Yeah "lets rewrite in Rust" has become a meme with negative connotations that will attract ridiculed or be ignored, but Rust clearly excels in this sort of tooling. Whether it makes sense to rewrite an existing codebase or pigeonhole it on the edges is another question.
Most innovations happens outside large organizations, then either replaces it or the ideas get stolen/absorbed into the parent. All the downsides and barriers of operating in a large legacy system are pretty well known, even if the maintainers are receptive, radical change will be DOA.
It's tricky to change up some parts of pip because it might break some existing users flows. It's really hard to test for this kind of infrastructure tool because it's so environment-dependent. The rewrite lets there be a clean slate. Meanwhile the target of being "compatible" with pip gives them a good idea of where to go, and they can focus a bit more on testing given the lessons learned.
Happy to report that this definitely isn't a paid ad. The Home Assistant team did this on their own. I helped out by building uv for some of the architectures they needed: https://github.com/astral-sh/uv/pull/2417
Astral is a company but that doesn’t make this an ad without evidence you didn’t include. Do you personally know that this was anything other than organic praise from a happy user?
I’m unclear on how you “port” a library from Python to Rust, it’s a ground up rewrite with a familiar interface to pip but fundamentally doesn’t share any code.
It's actually pretty easy to call python from rust and rust from python. An incremental port of a python library into rust would definitely be possible.
I'm not sure it would be a good idea. Python patterns don't necessarily make good rust patterns. Even ignoring larger code structure (e.g. single ownership*) you'd practically have to do a second pass to stop using python compatible types (except in the exposed interface) if you wanted an idiomatic rust library at the end of it. But it's definitely possible.
* Which you don't "have to" do in rust, you can use `Rc<RefCell>` everywhere and imitate python instead. It's just throwing some of the advantages of rust and adding some noise to type signatures.
> While uv supports a large subset of the pip interface, it does not support the entire feature set. In some cases, those differences are intentional; in others, they're a result of uv's early stage of development.
> For details, see our pip compatibility guide[1]
> Like pip-compile, uv generates a platform-specific requirements.txt file (unlike, e.g., poetry and pdm, which generate platform-agnostic poetry.lock and pdm.lock files). As such, uv's requirements.txt files may not be portable across platforms and Python versions.
i think now their current positioning is that the competition actually helped demonstrate the value of some kinds of speed, and that finally got those performance issues prioritized enough to work on by people with the right knowledge. Node doesn't lack for people who want to contribute, its more about people who -can- contribute well, and yes as other comment noted, its often to easier to try things out outside the system rather than work within it.
Python from the start has always been willing to use other languages and tools if it made sense. It's a pretty common pattern (at least in the data science and research circles I've been in) to write the original in python to see if it works, and then to port it over to a lower level language (C, Rust more and more these days) and expose an interface to it in Python.
For things like dependency resolution, where you're doing a lot of heavy CPU operations, there is a lot of room for optimization in lower level languages.
Bootstrapping is such an odd concept. Like, something that's self propagating is in a sense not fully reproducible and holonomic, unless you maintain the original non-bootstrapped version which would add tons of work.
A compiler bug could put a bug in the compiler and propagate itself indefinitely.
An implementation in some other languages that has an implementation in C means you could theoretically start from ASM and rebuild.
