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(I work on uv)

> The only advantage of uv is to have support for parallel async extraction.

This isn't true, the biggest speed ups are for the warm cases where we've already unpacked the files into the cache, as notatallshaw mentions above. We can also make low-level optimizations during resolution, e.g., in version parsing, that are not possible in pure Python code.

> If pip extracted multiple files/wheels in parallel without being blocked by the GIL, pip could easily match or outcompete uv.

I'm a bit confused by these claims about the GIL? The expensive IO operations release the GIL.

> When the user edits one file, all files are modified simultaneously

This is why we default to reflinks or copy-on-write semantics when creating environments, not all file systems support it but it's becoming more common.

> Hypothetically, a simple deduplication of binary files (.dll .so) should achieve 50% of the savings without significant drawbacks

We also explored this (see https://github.com/astral-sh/uv/pull/19694) and the linked pull request has a table comparing to this strategy.

> We have optimization for empty files (0 bytes) because there is nothing to write and checksum.

Interesting, I would be very surprised if this made a significant difference? but I'll take a look.

> I'm a bit confused by these claims about the GIL? The expensive IO operations release the GIL.

FYI: The extraction of files is largely python code that doesn't release the GIL. (cf. the zipfile class from the python interpreter has large layers of abstraction with a massive overhead in python code).

Regardless, python packages are thousands of tiny files, so pip never gets to release the GIL for any meaningful duration.

If you were writing an app that only extracted large GB files, you could take advantage of some I/O operations and some zlib operations freeing the GIL for a bit. Unfortunately pip is the opposite use case, lots of tiny files.

> Interesting, I would be very surprised if this made a significant difference? but I'll take a look.

Optimizing empty files was actually quite worthwhile for pip, because about 10% of python packages are empty init files.

This might not give the same result for uv though. pip is fully linear, every single open/read/write/stat operation we removed was a direct performance gain. uv does parallel async IO, you could very well remove 10% of filesystem calls and barely affect the overall duration. :D

> FYI: The extraction of files is largely python code that doesn't release the GIL. (cf. the zipfile class from the python interpreter has large layers of abstraction with a massive overhead in python code).

TIL. I ran some benchmarks and confirmed this is the case for many small files as you'd see in wheels — the GIL is released in a meaningful way for larger files though. Thanks!

> This might not give the same result for uv though.

Yeah, I built a prototype and ran some benchmarks. It makes a big difference if the entire wheel is empty files but for any real world examples it's within noise of the baseline.

> We can also make low-level optimizations during resolution, e.g., in version parsing, that are not possible in pure Python code.

As a complete aside, uv can and does do this, but for this particular optimization I'm not sure how much absolute time it ends up saving compared to pure Python in real world resolution scenarios.

uv's total memory usage isn't that much leaner than pip's, and for parsing speed it turned out that the library pip uses, packaging, was just very unoptimized at the time uv launched. This has been significantly addressed since then:

* We did a lot of work to make version parsing twice as fast: https://iscinumpy.dev/post/packaging-faster/

* Since that blog post I made typical version parse three times faster on top of that: https://github.com/pypa/packaging/pull/1082

* Also since that blog post version filtering has gone through multiple optimizations and in some cases will be more than 30x faster e.g. https://github.com/pypa/packaging/pull/1105, https://github.com/pypa/packaging/pull/1111, https://github.com/pypa/packaging/pull/1120

At this point large dependency resolves in pip are spending very little of their time doing things in packaging, like version parsing. The main non-IO time spent in large resolves is now in the core resolver, resolvelib, which I hope to one day replace with my experimental resolver nab: https://github.com/notatallshaw/nab. Nab scales to large resolves much more efficiently than resolvelib (in fact I've cross-ported some of the algorithmic efficiency gains to uv already ;o)).

We didn't compare to pip at the time, but it saved a lot of absolute time for us as reported in the benchmarks from the pull request (https://github.com/astral-sh/uv/pull/789) it improved a boto3 case by 3x (30s to 10s) and our "standard" solve benchmark by 2x. It's plausible some of those gains have been reduced by other optimizations in our solver since then though.

But this was just one example optimization, we do other low-level things, like zero-copy deserialization from our cache. The point is not that we do specific things, but that we have more levers to pull to improve performance. It's great to see all the improvements happening in pip performance regardless :)

> it improved a boto3 case by 3x (30s to 10s) and our "standard" solve benchmark by 2x. It's plausible some of those gains have been reduced by other optimizations in our solver since then though.

This makes a lot of sense because pubgrub makes heavy use of version comparison, compared to simple DFS algorithms like the one resolvelib uses. A lot of Pubgrub optimizations come from finding clever ways to not need to keep comparing versions.

> But this was just one example optimization, we do other low-level things, like zero-copy deserialization from our cache. The point is not that we do specific things, but that we have more levers to pull to improve performance.

Oh yes, I agree with the general point, I was just picking on the specific example for a fun exploration of performance optimizations.

Also, FWIW, I have in my professional career, not OSS work, implemented zero-copy deserialization from cache in pure Python, there are many surprising levers in Python when you are willing to explore the weird corners of the standard library.

There are a ton of advantages to using uv over pip. I don't use UV because it's faster, although I do appreciate that. I use it because it's smart enough to manage virtual environments for each environment and tool, it can isolate to different python versions trivially, and it handles locking in a way that is actual sane.

> The only advantage of uv is to have support for parallel async extraction

Maybe the only advantage in a particular area (installing)? Because there are many other advantages.

The rich lock file for instance allows for much better cross-platform tooling. I can build a linux Docker container from a macos build host, for example, without VMs or any other emulation - simply using the cross-platform details in uv.lock and the correct tooling. I can even cross-compile numpy and other native wheels (linux -> macos, macos -> linux).

Other locker tools provide similar cross-platform information (Poetry, PDM), but pip is still lacking.

it's very bizarre for a python developer like me to read comments like this

uv has been absolutely black magic for everyone in the way it just works and works much faster, but here you are telling us that actually no it's somehow not that good

I can say that I have used pip only and it does a package manager job well, it just works.

I’m not sure at what uv excel at but reading comments it seems uv is not only package manager but also venv manager.

Imho deduplication belongs at the filesystem level, so the user won't (directly) see it or even know about it. Modern file systems like btrfs have the api for it.

The dedup functionality in something like zfs or btrfs isn't all that great. It tends to be extremely memory hungry and to slow things down significantly. E.g. ZFS needs around 1-5GB of ram per TB of storage and writes need to be compared to a hash table to dedup properly.

Using hints or knowledge at the app level is a much better experience if the app can tell the FS that two files are identical. The FS doesn't have to worry about hashing blocks within the file, correcting alignments, etc.

Yes, but package managers are not that great either. Better keep them as simple as possible. And you don't have to do deduplication in an online fashion; you can do it overnight, if you want, as just a simple example.

Do you specific issues with uv that makes you distrust the implementation? Otherwise that reasoning is pretty weird - it's possible to write good software, even when the existing options aren't good. Why would we ask them to limit themselves to what might make sense for worse code?

You're missing the point. No normal user program can ever do what a filesystem can do: deduplicate in a way that is hidden for the user of the filesystem. Unless you want to change everything into a black box managed by the package manager, making everything confusing for users and also maintainers.

Why would the deduplication need to be hidden from the user? Why would everything need to become a blackbox?

Hardlinks have long been a well-documented feature of many file systems. I don't see how their use in a context users largely do not meddle with is too confusing or complicated.

Because why would you make everything more complicated if the file system can handle it just fine, plus you can automatically deduplicate other files that have nothing to do with package management?

Well uv runs on many systems and most don't have filesystem level dedupe configured, unfortunately.

Anyways it can be both? It could let the filesystem handle it if it detected the FS has file or block dedupe and fallback to hardlinks otherwise. It doesn't but that could be done if it showed it was worth it.

Because (going by scheme271's numbers) I might not want to waste lots of expensive RAM on something or mess around with offline deduplication, when a less resource-intensive alternative is entirely sufficient for whatever my use case is.

Maybe it's because I've been recently playing around with deduplicating subtrees in a different domain, but I don't see what makes this feature too complex to be worth the cost. Sure, symlinks and hardlinks require some additional care compared to plain files, but it's easy enough to add tests for those cases.

> ZFS needs around 1-5GB of ram per TB of storage

...per unique TB written to datasets with deduplication enabled, of course, not the pool as a whole (even though that ram cost is indeed at the pool level).

there are plenty of useful things to be done with zfs dedupe at practically no cost if you design for it ahead of time. unfortunately for some reason people parachute into every thread where zfs comes up to say you can never ever turn it on, so people are taught not to even try.

Just curious but what would be a good way to design for it ahead of time?

mainly i just mean dataset configuration and arrangement within the filesystem in general as it pertains to whatever you're actually doing. if you can reliably send writes with different storage characteristics (deduplicability, compressibility, etc.) to different datasets configured accordingly, you don't have to deal with the drawbacks of trying to dedupe service logs or installed packages (which will just slowly inflate the dedupe tables for no real benefit) or compress media/encrypted files (admittedly you can usually just bail out early compressing so this side doesn't matter much but imagine the next really good compression algorithm can't fail fast for some reason). so as long as you only enable dedupe where it will actually help you because you designated a dataset for some highly duplicated purpose (and a bunch of transient writes won't harm you by growing the tables needlessly), there's really no reason not to go for it unless tight write latency constraints are also in play.

for instance, i have a pool with plenty of datasets that have dedupe off and only a crucial few with it enabled: the ones that are nfs-mounted media storage volumes for fediverse instance containers that would otherwise all duplicate all media they see in common, which is most media posted to any of them and a good bit of the remote media received by any of them. the size of the dedupe tables in memory is purely a function of the number of blocks written to those few datasets and i expect to either sunset this laptop from 2012 or migrate the pool to a fresh one long before i would be worried about the memory cost of deduplication swelling a few orders of magnitude to where i'd have to care.

> The only advantage of uv is to have support for parallel async extraction. If pip extracted multiple files/wheels in parallel without being blocked by the GIL, pip could easily match or outcompete uv.

That sounds like it would be relatively easy to demonstrate: You could tweak uv to perform downloads/extractions sequentially and then compare its runtime with pip. Has any such comparison been done?

I haven't done the comparison but it shouldn't be so hard

- https://docs.astral.sh/uv/reference/environment/#uv_concurre...

Fun fact!

The original request to add a concurrency variable in uv was requested by me because, among other things, I wanted to measure the difference between uv and pip: https://github.com/astral-sh/uv/issues/3311

I'm not really following this performance discussion as I think it's gone off the rails.