| Also as a pip contributor. 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. I'm personally not looking forward to any deduplication/hardlink in pip. Hardlinks are very dangerous and system dependent. It's very dangerous for empty files (init.py, empty.log yet not written). When the user edits one file, all files are modified simultaneously, all venv ever created by the user can be broken by editing one file, which is quite catastrophic. It's also dangerous for small files with repeated content, for example random settings files that would contain a "1" or "true". Again, when the user edits one file, all files are edited and they were supposed to be different! Hypothetically, a simple deduplication of binary files (.dll .so) should achieve 50% of the savings without significant drawbacks I'd venture to say that pip extraction is more optimized than uv in at least one way. We have optimization for empty files (0 bytes) because there is nothing to write and checksum. uv doesn't seem to have the same optimizations, though I could be wrong, I just had a cursory look and my rust is not great. uv should probably review their treatment of empty files, it's counter productive to do any file system operation open/read/write because there is no content, it might be counterproductive to use any cache/comparison/hardlink if it takes more operations than doing nothing. |
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| ▲ | zanie 2 hours ago | parent | next [-] | | (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. | | |
| ▲ | user5994461 2 hours ago | parent | next [-] | | > 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 | |
| ▲ | notatallshaw 2 hours ago | parent | prev [-] | | > 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)). | | |
| ▲ | zanie 2 hours ago | parent [-] | | 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 :) | | |
| ▲ | notatallshaw an hour ago | parent [-] | | > 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. |
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| ▲ | tedivm an hour ago | parent | prev | next [-] | | 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. | |
| ▲ | jvolkman 2 hours ago | parent | prev | next [-] | | > 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. | |
| ▲ | amelius an hour ago | parent | prev | next [-] | | 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. | |
| ▲ | optionalsquid 2 hours ago | parent | prev [-] | | > 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? | | |
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