Nuitka VS FrameworkBenchmarks

Thanks for the feedback! I'm Syrus, main author of the work on py2wasm.

We already opened a PR into Nuitka to bring the relevant changes upstream: https://github.com/Nuitka/Nuitka/pull/2814

We envision py2wasm being a thin layer on top of Nuitka, as also commented in the article.

From what we gathered, we believe that there's usefulness on having py2wasm as a separate package, as py2wasm would also need to ship the precompiled Python distribution (3.11) for WASI (which will not be needed for the other Nuitka use cases), apart of also shipping other tools that are not directly relevant for Nuitka

This is a good place to mention https://nuitka.net/ which aims to compile python programs into standalone binaries.

For Python, you could make a proper deployment binary using Nuitka (in standalone mode – avoid onefile mode for this). I'm not pretending it's as easy as building a Go executable: you may have to do some manual hacking for more unusual unusual packages, and I don't think you can cross compile. I think a key element you're getting at is that Go executables have very few dependencies on OS packages, but with Python (once you've sorted the actual Python dependencies) you only need the packages used for manylinux [2], which is not too onerous.

[1] https://nuitka.net/

[2] https://peps.python.org/pep-0599/#the-manylinux2014-policy

glee is rich in blogging features but has some drawbacks. One of the main drawbacks is its compatibility with multiple operating systems and system architectures. We lost one potential customer due to glee incompatibility in macOS. Another major issue is the deployment time. We built the first version of glee entirely in Python and used nuitka, nuitka compiles Python programs into a single executable binary file. We need to create three separate stages for creating executable binaries for Windows, Mac, and Linux in deployment, and it takes around 20 minutes to complete.

There is already an AOT compiler for Python: Nuitka[0]. But I don't think it's much faster.

And then there is mypyc[1] which uses mypy's static type annotations but is only slightly faster.

And various other compilers like Numba and Cython that work with specialized dialects of Python to achieve better results, but then it's not quite Python anymore.

[0] https://nuitka.net/

[1] https://github.com/python/mypy/tree/master/mypyc

Nuitka deals pretty well with those in general: https://nuitka.net/

Hi HN,

Has anyone explored Nuitka [1] and developed understanding from a blank slate?

Is there any toy version of this, so that one can start playing with the language translation concepts?

Is there any underlying theory/inspiration upon which this project is built?

Are there any similar projects, in say other languages?

[1] https://github.com/Nuitka/Nuitka

That's more of cultural problem in the Python community.

If I provide an end user software to my client written an Python (so not a backend, not a lib...), I will compile it with nuitka (https://github.com/Nuitka/Nuitka) and hide the stack trace (https://www.bitecode.dev/p/why-and-how-to-hide-the-python-st...) to provide a stand alone executable.

This means the users don't have to know it's made with Python or install anything, and it just works.

However, Python is not like Go or Rust, and providing such an installer requires more than work, so a huge part of the user base (which have a lot of non professional coders) don't have the skill, time or resources to do it.

And few people make the promotion of it.

I should write an article on that because really, nobody wants to setup python just to use a tool.

As for a simpler option, you could use a "compiler": https://github.com/Nuitka/Nuitka

Neat. Thanks for sharing!

Interestingly, may-minihttp is faring very well in the TechEmpower benchmark [1], for whatever those benchmarks are worth. The code is also surprisingly straightforward [2].

[1] https://www.techempower.com/benchmarks/

[2] https://github.com/TechEmpower/FrameworkBenchmarks/blob/mast...

ntex was formed after a schism in actix-web and Rust safety/unsafety, with ntex allowing more unsafe code for better performance.

ntex is at the top of the TechEmpower benchmarks, although those benchmarks are not apples-to-apples since each uses its own tricks: https://www.techempower.com/benchmarks/#hw=ph&test=fortune&s...

Ruby is slow. Very slow. How much you may ask? https://www.techempower.com/benchmarks/#hw=ph&test=fortune&s... fastest Ruby entry is at 272th place. Sure, top entries tend to have questionable benchmark-golfing implementations, but it gives you a good primer on the overhead imposed by Ruby.

It is also not early 00s anymore, when you pick an interpreted language, you are not getting "better productivity and tooling". In fact, most interpreted languages lag behind other major languages significantly in the form of JS/TS, Python and Ruby suffering from different woes when it comes to package management and publishing. I would say only TS/JS manages to stand apart with being tolerable, and Python sometimes too by a virtue of its popularity and the amount of information out there whenever you need to troubleshoot.

If you liked Go but felt it being a too verbose to your liking, give .NET a try. I am advocating for it here on HN mostly for fun but it is, in fact, highly underappreciated, considered unsexy and boring while it's anything but after a complete change of trajectory in the last 3-5 years. It is actually the* stack people secretly want but simply don't know about because it is bundled together with Java in the public perception.

*productive CLI tooling, high performance, works well in a really wide range of workloads from low to high level, by far the best ORM across all languages and back-end framework that is easier to work with than Node.JS while consuming 0.1x resources

Although that seems to have improved in recent years.

https://www.techempower.com/benchmarks/#hw=ph&test=json§...

RoR and whatever C++ based web backend there is count as a valid comparison in my book. But comparing the languages itself is maybe a bit off.

On a side note, you can actually compare their performance here if you’re really curious. But take it with a grain of salt since these are synthetic benchmarks.

https://www.techempower.com/benchmarks

Most benchmarks you'll find essentially have someone's thumb on the scale (intentionally or unintentionally). Most people won't know the different languages well enough to create comparable implementations and if you let different people create the implementations, cheating happens. The TechEmpower benchmarks aren't bad, but many implementations put their thumb on the scale (https://www.techempower.com/benchmarks). For example, a lot of the Go implementations avoid the GC by pre-allocating/reusing structs or allocate arrays knowing how big they need to be in advance (despite that being against the rules). At some point, it becomes "how many features have you turned off." Some Go http routers (like fasthttp and those built off it like Atreugo and Fiber) aren't actually correct and a lot of people in the Go community discourage their use, but they certainly top the benchmarks. Gin and Echo are usually the ones that are well-respected in the Go community.

There is certainly a lot of speculation in Techempower benchmarks and top entries can utilize questionable techniques like simply writing a byte array literal to output stream instead of constructing a response, or (in the past) DB query coalescing to work around inherent limitations of the DB in case of Fortunes or DB quries.

And yet, the fastest Ruby entry is at 274th place while Rails is at 427th.

https://www.techempower.com/benchmarks/#hw=ph&test=fortune&s...

oh what machine? with how many workers? doing what?

search for "node" on this page: https://www.techempower.com/benchmarks/#section=data-r21

JustJS would like a word https://www.techempower.com/benchmarks/#section=data-r20&tes...

In terms of RPS, this web service is more-or-less the fortunes benchmark in the techempower benchmarks, once the data hits the cache: https://www.techempower.com/benchmarks/#section=data-r21

Or, at least, they would be after applying optimizations to them.

In short, both of these would serve more rps than you will likely ever need on even the lowest end virtual machines. The underlying API provider will probably cut you off from querying them before you run out of RPS.