PackageCompiler.jl VS actix-web

Yes, julia can be called from other languages rather easily, Julia functions can be exposed and called with a C-like ABI [1], and then there's also various packages for languages like Python [2] or R [3] to call Julia code.

With PackageCompiler.jl [4] you can even make AOT compiled standalone binaries, though these are rather large. They've shrunk a fair amount in recent releases, but they're still a lot of low hanging fruit to make the compiled binaries smaller, and some manual work you can do like removing LLVM and filtering stdlibs when they're not needed.

Work is also happening on a more stable / mature system that acts like StaticCompiler.jl [5] except provided by the base language and people who are more experienced in the compiler (i.e. not a janky prototype)

[1] https://docs.julialang.org/en/v1/manual/embedding/

[2] https://pypi.org/project/juliacall/

[3] https://www.rdocumentation.org/packages/JuliaCall/

[4] https://github.com/JuliaLang/PackageCompiler.jl

[5] https://github.com/tshort/StaticCompiler.jl

One of Julia's Achilles heels is standalone, ahead-of-time compilation. Technically this is already possible [1], [2], but there are quite a few limitations when doing this (e.g. "Hello world" is 150 MB [7]) and it's not an easy or natural process.

The immature AoT capabilities are a huge pain to deal with when writing large code packages or even when trying to make command line applications. Things have to be recompiled each time the Julia runtime is shut down. The current strategy in the community to get around this seems to be "keep the REPL alive as long as possible" [3][4][5][6], but this isn't a viable option for all use cases.

Until Julia has better AoT compilation support, it's going to be very difficult to develop large scale programs with it. Version 1.9 has better support for caching compiled code, but I really wish there were better options for AoT compiling small, static, standalone executables and libraries.

[1]: https://julialang.github.io/PackageCompiler.jl/dev/

Doesn’t work on Windows, but https://github.com/JuliaLang/PackageCompiler.jl does.

Also, you can precompile a whole package and just ship the binary. We do this all of the time.

https://github.com/JuliaLang/PackageCompiler.jl

And getting things precompiled: https://sciml.ai/news/2022/09/21/compile_time/

You can have a look at PackageCompiler.jl

I think by PackageManager here you mean package compiler, and yes these improvements do not need a 2.0. v1.8 included a few things to in the near future allow for building binaries without big dependencies like LLVM, and finishing this work is indeed slated for the v1.x releases. Saying "we are not doing a 2.0" is precisely saying that this is more important than things which change the user-facing language semantics.

And TTFP does need to be addressed. It's a current shortcoming of the compiler that native and LLVM code is not cached during the precompilation stages. If such code is able to precompile into binaries, then startup time would be dramatically decreased because then a lot of package code would no longer have to JIT compile. Tim Holy and Valentin Churavy gave a nice talk at JuliaCon 2022 about the current progress of making this work: https://www.youtube.com/watch?v=GnsONc9DYg0 .

This is all tied up with startup time and are all in some sense the same issue. Currently, the only way to get LLVM code cached, and thus startup time essentially eliminated, is to build it into what's called the "system image". That system image is the binary that package compiler builds (https://github.com/JuliaLang/PackageCompiler.jl). Julia then ships with a default system image that includes the standard library in order to remove the major chunk of code that "most" libraries share, which is why all of Julia Base works without JIT lag. However, that means everyone wants to have their thing, be it sparse matrices to statistics, in the standard library so that it gets the JIT-lag free build by default. This means the system image is huge, which is why PackageCompiler, which is simply a system for building binaries by appending package code to the system image, builds big binaries. What needs to happen is for packages to be able to precompile in a way that then caches LLVM and native code. Then there's no major compile time advantage to being in the system image, which will allow things to be pulled out of the system image to have a leaner Julia Base build without major drawbacks, which would then help make the system compile. That will then make it so that an LLVM and BLAS build does not have to be in every binary (which is what takes up most of the space and RAM), which would then allow Julia to much more comfortably move beyond the niche of scientific computing.

You're probably dancing around the edges of what [PackageCompiler.jl](https://github.com/JuliaLang/PackageCompiler.jl) is capable of targeting. There are a few new capabilities coming online, namely [separating codegen from runtime](https://github.com/JuliaLang/julia/pull/41936) and [compiling small static binaries](https://github.com/tshort/StaticCompiler.jl), but you're likely to hit some snags on the bleeding edge.

Actix Web Documentation: Detailed documentation on using Actix-web, including examples and best practices for building web applications with Rust.

I can't speak to the "is it any good" part, but (after a bit of research) I can share what I've found. I'll try to represent things as best as I understand, but I may have some finer details mixed up.

ntex is written by the same person that started actix-web, Nikolay Kim (fafhrd91 on GitHub). There was a bunch of drama a while back due to actix-web using (what many reasoned to be) avoidable unsafe code, which was later found to be buggy. Nikolay was pilloried online, resulting in him transferring leadership of actix-web to someone else. ntex is, as I understand it, essentially Nikolay picking back up on his ideals for what could have been actix-web, if people hadn't pushed him out of his own project.

How ntex compares to the pre-/post-leadership change of actix-web, I don't know.

Here are some jumping points if you want more of the backstory.

https://www.theregister.com/2020/01/21/rust_actix_web_framew...

https://steveklabnik.com/writing/a-sad-day-for-rust

https://github.com/actix/actix-web/issues/1289

Are you ready to embark on another journey in Rust? Today, we'll explore how to create a REST API that performs basic mathematical operations: addition, multiplication, and division. We'll use Actix, a powerful web framework for Rust, together with Rhai, a lightweight scripting language, to achieve our goal.

Within actix-web, middleware is used as a medium for being able to add general functionality to a (set of) route(s) by taking the request before the handler function runs, carrying out some operations, running the actual handler function itself and then the middleware does additional processing (if required). By default, actix-web has several default middlewares that we can use, including logging, path normalisation, access external services and modifying application state (through the ServiceRequest type).

Rust is a popular system programming language, known for its robust memory safety features and exceptional performance. While Rust was originally a system programming language, its application has evolved. Now you can see Rust in different app platforms, mobile apps, and of course, in web apps — both in the frontend and backend, with frameworks like Rocket, Axum, and Actix making it even easier to build web applications with Rust.

actix to handle HTTP requests