doctest VS Nuitka

I have never used "tools" for unit-tests, only web sites that show the results of the tests or code coverage. For C++ I prefer https://github.com/doctest/doctest but most companies I worked for use Catch2.

Do the README and tutorial not explain it well enough? It's a framework for automated unit testing.

This is, honestly, super easy to get going. Nowadays you have a ton of libraries and more-than-decent build systems. With Meson/CMake and Conan/Vcpkg I can set up a project with testing in 3 minutes. Also, I think that at the end of the day you want your tests to live somewhere else. But if you want to embed them, you also have https://github.com/doctest/doctest.

Write some tests. They can find bugs early and give you confidence that your code works so far. That doesn't have to be anything fancy, e.g. with doctest:

Keep in mind https://github.com/doctest/doctest/issues/554. Also, doctest lacks: - Matchers - Data generators - Benchmarking - ...

Your requirements can easily simulated on paper (like increase the speed once, twice, ...), then translated to unit-tests with a framework like https://github.com/doctest/doctest.

I like working in C++, after a decade of working in Java, Python, Javascript and Clojure, I find working in C++ (which I learned before these other languages) to be quite fun and pleasant, at least with relatively modern C++.

I've been, on and off, working on a little toy game engine, for a few years. Its a mix of keeping up with C++ advancements, learning various concepts like physically based rendering, and just the fun of crafting a big project, with no constraints other than my time and ability, no deadlines, no expectation of releasing anything. Its cathartic and enjoyable. I really do enjoy it.

Last September, I got frustrated with something I was working on in a more serious capacity. It was some server software, it responded to HTTP requests, it accessed third party services over HTTP and Websockets, it talked to a Postgres database. Overall it was an event driven system that transformed data and generated actions that would be applied by talking to third party services. The "real" version was written in Clojure and it worked pretty well. I really like Clojure, so all good.

But because I was frustrated with some things about how it ran and the resources it took up, I wondered what it would be like if I developed a little lean-and-mean version in C++. So I gave it a try as a side project for a few weeks. I used doctest[1] for testing, immer[2] for Clojure-like immutable data structures, [3] lager for Elm-like application state and logic management, Crow[4] for my HTTP server, ASIO[5] and websocketpp[6] for Websockets, cpp-httplib[7] as a HTTP client and PGFE[8] for Postgres, amongst some other little utility libraries. I also wrote it in a Literate Programming style using Entangled[9], which helped me keep everything well documented and explained.

For the most part, it worked pretty well. Using immer and lager helped keep the logic safe and to the point. The application started and ran very quickly and used very little cpu or memory. However, as the complexity grew, especially when using template heavy libraries like lager, or dealing with complex things like ASIO, it became very frustrating to deal with errors. Template errors even on clang became incomprehensible and segmentation faults when something wasn't quite right became pretty hard to diagnose. I had neither of these problems working on my game engine, but both became issues on this experiment. After a few weeks, I gave up on it. I do think I could have made it work and definitely could go back and simplify some of the decisions I made to make it more manageable, but ultimately, it was more work than I had free time to dedicate to it.

So my experience was that, yes, you can write high level application logic for HTTP web backends in C++. You can even use tools like immer or lager to make it feel very functional-programming in style and make the application logic really clean. Its not hard to make it run efficiently both in terms of running time and memory usage, certainly when comparing to Clojure or Python. However, I found that over all, it just wasn't as easy or productive as either of those languages and I spent more time fighting the language deficiencies, even with modern C++, than I do when using Clojure or Python.

I think I would think very long and hard before seriously considering writing a web backend in C++. If I had the time, I'd love to retry the experiment but using Rust, to see how it compares.

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

[2] https://github.com/arximboldi/immer

[3] https://github.com/arximboldi/lager

[4] https://github.com/CrowCpp/crow

[5] https://think-async.com/Asio/

[6] https://www.zaphoyd.com/projects/websocketpp/

[7] https://github.com/yhirose/cpp-httplib

[8] https://github.com/dmitigr/pgfe

[9] https://entangled.github.io/

For stuff like this which is very easy to test (very predefined input vs output), I highly suggest using some testing framework. Catch2 is great, but there is also doctest and good ole googletest. If you do this, it would also be a great intro to CI, where you do some plumbing on github or gitlab where every commit causes a build to happen on their servers and run through the unit tests, and if it passes it gets merged into master.

doctest is my favorite framework. Really simple to use, header only, supports compile-time tests, lots of features and it works well with cmake.

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

> Your comment would make sense if Python code could be compiled into x86 or ARM assembly in the first place.

It can actually be compiled (or transpiled) into C code [1] with few limitations, so I can't see why not.

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