This is another not-requested opinion on Deno! But what can I do? When I read “node replacement,” “TypeScript,” and “Rust,” I lose any inhibition. Therefore, I ported to Deno an old npm package and wrote a brief report on my experience. What I liked? What I disliked? Will Deno be succesful in the overcrowded world of programming platforms? These are my answers.

Today has been five years since Rust 1.0 release. I really want to wish happy birthday to this awesome language! I could write an article on it or I can link you to this wonderful post in the official Rust’s blog. Enjoy!

Game Development is one of the fields in which Rust can gain a lot of traction. As a modern compiled language with performances comparable to C++, Rust can finally free us from the tyranny of C++ bloated feature set, hard-to-link dependencies, and header/implementation file double-madness (I am obviously exaggerating, btw).

However, if this freedom arrive, it will be a very slow process. To make it slower, the feature of memory safety in videogames is not a huge priority compared to the ability to quickly prototype. The borrow-checker and the strict compiler are an obstacle in this regard. On the other hand, memory safety also means easier multi-threading. And this is sweet!

Fortunately, the annoyances of borrow-checker will get less in the way while people becomes more confident with the language, and while tooling gets better and better. I am confident we may see Rust carve out its space in this domain.

But this is the future. What about now?

You know I worked a lot with pathfinding. In academia, the MovingAI benchmark created by the MovingAI Lab of the University of Denver is a must for benchmarking pathfinding algorithms. It includes synthetic maps and maps from commercial videogames.

Parsing the benchmark data, the maps, creating the map data structure and more, is one of the most boring thing I needed to do for testing my algorithms. For this reason, I think a common library for working with the maps specifications it is a must.

One of the feature I like the most on Rust is automatic documentation. Documentation is a pillar in language ergonomic, and I love that Rust spend so much time into making documentation and documenting code a much pleasant experience.

Rust autogenerated documentation (with cargo doc) looks good, every crate on crates.io get its documentation published on docs.rs, and, most important, every code example in the code is compiled and run as a “unit test” making sure that all the examples are up-to date!

Yesterday I spent way more time than needed for compiling this dependency on Windows. The problem is that the error was not informative enough and hard to google, and, mostly, that there is no standardized way to look for installed libraries in Windows. Just to be clear, there is no reliable way to use pkg-config on Windows as in Unix-like systems.

The problem

The rusqlite crate is an ergonomic binding of SQLite in Rust. To work, it needs the original library installed. Unfortunately, on Windows is relatively hard to find the path or the list of the installed development libraries. It is not impossible, but it is harder because Windows ecosystem has not a standard software or API to obtain this information. If you try to blindly add rusqlite as a dependency and compile your crate you get a cryptic error:

Another year, another 5 promising programming languages you should keep an eye on in 2017. As usual, I’d like to write the warning I put here every year: in this list, you will not find programming languages for hiring purposes, but for very long-time investments and for pure programming fetish.

So, now that you know what I am talking about, here we go with the top 5 for 2017.

Top 5 Promising Programming Languages for 2017

Rust

You can follow the links to read the first part and the second part of this series.

In the previous part we have seen how to pass not trivial data to Rust functions such as a Python list. It is still not enough, though. In many cases we need to pass complex data structure back and forth from a Rust library. We may need to pass quaternions, 3D points, trees, a list of “books”… In short: anything.

Learning how to pass custom aggregated data types to Rust libraries (and back to Python) will be the focus of this part!

You can find the first part of this article HERE.

In the previous part we have seen how to run simple Rust functions with integer arguments. This is not enough, of course. We need to go further by passing Python lists to Rust functions.

The problem is that it is not possible to pass directly a Python list to a C interface. Python lists (we can call them Plists) are complicated beasts, you can easily see that they are objects full of methods, and attributes and… Stuff.

1
2

dir([])
['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']

We need first to convert this in something edible from a Rust library. But first things first.

Rust is an amazing language. It is one of the best potential alternatives to C and has been elected two times in a row as the most promising language of the year (by me, :P). However, because its strict compile-time memory correctness enforcement and because it is a low-level language, it is not the fastest way to build a prototype. But don’t worry! Rust is the perfect language for embedding fast-binary libraries in Python! In this way we can get the best of both worlds!

Writing Rust code that can be executed in Python is stupidly easy. Obviously, you have to well design the interface between the two languages, but this will improve your Python code in two ways: 1) You can execute CPU-intensive algorithms at binary speed and 2) use real threads instead of the Python “simulated” ones (and because Rust is designed to be memory safe, writing thread safe routines is much easier). Let’s see!