The compiler as we know it is generally attributed to Grace Hopper, who also popularized the notion of machine-independent programming languages and served as technical consultant in 1959 in the project that would become the COBOL programming language. The second part is not important for today's post, but not enough people know how awesome Grace Hopper was and that's unfair.

It's been at least 60 years since we moved from assembly-only code into what we now call "good software engineering practices". Sure, punching assembly code into perforated cards was a lot of fun, and you could always add comments with a pen, right there on the cardboard like well-educated cavemen and cavewomen (cavepeople?). Or, and hear me out, we could use a well-designed programming language instead with fancy features like comments, functions, modules, and even a type system if you're feeling fancy.

None of these things will make our code run faster. But I'm going to let you into a tiny secret: the time programmers spend actually coding pales in comparison to the time programmers spend thinking about what their code should do. And that time is dwarfed by the time programmers spend cursing other people who couldn't add a comment to save their life, using variables named var and cramming lines of code as tightly as possible because they think it's good for the environment.

The type of code that keeps other people from strangling you is what we call "good code". And we can't talk about "good code" without it's antithesis: "write-only" code. The term is used to describe languages whose syntax is, according to Wikipedia, "sufficiently dense and bizarre that any routine of significant size is too difficult to understand by other programmers and cannot be safely edited". Perl was heralded for a long time as the most popular "write-only" language, and it's hard to argue against it:

open my $fh, '<', $filename or die "error opening $filename: $!";
my $data = do { local $/; <$fh> };

This is not by far the worse when it comes to Perl, but it highlights the type of code you get when readability is put aside in favor of shorter, tighter code.

Some languages are more propense to this problem than others. The International Obfuscated C Code Contest is a prime example of the type of code that can be written when you really, really want to write something badly. And yet, I am willing to give C a pass (and even to Perl, sometimes) for a couple reasons:

• C was always supposed to be a thin layer on top of assembly, and was designed to run in computers with limited capabilities. It is a language for people who really, really need to save a couple CPU cycles, readability be damned.
• We do have good practices for writing C code. It is possible to write okay code in C, and it will run reasonably fast.
• All modern C compilers have to remain backwards compatible. While some edge cases tend to go away with newer releases, C wouldn't be C without its wildest, foot-meet-gun features, and old code still needs to work.

Modern programming languages, on the other hand, don't get such an easy pass: if they are allowed to have as many abstraction layers and RAM as they want, have no backwards compatibility to worry about, and are free to follow 60+ years of research in good practices, then it's unforgivable to introduce the type of features that lead to write-only code.

Which takes us to our first stop: Rust. Take a look at the following code:

let f = File::open("hello.txt");
let mut f = match f {
    Ok(file) => file,
    Err(e) => return Err(e),
};

This code is relatively simple to understand: the variable f contains a file descriptor to the hello.txt file. The operation can either succeed or fail. If it succeeded, you can read the file's contents by extracting the file descriptor from Ok(file), and if it failed you can either do something with the error e or further propagate Err(e). If you have seen functional programming before, this concept may sound familiar to you. But more important: this code makes sense even if you have never programmed with Rust before.

But once we introduce the ? operator, all that clarity is thrown off the window:

let mut f = File::open("hello.txt")?;

All the explicit error handling that we saw before is now hidden from you. In order to save 3 lines of code, we have now put our error handling logic behind an easy-to-overlook, hard-to-google ? symbol. It's literally there to make the code easier to write, even if it makes it harder to read.

And let's not forget that the operator also facilitates the "hot potato" style of catching exceptions¹, in which you simply... don't:

File::open("hello.txt")?.read_to_string(&mut s)?;

Python is perhaps the poster child of "readability over conciseness". The Zen of Python explicitly states, among others, that "readability counts" and that "sparser is better than dense". The Zen of Python is not only a great programming language design document, it is a great design document, period.

Which is why I'm still completely puzzled that both f-strings and the infamous walrus operator have made it into Python 3.6 and 3.8 respectively.

I can probably be convinced of adopting f-strings. At its core, they are designed to bring variables closer to where they are used, which makes sense:

"Hello, {}. You are {}.".format(name, age)
f"Hello, {name}. You are {age}."

This seems to me like a perfectly sane idea, although not one without drawbacks. For instance, the fact that the f is both important and easy to overlook. Or that there's no way to know what the = here does:

some_string = "Test"
print(f"{some_string=}")

(for the record: it will print some_string='Test'). I also hate that you can now mix variables, functions, and formatting in a way that's almost designed to introduce subtle bugs:

print(f"Diameter {2 * r:.2f}")

But this all pales in comparison to the walrus operator, an operator designed to save one line of code²:

# Before
myvar = some_value
if my_var > 3:
    print("my_var is larger than 3")

# After
if (myvar := some_value) > 3:
    print("my_var is larger than 3)

And what an expensive line of code it was! In order to save one or two variables, you need a new operator that behaves unexpectedly if you forget parenthesis, has enough edge cases that even the official documentation brings them up, and led to an infamous dispute that ended up with Python's creator taking a "permanent vacation" from his role. As a bonus, it also opens the door to questions like this one, which is answered with (paraphrasing) "those two cases behave differently, but in ways you wouldn't notice".

I think software development is hard enough as it is. I cannot convince the Rust community that explicit error handling is a good thing, but I hope I can at least persuade you to really, really use these type of constructions only when they are the only alternative that makes sense.

Source code is not for machines - they are machines, and therefore they couldn't care less whether we use tabs, spaces, one operator, or ten. So let your code breath. Make the purpose of your code obvious. Life is too short to figure out whatever it is that the K programming language is trying to do.

Footnotes

• 1: Or rather "exceptions", as mentioned in the RFC
• 2: If you're not familiar with the walrus operator, this link gives a comprehensive list of reasons both for and against.

April 21: see the "Update" section at the end for a couple extra details.

A very common operation when programming is iterating over elements with a nested loop: iterate over all entities in a collection and, for each element, perform a second iteration. A simple example in a bank setting would be a job where, for each customer in our database, we want to sum the balance of each individual operation that the user performed. In pseudocode, it could be understood as:

for each customer in database:
    customer_balance=0
    for each operation in that user:
        customer_balance = customer_balance + operation.value
    # At this point, we have the balance for this one customer

Of all the things that Python does well, this is the one at which Python makes it very easy for users to do it wrong. But for new users, it might not be entirely clear why. In this article we'll explore what the right way is and why, by following a simple experiment: we create an n-by-n list of random values, measure how long it takes to sum all elements three times, and display the average time in seconds to see which algorithm is the fastest.

values = [[random.random() for _ in range(n)] for _ in range(n)]

We will try several algorithms for the sum, and see how they improve over each other. Method 1 is the naive one, in which we implement a nested for-loop using variables as indices:

for i in range(n):
    for j in range(n):
        acum += values[i][j]

This method takes 42.9 seconds for n=20000, which is very bad. The main problem here is the use of the i and j variables. Python's dynamic types and duck typing means that, at every iteration, the interpreter needs to check...

• ... what the type of i is
• ... what the type of j is
• ... whether values is a list
• ... whether values[i][j] is a valid list entry, and what its type is
• ... what the type of acum is
• ... whether values[i][j] and acum can be summed and, if so, how - summing two strings is different from summing two integers, which is also different from summing an integer and a float.

All of these checks make Python easy to use, but it also makes it slow. If we want to get a reasonable performance, we need to get rid of as many variables as possible.

Method 2 still uses a nested loop, but now we got rid of the indices and replaced them with list comprehension

for row in values:
    for cell in row:
        acum += cell

This method takes 17.2 seconds, which is a lot better but still kind of bad. We have reduced the number of type checks (from 4 to 3), we removed two unnecesary objects (by getting rid of range), and acum += cell only needs one type check. Given that we still need checking for cell and row, we should consider getting rid of them too. Method 3 and Method 4 are alternatives to using even less variables:

# Method 3
for i in range(n):
    acum += sum(values[i])

# Method 4
for row in values:
    acum += sum(row)

Method 3 takes 1.31 seconds, and Method 4 pushes it even further with 1.27 seconds. Once again, removing the i variable speed things up, but it's the "sum" function where the performance gain comes from.

Method 5 replaces the first loop entirely with the map function.

acum = sum(map(lambda x: sum(x), values))

This doesn't really do much, but it's still good: at 1.30 seconds, it is faster than Method 3 (although barely). We also don't have much code left to optimize, which means it's time for the big guns.

NumPy is a Python library for scientific applications. NumPy has a stronger type check (goodbye duck typing!), which makes it not as easy to use as "regular" Python. In exchange, you get to extract a lot of performance out of your hardware.

NumPy is not magic, though. Method 6 replaces the nested list values defined above with a NumPy array, but uses it in a dumb way.

array_values = np.random.rand(n,n)
for i in range(n):
    for j in range(n):
        acum += array_values[i][j]

This method takes an astonishing 108 seconds, making it by far the worst performing of all. But fear not! If we make it just slightly smarter, the results will definitely pay off. Take a look at Method 7, which looks a lot like Method 5:

acum = sum(sum(array_values))

This method takes 0.29 seconds, comfortably taking the first place. And even then, Method 8 can do better with even less code:

acum = numpy.sum(array_values)

This brings the code down to 0.16 seconds, which is as good as it gets without any esoteric optimizations.

As a baseline, I've also measured the same code in single-threaded C code. Method 9 implements the naive method:

float **values;
// Initialization of 'values' skipped

for(i=0; i<n; i++)
{
    for(j=0; j<n; j++)
    {
    acum += values[i][j];
    }
}

Method 9 takes 0.9 seconds, which the compiler can optimize to 0.4 seconds if we compile with the -O3 flag (listed in the results as Method 9b).

All of these results are listed in the following table, along with all the values of n I've tried. While results can jump a bit depending on circumstances (memory usage, initialization, etc), I'd say they look fairly stable.

Final thoughts

I honestly don't know how to convey to Python beginners what the right way to do loops in Python is. With Python being beginner-friendly and Method 1 being the most natural way to write a loop, running into this problem is not a matter of if, but when. And any discussion of Python that includes terms like "type inference" is likely to go poorly with the crowd that needs it the most. I've also seen advice of the type "you have to do it like this because I say so and I'm right" which is technically correct but still unconvincing.

Until I figure that out, I hope at least this short article will be useful for intermediate programmers like me who stare at their blank screen and wonder "two minutes to sum a simple array? There has to be a better way!".

Further reading

If you're a seasoned programmer, Why Python is slow answers the points presented here with a deep dive into what's going on under the hood.

April 21 Update

A couple good points brought up by my friend Baco:

• The results between Methods 3, 4, and 5 are not really statistically significant. I've measured them against each other and the best I got was a marginal statistical difference between Methods 3 and 5, also known as "not worth it".
• Given that they are effectively the same, you should probably go for Method 4, which is the easiest one to read out of those three.
• If you really want to benchmark Python, you should try something more challenging than a simple sum. Matrix multiplication alone will give you different times depending on whether you use liblapack3 or libopenblas as a dependency. Feel free to give it a try!

If you're a software developer, you know the rules: new year, new programming language. For 2020 I chose Rust because it has a lot going for it:

• Memory safe and high performance
• Designed for low-level tasks
• Backed by Mozilla, of which I'm a fan
• Named "most loved programming language" for the fourth year in a row by the Stack Overflow Annual Survey

With all of these advantages in mind, I set to build something concrete: an implementation of the Aho-Corasick algorithm. This algorithm, at its most basic, builds a Trie and then converts it into an automaton, with the final result being efficient search of sub-strings (why? I hope I can write about why in the near future). It also seemed like the type of problem you'd like to tackle with Rust: implementing a Trie in C requires some liberal use of pointers, a task for which I had expected Rust to be the right tool (memory safety!). And since I need to run a lot of text through it, I need it to be as fast as possible.

So how did I fare? Two weeks into this project, Rust and I have... issues. More specifically, I'm having real trouble figuring out what is Rust good for.

Part I: Pointers and Strings are too complicated

Dealing with pointers is straight up painful, because allocating a piece of memory and linking it to something else gets very difficult very fast. I followed this book, titled "Learn Rust With Entirely Too Many Linked Lists", and the opening alone warns me that programming a linked list requires learning "the following pointer types: &, &mut, Box, Rc, Arc, *const, and *mut". A Reddit thread, on the other hand, suggests that a doubly-linked list is straightforward - all you need to do is declare your type as Option<Weak<RefCell<Node<T>>>>. Please note that neither Option, weak, nor RefCell are mentioned in the previous implementation...

So, pointers are out as killer feature. If optimizing memory usage is not its strong point, then maybe "regular" programming is? Could I do the rest of my text handling with Rust? Sadly, dealing with Strings is not great either. Sure, I get it, Unicode is weird. And I can understand why the difference between characters and graphemes is there. But if the Rust developers thought long and hard about this, why is "get me the first grapheme of this String" so difficult? And why isn't such a common operation part of the standard library?

For the record, this is a rhetorical question - the answer to "how do I iterate over graphemes" (found here) teaches us that...

• ... the developers don't want to commit to a specific method of doing this, because Unicode is complicated and they don't want to have to support it forever. If you want to do it, you have to pick an external library. But it won't be part of the standard library anytime soon. At the same time, ...
• ... they don't want to "play favorites" with any specific library over any other, meaning that no trace of a specific method is to be found in the official documentation.

The result, then, is puzzling: the experts who designed the system don't want to take care of it, the official doc won't tell you who is doing it right (or, more critical, who is doing it wrong and should be avoided), and you are essentially on your own.

Part II: the community

If we've learn anything from the String case, is that "just Google it" is a valid development strategy when dealing with Rust. This leads us inevitably to A sad day for Rust, an event that took place earlier this year and highlighted how bad the Reddit side of the community can be. To quote the previous article,

the Rust subreddit has ~87,000 subscribers (...) while Reddit is not official, and so not linked to by any official resources, it’s still a very large group of people, and so to suggest it’s "not the Rust community" in some way is both true and very not true.

So, why did I bring this up? Because the Reddit thread I mentioned above displays two hallmarks of the type of community I don't want to be a member of:

• the attitude of "it's very simple, all you need to create a new node is self.last.as_ref().unwrap().borrow().next.as_ref().unwrap().clone()"
• the other attitude, where the highest rated comment is the one that includes nice bits like "The only surprising thing about this blog post is that even though it's 2018 and there are many Rust resources available for beginners, people are still try to learn the language by implementing a high-performance pointer chasing trie data-structure". The fact that people may come to Rust because that's the type of projects a systems language is supposedly good for seems to escape them.

If you're a beginner like me, now you know: there is a good community out there. And it would be unfair for me to ignore that other forums, both official and not, are much more welcoming and understanding. But you need to double check.

Part III: minor annoyances

I really, really wish Rust would stop using new terms for concepts that already exist: abstract methods are "traits", static methods are "associated functions", "variables" are by default not-variable (and not to be confused with constants), and any non-trivial data type is actually a struct with implementation blocks.

And down to the very, very end of the scale, the trailing commas at the end of match expressions, the 4-spaces indentation, and the official endorsement of 1TBS instead of K&R (namely, 4-spaces instead of Tabs) are just plain ugly. Unlike Python, however, Rust does get extra points for allowing other, less-wrong styles.

Part IV: not all is hopeless

Unlike previous rants, I want to point out something very important: I want to like Rust. I'm sure it's very good at something, and I really, really want to find what it is. It would be very sad if the answer to "what is Rust good for?" ended up being "writing Rust compilers".

Now, the official documentation (namely, the book) closes with a tutorial on how to build a multi-threaded web server, which is probably the key point: if Rust claims that error handling and memory safety are its main priorities are true, then multi-threaded code could be the main use case. So there's hope that everything will get easier once I manage to get my strings inside my Trie, and iterating over Gb of text will be amazingly easy.

I'll keep you updated.

As I mentioned before, I have now replaced my blogging engine with Pelican. Now that I'm mostly done dealing with ensuring that everything behaves more-or-less as before, it's time to talk about Bash.

I don't really remember why I wanted to write my blog using Bash. In general, I think it was a combination of the following factors:

• I didn't want to install PHP. I have way too many memories of script-kiddies constantly probing my server, and that was never fun. Sure, I keep my server up-to-date, but why risking it? The less entry points for wannabe crackers, the better.
• I didn't really thought it would be possible. Sure, writing it at first was kind of fun, but once I started writing triple-nested-quotes (all of which needed to be escaped properly) things got weird. I probably should have called it a day at that point, but I was close enough to my goal to make it worth it to continue till the end.
• It was a good conversation starter - if you want to get a conversation rolling, bringing your terrible idea up is not the worst way to do it.

Of course, just because it was not the right tool for the job it doesn't mean that it was that bad. If anything, once I accepted that the heavy lifting should be made in a "proper" programming language (in this case, perl), using Bash to glue everything together worked surprisingly well.

With all of that in mind, I have now finally published the source code. I also plan on some light editing to make it friendlier, and an installation guide in case you really want to blog in a system where you have no permissions to install anything. The current template (just as my current blog) is based on Yahoo's Pure.css library library. In case you're not familiar with it, Pure.css is a set of CSS modules to make your project look good and responsive without a lot of effort, similarly to that other library whose name escapes me right now Bootstrap. I chose it specifically because I like to explore alternatives to the most popular projects, and Pure.css ended up being one of my favorites.

I was warned. I was told this wasn't a good idea. I mean, even the price should have pointed out that something was not what it seemed. But no, I had to go ahead. I had to be cooler than the other guys, and I was certainly going to prove everyone that you can't go wrong with a Nokia phone. So I got myself a Windows Phone 8.1 (again), and today I say to you, that was a stupid choice and I'm stupid for going with it.

I've chronicled in a previous blog^1 how much of a pain it was to try to develop for a Windows Phone (WP). That alone should have kept me away from buying a smartphone with a too-good-to-be-true price. But on I went, tricked by memories of me saying "this is not so bad after all", carefully ignoring that I only said that before I tried to write my own app. And here we are again.

This is, step by step, what my experience trying to develop a very simple app for my (Lumia 530) Windows Phone was like. Yours might vary, of course, but I'm not betting on it.

1. Install Visual Studio Community 2015 (free), following the Official WP develop guidelines
   Problem: all versions that could work in Windows 7 (the one I have) are deprecated for WP development one way or another: they don't have required tools, cannot be activated because the servers are gone, or just won't work. So get Windows 10 and that comes bundled with itwant it or not, and try again.

2. Update your Windows and Install Visual Studio Community 2015 (free), following the official WP develop guidelines
   Problem: the installer doesn't actually install the WP tools. Go back to the installer and modify your requirements.

3. Plug your phone to enable development
   Problem: you need to unlock your phone first.

4. Sign up to Microsoft's Dev Network to register as a developer to get your phone unlocked
   Problem: you need to pay €14, because reasons.

3.1 (Optional) Sign up on Dreamspark as a student to get a free developer account
Problem: the automatic verification process is broken. You need instead to send a copy of your ID, your student ID, and a transcript of your grades to Microsoft over unencrypted email. But don't worry, this data will be destroyed after they use it. "Anna" promised me so over email.

1. Start Visual Studio, create your first project, and add a couple text fields
   Problem: bugs! The auto-complete is buggy, running my app once would force me to restart every time, and adding a control on the GUI while the text cursor is in the wrong place can (and will) erase every other control. But hey, at least we are finally developing something.

2. Add a "select date" field to your project
   Problem: you need the DatePicker control to do this, but it's not included - I guess people don't select dates in phone apps. You have to follow several steps to get it working. However, as there's a library incompatibility somewhere, you'll get stuck anyway.

If you have been following all the steps you'll notice that, after several GiB of downloads and a lot of hours spent on internet forums, I have not yet managed to finish the first screen. I spent three days trying to get things running, which is the time I budgeted for the whole app, I'm out €14, and yet I haven't even managed to add a control that should have been there anyway. I also gave Microsoft quite some money, along with a lot of my personal information.

But the worst part is that I knew this is what development would be like, and yet I insisted on giving it another try. This is why I'm an idiot, and if you think your experience will be any better, it is my opinion that you are deluding yourself too.

Listen to my advice, dear reader: Windows Phone? Not even once.

Footnotes

^1 As of now I haven't restored the backup anywhere, but once I do you should see a link to that post here.