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Articles tagged with "python"

Write-only code

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.

Benchmarking Python loops

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.

Line graph of the table values

N=10 N=100 N=1000 N=10000 N=20000
Method 1 0.00001 0.00078 0.07922 8.12818 42.96835
Method 2 0.00001 0.00043 0.04230 4.34343 17.18522
Method 3 0.00000 0.00004 0.00347 0.33048 1.30787
Method 4 0.00000 0.00004 0.00329 0.32733 1.27049
Method 5 0.00000 0.00004 0.00329 0.32677 1.30128
Method 6 0.00003 0.00269 0.26630 26.61225 108.61357
Method 7 0.00001 0.00006 0.00121 0.06803 0.29462
Method 8 0.00001 0.00001 0.00031 0.03640 0.15836
Method 9 0.00000 0.00011 0.00273 0.22410 0.89991
Method 9b 0.00000 0.00006 0.00169 0.09978 0.40069

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!