Python’s standard datetime module is very powerful. However, it has a couple of annoying flaws.

Firstly, datetimes are considered a kind of date¹, which causes problems. Although datetime is a literal subclass of date so Mypy and isinstance believe a datetime “is” a date, you cannot substitute a datetime for a date in a program without provoking errors at runtime.

To put it more precisely, here are two programs which define a function with type annotations, that mypy finds no issues with. The first of which even takes care to type-check its arguments at run-time. But both raise TypeErrors at runtime:

Comparing datetime to date:

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from datetime import date, datetime

def is_after(before: date, after: date) -> bool | None:
    if not isinstance(before, date):
        raise TypeError(f"{before} isn't a date")
    if not isinstance(after, date):
        raise TypeError(f"{after} isn't a date")
    if before == after:
        return None
    if before > after:
        return False
    return True

is_after(date.today(), datetime.now())

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Traceback (most recent call last):
  File ".../date_datetime_compare.py", line 14, in <module>
    is_after(date.today(), datetime.now())
  File ".../date_datetime_compare.py", line 10, in is_after
    if before > after:
TypeError: can't compare datetime.datetime to datetime.date

Comparing “naive” and “aware” datetime:

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from datetime import datetime, timezone, timedelta

def compare(a: datetime, b: datetime) -> timedelta:
    return a - b

compare(datetime.now(), datetime.now(timezone.utc))

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Traceback (most recent call last):
  File ".../naive_aware_compare.py", line 6, in <module>
    compare(datetime.now(), datetime.now(timezone.utc))
  File ".../naive_aware_compare.py", line 4, in compare
    return a - b
TypeError: can't subtract offset-naive and offset-aware datetimes

In some sense, the whole point of using Mypy - or, indeed, of runtime isinstance checks - is to avoid TypeError getting raised. You specify all the types, the type-checker yells at you, you fix it, and then you can know your code is not going to blow up in unexpected ways.

Of course, it’s still possible to avoid these TypeErrors with runtime checks, but it’s tedious and annoying to need to put a check for .tzinfo is not None or not isinstance(..., datetime) before every use of - or >.

The problem here is that datetime is trying to represent too many things with too few types. datetime should not be inheriting from date, because it isn’t a date, which is why > raises an exception when you compare the two.

Naive datetimes represent an abstract representation of a hypothetical civil time which are not necessarily tethered to specific moments in physical time. You can’t know exactly what time “today at 2:30 AM” is, unless you know where on earth you are and what the rules are for daylight savings time in that place. However, you can still talk about “2:30 AM” without reference to a time zone, and you can even say that “3:30 AM” is “60 minutes after” that time, even if, given potential changes to wall clock time, that may not be strictly true in one specific place during a DST transition. Indeed, one of those times may refer to multiple points in civil time at a particular location, when attached to different sides of a DST boundary.

By contrast, Aware datetimes represent actual moments in time, as they combine civil time with a timezone that has a defined UTC offset to interpret them in.

These are very similar types of objects, but they are not in fact the same, given that all of their operators have slightly different (albeit closely related) semantics.

Using datetype

I created a small library, datetype, which is (almost) entirely type-time behavior. At runtime, despite appearances, there are no instances of new types, not even wrappers. Concretely, everything is a date, time, or datetime from the standard library. However, when type-checking with Mypy, you will now get errors reported from the above scenarios if you use the types from datetype.

Consider this example, quite similar to our first problematic example:

Comparing AwareDateTime or NaiveDateTime to date:

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from datetype import Date, NaiveDateTime

def is_after(before: Date, after: Date) -> bool | None:
    if before == after:
        return None
    if before > after:
        return False
    return True

is_after(Date.today(), NaiveDateTime.now())

Now, instead of type-checking cleanly, it produces this error, letting you know that this call to is_after will give you a TypeError.

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date_datetime_datetype.py:10: error: Argument 2 to "is_after" has incompatible type "NaiveDateTime"; expected "Date"
Found 1 error in 1 file (checked 1 source file)

Similarly, attempting to compare naive and aware objects results in errors now. We can even use the included AnyDateTime type variable to include a bound similar to AnyStr from the standard library to make functions that can take either aware or naive datetimes, as long as you don’t mix them up:

Comparing AwareDateTime to NaiveDateTime:

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from datetime import datetime, timezone, timedelta
from datetype import AwareDateTime, NaiveDateTime, AnyDateTime


def compare_same(a: AnyDateTime, b: AnyDateTime) -> timedelta:
    return a - b


def compare_either(
    a: AwareDateTime | NaiveDateTime,
    b: AwareDateTime | NaiveDateTime,
) -> timedelta:
    return a - b


compare_same(NaiveDateTime.now(), AwareDateTime.now(timezone.utc))

compare_same(AwareDateTime.now(timezone.utc), AwareDateTime.now(timezone.utc))
compare_same(NaiveDateTime.now(), NaiveDateTime.now())

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naive_aware_datetype.py:13: error: No overload variant of "__sub__" of "_GenericDateTime" matches argument type "NaiveDateTime"
...
naive_aware_datetype.py:13: error: No overload variant of "__sub__" of "_GenericDateTime" matches argument type "AwareDateTime"
...
naive_aware_datetype.py:16: error: Value of type variable "AnyDateTime" of "compare_same" cannot be "_GenericDateTime[Optional[tzinfo]]"
Found 3 errors in 1 file (checked 1 source file)

Telling the Difference

Although the types in datetype are Protocols, there’s a bit of included magic so that you can use them as type guards with isinstance like regular types. For example:

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from datetype import NaiveDateTime, AwareDateTime
from datetime import datetime, timezone

nnow = NaiveDateTime.now()
anow = AwareDateTime.now(timezone.utc)


def check(d: AwareDateTime | NaiveDateTime) -> None:
    if isinstance(d, NaiveDateTime):
        print("Naive!", d - nnow)
    elif isinstance(d, AwareDateTime):
        print("Aware!", d - anow)


check(NaiveDateTime.now())
check(AwareDateTime.now(timezone.utc))

Try it out, carefully

This library is very much alpha-quality; in the process of writing this blog post, I made a half a dozen backwards-incompatible changes, and there are still probably a few more left as I get feedback. But if this is a problem you’ve had within your own codebases - ensuring that dates and datetimes don’t get mixed up, or requiring that all datetimes crossing some API boundary are definitely aware and not naive, give it a try with pip install datetype and let me know if it catches any bugs!

In this post I’d like to convince you that you should be running Mypyc over your code¹ — especially if your code is a library you upload to PyPI — for both your own benefit and that of the Python ecosystem at large.

But first, let me give you some background.

Python is Slow, And That’s Fine, Because It’s Fast Enough

A common narrative about Python’s value proposition, from the very earliest days of the language², often recited in response to a teammate saying “shouldn’t we just write this in $HIGHER_PERFORMANCE_LANGUAGE instead?” goes something like this:

Sure, Python is slow.

But that’s okay, because it saves you so much time over implementing your code in $HIGHER_PERFORMANCE_LANGUAGE that you’ll have so much more time for optimizing those critical hot-spots where performance is really critical.

And if the language’s primitives are too slow to micro-optimize those hot-spots enough, that’s okay too, because you can always re-write just those small portions of the program as a C extension module.

Python’s got you covered!

There is some truth to this narrative, and I’ve quoted from it myself on many occasions. When I did so, I was not quoting it as some facile, abstract hypothetical, either. I had a few projects, particularly very early in my Python career, where I replaced performance-critical C++ code with a one tenth the number of lines of Python, and improved performance by orders of magnitude in the process³.

When you have algorithmically interesting, performance-sensitive code that can benefit from a high-level expressive language, and the resources to invest in making it fast, this process can be counterintuitively more efficient than other, “faster” tools. If you’re working on massively multiplayer online games⁴ or something equally technically challenging, Python can be a surprisingly good idea.

But… Is It Fine, Though?

This little nugget of folk wisdom does sound a bit defensive, doesn’t it? If Python were just fast, you could just use it, you wouldn’t need this litany of rationalizations. Surely if we believed that performance is important in our own Python code, we wouldn’t try to wave away the performance of Python itself.

Most projects are not massively multiplayer online games. On many straightforward business automation projects, this sort of staged approach to performance is impractical.

Not all performance problems are hot spots. Some programs have to be fast all the way through. This is true of some complex problems, like compilers and type checkers, but is also often the case in many kinds of batch processing; there are just a lot of numbers, and you have to add them all up.

More saliently for the vast majority of average software projects, optimization just isn’t in the budget. You do your best on your first try and hope that none of those hot spots get too hot, because as long as the system works within a painfully generous time budget, the business doesn’t care if it’s slow.

The progression from “idiomatic Python” to “optimized Python” to “C” is a one-way process that gradually loses the advantages that brought us to Python in the first place.

The difficult-to-reverse nature of each step means that once you have prototyped out a reasonably optimized data structure or algorithm, you need to quasi-permanently commit to it in order to squeeze out more straight-line performance of the implementation.

Plus, the process of optimizing Python often destroys its readability, for a few reasons:

1. Optimized Python relies on knowledge of unusual tricks. Things like “use the array module instead of lists”, and “use % instead of .format”.
2. Optimized Python requires you to avoid the things that make Python code nicely organized:
   1. method lookups are slow so you should use functions.
   2. object attribute accesses are slow so you should use tuples with hard-coded numeric offsets.
   3. function calls are slow so you should copy/paste and inline your logic
3. Optimized Python requires very specific knowledge of where it’s going to be running, so you lose the flexibility of how to run it: making your code fast on CPython might make it much slower on PyPy, for example. Native extension modules can make your code faster, but might also make it fail to run inside a browser, or add a ton of work to get it set up on a new operating system.

Maintaining good performance is part of your software’s development lifecycle, not just a thing you do once and stop. So by moving into this increasingly arcane dialect of “fast” python, and then into another programming language entirely with a C rewrite, you end up having to maintain C code anyway. Not to mention the fact that rewriting large amounts of code in C is both ludicrously difficult (particularly if your team primarily knows Python) and also catastrophically dangerous. In recent years, safer tools such as PyO3 have become available, but they still involve switching programming languages and rewriting all your code as soon as you care about speed⁵.

So, for Python to be a truly general-purpose language, we need some way to just write Python, and have it be fast.

It would benefit every user of Python for there to be an easy, widely-used way to make idiomatic, simple Python that just does stuff like adding numbers, calling methods, and formatting strings in a straight line go really fast — exactly the sorts of things that are the slowest in Python, but are also the most common, particularly before you’ve had an opportunity to cleverly optimize.

We’ve Been Able To At Least Make Do

There are also a number of tools that have long been in use for addressing this problem: PyPy, Pyrex, Cython, Numba, and Numpy to name a few. Their maintainers all deserve tremendous amounts of credit, and I want to be very clear that this post is not intended to be critical of anyone’s work here. These tools have drawbacks, but many of those drawbacks make them much better suited to specialized uses beyond the more general 80% case I’m talking about in this post, for which Mypyc would not be suitable.

Each one of these tools impose limitations on either the way that you write code or where you can deploy it.

Cython and Numba aren’t really “Python” any more, because they require special-purpose performance-oriented annotations. Cython has long supported pure-Python type annotations, but you won’t get any benefit from telling it that your variable is an int, only a cython.int. It can’t optimize a @dataclass, only a @cython.cclass. And so on.

PyPy gets the closest — it’s definitely regular Python — but its strategy has important limitations. Primarily, despite the phenomenal and heroic effort that went into cpyext, it seems like there’s always just one PyPy-incompatible library in every large, existing project’s dependency list which makes it impossible to just drop in PyPy without doing a bunch of arcane debugging first.

PyPy might make your program magically much faster, but if it doesn’t work, you have to read the tea leaves on the JIT’s behavior in a profiler which practically requires an online component that doesn’t even work any more. So mostly you just simplify your code to use more straightforward data structures and remove CPython-specific tricks that might trip up the JIT, and hope for the best.

PyPy also introduces platform limitations. It’s always — understandably, since they have to catch up after the fact — lagging a bit behind the most recently released version of CPython, so there’s always some nifty language feature that you have to refrain from using for at least one more release cycle.

It also has architectural limitations. For example, it performs quite poorly on an M1 Mac since it still runs under x86_64 emulation on that platform. And due to iOS forbidding 3rd-party JITs, it won’t ever be able to provide better performance in one of the more constrained environments that needs it more that other places. So you might need to rely on CPython on those platforms anyway… and you just removed all your CPython-specific hacks to try to please the JIT on the other platforms you support.

So while I would encourage everyone to at least try their code on PyPy — if you’re running a web-based backend, it might save you half your hardware budget⁶ — it’s not going to solve “python is slow” in the general case.

It’ll Eventually Be All Right

This all sounds pretty negative, so I would be remiss if I did not also point out that the core team is well aware that Python’s default performance needs to be better, and Guido van Rossum literally came out of retirement for one last job to fix it, and we’ve already seen a bunch of benefits from that effort.

But there are some fundamental limitations on the long-term strategy for these optimizations; one of the big upcoming improvements is a JIT, which suffers from some (but not all) of the same limitations as PyPy, and the late-bound, freewheeling nature of Python inherently comes with some performance tradeoffs.

So it would still behoove us to have a strategy for production-ized code that gives good, portable, ahead-of-time performance.

But What About Right Now?

Mypyc takes the annotations meant for Mypy and generates C with them, potentially turning your code into a much more efficient extension module. As part of Mypy itself, it does this with your existing Python type-hints, the kind you’d already use Mypy with to check for correctness, so it doesn’t entail much in the way of additional work.

I’d been curious about this since it was initially released, but I still haven’t had a hard real-world performance problem to really put it through its paces.

So when I learned about the High Throughput Fizzbuzz Challenge via its impressive assembler implementation that achieves 56GiB/s, and I saw even heavily-optimized Python implementations sitting well below the performance of a totally naïve C reference implementation, I thought this would be an interesting miniature experiment to use to at least approximate practical usage.

In Which I Design A Completely Unfair Fight Which I Will Then Handily Win

The dizzying heights of cycle-counting hand-tuned assembler implementations of this benchmark are squarely out of our reach, but I wanted to see if I could beat the performance of this very naïve C implementation with Python that was optimized, but at least, somewhat idiomatic and readable.

I am about to compare a totally naïve C implementation with a fairly optimized hand-tuned Python one, which might seem like an unfair fight. But what I’m trying to approximate here is a micro-instance of the real-world development-team choice that looks like this:

Since Python is more productive, but slower, the effort to deliver each of the following is similar:

1. a basic, straightforward implementation of our solution in C
2. a moderately optimized Python implementation of our solution

and we need to choose between them.

This is why I’ll just be showing naïve C and not unrolling any loops; I’ll use -O3 because any team moderately concerned with performance would at least turn on the most basic options, but nothing further.

Furthermore, our hypothetical team also has this constraint, which really every reasonable team should:

We can trade off some readability for efficiency, but it’s important that our team be able to maintain this code going forward.

This is why I’m doing a bit of optimizing in Python but not going all out by calling mmap or pulling in numpy or attempting to use something super esoteric like a SIMD library to emulate what the assembler implementations do. The goal is that this is normal Python code with a reasonable level of systems-level understanding (i.e. accounting for the fact that pipes have buffers in the kernel and approximately matching their size maximizes throughput).

If you want to see FizzBuzz pushed to its limit, you can go check out the challenge itself. Although I think I do coincidentally beat the performance of the Python versions they currently have on there, that’s not what I’m setting out to do.

So with that elaborate framing of this slightly odd experiment out of the way, here’s our naïve C version:

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#include <stdio.h>

int main() {
    for (int i = 1; i < 1000000000; i++) {
        if ((i % 3 == 0) && (i % 5 == 0)) {
            printf("FizzBuzz\n");
        } else if (i % 3 == 0) {
            printf("Fizz\n");
        } else if (i % 5 == 0) {
            printf("Buzz\n");
        } else {
            printf("%d\n", i);
        }
    }
}

First, let’s do a quick head-to-head comparison with a naïve Python implementation of the algorithm:

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def fizzbuzz() -> None:
    for counter in range(1, 1000000000):
        fizz = counter % 3 == 0
        buzz = counter % 5 == 0
        if fizz:
            print("Fizz", end="")
        if buzz:
            print("Buzz", end="")
        if not (fizz or buzz):
            print(counter, end="")
        print()

if __name__ == "__main__":
    fizzbuzz()

Running both of these on my M1 Max MacBook, the naïve C implementation yields 127 MiB/s of Fizzbuzz output. But, as I said, although we’re not going to have time for testing a more complex optimized C version, we would want to at least build it with the performance benefits we get for free with the -O3 compiler option. It turns out that yields us a 27 MiB/s speedup. So 154 MiB/s is the number we have to beat.⁷

The naïve Python version achieves a dismal 24.3 MiB/s, due to a few issues. First of all, although it’s idiomatic, print() is doing a lot of unnecessary work here. Among other things, we are encoding Unicode, which the C version isn’t. Still, our equivalent of adding the -O3 option for C is running mypyc without changing anything, and that yields us a 6.8MiB/s speedup immediately. We still aren’t achieving comparable performance, but a roughly 25% performance improvement for no work at all is a promising start!⁸

In keeping with the “some optimizations, but not so much that it’s illegible” constraint described above, the specific optimizations I’ve chosen to pursue here are:

1. switch to using bytes objects and sys.stdout.buffer to avoid encoding overhead
2. take advantage of the repeating nature of the pattern in FizzBuzz output and pre-generate a template rather than computing each line independently
3. fill out the buffer with the relevant integers from a sequence as we go
4. tune the repetition of that template to a size that roughly fills a pipe buffer on my platform of choice

Hopefully, with that explanation, this isn’t too bad:

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from sys import stdout
from typing import Tuple, Iterable


def precompute_template() -> Iterable[bytes]:
    for counter in range(1, 16):
        fizz = counter % 3 == 0
        buzz = counter % 5 == 0
        if fizz:
            yield b"Fizz"
        if buzz:
            yield b"Buzz"
        if not (fizz or buzz):
            yield b"%d"
        yield b"\n"


chunk_copies = 4
precomputed_template_chunks = list(precompute_template())
format_string = b"".join(precomputed_template_chunks)
number_indexes = [
    number_index
    for number_index, line_content in enumerate(format_string.split(b"\n"))
    if line_content == b"%d"
]
format_string *= chunk_copies


def fizzbuzz() -> None:
    num: int = 1
    output = stdout.buffer.write
    for num in range(1, 1000000001, 15 * chunk_copies):
        t: Tuple[int, ...] = tuple(
            (
                x + number_index
                for x in range(num, num + (15 * chunk_copies), 15)
                for number_index in number_indexes
            )
        )
        output(format_string % t)


if __name__ == "__main__":
    fizzbuzz()

Running this optimized version actually gets us within the ballpark of the naïve C version, even beating it by a hair; my measurement was 159 MiB/s, a small improvement even over -O3. So, per the “litany against C” from the beginning of this post, algorithmic optimization of Python really does help a lot; it’s not just a rationalization. This is a much bigger boost than our original no-effort Mypyc run, giving us more like an 85% speedup; definitely bigger than 25%.

But clearly we’re still being slowed down by Python’s function call overhead, object allocations for small integers, and so on, so Mypyc should help us out here: and indeed it does. On my machine, it nets a whopping 233 MiB/s. Now that we are accounting for performance and optimizing a bit, Mypyc’s relative advantage has doubled to a 50% improvement in performance on both the optimized-but-interpreted Python and naïve C versions.

It’s worth noting that the technique I used to produce the extension modules to test was literally pip install mypy; mypyc .../module.py, then python -c “import module”. I did already have a C compiler installed, but other than that, there was no setup.

I just wrote Python, and it just worked.

The Call To Adventure

Here’s what I want you to take away from all this:

1. Python can be fast.
2. More importantly, your Python can be fast.
3. For a fairly small investment of effort, your Python code can be made meaningfully faster.

Unfortunately, due to the limitations and caveats of existing powerful performance tools like Cython and PyPy, over the last few years in the Python community a passive consensus has emerged. For most projects, in most cases, it’s just not worth it to bother to focus on performance. Everyone just uses the standard interpreter, and only fixes the worst performance regressions.

We should, of course, be glad that the standard interpreter is reliably getting faster all the time now, but we shouldn’t be basing our individual libraries’ and applications’ performance strategies on that alone.

The projects that care the most about performance have made the effort to use some of these tools, and they have often invested huge amounts of effort to good effect, but often they care about performance too much. They make the problem look even harder for everyone else, by essentially stipulating that step 1 is to do something extreme like give up and use Fortran for all the interesting stuff.

My goal with this post is to challenge that status quo, spark interest in revisiting the package ecosystem’s baseline performance expectations, and to get more projects — particularly libraries on PyPI — to pick up Mypyc and start giving Python a deserved reputation for being surprisingly fast.

The Last Piece of the Puzzle

One immediate objection you might be thinking of is the fact that, under the hood, Mypyc is emitting some C code and building it, and so this might create a problem for deployment: if you’ve got a Linux machine but 30% of your users are on Windows, moving from pure-Python to this hybrid workflow might create installation difficulties for them, or at least they won’t see the benefits.

Luckily a separate tool should make that a non-issue: cibuildwheel. “CI Build Wheel”, as its name suggests, lets you build your wheels in your continuous integration system, and upload those builds automatically upon tagging a release.

Often, the bulk of the work in using it is dealing with the additional complexities involved in setting up your build environment in CI to make sure you’re appropriately bundling in any native libraries you depend upon, and linking to them in the correct way. Mypyc’s limitation relative to Cython is a huge advantage here: it doesn’t let you link to other native libraries, so you can always skip the worst step here.

So, for maintainers, you don’t need to maintain a pile of janky VMs on your personal development machine in order to serve your users. For users, nobody needs to deal with the nightmare of setting up the right C compiler on their windows machine, because the wheels are prebuilt. Even users without a compiler who want to contribute new code or debug it can run it with the interpreter locally, and let the cloud handle the complicated compilation steps later. Once again, the fact that you can’t require additional, external C libraries here is a big advantage; it prevents you from making the user’s experience inadvertently worse.

cibuildwheel supports all major operating systems and architectures, and supported versions of Python, and even lets you build wheels for PyPy while you’re at it.⁹

Putting It All Together

Using Mypyc and cibuildwheel, we, as PyPI package maintainers, can potentially produce an ecosystem of much faster out-of-the-box experiences via prebuilt extension modules, written entirely in Python, which would make the average big Python application with plenty of dependencies feel snappier than expected. This doesn’t have to come with the pain that we have unfortunately come to expect from C extensions, either as maintainers or users.

Another nice thing is that this is not an all-or-nothing proposition. If you try PyPy and it blows up in some obscure way on your code, you have to give up on it unless you want to fully investigate what’s happening. But if you trip over a bug in Mypyc, you can report the bug, drop the module where you’re having the problem from the list of things you’re trying to compile, and move on. You don’t even have to start out by trying to jam your whole project through it; just pick a few key modules to get started, and gradually expand that list over time, as it makes sense for your project.

In a future post, I’ll try to put all of this together myself, and hopefully it’s not going to be embarrassingly difficult and make me eat my words.

Despite not having done that yet, I wanted to put this suggestion out now, to get other folks thinking about getting started with it. For older projects¹⁰, retrofitting all the existing infrastructure to put Mypyc in place might be a bit of a challenge. But for new projects starting today, putting this in place when there’s very little code might be as simple as adding a couple of lines to pyproject.toml and copy-pasting some YAML into a Github workflow.

If you’re thinking about making some new open source Python, give Mypyc a try, and see if you can delight some users with lightning speed right out of the box. If you do, let me know how it turns out.

Acknowledgments

Thanks to Donald Stufft, Moshe Zadka, Nelson Elhage, Itamar Turner-Trauring, and David Reid for extensive feedback on this post. As always, any errors or inaccuracies remain my own.

This post recommends calling pygame.display.flip from a thread, which I tested extensively on mac, windows, and linux before posting, but after some feedback from readers, I realize that this strategy is not in fact cross-platform; specifically, the nvidia drivers on linux appear to either crash or display a black window if you try to do this. The SDL FAQ does say that you can’t call “video functions” from multiple threads, and flip does do that under the hood. I do plan to update this post again, either with a method to make it safe, or a method to use slightly more complex timing heuristics to accomplish the same thing. In the meanwhile, please be aware that this may cause portability problems for your code.

I’ve written about this before, but in that context I was writing mainly about frame-rate independence, and only gave a brief mention of vertical sync; the title also mentioned Twisted, and upon re-reading it I realized that many folks who might get a lot of use out of its technique would not have bothered to read it, just because I made it sound like an aside in the context of an animation technique in a game that already wanted to use Twisted for some reason, rather than a comprehensive best practice. Now that Pygame 2.0 is out, though, and the vsync=1 flag is more reliably available to everyone, I thought it would be worth revisiting.

Per the many tutorials out there, including the official one, most Pygame mainloops look like this:

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pygame.display.set_mode((320, 240))

while 1:
    for event in pygame.event.get():
        handleEvent(event)
    for drawable in myDrawables:
        drawable.draw()
    pygame.display.flip()

Obviously that works okay, or folks wouldn’t do it, but it can give an impression of a certain lack of polish for most beginner Pygame games.

The thing that’s always bothered me personally about this idiom is: where does the networking go? After spending many years trying to popularize event loops in Python, I’m sad to see people implementing loops over and over again that have no way to get networking, or threads, or timers scheduled in a standard way so that libraries could be written without the application needing to manually call them every frame.

But, who cares how I feel about it? Lots of games don’t have networking¹. There are more general problems with it. Specifically, it is likely to:

1. waste power, and
2. look bad.

Wasting Power

Why should anyone care about power when they’re making a video game? Aren’t games supposed to just gobble up CPUs and GPUs for breakfast, burning up as much power as they need for the most gamer experience possible?

Chances are, if you’re making a game that you expect anyone that you don’t personally know to play, they’re going to be playing it on a laptop². Pygame might have a reputation for being “slow”, but for a simple 2D game with only a few sprites, Python can easily render several thousand frames per second. Even the fastest display in the world can only refresh at 360Hz³. That’s less than one thousand frames per second. The average laptop display is going to be more like 60Hz, or — if you’re lucky — maybe 120. By rendering thousands of frames that the user never even sees, you warm up their CPU uncomfortably⁴, and you waste 10x (or more) of their battery doing useless work.

At some point your game might have enough stuff going on that it will run the CPU at full tilt, and if it does, that’s probably fine; at least then you’ll be using up that heat and battery life in order to make their computer do something useful. But even if it is, it’s probably not doing that all of the time, and battery is definitely a use-over-time sort of problem.

Looking Bad

If you’re rendering directly to the screen without regard for vsync, your players are going to experience Screen Tearing, where the screen is in the middle of updating while you’re in the middle of drawing to it. This looks especially bad if your game is panning over a background, which is a very likely scenario for the usual genre of 2D Pygame game.

How to fix it?

Pygame lets you turn on VSync, and in Pygame 2, you can do this simply by passing the pygame.SCALED flag and the vsync=1 argument to set_mode().

Now your game will have silky smooth animations and scrolling⁵! Solved!

But... if the fix is so simple, why doesn’t everybody — including, notably, the official documentation — recommend doing this?

The solution creates another problem: pygame.display.flip may now block until the next display refresh, which may be many milliseconds.

Even worse: note the word “may”. Unfortunately, behavior of vsync is quite inconsistent between platforms and drivers, so for a properly cross-platform game it may be necessary to allow the user to select a frame rate and wait on an asyncio.sleep than running flip in a thread. Using the techniques from the answers to this stack overflow answer you can establish a reasonable heuristic for the refresh rate of the relevant display, but if adding those libraries and writing that code is too complex, “60” is probably a good enough value to start with, even if the user’s monitor can go a little faster. This might save a little power even in the case where you can rely on flip to tell you when the monitor is actually ready again; if your game can only reliably render 60FPS anyway because there’s too much Python game logic going on to consistently go faster, it’s better to achieve a consistent but lower framerate than to be faster but inconsistent.

The potential for blocking needs to be dealt with though, and it has several knock-on effects.

For one thing, it makes my “where do you put the networking” problem even worse: most networking frameworks expect to be able to send more than one packet every 16 milliseconds.

More pressingly for most Pygame users, however, it creates a minor performance headache. You now spend a bunch of time blocked in the now-blocking flip call, wasting precious milliseconds that you could be using to do stuff unrelated to drawing, like handling user input, updating animations, running AI, and so on.

The problem is that your Pygame mainloop has 3 jobs:

1. drawing
2. game logic (AI and so on)
3. input handling

What you want to do to ensure the smoothest possible frame rate is to draw everything as fast as you possibly can at the beginning of the frame and then call flip immediately to be sure that the graphics have been delivered to the screen and they don’t have to wait until the next screen-refresh. However, this is at odds with the need to get as much done as possible before you call flip and possibly block for 1/60th of a second.

So either you put off calling flip, potentially risking a dropped frame if your AI is a little slow, or you call flip too eagerly and waste a bunch of time waiting around for the display to refresh. This is especially true of things like animations, which you can’t update before drawing, because you have to draw this frame before you worry about the next one, but waiting until after flip wastes valuable time; by the time you are starting your next frame draw, you possibly have other code which now needs to run, and you’re racing to get it done before that next flip call.

Now, if your Python game logic is actually saturating your CPU — which is not hard to do — you’ll drop frames no matter what. But there are a lot of marginal cases where you’ve mostly got enough CPU to do what you need to without dropping frames, and it can be a lot of overhead to constantly check the clock to see if you have enough frame budget left to do one more work item before the frame deadline - or, for that matter, to maintain a workable heuristic for exactly when that frame deadline will be.

The technique to avoid these problems is deceptively simple, and in fact it was covered with the deferToThread trick presented in my earlier post. But again, we’re not here to talk about Twisted. So let’s do this the no-additional-dependencies, stdlib-only way, with asyncio:

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import asyncio
import time
from math import inf

from pygame.display import set_mode, flip
from pygame.constants import SCALED
from pygame.event import get

event_handler = ...
drawables = [...]

async def pygame_loop(framerate_limit=inf):
    loop = asyncio.get_event_loop()
    screen_surface = set_mode(size=(480, 255), flags=SCALED, vsync=1)
    next_frame_target = 0.0
    limit_frame_duration = (1.0 / framerate_limit)

    while True:

        if limit_frame_duration:
            # framerate limiter
            this_frame = time.time()
            delay = next_frame_target - this_frame
            if delay > 0:
                await asyncio.sleep(delay)
            next_frame_target = this_frame + limit_frame_duration

        for drawable in drawables:
            drawable.draw(screen_surface)
        events_to_handle = list(get())
        events_handled = loop.create_task(handle_events(events_to_handle))
        await loop.run_in_executor(None, flip)
        # don’t want to accidentally start drawing again until events are done
        await events_handled

async def handle_events(events_to_handle):
    # note that this must be an async def even if it doesn’t await
    for event in events_to_handle:
        event_handler.handle_event(event)

asyncio.run(pygame_loop(120))

Go Forth and Loop Better

At some point I will probably release my own wrapper library⁶ which does something similar to this, but I really wanted to present this as a technique rather than as some packaged-up code to use, since do-it-yourself mainloops, and keeping dependencies to a minimum, are such staples of Pygame community culture.

As you can see, this technique is only a few lines longer than the standard recipe for a Pygame main loop, but you now have access to a ton of additional functionality:

• You can manage your framerate independence in both animations and game logic by just setting some timers and letting the frames update at the appropriate times; stop worrying about doing math on the clock by yourself!
• Do you want to add networked multiplayer? No problem! Networking all happens inside the event loop, make whatever network requests you want, and never worry about blocking the game’s drawing on a network request!
• Now your players’ laptops run cool while playing, and the graphics don’t have ugly tearing artifacts any more!

I really hope that this sees broader adoption so that the description “indie game made in Python” will no longer imply “runs hot and tears a lot when the screen is panning”. I’m also definitely curious to hear from readers, so please let me know if you end up using this technique to good effect!⁷

The essence of software engineering is solving problems.

The first impression of this insight will almost certainly be that it seems like a good thing. If you have a problem, then solving it is great!

But software engineers are more likely to have mental health problems¹ than those who perform mechanical labor, and I think our problem-oriented world-view has something to do with that.

So, how could solving problems be a problem?

As an example, let’s consider the idea of a bug tracker.

For many years, in the field of software, any system used to track work has been commonly referred to as a “bug tracker”. In recent years, the labels have become more euphemistic and general, and we might now call them “issue trackers”. We have Sapir-Whorfed² our way into the default assumption that any work that might need performing is a degenerate case of a problem.

We can contrast this with other fields. Any industry will need to track work that must be done. For example, in doing some light research for this post, I discovered that the relevant term of art in construction³ is typically “Project Management” or “Task Management” software. “Projects” and “Tasks” are no less hard work, but the terms do have a different valence than “Bugs” and “Issues”.

I don’t think we can start to fix this ... problem ... by attempting to change the terminology. Firstly, the domain inherently lends itself to this sort of language, which is why it emerged in the first place.

Secondly, Atlassian has desperately been trying to get everybody to call their bug tracker a “software development tool” where you write “stories” for years, and nobody does. It’s an issue tracker where you file bugs, and that’s what everyone calls it and describes what they do with it. Even they have to protest, perhaps a bit too much, that it’s “way more than a bug and issue tracker”⁴.

This pervasive orientation towards “problems” as the atom of work does extend to any knowledge work, and thereby to any “productivity system”. Any to-do list is, at its core, a list of problems. You wouldn’t put an item on the list if you were happy with the way the world was. Therefore every unfinished item in any to-do list is a little pebble of worry.

As of this writing, I have almost 1000 unfinished tasks on my personal to-do list.

This is to say nothing of any tasks I have to perform at work, not to mention the implicit א‎₀ of additional unfinished tasks once one considers open source issue trackers for projects I work on.

It’s not really reasonable to opt out of this habit of problematizing everything. This monument to human folly that I’ve meticulously constructed out of the records of aspirations which exceed my capacity is, in fact, also an excellent prioritization tool. If you’re a good engineer, or even just good at making to-do lists, you’ll inevitably make huge lists of problems. On some level, this is what it means to set an intention to make the world — or at least your world — better.

On a different level though, this is how you set out to systematically give yourself anxiety, depression, or both. It’s clear from a wealth of neurological research that repeated experiences and thoughts change neural structures⁵. Thinking the same thought over and over literally re-wires your brain. Thinking the thought “here is another problem” over and over again forever is bound to cause some problems of its own.

The structure of to-do apps, bug trackers and the like is such that when an item is completed — when a problem is solved — it is subsequently removed from both physical view and our mind’s eye. What would be the point of simply lingering on a completed task? All the useful work is, after all, problems that haven’t been solved yet. Therefore the vast majority of our time is spent contemplating nothing but problems, prompting the continuous potentiation⁶ of neural pathways which lead to despair.

I don’t want to pretend that I have a cure for this self-inflicted ailment. I do, however, have a humble suggestion for one way to push back just a little bit against the relentless, unending tide of problems slowly eroding the shores of our souls: a positivity journal.

By “journal”, I do mean a private journal. Public expressions of positivity⁷ can help; indeed, some social and cultural support for expressing positivity is an important tool for maintaining a positive mind-set. However, it may not be the best starting point.

Unfortunately, any public expression becomes a discourse, and any discourse inevitably becomes a dialectic. Any expression of a view in public is seen by some as an invitation to express its opposite⁸. Therefore one either becomes invested in defending the boundaries of a positive community space — a psychically exhausting task in its own right — or one must constantly entertain the possibility that things are, in fact, bad, when one is trying to condition one’s brain to maintain the ability to recognize when things are actually good.

Thus my suggestion to write something for yourself, and only for yourself.

Personally, I use a template that I fill out every day, with four sections:

• “Summary”. Summarize the day in one sentence that encapsulates its positive vibes. Honestly I put this in there because the Notes app (which is what I’m using to maintain this) shows a little summary of the contents of the note, and I was getting annoyed by just seeing “Proud:” as the sole content of that summary. But once I did so, I found that it helps to try to synthesize a positive narrative, as your brain may be constantly trying to assemble a negative one. It can help to write this last, even if it’s up at the top of your note, once you’ve already filled out some of the following sections.

• “I’m proud of:”. First, focus on what you personally have achieved through your skill and hard work. This can be very difficult, if you are someone who has a habit of putting yourself down. Force yourself to acknowledge that you did something useful, even if you didn’t finish anything, you almost certainly made progress and that progress deserves celebration.

• “I’m grateful to:”. Who are you grateful to? Why? What did they do for you? Once you’ve made the habit of allowing yourself to acknowledge your own accomplishments, it’s easy to see those; pay attention to the ways in which others support and help you. Thank them by name.

• “I’m lucky because:”. Particularly in post-2020 hell-world it’s easy to feel like every random happenstance is an aggravating tragedy. But good things happen randomly all the time, and it’s easy to fail to notice them. Take a moment to notice things that went well for no good reason, because you’re definitely going to feel attacked by the universe when bad things happen for no good reason; and they will.

Although such a journal is private, it’s helpful to actually write out the answers, to focus on them, to force yourself to get really specific.

I hope this tool is useful to someone out there. It’s not going to solve any problems, but perhaps it will make the world seem just a little brighter.

As I mentioned previously, I’ve recently been medicated for ADHD.

Everyone’s experience with medication, even the same medication, is different, but my particular experience — while hugely positive — has involved not so much a decrease in symptoms, but rather a shifting of my symptom profile. Some of my executive functions (particularly task initiation) have significantly improved, but other symptoms, such as time blindness have gotten significantly worse. This means, for example, I can now easily decide to perform a task, and actually maintain focus on that task for hours¹, but it’s harder to notice that it’s time to stop, and still somewhat difficult to tear myself away from it.

I’ve tried pomodoro timers before and I’ve had mixed success with them. While I tend to get more done if I set a pomodoro, it’s hard to remember to set the timers in the first place, and it’s hard to do the requisite time-keeping to remember how many pomodoros I’ve already set, how many more I’ll have the opportunity to set, etc. Physical timers have no associated automation and data recording, and apps can be so unobtrusive that I can easily forget about them entirely. I’ve long had an aspiration to eventually write my own custom-tailored app that addresses some of these issues.

As part of a renewed interest in ADHD management techniques, I watched this video about ADHD treatments from Dr. Russell Barkley, wherein he said (I’m paraphrasing) “if I don’t put an intervention into your visual field it might as well not exist”.

I imagined timer that:

1. was always clearly present in my visual field;
2. recorded the passage of intervals of time regardless of any active engagement from the user; the idea is to record the progress of the day, not give you a button you need to remember to push;
3. rewarded me for setting active intentions about what to do with those chunks of time, and allowed me to mark them as successful or failed.

So, last weekend, leveraging my newly enhanced task-initiation and concentration-maintenance abilities, I wrote it, and I’ve been using it all week. Introducing Pomodouroboros, the pomodoro timer that reminds you that the uncaring void marches on regardless of your plans or intentions.

I’ve been using it all week and preliminary results are extremely positive.

This thing is in an extremely rough state. It has no tests, no docs, and an extremely inscrutable UI that you need to memorize in order to use effectively. I need plenty of help with it. I contemplated keeping it private and just shipping a binary, but a realistic assessment of my limited time resources forced me to admit that it already kind of does what I need, and if I want to enhance it to the point where it can help other people, I’ll need plenty of help.

If this idea resonates with you, and you’re on macOS, check out the repo, make a virtualenv somehow, install its dependencies, I don’t know how you make virtualenvs or install dependencies, I’m not your dad², and run ./runme. If you’re on another platform, check out the code, ask me some questions, and maybe try to write a port to one of them.