Dates And Times And Types

Get a TypeError when using a datetime when you wanted a date.

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!


  1. But, in typical fashion, not a kind of time... 

You Should Compile Your Python And Here’s Why

write Python that’s faster than C by optimizing your code, adding standard type annotations, and using Mypyc.

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.


  1. Despite the fact that it is self-described “alpha” software; it’s clearly production-quality enough for Mypy itself to rely upon it, and to have thorough documentation, so if it has bugs that need fixing then it would be good to start discovering them. However, this whole post assumes that you do have good test coverage and you’ll be able to run it over your Mypyc-built artifacts; if you don’t, then this might be too risky. 

  2. I’d love to offer an attribution here, but I have no idea where it came from originally. It’s nearly impossible to search the web these days for things that people were saying in 2005... but as I recall, it grew up as a sort of oral tradition of call-and-response about performance complaints on forums and Usenet. 

  3. At least in the most favorable cases, of course. You can’t do this for everything, but in any sufficiently large C++ system you can always find some fun oversights

  4. as I was, at the time. 

  5. This is not to write off PyO3, which is an excellent tool. It has many uses beyond speed. Beyond the obvious one of “access to libraries in the excellent Rust ecosystem”, one of its important applications is in creating a safer lingua franca among high-level programming languages. If you have a large, complex, polyglot environment with some Ruby, some Java, some Python and some TypeScript, all of which need to share data structures, Rust is a much better option than C for the common libraries that you might need to bind in all of those languages. 

  6. It did for Twisted! We ran our website on absolutely ancient hardware for the better part of a decade and PyPy made it fast enough that nobody really noticed. When we were still using CPython, the site had become barely usable. 

  7. You can get this implementation and a table of the resgults here, on github

  8. I’m not sure that this is a meaningful comparison, but C’s no-cost optimization option of -O3 is a 20% improvement, so we’re in the same ballpark, which is interesting. 

  9. Interestingly, on PyPy, it might actually be faster to upload a pure-Python wheel anyway, since the higher cost of calling into a C module on that platform might negate any benefits of compiling it. But you’ll have to measure it and see. 

  10. not to put too fine a point on it, “like the ones that I maintain” 

A Better Pygame Mainloop

Fix your mainloop for smoother gameplay that takes less battery power.

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


  1. And, honestly, a few fewer could stand to have it, given how much unnecessary always-online stuff there is in single-player experiences these days. But I digress. That’s why I’m in a footnote, this is a good place for digressing. 

  2. “Worldwide sales of laptops have eclipsed desktops for more than a decade. In 2019, desktop sales totaled 88.4 million units compared to 166 million laptops. That gap is expected to grow to 79 million versus 171 million by 2023.” 

  3. At least, Nvidia says that “the world’s fastest esports displays” are both 360Hz and also support G-Sync, and who am I to disagree? 

  4. They’re playing on a laptop, remember? So they’re literally uncomfortable. 

  5. Assuming you’ve made everything frame-rate independent, as mentioned in the aforementioned post

  6. because of course I will 

  7. And also, like, if there are horrible bugs in this code, so I can update it. It is super brief and abstract to show how general it is, but that also means it’s not really possible to test it as-is; my full-working-code examples are much longer and it’s definitely possible something got lost in translation. 

Announcing Pomodouroboros

I wrote my own pomodoro timer which is also a meditation on mortality.

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.


  1. I cannot express how alien the sensation is to have conscious control over initiating this process; I’ve certainly experienced hyperfocus before but it’s always been something that happens to me and not something that I do 

  2. If I am your dad, come talk to me, based on your family history it’s quite likely that you do have ADHD and I’m happy to talk about how to get this installed for you offline. 

Interfaces and Protocols

Comparing zope.interface and typing.Protocol.

Some of you read my previous post on typing.Protocols and probably wondered: “what about zope.interface?” I’ve advocated strongly for it in the past — but now that we have Mypy and Protocols, is it simply a relic of an earlier time? Can we entirely replace it with Protocol?

Let’s have a look.

Typing in 2 dimensions

In the previous post I discussed structural versus nominal typing. In Mypy’s type system, most classes are checked nominally whereas Protocol is checked structurally. However, there’s another way that Protocol is distinct from a normal class: normal classes are concrete types, and Protocols are abstract.

Abstract types:

  1. cannot be instantiated: every instance of an abstract type is an instance of some concrete sub-type, and
  2. do not include (complete) implementation logic.

Concrete types:

  1. can be instantiated: they are complete descriptions of a type, and
  2. must include all their own implementation logic.

Protocols and Interfaces are both abstract, but Interfaces are nominal. The highest level distinction between the two is that when you have a problem that requires an abstract type, but nominal checking is preferable to structural, Interfaces are a better solution.

Python’s built-in Abstract Base Classes are technically abstract-and-nominal as well, but they’re in a strange halfway space; they’re formally “abstract” because they can’t be instantiated, but they’re partially concrete in that they can contain any amount of implementation logic themselves, and thereby making an object which is a subtype of multiple ABCs drags in all the usual problems of the conflicting namespaces within multiple inheritance.

Theoretically, there’s a way to treat ABCs as purely abstract — which is to use ABCMeta.register — but as of this writing (March 2021) it doesn’t work with Mypy, so within the context of “static typing in Python” we presently have to ignore it.

Practicalities

The first major advantage that Protocol has is that since it is now built in to Python itself, there’s no reason not to use it. When Protocol didn’t even exist, regardless of all the advantages of adding explicit abstract types to your project with zope.interface, it did still have the small down-side of requiring a new dependency, with all the minor headaches that might imply.

beyond the theoretical distinctions, there’s a question of how well tooling supports zope.interface. There are some clear gaps; there is not a ton of great built-in IDE support for zope.interface; less-sophisticated linters will sometimes still complain that Interfaces don’t take self as their first argument. Indeed, Mypy itself does this by default — although more on that in a moment. Less mainstream performance-focused type-checkers like Pyre and Pyright don’t support zope.interface, either, although their lack of support for zope.interface is just a part of a broader problem of their lack of extensibility; they also can’t support SQLAlchemy or the Django ORM without special-casing in the tools themselves.

But what about Mypy itself — if we have to discount ABCMeta.register due to practical tooling deficiencies even if they provide a built-in way to declare a nominal-but-abstract type in principle, we need to be able to use zope.interface within Mypy as well for a fair comparison with Protocol. Can we?

Luckily, yes! Thanks to Shoobx, there’s a fairly actively maintained Mypy plugin that supports zope.interface which you can use to statically check your Interfaces.

However, this plugin does have a few key limitations as of this writing (Again, March 2021), which makes its safety guarantees a bit lower-quality than Protocol.

The net result of this is that Protocols have the “home-field advantage” in most cases; out of the box, they’ll work more smoothly with your existing editor / linter setup, and as long as your project supports Python 3.6+, at worst (if you can’t use Python 3.7, where Protocol is built in to typing) you have to take a type-check-time dependency on the typing_extensions package, whereas with zope.interface you’ll need both the run-time dependency of zope.interface itself and the Mypy plugin at type-checking time.

So in a situation where both are roughly equivalent, Protocol tends to win by default. There are undeniably big areas where Interfaces and Protocols overlap, and in plenty of them, using Protocol is a fine idea. But there are still some clear places that zope.interface shines.

First, let’s look at a case which Interfaces handle more gracefully than Protocols: opting out of matching a simple shape, where the shape doesn’t fully describe its own meaning.

Where Interfaces work best: hidden and complex meanings

The string is a stark data structure and everywhere it is passed there is much duplication of process. It is a perfect vehicle for hiding information.

Alan Perlis, “Epigrams in Programming”, Epigram 34.

The place where structural typing has the biggest advantage is when the type system is expressive enough to fully encode the meaning of the desired behavior within the structure of the type itself. Consider a Protocol which describes an object that can add some integers together:

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class Math(Protocol):
    def add_integers(addend1: int, addend2: int) -> int:
        ...

It’s fairly unambiguous what adherents to this Protocol should do, and anyone implementing such a thing should be able to clearly tell that the method is supposed to add a couple of integers together; there’s nothing hidden about the structure of the integers, no constraints the type system won’t let us specify. It would be quite surprising if anything that didn’t have the intended behavior would match this Protocol.

A the other end of the spectrum, we might have a plugin Interface that has a lot of hidden structure. For this example, we have an Interface called IPlugin containing a method with an easy-to-conflict-with name (“name”) overloaded with very specific constraints on its return type: the string must contain the dotted-path name of a Python object in an import-able module (like, for example, "os.path.join").

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class IPlugin(Interface):
    def name() -> str:
        "Return the fully-qualified Python identifier of the thing to load."

With Protocols, you can work around these limitations, by manually making it harder to match; adding elements to the structure that embed names relevant to its semantics and thereby making the type behave more as if it were nominally typed.

You could make the method’s name long and ugly instead (plugin_name_to_load, let’s say) or add unused additional attributes (yep_i_am_a_plugin = Literal[True]) in order to reduce the risk of accidental matches, but these workarounds look hacky, and they have to be manually namespaced; if you want to mark it as having semantics associated with your specific plugin system, you have to embed the name of that system in your attributes themselves; here we’re just saying “plugin” but if we want to be truly careful, we have to embed the whole name of our project in there.

With Interfaces, the maintainer of each implementation must explicitly opt in, by choosing whether to specify that they are an @implementer(IPlugin). Since they had to import IPlugin from somewhere, this annotation carries with it a specific, namespaced declaration of semantic intent: “I know what the Interface IPlugin means, and I promise that I can provide it”.

This is the most salient distinction between Protocols and Interfaces: if you have strong reasons to want adherents to the abstract type to opt in, you want an Interface; if you want them to match automatically, you want a Protocol.

Runtime support

Interfaces also provide a more nuanced set of runtime checks.

You can say that an object directlyProvides an interface, allowing for some level of (at least runtime) type safety, and ask if IPlugin is .providedBy some object.

You can do most of this with Protocol, but it’s awkward. The @runtime_checkable decorator allows your Protocol to make isinstance(x, MyProtocol) work like IMyInterface.providedBy(x), but:

  1. you’re still missing directlyProvides; the runtime checking is all by type, not by the individual properties of the instance;
  2. it’s not the default, so if you’re not the one defining the Protocol, there’s no guarantee you’ll be able to use it.

With Interfaces, there’s also no mandatory relationship between the implementer (i.e. the type whose instances fit the specified shape) and the provider (the specific object which can fit the specified shape). This means you get features like classProvides and moduleProvides “for free”.

Interfaces work particularly well for communication between frameworks and application code. For example, let’s say you’re evolving the meaning of an Interface implemented by applications over time — EventHandler, EventHandler2, EventHandler3 — which have similarly named and typed methods, but subtly different expectations on their lifecycle or when precisely the methods will be called. A framework facing this problem can use a series of Interfaces, and check at runtime to see which of these the application implements, and be secure in the knowledge that the application has properly intentionally adopted the new interface, and doesn’t just happen to have a matching method name against an older version.

Finally, zope.interface gives you adaptation and adapter registries, which can be a useful mechanism for doing things like templating, like a much more powerful version of singledispatch from the standard library.

Adapter registries are nuanced, complex tools and unfortunately an example that captures the full utility of their power would itself be commensurately complex. However, the core of adaptation is the idea that if you have an arbitrary object x, and you want a provider of the interface IY, you can do the following:

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y = IY(x, None)

This performs a multi-stage check:

  1. If x already provides IY (either via implementer, provider, directlyProvides, classProvides, or moduleProvides), it’s simply returned; so you don’t need to special-case the case where you’ve already got what you want.
  2. If x has a __conform__(interface) method, it’ll be called with IY as the interface, and if __conform__ returns anything non-None that result will be returned from the call to IY.
  3. If IY has a specially-defined __adapt__ method, it can implement its own logic for this hook directly.
  4. Each globally-registered function in zope.interface’s adapter_hooks will be invoked to find a function that can transform x into an IY provider. Twisted has its own global registry in this list, which is what registerAdapter manipulates.

But from the perspective of the caller, you can just say “I want an IY”.

With Protocols, you can emulate this with functools.singledispatch by making a function which returns your Protocol type and registers various types to do conversion. The place that adapter registries have an advantage is their central nature and consistent idiom for converting to the target type; you can use adaptation for any Interface in the same way, and any type can participate in adaptation in the ways listed above via flexible mechanisms depending on where it makes sense to put your implementation, whereas any singledispatch function to convert to a Protocol needs to be bespoke per-Protocol.

Describing and restricting existing shapes

There are still several scenarios where Protocol’s semantics apply more cleanly.

Unlike Interfaces, Protocols can describe the types of things that already exist. To see when that’s an advantage, consider a sprawling application that uses tons of libraries and manipulates 3D spatial data points.

There’s a convention among these disparate libraries where they all represent a “point” as an object with .x, .y, and .z attributes which are all floats. This is a natural enough shape, given the domain, that lots of your libraries just fit it by accident. You want to write functions that can work with data output by any of these libraries as long as it plausibly looks like your own concept of a Point:

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class Point(Protocol):
    x: float
    y: float
    z: float

In this case, the thing defining the Protocol is your application; the thing implementing the Protocol is your collection of libraries. Since the libraries don’t and can’t know about the application — the dependency arrow points the other way — they can’t reference the Protocol to note that they implement it.

Using Protocol, you can also restrict an existing type to preserve future flexibility.

For example, let’s say we’re implementing a “mailbox” type pattern, where some systems deliver messages and other systems retrieve them later. To avoid mix-ups, the system that sends the messages shouldn’t retrieve them and vice versa - receivers only receive, and senders only send. With Protocols, we can describe this without having any new custom concrete types, like so:

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from typing import Protocol, TypeVar

T_co = TypeVar("T_co", covariant=True)
T_con = TypeVar("T_con", contravariant=True)

class Sender(Protocol[T_con]):
    def add(self, item: T_con) -> None:
        "Put an item in the slot."

class Receiver(Protocol[T_co]):
    def pop(self) -> T_co:
        "Retrieve an item from the PO box."

All of that code is just telling Mypy our intentions; there’s no behavior here yet.

The actual implementation is even shorter:

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from typing import Set

mailbox: Set[int] = set()

Literally no code of our own - set already does the job we described. And how do we use this?

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def send(sender: Sender[int]) -> None:
    sender.add(3)

def receive(receiver: Receiver[int]) -> None:
    receiver.pop()
    receiver.add(3)
    # Mypy stops us making this mistake:
    # "Receiver[int]" has no attribute "add"

send(mailbox)
receive(mailbox)

For its initial implementation, this system requires nothing beyond types available in the standard library; just a set. However, by treating their parameter as a Sender and a Receiver respectively rather than a Set, send and receive prevent themselves from using any functionality from the set passed in aside from the one method that their respective roles are supposed to “see”. As a result, Mypy will now tell us if any code which receives the sender object tries to remove objects.

This allows us to use existing data structures in libraries without the usual attendant problem of advertising to all clients that every tiny implementation detail of those existing structures is an intended part of the public interface. Python has always tried to make these sort of distinctions by leaving certain things undocumented or saying narratively which things you should rely on, but it’s always hit-or-miss (usually miss) whether library consumers will see those admonitions or not; by making it a feature of the programming environment, Mypy makes it harder to ignore.

Conclusions

In modern Python code, when you have an abstract collection of behavior, you should probably consider using a Protocol to describe it by default. However, Interface is also staying up to date with modern Python tooling by with Mypy support, and it can be worthwhile for more sophisticated consumers that want support for nominal typing, or that want to draw on its reach adaptation and component registration feature-set.