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I am no longer working on this project. It was an experiment which was useful for insight, but now this project is entirely superseded by Deadpool

cancelkulture

Cancellable subprocesses with timeouts in Python (including for asyncio.run_in_executor)

⚠️ Warning - experimental ⚠️

Do not use this for anything important if you don't understand what it's doing.

Demo

You know how in asyncio if you run something in a process pool executor using run_executor(), and then you try to cancel the task, but the underlying process doesn't actually stop? Also, it's super annoying you can't set a timeout on an executor job?

LET'S FIX THAT

Timeouts

import time
import asyncio
from cancelkulture import ProcessPoolExecutor, ProcessCancelTimeout


async def awork():
    loop = asyncio.get_running_loop()
    return await loop.run_in_executor(ProcessPoolExecutor(), time.sleep, 10.0)


async def main():
    try:
        await asyncio.wait_for(awork(), 5.0)
    except asyncio.TimeoutError:
        print('Timed out!')


asyncio.run(main())

Cancellation

import time
import asyncio
from cancelkulture import ProcessPoolExecutor, ProcessCancelTimeout


async def awork():
    loop = asyncio.get_running_loop()
    return await loop.run_in_executor(ProcessPoolExecutor(), time.sleep, 10.0)


async def main():
    t = asyncio.create_task(awork())

    # Let's wait a bit and cancel that task 
    loop = asyncio.get_running_loop()
    loop.call_later(5.0, t.cancel)

    try:
        await t
    except asyncio.CancelledError:
        print('Cancelled!')


asyncio.run(main())

Actually, there is also support for cancelling subprocesses in non-async code too. Read on to explore further examples.

How can this work?? Cancellation requires cooperative multitasking...

Haha lolno, we just SIGKILL the process running that task.

You're going to want to make sure your jobs are idempotent because in practice they will sometimes need to be restarted.

I know it sounds bad to SIGKILL a running process, but in reality, in production systems hardware and software (and humans) cause hard resets all the time. An extremely common example is simply redeploying a new version of a running application. Yes, your app will get a SIGTERM from Kubernetes, but if you have blocking CPU code running within many nested processes, it's a lot of work to orchestrate a controlled shutdown, especially within the narrow time window before Kubernetes does a SIGKILL anyway.

It's much easier to define idempotent work and design for restarts as necessary.

What exactly is provided in this library?

I first want to point out that this library is designed to compose with whatever you already have. Because I too have existing code and I can't just replace the world with every new thing.

Broadly, there are 3 features, and 3 supported use-cases. It's easier to go through the use-cases:

1. You have an asyncio program, and you need a ProcessPoolExecutor, and you'd like the executor jobs to actually get cancelled when you cancel a loop.run_in_executor() future.
2. You're working with regular sync code (not asyncio), and you need a ProcessPoolExecutor, and you would like to kill executor jobs. In particular during shutdown scenarios, e.g. for production redeployments of microservices.
3. You're stuck with using the built-in ProcessPoolExecutor from the concurrent.futures, but you would still like to be able to actually cancel running executor jobs.

We'll step throught the scenarios one-by-one.

1. asyncio: Cancellable executor jobs (and timeouts!)

We covered this in the demo section. But I just want to highlight a few things:

from cancelkulture import ProcessPoolExecutor, ProcessCancelTimeout


async def work():
    loop = asyncio.get_running_loop()
    return await loop.run_in_executor(
        ProcessPoolExecutor(),

        # Look here
        partial(time.sleep, 10.0),

        # And here
        ProcessCancelTimeout(5.0),
    )

I've made a subclass of the ProcessPoolExecutor from the concurrent.futures stdlib module. It is pretty close to the original, but it does some...extra things inside the submit() method to get the job done. You don't really have to worry about that, but what you do have to pay attention to is how to pass arguments. Parameters that are part of cancelkulture must be separate, they cannot be part of a partial.

This is also a good time to discuss why the timeouts are classes, like cancelkulture.ProcessCancelTimeout above. Usually, in such situations one would use kwargs to specify special parameters used by wrapper functions, and then inside the wrapper function you'd just omit them when calling the inner function. In my case my inner function is the original ProcessPoolExecutor.submit() method. Unfortunately, the design of asyncio is such that only *args can be passed to run_in_executor(). The docs specifically say to "use a partial" if we need to pass kwargs. That doesn't work for my wrapper use-case. So, instead I've made a new type for each timeout parameter, and then in my wrapper submit(), I check every value in *args to see whether the type matches on of the types used by cancelkulture. So that's why the classes.

Now is also a good time to discuss what the two timeouts do:

• ProcessTimeout: This is the allowed time for the job.
• ProcessCancelTimeout: This is the allowed time for the job after the job has been cancelled (e.g. fut.cancel()).

2. A richer ProcessPoolExecutor

The subclass provided by this package should be a drop-in replacement for the one in concurrent.futures, but it has 2 extra tricks. The first is that jobs can be cancelled.

Cancellation

This is an example from one of the tests. It uses a background thread to cancel a running future. This example demonstrates cancellation.

import time
import pytest
import threading
import cancelkulture


def delayed_cancel(fut, delay):
    time.sleep(delay)
    fut.cancel()


with cancelkulture.ProcessPoolExecutor() as exe:
    fut = exe.submit(
        time.sleep,
        10.0,
        cancelkulture.ProcessCancelTimeout(0.0),
    )
    t = threading.Thread(target=delayed_cancel, args=(fut, 0.5,), daemon=True)
    t.start()
    with pytest.raises(cancelkulture.ProcessCancelledError):
        _ = fut.result()

The job submitted to the executor would like to run for 10 seconds, but we cancel the future after 0.5 seconds.

Timeouts

With timeouts, there's more nuance. First, an example that shows how timeouts work, and then we'll discuss why I didn't use the existing timeout API when getting the result in fut.result(timeout).

    with cancelkulture.ProcessPoolExecutor() as exe:
        fut = exe.submit(
            time.sleep,
            10.0,
            cancelkulture.ProcessTimeout(0.5),
        )
        with pytest.raises(cancelkulture.ProcessTimeoutError):
            _ = fut.result()

The above is the way it currently works. However, if you're familiar with the stdlib concurrent.futures module, you will be aware that there is an existing feature to set a timeout parameter on the result call itself. For example, I could have gone with something like this:

    ### HYPOTHETICAL CODE ###

    with cancelkulture.ProcessPoolExecutor() as exe:
        fut = exe.submit(
            time.sleep,
            10.0,

                  <- See, no timeout parameter here

        )
        with pytest.raises(concurrent.futures.TimeoutError):
            _ = fut.result(timeout=5.0)

So why didn't I do this? It's because I didn't want to change compatibility with the stdlib ProcessPoolExecutor code. In the original, the timeout error that is raised does not affect the running task at all. It doesn't kill it, the task goes on running. My fear was that it might become difficult to know which executor is being used where, in more complicated programs with lots of moving parts.

Thus, the current design is that even for the ProcessPoolExecutor in cancelkulture, using fut.result(timeout) will not kill the task if that timeout is reached. It is to be used only as an alert, just like in the stdlib version.

3. Getting cancellation in stdlib ProcessPoolExecutor

This one is messy, but it also leads a path to explain how cancelkulture is made. The proverbial "sausage".

Again, an example from the tests:

import concurrent.futures
import multiprocessing
import time
import cancelkulture


ctx = multiprocessing.get_context("spawn")
with concurrent.futures.ProcessPoolExecutor(
    mp_context=ctx,
    initializer=initializer,
) as exe:
    fut = exe.submit(
        cancelkulture.killable_wrapper,
        time.sleep,
        10.0,
        killable_wrapper_timeout=5.0,
    )

    try:
        fut.result()
    except cancelkulture.ProcessTimeoutError:
        print('This happens after 5 seconds')

Observations:

• We're using the vanilla stdlib ProcessPoolExecutor here
• We're using the "spawn" context for subprocesses. All three methods provided by the multiprocessing module work, but I'm highlighting "spawn" because that one is the recommended one.

Here you can see we're passing cancelkulture.killable_wrapper as the target to the executor. This wrapper will be responsible for executing the actual function, in this example time.sleep().