How to combine python asyncio with threads?

Question

I have successfully built a RESTful microservice with Python asyncio and aiohttp that listens to a POST event to collect realtime events from various feeders.

It then builds an in-memory structure to cache the last 24h of events in a nested defaultdict/deque structure.

Now I would like to periodically checkpoint that structure to disc, preferably using pickle.

Since the memory structure can be >100MB I would like to avoid holding up my incoming event processing for the time it takes to checkpoint the structure.

I'd rather create a snapshot copy (e.g. deepcopy) of the structure and then take my time to write it to disk and repeat on a preset time interval.

I have been searching for examples on how to combine threads (and is a thread even the best solution for this?) and asyncio for that purpose but could not find something that would help me.

Any pointers to get started are much appreciated!

Answer 1

It's pretty simple to delegate a method to a thread or sub-process using BaseEventLoop.run_in_executor:

import asyncio
import time
from concurrent.futures import ProcessPoolExecutor

def cpu_bound_operation(x):
    time.sleep(x) # This is some operation that is CPU-bound

@asyncio.coroutine
def main():
    # Run cpu_bound_operation in the ProcessPoolExecutor
    # This will make your coroutine block, but won't block
    # the event loop; other coroutines can run in meantime.
    yield from loop.run_in_executor(p, cpu_bound_operation, 5)


loop = asyncio.get_event_loop()
p = ProcessPoolExecutor(2) # Create a ProcessPool with 2 processes
loop.run_until_complete(main())

As for whether to use a ProcessPoolExecutor or ThreadPoolExecutor, that's kind of hard to say; pickling a large object will definitely eat some CPU cycles, which initially would make you think ProcessPoolExecutor is the way to go. However, passing your 100MB object to a Process in the pool would require pickling the instance in your main process, sending the bytes to the child process via IPC, unpickling it in the child, and then pickling it again so you can write it to disk. Given that, my guess is the pickling/unpickling overhead will be large enough that you're better off using a ThreadPoolExecutor, even though you're going to take a performance hit because of the GIL.

That said, it's very simple to test both ways and find out for sure, so you might as well do that.

Answer 2

I also used run_in_executor, but I found this function kinda gross under most circumstances, since it requires partial() for keyword args and I'm never calling it with anything other than a single executor and the default event loop. So I made a convenience wrapper around it with sensible defaults and automatic keyword argument handling.

from time import sleep
import asyncio as aio
loop = aio.get_event_loop()

class Executor:
    """In most cases, you can just use the 'execute' instance as a
    function, i.e. y = await execute(f, a, b, k=c) => run f(a, b, k=c) in
    the executor, assign result to y. The defaults can be changed, though,
    with your own instantiation of Executor, i.e. execute =
    Executor(nthreads=4)"""
    def __init__(self, loop=loop, nthreads=1):
        from concurrent.futures import ThreadPoolExecutor
        self._ex = ThreadPoolExecutor(nthreads)
        self._loop = loop
    def __call__(self, f, *args, **kw):
        from functools import partial
        return self._loop.run_in_executor(self._ex, partial(f, *args, **kw))
execute = Executor()

...

def cpu_bound_operation(t, alpha=30):
    sleep(t)
    return 20*alpha

async def main():
    y = await execute(cpu_bound_operation, 5, alpha=-2)

loop.run_until_complete(main())

Answer 3

Another alternative is to use loop.call_soon_threadsafe along with an asyncio.Queue as the intermediate channel of communication.

The current documentation for Python 3 also has a section on Developing with asyncio - Concurrency and Multithreading:

import asyncio

# This method represents your blocking code
def blocking(loop, queue):
    import time
    while True:
        loop.call_soon_threadsafe(queue.put_nowait, 'Blocking A')
        time.sleep(2)
        loop.call_soon_threadsafe(queue.put_nowait, 'Blocking B')
        time.sleep(2)

# This method represents your async code
async def nonblocking(queue):
    await asyncio.sleep(1)
    while True:
        queue.put_nowait('Non-blocking A')
        await asyncio.sleep(2)
        queue.put_nowait('Non-blocking B')
        await asyncio.sleep(2)

# The main sets up the queue as the communication channel and synchronizes them
async def main():
    queue = asyncio.Queue()
    loop = asyncio.get_running_loop()

    blocking_fut = loop.run_in_executor(None, blocking, loop, queue)
    nonblocking_task = loop.create_task(nonblocking(queue))

    running = True  # use whatever exit condition
    while running:
        # Get messages from both blocking and non-blocking in parallel
        message = await queue.get()
        # You could send any messages, and do anything you want with them
        print(message)

asyncio.run(main())

How to send asyncio tasks to loop running in other thread may also help you.

If you need a more "powerful" example, check out my Wrapper to launch async tasks from threaded code. It will handle the thread safety part for you (for the most part) and let you do things like this:

# See https://gist.github.com/Lonami/3f79ed774d2e0100ded5b171a47f2caf for the full example

async def async_main(queue):
    # your async code can go here
    while True:
        command = await queue.get()
        if command.id == 'print':
            print('Hello from async!')
        elif command.id == 'double':
            await queue.put(command.data * 2)

with LaunchAsync(async_main) as queue:
    # your threaded code can go here
    queue.put(Command('print'))
    queue.put(Command('double', 7))
    response = queue.get(timeout=1)
    print('The result of doubling 7 is', response)