Why is my async function running synchronously Python3.9?

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I'm trying to run a function on separate threads using asyncio and futures. I have a decorator which takes the long running function and its argument asynchronously and outputs its value. Unfortunately the processes seem to not be working asynchronously.

def multiprocess(self, function, executor=None, *args, **kwargs):
    async def run_task(function, *args, **kwargs):
        @functools.wraps(function)
        async def wrap(*args, **kwargs):
            while True:
                execution_runner = executor or self._DEFAULT_POOL_
                executed_job = execution_runner.submit(function, *args, **kwargs)
                print(
                    f"Pending {function.__name__}:",
                    execution_runner._work_queue.qsize(),
                    "jobs",
                )
                print(
                    f"Threads: {function.__name__}:", len(execution_runner._threads)
                )
                future = await asyncio.wrap_future(executed_job)
                return future

        return wrap

    return asyncio.run(run_task(function, *args, **kwargs))

To call the decorator I have two functions _async_task and task_function. _async_task contains a loop that runs task_function for each document that needs to be processed.

@staticmethod
def _async_task(documents):
    processed_docs = asyncio.run(task_function(documents))
    return processed_docs

task_function processes each document in documents as below,

@multiprocess
async def task_function(documents):
    processed_documents = None
    try:
        for doc in documents:
            processed_documents = process_document(doc)
            print(processed_documents)
    except Exception as err:
        print(err)
    return processed_documents

The clue that this doesn't work asynchronously is that the diagnostics I have for the multithreading decorator print the following.

Pending summarise_news: 0 jobs
Threads: summarise_news: 2

Since there's no pending jobs and the entire process takes as long as the synchronous run, it's running synchronously.

concurrent.futures python-asyncio python-multithreading python-3.x python
1 Answers

I had some issues setting up your code, but I think I've come up with an answer.

First of all, as @dano mentioned in his comment, asyncio.run blocks until the coroutine running is completed. Thus, you won't get any speedup from using this approach.

I used a slightly modified multiprocess decorator

def multiprocess(executor=None, *args, **kwargs):
    def run_task(function, *args, **kwargs):
        def wrap(*args, **kwargs):

            execution_runner = executor or DEFAULT_EXECUTOR
            executed_job = execution_runner.submit(function, *args, **kwargs)
            print(
                f"Pending {function.__name__}:",
                execution_runner._work_queue.qsize(),
                "jobs",
            )
            print(
                f"Threads: {function.__name__}:", len(execution_runner._threads)
            )
            future = asyncio.wrap_future(executed_job)

            return future

        return wrap
    return run_task

As you can see, there's no asyncio.run here, and both the decorator and inner wrapper are synchronous since asyncio.wrap_future does not need await.

The updated multiprocess decorator is now used with process_document function. The reason for that is you won't get any benefit of parallelizing a function that calls blocking functions in a sequence. You have to convert your blocking function to be runnable in an executor instead.

NOTE that this dummy process_document is exactly like I described - fully blocking and synchronous.

@multiprocess()
def process_document(doc):
    print(f"Processing doc: {doc}...")
    time.sleep(2)
    print(f"Doc {doc} done.")

Now, to the last point. We already made process_document kind of asynchronous by converting it to be runnable in an executor, BUT it still matters HOW exactly you invoke it.

Consider the following examples:

for doc in documents:
    result = await process_document(doc)

results = await asyncio.gather(*[process_document(doc) for doc in documents])

In the former one, we will wait for coroutines sequentially, having to wait until one finishes before starting another. In the latter example, they will be executed in parallel, so it really depends on how exactly you invoke your coroutine execution.

Here's the full code snipped I used:

import asyncio
import concurrent.futures
import time


DEFAULT_EXECUTOR = concurrent.futures.ThreadPoolExecutor(max_workers=4)


def multiprocess(executor=None, *args, **kwargs):
    def run_task(function, *args, **kwargs):
        def wrap(*args, **kwargs):

            execution_runner = executor or DEFAULT_EXECUTOR
            executed_job = execution_runner.submit(function, *args, **kwargs)
            print(
                f"Pending {function.__name__}:",
                execution_runner._work_queue.qsize(),
                "jobs",
            )
            print(
                f"Threads: {function.__name__}:", len(execution_runner._threads)
            )
            future = asyncio.wrap_future(executed_job)

            return future

        return wrap
    return run_task


@multiprocess()
def process_document(doc):
    print(f"Processing doc: {doc}...")
    time.sleep(2)
    print(f"Doc {doc} done.")


async def task_function_sequential(documents):
    start = time.time()
    for doc in documents:
        await process_document(doc)

    end = time.time()
    print(f"task_function_sequential took: {end-start}s")


async def task_function_parallel(documents):
    start = time.time()

    jobs = [process_document(doc) for doc in documents]
    await asyncio.gather(*jobs)

    end = time.time()
    print(f"task_function_parallel took: {end-start}s")


async def main():
    documents = [i for i in range(5)]
    await task_function_sequential(documents)
    await task_function_parallel(documents)


asyncio.run(main())

Notice that the task_function_parallel example still takes around 4 seconds, instead of 2, because the thread pool is limited to 4 workers, and the number of jobs is 5, so the last job will be waiting for some workers to be available.

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