Asynchronous Execution with Python (Pt. 1)

For a general overview of the advantages and concepts behind async patterns, check out:

• Guide to Asyncio - Medium
• JavaScript EventLoop - MDN
• asyncio - PyDocs

With Python’s asyncio library you can write concurrent code for I/O-bound strategies. This differs from the multiprocessing library which focuses on using threads for CPU-bound strategies.

The asyncio library along with the async/await keywords enable this by using a concept called the "Event Loop". This provides an abstract way to create and schedule asynchronous loops and functions. JavaScript, being used in front-end interaction in browsers, is also based around the event loop concept making it a perfect language to compare/examine this concept.

Note: Some of the asyncio functions implemented in Python 3.7+ may be Deprecated in Python 3.10+

Synchronous Execution

Normally when programming in Python and most other Procedural languages, programs are executed from top to bottom (even if the code is not structured as such; see language interpretation/compilers). This means that each statement is executed after the completion of the prior statement(s).

In example, say we have an random_add function that sums a list of random integers after a specified delay (in seconds):

def random_add(delay: int) -> None:
    import time
    time.sleep(delay)
    # This retrieves a list of random ints
    n = random_number_list()
    print(f'Sum({n}) = {sum(n)}')

and we execute this function 3 times in succession:

if __name__ == "__main__":
    random_add(5) # call1: Delayed 5s
    random_add(8) # call2: Delayed 8s
    random_add(2) # call3: Delayed 2s

the total timing would be near 15s. With each call completing one after the next as shown below.

Even though the random_add function is not performing any calculations during its delayed period, it still has resources allocated for that period. This is why the time consumption of normal synchronous programs can typically be described as the sum of all of its procedures.

Synchronous Timing

Asynchronous Execution

We can reuse the prior random_add function and adapt it to execute asychronously:

import asyncio
async def random_add(delay: int) -> None:
    asyncio.sleep(delay)
    # This retrieves a list of random ints
    n = random_number_list()
    print(f'Sum({n}) = {sum(n)}')

now to execute this function 3 times just as before:

if __name__ == "__main__":
    await random_add(5)
    await random_add(8)
    await random_add(2)

Uh Oh! You should receive a SyntaxError similar to:

    await random_add(5)
    ^^^^^^^^^^^^^^^^^^^
SyntaxError: 'await' outside function

This issue may have even been detected by the IDE you are using before running the Python script. This is because await can only be used in async scopes and the main thread is synchronous without an event loop! To execute this code we must:

1. Wrap this code in a async function:
   

   async def async_test():
       await random_add(5)
       await random_add(8)
       await random_add(2)
   

2. Then utilize the asyncio.run function
   

   if __name__ == "__main__":
       asyncio.run(async_test())
   

Now the code should run happily. However, the code is still taking the same amount of time to execute. Why is that? Doesn't async make it work in parallel?

The reason is that the await keyword means the parent function will await a result before continuing to the next statement. Since the response is delayed it will wait for that period.

To execute the random_add function calls concurrently, we need to create Tasks to execute them in. This can be done via asyncio.create_task individually:

async def async_test_task():
    task1 = asyncio.create_task(random_add(5))
    task2 = asyncio.create_task(random_add(8))
    task3 = asyncio.create_task(random_add(2))
    await task1, await task2, await task3

or via asyncio.gather for lists of tasks:

async def async_test_gather():
    await asyncio.gather(
        random_add(5),
        random_add(8),
        random_add(2)
    )

For the rest of this post we will be utilizing asyncio.gather for simplicity. There are certain scenarios (eg. variable task creation) that asyncio.create_task is applicable.

Now that the code has been adjusted you will observe that the total execution time is only limited by the slowest task! Also, the order each task completes differs (shown below).

Asynchronous Timing

This is just the start of the features that are provided by async/await and the asyncio library in Python. Be sure to read Part 2 of this overview once it is released.