Python Async Functionality

Question

I'm trying to figure out how the async functionality works in Python. I have watched countless videos but I guess I'm not 'getting it'. My code looks as follows:

def run_watchers():
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    loop.run_until_complete(watcher_helper())
    loop.close()

async def watcher_helper():
    watchers = Watcher.objects.all()

    for watcher in watchers:
        print("Running watcher : " + str(watcher.pk))
        await watcher_helper2(watcher)

async def watcher_helper2(watcher):
    for i in range(1,1000000):
        x = i * 1000 / 2000

What makes sense to me is to have three functions. One to start the loop, second to iterate through the different options to execute and third to do the work.

I am expecting the following output:

Running watcher : 1
Running watcher : 2
...
...

Calculation done
Calculation done
...
...

however I am getting:

Running watcher : 1
Calculation done
Running watcher : 2
Calculation done
...
...

which obviously shows the calculations are not done in parallel. Any idea what I am doing wrong?

Mixing Django models (`Watcher`) with asyncio code kills concurrency. — Andrew Svetlov, May 02 '18 at 23:14

score 2 · Accepted Answer · answered May 02 '18 at 14:14

asyncio can be used only to speedup multiple network I/O related functions (send/receive data through internet). While you wait some data from network (which may take long time) you usually idle. Using asyncio allows you to use this idle time for another useful job: for example, to start another parallel network request.

asyncio can't somehow speedup CPU-related job (which is what watcher_helper2 do in your example). While you multiply some numbers there's simply no idle time which can be used to do something different and to achieve benefit through that.

Read also this answer for more detailed explanation.

Thanks, I understand what you are saying. I replaced the watcher_helper2 with the dummy code which does not make sense to use in async mode. For my case I found the solution, which is to use asyncio.wait(tasks) — ceds, May 02 '18 at 14:20

Python Async Functionality

1 Answers1