Process tasks in batchs in asyncio - python-3.x

Question

I have got a funcion that generates tasks (io bound tasks):
def get_task():
while True:
new_task = _get_task()
if new_task is not None:
yield new_task
else:
sleep(1)
And I am trying to write a consumer in asyncio that will be processing max 10 tasks at the time and one task is finished then will take new one.
I am not sure if I should use semaphores or is there any kind of asycio pool executor? I started to write a pseudocode with threads:
def run(self)
while True:
self.semaphore.acquire() # first acquire, then get task
t = get_task()
self.process_task(t)
def process_task(self, task):
try:
self.execute_task(task)
self.mark_as_done(task)
except:
self.mark_as_failed(task)
self.semaphore.release()
Could anyone help me? I have no clue where to put async/await keywords

Answer 1

Simple task cap using asyncio.Sepmaphore
async def max10(task_generator):
semaphore = asyncio.Semaphore(10)
async def bounded(task):
async with semaphore:
return await task
async for task in task_generator:
asyncio.ensure_future(bounded(task))
The problem with this solution is that tasks are being drawn from the generator greedily. For example, if generator reads from a large database, the program could run out of memory.
Other than that it's idiomatic and well-behaved.
A solution, that uses async generator protocol to pull new tasks on demand:
async def max10(task_generator):
tasks = set()
gen = task_generator.__aiter__()
try:
while True:
while len(tasks) < 10:
tasks.add(await gen.__anext__())
_done, tasks = await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED)
except StopAsyncIteration:
await asyncio.gather(*tasks)
It may be considered sub-optimal, because it doesn't start executing tasks until 10 are available.
And here's concise and magic solution using worker pattern:
async def max10(task_generator):
async def worker():
async for task in task_generator:
await task
await asyncio.gather(*[worker() for i in range(10)])
It relies on a somewhat counter-intuitive property of being able to have multiple async iterators over the same async generator, in which case each generated item is seen by only one iterator.
My gut tells me that none of these solutions behaves properly on cancellation.

Answer 2

Async isn't threads. If for example you have tasks that are file IO bound then write them async using aiofiles
async with aiofiles.open('filename', mode='r') as f:
contents = await f.read()
Then replace task with your tasks. If you want to only run 10 at a time await asyncio.gather every 10 tasks.
import asyncio
async def task(x):
await asyncio.sleep(0.5)
print( x, "is done" )
async def run(loop):
futs = []
for x in range(50):
futs.append( task(x) )
await asyncio.gather( *futs )
loop = asyncio.get_event_loop()
loop.run_until_complete( run(loop) )
loop.close()
If you can't write the tasks async and need threads this is a basic example using asyncio's ThreadPoolExecutor. Note that with max_workers=5 only 5 tasks are run at a time.
import time
from concurrent.futures import ThreadPoolExecutor
import asyncio
def blocking(x):
time.sleep(1)
print( x, "is done" )
async def run(loop):
futs = []
executor = ThreadPoolExecutor(max_workers=5)
for x in range(15):
future = loop.run_in_executor(executor, blocking, x)
futs.append( future )
await asyncio.sleep(4)
res = await asyncio.gather( *futs )
loop = asyncio.get_event_loop()
loop.run_until_complete( run(loop) )
loop.close()

Answer 3

As pointed out by Dima Tismek, using semaphores to limit concurrency is vulnerable to exhausting task_generator too eagerly, since there is no backpressure between obtaining the tasks and submitting them to the event loop. A better option, also explored by the other answer, is not to spawn a task as soon as the generator has produced an item, but to create a fixed number of workers that exhaust the generator concurrently.
There are two areas where the code could be improved:
there is no need for a semaphore - it is superfluous when the number of tasks is fixed to begin with;
handling cancellation of generated tasks and of the throttling task.
Here is an implementation that tackles both issues:
async def throttle(task_generator, max_tasks):
it = task_generator.__aiter__()
cancelled = False
async def worker():
async for task in it:
try:
await task
except asyncio.CancelledError:
# If a generated task is canceled, let its worker
# proceed with other tasks - except if it's the
# outer coroutine that is cancelling us.
if cancelled:
raise
# other exceptions are propagated to the caller
worker_tasks = [asyncio.create_task(worker())
for i in range(max_tasks)]
try:
await asyncio.gather(*worker_tasks)
except:
# In case of exception in one worker, or in case we're
# being cancelled, cancel all workers and propagate the
# exception.
cancelled = True
for t in worker_tasks:
t.cancel()
raise
A simple test case:
async def mock_task(num):
print('running', num)
await asyncio.sleep(random.uniform(1, 5))
print('done', num)
async def mock_gen():
tnum = 0
while True:
await asyncio.sleep(.1 * random.random())
print('generating', tnum)
yield asyncio.create_task(mock_task(tnum))
tnum += 1
if __name__ == '__main__':
asyncio.run(throttle(mock_gen(), 3))