Pool of asyncio coroutines with familiar interface. Supports python 3.5+ (including PyPy 6+, which is also 3.5 atm)
AioPool makes sure no more and no less (if possible) than size spawned coroutines are active at the same time. spawned means created and scheduled with one of the pool interface methods, active means coroutine function started executing it's code, as opposed to waiting -- which waits for pool space without entering coroutine function.
Read code doctrings for details.
Creates pool of size concurrent tasks. Supports async context manager interface.
Waits for pool space, then creates task for coro coroutine, returning future for it's result. Can spawn coroutine, created by cb with result of coro as first argument. ctx context is passed to callback as third positinal argument.
Waits for pool space, then creates task for coro, then waits for it to finish, then returns result of coro if no callback is provided, otherwise creates task for callback, waits for it and returns result of callback.
Creates waiting task for coro, returns future without waiting for pool space. Task is executed "in pool" when pool space is available.
Waits for all spawned (active and waiting) tasks to finish. Joining pool from coroutine, spawned by the same pool leads to deadlock.
Cancels spawned tasks (active and waiting), finding them by provided futures. If no futures provided -- cancels all spawned tasks.
Spawns coroutines created by fn function for each item in iterable with spawn, waits for all of them to finish (including callbacks), returns results maintaining order of iterable.
Spawns coroutines created by fn function for each item in iterable with spawn_n, returns futures for task results maintaining order of iterable.
itermap(fn, iterable, cb=None, ctx=None, *, flat=True, get_result=getres.flat, timeout=None, yield_when=asyncio.ALL_COMPLETED)
Spawns tasks with map_n(fn, iterable, cb, ctx), then waits for results with asyncio.wait function, yielding ready results one by one if flat == True, otherwise yielding list of ready results.
spawn and map methods is probably what you should use in 99% of cases. Their overhead is minimal (~3% execution time), and even in worst cases memory usage is insignificant.
spawn_n, map_n and itermap methods give you more control and flexibily, but they come with a price of higher overhead. They spawn all tasks that you want, and most of the tasks wait their turn "in background". If you spawn too much (10**6+ tasks) -- you'll use most of the memory you have in system, also you'll lose a lot of time on "concurrency management" of all the tasks spawned.
Play with python tests/loadtest.py -h to understand what you want to use.
Usage examples (more in tests/ and examples/):
async def worker(n): # dummy worker
await aio.sleep(1 / n)
return n
async def spawn_n_usage(todo=[range(1,51), range(51,101), range(101,200)]):
futures = []
async with AioPool(size=20) as pool:
for tasks in todo:
for i in tasks: # too many tasks
# Returns quickly for all tasks, does not wait for pool space.
# Workers are not spawned, they wait for pool space in their
# own background tasks.
fut = pool.spawn_n(worker(i))
futures.append(fut)
# At this point not a single worker should start.
# Context manager calls `join` at exit, so this will finish when all
# workers return, crash or cancelled.
assert sum(itertools.chain.from_iterable(todo)) == \
sum(f.result() for f in futures)
async def spawn_usage(todo=range(1,4)):
futures = []
async with AioPool(size=2) as pool:
for i in todo: # 1, 2, 3
# Returns quickly for 1 and 2, then waits for empty space for 3,
# spawns 3 and returns. Can save some resources I guess.
fut = await pool.spawn(worker(i))
futures.append(fut)
# At this point some of the workers already started.
# Context manager calls `join` at exit, so this will finish when all
# workers return, crash or cancelled.
assert sum(todo) == sum(fut.result() for fut in futures) # all done
async def map_usage(todo=range(100)):
pool = AioPool(size=10)
# Waits and collects results from all spawned workers,
# returns them in same order as `todo`, if worker crashes or cancelled:
# returns exception object as a result.
# Basically, it wraps `spawn_usage` code into one call.
results = await pool.map(worker, todo)
# await pool.join() # is not needed here, bcs no other tasks were spawned
assert isinstance(results[0], ZeroDivisionError) \
and sum(results[1:]) == sum(todo)
async def itermap_usage(todo=range(1,11)):
result = 0
async with AioPool(size=10) as pool:
# Combines spawn_n and iterwait, which is a wrapper for asyncio.wait,
# which yields results of finished workers according to `timeout` and
# `yield_when` params passed to asyncio.wait (see it's docs for details)
async for res in pool.itermap(worker, todo, timeout=0.5):
result += res
# technically, you can skip join call
assert result == sum(todo)
async def callbacks_usage():
async def wrk(n): # custom dummy worker
await aio.sleep(1 / n)
return n
async def cb(res, err, ctx): # callback
if err: # error handling
exc, tb = err
assert tb # the only purpose of this is logging
return exc
pool, n = ctx # context can be anything you like
await aio.sleep(1 / (n-1))
return res + n
todo = range(5)
futures = []
async with AioPool(size=2) as pool:
for i in todo:
fut = pool.spawn_n(wrk(i), cb, (pool, i))
futures.append(fut)
results = []
for fut in futures:
# there are helpers for result extraction. `flat` one will do
# exactly what's written below
# from asyncio_pool import getres
# results.append(getres.flat(fut))
try:
results.append(fut.result())
except Exception as e:
results.append(e)
# First error happens for n == 0 in wrk, exception of it is passed to
# callback, callback returns it to us. Second one happens in callback itself
# and is passed to us by pool.
assert all(isinstance(e, ZeroDivisionError) for e in results[:2])
# All n's in `todo` are passed through `wrk` and `cb` (cb adds wrk result
# and # number, passed inside context), except for n == 0 and n == 1.
assert sum(results[2:]) == 2 * (sum(todo) - 0 - 1)
async def exec_usage(todo=range(1,11)):
async with AioPool(size=4) as pool:
futures = pool.map_n(worker, todo)
# While other workers are waiting or active, you can "synchronously"
# execute one task. It does not interrupt others, just waits for pool
# space, then waits for task to finish and then returns it's result.
important_res = await pool.exec(worker(2))
assert 2 == important_res
# You can continue working as usual:
moar = await pool.spawn(worker(10))
assert sum(todo) == sum(f.result() for f in futures)
async def cancel_usage():
async def wrk(*arg, **kw):
await aio.sleep(0.5)
return 1
pool = AioPool(size=2)
f_quick = pool.spawn_n(aio.sleep(0.1))
f12 = await pool.spawn(wrk()), pool.spawn_n(wrk())
f35 = pool.map_n(wrk, range(3))
# At this point, if you cancel futures, returned by pool methods,
# you just won't be able to retrieve spawned task results, task
# themselves will continue working. Don't do this:
# f_quick.cancel()
# use `pool.cancel` instead:
# cancel some
await aio.sleep(0.1)
cancelled, results = await pool.cancel(f12[0], f35[2]) # running and waiting
assert 2 == cancelled # none of them had time to finish
assert 2 == len(results) and \
all(isinstance(res, aio.CancelledError) for res in results)
# cancel all others
await aio.sleep(0.1)
# not interrupted and finished successfully
assert f_quick.done() and f_quick.result() is None
cancelled, results = await pool.cancel() # all
assert 3 == cancelled
assert len(results) == 3 and \
all(isinstance(res, aio.CancelledError) for res in results)
assert await pool.join() # joins successfully
async def details(todo=range(1,11)):
pool = AioPool(size=5)
# This code:
f1 = []
for i in todo:
f1.append(pool.spawn_n(worker(i)))
# is equivalent to one call of `map_n`:
f2 = pool.map_n(worker, todo)
# Afterwards you can await for any given future:
try:
assert 3 == await f1[2] # result of spawn_n(worker(3))
except Exception as e:
# exception happened in worker (or CancelledError) will be re-raised
pass
# Or use `asyncio.wait` to handle results in batches (see `iterwait` also):
important_res = 0
more_important = [f1[1], f2[1], f2[2]]
while more_important:
done, more_important = await aio.wait(more_important, timeout=0.5)
# handle result, note it will re-raise exceptions
important_res += sum(f.result() for f in done)
assert important_res == 2 + 2 + 3
# But you need to join, to allow all spawned workers to finish
# (of course you can `asyncio.wait` all of the futures if you want to)
await pool.join()
assert all(f.done() for f in itertools.chain(f1,f2)) # this is guaranteed
assert 2 * sum(todo) == sum(f.result() for f in itertools.chain(f1,f2))