When can an asyncio Task be canceled? Or, more generally, when can an asyncio loop switch to a different Task? It's been really hard for me to use cancellation in my asyncio programs, because I don't know when a CancelledError can get thrown.
I was working on a bit of code earlier, with a context manager kinda like this:
#! /usr/bin/python3
import asyncio
class MyContextManager:
def __init__(self):
self.locked = False
async def __aenter__(self):
self.locked = True
async def __aexit__(self, *_):
self.locked = False
async def main():
async with MyContextManager():
print("Doing something that needs locking")
if __name__ == "__main__":
asyncio.run(main())
What happens if the task is cancelled during __aenter__? I need to make sure that self.locked is false whenever the async with section exits. (I'm using self.locked as a simplification of a more complex acquire/release algorithm here, which includes some steps that are necessarily async.)
The docs regarding async with say:
The following code:
async with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
aenter = type(manager).__aenter__
aexit = type(manager).__aexit__
value = await aenter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not await aexit(manager, *sys.exc_info()):
raise finally:
if not hit_except:
await aexit(manager, None, None, None)
If I'm reading this right, this means that there's a window between when await aenter is called and when the try:finally: block is set up. If a task is canceled at the time that aenter returns, then aexit will not be called.
Can a task be canceled on exit from an async function? Well, let's look at the docs for asyncio.shield:
The statement:
res = await shield(something())
is equivalent to:
res = await something()
except that if the coroutine containing it is cancelled, the Task running in something() is not cancelled. From the point of view of something(), the cancellation did not happen. Although its caller is still cancelled, so the “await” expression still raises a CancelledError.
This seems to imply that an await expression can raise a CancelledError, even if the task is not canceled during the evaluation of the underlying expression.
As an opposing view, when I looked at the source code for asyncio.shield, it looks like the CancelledError is raised within asyncio.shield, rather than at the time that the await expression returns.
The biggest advantage of coroutines over threads is that it's much easier to reason about parallelism: synchronous operations will complete serially, and it's only when you await that anything can change out from under you. I use this reasoning a lot in my code. But it's not clear exactly when that await expression can change something out from under you.
An asyncio task can only be canceled on the await, as that's the only point at which the event loop can be running instead of your code.
The event loop is given control when one of the tasks you await yields to it (this is usually done by something like awaiting a sleep, or a future). Do mind that this is not necessarily done for every await, so an await doesn't guarantee that the event loop will actually run (the simplest example would be a coro that only returns a constant).
With the python equivalent async context manager code, the only point where something can be cancelled before the try block is set up is within __aenter__ itself, as that's the only point that may actually yield control. If something in the __aenter__ is cancelled and propagates up as an exception, it wouldn't make sense to call the __aexit__ either.
For shield, what the docs are saying is that on cancellation, the shield's task will be cancelled, which is shown to the caller by rising CancelledError on await, but the task it's wrapping will continue executing without ever being aware of the shielded cancellation.
Related
I am learning python asyncio and testing a lot of code using them.
Below is a code where I try to subscribe multiple Websocket streaming using asyncio and aiohttp.
I do not understand why when coro(item1, item2): is executed as a task, it does not go into the async with ... block. (i.e "A" is printed but not "B").
Could anyone help me understand the reason for this?
(I have already got a working code but , I simply want to understand what the mechanism behind this is.)
Code
import aiohttp
import asyncio
import json
async def coro(
item1,
item2):
print("A")
async with aiohttp.ClientSession() as session:
async with session.ws_connect(url='URL') as ws:
print("B")
await asyncio.gather(ws.send_json(item1),
ws.send_json(item2))
print("C")
async for msg in ws:
print(msg)
async def ws_connect(item1,
item2):
task = asyncio.create_task(coro(item1, item2))
return task
async def main():
item1 = {
"method": "subscribe",
"params": {'channel': "..."}
}
item2 = {
"method": "subscribe",
"params": {'channel': "..."}
}
ws_task = await ws_connect(item1, item2)
print("D")
asyncio.run(main())
Output
D
A
B is never printed because you never await the returned task, only the method which returned it.
The subtle mistake is in return task followed by await ws_connect(item1, item2).
TL;DR; return await task.
The key to understand the program's output is to know that the context switches in the asyncio event loop can only occur at few places, in particular at await expressions. At this point, the event loop might suspend the current coroutine and continue with another.
First, you create a ws_connect coroutine and immedietely await it, this forces the event loop to suspend main and actually run ws_connect because there is not anything else to run.
Since ws_connect contains none of those points which allow context switch, the coro() function never actually starts.
Only thing create_task does is binding the coroutine to the task object and adding it to the event loop's queue. But you never await it, you just return it as any ordinary return value. Okay, now the ws_connect() finishes and the event loop can choose to run any of the tasks, it chose to continue with main probably since it has been waiting on ws_connect().
Okay, main prints D and returns. Now what?
There is some extra await in asyncio.run which gives coro() a chance to start - hence the printed A (but only after D) yet nothing forces asyncio.run to wait on coro() so when the coro yields back to the context loop through async with, the run finishes and program exits which leaves coro() unfinished.
If you add an extra await asyncio.sleep(1) after print('D'), the loop will again suspend main for at least some time and continue with coro() and that would print B had the URL been correct.
Actually, the context switching is little bit more complicated because ordinary await on a coroutine usually does not switches unless the execution really needs to block on IO or something await asyncio.sleep(0) or yield* guarantees a true context switch without the extra blocking.
*yield from inside __await__ method.
The lesson here is simple - never return awaitables from async methods, it leads to exactly this kind of mistake. Always use return await by default, at worst you get runtime error in case the returned object is not actually awaitable(like return await some_string) and it can easily be spotted and fixed.
On the other hand, returning awaitables from ordinary functions is OK and makes it act like the function is asynchronous. Although one should be careful when mixing these two approaches. Personally, I prefer the first approach as it shifts the responsibility on the writer of the function, not the user which will be warned linters which usually do detect non-awaited corountine calls but not the returned awaitables. So another solution would to make ws_connect an ordinary function, then the await in await ws_connect would apply to the returned value(=the task), not the function itself.
I'm trying to understand how asyncio works. As for I/O operation i got understand that when await was called, we register Future object in EventLoop, and then calling epoll for get sockets which belongs to Future objects, that ready for give us data. After we run registred callback and resume function execution.
But, the thing that i cant understant, what's happening if we use await not for I/O operation. How eventloop understands that task is complete? Is it create socket for that or use another kind of loop? Is it use epoll? Or doesnt it add to Loop and used it as generator?
There is an example:
import asyncio
async def test():
return 10
async def my_coro(delay):
loop = asyncio.get_running_loop()
end_time = loop.time() + delay
while True:
print("Blocking...")
await test()
if loop.time() > end_time:
print("Done.")
break
async def main():
await my_coro(3.0)
asyncio.run(main())
await doesn't automatically yield to the event loop, that happens only when an async function (anywhere in the chain of awaits) requests suspension, typically due to IO or timeout not being ready.
In your example the event loop is never returned to, which you can easily verify by moving the "Blocking" print before the while loop and changing main to await asyncio.gather(my_coro(3.0), my_coro(3.0)). What you'll observe is that the coroutines are executed in series ("blocking" followed by "done", all repeated twice), not in parallel ("blocking" followed by another "blocking" and then twice "done"). The reason for that was that there was simply no opportunity for a context switch - my_coro executed in one go as if they were an ordinary function because none of its awaits ever chose to suspend.
I'd like to know what guarantees python gives around when a event loop will switch tasks.
As I understand it async / await are significantly different from threads in that the event loop does not switch task based on time slicing, meaning that unless the task yields (await), it will carry on indefinitely. This can actually be useful because it is easier to manage critical sections under asyncio than with threading.
What I'm less clear about is something like the following:
async def caller():
while True:
await callee()
async def callee():
pass
In this example caller is repeatedly await. So technically it is yielding. But I'm not clear on whether this will allow other tasks on the event loop to execute because it only yields to callee and that is never yielding.
That is if I awaited callee inside a "critical section" even though I know it won't block, am I at risk of something else unexpected happening?
You are right to be wary. caller yields from callee, and yields to the event loop. Then the event loop decides which task to resume. Other tasks may (hopefully) be squeezed in between the calls to callee. callee needs to await an actual blocking Awaitable such as asyncio.Future or asyncio.sleep(), not a coroutine, otherwise the control will not be returned to the event loop until caller returns.
For example, the following code will finish the caller2 task before it starts working on the caller1 task. Because callee2 is essentially a sync function without awaiting a blocking I/O operations, therefore, no suspension point is created and caller2 will resume immediately after each call to callee2.
import asyncio
import time
async def caller1():
for i in range(5):
await callee1()
async def callee1():
await asyncio.sleep(1)
print(f"called at {time.strftime('%X')}")
async def caller2():
for i in range(5):
await callee2()
async def callee2():
time.sleep(1)
print(f"sync called at {time.strftime('%X')}")
async def main():
task1 = asyncio.create_task(caller1())
task2 = asyncio.create_task(caller2())
await task1
await task2
asyncio.run(main())
Result:
sync called at 19:23:39
sync called at 19:23:40
sync called at 19:23:41
sync called at 19:23:42
sync called at 19:23:43
called at 19:23:43
called at 19:23:44
called at 19:23:45
called at 19:23:46
called at 19:23:47
But if callee2 awaits as the following, the task switching will happen even if it awaits asyncio.sleep(0), and the tasks will run concurrently.
async def callee2():
await asyncio.sleep(1)
print('sync called')
Result:
called at 19:22:52
sync called at 19:22:52
called at 19:22:53
sync called at 19:22:53
called at 19:22:54
sync called at 19:22:54
called at 19:22:55
sync called at 19:22:55
called at 19:22:56
sync called at 19:22:56
This behavior is not necessarily intuitive, but it makes sense considering that asyncio was made to handle I/O operations and networking concurrently, not the usual synchronous python codes.
Another thing to note is: This still works if the callee awaits a coroutine that, in turn, awaits a asyncio.Future, asyncio.sleep(), or another coroutine that await one of those things down the chain. The flow control will be returned to the event loop when the blocking Awaitable is awaited. So the following also works.
async def callee2():
await inner_callee()
print(f"sync called at {time.strftime('%X')}")
async def inner_callee():
await asyncio.sleep(1)
TLDR: No. Coroutines and their respective keywords (await, async with, async for) only enable suspension. Whether suspension occurs depends on the framework used, if at all.
Third-party async functions / iterators / context managers can act as
checkpoints; if you see await <something> or one of its friends, then
that might be a checkpoint. So to be safe, you should prepare for
scheduling or cancellation happening there.
[Trio documentation]
The await syntax of Python is syntactic sugar around two fundamental mechanisms: yield to temporarily suspend with a value, and return to permanently exit with a value. These are the same that, say, a generator function coroutine can use:
def gencoroutine():
for i in range(5):
yield i # temporarily suspend
return 5 # permanently exit
Notably, return does not imply a suspension. It is possible for a generator coroutine to never yield at all.
The await keyword (and its sibling yield from) interacts with both the yield and return mechanism:
If its target yields, await "passes on" the suspension to its own caller. This allows to suspend an entire stack of coroutines that all await each other.
If its target returnss, await catches the return value and provides it to its own coroutine. This allows to return a value directly to a "caller", without suspension.
This means that await does not guarantee that a suspension occurs. It is up to the target of await to trigger a suspension.
By itself, an async def coroutine can only return without suspension, and await to allow suspension. It cannot suspend by itself (yield does not suspend to the event loop).
async def unyielding():
return 2 # or `pass`
This means that await of just coroutines does never suspend. Only specific awaitables are able to suspend.
Suspension is only possible for awaitables with a custom __await__ method. These can yield directly to the event loop.
class YieldToLoop:
def __await__(self):
yield # to event loop
return # to awaiter
This means that await, directly or indirectly, of a framework's awaitable will suspend.
The exact semantics of suspending depend on the async framework in use. For example, whether a sleep(0) triggers a suspension or not, or which coroutine to run instead, is up to the framework. This also extends to async iterators and context managers -- for example, many async context managers will suspend either on enter or exit but not both.
Trio
If you call an async function provided by Trio (await <something in trio>), and it doesn’t raise an exception, then it always acts as a checkpoint. (If it does raise an exception, it might act as a checkpoint or might not.)
Asyncio
sleep() always suspends the current task, allowing other tasks to run.
I was wondering what exactly happens when we await a coroutine in async Python code, for example:
await send_message(string)
(1) send_message is added to the event loop, and the calling coroutine gives up control to the event loop, or
(2) We jump directly into send_message
Most explanations I read point to (1), as they describe the calling coroutine as exiting. But my own experiments suggest (2) is the case: I tried to have a coroutine run after the caller but before the callee and could not achieve this.
Disclaimer: Open to correction (particularly as to details and correct terminology) since I arrived here looking for the answer to this myself. Nevertheless, the research below points to a pretty decisive "main point" conclusion:
Correct OP answer: No, await (per se) does not yield to the event loop, yield yields to the event loop, hence for the case given: "(2) We jump directly into send_message". In particular, certain yield expressions are the only points, at bottom, where async tasks can actually be switched out (in terms of nailing down the precise spot where Python code execution can be suspended).
To be proven and demonstrated: 1) by theory/documentation, 2) by implementation code, 3) by example.
By theory/documentation
PEP 492: Coroutines with async and await syntax
While the PEP is not tied to any specific Event Loop implementation, it is relevant only to the kind of coroutine that uses yield as a signal to the scheduler, indicating that the coroutine will be waiting until an event (such as IO) is completed. ...
[await] uses the yield from implementation [with an extra step of validating its argument.] ...
Any yield from chain of calls ends with a yield. This is a fundamental mechanism of how Futures are implemented. Since, internally, coroutines are a special kind of generators, every await is suspended by a yield somewhere down the chain of await calls (please refer to PEP 3156 for a detailed explanation). ...
Coroutines are based on generators internally, thus they share the implementation. Similarly to generator objects, coroutines have throw(), send() and close() methods. ...
The vision behind existing generator-based coroutines and this proposal is to make it easy for users to see where the code might be suspended.
In context, "easy for users to see where the code might be suspended" seems to refer to the fact that in synchronous code yield is the place where execution can be "suspended" within a routine allowing other code to run, and that principle now extends perfectly to the async context wherein a yield (if its value is not consumed within the running task but is propagated up to the scheduler) is the "signal to the scheduler" to switch out tasks.
More succinctly: where does a generator yield control? At a yield. Coroutines (including those using async and await syntax) are generators, hence likewise.
And it is not merely an analogy, in implementation (see below) the actual mechanism by which a task gets "into" and "out of" coroutines is not anything new, magical, or unique to the async world, but simply by calling the coro's <generator>.send() method. That was (as I understand the text) part of the "vision" behind PEP 492: async and await would provide no novel mechanism for code suspension but just pour async-sugar on Python's already well-beloved and powerful generators.
And
PEP 3156: The "asyncio" module
The loop.slow_callback_duration attribute controls the maximum execution time allowed between two yield points before a slow callback is reported [emphasis in original].
That is, an uninterrupted segment of code (from the async perspective) is demarcated as that between two successive yield points (whose values reached up to the running Task level (via an await/yield from tunnel) without being consumed within it).
And this:
The scheduler has no public interface. You interact with it by using yield from future and yield from task.
Objection: "That says 'yield from', but you're trying to argue that the task can only switch out at a yield itself! yield from and yield are different things, my friend, and yield from itself doesn't suspend code!"
Ans: Not a contradiction. The PEP is saying you interact with the scheduler by using yield from future/task. But as noted above in PEP 492, any chain of yield from (~aka await) ultimately reaches a yield (the "bottom turtle"). In particular (see below), yield from future does in fact yield that same future after some wrapper work, and that yield is the actual "switch out point" where another task takes over. But it is incorrect for your code to directly yield a Future up to the current Task because you would bypass the necessary wrapper.
The objection having been answered, and its practical coding considerations being noted, the point I wish to make from the above quote remains: that a suitable yield in Python async code is ultimately the one thing which, having suspended code execution in the standard way that any other yield would do, now futher engages the scheduler to bring about a possible task switch.
By implementation code
asyncio/futures.py
class Future:
...
def __await__(self):
if not self.done():
self._asyncio_future_blocking = True
yield self # This tells Task to wait for completion.
if not self.done():
raise RuntimeError("await wasn't used with future")
return self.result() # May raise too.
__iter__ = __await__ # make compatible with 'yield from'.
Paraphrase: The line yield self is what tells the running task to sit out for now and let other tasks run, coming back to this one sometime after self is done.
Almost all of your awaitables in asyncio world are (multiple layers of) wrappers around a Future. The event loop remains utterly blind to all higher level await awaitable expressions until the code execution trickles down to an await future or yield from future and then (as seen here) calls yield self, which yielded self is then "caught" by none other than the Task under which the present coroutine stack is running thereby signaling to the task to take a break.
Possibly the one and only exception to the above "code suspends at yield self within await future" rule, in an asyncio context, is the potential use of a bare yield such as in asyncio.sleep(0). And since the sleep function is a topic of discourse in the comments of this post, let's look at that.
asyncio/tasks.py
#types.coroutine
def __sleep0():
"""Skip one event loop run cycle.
This is a private helper for 'asyncio.sleep()', used
when the 'delay' is set to 0. It uses a bare 'yield'
expression (which Task.__step knows how to handle)
instead of creating a Future object.
"""
yield
async def sleep(delay, result=None, *, loop=None):
"""Coroutine that completes after a given time (in seconds)."""
if delay <= 0:
await __sleep0()
return result
if loop is None:
loop = events.get_running_loop()
else:
warnings.warn("The loop argument is deprecated since Python 3.8, "
"and scheduled for removal in Python 3.10.",
DeprecationWarning, stacklevel=2)
future = loop.create_future()
h = loop.call_later(delay,
futures._set_result_unless_cancelled,
future, result)
try:
return await future
finally:
h.cancel()
Note: We have here the two interesting cases at which control can shift to the scheduler:
(1) The bare yield in __sleep0 (when called via an await).
(2) The yield self immediately within await future.
The crucial line (for our purposes) in asyncio/tasks.py is when Task._step runs its top-level coroutine via result = self._coro.send(None) and recognizes fourish cases:
(1) result = None is generated by the coro (which, again, is a generator): the task "relinquishes control for one event loop iteration".
(2) result = future is generated within the coro, with further magic member field evidence that the future was yielded in a proper manner from out of Future.__iter__ == Future.__await__: the task relinquishes control to the event loop until the future is complete.
(3) A StopIteration is raised by the coro indicating the coroutine completed (i.e. as a generator it exhausted all its yields): the final result of the task (which is itself a Future) is set to the coroutine return value.
(4) Any other Exception occurs: the task's set_exception is set accordingly.
Modulo details, the main point for our concern is that coroutine segments in an asyncio event loop ultimately run via coro.send(). Initial startup and final termination aside, send() proceeds precisely from the last yield value it generated to the next one.
By example
import asyncio
import types
def task_print(s):
print(f"{asyncio.current_task().get_name()}: {s}")
async def other_task(s):
task_print(s)
class AwaitableCls:
def __await__(self):
task_print(" 'Jumped straight into' another `await`; the act of `await awaitable` *itself* doesn't 'pause' anything")
yield
task_print(" We're back to our awaitable object because that other task completed")
asyncio.create_task(other_task("The event loop gets control when `yield` points (from an iterable coroutine) propagate up to the `current_task` through a suitable chain of `await` or `yield from` statements"))
async def coro():
task_print(" 'Jumped straight into' coro; the `await` keyword itself does nothing to 'pause' the current_task")
await AwaitableCls()
task_print(" 'Jumped straight back into' coro; we have another pending task, but leaving an `__await__` doesn't 'pause' the task any more than entering the `__await__` does")
#types.coroutine
def iterable_coro(context):
task_print(f"`{context} iterable_coro`: pre-yield")
yield None # None or a Future object are the only legitimate yields to the task in asyncio
task_print(f"`{context} iterable_coro`: post-yield")
async def original_task():
asyncio.create_task(other_task("Aha, but a (suitably unconsumed) *`yield`* DOES 'pause' the current_task allowing the event scheduler to `_wakeup` another task"))
task_print("Original task")
await coro()
task_print("'Jumped straight out of' coro. Leaving a coro, as with leaving/entering any awaitable, doesn't give control to the event loop")
res = await iterable_coro("await")
assert res is None
asyncio.create_task(other_task("This doesn't run until the very end because the generated None following the creation of this task is consumed by the `for` loop"))
for y in iterable_coro("for y in"):
task_print(f"But 'ordinary' `yield` points (those which are consumed by the `current_task` itself) behave as ordinary without relinquishing control at the async/task-level; `y={y}`")
task_print("Done with original task")
asyncio.get_event_loop().run_until_complete(original_task())
run in python3.8 produces
Task-1: Original task
Task-1: 'Jumped straight into' coro; the await keyword itself does nothing to 'pause' the current_task
Task-1: 'Jumped straight into' another await; the act of await awaitable itself doesn't 'pause' anything
Task-2: Aha, but a (suitably unconsumed) yield DOES 'pause' the current_task allowing the event scheduler to _wakeup another task
Task-1: We're back to our awaitable object because that other task completed
Task-1: 'Jumped straight back into' coro; we have another pending task, but leaving an __await__ doesn't 'pause' the task any more than entering the __await__ does
Task-1: 'Jumped straight out of' coro. Leaving a coro, as with leaving/entering any awaitable, doesn't give control to the event loop
Task-1: await iterable_coro: pre-yield
Task-3: The event loop gets control when yield points (from an iterable coroutine) propagate up to the current_task through a suitable chain of await or yield from statements
Task-1: await iterable_coro: post-yield
Task-1: for y in iterable_coro: pre-yield
Task-1: But 'ordinary' yield points (those which are consumed by the current_task itself) behave as ordinary without relinquishing control at the async/task-level; y=None
Task-1: for y in iterable_coro: post-yield
Task-1: Done with original task
Task-4: This doesn't run until the very end because the generated None following the creation of this task is consumed by the for loop
Indeed, exercises such as the following can help one's mind to decouple the functionality of async/await from notion of "event loops" and such. The former is conducive to nice implementations and usages of the latter, but you can use async and await just as specially syntaxed generator stuff without any "loop" (whether asyncio or otherwise) whatsoever:
import types # no asyncio, nor any other loop framework
async def f1():
print(1)
print(await f2(),'= await f2()')
return 8
#types.coroutine
def f2():
print(2)
print((yield 3),'= yield 3')
return 7
class F3:
def __await__(self):
print(4)
print((yield 5),'= yield 5')
print(10)
return 11
task1 = f1()
task2 = F3().__await__()
""" You could say calls to send() represent our
"manual task management" in this script.
"""
print(task1.send(None), '= task1.send(None)')
print(task2.send(None), '= task2.send(None)')
try:
print(task1.send(6), 'try task1.send(6)')
except StopIteration as e:
print(e.value, '= except task1.send(6)')
try:
print(task2.send(9), 'try task2.send(9)')
except StopIteration as e:
print(e.value, '= except task2.send(9)')
produces
1
2
3 = task1.send(None)
4
5 = task2.send(None)
6 = yield 3
7 = await f2()
8 = except task1.send(6)
9 = yield 5
10
11 = except task2.send(9)
Yes, await passes control back to the asyncio eventloop, and allows it to schedule other async functions.
Another way is
await asyncio.sleep(0)
With python 3.5 or later, is there any difference between directly applying await to a future or task, and wrapping it with asyncio.wait_for? The documentation is unclear on when it is appropriate to use wait_for and I'm wondering if it's a vestige of the old generator-based library. The test program below appears to show no difference but that doesn't really prove anything.
import asyncio
async def task_one():
await asyncio.sleep(0.1)
return 1
async def task_two():
await asyncio.sleep(0.1)
return 2
async def test(loop):
t1 = loop.create_task(task_one())
t2 = loop.create_task(task_two())
print(repr(await t1))
print(repr(await asyncio.wait_for(t2, None)))
def main():
loop = asyncio.get_event_loop()
try:
loop.run_until_complete(test(loop))
finally:
loop.close()
main()
The wait_for gives two more functionalities:
allow to define timeout,
let you specify the loop
Your example:
await f1
await asyncio.wait_for(f1, None) # or simply asyncio.wait_for(f1)
besides an overhead of calling additional wrapper (wait_for), they're the same (https://github.com/python/cpython/blob/master/Lib/asyncio/tasks.py#L318).
Both awaits will wait indefinitely for the results (or exception). In this case plain await is more appropriate.
On the other hand if you provide timeout argument it will wait for the results with time constraint. And if it will take more than the timeout it will raise TimeoutError and the future will be cancelled.
async def my_func():
await asyncio.sleep(10)
return 'OK'
# will wait 10s
await my_func()
# will wait only 5 seconds and then will raise TimeoutError
await asyncio.wait_for(my_func(), 5)
Another thing is the loop argument. In the most cases you shouldn't be bothered, the use case is limited: inject different loop for tests, run other loop ...
The problem with this parameter is, that all subsequent tasks/functions should also have that loop passed along...
More info https://github.com/python/asyncio/issues/362
Passing asyncio loop by argument or using default asyncio loop
Why use explicit loop parameter with aiohttp?
Unfortunately the python documentation is a little bit unclear here, but if you have a look into the sources its pretty obvious:
In contrary to await the coroutine asyncio.wait_for() allows to wait only for a limited time until the future/task completes. If it does not complete within this time a concurrent.futures.TimeoutError is raised.
This timeout can be specified as second parameter. In your sample code this timeout parameter is None which results in exactly the same functionality as directly applying await/yield from.