Coroutines and Tasks

This section outlines high-level asyncio APIs to work with coroutines and Tasks.

Coroutines

Coroutines declared with the async/await syntax is the preferred way of writing asyncio applications. For example, the following snippet of code prints “hello”, waits 1 second, and then prints “world”:

>>> import asyncio

>>> async def main():
...     print('hello')
...     await asyncio.sleep(1)
...     print('world')

>>> asyncio.run(main())
hello
world

Note that simply calling a coroutine will not schedule it to be executed:

>>> main()
<coroutine object main at 0x1053bb7c8>

To actually run a coroutine, asyncio provides three main mechanisms:

  • The asyncio.run() function to run the top-level entry point “main()” function (see the above example.)

  • Awaiting on a coroutine. The following snippet of code will print “hello” after waiting for 1 second, and then print “world” after waiting for another 2 seconds:

    import asyncio
    import time
    
    async def say_after(delay, what):
        await asyncio.sleep(delay)
        print(what)
    
    async def main():
        print(f"started at {time.strftime('%X')}")
    
        await say_after(1, 'hello')
        await say_after(2, 'world')
    
        print(f"finished at {time.strftime('%X')}")
    
    asyncio.run(main())
    

    Expected output:

    started at 17:13:52
    hello
    world
    finished at 17:13:55
    
  • The asyncio.create_task() function to run coroutines concurrently as asyncio Tasks.

    Let’s modify the above example and run two say_after coroutines concurrently:

    async def main():
        task1 = asyncio.create_task(
            say_after(1, 'hello'))
    
        task2 = asyncio.create_task(
            say_after(2, 'world'))
    
        print(f"started at {time.strftime('%X')}")
    
        # Wait until both tasks are completed (should take
        # around 2 seconds.)
        await task1
        await task2
    
        print(f"finished at {time.strftime('%X')}")
    

    Note that expected output now shows that the snippet runs 1 second faster than before:

    started at 17:14:32
    hello
    world
    finished at 17:14:34
    
  • The asyncio.TaskGroup class provides a more modern alternative to create_task(). Using this API, the last example becomes:

    async def main():
        async with asyncio.TaskGroup() as tg:
            task1 = tg.create_task(
                say_after(1, 'hello'))
    
            task2 = tg.create_task(
                say_after(2, 'world'))
    
            print(f"started at {time.strftime('%X')}")
    
        # The wait is implicit when the context manager exits.
    
        print(f"finished at {time.strftime('%X')}")
    

    The timing and output should be the same as for the previous version.

    New in version 3.11: asyncio.TaskGroup.

Awaitables

We say that an object is an awaitable object if it can be used in an await expression. Many asyncio APIs are designed to accept awaitables.

There are three main types of awaitable objects: coroutines, Tasks, and Futures.

Coroutines

Python coroutines are awaitables and therefore can be awaited from other coroutines:

import asyncio

async def nested():
    return 42

async def main():
    # Nothing happens if we just call "nested()".
    # A coroutine object is created but not awaited,
    # so it *won't run at all*.
    nested()

    # Let's do it differently now and await it:
    print(await nested())  # will print "42".

asyncio.run(main())

Important

In this documentation the term “coroutine” can be used for two closely related concepts:

  • a coroutine function: an async def function;

  • a coroutine object: an object returned by calling a coroutine function.

Tasks

Tasks are used to schedule coroutines concurrently.

When a coroutine is wrapped into a Task with functions like asyncio.create_task() the coroutine is automatically scheduled to run soon:

import asyncio

async def nested():
    return 42

async def main():
    # Schedule nested() to run soon concurrently
    # with "main()".
    task = asyncio.create_task(nested())

    # "task" can now be used to cancel "nested()", or
    # can simply be awaited to wait until it is complete:
    await task

asyncio.run(main())

Futures

A Future is a special low-level awaitable object that represents an eventual result of an asynchronous operation.

When a Future object is awaited it means that the coroutine will wait until the Future is resolved in some other place.

Future objects in asyncio are needed to allow callback-based code to be used with async/await.

Normally there is no need to create Future objects at the application level code.

Future objects, sometimes exposed by libraries and some asyncio APIs, can be awaited:

async def main():
    await function_that_returns_a_future_object()

    # this is also valid:
    await asyncio.gather(
        function_that_returns_a_future_object(),
        some_python_coroutine()
    )

A good example of a low-level function that returns a Future object is loop.run_in_executor().

Creating Tasks

asyncio.create_task(coro, *, name=None, context=None)

Wrap the coro coroutine into a Task and schedule its execution. Return the Task object.

If name is not None, it is set as the name of the task using Task.set_name().

An optional keyword-only context argument allows specifying a custom contextvars.Context for the coro to run in. The current context copy is created when no context is provided.

The task is executed in the loop returned by get_running_loop(), RuntimeError is raised if there is no running loop in current thread.

Note

asyncio.TaskGroup.create_task() is a newer alternative that allows for convenient waiting for a group of related tasks.

Important

Save a reference to the result of this function, to avoid a task disappearing mid execution. The event loop only keeps weak references to tasks. A task that isn’t referenced elsewhere may get garbage-collected at any time, even before it’s done. For reliable “fire-and-forget” background tasks, gather them in a collection:

background_tasks = set()

for i in range(10):
    task = asyncio.create_task(some_coro(param=i))

    # Add task to the set. This creates a strong reference.
    background_tasks.add(task)

    # To prevent keeping references to finished tasks forever,
    # make each task remove its own reference from the set after
    # completion:
    task.add_done_callback(background_tasks.discard)

New in version 3.7.

Changed in version 3.8: Added the name parameter.

Changed in version 3.11: Added the context parameter.

Task Groups

Task groups combine a task creation API with a convenient and reliable way to wait for all tasks in the group to finish.

class asyncio.TaskGroup

An asynchronous context manager holding a group of tasks. Tasks can be added to the group using create_task(). All tasks are awaited when the context manager exits.

New in version 3.11.

create_task(coro, *, name=None, context=None)

Create a task in this task group. The signature matches that of asyncio.create_task().

Example:

async def main():
    async with asyncio.TaskGroup() as tg:
        task1 = tg.create_task(some_coro(...))
        task2 = tg.create_task(another_coro(...))
    print("Both tasks have completed now.")

The async with statement will wait for all tasks in the group to finish. While waiting, new tasks may still be added to the group (for example, by passing tg into one of the coroutines and calling tg.create_task() in that coroutine). Once the last task has finished and the async with block is exited, no new tasks may be added to the group.

The first time any of the tasks belonging to the group fails with an exception other than asyncio.CancelledError, the remaining tasks in the group are cancelled. No further tasks can then be added to the group. At this point, if the body of the async with statement is still active (i.e., __aexit__() hasn’t been called yet), the task directly containing the async with statement is also cancelled. The resulting asyncio.CancelledError will interrupt an await, but it will not bubble out of the containing async with statement.

Once all tasks have finished, if any tasks have failed with an exception other than asyncio.CancelledError, those exceptions are combined in an ExceptionGroup or BaseExceptionGroup (as appropriate; see their documentation) which is then raised.

Two base exceptions are treated specially: If any task fails with KeyboardInterrupt or SystemExit, the task group still cancels the remaining tasks and waits for them, but then the initial KeyboardInterrupt or SystemExit is re-raised instead of ExceptionGroup or BaseExceptionGroup.

If the body of the async with statement exits with an exception (so __aexit__() is called with an exception set), this is treated the same as if one of the tasks failed: the remaining tasks are cancelled and then waited for, and non-cancellation exceptions are grouped into an exception group and raised. The exception passed into __aexit__(), unless it is asyncio.CancelledError, is also included in the exception group. The same special case is made for KeyboardInterrupt and SystemExit as in the previous paragraph.

Sleeping

coroutine asyncio.sleep(delay, result=None)

Block for delay seconds.

If result is provided, it is returned to the caller when the coroutine completes.

sleep() always suspends the current task, allowing other tasks to run.

Setting the delay to 0 provides an optimized path to allow other tasks to run. This can be used by long-running functions to avoid blocking the event loop for the full duration of the function call.

Example of coroutine displaying the current date every second for 5 seconds:

import asyncio
import datetime

async def display_date():
    loop = asyncio.get_running_loop()
    end_time = loop.time() + 5.0
    while True:
        print(datetime.datetime.now())
        if (loop.time() + 1.0) >= end_time:
            break
        await asyncio.sleep(1)

asyncio.run(display_date())

Changed in version 3.10: Removed the loop parameter.

Running Tasks Concurrently

awaitable asyncio.gather(*aws, return_exceptions=False)

Run awaitable objects in the aws sequence concurrently.

If any awaitable in aws is a coroutine, it is automatically scheduled as a Task.

If all awaitables are completed successfully, the result is an aggregate list of returned values. The order of result values corresponds to the order of awaitables in aws.

If return_exceptions is False (default), the first raised exception is immediately propagated to the task that awaits on gather(). Other awaitables in the aws sequence won’t be cancelled and will continue to run.

If return_exceptions is True, exceptions are treated the same as successful results, and aggregated in the result list.

If gather() is cancelled, all submitted awaitables (that have not completed yet) are also cancelled.

If any Task or Future from the aws sequence is cancelled, it is treated as if it raised CancelledError – the gather() call is not cancelled in this case. This is to prevent the cancellation of one submitted Task/Future to cause other Tasks/Futures to be cancelled.

Note

A more modern way to create and run tasks concurrently and wait for their completion is asyncio.TaskGroup.

Example:

import asyncio

async def factorial(name, number):
    f = 1
    for i in range(2, number + 1):
        print(f"Task {name}: Compute factorial({number}), currently i={i}...")
        await asyncio.sleep(1)
        f *= i
    print(f"Task {name}: factorial({number}) = {f}")
    return f

async def main():
    # Schedule three calls *concurrently*:
    L = await asyncio.gather(
        factorial("A", 2),
        factorial("B", 3),
        factorial("C", 4),
    )
    print(L)

asyncio.run(main())

# Expected output:
#
#     Task A: Compute factorial(2), currently i=2...
#     Task B: Compute factorial(3), currently i=2...
#     Task C: Compute factorial(4), currently i=2...
#     Task A: factorial(2) = 2
#     Task B: Compute factorial(3), currently i=3...
#     Task C: Compute factorial(4), currently i=3...
#     Task B: factorial(3) = 6
#     Task C: Compute factorial(4), currently i=4...
#     Task C: factorial(4) = 24
#     [2, 6, 24]

Note

If return_exceptions is False, cancelling gather() after it has been marked done won’t cancel any submitted awaitables. For instance, gather can be marked done after propagating an exception to the caller, therefore, calling gather.cancel() after catching an exception (raised by one of the awaitables) from gather won’t cancel any other awaitables.

Changed in version 3.7: If the gather itself is cancelled, the cancellation is propagated regardless of return_exceptions.

Changed in version 3.10: Removed the loop parameter.

Deprecated since version 3.10: Deprecation warning is emitted if no positional arguments are provided or not all positional arguments are Future-like objects and there is no running event loop.

Shielding From Cancellation

awaitable asyncio.shield(aw)

Protect an awaitable object from being cancelled.

If aw is a coroutine it is automatically scheduled as a Task.

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.

If something() is cancelled by other means (i.e. from within itself) that would also cancel shield().

If it is desired to completely ignore cancellation (not recommended) the shield() function should be combined with a try/except clause, as follows:

try:
    res = await shield(something())
except CancelledError:
    res = None

Changed in version 3.10: Removed the loop parameter.

Deprecated since version 3.10: Deprecation warning is emitted if aw is not Future-like object and there is no running event loop.

Timeouts

coroutine asyncio.wait_for(aw, timeout)

Wait for the aw awaitable to complete with a timeout.

If aw is a coroutine it is automatically scheduled as a Task.

timeout can either be None or a float or int number of seconds to wait for. If timeout is None, block until the future completes.

If a timeout occurs, it cancels the task and raises TimeoutError.

To avoid the task cancellation, wrap it in shield().

The function will wait until the future is actually cancelled, so the total wait time may exceed the timeout. If an exception happens during cancellation, it is propagated.

If the wait is cancelled, the future aw is also cancelled.

Changed in version 3.10: Removed the loop parameter.

Example:

async def eternity():
    # Sleep for one hour
    await asyncio.sleep(3600)
    print('yay!')

async def main():
    # Wait for at most 1 second
    try:
        await asyncio.wait_for(eternity(), timeout=1.0)
    except TimeoutError:
        print('timeout!')

asyncio.run(main())

# Expected output:
#
#     timeout!

Changed in version 3.7: When aw is cancelled due to a timeout, wait_for waits for aw to be cancelled. Previously, it raised TimeoutError immediately.

Changed in version 3.10: Removed the loop parameter.

Waiting Primitives

coroutine asyncio.wait(aws, *, timeout=None, return_when=ALL_COMPLETED)

Run Future and Task instances in the aws iterable concurrently and block until the condition specified by return_when.

The aws iterable must not be empty.

Returns two sets of Tasks/Futures: (done, pending).

Usage:

done, pending = await asyncio.wait(aws)

timeout (a float or int), if specified, can be used to control the maximum number of seconds to wait before returning.

Note that this function does not raise TimeoutError. Futures or Tasks that aren’t done when the timeout occurs are simply returned in the second set.

return_when indicates when this function should return. It must be one of the following constants:

Constant

Description

FIRST_COMPLETED

The function will return when any future finishes or is cancelled.

FIRST_EXCEPTION

The function will return when any future finishes by raising an exception. If no future raises an exception then it is equivalent to ALL_COMPLETED.

ALL_COMPLETED

The function will return when all futures finish or are cancelled.

Unlike wait_for(), wait() does not cancel the futures when a timeout occurs.

Changed in version 3.10: Removed the loop parameter.

Changed in version 3.11: Passing coroutine objects to wait() directly is forbidden.

asyncio.as_completed(aws, *, timeout=None)

Run awaitable objects in the aws iterable concurrently. Return an iterator of coroutines. Each coroutine returned can be awaited to get the earliest next result from the iterable of the remaining awaitables.

Raises TimeoutError if the timeout occurs before all Futures are done.

Changed in version 3.10: Removed the loop parameter.

Example:

for coro in as_completed(aws):
    earliest_result = await coro
    # ...

Changed in version 3.10: Removed the loop parameter.

Deprecated since version 3.10: Deprecation warning is emitted if not all awaitable objects in the aws iterable are Future-like objects and there is no running event loop.

Running in Threads

coroutine asyncio.to_thread(func, /, *args, **kwargs)

Asynchronously run function func in a separate thread.

Any *args and **kwargs supplied for this function are directly passed to func. Also, the current contextvars.Context is propagated, allowing context variables from the event loop thread to be accessed in the separate thread.

Return a coroutine that can be awaited to get the eventual result of func.

This coroutine function is primarily intended to be used for executing IO-bound functions/methods that would otherwise block the event loop if they were run in the main thread. For example:

def blocking_io():
    print(f"start blocking_io at {time.strftime('%X')}")
    # Note that time.sleep() can be replaced with any blocking
    # IO-bound operation, such as file operations.
    time.sleep(1)
    print(f"blocking_io complete at {time.strftime('%X')}")

async def main():
    print(f"started main at {time.strftime('%X')}")

    await asyncio.gather(
        asyncio.to_thread(blocking_io),
        asyncio.sleep(1))

    print(f"finished main at {time.strftime('%X')}")


asyncio.run(main())

# Expected output:
#
# started main at 19:50:53
# start blocking_io at 19:50:53
# blocking_io complete at 19:50:54
# finished main at 19:50:54

Directly calling blocking_io() in any coroutine would block the event loop for its duration, resulting in an additional 1 second of run time. Instead, by using asyncio.to_thread(), we can run it in a separate thread without blocking the event loop.

Note

Due to the GIL, asyncio.to_thread() can typically only be used to make IO-bound functions non-blocking. However, for extension modules that release the GIL or alternative Python implementations that don’t have one, asyncio.to_thread() can also be used for CPU-bound functions.

New in version 3.9.

Scheduling From Other Threads

asyncio.run_coroutine_threadsafe(coro, loop)

Submit a coroutine to the given event loop. Thread-safe.

Return a concurrent.futures.Future to wait for the result from another OS thread.

This function is meant to be called from a different OS thread than the one where the event loop is running. Example:

# Create a coroutine
coro = asyncio.sleep(1, result=3)

# Submit the coroutine to a given loop
future = asyncio.run_coroutine_threadsafe(coro, loop)

# Wait for the result with an optional timeout argument
assert future.result(timeout) == 3

If an exception is raised in the coroutine, the returned Future will be notified. It can also be used to cancel the task in the event loop:

try:
    result = future.result(timeout)
except TimeoutError:
    print('The coroutine took too long, cancelling the task...')
    future.cancel()
except Exception as exc:
    print(f'The coroutine raised an exception: {exc!r}')
else:
    print(f'The coroutine returned: {result!r}')

See the concurrency and multithreading section of the documentation.

Unlike other asyncio functions this function requires the loop argument to be passed explicitly.

New in version 3.5.1.

Introspection

asyncio.current_task(loop=None)

Return the currently running Task instance, or None if no task is running.

If loop is None get_running_loop() is used to get the current loop.

New in version 3.7.

asyncio.all_tasks(loop=None)

Return a set of not yet finished Task objects run by the loop.

If loop is None, get_running_loop() is used for getting current loop.

New in version 3.7.

Task Object

class asyncio.Task(coro, *, loop=None, name=None)

A Future-like object that runs a Python coroutine. Not thread-safe.

Tasks are used to run coroutines in event loops. If a coroutine awaits on a Future, the Task suspends the execution of the coroutine and waits for the completion of the Future. When the Future is done, the execution of the wrapped coroutine resumes.

Event loops use cooperative scheduling: an event loop runs one Task at a time. While a Task awaits for the completion of a Future, the event loop runs other Tasks, callbacks, or performs IO operations.

Use the high-level asyncio.create_task() function to create Tasks, or the low-level loop.create_task() or ensure_future() functions. Manual instantiation of Tasks is discouraged.

To cancel a running Task use the cancel() method. Calling it will cause the Task to throw a CancelledError exception into the wrapped coroutine. If a coroutine is awaiting on a Future object during cancellation, the Future object will be cancelled.

cancelled() can be used to check if the Task was cancelled. The method returns True if the wrapped coroutine did not suppress the CancelledError exception and was actually cancelled.

asyncio.Task inherits from Future all of its APIs except Future.set_result() and Future.set_exception().

Tasks support the contextvars module. When a Task is created it copies the current context and later runs its coroutine in the copied context.

Changed in version 3.7: Added support for the contextvars module.

Changed in version 3.8: Added the name parameter.

Deprecated since version 3.10: Deprecation warning is emitted if loop is not specified and there is no running event loop.

cancel(msg=None)

Request the Task to be cancelled.

This arranges for a CancelledError exception to be thrown into the wrapped coroutine on the next cycle of the event loop.

The coroutine then has a chance to clean up or even deny the request by suppressing the exception with a try … … except CancelledErrorfinally block. Therefore, unlike Future.cancel(), Task.cancel() does not guarantee that the Task will be cancelled, although suppressing cancellation completely is not common and is actively discouraged.

Changed in version 3.9: Added the msg parameter.

Deprecated since version 3.11, will be removed in version 3.14: msg parameter is ambiguous when multiple cancel() are called with different cancellation messages. The argument will be removed.

The following example illustrates how coroutines can intercept the cancellation request:

async def cancel_me():
    print('cancel_me(): before sleep')

    try:
        # Wait for 1 hour
        await asyncio.sleep(3600)
    except asyncio.CancelledError:
        print('cancel_me(): cancel sleep')
        raise
    finally:
        print('cancel_me(): after sleep')

async def main():
    # Create a "cancel_me" Task
    task = asyncio.create_task(cancel_me())

    # Wait for 1 second
    await asyncio.sleep(1)

    task.cancel()
    try:
        await task
    except asyncio.CancelledError:
        print("main(): cancel_me is cancelled now")

asyncio.run(main())

# Expected output:
#
#     cancel_me(): before sleep
#     cancel_me(): cancel sleep
#     cancel_me(): after sleep
#     main(): cancel_me is cancelled now
cancelled()

Return True if the Task is cancelled.

The Task is cancelled when the cancellation was requested with cancel() and the wrapped coroutine propagated the CancelledError exception thrown into it.

done()

Return True if the Task is done.

A Task is done when the wrapped coroutine either returned a value, raised an exception, or the Task was cancelled.

result()

Return the result of the Task.

If the Task is done, the result of the wrapped coroutine is returned (or if the coroutine raised an exception, that exception is re-raised.)

If the Task has been cancelled, this method raises a CancelledError exception.

If the Task’s result isn’t yet available, this method raises a InvalidStateError exception.

exception()

Return the exception of the Task.

If the wrapped coroutine raised an exception that exception is returned. If the wrapped coroutine returned normally this method returns None.

If the Task has been cancelled, this method raises a CancelledError exception.

If the Task isn’t done yet, this method raises an InvalidStateError exception.

add_done_callback(callback, *, context=None)

Add a callback to be run when the Task is done.

This method should only be used in low-level callback-based code.

See the documentation of Future.add_done_callback() for more details.

remove_done_callback(callback)

Remove callback from the callbacks list.

This method should only be used in low-level callback-based code.

See the documentation of Future.remove_done_callback() for more details.

get_stack(*, limit=None)

Return the list of stack frames for this Task.

If the wrapped coroutine is not done, this returns the stack where it is suspended. If the coroutine has completed successfully or was cancelled, this returns an empty list. If the coroutine was terminated by an exception, this returns the list of traceback frames.

The frames are always ordered from oldest to newest.

Only one stack frame is returned for a suspended coroutine.

The optional limit argument sets the maximum number of frames to return; by default all available frames are returned. The ordering of the returned list differs depending on whether a stack or a traceback is returned: the newest frames of a stack are returned, but the oldest frames of a traceback are returned. (This matches the behavior of the traceback module.)

print_stack(*, limit=None, file=None)

Print the stack or traceback for this Task.

This produces output similar to that of the traceback module for the frames retrieved by get_stack().

The limit argument is passed to get_stack() directly.

The file argument is an I/O stream to which the output is written; by default output is written to sys.stderr.

get_coro()

Return the coroutine object wrapped by the Task.

New in version 3.8.

get_name()

Return the name of the Task.

If no name has been explicitly assigned to the Task, the default asyncio Task implementation generates a default name during instantiation.

New in version 3.8.

set_name(value)

Set the name of the Task.

The value argument can be any object, which is then converted to a string.

In the default Task implementation, the name will be visible in the repr() output of a task object.

New in version 3.8.