# Coroutines and Tasks

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

- Coroutines

- Awaitables

- Creating Tasks

- Sleeping

- Running Tasks Concurrently

- Shielding From Cancellation

- Timeouts

- Waiting Primitives

- Running in Threads

- Scheduling From Other Threads

- Introspection

- Task Object

## 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

## 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.

  Important

  Save a reference to the result of this function, to avoid a task disappearing mid execution.

  New in version 3.7.

  Changed in version 3.8: Added the name parameter.

  Changed in version 3.11: Added the context parameter.

## 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.

  Changed in version 3.10: Removed the loop parameter.

  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 CancelledError` … `finally` 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.