asynkit: a toolkit for coroutines

This module provides some handy tools for those wishing to have better control over the way Python's asyncio module does things

Installation

$ pip install asynkit

Coroutine Tools

eager() - lower latency IO

Did you ever wish that your coroutines started right away, and only returned control to the caller once they become blocked? Like the way the async and await keywords work in the C# language?

Now they can. Just decorate or convert them with acynkit.eager:

@asynkit.eager
async def get_slow_remote_data():
    result = await execute_remote_request()
    return result.important_data

async def my_complex_thing():
    # kick off the request as soon as possible
    future = get_slow_remote_data()
    # The remote execution may now already be in flight. Do some work taking time
    intermediate_result = await some_local_computation()
    # wait for the result of the request
    return compute_result(intermediate_result, await future)

By decorating your function with eager, the coroutine will start executing right away and control will return to the calling function as soon as it suspends, returns, or raises an exception. In case it is suspended, a Task is created and returned, ready to resume execution from that point.

Notice how, in either case, control is returned directly back to the calling function, maintaining synchronous execution. In effect, conventional code calling order is maintained as much as possible. We call this depth-first-execution.

This allows you to prepare and dispatch long running operations as soon as possible while still being able to asynchronously wait for the result.

asynckit.eager can also be used directly on the returned coroutine:

log = []
async def test():
    log.append(1)
    await asyncio.sleep(0.2) # some long IO
    log.append(2)

async def caller(convert):
    del log[:]
    log.append("a")
    future = convert(test())
    log.append("b")
    await asyncio.sleep(0.1) # some other IO
    log.append("c")
    await future

# do nothing
asyncio.run(caller(lambda c:c))
assert log == ["a", "b", "c", 1, 2]

# Create a Task
asyncio.run(caller(asyncio.create_task))
assert log == ["a", "b", 1, "c", 2]

# eager
asyncio.run(caller(asynkit.eager))
assert log == ["a", 1, "b", "c", 2]

eager() is actually a convenience function, invoking either coro_eager() or async_eager() (see below) depending on context. Decorating your function makes sense if you always intend To await its result at some later point. Otherwise, just apply it at the point of invocation in each such case.

coro_eager(), async_eager()

coro_eager() is the magic coroutine wrapper providing the eager behaviour:

1. It runs CoroStart.start() on the coroutine.
2. It returns CoroStart.as_future().

If the coroutine finished in step 1 above, the Future is a plain future and the result is immediately available. Otherwise, a Task is created continuing from the point where the coroutine initially suspended. In either case, the result is an awaitable.

async_eager() is a decorator which automatically applies coro_eager() to the coroutine returned by an async function.

CoroStart

This class manages the state of a partially run coroutine and is what what powers the coro_eager() function. It has the following methods:

• start() runs the coroutine until it either suspends, returns, or raises an exception. It is usually automatically invoked by the class Initializer
• resume() is an async function which continues the execution of the coroutine from the initial state.
• is_suspended() returns true if the coroutine start resulted in it becoming suspended.
• as_future() returns a future with the coroutine's results. If it finished, this is just a plain Future, otherwise, it is a Task.

Event loop tools

Also provided is a mixin for the built-in event loop implementations in python, providing some primitives for advanced scheduling of tasks.

SchedulingMixin mixin class

This class adds some handy scheduling functions to the event loop. They primarily work with the ready queue, a queue of callbacks representing tasks ready to be executed.

• ready_len(self) - returns the length of the ready queue
• ready_pop(self, pos=-1) - pops an entry off the queue
• ready_insert(self, pos, element) - inserts a previously popped element into the queue
• ready_rotate(self, n) - rotates the queue
• call_insert(self, pos, ...) - schedules a callback at position pos in the queue

Concrete event loop classes

Concrete subclasses of Python's built-in event loop classes are provided.

• SchedulingSelectorEventLoop is a subclass of asyncio.SelectorEventLoop with the SchedulingMixin
• SchedulingProactorEventLoop is a subclass of asyncio.ProactorEventLoop with the SchedulingMixin on those platforms that support it.

Event Loop Policy

A policy class is provided to automatically create the appropriate event loops.

• SchedulingEventLoopPolicy is a subclass of asyncio.DefaultEventLoopPolicy which instantiates either of the above event loop classes as appropriate.

Use this either directly:

asyncio.set_event_loop_policy(asynkit.SchedulingEventLoopPolicy())
asyncio.run(myprogram())

or with a context manager:

with asynkit.event_loop_policy():
    asyncio.run(myprogram())

Scheduling functions

A couple of functions are provided making use of these scheduling features. They require a SchedulingMixin event loop to be current.

sleep_insert(pos)

Similar to asyncio.sleep() but sleeps only for pos places in the runnable queue. Whereas asyncio.sleep(0) will place the executing task at the end of the queue, which is appropriate for fair scheduling, in some advanced cases you want to wake up sooner than that, perhaps after a specific task.

task_reinsert(task, pos)

Takes a runnable task (for example just created with asyncio.create_task() or similar) and reinserts it at a given position in the queue.
Similarly as for sleep_insert(), this can be useful to achieve certain scheduling goals.

task_switch(task, result=None, sleep_pos=None)

Immediately moves the given task to the head of the ready queue and switches to it, assuming it is runnable. When this call returns, returns result. if sleep_pos is not None, the current task will be put to sleep at that position, using sleep_insert(). Otherwise the current task is put at the end of the ready queue.

task_is_blocked(task)

Returns True if the task is waiting for some awaitable, such as a Future or another Task, and is thus not on the ready queue.

task_is_runnable(task)

Roughly the opposite of task_is_blocked(), returns True if the task is neither done() nor blocked and awaits execution.

create_task_descend(coro)

Implements depth-first task scheduling.

Similar to asyncio.create_task() this creates a task but starts it running right away, and positions the caller to be woken up right after it blocks. The effect is similar to using asynkit.eager() but it achieves its goals solely by modifying the runnable queue. A Task is always created, unlike eager, which only creates a task if the target blocks.

Runnable task helpers

A few functions are added to help working with tasks. They require a SchedulingMixin event loop to be current.

The following identity applies:

asyncio.all_tasks(loop) = asynkit.runnable_tasks(loop) | asynkit.blocked_tasks(loop) | {asyncio.current_task(loop)}

runnable_tasks(loop=None)

Returns a set of the tasks that are currently runnable in the given loop

blocked_tasks(loop=None)

Returns a set of the tasks that are currently blocked on some future in the given loop.

Coroutine helpers

A couple of functions are provided to introspect the state of coroutine objects. They work on both regular async coroutines, classic coroutines (using yield from) and async generators.

coro_is_new(coro)

Returns true if the object has just been created and hasn't started executing yet

coro_is_suspended(coro)

Returns true if the object is in a suspended state.

coro_is_done(coro)

Returns true if the object has finished executing, e.g. by returning or raising an exception.

coro_get_frame(coro)

Returns the current frame object of the coroutine, if it has one, or None.