A simple asyncio wrapper attempting to look like Trio

When going deeper and deeper with asyncio, and managing a lot of tasks
in parallel, you notice that on top of having a lot to deal with
to keep an eye on all your task, you also end up always doing the
same kind of boiler plate, and the code can become easily twisted and
unreadable.

Trio is there to make asynchronous
programming easy and more “for humans”.

Traio (as kind of Trio on top of Asyncio) let you use asyncio with a little of
the philosophy of Trio, mostly the Nursery concept (called Scope here).
It also synthesize the most common pattern we are using for handling async
tasks in a sane way.

Disclaimer

This is not a replacement for Trio: if you do a full Trio-like project,
just use Trio! It’s just truly awesome, but in some cases you get stuck
with asyncio, but still want to have a code you can read and manage…

Because we run on top of asyncio, we are quite limited in how we can handle
cancellation, scopes, and coroutines. This is just on top of asyncio, this
is not an alternative! But on the good side, you can mix this with regular
asyncio code, which can look appealing.

In future versions of asyncio, many of what we do here should hopefully become
unnecessary. In the mean time, there is no harm experimenting ;-)

Examples

Simple Scope

The main way to use the Scope is (like in Trio) as a context manager

import asyncio
from traio import Scope
async def fetch_url(x):
    # Do something long
    await asyncio.sleep(3)
async def main():
    async with Scope(timeout=10) as scope:
        for i in range(10):
            scope.spawn(fetch_url(i))

The Scope.spawn method, called on an awaitable thing, will spawn a task and
register it on the scope. An equivalent exist using the << operator (see next
example).

When reaching the end of the context block, the code will block until:

• all tasks are done
• or the timeout is over
• or the scope get’s cancelled.

You can also use the Scope without context manager:

import asyncio
from traio import Scope
async def fetch_url(x):
    # Do something long
    await asyncio.sleep(3)
async def main():
    # Equivalent to previous example
    scope = Scope(timeout=10)
    for i in range(10):
        scope << fetch_url(i)
    scope.finalize()
    await scope

Awaiting a scope will block until the scope is fully complete: all active tasks
have finished or the scope was cancelled. But unless scope.finalize() is called,
a scope will note stop on the last task being complete, only on cancellation.

The finalize method is called automatically when used as a context manager.

Names and logger

If you went deep enough in asyncio mysteries, you know that tracing code is
(for now) kind of a nightmare… For that reason, Scope as well as tasks can
be instantiated with a name. The Scope can also take a logger which will be
used for tracing most of the calls and task life cycle, mostly with debug level.

Special tasks

Scope.spawn can be called with different parameters with interesting
effects:

• The bubble boolean parameter controls task error bubbling. A task will
  bubble by default. This means that an error in the task will cause the task
  to stop (of course), but the scope will be cancelled as well and raise
  the given error. This is the default behavior.
  But it can be useful in some cases not to do that, and just ignore a task.
  Not that if you await manually a task, or add a done callback, this cancels
  bubbling automatically: if you take the pain of waiting for a task,
  it’s not to get all the rest cancelled!

import asyncio
from traio import Scope
async def fetch_url():
    # Do something long
    await asyncio.sleep(10)
async def trivial():
    await asyncio.sleep(0.01)
    raise ValueError('not interesting')
async def main():
    async with Scope(timeout=0.5) as n:
        # This will try to run for 10 seconds
        n << fetch_url()
        # This will run a bit then raise (but not 'bubble')
        n.spawn(trivial(), bubble=False)
    # Eventually after 0.5 seconds the Scope times out and
    # gives a TImeoutError

• A task can be marked as master, in that case the scope will die with the
  task when done. This is typically useful when you have one main task to be
  performed and other background ones, which have no meaning if the main one stops.

import asyncio
from traio import Scope
async def fetch_url():
    # Do something long
    await asyncio.sleep(10)
async def trivial():
    await asyncio.sleep(0.01)
async def main():
    async with Scope(timeout=0.5) as n:
        # This will try to run for 10 seconds
        n << fetch_url()
        # This will run a bit then scope gets cancelled when it ends
        n.spawn(trivial(), master=True)

• A task by default is awaited, which means the scope will wait
  for it to finish during finalisation stage, before exiting.
  It is possible to mark some tasks as not awaited if you want a task running,
  but not so essential that it should prevent cancellation.
  Typically, a background job which has no meaning alone.

import asyncio
from traio import Scope
async def background():
    while True:
        # Do something periodic and sleep
        await asyncio.sleep(1)
async def job():
    # Do the real work
    await asyncio.sleep(10)
async def main():
    async with Scope() as n:
        # Spawn a background job
        n.spawn(background(), awaited=False)
        # Do what you have to do.
        n << job()
    # At this point, job is done and background task was cancelled

Note you can combine all flags; If bubble is False and master is True,
the scope will exit silently when the task is done, even with an exception,
for example.

Nested Scope

That’s one of the most exciting ones: you can spawn a sub-scope from the
original one, which will follow the same rules as any Scope but will as well
die if the parent is cancelled!

import asyncio
from traio import Scope
async def fetch_url():
    # Do something long
    await asyncio.sleep(10)
async def main():
    async with Scope(timeout=0.2) as parent:
        async with parent.fork(timeout=0.5) as inner:
            # This will try to run for 10 seconds
            inner << fetch_url()
    # Here the outer Scope will timeout first and will cancel the inner one!

Running synchronous code with executors

It is possible to run synchronous code from a scope using the
loop.run_in_executor method; this would return a coroutine which
you can spawn as usual. But beware of using that method! The same way
interrupts handling is a mess in asyncio, using thread from asyncio is tricky.
Most OS don’t support cancelling running threads, so does Python.
As such, once your executor code is running, the scope has no way to
actually stop it; if you cancel the scope, the corresponding future will be
cancelled but the task will continue running if not yet finished, resulting in
various “fun” situations if that task has side effects…

So: yes, you can use this, but at your own risks!

import asyncio
import time
from traio import Scope
def fetch_url():
    # Do something long, sync!
    time.sleep(10)
async def main():
    async with Scope(timeout=0.2) as scope:
        scope << asyncio.get_event_loop().run_in_executor(None, fetch_url)

Getting current Scope

Using the awesome contextvars and
the current backport of it aiocontextvars,
we can keep track of the current active scope without necessarily passing the
scope variable all along the call chain.

async def handler(client):
    # This runs in the scope `main_scope`
    # Create a subscope of the current one
    async with Scope.get_current().fork() as s:
        # Here we are in the scope of `s`, as well as spawned tasks
        s << do_something(client)
        s << do_something_else(client)
async def server(main_scope):
    # This runs in the scope `main_scope`
    client = await new_connection()
    main_scope.spawn(handler(client))
async def main():
    async with Scope() as main_scope:
        main_scope << server(main_scope)
        main_scope << do_something_else()

Status

This is beta. We are not going to change the API (much) anymore.

TODOS

• write more examples
• extend the API:
  • new kinds of tasks to investigate, for example executors (although we have no way to stop
    a thread executor…)
• play more with the real Trio and get a better feeling of it
• get some user feedback if possible!

Inspiration

• The Trio presentation at PyCon Cleveland 2018 and more generally the Trio github. Trio is currently driving the innovation on async.
• Recent PyCon video from Yuri Selivanov about the status of asyncio and future development. Many promisses around getting a scope concept (the “supervisor”), task names, and event turning the holy CancelledError into a BaseException, which we are looking forward to!
• Interresting ideas in Ayo