Using GAsyncResult APIs with Python’s asyncio

With a GLib implementation of the Python asyncio event loop, I can easily mix asyncio code with GLib/GTK code in the same thread. The next step is to see whether we can use this to make any APIs more convenient to use. A good candidate is APIs that make use of GAsyncResult.

These APIs generally consist of one function call that initiates the asynchronous job and takes a callback. The callback will be invoked sometime later with a GAsyncResult object, which can be passed to a “finish” function to convert this to the result type relevant to the original call. This sort of API is a good candidate to convert to an asyncio coroutine.

We can do this by writing a ready callback that simply stores the result in a future, and then have our coroutine await that future after initiating the job. For example, the following will asynchronously connect to the session bus:

import asyncio
from gi.repository import GLib, Gio

async def session_bus():
    loop = asyncio.get_running_loop()
    bus_ready = loop.create_future()
    def ready_callback(obj, result):
        try:
            bus = Gio.bus_get_finish(result)
        except GLib.Error as exc:
            loop.call_soon_threadsafe(bus_ready.set_exception, exc)
            return
        loop.call_soon_threadsafe(bus_ready.set_result, bus)

    Gio.bus_get(Gio.BusType.SESSION, None, ready_callback)
    return await bus_ready

We’ve now got an API that is conceptually as simple to use as the synchronous Gio.bus_get_sync call, but won’t block other work the application might be performing.

Most of the code is fairly straight forward: the main wart is the two loop.call_soon_threadsafe calls. While everything is executing in the same thread, my asyncio-glib library does not currently wake the asyncio event loop when called from a GLib callback. The call_soon_threadsafe method does the trick by generating some dummy IO to cause a wake up.

Cancellation

One feature we’ve lost with this wrapper is the ability to cancel the asynchronous job. On the GLib side, this is handled with the GCancellable object. On the asyncio side, tasks are cancelled by injecting an asyncio.CancelledError exception into the coroutine. We can propagate this cancellation to the GLib side fairly seamlessly:

async def session_bus():
    ...
    cancellable = Gio.Cancellable()
    Gio.bus_get(Gio.BusType.SESSION, cancellable, ready_callback)
    try:
        return await bus_ready
    except asyncio.CancelledError:
        cancellable.cancel()
        raise

It’s important to re-raise the CancelledError exception, so that it will propagate up to any calling coroutines and let them perform their own cleanup.

By following this pattern I was able to build enough wrappers to let me connect to the D-Bus daemon and issue asynchronous method calls without needing to chain together large sequences of callbacks. The wrappers were all similar enough that it shouldn’t be too difficult to factor out the common code.

Exploring Github Actions

To help keep myself honest, I wanted to set up automated test runs on a few personal projects I host on Github.  At first I gave Travis a try, since a number of projects I contribute to use it, but it felt a bit clunky.  When I found Github had a new CI system in beta, I signed up for the beta and was accepted a few weeks later.

While it is still in development, the configuration language feels lean and powerful.  In comparison, Travis’s configuration language has obviously evolved over time with some features not interacting properly (e.g. matrix expansion only working on the first job in a workflow using build stages).  While I’ve never felt like I had a complete grasp of the Travis configuration language, the single page description of Actions configuration language feels complete.

The main differences I could see between the two systems are:

  1. A Github workflow is composed of multiple jobs right from the start.
  2. All jobs run in parallel by default.  It is possible to serialise jobs (similar to Travis’s stages) by declaring dependencies between jobs.
  3. Each job specifies which VM image it will run on, with a choice of Ubuntu, Windows, or MacOS versions.  If you choose Ubuntu, you can also specify a Docker container to run your build in, giving access to other Linux build environments.
  4. Each job can have a matrix attached, allowing the job to be duplicated according to a set of parameters.
  5. Jobs are composed of a sequence of steps.  Unlike Travis’s fixed set of build phases, these are generic.
  6. Steps can consist of either code executed by the shell or a reference to an external action.
  7. Actions are the primary extension mechanism, and are even used for basic tasks like checking out your repository.  Actions are either implemented in JavaScript or as a Docker container.  Only JavaScript actions are available for Windows and MacOS jobs.

The first project I converted over was asyncio-glib, where I was using Travis to run the test suite on a selection of Python versions.  My old Travis configuration can be seen here, and the new Actions workflow can be seen here.  Both versions are roughly equivalent, although the actions/setup-python@v1 action doesn’t currently make beta releases of Python available. The result of a run of the workflow can be seen here.

For a second project (videowhisk), I am running the tests against the VM’s default Python image.  For this project, I’m more interested in compatibility with the distro release’s GStreamer libraries than compatibility with different Python versions.  I suppose I could extend this using the matrix feature to test on multiple Ubuntu versions, or containers for other Linux releases.

While I’ve just been using this to run the test suite, it looks like Actions can be used for a lot more.  A project can have multiple workflows with different triggers, so it can also be used for automated triage of bugs or pull requests (e.g. request a review from a specific developer when a pull request is created that modifies files in a specific directory). It also looks like I could create a workflow to automatically publish to PyPI when I push a new tag to the repository that looks like a version number.

It will be interesting to see what this does to the larger ecosystem of “CI as a service” products built to work with Github.  On the one hand having a choice is nice, but on the other hand it’s nice to have something well integrated.  I really like Gitlab’s integrated CI system for projects I have hosted on various Gitlab instances, for example.

GLib integration for the Python asyncio event loop

As an evening project, I’ve been working on a small library that integrates the GLib main loop with Python’s asyncio. I think I’ve gotten to the point where it might be useful to other people, so have pushed it up here:

https://github.com/jhenstridge/asyncio-glib

This isn’t the only attempt to integrate the two event loops, but the other I found (Gbulb) is unmaintained and seems to reimplement a fair bit of the asyncio (e.g. it has its own transport classes). So I thought I’d see if I could write something smaller and more maintainable, reusing as much code from the standard library as possible.

My first step was writing an implementation of the selectors.BaseSelector interface in terms of the GLib main loop. The select() method just runs a GMainLoop with a custom source that will quit the loop if any of the file descriptors are ready, or the timeout is reached.

For the asyncio event loop, I was able to reuse the standard library asyncio.SelectorEventLoop with my new selector. In action, it looks something like this:

  1. Let the GMainLoop spin until any asyncio events come in.
  2. Return control to the asyncio event loop to process those events.
  3. Repeat

As far as testing goes, the Python standard library comes with a suite of tests parameterised on an event loop implementation. So I’ve just reused that as the bulk of my test suite, and done the same with the selector tests. There are a handful of test failures I still need to diagnose, but for the most part things just work.

Making an asyncio application use this event loop is simple:

import asyncio
import asyncio_glib
asyncio.set_event_loop_policy(asyncio_glib.GLibEventLoopPolicy())

The main limitation of this code is that it relies on asyncio running the GLib main loop. If some other piece of code runs the main loop, asyncio callbacks will not be triggered and will probably lead to busy looping. This isn’t a problem my project (an asyncio server making use of GStreamer), but would be a problem for e.g. a graphical application calling gtk_dialog_run().

Building IoT projects with Ubuntu Core talk

Last week I gave a talk at Perth Linux Users Group about building IoT projects using Ubuntu Core and Snapcraft. The video is now available online. Unfortunately there were some problems with the audio setup leading to some background noise in the video, but it is still intelligible:

The slides used in the talk can be found here.

The talk was focused on how Ubuntu Core could be used to help with the ongoing security and maintenance of IoT projects. While it might be easy to buy a Raspberry Pi, install Linux and your application, how do you make sure the device remains up to date with security updates? How do you push out updates to your application in a reliable fashion?

I outlined a way of deploy a project using Ubuntu Core, including:

  1. Packaging a simple web server app using the snapcraft tool.
  2. Configuring automatic builds from git, published to the edge channel on the Snap Store. This is also an easy way to get ARM builds for a package, rather than trying to deal with cross compilation tool chains.
  3. Using the ubuntu-image command to create an Ubuntu Core image with the application preinstalled

I gave a demo booting such an image in a virtual machine. This showed the application up and running ready to use. I also demonstrated how promoting a build from the edge channel to stable on the store would make it available to the system wide automatic update mechanism on the device.