Pyramid Tasks

Pyramid and Celery are both fantastic projects that compliment each other well: Pyramid processes synchronous web requests, while Celery performs asynchronous tasks in the background. Unfortunately, due to differences in structure and configuration, it's very difficult to integrate the two together. Configuration, clients, etc. available in Pyramid views may be unavailable in tasks, or may need to be accessed in a different way. Configuration and functionality may have to be duplicated in order to be shared between Pyramid and Celery. Pyramid Tasks aims to bridge this gap by creating a full Pyramid application in the Celery worker and providing a request object to every task. You can use the same configuration for Celery that you do Pyramid, including a Paste-style ini file. Bringing parity to Pyramid and Celery means you can write code for Pyramid and have the code just work in Celery.

To see Pyramid Tasks in action, check out the sample app.

Getting Started

To use Pyramid Tasks, you should first be familiar with Pyramid and Celery.

You can install Pyramid Tasks from PyPI:

pip install pyramid-tasks

Include Pyramid Tasks in your application using config.include, or add it to pyramid.includes in your ini file.

config.include('pyramid_tasks')

Configuring Celery

When you import Pyramid Tasks into your application, a new Celery application is created. All settings prefixed with celery. are put into Celery's configuration. As settings from a .ini file are all strings, values are coerced as necessary. Nested settings are supported by chaining dots, e.g. celery.broker_transport_options.queue_name_prefix.

For example, the following simple celeryconf.py:

broker_url = 'redis://'
broker_transport_options = {
    'visibility_timeout': 3600,
}
result_backend = 'redis://'

Would be translated into the following .ini file:

celery.broker_url = redis://
celery.broker_transport_options.visibility_timeout = 3600
celery.result_backend = redis://

Running a Worker

If you're running Pyramid via Paste (i.e. an ini file and possibly pserve), you can run a Celery worker using the same ini file.

celery -A pyramid_tasks --ini config.ini

This will create a Pyramid app via the same process pserve does, allowing you to share configuration between the two environments.

You can also create a Celery app using config.make_celery_app(), just like you use config.make_wsgi_app(). If you add app = config.make_celery_app() to celery.py in your project's package, you can invoke celery -A myproject worker to boot a worker.

To see both methods of running a worker in action, take a look at the sample app.

Registering Tasks

To register a new task, call config.register_task with the task function. You can also use the pyramid_tasks.task decorator as long as you run a scan (config.scan()) on the package, just like Pyramid's view_config decorator. register_task and @task take the same arguments as Celery.task.

For example, a simple Pyramid app with a task might look like the following:

from pyramid.config import Configurator


def add(request, x, y):
    return x + y


with Configurator() as config:
    config.register_task(add, name='add')

Invoking a Task

Once a task is registered, you can add it to the work queue using request.delay_task. This takes the task function or a string of the name of the task as the first argument. The remaining arguments (positional and keywork) will be passed to the task. When the task is invoked by a Celery worker, a request object will be created and passed as the first argument.

This request object will share the same configuration as requests in the Pyramid application. This means it will have the same or similar methods, registry, etc. However, it is not the request that invoked the task and properties such as url, GET, etc. will not be present. To use these values in your task, pass them in as arguments.

Let's take our simple Pyramid app and add a view that invokes the task.

from pyramid.config import Configurator


def add_view(context, request):
    request.delay_task(add, int(request.GET['x']), int(request.GET['y']))
    return 'OK\n'


def add(request, x, y):
    return x + y


with Configurator() as config:
    config.add_route('root', '/')
    config.add_view(add_view, route_name='root')
    config.register_task(add, name='add')

Getting Task Results

request.delay_task returns a Celery AsyncResult object. You can use this object to check if the task has completed (AsyncResult.ready()) and to get the return value of the task (AsyncResult.result). See the Celery docs for more information.

AsyncResult also has an id property. If you store this property somewhere, such as a client session, you can use request.get_task_result(id) to return a new AsyncResult object.

Periodic Tasks

Pyramid Tasks supports Celery Beat for running periodic tasks. After registering a task, use config.add_periodic_task to schedule the task. The arguments mirror Celery.add_periodic_task:

config.add_periodic_task(
    5.0,  # Run every five seconds
    'mytask',
    ('foo', 'bar'),  # Position arguments passed to task
    {'fizz': 'buzz'},  # Keyword arguments passed to task
)

You can also use celery.schedules.crontab as the first argument to use crontab-style scheduling.

You can run the Beat scheduler the same way you run the Celery worker.

celery -A pyramid_tasks beat --ini config.ini

To see Celery Beat in action, check out the beat sample app.

Acknowledgements

Pyramid Tasks is heavily inspired by the code of PyPA's Warehouse project.