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Description:

baseplatecelery 1.0.0a1

baseplate-celery
Baseplate Celery allows us to integrate Celery in Baseplate.py services. Celery is an open source asynchronous task queue. Integrating your Baseplate.py service with Celery can enable you to offload some tasks to run in the background, while leveraging some of Baseplate.py's configurations and telemetry.
Baseplate Celery will allow you to:

Instantiate an instance of Celery.
Identify background task functions within your application with a simple decorator.
Push background tasks to a message queue (or broker), currently supported broker is Redis.
Instantiate Celery workers to pick up tasks from the queue and execute in the background.

Usage
Install the library:
$ pip install baseplate-celery

1. Instantiate BaseplateCelery
Configure BaseplateCelery from the main application's configuration file.
[app:main]

...

# required: name of application
celery.service_name = helloworld

# required: the Redis instance to connect to as the message broker
celery.broker_url = redis://localhost:6379/1

...

It is recommended to create a dedicated module for BaseplateCelery in your application. For example, your project structure could look like:
helloworld/jobs/__init__.py
/celery.py
/tasks.py

Create the BaseplateCelery instance. To use BaseplateCelery within your application, import this instance.
helloworld/jobs/celery.py
from baseplate-celery import BaseplateCelery

celery_app = BaseplateCelery("helloworld")

2. Identify background tasks
Create a file called tasks.py in this module which contains all the functions you would like to register as Celery tasks. One option is to define all tasks directly in this file. Another option if the tasks are defined in various parts of your application is to import all of them into the tasks.py file.
helloworld/jobs/tasks.py
from .celery import celery_app

@celery_app.task
def add(ctx, x, y):
return x + y

All task functions pass the Baseplate RequestContext as a parameter to avoid issues of concurrency.
3. Configure BaseplateCelery workers
In your application's configuration .ini file, add a section for configuring the celery-worker.
[app:celery-worker]

# required: factory specifies the entry point for setting up Baseplate on the Celery worker
factory = helloworld:make_celery_worker

# required: the module which contains the `tasks.py` file
tasks = helloworld.jobs

# optional: additional command line arguments for starting the Celery worker
command = --loglevel INFO --pool gevent --time-limit 180 --concurrency 1

To start the celery worker, use the baseplate-serve command:
baseplate-serve example.ini --app-name celery-worker

Your application might already have a make_baseplate function doing all the Baseplate setup. The celery worker's entrypoint function should do the exact same Baseplate setup and then start the workers. For example:
def make_baseplate(app_config):
baseplate = Baseplate(app_config)
baseplate.configure_observers()
celery_app.set_baseplate(baseplate)
...

def make_celery_worker(app_config):
make_baseplate(app_config)
celery_app.run_workers(app_config)

4. Push background tasks to broker
Now that your application is set configured with a BaseplateCelery instance and the Celery workers are running, you can push a task to the broker by calling:
add.delay(4, 4)

Documentation:
See [the documentation] for more information.
[the documentation]:
Development
A Dockerfile is provided to get a development environment running. To use it,
build the base Docker image:
$ docker build -t baseplate_celery .

And then fire up the environment and use the provided Makefile targets to do
common tasks:
$ docker run -it -v $PWD:/src -w /src baseplate_celery
$ make fmt

The following make targets are provided:

fmt: Apply automatic formatting to the source code.
lint: Run linters on the code.
test: Run the test suite.
docs: Build the docs. Output can be found in build/html/.

Note: some tests are skipped by default locally because they are quite slow.
Enable these by setting CI=true in the environment: CI=true make test.