phillipemoreira / learning-celery

Learning celery

Learning celery

Simple repository designed to learn and test celery related stuff.

Prerequisites

At the bare minimum you'll need the following for your development environment:

1. Python 3.6
2. pyenv
3. pyenv-virtualenv or virtualenv
4. Docker
5. Docker Compose

Getting Started

Cloning

$ git clone [email protected]:phillipemoreira/learning-celery.git
$ cd learning-celery

Setting up python virtual environment

$ pyenv install 3.6.1
$ pyenv virtualenv 3.6.1 learning-celery
$ pyenv local learning-celery

Errors

If you receive one error of missing OpenSSL to run the pyenv install, you can try to fix running:

$ sudo apt install -y libssl1.0-dev

Installation

$ make install

Running

Celery requires a message broker and this project uses RabbitMQ (celery's default), but it does not require you to install in your host machine, it creates a docker container defined in the docker-compose configuration file. Therefore you are going to need to terminal instances, one for running the message broker, and another one for running the celery project itself.

RabbitMQ

On the first terminal instance:

$ docker-compose up

This will pull the docker image, setup and start a RabbitMQ Server.

Celery

on the second terminal instance:

$ cd app
$ celery -A tasks worker --loglevel=info

Testing

$ python
$ from tasks import add
$ add.delay(4, 4)

Expected result: The task has now been processed by the worker you started earlier and the result is available in console output. ex:

[2018-11-11 17:53:32,999: INFO/ForkPoolWorker-2] Task tasks.add[93a446f4-71e6-456d-b77d-32d5585f308e] succeeded in 0.0004594189813360572s: 8