Using Celery With Flask

Working with Flask and Celery

The integration of Celery with Flask is so simple that no extension is required. A Flask application that uses Celery needs to initialize the Celery client as follows:

from flask import Flask
from celery import Celery

app = Flask(__name__)
app.config['CELERY_BROKER_URL'] = 'redis://localhost:6379/0'
app.config['CELERY_RESULT_BACKEND'] = 'redis://localhost:6379/0'

celery = Celery(app.name, broker=app.config['CELERY_BROKER_URL'])
celery.conf.update(app.config)

As you can see, Celery is initialized by creating an object of class Celery, and passing the application name and the connection URL for the message broker, which I put in app.config under key CELERY_BROKER_URL. This URL tells Celery where the broker service is running. If you run something other than Redis, or have the broker on a different machine, then you will need to change the URL accordingly.

Flask Restful: Project Structure

There are many different ways to organize your Flask-RESTful app, but here we’ll describe one that scales pretty well with larger apps and maintains a nice level organization.

The basic idea is to split your app into three main parts: the routes, the resources, and any common infrastructure.

Here’s an example directory structure:

myapi/
    __init__.py
    app.py          # this file contains your app and routes
    resources/
        __init__.py
        foo.py      # contains logic for /Foo
        bar.py      # contains logic for /Bar
    common/
        __init__.py
        util.py     # just some common infrastructure

The common directory would probably just contain a set of helper functions to fulfill common needs across your application. It could also contain, for example, any custom input/output types your resources need to get the job done.

In the resource files, you just have your resource objects. So here’s what foo.py might look like:

from flask_restful import Resource

class Foo(Resource):
    def get(self):
        pass
    def post(self):
        pass

The key to this setup lies in app.py:

from flask import Flask
from flask_restful import Api
from myapi.resources.foo import Foo
from myapi.resources.bar import Bar
from myapi.resources.baz import Baz

app = Flask(__name__)
api = Api(app)

api.add_resource(Foo, '/Foo', '/Foo/<string:id>')
api.add_resource(Bar, '/Bar', '/Bar/<string:id>')
api.add_resource(Baz, '/Baz', '/Baz/<string:id>')

Flask: PyCharm Integration

Prior to PyCharm 2018.1, the Flask CLI features weren’t yet fully integrated into PyCharm. We have to do a few tweaks to get them working smoothly. These instructions should be similar for any other IDE you might want to use.

In PyCharm, with your project open, click on Run from the menu bar and go to Edit Configurations. You’ll be greeted by a screen similar to this:

Python REST APIs With Flask, Connexion, and SQLAlchemy

The goal of this article is to show you how to use Python 3, Flask, and Connexion to build useful REST APIs that can include input and output validation, and provide Swagger documentation as a bonus. Also included is a simple but useful single page web application that demonstrates using the API with JavaScript and updating the DOM with it.

The REST API you’ll be building will serve a simple people data structure where the people are keyed to the last name, and any updates are marked with a new timestamp.

This data could be represented in a database, saved in a file, or be accessible through some network protocol, but for us an in-memory data structure works fine. One of the purposes of an API is to decouple the data from the application that uses it, hiding the data implementation details.

The Flask Mega-Tutorial Part XIX: Deployment on Docker Containers

This is the nineteenth installment of the Flask Mega-Tutorial series, in which I’m going to deploy Microblog to the Docker container platform.

For your reference, below is a list of the articles in this series.

Deploy a Flask Application with Dokku

Dokku is a self-hosted Platform-as-a-Service (PaaS) that makes deploying applications simple using Git. Although Dokku’s implementation is similar to Heroku, it lacks certain key features such as auto-scaling. Dokku is an extremely powerful tool that automatically runs your application inside Docker and requires minimal configuration of web servers.

This guide demonstrates how to:

  • Create a Flask application that returns ‘Hello World!’ on the index page
  • Install Dokku on a Linode
  • Deploy a Flask application with a WSGI server inside a Docker container
  • Add an SSL certificate through Dokku with the Let’s Encrypt plugin