cookiecutter-flask-restful

Cookiecutter template for flask restful, including blueprints, application factory, and more

Introduction

This cookie cutter is a very simple boilerplate for starting a REST api using Flask, flask-restful, marshmallow, SQLAlchemy and jwt. It comes with basic project structure and configuration, including blueprints, application factory and basics unit tests.

Features

Simple flask application using application factory, blueprints
Flask command line interface integration
Simple cli implementation with basics commands (init, run, etc.)
Flask Migrate included in entry point
Authentication using Flask-JWT-Extended including access token and refresh token management
Simple pagination utils
Unit tests using pytest and factoryboy
Configuration using environment variables
OpenAPI json file and swagger UI

Used packages :

Installation

Install cookiecutter

Make sure you have cookiecutter installed in your local machine.

You can install it using this command : pip install cookiecutter

Create your project

Starting a new project is as easy as running this command at the command line. No need to create a directory first, the cookiecutter will do it for you.

To create a project run the following command and follow the prompt

cookiecutter https://github.com/karec/cookiecutter-flask-restful

Install project requirements

Let's say you named your app myapi and your project myproject

You can install it using pip :

cd myproject
pip install -r requirements.txt
pip install -e .

You have now access to cli commands and you can init your project

myapi db upgrade
myapi init

To list all commands

myapi --help

Configuration

Configuration is handled by environment variables, for development purpose you just need to update / add entries in .flaskenv file.

It's filled by default with following content:

FLASK_ENV=development
FLASK_APP="myapp.app:create_app"
SECRET_KEY=changeme
DATABASE_URI="sqlite:////tmp/myapp.db"
CELERY_BROKER_URL=amqp://guest:guest@localhost/  # only present when celery is enabled
CELERY_RESULT_BACKEND_URL=amqp://guest:guest@localhost/  # only present when celery is enabled

Avaible configuration keys:

FLASK_ENV: flask configuration key, enables DEBUG if set to development
SECREY_KEY: your application secret key
DATABASE_URI: SQLAlchemy connection string
CELERY_BROKER_URL: URL to use for celery broker, only when you enabled celery
CELERY_RESULT_BACKEND_URL: URL to use for celery result backend (e.g: redis://localhost)

Authentication

To access protected resources, you will need an access token. You can generate an access and a refresh token using /auth/login endpoint, example using curl

curl -X POST -H "Content-Type: application/json" -d '{"username": "admin", "password": "admin"}' http://localhost:5000/auth/login

This will return something like this

{
  "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ0eXBlIjoiYWNjZXNzIiwiaWRlbnRpdHkiOjEsImlhdCI6MTUxMDAwMDQ0MSwiZnJlc2giOmZhbHNlLCJqdGkiOiI2OTg0MjZiYi00ZjJjLTQ5MWItYjE5YS0zZTEzYjU3MzFhMTYiLCJuYmYiOjE1MTAwMDA0NDEsImV4cCI6MTUxMDAwMTM0MX0.P-USaEIs35CSVKyEow5UeXWzTQTrrPS_YjVsltqi7N4", 
  "refresh_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpZGVudGl0eSI6MSwiaWF0IjoxNTEwMDAwNDQxLCJ0eXBlIjoicmVmcmVzaCIsImp0aSI6IjRmMjgxOTQxLTlmMWYtNGNiNi05YmI1LWI1ZjZhMjRjMmU0ZSIsIm5iZiI6MTUxMDAwMDQ0MSwiZXhwIjoxNTEyNTkyNDQxfQ.SJPsFPgWpZqZpHTc4L5lG_4aEKXVVpLLSW1LO7g4iU0"
}

You can use access_token to access protected endpoints :

curl -X GET -H "Content-Type: application/json" -H "Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ0eXBlIjoiYWNjZXNzIiwiaWRlbnRpdHkiOjEsImlhdCI6MTUxMDAwMDQ0MSwiZnJlc2giOmZhbHNlLCJqdGkiOiI2OTg0MjZiYi00ZjJjLTQ5MWItYjE5YS0zZTEzYjU3MzFhMTYiLCJuYmYiOjE1MTAwMDA0NDEsImV4cCI6MTUxMDAwMTM0MX0.P-USaEIs35CSVKyEow5UeXWzTQTrrPS_YjVsltqi7N4" http://127.0.0.1:5000/api/v1/users

You can use refresh token to retreive a new access_token using the endpoint /auth/refresh

curl -X POST -H "Content-Type: application/json" -H "Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpZGVudGl0eSI6MSwiaWF0IjoxNTEwMDAwNDQxLCJ0eXBlIjoicmVmcmVzaCIsImp0aSI6IjRmMjgxOTQxLTlmMWYtNGNiNi05YmI1LWI1ZjZhMjRjMmU0ZSIsIm5iZiI6MTUxMDAwMDQ0MSwiZXhwIjoxNTEyNTkyNDQxfQ.SJPsFPgWpZqZpHTc4L5lG_4aEKXVVpLLSW1LO7g4iU0" http://127.0.0.1:5000/auth/refresh

this will only return a new access token

{
  "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ0eXBlIjoiYWNjZXNzIiwiaWRlbnRpdHkiOjEsImlhdCI6MTUxMDAwMDYxOCwiZnJlc2giOmZhbHNlLCJqdGkiOiIzODcxMzg4Ni0zNGJjLTRhOWQtYmFlYS04MmZiNmQwZjEyNjAiLCJuYmYiOjE1MTAwMDA2MTgsImV4cCI6MTUxMDAwMTUxOH0.cHuNf-GxVFJnUZ_k9ycoMMb-zvZ10Y4qbrW8WkXdlpw"
}

Running tests

Simplest way to run tests is to use tox, it will create a virtualenv for tests, install all dependencies and run pytest

tox

But you can also run pytest manually, you just need to install tests dependencies before

pip install pytest pytest-runner pytest-flask pytest-factoryboy factory_boy
pytest

With docker-compose and the Makefile

make tests

WARNING: you will need to set env variables

Installing a wsgi server

Running with gunicorn

This project provide a simple wsgi entry point to run gunicorn or uwsgi for example.

For gunicorn you only need to run the following commands

pip install gunicorn
gunicorn myapi.wsgi:app

And that's it ! Gunicorn is running on port 8000

If you chose gunicorn as your wsgi server, the proper commands should be in your docker-compose file.

Running with uwsgi

Pretty much the same as gunicorn here

pip install uwsgi
uwsgi --http 127.0.0.1:5000 --module myapi.wsgi:app

And that's it ! Uwsgi is running on port 5000

If you chose uwsgi as your wsgi server, the proper commands should be in your docker-compose file.

Using Flask CLI

This cookiecutter is fully compatible with default flask CLI and use a .flaskenv file to set correct env variables to bind the application factory. Note that we also set FLASK_ENV to development to enable debugger.

Using Celery

This cookiecutter has an optional Celery integration that let you choose if you want to use it or not in your project. If you choose to use Celery, additionnal code and files will be generated to get started with it.

This code will include a dummy task located in yourproject/yourapp/tasks/example.py that only return "OK" and a celery_app file used to your celery workers.

Running celery workers

In your project path, once dependencies are installed, you can just run

celery worker -A myapi.celery_app:app --loglevel=info

If you have updated your configuration for broker / result backend your workers should start and you should see the example task avaible

[tasks]
  . myapi.tasks.example.dummy_task

Running a task

To run a task you can either import it and call it

>>> from myapi.tasks.example import dummy_task
>>> result = dummy_task.delay()
>>> result.get()
'OK'

Or use the celery extension

>>> from myapi.extensions import celery
>>> celery.send_task('myapi.tasks.example.dummy_task').get()
'OK'

Using docker

WARNING both Dockerfile and docker-compose.yml are NOT suited for production, use them for development only or as a starting point.

This template offer simple docker support to help you get started and it comes with both Dockerfile and a docker-compose.yml. Please note that docker-compose is mostly useful when using celery since it takes care of running rabbitmq, redis, your web API and celery workers at the same time, but it also work if you don't use celery at all.

Dockerfile has intentionally no entrypoint to allow you to run any command from it (server, shell, init, celery, ...)

Note that you still need to init your app on first start, even when using compose.

docker build -t myapp .
...
docker run --env-file=.flaskenv myapp myapi init
docker run --env-file=.flaskenv -p 5000:5000 myapp myapi run -h 0.0.0.0
 * Serving Flask app "myapi.app:create_app" (lazy loading)
 * Environment: development
 * Debug mode: on
 * Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
 * Restarting with stat
 * Debugger is active!
 * Debugger PIN: 214-619-010

With compose

docker-compose up
...
docker exec -it <container_id> myapi init

With docker-compose and the Makefile

make init

Makefile usage

Initizalize the environment

make init

Build the containers

make build

Run the containers

make run

Create new database migration

make db-migrate

Apply database migrations

make db-upgrade

Run tests

make test

Using APISpec and Swagger

This boilerplate comes with pre-configured APISpec and swagger endpoints. Using default configuration you have two endpoints avaible:

/swagger.json: return OpenAPI specification file in json format
/swagger-ui: swagger UI configured to hit OpenAPI json file

This come with a very simple extension that allow you to override basic settings of APISpec using your config.py file:

APISPEC_TITLE: title for your spec, default to {{cookiecutter.project_name}}
APISPEC_VERSION: version of your API, default to 1.0.0
OPENAPI_VERSION: OpenAPI version of your spec, default to 3.0.2
SWAGGER_JSON_URL: Url for your JSON specifications, default to /swagger.json
SWAGGER_UI_URL: Url for swagger-ui, default to /swagger-ui
SWAGGER_URL_PREFIX: URL prefix to use for swagger blueprint, default to None

Changelog

18/01/2020

Added python 3.8 support
Upgraded to marshmallow 3
Added lint and tests envs to tox
Added black support
Improved travis tests
Updated Makefile to handle tests with celery
Updated tox to handle env variables for celery when runing tests
Added initial db migration instead of relying on db.create_all()
Added new step to create database in README
Various cleanup

08/2019

Added apispec dependencies
Registered users endpoints into swagger
New apispec extension
Added two new routes /swagger.json and /swagger-ui (configurable urls)
Added swagger html template
Add travis file

26/04/2019

Added docker and docker-compose support

24/04/2019

Update configuration to only use env variables, .flaskenv has been updated too
Add unit tests for celery
Add flake8 to tox
Configuration file cannot be overridden by MYAPP CONFIG env variable anymore
various cleanups (unused imports, removed configtest.py file, flake8 errors)

javigoncompte/cookiecutter-flask-restful