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Flask, SQLAlchemy and Celery integration tool.

Project description

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A Flask webapp project manager with built in ORM’ed database using SQLAlchemy and Celery backend support.

  • What Flasker is!

    • A transparent integration of Flask, SQLAlchemy and Celery which lets you configure these individually according to your project needs via a single .cfg file (cf. Config file API).
    • A simple pattern to organize your project via the current_project proxy. No more complicated import schemes!
    • A command line tool from where you can create new projects, launch the Flask buit in Werkzeug server, start Celery workers and the Flower tool, and run a shell in the current project context (inspired by Flask-Script).
  • What Flasker isn’t?

    • A simplified version of Flask, SQLAlchemy and Celery. Flasker handles the setup but intentionally leaves you free to interact with the raw Flask, Celery and database objects. Some knowledge of these frameworks is therefore required.

Flasker also comes with two optional extensions:


  • Installation:

    $ pip install flasker
  • To create a new project:

    $ flasker new basic

    This will create a project configuration file default.cfg in the current directory (cf Config file API for more info on the available configurations through the new command) and a basic Bootstrap themed app in an app folder (this can be turned off with the -a flag).

    Already, you should be able to run your app by running flasker server.

  • Editing your project:

    The flasker module exposes a current_project proxy which grants access to the Flask app, the Celery application and the SQLAlchemy database object respectively through its attributes app, celery, and db. Inside each project module (as defined by the MODULES option of the configuration file) you can then do, for example:

    from flasker import current_project
    app =
    # do stuff
  • Next steps:

    $ flasker -h

    This will list all available commands for that project:

    • Running the app server
    • Starting a worker for the Celery backend
    • Running the flower worker management app
    • Starting a shell in the current project context (useful for debugging)

    Extra help is available for each command by typing:

    $ flasker <insert_command> -h

Config file API

Here is what a minimalistic project configuration file looks like:

NAME: My Project
MODULES: app.views, app.tasks
DB_URL: sqlite:///db/db.sqlite
BROKER_URL: redis://

The following keys are valid in the PROJECT section:

  • NAME, name of the project
  • MODULES, modules to import on project load (comma separated list)
  • DB_URL, URL of database (defaults to the in memory sqlite://)
  • APP_FOLDER, path to Flask application root folder, relative to the configuration file (defaults to app/)
  • APP_STATIC_FOLDER, path to folder where the Flask static files lie, relative to the Flask root folder (defaults to static/)
  • APP_TEMPLATE_FOLDER, path to folder where the Flask template files lie, relative to the Flask root folder (defaults to templates/)
  • STATIC_URL, optional URL to serve static files

The APP section can contain any Flask configuration options (as defined here: and the CELERY section can contain any Celery configuration options (as defined here: Any options defined in either section will be passed along to the corresponding object.

There are two pregenerated configurations available through the flasker new command:

  • basic, minimal configuration
  • celery, includes default celery configuration with automatic worker hostname generation and task routing



Preface There exists a Flask API extension (Flask-Restless) that shares a similar purpose at first glance. However this extension was built with the goal to provide:

  • Faster queries: the ‘jsonification’ of model entities is heavily optimized for large queries.
  • More flexibility: API responses are not restricted to returning model columns but also return properties.
  • Convenient access to nested models: queries can go arbitrarily deep within nested models (the extension takes care of not repeating information). This is especially useful with a client-side library such as Backbone-Relational.
  • More endpoints: each one-to-many relation can have its own model specific endpoint.
  • Support for models with composite primary keys

Nevertheless this extension is much younger and currently lacks several great features offered by Flask-Restless (such as arbitrary queries and function evaluation).

How to use:

from flasker import current_project
from flasker.ext.api import APIManager

api_manager = APIManager()

This will add URL endpoints for all registered models and allow GET requests for each. Models are defined as subclasses of the flasker.util.Model class. They can have arbitrary keys and columns. For POST and PUT requests to work, the constructor must accept kwargs arguments (similar to the default implementation). You can also of course add models individually:

api_manager.add_model(YourModel, methods=['GET', 'POST'])

Options specified anew for a given model will override previous ones. add_model and add_all_models accept the same arguments:

  • Model, the model class
  • relationships, whether or not to create endpoints for one-to-many relationships on the model. Can be a list of keys, True (default) to create all or False to create none.
  • allow_put_many, allow PUT method for collections (defaults to True).
  • methods, list of allowed methods (defaults to ['GET', 'POST', 'PUT', 'DELETE']).

URLs are generated from the model’s tablename and relationship keys. For example, assume we have defined the following models:

from flasker.util import Model
from sqlalchemy import Column, ForeignKey, Integer, Unicode

class House(Model):

  id = Column(Integer, primary_key=True)
  address = Column(Unicode(128))

class Cat(Model):

  name = Column(Unicode(64), primary_key=True)
  house_id = Column(ForeignKey(''))

  house = relationship('House', backref='cats')

Calling api_manager.add(House) will create the following endpoints:

  • /api/houses/
  • /api/houses/<id>
  • /api/houses/<id>/cats/
  • /api/houses/<id>/cats/<position>

For convenience, the root url api/ yields a list of all endpoints, columns and relationships available through the API. Note that relationship endpoints for now only allow GET and PUT requests.


Adding the following code to any one of your modules will allow you to restrict access to your application:

from flasker import current_project
from flasker.ext.auth import GoogleAuthManager

auth_manager = GoogleAuthManager(
  AUTHORIZED_EMAILS=['', '', ...]


Here is the full list of options available to the GoogleAuthManager:

  • CLIENT_ID, your Google client ID (which can be found in the Google API console)
  • AUTHORIZED_EMAILS, a list or comma separated string of emails that can login (defaults to the empty string)
  • PROTECT_ALL_VIEWS, if True (default), all the views (not including statically served files) will have their access restricted to logged in users. If set to False, you should use the login_required decorator from Flask-Login to individually protect views
  • URL_PREFIX, the blueprint url prefix (defaults to /auth)
  • CALBACK_URL, the callback URL for Google OAuth (defaults to /oauth2callback). Note that this CALLBACK_URL is concatenated with the URL_PREFIX so that the full callback URL you should allow in the Google API console would by default be /auth/oauth2callback.

If you would like to include the parameters in the global configuration file (instead of passing them directly to the constructor as we did here), you can do that too by passing the corresponding section to the register_manager method (options specified here will override the ones from the previous method):

from flasker import current_project
from flasker.ext.auth import GoogleAuthManager

current_project.register_manager(GoogleAuthManager(), config_section='AUTH')

Where your config file looks something like this:

CLIENT_ID = your_google_client_id


  • Caching
    • cached_property
    • Cacheable
  • Jsonifying
    • jsonify
    • Jsonifiable
  • Logging
    • Loggable
  • Misc
    • Dict, dictionary with depth, width methods and flatten and unflatten classmethods. Also comes with the table method to transform nested dictionaries easily into HTML table headers.
    • SmartDictReader, like DictReader from csv but automatically converts fields from strings to other types (either by smart guessing or by passing the mapping as constructor argument)

Other stuff

  • Setting up Redis:

    $ curl -O
    $ tar xvzf redis-stable.tar.gz
    $ cd redis-stable
    $ make
    $ make test
    $ sudo cp redis-server /usr/local/bin/
    $ sudo cp redis-cli /usr/local/bin/

    To daemonize redis on a mac:

    Create a plist file:

    $ sudo vim /Library/LaunchDaemons/io.redis.redis-server.plist

    Copy the following contents:

    <?xml version="1.0" encoding="UTF-8"?>
    <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "">
    <plist version="1.0">
  • Running the server on Apache:

    Create a file called run.wsgi in the main directory with the following contents:

    # Virtualenv activation
    from os.path import abspath, dirname, join
    activate_this = abspath(join(dirname(__file__), 'venv/bin/'))
    execfile(activate_this, dict(__file__=activate_this))
    # Since the application isn't on the path
    import sys
    sys.path.insert(0, abspath(join(dirname(__file__)))
    # App factory
    from app import make_app
    application = make_app()

    Then add a virtualhost in your Apache virtual host configuration file (often found at /etc/apache2/extra/httpd-vhosts.conf) with the following configuration:

    <VirtualHost *:80>
      ServerName [server_name]
      WSGIDaemonProcess [process_name] user=[process_user] threads=5
      WSGIScriptAlias / [path_to_wsgi_file]
      <Directory [path_to_root_directory]>
          WSGIProcessGroup [process_name]
          WSGIApplicationGroup %{GLOBAL}
          Order deny,allow
          Allow from all
      ErrorLog "[path_to_error_log]"
      CustomLog "[path_to_access_log]" combined

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