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(2024-07-17 21:03)

This document contains additional information on contributing to dribdat as a developer. If you are just interested in connecting to dribdat's API, scroll down to the API overview

Developer Guide

For more background references, see the README.

Software Architecture

This code is originally based on cookiecutter-flask, which has a number of helpful developer features. It is a Python 3 project that uses the Flask microframework and SQLAlchemy for data modelling on the backend. A plain JSON API, along with Jinja templates and WTForms, serves data to a frontend based on Bootstrap 3 and jQuery.

Sketch of project architecture

Getting started

(1) Install Python (3.8+) and Poetry to start working with the code. Virtualenv and pip are also supported.

(2) You may need to install additional system libraries (libffi) for the misaka package, which depends on CFFI. You are likely to also need development headers for Python.

(3) Next, run the following commands from the repository root folder to bootstrap your environment with Poetry:

poetry shell
poetry install

Or using plain pip:

pip install -r requirements/dev.txt

(4) By default in a dev environment, a SQLite database will be created in the root folder (dev.db). You can also install and configure your choice of DBMS supported by SQLAlchemy. In production, the DATABASE_URL configures connectivity to an SQLAlchemy-compatible database engine. This requires a DRIBDAT_ENV=prod configuration.

Tip: Use .flaskenv or .env to store environment variables for local development. See the Configuration section for more details.

(5) Run the following to create your local SQLite database tables and perform the initial migration. Note that we avoid using the production migrations folder locally due to Flask-Migrate#61:

mv migrations migrations_prod
python manage.py db init
python manage.py db migrate
python manage.py db upgrade

(6) Install a local copy of frontend resources for offline development using npm or yarn. These will be used when FLASK_ENV=dev, otherwise a CDN will be used in production. I.e.:

npm install

(7) Finally, run this command (or just debug.sh) to start the server:

FLASK_DEBUG=1 python manage.py run

You should at this point see a welcome screen at http://127.0.0.1:5000 🎉

Follow the instructions to register your first user account, which will have admin access, and let you set up events.

Coding tips

This section has some advice for developers and operators.

Shell access

To open the interactive shell, run: python manage.py shell. By default, you will have access to the User model, as well as Event, Project, Category, Activity.

Using the Heroku toolchain, heroku run python manage.py shell, or using Docker, docker-compose run --rm dribdat python manage.py shell.

Running Tests

To run all tests, whose source is in the tests folder, run: python manage.py test

To run just a specific test, specify it by name, e.g. python manage.py test --name features

Migrations

Whenever a database migration needs to be made. Run the following commands:

python manage.py db migrate

This will generate a new migration script. Then run:

python manage.py db upgrade

To apply the migration. Watch out for any errors in the process.

For a full migration command reference, run python manage.py db --help.

API Guide

There are a number of API calls that admins can use to easily get to the data in Dribdat in various formats. The full list of calls is shown in the About or Search page in a running app.

Look up data on the current event with the Hackathon Schema.org Type:

For just basic information and projects from the current or another event:

Details on a project:

To get the full list of events, or all the latest activities:

To get your logged in user's full data:

To get just public data for any user:

To search through all project contents:

Use the limit query parameter to get more or less than 10 results.

Write access (beta)

If you would like to use external clients, like the chatbot, to remote control Dribdat you need to set DRIBDAT_APIKEY. The (experimental) call used to push data into projects is:

Technical details

For more details see api.py