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CambridgeSemiticsLab / nena

The North Eastern Neo-Aramaic Database Site
https://nena.ames.cam.ac.uk
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The North Eastern Neo-Aramaic Database

About

This is a Django web application that allows access to the database of grammatical descriptions of various North Eastern Neo-Aramaic dialects.

Development

Requirements

The application targets the Google Cloud Run for production and has been developed to work with Python 3.8 on Debian Stretch. If a local database is needed we use MariaDB 10.1.

You will need to have docker and docker-compose installed and working and have access to Docker Hub or have the images downloaded already.

You should create a .env file in the base directory, see ./.env.example for reference.

The actual data is not included in this repo and is only available on request from the Faculty of Asian and Middle-Eastern Studies at the University of Cambridge.

Creating a development environment with Google Cloud SQL and File Storage * RECOMMENDED

You need to add details of the google integration into your ./.env file. GS_ prefixed .env variables relate to Google Cloud Storage. The GOOGLE_APPLICATION_CREDENTIALS file is an key file for the Service Agent for the project and should be stored in ./secrets/ dir. See: https://cloud.google.com/sql/docs/mysql/sql-proxy#create-service-account

You can start the development environment with:

docker-compose up

Creating a development environment with local database and local files

In the database container, MariaDB data files are mounted into /var/lib/mysql from ./database in the checkout root, so you will need to create that directory or symlink it to a place that is suitable for the purpose.

You can start the development environment with:

docker-compose up --file=docker-compose-localdb.yml

The app should then appear at [https://localhost:8000].

To create Django's tables run migrations:

docker exec nena ./manage.py migrate

Production

Deploy to production

The application is deployed to a Google Cloud staging instance via a Github webhook which triggers a cloud build process on changes to the release branch. Cloud Run is then manually triggered to update to use this new image.