kekedouu / data-modelling-tool

A tool for modeling and presenting json-based data models

Data Modelling Tool

The data modelling tool is a tool for modelling complex domain models.

Some features:

• Create, view, and search models
• Create applications containing custom views, models, and actions
• Generate code that reflects models

Documentation

You can find the Data Modelling Tool documentation here; https://equinor.github.io/data-modelling-tool.

Developing

When running locally, in development mode, DMSS need to be running alongside DMT. Since DMT use the same virtual network as DMSS, DMSS needs to be started first.

Starting

cd ../data-modelling-storage-service
docker-compose up
cd ../data-modelling-tool
docker-compose up

The web app will be served at http://localhost

Import data and reset database

Import local documents to the configured DMSS_HOST (from /api/home directory).
Token is optional, but required if DMSS is configured with authentication.
Token can be acquired from the DMT Web application.

docker-compose run --rm api reset-app --token=Eyxx.xxxx.xxxx

If the data is corrupted or in a bad state, a hard reset of the DMSS is often a solution. This command will remove every mongo database using the same database host as the core, and upload DMSS's core documents.

docker-compose run --rm dmss reset-app

Running Tests

Unit tests:

docker-compose run --rm api pytest
docker-compose run --rm web yarn test

Integration tests:

docker-compose run --rm api behave

Pre-commit

We use pre-commit to do a minimum of checks on the developer pc before committing. The same checks, plus a few more are also run in the build pipeline.
You should catch any errors early to save time.

Setup;

pip install pre-commit  # Should be installed in global python environment
pre-commit install  # Pre-commit will now run on every commit (can be skipped with 'git commit --no-verify')

# To run manually on all files
pre-commit run -a 

Contributing

Read our contributors' guide to get started.