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

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

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.

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 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

Unit tests:

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

Integration tests:

docker-compose run --rm api behave

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 

Read our contributors' guide to get started.