This is a fork of the dbt-labs/jaffle_shop repo but to show off some Sainsbury's extensions.
This project has only been tested using Python 3.9 with the dependencies listed in the requirements files. Since this is a project just for testing, we're only using SQLite and dbt version 1.4.
Install Python 3.9 and the dependencies listed in the requirements files:
pip install -r requirements.txt
pip install -r requirements-dev.txt
pre-commit install --install-hooks
The jaffle_shop
directory has the usual dbt objects and can be run in the usual way. The unit tests for the dbt models are in the tests
subdirectory.
The package Bilbottom/dbt-unit-test-coverage will generate a coverage metric for the dbt models' unit tests. We can't measure line hits like we can with Python code, but we can measure the number of models that have unit tests and which of their CTEs have unit tests.
To run the code coverage report, compile the project and run the code coverage package:
dbt clean
dbt deps
dbt compile
dbt-unit-test-coverage
This builds off of another fork, this one from the EqualExperts/dbt-unit-testing repo:
This fork adds the ability to unit test particular CTEs in a model, provided you follow the dbt-labs style guide and include the select * from final
line at the bottom of the model.
With the CTE unit tests, we can then measure the number of (logical) CTEs that don't have tests which forms the coverage metric.