- add a Jupyter notebook that allows to install all project-external
dependencies regarding R and R packages
- adjust the GitHub Action workflow to also install R and the R packages
used within the project
- add a `init_r` module that initializes all R packages globally
once the `urban_meal_delivery` package is imported
- remove the factory functions for creating engines and sessions
- define global engine, connection, and session objects to be used
everywhere in the urban_meal_delivery package
- use Alembic to migrate the PostgreSQL database
+ create initial migration script to set up the database,
as an alternative to db.Base.metadata.create_all()
+ integrate Alembic into the test suite; the db_engine fixture
now has two modes:
* create the latest version of tables all at once
* invoke `alembic upgrade head`
=> the "e2e" tests are all run twice, once in each mode; this
ensures that the migration scripts re-create the same database
schema as db.Base.metadata.create_all() would
* in both modes, a temporary PostgreSQL schema is used to create the
tables in
=> could now run "e2e" tests against production database and still
have isolation
- make the configuration module public (to be used by Alembic)
- adjust linting rules for Alembic
- use SQLAlchemy (and PostgreSQL) to model the ORM layer
- add the following models:
+ Address => modelling all kinds of addresses
+ City => model the three target cities
+ Courier => model the UDP's couriers
+ Customer => model the UDP's customers
+ Order => model the orders received by the UDP
+ Restaurant => model the restaurants active on the UDP
- so far, the emphasis lies on expression the Foreign Key
and Check Constraints that are used to validate the assumptions
inherent to the cleanded data
- provide database-independent unit tests with 100% coverage
- provide additional integration tests ("e2e") that commit data to
a PostgreSQL instance to validate that the constraints work
- adapt linting rules a bit
- add the following file:
+ src/urban_meal_delivery/_config.py
- a config module is created holding two sets of configurations:
+ production => against the real database
+ testing => against a database with test data
- the module is "protected" (i.e., underscore) and imported at the
top level via a proxy-like object `config` that detects in which of
the two environments the package is being run
