Manually Constructed Engines¶
Due to the dynamic nature of the creation of the databases themselves, its
non-trivial for a user to know what the connection string, for example, would
be for the database ahead of time. Which makes testing code which manually
constructs its own
sqlalchemy.Engine objects internally more difficult.
Therefore, generally preferable way to use the fixtures is that you will be yielded a preconstructed engine pointing at the database to which your test is intended to run against; and to write your code such that it accepts the engine as a function/class parameter.
However, this is not always possible for all classes of tests, nor does it help for code which might already be written with a tightly coupled mechanism for engine creation.
For (contrived) example:
import psycopg2 import sqlalchemy def psycopg2_main(**config): conn = psycopg2.connect(**config) do_the_thing(conn) ... def sqlalchemy_main(**config): conn = sqlalchemy.create_engine(**config) do_the_thing(conn) ...
As you can see, in order to test these functions, we must pass in valid credentials rather than an engine itself.
Each of the fixtures you might create will attach a
attribute onto the engine it yields to the test which will be an instance of a
Attributes on this class include all the credentials required to connect to the
particular database. Additionally, there are convenience methods specifically meant to
coerce the credentials into a form directly accepted by common connection
from pytest_mock_resources import ( create_postgres_fixture, create_redshift_fixture, ) from package import entrypoint postgres = create_postgres_fixture() redshift = create_redshift_fixture() def test_psycopg2_main_postgres(postgres): credentials = postgres.pmr_credentials result = entrypoint.psycopg2_main(**credentials.as_psycopg2_connect_args()) assert result ... def test_sqlalchemy_main_postgres(postgres): credentials = postgres.pmr_credentials result = entrypoint.sqlalchemy_main(**credentials.as_url()) assert result ...