Materializing resource tests

When we discussed unit tests we showed how you can execute one or more assets together using dg.materialize(). We can still materialize our assets this way using mocks.

# /dagster_testing/assets/lesson_4.py
@patch("requests.get")
def test_state_population_api_assets(mock_get, example_response, api_output):
    mock_response = Mock()
    mock_response.json.return_value = example_response
    mock_response.raise_for_status.return_value = None
    mock_get.return_value = mock_response

    result = dg.materialize(
        assets=[
            lesson_4.state_population_api_resource,
            lesson_4.total_population_resource,
        ],
        resources={"state_population_resource": lesson_4.StatePopulation()},
    )
    assert result.success

    assert result.output_for_node("state_population_api_resource") == api_output
    assert result.output_for_node("total_population_resource") == 9082539

> pytest dagster_testing_tests/test_lesson_4.py::test_state_population_api_assets
...
dagster_testing_tests/test_lesson_4.py .                                                          [100%]

This uses the same patch and mocked object as before. The only difference is that because there is an asset that requires a resource, it must be initialized and set within dg.materialize().

Testing with materialize and config

We can also materialize resources and configs together. We will make a slight modification to our author_works_with_resource so it can take in a run configuration.

class StateConfig(dg.Config):
    name: str


@dg.asset
def state_population_api_resource_config(
    config: StateConfig, state_population_resource: StatePopulation
) -> list:
    return state_population_resource.get_cities(config.name)

Now it can be executed with the name of any state.

If we wanted to make a test using this new asset, we will need to specify both the resource and the run_config in dg.materialize().

@patch("requests.get")
def test_state_population_api_assets_config(mock_get, example_response, api_output):
    mock_response = Mock()
    mock_response.json.return_value = example_response
    mock_response.raise_for_status.return_value = None
    mock_get.return_value = mock_response

    result = dg.materialize(
        assets=[
            lesson_4.state_population_api_resource_config,
            lesson_4.total_population_resource_config,
        ],
        resources={"state_population_resource": lesson_4.StatePopulation()},
        run_config=dg.RunConfig(
            {"state_population_api_resource_config": lesson_4.StateConfig(name="ny")}
        ),
    )
    assert result.success

    assert result.output_for_node("state_population_api_resource_config") == api_output
    assert result.output_for_node("total_population_resource_config") == 9082539

> pytest dagster_testing_tests/test_lesson_4.py::test_state_population_api_assets_config
...
dagster_testing_tests/test_lesson_4.py .                                                          [100%]

dg.materialize() is very flexible for testing and allows us to do almost anything that is possible in the Dagster UI when executing assets.