@Scout Can you provide an example testing directory structure in comparison to the dagster assets directory?

Certainly! The documentation provides an example of a default project structure generated by the

dagster project scaffold

command, which includes directories for both your Dagster assets and tests. Here's a simplified version of that structure:

. ├── README.md ├── my_dagster_project │   ├── __init__.py │   └── assets.py ├── my_dagster_project_tests │   └── test_assets.py ├── pyproject.toml ├── setup.cfg ├── setup.py └── tox.ini

In this structure: -

my_dagster_project/

is the directory containing your Dagster code, including assets. -

my_dagster_project_tests/

is the directory where you would place your test code. The

assets.py

file within the

my_dagster_project

directory would contain your Dagster assets, which are software-defined objects that model data assets. The

test_assets.py

file within the

my_dagster_project_tests

directory would contain the tests for those assets. This structure helps maintain a clear separation between your production code and your test code, making it easier to manage and understand your project. It's also a common practice in Python projects to have a separate tests directory, which is recognized by most Python testing frameworks and tools.

@Scout How do the test modules import the assets?

In the test modules, you import the assets from your Dagster project's assets module. Here's an example of how you might structure your test module to import and test assets: Assuming you have an

assets.py

file in your

my_dagster_project

directory that looks like this:

python # my_dagster_project/assets.py  from dagster import asset  @asset def my_asset():     # Asset logic here     return "data"

Your test module in the

my_dagster_project_tests

directory might look like this:

python # my_dagster_project_tests/test_assets.py  from my_dagster_project.assets import my_asset  def test_my_asset():     # Here you can call the asset as a regular function and assert its behavior     assert my_asset() == "data"

In this example, the

test_my_asset

function in

test_assets.py

imports the

my_asset

function from

my_dagster_project/assets.py

and then calls it like a regular Python function to verify its behavior. When you run your tests using a test runner like

pytest

, it will recognize the

test_

prefix in both the file and function names and execute the test accordingly.

@Scout But doesn’t this mean the tests directory is importing by package? The developer would have to re-install the package everytime they fix something right? Or am I wrong?

You're correct that if you're importing the assets as a package, you would need to reinstall the package every time you make changes to the code for those changes to be reflected in the installed package. This can be cumbersome during development. To avoid this, you can use the "editable" install option provided by

pip

. This allows you to install the package in such a way that changes to the source code are immediately available without needing to reinstall the package. You can do this by running the following command from the root directory of your project:

shell
pip install -e .

The

-e

flag stands for "editable", and the

.

specifies the current directory (which should contain your

setup.py

or

pyproject.toml

file). With an editable install, you can import the assets directly from the source code, and any changes you make will be reflected immediately when you run your tests. This is a common practice for local development because it streamlines the development process and eliminates the need to reinstall the package after each change. load_assets_from_package_module generate... button Thank you for your feedback!