@Scout can i test just ohne

@graph_asset

without starting the whole Dagster env?

Yes, you can test individual

@graph_asset

without starting the entire Dagster environment. The documentation provides guidance on how to test assets, including graph assets, in a lightweight manner. You can execute a job within a Python process, without any persistent service up and running, which is ideal for testing purposes. To test an individual

@graph_asset

, you can use Python's standard testing tools like pytest. You would typically write a test function that materializes the asset and then perform assertions on the results. Here's an example from the documentation on how to test assets:

python from dagster import materialize from tutorial_project.assets import (     topstory_ids,     topstories,     topstories_word_cloud ) # Instead of importing one asset, import them all  def test_hackernews_assets():     assets = [topstory_ids, topstories, topstories_word_cloud]     result = materialize(assets)     assert result.success     df = result.output_for_node("topstories")     assert len(df) == 100

In this example, multiple assets are imported and tested together, but you can adapt it to test a single

@graph_asset

. After writing your test, you can run it using pytest or your preferred test runner without the need for the full Dagster environment to be running. Remember to navigate to the top-level directory of your project where the tests are located and run the tests using the following command:

shell pytest tutorial_project_tests

This will execute the tests and provide you with the output in your terminal. The documentation emphasizes that Dagster is designed to make testing easy in a domain where it has historically been difficult, and it provides APIs for testing every one of its abstractions.

@Scout how to provide an AssetSpec to a

@graph_asset

As of the last update in the documentation, there is no direct way to provide an

AssetSpec

to a

@graph_asset

using a decorator parameter. The

@graph_asset

decorator is used to define an asset that's computed using a graph of ops, and the dependencies between the ops are specified inside the body of the decorated function. However, you can define various aspects of an asset, such as its description, metadata, and group name, by using the

AssetOut

object when defining the outputs of the ops within the

@graph_asset

. Here's an example from the documentation:

python from dagster import AssetOut, graph_asset  @graph_asset(out={"my_asset": AssetOut(description="My asset description", metadata={"key": "value"})}) def my_graph_asset():     # ... define the computation ...     pass

In this example,

AssetOut

is used to specify the description and metadata for the asset produced by

my_graph_asset

. If you need to specify more complex asset specifications, you might need to use

AssetsDefinition.from_graph

to construct an asset with more detailed specifications, including checks and other configurations. If you're looking to provide checks or other configurations that are not directly supported by the

@graph_asset

decorator, you may need to use the

AssetsDefinition.from_graph

method, which allows for more detailed asset specifications. Here's an example from the documentation:

python from dagster import graph, AssetsDefinition, AssetOut  @graph def my_graph():     # ... define the computation ...  assets = AssetsDefinition.from_graph(     graph_def=my_graph,     keys_by_output_name={"result": AssetKey("my_asset")},     # ... other parameters like check_specs, group_name, etc. )

In this case, you can use the

check_specs

parameter to provide checks for the assets created from the graph. Please note that the exact way to provide an

AssetSpec

or equivalent configurations may depend on the version of Dagster you are using and the features available at the time. Always refer to the latest Dagster documentation for the most accurate and up-to-date information.